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P. Aisen1, J. Touchon2, R. Amariglio3, S. Andrieu4,5,6,7, R. Bateman8, J. Breitner9, M. Donohue1, B. Dunn10,  R. Doody11, N. Fox12, S. Gauthier9, M. Grundman13, S. Hendrix14, C. Ho15, M. Isaac16, R. Raman1,  P. Rosenberg17, R. Schindler18, L. Schneider19, R. Sperling3, P. Tariot20, K. Welsh-Bohmer21, M. Weiner22,  B. Vellas4,5,6 and Task Force Members*


*E.U./U.S. CTAD TASK FORCE: Abushakra Susan (Framingham), Agus Sam (Valby), Aisen Paul (San Diego), Amariglio Rebecca (Boston), Andrieu Sandrine (Toulouse), Bateman Randall (St. Louis), Bowman Gene (Lausanne), Breitner John (Montreal), Budd Haeberlein Samantha (Cambridge), Cantillon Marc (Princeton), Carrillo Maria (Chicago), Clavier Isabelle (Chilly Mazarin), Cummings Jeffrey (Las Vegas), Dedieu Jean-François (Chilly Mazarin), Donohue Michael (San Diego), Doody Rachelle (Basel), Downing Ann Catherine (Indianapolis), Dubé Sanjay (Aliso Viejo), Dunn Billy (Chicago), Eggermont Laura (Utrecht), Fox Nick (London), Gauthier Serge (Verdun), Goedkoop René (Issy Les Moulineaux), Grundman Michael (San Diego),  Hauge Vienna (Durham), Hendrix Suzanne (Salt Lake City), Ho Carole (South San Francisco), Krijezi Mubera (Basel), Lawson John (Malvern), Legrand Valérie (Nanterre), Malamut Rick (Aliso Viejo),  McCarthy Marie (Leopardstown), Megerian Thomas (Aliso Viejo),  Merdes Annette (Munich),  Nosheny Rachel (San Francisco), Olsson Tina (Cambridge), Ondrus Matej (Bratislava), Pueyo Maria (Suresnes), Rafii Michael (San Diego), Raman Rema (San Diego), Raunig David (Dublin), Rosenberg Paul (Baltimore), Salloway Stephen (Providence), Schindler Rachel (New York), Shin Paul (Aliso Viejo), Siffert Joao (Cambridge), Sims John (Indianapolis), Sperling Reisa (Boston), Stephens Andrew (Berlin), Suhy Joyce (Newark), Tariot Pierre (Phoenix), Touchon Jacques (Montpellier), Tsukii Katsuyoshi (Princeton),  Van Der Geyten Serge (Belgium), Vellas Bruno (Toulouse), Viglietta Vissia (Cambridge),  Vincenzetti Christine (Lausanne), Weiner Michael (San Francisco), Welsh-Bohmer Kathleen (Durham), Wendland Jens (Cambridge), Wessels Alette (Indianapolis), Wilson Louisa (Princeton), Wunderlich Glen (Ridgefield), Yang Jerry (New York), Zimmer Jennifer (Indianapolis), Zoia Vanessa (Utrecht)

1. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA; 2. University Hospital of Montpellier, 34025 Montepellier Cedex 5, and INSERM 1061, France; 3. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 4. UMR1027 Inserm, F-31073, Toulouse, France; 5. University of Toulouse III, F-31073, France; 6. Gerontopole Toulouse, Toulouse University Hospital, F-31000, Toulouse, France; 7. Department of Epidemiology and Public Health, CHU Toulouse, Toulouse France; 8. Washington University School of Medicine, St. Louis, MO, USA; 9.  McGill University, Montreal, CA; 10.  Neurology FDA; 11. F. Hoffmann-LaRoche Ltd., Basel, Switzerland; 12. University College London, UK; 13. Global R&D Partners, LLC, San Diego, CA, USA; 14. Pentara Corporation, Salt Lake City, UT, USA; 15. Denali Therapeutics, South San Francisco, CA, USA; 16. European Medicines Agency, London, UK; 17. Johns Hopkins University  School of Medicine, Baltimore, MD, USA; 18. Pfizer, Inc., New York, NY, USA; 19.  University of Southern California, Los Angeles, CA USA; 20. Banner Alzheimer’s Institute, Phoenix, AZ, USA; 21.  Duke University, Durham, NC, USA; 22. University of California, San Francisco, CA, USA

Corresponding Author: Paul Aisen, Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA,

J Prev Alz Dis 2017;4(2):117-125
Published online April 25, 2017,


At a meeting of the EU/US/Clinical Trials in Alzheimer’s Disease (CTAD) Task Force in December 2016, an international group of investigators from industry, academia, and regulatory agencies reviewed lessons learned from ongoing and planned prevention trials, which will help guide future clinical trials of AD treatments, particularly in the pre-clinical space. The Task Force discussed challenges that need to be addressed across all aspects of clinical trials, calling for innovation in recruitment and retention, infrastructure development, and the selection of outcome measures. While cognitive change provides a marker of disease progression across the disease continuum, there remains a need to identify the optimal assessment tools that provide clinically meaningful endpoints. Patient- and informant-reported assessments of cognition and function may be useful but present additional challenges. Imaging and other biomarkers are also essential to maximize the efficiency of and the information learned from clinical trials.

Key words: Alzheimer’s disease, clinical trials, secondary prevention trials, cognitive outcome measures, cognitive composites, patient-reported outcome measures, informant-reported outcome measures, molecular imaging, mild behavioral impairment.



Drug-development for Alzheimer’s disease (AD) has moved increasingly to the pre-dementia space, focusing on individuals in the preclinical and prodromal stages or with very mild dementia. Many trials are currently underway testing different candidate treatments in early disease populations enriched for different characteristics and employing different trial designs and outcome measures. The urgent need to identify an intervention that can delay or prevent AD has increased the mandate for investigators from industry and academia to share ideas, data, and resources, and build stronger global collaborative programs. With this in mind, the EU/US/Clinical Trials in Alzheimer’s Disease (CTAD) Task Force, an international collaboration of AD investigators from industry and academia, met in San Diego, California, USA, in December 2016 to review recent progress, identify gaps, and suggest opportunities for moving forward.
Past meetings of the Task Force have been helpful in reaching consensus and establishing guidelines for clinical trial endpoints and promoting collaborations to improve the efficiency of clinical trials and promote data sharing (1, 2). Yet many challenges remain in the pre-clinical space, where our understanding of pathological mechanisms is still limited and where current tools may lack the sensitivity needed to optimize dosing regimens and detect clinically meaningful change.


