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G.-X. Yu1,#, Y.-N. Ou2,#, Y.-L. Bi3, Y.-H. Ma2, H. Hu2, Z.-T. Wang2, X.-H. Hou2, W. Xu2, L. Tan1,2, J.-T. Yu4


1. Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, Dalian, China; 2. Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China; 3. Department of Anesthesiology, Qingdao Municipal Hospital, Qingdao University, China; 4. Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China; # Contributed equally to this work.

Corresponding Author: Prof. Jin-Tai Yu, Department of Neurology, Huashan Hospital, Fudan University, No. 12 Wulumuqi Road, Shanghai, China; Prof. Lan Tan, Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, No.5 Donghai Middle Road, Qingdao, China, E-mail addresses: (J.T. Yu); (L. Tan), Tel: +86 21 52888160; Fax: +86 21 62483421

J Prev Alz Dis 2021;
Published online September 20, 2021,



BACKGROUND: Studies suggested that vascular dysfunction might increase the risk of developing Alzheimer’s disease (AD), but the underlying mechanisms still remain obscure.
Objective: To evaluate the associations of vascular risk burden with AD core pathologies and investigate the effects of AD core pathologies on relationships between vascular risk burden and cognitive impairments.
Design: The Chinese Alzheimer’s Biomarker and LifestyLE (CABLE) study was principally focusing on aging, as well as the risk factors and biomarkers of AD initiated in 2017.
Setting: The CABLE study was a large cohort study established in Qingdao, China.
Participants: A total of 618 non-demented elders were obtained from CABLE study.
Measurements: The general vascular risk burden was assessed by the Framingham General Cardiovascular Risk Score (FGCRS). Multivariate linear regression analyses were performed to evaluate the associations of FGCRS with cerebrospinal fluid (CSF) AD biomarkers and cognition. Casual mediation analyses were performed to investigate the mediating effects of AD biomarkers on cognition.
Results: Increased FGCRS was related to higher levels of CSF total tau (t-tau, p < 0.001), phosphorylated tau (p-tau, p < 0.001) as well as the ratio of t-tau and amyloid-β 42 (t-tau/Aβ42, p = 0.010), and lower Chinese-Modified Mini-Mental State Examination (CM-MMSE, p = 0.010) score. Stratified analysis indicated that age modified the associations, with FGCRS being significantly associated with tau pathology (p < 0.001 for t-tau and p-tau) in middle-aged group (<65 years old), instead of older group. The influences of FGCRS on cognitive impairments were partially mediated by tau pathologies (a maximum proportion of 20.9%).
Conclusions: Tau pathology might be a pivotal mediator for effects of vascular risk on cognitive decline. Early and comprehensive intervention for vascular risk factors might be a potential approach to delaying or preventing cognitive impairment and AD.

Key words: Alzheimer’s disease, vascular risk burden, biomarkers, cognitive impairment, mediation.



Alzheimer’s disease (AD) is an age-related neurodegenerative disorder which is characterized by progressive cognitive impairment. The pathologic hallmarks of AD are neuritic plaques composed of aggregated amyloid-β (Aβ), and neurofibrillary tangles (NFT) harboring hyper-phosphorylated tau and diffuse plaques (1-3). AD has traditionally been regarded as a neurodegenerative disorder affecting neurons, and vascular damage has also been implicated in AD as a potentially modifiable factor of cognitive decline (4). Autopsy studies indicated that intracranial vascular damage often co-occurred with the AD core pathologies in sporadic late-onset AD and that vascular impairment might lower the threshold for dementia (5). In addition, considering the brain’s critical dependence on finely regulated blood supply and blood-brain barrier (BBB) exchange, the vascular alterations could play an essential part in neuronal dysfunction which is a mechanism underlying dementia (6). Accumulating evidence demonstrated that vascular risk factors had a key role in the progression of AD (7). Most vascular risk factors were implicated in AD, including hypertension, diabetes mellitus, smoking and hypercholesterolemia (8-11).
It has been a challenge to establish a direct causal relationship of vascular risk burden with human core AD pathologies. The underlying mechanisms by which these risk factors worsen cognition are still unclear. This might be achieved by their direct influences on AD-related neurodegeneration, or by leading to other cerebral damage which in conjunction with ongoing neurodegeneration could result in cognitive decline (12, 13). However, AD cerebrospinal fluid (CSF) biomarkers provide an opportunity to assess the relationships between vascular risk burden and AD core pathologies. According to the 2018 National Institute on Aging-Alzheimer’s Association (NIA-AA) Research Framework (14), the AD core biomarkers included CSF Aβ42, total tau (t-tau) and phosphorylated tau (p-tau). Previous studies showed that CSF Aβ42 or tau levels changed in twenty years before AD onset (15, 16). Moreover, cognitive decline was used to stage the severity of AD according to the NIA-AA Research Framework (14). Therefore, understanding the associations of vascular risk burden with CSF AD biomarkers and cognition is critical to establishing prevention strategies in preclinical AD.
However, there were inconsistent results on the associations of vascular risk burden with AD core pathologies (13, 17-21). Framingham General Cardiovascular Risk Score (FGCRS) was a well-validated, multivariable risk algorithms of vascular risk burden (22). Herein, in a cohort of non-demented Han Chinese elders, the purposes of our research were: 1) to assess whether FGCRS was associated with CSF AD biomarkers and cognition; 2) to investigate the influences of age and sex on the above associations; and 3) to examine whether AD core pathologies mediated the effects of FGCRS on cognitive impairments.




Non-demented northern Han Chinese participants were recruited from the Chinese Alzheimer’s Biomarker and LifestyLE (CABLE) study. CABLE study is a large cohort principally focusing on aging, as well as the risk factors and biomarkers of AD since 2017. Participants were patients in several departments of Qingdao Municipal Hospital. They signed informed consent at study entry, and agreed to provide cerebrospinal fluid (CSF) and blood samples for further detection, and underwent a series of clinical and neuropsychological assessments to evaluate their cognitive status. All participants were aged from 40 to 90 years. The exclusion criteria include: 1) cranial injury, infections of the central nervous system, epilepsy, multiple sclerosis or other major neurological diseases; 2) major psychological diseases; 3) severe systemic diseases which may have influences on AD biomarkers; and 4) family history of genetic diseases. Approval of CABLE study was obtained from the Institutional Review Board of Qingdao Municipal Hospital. The present study included non-demented participants who provided adequate information to calculate FGCRS and data of core CSF biomarkers. Their cognitive diagnoses were in compliance with the NIA-AA workgroup diagnostic criteria (23). The thresholds of the adapted Chinese-Modified Mini-Mental State Examination (CM-MMSE) to exclude participants with dementia tendency were 17 for illiterate participants, 20 for participants with 1 to 6 years of education, and 24 for participants with 7 or more years of education (24).

Framingham General Cardiovascular Risk Score

FGCRS was calculated based on a weighted summary of age, sex, systolic blood pressure, treatment for hypertension, smoking attitude, total cholesterol, high-density lipoprotein cholesterol and diabetes (22). The score for age ranges from 0 to 12; systolic blood pressure -3 to 7; total cholesterol 0 to 5; high-density lipoprotein cholesterol -2 to 2; smoker 0 to 3; and diabetes 0 to 4 in woman. And in man, the score for age ranges from 0 to 15; systolic blood pressure -2 to 5; total cholesterol 0 to 4; high-density lipoprotein cholesterol -2 to 2; smoker 0 to 4; and diabetes 0 to 3. The total FGCRS ranged from -5 to 33 for woman, and ranged from -4 to 33 for man. FGCRS was a multivariable risk factor algorithm for the prediction of 10-year risk of vascular events (22). When the risk is greater than 20% (FGCRS of 17 points for women and 14 points for men), professional intervention is warranted (22).

CSF AD biomarkers

In CABLE, CSF specimens were collected in 10 ml polypropylene tubes via lumbar puncture and then transported to the laboratory within 2 hours collection. These specimens were centrifuged at 2000×g for 10 minutes and stored in an enzyme-free EP (Eppendorf) tube at -80℃. CSF Aβ42, t-tau, and p-tau levels were measured by the ELISA kit (Innotest β-AMYLOID (1-42), hTAU-Ag, and PHOSPHO-TAU (181p); Fujirebio, Ghent, Belgium) on the microplate reader (Thermo Scientific™ Multiskan™ MK3). The mean intra-batch coefficient of variation (CV) was <5% (4.9% for Aβ42, 4.5% for t-tau, and 2.4% for p-tau). The mean inter-batch CV was <15% (13.3% for Aβ42, 13.8% for t-tau, and 10.9% for p-tau).

APOE and cognitive assessment

DNA was obtained from overnight fasting blood specimens using the QIAamp®DNA Blood Mini Kit (250). APOE ε4 genotyping was conducted using restriction fragment length polymorphism (RFLP) technology on the basis of 2 specific loci associated with APOE ε4 status, rs7412 and rs429358. Participants were finally divided into APOE ε4 carriers and non-carriers. The global cognitive functioning of all the participants was assessed by CM-MMSE score. Total CM-MMSE scores ranged from 0 to 30. The greater the total score was, the better the cognitive performance was.

Statistical Analyses

CSF values situated outside 3 standard deviations (SD) were excluded for further analysis. Participants were further dichotomized into high and vascular risk groups based on a cut-off of a predicted risk of 20%. Demographic factors were compared using Chi-square tests for categorical variables and Kruskal-Wallis test for continuous variables, respectively. The skewed independent or dependent variables were log10-transformed to normalize the distributions.
Multivariate linear regression analyses were used to evaluate the relationships of FGCRS with CSF AD biomarkers, with the score regarded both as continuous and dichotomous. We further calculated t-tau/Aβ42 and p-tau/Aβ42 which were regarded as better predictors of AD and cognitive impairment (25, 26). As age and sex were incorporated into FGCRS calculation, model 1 only included educational level, APOE ε4 status, CM-MMSE score, and history of stroke. We further additionally adjusted age and sex in the model 2. Age and sex were not only associated with AD, but also played an important part in vascular burden. Subgroup analyses stratified by age (mid-life stage and late-life stage based on a cut-off of 65 years old) and sex (female vs male) to investigate the effects of age and sex on the association between FGCRS and AD pathology were conducted.
We further evaluated the association between FGCRS and CM-MMSE score by multivariate linear regression analyses. Then the influences of CSF AD biomarkers on relationships between FGCRS and CM-MMSE score were assessed by a mediation analysis (27). If all the following 4 criteria were satisfied simultaneously, the mediation effects existed: 1) FGCRS was significantly associated with CSF AD biomarkers; 2) FGCRS was significantly associated with CM-MMSE score; 3) CSF AD biomarkers were significantly associated with CM-MMSE score; and 4) the relationship between FGCRS and CM-MMSE score was weakened after additional adjustment for CSF AD biomarkers. We further estimated the attenuation or indirect effect, with the significance determined using 10,000 bootstrapped iterations. Adjusted covariants included educational level, APOE ε4 status, and history of stroke in the above analyses. Statistical analyses were performed with R version 3.6.1 software. And a two-tailed p value < 0.05 was considered significant.



Characteristics of participants

The demographic characteristics of the total population included in our analysis were summarized in Table 1. A total of 618 non-demented participants were enrolled with an average age of 61.93±10.21 years, including 253 (40.9%) females and 93 (15.0%) APOE ε4 carriers. The mean FGCRS was 14.14±4.85 and there were 243 (39.3%) individuals in high vascular risk burden group.

Table 1. Basic demographic information of the analytical population

P values of between-group comparisons were obtained using the Chi-square tests for categorical variables and Kruskal-Wallis test for continuous variables; Abbreviations: APOE, apolipoprotein E; CM-MMSE, Chinese-modified mini-mental state examination; Total-C, total cholesterol; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; FGCRS, Framingham General Cardiovascular Risk Score; Aβ42, β-amyloid 42; t-tau, total tau; p-tau, phosphorylated tau.


Associations between FGCRS and CSF AD biomarkers

We found that increased FGCRS was related to higher levels of CSF t-tau (β = 0.190, p <0.001), p-tau (β = 0.099, p <0.001) and t-tau/Aβ42 (β = 0.121, p = 0.010) after adjustment for educational level, APOE ε4 status, CM-MMSE score, and history of stroke (Model 1 in Table 2). No significant associations between FGCRS and levels of CSF Aβ42 (β = 0.069, p = 0.113) or p-tau/Aβ42 (β = 0.030, p = 0.488) were observed. Moreover, high vascular risk showed closer associations with increased levels of CSF t-tau (β = 0.046, p < 0.001), p-tau (β = 0.020, p = 0.007) and t-tau/Aβ42 (β = 0.049, p = 0.002) than low vascular risk (Figure 1B, 1C and 1D). High vascular risk group had non-significant associations with CSF Aβ42 (β = -0.003, p = 0.850) and p-tau/Aβ42 (β = 0.023, p = 0.111) levels compared with low vascular risk group (Figure 1A, 1E). In the fully adjusted model, increased FGCRS was still related to higher CSF p-tau (β = 0.059, p = 0.044; Model 2 in Table 2). The associations of FGCRS with other AD CSF biomarkers became non-significant.

Table 2. Associations of FGCRS with CSF AD biomarkers and cognition

Model 1 was adjusted for education level, APOE ε4 status, CM-MMSE score, and history of stroke. Model 2 was adjusted for age, sex, educational level, APOE ε4 status, CM-MMSE score, and history of stroke; * Model 1 was adjusted for education level, APOE ε4 status, and history of stroke. Model 2 was adjusted for age, sex, educational level, APOE ε4 status, and history of stroke. Abbreviations: FGCRS, Framingham General Cardiovascular Risk Score; AD, Alzheimer’s disease; CSF, cerebrospinal fluid; Aβ42, β-amyloid 42; t-tau, total tau; p-tau, phosphorylated tau, CM-MMSE, Chinese-modified mini-mental state examination; APOE, apolipoprotein E.


Figure 1. Associations between vascular risk burden and CSF AD biomarkers

Compared to low vascular risk group, significant associations of increased FGCRS with higher t-tau (B), p-tau (C) and t-tau/Aβ42 (D) levels were found, but no significant relationships with Aβ42 (A) or p-tau/Aβ42 (E) levels were found. Abbreviations: FGCRS, Framingham General Cardiovascular Risk Score; AD, Alzheimer’s disease; CSF, cerebrospinal fluid; Aβ42, β-amyloid42; t-tau, total tau; p-tau, phosphorylated tau.


Subgroup analyses stratified by age and sex

Considering that age and sex not only associate with AD, but also play important roles in vascular risk burden, we conducted subgroup analyses stratified by age (mid-life stage and late-life stage based on a cut-off of 65 years old) and sex (female vs male) to investigate the effects of age and sex on the association between FGCRS and AD pathology. Results indicated that FGCRS was significantly associated with tau pathology (β = 0.159, p < 0.001 for t-tau; β = 0.120, p < 0.001 for p-tau) in middle-aged group, instead of older group (Table 3). However, sex didn’t modify the above associations. Higher FGCRS was associated with tau pathology in both female (β = 0.179, p < 0.001 for t-tau; β = 0.097, p = 0.001 for p-tau) and male (β = 0.275, p < 0.001 for t-tau; β = 0.130, p = 0.001 for p-tau) participants. FGCRS was also associated with t-tau/Aβ42 (β = 0.246, p = 0.002) in the male participants.

Table 3. Associations of FGCRS with CSF AD biomarkers and CM-MMSE score in age and sex subgroups

NOTE: Associations of FGCRS between CSF AD biomarkers and CM-MMSE score were accessed by multiple linear regression models; All models were adjusted for education level, CM-MMSE score, history of stroke and APOE ε4 status; * CM-MMSE was adjusted for education level, history of stroke and APOE ε4 status; Abbreviations: FGCRS, Framingham General Cardiovascular Risk Score; AD, Alzheimer’s disease; CSF, cerebrospinal fluid; Aβ42, β-amyloid42; t-tau, total tau; p-tau, phosphorylated tau; CM-MMSE, China-modified mini-mental state examination; APOE, apolipoprotein E.


Causal Mediation Analyses

In all individuals, there was a significant association between FGCRS and CM-MMSE score (β = -0.019, p = 0.010) after adjustment for education level, APOE ε4 status, and history of stroke. According to the above analyses, we found FGCRS was significantly associated with both tau pathologies and cognition. We further explored whether the FGCRS contributed to cognitive decline via mediating tau pathologies (Figure 2). The association of FGCRS and CM-MMSE score was weakened after separately additional adjustment for CSF t-tau, p-tau and t-tau/Aβ42 levels. Thus, tau pathology was identified as a significant mediator for effects of vascular risk burden on cognitive impairments. Moreover, we considered the effects as partial mediation. The mediation proportions were 20.9% for p-tau with a significant indirect effect (p = 0.006), 10.9% for t-tau/Aβ42 with a significant indirect effect (p = 0.017), and 15.9% for t-tau with a marginally significant indirect effect (p = 0.064).

Figure 2. Causal Mediation Analyses

The association between FGCRS and CM-MMSE score was mediated by tau pathologies; Abbreviations: IE, indirect effect; FGCRS, Framingham General Cardiovascular Risk Score; CM-MMSE, China-modified mini-mental state examination; Aβ42, β-amyloid42; t-tau, total tau; p-tau, phosphorylated tau.



This is a population-based cross-sectional study, which aimed to explore the associations between FGCRS and a series of AD CSF biomarkers in a cohort of non-demented Han Chinese elders. The primary findings of our research were as follows: 1) FGCRS was positively associated with tau pathologies, which was more evident in the middle-aged individuals; 2) FGCRS was negatively associated with cognitive performance; 3) the influence of FGCRS on cognitive impairments was partially mediated by tau pathologies.
Our results are in line with the finding of many previous studies that individual vascular risk burden could lead to increased tau pathologies (18, 28). Long-term exposure to vascular risk factors can lead to cerebral small vessel diseases (CSVDs), including lacunar infarcts, white matter hyperintensities (WMHs), microinfarcts, and BBB disruption. These diseases are important mechanisms underlying cognitive impairment and dementia. Increased vascular risk burden might lead to atherosclerosis, infarction and other vascular damage, therefore lowering cerebral blood flow (CBF) (29) and further resulting in increased tau pathology (30-32). Our results raised the possibility that cerebrovascular dysfunction might be associated with pre-symptomatic AD pathology (33). Nevertheless, we found no clear association between vascular risk burden and Aβ burden, which was consistent with previous studies (18, 21, 28). Our findings supported the possibility that separate amyloid and vascular pathways might both enhance neurodegeneration (34, 35). Furthermore, CSF t-tau/Aβ42 ratio has been proposed to provide more accurate risk assessments for the development of AD (26, 36). The ratio reflects two aspects of AD pathology: amyloid plaques (Aβ42) and neurodegeneration (tau) (37). P-tau is a marker of axonal damage and neuronal degeneration, and it has stronger associations with AD pathophysiology and the formation of neurofibrillary tangles than t-tau (38). However, our study found that FGCRS was related to higher levels of t-tau/Aβ42 rather than p-tau/Aβ42, possibly because a composite of vascular risk factors in the FGCRS may affect the effects of individual risk factors. Using the same analytical population of CABLE database, previous studies have found that blood pressure(39) and dyslipidemia (40) mainly affect tau pathology, whereas blood glucose (41) affects Aβ pathology. Notably, the underlying mechanisms in which vascular risk mediates Aβ or tau pathology warrant further investigation.
Higher FGCRS was associated with cognitive decline, which was in line with several large longitudinal studies (17, 18, 42). Using casual mediation analysis, we further found that tau pathology was a mediator of the effect of FGCRS on cognitive impairment. Imaging studies suggested that the influences of CBF and soluble platelet-derived growth factor receptor beta (sPDGFRβ), two biomarkers of vascular health, on global cognition were partially mediated by tau pathologies (43). Several potential mechanisms in which tau pathologies mediate the association of vascular risk burden with cognitive impairments have been identified. According to a neuropathological study, microvessels obtained from human AD prefrontal cortex with increased tau pathology upregulate genes participating in endothelial senescence and recruitment of leukocytes into the endothelium, contributing to AD-related cerebrovascular damage and decreased CBF (44). Studies also found that reduced CBF was associated with cognitive decline (45, 46). Moreover, previous studies found that vascular risk burden would become more related to CSF neurofilament light (NFL) in the context of greater CSF t-tau or p-tau levels (47). NFL was also supposed to be an AD biomarker and higher CSF NFL levels were posited to reflect axonal injury and cognitive impairment (48, 49). In conjunction with vascular risk burden, tau pathology could aggravate the impairment of nerve function. Furthermore, greater tau levels were related to decreased levels of claudin-5 (CLDN5) and occludin (OCLN) which played an important part in regulating endothelial barrier integrity (50). Decreased levels of CLDN5 and OCLN indicated BBB damage which was found to be associated with human cognitive dysfunction (51).
Although single vascular risk factors could be particularly effective in the neurovascular unit, but a cluster of them could lead to the final vascular derangement (52, 53). FGCRS was a simple and reliable instrument for assessing the general vascular risk burden, in which most indicators were preventable. It has been confirmed that FGCRS is superior to the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) dementia risk score for the application in prevention programs for evaluating cognitive impairments and targeting modifiable factors (54). Moreover, studies have shown that AD has a long asymptomatic period during which there is accumulation and progression of pathologies and brain structural changes. Symptoms appear when compensatory mechanisms have been overcome, initially as mild cognitive impairment (MCI) and ultimately as dementia (2). This present study focused on the non-demented elders might give us a hint that vascular risk factors may be mainly associated with higher CSF biomarkers in the stage without severe cognitive impairment. Till now, there is a lack of effective drugs to prevent or treat AD. Therefore, findings from this work might highlight the importance of early and integrated management of vascular risk burden to protect cognitive health or delay dementia.
There were several limitations that should be mentioned. Firstly, the associations of FGCRS with CSF AD biomarkers and cognition were only evaluated cross-sectionally in the CABLE cohort. Therefore, the temporality for the associations is unclear. Secondly, some data were obtained from self-reports of participants, such as the history of stroke and diabetes, which might lead to reporting bias. Thirdly, the CABLE is a hospital registry-based study with a specific population profile. The conclusions have a limited power to be generalizable to the general population, but the present findings may have important implications for AD prevention and early warning. Moreover, it is still controversial whether to adjust for age and sex when using the FGCRS, because these variables have been controlled for within the FGCRS calculation. Our results were obtained without correction for age and sex. Lastly, ethnic homogeneity of Chinese Northern Han subjects limited the generalizability of our findings to other studies with different ethnicities. It is necessary to further confirm our results in longitudinal cohorts with multiracial participants.
In summary, our study emphasized the close associations of vascular risk burden with tau pathologies and cognitive impairments. Tau pathologies partially mediated the influences of vascular risk burden on cognitive impairments. Our findings suggesting that early and comprehensive intervention for vascular risk factors might be a potential approach to delaying or preventing cognitive impairment and AD.


Acknowledgements: The authors thank all participants of the present study as well as all members of staff of the CABLE study for their role in data collection.

Fundings: This study was supported by grants from the National Natural Science Foundation of China (81971032), the National Key R&D Program of China (2018YFC1314700), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University.

Conflict of interest: The authors declare that they have no competing interests.

Ethical Standards: The CABLE database was conducted in accordance with the Helsinki declaration, and the research program was approved by the Institutional Ethics Committee of Qingdao Municipal Hospital. All subjects or their proxies provided written consents.



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M. Milà-Alomà1,2,3,4, G.Salvadó1,2, M. Shekari1,2,3, O. Grau-Rivera1,2,4,5, A. Sala-Vila1,2, G. Sánchez-Benavides1,2,4, E.M. Arenaza-Urquijo1,2,4, J.M. González-de-Echávarri1,2, M. Simon6, G. Kollmorgen7, H. Zetterberg8,9,10,11, K. Blennow8,9, J.D. Gispert1,2,3,12, M. Suárez-Calvet1,2,4,5,
J.L. Molinuevo1,2,3,4 for the ALFA study†


1. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation. Barcelona, Spain; 2. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; 3. Universitat Pompeu Fabra, Barcelona, Spain; 4. Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain; 5. Servei de Neurologia, Hospital del Mar, Barcelona, Spain; 6. Roche Diagnostics International Ltd, Rotkreuz, Switzerland; 7. Roche Diagnostics GmbH, Penzberg, Germany; 8. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Sweden;
9. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; 10. Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, United Kingdom; 11. UK Dementia Research Institute at UCL, London, United Kingdom; 12. Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain; †The complete list of collaborators of the ALFA Study can be found in the acknowledgements section.

Corresponding Authors: José Luis Molinuevo, Alzheimer Prevention Program – Barcelonaβeta Brain Research Center, Wellington 30, 08005, Barcelona, Spain, +34933160990, E-mail:; Marc Suárez-Calvet, Alzheimer Prevention Program – Barcelonaβeta Brain Research Center, Wellington 30, 08005, Barcelona, Spain
+34933160990, E-mail:

J Prev Alz Dis 2021;1(8):68-77
Published online September 29, 2020,



Amyloid-β (Aβ) positivity is defined using different biomarkers and different criteria. Criteria used in symptomatic patients may conceal meaningful early Aβ pathology in preclinical Alzheimer. Therefore, the description of sensitive cutoffs to study the pathophysiological changes in early stages of the Alzheimer’s continuum is critical. Here, we compare different Aβ classification approaches and we show their performance in detecting pathophysiological changes downstream Aβ pathology.
We studied 368 cognitively unimpaired individuals of the ALFA+ study, many of whom in the preclinical stage of the Alzheimer’s continuum. Participants underwent Aβ PET and CSF biomarkers assessment. We classified participants as Aβ -positive using five approaches: (1) CSF Aβ42 < 1098 pg/ml; (2) CSF Aβ42/40 < 0.071; (3) Aβ PET Centiloid > 12; (4) Aβ PET Centiloid > 30 or (5) Aβ PET Positive visual read. We assessed the correlations between Aβ biomarkers and compared the prevalence of Aβ positivity. We determined which approach significantly detected associations between Aβ pathology and tau/neurodegeneration CSF biomarkers.
We found that CSF-based approaches result in a higher Aβ-positive prevalence than PET-based ones. There was a higher number of discordant participants classified as CSF
Aβ-positive but PET Aβ-negative than CSF Aβ-negative but PET Aβ-positive. The CSF Aβ 42/40 approach allowed optimal detection of significant associations with CSF p-tau and t-tau in the Aβ-positive group.
Altogether, we highlight the need for sensitive Aβ -classifications to study the preclinical Alzheimer’s continuum. Approaches that define Aβ positivity based on optimal discrimination of symptomatic Alzheimer’s disease patients may be suboptimal for the detection of early pathophysiological alterations in preclinical Alzheimer.

Key words: Alzheimer’s disease, biomarkers, cerebrospinal fluid, positron emission tomography, preclinical Alzheimer.



Alzheimer’s disease (AD) is preceded by a long preclinical phase, which is characterized by the emergence of pathological brain changes in the absence of evident clinical symptoms. Cognitive symptoms may arise decades after the first pathological brain changes and can eventually progress to dementia. AD is therefore understood as a continuum (the Alzheimer’s continuum) entailing a preclinical (i.e., asymptomatic) and a clinical (i.e., symptomatic) phase (1, 2). Following the latest NIA-AA guidelines, the term ‘Alzheimer’s disease’ should be applied when there is biomarker evidence of both amyloid-β (Aβ) and tau pathologies (2, 3).
A key aspect in preclinical Alzheimer’s studies is the biomarker-based definition of Aβ pathology. Currently established Aβ pathology biomarkers can be divided in two main categories: fluid [mainly cerebrospinal fluid (CSF) and, more recently, also blood] and neuroimaging (Aβ PET) biomarkers (4, 5). Although all these biomarkers may be used on a continuous scale, cutoffs are needed to dichotomize biomarker values for diagnosis or research individual categorization purposes. Different approaches may be used to establish biomarker cutoffs. These mainly include: (1) comparison against a gold standard (neuropathology, other validated biomarkers reflecting pathology), (2) optimal discrimination between a control and a pathological group (case-control studies), (3) prediction of disease progression, or (4) description of abnormality based on population percentiles in a ‘normal’ or reference population (e.g., 95% of the unaffected population have lower/higher values) (2, 6, 7).
Regarding CSF Aβ biomarkers, a decrease in CSF Aβ42 is the widely known pattern in AD, which also correlates with increased Aβ PET uptake (8, 9). In addition, CSF Aβ42 cutoffs have been validated against Aβ PET as gold standard (10, 11). Nevertheless, the use of CSF Aβ42 may be limited by interindividual differences in Aβ42 production, and by the impact of pre-analytical and assay-related factors. To overcome some of these limitations, normalization of Aβ42 with Aβ40, by using the Aβ42/40 ratio, was proposed (12). Studies have shown that the CSF Aβ42/40 ratio is a better predictor of abnormal Aβ PET and that the ratio allows a more accurate identification of AD patients than CSF Aβ42 (13, 14).
In the clinical setting, Aβ PET imaging is used to detect Aβ pathology in patients with cognitive impairment suspected to be AD-related. The definition of a positive or negative scan is done through visual read (VR) by trained specialists, who categorize images according to well-established criteria. The VR method has been validated against neuropathology and has shown high concordance with quantitative methods (15–17). Aβ PET images can be quantified using standardized uptake value ratios (SUVR), as well as with the “Centiloid” (CL) scale. The CL scale is a method for standardizing measurements to a uniform scale, thus facilitating comparisons across radiotracers and also across studies (18), and has a robust association with pathology (19,20).
It is important to note that, although binary cutoffs allow us to stratify patients or research participants according to their biomarker profile, there is a continuum of Aβ pathology changes, that is a dynamic transition from Aβ-negative to Aβ-positive states. Therefore, binarization may leave emerging Aβ pathology undetected. This is especially important in the study of preclinical Alzheimer, in which the use of cutoffs that have been defined for a clinical use possibly conceals meaningful amounts of early Aβ pathology. Thus, the description of cutoffs that are sensitive enough to detect subtle pathological changes in the very early stage of the Alzheimer’s continuum is of great importance (21, 22).
The aim of this study is to describe and compare different Aβ classification approaches, derived through both CSF and PET measures, and to show their performance in the detection of other early pathophysiological changes in the Alzheimer’s continuum. We hypothesize that an optimal Aβ classification approach at this early stage would set the point where Aβ-downstream pathophysiological changes start.




The ALFA+ cohort is a nested longitudinal study of the ALFA (for ALzheimer and FAmilies) study, which aims at characterizing the preclinical stage of the Alzheimer’s continuum (23). The ALFA study includes 2,743 cognitively unimpaired individuals, aged between 45 and 75 years old, which are enriched for family history of AD and APOE-ε4 carriership. In the ALFA+ cohort, participants undergo a more detailed characterization, which entails acquisition of CSF samples for biomarker levels determination, neuroimaging biomarkers (MRI and FDG and Aβ PET), APOE genotyping and cognitive assessments. Among the ALFA+ participants, 381 had available CSF biomarker measurements. Since we aimed at studying participants in the Alzheimer’s continuum, we excluded 13 participants that had a CSF biomarker of suspected non-Alzheimer’s pathology (2), as defined by CSF Aβ42/40 ≥ 0.071 and CSF p-tau >24 pg/ml. None of them was Aβ-positive using Aβ PET in any of the approaches defined below. Thus, 368 participants were finally included in the present study. Of those, a subset of 303 and 301 participants also had CL quantification or VR assessment of Aβ PET imaging, respectively. For all participants, the time difference between CSF collection and Aβ PET assessments was less than one year.

CSF biomarkers assessment

CSF samples were obtained by lumbar puncture and collected, processed and stored following standard procedures (24). Total tau (t-tau) and phosphorylated tau (p-tau) measurements were performed using the electrochemiluminiscence Elecsys® Total-Tau CSF and Phospho-Tau(181P) CSF immunoassays, respectively. CSF Aβ42 and Aβ40 were measured with the prototype NeuroToolKit (Roche Diagnostics International Ltd.). All assays were performed on a fully automated cobas e 601 or e 401 instrument (Roche Diagnostics International Ltd.) at the Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.

PET imaging acquisition and processing

Imaging acquisition and preprocessing protocols have been described previously (20). In brief, a T1-weighted MRI and an [18F] Flutemetamol PET scan was acquired in all participants. The T1-weighted 3D-TFE sequence was acquired in a Philips 3 T Ingenia CX scanner. PET imaging was conducted in a Siemens Biograph mCT, following a cranial CT scan for attenuation correction. Participants were injected with 185 MBq (range 166.5–203.5 Mbq) of [18F] Flutemetamol, and 4 frames of 5 min each were acquired 90 min post-injection.
PET processing was performed following the standard CL pipeline (18) using SPM12. In brief, PET images are first coregistered to their respective T1-weighted images and, afterwards, moved to MNI space using the normalization transformation derived from the segmentation of the T1-weighted image. PET images were intensity normalized using whole cerebellum as reference region. CL values were calculated from the mean values of the standard CL target region ( using the transformation previously calibrated (20).
A nuclear medicine physician visually rated the scans as Aβ-positive or Aβ-negative using standard clinical criteria as specified in the Summary of Product Characteristics of the tracer (

Aβ pathology biomarkers and cutoffs

We classified the participants as Aβ-positive or Aβ-negative using five different Aβ biomarker/cutoff combinations (Aβ classification approaches; Table 1). Each of the approaches were determined as follows. In approach 1, participants with a CSF Aβ42 < 1098 pg/ml were considered as Aβ-positive. This cutoff had been previously established in concordance with Aβ PET in a similar cohort and had area under the ROC curve (AUC) of 0.85 to discriminate between Aβ PET-positive or negative (10). In approach 2, we used CSF Aβ42/40 as a biomarker of Aβ pathology and we determined a cutoff using a Gaussian mixture model (GMM). The cutoff was defined as the mean minus 2 standard deviations (SD) of the non-pathologic Gaussian distribution. As the fitting of the GMM can vary depending on initial conditions, the process was repeated 1000 times with random initial conditions. The final cutoff for CSF Aβ42/40, derived as the median value of the 1000 repetitions, was 0.071. In both approaches 3 and 4, we used the Aβ PET CL scale as biomarker of Aβ pathology, but with different cutoffs. In approach 3, we defined Aβ positivity when CL > 12; this cutoff has been previously proposed to detect subtle Aβ pathology in a comparison against CSF Aβ42 (20). In approach 4, we defined Aβ positivity when CL > 30, a cutoff that falls within the optimal range of agreement with VR method in clinical populations (24 – 35 CL), and has been proposed to detect established Aβ pathology (19,20,25–27). Finally, in approach 5, we categorized participants as Aβ-positive or Aβ-negative using the Aβ PET VR, the approach most commonly used in the clinical setting.

Table 1. Summary of Aβ biomarkers/cutoffs combinations used in each Aβ classification approach

Abbreviations: Aβ, amyloid-β; CSF, cerebrospinal fluid; CL: Centiloids.

Statistical analysis

CSF biomarker distributions were tested for normality with the Kolmogorov-Smirnov test and histograms visual inspection. CSF Aβ42, p-tau and t-tau did not follow a normal distribution and were thus log10-transformed. In contrast, CSF Aβ42/40 ratio followed a normal distribution and was not transformed. CSF biomarker extreme values were identified following Tukey criterion (i.e., values falling at over three times the interquartile range below the first quartile or above the third quartile) and excluded from the analyses.
Differences in demographic characteristics and CSF biomarker values as a function of Aβ classification schemes were assessed by means of chi-squared or one-way analysis of variance (ANOVA) tests, as appropriate. Correlations between Aβ biomarkers were tested using partial Spearman rank correlation, corrected by age. We used a chi-squared analysis in order to test significant differences in Aβ-positive prevalence using different Aβ classification approaches.
We tested the association between tau and neurodegeneration CSF biomarkers (i.e., CSF p-tau, t-tau) and the Aβ biomarker (i.e., CSF Aβ42, Aβ42/40 or CL scale) in a linear model, adjusting by the effect of age and sex. These analyses were performed separately in Aβ-positive or Aβ-negative groups as defined by each approach. Moreover, in order to determine which of the approaches was more sensitive to detect early pathophysiological changes, we conducted the same analyses in the whole cohort but additionally introducing in the model the interaction term ‘Aβ biomarker x Aβ group’. A significant interaction term denotes that the association of the Aβ biomarker with CSF p-tau and t-tau differs between the Aβ-negative and Aβ-positive groups, as defined by that approach. These analyses were performed in each of the five Aβ classification approaches.
All statistical models were adjusted for the effects of age and sex. For all analysis, we applied a false discovery rate (FDR) multiple comparison correction following the Benjamini-Hochberg procedure (28). All tests were 2-tailed, with a significance level of α = 0.05.



