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M. Senda1, K. Ishii2, K. Ito3, T. Ikeuchi4, H. Matsuda5, T. Iwatsubo6, A. Iwata7, R. Ihara7, K. Suzuki8, K. Kasuga4, Y. Ikari1,9, Y. Niimi6, H. Arai10, A. Tamaoka11, Y. Arahata3, Y. Itoh12, H. Tachibana13, Y. Ichimiya14, S. Washizuka15, T. Odawara16, K. Ishii17, K. Ono18, T. Yokota19, A. Nakanishi20, E. Matsubara21, H. Mori12, H. Shimada12


1. Kobe City Medical Center General Hospital, Japan; 2. Tokyo Metropolitan Institute of Gerontology, Japan; 3. National Center for Geriatrics and Gerontology, Japan; 4. Niigata University, Japan; 5. National Center of Neurology and Psychiatry, Japan; (currently, Southern Tohoku Drug Development and Cyclotron Research Center, Japan); 6. The University of Tokyo, Japan; 7. The University of Tokyo, Japan; (currently, Tokyo Metropolitan Geriatric Hospital, Japan); 8. The University of Tokyo, Japan; (currently, National Defense Medical College, Japan); 9. Osaka University, Japan; 10. Tohoku University, Japan; 11. University of Tsukuba, Japan; 12. Osaka City University, Japan; 13. Kobe University, Japan; 14. Juntendo Tokyo Koto Geriatric Medical Center, Japan; 15. Shinshu University, Japan; 16. Yokohama City University, Japan; 17. Kindai University, Japan; 18. Showa University, Japan; 19. Tokyo Medical and Dental University, Japan; 20. Osaka City Kosaiin Hospital, Japan; 21. Oita University, Japan

Corresponding Author: Michio Senda, Division of Molecular Imaging Research Kobe City Medical Center General Hospital (KCGH), 2-1-1 Minatojima-Minamimachi, Chuo-ku, Kobe 650-0047 Japan, E-mail:, Phone: 81-78-304-5212, Fax: 81-78-304-5201.



BACKGROUND: PET (positron emission tomography) and CSF (cerebrospinal fluid) provide the “ATN” (Amyloid, Tau, Neurodegeneration) classification and play an essential role in early and differential diagnosis of Alzheimer’s disease (AD).
OBJECTIVE: Biomarkers were evaluated in a Japanese multicenter study on cognitively unimpaired subjects (CU) and early (E) and late (L) mild cognitive impairment (MCI) patients.
MEASUREMENTS: A total of 38 (26 CU, 7 EMCI, 5 LMCI) subjects with the age of 65-84 were enrolled. Amyloid-PET and FDG-PET as well as structural MRI were acquired on all of them, with an additional tau-PET with 18F-flortaucipir on 15 and CSF measurement of Aβ1-42, P-tau, and T-tau on 18 subjects. Positivity of amyloid and tau was determined based on the positive result of either PET or CSF.
RESULTS: The amyloid positivity was 13/38, with discordance between PET and CSF in 6/18. Cortical tau deposition quantified with PET was significantly correlated with CSF P-tau, in spite of discordance in the binary positivity between visual PET interpretation and CSF P-tau in 5/8 (PET-/CSF+). Tau was positive in 7/9 amyloid positive and 8/16 amyloid negative subjects who underwent tau measurement, respectively. Overall, a large number of subjects presented quantitative measures and/or visual read that are close to the borderline of binary positivity, which caused, at least partly, the discordance between PET and CSF in amyloid and/or tau. Nine subjects presented either tau or FDG-PET positive while amyloid was negative, suggesting the possibility of non-AD disorders.
CONCLUSION: Positivity rate of amyloid and tau, together with their relationship, was consistent with previous reports. Multicenter study on subjects with very mild or no cognitive impairment may need refining the positivity criteria and cutoff level as well as strict quality control of the measurements.

Key words: Alzheimer’s disease, PET, CSF biomarker, amyloid, tau.




Early and differential diagnosis of Alzheimer’s disease (AD) has been drawing more and more attention these days as the target population of the therapeutic trials has shifted toward the early phases of the AD continuum. Biomarkers including PET, MRI and cerebrospinal fluid(CSF)/plasma play an essential role in such early phases, where clinical manifestation and behavioral findings are limited. Jack et al (1) extracted three markers, i.e., amyloid (A), tau (T) and neurodegeneration (N), and proposed the “ATN” classification for differential diagnosis of AD continuum. PET provides imaging and quantification of amyloid and tau deposition as well as neurodegeneration evaluable with 18F-fluorodeoxyglucose(FDG)-PET. Amyloid and tau can also be evaluated with CSF sampling, and recently with plasma as well, and MRI volumetry has also been used as a marker of neurodegeneration.
In Japan, a large-scale prospective observational study called J-ADNI (Japanese Alzheimer’s Disease Neuroimaging Initiative) was completed (2), in which a total of 537 subjects were enrolled, comprising 154 cognitively unimpaired subjects (CU), 234 MCI and 149 AD patients.
Then, a new version of J-ADNI was designed by the same group, named “AMED Preclinical AD Study”, which focused on CU and MCI and acquired amyloid-PET and FDG-PET on all subjects. Part of the subjects also underwent a tau-PET scan and/or a CSF sampling. The objective of the study was to evaluate PET and MRI images and CSF biomarkers in CU and MCI subjects in Japan, compare those biomarkers between modalities to explore their reliability and usefulness in such early-phase subjects, and obtain a rough idea of the fractions of ATN-based classifications. This report summarizes the results of the study that was recently completed.




The study was a non-randomized prospective observational study, and was designed and conducted in accordance with the ethical principles as proclaimed in the Declaration of Helsinki. The study protocol was first approved by Ethical Committee of Osaka City University Graduate School of Medicine (site of leading PI) and registered as UMIN000019926, and was later re-approved by Osaka City University Hospital Certified Review Board when the Japanese Law on Clinical Research was enacted, and was registered as jRCTs051180239. The protocol was also approved by each participating site according to the Japanese regulations and ethics guidance. The tau-PET portion of the study was designed as a nominally separate add-on study when tau-PET became available, though limited, later in the course of the research project, and was approved and registered as jRCTs051190065.
The subjects were enrolled at a total of 14 clinical sites and consisted of 26 CU subjects and 12 MCI patients (7 early (E) MCI and 5 late (L) MCI as classified below) based on the neuropsychological tests.
Written informed consent was obtained from each subject and the study partner such as a family member of the subject.
The CU subjects were 65-82 years old without any memory problem and CDR-J=0. The MMSE-J score was 25-30 (higher than or equal to 24), and the delayed recall score of WMS-R logical memory (WMS-R LM II) ranged from 4 to 19 except for one subject (See footnote of Table 1).
The MCI subjects were 65-84 years old with objective persistent memory impairment reported by the study partner and CDR-J=0.5 with memory box score being 0.5 or higher. The MMSE-J score was 27-30 (higher than or equal to 24). The WMS-R LM II was used to classify the MCI subjects into EMCI (WMS-R LM II = 3-6, 5-9, 9-11) and LMCI (≤2, ≤4, ≤8) depending on the educational years (0-7, 8-15, ≥16 years, respectively), and was 6-18 for EMCI and 0-8 for LMCI.

CSF measurements and genotyping

CSF was collected from 18 subjects by lumbar puncture and stored in polypropylene tubes at -80℃ until biochemical analysis. CSF concentration of Aβ1-42 was analyzed using V-PLEX Aβ Peptide Panel 1 kit with MESO QuickPlex SQ120 (MesoScale Discovery, Rockville, MD). CSF phosphorylated tau (P-tau) and total tau (T-tau) were measured using commercially available ELISA kits, INNOTEST hTAU and PHOSPHO-TAU (181P) (Fujirebio Europe, Belgium), respectively, according to the manufacturer’s instructions. Stability of the results was monitored in the Alzheimer’s Association QC program. Cutoff values (Aβ42<378.7 pg/mL, P-tau>29.1 pg/mL, and T-tau>88.8 pg/mL) that best discriminated PiB-PET positive AD patients from PiB-PET negative CU subjects were determined using independent J-ADNI cohort (2). Because the CSF assays used in this study were different from those used in J-ADNI study, calibration between two assays were performed.
APOE genotyping (rs429358 and rs7412) was performed by Taq-Man based assay using blood samples.

MRI imaging

The brain MRI was acquired for each subject using a 3-Tesla or 1.5-Tesla scanner. The structural 3D-T1 images (MP-RAGE or IR-SPGR) were analyzed with FreeSurfer (Ver. 6.0) to measure the regional cerebral gray-matter volumes. Because the absolute volumetry depends on the version of the software and other conditions, the regional atrophy of the subject was derived as z-score using mean and SD of the baseline scan for the 26 CU subjects of this study. The volume of 8 regions in the temporal lobe (right and left entorhinal cortex, parahippocampal gyrus, hippocampus, and amygdala) were summed up and the z score was derived as a measure of the temporal lobe atrophy for each subject.

PET image acquisition

All subjects underwent an amyloid-PET and an FDG-PET. Each PET imaging site, together with the PET camera, was qualified, in which the reconstruction parameters were determined for each PET camera so that all the PET cameras satisfied the image quality criteria with the Hoffman 3D brain phantom and the uniform cylindrical phantom (3).
For amyloid PET, either 11C-PiB (PiB), 18F-florbetapir (FBP) or 18F-flutemetamol (FMM) was used for 23, 13, and 2 subjects, respectively. The injection activity was 555MBq, 370MBq, 185MBq, the uptake time (start of emission scan post injection) was 50min, 50min, 90min, and the scan duration was 20min, 20min, 30min, for PiB, FBP, and FMM, respectively.
For the FDG scan, after at least 4 hours of fasting, the subject was administered with 185 MBq of 18F-FDG in a quiet, dimly lit room while resting in a reclining chair or bed, and the subject remained in the condition until several minutes before the start of the scanning session. The PET emission data was acquired for 30 minutes starting at 30 minutes post injection.
Tau-PET was performed with 18F-flortaucipir (FTP) on 15 subjects. Because tau-PET was not ready until late in the course of the research project, the time span from amyloid to tau-PET ranged from 1.0 to 2.0 (mean 1.56) years. The subject was administered with 240.5 MBq of FTP and a 30 min emission scan started 75 min post injection.
No adverse effects were observed at the PET scans of this study.

PET image analysis

The amyloid PET images were binary interpreted visually in a blind manner by the readers who were qualified for this study, and the adjudicator (K.I.) confirmed them. The PiB images were interpreted visually using the criteria adopted in J-ADNI (4), and the FMM and FBP images were interpreted with each vendor’s criteria.
As a quantitative analysis of the amyloid PET, mean cortical standardized uptake value ratio (mcSUVR) of PiB images was computed using the cerebellar cortex as a reference based on the method of J-ADNI, and the cutoff value of 1.5 was used to determine the quantitative positivity (4). The FMM images were analyzed with CORTEX ID (GE Healthcare) to derive mcSUVR using the pons as a reference, for which the cutoff value of 0.58 was used for the quantitative positivity (5). The FBP images were analyzed with MIMneuro (MIM Software) to derive mcSUVR using the whole cerebellum as a reference, for which the cutoff value of 1.10 was used for the quantitative positivity (6).
The FDG images together with the semiquantitative 3D-SSP results were visually interpreted by three independent readers followed by a consensus read in the same way as J-ADNI (7), and the images were classified into N1 (normal), N2 (reflecting atrophy), N3, P1 (AD pattern), P2 (FTD pattern), P3, and P1+ (DLB pattern) (8). No one presented N3 or P3 in this study. The DLB pattern criteria was interpreted in a broader sense to include cases with occipital hypometabolism extending to neighboring areas even if typical temporoparietal hypometabolism was not observed. The FDG images were also quantified with AD t-sum (9) using the module PALZ in the PMOD software package (Ver. 3.2; PMOD Technologies, Zurich, Switzerland), which were then converted into PET score [10] that reflects the severity of temporoparietal hypometabolism (AD pattern).
The FTP images were interpreted and classified into AD negative, AD+ and AD++, according to the vendor’s criteria that regards cortical uptake except anterior temporal as AD-related ( The FTP-PET was also analyzed with MUBADA-PERSI method to derive SUVR over the area affected by AD process (posterior temporal, occipital, parietal and part of frontal cortex) with white matter as a reference (11, 12).

Follow up

Whenever possible, each subject was followed up every year with a general clinical interview with neuropsychological tests, an MRI scan, and an FDG-PET scan. As a result, 1-year follow-up data were acquired on 32 subjects, and 2-year follow-up on 5 subjects.

Statistical methods

Because the number of subjects was small, descriptive results were presented in general. Proportion of positivity was compared between groups using chi-square tests, in which EMCI and LMCI were combined to increase the number of observations. Statistical tests were also performed on the Pearson correlation coefficient between two variables.



Findings of each subject

Table 1 describes findings of each subject as classified according to the ATN concept. In this study, amyloid (A) was interpreted as positive (A+) when either PET or CSF Aβ was positive. Tau (T) was interpreted as positive (T+) when either PET or CSF P-tau was positive; negative (T-) when either of them was obtained and neither of them were positive; and was “na” (not available) (Tna) when neither of them were obtained. Neurodegeneration (N) was interpreted as positive (N+) when the consensus visual read of FDG-PET showed a progressive pattern (P1, P2, or P1+), and negative (N-) when it was a non-progressive pattern (N1 or N2).

Table 1. Findings for each subject and ATN classification


The amyloid positivity rate was 13/38 overall (6/26 CU, 4/7 EMCI, 3/5 LMCI, p>0.05 between CU and MCI), while it was 8/38 based on the PET alone (3/26 CU, 2/7 EMCI, 3/5 LMCI, p>0.05 between CU and MCI).
Tau was positive for 7, negative for 2 and not available for 4 out of the 13 A+ subjects, being 2, 2, 2 and 5, 0, 2 out of the 6 A+ CU and 7 A+ MCI subjects, respectively.
FDG-PET showed a progressive pattern in 6/13 A+ subjects (3/6 CU, 1/4 EMCI, 2/3 LMCI) as compared to 3/25 A- subjects (0/20 CU, 1/3 EMCI, 2/2 LMCI). Significant difference was observed in the FDG-PET positivity (N+) proportion between A+ and A- (p<0.05) as well as between CU (3/26) and MCI (6/12) (p<0.05).
Of interest, tau was positive for as many as 8 (negative for 8, not available for 9) out of the 25 amyloid negative subjects, indicating tau deposition without AD pathological process. It should be noted that all the 8 A-T+ subjects was tau positive due to CSF test, in spite of negative tau PET for two of them.
Association of APOE genotypes with amyloid PET (p>0.6) or CSF Aβ (p>0.5), or with any other biomarkers, was not observed for the presence of E4, probably due to the small number of subjects.

Representative cases

Figures 1 (#24, LMCI) and 2 (#22, CU) depict a case with prodromal AD (A+T+N+) and preclinical AD (A+T+N-), respectively. PET and CSF were discordant for “A” and/or “T” in both cases, which may be related to visually equivocal images and near-cutoff level quantified values. In the case of Figure 2, CSF P-tau was positive while tau PET was negative, consistent with the report of earlier and more sensitive positivity of CSF P-tau than tau-PET in the AD continuum (13).
Four cases (1 CU, 1 EMCI, 2 LMCIs) showed a mild/partial DLB pattern in FDG-PET marked with “P1+” in Table 1, featuring hypometabolism in the occipital cortex extending into surrounding areas but not showing a typical AD pattern of temporo-parietal hypometabolism. Amyloid was positive for 3/4 and tau was positive for 4/4. Figure 3 (#26, CU) depicts one of them.

Figure 1. PiB, FTP and FDG-PET of a female LMCI patient in her 70s (#24) interpreted as prodromal AD

Amyloid PET with PiB was visually negative, as the left parietal mild accumulation did not reach the cortical surface (arrow). However, the subject was classified as “A+” because quantitative analysis revealed SUVR (1.57) above cutoff. The CSF Aβ was negative (399.8 pg/mL). The FTP-PET showed abnormal tau accumulation in the left posterior temporal lobe (arrow), typical of AD process. Note off-target uptake of FTP in choroid plexus (arrowheads), substantia nigra, and striatum. The FDG-PET was read as temporo-parietal hypometabolism indicating AD pattern in the baseline that progressed in two years (arrows). PET score and MRI z-score also increased in two years: from 0.76 to 1.08 and from 2.7 to 3.2, respectively.

Figure 2. PiB, FTP and FDG-PET of a female CU subject of her 70s (#22) interpreted as preclinical AD

PiB-PET revealed positive amyloid accumulation in the left temporal and parietal areas (arrows). Tau PET with FTP acquired 1 year later was negative, because mild activity along the cortical rim was interpreted as off-target uptake by the meninges (short arrows) and that the left anterior temporal uptake was considered non-pathological within the AD continuum (long arrow). CSF P-tau was positive. FDG-PET showed a normal pattern.

Figure 3. FBP, FTP and FDG-PET of a female CU subject of her 70s (#26)

FBP-PET presented negative amyloid, and tau was negative in FTP-PET, although CSF showed positive Aβ (317.9pg/mL) and P-tau (38.2pg/mL). FDG-PET revealed a DLB pattern, presenting occipital hypometabolism (long arrows) extending to the right temporal and parietal cortex (short arrows), which progressed 1 year later. Note cingulate island sign denoting preserved metabolism in the posterior cingulate cortex (arrowheads).


Association between PET and CSF

For the 18 subjects, in which CSF data were obtained, amyloid positivity by CSF agreed with that by PET in 12 cases while 6 showed a discordance (Table 1). The rate of discordance was consistent with previous reports and may be caused by various factors (13).
Quantified tau uptake (SUVR) measured with FTP-PET using MUBADA-PERSI method was significantly correlated with CSF P-tau (r=0.92, p<0.001, n=8) (Figure 4). Although the cutoff for SUVR with MUBADA-PERSI SUVR is not established yet, the visual read of the FTP-PET was positive only for two of them. In CSF P-tau, however, 7 out of the 8 subjects showed P-tau above the cutoff level, indicating a discordance in the tau positivity between PET and CSF. This is consistent with recent investigations that reported earlier or more sensitive positivity of CSF P-tau than tau-PET in the AD continuum (i.e. in amyloid positive subjects), because secretion of soluble p-tau to CSF is increased by Aβ pathology before tau begins to accumulate in the brain (14).

Figure 4. Scatter plot of tau uptake (SUVR) quantified with FTP-PET and CSF P-tau

Red marks indicate PET-positive cases by visual read. Arrow indicates cutoff value for CSF P-tau.



PET/CSF discordance for amyloid and tau

This study suffers limitations such as the small number of subjects, poor follow-up records, and lack of tau-PET and CSF measurement for a large fraction of the subjects. However, some findings are notable.
The rate of amyloid positivity based on the combination of PET and CSF (6/26=23% for CU, 7/12=58% for MCI) was consistent with previous reports including J-ADNI. Discordance of positivity between amyloid-PET and CSF Aβ was observed in 6 subjects (5 PET-/CSF+, 1 PET+/CSF-), suggesting higher sensitivity of CSF, which was also consistent with the ADNI data on CU and MCI (13).
The rate of tau positivity was 4/6 for amyloid PET-/CSF Aβ+ or amyloid PET+/CSF Aβ-, and 2/2 for amyloid PET+/CSF Aβ+ in this study (Table 1). This was agreeable with the above ADNI data, in which the former two groups presented significantly lower CSF p-tau and PET-measured tau deposition than the latter and suggested earlier manifestations of AD process (13).
It is known that CSF p-tau is quantitatively associated with PET-measured tau deposition, especially in the AD continuum, and that CSF p-tau rises in the earlier stage than the pathological uptake of FTP-PET accrues (14). The result of the present study indicated a similar association in spite of the small number of subjects (Figure 4), in which FTP-PET was quantified with MUBADA, and that CSF p-tau was more sensitive than visual read of FTP-PET. In the early phase of AD continuum, CSF p-tau and FTP-PET could be considered to reflect different pathological changes, as the former may indicate excess secretion of p-tau to CSF and the latter represents tissue tau deposition. The present study adopted the criteria of visual FTP-PET interpretation to determine the positivity, by which the anterior temporal FTP uptake was considered insignificant. Because MUBADA VOI covers wide cortical areas, it may not be a sensitive measure of early tau deposition in the AD continuum that begins in the temporal cortex. Although a recent study suggests the earliest tau deposition at the rhinal cortex located in the anteromedial temporal lobe (15), it was hard to quantify the pathological FTP uptake in that region due to off-target binding to the choroid plexus in the present study (Figure 2). In that sense, use of FTP was another limitation of this study. Tau PET drugs with little off-target binding such as 18F-MK-6240 or 18F-PI-2620 may be more suitable for evaluation of the earliest stage of AD.

Neurodegeneration marker

In this study only visual assessment of FDG-PET was used to evaluate the “N” (neurodegeneration) marker, and quantitative PET score was not used so that one “N-” subject presented a high PET score (#4). Although CSF T-tau and MRI-volumetry are also regarded as an N-marker, their association with FDG-PET remains to be investigated as they represent different pathophysiology.
Hypometabolism depicted by FDG-PET reflects reduced neuronal activity in general, regardless of pathophysiology. No subjects showed A+T-N+ in this study, which agrees with the concept of tau deposition leading to neurodegeneration in AD continuum, although such manifestation, if occurred, might have suggested combined AD and non-AD processes. Outside the AD continuum (A-), however, FDG-PET neurodegeneration was positive in 3/5 MCI (2/2 LMCI) subjects and was not observed in CU subjects (0/20), which is consistent with the above notion and agrees with previous reports (16).

Binary criteria

A large number of subjects presented quantitative measures and/or visual read that are close to the borderline of binary positivity in the present study, which caused, at least partly, discordance between PET and CSF in amyloid and/or tau, such as the cases in Figures 1 and 2. This is understandable because most of the subjects in the present study were in the early phase of AD continuum or of a non-AD disease if any, and that the current criteria and cutoff level have been derived from differential diagnosis of AD patients from CU subjects. To deal with early-phase subjects having no or very mild cognitive impairment, the criteria and cutoff level of the biomarkers may need refining, and the data acquisition may need strict quality control.

Non-AD disorders

The present study revealed 8 A-T+ subjects. Because two of them (#37, #38) were LMCI patients and showed AD or DLB pattern in FDG-PET, they are considered SNAP and to have cognitive impairment due to non-AD disorders (17). The other six are CU subjects and had non-progressive FDG pattern, and may suggest a very early stage of various non-AD tauopathy such as PART or normal aging process (17). There is also a possibility of false-positive CSF P-tau as the value was close to the cutoff, ranging 31.8-38.2 pg/mL for 5 of the 6 subjects. Since biomarkers in non-AD tauopathies are not well understood, further investigations are needed.
Another EMCI subject (#36) presented “A-T-N+” with FTD pattern in FDG-PET, and was suspected of early stage of non-amyloid non-tau FTLD.
The present study also revealed 4 subjects presenting DLB pattern in FDG-PET, with occipital hypometabolism extending to surrounding areas (Figure 3). Three of them were amyloid positive and all were tau positive. It is not clear whether they were preclinical or prodromal stage of atypical AD, or DLB with or without amyloid deposition.
These findings suggest that a significant fraction of the subjects in this study might be related to non-AD disorders such as DLB, SNAP, PART, argyrophilic grain disease (AGD), and TDP-43 proteinopathy (like LATE) (17). Even if they are amyloid positive, there is a possibility of incidental amyloid deposition. Therefore, possibility of non-AD disorders should always be considered when clinical trials targeting preclinical or prodromal AD are designed and subjects are selected based on the biomarkers.



In conclusion, this study confirmed the known changes of PET and CSF biomarkers in preclinical and prodromal AD, and at the same time, suggested difficulties of determining the criteria and cutoff level of those biomarkers to evaluate such subjects as well as the possibility of unsolicited inclusion of early-phase non-AD disorders.


Acknowledgements: We are grateful for the materials and technical supports for the PET imaging by Fujifilm Toyama Chemical, Avid Radiopharmaceuticals/Eli Lilly Japan, and GE Healthcare. PET centers that imaged the subjects but did not belong to the clinical site that enrolled the subjects are also acknowledged, including Tohoku University Cyclotron and Radioisotope Center (CYRIC), Tsukuba Advanced Imaging Center (AIC), Tokyo Metropolitan Institute of Gerontology (TMIG), Aizawa Hospital, MI Clinic, and Kobe City Medical Center General Hospital (KCGH). We thank all the people who participated in this study in the clinical and imaging sites as well as in the Core sites.

Conflict of interest: The following conflicts of interest are disclosed by the authors. Senda reports provision of devices, cassettes, and precursors from Avid/Eli Lilly Japan and GE, funding as PI of clinical trials sponsored by Eli Lilly, Eisai, Biogen, Cerveau and Merck, as well as leadership role in the Japanese Society of Nuclear Medicine as board member, congress chair and committee chair. Ikeuchi reports grants from AMED (JP19dk0207020, JP20dk0202028, JP20dm0207073). Matsuda reports a grant from AMED (19dk0207020h0005), intramural grants from National Center of Neurology and Psychiatry, and an entrusted research fund from Nihon Medi-Physics Co. Ltd. Iwatsubo reports a grant from an anonymous Foundation. Iwata reports grants from AMED (19dk0207020h0005, 16dk0207028h0001). Ikari is a full time employee of CMIC Inc. as well as graduate student of Osaka University. Washizuka reports research funding from AMED and pharmaceutical companies including Otsuka, Eisai, Pfizer, Daiichi-Sankyo, Tsumura, Mochida, Astellas, Shionogi, Takeda, Sumitomo-Dainippon, as well as honoraria from such pharmaceutical companies. Kazunari Ishii reports honoraria from Nihon Medi-Physics. Yokota reports licensing and collaboration research with Takeda Pharmaceutical Company. Nakanishi reports research funding from Eisai and Elli Lilly Japan as well as leadership role as a director in the Japan Society for Dementia Research. Shimada reports grants from AMED (19dk0207020h0005, 20dk0207028h0005). The other authors have nothing to disclose.

Funding: This study was financially supported by grants from Japan Agency for Medical Research and Development (AMED) 19dk0207020h0005, 20dk0202028h0005 and 20dm0207073h003, as well as by an anonymous Foundation.



1. Jack CR, Jr., Bennett DA, Blennow K, et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 2016;87:539-547.
2. Iwatsubo T, Iwata A, Suzuki K, et al. Japanese and North American Alzheimer’s Disease Neuroimaging Initiative studies: Harmonization for international trials. Alzheimers Dement 2018;14:1077-1087.
3. Ikari Y, Akamatsu G, Nishio T, et al. Phantom criteria for qualification of brain FDG and amyloid PET across different cameras. EJNMMI physics 2016;3:23.
4. Yamane T, Ishii K, Sakata M, et al. Inter-rater variability of visual interpretation and comparison with quantitative evaluation of (11)C-PiB PET amyloid images of the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) multicenter study. Eur J Nucl Med Mol Imaging 2017;44:850-857.
5. Thurfjell L, Lilja J, Lundqvist R, et al. Automated quantification of 18F-flutemetamol PET activity for categorizing scans as negative or positive for brain amyloid: concordance with visual image reads. J Nucl Med 2014;55:1623-1628.
6. Clark CM, Pontecorvo MJ, Beach TG, et al. Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid-β plaques: a prospective cohort study. The Lancet Neurology 2012;11:669-678.
7. Yamane T, Ikari Y, Nishio T, et al. Visual-statistical interpretation of (18)F-FDG-PET images for characteristic Alzheimer patterns in a multicenter study: inter-rater concordance and relationship to automated quantitative evaluation. AJNR Am J Neuroradiol 2014;35:244-249.
8. Silverman DHS, Small GW, Chang CY, et al. Positron Emission Tomography in Evaluation of DementiaRegional Brain Metabolism and Long-term Outcome. JAMA 2001;286:2120-2127.
9. Herholz K, Salmon E, Perani D, et al. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. NeuroImage 2002;17:302-316.
10. Herholz K, Westwood S, Haense C, Dunn G. Evaluation of a calibrated (18)F-FDG PET score as a biomarker for progression in Alzheimer disease and mild cognitive impairment. J Nucl Med 2011;52:1218-1226.
11. Southekal S, Devous MD, Sr., Kennedy I, et al. Flortaucipir F 18 Quantitation Using Parametric Estimation of Reference Signal Intensity. J Nucl Med 2018;59:944-951.
12. Devous MD, Sr., Joshi AD, Navitsky M, et al. Test-Retest Reproducibility for the Tau PET Imaging Agent Flortaucipir F 18. J Nucl Med 2018;59:937-943.
13. Reimand J, Collij L, Scheltens P, Bouwman F, Ossenkoppele R, Alzheimer’s Disease Neuroimaging I. Association of amyloid-beta CSF/PET discordance and tau load 5 years later. Neurology 2020;95:e2648-e2657.
14. Mattsson-Carlgren N, Andersson E, Janelidze S, et al. Aβ deposition is associated with increases in soluble and phosphorylated tau that precede a positive Tau PET in Alzheimer’s disease. Science advances 2020;6:eaaz2387.
15. Sanchez JS, Becker JA, Jacobs HIL, et al. The cortical origin and initial spread of medial temporal tauopathy in Alzheimer’s disease assessed with positron emission tomography. Science translational medicine 2021;13:eabc0655.
16. Coutinho AM, Busatto GF, de Gobbi Porto FH, et al. Brain PET amyloid and neurodegeneration biomarkers in the context of the 2018 NIA-AA research framework: an individual approach exploring clinical-biomarker mismatches and sociodemographic parameters. Eur J Nucl Med Mol Imaging 2020;47:2666-2680.
17. Jack CR, Jr., Knopman DS, Chételat G, et al. Suspected non-Alzheimer disease pathophysiology–concept and controversy. Nature reviews Neurology 2016;12:117-124.