Prevention trials

Prevention trials are underway across the spectrum of AD, from autosomal-dominant to sporadic, including populations with risk factors that increase the probability they will develop cognitive decline. Task Force participants described these complementary trials, emphasizing the cooperation, collaboration, and data sharing initiatives that have emerged.

CAP – The Collaboration for Alzheimer’s Prevention

The Collaboration for Alzheimer’s Prevention (CAP) is a partnership of the Alzheimer’s Association, National Institute on Aging, Fidelity Foundation, US Food and Drug Administration (FDA), and four groups that are sponsoring five trials: the Alzheimer’s Prevention Initiative (API), the Dominantly Inherited Alzheimer’s Network Trials Unit (DIAN-TU), the Alzheimer’s Therapeutic Research Institute (ATRI), and the TOMMORROW study. CAP brings these groups together under one umbrella to harmonize biomarkers, clinical, and cognitive measures, and align data- and sample-sharing approaches used in these trials so that findings can be compared to inform the entire community (3).


While less than 1 percent of AD cases result from autosomal dominant mutations in three genes that are directly involved in Aβ production, the predictable course of disease in these individuals provides an opportunity to model the disease, predict time of clinical onset, and intervene at any time point in the disease course due to the predictable time to biomarker changes and clinical symptom onset (4, 5). Findings from these autosomal dominant cases may also be translatable to sporadic populations (6).
DIAN, initiated as an observational study with the aim of characterizing the disease, has provided data in support of hypothetical models of disease progression (6, 7). This led to the idea that individuals could be targeted at various stages of disease. DIAN-TU has developed two trials:
The DIAN-TU-001 trial is a Phase 2/3 placebo-controlled, double-blinded, cognitive outcome trial with biomarker interim analyses. Participants are mutation carriers or non-carriers (placebo controls only) between -15 to +10 years of estimated symptom onset with a global CDR of 0, 0.5, or 1. Enrollment has been completed for this study. Mutation carriers were randomized to one of three arms: two different treatment arms (gantanerumab, solanezumab), or placebo in a 3:1 ratio of active to placebo. Drug treatment will continue for at least four years. The first two years are expected to enable establishment of a biomarker endpoint, and a cognitive endpoint will be compared after four years.
DIAN-TU will add two or more disease-modifying therapeutics to the platform in a trial called the Next Generation (NexGen) prevention trial, which will run in parallel and use an adaptive design (8). With a grant from the Alzheimer’s Association, NexGen will add two new treatment arms, employ novel biomarkers, home-based cognitive testing, maximally effective dose adjustment, and may conduct a cognitive interim analysis. The disease-progression model used in the design of this study estimated decline based on observational cognitive data from presymptomatic participants in DIAN. DIAN-TU currently has trial performance sites in seven countries.
DIAN has already demonstrated that it is possible to predict clinical onset in those with ADAD mutations, allowing targeting of treatment to specific stages of disease. The DIAN-TU trial has highlighted other issues that are relevant to secondary prevention trials:
•    Since potential participants do not have disease and may not have been involved in previous clinical trials, involving them in the design of the trial — including decisions about enrollment and implementation of trial — maximizes participant recruitment and retention.
•    In addition to participants, family members, advocacy organizations, and pharmaceutical partners should be engaged in the development of the trial.
•    Participant registries and cohorts developed from these registries are essential for efficient recruitment.
•    Use of a defined population –such as those with ADAD mutations — results in low rates of screen failures and thus can maximize the productivity of a trial.
•    Attrition can be minimized by choosing expert trial sites with full commitment to the trial.
•    Including biomarkers is essential to learn more about the effects of drugs.

A4 and EARLY

The Anti-Amyloid Treatment in Asymptomatic Alzheimer’s (A4) study is a Phase 3 secondary prevention trial being conducted in partnership with Eli Lilly (9). It is enrolling clinically normal participants aged 65 to 85 thought to be at risk of developing cognitive decline due to AD based on evidence from an amyloid PET scan showing amyloid deposition in the brain. With support from Alzheimer’s Association, A4 investigators will also follow a cohort of individuals with normal PET amyloid in the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study; and with support from the National Institutes of Health’s Accelerating Medicines Partnership (AMP), a subset of A4 participants will receive tau PET scans.
As of December 2016, enrollment for A4 has begun at 67 sites in the US, Canada, and Australia. More than 5,000 participants have been screened and 815 randomized. When enrollment is complete, 1150 participants will take part in the study. The trial is coordinated by the University of Southern California’s ATRI.
A4 will utilize the Preclinical Alzheimer’s Cognitive Composite (PACC) as the primary outcome measure (10). In parallel, the Harvard Aging Brain Study is evaluating a modification of the PACC that incorporates both the free and total scores of the free and cued selective reminding test (FCSRT) to see if they add power in early stage disease. Working with Janssen Pharmaceuticals, the ATRI team has also worked to initiate a global prevention study called EARLY in participants identified as amyloid positive by either PET scan or CSF analysis. This study will also include participants as young as age 60 with additional risk factors.
Lessons learned from A4 and EARLY include:
•    Site start up and enrollment is challenging and has taken longer than was anticipated.
•    Building infrastructure and trial-ready cohorts is essential to ensure that prevention studies can be completed in a reasonable time frame.