Participants’ demographics

Participants’ characteristics are depicted in Table 2. We compared the demographical characteristics of the Aβ-positive and Aβ-negative groups defined by the different Aβ classification approaches. We observed that Aβ-positive participants were significantly older using PET-based versus CSF-based approaches. There were not significant differences in years of education, frequency of the APOE-ε4 allele or sex between the different Aβ-classification approaches.
Most of the AD biomarkers significantly differed between the five Aβ classification approaches within the Aβ positive or Aβ-negative group (Table 2). PET-based Aβ classification approaches (namely, approach 3, 4 and 5) had a more pathologic biomarker profile (that is, lower CSF Aβ42 and CSF Aβ42/40 ratio, but higher CL scale and CSF p-tau and t-tau), than the CSF-based Aβ classification approaches (namely, approach 1 and 2). These results suggest that CSF-based Aβ biomarkers are capturing Aβ accumulation at an earlier stage of the Alzheimer’s continuum. This idea is supported by the fact that Aβ-negative groups defined by PET-based approaches had higher levels of CSF p-tau and t-tau (Table 2).

Table 2. Participants’ demographic characteristics and biomarker levels by five Aβ classification approaches

Data are expressed as mean (M) and standard deviation (SD) or number of participants (n) and percentage (%), as appropriate. One-way ANOVA followed by Tukey corrected post hoc comparisons was used to compared age and education and Pearson’s Chi-square test to compare sex and APOE-ε4 status between approaches within each Aβ-group. CSF biomarkers and centiloid scale were compared with an ANCOVA adjusted by age and sex followed by Tukey corrected post hoc comparison. The P-values indicated in the last column refer to the approach main effect. All P-values are corrected for multiple comparisons using the FDR method; Abbreviations: Aβ42, amyloid-β 42; p-tau, phosphorylated tau; t-tau, total tau; * P<0.05 vs approach 1; † P<0.001 vs approach 1; ‡ P<0.01 vs approach 2; § P<0.01 vs approach 1; ||P<0.0001 vs approach 1; { P<0.0001 vs approach 2; # P<0.001 vs approach 2; ** P<0.05 vs approach 2; ‡‡ P< 0.05 vs approach 4


Correlations between Aβ biomarkers

We tested the correlations between CSF and PET Aβ biomarkers, corrected by age. There was a moderate correlation between CSF Aβ42 and CL values (Spearman’s rho = -0.41; P<0.0001; Figure 1A and B) and CSF Aβ42/40 and CL values (Spearman’s rho = -0.54; P<0.0001; Figure 1C and D). There was also a high correlation between CSF Aβ42 and CSF Aβ42/40 ratio (Spearman’s rho = -0.75; P<0.0001; Figure 1E). Prevalence of Aβ positivity using different Aβ classification approaches We next determined the prevalence of Aβ-positive and Aβ-negative participants according to each Aβ classification approach and, remarkably, we found a wide range of Aβ-positive prevalences ranging from 8.25 to 44.3% (Figure 2; Table 3). Approach 1 (i.e., CSF Aβ42) resulted in the highest prevalence of Aβ-positive participants (N = 163, 44.3%), followed by approach 2 (CSF Aβ42/40 ratio; Aβ-positive: N = 131, 35.6%), approach 3 (CL > 12; Aβ-positive: N = 49, 16.2%), approach 5 (VR; Aβ-positive: N = 40, 13.3%) and, finally, approach 4 (CL > 30; Aβ-positive: N = 25, 8.25%) (Figure 2). The prevalence of Aβ-positive participants in CSF-based classification approaches (approach 1 and 2) was significantly higher than that in PET-based classification approaches (approach 3, 4 and 5, P < 0.0001; Figure 2). Notably, approach 3 (CL > 12) showed a statistically significant higher prevalence of Aβ-positive participants compared to approach 4 (CL > 30, P = 0.043, Figure 2).

Figure 1. Correlation between Aβ classification approaches

Scatter plots representing the associations between each Aβ biomarker. Each point depicts the value of the Aβ biomarker of an individual. Green areas depict discordant subjects and percentage of them is shown in red. The red dashed lines indicate the Aβ biomarker cutoffs used in each Aβ classification approach: CSF Aβ42 = 1098 pg/ml, CSF Aβ42/40 = 0.071, CL = 12 and CL = 30. A, B. Correlation and comparison between CSF Aβ42 and CL; cutoff CL = 12 (A) and CL = 30 (B). C, D. Correlation and comparison between CSF Aβ42/40 and CL; cutoff CL = 12 (C) and CL = 30 (D). E. Correlation and comparison between CSF Aβ42 and CSF Aβ42/40. F. Comparison between CSF Aβ42 and VR. G. Comparison between CSF Aβ42/40 and VR. H, I. Comparison between VR and CL; cutoff CL = 12 (H) and CL = 30 (I). Abbreviations: Aβ, amyloid β; CL, Centiloids; CSF, Cerebrospinal fluid; PET, Positron Emission Tomography


Figure 2. Prevalence of Aβ-negative and Aβ-positive groups in each Aβ classification approach

The graph depicts the prevalence of the Aβ-positive (Aβ+; color) and Aβ-negative (Aβ-; grey) individuals in each Aβ classification approach. We conducted a pair wise chi squared comparison between all the approaches and we found significant statistical differences between them (P < 0.0001). Bonferroni-corrected pair-wise post hoc comparisons: * P<0.0001 vs approach 1 and approach 2¸† P<0.05 vs approach 3. Abbreviations: Aβ, amyloid-β; CL, Centiloids; CSF, Cerebrospinal fluid; PET, Positron Emission Tomography; VR, Visual Reads.


We assessed the differences between the CSF- and PET-defined Aβ-positive participants (i.e., CSF/PET-discordant participants) within each Aβ classification approach (shown in Table 3 and Figure 1). The participants that were classified as CSF Aβ-positive but PET Aβ-negative range from 19.8 to 37.0% (Table 3; green areas in Figure 1). The highest discordance occurred between Aβ classification approach 4 (CL < 30) and approach 1 (CSF Aβ42; 37.0% discordance; Figure 1B), and the lowest occurred between approach 3 (CL < 12) and approach 2 (CSF Aβ42/40; 19.8% discordance, Figure 1C). By contrast, fewer participants showed discordance in the other direction, i.e., a CSF Aβ-negative but a PET Aβ-positive biomarker (range: 0.33% to 2.99%; Figure 1; Table 3). Of note, the proportion of discordant participants was higher for CSF Aβ42 (approach 1) than for CSF Aβ42/40 ratio (approach 2) in all comparisons. Association of Aβ biomarkers with tau pathology and neurodegeneration CSF biomarkers We next assessed whether changes in tau pathology and neurodegeneration CSF biomarkers as a function of Aβ pathology differed before and after the computed Aβ cutoffs. We hypothesized that an optimal Aβ biomarker cutoff would discriminate between an Aβ-negative state (where Aβ pathology is not associated with downstream tau pathology and neurodegeneration changes) and an Aβ-positive state (where those downstream changes arise). For these purposes, we computed the association between each Aβ biomarker and CSF p-tau and t-tau in Aβ-negative and Aβ-positive groups defined by the different Aβ classification approaches. We also computed the interaction term ‘Aβ biomarker x Aβ group’. The results of the analyses are summarised in Table 4 and Figure 3. Remarkably, robust statistically significant interactions occurred using approach 2. In other words, the cutoff derived from the CSF Aβ42/40 ratio significantly discriminated between two states: (1) an Aβ-negative state where tau and neurodegeneration CSF biomarkers were not associated with CSF Aβ42/40 levels and (2) an Aβ-positive state where CSF p-tau and t-tau significantly increase as a function of higher Aβ pathology (i.e., decreased CSF Aβ42/40 ratio). In approach 1 (i.e., CSF Aβ42), we also observed statistical significance in the interaction term ‘Aβ biomarker x Aβ group’ for CSF p-tau and t-tau. Nevertheless, both CSF p-tau and t-tau were positively associated with CSF Aβ42 in the Aβ-negative group. None of the Aβ-positive and Aβ-negative groups defined with Aβ PET-based approaches differed in their association between CL scale and CSF p-tau and t-tau. However, there was a tendency to statistical significance in the interaction term ‘Aβ biomarker x Aβ group’ for CSF p-tau in approach 3 (i.e., CL > 12). Moreover, there was a significant association between CSF p-tau and t-tau with CL scale in the Aβ-positive group using approach 3 and approach 5 (i.e., VR).

Table 3. Contingency table with the percentage of agreement and discordance between the different Aβ classification approaches

Data are expressed as percentage (%) of participants in each category. Bold letters indicate discordance. We performed a chi-square test of independence to examine the relation between Aβ positivity and the Aβ classification approach used and we observed that the percentage of Aβ positivity was not equally distributed between Aβ-classification approaches X2 (4, N = 301) = 176, P < 0.0001. A Bonferroni corrected pair-wise post hoc comparisons showed significant differences between the approach 1 and approach 3, 4 and 5 (P < 0.0001)¸ between the approach 2 and approach 3, 4 and 5 (P < 0.0001), and between approach 3 and 4 (P < 0.05). Abbreviations: Aβ, amyloid-β; Aβ+, amyloid-β positive; Aβ-, amyloid-β negative; CL, Centiloids; VR, Visual Read.

Figure 3. Association of each Aβ biomarker with CSF p-tau and t-tau by Aβ classification approach

Scatter plots representing the associations of CSF p-tau and t-tau with each Aβ biomarker in Aβ-positive and Aβ-negative groups. Each point depicts the value of the CSF biomarker of an individual and the solid lines indicate the regression line for each group. The P-values shown for the ‘Aβ biomarker x Aβ group’ interaction were computed using a linear model adjusting for age and sex. All P-values are corrected for multiple comparisons using the FDR method. The dashed lines indicate the Aβ biomarker cutoffs used in each Aβ classification approach: CSF Aβ42 = 1098 pg/ml, CSF Aβ42/40 = 0.071, CL = 12 and CL = 30; Abbreviations: Aβ, amyloid β; CL, Centiloids; CSF, Cerebrospinal fluid; PET, Positron Emission Tomography; p-tau, phosphorylated tau; t-tau, total tau; VR, Visual Reads.


Table 4. CSF p-tau and t-tau associations with Aβ biomarkers by Aβ classification approach

CSF p-tau or t-tau were assessed by a linear model with the Aβ biomarker (CSF Aβ42, CSF Aβ42/40 or CL) as main effect and age and sex as covariates. This analysis was performed separately in Aβ-negative or Aβ-positive groups as defined by each approach. Moreover, we added the interaction term ‘Aβ biomarker x Aβ group’ in order to test statistical differences in the regression slopes between Aβ-negative and –positive groups. The standardized regression coefficients (β) and standard errors (SE) are depicted. P-values are corrected for multiple comparisons using FDR method. *Significant values; Abbreviations: Aβ42, amyloid-β 42; CL, Centiloids; p-tau, phosphorylated tau; t-tau, total tau; VR, Visual Read.



In this study, we compared five different approaches to classify cognitively unimpaired individuals as Aβ-positive or Aβ-negative, and we found considerable differences between them. Specifically, our results show that: (1) Aβ-positive prevalence vary from 8 to 44% depending on the approach used; (2) Aβ CSF-based approaches result in the highest prevalence of Aβ-positive subjects compared with PET-based approaches; (3) Discordant classification appears more frequently as a CSF Aβ-positive but PET Aβ-negative (19.8 – 37.0%), while only a few individuals were CSF Aβ-negative but PET Aβ-positive (0.33 – 2.99%); (4) In Aβ PET, the use of a lower CL cutoff (12 CL instead of 30 CL) increases the detection of Aβ-positive individuals based on CSF levels; (5) among all Aβ classification approaches assessed, the CSF Aβ42/40 ratio cutoff shows the greater concordance with Aβ PET imaging (using the 12 CL cutoff), and the most significant difference of regression slopes between the Aβ-positive and Aβ-negative groups in the association with Aβ downstream pathology (e.g., tau pathology and neurodegeneration).
The ATN classification is widely used in AD research to classify patients or study participants based on biomarker evidence of Aβ pathology (A), tau pathology (T) or neurodegeneration/neuronal injury (N) (7). Each biomarker group can be defined with different biomarkers. For Aβ pathology (A), the most accepted biomarkers are CSF Aβ42, the CSF Aβ42/40 ratio and Aβ PET.
In regard to CSF Aβ biomarkers, CSF Aβ42 has been the most widely used, and cutoffs have been established against Aβ PET (10,11). However, increasing evidence favors the use of CSF Aβ42/40 ratio to normalize both preanalytical and interindividual differences, which provides a higher reproducibility and specificity for AD pathology (13,14). We show that the CSF Aβ42 approach results in a higher number of Aβ-positive participants than any other approach. However, CSF Aβ42 approach also results in a higher frequency of discrepancies with the PET-based approaches compared to the CSF Aβ42/40 ratio approach. This observation is in line with the idea that the CSF Aβ42/40 ratio predicts more accurately Aβ PET than CSF Aβ42. Furthermore, a high concordance between Aβ PET VR and ratios of CSF biomarkers have been shown to best distinguish Aβ-positive from negative individuals in both cognitively unimpaired and impaired populations. The CSF p-tau/Aβ42 ratio has a high diagnostic clinical utility as its cutoff has been derived using Aβ PET VR concordance (10). However, we have not used this ratio in this setting because it represents a mixed pathology of Aβ and tau, and tau biomarkers become abnormal later in the disease stage. For CSF Aβ42, we used the cutoff of 1098 pg/ml, that was previously defined in a study where CSF Aβ42 was measured by a Roche Elecsys assay and a ROC curve was built in comparison against Aβ PET. This cutoff is also very similar to that obtained in the BioFINDER study (1100 pg/ml) which is anchored to a positive Aβ PET VR (11).
For CSF Aβ42/40, we rendered a cutoff with a different approach. Instead of comparing the CSF values with Aβ PET as the gold standard, we applied a data-driven approach, namely a Gaussian Mixture Modelling (GMM). This method allows to describe a normal range of CSF Aβ42/40 ratios in the non-pathological group and derive a cutoff defined as the mean minus 2 standard deviations of this Gaussian distribution. By definition, under this criterion, 97.7% of Aβ-negative subjects are expected to display high CSF Aβ42/40 values. In other words, this criterion is highly specific by design. We hypothesised that this method would be more sensitive to detect incipient Aβ pathology in cognitively unimpaired individuals than criteria based on discriminating asymptomatic from symptomatic AD populations. In fact, GMM has already been used to derive cohort-specific CSF Aβ cutoffs, showing a robust ability to discriminate normal and pathologic distributions of Aβ42 (29–31). Our results confirm the suitability of both using the CSF Aβ42/40 ratio and deriving its cutoff with a GMM method. In fact, approach 2 shows the lowest number of discordant values compared with any other approach. According with our hypothesis, we observed that this approach provides optimal discrimination between Aβ-negative participants, showing no associations with Aβ-downstream pathologies (as measured by CSF p-tau and t-tau), and Aβ-positive participants, which display significant association with them. This is shown by the fact that this approach results in a significant difference of regression slopes between the Aβ-positive and Aβ-negative groups in the association between CSF p-tau or t-tau as a function of Aβ pathology. In fact, visual inspection of Figure 4 shows that the CSF Aβ42/40 cutoff we have computed by GMM falls close to the intersection of the regression lines in the Aβ-positive and Aβ-negative groups. Remarkably, this result is not seen when using the CSF Aβ42 approach. In this approach, we found unexpected significant positive correlations between CSF Aβ42 and both CSF p-tau and t-tau in the Aβ-negative group. Without the Aβ40 normalization, CSF Aβ42 may unspecifically correlate with other proteins because of the abovementioned limitations in terms of interindividual differences (high and low Aβ producers) and preanalytical variability.
In relation to Aβ PET biomarkers, Aβ PET classification can be performed through VR, widely used in the clinical practice, or using a quantitative approach such as the CL scale. This scale is expressed as universal units and allows the comparison among different tracers and studies (18). Different cutoffs to categorize participants according to CL might be used, depending on the sample and on the study objectives. Optimal cutoffs for CL against VR typically fall within the range of 24 – 35 CL in clinical populations (25–27). However, these VR-based CL cutoffs might not be optimal to study the preclinical stage of the Alzheimer’s continuum, when the goal is to detect subtle Aβ accumulation. Therefore, here we compared three different PET-based approaches: CL > 12, which was established as a cutoff with an optimal agreement against the standard cutoff of CSF Aβ42 < 1098 pg/ml (20) and is expected to detect subtle early Aβ changes; CL > 30, a cutoff falling within the range proposed to robustly detect established Aβ pathology (19,20,25–27); and VR, expected to represent a cutoff closer to the clinical practice. As expected, a cutoff of CL > 12 showed a higher prevalence of Aβ-positive individuals than the other two PET-based approaches. The slopes of CSF p-tau or t-tau as a function of CL did not significantly differ between the Aβ-positive and Aβ-negative groups defined by the CL12 approach, although tendency to this direction is observed, especially for CSF p-tau. However, the CL12 approach allowed the detection of significant associations of CSF p-tau and t-tau in the Aβ-positive group, an association that is also observed with the VR approach, but not with the CL30 one. These results suggest that the CL12 approach may be detecting Aβ deposition that is already associated with increasing tau pathology and neurodegeneration biomarkers. On the contrary, classifying Aβ groups using CL > 30 as cutoff, resulted in the lowest prevalence of Aβ-positive individuals and did not allow the discrimination of changes in Aβ-downstream CSF biomarkers changes between the two Aβ-groups. This suggests that 30 CL is a late cutoff to apply in preclinical Alzheimer’s studies, especially in cohort such as ALFA+, comprised by individuals in the very earliest phase of the Alzheimer’s continuum. Aβ-positive subjects according to the CL30 criterion are probably in a more advanced stage of Aβ pathology. We did not find a significant association between either CSF t-tau or p-tau with CL in the Aβ-positive group defined by the CL30 approach. This might be explained by the reduced sample size in that Aβ-positive group (n = 25), but also because Aβ-pathology might start to plateau in this late stage of preclinical Alzheimer. Conversely, a positive association is observed in the Aβ-negative group, supporting the idea that CL30 is a late cutoff and that Aβ-downstream processes have arisen before this cutoff. Interestingly, the VR approach rendered an Aβ-positive prevalence closer to the CL12 approach than to the CL30 approach. Of note, the raters in our study are highly trained to detect early Aβ deposition, but may differ in other studies or in the clinical setting. Similarly to the CL30 approach, the VR approach does not discriminate between the associations of the CL scale with CSF t-tau or p-tau in the Aβ-positive and Aβ-negative groups, although in this case positive associations are found in the Aβ-positive group.
Finally, we found obvious differences between the CSF- and PET-based Aβ classification approaches. Our results show only a moderate correlation between Aβ CSF and Aβ PET, which is not surprising as they may reflect different aspects of Aβ pathology, i.e., soluble Aβ and fibrillar Aβ aggregates, respectively (1). Noteworthy, the proportion of discordant values that were CSF Aβ-positive but PET Aβ-negative was considerably higher than those that were CSF Aβ-negative but PET Aβ-positive. This is consistent with the idea that Aβ CSF biomarkers detect earlier stages of Aβ pathology than Aβ PET, that is Aβ dysregulation that precedes Aβ plaques formation (32). Furthermore, this earlier dysregulation seems to be already associated to changes in Aβ-downstream events (i.e., tau pathology and neurodegeneration), thus proving the importance of studying this very early stage.
Our study has some limitations: (1) it is a cross sectional study and we could not track the progression of Aβ biomarkers, which would be particularly relevant in the Aβ-discordant biomarkers individuals; (2) it is a comparative analysis between different approaches, lacking a gold standard, so conclusions on sensitivity and specificity for each approach cannot be drawn. However, since comparisons among Aβ positivity criteria are made in the same dataset, conclusions on the relative sensitivity to detect associations with downstream pathophysiological events are valid; (3) results might not be generalizable to other cohorts because ALFA+ is a very specific cohort aimed at studying the preclinical stage of the Alzheimer’s continuum.
Overall, in this study we show that, in the preclinical stage of the Alzheimer’s continuum, there are important differences in the prevalence and the characteristics of Aβ-positive and Aβ-negative study participants depending on the Aβ classification approach used for the dichotomization. This emphasizes the importance of considering the sample and the study aims when deriving cutoffs for participant dichotomization. This is particularly important when studying the preclinical Alzheimer’s ccontinuum stage, where the approaches usually used in symptomatic AD patients may be not suitable to detect subtle changes in Aβ pathology.

Acknowledgements: This publication is part of the ALFA study (ALzheimer and FAmilies). The authors would like to express their most sincere gratitude to the ALFA project participants and relatives without whom this research would have not been possible.

Collaborators of the ALFA study are: Annabella Beteta, Raffaele Cacciaglia, Alba Cañas, Carme Deulofeu, Irene Cumplido, Natalia Vilor Tejedor, Ruth Dominguez, Maria Emilio, Carles Falcon, Karine Fauria, Sherezade Fuentes, Laura Hernandez, Gema Huesa, Jordi Huguet, Paula Marne, Tania Menchón, Carolina Minguillon, Grégory Operto, Albina Polo, Sandra Pradas, Anna Soteras, and Marc Vilanova. The authors thank Roche Diagnostics International Ltd. for providing the kits to measure CSF biomarkers and GE Healthcare for kindly providing 18F-flutemetamol doses of ALFA+ study participants. ELECSYS, COBAS, and COBAS E are registered trademarks of Roche. The Elecsys β-Amyloid (1-42) CSF immunoassay in use is not a commercially available IVD assay. It is an assay that is currently under development and for investigational use only. The measuring range of the assay is 200 (lower technical limit) – 1700 pg/mL (upper technical limit). The performance of the assay beyond the upper technical limit has not been formally established. Therefore, use of values above the upper technical limit, which are provided based on an extrapolation of the calibration curve, is restricted to exploratory research purposes and is excluded for clinical decision making or for the derivation of medical decision points.

Funding: The project leading to these results has received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300004 and the Alzheimer’s Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa-17-519007). Additional support has been received from the Universities and Research Secretariat, Ministry of Business and Knowledge of the Catalan Government under the grant no. 2017-SGR-892. OGR is supported by the Spanish Ministry of Science, Innovation and Universities (FJCI-2017-33437). ASV is the recipient of an Instituto de Salud Carlos III Miguel Servet II fellowship (CP II 17/00029). EMAU is supported by the Spanish Ministry of Science, Innovation and Universities – Spanish State Research Agency (RYC2018-026053-I). JDG is supported by the Spanish Ministry of Science and Innovation (RYC-2013-13054). MSC received funding from the European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie action grant agreement No 752310, and currently receives funding from Instituto de Salud Carlos III (PI19/00155) and from the Spanish Ministry of Science, Innovation and Universities (Juan de la Cierva Incorporación grant IJC2018-037478-I). KB is supported by the Swedish Research Council (#2017-00915), the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615), the Swedish Alzheimer Foundation (#AF-742881), Hjärnfonden, Sweden (#FO2017-0243), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986), and European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236). HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018-02532), the European Research Council (#681712), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-720931), the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862), and the UK Dementia Research Institute at UCL.

Conflicts of Interest: JLM has served/serves as a consultant or at advisory boards for the following for-profit companies, or has given lectures in symposia sponsored by the following for-profit companies: Roche Diagnostics, Genentech, Novartis, Lundbeck, Oryzon, Biogen, Lilly, Janssen, Green Valley, MSD, Eisai, Alector, BioCross, GE Healthcare, ProMIS Neurosciences. JDG has given lectures in symposia sponsored by the following for-profit companies: General Electric, Philips and Biogen. KB has served as a consultant or at advisory boards for Abcam, Axon, Biogen, Lilly, MagQu, Novartis and Roche Diagnostics, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. HZ has served at scientific advisory boards for Denali, Roche Diagnostics, Wave, Samumed and CogRx, has given lectures in symposia sponsored by Fujirebio, Alzecure and Biogen, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, a GU Ventures-based platform company at the University of Gothenburg. GK is a full-time employee of Roche Diagnostics GmbH. MS is a full-time employee of Roche Diagnostics International Ltd. The remaining authors declare that they have no conflict of interest.

Ethical Standards: The ALFA+ study (ALFA-FPM-0311) was approved by the Independent Ethics Committee “Parc de Salut Mar,” Barcelona, and registered at (Identifier: NCT02485730). All participants signed the study’s informed consent form that had also been approved by the Independent Ethics Committee “Parc de Salut Mar,” Barcelona.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.


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F. Guerville1, P. De Souto Barreto1,2, I. Ader3, S. Andrieu2,4, L. Casteilla3, C. Dray5, N. Fazilleau1,6, S. Guyonnet1,2, D. Langin5,7, R. Liblau6, A. Parini5, P. Valet5, N. Vergnolle8, Y. Rolland1,2, B. Vellas1,2


1. Gerontopole of Toulouse, Institute of Ageing, Toulouse University Hospital (CHU Toulouse), F-31000 Toulouse, France; 2.  Inserm UMR1027, University of Toulouse III, F-31000 Toulouse, France; 3. STROMALab, University of Toulouse, CNRS ERL5311, EFS, ENVT, Inserm U1031, Université Paul Sabatier, F-31432 Toulouse, France; 4. Department of Epidemiology and Public Health, CHU Toulouse, Toulouse, France; 5. Institut des Maladies Métaboliques et Cardiovasculaires, Inserm UMR1048, University of Toulouse III, F-31000 Toulouse, France; 6. Centre de Physiopathologie de Toulouse Purpan, Inserm UMR1043, CNRS UMR5282, University of Toulouse III, F-31000 Toulouse, France; 7. Department of Medical Biochemistry, Toulouse University Hospital (CHU Toulouse), F-31000 Toulouse, France; 8. Institut de Recherche en Santé Digestive, Inserm UMR1220, INRA UMR1416, University of Toulouse III, F-31000 Toulouse, France

Corresponding Author: Florent Guerville, Institut du Vieillissement, Gérontopôle de Toulouse, 37 allée Jules Guesde, 31000 Toulouse, France. Email:, Tel: 0033561145664. Fax: 0033561145640

J Prev Alz Dis
Published online December 16, 2019,



The Geroscience aims at a better understanding of the biological processes of aging, to prevent and/or delay the onset of chronic diseases and disability as well as to reduce the severity of these adverse clinical outcomes. Geroscience thus open up new perspectives of care to live a healthy aging, that is to say without dependency. To date, life expectancy in healthy aging is not increasing as fast as lifespan. The identification of biomarkers of aging is critical to predict adverse outcomes during aging, to implement interventions to reduce them, and to monitor the response to these interventions. In this narrative review, we gathered information about biomarkers of aging under the perspective of Geroscience. Based on the current literature, for each hallmark of biological aging, we proposed a putative biomarker of healthy aging, chosen for their association with mortality, age-related chronic diseases, frailty and/or functional loss. We also discussed how they could be validated as useful predictive biomarkers.

Key words: Biomarkers, biological age, healthy aging, frailty, geroscience.


Geroscience from lifespan to healthy aging

The emerging field of Geroscience aims at a better understanding of the biological processes of aging, in order to reduce the burden of age-related diseases, slow functional decline and promote healthy aging (1–3). Human life expectancy remarkably increased worldwide during the past century and this rise is projected to continue (4). This is accompanied by an increasing prevalence of chronic diseases, including diabetes, cardio-vascular, neurodegenerative or kidney diseases and cancer, which share age as a common strong risk factor (5). Another critical challenge to societies is the amount of disability generated by these changes (6). Thus, healthy aging, the portion of life free of major chronic disease and disability, is not increasing to the same extent as lifespan. Indeed, recent increase in life expectancy is thought to be mainly due to prolonged survival with chronic disease(s) and/or disability, rather than to healthy aging. Therefore, the compression of comorbidity (7), i.e. delaying chronic diseases as close as possible to natural death, has become a major goal to achieve. Another major obstacle to increase healthy aging is the decline in physiological (including physical and cognitive) functions that occurs with aging, with a strong negative impact on quality of life, independency and survival. Functional decline may be a consequence of chronic diseases, but may also occur independently of them (8). Thus, delaying, minimizing or even preventing functional decline are also major aims for Geroscience.


The need for biomarkers of healthy aging

“If you cannot measure it, you cannot improve it”, stated William Thomson, the great Irish physicist better known as Lord Kelvin. Following this principle, the identification of biomarkers of healthy aging is critical to predict adverse outcomes in late life, to implement interventions aiming at increasing healthy aging, and monitor the response to these interventions.
We especially need biological biomarkers that could capture the inter-individual variability of biological processes of aging before it becomes clinically detectable. Indeed, interventions to promote healthy aging might be more effective in people at risk for functional decline than in those already engaged in the disability process (9,10) Targeting proper interventions on people at risk would also reduce unnecessary health care costs on healthy individuals. For clinical trials, risk stratification based on biology would also be helpful to reduce sample size and study time period, through selection of participants with a high risk of clinical adverse outcomes. Furthermore, research on the biology of aging is probably more likely to identify shared molecular and cellular mechanisms of multiple age-related diseases and functional loss, thereby paving the way to targeted and personalized interventions (1,2,11).
One of the difficulties in identifying biomarkers of aging is that there is no consensus about an operational definition of biological aging. The American Federation of Aging Research (AFAR) defined 3 criteria that a biomarker of aging should ideally meet: mark the individual stage of aging and predict mortality better than chronological age; monitor aging in a range of systems and not the effects of diseases; and allow longitudinal non-invasive tracking in animals and humans (12). Then, which event(s) should be predicted by an ideal biomarker or set of biomarkers? Death is obviously a significant outcome, but can be preceded by a long period of multi-morbidity and disability, so time-to-death per se is not a relevant outcome for a biomarker of healthy aging. Age-related diseases are to be considered but this disease-centered approach may focus research on a specific organ or on one limited physiological system. Frailty, conceptually defined as an age-associated state of increased vulnerability to stressors, can be considered as a clinical metric of biological aging. Indeed, operational definitions of frailty were widely validated as predictive of hospitalizations, disability, and death (13). There is also growing interest in measuring intrinsic capacity, a composite of all the physical and mental capacities of an individual (14), as a key determinant of functional ability.


Bibliography methodological approch

For this narrative review, our search for putative biomarkers of healthy aging was based on the following criteria:
(I) In the absence of a consensual operational definition of biological aging, we searched for biomarkers associated with survival, several aging-related diseases, frailty and/or functional loss.
(II) Putative biomarkers should have been studied in humans. Whenever available, animal data were also considered.
(III) To cover the main domains of aging biology, we chose to report at least one putative biomarker of healthy aging for each of the nine hallmarks of aging proposed by Lopez-Otin et al. (15): genomic instability, telomere attrition, epigenetic changes, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem-cell exhaustion and altered intercellular communication. In Lopez-Otin et al.’s review, which was focused on mammals, each hallmark should ideally fulfill the following criteria: it should manifest during normal aging, its experimental aggravation should accelerate aging and its experimental amelioration should delay the normal aging process and thus increase healthy aging. Thus, there is causal evidence for the implication of these biological mechanisms in the aging process, and associated therapeutic potential.
(IV) In a feasibility purpose, we chose only non-invasive biomarkers.
(V) In a discovery purpose, we focused on the literature published after the review by Lopez-Otin et al. (2013).
The search was performed on PubMed in April 2019 using the following terms: “biomarker” and (“aging” or “frailty” or “functional decline” or “genomic instability” or “telomere attrition”, or “epigenetic changes” or “loss of proteostasis” or “deregulated nutrient sensing” or “mitochondrial dysfunction” or “cellular senescence” or “stem-cell exhaustion” or “inflammaging”). The nine latter keywords were selected based on the nine hallmarks of aging proposed by Lopez-Otin et al. (15).
We selected biomarkers that fulfilled criteria (I) (association with at least one cited outcome), (II), (III) and (IV). Criterion (V) was optional, because we found relevant literature published before 2013.


Putative biomarkers of healthy aging

The results of our search are summarized in the Table. Only blood-based biomarkers met our selection criteria. We present below putative biomarkers for each hallmark of aging.

Table 1. Putative biomarkers of healthy aging

Table 1. Putative biomarkers of healthy aging

MCI, mild cognitive impairment. GWAS, genome-wide association studies

Genomic instability: Micronucleus assay

Genetic damage accumulates with aging, due to extrinsic and intrinsic factors, and genomic instability results from the imbalance between DNA damage and repair (16,17). Chromosome damage can be assessed with the micronucleus assay, which measures chromosome loss and breakage (18). Micronuclei are formed from chromosome fragments or whole chromosomes left out during cell division. From a minimum of 2000 cells, the percentage of micro-nucleated cells is measured via automatic microscope scoring and reviewed by an experienced scorer (19). Due to their non-invasive availability, peripheral blood and exfoliated buccal cells are the preferred material for this assay. The percentage of micro-nucleated cells increases with age, cancer, neurodegenerative diseases, tobacco use, and decreases with fruit consumption (20,21).
In 257 persons aged 65 and older from Galicia (Spain), Sanchez-Flores et al. recently reported a cross-sectional association between frailty and the micronucleus assay performed in peripheral blood lymphocytes (22). Interestingly, in this study, a higher micronucleus frequency was associated with 4 over 5 criteria of Fried frailty phenotype (except unintentional weight loss), with malnutrition or risk of malnutrition according to the Mini Nutritional Assessment score and with cognitive impairment according to the Mini-Mental Status Examination score. Longitudinal studies are required to validate the micronucleus assay as a healthy aging biomarker. In animals, the micronucleus assay has been widely used as a genotoxicity test (23), but not as a biomarker of healthy aging.

Telomere attrition

Some chromosome regions are particularly susceptible to age-related damage: telomeres are repetitive DNA sequences capping chromosomes, which shortens every time cells divide. It is probably the most studied hallmark of aging, with more than 8000 publications referenced in PubMed to date. The two main historical methods used to measure telomere length are the Southern blot (measuring the size of enzymatically-cleaved telomere fragments (24) and the quantitative polymerase chain reaction (qPCR), which reports a telomere/single copy gene signals ratio (25).
In a recent meta-analysis of twenty-five studies (n=121749, 21763 deaths) telomere attrition was predictive of all-cause mortality: subjects with telomere length in the lowest quartile had a 26% (95% CI 15-38%) higher hazard of death (26). The relation with frailty is less clear: in a meta-analysis of nine studies (n=10079 older subjects), Araujo-Carvalho et al. reported a borderline positive association between telomere attrition and Fried frailty phenotype (standard mean difference -0.56, 95% IC -1.12 to 0.00) and a statistically significant but weak positive association between telomere attrition and frailty index (standard mean difference 0.06; 95% IC -0.10 to -0.01) (27). The authors concluded that telomere length may not be a meaningful biomarker for frailty.
Nevertheless, attrition is not the only telomere modification observed during aging. Indeed, data from human and mice suggest a contribution of telomere damage to lung and cardiomyocyte aging, independently of telomere length (28,29). Interestingly, these works highlight molecular links between several hallmarks of aging: telomere damage is driven by mitochondrial dysfunction (through reactive oxygen species) and contributes to cellular senescence. Further investigations are needed to assess if telomere damage, detected noninvasively, could predict health outcomes during aging.