R.E. Amariglio1,2, S.A.M. Sikkes6, G.A. Marshall1,2, R.F. Buckley2,7,8, J.R. Gatchel3,4, K.A. Johnson1,5, D.M. Rentz1,2, M.C. Donohue9, R. Raman9, C.-K. Sun9, R. Yaari10, K.C. Holdridge10, J.R. Sims10, J.D. Grill11, P.S. Aisen9, R.A. Sperling1,2 and the A4 Study Team


1. Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA; 2. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; 3. Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; 4. Division of Geriatric Psychiatry, McLean Hospital, Belmont Massachusetts, USA; 5. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA; 6. Alzheimer Center Amsterdam, Department of Neurology, Amsterdam University Medical Centers, Amsterdam, Netherlands; 7. Florey Institute, University of Melbourne, Parkville, Victoria, Australia; 8. Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia; 9. Alzheimer’s Therapeutic Research Institute, Keck School of Medicine of the University of Southern California, San Diego, CA, USA; 10. Eli Lilly and Company, Indianapolis, IN, USA; 11. University of California Irvine, Irvine, USA

Corresponding Author: R.E. Amariglio, Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, 02115, USA,

J Prev Alz Dis 2021;3(8):257-262
Published online March 5, 2021,



Background: Greater subjective cognitive changes on the Cognitive Function Index (CFI) was previously found to be associated with elevated amyloid (Aß) status in participants screening for the A4 Study, reported by study partners and the participants themselves. While the total score on the CFI related to amyloid for both sources respectively, potential differences in the specific types of cognitive changes reported by either participants or their study partners was not investigated.
Objectives: To determine the specific types of subjective cognitive changes endorsed by participants and their study partners that are associated with amyloid status in individuals screening for an AD prevention trial.
Design, Setting, Participants: Four thousand four hundred and eighty-six cognitively unimpaired (CDR=0; MMSE 25-30) participants (ages 65-85) screening for the A4 Study completed florbetapir (Aß) Positron Emission Tomography (PET) imaging. Participants were classified as elevated amyloid (Aß+; n=1323) or non-elevated amyloid (Aß-; n=3163).
Measurements: Prior to amyloid PET imaging, subjective report of changes in cognitive functioning were measured using the CFI (15 item questionnaire; Yes/Maybe/No response options) and administered separately to both participants and their study partners (i.e., a family member or friend in regular contact with the participant). The impact of demographic factors on CFI report was investigated. For each item of the CFI, the relationship between Aß and CFI response was investigated using an ordinal mixed effects model for participant and study partner report.
Results: Independent of Aß status, participants were more likely to report ‘Yes’ or ‘Maybe’ compared to the study partners for nearly all CFI items. Older age (r= 0.06, p<0.001) and lower education (r=-0.08, p<0.001) of the participant were associated with higher CFI. Highest coincident odds ratios related to Aß+ for both respondents included items assessing whether ‘a substantial decline in memory’ had occurred in the last year (ORsp= 1.35 [95% CI 1.11, 1.63]; ORp= 1.55 [95% CI 1.34, 1.79]) and whether the participant had ‘seen a doctor about memory’ (ORsp= 1.56 [95% CI 1.25, 1.95]; ORp =1.71 [95% CI 1.37, 2.12]). For two items, associations were significant for only study partner report; whether the participant ‘Repeats questions’ (ORsp = 1.30 [95% CI 1.07, 1.57]) and has ‘trouble following the news’ (ORsp= 1.46[95% CI 1.12, 1.91]). One question was significant only for participant report; ‘trouble driving’ (ORp= 1.25 [95% CI 1.04, 1.49]).
Conclusions: Elevated Aβ is associated with greater reporting of subjective cognitive changes as measured by the CFI in this cognitively unimpaired population. While participants were more likely than study partners to endorse change on most CFI items, unique CFI items were associated with elevated Aß for participants and their study partners, supporting the value of both sources of information in clinical trials.

Key words: Subjective cognitive cecline, amyloid, clinical trial, Alzheimer’s disease.



After a series of disappointing results at the symptomatic stages of Alzheimer’s disease (AD), therapeutic trials are increasingly moving towards prevention at the preclinical stage (1). Individuals enrolled in a secondary prevention trial are characterized as clinically normal, but are considered at increased risk of AD due to elevated biomarkers, such as amyloid (Aß) Positron Emission Tomography (PET) imaging. By shifting the focus earlier in the disease, however, demonstrating clinically meaningful treatment effects is challenging, since most individuals at the preclinical stage are not expected to demonstrate overt cognitive and functional impairment during the course of a trial (2). As such, efforts to identify new methods to capture subtle changes in cognitive functioning prior to the onset of objective clinical impairment are needed to quantify treatment effects with greater resolution.
Subjective report of everyday high-level cognitive functioning from both the individual and a close family member or friend, may offer a window into early cognitive changes along the preclinical stage. Indeed, prior studies have shown that both greater cognitive complaints from the participant, as well as from a study partner, are associated with higher likelihood of subsequent cognitive decline and clinical progression (3-6).
In addition to serving as outcome measures, subjective cognitive assessments may also facilitate the process of identifying individuals who meet criteria for preclinical AD. As individuals age, cognitive complaints become increasingly common and are not necessarily specific to a pathological process (7). Elucidating particular patterns of complaints from the participant and the study partner that relate to AD biomarkers may enhance the utility of subjective report that is sometimes dismissed for being non-specific (8, 9). Further, better characterization of the subtle changes that are observed at the preclinical stage may ultimately help to identify specific targets for therapeutic intervention.
In the current study, we examined data from individuals screening for the Anti-Amyloid Asymptomatic Alzheimer’s (A4) Study testing solanezumab, an anti-amyloid antibody, in a secondary AD prevention trial. In particular, we sought to build upon previous findings in the A4 screen data that found both participant and study partner report related to Aß on the total score of the Cognitive Function Index (CFI) (10), a subjective questionnaire that asks a participant and study partner about change in the participant’s cognitive functioning over the last year (3, 11). Here, we investigated the potential impact of demographic factors on participant vs. study partner report on the total score of the CFI, as well as which specific individual items on the CFI related to amyloid burden on PET. In this way, we aimed to elucidate the pattern of cognitive complaints at the preclinical stage of AD from both the perspective of the participant and study partner.



Data presented here come from participants who were screened for the A4 Study. In brief, the A4 Study is a preclinical stage treatment trial that is being conducted at 67 clinical trial sites in the U.S., Canada, Japan, and Australia, among participants with elevated Aß as determined by florbetapir PET. Participants first underwent an initial clinic screening visit and if eligibility criteria were met, subsequently underwent Aß PET imaging at a second screening visit. Participants who completed screening for the A4 study, were ages 65-85 years and were considered cognitively unimpaired, based on a global CDR (12) score of 0, Mini-Mental State Exam (MMSE) (13) score of 25-30, and Logical Memory II subscale delayed paragraph recall (LM-IIa) of the Wechsler Memory Scale-Revised (WMS-R) (14)score of 6-18. Moreover, participants did not have unstable or exclusionary medical or psychiatric problems. Participants had adequate literacy in English, Spanish, or Japanese, and had adequate vision and hearing to complete the required cognitive tests. Participants were required to have a study partner who was willing to provide collateral information about the participant’s everyday cognitive functioning; study partners were required to have at least weekly contact with participants in person, by phone, or by email. Key exclusion criteria for participants were diagnosis of cognitive impairment or dementia, use of AD medications, unstable anxiety or depression, or other unstable medical conditions, although participants with treated hypertension, diabetes, and other common medical ailments were permitted. Four thousand four hundred and eighty-six participants meeting these criteria then underwent florbetapir PET imaging.

Cognitive Function Index

The CFI was originally developed as a 14-item, self-administered mail-in screening instrument for AD diagnostic evaluation in prevention trials (10). The CFI has a participant version in which participants report on their own cognitive functioning, as well as a study partner version in which study partners report on the participants’ cognitive functioning. The CFI was previously found to have adequate validity and reliability (6, 10). All questions on the CFI ask about cognitive changes over the last year with response options that include Yes (2), No (0), and Maybe (1). Questions range from cognitive items (e.g., “Compared to one year ago, do you feel that your memory has declined substantially?”) to functional items (e.g., “Compared to one year ago, do you have more difficulty managing money?”). The A4 version of the CFI added an additional question, “In the past year, have you seen a doctor about memory concerns?” with response options: Yes (1) or No (0). On a few questions, (e.g., “Has your work performance (paid or volunteer) declined significantly, compared to one year ago?”), Non-Applicable (N/A) was also a response option. A total CFI score can be derived by summing each item of the participant and study partner versions of the questionnaire respectively (the range is 0-29 with higher scores indicating greater complaints about cognitive functioning difficulties). The CFI was administered separately to both the participant and their study partner at the first screen visit prior to florbetapir PET imaging at the second screening visit.

Amyloid PET Imaging

Florbetapir PET was acquired 50-70 minutes after injection of 10 mCi of florbetapir F 18. Amyloid eligibility (elevated [Aß+] and eligible to continue in screening vs. not elevated [Aß-] and ineligible) was assessed using an algorithm combining both quantitative standardized uptake value ratio (SUVr) methodology and qualitative visual read performed at a central laboratory. Mean cortical standardized uptake value ratio [SUVr] using a whole cerebellar reference region of ≥1.15 was utilized to define elevated amyloid as the primary criterion, as quantitative assessment was thought to be more sensitive to the presence of early amyloidosis in the preclinical stage of AD. A SUVr between 1.10 and 1.15 was considered to be elevated amyloid only if the visual read was considered positive by a two independent-reader consensus determination (10).

Statistical Analyses

Demographic factors of the participant (e.g., sex, age, education) and of the study partner (e.g., partner sex, partner age, living status with participant) were summarized by Aß status with means, standard deviations, and two-sample t-test; or counts, percentages, and Fisher’s Exact test. Pearson’s correlation coefficients were calculated for continuous characteristics and CFI scores. Linear regression models were used to determine whether there was an interaction between Aß status and each participant and study partner characteristic on the CFI score for the participant and study partner report.
To compare level of endorsement on each item of the CFI between participant and study partner, a cumulative odds mixed effects model was employed with CFI response (No=0, Maybe=1,Yes= 2) as the outcome and CFI source (participant or study partner), age, sex, and education as the predictors; and dyad-specific random intercepts. Non-applicable data was treated as missing. Item level missing data was rare and was not analyzed with imputation. To determine the relationship between Aß status and level of endorsement on each item, separate cumulative odds models were fit for each CFI item. CFI response for participant and study partner were run separately as the dependent variable and Aß status as the predictor controlling for age, education, and sex. The false discovery rate for item level analysis was adjusted within source (participant vs. study partner) by the method of Benjamini and Hochberg (1995) (15). Statistical analyses were performed using the R software (


Of the 4486 participants, 1322 were categorized as Aß+ (29.5%) and 3163 were Aß-. Aß+ participants were slightly older than Aß- (see table 1). No differences between Aß groups were observed by sex, years of education, or marital or retirement status. Study partners were majority spouses (62%) and female (60%) and were age 65.8±11.2 years. There were no differences in relationship to study partner by Aß group.

Table 1. Demographic variables of all participants with comparison of Not elevated (Aß-) and elevated amyloid (Aß+) groups

1. Fisher’s Exact test

Higher score on the participant CFI was associated with older age (r= 0.06, p<0.001) and lower education (r=-0.08, p<0.001), but was not associated with sex (t=-2.68, p=0.79). Study partner CFI score was higher for female study partners (meanfemale= 2.94, meanmale= 2.54; t= 3.5, p=0.0005) and if a study partner lived with the participant (meanlive with= 2.92, meanlive separate= 2.49; t=-3.7, p=0.0003). Older age of the participant related to higher study partner CFI score (r= 0.07, p<0.001).
Next, we were interested in whether demographic factors modified the relationship between Aß and the CFI total score for participant report and study partner report respectively. When examining participant report, there was not a significant interaction between Aß and sex (ß= -0.14, p=0.30), Aß and age (ß= 0.015, p=0.27), nor Aß and education level of participant (ß= 0.106, p= 0.47) to predict participant CFI score. For study partners, there was not a significant interaction between Aß and sex of the study partner (ß= 0.05, p =0.85) nor Aß and living situation of study partner with participant (ß= 0.17, p=0.53). The interaction between Aß and age of study partner to predict study partner CFI score was at the significance nominal threshold (ß= 0.02, p= 0.05). Specifically, among Aß-, younger study partners tended to report higher CFI scores. However, among Aß+, age of study partner did not modify CFI report.
At the item level, the 3 most commonly endorsed (‘Yes’ or ‘Maybe’ response) items were the same for both participant and study partner report (see figure : ‘trouble with names and words’ ‘relying on written reminders’ and ‘misplacing things’ (See figure 1, supplemental table 1). The 3 least endorsed items were also the same for participant and study partner report: ‘managing money’ ‘difficulty with hobbies’ ‘difficulty with appliances’. For one item, ‘trouble with work performance,’ 18% of participants and 25% of study partners did not find this item applicable.
In general, greater endorsement was found for participants compared to their study partners (see figure 1). The only items that did not significantly differ in level of endorsement between participant and study partner included ‘Seen a doctor about memory,’ ‘Repeating questions,’ ‘Difficulty with appliance and electronic devices.’

Figure 1. Percentage of endorsement on CFI for participant and study partner report at the item level


As was previously reported (10), Aß+ participants reported higher total CFI (Cohen d = 0.31, p<0.001) and study partners reported Aß+ participants had higher total CFI (Cohen d = 0.23, p<0.001). Here, we examined the relationship between Aß status and level of endorsement for each item. For 7 of the 15 items, Aß+ was associated with a higher odds of endorsement on CFI for both participant and study partner report and included: ‘Seen a doctor about memory,’ ‘Substantial memory decline,’ ‘Misplacing things,’ ‘Help to remember appointments,’ ‘Disoriented when traveling,’ ‘Trouble with names and words,’ and ‘Relying on written reminders.’ For participant report, but not study partner, Aß+ was associated with greater endorsement on ‘trouble with driving.’ For study partner report but not participant, Aß+ was associated with ‘trouble following the news’ and ‘repeating questions’ (see figure 2). Additionally, Aß+ was associated with ‘decline in work performance’ but as noted above 25% of study partners answered ‘not applicable’ leading to less stable estimates on this item compared to other items on the CFI. All items significantly associated with Aß+ remained significant after an FDR adjustment.

Figure 2. CFI items and odds of endorsement related to Aß

Items presented highest odds of endorsement and Aß+ to lowest for participant and study partner report. * = significant association of item and Aß+



Extending previous findings from the A4 screen data that found for Aß+ participants, participant and study partner CFI scores were higher than for Aß- participants (10), here we found at an item-level, 7 of the 15 CFI items were related to Aß+ for participant and study partner report. Further, the odds of endorsement associated with elevated Aß was numerically higher for participant report compared to study partner report. Items that were related to both study partner and participant report reflected predominantly cognitive changes (e.g., ‘Substantial memory decline,’ ‘Misplacing things’). Functional items (e.g., ‘Hobbies more difficult,’ ‘Reduced social activities,’ ‘Difficulty managing money,’ and ‘Difficulty with appliances’) were much less likely to be endorsed by either a participant or a study partner and were not associated with Aß+.
Additionally, the item ‘seen a doctor about memory’ related to Aß+ for both participant and study partner report. Unlike the other CFI items, this question is based on a specific event rather than an overall subjective experience. The association of ‘seen a doctor about memory’ with Aß is consistent with previous work demonstrating individuals recruited from a memory clinic are more likely Aß+ compared to community-based individuals with subjective memory complaints (9, 16).
Several items were only related to study partner or participant report alone. Specifically, two items were related to Aß+ for study partner report (i.e., ‘Repeating questions’ and ‘trouble following the news’). Interestingly, the fact that study partners report ‘repeating questions,’ but not participants themselves is consistent with clinical observations, in that the participant may not realize they are repeating themselves and would be less likely to endorse this item.
These findings suggest that, even at the preclinical phase, subtle changes in cognitive functioning are recognized by and at the level of awareness of some participants and some study partners. Participant report, while potentially sensitive at the earliest stages of disease, has faced criticism as an outcome in clinical trials as changes in self-awareness (i.e., anosognosia) in patient reported outcomes can occur, particularly by the stage of dementia (17). Thus, there have been concerns as to whether participant report can reliably serve as an indicator of symptom progression for the duration of a trial as individuals move towards clinical impairment. Conversely, it has been unclear what changes if any, a study partner can observe at the preclinical stage when individuals are entirely independent in their daily activities. Importantly, in this study of individuals screening for a prevention trial, study partner report was consistent with participant report, despite the fact that individuals had a global CDR score of 0.
We also examined the impact of demographic features of participants and their study partners on the relationship between Aß status and CFI total score. For participants, while there was a higher level of endorsement for CFI that related to older age and lower education, these demographic variables did not significantly modify the relationship between Aß status and CFI score. Likewise, there was a higher level of endorsement for study partner-reported CFI if the study partner was female or lived with the participant, however these demographic variables did not significantly modify the relationship between Aß status and CFI score. Finally, while age of the study partner did not relate to study partner reported CFI score, there was a nominally significant modifying effect of study partner age on the relationship between Aß status and CFI score. Taken together, the associations between Aß+ and CFI seem to remain even after after adjusting for age, education, sex, and living situation of study partner with participant.
A few limitations to this study a worth noting. Participants in this study were highly educated with limited ethnic and racial diversity, typical for clinical trial populations. Thus, it remains unknown how these findings might generalize to the larger population, as there may be educational and cultural differences in the value of subjective reporting as it relates to risk for AD.
Our findings are in support of subjective report of cognitive functioning to characterize early manifestations of AD among those in an early-treatment trial. While participant report appears to be numerically higher as it relates to Aß status compared study partner report, study partners are also observing similar changes as they relate to Aß status. In the future, examining items longitudinally with tau PET in combination with amyloid PET will also help to better approximate the optimal utility of participant and study partner report as individuals decline or improve during the course of a trial.


Funding: The A4 Study is a secondary prevention trial in preclinical Alzheimer’s disease, aiming to slow cognitive decline associated with brain amyloid accumulation in clinically normal older individuals. The A4 Study is funded by a public-private-philanthropic partnership, including funding from the National Institutes of Health-National Institute on Aging (U19 AG010483, U24AG057437, R01 AG063689), Eli Lilly and Company, Alzheimer’s Association, Accelerating Medicines Partnership, GHR Foundation, an anonymous foundation and additional private donors, with in-kind support from Avid and Cogstate. The companion observational Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study is funded by the Alzheimer’s Association (LEARN-15-338729) and GHR Foundation. The A4 and LEARN Studies are led by Dr. Reisa Sperling at Brigham and Women’s Hospital, Harvard Medical School and Dr. Paul Aisen at the Alzheimer’s Therapeutic Research Institute (ATRI), University of Southern California. The A4 and LEARN Studies are coordinated by ATRI at the University of Southern California, and the data are made available through the Laboratory for Neuro Imaging at the University of Southern California. The participants screening for the A4 Study provided permission to share their de-identified data in order to advance the quest to find a successful treatment for Alzheimer’s disease.

Acknowledgements: We would like to acknowledge the dedication of all the participants, the site personnel, and all of the partnership team members who continue to make the A4 and LEARN Studies possible. The complete A4 Study Team list is available on:
Conflict of Interest: R. Amariglio has nothing to disclose. J. Grill has nothing to disclose. D. Rentz has nothing to disclose. G Marshall reports personal fees and institutional support from Eisai Inc., institutional support from Eli Lilly and Company, Janssen Alzheimer Immunotherapy, Novartis, and Genentech, and personal fees from Grifols Shared Services North America, Inc, Pfizer outside the submitted work. R. Buckley has nothing to disclose. R. Yaari reports personal fees from Eli Lilly and Company during the conduct of the study. R. Raman reports grants from NIA, grants from Eli Lilly during the conduct of the study, grants from Janssen and grants from Eisai, outside the submitted work. CK. Sun reports grants from NIA and grants from Eli Lilly and Company during the conduct of the study. J. Sims he is an employee and stock holder of Eli Lilly and Company outside the submitted work. M. Donohue reports grants from NIH, grants and personal fees from Eli Lilly and Company during the conduct of the study, personal fees from Roche, personal fees from Biogen, personal fees from Neurotrack, other from Janssen, personal fees from Vivid Genomics outside the submitted work. P. Aisen reports grants from Janssen, grants from NIA, grants from FNIH, grants from Alzheimer’s Association, grants from Eisai, personal fees from Merck, personal fees from Biogen, personal fees from Roche, personal fees from Lundbeck, personal fees from Proclara, personal fees from Immunobrain Checkpoint outside the submitted work. K. Holdrige reports she is an employee and minor stockholder of Eli Lilly and Company. S Sikkes reports grants from Zon-MW OffRoad, grants from EU-JPND, institutional support from Lundbeck, Boehringer, and Toyama outside the submitted work. Dr. Gatchel reports grants from Alzheimer’s Association, grants from NIH/NIA, personal fees from Huron , grants from Merck , outside the submitted work.

Ethical Standards: Study protocols were approved by the Partners Institutional Review Board, and all participants provided written informed consent before undergoing any study procedures.”

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.



1. Jack C, Bennett D, Blennow K, et al. 2018 NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disese. Alzheimers Dement 2018; 14: 535-562.
2. Administration USFD: Alzheimer’s Disease: Developing Drugs for Treatment Guidance for Industry 2018.
3. Amariglio RE, Donohue MC, Marshall GA, et al. Tracking early decline in cognitive function in older individuals at risk for Alzheimer disease dementia: the Alzheimer’s Disease Cooperative Study Cognitive Function Instrument. JAMA Neurol 2015; 72:446-454.
4. Gifford KA, Liu D, Carmona H, et al. Inclusion of an informant yields strong associations between cognitive complaint and longitudinal cognitive outcomes in non-demented elders. J Alzheimers Dis 2015; 43:121-132.
5. Nuño, MM, Gillen D, Grill J. Study partner types and prediction of cognitie performance: implications to preclinical Alzheimer’s trials. Alzheimer’s Research & Therapy 2019; 11:92.
6. Li C, Neugroschl J, Luo X, et al. The utility of the Cognitive Function Instrument (CFI) to detect cognitive decline in non-demented older adults. J Alzheimers Dis 2019; 60: 427-437.
7. Zwan MD, Villemagne VL, Dore V, et al. Subjective Memory Complaints in APOEvarepsilon4 Carriers are Associated with High Amyloid-beta Burden. J Alzheimers Dis 2016; 49:1115-1122.
8. Buckley, R, Ellis, KA, Ames, D, et al. Phenomenological characterization of memory complaints in preclinical and prodromal Alzheimer’s disease. Neuropsychology 2015; 29:571-81.
9. La Joie R, Perrotin A, Egret S, et al. Qualitative and quantitative assessment of self-reported cognitive difficulties in nondemented elders: Association with medical help seeking, cognitive deficits, and beta-amyloid imaging. Alzheimers Dement (Amst) 2016; 5:23-34.
10. Sperling RA, Donohue MC, Raman R, et al. Association of Factors With Elevated Amyloid Burden in Clinically Normal Older Individuals. JAMA Neurol 2020; e pub.
11. Walsh SP, Raman R, Jones KB, et al. ADCS Prevention Instrument Project: the Mail-In Cognitive Function Screening Instrument (MCFSI). Alzheimer Dis Assoc Disord 2006; 20:S170-17811.
12. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993; 43:2412-2414.
13. Folstein MF, Folstein SE, McHugh PR. «Mini-mental state». A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12:189-198.
14. Wechsler D. WMS-R Wechsler Memory Scale Revised Manual, New York, The Psychological Corporation, Harcourt Brace Jovanovich, Inc, 1987
15. Benjamini, Y., & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 1995; 57: 289-300.
16. Snitz BE, Wang, T, Cloonan YK, et al. Risk of progression from subjective cognitive decline to mild cognitive impairment: The role of study setting. Alz & Dement 2018; 14: 734-742.
17. Frank L, Lenderking WR, Howard K, et al. Patient self-report for evaluating mild cognitive impairment and prodromal Alzheimer’s disease. Alzheimers Res Ther 2011; 3:35.



G. Klein1, P. Delmar1, G.A. Kerchner1, C. Hofmann1, D. Abi-Saab1, A. Davis2, N. Voyle2, M. Baudler1,3, P. Fontoura1, R. Doody1,3


1. F. Hoffmann-La Roche Ltd, Basel, Switzerland; 2. Roche Products Ltd, Welwyn Garden City, UK; 3. Genentech Inc., South San Francisco, CA, USA.

Corresponding Authors: Gregory Klein, Biomarkers and Translational Technology, Neuroscience and Rare Diseases, Basel, Switzerland. Email:, Phone: (+41) 616820759

J Prev Alz Dis 2021;1(8):3-6
Published online December 3, 2020,



Previous findings from the positron emission tomography (PET) substudy of the SCarlet RoAD and Marguerite RoAD open-label extension (OLE) showed gantenerumab doses up to 1200 mg every 4 weeks administered subcutaneously resulted in robust beta-amyloid (Aβ) plaque removal over 24 months in people with prodromal-to-moderate Alzheimer’s disease (AD). In this 36-month update, we demonstrate continued reduction, with mean (standard error) centiloid values at 36 months of -4.3 (7.5), 0.8 (6.7), and 4.7 (8.0) in the SCarlet RoAD (double-blind pooled placebo and active groups), Marguerite RoAD double-blind placebo, and Marguerite RoAD double-blind active groups respectively, representing a change of -57.0 (10.3), -90.3 (9.0), and -74.9 (10.5) centiloids respectively. These results demonstrate that prolonged gantenerumab treatment, at doses up to 1200 mg, reduces amyloid plaque levels below the amyloid positivity threshold. The ongoing GRADUATE Phase III trials will evaluate potential clinical benefits associated with gantenerumab-induced amyloid-lowering in people with early (prodromal-to-mild) AD.

Key words: Gantenerumab, Alzheimer’s disease, positron emission tomography, amyloid.




Alzheimer’s disease (AD) accounts for 60–80% of all cases of dementia globally (1). Currently, the available treatments for AD offer only limited benefits and there is an urgent need for disease-modifying therapies that reverse neuropathologic changes, or slow or stop neurodegeneration (1, 2).
AD pathogenesis is driven by the gradual accumulation of beta-amyloid (Aβ) plaques and neurofibrillary tangles (NFTs) in the brain (1, 3). In vitro and in vivo evidence suggests that soluble Aβ oligomers and insoluble Aβ plaques contribute to cognitive failure by causing neuronal loss, synaptic dysfunction and disconnection syndromes (4, 5). The recognition of Aβ accumulation as the earliest identifiable marker of AD has led to the development of amyloid positron emission tomography (PET), a neuroimaging technique that can be utilized to visualize Aβ accumulation that helps improve diagnostic accuracy and may also facilitate appropriate participant selection in clinical trials (6).
Gantenerumab is a fully humanized, anti-Aβ immunoglobulin (Ig) G1 that binds to Aβ species with high affinity for aggregated forms, including oligomers and plaques, and is thought to remove Aβ via microglia-mediated phagocytosis (7, 8). The long-term, pharmacodynamic effect of gantenerumab-induced Aβ plaque removal in participants with prodromal-to-mild and mild-to-moderate AD is currently being investigated in the PET substudies of the Phase III SCarlet RoAD (SR; NCT01224106) and Marguerite RoAD (MR; NCT02051608) open-label extension (OLE) studies, respectively (7). Interim results showed robust Aβ plaque removal with gantenerumab doses up to
1200 mg administered subcutaneously, with mean amyloid reductions of 59 centiloids and 51% of participants below the Aβ positivity threshold after 24 months (7). Here, we tested whether amyloid signal plateaus or continues to decline with continued therapy in the 36-month results of the ongoing OLE PET substudy.



Participants and study design

Complete details of the study designs and methodologies of SR and MR, the associated OLE studies, and the OLE PET substudies have been previously reported (7-9). Briefly, participants in the SR trial who received double-blind treatment and had ≥1 follow-up visit and those who were currently enrolled in the MR trial were eligible for participation in the OLE. Various titration schemes were used to allow OLE participants to gradually reach the target dose of gantenerumab 1200 mg per month while decreasing the risk of amyloid-related imaging abnormality (ARIA)-related adverse events. The target gantenerumab dose was reached within 6 to 10 months for SR OLE participants, and 2 to 6 months in MR OLE participants.
Participants of the OLE substudy were divided into three cohorts based on their prior exposure to gantenerumab and their stage of AD. The SR cohort included SR participants with all SR treatment arms pooled together (received gantenerumab 105 mg or 225 mg or placebo every 4 weeks during the double-blind phase), all participants in the SR cohort were off treatment for 16 to 19 months prior to OLE higher dosing. The MR double-blind placebo cohort (MR-DBP) included participants in the MR trial who received placebo during the double-blind phase and the MR double-blind active cohort (MR-DBA) included participants of the MR trial who received either 105 or 225 mg gantenerumab during the double-blind phase.

Amyloid-β plaque PET imaging and quantification

Amyloid PET scans were obtained at baseline and at 12, 24, and 36 months after baseline using intravenous
370 MBq 18F-florbetapir, with each 15-minute scan obtained 50 minutes after 18F-florbetapir injection. Participants who received a PET scan during the double-blind phase, within 9 to 12 months of OLE dosing, were not scanned at OLE baseline to minimize participant burden.
Volume-weighted, gray matter-masked standard uptake value ratios (SUVR) were calculated for six bilateral cortical regions using the Automated Anatomical Labeling (AAL) template, normalized by a cerebellar cortex reference region (10, 11). SUVR values were then converted to centiloid values as previously described, using the following linear transformation: Centiloid = SUVR*184.12 – 233.72 (7, 12). The threshold for amyloid positivity has been previously established as 24 centiloids, which corresponds to 1.40 SUVR units. The amyloid positivity threshold represents the quantitative threshold that best discriminates pathologically verified absence of plaques or sparse plaques from moderate-to-frequent plaques (13).

Statistical analysis

This analysis included all study participants who had a PET scan at OLE baseline (or 9–12 months prior to OLE dosing) and received ≥1 follow-up scan. PET centiloid values were analyzed using a mixed model for repeated measures (MMRM), with treatment visit, treatment group, and the interaction for treatment group by visit as independent variables. An unstructured covariance matrix was used to capture within-participant correlation.



Participant characteristics

A total of 67 participants with at least 1 post-baseline scan were enrolled in the OLE PET substudy (SR, n = 19; MR-DBP, n = 27; MR-DBA, n = 21). A total of 30 participants completed the 36-month scan (SR, n = 10; MR-DBP, n = 12; MR-DBA, n = 8). The baseline characteristics for both the overall population and the 36-month completers are shown in Table 1. More than half of the participants in each cohort were Apolipoprotein E (APOE) ε4 carriers (SR, 89%; MR-DBP, 67%; MR-DBA, 52%). Across all three cohorts, the mean [SE] baseline amyloid burden in centiloids was above the positivity threshold (SR, 49.6 [12.1]; MR-DBP, 91.1 [9.6]; MR-DBA, 79.6 [10.9]).