The Alzheimer’s Prevention Initiative, established by the Banner Alzheimer’s Institute in Phoenix, Arizona, to evaluate disease-modifying treatments for Alzheimer’s disease (11), has launched two trials in cognitively unimpaired people who are at high imminent risk at the time of enrollment. The first of these trials — the API-ADAD trial (NCT01998841)– enrolled individuals from large kindreds in Antioquia, Colombia, with the autosomal dominant PSEN1 E280A mutation, which virtually ensures that carriers will develop early onset AD. Both mutation carriers and non-carriers are enrolled, although their mutation status is not disclosed through an interesting design that embeds two substudies: 1) a randomized clinical trial in which only mutation carriers are randomized to receive either placebo or treatment; and 2) a cohort study that compares mutation carriers and non-carriers receiving placebo. The 60-month study was launched in 2013.
Lessons learned from API include:
•    The important enabling role of Health Authorities
•    The importance of existing and new data, including biomarker data, upon which to base the design
•    The value of a registry for recruitment, which allowed balancing of carriers and non-carriers referred to the study while maintaining blind to genotype
•    Pre-screen fail rates were high because of prohibited medical conditions, mild cognitive impairment, illiteracy, low MMSE, and scheduling.
•    Screen fail rates were also higher than predicted because of labs, medical conditions, inability to comply with the protocol, and MMSE.
•    Participants were exceptionally motivated and the team implemented well-planned adherence and retention strategies that resulted in only 2.6% drop out rate compared to 25% predicted. This has helped to preserve the power of the study.
•    Collaboration with colleagues at the Grupo Neurosciences de Antioquia (GNA) was essential to address substantial cultural, ethnic, and language issues. This included setting up a “health plan” in Colombia to assure access to health care and a “social plan” to support families regardless of whether they were participating in the study.
•    Flexibility was required in terms of adapting the trial to new findings (e.g., increasing the doses of crenezumab and embedding tau PET), adapting to changes in the sponsor team over time, the continuing need for funding, and responding to media attention.

Unanticipated issues that arose included low vitamin B12 levels, low thyroid function tests, and a high prevalence of people with limited formal education that produced challenges in obtaining proper informed consent. To accommodate those with low literacy, the study team created an informed consent form in the form of an illustrated companion guide.
Moving forward, the API-ADAD team may need to address a variety of issues, including responding to changes in community standards regarding genetic testing and disclosure, determining when and how to implement new studies, extending the trial in a way that maximizes power and retention but minimizes disclosure of genetic status, following participants after the conclusion of the study, establishing the clinical meaningfulness of differences in cognition, and introducing the possibility of autopsy studies.
The second API study – the Generation Study – will enroll about 1340 participants between the ages of 60 and 75 who are cognitively unimpaired and homozygous for the ApoEε4 allele, which dramatically increases an individual’s risk of developing late onset AD.


The Multi-domain Alzheimer’s Prevention Trial (MAPT) is a Phase 3 randomized, placebo-controlled intervention study conducted at multiple sites across France, which tested a multi-domain intervention comprising nutrition, physical exercise, cognitive and social activities, and an increased intake of omega-3 polyunsaturated fatty acid in frail older adults at risk of cognitive decline (12). 1680 adults age 70 or older with subjective memory complaints but no dementia and living in community settings were enrolled in the 5-year study that included 3 years of intervention plus 2 years of additional observation. The primary outcome measure was cognitive decline, assessed using an adapted version of the PACC. Subgroups of participants also had imaging studies. Preliminary results suggest that the multidomain intervention slowed cognitive decline compared to the placebo group, although the primary outcome was not significant after adjusting for multiple comparisons. Placebo group data also demonstrated increased cognitive decline in participants who were amyloid-positive, ApoEε4 carriers, those who had a baseline CDR of 0.5 suggesting mild cognitive impairment (MCI), older individuals (+75 yrs), and those with lower blood levels of Docosahexaenoic acid (DHA) and Eicosapentaenoic acid (EPA) (13).
Lessons learned from MAPT include:
•    Excluding participants who are less likely to decline can increase the ability of the trial to detect an effect.
•    Including participants with early MCI can increase the power of the trial because they are more likely to decline without intervention.
•    Cognitive composites are useful; however learning effects are important and need to be controlled for. Practice sessions before randomization are recommended.
•    Local and regional networks of research centers, memory clinics, and family practitioners are essential for recruitment. Mobile research teams may augment these sites.
•    Home-based visits may limit the number of dropouts.

Further trials are currently in development to follow up on the MAPT results:
–    LowMapt is a randomized placebo control study targeting those with low DHA in red blood cells. The objective is to replicate the cognitive effect observed in MAPT subjects with low DHA/EPA. Target population: Older adults 70 yrs old +, N= 400, with low DHA/EPA RBC < 4.83%; Intervention: DHA 800 mg/EPA 500 mg  vs placebo. Duration: 18 months, plus supplementation for 18 months, total 36 months. Delayed start analysis. Primary Criteria: Cognitive Composite score
–    Nolan trail: The objective is to prevent cognitive decline in older adults with memory complaint with  a Brain Protector Blend (Nestle Research center) versus placebo. Target population: 2080 subjects, + 70 yrs with Memory complaints but no dementia. 4 years of follow-up. Co-primary subgroup sizes: Low DHA/EPA subgroup: n=646 CDR 0.5 subgroup: n=580. Primary criteria: MAPT Cognitive Composite Score.
–    MAPT – e-Study. The objective is to replicate the multi-domain intervention observed effect using new technologies. Target population: Older adults 70 + yrs old with memory complaint, N = 120. R.C.T:  e-Multi-domain intervention, using e-platform, and e-coach vs usual follow up. Duration: 6 months. Primary Criteria: cognitive composite score, both paper and electronic.