Epigenetic alterations: DNA methylation clocks

Changes in DNA sequence are not the only age-related genomic alterations. Epigenetic modifications such as DNA methylation, histone modification, chromatin remodeling, that influence gene expression, are also features of aging (15). Among them, changes in methylation of CpG islets are major regulators of gene expression. Based on these changes, relatively constant between individuals, several groups identified “DNA methylation clocks” that accurately predicts the chronological age of the donor (30,31). The clock by Hannum et al., developed from whole blood DNA and measuring methylation fraction of 25000 CpG islets, has a correlation coefficient with age >0.9 and an average error in age prediction <5 years. Nevertheless, a healthy aging biomarker should measure biological age rather than chronological age. Interestingly, DNA methylation clocks are considered as hybrid measurement, involving both chronological and biological elements (32).
Indeed, biological age may be reflected by the difference between true chronological age and DNA methylation age (i.e. age predicted by a DNA methylation clock). In four cohorts of older persons from Scotland and USA (n=4658), this difference (Δage) was found predictive of mortality: a 5-year Δage was associated with a 16% increase in mortality risk, independently of age, education, social class and comorbidity (33). A simpler score, based on methylation of only 10 CpG sites, was also reported predictive of all-cause, cardio-vascular and cancer mortality in two independent cohorts (34). Furthermore, in 1091 septuagenarians participating in one of the Scottish cohort cited above (LBC1936), Marioni et al. reported a cross-sectional negative association between Δage, cognition (6 tests from the Wechsler Adult Intelligence Scale-III) and physical function (grip strength) (35). Nevertheless, neither Δage, nor its longitudinal change, were found predictive of cognitive or physical decline.

Loss of proteostasis: Clusterin

Intracellular protein homeostasis, or proteostasis, is maintained through several quality control mechanisms: protein refolding by chaperone proteins and degradation by the ubiquitin-proteasome system or lysosomal pathways (autophagy). Due to cellular stress increasing protein misfolding, and/or failure of quality control mechanisms, aggregation of misfolded proteins are features of aging and age-related diseases, such as Alzheimer’s (36, 37)
The soluble form of Clusterin (sCLU, also known as Apolipoprotein J) protects from protein aggregation and precipitation (38). Using different techniques, several groups reported associations between Clusterin and age-related diseases.
Using ultracentrifugation or gel filtration, Riwanto et al. isolated serum HDL-associated Clusterin and reported a decreased level in patients with coronary artery disease compared to healthy controls from Switzerland (39). In an elegant biomarker discovery report, Thambisetty et al. provided more insight about the potential role of Clusterin in Alzheimer’s disease (40). In the discovery phase of the study, proteomic analyses revealed a positive association between serum Clusterin and (a) hippocampal atrophy measured with MRI in 44 subjects with mild cognitive impairment (MCI) or mild to moderate AD from the KCL-ART study (London), and (b) disease progression speed according to the clinical ADAS-cog scale in 51 AD patients from the AddNeuroMed European cohort. In the validation phase, serum Clusterin (as measured by an ELISA technique) was positively associated with atrophy of the entorhinal cortex (as measured with MRI), severity of cognitive impairment and speed of progression in AD (as measured with MMSE before or after blood sampling) in 689 participants of the KCL-ART or the AddNeuroMed study. Furthermore, in 60 non-demented participants of the Baltimore Longitudinal Study of Aging, serum Clusterin was positively associated with fibrillar amyloid burden in the entorhinal cortex, as measured with PET imaging 10 years after blood sampling. Finally, in a mouse model of AD, serum Clusterin was higher than in wild-type mice, cortical plaques contained both Amyloid-β protein and Clusterin, and the cortical loads of the 2 proteins were highly positively correlated.
Using an APO multiplex bead fluorescence immunoassay technique in 664 participants (257 with MCI) of the Sydney Memory and Aging Study, Song et al. reported higher levels of serum Clusterin/APOJ in subjects with MCI, and a negative correlation between APOJ levels and cognitive scores (41).
In two genome-wide association studies (>14000 people in France, Belgium, Italy, Finland and Spain and 16000 people in UK, Germany and USA), polymorphisms in the clusterin gene were found strongly associated with Alzheimer’s disease (AD), as was the well-established susceptibility locus APOE (42,43).
However, given the opposite direction of associations between serum Clusterin and coronary artery disease and AD, further research is needed to determine if Clusterin could be a biomarker of healthy aging.

Deregulated nutrient sensing: Sirtuins

Mammals’ somatotrophic axis comprises the growth hormone and the insulin-like growth factor (IGF-1), which shares downstream intracellular pathway with insulin, thereby signaling nutrient abundance and anabolism. Decline in this axis is one of the major features of metabolic aging (44). Besides the insulin and IGF-1 signaling pathway, sirtuins are other nutrient sensors with an opposite effect: they signal nutrient scarcity and catabolism. Thus, activation of sirtuins mimics calorie restriction and improves lifespan and health in animals (45).
Performing RT-PCR on whole blood cells from 350 community-dwellers participating to the Toledo Study for Healthy Aging, El Assar et al. recently tested the association between the transcription of 21 genes involved in response to stress and malnutrition risk assessed with the Mini-Nutritional Assessment score. The expression of sirt1, coding for sirtuin-1, was negatively associated with malnutrition risk, independently of age, comorbidity, frailty and diet (46). No associations were found between other genes and malnutrition risk. In addition, sirt1 plays a central role in survival and regeneration of skeletal muscle cells, as reviewed by Sharples et al. (47).
Sirtuin-1 was originally described as a nuclear protein, but was more recently reported detectable in human serum using ELISA, surface plasmon resonance and Western blot (48). In this first study, lower serum sirtuin-1 levels were found in healthy older (n=22) individuals and in MCI (n=9) or AD (n=40) patients than in young controls (n=22). In 200 Indian outpatients of a Geriatric Medicine Department, the same group reported lower serums sirtuins 1, 2 and 3 (as measured with surface plasmon resonance and Western blot) as independently associated with Fried frailty phenotype. A better diagnostic accuracy was found for sirtuin-1 (receiver operating characteristic’s area under curve = 0.9) (49). Despite external replication of the detection of sirtuin-1 in human serum (50), it is still unknown how and why this nuclear protein is released in the extracellular compartment.

Mitochondrial dysfunction: Growth Differentiation Factor 15 and Apelin

Human aging is generally linked to a progressive mitochondrial dysfunction (51). Among the important parameters involved in this dysfunction, the decrease in the efficacy of the respiratory chain observed in aging is characterized by increased reactive oxygen species (ROS) production, mitochondrial integrity defects and reduced mitochondrial biogenesis (controlled, among others, by sirtuins). Nevertheless, higher mitochondrial oxidative stress increases lifespan in rodents. These paradoxical effects of ROS on aging can be harmonized if their production is seen as a stress-compensatory mechanism to maintain survival, which becomes detrimental if excessive and sustained (52).
Growth differentiation factor 15 (GDF-15) is a stress-induced cytokine and member of the transforming growth factor β superfamily. GDF-15 has emerged as a biomarker of cellular stress than can be produced by a number of organs such as lung, kidney and liver (53). It is also considered as a diagnostic marker for inherited mitochondrial diseases, and potentially as a marker of mitochondrial dysfunction (54). GDF-15 has negative effects on appetite and weight in mice and is associated with weight loss in patients with cancer (55). Furthermore, its overexpression increases lifespan in mice, especially on a high-fat diet (56). In two Swedish cohorts (n=1200), higher GDF-15 serum levels (measured by ELISA) was associated with cardio-vascular, cancer and all-cause 5- and 12-year mortality, independently of telomere length, IL-6 and CRP (57). Measured by an immunoradiometric assay in frozen plasma in 1000 septuagenarians participants of the PIVUS study (Sweden), longitudinal increase in GDF-15 levels was associated with a 4-fold increase in the 5-years mortality hazard (58). Finally, in 1037 non demented community-dwellers >70 yo participants to the Sydney Memory and Aging Study, higher serum GDF-15 (measured by ELISA) was associated with MCI/dementia incidence, independently of cardiovascular comorbidity, APOE genotype and inflammation parameters (59). Even if expression and secretion of GDF-15 are increased in response to deterioration of energy metabolism in a cellular model of mitochondrial disease (54), the physiological link between GDF-15 and mitochondrial dysfunction, especially during aging, remains to be determined.
Recent findings suggest that apelin, an exercise-induced myokine, may also be considered as a putative biomarker of healthy aging related to mitochondrial dysfunction (60). Among 61 participants of the French MAPT study aged 70 and older, baseline serum apelin (measured with ELISA) was positively associated with muscle mass (measured using dual energy X-ray absorptiometry), independently of age, sex and BMI. Moreover, increase of serum apelin over 6 month was positively correlated to physical function improvement (SPPB score) in 34 participants >70 yo of the physical activity LIFE-P trial. In the same work, apelin production by muscle declined with aging in mice while sarcopenia was exacerbated in apelin-deficient mice and was reversed by apelin supplementation or overexpression. In those experiments, apelin enhanced muscle function through mitochondriogenesis, but also other pathways related to hallmarks of aging: autophagy, inflammation and muscle stem cells. It remains to be determined whether apelin could predict other outcomes than sarcopenia and response to exercise, such as Alzheimer’s disease (61), and could be considered as a broader biomarker of healthy aging.

Cellular senescence: p16Ink4A

Cellular senescence is a state of stable arrest of the cell cycle coupled to phenotypic changes, including the production of several molecules (especially matrix metalloproteases and pro-inflammatory cytokines) collectively known as the senescence-associated secretory phenotype (SASP) (62). The SASP mediates senescence spreading to adjacent cells, inflammation, and tissue dysfunction. Seen as a compensatory mechanism aimed at avoiding proliferation of damaged cells, cellular senescence is induced by age-associated stimuli: telomere attrition, DNA damage and excessive mitogenic signaling, particularly by the p16Ink4a tumor suppressor protein, upon epigenetic de-repression of the ink4/ark locus (63).
p16Ink4A positively correlates with age in various tissues in mice and in human skin (64,65). Measured by RT-PCR in peripheral blood T lymphocytes from 170 donors of 2 independent US cohorts, the transcription of p16Ink4a was positively associated with age, tobacco use and physical inactivity (66). Moreover, in a meta-analysis of 372 GWAS studies aiming at identifying susceptibility polymorphisms for age-associated diseases, the ink4/ark locus was linked to the highest number of diseases, including Alzheimer’s, cardio-vascular diseases, cancer and type 2 diabetes (67).
To our knowledge, an association between a marker of cellular senescence and functional loss, frailty or aging phenotype has not yet been reported. As recently suggested, a set of biomarkers would be more efficient to capture the accumulation of senescent cells during aging (68). Given the central role of the SASP in consequences of cellular senescence, a systemic measurement of key components of the SASP in an available sample (like blood) would be, if associated with functional loss or an aging phenotype, an interesting biomarker of healthy aging. In view of the association between senescent cells accumulation and several age-associated diseases, removing senescent cells from tissues is a promising pharmacological target (69).

Stem cell exhaustion: Circulating osteogenic progenitors

The repair and regenerative potential of many tissues declines with aging, due to functional attrition in several stem cell compartments (e.g. hematopoietic, neural, mesenchymal and intestinal epithelial stem cells, as well as satellite cells in muscles). Adult stem cells are present in every tissues and organs after development and regenerate damaged tissues throughout life. During aging, the function of stem cells decline (70). Stem cell exhaustion is seen as an integrative consequence of several hallmarks of aging described above, including DNA damage, epigenetic alterations, telomere shortening, cellular senescence and mitochondrial dysfunction (15).
However, stem cell exhaustion is difficult to measure non-invasively before the onset of its clinical consequences, such as anemia and other cytopenias for hematopoietic stem cells, but also sarcopenia for muscle stem cells/satellite cells, and decreased intestinal function for intestinal epithelial stem cells. So far, data is scarce on potential biomarkers for this hallmark of aging. Circulating osteogenic progenitors (COP) cells were proposed as a surrogate marker of the mesenchymal stem cell population within the bone marrow (71). Their ability to differentiate, not only into bone, but other mesenchymal tissues, including muscle, offers perspectives in regenerative medicine for musculoskeletal diseases (72). In 77 participants of the Nepean osteoporosis and Frailty study older than 65 yo, the proportion of COP cells among peripheral blood mononuclear cells was measured using flow cytometry, as double positive cells for CD45 (an hematopoietic marker) and osteocalcin (a marker of bone formation). COP cell percentage was inversely correlated with age. Lower COP cell percentages were associated with frailty, lower physical performance (measured by grip strength and gait speed) and disability, independently of age and comorbidity (73). Nevertheless, there is currently no consensual phenotype to specifically identify these cells in blood (72) and no longitudinal associations with lifespan or healthy aging have so far been reported.

Altered intercellular communication: Inflammasomes and IMM-AGE score

Aging is associated with changes in communications between cells, mainly driven by a chronic low-grade systemic inflammation named inflammaging (15,74). This inflammation is seen as a consequence of several hallmarks of aging described above, including cellular senescence (through the SASP) and loss of proteostasis, because misfolded proteins constitute a danger signal that triggers the innate immune response (75). A large body of literature links inflammaging to age-associated diseases, functional decline, and frailty (76).
One of the major pathways of inflammaging is the inflammasome pathway. Firstly described in innate immune cells (77), the inflammasome describes a complex system of intracellular proteins that assembly upon detection of stress/danger signals and trigger maturation and release of pro-inflammatory cytokines (namely interleukin-1β and interleukin-18). Mouse models lacking the NLRP3 inflammasome exhibit less inflammaging, glucose intolerance, hippocampal degenerescence, neuroinflammation, cognitive and physical decline (78). In participants of the Stanford-Ellison cohort aged 60 to >90 yo, inflammasome activation (measured by nlcrc4 and nlrc5 genes expression in whole blood cells and interleukin-1β production) was positively associated with hypertension and arterial stiffness and negatively associated with personal and familial longevity (79). As cholesterol crystals and β-amyloid proteins can trigger assembly of inflammasome complexes, this pathway is involved in atherosclerosis lesion progression (80) and neuro-inflammation in AD (81,82). Thus, inflammasome inhibitors are promising drugs in age-related diseases (83–85).
Beyond inflammaging, immunosenescence encompasses quantitative and functional changes of multiple actors of both the innate and adaptive arms of the immune system (86). Immunosenescence may aggravate the aging process related to hallmarks of aging described above, notably because of the failure to eliminate pathogens, but also pre-malignant cells, senescent cells and misfolded proteins (15,75). Using an integrative and longitudinal “multi-omics” approach from peripheral blood, Alpert et al. recently captured the immune system trajectories in 135 healthy older individuals (87). Moreover, they derived a simplified “IMM-AGE” score based on baseline expression of 57 immune genes that predicted all-cause mortality over 7 years, independently of cardio-vascular risk factors and disease, in >2000 participants of the Framingham Heart Study. Survival was far more significantly associated with the IMM-AGE score than with the DNA methylation age in the same population. This work provides major contributions, especially regarding inter-individual variability of immunosenescence trajectories and their prognostic value.


Conclusions and perspectives

To date, no (set of) biomarker(s) has been reported to fulfill ideal criteria for biomarkers of healthy aging: measuring aging in a range of systems, non-invasively in humans and animals, predicting mortality, age-related diseases and loss of functions. Nevertheless, we report here several putative blood biomarkers that were shown predictive of mortality and/or associated with age-related chronic diseases and/or functional decline. Some of them (e.g. DNA methylation clocks) were externally validated, but most of them were not. Above all, associations between these putative biomarkers and frailty or loss of functions are mostly cross-sectional. Therefore, there is a major need for longitudinal studies with repeated measures of physical and mental functions in participants of a wide range of age and health status. Especially, cohorts including middle-aged persons would allow the identification of early biomarkers of healthy aging, whereas such biomarkers could be missed in studies focusing on older people, due to selection bias. Longitudinal assessment of putative biomarkers would also allow studying their dynamic. This would certainly provide more insight in the biological processes of aging and their heterogeneity across individuals (87).
Giving the complexity of the aging process, the probability that a single biomarker will ever meet those ideal criteria seems very low. At the opposite, the availability of “omics” approaches now allows hypothesis-free identification of potential biomarkers, not only among genes, transcripts and proteins, but also among non-coding RNA and metabolites (88–90). How to integrate, with a physiological perspective, hypothesis-driven approaches focused on a single biological pathway and multi-omics approaches is probably one of the major challenge for future research on biomarkers of healthy aging. In that purpose, artificial intelligence has already been used to provide biological age prediction tools and its convergence with Geroscience is expected to grow (91). Another major, often underestimated, challenge in biomarker development is to meet the standards for widespread use in laboratory medicine (92,93).
We chose to focus on the nine hallmarks of aging proposed by Lopez-Otin et al. (15), but new hallmarks may emerge. For example, all the studies described above concentrated their efforts on investigating host biomarkers of healthy aging. A growing field related to the identification of microbial strains (bacteria, virus, parasites, fungi) could soon add more candidates to the list of possible healthy aging biomarkers. Most work thus far has been rather descriptive. Gut microbiota dysbiosis has been associated with a number of diseases, but also with aging (94,95). Recent studies using turquoise killifish demonstrated that transfer of the gut microbiome from young to middle-aged killifish resulted in an increase in lifespan and a delayed behavioral decline, compared to fish that received the microbiota from middle-aged fish (96). The composition of human gut microbiome changes heavily from one individual to another and is also sensitive to many environmental factors, including diet or medication, which are important actors in aging individuals. Further experimental and clinical studies are needed to explore the role of microbiome (from the gut, but also potentially from the skin or the mouth) in aging, and to identify microbial healthy aging markers. This is a completely unexplored domain for the moment, which could well complement the search of host healthy aging biomarkers.
The use of animal models that age faster than humans and are more suitable for experimental modification of biologic pathways or life conditions is essential for biomarker discovery and validation. The five main model organisms used in aging-related research are budding yeast Saccharomyces cerevisiae, nematodes Caenorhabditis elegans, fruit flies Drosophila melanogaster, fishes Nothobranchius furzeri and laboratory mice Mus musculus. Numerous studies on these different animal models have identified several orthologous genes that modulate longevity in the same way over a long evolutionary distance (97). Of note, some vertebrates, like the African Killifish, age even faster than mice and are useful models to study the biology of aging (11). Each of these species has its limits and strengths as a model for human aging, and it is important to consider the way they look alike but also how they differ in physiology, longevity, and aging traits.
Efforts are needed to reduce differences between animal lab life and human real life. Indeed, lab animals usually have a homogeneous or even identical genetic background and live in pathogen-controlled conditions. Working on inbred or outbred mice to study biological processes of aging remains an open question (98). Their food intake has often been modified to influence the aging process (99), contrarily to their physical activity, despite possible effects on healthy aging (100). Finally, the development of preclinical models of frailty is an extremely important step in Geroscience research. Despite efforts in this direction in mice (101–103), translational studies on the mechanisms of aging in animals and humans have yet to be conducted.
Several non-pharmacological interventions, including diet and exercise, may influence lifespan and healthy aging through effects on several hallmarks of aging (99,104–106). It will be useful to take those parameters into account in human and animal studies designed to discover and validate biomarkers of healthy aging. A better understanding of the biology of aging also paves the way to potentially promising pharmacological interventions linked to several hallmarks of aging, including senolytics (69), inflammasome inhibitors (84), metformin (107), rapamycin (108), resveratrol  (109,110) and mesenchymal stem cells (111). One could argue that improving the detection of frailty and functional loss and the implementation of personalized non-pharmacological interventions is more likely to increase healthy aging in populations than new or repurposed drugs (112). Biomarkers of healthy aging could nevertheless become useful as complementary tools to stratify the risk of functional loss and to monitor response to lifestyle interventions.


Conflict of interest/disclosures:  The authors have no conflict of interest to disclose.

Funding: For this work, Florent Guerville received a grant (Bourse de Mobilité) from Bordeaux University Hospital.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.



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S.C. Burnham1, P.M. Coloma2, Q.-X. Li3, S. Collins4, G. Savage5, S. Laws6,7, J. Doecke8, P. Maruff9, R.N. Martins6,10, D. Ames11, C.C. Rowe12,13, C.L. Masters3, V.L. Villemagne3,12,13


1. CSIRO Health & Biosecurity, Parkville, Victoria, Australia; 2. Personalised Health Care – Data Science, F. Hoffmann-La Roche Ltd, Basel, Switzerland; 3. Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia; 4. Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia; 5. ARC Centre of Excellence in Cognition and its Disorders (CCD) and Department of Psychology, Macquarie University, Sydney, New South Wales, Australia; 6. School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia; 7. School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Western Australia, Australia; 8. CSIRO Health & Biosecurity, Herston, Queensland, Australia; 9. Cogstate Ltd, Melbourne, Victoria, Australia; 10. Department of Biomedical Sciences, Macquarie University, Sydney, New South Wales, Australia; 11. National Ageing Research Institute (NARI), The University of Melbourne, Parkville, Victoria, Australia; 12. Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia; 13. Department of Medicine, Austin Health, Heidelberg, Victoria, Australia

Corresponding Author: Samantha C. Burnham, CSIRO, 343 Royal Parade, Parkville, VIC 3052, Australia, Email:, Tel.: +61399627162

J Prev Alz Dis
Published online May 17, 2019,



BACKGROUND: The National Institute on Aging and Alzheimer’s Association (NIA-AA) have proposed a new Research Framework: Towards a biological definition of Alzheimer’s disease, which uses a three-biomarker construct: Aß-amyloid, tau and neurodegeneration AT(N), to generate a biomarker based definition of Alzheimer’s disease.
OBJECTIVES: To stratify AIBL participants using the new NIA-AA Research Framework using cerebrospinal fluid (CSF) biomarkers. To evaluate the clinical and cognitive profiles of the different groups resultant from the AT(N) stratification. To compare the findings to those that result from stratification using two-biomarker construct criteria (AT and/or A(N)).
DESIGN: Individuals were classified as being positive or negative for each of the A, T, and (N) categories and then assigned to the appropriate AT(N) combinatorial group: A-T-(N)-; A+T-(N)-; A+T+(N)-; A+T-(N)+; A+T+(N)+; A-T+(N)-; A-T-(N)+; A-T+(N)+. In line with the NIA-AA research framework, these eight AT(N) groups were then collapsed into four main groups of interest (normal AD biomarkers, AD pathologic change, AD and non-AD pathologic change) and the respective clinical and cognitive trajectories over 4.5 years for each group were assessed. In two sensitivity analyses the methods were replicated after assigning individuals to four groups based on being positive or negative for AT biomarkers as well as A(N) biomarkers.
SETTING: Two study centers in Melbourne (Victoria) and Perth (Western Australia), Australia recruited MCI individuals and individuals with AD from primary care physicians or tertiary memory disorder clinics. Cognitively healthy, elderly NCs were recruited through advertisement or via spouses of participants in the study.
PARTICIPANTS: One-hundred and forty NC, 33 MCI participants, and 27 participants with AD from the AIBL study who had undergone CSF evaluation using Elecsys® assays.
INTERVENTION (if any): Not applicable.
MEASUREMENTS: Three CSF biomarkers, namely amyloid β1-42, phosphorylated tau181, and total tau, were measured to provide the AT(N) classifications. Clinical and cognitive trajectories were evaluated using the AIBL Preclinical Alzheimer Cognitive Composite (AIBL-PACC), a verbal episodic memory composite, an executive function composite, California Verbal Learning Test – Second Edition; Long-Delay Free Recall, Mini-Mental State Examination, and Clinical Dementia Rating Sum of Boxes scores.
RESULTS: Thirty-eight percent of the elderly NCs had no evidence of abnormal AD biomarkers, whereas 33% had biomarker levels consistent with AD or AD pathologic change, and 29% had evidence of non-AD biomarker change. Among NC participants, those with biomarker evidence of AD pathology tended to perform worse on cognitive outcome assessments than other biomarker groups. Approximately three in four participants with MCI or AD had biomarker levels consistent with the research framework’s definition of AD or AD pathologic change. For MCI participants, a decrease in AIBL-PACC scores was observed with increasing abnormal biomarkers; and increased abnormal biomarkers were also associated with increased rates of decline across some cognitive measures.
CONCLUSIONS: Increasing biomarker abnormality appears to be associated with worse cognitive trajectories. The implementation of biomarker classifications could help better characterize prognosis in clinical practice and identify those at-risk individuals more likely to clinically progress, for their inclusion in future therapeutic trials.

Key words: Alzheimer’s disease, biomarkers, progression, longitudinal.



Alzheimer’s disease (AD) is a progressive, neurodegenerative disease characterized by neurodegeneration, synaptic loss, and the accumulation of extracellular-amyloid plaques and tau intracellular neurofibrillary tangles (1, 2). Several key imaging and cerebrospinal fluid (CSF) biomarkers have been identified in AD (3, 4). Deposition of beta-amyloid (Aβ-amyloid) plaques is one of the most important pathologic hallmarks of AD and is widely thought to be the initiating and primary driver of disease (amyloid hypothesis) (5, 6). Measures of Aβ-amyloid include amyloid imaging with positron emission tomography (PET) as well as CSF Aβ1-42, and studies have shown that these markers may be detectable over a decade before symptom onset (6, 7). Neurodegeneration and synaptic loss are also apparent prior to symptom onset, and may be visible on brain magnetic resonance imaging (MRI) as structural atrophy in regions consistent with AD (3). Other methods of assessing neurodegeneration include fluorodeoxyglucose [FDG]-PET, which measures brain metabolism as an indicator of synaptic activity (8, 9) and CSF total tau (t-tau), which is also indicative of synaptic loss and neurodegeneration (4, 10). Finally, tau pathology may be assessed using tau PET or CSF phosphorylated tau (p-tau), which has shown utility for predicting progression from mild cognitive impairment (MCI) to AD dementia as well as differentiating AD from other forms of dementia (3, 4, 11, 12).
Based on these biomarkers of Aβ-amyloid (CSF Aβ1–42), neurodegeneration (t-tau) and tau pathology (p-tau), various constructs have been developed to accurately identify individuals in the earliest (pre-symptomatic) stages of disease who are likely to progress to MCI and AD. Initial diagnostic research criteria developed by the National Institute on Aging and Alzheimer’s Association (NIA-AA) classified individuals with evidence of Aβ-amyloid pathology (i.e., abnormal Aβ-amyloid PET and CSF Aβ-amyloid) into three stages of preclinical AD based on the presence or absence of markers of neuronal injury (i.e., FDG-PET, structural MRI, or measures of tau) and evidence of subtle cognitive change (13). The criteria were further expanded to include two additional categories for cognitively normal individuals, including those with no biomarkers of AD (i.e., normal Aβ-amyloid, neurodegeneration, and tau) and those without evidence of Aβ-amyloid pathology but who are positive for other markers of neuronal injury, also referred to as suspected non-AD pathophysiology (SNAP) (14). These classifications were able to characterize 97% of cognitively normal individuals from a population-based sample (14) and have been shown to correlate with the cognitive trajectories and disease progression of individuals over time (15, 16).
While previous iterations of the NIA-AA criteria were based on a two-marker construct using evidence of Aβ-amyloid pathology and neurodegeneration as a single category, it is thought that segregating measures of pathologic tau (i.e., tau PET, CSF p-tau) from other markers of neuronal injury may help to better distinguish AD-related pathology from other neurodegenerative conditions (3). The recent NIA-AA Research Framework: Towards a biological definition of Alzheimer’s disease (4) is therefore based on a three-marker construct. The recent framework uses normal (-) or abnormal (+) levels of Aβ-amyloid deposition (“A”), pathologic tau (“T”), and neurodegeneration (“(N)”) as constructs to create the AT(N) classification system. In this contribution, we interrogated the AT(N) classification system to improve understanding for its implementation and applicability in characterizing and understanding the pathogenesis of AD. Firstly, we apply the AT(N) classification system to CSF biomarkers from well-characterized participants in the longitudinal Australian Imaging, Biomarker & Lifestyle (AIBL) Flagship Study of Ageing. Secondly, we describe the long-term clinical and cognitive trajectories of AIBL elderly cognitively normal controls (NCs) as well as AIBL MCI individuals, using the three-marker construct.



The AIBL cohort

The AIBL cohort study of aging combines data from neuroimaging, biomarkers, lifestyle, clinical, and neuropsychological assessments. Two study centers in Melbourne (Victoria) and Perth (Western Australia), Australia recruited individuals with MCI and with AD from primary care physicians or tertiary memory disorders clinics. Cognitively healthy NC participants were recruited through advertisement or via spouses of participants in the study. Exclusion criteria included a history of non-AD dementia, Parkinson’s disease, schizophrenia, bipolar disorder, current depression, cancer in the past 2 years (with the exception of basal-cell skin carcinoma), symptomatic stroke, uncontrolled diabetes, or current regular alcohol use. Between November 3, 2006, and October 30, 2008, AIBL recruited 1112 eligible volunteers who were at least 60 years old and fluent in English. Full details on the study design and inclusion criteria have been reported elsewhere (17). An enrichment cohort of 86 participants with AD, 124 MCI participants, and 389 NC participants were recruited by AIBL between March 30, 2011, and June 29, 2015. At baseline, the AIBL study participants had an average age of 72 years, 58% were female, and 36% were Apolipoprotein E (APOE) ε4 carriers. APOE ε4 carriage was determined as previously described (18). Two hundred AIBL participants (140 NC, 33 MCI and 27 AD) with a mean age of 73 (50% Males) who had undergone lumbar puncture were included in the current study.

Assessment of CSF biomarkers

Lumbar puncture was used to collect CSF from 200 AIBL participants in the morning after overnight fasting, with a protocol aligned to the Alzheimer’s Biomarkers Standardization Initiative (ABSI). Lumbar puncture was performed in the sitting position using a strictly aseptic technique and gravity drip collection. CSF was collected into a polypropylene tube and placed on ice prior to centrifugation (2000 ×g at 4°C for 10 minutes), and the supernatant was transferred to a second polypropylene tube and gently inverted. Samples were aliquoted (500 μL) into Nunc cryobank polypropylene tubes (NUN374088) and stored in liquid nitrogen vapor tanks within 1 hour (kept on dry ice prior to storage) and only thawed once, immediately before analysis. CSF levels of Aβ1-42, t-tau, and p-tau were measured by electrochemiluminescence Elecsys® immunoassay (Roche Diagnostics, Penzberg, Germany) that uses a quantitative sandwich principle. Levels were measured using the Roche cobas® e601 analyzer (Roche Diagnostics) with a total assay duration of 18 minutes.

Application of the NIA-AA Research Framework

The NIA-AA Research Framework (4), details grouping of individuals based on AT(N) criteria, where: ‘A’ represents Aβ-amyloid or associated pathologic state—here ‘A’ is defined using CSF Aβ1-42; ‘T’ represents aggregated tau (neurofibrillary tangles) or associated pathologic state—in this current study ‘T’ is defined using CSF p-tau; ‘(N)’ represents neurodegeneration or neuronal injury—here ‘(N)’ is defined using CSF t-tau. Individuals were classified as being positive or negative for each of the A, T, and (N) criteria. A+ was defined as having a CSF Aβ1-42 level ≤1054.00pg/mL and A- as having a CSF Aβ1-42 level >1054.00 pg/mL. T+ was defined as having a CSF p-tau level ≥21.34 pg/mL and T- as having a CSF p-tau level

Cognitive markers

All participants underwent extensive neuropsychological testing, as previously described (17). Briefly, the tests comprising the AIBL clinical and neuropsychological battery were selected to cover the main domains of cognition affected by AD and other dementias, and are all internationally recognized as having good reliability and validity. The full battery comprised: the Clinical Dementia Rating (CDR) Scale, Mini-Mental State Examination (MMSE) (19), Clock-Drawing Test, California Verbal Learning Test – Second Edition (CVLT-II) (20), Logical Memory (LM) I and II (Wechsler Memory Scale [WMS]-III; Story A only) (21-23), Delis–Kaplan Executive Function System (D-KEFS) verbal fluency (24), 30-item Boston Naming Test (BNT) (25), the Stroop Test (Victoria version) (22), the Rey Complex Figure Test (RCFT) (26), Digit Span and Digit Symbol-Coding subtests of the Wechsler Adult Intelligence Scale – Third Edition (WAIS–III) (27), the Wechsler Test of Adult Reading (WTAR) (28), the Hospital Anxiety and Depression Scale (HADS), and the Geriatric Depression Scale (GDS).
Clinical and cognitive trajectories were evaluated using the AIBL-Preclinical Alzheimer Cognitive Composite (AIBL-PACC) (29), a verbal episodic memory composite, an executive function composite (30), CVLT-II Long-Delay Free Recall (CVLT-II LDFR), MMSE, and CDR Sum of Boxes (CDR SoB) measures. The AIBL-PACC was constructed by summing Z-score measures of CVLT-II LDFR, LM-II, MMSE, and Digit Symbol-Coding. The verbal episodic memory composite was created from Z-scores of CVLT-II LDFR, CVLT-II recognition false positives, and LM-II, and the executive function composite was generated from Z-scores of D-KEFS letter fluency and category switching totals as well as the colors/dots interference measure from the Stroop Test (Victoria version).


Demographic information was assessed across clinical classifications for 200 AIBL participants who had undergone CSF evaluation. Participants were classified into one of eight categories based on the three-construct model of AT(N) in the NIA-AA Research Framework. The prevalence of the AT(N) groups was assessed across the clinical classification groups. The eight AT(N) groups were then collapsed into four main groups of interest: those with normal AD biomarkers, those with non-AD pathologic change, those with AD pathologic change, and those with AD. Baseline cognitive performance was assessed across these four groups within the NC and MCI clinical classification groups using boxplots and one-way t-tests. Longitudinal change in cognitive performance over time, separately for the NC and MCI, was assessed using boxplots and one-way t-tests of the random slopes obtained from linear mixed-effect models. In the linear mixed-effect models, the cognitive measure represented the dependent variable; age, sex, and APOE ε4 status were included as interacting independent factors and time since CSF evaluation was included as a random factor. The dependent variable was evaluated every 18 months for a mean follow-up of 4.5 years. The number of participants progressing towards more advanced disease (i.e., NC to MCI/AD and MCI to AD) within each of these four groups was also evaluated using descriptive statistics, due to the small number of conversions more sophisticated analyses such as Cox proportional hazards analyses could not be undertaken.

Sensitivity Analysis I

Participants were assigned to one of four groups (A-T-; A+T-; A-T+; A+T+) based on their CSF Aβ1-42 and p-tau levels as described above. Baseline cognitive performance was assessed across these four AT groups within each clinical classification group using boxplots and one-way t-tests. Longitudinal change in cognitive performance over time was assessed using boxplots and one-way t-tests of the random slopes obtained from linear mixed-effect models. In the linear mixed-effect models, the cognitive measure represented the dependent variable; age, sex, and APOE ε4 status were included as interacting independent factors and time since CSF evaluation was included as a random factor.

Sensitivity Analysis II

Participants were assigned to one of four groups (A-N-; A+N-; A-N+; A+N+) based on their CSF Aβ1-42 and t-tau levels as described above. Baseline cognitive performance was assessed across these four A(N) groups within each clinical classification group using boxplots and one-way t-tests. Longitudinal change in cognitive performance over time was assessed using boxplots and one-way t-tests of the random slopes obtained from linear mixed-effect models. In the linear mixed-effect models, the cognitive measure represented the dependent variable; age, sex, and APOE ε4 status were included as interacting independent factors and time since CSF evaluation was included as a random factor.




The majority of participants (140/200) were cognitively healthy (NC) and the remaining comprised MCI or AD (n=33 and n=27, respectively) (Table 1). There was a higher prevalence of males in the MCI and AD samples compared to the NC sample. Reported ages at baseline did not differ across the three samples (averaging around 73 years). The NC participants had a higher level of education and had fewer APOE ε4 carriers. The mean duration of follow-up for all participants was 4.54 years.

Table 1. Demographics

Table 1. Demographics

AD, Alzheimer’s disease; APOE, Apolipoprotein E; MCI, mild cognitive impairment; NC, normal control; SD, standard deviation.


Prevalence of AT(N) groups

The prevalence of each of the eight AT(N) classifications within the AIBL NC, MCI, and AD samples are given in Figure 1. The highest proportion of NC participants (38%) had normal AD biomarkers; 13% had AD pathologic change, 20% have AD, and 29% had non-AD pathologic change. In the MCI and AD samples, 75% and 70% of participants had AD pathologic change, respectively.

Figure 1. Prevalence of the AT(N) groups across clinical classifications

Figure 1. Prevalence of the AT(N) groups across clinical classifications

AD, Alzheimer’s disease; MCI, mild cognitive impairment


Cross-sectional cognitive performance in NC

In general, NC participants with biomarkers consistent with AD performed the worst on the cognitive composite markers and MMSE (Figure 2A‒C and E). Differences were not observed for CDR SoB with all NCs scoring 0 on this test (Figure 2D). The NC participants with normal AD biomarkers had the lowest scores on the CVLT-II LDFR (Figure 2F). In general, within the NC sample those classified as having non-AD pathologic change had similar scores to those with normal AD biomarkers. Regarding the sensitivity analyses, The A+T+ group had significantly (p=0.03) lower baseline scores for AIBL-PACC in comparison to the A-T- group and the A+T+ group had significantly lower baseline scores for the Verbal Episodic Memory composite than the A-T+ group. Also, the A+N+ group had significantly lower baseline scores for the Verbal Episodic Memory composite than the A-N+ group. No other differences were observed in the sensitivity analyses of differences in the NC at baseline.