Table 1. Baseline characteristics of participants enrolled in the SR, MR-DBP, and MR-DBA cohorts, including 36-month completers

APOEε4, Apolipoprotein E; IQR, Interquartile range; MMSE, Mini-Mental State Examination; MR-DBA, Marguerite RoAD double-blind active; MR-DBP, Marguerite RoAD double-blind placebo; OLE, open-label extension; SE, standard error; SD, standard deviation; SR, SCarlet RoAD.


Amyloid PET results

Consistent with our previous report, reductions in mean amyloid burden were observed across cohorts after 12 and 24 months of open-label therapy, with 37% and 52% of participants, respectively, reaching levels below the amyloid positivity threshold (Figure 1) (7). Continued reductions beyond 24 months were observed after 36 months, with mean amyloid levels approaching zero centiloids across all cohorts. The absolute mean (SE) amyloid burden after 36 months were -4.3 (7.5), 0.8 (6.7), and 4.7 (8.0) centiloids for the SR, MR-DBP and MR-DBA cohorts, respectively, representing a change of -57.0 (10.3), -90.3 (9.0), -74.9 (10.5) centiloids respectively. Furthermore, the proportion of participants below the amyloid positivity threshold was 24 of 30 participants (80%) at 36 months (Figure 1).

Figure 1. Reduction of amyloid burden towards zero centiloids after 36 months of open-label therapy

*LS mean (SE); †Analyzed using an MMRM; LS, least-squares; MMRM, mixed model for repeated measures; MR-DBA, Marguerite RoAD double-blind active; MR-DBP, Marguerite RoAD double-blind placebo; SE, standard error; SR, SCarlet RoAD; SUVR, standard uptake value ratio.



This 36-month OLE PET substudy investigated the effect of gantenerumab on Aβ plaque removal on participants with prodromal-to-moderate AD. Prior results have shown that while the three cohorts began with considerably different mean baseline centiloid values, all three cohorts demonstrated a mean centiloid value just below the amyloid positivity threshold after
24 months of treatment with gantenerumab 1200 mg every 4 weeks. The latest results showed continued Aβ reduction with gantenerumab treatment below the amyloid positivity threshold, without plateau, with 80% of completers below the amyloid positivity threshold after 36 months of open-label therapy. Mean centiloid values of all three cohorts at this time are near a value of zero, which represents the mean amyloid burden expected in a healthy control group (12). Given that the SR and MR-DBA groups may have experienced some amyloid reduction due to low-dose gantenerumab treatment during the double-blind period of the SR and MR studies, the 90-centiloid reduction seen in the MR-DBP group represents the amyloid reduction that could be expected in a treatment-naïve population. The consistent reduction in Aβ suggests that gantenerumab is able to remove Aβ species successfully.
These findings may translate to clinical benefit in people with prodromal-to-mild AD as other studies with aducanumab and lecanemab (BAN2401) have observed amyloid PET reduction as well as clinical efficacy (7, 14, 15). Specifically, in a Phase Ib placebo-controlled study, aducanumab demonstrated reduced brain amyloid plaque levels after 24 months with a reduction in clinical decline as measured by the Clinical Dementia Rating–Sum of Boxes (CDR-SB) and Mini-Mental State Examination (MMSE) (14). In a Phase II placebo-controlled study, lecanemab produced a dose-dependent reduction in amyloid plaque levels after
18 months and a reduction in clinical decline as measured by AD Composite Score (15). In light of these studies, the current PET results suggest that the process of Aβ reduction at the gantenerumab dose of 1200 mg every
4 weeks has the potential to produce clinical benefits. The precise relation between amyloid reduction and clinical benefit is still an open question, including the question of whether a reduction to below amyloid positivity or to centiloid zero makes a difference in the clinical outcome and management of patients with early AD. The ongoing GRADUATE Phase III program evaluates the safety and efficacy of gantenerumab, subcutaneously administered, in participants with early AD. This program includes two global, double-blind, placebo-controlled trials in people with early AD, designed to maximize exposure to gantenerumab and to prospectively examine the correlation between amyloid-lowering and clinical outcomes.


Funding: This study was sponsored by F. Hoffmann-La Roche Ltd, Basel, Switzerland.

Acknowledgments: We would like to thank all the participants and their families, the investigators and site staff, and the entire study team for their time and commitment to the SCarlet RoAD and Marguerite RoAD OLE studies. Medical writing support was provided by Joshua Quartey, BSc, of Health Interactions and was funded by F. Hoffmann-La Roche Ltd.

Conflict of interest disclosures: GK, PD, GAK, CH, DA-S and PF were full-time employees of F. Hoffmann-La Roche Ltd during the conduct of the study. GK, PD, GAK, CH, DA-S, NV and PF are shareholders in F. Hoffmann-La Roche Ltd. AD and NV were full-time employees of Roche Products Ltd during the conduct of the study. AD is currently employed at the MRC Clinical Trials Unit at UCL. MB and RD are full-time employees and shareholders in F. Hoffmann-La Roche Ltd and Genentech Inc. CH has an Alzheimer’s disease-related patent planned which is relevant to this study.

Ethical standards: Institutional Review Boards (IRBs) approved the SCarlet RoAD and Marguertie RoAD studies, and all participants gave informed consent before participating.

Data sharing statement: Qualified researchers may request access to individual patient-level data through the clinical study data request platform: https://vivli. org. Further details on Roche’s criteria for eligible studies are available here: For further details on Roche’s Global Policy on the Sharing of Clinical Information and how to request access to related clinical study documents, see here: development/who_we_are_how_we_work/clinical_trials/our_commitment_to_ data_sharing.htm

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.


1. Alzheimer’s Association. 2019 Alzheimer’s disease facts and figures. Alzheimers Dement 2019; 15:321-387.
2. Wang S, Mims PN, Roman RJ, et al. Is beta-amyloid accumulation a cause or consequence of Alzheimer’s disease? J Alzheimers Parkinsonism Dement 2016; 1:007.
3. Lee JC, Kim SJ, Hong S, et al. Diagnosis of Alzheimer’s disease utilizing amyloid and tau as fluid biomarkers. Exp Mol Med 2019; 51:1-10.
4. Mucke L & Selkoe D. Neurotoxicity of amyloid β-protein: Synaptic and network dysfunction. Cold Spring Harb Perspect Med 2012; 2:a006338.
5. Delbeuck X, Van der Linden M, Collette F. Alzheimer’s disease as a disconnection syndrome? Neuropsychol Rev 2003; 13:79-92.
6. Ishii K. Amyloid positron emission tomography in the therapeutic strategies for Alzheimer’s disease. Brain Nerve 2017; 69:809-818.
7. Klein G, Delmar P, Voyle N, et al. Gantenerumab reduces amyloid-β plaques in patients with prodromal to moderate Alzheimer’s disease: A PET substudy interim analysis. Alzheimers Res Ther 2019; 11:101.
8. Ostrowitzki S, Lasser RA, Dorflinger E, et al. A phase III randomized trial of gantenerumab in prodromal Alzheimer’s disease. Alzheimers Res Ther 2017; 9:95.
9. Abi-Saab D, Andjelkovic M, Pross N, et al. MRI findings in the open label extension of the Marguerite RoAD study in patients with mild Alzheimer’s disease. J Prev Alz Dis 2017; 4:339 (P336).
10. Fleisher AS, Chen K, Liu X, et al. Using positron emission tomography and florbetapir F18 to image cortical amyloid in patients with mild cognitive impairment or dementia due to Alzheimer disease. Arch Neurol 2011; 68:1404-1411.
11. Barthel H, Gertz HJ, Dresel S, et al. Cerebral amyloid-β PET with florbetaben (18F) in patients with Alzheimer’s disease and healthy controls: A multicentre phase 2 diagnostic study. Lancet Neurol 2011; 10:424-435.
12. Klunk WE, Koeppe RA, Price JC, et al. The Centiloid Project: Standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement 2015; 11:1-15.e11-14.
13. Navitsky M, Joshi AD, Kennedy I, et al. Standardization of amyloid quantitation with florbetapir standardized uptake value ratios to the Centiloid scale. Alzheimers Dement 2018; 14:1565-1571.
14. von Rosenstiel P, Gheuens S, Chen T, et al. Aducanumab titration dosing regimen: 24-month analysis from PRIME, a randomized, double-blind, placebo-controlled Phase 1b study in patients with prodromal or mild Alzheimer’s disease. Neurology 2018; 90:Abstract S2.003.
15. Swanson C, Zhang Y, Dhadda S, et al. Treatment of early AD subjects with BAN2401, an anti-Aβ protofibril monoclonal antibody, significantly clears amyloid plaque and reduces clinical decline. Alzheimers Dement 2018; 14 (Suppl):P1668. DT-1601-1607.



D. Michelson1, M. Grundman2, K. Magnuson1, R. Fisher1, J.M. Levenson1, P. Aisen3, K. Marek4, M. Gray1, F. Hefti1


1. Proclara Biosciences, USA; 2. Global R&D Partners, LLC and University of California, San Diego, USA; 3. University of Southern California, USA; 4. Invicro LLC, USA

Corresponding Author: Richard Fisher, 125 Cambridgepark Dr. Ste 301, Cambridge MA 02140, USA, Tel: 1-857-998-1664, Email:, FAX: 1-857-320-4020

J Prev Alz Dis 2019;
Published online October 4, 2019,



The engineered fusion protein NPT088 targets amyloid in vitro and in animal models of Alzheimer’s disease. Previous studies showed that NPT088 treatment reduced β-amyloid plaque and tau aggregate loads in mouse disease models. Here, we present the results from an initial clinical study of NPT088 in patients with mild to moderate Alzheimer’s disease. Patients were treated with 4 dose levels of NPT088 for 6 months to evaluate its safety and tolerability.  Exploratory measurements included measurement of change in β-amyloid plaque and tau burden utilizing Positron Emission Tomography imaging as well as measures of Alzheimer’s disease symptoms. At endpoint NPT088 was generally safe and well-tolerated with the most prominent finding being infusion reactions in a minority of patients.  No effect of NPT088 on brain plaques, tau aggregates or Alzheimer’s disease symptoms was observed.

Key words:  Amyloid, Alzheimer’s Disease, β-amyloid plaques, tau tangles.



Current state of research in early detection of Alzheimer’s disease

The characteristic pathological findings in Alzheimer’s disease (AD) are extracellular β-amyloid plaques and intracellular tau tangles.  These deposits have been hypothesized to play an important role in the pathophysiology of AD, and removing plaques and/or tangles has been proposed as a potential treatment for AD.  Multiple efforts have focused on β-amyloid, aiming to block production within the amyloid processing pathway or to remove β-amyloid plaques from the brain.  Tau pathology has also been targeted, but fewer candidate treatments have been brought forward compared with those directed at β-amyloid.
Most efforts targeting amyloid have employed either antibodies or small molecules aimed specifically at either the β-amyloid or tau pathway, but not both.  However, a defining feature of amyloid, including β-amyloid in plaque and tau tangles, irrespective of the specific underlying misfolded protein, is the characteristic structure based on β-pleated sheets (1).  By targeting and binding this common structure, it may be possible with a single molecule to reduce multiple different forms of brain amyloid.  The serendipitous discovery of amyloid binding by the bacteriophage M13 and the isolation of this activity to a specific domain of a capsid protein led to development of novel therapeutic proteins as investigational treatments for AD and other disorders associated with pathological amyloid deposition (2).
NPT088 is a fusion protein that in vitro and in animals binds multiple species of amyloid via its binding to the canonical amyloid fold (2,3).   Briefly, NPT088 is composed of a binding domain derived from a minor capsid protein of the bacteriophage M13 fused to a human IgG1 Fc that is intended to mobilize clearance mechanisms following amyloid binding.  In transgenic mouse models of neurodegenerative diseases, NPT088 reduces brain deposits of β-amyloid plaque and tau aggregate burden (4).  Thus, unlike most antibodies, which target β-amyloid or tau but not both, NPT088 could potentially reduce both β-amyloid and tau burden in patients by virtue of its ability to bind broadly across amyloid species.
Single doses of NPT088 up to 30 mg/kg administered to healthy volunteers were well-tolerated with a plasma half-life of ~12 days (Proclara, unpublished data).  We report here results of a multiple dose study in patients with AD assessing the safety and tolerability of NPT088, and exploring whether the reductions in β-amyloid plaque and tau observed in animal models could be translated into humans with AD.



The study was conducted at 19 sites in the United States.  Participants were men and women 50-85 of years of age with a Mini-Mental State Exam (MMSE) score between 16 and 27 inclusive and a clinical diagnosis of probable AD (5) confirmed by florbetapir PET scan with either a composite SUVr > 1.2 or a positive central visual read (6).  Symptomatic medications for AD were permitted provided the dose had been stable for at least 60 days.  All patients also underwent a screening MRI to rule out conditions that could confound the diagnosis of AD as the primary cause of dementia.  This was a double-blind, placebo-controlled study consisting of a 6-month treatment period and a 2-month safety follow-up.  Four doses of NPT088 or placebo, intravenously administered monthly with a randomization ratio of NPT088:placebo of 2:1, were examined in sequential cohorts.  The initial 2 cohorts (0.6 mg/kg and 2.0 mg/kg, respectively) were smaller and intended to provide initial safety and tolerability prior to dosing the 3rd and 4th cohorts (6 mg/kg and 20 mg/kg respectively).  In addition to adverse event assessment, safety measures included MRI at baseline, 3 months and endpoint, and routine laboratory examinations.  PET imaging with florbetapir-F18 PET imaging was repeated at Week 24 to evaluate potential changes in β-amyloid plaques, administered as a single intravenous bolus of 10 mCi (370 MBq) (± 10%) followed by acquisition of dynamic PET florbetapir PET scans at 50 to 65 minutes post-administration. Florbetapir binding was measured using PMOD software (PMOD Technologies, Zurich, Switzerland) to determine the composite cortical standard uptake value (SUV) ratio compared to a cerebellar reference region. Tau PET imaging was conducted at screening and then again at week 24 in a subset of patients to evaluate potential changes in brain tau aggregate loads.  The investigational radiopharmaceutical MNI-960 (PI2620 under development by Life Molecular Imaging and Invicro) (7) was administered as a bolus of no more than 10 mCi followed by serial dynamic 3-D brain PET acquired for up to 180 minutes. MNI-960 binding was measured using PMOD software (PMOD Technologies, Zurich, Switzerland) to determine the both the regional standard uptake value (SUV) ratio compared to a cerebellar reference region. Other exploratory measures included measures of cognitive and functional change, including ADAS-Cog 13, ADCS-ADL, and CDR-SB at weeks 12 and 24.  Plasma pharmacokinetics and anti-drug antibodies were also assessed.  The study was reviewed and approved by each site’s institutional review board, and each patient provided written informed consent to participate.  The study was monitored by an independent data monitoring committee that that reviewed each cohort’s data prior to approving initiation of the next dose.
The protocol-specified primary objective was to evaluate the safety and tolerability of multiple doses of NPT088. β-amyloid and tau PET and cognitive and functional measures were exploratory endpoints.  The protocol had 80% power to detect adverse events that occurred in 9.6% or more patients in either of the two lower dose cohorts or in 3.6% or more of patients in the combined higher dose cohorts.  Based on the results of Sevigny et al (8), at the planned sample size of 16 active and 8 placebo patients for each of the higher dose cohorts, the study was expected to have a power of 88% to detect a mean difference of 0.15 SUVr units in β-amyloid  plaque between the active and placebo groups, assuming a dropout rate of up to 15% in the florbetapir PET analysis.  For safety analyses, all randomized patients who received at least one dose of study drug were included.  For PET and symptom measures all randomized patients who had at least one post-baseline measurement were included and were analyzed by ANCOVA that included MMSE strata as a covariate.  A mixed model repeated measure approach was used for outcomes measured at more than one timepoint.



A total of 85 patients were randomized to treatment.  Of these, 83 (27M/56F) received study drug and were included in the safety analysis population, and 66 (78%) completed the study.  Patient characteristics are summarized in Table 1.


Table 1. Patient Characteristics and Key Outcomes

Table 1. Patient Characteristics and Key Outcomes


Safety and tolerability

Adverse events were generally consistent with those expected in a population of AD patients and did not suggest differences between placebo and NPT088 with the exception of systemic hypersensitivity reactions related to infusion reactions, of which 17 were reported in 12 unique patients, all of whom received NPT088 (0.6 mg/kg: 2/6 patients (33%); 2.0 mg/kg: 2/6 patients (33%); 6.0 mg/kg: 6/37 patients (16%); 20 mg/kg: 2/30 patients (7%)).  Using the National Cancer Institute’s common terminology criteria for adverse events, version 4.03, 11 events were graded as mild, 3 as moderate and 1 as severe, and resulted in 5 of these 12 patients discontinuing the study early.  Routine safety laboratories, including chemistry, hematology and ECG, did not suggest meaningful differences between groups.  NPT088 was not associated with an increase in treatment emergent ARIA-E or ARIA-H compared with placebo.

Pharmacokinetic Results and Anti-drug Antibodies

NPT088 Cmax and AUC increased with increasing dose in a dose proportional manner.  At the 20 mg/kg dose, mean (SD) Cmax was 538 (139) µg/ml after the final dose, mean plasma half-life was approximately 10 days, and drug did not accumulate with repeated dosing.  Anti-drug antibodies were undetectable or low in most patients, and no difference in plasma NPT088 concentrations was observed between those patients in whom antibodies were detected and those without detectable antibodies.

PET Scans

At endpoint, the results of the florbetapir and MNI-960 PET scans did not demonstrate an effect of NPT088 on either β-amyloid plaque or tau aggregates (Figure 1).
Cognitive and Functional measures: Results for cognitive and functional measures did not demonstrate an effect of NPT088 (Table 1).

Figure 1. PET change from baseline

Figure 1. PET change from baseline

Legend: Change from baseline SUVr in β-amyloid (L) and tau burden (R) with mean (95% CI) after 24 weeks



This study was designed to assess the safety and tolerability of multiple doses of NPT088, and to explore whether preclinical data demonstrating effects on β-amyloid plaque and tau aggregates could be demonstrated in humans with AD.  The results of the study showed that NPT088 was generally safe and well-tolerated.  The only apparently drug-specific adverse effects were systemic infusion reactions, a predicted risk of administration of a drug derived from a non-human phage protein.  Reactions occurred in approximately 20% of patients assigned to active drug, and the majority were mild and did not preclude further treatment.  One severe reaction was observed in a patient who experienced a drop in blood pressure, loss of consciousness and seizure-like activity during an episode that resolved within minutes without treatment and without sequelae.
The study did not demonstrate an effect of NPT088 to reduce either β-amyloid plaque or tau aggregate burden. With respect to β-amyloid, the failure to translate the animal findings into humans could be due to several factors.  One possibility is that small effects were present but undetected due to sample size or other unknown issues.   It may also be that the ability of NPT088 to bind β-amyloid plaque in humans differs from that in animal models, and that NPT088 was ineffective as a result, or that exposure to drug was suboptimal.  In a single dose study in healthy humans, cerebrospinal fluid (CSF) exposures of NPT088 were 0.1%-0.25% of those in plasma (Proclara, unpublished data).  The peak mean concentration of 538 µg/ml after the final dose in the 20 mg/kg group would correspond to ~5 nM in CSF, a concentration at which efficacy was observed in animals.  However, trough concentrations of NPT088 were negligible at all doses, and if exposures continuously at or near Cmax are required for efficacy, this could account for the absence of a positive finding.
With respect to tau, the study encountered execution challenges related to limited MNI-960 production facilities, and the number of patients who received tau scans was much smaller than originally anticipated, making detection of any but the largest effects unlikely.  Although uninformative about treatment effects, the results do provide data about the performance characteristics of MNI-960 in a patient population.  A detailed discussion of these results will be the topic of a separate report.
In the absence of effects on either β-amyloid plaques or tau, the absence of changes in cognition or function is unsurprising.  Given the relatively small number of patients in each group and the 6-month treatment duration, changes relative to placebo in cognitive and functional effects were not expected unless the drug led to dramatic improvements over baseline or, alternatively, a marked worsening.  As evidenced by the results, neither of these occurred, although we cannot definitively rule out the possibility that smaller, undetected changes were present.
In summary, the results of this study indicate that apart from hypersensitivity reactions NPT088 is well tolerated in an AD population.  Exploratory analyses did not yield evidence that NPT088 reduces β-amyloid or tau burden in humans with AD.


Acknowledgments:  The authors wish to thank the patients, families and investigative site staff who participated in this study and without whose generous contribution of time and effort the study would not have been possible.

Funding: This study was funded by Proclara Biosciences and by Part the Cloud grant PTC-17-442898 from the Alzheimer’s Association

Disclosures: David Michelson, Richard Fisher, Franz Hefti, Michelle Gray, and Jonathan Levenson are all current or former employees of Proclara Biosciences.  Michael Grundman and Paul Aisen were paid consultants to Proclara Biosciences.  Kenneth Marek is a former employee of Invicro and has served as a paid consultant to Proclara Biosciences.



1.     Toyama BH, Weissman JS. Amyloid structure: conformational diversity and consequences. Ann Rev Biochem 2011; 80: 557–85.
2.     Krishnan R, Tsubery H, Proschitsky MY, Asp E, Lulu M, Gilead S, Gartner M, Waltho JP, Davis PJ, Hounslow AM, Kirschner DA, Inouye H, Myszka DG, Wright J, Solomon B, Fisher RA. A bacteriophage capsid protein provides a general amyloid interaction motif (GAIM) that binds and remodels misfolded protein assemblies. J Mol Biol 2014; 426:2500-19
3.     Asp E, Proschitsky M, Lulu M, Rockwell-Postel C, Tsubery H, Krishnan R. Stability and inter-domain interactions modulate amyloid binding activity of a General Amyloid Interaction Motif. J Mol Biol 2019; 431:1920-39.
4.     Levenson JM, Schroeter S, Carroll JC, Cullen V, Asp E, Proschitsky M, Chung CH, Gilead S, Nadeem M, Dodiya HB, Shoaga S, Mufson EJ, Tsubery H, Krishnan R, Wright J, Solomon B, Fisher R, Gannon KS. NPT088 reduces both amyloid-β and tau pathologies in transgenic mice. Alzheimers Dement 2016; 2:141-155.
5.     McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH.  The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011; 7:263-9.
6.     Doraiswamy PM, Sperling RA, Johnson KA, Reiman EM, Davis MD, Grundman M, Sabbagh MN, Sadowsky CH, Fleisher AS, Carpenter A, Clark CM, Joshi AD, Mintun MA, Skovronsky DM, Pontecorvo MJ. Amyloid b assessed by florbetapir F18 PET and 18 month cognitive decline. Neurology 2012; 79:1636-44.
7.     Kroth H, Oden F, Molette J, Schieferstein H, Capotosti F, Mueller A, Berndt M, Schmitt-Willich H, Darmency V, Gabellieri E, Boudou C, Juergens T, Varisco Y, Vokali E, Hickman DT, Tamagnan G, Pfeifer A, Dinkelborg L, Muhs A, Stephens A. Discovery and preclinical characterization of [18F]PI-2620, a next-generation tau PET tracer for the assessment of tau pathology in Alzheimer’s disease and other tauopathies. Eur J Nucl Med Mol Imaging 2019; 46:2178-89.
8.     Sevigny J, Chiao P, Bussière T, Weinreb PH, Williams L, Maier M, Dunstan R, Salloway S, Chen T, Ling Y, O’Gorman J, Qian F, Arastu M, Li M, Chollate S, Brennan MS, Quintero-Monzon O, Scannevin RH, Arnold HM, Engber T, Rhodes K, Ferrero J, Hang Y, Mikulskis A, Grimm J, Hock C, Nitsch RM, Sandrock A. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature. 2016; 537(7618):50-6.



S. Gauthier1, J. Alam2, H. Fillit3, T. Iwatsubo4, H. Liu-Seifert5, M. Sabbagh6, S. Salloway7, C. Sampaio8, J.R. Sims5, B. Sperling9, R. Sperling10, K.A. Welsh-Bohmer11, J. Touchon12, B. Vellas13, P. Aisen14 and the 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 (Verdun); 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 (New York);  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. McGill Center for Studies in Aging, Verdun QC, Canada;  2. EIP Pharma Inc., Cambridge MA, USA; 3. The Alzheimer’s Drug Discovery Foundation, New York NY, USA; 4. University of Tokyo, Japan; 5. Eli Lilly and Company, Indianapolis IN, USA; 6L Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas NV, USA; 7. The Warren Alpert Medical School of Brown University, Providence RI, USA; 8. CHDI Foundation, Princeton NJ, USA; 9. Lundbeck, Valby 2500 Denmark; 10. Brigham and Women’s Hospital, Boston MA, USA; 11. Duke University, Durham NC, USA; 12. University Hospital of Montpellier, 34025 Montpellier Cedex 5, and INSERM 1061, France; 13. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France; 14. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego CA, USA

Corresponding Author: Serge Gauthier, McGill Center for Studies in Aging, Verdun QC, Canada,

J Prev Alz Dis
Published online April 18, 2019,



Combination therapy is expected to play an important role for the treatment of Alzheimer’s disease (AD). In October 2018, the European Union-North American Clinical Trials in Alzheimer’s Disease Task Force (EU/US CTAD Task Force) met to discuss scientific, regulatory, and logistical challenges to the development of combination therapy for AD and current efforts to address these challenges. Task Force members unanimously agreed that successful treatment of AD will likely require combination therapy approaches that target multiple mechanisms and pathways. They further agreed on the need for global collaboration and sharing of data and resources to accelerate development of such approaches.

Key words: Alzheimer’s disease, amyloid, tau, therapeutics, trial design.



Combination therapy has resulted in improved outcomes for many of the world’s most significant and complex diseases, including cancer, AIDS, and cardiovascular disease, and the prospect of combination therapy has also gained traction in the Alzheimer’s disease (AD) field (1-3). The reasons for pursuing combination therapy for AD go beyond the disappointing track record in developing effective treatments for this disease that is likely to affect more than 150 million people worldwide by 2050 (4-6). As with many other complex diseases, AD arises from a series of pathological changes and the involvement of many pathogenic pathways that begin well before symptoms appear (7), suggesting that effective treatment will require targeting multiple pathways, either simultaneously or sequentially. However, the complexity of AD pathophysiology also introduces substantial hurdles to the development of combinatorial approaches. To better understand current efforts to develop such approaches and the steps that need to be taken to expedite this process, the European Union-North American Clinical Trials in Alzheimer’s Disease Task Force (EU/US CTAD Task Force) discussed combination therapy for AD at its 2018 meeting. The Task Force brings together investigators from industry, academia, and regulatory agencies to build consensus and promote collaboration and information sharing on issues important for the development of effective Alzheimer’s treatments. Many Task Force members expect combination therapy to play an important role in treating AD and call for global collaboration to develop combination therapies (8, 9), but agree that the path forward has yet to be clearly defined.


Best candidates for combination therapy

Combination therapy for AD could involve interrupting a single important pathogenic pathway (such as amyloid or tau) at multiple points or targeting two or more pathways together (such as amyloid plus tau). Despite the many disappointing clinical trials of disease-modifying therapies targeting amyloid, it remains a promising target for disease modification, in particular for prevention studies. The rationale for targeting amyloid is strong (10). Most known genetic mutations related to AD are involved in amyloid production or processing. This includes mutations in the Presenilin 1 and 2 and amyloid precursor protein (APP) genes, and Down syndrome, the most common cause of early-onset AD, which is caused by a trisomy of chromosome 21 where the APP gene resides. In addition, a mutation in the APP gene known as the Icelandic mutation (A673T) has been shown to be protective against AD and cognitive decline (11).
Moreover, there is abundant evidence that Aβ oligomers and amyloid plaques are toxic (12, 13), and encouraging although preliminary evidence that removing plaques may be associated with improved cognition and clinical outcomes.
The APP molecule undergoes sequential cleavage via β- and γ-secretases to produce amyloidogenic fragments. Amyloid peptides take on monomeric, oligomeric, and fibrillar forms that may cause toxicity through a variety of mechanisms including oxidative stress, excitotoxicity, synaptic failure, and other mechanisms associated with neuronal death (14). This complex pathway from APP to toxicity thus creates multiple potential therapeutic targets (Figure 1). Antibodies directed at different amyloid fragments have been developed as potential treatments against AD with varying degrees of success at removing amyloid and halting the disease process; secretase inhibitors have also been effective at reducing amyloid load but have been associated with cognitive worsening and other adverse events (15, 16).

Figure 1. Opportunities for amyloid-based combination therapies based on therapeutics currently in clinical development

Figure 1. Opportunities for amyloid-based combination therapies based on therapeutics currently in clinical development


A workgroup of the National Institutes on Aging and the Alzheimer’s Association (NIA-AA) recently published a research framework that defines and stages the disease according to the presence of Amyloid (A), Tau (T) and neurodegeneration (N) biomarkers (17).  Yet while the AD disease-modifying drug development pipeline continues to reflect the predominance of the amyloid pathway, there has recently been an increase in the number of drug trials testing non-amyloid mechanisms (18, 19). In agreement with the NIA-AA Research Framework, the Task Force recognized the need to add biomarkers of other pathologies commonly seen in the brains of people with AD, such as vascular pathology (V), inflammation (I), and Lewy bodies (L).