The TOMMORROW study is a multi-national, randomized, placebo-controlled trial designed to simultaneously test two co-primary hypotheses. The first is whether low-dose pioglitazone, which modulates the transcription of genes involved in glucose and lipid metabolism, can delay the onset of MCI due to AD in a population enriched for those for carriers of the TOMM40 rs10524523 gene and the ApoEε4 allele, which increases their risk of cognitive decline. The second aim is to assess the predictive utility of a genetic biomarker algorithm comprised of age, APOE and TOMM40 geneotypes in the near-term onset of symptoms due to MCI due to AD.  The study uses a time to event design for both aims.  The primary endpoint is a clinical diagnosis of MCI due to AD  which uses operationalized criteria that have been cross-culturally validated allowing for harmonized diagnostic assignment across the nearly 60 sites involved in the global trial.
Lessons learned from TOMMORROW include:
•    Use of a streamlined battery of neuropsychological tests, akin to clinical practice, tapping domains of verbal and visual memory, language, visuospatial function, executive control, and attention was intended to capture the heterogeneity in early MCI due to AD and may provide new insights into the earliest cognitive manifestations of emerging MCI due to AD
•    Variability in cognitive measurement can be a limitation in clinical trials that rest on these endpoints. The TOMMORROW study with its diagnostic endpoint requires clinical neuropsychologists at each site and provides tight external quality assurance provided through study vendors. The latter ensures standardized administration of all measures across sites, and provides centralized scoring of measures which are inherently more variable in their scoring (e.g  visuoconstruction and visual memory measures)
•    A clinical diagnosis of MCI due to AD based on the clinical criteria of the 2011 NIA-Alzheimer’s Association (14) is a novel endpoint in trials.  This diagnosis has been rigorously operationalized for global use and is defined as
o    a decline from a baseline CDR score of 0 to a score of 0.5 and
o    failure either on one of two memory tests (-1.5 SD below an age adjusted mean and a change from baseline) or failure in 2 of 12 measures in separate domains of which one is memory (-1.3 SD below normative mean and a change from baseline)
o    exclusion of competing medical explanations

•    And importantly, to be a confirmed  MCI-AD endpoint the clinical diagnosis must be confirmed across two consecutive observations 6 months apart. And all primary endpoint events are affirmed by an independent, blinded adjudication panel, allowing harmonization in the diagnosis across clinicians, languages, and cultures.
•    Selecting sites on the basis of access to a large population of healthy elderly, the availability of site registries, and dedicated staff able to manage a high number of participant visits can maximize the success of enrollment.
•    Developing a customized recruitment strategy for each site may be needed.
•    Validating instruments and establishing normative cutpoints in different languages and communities is needed for multi-national studies.


Outcome measures for prevention trials

Cognitive change is an early change that can be detected in preclinical AD and is a manifestation of AD, making it possibly the best “biomarker” for AD trials, including preclinical trials. Assessing cognition represents a unifying approach to measurement of disease progression and can be adapted as an outcome measure for clinical trials, since it has face validity and directional hypotheses can be postulated a priori. However, there remain concerns about the clinical meaningfulness of some cognitive measures since points on scales do not always correspond to a clinically meaningful benefit. The sensitivity of individual cognitive measures has also been called into question in the earliest stages of disease. Regulatory agencies including the FDA and the European Medicines Agency (EMA) have issued draft guidance on developing treatments for early stage disease that require endpoints to include functional and global measures in addition to cognition (15, 16), and multiple analyses have concluded that composites incorporating both cognitive and functional measures may increase power in a trial in preclinical AD (17, 18). Clinical endpoints, based on a diagnosis of dementia or MCI have also been used in some trials, including the TOMMORROW trial.

Cognitive composites

Several different cognitive batteries and composites have been created for the prevention trials described above. There are significant similarities among these composites in terms of domains and constructs, although they may use different instruments to assess episodic memory, executive function, orientation, and other domains. Some of the composites include semantic measures like category fluency. Composite measures, and the weights assigned to different components, may be theoretically or empirically driven, or may have elements of both approaches. They can be optimized for clinical progression or for different stages of disease. The similarity among these composites supports the notion that cognition is a special marker in the AD field and that it is useful across the entire spectrum of disease including the preclinical stage. Whether such composites are useful in primary prevention studies remains to be determined.
The PACC includes, in addition to cognitive measures across multiple domains, the mini-mental status exam (MMSE) to assess global functioning and mental status. An analysis of scores from individuals in the AIBL study with elevated Aβ suggested that dropping the MMSE improves sensitivity in the preclinical stage of disease (19). However, studies in other populations, including DIAN, ADNI, API, and PAQUID, indicate that MMSE scores separate 6-9 years before dementia diagnosis even in people with elevated Aβ (6, 20-22). The APCC includes only the orientation to time from the MMSE, based on data from three combined studies: Rush Alzheimer’s Disease Center’s Religious Orders Study [ROS], Memory and Aging Project [MAP], and the Minority Aging Research Study [MARS], which indicated that other MMSE items did not improve sensitivity to progression in preclinical stages (23). These different conclusions, and in particular a study conducted by Donohue and colleagues (21), suggest that cross-validation should be conducted when considering changes to composite measures.
There may be additional cognitive components that are not captured by current composites, such as differentiating between processing speed, difficulty with a task, and the ability to learn new words. In addition, cognitive composites fail to capture declines in social functioning such as participating in conversations and navigating social situations.
Cognitive composites and online tools for assessing cognition may also be useful to gather data in general populations as a screening tool.

Computerized cognitive assessments

Computerized cognitive assessments and computerized cognitive batteries have been suggested as providing more reliable and efficient means of assessing cognition compared to paper and pencil measures. In a pilot study of clinically normal older adults comparing two computerized batteries — the NIH Toolbox Cognition Battery (NIHTB-CB) and the Cogstate iPad C3 battery – to the PACC, both computerized batteries showed promise. The Cogstate-C3 provides two distinct composites; one measuring logical memory and the other measuring processing speed and attention.  Both the NIHTB-CB and the C3 Learning-Memory composites correlated well with the PACC, and the C3 Learning-Memory composite also identified subtle cognitive impairment with the greatest sensitivity and specificity. The NIHTB-CB showed the strongest overall clustering and alignment with the PACC. The authors concluded that further testing will be needed before these measures can be used in large scale prevention trials (24).