Figure 2. Cross-sectional performance on the six cognitive measures (A: AIBL-PACC; B: Verbal Episodic Memory; C: Executive Function; D: CDR Sum of Boxes; E: MMSE; F: CVLT-II LDFR) for the four contracted AT(N) groups in NC

Figure 2. Cross-sectional performance on the six cognitive measures (A: AIBL-PACC; B: Verbal Episodic Memory; C: Executive Function; D: CDR Sum of Boxes; E: MMSE; F: CVLT-II LDFR) for the four contracted AT(N) groups in NC

AD, Alzheimer’s disease; AIBL-PACC, Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing – Preclinical Alzheimer Cognitive Composite; CDR, Clinical Dementia Rating; CVLT-II LDFR, California Verbal Learning Test – Second Edition; Long-Delay Free Recall; MMSE, Mini-Mental State Examination; NC, normal control; SD, standard deviation.


Cross-sectional cognitive performance in MCI

For MCI participants there was a decrease in performance from those with normal AD biomarkers, to those with AD pathologic change and then AD for the AIBL-PACC (Figure 3A). This trend was not observed in the other five clinical and cognitive markers considered (Figure 3B–F). No baseline differences were obsevered for the MCI in the sensitivity analyses.

Figure 3. Cross-sectional performance on the six cognitive measures (A: AIBL-PACC; B: Verbal Episodic Memory; C: Executive Function; D: CDR Sum of Boxes; E: MMSE; F: CVLT-II LDFR) for the four contracted AT(N) groups in MCI

Figure 3. Cross-sectional performance on the six cognitive measures (A: AIBL-PACC; B: Verbal Episodic Memory; C: Executive Function; D: CDR Sum of Boxes; E: MMSE; F: CVLT-II LDFR) for the four contracted AT(N) groups in MCI

AD, Alzheimer’s disease; AIBL-PACC, Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing – Preclinical Alzheimer Cognitive Composite; CDR, Clinical Dementia Rating; CVLT-II LDFR, California Verbal Learning Test – Second Edition; Long-Delay Free Recall; MMSE, Mini-Mental State Examination; MCI, mild cognitive impairment; SD, standard deviation.


Longitudinal change in cognitive performance

For both the NC and MCI participants, systematic differences were not observed in the rates of decline for the four groups considered (Supplementary Figures 1 and 2). No differences were observed in the sensitivity analyses.

Progression to disease

Over the period of follow-up (mean=4.54 years), of the 53 NC individuals with normal AD biomarkers, one progressed to MCI due to AD and one progressed to MCI not due to AD. Of the 18 NC individuals with AD pathologic change, two progressed to MCI due to AD. Of the 28 NC individuals with AD biomarkers, one participant died and there were no other transitions. Of the 41 individuals with non-AD pathologic change, one participant died, one progressed to MCI, and one progressed to vascular dementia. Of the nine MCI individuals with AD pathologic change, one progressed to AD. Of the 13 MCI individuals with AD biomarkers, two participants died and two progressed to AD. There were not enough events of progression to ascertain any statistically significant differences in progression between the groups.



This analysis evaluated the AT(N) classification system in a well-characterized population from the AIBL cohort, including cognitively healthy NC participants as well as those with MCI and AD. Approximately two in five of the elderly NC had no evidence of abnormal AD biomarkers, whereas one in three had biomarker levels consistent with AD or AD pathological change and almost one in three had evidence of non-AD pathological change. Twenty-three percent of the NC participants had biomarker levels aligned with the SNAP category (A-(N+)), which aligns with other reports in the literature (3, 16).
Among NC participants, those with biomarker evidence of AD pathology tended to perform worse on composite cognitive outcome assessments and the MMSE compared with other biomarker groups. Participants with abnormal non-AD-specific biomarkers performed similarly to those with or without normal AD biomarkers across endpoints. No differences were observed across the four biomarker groups with respect to rate of decline on any outcome assessment.
Approximately three in four participants with MCI or AD had biomarker levels consistent with AD or AD pathologic change. For MCI participants, a decrease in AIBL-PACC scores was observed with increasing abnormal biomarkers; increased abnormal biomarkers were also associated with increased rates of decline across some cognitive measures. There were not enough events of disease progression (i.e., NC to MCI/AD or MCI to AD) to draw any conclusions about the risk of disease progression based on the biomarker constructs.
Despite the lack of statistically significant trends, which is likely to be related to the small numbers of participants included, observations from the current study are qualitatively consistent with previous work showing that biomarkers of AD evident before clinical symptoms appear to predict cognitive deficit. In a natural history study classifying NC participants (N=166) with a two-marker construct, using Aβ-amyloid (assessed using amyloid PET imaging) and markers of neurodegeneration (hippocampus volume seen on MRI, FDG-PET), those with normal AD biomarkers showed improvement over time on a composite cognitive measure derived from eight neuropsychological tests, likely due to practice effects (15). Conversely, participants who either had evidence of Aβ-amyloid pathology or were considered SNAP participants had reduced practice effects, and those positive for both Aβ-amyloid pathology and markers of neurodegeneration showed cognitive decline (15). An analysis of a larger group of NC individuals from the AIBL cohort (N=573) also applied the two-marker construct, using amyloid PET as a marker of Aβ-amyloid pathology and hippocampal volume on MRI to assess neurodegeneration, and showed that amyloid-PET positivity conferred significant risk for cognitive decline, with structural evidence of neurodegeneration further compounding this risk (16). Applying this two-marker construct here in a sensitivity analysis, highlighted some baseline differences: individuals with abnormal CSF levels for Aβ-amyloid and one of the tau markers performed worse than participants with less biomarker abnormality on two of the cognition measures. No longitudinal differences were observed in the sensitivity analysis.
The composite AT(N) system for classifying AD used in the present analysis separates markers of tau pathology from other neurodegenerative markers which is thought to improve specificity in terms of differentiating patients with AD vs. non-AD pathology. However, our inconclusive findings suggest that further study of the AT(N) classification system and its comparison to the two-biomarker constructs in larger groups of participants across the disease spectrum is needed.
Our construct employed CSF-based immunoassay measures for determining A, T, and (N) status, in comparison to the imaging metrics employed in the previous studies discussed (15,16). The availability of immunoassay methodology for evaluating AD and neurodegeneration biomarkers could have important implications for clinical practice as this type of testing may be more widely accessible and cheaper than imaging-based methodologies. In turn, this potential for great accessibility vs. imaging methodologies may facilitate wider application of AT(N) classification in clinical trial methodology to screen more potential participants and further enrich study populations with AD biomarker-positive individuals who are most likely to show AD-related disease progression within the duration of the study. A much wider application would be achievable once blood biomarkers become available.
There are a number of limitations to this study, including the small sample size, which may preclude any statistically significant differences being observed. Further, only a small number of disease progression events occurred precluding any evaluations to be made regarding the power of the AT(N) criteria to predict progression to disease. The participants were volunteers who were not randomly selected from the community, and were generally well educated; thus, these findings might only be valid in similar cohorts and this limitation precludes the generalization of the findings. In view of the stringent selection criteria in AIBL, which excluded individuals with cerebrovascular disease or other dementias, the effect of other comorbidities on the trajectories might be underestimated. Longitudinal cognitive performance was based on three composite measures as well as two clinical scores and one standard measure, which were corrected using within-study norms; however, other cognitive tests, or combinations thereof, might yield different results. Further, biomarker levels were obtained from a CSF immunoassay and different techniques may yield different results. The cut-offs used for dichotomous stratification were somewhat arbitrary and continuous variables might provide better predictors of progression. Another potential limitation is the non-specificity of t-tau for the (N) classification and other markers, such as neurofilament light, either in CSF of plasma, may provide a more robust assessment of (N).
In conclusion, increasing CSF biomarker abnormality appears to be associated with worse cognitive trajectories. The implementation of the AT(N) classification could help better characterize prognosis in clinical practice and identify those at-risk individuals more likely to progress, for inclusion in future therapeutic trials. However, our inconclusive findings suggest that further study of the AT(N) classification system in larger groups of participants is warranted.


Funding: Core funding for the AIBL study was provided by the CSIRO Flagship Collaboration Fund and the Science and Industry Endowment Fund (SIEF) in partnership with the CRC for Mental Health, Edith Cowan University (ECU), Mental Health Research Institute (MHRI), Alzheimer’s Australia (AA), National Ageing Research Institute (NARI), Austin Health, Macquarie University, CogState Ltd, Hollywood Private Hospital, and Sir Charles Gairdner Hospital. The study also received funding from the National Health and Medical Research Council (NHMRC), Dementia Collaborative Research Centre (DCRC) program, and McCusker Alzheimer’s Research Foundation, and operational infrastructure support from the Government of Victoria. This specific study was funded in part by F. Hoffmann-La Roche Ltd, Basel, Switzerland. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Acknowledgments: We thank the participants who took part in the AIBL study and their families. Editorial assistance was provided by Liz LaFlamme, PhD, and Rachel Johnson, PhD, of Health Interactions, funded by F. Hoffmann-La Roche Ltd, Basel, Switzerland.

Conflict of interest: Samantha C. Burnham: reports speaker honoraria from Novartis outside the scope of the submitted work and research funding paid to her employers from F. Hoffmann-La Roche Ltd. Preciosa M. Coloma: is a full-time employee of, and own shares in, F. Hoffmann-La Roche Ltd. Simon Laws: received personal fees from Alzhyme outside the scope of the submitted work. James Doecke: reports research funding paid to his employers from F. Hoffmann-La Roche Ltd. David Ames: reports receipt of financial assistance to his employer to assist with an international drug trial of an anti-Alzheimer’s agent, owned by Eli Lilly. Christopher C. Rowe: reports speaker honoraria from GE Healthcare and Avid Radiopharmaceuticals, consulting fees from Avid Radiopharmaceuticals, AstraZeneca, and Piramal Imaging, and research grants from Avid Radiopharmaceuticals, GE Healthcare, and Piramal Imaging all outside the scope of the submitted work. Colin L. Masters: reports personal fees from Prana Biotechnology, Eli Lilly, and Actinogen outside the scope of the submitted work. Victor L. Villemagne: reports speaker honoraria from GE Healthcare, Piramal Imaging, and Avid Radiopharmaceuticals, and consulting fees from Lundbeck, AbbVie, Shanghai Green Valley Pharmaceutical Co. outside the scope of the submitted work and consulting fees from F. Hoffmann-La Roche Ltd. All other authors declare no conflicts of interest

Ethical standards: This work was conducted in accordance with the principles set forth by the Declaration of Helsinki. The institutional ethics committees of Austin Health, St Vincent’s Health, Hollywood Private Hospital, and Edith Cowan University in Australia approved the AIBL study, and all volunteers gave written informed consent before participating.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.





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J. Cummings1, K. Blennow2, K. Johnson3, M. Keeley4, R.J. Bateman5, J.L. Molinuevo6, J. Touchon7, P. Aisen8, B. Vellas9 and the EU/US/CTAD Task Force*


* EU/US/CTAD TASK FORCE: EU/US/CTAD TASK FORCE: Bjorn Aaris Gronning (Valby); Paul Aisen (San Diego); John Alam (Cambridge); Sandrine Andrieu (Toulouse), Randall Bateman (St. Louis); Monika Baudler (Basel);  Joanne Bell (Wilmington); Kaj Blennow (Mölndal); Claudine Brisard (Blue Bell); Samantha Budd-Haeberlein (USA); Szofia Bullain (Basel) ; Marc Cantillon (Princeton) ; Maria Carrillo (Chicago);  Gemma Clark (Princeton); Jeffrey Cummings (Las Vegas); Daniel Di Giusto (Basel); Rachelle Doody (Basel); Sanjay Dubé (Aliso Viejo); Michael Egan (North Wales); Howard Fillit (New York); Adam Fleisher (Philadelphia); Mark Forman (North Wales); Cecilia Gabriel-Gracia (Suresnes); Serge Gauthier (Montreal); Jeffrey Harris (South San Francisco); Suzanne Hendrix (Salt Lake City); Dave Henley (Titusville); David Hewitt (Blue Bell); Mads Hvenekilde (Basel); Takeshi Iwatsubo (Tokyo); Keith Johnson (Boston); Michael Keeley (South San Francisco); Gene Kinney (South San Francisco); Ricky Kurzman (Woodcliffe Lake); Valérie Legrand (Nanterre); Stefan Lind (Valby); Hong Liu-Seifert (Indianapolis); Simon Lovestone (Oxford); Johan Luthman (Woodcliffe); Annette Merdes (Munich); David Michelson (Cambridge); Mark Mintun (Philadelphia); José Luis Molinuevo (Barcelona); Susanne Ostrowitzki (South San Francisco); Anton Porsteinsson (Rochester);  Martin Rabe (Woodcliffe Lake); Rema Raman (San Diego); Elena Ratti (Cambridge);  Larisa Reyderman (Woodcliffe Lake); Gary Romano (Titusville); Ivana Rubino (Cambridge); Marwan Noel Sabbagh (Las Vegas);  Stephen Salloway (Providence); Cristina Sampaio (Princeton); Rachel Schindler (USA); Peter Schüler (Langen); Dennis Selkoe (Boston); Eric Siemers (Indianapolis);  John Sims (Indianapolis); Heather Snyder (Chicago); Georgina Spence (Galashiels); Bjorn Sperling (Valby); Reisa Sperling (Boston); Andrew Stephens (Berlin); Joyce Suhy (Newark); Gilles Tamagnan (New Haven); Edmond Teng (South San Francisco); Gary Tong (Valby); Jan Torleif Pedersen (Valby); Jacques Touchon (Montpellier); Bruno Vellas (Toulouse ); Vissia Viglietta (Cambridge) ; Christian Von Hehn (Cambridge); Philipp Von Rosenstiel (Cambridge) ; Michael Weiner (San Francisco); Kathleen Welsh-Bohmer (Durham);  Iris Wiesel (Basel); Haichen Yang (North Wales);  Wagner Zago (South San Francisco); Beyhan Zaim (Woodcliffe Lake); Henrik Zetterberg (Mölndal)

1. University of Nevada Las Vegas, School of Allied Health Sciences and Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada, USA; 2. Inst. of Neuroscience and Physiology, University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden; 3. Massachusetts General Hospital, Harvard Medical School, Boston MA, USA; 4. Genentech Research and Early Development, So. San Francisco, CA, USA; 5. Washington University School of Medicine, St. Louis, MO, USA; 6. BarcelonaBeta Brain Research Center Pasqual Maragall Foundation, Barcelona, Spain; 7. Montpellier University, and INSERM U1061, Montpellier, France; 8. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA; 9. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France

Corresponding Author: Jeffrey Cummings, University of Nevada Las Vegas, School of Allied Health Sciences and Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada, USA,

J Prev Alz Dis 2019;
Published online April 18, 2019,



Efforts to develop effective disease-modifying treatments for Alzheimer’s disease (AD) have mostly targeted the amyloid β (Aβ) protein; however, there has recently been increased interest in other targets including phosphorylated tau and other forms of tau. Aggregated tau appears to spread in a characteristic pattern throughout the brain and is thought to drive neurodegeneration. Both neuropathological and imaging studies indicate that tau first appears in the entorhinal cortex and then spreads to the neocortex. Anti-tau therapies currently in Phase 1 or 2 trials include passive and active immunotherapies designed to prevent aggregation, seeding, and spreading, as well as small molecules that modulate tau metabolism and function. EU/US/CTAD Task Force members support advancing the development of anti-tau therapies, which will require novel imaging agents and biomarkers, a deeper understanding of tau biology and the dynamic interaction of tau and Aβ protein, and development of multiple targets and candidate agents addressing the tauopathy of AD. Incorporating tau biomarkers in AD clinical trials will provide additional knowledge about the potential to treat AD by targeting tau.

Key words: Alzheimer’s disease, tau, tauopathy, therapeutics, biomarkers.



No new drugs have been approved by the US Food and Drug Administration (FDA) for the treatment of Alzheimer’s disease (AD) since 2003 (1) despite the fact that an estimated 5.7 million Americans and 50 million people worldwide have AD today, and the prevalence is expected to grow to 152 million worldwide by 2050 (2, 3).  AD clinical trials have failed at a very high rate: between 2002 and 2012, 99.6% of AD drugs tested failed to demonstrate clinical efficacy (1). Possible reasons for the high failure rate include targeting the wrong pathology or the wrong stage of disease (4, 5). Inappropriately designed trials and other methodological or unknown factors may have also contributed to treatment failures (6).
Despite the disappointments of the past 20 years, many experts in the Alzheimer’s community see reasons for optimism, including the emergence of novel drugs addressing a broader array of mechanisms than in the past (7). A recent report on the status of the AD drug development pipeline identified 112 agents: 26 in Phase 3 studies, 75 in Phase 2 studies, and 23 in Phase 1 studies (8). Moreover, whereas most of the negative studies in recent years targeted brain amyloidosis and amyloid β (Aβ), current studies are targeting a broader repertoire of mechanisms, including tau pathology. Of the 26 agents in Phase 3, only one targets tau, while 9 of the agents in Phase 2 (5 immunotherapies and 4 anti-aggregation agents) target tau (8).


Biology of tau and anti-tau therapeutics

The microtubule-associated protein tau, commonly referred to simply as tau, is found in a hyperphosphorylated form as insoluble, filamentous tangles and neuropil threads as well as dystrophic neurites in the AD brain (9). Along with plaques made up of aggregated Aβ protein, neurofibrillary tangles (NFTs) represent one of the hallmark pathologies of AD. Like Aβ, tau is found in several forms in the brain including monomers, oligomers, and fibrillary tangles (10).  Tau pathology also plays a central role in other neurodegenerative diseases known collectively as tauopathies, including the primary tauopathies frontotemporal lobar degeneration with tau inclusions (FTLD-tau), Pick’s disease, progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), argyrophilic grain disease (AGD), and chronic traumatic encephalopathy (CTE) (11).
Six isoforms of tau exist; it binds to several other proteins; and it undergoes many post-translational modifications, all of which contribute to its multiple functions in the brain. Tau protein plays important roles in cytoskeletal stability, cell signaling, synaptic plasticity, and neurogenesis (12, 13). In the AD brain, NFTs and neuropil threads composed of aggregated hyperphosphorylated tau are thought to be the primary drivers of neurodegeneration, although the mechanisms underlying the pathogenic process and the exact relationships of tau to Aβ remain unclear. Evidence also strongly suggests that tau propagates or spreads between cells (14) and that neuroinflammation triggered by microglial activation and astrogliosis contributes to tau-associated pathogenesis. Microglia may contribute to tau spreading (15). While postmortem and tau positron emission tomography (PET) studies indicate that tau spreading is associated with disease progression (16, 17), there are many unanswered questions regarding the rate of seeding or the effects of tau spreading on neuronal biology. If the spread of tau is driving clinical and cognitive changes, this would support intervening at the earliest stages of the tau-related disease process.
Neuropathological and imaging studies using PET suggest that tau aggregates are found in the entorhinal cortex and then the neocortex.  If and how this drives neurodegeneration, what forms of tau are toxic, and the relationship of tau to amyloid in terms of toxicity remain unanswered questions. Tau pathology correlates much more closely to cognitive decline than does amyloid pathology (18, 19), and a recent study suggests that tau aggregation is linked to neurodegeneration and clinical manifestations of AD (20).
The complexity of tau biology provides many potential therapeutic targets to prevent tau production, aggregation, or spread at the level of transcription, phosphorylation, depolymerization, and transport. For example, preclinical studies indicate that antibodies against tau can prevent the trans-synaptic transmission of tau between neurons (21). A Phase 1 study of the humanized monoclonal antibody ABBV-8E12 showed acceptable safety and tolerability, which provided the basis for initiating a Phase 3 study in PSP patients to assess dose-related efficacy (22). The antibody is intended to prevent the trans-neuronal spread of the tau protein. Other monoclonal antibodies being assessed in early phase studies and targeting aspects of the tau protein include BIIB092, LY3303560, and RO7105705 (Table 1).

Table 1. Phase 1 and 2 clinical Trials targeting tau in AD populations

Table 1. Phase 1 and 2 clinical Trials targeting tau in AD populations

ADAS-cog = Alzheimer’s Disease Assessment Scale, cognition subscale, ADCS-ADL = Alzheimer’s Disease Cooperative Study – Activities of Daily Living Inventory, AEs= adverse events, CDR-SB = Clinical Dementia Rating Scale Sum of Boxes, CGIC = Clinical Global Impressions Scale, FAQ = functional activities questionnaire, fMRI = functional magnetic resonance imaging, HAM-D = Hamilton Psychiatric Rating Scale for Depression, iADRS=integrated AD rating scale, MMSE = Mini-mental state examination, NPI = neuropsychiatric inventory, NPS battery = neuropsychiatric symptoms battery, RBANS= Repeatable Battery for the Assessment of Neuropsychological Status, TEAEs = treatment-emergent adverse events. UPSA=University of California Performance Based Skills Assessment, brief version


Current status of anti-tau therapies in the AD treatment pipeline

One putative anti-tau agent, TRx0237 was studied in a Phase 3 trial and failed to show a difference between different doses.  Studies in mouse models suggested that the agent functioned as an aggregation inhibitor and reduced the number of tau positive neurons (23); no target engagement biomarker was included in trial to determine if this was achieved in humans (24). Subgroup analyses suggest that some patients may have benefited from therapy and further studies of this compound are underway (24). Table 1 summarizes the anti-tau agents that are currently being tested in Phase 1 or Phase 2 clinical trials. These include both passive and active immunotherapies with monoclonal antibodies as well as drugs that affect the molecular structure of tau to modulate its function or prevent phosphorylation.
Other anti-tau drugs are also in development for AD including epigallocatechin-3 gallate (EGCG), a polyphenolic flavanoid extracted from green tea 25, and AC Immune’s tau morphomers, small molecules designed to inhibit aggregation and seeding and disaggregate already formed tau aggregates. Preclinical studies suggest that tau morphomers reduce pathological tau, improve cognition and function, and reduce microglia activation. Importantly, they are capable of crossing the blood-brain barrier.


Outcome measures and biomarkers

Tau PET imaging

Tau and Aβ aggregates in the brain have been investigated in several cohort studies, both neuropathologically at autopsy and in living people using PET (26-28). The overall picture emerging from these studies is that among cognitively normal individuals, about one-third have high amyloid, and among those with high amyloid about half also have high tau loads. A minority of cognitively normal individuals have sub-threshold levels of amyloid and high tau. The anatomic location of tau deposition may be important. These observations raise the possibility that quantifying progression of tau pathology may provide an early indicator of disease.
Johnson and colleagues have investigated the anatomical variability of amyloid and tau deposition in more than 400 individuals. These data indicate that distribution of tau in the rhinal cortex correlates with amyloid burden and that low amyloid individuals just starting to show elevations in tau are those most likely to be on the way to neocortical tauopathy. By the time tau levels have increased in the inferior temporal cortex, individuals may show significant impairments. These data support the hypothesis that amyloid is associated with tau spread.
Longitudinal data also provide support for these measures as useful for staging in order to establish a basis on which to measure change in serial imaging that could be useful in the clinical and clinical trial settings. Four stages were proposed:
Stage 0 – No signal exceeding background, consistent with Braak 0.
Stage 1 – Rhinal cortex signal emerging in a minority of low-amyloid clinically unimpaired individuals (allocortex, MTL) consistent with Braak I/II
Stage 2 – Inferior temporal signal emerging in the presence of high levels of fibrillar amyloid in clinically unimpaired individuals (corresponding to Braak stages III/VI)
Stage 3 – Additional neocortical binding in mild cognitive impairment (MCI) and AD patients (beyond inferior temporal; corresponding to Braak stages V/VI)
Figure 1 provides an example of images with high and low tau burden.

Figure 1. Flortaucipir images with low (Braak I/II) and high (Braak III/VI) levels of tau. The individual whose image is shown on the left had low amyloid levels; the one shown on the right had high amyloid levels (images courtesy of Keith Johnson)

Figure 1. Flortaucipir images with low (Braak I/II) and high (Braak III/VI) levels of tau. The individual whose image is shown on the left had low amyloid levels; the one shown on the right had high amyloid levels (images courtesy of Keith Johnson)


CSF and blood biomarkers of tau

A systematic review and meta-analysis of cerebrospinal fluid (CSF) and blood biomarkers showed that CSF levels of total tau (T-tau), phosphorylated tau (p-tau), Aβ42, and neurofilament light (NfL), and plasma levels of T-tau were associated with AD and MCI due to AD but with quite pronounced, assay-dependent variation between studies, and no or only weak correlation with CSF T-tau levels (29-31). With regard to P-tau, a semi-sensitive assay for tau phosphorylated at threonine 181 (similar to the most employed CSF test) with electrochemiluminescence detection has been developed (32). Using this assay, plasma P-tau concentration was higher in AD dementia patients than controls. Plasma P-tau concentration was associated with both Aβ and tau PET and more AD-associated than the corresponding plasma T-tau test, which are promising results in need of replication. While conventional plasma measures of Aβ42 and Aβ40 by ELISA do not show a consistent change in clinically diagnosed AD cases as compared with cognitively unimpaired elderly (29), recent studies of blood Aβ using single molecule array (Simoa) or mass spectrometry have shown a relationship between blood levels of Aβ 40/42 ratios and the brain burden of Aβ (33-35). NfL indicates axonal damage and can also be measured in blood (36). Blood NfL shows particular promise as a biomarker of neurodegeneration in AD (37, 38) but high levels are also found in many other disorders characterized by neurodegeneration (39, 40). Given that NfL is a general neurodegeneration marker and not specifically involved in AD pathophysiology, it may give more unbiased information than tau biomarkers in clinical trials. Furthermore, the correlation between CSF and blood levels of NfL is very high (36), which is not the case for blood measures of tau (30). Synaptic proteins, including dendritic protein neurogranin and the pre-synaptic growth-associated protein 43 (GAP-43), show marked increases in CSF and are seemingly specific for AD (41, 42). Emerging CSF biomarkers including neuron-specific enolase (NSE), visinin-like protein 1 (VLP-1), heart fatty acid binding protein (HFPAP), and YKL-40 (a marker of glial activation) show moderate associations with AD (29, 43).
CSF tau comprises many different tau fragments that reflect processing of secreted tau, and some of these fragments may prove to be useful diagnostically (44) or provide information about tau kinetics in neurons (45). New assays are being developed to measure additional endogenous tau fragments that may correlate with tau pathology. For example, one of these tau fragments, tau368, results from cleavage of tau by asparagine endopeptidase (AEP) at position 368. The result of this is tau hyperphosphorylation, impaired microtubule assembly, and aggregation of truncated tau in neurofibrillary tangles (46).  Inhibiting AEP may represent a novel therapeutic strategy for neurodegenerative disease (47).  Tau368 can be measured in CSF and a first small study shows an association with longitudinal increase in tau PET tracer retention (48). Further, mass spectrometry studies show that CSF tau is specifically cleaved to a mid-domain fragment between amino acids 222-225 (45). Using an assay based on an end-specific tau x-224 monoclonal antibody, increased CSF levels were found in AD, while tau224 levels were low in other tauopathies (49). Exosomal tau has been evaluated as a biomarker but the studies have not been replicated and it is presently not possible to draw any conclusion on whether or not exosomal tau is a biomarker for AD.
The varying measures of tau report on different aspects of AD biology. In the amyloid, tau, neurodegeneration (ATN) Framework for AD diagnosis (50), tau PET and CSF p-tau are viewed as reporters of the presence and spread of tau pathology, whereas CSF t-tau, fluorodeoxyglucose PET, and MRI atrophy are seen as reporters of neurodegeneration. Recent evidence suggests that the soluble forms of tau are increased in production with greater amyloid plaque burden (45), while aggregated forms of tau appear at later stages of AD pathophysiology, closer to symptom onset. Tau markers — tau PET, p-tau, t-tau — measure different aspects of AD from this perspective.
For use in clinical trials of anti-tau agents, CSF biomarkers of amyloid and tau are needed to provide evidence of target engagement, enable enrichment of trials with appropriate participants, and show downstream effects of treatment (51). Lowering of CSF p-tau may suggest an effect on tau phosphorylation; however, more studies are needed to evaluate how CSF p-tau relates to brain pathology. Biomarker studies in recent clinical trials of the anti-amyloid antibodies bapineuzumab, gantenerumab, and BAN-2401 suggest that declines in CSF p-tau, t-tau, neurogranin and NfL indicate a downstream effect of Aβ immunotherapy on neurodegeneration, tau pathology, and synaptic degeneration (52-54).
Fully automated CSF immunoassays of AD biomarkers are now available, and in a study comparing fully automated CSF immunoassay with amyloid PET imaging, a multinational group of investigators found that the CSF tau/Aβ ratio was as accurate as amyloid PET in predicting clinical progression among patients with MCI (55) .


Challenges and unanswered questions

While the development of tau-targeted therapies is seen by many in the AD research community as one of the highest priority efforts, the complexity of tau protein processing gives rise to many challenges that have slowed development of tau-based therapies (56). Among the questions raised by the Task Force were these:
•    What is known about the normal physiological function of tau, and are there potential negative/untoward consequences of reducing tau?
•    What is the effect of tau suppression on spatiotemporal deposition of tau?
•    What degree of tau lowering should be targeted to achieve an optimal therapeutic effect?
•    What other factors may contribute to tau-based neurodegeneration (e.g., inflammation, aging, or vascular factors?)
•    What is the relationship of amyloid-beta and tau?
•    What is the relationship of soluble forms of tau and aggregated tau deposits?
•    What is the role of microglia activation in the development of tau pathology?
•    Since most tau is intracellular, will targeting it extracellularly be sufficient; or is there a window of time during which limiting extracellular tau would show a treatment benefit?
•    What happens downstream when an antibody binds to tau? Is it sequestered or disposed of through cellular mechanisms or the glymphatic system 57 and does this result in downstream preservation of neurons?
•    What are the best tau epitopes or tau fragments to target?
•    Which tau fragments correlate best with AD-type neurodegeneration in CSF or in plasma?
•    Which p-tau variants in CSF or blood correlate best with tau pathology in AD, or can differentiate AD from other tauopathies?
•    Are there differential rates of change in tau deposition across the anatomy?
•    What regions should tau PET target to demonstrate target engagement, and how should tau PET be developed for use in clinical trials to predict treatment response or measure treatment effect?
•    What will be required to make tau PET useful clinically for diagnosis, prognosis, or prediction of treatment response?
•    Do trials for anti-tau agents require similar structures as for Aβ-targeting agents even though the dynamics of the protein are different?
•    What is the best population, taking into account the ATN stage, to target?
•    Should anti-tau clinical trials focus on subpopulations and if so, which subpopulations?
•    Would the best path forward for anti-tau agents be to test them in combination trials with Aβ-targeting agents or drugs that target other pathologies such as neuroinflammation?
•    How can tau-PET be used to stage AD?
•    What are the best tau-related outcomes for AD trials?



Anti-tau therapies are beginning to populate the AD drug development pipeline, mostly in Phase 1 and Phase 2 trials. However, anti-tau treatments have not yet shown evidence of a treatment effect in patients. The Task Force concluded that the development of anti-tau treatment will be determined by multiple trials and will require contributions from industry, academia, and advocacy groups.
The Task Force also called for incorporating CSF tau measures in all anti-tau trials. At a later date, tau PET may also be a viable option. For a biomarker to accurately assess target engagement and for pharmacodynamic studies, assays need to be designed specifically for the therapeutic antibody in addition to general tau-based assays. Such assays would enable exploration of whether a change in a specific tau species indicates that the therapeutic antibody binds tau in the brain parenchyma and if bound tau is secreted into the CSF.
Most Task Force members agreed that anti-tau trials are justified because AD symptoms are likely driven by the spread of tau and its degenerative effects, as well as by amyloid. However, most members also agreed that the specific tau-based mechanisms that will likely provide a treatment effect from anti-tau therapy are unclear and that significant observational and trial related studies will help better inform which tau targets will be most effective.


Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript.

Conflicts of interest: The Task Force was partially funded by registration fees from industrial participants. These corporations placed no restrictions on this work. Dr. Cummings is the Chief Scientific Officer of CNS Innovations. He acknowledges funding from the National Institute of General Medical Sciences (Grant: P20GM109025) and support from Keep Memory Alive; Consultation for Pharmaceutical Companies: Dr. Cummings has provided consultation to Acadia, Accera, Actinogen, AgeneBio, Alkahest, Allergan, Alzheon, Avanir, Axsome, Binomics, BiOasis Technologies, Biogen, Bracket, Denali, Diadem, EIP Pharma, Eisai, Genentech, Green Valley, Grifols, Hisun, Idorsia, Lundbeck, MedAvante, Merck, Otsuka, Pain Therapeutics, Probiodrug, Proclara, QR, Resverlogix, Roche, Samumed, Shinkei Therapeutics, Sunovion, Suven, Takeda, and United Neuroscience pharmaceutical and assessment companies. Consultation for Foundations: Dr. Cummings has provided consultation to Global Alzheimer Platform (GAP). Stock: Dr. Cummings owns stock in ADAMAS, BioAsis, Prana, MedAvante, Neurokos, and QR Pharma. Board member: None. Speaker/lecturer: None. Other: Dr. Cummings owns the copyright of the Neuropsychiatric Inventory (NPI). Dr. Cummings is the Chief Scientific Officer of CNS Innovations.Expert witness/legal consultation: None. NIH support: COBRE grant # P20GM109025; TRC-PAD # R01AG053798; DIAGNOSE CTE # U01NS093334.Research Support: None. Spousal ownership or significant financial interest in a relevant company: CNS Innovations. Dr. Johnson has consulted for Merck, Eli Lilly, Novartis, Biogen, Takeda, Roche, Eisai, Piramal, and GE. Dr. Keeley reports that he is an employee of Genentech. Dr. Bateman reports grants from BrightFocus Foundation, Pharma Consortium (Abbvie, AstraZeneca, Biogen, Eisai, Eli Lilly and Co., Hoffman La-Roche Inc., Janssen, Pfizer, Sanofi-Aventi),  the Tau SILK/PET Consortium (Biogen/Abbvie/Lilly), Association for Frontotemporal Degeneration FTD Biomarkers Initiative, Anonymous Foundation, GHR Foundation, NIH, Alzheimer’s Association, Lilly, Rainwater Foundation Tau Consortium, and Cure Alzheimer’s Fund, grants, personal fees and non-financial support from Roche and Janssen, personal fees and non-financial support from Pfizer, Eisai, and Merck, and non-financial support from Avid Radiopharmaceuticals outside the submitted work. Washington University, Dr. Bateman, and David Holtzman have equity ownership interest in C2N Diagnostics and receive royalty income based on technology (stable isotope labeling kinetics and blood plasma assay) licensed by Washington University to C2N Diagnostics. RJB receives income from C2N Diagnostics for serving on the scientific advisory board. Washington University, with RJB as co-inventor, has submitted the US nonprovisional patent application “Methods for Measuring the Metabolism of CNS Derived Biomolecules In Vivo” and provisional patent application “Plasma Based Methods for Detecting CNS Amyloid Deposition”. Dr. Molinuevo reports personal fees from Alergan, from Oryzon, from Genentech, from Novartis, from Lundbeck, from Biogen, from Lilly, from Janssen, Green Valley, from MSD, from Eisai, from Alector and from Raman Health,  outside the submitted work. Dr. Touchon has nothing to disclose. Dr. Aisen reports grants from Lilly, personal fees from Proclara, other from Lilly, other from Janssen, other from Eisai, grants from Janssen, grants from NIA, grants from FNIH, grants from Alzheimer’s Association, personal fees from Merck, personal fees from Roche, personal fees from Lundbeck, personal fees from Biogen, personal fees from ImmunoBrain Checkpoint,  outside the submitted work. Dr. Vellas reports grants from Lilly, Merck, Roche, Lundbeck, Biogen, grants from Alzheimer’s Association, European Commission, personal fees from Lilly, Merck, Roche, Biogen,  outside the submitted work.