Possible combination trial designs that target amyloid

Preclinical AD is marked primarily by amyloid accumulation, with cognitive performance and biomarkers of neurodegeneration, tau, and cerebral metabolism increasing markedly only in the clinical stages of disease (20). This suggests that a vigorous attack on amyloid using multiple agents simultaneously to target different steps in the amyloid pathway may slow, stop, or reverse the progression of AD.
However, an even more promising approach may be attacking the amyloid pathway sequentially at different times and disease stages. Sequential therapy offers efficiency advantages by enabling the assessment of individual adverse events and benefits more readily. One potential sequential therapy design using an induction/maintenance approach would be to start treating with an inhibitor of Aβ production, such as a beta-secretase inhibitor (BACEi), before there is any detectable amyloid; and then introducing amyloid-reducing antibodies when amyloid becomes elevated but before neuronal damage has begun. This approach could reduce the number of anti-amyloid antibody infusions required, thus saving costs and reducing exposure. However, designing a trial using this strategy could become very complicated.
An alternative would be to start with an anti-amyloid antibody first to induce an amyloid-free state for 3 months to 1 year (long enough to see cognitive benefit in early stage), and then push backwards and treat with BACEi as maintenance therapy. Although BACEi have shown significant adverse events in several trials, a lower dose (e.g. inhibiting only ≤30% of BACE) may improve the risk/benefit calculation. Other secretase modulators, antibodies that target diffusible amyloid, or amyloid active vaccine may also be used for maintenance.
A combination study including both anti-tau and anti-amyloid drugs also has been suggested although many questions remain about the efficacy of anti-tau agents, the best tau epitopes to target, the optimal stage of disease to treat, how to establish target engagement, and how to design anti-tau trials (31).  Another combination clinical trial that combines two non-amyloid approaches is also underway at Amylyx Pharmaceuticals in partnership with the Alzheimer’s Drug Discovery Foundation and the Alzheimer’s Association. This Phase 2 trial of AMX0035 combines  sodium phenylbutyrate, which is approved for the treatment of urea cycle disorders, and tauroursodeoxycholic acid (TUDCA), a bile acid that supports mitochondrial energetics (19). The combination is expected to protect neurons from inflammation and oxidative stress.


Best target populations and study designs

For clinical trials of combination therapies such as those described above, the stage of disease and study design for proof-of-concept and Phase 3 studies will be determined by a medication’s mode of action on disease pathophysiology. For example, trials designed to treat patients in early disease stages, i.e., symptomatic with a CDR 0.5 or 1, should maximize the likelihood of detecting disease progression during the trial and demonstrating a slowing of progression if the treatment is efficacious. Enabling optimal designs and optimizing treatment assignment will require that participants have adequate biological characterization with biomarkers.
The most informative trial design for a two-agent combination therapy trial would employ a 2 x 2 factorial structure where each agent is tested alone and in combination (21). A more efficient approach, however, would be a 2-arm trial of the combination vs. placebo, with deconvolution of the contribution of each agent should the initial approach be successful. In either case, selecting dose and treatment regimens for combination studies is complicated and often leads investigators to take shortcuts, which can lead to misleading results or unacceptable risks to participants. The statistical and regulatory implications of various trial designs are discussed below.
For trials in patients with AD dementia, since many individuals will already be taking acetylcholinesterase inhibitors (e.g., donepezil) and/or memantine (22), add-on designs that combine the standard treatment plus the disease-modifying agent being tested may be necessary. To test combinations in early AD patients, a different type of add-on design could provide more precision. For this type of study, participants would be randomized first to induction therapy with an agent that targets the most prominent apparent pathology (amyloid for most, tau for a few); then after a pre-determined time period (e.g., 6 months), a second treatment is added that targets the second most predominant pathology (e.g., tau, amyloid, inflammation, Lewy bodies, or vascular).
Open perpetual platform trials using a master protocol with defined inclusion and exclusion criteria may be the most efficient way to conduct combination trials. The Dominantly Inherited Alzheimer’s Network Trials Unit (DIAN-TU) has developed such a platform for testing a variety of therapeutics in people with autosomal dominant AD (23). Such platforms enable testing of multiple active treatment arms with shared control arm, and they allow for: 1) pooling of placebo groups, 2) the discontinuation of arms for futility, 3) the addition of new arms including either new drugs or new doses, 4) adaptive randomization, and 5) personalization of arms to specific subgroups (24).


Regulatory issues

Regulatory authorities encourage innovative development approaches for delivering combination therapies for AD. In 2013, the U.S. Food and Drug Administration (FDA) published guidance for co-development of two or more new investigational drugs for use in combination (25). According to this guidance, combination therapy is justified for treating serious diseases with unmet medical needs when there is a strong rationale and strong preclinical data for the combination, and when there is a compelling reason for developing the two drugs in tandem rather than independently.
Selecting agents to combine begins with assessing and characterizing whether the interaction between the components is additive, synergistic, or antagonistic. In addition, since most amyloid treatments activate the immune system, nonclinical studies are needed to assess the interaction of combinations with immune mechanisms. How the effectiveness of the combination is defined affects the study design and may depend on the stages of development of the components.  Thus, if one component is already approved, it may be sufficient to demonstrate how much greater is the effect of the combination of new drug plus the approved drug compared to the effect of the approved drug alone. If both components are novel, however, a full factorial design may be needed to understand contributions of the different agents to the treatment response. Additive or synergistic effects may be demonstrated.
Both FDA and the European Medicines Agency (EMA) require preclinical studies of the combination. In some cases, toxicology of the combination will need to be tested, although there have been some studies where regulators were sufficiently confident that a combination would be safe and allowed advancing to Phase 2.


Blazing the trail to combination therapy

In December 2017, Lilly launched the multi-site TRAILBLAZER-ALZ Phase 2 trial, which combined a BACEi with the anti-amyloid monoclonal antibody LY3002813 (NCT03367403), a humanized IgG1 antibody directed at N3pG, an amyloid epitope that is present only in amyloid plaques (26). In preclinical studies, LY3002813 was shown to remove amyloid plaque through microglial-mediated clearance (27). In the PDAPP mouse model, an antiN3pG plus a BACEi removed most pre-existing plaque and improved neuronal health in a synergistic, dose-dependent manner (28).
A phase 1 study of the monoclonal antibody demonstrated a significant reduction in brain amyloid by florbetapir positron emission tomography (PET); and a phase 1 study of the BACEi demonstrated a lowering of cerebrospinal fluid (CSF) Aβ, with no safety or tolerability concerns. These results in Phase 1, combined with the preclinical data, prompted Lilly to plan the TRAILBLAZER trial that would include three arms: 1) placebo, 2) N3pG monoclonal antibody alone, and 3) N3pG mAb plus BACEi. Rather than using a full factorial design, external data from multiple ongoing BACEi studies would be used to demonstrate the efficacy of the BACEi alone.
The study enrolled participants with early symptomatic AD who are amyloid positive with a low-to-medium tau burden, randomized to the three arms. These inclusion/exclusion criteria were selected to produce a relatively homogeneous population. A composite scale of cognition and function was selected as the primary outcome, and a robust biomarker strategy was planned to demonstrate the contribution of each component of the combination (29). Other cognitive and functional measures as well as amyloid PET, tau PET, and volumetric magnetic resonance imaging (MRI) were included as secondary outcome measures.
The combination BACEi plus N3pG mAb arm of the trial was subsequently discontinued based on data from multiple sources that raised concerns about the risk/benefit profile of BACEi (30). Nonetheless, the study design holds lessons for future trials of combination therapies, including the use of preclinical data in animal models to demonstrate synergy, the use of robust Phase 1 data to simplify Phase 2 combination designs, and the importance of early interaction with regulators to design toxicology and clinical studies.


Moving forward

Despite the discontinuation of the combination therapy arm in the TRAILBLAZER trial, Task Force members unanimously agreed that successful treatment of AD will require combination approaches that target multiple mechanisms and pathways. However, many questions remain regarding how best to move forward in the development of combination therapies.
Task Force members suggested several steps that should be taken to expedite the development of combination therapies:
•    Establish thresholds for pathologies beyond amyloid and tau, including inflammation and vascular load.
•    Pool observational studies to determine natural history of various combinations of pathologies.
•    Negotiate a DIAN-like structure with global resources from companies and academia, for example through the European Prevention of Alzheimer’s Dementia Consortium (EPAD).
•    Enlarge the dialogue about combination therapy to include disease modifying as well as symptomatic treatments and mechanisms that address the neurodegenerative process.
•    Pool resources, for instance by testing add-on compounds in participants enrolled in preclinical or Phase 2 clinical trials with a single agent, having completed the double-blind placebo-controlled phase of the study.

Task Force members also agreed that patient engagement is key to the development of combination therapies, particularly for treatments intended for the presymptomatic stages of the disease.


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. Gauthier reports personal fees from TauRx, Lundbeck Institute, and Esai; and grants from Lilly and Roche, outside the submitted work. Dr. Alam reports personal fees (employment) from EIP Pharma, Inc,  outside the submitted work. Dr. Fillit discloses the following consulting relationships during the past 3 years: Axovant, vTv, Lundbeck, Otsuka, Lilly, RTI, Roche, Genentech, Merck, Samus, Pfizer. He reports no conflicts of interest related to these disclosures that are relevant to this publication. Dr. Iwatsubo has nothing to disclose. Dr. Liu-Seifert reports other from Lilly,  outside the submitted work. Dr. Sabbagh has consulted for Allergan, Biogen, Bracket, Neurotrope, Cortexyme, Roche, Grifols, Sanofi, VTV therapeutic, and Alzheon. Dr Sslloway has nothing to disclose; Dr. Sims, employee of Eli Lilly and Company and holder of stock in Eli Lilly and Company. Dr. Sperling is an employee of H. Lundbeck A/S,  outside the submitted work. Dr. Sperling reports grants from Janssen, during the conduct of the study; personal fees from AC Immune, personal fees from Biogen, personal fees from Roche, personal fees from Eisai, personal fees from Insightec, personal fees from Takeda, personal fees from Merck, personal fees from General Electric, outside the submitted work. Dr. Welsh-Bohmer has contracts with Takeda Pharmaceutical Company and with VeraSci where she is the VP for Neurodegenerative Disorders. Dr. Touchon has nothing to disclose; 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; 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.



1.    Hendrix JA, Bateman RJ, Brashear HR, et al. Challenges, solutions, and recommendations for Alzheimer’s disease combination therapy. Alzheimers Dement 2016;12:623-630.
2.    Perry D, Sperling R, Katz R, et al. Building a roadmap for developing combination therapies for Alzheimer’s disease. Expert Rev Neurother 2015;15:327-333.
3.    Stephenson D, Perry D, Bens C, et al. Charting a path toward combination therapy for Alzheimer’s disease. Expert Rev Neurother 2015;15:107-113.
4.    Alzheimer’s Association. 2018 Alzheimer’s disease facts and figures. Alzheimers Dement 2018;14:367-429.
5.    Alzheimer’s Disease International. World Alzheimer’s Report 2018. The state of the art of dementia research: New frontiers. London2018.
6.    Cummings J, Lee G, Mortsdorf T, Ritter A, Zhong K. Alzheimer’s disease drug development pipeline: 2017. Alzheimers Dement (N Y) 2017;3:367-384.
7.    Bateman RJ, Xiong C, Benzinger TLS, et al. Clinical, cognitive, and biomarker changes in the Dominantly Inherited Alzheimer Network. The New England journal of medicine 2012;367:795-804.
8.    Schindler RJ. Study design considerations: conducting global clinical trials in early Alzheimer’s disease. J Nutr Health Aging 2010;14:312-314.
9.    Sperling R, Cummings J, Donohue M, Aisen P. Global Alzheimer’s Platform Trial Ready Cohorts for the Prevention of Alzheimer’s Dementia. J Prev Alzheimers Dis 2016;3:185-187.
10.    Selkoe DJ, Hardy J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol Med 2016;8:595-608.
11.    Jonsson T, Atwal JK, Steinberg S, et al. A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline. Nature 2012;488:96-99.
12.    Serrano-Pozo A, Betensky RA, Frosch MP, Hyman BT. Plaque-Associated Local Toxicity Increases over the Clinical Course of Alzheimer Disease. Am J Pathol 2016;186:375-384.
13.    Benilova I, Karran E, De Strooper B. The toxic Abeta oligomer and Alzheimer’s disease: an emperor in need of clothes. Nat Neurosci 2012;15:349-357.
14.    O’Brien RJ, Wong PC. Amyloid precursor protein processing and Alzheimer’s disease. Annu Rev Neurosci 2011;34:185-204.
15.    Sims JR, Selzler KJ, Downing AM, et al. Development Review of the BACE1 Inhibitor Lanabecestat (AZD3293/LY3314814). J Prev Alzheimers Dis 2017;4:247-254.
16.    Budd Haeberlein S, O’Gorman J, Chiao P, et al. Clinical Development of Aducanumab, an Anti-Abeta Human Monoclonal Antibody Being Investigated for the Treatment of Early Alzheimer’s Disease. J Prev Alzheimers Dis 2017;4:255-263.
17.    Jack CR, Jr., Bennett DA, Blennow K, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018;14:535-562.
18.    Cummings J, Lee G, Ritter A, Zhong K. Alzheimer’s disease drug development pipeline: 2018. Alzheimers Dement (N Y) 2018;4:195-214.
19.    Hara Y, McKeehan N, Fillit HM. Translating the biology of aging into novel therapeutics for Alzheimer disease. Neurology 2019;92:84-93.
20.    Jack CR, Jr., Knopman DS, Weigand SD, et al. An operational approach to National Institute on Aging-Alzheimer’s Association criteria for preclinical Alzheimer disease. Ann Neurol 2012;71:765-775.
21.    Tomaszewski S, Gauthier S, Wimo A, Rosa-Neto P. Combination Therapy of Anti-Tau and Anti-Amyloid Drugs for Disease Modification in Early-stage Alzheimer’s Disease: Socio-economic Considerations Modeled on Treatments for Tuberculosis, HIV/AIDS and Breast Cancer. J Prev Alzheimers Dis 2016;3:164-172.
22.    Hendrix S, Ellison N, Stanworth S, Otcheretko V, Tariot PN. Post Hoc Evidence for an Additive Effect of Memantine and Donepezil: Consistent Findings from DOMINO-AD Study and Memantine Clinical Trial Program. J Prev Alzheimers Dis 2015;2:165-171.
23.    Bateman RJ, Benzinger TL, Berry S, et al. The DIAN-TU Next Generation Alzheimer’s prevention trial: Adaptive design and disease progression model. Alzheimers Dement 2017;13:8-19.
24.    Saville BR, Berry SM. Efficiencies of platform clinical trials: A vision of the future. Clin Trials 2016;13:358-366.
25.    Food and Drug Administration. Guidance for industry: Codevelopment of two or more unmarketed investigational drugs for use in combination.. Accessed online 4/13/14 at, 2013.
26.    Irizarry MC, Fleisher AS, Hake AM, et al. TRAILBLAZER-ALZ (NCT03367403): A Phase 2 disease-modification combination therapy trial targeting multiple mechanisms of action along the amyloid pathway. Alzheimer Dement 2018;14:P1622-P1623.
27.    DeMattos RB, Lu J, Tang Y, et al. A plaque-specific antibody clears existing beta-amyloid plaques in Alzheimer’s disease mice. Neuron 2012;76:908-920.
28.    DeMattos R, May P, Racke M, et al. Combination therapy with a plaque-specific Abeta antibody and BACE inhibitor results in dramatic plaque lowering in aged PDAPP transgenic mice. Alzheimer Dement 2014;10:P149.
29.    Wessels AM, Siemers ER, Yu P, et al. A Combined Measure of Cognition and Function for Clinical Trials: The Integrated Alzheimer’s Disease Rating Scale (iADRS). J Prev Alzheimers Dis 2015;2:227-241.
30.    Panza F, Lozupone M, Solfrizzi V, et al. BACE inhibitors in clinical development for the treatment of Alzheimer’s disease. Expert Rev Neurother 2018;18:847-857.
31.    Vellas B, Bain LJ, Touchon J, et al. Advancing Alzheimer’s Disease Treatment: Lessons from CTAD 2018 . J Prev Alzheimers Dis. 2019; in press



J. Cummings1, N. Fox2


1. the 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, United Kingdom

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 2017;4(2):109-115
Published online April 25, 2017,




Background: Disease-modifying therapies (DMTs) are urgently needed to treat the growing number of individuals with Alzheimer’s disease (AD) or at immanent risk for AD.  A definition of DMT is required to facilitate the process of DMT drug development.
Process: This is a review of the state of the science with regard to definition and development of DMTs.
Results: A DMT is 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 that lead to cell death.  Demonstration of DMT efficacy is garnered through clinical trial designs and biomarkers. Evidence of disease modification in the drug development process is based on trial designs such as staggered start and delayed withdrawal showing an enduring effect on disease course or on combined clinical outcomes and correlated biomarker evidence of an effect on the underlying pathophysiological processes of the disease.  Analytic approaches such as showing change in slope of cognitive decline, increasing drug-placebo difference over time, and delay of disease milestones are not conclusive by themselves but support the presence of a disease modifying effect.  Neuroprotection is a related concept whose demonstration depends on substantiating disease modification.  No single type of evidence in itself is sufficient to prove disease modification – consistency, robustness, and variety of sources of data will all contribute to convincing stakeholders that an agent is a DMT.
Conclusion: DMT is defined by its enduring effect on processes leading to cell death.  A variety of types of data can be used to support the hypothesis that disease modification has occurred.

Key words: Alzheimer’s disease, biomarker, amyloid, disease modifying therapy, staggered start.




Disease-modifying therapy (DMT) is a major goal of research in Alzheimer’s disease (AD) therapeutics.  People with or at risk for AD, caregivers and family members, academic scientists, advocacy groups, biopharma industry scientists, the National Institutes of Health and other funders, and regulatory agencies are all stakeholders in the search for drugs or other interventions that can prevent or defer the onset or slow the decline of AD.  Putative DMTs build on an increasingly sophisticated neurobiological understanding of AD and intervene in steps thought to be critical to the pathophysiological process leading to cell death and expressing itself clinically as disease progression. The search for DMTs has taken on increased urgency as the world faces the tsunami of AD occurring with the aging of the global population (1). Simultaneously, the discovery of the long preclinical phase of AD has revealed the great number of people who have AD-type changes in the brain and are at risk for the emergence of clinical manifestations (2, 3).  DMTs are warranted in this population to prevent or defer disease emergence.
Despite the obvious need for treatments that will change the course of AD, the large number of programs directed at finding such agents, and the size of the population involved or at risk, there has been relatively little discussion or consensus building about the definition of DMT or the data needed to meet the definition.  Much of the information is in regulatory documents (4, 5). A definition is necessary to identifying appropriate clinical outcomes, develop biomarkers, and design trials that will demonstrate disease modification and meet agreed upon criteria.
In this paper, we address key issues of the concept of disease modification and DMTs and offer a framework for collecting data in clinical trials supportive of disease modification by the candidate therapy.


Defining Disease-Modifying Therapy

The three key elements of DMT are disease, modifying, and therapy (4-6).
”Disease” includes the preclinical phase of AD when there is evidence of fibrillar amyloid deposition in the brain on amyloid imaging or abnormally low levels of the 42 amino acid amyloid beta protein (AB42) in cerebrospinal fluid (CSF) in a subject who has normal cognition and function; prodromal AD identified by the presence of the same Aß abnormalities in individuals who have some degree of cognitive impairment but do not meet criteria for dementia; and individuals with biological and clinical evidence of AD dementia (7, 8). Figure 1 shows the populations of AD in which DMT development is being pursued.  There is a lack of consensus on whether the preclinical phase as described here is properly considered a disease since there are no symptoms, but treatments provided in this stage are aimed at preventing the symptomatic phases of AD and can be considered as DMTs.  Preclinical applications of DMTs can include both primary prevention, beginning with individuals who have no evidence of AD pathology, or secondary prevention in individuals who are cognitively normal but have positive amyloid imaging or other biomarker evidence of the presence of AD pathology.

Figure 1. Stages of Alzheimer’s disease applicable to development of disease modifying therapies

Figure 1. Stages of Alzheimer’s disease applicable to development of disease modifying therapies

“Modifying” is the key word in the definition of DMT and is considered in more detail throughout this paper.  The concept of modification is based on extrapolation from basic science observations that have identified processes in cellular studies, animal models, or human pluripotent stem cells (iPSCs) that result in AD-like changes and that can be altered by treatment (9, 10). For human application, “modification” refers to a change in the underlying disease process that produces an enduring effect on the clinical course of AD. Pathological processes of AD possibly contributing to cell death and representing targets for DMT’s include amyloid toxicity, tau-related cytopathy, disruption of membrane integrity, inflammation, oxidation, apoptosis, mitochondrial dysfunction, synaptic loss, cell loss, heavy metal-related facilitation of neuronal injury, demyelination, and possibly other processes yet to be identified.
“Therapy” refers to a structured intervention that might include a pharmacologic agent, device, or nonpharmacologic activity such as exercise (11).
Disease modification is an inferential concept based on trial-derived data since direct observation of the changes in the brain is not feasible. The data necessary to provide evidence of an enduring clinical effect and disease modification are generated in clinical trials using clinical outcomes and biomarkers.  Trial design and analytic strategies are used to optimize the ability to demonstrate drug-placebo differences supportive of disease-modification.
The concept of DMT stands in contrast to the idea of “symptomatic” therapy defined as interventions that improve cognition, defer cognitive or functional decline, or ameliorate symptoms such as agitation, depression or delusions without altering the underlying disease processes that comprise AD pathogenesis and without producing enduring changes that persist when the treatment is withdrawn.  Symptomatic therapies may be based on disease-related concepts such as a cholinergic deficiency but are not intended to interrupt processes leading to cell death.  Symptomatic treatments such as cholinesterase inhibitors have been shown to delay disease progression as measured by cognitive and functional measures (12); delay of symptoms does not constitute proof of disease-modification.
Based on the critical elements, 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.  Data supporting an intervention as a DMT would meet one of two criteria: 1) the intervention produces a significant drug-placebo difference on accepted clinical outcome(s) and has a consistent effect on one or more validated biomarkers considered fundamental to AD pathophysiology, or 2) the intervention produces a positive outcome on a staggered start or delayed withdrawal clinical trial design consistent with an enduring change in clinical course. The interpretation of biomarker results will depend on the repertoire of biomarkers tested, their internal consistency, relationship to the proposed mechanism of action, dose-response observations, and effect on proposed “downstream” events.  This DMT definition is not the same as specifying the requirements for approval of a DMT by a regulatory body; regulatory agencies address such issues as the necessary number of trials, quality of trials and trial data, and clinical meaningfulness of the observations.
Disease modification has synergies with the concept of “neuroprotetction.”  The latter refers to interventions that favorably influence the disease process or underlying pathogenesis to produce enduring benefits for patients (13, 14).  The clinical benefit is achieved by forestalling onset of illness or clinical decline. Effective neuroprotection results in disease modification and efficacious neuroprotective therapies are disease-modifying. Neuroprotection may be primary if the mechanism of action is directly on the neuron (e.g., mitochondrial agents) or secondary if the protection is derived from an action on an intermediary that compromises neuronal function.  Neuroprotection achieved with multiple sclerosis therapies, for example, is proposed as secondary neuroprotection from effects on inflammation.
Neuroprotection and disease modification are concepts that apply broadly across neurodegenerative diseases (15, 16).


Regulatory Views of Disease Modification

In its guidance on “Alzheimer’s Disease:  Developing Drugs for Treatment of Early Stage Disease” (4) the US Food and Drug Administration (FDA) described two approaches to demonstrating disease modification:  1) clinical benefit supported by a meaningful effect on a biomarker, or 2) clinical trial design suited to demonstrating a lasting effect on the disease course.  They stated that a divergence of slope of decline might be produced by a pharmacologically reversible effect and is not by itself evidence of disease modification.  They noted that a biomarker effect cited in support of disease modification must reflect a pathophysiological entity that is fundamental to the underlying disease process.  They observed that there is currently insufficient evidence on which to base a hierarchical structuring of biomarkers and encouraged trial sponsors to analyze the results of biomarkers independently.  The FDA guidance observed that randomized start and randomized withdrawal trial designs with clinical outcomes can provide evidence of enduring effects consistent with disease modification. They stated that for ethical reasons, the randomized start design would be most appropriate for trials of patients with AD.
The European Medicines Agency (EMA) discussion of AD therapy states that a medicinal product can be considered to be disease modifying when it delays the underlying pathological or pathophysiological disease processes (5). It states that this can be demonstrated by results that show slowing of the rate of decline of clinical signs or symptoms when these results are linked to a significant effect on adequately validated biomarkers that reflect key pathophysiological aspects of the underlying disease process.  EMA noted that change in rate of decline as shown by slope analysis and increasing drug-placebo difference are analyses that can support a disease-modifying effect.  Delayed start or withdrawal designs were described as options to enhance the data derived from a trial intended to show disease modification.  EMA suggested that if biomarker results are unclear, an alternative treatment labeling such as “delay or slowing in rate of decline” may be acceptable if effects on cognition and function are demonstrated.


Trial Design and Data Analysis to Support Disease Modification

Delayed start and randomized withdrawal are clinical trial designs that provide evidence of disease modification (17). In the delayed  start design, one subject group is started on treatment later than another and the failure to “catch up” with the first indicates that there has been an enduring effect on the disease and the effect of the drug is more than symptomatic.  In the randomized withdrawal design, a treated subject group is withdrawn from therapy and if they do not assume the same level of function as an untreated group, then the disease has been modified (18). There are substantial uncertainties with these proposed designs such as the appropriate duration of the period between the start of treatment of groups 1 and 2 in the delayed start and the required duration of the period of observation of the withdrawn group in the randomized withdrawal design.  No study has successfully utilized these designs to establish disease-modification in a neurodegenerative disorder.
In addition to the trial design, strategies for data analysis can contribute to supporting the presence of disease-modification in trials of DMTs.  Four major analytic approaches have been used in clinical trials:  1) drug-placebo difference at trial end; 2) delay to milestone (e.g, progression to Clinical Dementia Rating [CDR] (19) to 1 from 0.5 or to 2 from 1); 3) increasing drug placebo difference over time; and 4) change in slope of decline.  Drug-placebo difference at trial end and delay to milestone are not unique to disease modification and can be produced by symptomatic agents (12). Change in slope from more acute to less acute with successful intervention and increasing drug-placebo difference over time are supportive of disease-modification (Figure 2) (18) Neither of these are sufficiently informative by themselves to establish disease modification.  These observations are expected in DMT trials and can add support to the case for disease modification.  EMA has suggested that these analyses might be supportive of a “slowing in rate of decline” (5); that could serve as an alternative label for a candidate agent that failed to meet all criteria for a DMT.


Figure 2. Analytic observations consistent with disease modification

Figure 2. Analytic observations consistent with disease modification

Clinical Outcomes to Demonstrate Disease Modification

A successful DMT must produce meaningful clinical benefit.  New outcome instruments are required to show the clinical dimension of disease-modification in recently identified trial populations such as those with normal cognition in prevention trials and those with mild cognitive changes in prodromal AD trials.  In primary prevention trials, DMTs would be expected to delay the development of biomarker evidence of AD (e.g, delay of amyloid accumulation as shown by amyloid imaging) and thereby to delay the onset of cognitive decline.  No biomarker has been shown to be a surrogate of clinical decline in AD and trials will be forced to use biomarkers that are “reasonably likely” to predict clinical benefit.  Such approaches have been used in development of drugs for other disease states such as human immunodeficiency virus infections (20).  Confirmation of the disease modifying effect on cognition would require observation of the treated patients for long periods of time. Primary prevention might be instituted at age 50 or 55, delayed amyloid deposition might be evident after 4-5 years of treatment, but delay of cognitive decline would require observation until age 65 or 70 – 20 years after the initiation of therapy. These timeframes will require use of putative fit-for-purpose biomarkers that may eventually be shown to be predictive of cognitive function.  Biomarker effects by themselves if considered reasonably likely to predict clinical benefit might be sufficient for accelerated approval requiring demonstration of cognitive effects with longer term observation after approval.
Secondary prevention trials enroll individuals who are cognitively normal but who have biomarker evidence (e.g., positive amyloid imaging; CSF signature of AD) of being at high risk for the development of cognitive decline.  The combination of delay of onset of cognitive decline compared to placebo and biomarker changes supportive of an impact on the fundamental pathophysiology of AD would be key to establishing an agent as a DMT.
Prodromal AD is the predementia stage of AD in which patients have cognitive impairment without functional deficits, do not meet criteria for dementia, and have a biomarker indicative of the presence of AD pathology (positive amyloid imaging; CSF signature of AD) (8). The FDA has issued a guidance concerning trials in this population and has suggested that composite measures such as the Clinical Dementia Rating – Sum of Boxes (CDR-sb) (19) could function as a single trial outcome although benefit on both the cognitive and functional portions of the measure is expected (4).  Several alternative composites have been proposed for this role including the integrated AD Rating Scale (iADRS) (21) and the AD Composite Scale (ADCOMS)(22). Support for disease modification could be obtained by showing a drug-placebo difference at trial end on the composite score and on a suite of biomarkers, or the composite could be used as the outcome in a staggered start or randomized withdrawal trial design.  Expectations for functional outcomes in this population are ambiguous since the prodromal population by definition lacks functional impairment.
DMTs would also be appropriate for treatment of AD dementia especially in early stages when cognitive and functional deficits remain compatible with acceptable quality of life.  In some studies, prodromal and mild AD dementia are included in the same trial population in recognition of the arbitrary nature of dividing the seamless spectrum of progressive cognitive decline that characterizes AD.  Clinical outcomes in these populations would require showing a drug-placebo benefit on cognitive and functional or global outcomes. The clinical outcomes would require support of concurrent biomarker differences to establish an agent as a DMT.  Alternatively, the outcomes could be used in staggered start or randomized withdrawal designs.
Establishment of disease modification is dependent on the measurement characteristics of the tools (clinical and biomarker) used as well as the quality of execution of the clinical trial.