Patient and informant-reported outcomes

Given the need for outcome measures that are clinically meaningful, other options that have been considered include performance-based functional measures and informant- or patient-reported activities of daily living (ADL) or instrumental activities of daily living (iADL) scales. Performance-based functional measures include assessments of financial capacity (25), ability to perform an automated phone task (26, 27), and a virtual reality simulation of functional abilities related to shopping (taking  a bus, shopping, managing money) (28). The latter was developed for schizophrenia, not dementia trials. Patient- and informant-based scales include the ADCS-ADL scale (29), the Everyday Cognition (E-Cog) scale (30), the Cognitive Function Instrument (31), the Functional Activities Questionnaire (FAQ) (32), and the Amsterdam IADL Questionnaire (33). A major advantage of performance-based measures is that they capture changes in everyday function, which reflect clinically meaningful deterioration. Patient- and informant-rated outcomes (PROs and IROs) may be easier to administer and can cover a broad range of everyday tasks that include both cognitive (e.g., repeating oneself) and functional changes (e.g., difficulty with driving). Initially, individuals may notice changes that are imperceptible to others, making them especially useful in early disease stages. However, as the disease progresses, patients may lose awareness of their impairments, making IROs potentially more useful, although the point along the trajectory where this happens is unclear and variable. Switching from PROs to IROs as disease progresses in a clinical trial could be particularly challenging.
Self-reported measures of subjective cognitive decline have also been proposed by an international working group (34). A review of self-report measures used in 19 international research studies reported wide heterogeneity across measures (35). To develop a more reliable subjective cognitive decline measure, the working group recommended asking specific rather than broad questions, with specific time references (e.g., change from one year ago), and including questions about mood, personality, and health factors. However, subjective cognitive decline measures are sensitive to various biases. In recent analyses by the Harvard Aging Brain Study, the relationship between subjective cognitive decline and cognition was shown to be stronger among Caucasians than African Americans; and the relationship between subjective report and amyloid burden was shown to be stronger in those with more education compared to those with less education (36).


Imaging provides structural, molecular, and functional information about AD that can help guide decisions about potential clinical benefits of treatments and provide information on mechanisms and safety. These measures can thus be used either as inclusion criteria or outcomes. Most of the prevention studies discussed above incorporate structural MRI as well as amyloid and tau PET. DIAN-TU and API-ADAD also include fluorodeoxyglucose (FDG) PET, which measures brain metabolism; and A4 and DIAN-TU add task-free functional MRI studies to assess the functioning of neural networks.
Selection of imaging endpoints as outcome measures in trials depends on the treatment mechanism (e.g., targeting amyloid, tau, neuroinflammation, or neurodegeneration); the aim of the study (e.g., primary, secondary, or tertiary prevention); the desired outcome (e.g., slowing, stopping, or reversing accumulation of tau or amyloid or neurodegeneration); and subject selection (i.e., pathology and stage of disease). A single imaging marker such as amyloid deposition may show the presence of disease, but the long period (10-15 years) when preclinical individuals may have evidence of cerebral amyloid means that using amyloid as the sole inclusion measure can lead to substantial heterogeneity, thus reducing statistical power (37). This can be mitigated by combining imaging and other biomarkers (38). Combining biomarkers may provide a better understanding of the effects of therapy.
A wide range of new imaging markers of molecular pathology and neurodegeneration are becoming available, such as PET ligands that enable assessment of neuroinflammation and synaptic density. These new markers may enable trials targeted more specifically to certain types of treatment and stages of disease, but will require the field to share data and align on standardized methods and develop an evidence base demonstrating optimal sample sizes to predict potentially clinically meaningful benefit.

Early behavioral disorders in preclinical AD

Early neuropsychiatric and behavioral symptoms and disorders may also be useful indicators of preclinical AD, a concept that has been termed “Mild Behavioral Impairment” (MBI), akin to MCI and with recent publication of provisional criteria and a checklist (39, 40). Multiple studies have demonstrated an association between neuropsychiatric symptoms and an increased risk of dementia and AD (41-44), although the mechanisms underlying this association remain unclear. While some studies have suggested that depression is associated with an increase in accumulation of brain amyloid, neurofibrillary tangles, or hippocampal atrophy (45-47), others have shown no association between dementia-related markers of pathology and depression (48). Anxiety has also been linked to increased levels of plaques and tangles (46). Self-reported loneliness has also been associated with elevated brain amyloid (49), suggesting that measurement of loneliness and other neuropsychiatric symptoms not captured by currently used measures such as the Neuropsychiatric Inventory (NPI), Geriatric Depression Scale, or Patient Health Questionnaire-9 (50) may add to the armamentarium of tools to detect preclinical AD.

Regulatory considerations

While cognition is unquestionably important, regulators continue to express concerns about the assessment tools currently available and their ability to identify clinically meaningful change in the early stages of AD. Likewise, there is a need for more sensitive measures of functional impact that reflect cognitive domains disturbed in early disease. Bridging the space between cognition and function is critical, leading to an increased reliance on performance-based and patient-reported outcomes. However, demonstrating the reliability of data collected using these measures remains a challenge. Moreover, given that clinical meaningfulness may change across the continuum of the disease, outcome measures used in clinical trials may also need to change depending on the stage of disease. Safety is another important criterion for regulators; however, risk-tolerance and the risk/benefit tradeoff may also change as the disease progresses, adding further complexity to regulatory decisions. This requires the inclusions of patients and patient representatives in the decision-making process.