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J.T. Fuller1,2, A. Cronin-Golomb1, J.R. Gatchel2,3, D.J. Norton4, E. Guzmán-Vélez2, H.I.L. Jacobs5,6,7, B. Hanseeuw5,8,9, E. Pardilla-Delgado2, A. Artola2, A. Baena10, Y. Bocanegra10, K.S. Kosik11, K. Chen12, P.N. Tariot12, K. Johnson5,13, R.A. Sperling8,13, E.M. Reiman11, F. Lopera10,*, Y.T. Quiroz2,10,*


1. Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA; 2. Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 3. Division of Geriatric Psychiatry, McLean Hospital, Belmont, MA, USA; 4. Department of Psychology, Williams College, Williamstown, MA, USA; 5. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 6. School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht, University, Maastricht, The Netherlands; 7. Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands; 8. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 9. Department of Neurology, Université Catholique de Louvain, Brussels, Belgium; 10. Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellín, Colombia; 11. Neuroscience Research Institute and Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, USA; 12. Banner Alzheimer’s Institute, Phoenix, Arizona, USA; 13. Center for Alzheimer’s Research and Treatment, Brigham and Women’s Hospital, Boston, MA, USA

Corresponding Author: Yakeel T. Quiroz, PhD Assistant Professor, Harvard Medical School, Departments of Psychiatry and Neurology, Massachusetts General Hospital, 100 1st Avenue, Building 39, Suite 101, Charlestown, MA 02129, Phone (617) 643-5944; Fax: (617) 726-5760, E-mail:

J Prev Alz Dis 2019;2(6):112-120
Published online February 11, 2019,



The study of individuals with autosomal dominant Alzheimer’s disease affords one of the best opportunities to characterize the biological and cognitive changes of Alzheimer’s disease that occur over the course of the preclinical and symptomatic stages. Unifying the knowledge gained from the past three decades of research in the world’s largest single-mutation autosomal dominant Alzheimer’s disease kindred — a family in Antioquia, Colombia with the E280A mutation in the Presenilin1 gene — will provide new directions for Alzheimer’s research and a framework for generalizing the findings from this cohort to the more common sporadic form of Alzheimer’s disease. As this specific mutation is virtually 100% penetrant for the development of the disease by midlife, we use a previously defined median age of onset for mild cognitive impairment for this cohort to examine the trajectory of the biological and cognitive markers of the disease as a function of the carriers’ estimated years to clinical onset. Studies from this cohort suggest that structural and functional brain abnormalities — such as cortical thinning and hyperactivation in memory networks — as well as differences in biofluid and in vivo measurements of Alzheimer’s-related pathological proteins distinguish Presenilin1 E280A mutation carriers from non-carriers as early as childhood, or approximately three decades before the median age of onset of clinical symptoms. We conclude our review with discussion on future directions for Alzheimer’s disease research, with specific emphasis on ways to design studies that compare the generalizability of research in autosomal dominant Alzheimer’s disease to the larger sporadic Alzheimer’s disease population.

Key words: Autosomal dominant Alzheimer’s disease, dementia, biomarkers, cognitive markers.



Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is associated with the accumulation of amyloid-beta (Aβ1-42) plaques and intra-neuronal tau tangles which are hypothesized to damage brain structures, impair brain metabolism, and result in the clinical manifestation of the disease (1, 2). Most individuals develop the sporadic form of AD, which for the purpose of this review we operationally define as the form of AD with a late onset of clinical symptoms (i.e., >65 years old) and an etiology that is believed to not solely be determined by genetic influences (3). A small percentage of AD cases, however, are caused by autosomal dominant mutations that typically result in an earlier onset of clinical symptoms (i.e. <65 years of age) (4), with age of symptom onset varying by mutation type (5–7). Autosomal dominant mutations in genes such as the Presenilin1 (PSEN1) are nearly 100% penetrant and confer the development of AD with virtual certainty by midlife (8, 9), providing a unique opportunity to study the biological and cognitive markers of AD across the preclinical and symptomatic stages of the disease. Beyond aiming to find treatments that benefit mutation-carriers, ADAD investigators work to elucidate the trans-diagnostic disease mechanisms between ADAD and the more common sporadic form of the disease. As ADAD kindreds provide a quasi-experimental framework to investigate AD mechanisms and treatments, to the degree which ADAD and sporadic AD are similar it is possible through ADAD research to generate testable research hypotheses for new ways to identify and treat individuals at risk for the sporadic form of AD.
The largest identified single-mutation ADAD kindred in the world is a family of individuals with the E280A mutation in PSEN1 from Antioquia, Colombia (8–10). Over 1,800 Colombian PSEN1 E280A carriers and 4,000 non-carrier family members who trace their lineage back to a single-common ancestor with the mutation participate in research at the University of Antioquia and collaborating sites in the United States (11). As research in this cohort progresses – including a five-year phase 2 clinical trial of the anti-amyloid agent Crenezumab involving 300 Colombian kindred members, 200 of whom are mutation carriers (12) – a review that unifies our understanding of the PSEN1 E280A literature is needed.
Here, we synthesize all previous studies from the Colombian kindred, using a previously determined median estimated years until onset (EYO) of mild cognitive impairment (MCI) for this cohort (44 years) as a reference point to understand the progression of the cognitive and biological makers of the disease [10]; importantly, negative EYO indicates years before the median age of clinical onset of MCI, while positive EYO represents years after the median age of onset of MCI. We also provide directions for future research in this and other ADAD cohorts, including suggestions for generalizing ADAD findings to sporadic AD.



Thirty-four original reports (including published abstracts) on the cognitive and biological markers of PSEN1 E280A were identified, dating from 1997 to 2018. Studies from this cohort were defined using cohort characteristic criteria described in Lopera et al. (1997) and Acosta-Banea et al. (2011) (10, 13). All studies were found using Google Scholar, PubMed, and PsycInfo searches between November, 2017 and May, 2018. Search terms included: Presenilin1 E280A, Colombian kindred, autosomal dominant Alzheimer’s disease, familial Alzheimer’s disease. Articles found included works in both English and Spanish. All published reports on the biological and cognitive markers of this cohort were included in this review. We break down our review by the type of measurement used in each study: clinical/cognitive, psychophysiological, functional neuroimaging, structural neuroimaging, and fluid and brain-based markers of AD pathology.
As all the studies included in this review draw upon participants in the same cohort, identifying the exact number of unique participants is challenging. We can infer that some participants, such as those in the studies of child mutation carriers, were not included in other studies (i.e., studies of carriers with MCI or due to AD), but otherwise the number of unique participants across all studies is difficult to identify. The median sample sizes of carriers and non-carriers across the cognitive studies were 40 and 30, respectively, including two large (n > 400 carriers) database-wide retrospective clinical and cognitive studies (10, 14). In comparison, for the psychophysiological studies the median sample size of carriers and non-carriers were 15 individuals in each group. Across the functional imaging reports, there were median sample sizes of 21 carriers and 20 non-carriers. A median of 29 carriers and 23 non-carriers were included in the structural imaging studies. A median of 25 carriers and 21 non-carriers were used in the studies of the plasma, cerebral spinal fluid, and in vivo studies of this cohort. Table 1 provides a summary of the characteristics (number of published reports, EYO range, median sample size) and outstanding research questions for each cognitive and biological marker domain. For a synopsis statement on each published report from specific studies with the Colombian kindred, please see Supplementary Table A.

Table 1. Summary of Characteristics of Published Cognitive and Biological Marker Reports of PSEN1 E280A ADAD

Table 1. Summary of Characteristics of Published Cognitive and Biological Marker Reports of PSEN1 E280A ADAD

*Some reports share overlapping modalities (e.g., brain function and biomarkers); **The median sample size of the studies of cognitive testing is driven by two large (n > 400) retrospective studies; Abbreviations: EYO = estimated years until onset of mild cognitive impairment; EEG = electroencephalogram; fMRI = functional magnetic resonance imaging; SPECT = single-photon emission computed tomography; FDG-PET = fludeoxyglucose positron emission tomography; MRI = magnetic resonance imaging; DTI = diffusion tensor imaging; CSF = cerebral spinal fluid; PET = position emission tomography



Cognitive Function

The age of onset of dementia and rate of disease progression in this kindred was found to be variable (15), but a large, retrospective study of 449 mutation carriers identified a median age of onset of MCI of 44 years old, with a symptomatic disease course averaging 15 years thereafter (10). Verbal memory deficits appeared in carriers between -9 to -14 EYO (10, 16–20). In addition, subjective memory complaint (SMC) scores based on study-partner report began to differ from non-carriers approximately -6 EYO and followed a linear function, correlating with hippocampal volume in these carriers in the preclinical stage of the disease (21). Self-reported SMC was elevated in preclinical carriers relative to non-carriers, though a linear relationship with age was not found (21).
Poor performance on tests of attention, concentration, and semantic recognition was also observed in carriers approximately -5 EYO (16, 17, 22). As mutation carriers progressed to dementia, memory and language problems worsened, and changes in personality, behavior and physical symptoms occurred (13). Carriers of the PSEN1 E280A mutation with mild dementia (mean EYO from MCI = 5 years) performed worse than non-carriers on two thirds of tests from a battery of 43 neuropsychological variables (23).
The visual short-term memory test by Parra and colleagues (24) also demonstrated potential as a possible marker of early cognitive decline in preclinical carriers (24–26). Performance on the color-shape binding condition of this test reliably discriminated preclinical carriers from non-carriers, even when other traditional neuropsychological tests could not, though a precise age at which this test begins to reveal deficits in carriers was not identified (24).

Psychophysiological Measures

Electroencephalogram (EEG) studies in the Colombian kindred, when considered together, provide evidence of a shift from hyperactivity to hypoactivity among frontal (27), perceptual (28, 29), and memory-related (27, 30–32) brain networks in carriers in the preclinical stage of the disease. Increased theta synchronization (33) and alterations in beta-band frequencies (34) in frontal regions of preclinical carriers occurred approximately -10 EYO (27, 35), suggesting that abnormal cortical activity may occur in younger mutation carriers, despite similar cognitive performance to non-carriers. The right visuo-perceptual area, which showed greater activity in younger carriers, was conversely found to have decreased activity in carriers close to clinical onset (29). In the context of other markers of AD, the change from EEG-measured cortical hyperactivity to hypoactivity in PSEN1 E280A carriers appeared to coincide with elevated levels of in vivo cortical Aβ1-42 in mutation carriers (36, 37).

Structural Imaging

Structural magnetic resonance imaging (MRI) studies across ADAD mutations consistently suggest a pattern of decreased cortical thickness in the posterior parietal lobe of PSEN1 E280A mutation carriers before significant atrophy in other memory-related brain structures (e.g., the hippocampus and perihippocampus). The initial MRI study of kindred members showed that perihippocampal fissures and the distance between the unci of the temporal lobe distinguished symptomatic mutation carriers from cognitively unimpaired carriers and non-carrier family members, and that temporal lobe atrophy and ventricular enlargement (likely driven by the grade of atrophy) also correlated with disease severity (38). Subsequent studies revealed decreased cortical thickness in the parietal lobe of young carriers (-18 to -26 EYO) (39) and broader temporal and parietal reductions in thickness in carriers a decade before the median age of onset of MCI (40, 41). Complementing these studies of grey matter volume, one diffusion tensor imaging study measuring white matter integrity found significantly degenerated white matter only in carriers with dementia due to AD (mean EYO = 3 years) relative to non-carriers and preclinical carriers (26). Decreased white matter integrity was observed most saliently in the mid-frontal lobe and the genu of the corpus callosum (26).
Child PSEN1 E280A carriers (9-17 years old, -27 to -35 EYO) paradoxically showed greater grey matter volume compared to age-matched non-carriers in parietal and temporal areas (42), consistent with what has been observed in infant carriers of the E4 variant of the Apolipoprotein (ApoE4) who have greater grey matter in frontal areas relative to non-ApoE4 carrier infants (43). Evidence from the ApoE4 literature is mixed about whether greater frontal lobe matter in ApoE4-carrying infant relates to a potential executive-function-related compensatory neurodevelopmental process in these individuals at-risk for AD (44, 45). A study of child PSEN1 E280A carriers is currently underway to attempt to similarly determine if Colombian kindred child mutation carriers who have greater grey matter in the parietal and temporal lobes show any cognitive deficits or strengths relative to non-carrier children.

Functional Imaging

The use of functional MRI (fMRI), hexamethylpropyleneamine oxime spectroscopy (SPECT), and fludeoxyglucose PET (FDG-PET) propelled the discovery of AD-related disruptions in the metabolism and activity of memory-related brain structures (e.g., the hippocampus) and in connectivity within networks associated with memory performance (e.g., the default mode network, [DMN]) in PSEN1 E280A carriers. Prior studies have shown that, relative to non-carriers, increased blood oxygenation level-dependent (BOLD) fMRI activation during the encoding phase of a face-name association task was evident in the right cingulate gyrus and the right anterior hippocampus in mutation carriers a mean of -11 EYO (46). Younger carriers (-18 to -26 EYO) also showed greater BOLD activation during encoding in the right hippocampus and parahippocampal gyrus on this task, as well as less deactivation in the precuneus and posterior cingulate cortex relative to matched non-carriers (39). Child carriers (-27 to -35 EYO) similarly had less encoding-related deactivation of parietal regions and increased connectivity in the DMN between the posterior cingulate cortex and medial temporal lobe relative to non-carriers during this task (42). A scene-encoding BOLD fMRI task yielded similar encoding-related hippocampal and parahippocampal gyri hyperactivation patterns as the face-name task, discriminating mutation carriers in the preclinical stage with a median of -10 EYO from non-carriers (47).
In a SPECT study, preclinical mutation carriers in the decade before the estimated onset of MCI exhibited decreased cerebral perfusion in the posterior parietal lobe, the anterior frontal lobe, the posterior and anterior cingulate cortex, and throughout the hippocampal complex (48). FDG-PET was used in a different study with adult carriers and revealed lower glucose metabolism in temporal and parietal regions (e.g., the posterior cingulate and precuneus) among carriers with approximately -18 EYO relative to non-carriers (49). Carriers with dementia showed a similar pattern of decreased glucose metabolism as preclinical carriers (49), but had further decreased cerebral perfusion in the superior frontal cortex and posterior parietal lobe (48). Findings of brain hypometabolism among PSEN1 E280A carriers are generally consistent with the literature from individuals at risk for sporadic AD (50).

Plasma, Cerebral Spinal Fluid, and other in vivo markers of AD pathology

Plasma, cerebral spinal fluid (CSF), and in vivo PET studies provide evidence of AD pathogenesis in mutation carriers decades before the onset of clinical symptoms. Increased CSF Aβ1-42 distinguished young adult mutation carriers (-18 to -26 EYO) from non-carriers (39) while child mutation carriers (-27 to  -35 EYO) showed elevated plasma Aβ1-42 and elevated ratios of plasma Aβ1-42 to Aβ1-40 when compared to non-carrier children (42). Florbetapir PET imaging revealed Aβ1-42 aggregation in carriers with an average of -16 EYO, which increased steeply for almost 10 years until plateauing at -7 EYO (36). Aβ1-42 aggregation was most evident in the anterior cingulate and precuneus (36). Consistent with this initial study, a recent report using the Pittsburgh-compound-B (PiB) tracer showed cortical Aβ1-42 aggregation beginning in carriers with an average of -15 EYO (37). Flortaucipir PET imaging of tau in the brain of carriers has shown progressive accumulation of tau in the entorhinal cortex and inferior temporal lobe an average of -6 EYO, and an estimated 10 years after the first evidence of cortical Aβ1-42 (37). Tau aggregation was strongly related to performance on tests of verbal memory and global cognition, and could be seen in the junctions of the parietal, temporal, and occipital lobes, as well as in the posterior cingulate cortex and precuneus among carriers with MCI (37).
A single study extracted the sequential change points (and their 95% confidence intervals) for the CSF and brain biomarkers in carriers, revealing that the CSF Aβ1-42  was the earliest biomarker to show abnormality at -19 EYO, followed by PET markers of in vivo AD pathology, and finally hippocampal volume at -5 EYO (51). Further work with biomarkers has shown that baseline cortical Aβ1-42 and CSF phosphorylated-tau/Aβ1-42 ratios predicted cognitive decline over 2-3 years among carriers with EYOs ranging from -24 to 0, whereas CSF Aβ1-42 and tau measurement levels (total tau and phosphorylated-tau [p-tau]) did not (52).



The near 100% penetrance of the PSEN1 E280A ADAD mutation has provided investigators with a unique framework to examine the course of the cognitive and biological markers of AD across the preclinical and symptomatic stages of the disease, something that cannot be readily done in sporadic AD populations without multi-decade longitudinal studies. Research in the Colombian kindred has included participants with a wide age range, including children, young cognitively unimpaired adults, and individuals with MCI and dementia due to AD. Figure 1 draws from the cumulative studies across this wide age range and provides a cross-sectional synthesis of the findings from Colombian ADAD kindred based on the EYO of MCI for carriers of the PSEN1 E280A mutation. Ordering these findings based on EYO suggests that plasma and CSF Aβ1-42 are clinically abnormal early in the lives of PSEN1 E280A carriers, corresponding with early-life increases in connectivity (as seen on fMRI) within the DMN and hyperactivity (as measured by EEG) throughout the brain. Cortical Aβ1-42 deposition becomes evident a decade-and-a-half later (~ -16 EYO), followed by decreased cerebral perfusion and glucose metabolism in brain regions impacted by cortical Aβ deposition, suggesting a potential connection between cortical Aβ1-42 and decreased parietal, frontal, and temporal metabolic activity in preclinical ADAD. Significantly decreased cortical thickness in parietal regions was also evident as carriers approached the median age of onset of MCI, while child mutation carriers showed paradoxically greater grey matter than non-carriers in regions impacted by AD pathology.

Figure 1. Hypothetical Model of Progression of Biological Markers of PSEN1 E280A Autosomal Dominant Alzheimer’s Disease Relative to Earliest Known Signal of Cognitive Decline

Figure 1. Hypothetical Model of Progression of Biological Markers of PSEN1 E280A Autosomal Dominant Alzheimer’s Disease Relative to Earliest Known Signal of Cognitive Decline

A synthesis of the Colombian kindred biological marker literature is presented as a function of the estimated years until the median age of onset of mild cognitive impairment in this cohort. The hypothetical trajectories of the biological markers of ADAD in this cohort are displayed relative to the earliest known signs of cognitive decline at a median age of -12 EYO (dashed line; Acosta-Baenta et al., 2011).  Note: fMRI = EEG = electroencephalogram; FDG-PET = fludeoxyglucose positron emission tomograph; SPECT = single-photon emission computerized tomography; FMRI = functional magnetic resonance imaging; DMN = default mode network; MRI = structural magnetic resonance imaging; WM = white matter; DTI = diffusion tensor imaging; Aβ = amyloid-beta; PET = positron emission tomography; MTL = medial temporal lobe; CSF = cerebral spinal fluid.


The differences seen between younger and older carriers of the PSEN1 E280A mutation suggest that the course of this ADAD mutation has a temporal order in which biofluid markers and electrophysiological measures may become abnormal early in the disease process. Structural imaging markers (e.g., grey matter volume, white matter integrity) provide evidence of degeneration much later in the preclinical stage of the disease close to the onset of clinical symptoms, although child mutation carriers show paradoxically greater cortical thickness in temporal and parietal regions. Figure 2 illustrates the course of structural MRI and fMRI signatures of PSEN1 E280A from childhood into adulthood, while Figure 3 shows the spatial pattern of temporal lobe tau deposition, as staged by level of neocortical Aβ1-42, from the late preclinical stage of the disease to MCI. Synthesizing findings across PSEN1 E280A ADAD is essential to future research that will compare findings from this mutation to sporadic AD. Until longitudinal data are available for this cohort, using cross-sectional findings and EYO to model the hypothetical progression of disease is the best way to understand the earliest biological and cognitive events in ADAD at which pharmacological and non-pharmacological interventions may be most efficacious.

Figure 2. Structural and Functional Neuroimaging of Child and Adult PSEN1 E280A Carriers

Figure 2. Structural and Functional Neuroimaging of Child and Adult PSEN1 E280A Carriers

A) Child mutation carriers (-36 to -27 EYO), relative to non-carrier children, show paradoxically greater cortical thickness in the parietal and temporal lobe, as indicated by the color blue; cognitively unimpaired adult mutation carriers, however, exhibit decreased cortical thickness relative to non-carriers in the posterior parietal lobe, as indicated by the colors red and yellow. B) Relative to non-carrier children, child PSEN1 mutation carriers show decreased deactivation of the posterior parietal lobe and increased hippocampal activation during memory encoding (Quiroz et al., 2015); cognitively unimpaired adult PSEN1 mutation carriers also exhibit decreased deactivation of the posterior parietal lobe and increased hippocampal activation during memory encoding (Reiman et al., 2012).

Figure 3. Comparison of the spatial patterns of tau deposition in PSEN1 E280A mutation carriers

Figure 3. Comparison of the spatial patterns of tau deposition in PSEN1 E280A mutation carriers

Amyloid-positive PSEN1 mutation carriers display greater tau levels in the entorhinal cortex and regions of the inferior temporal lobe; deposition is believed to begin around -6 EYO (Quiroz et al., 2018).


Through the cumulative, synthesized findings of our review, we propose a hypothetical “tipping point” of hyper to hypo-activity in memory and visuo-perceptual regions in carriers in their mid-thirties (~10 -EYO), which aligns with the mid-point between cortical Aβ deposition (median of -16 EYO) and regional tau deposition (median of -6 EYO) (36, 37). Significant debate persists, however, about whether changes in cortical activation and functional activity are not compensatory, but reflective of poor brain maintenance. A prevailing counter-argument to the posterior-to-anterior compensatory shift in preclinical AD is that increased prefrontal activity in healthy older adults corresponds with decreased efficiency in neural networks (53). While this question is still unresolved in the fields of sporadic AD and ageing, we note that the cognitively unimpaired PSEN1 E280A mutation carriers who participated in the studies of brain function were between 20-30 years younger than the earliest age at which one can be diagnosed with late-onset sporadic AD (65 years old) and were likely unaffected by ageing processes discrete from AD pathology. Studying cognitively unimpaired adult ADAD mutation carriers from this kindred mitigates many of the confounds of advancing age that are common in sporadic AD research.
Several questions remain about how cortical Aβ1-42  relates to changes in cortical activation and functional connectivity in preclinical AD. For example, little is known about the deposition of Aβ1-42 or tau in the occipital lobe, so understanding changes in occipital lobe functioning in the context of AD pathologic change is an important area of future work in in the Colombian kindred. Grey and white matter degeneration and the aggregation of cortical Aβ1-42 and temporal lobe tau are also likely interrelated in ADAD; future work should further explore the relation between white matter integrity and AD pathologic change. A longitudinal study of members from the Colombian kindred that examines plasma, CSF, imaging, and cognitive function is currently underway with the hope of more definitively addressing these questions and elucidating the temporal relations between the cognitive and biological markers of preclinical ADAD. Similarly, studying EEG and in vivo brain pathology in the same sample could determine whether EEG hyper- or hypoactivity can reliably serve as a proxy for the aggregation of AD-related cortical Aβ1-42. The implications of such a study could facilitate the potential clinical and research use of EEG as a precursor assessment to more expensive (and less widely available) positron emission tomography (PET) imaging. Future research should also seek to integrate new behavioral measures, biomarkers, and imaging methods that will continue to expand our knowledge about the progression of AD, such as functional near-infrared spectroscopy (fNIRS) and PET imaging of other AD-related proteins. Understanding how health behaviors, like aerobic fitness and sleep hygiene, impact the course of AD is also a goal of ongoing research efforts in this cohort; two studies are currently underway to explore how these health behaviors impact the biological and cognitive course of AD in PSEN1 E280A carriers.
In addition to identifying the earliest imaging marker abnormalities of ADAD, cognitive and other clinical markers can play an important role as well. Studies from this cohort have suggested that short-term memory deficits in verbal (14) and visual memory (24–26) strongly discriminate preclinical PSEN1 E280A carriers from non-carriers, and are likely to be cost-effective screeners for preclinical AD. Continued detailed investigation of cognitive and behavioral markers sensitive to changes early in preclinical AD is warranted, with specific emphasis on exploring preclinical subclinical changes (i.e., close to the threshold but below cutoffs for statistical significance) in cognitive domains, such as executive function, that may underpin episodic memory problems in the early symptomatic stage of the disease.
Our review also highlights the utility of examining PSEN1 E280A ADAD fluid and imaging progression in children younger than 8 years old, even younger than those investigated by Quiroz and colleagues (2015) (42). Extending ADAD research with larger samples and longitudinal follow up in younger children could provide the strongest evidence for neurodevelopmental changes in PSEN1 E280A ADAD.  If such changes were identified, this would underscore the importance of developing early-life interventions for these individuals. Extrapolating from the EYO of children carriers, ADAD-related abnormal memory network activity, as well as elevations of plasma Aβ1-42 and elevated ratios of plasma Aβ1-42 to Aβ1-42 may inform potential longitudinal or retrospective studies of sporadic AD that explore these abnormalities approximately 30 years before the onset of clinical symptoms.
Like other narrative reviews, our approach is limited in that we pool research studies from the same kindred that vary in methodological design and participant age to extrapolate a hypothesized sequential change model across the disease. In addition, the normally inferable strength of replication by multiple studies is weakened in the set reviewed here, due to the high likelihood that some participants were measured in several studies. Nonetheless, given the robust characterization of this cohort and, in particular, the virtual guarantee that carriers of this mutation will develop clinical symptoms by mid-life, the data reviewed here can inform ongoing research on ADAD and sporadic AD.
Findings to date from this PSEN1 E280A kindred generally support the prevailing models of AD pathogenesis and largely align with research from other ADAD groups, such as the Dominantly Inherited Alzheimer’s Network (DIAN), which suggest that CSF markers of amyloid-beta change earliest in the disease process, followed by decline in brain metabolism, and lastly by atrophy in memory-related brain structures (e.g., the hippocampus) which is closer to the age of onset of cognitive decline (54). Reports from the Colombian kindred also generally align with studies of sporadic AD (55, 56). The most precise understanding of disease-course will, however, come from longitudinal studies. We are collecting such data now, and these data as well as those from DIAN other ADAD research groups will facilitate comparisons with longitudinal studies of sporadic AD (e.g., the Harvard Aging Brain Study, the Alzheimer’s Disease Neuroimaging Initiative, and the Australian Imaging, Biomarker & Lifestyle study). Studies that compare data from ADAD kindreds and sporadic AD groups should ensure that, when feasible, parallel protocols for imaging, biomarker collection, and cognitive testing are followed. While the clinical and cognitive course of ADAD appears to follow a similar course to that in sporadic AD, initial studies comparing the two etiologically distinct forms of AD should prioritize testing whether specific biological and cognitive markers in the preclinical stage of ADAD occur at similar points before clinical onset and predict risk for clinical progression in sporadic AD populations. Studying ADAD kindreds, like the Colombian PSEN1 E280A cohort, is an approach that affords an understanding of the cognitive and biological course of AD separate from age-related processes and co-morbidities (e.g., stroke and white matter hyperintensities). As carriers of the PSEN1 E280A mutation are virtually guaranteed to develop MCI and dementia due to AD by their mid-to-late 40s, the effects age-related processes on the cognitive and biological changes seen in ADAD are believed to be low to non-existent.
Over the next decade, collaborations between ADAD and sporadic AD research groups may substantially advance our understanding of the generalizability of ADAD findings to the more common sporadic form of the disease, while also highlighting those changes that are specific to early onset forms of the disease. As we approach the new frontier of AD research that aims to find an effective prevention or treatment for AD by 2025 (57), longitudinal research and comparisons between PSEN1 E280A ADAD and sporadic AD cohorts will be essential to advancing our understanding of when the AD pathophysiological process begins, how to most accurately identify individuals at risk for AD in late life early on, where in the disease process intervention will be most efficacious, and what the most effective and stage-specific interventions might be.



Significant abnormalities in plasma, CSF, and brain-based AD pathology, as well as differences in brain structure and function (despite preserved cognition) are evident in carriers of the PSEN1 E280A mutation as early as three-and-a-half decades before the median age of onset of AD-related cognitive decline. Findings from the Colombian kindred have laid the groundwork for better understanding the prognostic value of fluid and in vivo imaging markers of ADAD.  However, continued integration and comprehensive evaluation of the biological and cognitive markers of ADAD will inform the search for the earliest sensitive and specific markers of the disease and direct the development of interventions for the preclinical stage of AD. Generalizing findings from the Colombian kindred to sporadic AD will be the next wave of research in this kindred, carrying significant implications for clinical trials, research, and treatment of the disease.


Funding and financial disclosures: Research reported in this review was supported by grants from the NIH Office of the Director (DP5OD019833 to YTQ); the NIH National Institute on Aging (R01AG054671 to YTQ; 1RF1AG041705 to EMR, FL, and PNT; 1R01AG055444 to EMR, FL, and PNT; AG058805-01 to JRG; Massachusetts General Hospital ECOR (1200-228010 and 1200-228767 to YTQ) and Rappaport Fellowship (JRG); the Alzheimer’s Association Clinical Fellowship (AACF-16-440965 to JRG); the Alzheimer’s Association (EMR, KJ); project 111565741185 from the Administrative Department of Science, Technology, and Innovation (Colciencias Colombia) (FL); the European Union¹s Horizon 2020 Research and Innovation Programme (Marie Sklodowsk; a-Curie Grant agreement [IF-2015-GF, 706714] to HILJ); the Belgian Foundation for Scientific Research (FNRS grant# SPD28094292 to BJH); the Belgian Foundation for Alzheimer Research (SAO-FRA grant# P16.008 to BJH); the Banner Alzheimer’s Foundation (EMR); GHR Foundation (EMR); F-Prime Biosciences Research Initiative (EMR); NOMIS Foundation (EMR), the FIL Foundation (EMR), Fidelity Biosciences (KJ), Harvard Neurodiscovery Center (KJ), the Marr Foundation (KJ and RAS) and the Phi Kappa Phi Graduate Student Fellowship (JTF). PNT reports receiving consulting fees from Abbott Laboratories, AbbVie, AC Immune, Acadia, Auspex, Boehringer Ingelheim, Chase Pharmaceuticals, Corium, Eisai, GliaCure, INSYS Therapeutics, Pfizer, T3D; receiving consulting fees and research support from AstraZeneca, Avanir, Biogen, Cognoptix, Eli Lilly, H. Lundbeck A/S, Merck and Company, Roche, and Takeda; receiving research support only from Amgen, Avid, Functional Neuromodulation, GE Healthcare, Genentech, Novartis, Roche, Targacept, the National Institute on Aging, and the Arizona Department of Health Services; owning stock options in Adamas; and being listed as a contributor to a patent owned by the University of Rochester. Banner Alzheimer’s Institute also has contracts with Genentech/Roche, Novartis/Amgen and Avid/Lilly. KJ has provided consulting services for Lilly, Novartis, Janssen, Roche, Piramal, GE Healthcare, Siemens, ISIS Pharma, AZTherapy, and Biogen; has received support from a joint National Institutes of Health– and Lilly-sponsored clinical trial (Anti-Amyloid Treatment in Asymptomatic Alzheimer’s [A4] Study); and has received research support from the National Institute on Aging (grants U19AG10483 and U01AG024904-S1), Fidelity Biosciences, the Michael J. Fox Foundation, and the Alzheimer’s Association. RAS receives research support from grants U01 AG032438, U01 AG024904, R01 AG037497, R01 AG034556, and U19 AG010483 from the National Institutes of Health. She is also a site principal investigator or coinvestigator for Avid, Bristol-Myers Squibb, Pfizer, and Janssen Alzheimer Immunotherapy clinical trials. EMR reports that he is a compensated Scientific Advisor with: Alkahest, Alzheon, Axovant, Denali, Green Valley, United Neuroscience and Zinfandel Pharma. Authors JTF, ACG, JRG, DJN, EGV, EPD, HILJ, BJH, AA, AB, YB, KK, KC, FL, and YTQ report no financial disclosures.

Acknowledgements: We thank the members of the Colombian families with ADAD for their invaluable dedication to research and inspiration.

Author contributions:  Review conception and design: Fuller, Cronin-Golomb, Lopera, Quiroz. Review supervision: Cronin-Golomb, Lopera, Quiroz. Database search: Fuller, Lopera, Quiroz. Interpretation and synthesis of the literature: Fuller, Lopera, Quiroz. Drafting the manuscript: Fuller, Cronin-Golomb, Gatchel, Norton, Guzmán-Vélez, Jacobs, Hansseuw, Pardilla-Delgado, Lopera, Quiroz.

Preparation of figures and tables: Fuller, Guzmán-Vélez, Quiroz

Critical revision of the manuscript for important intellectual content: All co-authors

Conflict of interest: Dr. Hanseeuw reports grants from Belgian National Fund for Scientific Research (FNRS), grants from Belgian Foundation for Alzheimer Research (SAO-FRA), personal fees from GE Healthcare,  all outside the submitted work. Dr. Sperling reports grants from Janssen during the conduct of the study; personal fees from AC Immune, personal fees from Roche, personal fees from Eisai, personal fees from Insightec, personal fees from Takeda, personal fees from from Merck, personal fees from General Electric, all outside of the submitted work. Dr. Gatchel reports grants from BrightFocus Foundation, grants from Alzheimer’s Association, and grants from NIH/NIA, all outside the submitted work. Dr. Tariot reports personal fees from AbbVie, AC Immune, Acadia, Auspex, Boehringer Ingelheim, Chase Pharmaceuticals, Corium, Eisai, GliaCure, INSYS Therapeutics, Pfizer, and T3D, all outside of the submitted work; he has also received grants and personal fees from AstraZeneca, Avanir, Biogen, Eli Lilly, H. Lundbeck A/S, Merck and Company, Roche, and Takeda, all outside of the submitted work. Dr. Tariot received grants from Amgen, Avid, GE Healthcare, Genentech, Novartis, National Institute on Aging, and the Arizona Department of Health Services. Dr. Tariot owns stock options in Adamas. In addition, Dr. Tariot is a contributor on a patent owned by the University of Rochester, outside of the submitted work. Dr. Reiman reports grants from the National Institute on Aging, Novartis/Amgen, Banner Alzheimer’s Foundation, Alzheimer’s Association, GHR Foundation, F-Prime Biosciences Research Initiative and NOMIS Foundation.  He also reports that he is a compensated Scientific Advisor with: Alkahest, Alzheon, Aural Analytics, Denali, Green Valley, Roche (Travel Expenses only), United Neuroscience, and Zinfandel Pharma.  Banner Alzheimer’s Institute has contracts with Genetech/Roche, Novartis/Amgen and Avid/Lilly. Dr. Jacobs reports support by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant agreement [IF-2015-GF, 706714]. Dr. Quiroz reports grants from the National Institute on Aging, and Alzheimer’s Association. All other co-authors report nothing to disclose.

Ethical standards: The studies described in this manuscript were conducted in accordance with ICH-Good Clinical Practice guidelines and the Declaration of Helsinki and were approved by the appropriate institutional review committees and regulatory agencies.  Written informed consent was obtained from each participant before any study procedures.