Biomarkers to Demonstrate Disease Modification

There is currently a limited repertoire of biomarkers of AD; none have achieved the status of a surrogate marker that is known to reliably predict clinical outcomes and can be substituted for clinical measures in trials.  Two biomarkers have been qualified by the EMA and can be used in clinical trials without re-qualification for individual trials. These are low CSF Aß42 and hippocampal atrophy as measured on magnetic resonance imaging (MRI ) (23, 24). No AD biomarkers have been qualified by the FDA, and biomarkers used as outcome measures in trials of DMTs must be established as fit-for-purpose for each trial (25).
Biomarkers can be divided into diagnostic biomarkers that identify the presence of AD type pathology and pathophysiological biomarkers that reflect disease progression (8). Recognized diagnostic biomarkers include amyloid imaging documenting abnormal amyloid levels and the CSF signature of AD comprised of low Aß42 and high tau or phospho-tau (p-tau). Biomarkers reflecting disease progression include measures of brain atrophy using MRI, assessment of cerebral metabolism using fluorodeoxyglucose (FDG) positron emission tomography (PET), CSF levels of tau and p-tau, and tau PET.
Effects on diagnostic biomarkers do not by themselves support disease-modification.  Bapineuzumab and AN-1792 are examples of immunotherapies that reduced plaque burden in clinical trials and did not affect the course of decline in AD (26, 27). Removal of plaque amyloid documents an effect of treatment on fibrillar amyloid but not necessarily an effect on processes leading to cell death.
Effects on biomarkers of disease progression could be regarded as evidence in support of disease-modification.  Reduction of whole brain or hippocampal atrophy compared to atrophy in a placebo group would be regarded as supportive of disease-modification if seen in conjunction with clinical benefit.  Several trials have shown greater atrophy in groups treated with putative DMTs compared to placebo controls and a comprehensive understanding of the influences on this measure has yet to be achieved (28, 29). Less elevation of CSF tau or p-tau in the treatment group compared to the placebo or reduced tau accumulation as seen on tau imaging would be regarded as evidence of disease modification (30). Less marked reduction of cerebral metabolism on FDG PET would support a disease-modifying effect; this evidence should be viewed with caution as changes on FDG PET can be produced by symptomatic agents such as cholinesterase inhibitors (31).
FDA has suggested that multiple biomarkers should be collected in clinical trials.  Assessing amyloid imaging and CSF Aß42 could provide internally consistent evidence of an effect on amyloid physiology.  Similarly, collecting simultaneous measures of tau abnormalities – tau imaging, CSF tau/p-tau – might convincingly support a tau-related drug effect (32).
Pathophysiological events in AD are hypothesized to be linked, with amyloid changes being “upstream” and tau alterations, inflammation and cell loss being “downstream” (33).  Measures of several biomarkers that indicate effects on different elements of the pathophysiology would be supportive of a disease-modifying effect independent of the veracity of our current disease models.  Thus, measures of Aß42, CSF tau/p-tau, and whole brain atrophy on MRI could provide a more comprehensive view of the effects of a DMT and a more compelling suite of observations.  The interpretation of biomarker results will depend on the repertoire of biomarkers tested, their internal consistency, relationship to the proposed mechanism of treatment action, dose-response observations, and effect on proposed “downstream” events.
Linking the observed biomarker effect to the observed clinical effect is important to support the effects of a DMT.  Biomarker and clinical effects could be achieved through different mechanisms of drug action (20). A relationship between the clinical and biological effects is indicated by correlations between the magnitude of change on clinical and biomarker measures.  A dose-response relationship between administered dose or serum level and biomarker effect would provide further evidence of a causative relationship.


Classifying Evidence in Support of Disease Modification

Data supporting disease modification are inferential based on biomarkers of biological effects; no direct measures of disease modification are available.  No single piece of evidence will prove that an agent has produced disease modification.  Synthesis of clinical outcomes, biomarker outcomes, trial designs, and analytic strategies supported by non-clinical studies of mechanism of drug action will be required to provide compelling support for disease modification (Table 1).

Table 1. Data supporting disease-modification by a putative DMT

Table 1. Data supporting disease-modification by a putative DMT

The combination of clinical and biological observations allows the construction of levels of evidence in support of disease modification collected in trials.  Staggered start and randomized withdrawal evidence is more compelling than parallel group designs, and effects on multiple independent biomarkers are more compelling that effects on single biomarkers or related biomarkers.  Table 2 presents an approach to classification of levels of evidence in support of a DMT for mild-moderate AD trial outcomes; similar approaches could be applied to prevention trials and trials involving prodromal AD.

Table 2. Approach to levels of clinical and biomarker evidence in support of disease modification

Table 2. Approach to levels of clinical and biomarker evidence in support of disease modification


AD is increasingly well understood from a neurobiological perspective.  New targets are being identified as the processes involved in the disease are better defined.  More candidate molecules are being identified and entered into the AD therapeutic pipeline (34). Many of these agents are intended to be DMTs that will prevent, delay or slow the progression of AD.  Success in developing DMTs grows more urgent as the population of those with or at risk for AD increases. A definition of DMT is key to advancing a therapeutic agenda. The recommendations offered here for defining DMTs and providing data to support identification of DMTs are intended to assist in the critical process of developing DMTs.


Funding: JC acknowledges the funding from the National Institute of General Medical Sciences (Grant: P20GM109025) and support from Keep Memory Alive. NF acknowledges the support of the Leonard Wolfson Experimental Neurology Centre, the NIHR Queen Square Dementia Biomedical Research Unit, and UCL/UCLH Biomedical Research Centre. The Dementia Research Centre is supported by Alzheimer’s Research UK, Brain Research Trust, and The Wolfson Foundation. The Dementia Research Centre is an Alzheimer’s Research Centre Co-ordinating Centre. This work was supported by The Dunhill Medical Trust [grant number R337/0214]; Alzheimer’s Society (AS-PG-14-022); ESRC/NIHR (ES/L001810).

Acknowledgements: None.

Disclosures: JC has provided consultation to Abbvie, Acadia, Actinogen, Alzheon, Anavex, Avanir, Axovant,  Boehinger-Ingelheim, Bracket, Eisai, Forum, GE Healthcare, Genentech, Intracellular Interventions, Lilly, Lundbeck, Medavante, Merck, Neurocog, Novartis, Orion, Otsuka, Pfizer, Piramal, QR, Roche, Suven, Sunovion, Takeda and Toyama pharmaceutical and assessment companies. NF consults for Eli Lilly, Novartis, Sanofi, Roche, and GlaxoSmithKline GSK. NF consults for Eli Lilly, Novartis, Sanofi, Roche, and GlaxoSmithKline GSK.

Ethical standards: This review paper did not involve new subject testing and did not compromise ethical standards.



1.     Sosa-Ortiz AL, Acosta-Castillo I, Prince MJ. Epidemiology of dementias and Alzheimer’s disease. Arch Med Res 2012;43:600-608.
2.    Pietrzak RH, Lim YY, Ames D, et al. Trajectories of memory decline in preclinical Alzheimer’s disease: results from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of ageing. Neurobiol Aging 2015;36:1231-1238.
3.    Bateman RJ, Xiong C, Benzinger TL, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 2012;367:795-804.
4.    U.S. Food and Drug Administration. Guidance for Industry Alzheimer’s Disease:  Developing drugs for the treatment of early stage disease Washington, D.C.2013 [updated 2/2013]. Available from:
5.    European Medicine Agency; Committee For Medicinal Products For Human Use. Draft guideline on the clinical investigation of medicines for the treatment of Alzheimer’s disease and other dementias. 2016; EMA/CHMP/539931/2014.
6.    Cummings JL. Defining and labeling disease-modifying treatments for Alzheimer’s disease. Alzheimers Dement 2009;5:406-418.
7.    McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011;7:263-269.
8.    Dubois B, Feldman HH, Jacova C, et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 2007;6:734-746.
9.    Choi SH, Kim YH, Hebisch M, et al. A three-dimensional human neural cell culture model of Alzheimer’s disease. Nature 2014;515:274-278.
10.    Hurtado DE, Molina-Porcel L, Iba M, et al. A{beta} accelerates the spatiotemporal progression of tau pathology and augments tau amyloidosis in an Alzheimer mouse model. Am J Pathol 2010;177:1977-1988.
11.    Duara R, Barker W, Loewenstein D, Bain L. The basis for disease-modifying treatments for Alzheimer’s disease: the Sixth Annual Mild Cognitive Impairment Symposium. Alzheimers Dement 2009;5:66-74.
12.    Mohs RC, Doody RS, Morris JC, et al. A 1-year, placebo-controlled preservation of function survival study of donepezil in AD patients. Neurology 2001;57:481-488.
13.    Ravina BM, Fagan SC, Hart RG, et al. Neuroprotective agents for clinical trials in Parkinson’s disease: a systematic assessment. Neurology 2003;60:1234-1240.
14.    Wiendl H, Elger C, Forstl H, et al. Gaps between aims and achievements in therapeutic modification of neuronal damage («neuroprotection»). Neurotherapeutics 2015;12:449-454.
15.    Whitcup SM. Clinical trials in neuroprotection. Prog Brain Res 2008;173:323-335.
16.    Dunkel P, Chai CL, Sperlagh B, Huleatt PB, Matyus P. Clinical utility of neuroprotective agents in neurodegenerative diseases: current status of drug development for Alzheimer’s, Parkinson’s and Huntington’s diseases, and amyotrophic lateral sclerosis. Expert Opin Investig Drugs 2012;21:1267-1308.
17.    Bodick N, Forette F, Hadler D, et al. Protocols to demonstrate slowing of Alzheimer disease progression. Position paper from the International Working Group on Harmonization of Dementia Drug Guidelines. The Disease Progression Sub-Group. Alzheimer Dis Assoc Disord 1997;11 Suppl 3:50-53.
18.    Cummings JL, Zhong K. Clinical trials and drug development in neurodegenerative diseases: unifying principles. In: Cummings JL, Pillai JA, editors. Neurodegenerative Diseases Unifying Principles, 2017 in press. Oxford University Press, New York, NY, pp. 323-336.
19.    Williams MM, Storandt M, Roe CM, Morris JC. Progression of Alzheimer’s disease as measured by Clinical Dementia Rating Sum of Boxes scores. Alzheimers Dement 2013;9:S39-44.
20.    Katz R. Biomarkers and surrogate markers: an FDA perspective. NeuroRx 2004;1:189-195.
21.    Wessels AM, Siemers ER, Yu P, et al. A combined measure of cognition and function for clinical trials: The integrated Alzheimer’s Disease Rating Scale (iADRS). J Prev Alzheimers Dis 2015;2:227-241.
22.    Wang J, Logovinsky V, Hendrix SB, et al. ADCOMS: a composite clinical outcome for prodromal Alzheimer’s disease trials. J Neurol Neurosurg Psychiatry 2016.
23.    Committee For Medicinal Products For Human Use. Qualification opinion of low hippocampal volume (atrophy) by MRI for use in clinical trials for regulatory purpose – in pre-dementia stage of Alzheimer’s disease.: European Medicines Agency; 2011 [updated 06 Dec 2016December 6, 2016]. Available from:
24.    European Medicine Agency; Committee For Medicinal Products For Human Use. Qualification opinion of novel methodologies in the predementia stage of Alzheimer’s disease: cerebro-spinal fluid related biomarkers for drugs affecting amyloid burden. 2011; EMA/CHMP/SAWP/102001/2011.
25.    Amur SG, Sanyal S, Chakravarty AG, et al. Building a roadmap to biomarker qualification: challenges and opportunities. Biomark Med 2015;9:1095-1105.
26.    Liu E, Schmidt ME, Margolin R, et al. Amyloid-beta 11C-PiB-PET imaging results from 2 randomized bapineuzumab phase 3 AD trials. Neurology 2015;85:692-700.
27.    Holmes C, Boche D, Wilkinson D, et al. Long-term effects of Abeta42 immunisation in Alzheimer’s disease: follow-up of a randomised, placebo-controlled phase I trial. Lancet 2008;372:216-223.
28.    Novak G, Fox N, Clegg S, et al. Changes in brain volume with bapineuzumab in mild to moderate Alzheimer’s disease. J Alzheimers Dis 2015;49:1123-1134.
29.    Fox NC, Black RS, Gilman S, et al. Effects of Abeta immunization (AN1792) on MRI measures of cerebral volume in Alzheimer disease. Neurology 2005;64:1563-1572.
30.    Blennow K, Zetterberg H, Fagan AM. Fluid biomarkers in Alzheimer disease. Cold Spring Harb Perspect Med 2012;2:a006221.
31.    Mega MS, Dinov ID, Porter V, et al. Metabolic patterns associated with the clinical response to galantamine therapy: A fludeoxyglucose f 18 positron emission tomographic study. Arch Neurol 2005;62:721-728.
32.    Medeiros R, Baglietto-Vargas D, LaFerla FM. The role of tau in Alzheimer’s disease and related disorders. CNS Neurosci Ther 2011;17:514-524.
33.    Selkoe DJ, Hardy J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol Med 2016;8:595-608.
34.    Cummings J MT, Lee G . Alzheimer’s drug development pipeline: 2016. Alzheimer’s & Dementia 2016;2:222-232.



N. Ketter1, E. Liu1, J. Di1, L.S. Honig2, M. Lu1, G. Novak1, J. Werth3, G. LePrince Leterme4, A. Shadman1, H.R. Brashear1


1. Janssen Alzheimer’s Immunotherapy Research & Development, LLC, South San Francisco, CA, USA; 2. Columbia University College of Physicians & Surgeons, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, New York, NY, USA; 3. Pfizer R&D, Collegeville, PA, USA; 4. Pfizer Global Research and Development, Paris, France

Corresponding Author: Dr. Nzeera Ketter, Janssen Alzheimer Immunotherapy Research & Development, 700 Gateway Boulevard, South San Francisco, California 94080, Tel: 510 248 2708, Fax: 510 248 2560, Email:


J Prev Alz Dis 2016;3(4):192-201

Published online October 24, 2016,



Background: Vanutide Cridificar (ACC-001), a novel investigational immunotherapeutic vaccine designed to elicit antibodies against the N-terminal peptide 1-7 of the amyloid-beta peptide, believed to be important in the pathogenesis of Alzheimer’s disease (AD).
Objectives: To evaluate the immunogenicity, safety and impact of ACC-001 with Quillaja saponaria (QS-21) adjuvant on the reduction of brain fibrillar amyloid burden, assayed by positron emission tomography (PET) imaging, in patients with mild to moderate AD.
Design: Randomized, phase 2, interventional study. Trial registration: Identifier: NCT01284387.
Participants: Individuals with mild to moderate Alzheimer’s disease (Mini-Mental State Examination scores 18-26; measurable amyloid burden in the expected range, on the screening 18F-florbetapir PET scan; and a Rosen modified Hachinski ischemic score ≤4).
Intervention: Participants were randomized to 3 μg or 10 μg ACC-001 (each in combination with 50 μg QS-21) or placebo (without QS-21).
Measurements: Primary endpoint was the change from baseline to week 104 in cerebral amyloid burden as measured by the global cortical average (GCA) standard value uptake ratio (SUVR) based on the brain 18F-florbetapir PET composite cortical SUVR between each ACC-001+QS-21 dose compared with placebo. Secondary endpoints included safety, immunogenicity and pharmacodynamics. Exploratory endpoints included cognitive and functional efficacy, and health outcome measures.
Results: Of 126 randomized patients (placebo: 40; ACC-001 3 µg+QS-21: 43; and ACC-001 10 µg+QS-21: 43), 125 received study treatment; 92 (73%) completed the study. Change in 18F-florbetapir PET GCA SUVR, was not significantly different between either of the two ACC-001+QS-21 treatment groups and placebo (3 μg +QS-21 vs. placebo diff=-0.03, p=0.54; 10 μg +QS-21 vs. placebo diff=-0.08, p=0.07), but the trend was numerically consistent with a dose response. The geometric mean peak anti-Aβ IgG titers were slightly higher in the 10 μg than the 3 μg group. The proportion of responders was similar in both dose groups of ACC-001+QS-21. The cerebrospinal fluid (CSF) p-tau changes from baseline in both active treatment groups were not statistically different from placebo, but were numerically consistent with a dose response (3 μg +QS-21 vs. placebo diff=-3.2, p=0.57; 10 μg +QS-21 vs. placebo diff=-7.0, p=0.19). The vMRI showed statistically significant faster treatment-related decrease in brain volume in the 10 μg group but was not significant in the 3 μg group, compared with placebo (3 μg diff =-1.3 mL/year, p=0.50; 10 μg diff=-4.2 mL/year, p=0.02). Measured plasma Aβ levels increased in parallel with peak anti-Aβ titers after each injection. Amyloid-related imaging abnormalities with edema/effusion (ARIA-E) were more frequent in patients who received ACC-001+QS-21 than placebo (6% vs. 0%) but none were symptomatic. The most common treatment-emergent adverse events in the active groups were injection reactions, and occurred more frequently in the ACC-001+QS-21 groups than the placebo (48% vs 8%), the majority of which were mild and transient.
Conclusions: Primary biomarker efficacy endpoints were not statistically significant in either dose group. The numerical decreases in 18F-florbetapir PET GCA SUVR suggests a dose-related trend for greater reductions in fibrillar amyloid burden in the ACC-001+QS-21 10 μg group compared with placebo. Likewise, while not significant, there was a numerical trend of decreased CSF p-tau levels with ACC-001, possibly consistent with a downstream effect in the ACC-001+QS-21 group. Insufficient antibody titers or quality, insufficient power to detect a difference, or too short duration of follow up may be reasons why a statistically significant response was not observed. Brain volume measures showed faster volume loss in the 10 µg treatment group, similar to the effect seen in few earlier AD immunotherapy trials which may suggest removal of amyloid and resultant decrease in inflammation. No new, unexpected safety signals were detected.

Key words: Alzheimer’s disease, amyloid, mild to moderate AD, PET, vaccine, vanutide cridificar.



Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, characterized by specific neuropathological changes, including intraneuronal (neurofibrillary tangles) and extracellular protein aggregates (neuritic plaques). The predominant components of neuritic plaques are amyloid beta (Aβ) peptides, particularly the 42 amino acid isoform (Aβ42) that is derived from a larger amyloid precursor protein (1). The “Aβ-cascade” hypothesis of AD pathogenesis addresses early events in the pathological process leading to amyloid deposition, which drives subsequent abnormal tau phosphorylation, neurofibrillary tangle formation, and ultimately neuronal death (2, 3). Multiple lines of evidence support aberrant Aβ42 production or clearance in the pathogenesis of AD, with a likely progression in neuronal dysfunction and neurodegeneration, and ensuing decline in cognition and function (4, 5).          
Currently available therapies for AD are symptomatic in nature, and do not show evidence of slowing disease progression or neurodegeneration (6, 7). Hence, there is an unmet need to develop therapeutics that can alter the underlying progressive disease pathology and pathophysiology of AD, and potentially slow clinical decline.
Vanutide cridificar (ACC-001) is an investigational therapeutic vaccine comprised of the N-terminal 1-7 amino acids of the Aβ peptide conjugated to a mutant diphtheria toxin carrier protein, CRM197, which is designed to stimulate the systemic production of anti-Aβ antibodies against the N-terminus 1-7 of Aβ. ACC-001 elicited anti-Aβ titers in non-human primates without evidence of a cytotoxic T-cell response when administered with or without the adjuvant Quillaja saponaria (QS-21). QS-21 is a highly potent stimulator of antigen-specific immune responses, and higher antibody titers are elicited when the ACC-001 vaccine is combined with QS-21 in individuals with mild to moderate AD (8-10). Preclinical studies demonstrated that ACC-001 with adjuvant immunostimulatory agent QS-21 is capable of inducing significant anti-Aβ antibody responses (8, 10, 11).
An earlier study with active Aβ immunotherapy, AN1792 with adjuvant QS-21, elicited anti-Aβ antibodies in 20% of patients with AD, but was associated with meningoencephalitis (12). An Aβ-specific T-cell response has been strongly suspected as the cause of this adverse event (AE) because the vaccine contained T-cell epitopes (7, 13, 14). Based on these data, ACC-001 was developed to restrict the immunologic response to a more defined portion of the Aβ molecule (N-terminal amino acids 1-7), which has been shown to have plaque-clearing activity in mice, and to avoid potential stimulation cytotoxic T-cells (10).
This phase 2, randomized, double-blind, placebo-controlled study was designed to evaluate the effect of ACC-001 with the adjuvant QS-21 compared with placebo in reducing cerebral amyloid burden, as well as safety, in patients with mild to moderate AD. Exploratory objectives were to evaluate the effect of ACC-001 on other biomarkers, immunogenicity, pharmacodynamic measures, efficacy, and health outcome measures.




Eligible participants were men and women, aged 50−89 years (inclusive), with a diagnosis of probable AD according to the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria; with a magnetic resonance imaging (MRI) scan not consistent with AD; a Mini–Mental State Examination (MMSE) score of 18 to 26 (inclusive); measurable amyloid burden on the screening 18F-florbetapir positron emission tomography (PET) in the range expected for AD patients (visual analysis); and a Rosen modified Hachinski ischemic score of ≤4.
Exclusion criteria included significant neurological or medical disease other than AD that could affect cognition; evidence of any abnormality on central read MRI including but not limited to ≥2 microhemorrhages, history or evidence of a prior hemorrhage >1 cm3, ≥2 lacunar infarcts, a prior infarct >1 cm3, cerebral contusion, encephalomalacia, aneurysms, vascular malformations, subdural hematoma, or space-occupying lesions; major psychiatric disorder; history of seizures or stroke; and history of clinically significant carotid or vertebrobasilar stenosis and other risk factors for thromboembolic stroke.
The Independent Ethics Committee or Institutional Review Board at each study site reviewed and approved the protocol, and the study was conducted in accordance with the ethical principles that have their origin in the Declaration of Helsinki and that are consistent with International Conference on Harmonization for Good Clinical Practices guidelines and applicable local and regulatory requirements. Written informed consent was obtained from each patient or their legally acceptable representatives before enrollment.

Study design and treatment

This phase 2, double-blind, randomized, placebo-controlled, parallel-group, study ( Identifier: NCT01284387) was conducted across 22 sites in the United States from February 2011 to January 2014. The study duration was approximately 26 months comprising a 7-week screening phase, followed by a 104-week (about 24-months) double-blind treatment phase (including 78 weeks [about 18 months] of dosing followed by an additional 6 months safety follow up after the last injection). At the beginning of the double-blind phase, eligible patients were randomized (1:1:1) to the three treatment arms: 3 μg or 10 μg of ACC-001 (each in combination with 50 μg QS-21 adjuvant) or placebo (without QS-21), based on a computer-generated randomization schedule stratified by APOE status (APOE*E4 carrier vs. non carrier). The investigational product or placebo was administered intramuscularly on day 1 and at weeks 4, 12, 26, 52, and 78 (total of 6 injections per patient) (Figure 1). The investigator, all study staff (except the dispensing pharmacists) and the patients were blinded to the treatment allocation.

Figure 1. Study Design and Patient Disposition

ADAS-Cog/11, Alzheimer’s Disease Assessment Scale – Cognitive subscale (11-item version); AD MACQ, AD Medication Administration Concerns Questionnaire; CDR-SB, Clinical Dementia Rating Sum of Boxes; DAD, Disability Assessment for Dementia; DS, Dependence Scale; FAQ, Functional Activities Questionnaire; MMSE, Mini-Mental State Examination; NPI, Neuropsychiatric Inventory; NTB, Neuropsychological Test Battery; PET, Positron emission tomography; RUD-Lite V.2.4, Resource Utilization in Dementia version 2.4; vMRI, Volumetric magnetic resonance imaging; w, week. Anti- Aβ titers were collected at the dosing visit and then 2 weeks


Other experimental medications and devices, immunosuppressants, chemotherapeutic agents, anticonvulsants, anticoagulants, and opioid pain relievers were not allowed during the study. Routine vaccinations with commercially available products could be given only 2 weeks after the administration of the investigational product/placebo.



The primary evaluation was the brain amyloid accumulation as measured by change from baseline to week 104 in global cerebral amyloid burden as measured by the global cortical average (GCA) which was calculated as the average standardized uptake value ratio (SUVR) based on the brain 18F-florbetapir PET scan using cerebellar gray matter as the reference region.


Safety evaluations included evaluation of AEs, deaths, prespecified events of special circumstance (ESCs) which included amyloid-related imaging abnormalities associated with edema or effusion (ARIA E), vasculitis, and intracranial hemorrhage, adverse drug reactions (ADRs), vital signs, clinical laboratory tests, electrocardiograms (ECGs), brain MRIs, suicidality assessments, physical and neurological examinations.


Exploratory evaluations included assessment of the effect of ACC-001+QS-21 on the following: a). other biomarkers (PET amyloid signal in individual regions of interest [ROIs], whole brain, ventricular, and hippocampal volume as measured by brain boundary shift integral [BBSI], ventricular boundary shift integral [VBSI], hippocampal boundary shift integral [HBSI], and cerebrospinal fluid [CSF] biomarkers [Aβx-42 and Aβx-40 concentration, phosphorylated tau (p-tau) and total tau (T-tau)]); b). immunogenicity (anti-Aβ [IgG and IgM] titer, CSF anti-Aβ titer); c). pharmacodynamic measures (plasma Aβx-40 concentrations); d). clinical efficacy (Alzheimer’s Disease Assessment Scale-Cognitive subscale [ADAS-Cog 11 items], Clinical Dementia Rating sum of Boxes [CDR SB], Disability Assessment for Dementia [DAD], Functional Activities Questionnaire [FAQ], MMSE, neuropsychological test battery [NTB], and Neuropsychiatric Inventory [NPI]); e). health outcome measures (Dependence Scale [DS], the Resource Utilization in Dementia Lite version 2.4 [RUD-Lite V2.4], and the AD Medication Administration Concerns Questionnaire [AD MACQ]).


Blood samples for all analysis (efficacy, safety and exploratory) were collected at specified time intervals from baseline to the end of 104 weeks. 18F-florbetapir PET scans were planned to be obtained for all the patients at 4 time points: baseline and at 50, 78, and 104 weeks (or early termination, ET). Amyloid-PET images were acquired 50 minutes after the intravenous injection of 10 (370 MBq) ±1 mCi of florbetapir during screening phase and at week 50. Visual assessment of the PET scan was performed by expert readers at a central imaging core laboratory. The scans were also analyzed quantitatively by the same core laboratory. The primary amyloid PET endpoint was the GCA which was calculated as the average SUVR of five cortical ROIs: anterior cingulate, frontal cortex, lateral temporal cortex, parietal cortex, and posterior cingulate/precuneus. Exploratory amyloid PET endpoints consisted of SUVRs for specific ROIs. Brain MRI scans was acquired at screening and at weeks 2, 10, 24, 50, 76, 102 or at ET of the study in all patients for safety. The volumetric analysis of MRIs from screening and weeks 24, 50, 76 and 102 was performed.
An optional CSF substudy was included such that in consenting patients, lumbar puncture was performed at screening visit and at week 76, and CSF samples were analyzed for p-tau, T-tau and Aβ by enzyme-linked immunosorbent assay (ELISA). Plasma Aβ concentration were measured at screening and at weeks 2, 4, 6, 12, 14, 28, 54, 78, 80, 104 or at ET visit. Plasma and CSF Aβx-40 and Aβx-42 concentrations were determined using validated ELISA methodologies developed in compliance with Janssen AI Bioanalytical Development operating procedures and documented in method validation reports on Meso Scale Discovery (Meso Scale Diagnostics, Rockville, MD) technology and plate reader. Serum anti-Aβ IgG (total) and anti-Aβ IgM titers were evaluated by an ELISA assay at baseline, prior to and 2 weeks after each immunization at weeks 4, 6, 12, 14, 26, 28, 52, 54, 78, 80, 104 or at ET. The proportion of serological responders (patients with serum anti-Aβ IgG titer ≥300 U/mL no later than 30 days after the administration of the third injection of active investigational product) at each visit was analyzed at study end. The efficacy and health outcome measures were evaluated at baseline and at specified visits.


Statistical methods

Sample size determination

A total of 108 patients were planned to be enrolled in the study. The sample size calculation assumed a GCA SUVR standard deviation of 0.154 based on a prior study of bapineuzumab, using 11C-PIB as the tracer (15). Assuming maximum dropout rate of 30% in any treatment group, the planned sample size provided 94% power (2-sided alpha=0.05) to detect a difference of 0.134 between the pooled ACC-001+QS-21 group and placebo in the mean change from baseline at week 104 PET GCA SUVR. For comparing individual ACC-001+QS-21 dose and placebo, the power was 85% under same assumptions.

Analysis populations

The 18F-florbetapir PET analysis population included all randomized and dosed patients who had a baseline and at least 1 postbaseline assessment of cerebral amyloid burden (GCA SUVR) using 18F-florbetapir PET. The safety analysis set included all randomized patients who received at least one dose of the study medication. Immunogenicity analysis was based on the safety population.

Statistical analysis

All endpoints were summarized by treatment group using descriptive statistics. Primary analysis used a mixed-effect model for repeated measures (MMRM) to compare between treatment groups change from baseline in the GCA SUVR at week 104. This model included fixed effects of treatment, baseline GCA SUVR, baseline age, APOE*E4 status stratum, visit, treatment by visit interaction. An unstructured variance-covariance structure was used to model the within-patient errors.
Similar MMRM models were used for the analysis of exploratory clinical endpoints. For vMRI biomarkers, the MMRM for BBSI rate used baseline WBV as the baseline assessment, the MMRM for left/right HBSI rate used baseline left/right HCV as the baseline assessment, but the MMRM for VBSI rate did not include any baseline assessment. The change from baseline in p-tau, T-tau, and Aβ (x-40 and x-42) concentrations at week 76 were analyzed using an analysis of covariance (ANCOVA) model that included treatment, corresponding baseline assessment, baseline age, and APOE*E4 status stratum as covariates. Anti-Aβ titer was measurable in serum, and the proportion of serological responders (defined by a ≥300 U/ml serum anti-Aβ IgG titer no later than 30 days after the administration of the 3rd injection of active study medication) was summarized by treatment group with 95% confidence intervals calculated using the exact Binomial method. Anti-Aβ titer could not be successfully measured in the CSF due to lack of sensitivity. In addition to the comparisons of individual dose groups with placebo, the two active dose groups were combined and compared with placebo. All statistical tests were conducted with a two-sided type I error rate of 0.05 without adjustment for multiple comparisons.



Unless otherwise specified, analysis results were presented in the form of mean (standard deviation [SD]) for numerical variables and count (%) for categorical variables. Out of 126 patients enrolled and randomized in three treatment groups (placebo: n=40; ACC-001 3 µg+QS-21: n=43; and ACC-001 10 µg+QS-21: n=43), 125 patients received study treatment (placebo: n=39; ACC-001 3 µg+QS-21: n=43; and ACC-001 10 µg+QS-21: n=43). A total of 92 (73%) patients completed the study. There were 115 screen failures excluded because of clinical and concomitant medication (n=65 [56%]) MMSE out of range (n=33 [28%]), amyloid below the threshold (n=18 [15%]) and miscellaneous. In all 3 treatment groups, the most common reasons for withdrawal were AEs and withdrawal due to patients’ request. The percentages of patients who were withdrawn were higher in the ACC-001 10 µg +QS-21 group (n=14 [33%]) compared with the 3 µg+QS-21 (n=12 [28%]) and placebo group (n=8 [20%]). The mean duration of AD was slightly shorter in the placebo group (1.9 [1.52]) than in the two ACC-001 groups (ACC-001 3 µg+QS-21: 2.8 [2.79]; ACC-001 10 µg+QS-21: 2.4 [2.07]). Otherwise, the overall demographic characteristics were similar across all treatment groups (Table 1).