There are reasons for optimism regarding drug development for AD, including an improved understanding of the biological mechanisms underlying early stage disease, more data sharing and collaboration, new assessment tools and biomarkers (e.g. tau PET, remote cognitive assessments), and the establishment of several different registries of potential trial participants. Approval and acceptance of a central IRB mechanism, which should markedly improve trial enrollment, is expected in 2017.
However, many challenges remain. Trials continue to take too long and cost too much. Phase 2 studies continue to be poor at predicting success in Phase 3. Tackling the problem of high screen failure rates, resulting from exclusions for co-morbidities or the presence or absence of genetic factors or mild cognitive impairment, will be essential to enable the enrollment of study populations that reflect the real-world population that preventive interventions are designed for. Including individuals with diabetes, cerebrovascular disease, and other risk factors will be necessary, but will require complex multivariate analyses.
Biomarker disappointments suggest a lack of shortcuts to demonstrating efficacy as well as the need for further standardization. Validating biomarkers is a key necessary step to facilitate future studies. While PET imaging has been incorporated into many prevention trials, CSF studies offer a potentially less expensive alternative to assess amyloid or tau load. In many countries outside of the US, lumbar puncture has a higher degree of acceptability among both patients and practitioners. However, since CSF and PET studies provide different information about pharmacodynamics and accumulation of amyloid, they are not completely interchangeable. There is also a need for more sensitive biomarkers that are pathway-independent but could assess cognitive loss, such as markers of synaptic function, axon degeneration, neuroinflammation. Alignment of the research community around some lead candidates to incorporate into studies could accelerate the identification and development of these novel biomarkers.
Since no one drug is likely to work across all population groups, tools need to be adapted to assess change in trajectory across different disease stages and population groups.  In designing a trial and selecting the most appropriate assessment tools, trialists should keep in mind that the best trial may be the simplest trial, since burden on participants, families, sites, and operational teams can sink an otherwise excellent trial design.
Improved infrastructure is needed. Building a network of trial sites that use simplified contract language, a common, standardized set of methods, have pre-trained raters and other personnel, and have contracts in place so they can start trials quickly could reduce lengthy start-up times and optimize data management. As part of a new paradigm for Alzheimer’s prevention, one suggestion was to establish Alzheimer’s prevention programs that are independent of hospital-based memory centers and more focused on health promotion. Another suggestion was to create trial-ready populations within existing health care systems, despite the challenges of conducting studies in clinical care settings, including issues related to reimbursement when services are provided in the context of a clinical trial. Trial-ready organizations that can be quickly responsive are clearly needed. The GAP-Net program  has infused $100,000 into 11 sites to try to develop a science of recruitment; 43 additional sites will be activated this year, 60 percent of these academic and 40 percent commercial. A central IRB is also crucial, and NIH has helped by requiring it for multi-site NIH studies. In the US, the NIA recently announced a $70 million five-year award to establish an Alzheimer’s Clinical Trials Consortium (ACTC) that will include multiple trial sites. Cooperation between ACTC, the European Prevention of Alzheimer’s Disease (EPAD) consortium , and other studies will be needed to ensure synergy with European efforts.
Resources are also needed for outreach and recruitment, recognizing that different approaches may be appropriate for different populations. One example of progress in this area was reported by the Arizona Alzheimer’s Consortium , which created a registry with multiple goals: increasing awareness of AD research and prescreening, screening and referring eager registrants to studies (51). API has also created a registry, the Alzheimer’s Prevention Registry , which has demonstrated recruitment success at local events. National branding efforts can also be useful if they match what is being done at the local level. Working with the media can also boost recruitment, but sites must be prepared to respond quickly to possibly hundreds of calls when a major story comes out in the news. Registries also offer opportunities to collect pre-randomization cognitive or functional data that can help document disease trajectories in cohorts before they develop disease.
Thinking of AD as a single disease occurring across a continuum also can provide a regulatory benefit by allowing trials to combine participants at different stages of disease. In addition, both the FDA and EMA have created mechanisms that allow the approval or conditional approval of a drug if it is “reasonably likely” that a positive signal on a cognitive measure or biomarker will translate into a clinically meaningful benefit even in the absence of two confirmatory pivotal trials establishing efficacy. These mechanisms typically require post-approval studies showing functional benefits, which may also provide data for payers about the real-world benefits of a treatment. Indeed, since the ultimate goal of drug development is to move a drug to market so it can provide benefits to patients, attention to payer considerations is needed throughout the drug development process. In this regard, considering cost savings as an outcome measure may provide data important to payers.
Finally, the EU US CTAD Task Force recommended investing in the next generation to train and encourage them to become clinicians, neuropsychologists, quantitative researchers, and clinical trialists, since these professionals will be essential for sustaining long-duration prevention studies and continuing progress toward effective treatments and cures.


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

Conflict of interest: Authors have no conflicts of interest with this paper. The Task Force was partially funded by registration fees from industrial participants. These corporations placed no restrictions on this work.



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L. Ruffini1, F. Lauretani2, M. Scarlattei1, A. Ticinesi2, T. Meschi2, C. Ghetti4, G. Serreli1, M. Maggio3, P. Caffarra5


1. Nuclear Medicine Unit, Academic Hospital of Parma, Parma, Italy; 2. Geriatric Rehabilitation Department, Academic Hospital of Parma, Parma, Italy; 3. Department of Clinical and Experimental Medicine, Section of Geriatrics, University of Parma; 4. Medical physics, Academic Hospital of Parma, Parma, Italy; 5. Department of Neuroscience, University of Parma, Parma, Italy

Corresponding Author: Livia Ruffini, MD, Nuclear Medicine Unit, Academic Hospital of Parma, Parma, Italy, E-mail:

J Prev Alz Dis 2016;3(3):127-132
Published online May 31, 2016,



A significant progress has been made in the understanding of the neurobiology of Alzheimer’s disease. The post-mortem studies are the gold standard for a correct histopathological diagnosis, contributing to clarify the correlation with cognitive, behavioral and extra-cognitive domains. However, the relationship between pathological staging and clinical involvement remains challenging.
Neuroimaging, including positron emission tomography (PET) and magnetic resonance, could help to bridge the gap by providing in vivo information about disease staging. In the last decade, advances in the sensitivity of neuroimaging techniques have been described, in order to accurately distinguish AD from other causes of dementia.
Fluorodeoxyglucose-traced PET (FDG-PET) is able to measure cerebral metabolic rates of glucose, a proxy for neuronal activity, theoretically allowing detection of AD. Many studies have shown that this technique could be used in early AD, where reduced metabolic activity correlates with disease progression and predicts histopathological diagnosis. More recently, molecular imaging has made possible to detect brain deposition of histopathology-confirmed neuritic β-amyloid plaques (Aβ) using PET. Although Aβ plaques are one of the defining pathological features of AD, elevated levels of Aβ can be detected with this technique also in older individuals without dementia. This raises doubts on the utility of Aβ PET to identify persons at high risk of developing AD.
In the present case-series, we sought to combine metabolic information (from FDG-PET) and amyloid plaque load (from Aβ PET) in order to correctly distinguish AD from other forms of dementia. By selecting patients with Aβ PET + / FDG-PET + and Aβ PET – / FDG-PET +, we propose an integrated algorithm of clinical and molecular imaging information to better define type of dementia in older persons.