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J. Cummings1, N. Fox2, B. Vellas3, P. Aisen4, G. Shan5 for the EU/US Alzheimer’ Disease Task Force


1. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; 2. Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK; 3. Department of Geriatric Medicine, University Toulouse III, Toulouse, France; 4. Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA; 5. Department of Environmental and Occupational Health, Epidemiology and Biostatistics Program, School of Community Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA

Corresponding Author: Jeffrey Cummings, MD, ScD, Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W Bonneville Ave, Las Vegas, NV, 89106, USA, T: 702.483.6029, F: 702.722.6584, Email:

J Prev Alz Dis 2018;5(2):103-109
Published online March 16, 2018,



BACKGROUND:  Disease-modifying therapies are urgently needed for the treatment of Alzheimer’s disease (AD).  The European Union/United States (EU/US) Task Force represents a broad range of stakeholders including biopharma industry personnel, academicians, and regulatory authorities.
OBJECTIVES:  The EU/US Task Force represents a community of knowledgeable individuals who can inform views of evidence supporting disease modification and the development of disease-modifying therapies (DMTs).  We queried their attitudes toward clinical trial design and biomarkers in support of DMTs.
DESIGN/SETTING/PARTICIANTS:  A survey of members of the EU/US Alzheimer’s Disease Task Force was conducted.  Ninety-three members (87%) responded.  The details were analyzed to understand what clinical trial design and biomarker data support disease modification.
MEASUREMENTS/RESULTS/CONCLUSIONS:  Task Force members favored the parallel group design compared to delayed start or staggered withdrawal clinical trial designs to support disease modification.  Amyloid biomarkers were regarded as providing mild support for disease modification while tau biomarkers were regarded as providing moderate support.  Combinations of biomarkers, particularly combinations of tau and neurodegeneration, were regarded as providing moderate to marked support for disease modification and combinations of all three classes of biomarkers were regarded by a majority as providing marked support for disease modification.  Task Force members considered that evidence derived from clinical trials and biomarkers supports clinical meaningfulness of an intervention, and when combined with a single clinical trial outcome, nearly all regarded the clinical trial design or biomarker evidence as supportive of disease modification.  A minority considered biomarker evidence by itself as indicative of disease modification in prevention trials.  Levels of evidence (A,B,C) were constructed based on these observations.
CONCLUSION:  The survey indicates the view of knowledgeable stakeholders regarding evidence derived from clinical trial design and biomarkers in support of disease modification.  Results of this survey can assist in designing clinical trials of DMTs.

Key words: Alzheimer’s disease, clinical trials, biomarkers, EU/US Task Force.




Disease-modifying therapies (DMTs) for Alzheimer’s disease (AD) are an increasingly important goal for drug development programs in an effort to prevent or delay the onset or slow the progression on the disease (1).  Analyses suggest that a delay of 5 years in the onset of AD by 2025 would decrease the frequency of the illness by 40% in 2035 and would save an estimated $367 billion US dollars by 2050 (2).  A DMT can be defined as an intervention that produces an enduring change in the clinical progression of AD by interfering in the underlying pathophysiological mechanisms of the disease process leading to cell death (3, 4).  Sources of data in support of disease modification (DM) include observations derived from trial designs and data from biomarkers collected in the course of clinical trials (3, 4).
To better understand how critical stakeholders view the strength of evidence used to support the concept of DM and to construct levels of evidence of DM, we conducted a survey of members of the European Union/United States (EU/US) Alzheimer’s Disease Task Force including individuals with biopharma industry, academic, and regulatory backgrounds relevant to AD drug development.  The Task Force has a history of convening, discussing, and recommending action for issues related to clinical trials in AD [5-9].   Based on the results of the survey we suggest levels of evidence for DMT clinical trials as ranked by Task Force members.  The purpose of the survey and data organization/presentation is to assist those involved in DMT drug development to choose trial designs and biomarkers for trials that will best demonstrate DM.


Research Methods

The survey was conducted between June 26 and July 11, 2017 and consisted of 4 rounds of requests to all members of the EU/US Task Force to complete the survey.  The purpose of the survey and the proposed use of the data collected were explained in the request soliciting the response.  Ninety-three unique individuals responded to the survey of 107 active members of the Task Force for a response rate of 87 %.
Most of the questions (29/35) querying trial design and biomarker support for DMTs were framed as offering “no”, “mild”, “moderate”, or “marked” support for DM. Two participation questions, 3 demographic questions, and 3 yes/no questions were also posed.  The survey reporting adheres to established guidelines (10).  Statistical comparisons used the Bonferroni correction for multiple comparisons (6 possible combinations) within the question with an adjusted p-value of 0.05/6=0.0083 (PercD).
The survey was reviewed and a waiver for informed consent obtained from the Cleveland Clinic Institutional Review Board.  All survey respondents agreed to take the survey and to have their responses used in a publication.  All responses were anonymous.  Not all respondents answered all questions; no questions had fewer than 70 respondents and most had 80 or more.



The survey included demographic information regarding the participants. Of respondents, 48.91% were members of the biopharma industry, 38.04% were primarily academicians with expertise in AD and DMT, and 13.04% were from other sectors including regulatory authorities. Of the participating Task Force members, 30.34%% were active primarily in the EU, 55.43%% in North America, and 14.13% in other world regions.
Data were collected on the experience of those responding to the survey; 6.59% were relatively new to drug development with 0-5 years of experience, 10.99% had 6-10years, 16.48% had 11-15 years, 21.98% in 16-20 years, and 43.96% had more than 20years of drug development experience.  Cumulatively the respondents had a minimum of 1478.1 years of experience with clinical trials and drug development and more than 80% had at least 10 years of drug development experience.
The survey queried the respondents on the strength of data offered by types of trial design for DM (Table 1).  Staggered start and delayed withdrawal designs have been proposed as means of supporting DM (11-13).  Of the respondents, 10.71%, 33.33%, 36.90% and 19.05% thought the delayed start design with failure to catch up to the group treated first by the group treated after a delay offered no, mild, moderate, and marked support, respectively. The percentage difference (PercD) between mild and no support was 22.62% with a 95% confidence interval (CI) of 9.28% – 35.96%, and the PercD between moderate and no support was 26.19% with a 95% CI of 12.53% – 39.84%. Similarly, 9.88%, 41.98%, 32.10%, and 16.05% of the respondents thought that the failure to fall to baseline levels by the withdrawn group in the delayed withdrawal design offered no, mild, moderate, and marked support for DM.  A parallel group design with a drug-placebo difference at trial conclusion was considered to offer no support for DM by 32.94%; mild support by 17.65%, moderate support by 16.47%, and marked support by 32.94%.

Table 1. Ratings of clinical trial design evidence in support of disease-modification

Table 1. Ratings of clinical trial design evidence in support of disease-modification


The survey queried observations regarding the slope of decline expected with DMTs.  A change in slope of decline was considered to offer no, mild, moderate, and marked support for DM by 10.47%, 24.42%, 38.37%, and 26.74% of the respondents.  An increasing drug-placebo difference over time was thought to offer no, mild, moderate, and marked support for DM by 9.30%, 19.77%, 34.88%, and 36.05% of respondents.  Delay to milestone observations (e.g, delay to reach Clinical Dementia Rating (CDR) scores of 1.0 from a CDR of 0.5 at baseline) were regarded as supporting no, mild, moderate, and marked support of DM by 19.77%, 29.07%, 36.05%, and 15.12% of survey respondents.
The survey interrogated the support offered for DM by commonly used biomarkers including amyloid and tau imaging and cerebrospinal fluid (CSF) measures of amyloid beta protein 1-42 (Aß), total tau, and phospho-tau (p-tau) (Table 2).  Reduction in amyloid burden as shown by amyloid imaging was regarded as offering no, mild, moderate, and marked support by 6.25%, 52.50%, 30.00%, and 11.25% of respondents (mild support VS no support: PercD=46.25%, 95% CI=32.86% – 59.64%; mild support VS marked support: PercD=41.25%, 95% CI=26.27% – 56.23%).  CSF measures of Aß were regarded as offering less support by most respondents (20.25%, 49.37%. 24.05%, 6.33%).

Table 2. Ratings of biomarker evidence in support of disease-modification

Table 2. Ratings of biomarker evidence in support of disease-modification

Table 3. Rating of biomarker Evidence of Disease-Modification using the A, T, N approach (A - amyloid biomarkers, T–tau biomarkers, N-neurodegeneration biomarkers)

Table 3. Rating of biomarker Evidence of Disease-Modification using the A, T, N approach (A – amyloid biomarkers, T–tau biomarkers, N-neurodegeneration biomarkers)


Changes in tau imaging were regarded as indicative of DM by many respondents (7.69%, 26.92%, 46.15%, 19.23% considered reduction of tau burden on tau imaging as offering no, mild, moderate, and marked support for DM).  Drug-placebo differences in total tau and p-tau were considered to offer similar levels of support for DM.  Differences in CSF total tau were considered by 9.09%, 54.55%, 28.57%, and 7.70% of respondents to offer no, mild, moderate and marked support for DM.  For p-tau, 7.69%, 50.00%, 32.05%, and 10.26% considered drug-placebo difference to be indicative of no, mild, moderate, and marked support for DM.
Drug-placebo differences in fluorodeoxyglucose (FDG) positron emission tomography (PET) were considered by 16.46%, 41.77%, 31.65%, and 10.13% of respondents to be indicative of DM.
Individuals thought changes in volumetric magnetic resonance imaging (MRI) supported the occurrence of DM more than drug-placebo differences in functional MRI.  Of the respondents, 7.69%, 35.90%, 38.46%, and 17.95% thought drug-placebo differences on volumetric MRI to offer no, mild, moderate, and marked support for DM.  Drug-placebo differences in fMRI were generally considered less indicative of DM (22.08%, 51.95%, 20.78%, and 5.19% considered fMRI differences between drug and placebo to indicate no, mild, moderate, and marked support for DM).
The survey also queried the combinations of biomarkers that might be supportive of DM by a potential DMT.  When considering drug-placebo differences on two amyloid biomarkers (e.g, amyloid imaging and CSF Aß), 7.59%, 36.71%, 36.71%, and 18.99% ranked the changes as offering no, mild, moderate, and marked support for DM).  This compares to drug-placebo differences in two tau-related measures (e.g, tau imaging and CSF tau or p-tau) that was seen by 7.79%, 20.78%, 49.35%, and 22.08% of respondents as supportive of DM. The percentage of moderate support is significantly higher than that of no support (PercD=41.56%, 95% CI=27.45% – 55.66%), mild support (PercD=28.57%, 95% CI=10.99% – 46.15%), and marked support (PercD=27.27%, 95% CI=9.41% – 45.14%). Drug-placebo differences of combinations of amyloid-related plus tau-related measures were considered more indicative of DM:  5.33%, 12.00%, 49.33%, and 33.33% regarded the combination as indicating no, mild, moderate, and marked support.  The percentage of moderate support or marked support is statistically significantly greater than that of no support or mild support. Therapies that produce changes in amyloid measures and volumetric MRI were considered by 2.56%, 11.54%, 53.85%, and 32.05% as indicative of no, mild, moderate, or marked support of DM.  Drug-placebo differences on the combination of volumetric MRI and measures of tau was regarded by 2.53%, 6.33%, 48.10%, and 43.04% of respondents as indicative of DM.  Drug-placebo differences on the combination of all three types of measures (amyloid, tau, MRI) was most likely to be seen by the respondents as supportive of DM:  2.53%, 6.33%, 30.38%, and 60.76% thought this combination to support DM. The percentage of marked support is significantly greater than that of no support (PercD=58.22%, 95% CI=46.27% – 70.18%), mild support (PercD=54.43%, 95% CI=40.93% – 67.93%), and moderate support (PercD=30.38%, 95% CI=10.42% – 50.34%).
The survey also sought to understand respondents’ views of biomarkers using the Amyloid (A), Tau (T), and Neurodegeneration (N) classification of biomarkers (14).  Drug-placebo differences in A were regarded as indicative of DM by 7.79%, 55.84%, 29.87%, and 6.49% (no, mild, moderate, marked) of respondents.  Drug-placebo differences in T were regarded as indicative of DM by 5.19%, 35.06%, 50.65%, and 9.09% of respondents.  Drug-placebo differences in N were perceived as indicative of DM by 2.67%, 34.67%, 41.33%, and 21.33% of respondents.
The survey also approached drug-placebo differences in combinations of biomarkers using the A, T, N approach.  Drug-placebo difference in A plus T were regarded as indicative of DM by 4.00%, 20.00%, 53.33%, and 22.67% of respondents (no, mild, moderate, marked).  Differences in A plus N were perceived as supporting DM by 1.35%, 17.57%, 54.05%, and 27.03% of surveyed individuals (no, mild, moderate, marked).  Of respondents, 1.32%, 15.79%, 42.11%, and 40.79% thought drug-placebo differences on T plus N to indicate DM (no, mild, moderate, marked).  Drug-placebo differences on the combination of all three biomarker classes — A, T, N — were considered by 1.32%, 6.58%, 26.32%, and 65.79% to support DM (no, mild, moderate, marked).
The survey queried respondents on how biomarker and clinical trial design observations related to other types of data in establishing clinical meaningfulness (Figure 1). Respondents were asked whether they thought evidence of DM (from trial designs or biomarkers) supported the concept of clinical meaningfulness of an intervention. Two-thirds (67.53%) said “yes” (Figure 1a).  Respondents were asked if evidence of DM (from trial designs or biomarkers) support clinical meaningfulness of an intervention if paired with a positive clinical outcome (e.g, AD Assessment Scale-cog subscale, CDR- sum of boxes, Preclinical Alzheimer Cognitive Composite, etc) in a trial of a DMT.  Ninety percent (90.67%) responded “yes” to this probe (Figure 1b).  Considering the role of biomarkers in prevention trials, respondents were asked if biomarker evidence of successful intervention in the fundamental process of cell death could serve as a single primary outcome in prevention trials of participants with normal cognition and biomarker evidence of AD pathology.  Approximately 40 percent (42.11%) said “yes” (Figure 1c).

Figure 1a. Percent of respondents with “yes” and “no” answers to the probe question

Figure 1a. Percent of respondents with “yes” and “no” answers to the probe question


Figure 1b. Percent of the respondents with “yes” and “no” answers to the probe question

Figure 1b. Percent of the respondents with “yes” and “no” answers to the probe question


Figure 1c. Percent of the respondents with “yes” and “no” answers to the probe question

Figure 1c. Percent of the respondents with “yes” and “no” answers to the probe question



Observations from this survey reflect the opinions of a majority of members of the EU/US Alzheimer’s Disease Task Force.  Together the respondents represent in excess of 1400 cumulative years of drug development experience.  The survey specifically focused on the roles of clinical trial design and of biomarkers in supporting DM in clinical trials of AD DMTs.  The survey also queried the roles of trial designs and biomarkers in regulatory discussions of clinical meaningfulness.
Delayed start and staggered withdrawal designs are frequently discussed as means of showing DM in clinical trials (3, 11-13). There was some ambiguity in the responses obtained for trial design.  Although, a minority of EU/EU Task Force members considered these designs as offering marked support for DM (19.05% and 16.05% respectively) and parallel group designs showing a drug-placebo difference at trial termination were seen as offering marked support for DM (32.94%), the three trial designs had very similar percentages when moderate and marked support were combined (55.95% for delayed start; 48.15% for staggered withdrawal; 49.41% for parallel group). A drug-placebo difference at trial termination in a parallel group trial was regarded as showing marked support or no support for DM by exactly equal numbers of Task Force members (32.94% and 32.94%).
A number of secondary observations have been suggested as supportive of DM (3, 4).  When considering moderate and marked support together, a majority of Task Force members thought that a change in slope of decline and an increasing drug-placebo difference over time offered substantial support for DM (65.11% and 70.93%).  Delay-to-milestone observations were thought to provide mostly mild (29.07%) and moderate (36.05%) support for DM.
Reductions in amyloid on amyloid imaging and drug-placebo differences in CSF Aß were considered to provide marked support for DM by a minority of Task Force members (11.25% for amyloid imaging, 6.33% for CSF Aß, and 6.49% for A in the A, T, N classification).  These measures were thought to offer moderate support by 30.00%, 24.05%, and 29.87%.
Tau imaging was perceived as providing stronger evidence of DM than amyloid imaging.  Of the respondents, 19.23% thought tau imaging offered marked support for DM and 46.15% thought it offered moderate support (65.38% together).  CSF measures of tau were regarded less confidently as supporting DM; total tau was seen as offering marked and moderate support by 7.79% and 28.57%, while p-tau was perceived as offering marked and moderate support by 10.26% and 32.05%.  Drug-placebo differences in T of the A, T, N classification were considered to offer marked and moderate support for DM by 9.09% and 50.65% (59.74% together).
Drug-placebo differences on volumetric MRI were considered more supportive of DM than either amyloid or tau measures; 17.95% thought if offered marked support for DM and 38.46% thought it moderately supportive (56.41% together).  Drug-placebo differences on N of the A, T, N classification were perceived as offering marked and moderate support by 21.33% and 41.33% (62.66% together).  Drug-placebo difference on FDG PET and fMRI were most commonly considered mildly supportive of DM (41.77%, 51.95%).
Drug-placebo differences on a combination of two types of amyloid biomarkers were considered somewhat supportive of DM (18.99% marked support, 36.71% moderate support).  Drug-placebo difference on two types of tau biomarkers changed little from the confidence in DM derived from tau imaging by itself (22.08% marked and 49.35% moderate support for the combination; 19.23% marked and 46.15% moderate for tau imaging by itself).  Drug-placebo differences in combinations of biomarkers assessing different pathologies were perceived as supporting DM more strongly than individual or combinations of biomarkers measuring a single category of pathological change.  Amyloid plus tau provided marked support for 33.33% of Task Force members and 49.33% thought it provided moderate support (81.69% together); for amyloid plus MRI changes 32.05% and 53.85% thought the combination provided marked and moderate support (85.90% together); for tau plus MRI 44.04% and 48.10% thought the combination provided marked and moderate support (91.14% together).  Drug-placebo difference on the combination of all three types of biomarkers was thought to be most supportive of DM (60.67% marked support).  Overall, the trend was to regard the combination of biomarkers to be more indicative of DM than any single biomarker and confidence in DM increased with the number of biomarkers demonstrating a drug-placebo difference.
The A, T, N approach yielded results similar to the biomarker-specific approach (for marked support: A+T = 22.67%; A + N = 27.03%; T + N = 40.79% and A+T+N = 65.79%).  The concordance between the biomarker-specific rankings and the A, T, N rankings supports the internal validity of the survey.
Most Task Force members considered trial and biomarker evidence of DM to be clinically meaningful (67.53%).  A substantial majority (90.67%) thought that the combination or a clinical measure (ADAS-cog, CDR-sb, PACC) plus trial design or biomarker evidence of DM would support the clinical meaningfulness of an intervention.  A minority of Task Force members (42.11%) thought that biomarker evidence of DM would serve as a single primary outcome in prevention trials.
Limitations of the survey include the relatively small size of the Task Force; the fact that not all respondents answered all questions; the possible ambiguity or misinterpretation of some questions; and possible subjective variability in defining “mild”, “moderate”, and “marked”.
Strengths include the experience of the stake holders; the high rate of response among Task Force members; and the internal consistency shown across the two biomarker classification systems used.
One of the goals of the survey was to allow construction of levels of evidence in support of DM to assist in planning trials of DMTs.  Table 4 shows how the survey would guide the assignment of levels of evidence supportive of DM.   Based on the percent of Task Force members indicating that a trial design or biomarker had marked, moderate or mild support for DM, the survey indicates that the trial design most commonly chosen to support DM was the parallel group approach (Class A); delayed start and delay-to-milestone designs offered moderate support (Class B); and staggered withdrawal designs offered mild support (Class C).  Class A biomarker support for DM was based on the combination of A+T+N; Class B evidence included T+N, A+N, A+T, two T, two A, volumetric MRI, and tau imaging; Class C evidence included FDG, fMRI, CSF tau, CSF p-tau, CSF Aß, and amyloid imaging.

Table 4. Levels of evidence in support of DM as derived from the survey of the EU/US Alzheimer’s Disease Task Force. In each case, the hierarchy is derived from the survey by which observation had marked support (Level A), moderate support (Level B), or mild support (Level C)

Table 4. Levels of evidence in support of DM as derived from the survey of the EU/US Alzheimer’s Disease Task Force. In each case, the hierarchy is derived from the survey by which observation had marked support (Level A), moderate support (Level B), or mild support (Level C)

*Ambiguities concerning this recommendation are provided in the discussion; **2 A biomarkers were equally ranked for moderate and mild support of DM; A = amyloid, T= tau, N = neurodegeneration



This survey of EU/US Alzheimer’s Disease Task Force members provides insight into the opinions of those experienced with development of DMTs for AD.  Cumulatively the respondents had more than 1400 years of drug development experience.  Some clear trends were evident in the survey such as the perception that drug-placebo differences in several types of biomarkers offers more support for DM than combinations of any two or any single biomarker.  The survey allowed the construction of classes of evidence in support of DM (Table 4).  The survey was designed to assist those involved in development of DMTs for AD to choose designs and biomarkers perceived by the stakeholders as indicative of DM.  These observations may assist in constructing trials and marshalling evidence of DM in trials of urgently needed DMTs for AD.


Funding: JC acknowledges support of a COBRE grant from the NIH/NIGMS (P20GM109025) and Keep Memory Alive.

EU/US Task Force members who agreed to be acknowledged: Cosma-Roman D, Avanir Pharmaceuticals, USA; Dekosky S, University of Florida College of Medicine, USA; Delrieu J, CHU La Grave – Casselardit, France ; Donohue M, Keck School of Medicine, University of Southern California, USA; Dube S,  Avanir Pharmaceuticals, USA ; Dubois B, Salpetriere Hospital, France; Frisoni G, IRCCS San Giovanni Dio, Italy; Fullerton T, Pfizer, USA; Gauthier S, McGill Center for Studies in Aging, Canada; Goedkoop R,  Pharnext SA, France; Grundman M, Global R&D Partners, USA; Guthrie S, UTHRIE Spencer, Prothena Biosciences, USA; Ho C, Denali Therapeutics, USA; Tome M, European Medicines Agency, United Kingdom; Lawson J, Fujirebio, USA; Lovestone S, University of Oxford, United Kingdom; Lyketsos C, Johns Hopkins University, USA; Malamut R, Avanir Pharmaceuticals, USA ; Merdes A, Servier Forschung GmbH, Germany; Mintzer J, Roper St. Francis CBRT, USA; Molinuevo JL, ICN Hospital Clinic, Spain; Olsson T, Biogen, USA; Ousset PJ,  CHU La Grave – Casselardit, France ; Peskind E, University of Washington School of Medicine, USA; Pollentier S, Boehringer Ingelheimer Pharma GmbH & Co. KG, Germany; Porteinsson A, University of Rochester School of Medicine and Dentistry, USA; Pueyo M, Institut de Recherches Internationales SERVIER, France ; Rafii M, Keck School of Medicine, University of Southern California, USA; Raman R, Keck School of Medicine, University of Southern California, USA; Rosenberg P, John Hopkins University School of Medicine, USA; Rouru J, Orion Pharma, Finland; Rubino I, Biogen, USA; Salloway S, Warren Alpert Medical School of Brown University, USA; Scheltens P, VU University Medical Center, The Netherlands; Siemers E, Eli Lilly and Company, USA; Siffert J, Nestlé Health Science, USA; Sims J, Eli Lilly and Company, USA; Smith J, Roche Products Ltd, United Kingdom; Sperling B, Biogen, USA; Sperling R, Harvard Medical School, USA; Touchon J, Jacques Touchon Conseil, France; Van der Geyten S, Janssen Research & Development, Belgium; Weiner M, San Francisco Veterans Affairs Medical Center, USA; Wilcock G, University of Oxford, United Kingdom.

Disclosures: JC has provided consultation to Abbvie, Acadia, Actinogen, Alzheon, Anavex, Avanir, Axovant,  Boehinger-Ingelheim, Bracket, Dart, Eisai, Genentech, Intracellular Interventions, Lilly, Lundbeck, Medavante, Merck, Neurocog, Novartis, Orion, Otsuka, Pfizer, QR, Roche, Suven, Sunovion, Takeda and Toyama pharmaceutical and assessment companies. NF consults for Eli Lilly, Novartis, Sanofi, Roche, and GlaxoSmithKline GSK. BV reports grants from Pierre Fabre, Avid, Exonhit, AbbVie, Lilly, Lundbeck, MSD, Otsuka, Regenron, Sanofi, Roche, AstraZeneca, LPG Systems, Nestlé, and Alzheon, and personal fees from Lilly, Lundbeck, MSD, Otsuka, Roche, Sanofi, Biogen, Nestlé, Transition Therapeutics, and Takeda. PA reports being a consultant to NeuroPhage, Merck, Roche, Novartis, Lundbeck, Biogen, Probiodrug, Anavex, and Abbvie; and receiving grants from Eli Lilly and Company, Janssen, the Alzheimer’s Association, and the NIH. GS reports no conflicts of interest.

Ethical standards: The survey was approved and requirement for informed consent waived by the Cleveland Clinic Institutional Review Board. All participants agree to the survey and to have the results published.  All Task Force members listed agreed to be acknowledged.



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R. Doody


Corresponding Author: R. Doody on behalf of the Roche/Genentech Alzheimer’s Molecule and Diagnostics Teams Product Development, Neuroscience, F. Hoffman-La Roche Ltd, Basel, Switzerland,, Tel: +41 61 687 09 46

J Prev Alz Dis 2017;4(4):264-272
Published online September 27, 2017,



Alzheimer’s disease (AD) is a chronic neurodegenerative disease for which no preventative or disease-modifying treatments currently exist. Pathological hallmarks include amyloid plaques and neurofibrillary tangles composed of hyper-phosphorylated tau protein. Evidence suggests that both pathologies are self-propagating once established. However, the lag time between neuropathological changes in the brain and the onset of even subtle clinical symptomatology means that patients are often diagnosed late when pathology, and neurodegeneration secondary to these changes, may have been established for several years. Complex pathological pathways associated with susceptibility to AD and changes that occur downstream of the neuropathologic process further contribute to the challenging endeavour of developing novel disease-modifying therapy. Recognising this complexity, effective management of AD must include reliable screening and early diagnosis in combination with effective therapeutic management of the pathological processes. Roche and Genentech are committed to addressing these unmet needs through developing a comprehensive portfolio of diagnostics and novel therapies. Beginning with the most scientifically supported targets, this approach includes two targeted amyloid-β monoclonal antibody therapies, crenezumab and gantenerumab, and an anti-tau monoclonal antibody, RO7105705, as well as a robust biomarker platform to aid in the early identification of people at risk or in the early stages of AD. Identification and implementation of diagnostic tools will support the enrolment of patients into clinical trials; furthermore, these tools should also support evaluation of the clinical efficacy and safety profile of the novel therapeutic agents tested in these trials. This review discusses the therapeutic agents currently under clinical development.

Key words: Alzheimer’s disease, disease-modifying therapy, biomarkers, diagnosis.



Roche/Genentech in Neuroscience

The management of neurological diseases is associated with substantial unmet medical need (1, 2). It is expected that global costs of neurological diseases and their management will increase from $2.5 trillion in 2010 to $6 trillion by 2030 (2). Research and development (R&D) efforts at Roche/Genentech aim to address challenges in important areas in neuroscience, including neuroinflammatory, neurodegenerative, neuromuscular and movement disorders, as well as psychiatric disorders and pain. Ultimately, the goal of these efforts is to bring molecules with disease-modifying properties to patients in therapeutic areas in which there is great medical need but few or no treatment options. In other words, Roche/Genentech aim to develop transformational therapies.
Some of the key disease areas that Roche/Genentech are investigating include multiple sclerosis (MS), Alzheimer’s disease (AD), spinal muscular atrophy, Parkinson’s disease, amyotrophic lateral sclerosis, autism spectrum disorder and Huntington’s disease. Currently, Roche/Genentech have 13 molecules in neuroscience in clinical development; seven products in early stage and six products in later stage clinical trials. In addition, ocrelizumab (Ocrevus) was recently approved for the treatment of relapsing-remitting MS and as the first agent for the treatment of primary progressive MS. Learnings across the neuroscience space inform Roche/Genentech’s approaches to AD, and enhance the development of other molecules.


Challenges in AD

AD represents one of the most challenging areas in healthcare, and the magnitude of the problem is expected to grow with the ageing worldwide population. As of 2015, there were 46.8 million people worldwide with dementia, and by 2050, this number is expected to reach 131.5 million (3). AD is the most common type of dementia and makes up at least 60–80% of these cases (3). Currently, approved treatments for AD are limited to drugs with symptomatic benefit. While these symptomatic benefits may be sustained for several years, current treatments are unable to slow the ultimate progression of the disease, thus highlighting the need for disease-modifying therapies (DMTs) (4). The challenges associated with development of DMTs for AD arise from the inherent difficulty of detecting early, clinically silent disease and from the lack of reliable biomarkers tied to pathogenesis. Finding improved biomarkers will not only help to ensure timely diagnosis of AD, but will also aid in the accurate stratification and staging of patients across the spectrum of AD, which is important for optimising clinical trial designs. In recent years, AD has been described as encompassing a disease continuum from clinically silent, regionally limited pathologic changes in the brain (many years prior to the appearance of symptoms) through subtle cognitive changes and on to the dementia stage (5). Symptoms are caused by the accumulation of brain changes in combination with the loss of brain compensation or cognitive reserve over time. The nosology and diagnostic classification systems encompassing the AD-specific disease continuum continue to evolve.
The International Working Group for New Research Criteria for the Diagnosis of Alzheimer’s disease requires supporting biomarker evidence in addition to subtle cognitive changes for the diagnosis of prodromal AD (6). The National Institute on Aging and the Alzheimer’s Association (NIA-AA, 2011) have established separate criteria for the diagnosis of preclinical AD, mild cognitive impairment (MCI) due to AD, and AD dementia (7). The diagnosis of preclinical AD should be applied to those patients who have no noticeable symptoms such as memory loss, but do show fluid and/or blood biomarkers to indicate the earliest signs of disease. MCI due to AD (representing a stage like prodromal AD), may be diagnosed following three clinical steps: expression of concern over cognitive performance; assessment of cognition and function; and establishing an aetiology of MCI due to AD. A fourth step, application of biomarker evidence, is currently restricted to research purposes (7).
The push to include biomarkers in detection, classification and diagnosis of AD cases continues with the most recent iteration of a research framework. The NIA-AA has called for public engagement to develop a new research framework that places an even greater focus on AD as a continuum rather than a discrete process (5). This framework proposes to stage AD based on an individual’s biomarker profile (e.g. incorporating biomarker status for amyloid-β [A], tau [T] and neurodegeneration [N]) as well as cognitive status (cognitively impaired, MCI, dementia). In combination, these disease features offer 12 potential categories that span the disease continuum (5).
Accumulation of nonclinical and clinical experience in AD suggests that, in order to maximally salvage cognition and functional ability, interventions against AD should begin in the pre-dementia period (8, 9). However, AD remains both underdiagnosed and is most often diagnosed at a late stage when cognitive and functional abilities are already significantly compromised (10). Existing neuroimaging and laboratory tests used in clinical practice are insufficient on their own to diagnose AD and as such are primarily used to rule out other organic causes of dementia (10). It is likely that significant advances in screening and early detection will not come until reliable diagnostic tests are available to aid this process.


Roche/Genentech Commitment to AD

The management of AD in the future will likely employ multimodal pharmacological and non-pharmacological approaches. These will potentially combine various DMTs against target pathological pathways at different stages of disease, in combination with lifestyle modifications and treatment of medical comorbidities that increase the risk for developing AD. Roche/Genentech are therefore investing in a comprehensive portfolio to transform clinical paradigms in the management of AD, from screening, to diagnosis, to prevention and treatment.
Roche/Genentech’s portfolio includes monoclonal antibody (mAb) therapies that target pathological processes to slow the progression of disease. In conjunction, biomarkers will continue to play a significant role in R&D for AD. Biomarkers will aid in refining targets, proving target engagement, and will become more relevant in clinical practice by enabling screening, diagnosis and timely intervention. Roche/Genentech are developing cerebrospinal fluid (CSF) in vitro diagnostics and tau positron emission tomography (PET) tracers.


Development of AD Therapeutics

Development of AD therapeutics is challenging because of the complexity of the pathogenic mechanisms, which suggest multiple targets (4), and the long period of clinical silence during the development of this disease. The amyloid hypothesis is the longest-established hypothesis for the aetiology of AD. This hypothesis is based on the initial discovery of amyloid plaques in the brains of deceased patients (11), with evidence suggesting that amyloid (in the form of extracellular amyloid plaques and amyloid-β soluble oligomers) plays a key role in AD pathophysiology (12). Neurofibrillary tau tangles (NFTs) and soluble oligomers of tau are another hallmark of AD that drives the pathogenesis of this disease (13). Tau pathology appears to correlate more strongly with specific neuropsychological deficits and with cognitive decline than amyloid-β accumulation (14); however, amyloid-β accumulation precedes tau tangle formation and emerging reports propose it may enhance the pathological spread of tau (15-17). Targeted therapies against both amyloid and tau have thus become the mainstay of R&D efforts in AD.


Anti-amyloid Therapy

The goal of anti-amyloid therapy is to modify the course of AD by interrupting stages of amyloid-β accumulation and/or facilitating the removal of amyloid-β species (18, 19). Clinical data suggest that anti-amyloid mAbs may have disease-modifying properties, whether by targeting the soluble form of amyloid-β (without removing existing plaques) and/or by targeting the plaques themselves (plaque removal) (20-22). Roche/Genentech currently have clinical development programmes for two differentiated anti-amyloid mAbs, crenezumab and gantenerumab (Figure 1). Late phase clinical trials are ongoing, and the key findings from nonclinical and clinical studies reported to date are summarised below. Our clinical trials have been informed by our earlier efforts, and have also incorporated the latest industry-wide clinical development insights regarding patient selection, pharmacokinetic/pharmacodynamic (PK/PD) modelling, dosing and clinical trial operations to optimise clinical development of crenezumab and gantenerumab. These insights have and will continue to be applied to the anti-tau AD programme and across Roche/Genentech’s neuroscience portfolio.

Figure 1. Roche/Genentech’s anti-amyloid-β monoclonal antibodies under clinical development target different species of amyloid-β within the brain

Figure 1. Roche/Genentech’s anti-amyloid-β monoclonal antibodies under clinical development target different species of amyloid-β within the brain


Mode of action

Crenezumab is a humanised amyloid-β immunoglobulin G4 (IgG4) mAb that has a proposed mechanism of action that focusses on neutralising oligomers of amyloid-β with a reduced risk of amyloid-related imaging abnormalities (ARIA) compared with other mAbs under development (21, 23). The crenezumab antibody binds monomeric and aggregated forms of amyloid-β, with highest affinity for oligomers, a form hypothesised to mediate neuronal toxicity (21, 23). The low effector function of the IgG4 backbone and lack of binding to vascular amyloid are hypothesised to minimise inflammation at brain vasculature and thereby lower the risk of localised microvascular damage and induction of ARIA (21, 23).

Nonclinical data

The crystal structure of crenezumab is consistent with high affinity and versatile interactions with amyloid-β, with the ability to inhibit amyloid-β aggregation (21, 23). Crenezumab has been shown to inhibit amyloid-β oligomer-induced neuronal toxicity in vitro and to promote oligomer removal via microglial phagocytosis, with minimal inflammatory activation of microglia (21). Furthermore, following administration to AD transgenic mice, crenezumab localised to those brain areas with putative high concentrations of amyloid-β oligomers, i.e. hippocampal mossy fibres and the periphery of amyloid plaques, but not to the dense core of plaques or vascular amyloid (24).