Table 1. Demographic and Baseline Characteristics (All Randomized Analysis Population)

† Use of baseline treatment of cholinesterase inhibitors or memantine;  ‡  placebo group: n=39; total: n=125; Aβ42, Amyloid beta peptide 42-amino acid isoform;  AD, Alzheimer’s disease; ADAS-Cog/11, Alzheimer’s Disease Assessment Scale – Cognitive subscale (11-item version); APOE*E4, apolipoprotein E E4 allele; BMI, Body mass index; CDR-SB, Clinical Dementia Rating Sum of Boxes; CSF, Cerebrospinal fluid; MMSE, Mini-Mental State Examination; N, Number of patients in each treatment group, which was used as the denominators for percentage; n, number of patients in group with test result for that visit; PET GCA SUVR, Positron emission tomography global cortical average standard uptake value ratio;  p-tau, phosphorylated tau; SD, Standard deviation. 


A large proportion of subjects received concomitant medications for the symptomatic treatment of AD, including cholinesterase inhibitors (placebo: n=37 [95%]; ACC-001 3 µg+QS-21: n=37 [86%]; and ACC-001 10 µg+QS-21: n=34 [79%]) and memantine (placebo: n=22 [56%]; ACC-001 3 µg+QS-21: n=30 [70%]; and ACC-001 10 µg+QS-21: n=23 [54%]).
The mean MMSE total score was 22.0 (range: 17-27), and 85 (68%) patients had a baseline MMSE total score of ≥21 (ie, mild AD). Compared with the placebo group, the ACC-001+QS-21 groups appeared to have slightly higher mean values for ADAS-Cog/11 and CDR-SB total scores and slightly higher PET GCA SUVR, indicating greater impairment. However, baseline conditions were similar across treatment groups based on all randomized and Florbetapir PET analysis population (placebo: n=1.8 [0.32]; ACC-001 3 µg+QS-21: n=1.8 [0.29]; and ACC-001 10 µg+QS-21: n=1.9 [0.26]).

Primary endpoint

The MMRM-based least square mean (LSM) change from baseline in PET GCA SUVR  indicated a reduction in fibrillar amyloid signal in each treatment group at week 104, and the change from baseline was statistically significant for the ACC-001 10 µg +QS-21 group and the pooled active-treatment group. Although none of the differences compared with placebo were statistically significant (3 μg +QS-21 vs. placebo diff=-0.03, p=0.54; 10 μg +QS-21 vs. placebo diff=-0.08, p=0.07, figure 2), the numerical differences appeared to be consistent with a dose response.


Figure 2. Florbetapir PET GCA SUVR (CGM-Ref): Change from Baseline over Time (Mixed Model for Repeated Measures Analysis) (Florbetapir PET Analysis Population)

 GCA , global cortical average; PET, Positron emission tomography; SUVR, standard value uptake ratio ; PET SUVR calculated using cerebellar gray matter as reference region.


The vMRI showed incrementally faster treatment-related decrease in brain volume. The rate of change in whole brain volume as measured by annualized BBSI rate was statistically significant in the 10 μg group at week 102 (3 μg diff =-1.3 mL/year p=0.50; 10 μg diff=-4.2 mL/year p=0.02) compared with placebo, while the change in ventricular volume and left/right hippocampal volume was not statistically significant albeit showing numerically consistent dose-dependent trends (Figure 3). The CSF p-tau changes from baseline in both the active treatment groups were not statistically different from placebo, but were numerically consistent with a dose response (3 μg diff=-3.24 p=0.57; 10 μg diff=-7.02 p=0.19) (Table 2). There were no statistically significant differences from placebo for either of the ACC-001 groups in the changes from baseline to week 76 in CSF T-tau, Aβx-40, or Aβx-42 levels.


Table 2. Summary of Biomarker and Clinical Endpoints

ADAS-Cog/11, Alzheimer’s Disease Assessment Scale – Cognitive subscale (11-item version); BBSI, brain boundary shift integral; CDR-SB, Clinical Dementia Rating Sum of Boxes; CSF, Cerebrospinal fluid; DAD, Disability Assessment for Dementia; DS, Dependence Scale; FAQ, Functional Activities Questionnaire; MMSE, Mini-Mental State Examination; NPI, Neuropsychiatric Inventory; NTB, Neuropsychological Test Battery; p-tau, phosphorylated tau; VBSI, Ventricular Boundary Shift Integral; SD, Standard Deviation; t-tau, total tau.


Figure 3. vMRI Annualized BBSI Rate (mL/Year): Values over Time (Mixed Model for Repeated Measures Analysis) (vMRI Analysis Population)

 BBSI, brain boundary shift integral; VMRI, volumetric brain MRI


Mean serum anti-Aβ antibody titers indicated that immunization with ACC-001 in combination with QS-21 produced both IgG and IgM antibody responses (Figure 4A & 4B). After 1 year of treatment, geometric mean serum anti-Aβ IgG titers were slightly higher in the 10 µg group than in the 3 µg group of ACC-001+QS-21. None of the patients in the placebo group had measurable anti-Aβ IgG titers except for 2 patients each at week 52 and 54 when levels were slightly above the lower limit of quantitation. More than 90% of the patients in each ACC-001 groups were considered serological responders (anti-Aβ IgG titer ≥ 300 U/mL no later than 30 days after administration of the 3rd dose). At week 6, the number of patients with anti-Aβ IgG titer ≥ 300 U/mL were 31 (74%) patients in the ACC-001 3 µg + QS-21 group and 29 (67%) patients in the ACC-001 10 µg + QS-21 group, whereas at week 14, the counts became 36 (90%) in the ACC-001 3 µg+QS-21 group and 38 (95%) in the ACC-001 10 µg+QS-21 group.


Figure 4A. Serum Anti-Aβ IgG ELISA Titer (U/mL): Geometric Mean over Time

 Aβ, beta amyloid protein; ELISA, enzyme-linked immunosorbent assay; IgG, immunoglobulin G


Figure 4B. Serum Anti-Aβ IgM ELISA Titer (U/mL): Geometric Mean over Time (Safety Analysis Population)

Aβ, beta amyloid protein; ELISA, enzyme-linked immunosorbent assay; IgM, immunoglobulin M


Plasma Aβx-40 levels increased with the changes in anti-Aβ IgG titers in the active treatment groups, were much higher with ACC-001 than placebo, and were similar for the two ACC-001 dose levels (Figure 5).


Figure 5. Plasma Aβx-40 (pg/mL): Geometric Mean over Time (Pharmacodynamic Analysis Population)

Aβ, beta amyloid protein


The exploratory efficacy evaluations (cognitive [ADAS Cog/11 score, NTB score, and MMSE score]; global [CDR-SB score]; functional [DAD score and FAQ score]; and behavioral [NPI score]) showed no statistical differences between ACC-001 (either dose) and placebo groups (Table 2). There were no statistically significant or clinically meaningful effects of ACC-001 on health outcome measures.


Table 3. Treatment-Emergent Adverse Events in ≥10% of Patients in any Treatment Group

TEAE, treatment-emergent adverse event.



Overall, ≥93% of patients in each treatment group experienced at least 1 treatment emergent adverse event (TEAE), most of which were mild to moderate in severity. The most common TEAEs (≥10% in pooled active-treated group) and those at a rate that was ≥5% higher than in the placebo group were injection site pain, injection site reaction, urinary tract infection, behavioral and psychiatric symptoms of dementia, depression and fall. Except for injection site reactions, there were no apparent dose-related trends in AE incidence (Table 3). For injection site pain, although the ACC-001 3 µg and 10 µg groups were similar in terms of frequency (ACC-001 3 µg+QS-21: n=12 [28%]; ACC-001 10 µg+ QS-21: n=12 [28%]), the severity of the events was higher in the ACC-001 10 µg+ QS-21 group. Two deaths were reported in ACC-001 10 µg+QS-21 group, which were due to serious TEAEs (sepsis and progression of AD) and not considered to be related to the study medication. The TEAEs leading to early discontinuation of study medication or the study occurred in higher percentages in the ACC-001 groups (ACC-001 3 µg+QS-21: n=4 [9%]; ACC-001 10 µg+QS-21: n=7 [16%]) compared to the placebo group (n=2 [5%]). The only TEAE leading to early discontinuation of the study medication in >1 patient was ARIA-E (n=4). Pooled ACC-001 treatment group showed a 6% (n=5) incidence of asymptomatic ARIA-E, not seen with placebo (ACC-001 3 µg+QS-21: n=2 [5%]; ACC-001 10 µg+QS-21: n=3 [7%]). These events were considered to be mild or moderate in severity in all patients as reported by the investigator. None of these patients received additional doses of investigational product after ARIA-E had been confirmed. Because ARIA-E was confirmed retrospectively in 1 case, a single patient received a single dose of ACC-001 3 µg after the onset of ARIA-E.

The frequency of treatment-emergent serious AEs occurring during study treatment was similar across all the treatment groups. The most common ADR noted was injection site reaction, which showed an increase in the pooled ACC-001 treatment group as compared to placebo (48% vs 8%), the majority of which were mild and transient. No clinically relevant changes were observed in hematology, urinalysis and vital parameters in any of the treatment groups.



This was the first study designed to assess the efficacy of ACC-001+QS-21 vaccine in reducing cerebral fibrillar amyloid burden in patients with mild to moderate AD. The safety profile of the vaccine (antigen with adjuvant) was assessed against saline for this early study to assess local and systemic reactogenicity against saline. Should the vaccine have been developed further the placebo would have included adjuvant to ensure that the appearance of active and placebo and the differential reactogencity did not unblind patients or the staff.   Both doses of ACC-001 (3 and 10 µg) revealed small numerically but not statistically significant, progressive reductions in amyloid-PET signal compared with the placebo, suggestive of target engagement.
The earlier studies in patients with AD had reported an increased 18F-florbetapir PET GCA SUVR over time in the placebo group (15, 16). The numerical decline in 18F-florbetapir PET GCA SUVR in the placebo group reported in this study was not statistically significant or clinically meaningful. This could be attributed to an increase in the amyloid burden in the cerebellar grey reference region resulting in a decrease in the ratio as a true reduction of cortical amyloid in untreated patients with AD does not appear plausible. The results may also represent a regression to the mean, but such a change would be unlikely to fully offset disease progression. Nonetheless, this decline in SUVR in the placebo group raises questions about the interpretability of the results and raises the possibility that the treatment effect of ACC-001+QS-21 may have been underestimated.
The rate of brain atrophy was consistent with typical AD progression. Brain volume measurements showed greater volume loss in the treatment groups, consistent with prior AD immunotherapy trials (16, 17); the etiology of this volume loss is unknown but possible explanations include amyloid removal, fluid shifts, reduced inflammation or neuronal loss (18).
There were larger decreases in CSF p-tau levels from baseline to week 76 in the ACC-001 groups than in the placebo group, consistent with potential downstream effect of ACC-001. The effects were small but showed a trend indicative of dose response, while no reductions in CSF T-tau levels were observed. However, it should be emphasized that T-tau is a marker of general neurodegeneration that can occur in other dementias and neurodegenerative diseases, whereas p-tau is a marker of the hyperphosphorylation state of tau proteins that is more specific to AD. No difference in the CSF T-tau levels were observed between placebo and the anti-amyloid antibody bapineuzumab in a phase 3 study in patients with mild-to-moderate AD (16), whereas a dose-related trend was observed in another phase 2 bapineuzumab study in a similar patient population (19). These differences in results may be due to the smaller sample size and hence need replication with larger sample sets.
Immunization with ACC-001+QS-21 stimulated the production of both IgG and IgM antibody responses, with moderately higher serum anti-Aβ IgG and IgM titers with the 10 µg dose than with the 3 µg dose. A greater peak dose response was reported after the 12 month boost. An earlier phase 2 ACC-001 study conducted in Japan without an adjuvant showed a much greater dose response (8). A dose response was also reported in another study of CAD106 administered without an adjuvant (20). The addition of QS-21 adjuvant has shown to increase the immunogenicity of the ACC-001 so that a significant dose response was not observed in geometric mean titer (8, 11).
In this study, more than 90% of the patients in each ACC-001 group were considered serological responders. The plasma Aβx-40 levels paralleled the changes in anti-Aβ IgG titers in the active treatment groups, were consistently higher and similar in the ACC-001+QS-21 groups than in the placebo group from week 14 through the end of the study. The decrease in titers at week 104 was largely attributed to the fact that the last injection of ACC-001+QS-21 was given 6 months prior to that evaluation. No apparent, consistent correlations between plasma Aβx-40 AUC and 18F-florbetapir PET GCA SUVR were reported for any of the treatment groups.
The demonstration of a prime response with IgM and prolonged elevation of IgM compared to that achieved with standard prophylactic vaccines is unusual but the mechanism or significance is unknown. The epitopes present in the vaccine are also present in endogenous Aβ in patients with AD. The inclusion of CRM in the ACC-001 vaccine construct may have resulted in a novel presentation of these vaccine antigens, resulting in the production of IgM usually observed when an antigen is presented for the first time.
The effect on biomarkers seen with ACC-001+QS-21 is small and may not represent sufficient alteration of the underlying disease processes to translate to clinical efficacy, which is consistent with the study outcomes. These results are consistent with the earlier studies in subjects with mild-moderate AD and with early AD (8, 11, 21). The lack of clear response differences in exploratory clinical efficacy measures in these studies could be due to the small sample size or the short duration of follow up.
No new safety signals for ACC-001 were detected in this study. The TEAEs that occurred were typical for a long-duration vaccine study in elderly patients with mild to moderate AD. The TEAEs were more frequent in the ACC-001 groups than in the placebo group, largely reflecting a difference in the incidence of injection site reactions, which were transient and mostly mild in severity. No meningoencephalitis or autoimmune diseases were reported in this study. The incidence of ARIA-E was low and only occurred in the ACC-001+QS-21 treatment groups, suggesting that the antibody had crossed the blood brain barrier. A relationship to APOE*E4 genotype has been seen in studies of monoclonal anti-amyloid antibodies (22), but among these 5 cases, 4 out of 5 were APOE*E4 non-carriers (1 in the 3 μg group and 3 in the 10 μg group).   The frequency of ARIA-E was too low and the study too small to draw firm  conclusions.



Amyloid PET imaging results did not show significant change in GCA SUVR. There was a numerical dose-response trend suggestive of target engagement. These results were consistent with CSF p-tau levels but not T-tau levels. Brain volume measurementss showed a faster volume loss in the ACC-001+QS-21 treatment groups, consistent with prior AD immunotherapy trials. The rate of brain volume decrease was consistent with typical AD progression. An acceptable safety and tolerability profile was reported. The incidence of ARIA-E was very low and therefore no relationship with risk factors such as APOE*E4 status was possible. All ARIA-E cases were asymptomatic and radiologically mild, despite all patients having detectable amyloid burden at study entry.

Acknowledgments: We acknowledge Rishabh Pandey (SIRO Clinpharm Pvt. Ltd.) for providing writing assistance (funded by Janssen Research & Development, LLC) and Dr. Bradford Challis (Janssen Research & Development, LLC) for additional editorial support for the development of this manuscript. Authors thank Bioclinica for the interpretation of the MRI scans and MNI for the interpretation of the PET scans. Authors also thank the study participants and the investigators for their participation in this study.

Funding: This study was sponsored by Janssen Research & Development, LLC and Pfizer, Inc. Writing assistance for this manuscript was funded by Janssen Research & Development, LLC.

Trial Registration: Identifier: NCT01284387

Declaration of Conflicting Interests: Dr. Honig was an investigator for this study. He received research funding, but no personal compensation for this study. He has received personal compensation from Janssen in his role as a member of the Steering Committee of a different Janssen study. Drs. Ketter, Lu, Di, Novak and Brashear are employees of Janssen Research & Development, LLC and hold stocks in the company. Dr. Liu and Ms. Shadman were at Janssen Research & Development, LLC during the time the study was conducted, but now are affiliated with Prothena and Amgen respectively. Drs. Werth and LePrince Leterme are employees of Pfizer Clinical Sciences. All authors meet ICMJE criteria and all those who fulfilled those criteria are listed as authors. All authors had access to the study data and made the final decision on where to publish these data.

Author Contributions:  Drs. Ketter, Liu, Novak, Brashear and Ms. Shadman participated in the study design, data collection, and conduct of the trial, data analysis plan, interpretation, and review of the manuscript. Dr. Honig was an investigator in this study and involved in patient recruitment, study operation, study management, and data collection, and was the primary scientific reviewer involved in review and finalization of study report. Drs. Werth, LePrince, participated in the data analysis plan, interpretation and review of the manuscript. Dr. Ketter was the study responsible physician and was involved in the execution, data analysis plan, interpretation of data, and review of the manuscript. Dr. Liu was the biomarker lead and had a primary role in the study design and data interpretation. Dr. Brashear was the medical lead for the program and had a primary role in the study design and data interpretation. Dr. Novak was involved in data analysis plan and interpretation. Dr. Lu was the clinical pharmacologist who contributed to the data analysis and review. Dr. Di was the project statistician who made contributions to the study design, data analysis plan, and interpretation of the results.



1.    Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002; 297:353-356.
2.    Armstrong RA. The pathogenesis of Alzheimer’s disease: A reevaluation of the «amyloid cascade hypothesis». Int J Alzheimers Dis 2011; 2011:630865.
3.    Pimplikar S.W. Reassessing the amyloid cascade hypothesis of Alzheimer’s disease. Int J Biochem Cell Biol 2009; 41:1261-1268.
4.    Haass C, Selkoe DJ. Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer’s amyloid beta-peptide. Nat Rev Mol Cell Biol 2007; 8:101- 112.
5.    Thal DR, Griffin WS, de Vos RA, Ghebremedhin E. Cerebral amyloid angiopathy and its relationship to Alzheimer’s disease. Acta Neuropathol 2008; 115:599-609.
6.    Citron M. Alzheimer’s disease: strategies for disease modification. Nat Rev Drug Discov 2010; 9:387-398.
7.    Winblad B, Graf A, Riviere ME, Andreasen N, Ryan JM. Active immunotherapy options for Alzheimer’s disease. Alzheimers Res Ther 2014; 6:7.8.    
8.    Arai H, Suzuki H, Yoshiyama T. Vanutide cridificar and the QS-21 adjuvant in Japanese subjects with mild to moderate Alzheimer’s disease: results from two phase 2 studies. Curr Alzheimer Res 2015;12:242-254.
9.    Gin DY, Slovin SF. Enhancing Immunogenicity of Cancer Vaccines: QS-21 as an Immune Adjuvant. Curr Drug ther 2011; 6:207-212.
10.    Hagen M, Seubert P, Jacobsen S, et al. The Aβ peptide conjugate vaccine, ACC-001, generates N-terminal anti-Aβ antibodies in the absence of Aβ directed T-cell responses [abstract]. Alzheimers Dement 2011;7:S460-S461.
11.    Arai H, Suzuki H, Yoshiyama T, et al. Safety, tolerability, and immunogenicity of an immunotherapeutic vaccine (vanutide cridificar [ACC-001]) and the QS-21 adjuvant in Japanese subjects with mild to moderate Alzheimer’s disease: a phase 2a, multicenter, randomized, adjuvant- and placebo-controlled, multiple-ascending-dose study [abstract P1-338]. Alzheimers Dement 2013;.9:282.
12.    Orgogozo JM, Gilman S, Dartigues JF, et al. Subacute meningoencephalitis in a subset of patients with AD after Aβ42 immunization. Neurology 2003; 61:46-54.
13.    Cribbs DH, Ghochikyan A, Vasilevko V, et al. Adjuvant-dependent modulation of Th1 and Th2 responses to immunization with beta-amyloid. Int Immunol 2003;15:505-514.
14.    Nicoll JA, Wilkinson D, Holmes C, Steart P, Markham H, Weller RO. Neuropathology of human Alzheimer disease after immunization with amyloid-beta peptide: a case report. Nat Med 2003; 9:448-452.
15.    Rinne JO, Brooks DJ, Rossor MN, et al. 11C-PiB PET assessment of change in fibrillar amyloid-beta load in patients with Alzheimer’s disease treated with bapineuzumab: a phase 2, double-blind, placebo-controlled, ascending-dose study. Lancet Neurol 2010; 9: 363-372.
16.    Salloway S, Sperling R, Fox NC, et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N Engl J Med 2014; 370:322-33.
17.    Gilman S, Koller M, Black RS, et al. Clinical effects of Aβ immunization (AN1792) in patients with AD in an interrupted trial. Neurology 2005; 64: 1553-1562.
18.    Lemere CA, Masliah E. Can Alzheimer disease be prevented by amyloid-beta immunotherapy? Nat Rev Neurol 2010; 6:108-119.
19.    Blennow K, Zetterberg H, Rinne JO, et al. Effect of immunotherapy with bapineuzumab on cerebrospinal fluid biomarker levels in patients with mild to moderate Alzheimer disease. Arch Neurol 2012; 69:1002-1010.
20.    Winblad B, Andreasen N, Minthon L, et al. Safety, tolerability, and antibody response of active Abeta immunotherapy with CAD106 in patients with Alzheimer’s disease: randomised, double-blind, placebo-controlled, first-in-human study. Lancet Neurol 2012; 11:597-604.
21.    Christopher H, van Dyck CS, Leterme GLP, et al.Vanutide cridificar (ACC-001) and QS-21 adjuvant in individuals with early Alzheimer’s disease: amyloid imaging positron emission tomography and safety results from a phase 2 study. J Prev Alz Dis 2016; Accepted.
22.    Salloway S, Sperling R, Gilman S, et al. A phase 2 multiple ascending dose trial of bapineuzumab in mild to moderate Alzheimer disease. Neurology 2009; 73:2061-2070.



C.H. van Dyck1, C. Sadowsky2, G. Le Prince Leterme3, K. Booth4, Y. Peng5, K. Marek6, N. Ketter7, E. Liu7, B.T. Wyman8, N. Jackson5, M. Slomkowski4, J. M. Ryan5

1. Yale University School of Medicine, New Haven, CT, USA; 2. Premiere Research Institute, Palm Beach Neurology, Nova Southeastern University, West Palm Beach, FL, USA; 3. Pfizer Global Research and Development, Paris, France, USA; 4. Pfizer Inc, Collegeville, PA, USA; 5. Pfizer Vaccine R&D, Collegeville, PA, USA; 6. Molecular NeuroImaging, New Haven, CT, USA; 7. Janssen Research and Development, San Diego, CA, USA; 8. Pfizer Inc, Groton, CT, USA

Corresponding Author: Christopher H. van Dyck, MD, Professor of Psychiatry, Neurology, and Neurobiology, Director, Alzheimers Disease Research Unit, Yale University School of Medicine, One Church Street, Suite 600, New Haven, CT 06510 USA, Tel: 1-203-764-8100, Fax: 1-203-764-8111, E-mail:

J Prev Alz Dis 2016;3(2):75-84
Published online February 3, 2016,


BACKGROUND: ACC-001 is an investigational therapeutic vaccine designed to elicit antibodies against the N-terminal peptide 1-7 of the amyloid-beta peptide, believed to be important in the pathogenesis of Alzheimer’s disease. 

OBJECTIVES: To evaluate safety, immunogenicity, impact on brain amyloid, and other exploratory endpoints in participants receiving ACC-001. 

DESIGN: Randomized, phase 2, interventional study. Trial registration: ID NCT01227564.

PARTICIPANTS: Individuals with early Alzheimer’s disease (Mini-Mental State Examination scores ≥25, a global Clinical Dementia Rating of 0.5, and evidence of elevated baseline brain amyloid burden).

INTERVENTION: Participants were randomized to ACC-001 3 µg or 10 µg with QS-21 adjuvant (50 µg), or placebo.

MEASUREMENTS: The primary endpoint was change in brain amyloid burden by 18F-florbetapir positron emission tomography in composite cortical standard uptake value ratio.

RESULTS: A total of 63 participants were randomized and 51 completed the study. At week 104, no significant differences were observed in 18F-florbetapir positron emission tomography composite cortical standard uptake value ratio between either ACC-001 dose compared with placebo. In both ACC-001 + QS-21 treatment groups, following the initial immunization, the anti-amyloid-beta geometric mean titers increased after each subsequent vaccination and then declined, with less apparent decline after the later compared with earlier immunizations. The majority of treatment-emergent adverse events in the ACC-001 + QS-21 groups were injection site reactions, which occurred at a greater rate in active treatment groups than in the placebo group. No amyloid-related imaging abnormalities of edema or effusion were reported.

CONCLUSION: No statistically significant differences were observed between groups in the change from baseline brain amyloid burden despite apparently robust systemically measured anti-amyloid-beta antibody response at both dose levels. Insufficient antibody titers, poor quality immune response, short duration of treatment, or small sample size may have resulted in these findings. The safety and tolerability profile was acceptable.

Key words: Alzheimer’s disease, amyloid, early AD, PET, vaccine.  



Alzheimer’s disease (AD) is characterized by senile plaques, with predominant components of 2 amyloid-beta (Aβ42, Aβ40) peptides, which are derived from the amyloid precursor protein (1-3). The “Aβ-cascade” hypothesis of AD pathogenesis addresses early events in the pathological process leading to amyloid deposition, which drives subsequent abnormal tau phosphorylation, neurofibrillary tangle formation, and ultimately neuronal death (1, 2). Immunotherapy directed against Aβ may result in a reduction of brain amyloid burden, and has the potential to alter disease progression (4).
Passive immunotherapies directed against Aβ (eg, bapineuzumab and solanezumab) have failed to show an impact on cognitive and functional endpoints in phase 3 clinical trials of participants with mild to moderate AD (5, 6). Nonetheless, an impact on selected biomarkers (eg, increase in cerebrospinal fluid [CSF] levels of free Aβ40 (5), reduction in CSF phosphorylated-tau [p-tau] in apolipoprotein E [ApoE] ε4 carriers (6)) has been detected in these studies, and brain amyloid reduction was also observed, which is consistent with antibody-target engagement (7). Notably, among ApoE ε4 carriers treated with bapineuzumab, a reduction in cortical fibrillar Aβ accumulation has been detected using carbon-11-labelled Pittsburgh compound B (¹¹C-PiB) and positron emission tomography (PET) (6, 7). The method thus has implications for studying the impact of antibody therapies on Aβ accumulation in the brain and its clinical effect (6, 8). Although bapineuzumab and solanezumab failed to meet their primary endpoints across the full spectrum of mild to moderate AD, subgroup analyses in the solanezumab studies suggest that some outcomes may show treatment effects in mild-stage participants (5) and the effects of bapineuzumab on brain amyloid burden was more pronounced in the subpopulation with mild AD (7). These results have supported the possibility that anti-amyloid therapies may be more effective in participants with mild-stage dementia and, by extension, in those with still earlier, prodromal symptoms who have less extensive brain pathology (4-6, 9). Vanutide cridificar (ACC-001, PF-05236806) is an investigational therapeutic vaccine comprised of multiple copies (approximately 15) of the N-terminal 1-7 amino acids of the Aβ peptide conjugated to a mutant diphtheria toxin carrier protein, CRM197, which is designed to stimulate the systemic production of anti-Aβ antibodies against the N-terminus 1-7 of Aβ. ACC-001 elicited anti-Aβ titers in non-human primates without evidence of a cytotoxic T-cell response when administered with or without the adjuvant Quillaja saponaria (QS-21). QS-21 is a highly potent stimulator of antigen-specific immune responses, and higher antibody titers are elicited when the ACC-001 vaccine is combined with QS-21 in individuals with mild to moderate AD (10-12). The present study evaluated the safety of ACC-001 with QS-21 adjuvant and the impact of treatment compared with placebo to reduce brain amyloid burden, as measured by 18F-florbetapir PET imaging in individuals with early AD defined by cognitive criteria and who had evidence of pathological brain amyloid burden. Additional exploratory endpoints included immunogenicity, CSF biomarkers, brain volume, and cognitive and functional efficacy assessments.


Study Design and Conduct

This was a phase 2, multicenter, 24-month, randomized, third-party unblinded, placebo-controlled, parallel-group brain amyloid imaging (PET), and safety trial of ACC-001 + QS-21 adjuvant in participants with early AD ( ID: NCT01227564) conducted in the United States. The study was conducted in compliance with the ethical principles according to the Declaration of Helsinki and in compliance with all International Conference on Harmonisation Good Clinical Practice guidelines, and the study protocol was approved by the institutional review board(s) and/or independent ethics committee(s) for each investigational site. Written informed consent was obtained before performance of any study-related procedures. The study safety results were monitored by an external data monitoring committee that was independent of the study investigators, sponsor, and regulatory agency personnel. The initial target sample size of 108 participants was reduced to approximately 60 participants following a protocol amendment based on preliminary negative cognitive and biomarker results of other ACC-001 trials.

Selection of Participants

Participants were enrolled if they were aged 50 to 80 years inclusive, with early AD defined as meeting the following main criteria: concerns about cognitive changes but not demented, with a baseline Mini-Mental State Examination (MMSE) score ≥25, global Clinical Dementia Rating (CDR) of 0.5, with a memory box score of 0.5, and an elevated baseline brain amyloid burden (18F-florbetapir-PET with composite cortical standard uptake value ratio [SUVr] ≥1.45). Exclusion criteria included presence of significant neurologic disease other than early AD that could affect cognition; history of or screening visit brain magnetic resonance imaging (MRI) scan indicative of any other significant abnormality, including but not limited to, multiple microhemorrhages (2 or more), history or evidence of a single prior hemorrhage >1 cm3, multiple lacunar infarcts (2 or more) or evidence of a single prior infarct >1 cm3, evidence of a cerebral contusion, encephalomalacia, aneurysms, vascular malformations, subdural hematoma, or space-occupying lesions.

Randomization and Treatment

Participants were randomized 1:1:1 to receive ACC-001 3 µg + QS-21 50 µg, ACC-001 10 µg + QS-21 50 µg, or phosphate-buffered saline as placebo, stratified by ApoE ε4 carrier status. All parties were blinded to treatment allocation with the exception of the dispensing pharmacists, who did not participate in any evaluations. Intramuscular injections were administered at day 1, and week 4, 12, 26, 52, and 78, and a blood draw to assess immunogenicity was performed just prior to, and 2 and 8 weeks after each injection. Participants were followed for an additional 6 months after the last injection. Treatment and assessment schedules are shown in Supplemental Figure 1.