Key words: Molecular imaging, amyloid PET, 18F-FDG PET, Alzheimer disease.



Beta amyloid deposition in cerebral tissue is one of the major histopathological hallmarks of Alzheimer’s disease (AD) and the development of radiotracers to visualize β-amyloid plaques in vivo has been proposed as a biomarker of AD.
The Cochrane Library recent reports examined the utilization of the (11)C-PIB-PET for the early diagnosis of AD and other types of dementia in adult persons (1). The conversion from mild cognitive impairment (MCI) to AD was evaluated in nine of the examined studies. Of the 274 participants included in the meta-analysis, 112 developed Alzheimer’s dementia. Sensitivity ranged between 83% and 100% while specificity between 46% and 88%. They concluded that given the heterogeneity in the conduct and interpretation of the test and the lack of defined thresholds for determination of test positivity, its routine use in clinical practice cannot be recommended. However, a recent meta-analysis (2), showed that, among participants with dementia, the prevalence of amyloid positivity was associated with clinical diagnosis, age, and APOE genotype. These findings indicate the potential clinical utility of amyloid imaging for differential diagnosis in early-onset dementia. This technique can also support the clinical diagnosis in subjects who are negative for APOE ε4 status and older than 70 years (2). Moreover, among persons without dementia, the prevalence of cerebral amyloid pathology as determined by positron emission tomography (PET) or cerebrospinal fluid findings was associated with age, APOE genotype, and presence of cognitive impairment. These findings suggest a 20- to 30-year interval between first development of amyloid positivity and the onset of dementia (3). A robust difference was also found in the amyloid PET tracer PIB retention in cerebral tissue between AD patients and healthy controls (HC).
However, the utilization of FDG-PET for the diagnosis of AD is still debated. The variability of specificity values and the absence of defined thresholds for determination of test positivity among different studies is still a major issue (1). Thus, current evidence does not support the routine use of FDG-PET scans in clinical practice in persons with MCI (4). FDG PET scan is also an expensive test, therefore it is important that its accuracy is clearly demonstrated and its protocol adequately standardized before it can be widely used. More uniform approaches to thresholds sufficient sample sizes are needed in future studies to make definitive recommendations (5).
In the present case-series, we combined metabolic (from FDG-PET) and amyloid plaque load (from Aβ PET) information in older individuals with dementia, in order to: a) identify AD from other forms of dementia, b) select patients with Aβ PET + / FDG-PET + and Aβ PET – / FDG-PET + and c) propose an algorithm to integrate clinical and imaging information to better define different types of dementia in older persons.



Patients were evaluated at the Center for Cognitive Disorders, AUSL of Parma, while the PET scans were performed at the Nuclear Medicine Department of University-Hospital of Parma (Parma, Italy). Patients were first evaluated by neurologists with a standard clinical evaluation (6) and then referred to a neuropsychologist with long-term experience in clinical and experimental neuropsychology of degenerative diseases (7). The diagnosis of mild cognitive impairment (MCI) (8) and dementia syndrome, independent of etiology, were established using a standard evaluation protocol based on the 1984 NINCDS–ADRDA criteria (9). The neuropsychological battery included test assessing abstract reasoning, memory, attention, language, praxis and visuo-perceptive functions. All tests included in the neuropsychology battery have normal ranges and cut-offs available for the Italian population (10-12). Depressive  symptoms were assessed by the 15-item Geriatric Depression Scale (GDS-15) which is a widely used screening instrument for depressive symptoms in the elderly. The 15-item Geriatric Depression Scale (GDS-15) detects changes in depressive symptoms after a major negative life event (13). All patients underwent a brain MRI or CT scan in the previous 3 months. Chronic drug treatment were recorded. Missing data were integrated by checking in original clinical sheet. The data were treated in agreement with Italian law for the privacy guaranty. The study received approval by the institutional review board, and all patients signed informed consent.

PET/CT imaging

Both 18F-FDG and amyloid PET scans were performed using a whole-body hybrid system Discovery IQ (GE Healthcare) operating in three-dimensional detection mode, in two different days. Head holder was used to restrict patient and head movement was checked on a regular basis.


After an overnight fast, 200 MBq of 18F-FDG  were administered intravenously in a quiet, dimly lit examination room. The brain PET/CT recording was started 30 min after tracer injection. During the 30-minute uptake period, participants were left undisturbed in a darkened room and instructed to rest quietly without activity with their eyes close, as commonly recommended.
The brain CT was first recorded to provide the attenuation correction map (140 kV, 25 mA, 512×512 matrix, 3.75-mm slice thickness, scan Type Helical full 0.8 s, No of images 79, Rec Fov 30 cm, recon type standard). CT was immediately followed by a 3D-PET recording during a 10-min period (FOV 30 cm, recon type QCHD and VPHD, matrix size 256×256).
Quantitative analysis was performed using the SPM5 software implemented in Matlab R2014a (12). The patient PET dataset was spatially normalized using the SPM5 PET template and smoothed with a Gaussian filter of 8 mm FWHM. Differences in CMRglc (patient  vs. normal) were assessed on a voxel-by-voxel basis, using a paired t-test. The results were displayed on the Tailarach atlas.

18F-fluorbetaben PET

All cerebral emission scans began 90 minutes after a mean injection of 4 MBq/kg weight (240-360 MBq) of 18F-fluorbetaben. For each subject, 10-minute frames were acquired to ensure movement-free image acquisition. All PET sinograms were reconstructed with a 3-D iterative algorithm, with corrections for randomness, scatter, photon attenuation and decay, which produced images with an isotropic voxel of 2×2×2 mm and a spatial resolution of approximately 5-mm full-width at a half-maximum at the field of view center.
PET images were assessed visually by two trained, independent readers blinded each other with a  previously described technique (14-15).
Visual assessment of florbetaben PET images was performed by a three-grade scoring system (RCTU – regional cortical tracer uptake) comparing the activity in cortical gray matter (frontal cortex, posterior cingulate cortex/precuneus; lateral temporal cortex, and parietal cortex) with activity in adjacent cortical white matter. The RCTU scores (1 = no binding, 2 = minor binding, and 3 = pronounced binding) were then condensed into a single three-grade scoring system for each PET scan, the BAPL score: 1 = no b-amyloid load, 2 = minor b-amyloid load, 3 = significant b-amyloid load, with the resulting scores condensed into a binary interpretation (score 1 = negative; score 2 or 3 = positive).