Clinical data

A randomised, double-blind, placebo-controlled, single-dose followed by multiple-dose phase I study ( NCT00736775) (25) investigated the safety and tolerability of crenezumab in patients with mild-to-moderate AD. In the single-dose stage, 25 patients were randomised to receive either placebo or crenezumab 0.3–10 mg/kg intravenously (IV) and in the multi-dose stage, 31 patients were randomised to receive placebo or crenezumab at 0.5–5.0 mg/kg IV every 4 weeks (25). Crenezumab showed an encouraging safety profile, with no reported increase in ARIA due to vasogenic oedema (ARIA-E) following either a single dose (0.3–10 mg/kg IV) or multiple (four) ascending doses (0.5–5 mg/kg IV) (21).
ABBY was a randomised, double-blind, placebo-controlled, multicentre phase II study ( NCT01343966) (26) of crenezumab in patients with mild-to-moderate AD (Mini-Mental State Examination [MMSE] 18–26). Patients were randomised to receive low-dose subcutaneous (SC) crenezumab 300 mg every 2 weeks, high-dose SC crenezumab 15 mg/kg every 4 weeks for 68 weeks or placebo. The co-primary endpoints using the Alzheimer’s Disease Assessment Score-Cognitive (ADAS-Cog12) and Clinical Dementia Rating-Sum of Boxes (CDR-SB) were not met; however, exploratory analyses showed a significant change in ADAS-Cog12 score in a subset of patients with mild AD (baseline MMSE score 22–26) who received the higher dose of crenezumab
(15 mg/kg IV). Additional exploratory analyses showed larger effects in progressively milder AD (MMSE 20–26) subpopulations (unpublished data).
To assess the possibility that targeting a pre-plaque species of amyloid-β might lower brain amyloid levels, a randomised, double-blind, placebo-controlled, multicentre, parallel group phase II study, BLAZE ( NCT01397578) (27), evaluated the effects of crenezumab on brain amyloid plaque load as measured using biomarkers and florbetapir-PET in patients with mild-to-moderate AD. In part 1, patients were randomised to receive 300 mg crenezumab SC or placebo every 2 weeks for 68 weeks (low-dose SC cohort). In part 2, patients were randomised to receive 15 mg/kg IV crenezumab or placebo every 4 weeks for 68 weeks (high-dose IV cohort). The primary endpoint of crenezumab-related change in brain amyloid relative to placebo, as measured using prespecified standardised uptake value ratio (SUVr) with cerebellar grey reference region, was not met. However, amyloid florbetapir-PET exploratory analyses using two independent subcortical white matter reference regions showed trends toward reduced amyloid accumulation and reduced longitudinal variability in the higher dose 15 mg/kg IV cohort (unpublished data). Because crenezumab does not bind with high affinity to the fibrillary amyloid-β found in the dense core of plaques, it may not lead to brain amyloid reduction on PET and may explain why changes to amyloid burden were not seen. It is, however, hypothesised that further dose increases in patients at an earlier disease stage may yield greater clinical efficacy and possibly affect brain amyloid load.
Importantly, no increase in the risk of ARIA-E or ARIA due to cerebral microhemorrhage/superficial haemosiderosis (ARIA-H) has been observed with crenezumab compared to placebo to date (28). In the phase II studies (ABBY and BLAZE), only one case (corresponding to 0.3% of the patients that received crenezumab) of ARIA-E has been reported. Rates of ARIA-H were balanced between crenezumab and placebo groups. Furthermore, overall adverse event rates were within expected background rates for the study population and balanced across placebo and drug groups (unpublished data). ARIA events have been reported in several amyloid-modifying therapy trials for AD (29) and must be considered when determining dosing schedules of these treatments.
A mathematical model developed to investigate the target engagement of crenezumab supported the hypothesis that its target is not saturated at crenezumab doses of 15 mg/kg; a higher dose of 60 mg/kg was predicted to have greater amyloid-β oligomer engagement (30). With the prospect of conducting new studies with higher doses of crenezumab, a phase Ib dose-escalation study in 52 patients with mild-to-moderate AD ( NCT02353598) (31) was conducted to assess the safety and tolerability of higher IV doses of crenezumab (40, 60 and 120 mg/kg) administered four times over 13 weeks (31). In this study none of the patients developed ARIA-E and six developed ARIA-H (28). Together with the data supporting efficacy, these findings led to the decision to investigate a 60 mg/kg IV dose of crenezumab in the ongoing phase III studies (28, 30).

Present and future of crenezumab

CREAD 1 and 2 ( NCT02670083 and NCT03114657) (32, 33) are ongoing, global, pivotal phase III studies that are investigating the safety and efficacy of crenezumab 60 mg/kg in patients with prodromal and mild AD (32, 33). Patients in both trials demonstrate impaired memory and are amyloid positive by CSF or PET scans. These studies will evaluate the effect of crenezumab on change from baseline in the CDR-SB over 2 years.


Crenezumab is a humanised anti-amyloid-β mAb in phase III development for the treatment of prodromal to mild AD. Crenezumab’s proposed mode of action differs from that of most anti-amyloid-β mAbs in that it primarily targets soluble, monomeric amyloid-β and oligomers. Also important to note is that its IgG4 backbone may be associated with a reduced risk of ARIA (21, 23). To date, crenezumab has a favourable safety profile; dose-limiting ARIA events have not been reported (28), supporting the use of the higher 60 mg/kg dose rather than the 15 mg/kg dose used in the phase II trials. Moreover, consistent with prevailing hypotheses that early intervention and high doses may maximise the benefit of anti-amyloid drugs, exploratory analysis of a subset of patients with mild AD receiving the highest dose of crenezumab showed clinical efficacy signals. Building on key learnings from phase II trials, the CREAD pivotal phase III studies have been designed to investigate the safety and efficacy of higher doses of crenezumab in patients with prodromal to mild AD.


Mode of action

Gantenerumab is a fully human amyloid-β IgG1 mAb that is administered by SC injection and is currently in late clinical development to assess its potential as a DMT (22). Gantenerumab was designed to bind a conformational epitope comprising N-terminal and central parts of the amyloid-β peptide (amino acids 3–11 and 19–26). This epitope is present on aggregated amyloid-β in the brain, meaning that gantenerumab specifically recognises amyloid-β oligomers and fibrils with high nanomolar affinity (22). Notably, the IgG1 backbone of gantenerumab was selected to fully support and promote removal of aggregated amyloid-β by Fcγ receptor-mediated cellular phagocytosis (18, 34).

Nonclinical efficacy data

Nonclinical studies in transgenic mice have reported efficient target engagement of gantenerumab at amyloid plaques in vivo. In these studies, binding of gantenerumab to amyloid-β plaques was dose-dependent and could be measured up to 9 weeks after a single IV administration (18). This observation is particularly relevant considering that the plasma half-life of gantenerumab was limited to 6.5 days in mice. Despite this short half-life, gantenerumab that entered the brain accumulated in a dose-dependent manner, remained present for a prolonged period, and was associated with amyloid plaques that were subsequently cleared, as confirmed by chronic studies in different transgenic models (18, 35).
Penetration of the blood-brain barrier by gantenerumab has been demonstrated in both monkeys and transgenic mice, as evidenced by analyses of CSF samples and immune-decoration of plaques in vivo, respectively (18). In the brain of transgenic PS2APP mice, gantenerumab caused plaque reduction through microglial cell-mediated clearance (18). In transgenic mice, gantenerumab showed a high degree of plaque binding in vivo similar to an analogue of aducanumab, another plaque-targeting mAb (36). Concentration-dependent clearance of human amyloid-β plaques by primary human macrophages and microglia in human AD brain slices further supported gantenerumab’s ability to clear amyloid plaques via Fcγ receptor-mediated cellular phagocytosis (18). Notably, in contrast to some other amyloid-β antibodies, gantenerumab did not alter plasma amyloid-β as expected, due to its low affinity against monomeric amyloid-β. The low level of interaction with monomeric amyloid-β suggests that binding to disease-relevant aggregated amyloid-β species is thermodynamically preferred. Overall, nonclinical studies with gantenerumab confirmed its potential as a DMT in AD and indicated that the safety/tolerability profile of gantenerumab was favourable, supporting entry into a clinical phase of development.

Clinical data

In a multicentre, randomised, double-blind, placebo-controlled, multiple ascending dose PET phase I study ( NCT00531804) (37) in patients with mild or moderate AD,  patients were randomised to receive either placebo or gantenerumab at 60 mg IV or 200 mg IV every 4 weeks. Gantenerumab administration resulted in a rapid reduction in brain amyloid load over 6 months and was associated with a dose-dependent cortical amyloid SUVr reduction compared with placebo (22). During this phase I trial, two patients in the gantenerumab 200 mg IV cohort presented transient and focal areas of ARIA-E in magnetic resonance imaging (MRI) scans.
SCarlet RoAD ( NCT01224106) (38) was a phase III clinical trial to investigate the safety and efficacy of gantenerumab (105 mg or 225 mg administered SC) in patients with prodromal AD. CDR-SB score changes from baseline in treated versus placebo groups were used as a single primary endpoint. Patient inclusion criteria required low CSF amyloid-β1–42 to ensure the presence of target pathology and to increase the diagnostic accuracy of AD (38). The maximum dose of gantenerumab in this study was kept at 225 mg due to reports of ARIA in association with other mAbs under development (29). The dosing was halted after a pre-planned futility analysis showed the study dose was futile. The trial was converted into an open-label extension study with the purpose of evaluating the safety and biological effects of higher doses of gantenerumab (up to 1,500 mg SC) using different titration schedules. Results of exploratory analyses of the halted trial showed dose-dependent amyloid removal by PET imaging and lower levels of biomarkers associated with neurodegeneration in the CSF, including total tau (t-tau), phosphorylated-tau (p-tau) and neurogranin (39).
Additional exploratory analyses of patients with ‘fast’ disease progression as per the Delor et al. (40) criteria showed that gantenerumab slowed clinical decline using the ADAS-Cog13, Cambridge Neuropsychological Test Automated Battery and MMSE measures (unpublished data). Furthermore, exposure-dependent effects on select clinical and biomarker endpoints suggested that higher dosing with gantenerumab may be necessary to achieve clinical efficacy (41). Safety data collected during this trial revealed the occurrence of dose-, time- and apolipoprotein epsilon 4 (APOε4) genotype-related ARIA-E (41). Most of the cases of ARIA were asymptomatic and rated by the investigators as mild or moderate. Furthermore, most patients could continue treatment, albeit at a lower (halved) dose, with a low rate of recurrence (unpublished data).
To further understand the safety profile of gantenerumab, scientists at Roche/Genentech developed a PK/PD model to assess rates of amyloid removal and ARIA-E incidence predicted with different doses of gantenerumab. Using publicly available data on other mAbs and SCarlet RoAD data, this model showed that higher target doses of gantenerumab could be tested without major safety concerns and was able to predict the degree of amyloid reduction within the brain that was later observed in the open-label extension study (42, 43).
Marguerite RoAD ( NCT02051608) (44) was a randomised, double-blind, placebo-controlled phase III study investigating the efficacy and safety of gantenerumab in patients with mild AD. Patients were randomised to monthly SC injections of either placebo or gantenerumab starting at 105 mg/kg for 24 weeks, followed by gantenerumab 225 mg/kg (44). Following the preplanned futility analysis of SCarlet RoAD, recruitment to the Marguerite RoAD trial was halted. As with SCarlet RoAD, this study was converted into an open-label extension trial to evaluate the safety and biological effects of higher doses of gantenerumab (up to 1,200 mg SC) using different titration schedules.
In the open-label extension to the Marguerite RoAD and SCarlet RoAD trials, patients were assigned to different gantenerumab titration schedules depending on their APOε4 genotype (carrier vs. non-carrier) and their treatment during the double-blind trial (gantenerumab vs. placebo): non-carriers previously on 225 mg of gantenerumab were assigned to the fastest titration schedule (2 months), whereas carriers previously on placebo or 105 mg of gantenerumab were assigned to the slowest titration schedule (6 months) (43).

Present and future of gantenerumab

The findings reported in both SCarlet RoAD and Marguerite RoAD point toward greater efficacy of gantenerumab at higher doses. Preliminary results from the open-label extension studies indicate that the safety profile of gantenerumab at higher doses remains unchanged, with no new safety signals identified (43). Moreover, the PD data from the open-label extension studies support the use of higher doses compared with those used in SCarlet RoAD and Marguerite RoAD (unpublished data). Based on these results, two new phase III trials (GRADUATE 1 and 2) are planned to start later in 2017. These global, randomised, double-blind, placebo-controlled studies will independently investigate the efficacy and safety of gantenerumab in patients with prodromal to mild AD over 2 years. Patients will have to demonstrate memory impairment and amyloid-β pathology (by CSF or amyloid PET) in both trials. Roche/Genentech is also exploring gantenerumab effects on tau burden using Genentech Tau Probe 1 (GTP1) tau PET imaging in these studies.


Gantenerumab is an investigational fully human anti-amyloid-β monoclonal IgG antibody that binds with high affinity to aggregated amyloid-β and can remove amyloid plaques via microglia-mediated phagocytosis. Currently in phase III clinical development for prodromal and mild AD, gantenerumab is administered once monthly via a SC injection. Clinical data with gantenerumab have demonstrated dose-dependent signals of efficacy across different endpoints including cognitive (ADAS-Cog13, MMSE) and biological (amyloid PET, CSF and tau) assessments. Additionally, removal of brain amyloid by gantenerumab is associated with effects on target and downstream biomarkers of neurodegeneration, suggesting a disease-modifying effect (unpublished data). Analyses of safety data from the open-label extension trials with higher doses of gantenerumab are consistent with previous findings at lower doses (43). Overall, the outcomes of these and other clinical trials in AD support the further investigation of higher dose gantenerumab.

Early intervention trials with anti-amyloid mAbs

Early intervention with these anti-amyloid therapies will likely prove to be key to their success. As such, Roche/Genentech is involved in two landmark collaboration projects: the Alzheimer’s Prevention Initiative (API) and Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU). These studies are investigating interventions to delay or prevent the clinical onset of AD in individuals with autosomal dominant genetic mutations that place them at a high risk of developing early-onset AD. DIAN-TU ( NCT01760005) (45) will assess the safety and tolerability of investigational products in these patients and will assess the biomarker and cognitive efficacy of novel therapies when administered during asymptomatic and very early disease stages (45). It is hoped that this study will also determine if early intervention can improve disease-related biomarker profiling. The API study ( NCT01998841) (46) is a randomised, double-blind, placebo-controlled, parallel group, adaptive study to investigate the safety and efficacy of crenezumab in patients carrying the PSEN1 E280A mutation and who do not meet the criteria for MCI due to AD or dementia due to AD and are thus in a preclinical phase of AD. This trial includes non-carriers who are randomised to placebo, so that carrier status is not revealed to trial participants (46).


Anti-tau therapy

The progression of tau pathology is proposed to be mediated by the cell-to-cell spread of toxic soluble tau via the extracellular space. Thus, in addition to anti-amyloid therapies, Roche/Genentech are also developing an anti-tau therapy designed to intercept tau in the brain extracellular environment and halt or slow spread of toxic soluble tau. An IgG4 backbone was selected for this molecule based on the discovery that effector function was not required for efficacy in preclinical models and to minimise potential risks associated with full effector function mAbs. The tighter clinical correlations between neuropsychological profile and tau pathology suggest that treatment benefits may differ from those of anti-amyloid therapies.

Monoclonal antibody RO7105705

RO7105705 is an anti-tau therapy designed to intercept tau in the extracellular environment and slow the spread of toxic soluble tau. It binds to all tau isoforms and species regardless of phosphorylation state, with strong avidity to oligomeric tau (47).

Nonclinical data

In neuron-microglia cocultures, an anti-tau antibody with attenuated effector function, but not a comparable antibody with full effector function, was shown to protect neurons against oligomeric tau-induced toxicity (48). A murine surrogate of RO7105705 reduced the accumulation of tau pathology in a transgenic mouse model that overexpresses human tau (47).

Clinical experience

A randomised, placebo-controlled, double-blind, single and multiple ascending dose phase I study ( NCT02820896) (49) was carried out to assess the safety, tolerability and PK of IV and SC RO7105705 in healthy volunteers and patients with mild-to-moderate AD (49). This study reported that RO7105705 was safe and well tolerated up to the highest dose tested: single doses of up to 16,800 mg IV in healthy volunteers and multiple doses of
8,400 mg IV every week (up to a maximum of four doses) in healthy volunteers and patients with mild-to-moderate AD (50). Additionally, RO7105705 was safe and well tolerated when administered SC, with a bioavailability of approximately 70% compared with IV administration. Finally, RO7105705 was detectable in the CSF, indicating central nervous system exposure to the drug (50).
Based on the results of this phase I trial, a phase II study is planned to start in 2017 ( NCT03289143) (51) to test the safety and efficacy of RO7105705 in patients with prodromal-to-mild AD. This trial will include an 18-month double-blind, placebo-controlled evaluation of three active doses of RO7105705 versus placebo, followed by an open-label extension. In this study, the pathological burden of tau will be assessed at baseline and longitudinally with [18F] GTP1 tau PET imaging, an important exploratory biomarker of target engagement by RO7105705.


Development of Diagnostics

The gantenerumab and crenezumab clinical development programmes have incorporated a range of assessment tools, neuroimaging and other biomarkers to better understand their impact on case selection and disease progression, and to demonstrate evidence of disease modification. These include volumetric MRI, florbetapir-PET and CSF biomarkers (t-tau, p-tau and amyloid-β1–42 levels). Roche/Genentech are therefore developing a toolbox of prototype biomarker assays to use alongside the Roche Diagnostics Cobas® analyser series (a platform created to streamline and improve laboratory sample analysis) to provide basic and clinical researchers with the capability to perform sample profiling using robust biomarker assays.
Roche/Genentech have also developed two tau tracers, GTP1 and Roche RO6958948, which use PET to perform selective imaging of NFTs in the brain. These tracers are currently being used to further explore how tau pathology spreads throughout the brain in patients with AD (52). GTP1 is also being used in the Roche/Genentech phase II and III clinical trials to explore the utility of tau PET imaging in patient selection, prediction of response and evidence of therapeutic effect.
Three neuroimaging measures included as biomarkers in research diagnostic criteria for AD are regularly incorporated into clinical trials: hippocampal atrophy measured by MRI; amyloid uptake as measured by amyloid tracer in PET (amyloid-PET); and decreased fluorodeoxyglucose (18F) uptake as measured by PET (FDG-PET) (53). In conjunction, three CSF biomarkers have now been included in research diagnostic criteria for AD and are regularly incorporated into clinical trials: amyloid-β42; t-tau; and p-tau. CSF amyloid-β42 may indicate the presence of AD pathology up to several years prior to emergence of symptoms (54). In therapeutic studies, a clear relationship between changes in fluid biomarkers and clinical benefit has yet to be established.
Roche/Genentech is committed to the development of advanced screening and diagnostic tools to allow for earlier AD identification, the development of biomarkers of target engagement and downstream effects, and the development of surrogate endpoints to speed the clinical development process and to help deliver personalised healthcare (55, 56). Studies of GTP1 and RO6958948 in AD and healthy volunteers have shown that these PET biomarkers can identify patients with tau pathology. A phase I study is ongoing to evaluate longitudinally the use of GTP1 for detecting tau pathology progression and its relationship to CSF biomarkers and clinical endpoints in patients with prodromal, mild and moderate AD and healthy volunteers ( NCT02640092) (55); the study is due to complete in 2019. In addition, the BioFINDER study 2 ( NCT03174938) (56) is a longitudinal cohort study comprising 1,315 individuals across the different stages of AD (cognitively healthy, subjective to cognitive decline, MCI, dementia due to AD) and also includes subjects suffering from other types of dementias. Patients will be followed for 2–8 years with extensive clinical evaluation (general neurological, cognitive, functioning and behavioural) as well as biomarker testing (CSF amyloid and tau, amyloid and tau PET imaging, FDG PET and MRI) (56). The Roche Elecsys® CSF assay platform and the Roche tau PET ligand 18-F-RO6958948 will be used to characterise individuals within this cohort.



In summary, the complexities of diagnosing and defining the stages of AD are undoubtedly associated with some of the past DMT failures in this field. Roche/Genentech are committed to development of a comprehensive therapeutic portfolio that aims to address these challenges. Gantenerumab and crenezumab are anti-amyloid-β mAbs that bind with high affinity to various species of amyloid-β.  In addition, RO7105705 is an anti-tau therapy designed to intercept tau pathology and slow the spread of toxic soluble tau. Moreover, Roche/Genentech are developing diagnostic tools and biomarkers to aid the identification of specific patient groups, to evaluate clinical outcomes of DMTs, and to compliment the development of novel therapeutic agents that tackle the most fundamental pathologic processes that underlie AD. Identifying patients for treatment during the early disease stages may prove critical in the success of DMTs. Roche/Genentech’s commitment to developing treatments aimed at multiple pathological targets, combined with the development of a robust biomarker platform, promises much potential for clinical trials in the present and for AD patients in the future.


Funding: The studies described in this review were funded by F. Hoffmann-La Roche.

Acknowledgments: We would like to thank members of the Roche/Genentech Alzheimer’s Molecules and Diagnostics teams including: Jasvinder Atwal PhD; Gai Ayalon PhD; Richard Batrla-Utermann MD, PhD; Monika Baudler PhD; Tobias Bittner PhD; Bernd Bohrmann PhD; Paul Delmar PhD; Rachelle Doody MD, PhD; Paulo Fontoura MD, PhD; Corinne Foo-Atkins MD, MBA; Reina Fuji VMD, PhD; Mads Hvenekilde, PhD; Geoffrey Kerchner MD, PhD; Gregory Klein PhD; Howard Mackey PhD; Ferenc Martenyi MD, PhD; Smiljana Milosavljevic-Ristic MD; Laura Murray, MS; Susanne Ostrowitzki MD, PhD; Youssef Saidi PhD; Barbara Schaeuble MD, PhD; Andres Schneider MD; Sheila Seleri MD, PhD; Jan Shadrack, MD; Jillian Smith, PhD and Chia-Wen Wu PhD for their input and help with the development of this manuscript. We thank all the patients with Alzheimer’s disease and their caregivers who participated in the trials described in this review. We also thank our partners at each of the participating study sites. Editorial support in the form of medical writing was provided by Medologie, part of the Bioscript® group, and was funded by F. Hoffmann-La Roche.

Conflict of interest disclosure statement: Dr. Rachelle Doody is a current employee of F. Hoffman-La Roche Ltd.



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O. Rodriguez-Gomez1, A. Sanabria1, A. Perez-Cordon1, D. Sanchez-Ruiz1, C. Abdelnour1, S. Valero1,2, I. Hernandez1, M. Rosende-Roca1, A. Mauleon1, L. Vargas1, M. Alegret1, A. Espinosa1, G. Ortega1, M. Guitart1, A. Gailhajanet1, O. Sotolongo-Grau1, S. Moreno-Grau1, S. Ruiz1, M. Tarragona1, J. Serra1, E. Martin1, E. Peleja1, F. Lomeña3, F. Campos3, A. Vivas4, M.Gomez-Chiari4, M.A. Tejero4, J. Giménez4, P. Pesini5, M. Sarasa5, G.Martinez1,6,7, A. Ruiz1, L. Tarraga1, M.Boada1

1. Fundació ACE. Alzheimer Treatment and Research Center. Barcelona, Spain; 2. Psychiatry Department, Hospital Universitari Vall d’Hebron, CIBERSAM, Universitat Autonoma de Barcelona,Barcelona, Spain; 3. Servei de Medicina Nuclear, Hospital Clínic i Provincial. Barcelona, Spain; 4. Departament de Diagnòstic per la Imatge. Clínica Corachan, Barcelona, Spain; 5. Araclon Biotech©. Zaragoza, Spain; 6. Iberoamerican Cochrane Centre, Barcelona, Spain; 7. Faculty of Medicine and Dentistry, Universidad de Antofagasta, Antofagasta, Chile

Corresponding Author: Octavio Rodriguez-Gomez, MD., Gran Via De Carles III, 85 BIS. CP: 08028. Barcelona. Spain, E-mail:, Fax: 0034 934193542, Telephone number: 0034 934304720

J Prev Alz Dis 2017;4(2):100-108
Published online November 15, 2016,


Background: Long-term longitudinal studies with multimodal biomarkers are needed to delve into the knowledge of preclinical AD. Subjective cognitive decline has been proposed as a risk factor for the development of cognitive impairment. Thus, including individuals with SCD in observational studies may be a cost-effective strategy to increase the prevalence of preclinical AD in the sample.
Objectives: To describe the rationale, research protocols and baseline characteristics of participants in the Fundació ACE Healthy Brain Initiative (FACEHBI).
Design: FACEHBI is a clinical trial (EudraCT: 2014-000798-38) embedded within a long-term observational study of individuals with SCD.
Setting: Participants have been recruited at the memory clinic of Fundació ACE (Barcelona) from two different sources: patients referred by a general practitioner and individuals from an Open House Initiative.
Participants: 200 individuals diagnosed with SCD with a strictly normal performance in a comprehensive neuropsychological battery.
Measurements: Individuals will undergo an extensive neuropsychological protocol, risk factor assessment and a set of multimodal biomarkers including florbetaben PET, structural and functional MRI, diffusion tensor imaging, determination of amyloid species in plasma and neurophthalmologic assessment with optical coherence tomography.
Results: Two hundred individuals have been recruited in 15 months. Mean age was 65.9 years; mean MMSE was 29.2 with a mean of 14.8 years of education.
Conclusions: FACEHBI is a long-term study of cognition, biomarkers and lifestyle that has been designed upon an innovative symptom-based approach using SCD as target population. It will shed light on the pathophysiology of preclinical AD and the role of SCD as a risk marker for the development of cognitive impairment.

Key words: Subjective cognitive decline, biomarkers, preclinical AD, longitudinal study.


The prevalence of dementia is increasing in developed societies due to social and demographic changes, and this trend is expected to worsen within the next decades. This epidemic progression could pose a threat to public health, to such an extent that the World Health Organization has declared dementia control a global health prority (1). The disappointing results of the clinical trials in patients with Alzheimer´s disease (AD) dementia (2) or even mild cognitive impairment (MCI) have highlighted the necessity to act earlier (3). In this context, the earliest stages of AD are becoming a topic of major scientific interest.  Nowadays, advancing research has provided a large amount of knowledge of the phenomena involved in the transition from mild cognitive impairment to dementia, but much less is known about the events that lead individuals that are strictly normal from a cognitive viewpoint to develop cognitive impairment (4). In this regard, strong evidence exists that the pathophysiological process of Alzheimer´s disease (AD) begins many years before the onset of the clinical symptoms, leading to the formulation of the biomarker-defined construct of preclinical AD (5). Deep knowledge of this process is essential to develop diagnostic and prognostic markers. Additionally, it will allow a  better selection of individuals at risk for preventive trials and monitorization of the efficacy of treatments intended to modify the course of the disease. However, our present understanding of preclinical AD is far from complete and the very definition of the concept is controversial to date (5-7). Currently, we do not have enough knowledge of the prognostic implications of biomarker positivity for cognitively unimpaired individuals (8). This can lead to problems regarding disclosure of biomarker results and may raise ethical concerns when implementing clinical trials with potentially harmful drugs in this population (9). Furthermore, we need to deepen our understanding of the dynamics of the different biological processes and their related biomarkers along the disease continuum. The more accepted model to date relies largely on studies focused on individuals with rare dominantly inherited variants of AD (10), and we cannot postulate that this model necessarily fits into the typical forms of the disease. In fact, cohorts of late onset AD (LOAD) have shown that not all  the  patients follow the same sequence of events (11).
Given all these gaps in the understanding of the preclinical stage of AD, long-term longitudinal studies are needed and, in fact, significant efforts have been made to clarify the more relevant etiological and pathophysiological aspects of the disease. Studies that include cohorts of healthy controls such as the Alzheimer´s Disease Neuroimaging Initiative (ADNI) (12), Australian Imaging, Biomarkers & Lifestyle Flagship Study of Aging (AIBL) (13) or Mayo Clinic Study of Aging (MCSA) (14) are crucially helping  broaden the knowledge of the field. Despite this, new studies with innovative designs and protocols are needed to address the multiple questions that still remain unanswered.
Metacognition is a research topic that is receiving increasing attention because evidence suggests that some individuals with preclinical AD are able to perceive and report a sensation of loss of cognitive abilities. Thus, many studies in individuals without cognitive impairment show a cross-sectional correlation between the presence of subjective cognitive decline (SCD) and AD biomarkers positivity (15). Longitudinal studies also found that individuals with SCD have a higher risk of developing MCI and dementia (16). Hence, including individuals with SCD in observational studies may be a cost-effective strategy to increase the prevalence of preclinical AD in the sample.
Here we present the Fundació ACE Healthy Brain Initiative (FACEHBI), a long-term observational study carried out with a sample of individuals with SCD. We use a multimodal biomarker approach intended to capture the more relevant molecular, structural and functional processes present in the earliest phases of AD combined with a highly comprehensive and sensitive neuropsychological protocol. Special attention is also paid to lifestyle, personality traits, psychological symptoms, and modifiable risk factors that have been linked to AD in several epidemiological studies (17). In the present work we will describe the rationale, design and baseline demographic characteristics of FACEHBI.



FACEHBI is a single-center prospective observational study. The FACEHBI protocol received the approval from the Spanish Drug Agency (AEMPS for its initials in Spanish) and has been registered as phase 1 clinical trial (CT) (EudraCT: 2014-000798-38, approval date 26th September 2014). Due to regulatory requirements we had to request approval from the AEMPS because at the moment of study design florbetaben (FBB) had not yet been approved for clinical use in Europe. The duration of the CT will be two years, although FACEHBI  was intended to be a long-term study.


The general aims of the FACEHBI long-term study are: a) to determine which clinical, neuropsychological, genetic, biochemical and neuroimaging variables better correlate with the amyloid burden measured with FBB PET in a cross-sectional manner; b) to determine in a longitudinal way which clinical, genetic, neuropsychological, biochemical  and neuroimaging variables better predict the development of cognitive and functional impairment in individuals with SCD; c) to explore the relationships between the different biomarkers at different points in time and the longitudinal evolution of each biomarker in the transition from cognitive normality to cognitive impairment; d) to explore cross-sectionally which clinical, biomarker and  psychological variables better correlate with subjective cognition measures; e) to explore if subjective cognition measures can be a useful tool to predict the development of cognitive impairment in individuals with normal objective cognition.
The primary objective of the CT is to determine if elevated baseline levels of brain ß amyloid measured with FBB PET are correlated with a greater decline in Face Name Associative Memory Exam (FNAME) (18) scores after two years of follow- up.


FACEHBI will use a convenience sample of 200 individuals diagnosed with SCD at Fundació ACE. SCD is defined by the coexistence of cognitive complaints and a strictly normal performance in a comprehensive neuropsychological battery. The sample has been obtained from two different sources: individuals referred by their physicians to our memory clinic for study of cognitive impairment and individuals who came to our institution through an Open House Initiative (OHI) (19). Since the inception of OHI in 2008, Fundació ACE has been holding open house days in which any citizen of Barcelona can sign up for free cognitive screening without the need of physician referral. OHI encompasses a community service and a recruitment strategy for research studies.
Inclusion criteria were: a) subjects older than 49 years; b) subjective cognitive complaints defined as a score of ≥ 8 on MFE-30, the Spanish version of the Memory Failures in Everyday Life Questionnaire (20); c) MMSE ≥ 27; d) CDR=0; e) performance in Fundació ACE Neuropsychological Battery (NBACE) (21) within the normal range for age and educational level; f) literate.
Exclusion criteria were: a) evidence of impairment in daily life activities; b) relevant symptoms of anxiety or depression defined as a score of ≥ 11 on Hospital Anxiety and Depression Scale (HADS) (22); c) presence of other psychiatric diagnosis; d) history of alcoholism and epilepsy; e) presence of auditory or visual impairment sufficient to interfere with neuropsychological assessment; f) known renal or liver failure (due to lack of data on FBB pharmacokinetics in this clinical scenario).

Visits and procedures scheduled

The first phase of FACEHBI in the framework of the CT will have a duration of  two years for each subject, comprising three visits with one-year interval between them (Figure 1).

Figure1. Shows a briefing of FACEHBI flowchart

The baseline visit and visit two (for the CT) are identical and include exhaustive anamnesis, physical and neurological examination, an extensive neuropsychological protocol and a set of self-administered questionnaires that explore issues related to personality and lifestyle. These visits also comprise a battery of multimodal biomarkers including: a) FBB PET; b) structural MRI, functional MRI, diffusion tensor imaging DTI; c) blood extraction for standard biochemical analysis, genetic analysis and determination of amyloid species; and d) neurophthalmologic assessment with retinal OCT.
The intermediate visit consists of a neurological visit, an abbreviated neuropsychological protocol including NBACE, HADS and MFE-30, blood extraction for amyloid determination and neurophthalmologic assessment with OCT.
All the procedures of each visit should be done within a time window of three months.

Neuropsychological assessment

The baseline visit and visit two include an extensive neuropsychological protocol that examines exhaustively all the domains of cognition (Table 1).

Table 1. Shows the different neuropsychological tests and the cognitive functions explored


In addition, the participants will be offered a set of self-administered questionnaires to be filled in at home and delivered later (Table 2).

Table 2. Shows the different self-administered questionnaires of FACEHBI

MRI acquisition

All MRI scans will be acquired prior to FBB PET. MRI will be performed on a 1,5 T Siemens Magneton Aera (Erlangen, Germany) using 32-channel head coil. Anatomical T1-weighted images for voxel-based morphometry (VBM) will be acquired using a rapid acquisition gradient-echo 3D MPRAGE sequence with the following parameters: TR 2.200ms, TE 2,66ms, TI 900ms,  slip angle 8º, FOV 250mm, slice thickness 1mm and isotropic voxel size1x1x1mm. DTI (Diffusion Tensor Images) scans will be acquired using EPI (Echo-planar images) diffusion-weighted sequences with 64 encoding directions, b=0 and 1000 s/mm2.  Resting State functional MRI study (rsfMRI) will be obtained using a BOLD signal sequence with prospective correction movement for posterior processing. In addition to the MRI Imaging protocol, the subjects will receive axial T2-Weighted, 3D isotropic FLAIR and axial T2*Weighted sequences to detect significant vascular pathology or microbleeds.

FBB PET acquisition

FBB-PET scans will be obtained with a Siemens© Biograph molecular-CT machine. PET images will be acquired in 20 minutes starting from 90 minutes after intravenous administration of 300 Mbq of florbetaben(18F) radio tracer (NeuraCeq©), administered as a single slow intravenous bolus (6 sec/mL) in a total volume of up to 10 mL.

Neuroimaging processing

MRI cortical and subcortical segmentation will be carried out with Freesurfer 5.3. The Freesurfer cortical thickness pipeline involves intensity normalization, registration to Talairach space, skull stripping, segmentation of white matter (WM), tessellation of the WM boundary, and automatic correction of topological defects. Hippocampus volume, cortex mean thickness and white matter hypointensities (WMH) will be determined from the segmentation.
DTI images will be also processed with FSL. The images will be eddy-corrected, skull-stripped, fitted to a diffusion tensor model for each voxel and co-registered to the standard space template FMRIB58. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD) and radial diffusivity (RD) will be calculated for the regions of the white matter John Hopkins University Atlas.
fMRI imaging will be processed with AFNI 16.0.19 and FSL 5.0. Scans will be converted to NIfTI-1 format and then a procedure for slice-timing correcting, motion correcting (realignment), despiking, realigment to anatomic space and noise correction will be applied. The resulting images will be registered to the MNI space and the default network will be calculated between the ROI defined by the Destrieux Atlas.
FBB-PET will be processed with FSL 5.0 suite. The FBB-PET images will be co-registered onto structural images. Amyloid cortical retention SUVR will be determined as the mean value of the cortical regions segmented on MRI and normalized by the cerebellum gray matter uptake. A cutoff of  SUVR = 1.45 has been selected as amyloid positivity criterion, based in previous studies (35). FBB-PET images will only be interpreted by readers trained in the interpretation of PET images with FBB-PET

Blood sampling, APOE genotyping, blood amyloid species determination

Blood samples after fasting will be obtained in all the visits for standard biochemical analysis, determination of blood amyloid species, APOE genotyping and DNA banking.
Genomic DNA will be extracted from 200µl of human whole blood using Maxwell® 16 Blood DNA purification kit (Promega) according to the manufacturer’s instructions. APOE rs7412 and rs42358 markers will be genotyped using real-time PCR. PCR reactions will be performed in a final volume of 5µl, using 11ng of genomic DNA, 0.3µM of each amplification primer, and  2.65µl of 2X SYBR Fast Master Mix (Kapa Biosystems). We will use an initial denaturation step at 95 °C for 2 min, followed by 33 cycles at 95 °C for 10 s, and at 69 °C for 30 s. Melting curves will be 95 °C for 15 s (ramping rate 5.5 °C s), 45 °C for 15 s (ramping rate of 5.5°C s−1) and 95 °C for 15 s (ramping rate of 5.5°C s−1). In the last step of each melting curve a continuous fluorimetric register will be performed by the system at one acquisition register per each degree Celsius. Melting peaks and genotype calls will be obtained using the Eco Real-Time PCR system (Illumina).
Blood samples for amyloid assays will be collected in polypropylene vials with EDTA; they will be immediately centrifuged and aliquoted. The aliquots of plasma and the remaining cell pellet will be immediately frozen at −80◦C and sent to the Araclon laboratory. Aβ1-40 and Aβ1-42 will be measured in plasma using two specific sandwich ELISA kits, ABtest 40 and ABtest 42 (Araclon Biotech, Zaragoza, Spain) according to the supplier’s instructions. Before analysis, each plasma sample will split in an undiluted aliquot and another aliquot pretreated by 1 : 3 dilution in a formulated sample buffer intended to break interactions of Aβ with other plasma components. Thus, levels of free and total Aβ1- 40 and Aβ1-42 will be separately determined in undiluted plasma and diluted plasma, respectively.