The primary endpoints were change from baseline to week 104 in brain amyloid burden by 18F-florbetapir PET as measured by composite cortical SUVrs, the average of 6 regions of interest (ROIs): Frontal, parietal, lateral temporal, anterior and posterior cingulate, and occipital cortices, and safety and tolerability measures. The targeted dose of florbetapir was 370 ± 37 MBq (10 mCi ± 10%); lower limit: 185 MBq (5 mCi), upper limit: 407 MBq (11 mCi). Quantitative PET image analysis was centrally performed at the Imaging Core Lab by experts who were blinded to the participants’ clinical diagnoses. An automated segmentation procedure was applied to the 3D T1-weighted MRI to remove pixels of high intensity, such as white matter, and/or pixels of low intensity, such as CSF. An individualized MR-based definition of the gray matter was provided. The segmented MRI scan and each PET image were then co-registered using a standard mutual information algorithm and spatially normalized. A modified automated anatomical labeling template was subsequently applied for standardized, regional brain volume of interest sampling of count densities. All analyses were completed in a customized version of PMOD (PMOD Technologies, Zurich, Switzerland). SUVrs were calculated using the cerebellar gray matter as the reference region. The composite SUVr was the average of the SUVrs over the ROIs described above. 18F-florbetapir PET scans were obtained at baseline and at week 52, 78, and 104. PET scan management and quantitative analyses were performed centrally.
Safety evaluations included treatment-emergent adverse events (TEAEs), serious AEs (SAEs), AEs of special circumstance, a systematic assessment of injection site reactions (ISRs), clinical brain MRI, laboratory assessments, physical and neurological exams, electrocardiograms, and suicidality assessments. All clinical MRI scans were centrally read. AEs of special circumstance included amyloid-related imaging abnormalities of edema or effusion (ARIA-E), intracranial and cerebral hemorrhage, vasculitis, and immune-mediated events.
Exploratory biomarker and clinical endpoints included change from baseline in the CSF p-tau and total tau (t-tau); volumetric MRI (vMRI) using the boundary shift integral (BSI) method; and in clinical assessments of CDR-Sum of Boxes (CDR-SB), Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog 13 items) total score, Neuropsychological Test Battery (NTB) composite score, Functional Activities Questionnaire (FAQ) total score, and MMSE total score. BSI measures were obtained by subtracting the area under the intensity profile across a boundary in the repeat scans from the initial scan (baseline) in registered MRI scan pairs for brain boundary (BBSI), ventricular boundary (VBSI), and hippocampal boundary (HBSI). Exploratory immunogenicity of ACC-001 + QS-21 was assessed by anti-Aβ antibody titers on enzyme-linked immunosorbent assay (ELISA). A participant was defined as a serological immune responder if the anti-Aβ IgG titer was ≥300 U/ml, which is 3x the lower limit of detection for the assay, at a time point not later than 30 days from administration of the third injection. CSF t-tau and tau phosphorylated at threonine 181 (p-tau) was measured using a sandwich ELISA method (Innogenetics, Ghent, Belgium). Plasma Aβx-40 samples and CSF Aβx-40 and Aβx-42 concentrations were determined using validated ELISA methodologies developed in compliance with Janssen AI Bioanalytical Development operating procedures and documented in method validation reports on Meso Scale Discovery (Meso Scale Diagnostics, Rockville, MD) technology and plate reader.

Study Size and Statistical Analysis

Assuming that about 30% of the 108 randomized participants in the original planned analysis would not complete the study, with a 2-sided t-test at an alpha level of 0.05, the study would have at least 80% power to detect a true treatment difference of 0.130 between each dose of ACC-001 and control in the mean change from baseline at week 104 in amyloid burden as measured by 18F-florbetapir PET. The decision to reduce sample size to 60 participants increased the minimal detectable true between-group difference to 0.169 with 80% power. The sample size calculation also assumes a standard deviation (SD) of 0.16 based on an internal phase 2 study of bapineuzumab using PiB PET data. Descriptive summary statistics were provided for safety results based on the safety population, which consisted of all participants with documented use of at least 1 dose of study medication. Mixed-effects models for repeated measures (MMRM) were used to analyze the primary endpoint, exploratory longitudinal biomarkers, and clinical endpoints based on a full analysis set population, which consisted of all randomized participants who had at least 1 dose of study medication and at least 1 postbaseline efficacy assessment. The MMRM model for each endpoint included the corresponding baseline assessment and ApoE ε4 status as covariates, and visit, treatment, and visit by treatment interaction as fixed effects, with variance-covariance structure set to unstructured. For CSF biomarkers, as only 1 post-baseline sample was collected at week 80 or 104, a similar analysis of covariance (ANCOVA) model was used to analyze each CSF parameter, with corresponding baseline assessment included as a covariate in the model. Results from individual ACC-001 dose groups were compared with those of the placebo group. Unadjusted nominal P-values from the model-based analyses are reported in the results section. Descriptive summary statistics are provided for immunogenicity results.


Participant Populations and Disposition

A total of 63 participants were randomized and received study treatment; 56 (88.9%) completed study treatment (week 78) and 51 (81.0%) completed the study (week 104). The most common reason for discontinuation of treatment (5 participants, 7.9%) and the study (11 participants, 17.5%) was “no longer willing to participate in the study.” Subject disposition by treatment group is shown in Figure 1. A total of 61 (96.8%) participants were included in the PET amyloid SUVr analysis and all 63 randomized participants were included in the safety analysis.

Demographics and Baseline Characteristics

Among participants enrolled in the study, all were white, 33 (52.4%) were female, and mean age was 68.3 years. Cognitive symptoms were present for a mean of 4.4 years at the time of randomization, and 39 (61.9%) were taking cognitive enhancing concomitant medication. Mean baseline MMSE score for all randomized participants was 27.6 (SD 1.58). Overall, 41 (65.1%) participants were ApoE ε4 carriers; 31 (49.2%) were heterozygous and 10 (15.9%) were homozygous for the ApoE ε4 allele. Mean baseline 18F-florbetapir PET cortical composite SUVr was 1.759 (SD 0.2354). Demographic and clinical information by treatment group is shown in Table 1.

Table 1. Participant demographic and clinical information (all randomized participants)

N, number of participants in each treatment group, which was used as the denominator for percentages; n, number of participants in group with test result for that visit.


Figure 1. Participant disposition

*Participant moved to another town and was no longer able to participate.


Biomarker and Clinical Efficacy Evaluations

Primary Endpoint: Brain Fibrillar Aβ

No statistically significant differences were observed in the change from baseline in 18F-florbetapir PET cortical composite SUVr between each ACC-001 dose group and placebo at week 104 (Figure 2). The 2-sided nominal P-values for comparisons between each ACC-001 dose group and placebo from the MMRM analysis were 0.3826 and 0.3912, respectively, at week 104. Subgroup analyses by ApoE ε4 status and cognitive enhancement concomitant medication status did not show any differences between treatments within each subgroup (not shown). As only 1 participant was not classified as a serologic antibody responder (see below) in the ACC-001 + QS-21 groups, subgroup analysis with serologic responders only would yield similar results as the one with all participants included.


Figure 2. Brain fibrillar Aβ measured by PET composite SUVr adjusted mean change from baseline with 95% CI (MMRM analysis, full analysis set).


The LSM change from baseline in the composite SUVrs and the associated 95% CI were obtained from MMRM that includes baseline assessment and ApoE ε4 status as covariates, and visit, treatment, and visit by treatment interaction as fixed effects, with variance-covariance structure set to unstructured. LSM difference between ACC-001 3 μg + QS-21 and placebo, and ACC-001 10 μg + QS-21 and placebo did not show any statistically significant differences; 2-sided nominal P-values (unadjusted for multiplicity) were 0.3826 and 0.3912, respectively, at week 104.


Exploratory Endpoints

Regional Brain Fibrillar Aβ Burden

At week 104, there were no statistically significant differences of the least squares mean (LSM) changes from baseline in any of the regional SUVrs (frontal, parietal, lateral temporal, anterior and posterior cingulate, and occipital cortices) between ACC-001 3 μg + QS-21 and placebo, or between ACC-001 10 μg + QS-21 and placebo (Supplemental Table 1).


For the parameter CSF p-tau, none of the LSM differences in the change from baseline to week 80/104 between ACC-001 3 μg + QS-21 or ACC-001 10 μg + QS-21 and placebo were statistically significant (Table 2). For CSF t-tau, the LSM difference in the change from baseline to week 80/104 was statistically significant for the comparison between the higher active dose, ACC-001 10 μg + QS-21, and placebo groups (unadjusted nominal P=0.0327), but not between ACC-001 3 μg + QS-21 and placebo groups. None of the LSM differences between individual dose groups of ACC-001 + QS-21 and the placebo group were statistically significant for any analyzed vMRI brain volumes (BBSI, VBSI, HBSI); 2-sided unadjusted nominal P-values for brain volumes are shown in Table 2.


Table 2. Model-based summary of change from baseline results in exploratory biomarker and clinical endpoints (full analysis set)

*All differences reflect comparison with placebo (ACC-001 – placebo). The LSM difference, 95% CI, and P-values for CSF p-tau and t-tau use ANCOVA models that include the associated baseline assessment, ApoE ε4 status, and treatment. The LSM differences, 95% CIs, and P-values for brain volumes and clinical assessments use MMRM analyses that include baseline assessment and ApoE ε4 status as covariates, and visit, treatment, and visit by treatment interaction as fixed effects, with variance-covariance structure set to unstructured; †For participants who had a CSF already drawn at week 80, no CSF sample was drawn at week 104, nor at the early termination visit. For participants who early terminated the study before week 80, CSF was drawn at the early termination visit.

Clinical Assessments

No statistically significant differences were observed for any of the LSM differences in the change from baseline to week 104 between individual ACC-001 + QS-21 dose groups and placebo for any of the clinical assessments, including CDR-SB, ADAS-cog, NTB, FAQ, and MMSE (Table 2).

Plasma Aβx-40

The time course of the mean change from baseline of plasma Aβx-40 is shown in Figure 3. The plasma Aβ increased for both ACC-001 + QS-21 dose levels, until week 52 when a plateau was reached. A numerical dose-dependent trend was observed across the study period. LSM difference between ACC-001 10 µg + QS-21 and placebo was statistically significant beginning at the week 26 assessment, and remained so through week 104.


Figure 3. Plasma Aβx-40 adjusted mean percentage change from baseline with 95% CI (MMRM analysis, full analysis set)

LSM difference and 95% CI were obtained from MMRM analysis that includes baseline assessment and ApoE ε4 status as covariates, and visit, treatment, and visit by treatment interaction as fixed effects, with variance-covariance structure set to unstructured (2-sided nominal P-value unadjusted for multiplicity): ACC-001 3 μg + QS-21, difference compared with placebo at week 104 = 27.07%, 2-sided P=0.0598; ACC-001 10 μg + QS-21, difference compared with placebo at week 104 = 43.12%, 2-sided P=0.0077.


For all treatment groups, baseline anti-Aβ IgG titers were below the lower limit of quantification of the assay (Figure 4). After the first immunization, geometric mean titers (GMTs) increased after each subsequent immunization in both ACC-001 + QS-21 groups followed by a decline over time, with a trend toward less decline over time after the last 2 immunizations compared with the preceding immunizations and no clear evidence of a dose response. Overall, 97.6% of participants in the ACC-001 + QS-21 groups (100% in the ACC-001 3 µg + QS-21 group and 95% in the ACC-001 10 µg + QS-21 group) and none in the placebo group met the criteria for serologic response.


Figure 4. Geometric mean anti-Aβ IgG titers by study week (full analysis set)

Arrows indicate the time points of immunization. Lower limit of quantification (LLOQ) determined for this assay was 100 U/mL. For any anti-Aβ IgG antibody level that was below the LLOQ (100 U/mL), the lower limit of detection defined as 0.5*LLOQ was assigned.


Nearly all participants across treatment groups reported an AE during the study, and all but 2 of these were TEAEs (Table 3). Treatment with ACC-001 + QS-21 was not associated with a higher rate of SAEs compared with placebo. No ARIA-E, intracranial or cerebral hemorrhage, vasculitis, or immune-mediated events were reported during the study. There were no AEs leading to death during the study.


Table 3. Overview of adverse events (safety population)

*TEAEs of special circumstance: ARIA-E, intracranial hemorrhage (including intraparenchymal, subdural, epidural, intraventricular, and subarachnoid bleeding), vasculitis, and the following immune-mediated events following an injection: Anaphylaxis, angioedema, urticaria, and clinical syndrome diagnostic of serum sickness.

Table 4. TEAEs in ≥5% of participants (safety population)

The majority of TEAEs in the ACC-001 + QS-21 groups were ISRs, which occurred at a greater rate in these groups than in the placebo group (Table 4). Most ISRs were mild or moderate in severity, less than 7 days in duration, and occurred mainly after the first or second injection. The most common ISR symptoms were pain, swelling, and redness (68.4%, 36.8%, and 34.2%, respectively).


Studies of passive anti-Aβ immunotherapy to reduce brain amyloid have been conducted in mild to moderate AD dementia, and have not thus far demonstrated a significant benefit of therapy on cognitive and functional assessments. These studies were in later-stage populations who meet the criteria for dementia, but subgroup analyses from 1 of these studies have suggested that some outcomes may show treatment effects in a mild-stage AD population (5), and this has led to additional larger studies in mild-stage AD dementia and in preclinical populations.
The use of PET to examine brain amyloid burden as a biomarker outcome in AD studies has shown promise as a method of evaluating the effects of treatment in smaller phase 2 studies, which have not been replicated yet in larger phase 3 programs. Two small, early-phase studies of bapineuzumab and gantenerumab both suggested brain amyloid reductions as measured by ¹¹C-PiB PET in conjunction with active treatment (8, 13). However, in larger phase 3 studies, while among ApoE ε4 carriers, bapineuzumab treatment was associated with significantly less amyloid accumulation than placebo treatment, there was no amyloid reduction from baseline and no treatment effect was observed in ApoE ε4 noncarriers (6). Upon further analyses, a significant effect of bapineuzumab on brain amyloid was observed in the subpopulation with mild AD, in both the ApoE ε4 carriers and non-carriers (7). In addition, phase 3 studies with solanezumab failed to show treatment effects on amyloid accumulation or reduction as measured by 18F-florbetapir PET (5). The gantenerumab phase 3 trial in mild AD, which includes a PET substudy, is ongoing ( ID: NCT02051608), although the program in early (pre-dementia) AD has been discontinued, (Roche Media Release, 19 December 2014).
In the current study of ACC-001 + QS-21 in early AD, brain amyloid imaging using 18F-florbetapir PET did not show a significant change in cortical composite SUVr, or in any of the exploratory assessments by region; however, numerical trends suggest an increase with placebo and decrease with ACC-001 + QS-21. The reduction in CSF t-tau with the higher dose level of ACC-001 + QS-21 suggests a downstream effect on a relevant pathological marker. Anti-Aβ antibodies were elicited by both doses of ACC-001 + QS-21 to a similar degree and were sustained for the 6-month follow-up period following the last administration. The increase observed in plasma Aβ1-40 indicates peripheral target engagement.
There are several possible explanations for the negative PET findings. Active immunotherapy requires the recipient’s own immune system to mount a response, which is innately variable between individuals and may be inadequate with age-related decline in immune responses (14). The systemic humoral immune response may not have been sufficiently high to allow an adequate quantity of antibody to cross the blood-brain barrier (BBB). While monoclonal antibodies have been shown to cross the BBB (~0.3% of serum levels for bapineuzumab and total IgG (15, 16)), the measurement of CSF levels of the antibody titers elicited by ACC-001 was not possible due to the lack of a validated assay. In addition, because of the delay associated with mounting an immune response after vaccination, the duration of treatment may not have been long enough to observe a biomarker effect. Anti-Aβ antibody titers were elicited with ACC-001 + QS-21, but the titer required over a given period for a clinical effect or for central target engagement is unknown. The quality and functional nature of the elicited antibodies were also not evaluated, and may not have been of sufficient potency to robustly bind and effect removal of brain amyloid; high titers may not necessarily include high-quality antibodies, which is a risk inherent to therapeutic vaccine development. Since ACC-001 elicits a polyclonal antibody response, replication of the results seen with passive immunotherapy with monoclonal antibodies, which may be given at higher doses, is not guaranteed and may account for differences in central target engagement. Multiple ascending-dose studies showed high rates of immune response to ACC-001 + QS-21, but did not include an evaluation of brain amyloid load (12). Other epitopes have been selected for vaccines targeted at amyloid reduction and have shown an immune response, but not evidence of efficacy in phase 1/2 studies (17). It remains to be seen whether these results will extend to other vaccines. Amyloid PET imaging is a semi-quantitative method (18) and several variables may greatly impact the measurement methodology. Use of a white matter reference region has recently been shown to reduce variability and increase the power to detect longitudinal changes in Aβ PET SUVrs (19, 20). Use of a gray matter reference region was prespecified in this study at the time of its design, and while it is not clear whether the results would have been affected by the use of a white matter reference region, evolving improvements in methods can be expected during the course of any study.
The study population was younger (5, 6) than those previously studied (mean age 67-70 years), with earlier-stage AD (MMSE score 27.6 ± 1.58 [SD] at baseline, and excluding CDR >0.5, since MMSE score cutoffs are not absolute and definitive for defining AD stage). Increased penetrance of the BBB with disease might facilitate greater antibody access to the CNS, and consequently greater central target engagement; however, this has not been well studied in AD and results have been variable (21, 22). This could in part also explain why no ARIA-E were observed in the current study (see below); however, BBB permeability was not evaluated. The study was initially designed to include 108 participants, which was already small, and the protocol was amended with a reduced sample size; the smaller final sample size of 63 participants could have contributed to the negative results, although variability around the point estimates of change from baseline (SD ranged from 0.1413 to 0.1949 across groups) was consistent with sample size calculation assumptions. In addition, a total of 42 participants completed the study, had the final assessment, and were included in the analysis of the primary endpoint, which is consistent with the anticipated 30% discontinuation rate used in the statistical power calculation for the minimum detectable true between-group difference of 0.169. However, the observed between-group differences in this study were much smaller than the minimum detectable difference, even based on the original sample size (minimum detectable difference of 0.130 for 108 participants). These findings suggest that future therapeutic vaccine studies in early AD as defined here should be much larger to detect a smaller difference at 2 years, or be of longer duration to test for increasing separation between groups over time.
No greater brain volume loss was observed in the treatment groups, which is inconsistent with prior immunotherapy trials in mild to moderate AD and may reflect the earlier stage of AD in this population. The lack of changes in the assessed clinical scales is consistent with the biomarker results in this early AD clinical trial, which should be put into perspective in the context of small sample size, the fact that the study was not powered for clinical assessments, and the inherent variability of a multicenter study. ISR was the most common TEAE, which is consistent with other studies of ACC-001 (12, 23). ISRs were largely manageable, without clinical consequence, and of a severity no higher than that observed previously.
ARIA-E have been noted with variable frequency with passive immunotherapy in mild to moderate AD depending on the monoclonal antibody used (6, 24, 25). Bapineuzumab, which is N-terminal specific, was associated with rates of ARIA-E between 5.6% in ApoE ε4 non-carriers and 21.2% in ApoE ε4 carriers in a retrospective central MRI read (6). ACC-001 + QS-21 had an acceptable safety and tolerability profile in this early AD population, without reports of ARIA-E. In the parallel biomarker study ( ID: NCT01284387) in mild to moderate AD, where MRI scans were centrally read, the rate of ARIA-E was approximately 5%, suggesting that antibodies generated by the vaccine entered the CNS. Even if ARIA-E were underestimated in the multiple ascending dose studies, where they were observed in ≤1.1% of participants (12, 23), they should have been seen in this study, where MRI scans were also centrally read. The lack of ARIA-E in this early population and this small study does not necessarily reflect a lack of target engagement but may be due to chance, underestimated because of small sample size, exclusion criteria employed in the study, and potentially a less permeable BBB in this population. It is also possible that the polyclonal antibody response to ACC-001 is qualitatively different than delivering a passive monoclonal antibody. A phase 1/2 study of CAD106, an investigational vaccine targeting Aβ1-6, also showed no evidence of ARIA-E in a mild AD population (17). Further study will be needed to evaluate this issue.


This study of ACC-001 + QS-21 treatment did not meet the primary objective of reduction in brain amyloid cortical composite SUVr, despite eliciting a systemic immune response and evidence for peripheral target engagement. Similarly, ARIA-E was not observed in this study. The antibody levels may not have been high enough or lacking a sufficient functional potency to remove sufficient amounts of amyloid over the given period of treatment to be detectable by amyloid PET scans. Failure to adequately penetrate the BBB, resulting in insufficient anti-Aβ antibody, polyclonal antibody response, insufficient duration to produce a detectable central target engagement, and small sample size may also be factors impacting the results. Additional research is needed to examine the quality of antibodies elicited by therapeutic vaccines for early AD, the optimal treatment period, and methods to directly determine central target engagement before definitive conclusions can be drawn about this therapeutic approach in the early AD population.

Acknowledgments: These studies were sponsored by Pfizer Inc and Janssen Alzheimer Immunotherapy, R&D, LLC. Editorial writing support was provided by Marsha Scott, PhD, at Phase Five Communications, and was funded by Pfizer Inc.

Funding: C.H. van Dyck: Data collection/interpretation; drafting/revising of manuscript; final review/approval of manuscript; C. Sadowsky: Data collection/analysis/interpretation; drafting/revising of manuscript; final review/approval of manuscript; G. Le Prince Leterme: Data collection/interpretation; drafting/revising of manuscript; final review/approval of manuscript; K. Booth: Data collection/interpretation; drafting/revising of manuscript; final review/approval of manuscript; Y. Peng: Study design; data collection/analysis/interpretation; drafting/revising of manuscript; final review/approval of manuscript; K. Marek: Data collection/interpretation; drafting/revising of manuscript; final review/approval of manuscript; N. Ketter: Reviewed interim and final analyses; drafting/revising of manuscript; final review/approval of manuscript; E. Liu: Study design; interim and final analyses review; data interpretation; drafting/revising of manuscript; final review/approval of manuscript; B.T. Wyman: Study design; data collection/analysis/interpretation; drafting/revising of manuscript; final review/approval of manuscript; N. Jackson: Study design; data interpretation; drafting/revising of manuscript; final review/approval of manuscript; M. Slomkowski: Study design; data collection; drafting/revising of manuscript; final review/approval of manuscript; J.M. Ryan: Study design; data collection/interpretation; drafting/revising of manuscript; final review/approval of manuscript.

Conflicts of interest: C.H. van Dyck reports grants from Pfizer Inc during the conduct of the study. He also reports grants from Baxter Pharmaceuticals, Biogen Idec, Eisai, Inc., Eli Lilly and Company, Forum Pharmaceuticals, Genentech, Inc., Merck & Co. Inc., Pfizer Inc, TauRx, Toyama Chemical Co., Ltd., and Wyeth Research; grants and personal fees from Bristol-Myers Squibb Company, Janssen, and Roche Pharmaceuticals; and personal fees from AbbVie, outside the submitted work. C. Sadowsky reports financial relationships for advisory boards with Alzheon Pharmaceutical and Cognoptix Pharmaceutical and for speaker bureaus with Novartis Pharmaceuticals Corporation, Lilly, Pam Lab, and Forest. G. Le Prince Leterme is an employee of Pfizer Inc. K. Booth is an employee of Pfizer Inc, has stock options, and owns company stock. Y. Peng is an employee of Pfizer Inc. K. Marek reports personal fees from Molecular Neuroimaging, and owns company stock. He is a consultant for Bristol-Myers Squibb Company, GE Healthcare, Lilly, Lysosomal Therapeutic, Inc., Merck & Co. Inc., Pfizer Inc, Piramal, Prothena, Oxford Biomedica, and Roche Pharmaceuticals. N. Ketter is an employee of Janssen R&D. E. Liu was an employee of Janssen R&D at the time the study was conducted and manuscript developed. B.T. Wyman was an employee of Pfizer Inc at the time the study was conducted and owns company stock. N. Jackson was an employee of Pfizer Inc at the time the study was conducted. M. Slomkowski was an employee of Pfizer Inc at the time the study was conducted. J.M. Ryan was an employee of Pfizer Inc at the time the study was conducted.


1.    Armstrong RA. The pathogenesis of Alzheimer’s disease: a reevaluation of the «amyloid cascade hypothesis». Int J Alzheimers Dis 2011;2011:630865.
2.    Pimplikar SW. Reassessing the amyloid cascade hypothesis of Alzheimer’s disease. Int J Biochem Cell Biol 2009;41:1261-1268.
3.    Gralle M, Ferreira ST. Structure and functions of the human amyloid precursor protein: the whole is more than the sum of its parts. Prog Neurobiol 2007;82:11-32.
4.    Winblad B, Graf A, Riviere ME, Andreasen N, Ryan JM. Active immunotherapy options for Alzheimer’s disease. Alzheimers Res Ther 2014;6:7.
5.    Doody RS, Thomas RG, Farlow M, et al. Phase 3 trials of solanezumab for mild-to-moderate Alzheimer’s disease. N Engl J Med 2014;370:311-321.
6.    Salloway S, Sperling R, Fox NC, et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N Engl J Med 2014;370:322-333.
7.    Liu E, Schmidt ME, Margolin R, et al. Amyloid-β 11C-PiB-PET imaging results from 2 randomized bapineuzumab phase 3 AD trials. Neurology 2015;85:692-700.
8.    Rinne JO, Brooks DJ, Rossor MN, et al. 11C-PiB PET assessment of change in fibrillar amyloid-beta load in patients with Alzheimer’s disease treated with bapineuzumab: a phase 2, double-blind, placebo-controlled, ascending-dose study. Lancet Neurol 2010;9:363-372.
9.    Moreth J, Mavoungou C, Schindowski K. Passive anti-amyloid immunotherapy in Alzheimer’s disease: what are the most promising targets? Immun Ageing 2013;10:18.
10.    Hagen M, Seubert P, Jacobsen S, et al. The Aβ peptide conjugate vaccine, acc-001, generates n-terminal anti- Aβ antibodies in the absence of Aβ directed t-cell responses. Alzheimers Dement 2011;7:S460–S461.
11.    Gin DY, Slovin SF. Enhancing immunogenicity of cancer vaccines: QS-21 as an immune adjuvant. Curr Drug Ther 2011;6:207-212.
12.    Arai H, Yoshiyama T, Suzuki H. Vanutide cridificar and the QS-21 adjuvant in Japanese subjects with mild to moderate Alzheimer’s disease: results from two Phase 2 studies. Curr Alzheimer Res 2015;12:242-254.
13.    Ostrowitzki S, Deptula D, Thurfjell L, et al. Mechanism of amyloid removal in patients with Alzheimer disease treated with gantenerumab. Arch Neurol 2012;69:198-207.
14.    Grubeck-Loebenstein B, Della Bella S, Iorio AM, Michel JP, Pawelec G, Solana R. Immunosenescence and vaccine failure in the elderly. Aging Clin Exp Res 2009;21:201-209.
15.    Blennow K, Zetterberg H, Rinne JO, et al; AAB-001 201/202 Investigators. Effect of immunotherapy with bapineuzumab on cerebrospinal fluid biomarker levels in patients with mild to moderate Alzheimer disease. Arch Neurol 2012;69:1002-1010.
16.    Blennow K, Fredman P, Wallin A, et al. Protein analysis in cerebrospinal fluid. II. Reference values derived from healthy individuals 18-88 years of age. Eur Neurol 1993;33:129-133.
17.    Farlow MR, Andreasen N, Riviere ME, et al. Long-term treatment with active Aβ immunotherapy with CAD106 in mild Alzheimer’s disease. Alzheimers Res Ther 2015;7:1-13.
18.    Schmidt ME, Chiao P, Klein G, et al. The influence of biological and technical factors on quantitative analysis of amyloid PET: Points to consider and recommendations for controlling variability in longitudinal data. Alzheimers Dement 2015;11:1050-1068.
19.    Chen K, Roontiva A, Thiyyagura P, et al. Improved power for characterizing longitudinal amyloid-β PET changes and evaluating amyloid-modifying treatments with a cerebral white matter reference region. J Nucl Med 2015;56:560-566.
20.    Brendel M, Högenauer M, Delker A, et al; Alzheimer’s Disease Neuroimaging Initiative. Improved longitudinal [(18)F]-AV45 amyloid PET by white matter reference and VOI-based partial volume effect correction. Neuroimage 2015;108:450-459.
21.    Marques F, Sousa JC, Sousa N, Palha JA. Blood-brain-barriers in aging and in Alzheimer’s disease. Mol Neurodegener 2013;8:38.
22.    Montagne A, Barnes SR, Sweeney MD, et al. Blood-brain barrier breakdown in the aging human hippocampus. Neuron 2015;85:296-302.
23.    Pasquier F, Sadowsky C, Holstein A, et al. Two phase 2 multiple ascending–dose studies of vanutide cridificar (ACC-001) and QS-21 adjuvant in mild-to-moderate Alzheimer’s disease. J Alzheimers Dis. Submitted.
24.    Sperling RA, Jack CR, Black SE, et al. Amyloid related imaging abnormalities (ARIA) in amyloid modifying therapeutic trials: recommendations from the Alzheimer’s Association Research Roundtable Workgroup. Alzheimers Dement 2011;7:367-385.
25.    Sperling R, Salloway S, Brooks DJ, et al. Amyloid-related imaging abnormalities in patients with Alzheimer’s disease treated with bapineuzumab: a retrospective analysis. Lancet Neurol 2012;11:241-249.

Supplemental Figure 1. Study design and assessment timing


Supplemental Table 1. Regional brain fibrillar Aβ burden (PET SUVrs*) change from baseline based on MMRM at week 104 and difference compared with placebo (full analysis set)



S. Villeneuve, W.J. Jagust


Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley CA, USA. 

Corresponding Author: Sylvia Villeneuve, Helen Wills Neuroscience Institute, 132 Barker Hall, MC# 3190, University of California, Berkeley, CA 94720 USA,
E-mail:, Telephone: 510-643-6616, Fax: 510-642-3192

J Prev Alz Dis 2015;2(1):64-70
Published online Januay 29, 2015,


Vascular risk factors (e.g. hypertension, dyslipidemia and diabetes) are well known risk factors for Alzheimer’ disease. These vascular risk factors lead to vascular brain injuries, which also increase the likelihood of dementia. The advent of amyloid PET imaging has helped establish that vascular risk factors also lead to Alzheimer’s disease via pathways that are independent from vascular brain injuries, at least, when vascular brain injuries are measured as white matter lesions and infarcts. While vascular brain injuries (white matter lesions and infarcts) do not seem to influence amyloid pathology, some evidence from amyloid imaging suggests that increased vascular risk is related to increased amyloid burden. Furthermore, while vascular brain injuries and amyloid have an additive and independent impact on brain integrity, vascular risk factors might potentiate the impact of amyloid on cortical thickness on brain regions vulnerable to Alzheimer’s disease. New research should further explore and confirm, or refute, possible interactions between amyloid and vascular risk factors on brain integrity and cognition. Neuroimaging tools used to assess vascular brain integrity should also be expanded. Measuring cortical blood flow or damage to the capillary system might, for instance, give insight about how vascular risk factors can be associated to amyloid burden and impact. These findings also stress the need for monitoring vascular risk factors in midlife as a strategy for Alzheimer’s disease prevention. .

Key words: Alzheimer’ disease, amyloid, vascular brain injuries, vascular risk factors, treatment.


How do vascular factors, such as vascular diseases or vascular brain injuries (VBI, also often called cerebrovascular disease), increase the risk of Alzheimer disease (AD)? Are Alzheimer and vascular pathologies independent diseases, or does the presence of one pathology influence the presence and the impact of the other? Can vascular risk factors be good preventive targets for AD, and if so, why and when should they be targeted? Although the answers to these questions remain unclear, they represent some of the oldest issues in understanding relationships between Alzheimer and vascular diseases. Neuroimaging has long helped us to detect and quantify brain vascular diseases. The more recent advent of amyloid imaging now permits the detection and quantitation of amyloid-beta (Aβ), permitting new types of studies to explore complex relationships between Alzheimer and vascular pathologies. The current review first presents a brief overview of knowledge about the association between AD pathology and vascular factors (both VBI and vascular risk factors) from epidemiology and autopsy studies. We subsequently address what has been learned since the advent of in-vivo Aβ imaging. Possible avenues for prevention and treatments are also explored along with future research directions. This review does not intend to be an exhaustive review of the literature, but more an overview of where we are and where we should go next.    


The cause(s) of Alzheimer’s disease

The major obstacle to AD prevention and treatment is that the cause(s) of the disease is still unknown. In 1991, it was proposed that cerebral amyloid deposition represents the key pathogenic mechanism of AD development (1). The amyloid hypothesis suggested that amyloid initiates a cascade of pathological events, including the overexpression of neurofibrillary tangles, that lead to neurodegeneration and cognitive decline (2). The amyloid hypothesis finds its strongest support in the several varieties of familial AD that invariably result from genetic mutations which influence amyloid accumulation. In late onset AD, however, the causes likely include a combination of genetic, environmental, and lifestyle factors that act in concert to influence individual risk for development of disease and its associated symptoms. Specifically, while amyloid deposition seems still to be a key feature of late disease, other factors moderate its impact on brain integrity and cognition. Also, because late onset AD patients do not have a genetic mutation that causes early Aβ production, other genetic and environmental factors must influence Aβ accumulation. Identifying these factors and understanding the mechanism by which they influence the risk of AD is important from a prevention point of view, but also to guide new drug development.

Vascular and cerebrovascular diseases as risk factors for Alzheimer’ disease: knowledge from epidemiology and autopsy research studies

Vascular risk factors such as hypertension, dyslipidemia and diabetes are well known risk factors for AD (3). When looking at the prevalence of vascular factors compared to other risk factors (Table 1), it is evident that vascular factors should be a particular target for AD prevention. Furthermore, individuals with multiple vascular risks have more than twice the risk of developing dementia associated with AD compared to elderly without vascular risk factors (4). These vascular risk factors lead to VBI (e.g. white matter lesions and infarcts), which also increase the likelihood of dementia (3, 5). In fact, autopsy studies suggest that the most prevalent cause of dementia is mixed dementia, often defined by the presence of Alzheimer plus vascular pathologies (6). Autopsy studies further suggest that, while about a quarter of people can be free from dementia when presenting with Alzheimer pathology with no other comorbidity, very few persons (less than 7%) can stay free from dementia when both Alzheimer and vascular pathologies are present (6). Interestingly, autopsy studies also showed that less severe Alzheimer’s pathology is needed to develop Alzheimer’s dementia in the presence of infarcts or white matter lesions (7). Given the strong co-occurrence between both diseases, Alzheimer’s and vascular dementia are often presented as a continuum: with pure Alzheimer’s or vascular dementia representing the two extremes, and ‘mixed’ dementia in between and representing most older people with dementia.

Table 1. Risk factors for Alzheimer’ disease

Presented are common risk factors for AD; 1. Factors that have additionally been associated with increased brain Aβ. For hypercholesterolemia, both low HDL and high LDL cholesterol, but not total cholesterol, have been associated with increase Aβ (34). Aggregate vascular risk has also been associated with increased Aβ (33).

Because both pathologies frequently occur together, it is a major challenge to assign the degree of importance to either of them with regard to their effects on brain and cognitive integrity. Before Aβ-imaging, assessing the respective impact of both pathologies was only possible in autopsy-defined groups. However, even with the availability of autopsy data, or now with the availability of quantitative measures of Aβ deposition, assigning a role to each pathology when they are mixed is problematic. This is because such effects likely depend on the amount of each pathology, the length of time the pathology has been present, the location of pathology (particularly true for cerebrovascular disease which can be more focal than Aβ), and many aspects of the individual subject’s genetic, medical, and environmental background that could increase or limit susceptibility to each pathological process. Another challenging question, based on the strong associations between Alzheimer’s and vascular pathologies, is whether the impact of both pathologies are independent and additive, or if the presence of one pathology influences the presence and the impact of the other. It is possible that 1) both pathologies share common drivers (i.e. age, Apolipoprotein E (ApoE)) but act via independent pathways, 2) that one pathology drives the other pathology and/or 3) that both pathologies interact and that the join effect of both pathologies on brain and cognition is greater than their sum. Autopsy studies reported insufficient data supporting a direct link (options 2 and 3 above) between Alzheimer and vascular pathologies (8). It has therefore been assumed that both diseases occur and act independently, and additively increase the risk of dementia. Figure 1 schematises these independent pathways.

Figure 1. Independent Alzheimer and vascular pathways: an autopsy based model

Autopsy studies suggest that Alzheimer and vascular pathologies increase the risk of AD via independent and additive pathways. Because both pathologies frequently co-occur and because vascular risk factors such as hypertension and diabetes are well known risk factors for AD, mixed dementia is often considered the most frequent type of dementia. VBI : vascular brain injuries. Aβ: amyloid-beta


Aβ imaging

In 2004, the first in-vivo radiotracer to specifically track brain Aβ was reported (9). The Pittsburgh Compound B (PIB)-PET tracer is a 11C radiotracer that binds to fibrillar deposits of Aβ protein in plaques and cerebrovascular amyloid (CAA). Since then several 18F-labeled (half-life of 110 min) compounds have been created. Using these radiotracers it is now possible to track brain and cognitive changes associated with “pure” Alzheimer or vascular dementia, as well as subtle cognitive changes that are independent from both pathologies, which might include what is often termed normal aging. It is also possible to assess the relationship between Aβ, VBI and vascular risk factors in-vivo and test if Aβ and vascular factors act via independent or common pathways.

Figure 2. Alzheimer and vascular independent and shared pathways : an in-vivo based model

Proposed conception of the relationship between AD and vascular factors. While Aβ burden and vascular brain injuries (VBI, white matter lesions and infarcts) still have distinct pathways, vascular risk factors are associated with both Aβ burden and VBI. Vascular risk factors are also associated with brain integrity via a pathway that is independent from Aβ and VBI. Accordingly, vascular risk factors should be a particular target for prevention. Aβ: amyloid-beta. BBB: blood brain carrier. CBF: cerebral blood flow


Aβ and vascular brain injuries: independent or dependant pathways?

Supporting autopsy findings, many in-vivo studies assessing a relationship between Aβ and VBI (white matter lesions or infarcts) found no or slight correlation between the two factors in cognitively normal older adults, or older adults in preclinical or clinical phases of AD (10-18), even though increased white matter lesions have sometimes been reported in AD patients (14, 15).  Increased PIB-PET signal has in turn been associated with increased white matter lesions in persons presenting cerebral amyloid angiopathy (CAA) (19). Therefore CAA might have a stronger relationship with VBI than parenchymal Aβ. Whether transient Aβ increase follows an acute vascular event in humans, as has been suggested in rodents (20), still needs to be tested.

Concerning the impact of Aβ and VBI on brain and cognitive integrity, it seems that both factors mainly act via independent pathways, which is also in line with autopsy studies. Lower cerebrospinal fluid Aβ (which is inversely associated with brain Aβ) has been associated with decreased temporoparietal metabolism while greater white matter lesions have been associated with decreased frontal metabolism in individuals with mild cognitive impairment that subsequently progressed to dementia (21). Hippocampal volume and precuneus thickness have further been found to mediate (account for) the relationship between Aβ and memory (22-24), while frontal thickness has been reported to mediate the relationship between VBI and executive function (22) in cognitively impaired patients. These results do not imply that VBI cannot target brain regions typically affected by AD pathology (18, 25), but they suggest that VBI has a predominant impact on frontal functions. Similarly, while VBI is primarily associated with executive dysfunctions, it is not restricted to them, or to the impact of frontal-executive dysfunctions on other cognitive domains (10, 11, 26).  

The association between white matter structural integrity, measured with diffusion tensor imaging (DTI), and Aβ needs to be further explored given the inconsistent results reported in the literature (13, 27). Furthermore, even an association between these DTI changes and white matter lesions (13, 27) does not exclude the possibility that they are not all from vascular origin. The question of whether VBI potentiates the association between Aβ and functional connectivity, or if VBI and Aβ affect different brain networks, also needs further exploration. Indeed, while evidence suggests a link between Aβ and brain network functions measured with functional MRI (26), the independent or shared impact of white matter lesions on brain connectivity is unknown.

Figure 3. Impact of Aβ, VBI and vascular risk factors on cortical thickness in older adults with a spectrum of vascular diseases

Legend: Statistical cortical maps showing the association among Aβ, VBI (white matter hyperintensity), vascular risk (FCRP score) and cortical thickness in a sample of 66 older (64 for VBI) adults enriched for vascular diseases. Results suggest that increased vascular risk, increased Aβ burden and increased VBI are associated with thinner cortex. Statistical surface maps were created using a vertex-wise statistical thresholds of p < 0.05. The analyses are corrected for age, cognitive status, and multiple comparisons. This figure is based on a previously publication (38) 

With the constant improvement of neuroimaging tools, it is now possible to go beyond the assessment of WML/infarcts (or DTI) and explore other cerebrovascular mechanisms that might be related to Aβ. Indeed the multiple pathways by which amyloid and vascular factors could be linked do not necessary involve white matter lesions or infarcts (3, 28, 29), a fact that should be keep in mind and further explored. A recent study suggested for instance an association between Aβ and lower cerebral blood flow assessed using MRI-based arterial spin labelling (30). One explanation might be that lower blood flow diminishes Aβ clearance, which in turn reduces cerebral blood flow via a harmful vicious cycle (28). Assessing the integrity of the blood brain barrier using an MR contrast (28), brain vasoreactivity using carbon dioxide inhalation (31), or cerebral blood volume (as a proxy of capillary density) using a contrast agent and functional MRI (32), in relationship to Aβ would also be of interest. Even if still difficult to examine using existing neuroimaging tools, assessing the link between Aβ and microinfarcts might lead to new insight about the relationship between vascular factors and Aβ.

Aβ and vascular risk factors: independent or dependant pathways?

Although VBI and vascular risk factors, such as hypertension, cholesterol and diabetes, are linked, vascular risk factors can occur in the absence of VBI and vice versa. Vascular risk factors and VBI should therefore be considered and treated as two separate entities. While no clear association has been found between Aβ and VBI, there is strong evidence suggesting that vascular risk factors (aggregate or independent risk) are associated with increased brain Aβ (33-36). Importantly, some of these observations were found in late middle age subjects (36), suggesting that intervention targeting vascular risk factors should probably be started in midlife. Supporting that idea, the impact of vascular risk factors on brain integrity can already be detected in young adults (37). While the process by which vascular risk factors might lead to Aβ are mainly unknown, assessing these “other cerebrovascular mechanisms” are of interest since changes in cerebral blood flow, diminution of blood brain barrier permeability and vascular oxidative metabolism are all possible mechanisms by which vascular risk factors might increase Aβ burden (28).

In one of our previous studies we suggested that vascular risk factors interact with Aβ to reduce cortical thickness in brain regions known to be vulnerable to AD (38). This observation was independent of VBI and found when looking at aggregate vascular burden (assessed using the total Framingham cardiovascular risk profile, FCRP, score) or levels of circulating high-density lipoprotein (HDL) cholesterol. These data suggest that the impact of Aβ on cortical thickness might be potentiated by the presence of vascular burden and/or vice versa. In this same study, we also presented results suggesting that vascular risk factors can be associated with cortical thinning independently of Aβ and VBI. Therefore, vascular risk factors could influence AD risk via at least three pathways: 1) by increasing VBI, 2) by facilitating Aβ burden (and having a synergistic effect with it on brain integrity), and 3) by direct effects on the brain independently of Aβ and VBI (Figure 2). This last pathway should not be neglected as vascular risk factors can start early in life and therefore probably have a wide spread impact by the time a person reaches 65 years old, as suggested in Figure 3. Even if vascular risk factors do not lead to dementia by themselves, they probably diminish the “brain reserve”, conceptualised as a buffer that allows individuals to stay free from cognitive impairment in the presence of brain pathology. These “frail” brains might also be more vulnerable to other brain pathologies, as the interaction with Aβ suggests (38).

In this same study, it was also suggested that cholesterol-lowering medications might be protective against the negative impact of vascular risk factors and Aβ on cortical thickness. Both higher FCRP and higher Aβ burden were associated with less cortical thinning in subjects that were taking cholesterol-lowering drugs when compared with subjects who were not taking cholesterol drugs. This finding, which needs replication, is in line with other studies suggesting that statins confer some level of neuro-protection against late-life development of AD (39, 40, see also 41). Given that statin treatment has shown no reliable effect on clinical symptoms in subjects with dementia, it is more than plausible that statins only have an impact when started in midlife. Also, not all classes of statins necessarily confer the same protective benefit (40), an effect that needs to be better understood. 

Table 2. Preventive and treatment targets for Alzheimer’ disease

Presented are prevention and treatment targets for AD.  This table is intended to present avenues that should be explored and is not restricted to available treatments or treatments that have been found to be beneficial. 


Apolipoprotein E, Aβ and vascular factors

ApoE is a well-known genetic risk factor for AD (3, 42), with ~ 60% of AD patients presenting at least one ε4 allele (43). Interestingly, ApoE seems to be a common upstream driver to both Aβ and vascular burden, reinforcing the association between these two factors. ApoE, for example has been suggested to play a key role in Aβ accumulation and clearance, with ApoE4 being associated with increased Aβ burden (44) and ApoE2 being associated with lower Aβ burden (45). Because of its role in lipid metabolism regulation, ApoE4 also influences vascular risk factors and cardiovascular diseases (46), which in turn affects the risk of AD, as presented previously. Other mechanisms by which ApoE4 might influence the clinical expression of AD include neuronal inflammation, less efficient neuronal repair, diminished blood barrier integrity, increased tau phosphorylation, neurofibrillary tangle formation, neuronal mitochondrial dysfunction, and decreased GABAergic interneuron selectivity (47, 48). Given its wide range of functions, ApoE is probably a key factor to target for AD prevention and treatment.

Alzheimer’s disease prevention and treatment

Because the disease starts up to 30 years before the onset of dementia (49), and because vascular risk factors already impair the brain in middle age (37), preventive strategies for AD (table 2) should be implemented as early as possible. Most of these strategies should also be adopted in late life as they may still confer a benefit. For instance, while monitoring vascular risk may be a good prevention target in midlife, treating vascular risk in late life as been found to improve cognition in individuals with mild cognitive impairment (50).

In addition to targeting vascular risk factors, one obvious treatment target for AD is anti-amyloid therapies. Even if these therapies failed in dementia patients, they might have beneficial impact at preclinical or presymptomatic stages of the disease (51). Clinical trials enrolling subjects with autosomal-dominant familial AD and cognitively normal amyloid-positive older adults from the general population are currently ongoing. Given the possible vascular side effect of anti-amyloid therapies (52), vascular brain health will be monitored closely in these trials as they may predict adverse side effects.

Several other avenues should be tested for AD prevention and treatment in addition to Aβ and vascular therapies as the disease is multifactorial. More importantly research should be done to assess the mechanism by which these other factors can diminish the risk of AD since it might lead to new treatments. For instance, depression is a well-known risk factor for dementia. More recently it was suggested that individuals with a lifetime history of depression present increased brain Aβ (53). While depression might be secondary to Aβ accumulation, it is also important to assess if anti-depresant medication can slow Aβ accumulation, and hopefully AD progression, as was recently suggested (54). Similarly, enhanced lifetime cognitive activity has been shown to buffer the effect of ApoE4 on Aβ burden (55). While this information is valuable by itself for preventative strategies, understanding the mechanism by which cognitive activity might influence Aβ burden could point to new treatment strategies. Such strategies could include approaches such as the antiepileptic levetiracetam (56) (assuming that cognitive activity attenuate Aβ secretion via modulation of neural activity) or cognitive training protocols (57).

As mentioned previously, ApoE is a major risk factors for both Alzheimer and vascular pathologies. Increasing effort should therefore focus on developing and testing drugs that modify ApoE expression and function. Promoting ApoE levels, increasing ApoE receptor 2, blocking domain interaction in ApoE4, or restoring brain vascular integrity are all potential interesting targets (48, 58-60).


The absence of a relationship between Aβ and VBI (here defined as white matter lesions and infarcts), as well as their independent impact on cognition and brain integrity, suggests that both factors mainly act via independent pathways. Vascular risk factors however, seem to have a more direct impact on AD since increased vascular risk factors have been associated with increased Aβ burden. Furthermore, increased vascular risk factors might potentiate the impact of Aβ burden on cortical thickness. Given these findings, and the fact that vascular risk factors are often treatable, they should represent key factors for prevention.

Finally, while everyone is impatiently awaiting the results of anti-amyloid therapies in asymptomatic individuals, other treatments strategies should also be targeted. Among them, drugs that modify ApoE metabolism and function might be promising. Effort should also be made to understand how protective and risk factors such as lifestyle, psychiatric symptoms and sleep, influence AD risk.

Acknowledgements: The authors would like to thank J. Vogel for his review of the manuscript prior to publication.

Funding: SV is supported by a Canadian Institutes of Health Research post-doctoral fellowship and WJJ is supported by a NIH grants AG034570.

Conflict of interest: The authors report no conflict of interest.


1. Selkoe DJ. The Molecular Pathology of Alzheimer’s Disease. Neuron 1991;6:487-498.

2. Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002;297:353-356.

3. Kalaria RN, Akinyemi R, Ihara M. Does vascular pathology contribute to Alzheimer changes? Journal of the neurological sciences 2012;322:141-147.

4. Luchsinger JA, Reitz C, Honig LS, Tang MX, Shea S, Mayeux R. Aggregation of vascular risk factors and risk of incident Alzheimer disease. Neurology 2005;65:545-551.

5. Schneider JA, Wilson RS, Bienias JL, Evans DA, Bennett DA. Cerebral infarctions and the likelihood of dementia from Alzheimer disease pathology. Neurology 2004;62:1148-1155.

6. Schneider JA, Arvanitakis Z, Bang W, Bennett DA. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology 2007;69:2197-2204.

7. Snowdon DA, Greiner LH, Mortimer JA, Riley KP, Greiner PA, Markesbery WR. Brain infarction and the clinical expression of Alzheimer disease. The Nun Study. JAMA 1997;277:813-817.

8. Chui HC, Zheng L, Reed BR, Vinters HV, Mack WJ. Vascular risk factors and Alzheimer’s disease: are these risk factors for plaques and tangles or for concomitant vascular pathology that increases the likelihood of dementia? An evidence-based review. Alzheimers Res Ther 2012;3:36.

9. Klunk WE, Engler H, Nordberg A, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Annals of neurology 2004;55:306-319.

10. Hedden T, Mormino EC, Amariglio RE, et al. Cognitive profile of amyloid burden and white matter hyperintensities in cognitively normal older adults. The Journal of neuroscience : the official journal of the Society for Neuroscience 2012;32:16233-16242.

11. Marchant NL, Reed BR, DeCarli CS, et al. Cerebrovascular disease, beta-amyloid, and cognition in aging. Neurobiology of aging 2012;33:1006 e1025-1036.

12. Marchant NL, Reed BR, Sanossian N, et al. The Aging Brain and Cognition: Contribution of Vascular Injury and Abeta to Mild Cognitive Dysfunction. JAMA Neurol 2013:1-8.

13. Chao LL, Decarli C, Kriger S, et al. Associations between white matter hyperintensities and beta amyloid on integrity of projection, association, and limbic fiber tracts measured with diffusion tensor MRI. PloS one 2013;8:e65175.

14. Provenzano FA, Muraskin J, Tosto G, et al. White matter hyperintensities and cerebral amyloidosis: necessary and sufficient for clinical expression of Alzheimer disease? JAMA Neurol 2013;70:455-461.

15. Zhou Y, Yu F, Duong TQ, for the Alzheimer’s Disease Neuroimaging I. White matter lesion load is associated with resting state functional MRI activity and amyloid pet but not FDG in mild cognitive impairment and early alzheimer’s disease patients. Journal of magnetic resonance imaging : JMRI 2013;9:102-109..

16. Park JH, Seo SW, Kim C, et al. Effects of cerebrovascular disease and amyloid beta burden on cognition in subjects with subcortical vascular cognitive impairment. Neurobiology of aging 2014;35:254-260.

17. Grimmer T, Faust M, Auer F, et al. White matter hyperintensities predict amyloid increase in Alzheimer’s disease. Neurobiology of aging 2012;33:2766-2773.

18. Hedden T, Schultz AP, Rieckmann A, et al. Multiple Brain Markers are Linked to Age-Related Variation in Cognition. Cerebral cortex 2014; pii:bhu238. [Epub ahead of print]

19. Gurol ME, Viswanathan A, Gidicsin C, et al. Cerebral amyloid angiopathy burden associated with leukoaraiosis: a positron emission tomography/magnetic resonance imaging study. Annals of neurology 2013;73:529-536.

20. Garcia-Alloza M, Gregory J, Kuchibhotla KV, et al. Cerebrovascular lesions induce transient beta-amyloid deposition. Brain : a journal of neurology 2011;134:3697-3707.

21. Haight TJ, Landau SM, Carmichael O, et al. Dissociable effects of Alzheimer disease and white matter hyperintensities on brain metabolism. JAMA Neurol 2013;70:1039-1045.

22. Ye BS, Seo SW, Kim GH, et al. Amyloid burden, cerebrovascular disease, brain atrophy, and cognition in cognitively impaired patients. Alzheimer’s & dementia : the journal of the Alzheimer’s Association 2014; ;pii: S1552-5260(14)02419-4. [Epub ahead of print]

23. Mormino EC, Kluth JT, Madison CM, et al. Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. Brain : a journal of neurology 2009;132:1310-1323.

24. Villeneuve S, Reed BR, Wirth M, et al. Cortical thickness mediates the effect of beta-amyloid on episodic memory. Neurology 2014;82:761-767.

25. Wirth M, Villeneuve S, Haase CM, et al. Associations between Alzheimer disease biomarkers, neurodegeneration, and cognition in cognitively normal older people. JAMA Neurol 2013;70:1512-1519.

26. Jagust W. Vulnerable neural systems and the borderland of brain aging and neurodegeneration. Neuron 2013;77:219-234.

27. Glodzik L, Kuceyeski A, Rusinek H, et al. Reduced glucose uptake and Abeta in brain regions with hyperintensities in connected white matter. NeuroImage 2014;100:684-691.

28. Zlokovic BV. Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nature reviews Neuroscience 2011;12:723-738.

29. de la Torre JC. Is Alzheimer’s disease a neurodegenerative or a vascular disorder? Data, dogma, and dialectics. Lancet neurology 2004;3:184-190.

30. Mattsson N, Tosun D, Insel PS, et al. Association of brain amyloid-beta with cerebral perfusion and structure in Alzheimer’s disease and mild cognitive impairment. Brain : a journal of neurology 2014;137:1550-1561.

31. Glodzik L, Randall C, Rusinek H, de Leon MJ. Cerebrovascular reactivity to carbon dioxide in Alzheimer’s disease. Journal of Alzheimer’s disease : JAD 2013;35:427-440.

32. Brickman AM, Khan UA, Provenzano FA, et al. Enhancing dentate gyrus function with dietary flavanols improves cognition in older adults. Nature neuroscience 2014;17:1798-1803.

33. Reed BR, Marchant NL, Jagust WJ, DeCarli CC, Mack W, Chui HC. Coronary risk correlates with cerebral amyloid deposition. Neurobiology of aging 2012;33:1979-1987.

34. Reed B, Villeneuve S, Mack W, Decarli C, Chui HC, Jagust W. Associations between serum cholesterol levels and cerebral amyloidosis. JAMA Neurol 2014;71:195-200.

35. Rodrigue KM, Rieck JR, Kennedy KM, Devous MD, Diaz-Arrastia R, Park DC. Risk Factors for beta-Amyloid Deposition in Healthy Aging: Vascular and Genetic Effects. JAMA Neurol 2013:1-7.

36. Langbaum JB, Chen K, Launer LJ, et al. Blood pressure is associated with higher brain amyloid burden and lower glucose metabolism in healthy late middle-age persons. Neurobiology of aging 2012;33:827 e811-829.

37. Maillard P, Seshadri S, Beiser A, et al. Effects of systolic blood pressure on white-matter integrity in young adults in the Framingham Heart Study: a cross-sectional study. Lancet neurology 2012;11:1039-1047.

38. Villeneuve S, Reed BR, Madison CM, et al. Vascular risk and Abeta interact to reduce cortical thickness in AD vulnerable brain regions. Neurology 2014;83:40-47.

39. Jick H, Zornberg GL, Jick SS, Seshadri S, Drachman DA. Statins and the risk of dementia. Lancet 2000;356:1627-1631.

40. Wolozin B, Wang SW, Li NC, Lee A, Lee TA, Kazis LE. Simvastatin is associated with a reduced incidence of dementia and Parkinson’s disease. BMC medicine 2007;5:20.

41. Leduc V, De Beaumont L, Theroux L, et al. HMGCR is a genetic modifier for risk, age of onset and MCI conversion to Alzheimer’s disease in a three cohorts study. Molecular psychiatry 2014doi: 10.1038/mp.2014.81. [Epub ahead of print]

42. Mahley RW, Huang Y. Alzheimer disease: multiple causes, multiple effects of apolipoprotein E4, and multiple therapeutic approaches. Annals of neurology 2009;65:623-625.

43. Ward A, Crean S, Mercaldi CJ, et al. Prevalence of apolipoprotein E4 genotype and homozygotes (APOE e4/4) among patients diagnosed with Alzheimer’s disease: a systematic review and meta-analysis. Neuroepidemiology 2012;38:1-17.

44. Strittmatter WJ, Saunders AM, Schmechel D, et al. Apolipoprotein E: high-avidity binding to beta-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc Natl Acad Sci U S A 1993;90:1977-1981.

45. Jiang Q, Lee CY, Mandrekar S, et al. ApoE promotes the proteolytic degradation of Abeta. Neuron 2008;58:681-693.

46. Villeneuve S, Brisson D, Marchant NL, Gaudet D. The potential applications of Apolipoprotein E in personalized medicine. Frontiers in aging neuroscience 2014;6:154.

47. Mahley RW, Huang Y. Apolipoprotein e sets the stage: response to injury triggers neuropathology. Neuron 2012;76:871-885.

48. Liu CC, Kanekiyo T, Xu H, Bu G. Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nature reviews Neurology 2013;9:106-118.

49. Villemagne VL, Burnham S, Bourgeat P, et al. Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet neurology 2013;12:357-367.

50. Chertkow H, Massoud F, Nasreddine Z, et al. Diagnosis and treatment of dementia: 3. Mild cognitive impairment and cognitive impairment without dementia. CMAJ : Canadian Medical Association journal = journal de l’Association medicale canadienne 2008;178:1273-1285.

51. Sperling RA, Jack CR, Jr., Aisen PS. Testing the right target and right drug at the right stage. Science translational medicine 2011;3:111cm133.

52. Yamada M. Predicting cerebral amyloid angiopathy-related intracerebral hemorrhages and other cerebrovascular disorders in Alzheimer’s disease. Frontiers in neurology 2012;3:64.

53. Wu KY, Hsiao IT, Chen CS, et al. Increased brain amyloid deposition in patients with a lifetime history of major depression: evidenced on 18F-florbetapir (AV-45/Amyvid) positron emission tomography. European journal of nuclear medicine and molecular imaging 2014;41:714-722.

54. Sheline YI, West T, Yarasheski K, et al. An antidepressant decreases CSF Abeta production in healthy individuals and in transgenic AD mice. Science translational medicine 2014;6:236re234.

55. Wirth M, Villeneuve S, La Joie R, Marks SM, Jagust WJ. Gene-environment interactions: lifetime cognitive activity, APOE genotype, and beta-amyloid burden. The Journal of neuroscience : the official journal of the Society for Neuroscience 2014;34:8612-8617.

56. Bakker A, Krauss GL, Albert MS, et al. Reduction of hippocampal hyperactivity improves cognition in amnestic mild cognitive impairment. Neuron 2012;74:467-474.

57. Belleville S, Clement F, Mellah S, Gilbert B, Fontaine F, Gauthier S. Training-related brain plasticity in subjects at risk of developing Alzheimer’s disease. Brain : a journal of neurology 2011;134:1623-1634.

58. Bu G. Apolipoprotein E and its receptors in Alzheimer’s disease: pathways, pathogenesis and therapy. Nature reviews Neuroscience 2009;10:333-344.

59. Champagne D, Pearson D, Dea D, Rochford J, Poirier J. The cholesterol-lowering drug probucol increases apolipoprotein E production in the hippocampus of aged rats: implications for Alzheimer’s disease. Neuroscience 2003;121:99-110.

60. Mahley RW, Huang Y. Small-molecule structure correctors target abnormal protein structure and function: structure corrector rescue of apolipoprotein E4-associated neuropathology. Journal of medicinal chemistry 2012;55:8997-9008.