Neuro-ophthalmologic assessment

Subjects will undergo an ophthalmologic assessment in all the visits including a brief structured ophthalmologic anamnesis, visual acuity assessment with ETDRS and intraocular pressure determination with Goldmann applanation tonometer. OCT will be performed with a 3D OCT-1 Maestro system (Topcon Inc, Tokyo, Japan) using the Radial (Dia.6.0mm Overlap 4), 3D Macula(V) (7.0×7.0mm) and 3D Disc (6.0×6.0mm) protocols. An ophthalmologist will review all the clinical records in order to rule out ophthalmologic pathologies and ensure quality of the images.

Sample size and power calculation

Assuming that the prevalence of amyloid positivity will be about 10% in our sample (preliminary data) (36), FACEHBI has a statistical power of 80% to detect differences bigger than 9.5 points in total FNAME score between PET positive and PET negative subjects.

Statistical analysis

In this work we present the baseline socio-demographic characteristics of the sample. We calculated mean and standard deviation for continuous variables using SPSS 20 (SPSS Inc.Chicago, IL). Dichotomous variables are presented as percentages.

Ethical standards

The FACEHBI protocol received approval from the ethics committee of the Hospital Clinic i Provincial (Barcelona, Spain) (EudraCT: 2014-000798-38). All the participants signed written informed consent prior to any evaluation according to Spanish biomedical laws and to the principles of the Declaration of Helsinki. It is worth noting that, according to the decision of the local ethics committee,  amyloid status will not be disclosed to participants.


The recruitment of subjects began in December 2014 and by the end of March 2015 all the individuals had undergone all the procedures of the baseline visit. (See figure 2).

Figure 2. Shows the rhythm of recruitment and completion of procedures of the baseline visit (FBB PET is the last procedure for each subject in this visit).

215 individuals were recruited. There were four screening failures: two due to inability to undergo MRI and two more due to evidence of cognitive impairment in neuropsychological assessment. 11 individuals withdrew informed consent prior to undergoing all baseline procedures. Finally, 200 subjects completed the baseline visit and will be followed yearly according to schedule. There were no serious adverse events related to florbetaben. Baseline socio-demographic characteristics of the sample are shown in table 3.

Table 3. Shows the baseline demographic characteristics of the FACEHBI participants

The majority of the participants (62.5%) were women, with a mean of 14.76 years of education. It is worth noting that mean educational level of the patients with SCD referred to Fundació ACE by their physician was lower: 10.70 years. First degree family history of dementia was frequent among FACEHBI participants reaching the 60%. All the participants were fluent in Spanish and a high percentage (91%) was bilingual Spanish-Catalan. The most frequent vascular risk factor was dyslipidemia (41.5%) followed by HTA (31.5%), whereas only 4% of the subjects were diabetic. Comparing the two different recruitment methods, the majority of individuals (69%) came from OHI, the rest had been referred by other physicians to our memory clinic.


Long-term longitudinal studies with multimodal biomarkers are needed to delve into the knowledge of the first stages of AD pathophysiology. These studies will deepen understanding of the temporal and causal relationship of the different biological processes involved in neurodegeneration and ultimately determine which biomarker or biomarkers are more accurate and cost-effective to predict the development of cognitive impairment. Advancing research suggests that in the design of new studies we must take into account the high etiological and biological complexity involved in the clinical expression of cognitive impairment. For example, focusing the study exclusively on amyloid cascade-related events is a biased approach since clinic-pathological studies show that in the majority of individuals clinically diagnosed with AD there is actually a mix of different pathologies, vascular damage being especially frequent in the elderly (37). Furthermore, cognitive impairment should not be considered as the result of a specific pathology (or pathologies) in a direct and proportional way, because there is strong evidence that different individuals with the same degree of pathologic burden display different levels of cognitive impairment (38). Thus, cognitive performance can be conceptualized as a result of equilibrium between brain pathology and factors that promote general brain resilience and cognitive reserve (39). Therefore, factors related to cognitive reserve should be taken into account when designing studies to understand the dynamics of the transition from normality to cognitive impairment. Moreover, this research field should not be limited to tracking the measurable biological processes, but it must also consider the different modifiable risk factors (17) in order to understand their mechanism of action.
FACEHBI is an initial clinical trial embedded within a more ambitious long-term longitudinal study of risk factors, lifestyle, biomarkers and cognition carried out in a cohort of individuals without objective cognitive impairment.
The FACEHBI protocol includes a set of multimodal biomarkers that will allow measuring longitudinally different pathophysiological processes that coexist in preclinical AD. We will measure baseline brain amyloidosis and longitudinal amyloid deposit through FBB-PET. FBB is an amyloid ligand that showed to be highly sensitive and specific for the detection of amyloid plaques in the brain, taking brain pathology as the gold standard (40).
Structural magnetic resonance imaging (sMRI) will allow us to precisely determine the degree of atrophy in different brain regions and the pattern of cortical thickness. T2 weighted and magnetic susceptibility sequences will be helpful in detecting ischemic and hemorrhagic brain damage that exists to some degree in the majority of cases of late onset AD and that adds to neurodegeneration as pathological substrate of cognitive impairment (37). Additionally, thanks to diffusion-weighted sequences we will be able to quantify the degree of disruption of white matter microstructure, which is an important substrate of the structural connectivity. DTI measures have been shown to be altered early in preclinical AD, even when volumetric measures remain unchanged (41). FACEHBI will explore the value of this promising tool as an early prognostic and diagnostic marker. Thanks to resting-state functional MRI, we will be able to measure the activation of different brain regions and functional networks, thus assessing the functional connectivity that has been shown to be altered in preclinical AD (42). The correlations between fMRI, structural measures, cognitive performance and amyloid burden can improve our understanding of the functional brain mechanisms underlying cognitive reserve (43).
However, PET and MRI are relatively expensive and time-consuming techniques, thus not suitable for the screening of big populations. In the face of the epidemic progression expected for AD, the development of economic and innocuous biomarkers is of great interest from a public health point of view. In this regard, our protocol includes determination of amyloid species in blood that has shown promising results (44). Optical coherence tomography (OCT) of the retina is another inexpensive, quick and innocuous biomarker included in the FACEHBI protocol. Several studies showed that the inner retinal layers are thinner in AD and MCI compared to healthy controls (45). Nevertheless, the possible alteration of retinal structures in preclinical AD has not been studied to date.
We propose an extensive neuropsychological protocol intended to track the subtle cognitive changes that occur in preclinical AD. FACEHBI will assess the diagnostic value of FNAME in particular, which is a new sensitive neuropsychological tool specifically designed to detect individuals with preclinical AD. FNAME score has been shown to be correlated to amyloid burden in cognitively unimpaired individuals (18). The correlation between biomarkers and the different cognitive measures will allow us to construct neuropsychological composites to better predict amyloid positivity and longitudinal decline. Special attention will be paid  to language assessment in FACEHBI. Early and subtle language impairments have so far been explored less intensively than memory and executive function in the context of preclinical AD (46). Our sample is composed of a majority of bilingual individuals, including simultaneous, early and late bilinguals. We will explore the suggested relationship between bilingualism, cognitive reserve and risk of AD (47).
AD research in the last decades has been reductionist, focusing on neurodegenerative processes related to amyloid cascade hypothesis and excluding from the studies patients with other causes for brain damage. However, clinic-pathological studies show us that the majority of clinically diagnosed AD cases have actually mixed brain pathology (37). FACEHBI has broad inclusion criteria not excluding a priori individuals with vascular brain burden. We believe that this design allows for a more naturalistic approach to the processes involved in late life cognitive impairment. Another important decision was to exclude individuals with relevant symptoms of anxiety and depression. The relationship between psychiatric symptoms and cognitive impairment is complex. It is well known that psychiatric symptoms can affect cognitive performance in the absence of underlying neurodegenerative disease; conversely, late life depression has been robustly reported as a risk factor for developing AD (48). Specifically in the context of SCD the vast majority of studies show that self-perception of cognitive decline is more strongly correlated to anxiety and depression than to neurodegenerative diseases. The international SCD initiative (SCD-I) in his consensus conceptual framework for research on SCD in preclinical AD recommends excluding individuals with severe anxiety or depression symptoms (15). With this in mind, we decided to establish a cut-off point of 11 in HADS as an exclusion criterion.
Several methodological problems arise when designing observational studies on preclinical AD, because this condition is a biomarker-defined construct for which clinical diagnosis is elusive. Hence, the challenge is to find the way to increase the prevalence of preclinical AD in the sample without using expensive biomarkers as inclusion criteria. Enrichment strategies based on genetic factors such as APOE may pose ethical problems and can lead to biological bias, since a big proportion of individuals that will suffer AD despite being negative for this genetic factor are excluded from the study. The age can be effective as enrichment strategy (49); however, the establishment of an advanced age as inclusion criterion precludes a long observation window hampering the study of some slowly progressive processes that define AD pathophysiology.
FACEHBI is designed upon an innovative symptom-based approach for enrichment using SCD as target population. The results will clarify if such a strategy can be useful to increase the prevalence of preclinical AD in the sample. In addition, this project will broaden the knowledge of the nosology of SCD. Most studies agree that subjective cognition is strongly related to personality traits and psychiatric symptoms such as anxiety and depression (15). The FACEHBI protocol includes comprehensive scales to measure all these factors. In individuals with SCD, informant-based reporting of cognitive symptoms has been shown to be better correlated with objective cognition and biomarkers than self-reporting (28, 50). FACEHBI addresses this issue using SCD-Q that includes an informant-completed questionnaire (28). Ultimately, the usefulness of subjective cognition measures to predict cognitive impairment in apparently healthy individuals is a key research topic that this project will help clarify.
Our experience with FACEHBI shows that innovative strategies of patient engagement such as Open House Initiative (OHI) are successful at recruiting individuals with SCD. We have been able to recruit more than 200 individuals from a single site in 15 months, most of whom (almost 70%) came from OHI.
The socio-demographic characteristics of our sample confirm previous findings of other groups showing that individuals who are more likely to volunteer for research studies are women and tend to have a higher level of education and a particular interest in the issue being studied (51). In the case of FACEHBI, this interest can be easily explained by the high frequency (60.5%) of family history of dementia. Our sample is strikingly homogeneous regarding ethnicity, probably reflecting that immigration from other countries is a very recent phenomenon in the history of Spain. The prevalence of vascular risk factors in our sample is relatively low compared with other studies of cognitive aging like BMI (13, 14). Mean age of our sample is also relatively young; this means that a long follow-up period will be required to obtain relevant results. On the other hand, this young sample followed up for a long time with biomarkers will allow capturing very early phenomena in the continuum of preclinical AD. The prevalence of APOEε4 carriers in our sample (26%) is higher compared with our control population (18.5%) (52), reflecting that participants in FACEHBI as a whole have a higher risk of developing AD.
We acknowledge limitations in the design of FACEHBI. CSF analysis is not included in the protocol, which can lead to the loss of relevant information. Nevertheless, FACEHBI includes a comprehensive set of biomarkers of amyloidosis and neurodegeneration. Furthermore, lumbar puncture is an invasive process, not completely innocuous, that can cause pain or discomfort to the patient. Thus, the inclusion of mandatory lumbar puncture in the protocol may result in subject´s reluctance to participate. We have chosen to prioritize subject´s comfort to ensure long-term adherence to the protocol, which is an essential goal of a study of this nature. We are aware of the fact that the strict neuropsychological inclusion criteria of FACEHBI (scores lower than -1.5 SD in any cognitive measure of NBACE are not allowed) result in a lower prevalence of amyloid positive subjects at baseline compared to a more liberal definition of SCD. However, this approach ensures that the sample does not include any individual with MCI at baseline and allows capturing early phenomena present in the pre-MCI stage of SCD. Participants in FACEHBI make up a non-homogeneous convenience sample recruited through two different methods: referrals from other physicians and an Open House Initiative. We recognize that from a methodological point of view it can lead to several biases that would be avoidable using a probabilistic sampling. Nevertheless, some of these biases can probably lead to a higher prevalence of preclinical AD in convenience samples (19). Despite the fact that the impact of the recruitment methods has not been studied specifically in SCD, a study that compared the rate of hippocampal atrophy in two samples of healthy controls from two different settings (volunteers and random sampling from the whole population) found a higher rate of atrophy in volunteers (53). Hence, in studies of SCD addressing preclinical AD a population-based approach is probably not the better design when the sample is relatively small because a low prevalence of preclinical AD in the sample can result in a lack of statistical power. Moreover, the fact that FACEHBI uses two different recruitment strategies will be useful to evaluate the influence of these sampling methods in terms of preclinical AD enrichment. We hope these results will help other groups  design studies of cognitively healthy individuals.
In conclusion, FACEHBI is an innovative longitudinal study designed to delve into the knowledge of the pathophysiology of preclinical AD. We are confident that this project will shed light on the evolution of subjective and objective cognition along AD continuum and the role that SCD can play as a risk marker of AD.

Acknowledgements: We acknowledge all FACEHBI participants for their generosity and their trust in our institution. We also want to thank our sponsors for making this project possible and all of the investigators from the Fundació ACE Barcelona Alzheimer Treatment and Research Center, Hospital Clinic and Clínica Corachan for their close collaboration and continuous intellectual input. We are indebted to Trinitat Port-Carbó and her family for their support to Fundació ACE research programs.

Funding: Funds from Fundació ACE Insitut Catalàde Neurociències  Aplicades , Grifols ®, Piramal ® and Araclon Biotech® are supporting the FACEHBI study. The sponsors were not involved in the study design, data collection, analysis or interpretation. The only exception is Araclon Biotech©, who is in charge of the determination of amyloid species in blood. The sponsors have reviewed the manuscript and have given their approval.

The FACEHBI study group: Merce Boada1, Agustin Ruiz1, Lluis Tarraga1, Octavio Rodriguez-Gomez1, Isabel Hernandez1, Maitee Rosende-Roca1, Ana Mauleon1, Liliana Vargas1, Domingo Sanchez-Ruiz1, Carla Abdelnour1, Asunción Lafuente1, Montserrat Alegret1, Angela Sanabria1, Alba Perez-Cordon1, Ana Espinosa1, Gemma Ortega1, Susana Ruiz1, Marina Tarragona1, Oscar Sotolongo-Grau1, Sonia Moreno-Grau1, Sergi Valero1,2, Judit Serra1, Elvira Martin1, Esther Peleja1, Marina Guitart1, Anna Gailhajanet1, Susana Diego1, Marta Ibarria1, Pilar Cañabate1, Mariola Moreno1, Silvia Preckler1, Mar Buendia1, Ana Pancho1, Gabriel Martinez1, Miguel Castilla-Marti1,3, Assumpta Vivas4, Marta Gomez-Chiari4, Miguel Angel Tejero4, Joan Gimenez4, Francisco Lomeña5, Francisco Campos5, Javier Pavia5, Rosella Gismondi6, Santiago Bullich6, Manuel Sarasa7, Pedro Pesini7,Inmaculada Monleon7, Virginia Pérez-Grijalba7, Noelia Fandos7, Judith Romero7, Marcelo Berthier8 (1. Fundació ACE. Alzheimer Treatment and Research Center. Barcelona, Spain; 2. Psychiatry Department, Hospital Universitari Vall d’Hebron, CIBERSAM, Universitat Autonoma de Barcelona,Barcelona, Spain; 3. Valles Ophthalmology Research (VOR), Sant Cugat del Vallés, Barcelona, Spain; 4. Departament de Diagnòstic per la Imatge. Clínica Corachan, Barcelona, Spain; 5. Servei de Medicina Nuclear, Hospital Clínic i Provincial. Barcelona, Spain.6 Piramal Imaging GmbH, Berlin, Germany; 7. Araclon Biotech©. Zaragoza, Spain; 8. Cognitive Neurology and Aphasia Unit (UNCA). University of Malaga).

Conflict of Interest: M. Sarasa and P. Pesini are employees of Araclon Biotech.


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J.C.S. Breitner1,2, J. Poirier1,2, P.E. Etienne1,2, J.M. Leoutsakos3 for the PREVENT-AD Research Group1


1. Douglas Mental Health University Institute, Montreal, QC, Canada; 2. McGill University Faculty of Medicine Montreal, QC, Canada; 3. Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Corresponding Author: John C. S. Breitner, MD, MPH, Director, Centre for Studies on Prevention of Alzheimer’s disease, Douglas Hospital Research Centre, 6875 Blvd Lasalle Montreal, QC H4H 1R3, Canada,, (01)-514-761-6131, ext 3940, fax (01)-514-221-4700

J Prev Alz Dis 2016;3(4):236-242
Published online October 25, 2016,



We describe events spanning over 20 years that have shaped our approach to identification of interventions that may delay symptoms in Alzheimer’s disease (AD).  These events motivated the development of a new Centre for Studies on Prevention of AD that includes an observational cohort of cognitively normal high-risk persons and INTREPAD, a nested two-year randomized placebo-controlled trial of the non-steroidal anti-inflammatory drug naproxen sodium.  INTREPAD enrolled 217 persons and will follow 160 in a modified intent-to-treat analysis of persons who remained on-protocol through at least one follow-up evaluation.  The trial employs dual endpoints: 1) a composite global cognitive score generated by a battery of 12 psychometric tests organized into five subscales; and 2) a summary Alzheimer’s Progression Score derived from latent variable modeling of multiple biomarker data from several modalities.  The dual endpoints will be analyzed by consideration of their joint probability under the null hypothesis of no treatment effect, after allowing appropriately for their lack of independence.  
We suggest that such an approach can be used economically to generate preliminary data regarding the efficacy of potential prevention strategies, thereby increasing the chances of finding one or more interventions that successfully prevent symptoms.

Key words: Prevention trials, biomarkers, Alzheimer’s disease, pathogenesis.



We describe events leading to the development of a new Centre for Studies on Prevention of Alzheimer’s Disease (AD) deriving from observations over 30 years.  We review some history and key constructs that motivate our present activities.  We then offer a description of our methods and the sorts of data expected over the coming two years.          


Historical development and rationale

In the 1980’s, studies of familial aggregation in AD led to a concept of disease risk as a monotonically increasing function of time (1-4).  This notion provoked consideration to the effects of delayed onset on the lifetime incidence of AD dementia.  In 1991, one of us (JCSB) suggested that a five-year delay in onset should reduce the lifetime incidence of disease by 37% (5). A year later, Zaven Khachaturian correctly noted that later onset would result not only in reduced incidence but also in decreased duration of illness, and offered an improved estimate that a five-year delay in onset would decrease the population burden of disease by 50%, while a ten-year delay would reduce it by another half (6). The latter estimate was subsequently corroborated using more sophisticated methods by Brookmeyer et al. (7).  Thus delay in onset became a prime strategy for prevention of symptoms.


Enter APOE

In the early 1990s we had little sense of the sorts of intervention that might achieve this sort of delay.  In 1993, however, the laboratories of Drs. Allen Roses and Judes Poirier established a strong relationship between polymorphism at APOE and the risk of later-onset AD  dementia (8, 9). The Roses group showed a specific association between APOE polymorphism and the distribution of AD onsets in a collection of families with multiple cases of AD (10). This latter report showed five- or greater-year differences in typical onset of AD dementia.  Shortly thereafter, epidemiological studies in representative populations showed substantial APOE-related alterations in population prevalence (11, 12) and incidence of AD dementia (11, 13).

Awareness of ‘pre-clinical’ AD

Robert Katzman is often credited with the idea that Alzheimer’s disease is a chronic illness with a pre-clinical (we prefer the more precise term pre-symptomatic) stage.  Logically, pre-symptomatic AD should represent a window of opportunity for preventive interventions. Just as it became evident that APOE polymorphism modified the timing of AD symptom appearance (14), other factors might modify the incidence of AD dementia by affecting the rate at which the pre-symptomatic disease evolved, and thus the age at which symptoms emerged (15). Among strategies to accomplish this, one might attempt to simulate the effects of the less risky APOE alleles (especially ε2) in pre-symptomatic AD.

Influence of new theories on pathogenesis

Most work in this area drew on the amyloid cascade hypothesis (16) or the potential importance of hyperphosphorylated tau proteins (17). The main focus was treatment of symptomatic disease rather than prevention, but several major prevention programs are now pursuing strategies aimed at preventing overproduction of Aβ peptides (18, 19).  At least one randomized trial of an anti-tau treatment has recently been reported (20, 21).  Some concern about these efforts (possibly excepting the API FAD trial) has arisen with recent understanding that fibrillary amyloid deposition in Late Onset AD may result more from diminished clearance than from overproduction of Aβ peptides (22, 23).

Other strategies, including anti-inflammatory treatments

Our group has concentrated on interventions not based on the amyloid or tau theories of AD pathogenesis, which are being pursued vigorously elsewhere.  Prominent among these have been several trials of various anti-inflammatory agents (24), motivated largely by epidemiological studies from the 1990’s suggesting reduced incidence of AD dementia in aging persons exposed to anti-inflammatory treatments, primarily NSAIDs.  One such trial was the Alzheimer Disease Anti-inflammatory Prevention Trial (ADAPT), a double-masked pharmaco-prevention trial of the conventional NSAID naproxen and the selective COX-2 inhibitor celecoxib (25). An initial report from ADAPT described its results as null (26).  However, that report described an analysis which excluded several persons with prevalent dementia (undetected by the trial’s screening procedures), and even this analysis suggested a worrisome increase in the incidence of AD dementia  with both treatments in the first 2 – 3  years following randomization.  Later analyses (27) suggested that ADAPT participants exposed to naproxen developed no new cases of dementia in the 2 – 3 years thereafter (in contrast to celecoxib- or placebo-assigned persons), and a subsequent growth mixture modeling analysis suggested similar results (28).  Perhaps more importantly, CSF from almost 200 lumbar punctures (LPs) obtained between 21 and 41 months following the termination of treatments showed a substantially lower ratio in the CSF of total tau (t-tau) / Aβ1-42 concentrations – a widely recognized marker of disease progression – in participants originally assigned to naproxen (27).  Predictably, however, still later analyses of the ADAPT primary outcomes (AD dementia incidence, cognitive decline) showed no long-term benefit of either NSAID (29).

Delayed-washout evidence of disease modification?

An unintended aspect of the termination of the ADAPT treatments was its realization of a “delayed washout” design of the sort typified by the DATATOP trial of deprenyl for Parkinson’s disease (30), and discussed in Paul Leber’s classic paper on differentiation of symptomatic vs. disease-modifying treatments for AD (31). By the time the ADAPT treatments had been stopped, cumulative hazard analyses of ADAPT suggested that the incidence of dementia in the placebo-assigned group had “caught up” with the (elevated) incidence among those assigned to the NSAID treatments (27).  Only after this point, however, did naproxen-assigned subjects appear to show reduced incidence of dementia for several years. Likewise, a contrast in CSF biomarkers favoring naproxen-treated subjects was observed in the years after termination of treatments (27). These observations well after the (unplanned) interruption of the ADAPT treatments suggest that a delayed-washout design may be used for other tests of interventions in the pre-symptomatic space.


Development of a new Centre for Studies on Prevention of AD

In 2006 an influential paper by Leon Thal emphasized the potential importance of safety concerns and advocated prevention approaches that relied on behavioural or “lifestyle” interventions (32).  Several prevention trials of this sort have now been undertaken, some with encouraging results (33-35). Concerns remain, however about the feasibility of persuading large numbers of people to change their behaviour in accord with these trials’ intervention strategies, and a search for pharmaco-prevention strategies remains important (consider, by analogy, the success of medical vs. behavioural treatments for Type II diabetes, hyperlipidemias, or hypertension).  
By contrast, the potential safety concerns of drug treatments tend to be problematic for long-term prevention trials, where there is no proven benefit to offset risks that become apparent in “real time” (36).  More generally, it is difficult to identify the most promising candidate pharmaco-preventive interventions.  In late 2010, therefore, we began an ambitious new initiative aimed at facilitating the discovery of safe and promising pharmacologic interventions that appeared to slow the progression of pre-symptomatic AD. We began with several premises:
1.    To prevent AD symptoms, one must slow (or arrest) the progress of the disease in its pre-symptomatic stage.  The latter may even include times when functional or chemical change is demonstrable before there is evident structural pathology (e.g., fibrillar Aβ deposits).
2.    Working in this pre-symptomatic space, one cannot rely on occurrence or progression of typical AD symptoms (excepting, at least in theory, cognitive decline over time).
3.    By elimination, one must therefore concentrate on biomarkers of AD progression.
4.    We did not know which biomarkers would best serve this purpose, but it seemed likely that (given the heterogeneity in AD progression) multiple markers would be advantageous.  
5.    Such study of multiple markers was certain to create a challenge in finding methods of analysis to measure treatment effects on the pathogenesis of the disease.  We thought it logical, nonetheless, to begin our inquiries with several markers most robustly associated with the later, symptomatic stages of the disease, realizing that at some later point we would need to validate the chosen markers as being indicative of the pre-symptomatic disease process.
6.    For practical reasons, we recognized that we would need sufficient precision in the measurement of change in the marker indicators over a relatively brief interval (we set a goal of two years for robust detection of such change).
7.    Our research subjects needed to be at substantially elevated risk of AD dementia.  Ideally, a substantial majority of them should be at some stage of pre-symptomatic AD.
8.    We preferred, however, to avoid restriction of participation to “special populations”, because we wanted results that could plausibly apply to typical populations in general (or at least to a substantial element of such populations). For this reason, we did not wish to resort to APOE genotyping as a means of “enriching” our sample for persons with pre-symptomatic AD.
9.    Within limits of available resources, we wished to measure as many biomarkers as possible at regular intervals (in the parlance of the UK Dementia Platform, “Deep and Frequent Phenotyping”).  Because we wanted ideally to find markers that changed over two years, we planned to examine participants annually.
10.    To the extent possible, our hope was to “let the data tell us” which markers show most readily detectable change.

Complementing these ideas, we believed it would be important to assess whether the encouraging biomarker results with naproxen from ADAPT could be corroborated.  We therefore elected to undertake a biomarker-endpoint trial of naproxen as a practical way to test the feasibility of this approach.


Thus, in late 2011, our Centre began to assemble a cohort of participants for PRe-symptomatic EValuation of Experimental or Novel Treatments for AD (PREVENT-AD).  Participants in this Cohort have either a parental history of Alzheimer-type dementia or multiple siblings affected by the disease.  Other entry criteria were designed to assure the possibility of long-term follow-up studies using multiple biomarker endpoints, and the design called for annual follow-up examinations using cognitive and multiple-biomarker measures to indicate apparent progression of pre-symptomatic AD.  A subset of this longitudinal cohort study were enrolled in a two-year biomarker-endpoint trial of the traditional (dual-inhibitor) NSAID naproxen sodium 220 mg tablets (available without prescription in Canada). After receiving approval from Health Canada and our institutional ethics committee, we began enrollment of a target of 200 persons for this randomized, placebo-controlled, double-masked trial, which we named INTREPAD (Impact of Naproxen TREatment in Pre-symptomatic Alzheimer’s Disease; NCT 02702817).  Naproxen sodium 220 mg tablets and matching placebo for this double-blind, randomized trial were generously provided by Pharmascience, Inc., a prominent Canadian manufacturer of generic pharmaceuticals.  
All participants in both the PREVENT-AD Cohort and its sister INTREPAD trial provided written informed consent prior to enrollment.  Characteristics of these participants are shown in Table 1.  Table 2 enumerates the biomarker endpoints under investigation by these efforts.  At each point of evaluation INTREPAD participants (but not other members of the non-trial PREVENT-AD cohort) were asked to donate CSF via LP, and over half of them agreed to a series of four CSF donations over a two-year interval (Figure 1).


Figure 1. Completed Lumbar Punctures

Legend:  INTREPAD participants were asked to volunteer for a series of lumbar punctures at Baseline (BL) and at specified follow-up intervals of 3, 12, and 24 months when they return for examination.  Of the 103 who volunteered for LPs, 94 stayed on protocol for at least three months and, as members of the m-ITT pool, were asked to continue the series.  Over half the INTREPAD m-ITT participants have now completed their 24-month follow-up visit and are off study drug.  Of these, 24 have returned for annual assessment in the post-treatment “delayed washout” phase of the trial. About 80 persons are expected eventually to complete follow-up by spring 2017.


Value of simultaneous studies among INTREPAD and non-trial PREVENT-AD participants

As these studies were designed, we were uncertain which among the multiple markers to be followed in INTREPAD would best serve as endpoints for the trial.  We therefore chose to “let the data (from longitudinal observations in the non-trial PREVENT-AD Cohort) tell us” which markers or combinations of markers would most likely indicate the progress of pre-symptomatic disease.  While remaining committed to the principle that data collection methods must be fully specified before the trial began, we nonetheless opted to suspend declaration of the trial’s outcomes pending observation of substantial longitudinal data from the non-trial cohort.  It was hoped that the latter could reveal in “real time” which markers (excluding CSF analytes that were not available) would best serve as outcomes for the double-masked INTREPAD trial.  Analysis of these data then permitted declaration of outcomes (see below) before beginning analyses of trial treatment effects.


Table 1. Selected demographics of the observational PREVENT-AD Cohort, the nested INTERPAD trial, and its panel of serial CSF donors

mITT = modified Intend-to-Treat (subjects with at least one follow-up data point)


Consideration of novel marker systems

We have chosen to pursue several CSF and either structural or functional imaging variables for investigation as potential markers of pre-symptomatic disease progression. Table 2 shows a number of traditional and non-traditional marker systems, including two sensori-neural abilities as candidate pre-symptomatic AD biomarkers.  Abilities in olfactory identification are impaired in Alzheimer’s dementia and MCI (37, 38) and prodromal AD (39), and may even show some relationship to treatment response to cholinesterase inhibiting drugs (40). We therefore added the University of Pennsylvania Smell Identification Test (UPSIT) to our annual assessment battery (41) finding substantial correlations of olfactory identification with age, and with several other biomarker endpoint assessments reported at the 2015 and 2016 AAIC meetings.  As well, we explored the utility of two tests of central auditory processing (CAP) as possible indicators of pre-clinical disease progression, relying especially on a powerful observational study by Gates et al. (42), that identified subjects with impaired CAP among cognitively normal members of the Adult Changes in Thought (ACT) study directed by Eric Larson.  Over four years of follow-up, ACT participants with impaired CAP had shown up to nine-fold increases in incidence of AD dementia.


Table 2. Multiple metrics for analysis as indicators of AD pathogenesis


A principal outcome conjoining two main outcomes

As an exception to the “let the data tell us” approach, we committed a priori to consider global cognitive ability as an important trial endpoint.  We reasoned that, even without a demonstrable effect on biologically oriented measures, a treatment effect of diminished cognitive decline would relate intuitively to prevention of cognitive disorder, and would anyway be valuable in its own right.  For such a cognitive endpoint, we chose total score on the 30-40 minute Repeatable Battery for Assessment of Neuropsychological Status (43). This battery is available in four equivalent versions in Canadian French and is suited to the study of cognitive abilities in persons without dementia (for example, having good sensitivity in separating MCI from normal cognition, it is now being used as an entry criterion in three pharmaceutical trials with MCI endpoints). The RBANS comprises 12 individual tests that generate scores on five scales. The global and all scale scores have psychometric properties modeled on the WAIS (typical median score 100; s.d. = 15 points).
For the remaining (non-cognitive) main outcome we needed a summary variable that could provide a digest of the multimodal biomarker and sensori-neural marker data available to indicate the degree of pre-symptomatic disease progression.  The idea of a single such summary score grew from our observation of the inter-relatedness, in the non-trial PREVENT-AD cohort, of many of the markers shown in Table 2 (data not shown). Most of these correlated strongly with age, but the latter correlation was typically vitiated when other presumed AD markers were included in multivariable models.  By contrast, strong apparent correlations among these biomarkers suggested that many of them were common effects of an underlying “driver,” and we hypothesized that the latter might in fact be the progression of pre-symptomatic AD pathogenesis.  We therefore developed a latent variable modeling method that yields a single ‘Alzheimer Progression Score.’ The development and validation of this APS is described in an accompanying paper (45). Briefly, the score is estimated using item response theory to analyze the data on multiple individual disease markers, including CSF analytes when available.
How, then, to consider the two main outcomes together for examination of treatment effects?  If we accepted either outcome as being a sufficient test of the null hypothesis (of no treatment effect), this would require correction for multiple comparisons with resulting loss of statistical power.  By contrast, requiring that both achieve some critical p-value (alpha) would be too stringent (again, with risk of type II error).  If the two main outcomes were independent (they are not), one might avoid this conundrum by calculating their joint probability using simple multiplication (e.g., two observed p-values of 0.1 would result in a combined p = 0.01).  Instead , we have adapted a method (to be discussed in a forthcoming report) that considers the joint probabilities of both main outcomes while taking into account the correlation between them.   We suggest that this approach should achieve the “best of both worlds” as it considers cognition separately from biological markers but uses information from both to estimate disease progress.


Looking forward . . .

It will be about six months before the last-enrolled participant in INTREPAD will undergo his/her final assessment on-treatment, after which we shall unmask treatment assignment.  Should the trial show a treatment effect of naproxen, we shall (of course) be strongly motivated to continue to study anti-inflammatory treatments as a route to the prevention of AD.  We plan also to extend the observations of the trial cohort off-treatment for another two years, with annual assessments, to ascertain whether any observed treatment effects (whatever their level) are sustained, as would be expected if naproxen did indeed have a disease-modifying influence on the pathogenesis of pre-clinical AD (31).
In analogous fashion, we have begun a new program of evaluation of the potential as an AD preventive of the retired cholesterol-lowering drug probucol as a putative stimulator of increased availability of the apoE protein.  And, in separate studies, we are using high-throughput technology from both Millipore and MesoScale Discovery to analyze up to 45 different markers of inflammation and immune activity in the CSF of those INTREPAD participants who have undergone multiple LPs (44).  
We suggest, however, that the most important result of our work will not be its answer regarding the apparent efficacy of naproxen (or probucol) as inhibitors of pre-clinical disease progression.  Whatever the results of these trials, their successful completion should provide a demonstration of the feasibility of the described method of biomarker-endpoint trials in high-risk individuals for economical testing of the likely success of any individual candidate preventive agent.   To date, for example, the work on INTREPAD has cost approximately $3.0 million CAD (US$ 2.3 million), and we expect to complete it with other funds totalling no more than $2.0 million.  Considering the many years and scores of millions of dollars required to undertake a Phase III prevention trial using incident illness (Alzheimer’s dementia or even MCI/AD) as an endpoint, we suggest that the described method may provide important preliminary data at much lower cost to justify this sort of investment.  Even without confirmation of a naproxen effect on AD pathogenesis, we suggest that the data from INTREPAD, as well as the remainder of the PREVENT-AD Cohort will prove valuable for analyses of the signs of AD pathogenesis in high-risk older persons.  Thus the work may provide valuable information along many dimensions.  We trust that the resulting data and results of their analyses may provide helpful examples more broadly for the field of AD prevention.  


Funding: Funded by generous support from McGill University, the government of Canada, an unrestricted gift from Pfizer Canada, the Canada Fund for Innovation, the Douglas Hospital Research Centre, the Levesque Foundation.  Naproxen sodium 220 mg tablets and matching placebo and probucol 300 mg scored tablets and their placebo have been generously provided by Pharmascience Inc.  The sponsors had no role in the design and conduct of the studies; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Conflicts: The authors declare that they have no conflicts of interest with this work.

Acknowledgements: The entire PREVENT-AD Research Group, past and present, is listed at We are deeply grateful for the tireless generosity and commitment of the participants in our work, and for helpful comments and the personal commitment of Prof. Alan Evans and Mr. Samir Das of the McConnell Brain Imaging Center, Montreal Neurological Institute.  We also thank Prof. Remi Quirion, former Dean Richard Levin, and Mr. Jacques Hendlisz, without whose encouragement and commitment this work would not have been undertaken, and Drs. Alain Gratton, Mimi Israel, and Gustavo Turecki for their key efforts to help along the way.   

Ethical standards: All research was conducted under the principles of the World Medical Association Declaration of Helsinki, and all procedures were approved by the IRB of the McGill University Faculty of Medicine. Experimental administration of both naproxen sodium and probucol for this work was approved by Health Canada.



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