The complexity of the diagnosis of dementia in older persons: three suggestive case reports

First Case (probable AD)

A 74-year-old woman with memory complaints was evaluated. Her medical history was characterized only by hypertensive cardiomyopathy.  At the initial cognitive assessment, she showed a nearly normal Mini Mental Examination State (MMSE 23.4/30 adjusted for age and education). Cerebral MRI showed mild cerebral atrophy and slight hippocampal atrophy. The neurological examination was free of neurological signs. At the Geriatric Depression Scale (GDS) she scored 1/15 and her Neuropsychiatric Inventory score was 0/144. The neuropsychological evaluation showed deficits in immediate and cue total verbal recall and constructional apraxia. The FDG-PET scans showed a significantly hypo-metabolism involving frontal, temporal and parietal lobe (Figure 1a). The amyloid PET was then performed, indicating a significant presence of amyloid plaques (BAPL 3) (Figure 1b). The diagnosis of probable Alzheimer’s disease was thus established.

Figure 1a. Metabolic PET. Tracer: 18F-FDG

a) Quantitative assessment of FDG uptake: SPM results on a voxel-by-voxel basis; b) Results of the analysis displayed on the Tailarach atlas


Figure 1b. Amyloid PET (axial sections). Tracer: 18F-Fluorbetaben


Second Case (probable FTD)

A 75-year-old woman with “hesitant and agrammatical” language was evaluated. Her medical history was null. Her cerebral CT scan was normal. At the initial cognitive assessment, she showed a pathological Mini Mental Examination State (MMSE 21.4/30 adjusted for age and education). The neurological examination was normal. At the cognitive evaluation she was found having both memory and extra-amnesic disorders relating to consistent difficulties on speech production. The FDG-PET scans showed a significantly reduced metabolic activity in many cerebral areas, including prevalently the frontal lobe (Figure 2a). The amyloid PET was negative for brain amyloid deposition  (Figure 2b). The diagnosis of primary progressive aphasia due to probable Fronto-Temporal Dementia was established.

Figure 2a. Metabolic PET. Tracer: 18F-FDG

Quantitative assessment of FDG uptake: SPM results on a voxel-by-voxel basis.


Figure 2b. Amyloid PET/CT. Tracer: 18F-Fluorbetaben


Third Case (probable vascular dementia, VaD)

A 79-year-old men with history of hypertension and type 2 diabetes was evaluated for progressive loss of language. At the initial cognitive assessment, he showed a slight deficit on Mini Mental Examination State (MMSE 22.3/30 adjusted for age and education). His cerebral MRI was positive for the presence of small vascular lesions (not of lacunar type), in peri-ventricular and peri-trigonal areas and cortical atrophy. At the neurological examination bilateral Babinski sign and palmo-mental reflex were present. The cognitive evaluation showed multiple deficits on abstract reasoning, verbal fluency, executive and constructional functions, delayed memory recall difficulties. The initial diagnosis was of mild cognitive impairment due to cerebral vascular pathology. The FDG-PET scans however showed a significantly hypo-metabolism in different cerebral areas involving the frontal, temporal and parietal lobes (Figure 3a). The amyloid PET showed a significant presence of amyloid plaques (BAPL 3) (Figure 3b). The conclusive diagnosis was therefore of probable Alzheimer’s disease with associated cerebral vascular lesions.

Figure 3a. Metabolic PET. Tracer: 18F-FDG

a) Quantitative assessment of FDG uptake: SPM results on a voxel-by-voxel basis; b) Results of the analysis displayed on the Tailarach atlas


Figure 3b. Amyloid PET (axial sections). Tracer: 18F-Fluorbetaben


Figure 4. PET Algorithm Proposed



Discussion and proposition of a possible algorithm for the diagnosis of dementia, integrating neuropsychological and neuroimaging information

We briefly described three case reports underlying the utility of PET imaging in the differential diagnosis of dementia types. FDG-PET and amyloid-PET scans seem to be  useful  in the diagnostic algorithms of dementia, even if some limitations are present.
Our case-series introduce some considerations for a probably more appropriate prescription of amyloid and FDG-PET (Figure 4). When well defined neuropsychological deficits, regarding memory plus other cognitive domains, are present and the available international criteria are satisfied, an amyloid PET scan positive for a significant plaque deposition is suggestive for the diagnosis of AD. Conversely, when the amyloid PET is negative, the FDG-PET results should be considered. If the FDG-PET is indicative of hypomethabolism in the frontal areas, a diagnosis of FTD is highly probable (case report two). Moreover, the execution of the amyloid PET might also help in selected cases when the differential diagnosis between VaD and AD is not clear. In the frequent cases where MRI scans are positive for diffuse small vascular lesions, not clearly of lacunar origin, the amyloid PET might contribute to confirm a degenerative disease, especially when the clinical history is not suggestive of VaD. On the contrary, even in the presence of vascular lesions, a positive amyloid PET allows the clinical diagnosis of AD, supporting the hypothesis of a primary neurodegenerative diseases overlapping to cerebral vascular lesions (case report three).
Our case-series highlights the need of establishing a clear diagnostic algorithm emphasizing the role of the neuropsychological and clinical assessment combined with the information derived from nuclear medicine in order to achieve an early diagnosis allowing the most accurate and effective therapeutic options.
Since the burden of dementia in older persons is predicted to increase, it is imperative to develop accurate diagnostic techniques, particularly in older individuals where cerebrovascular lesions and neurodegenerative diseases may coexist.
The application of FDG-PET study to evaluate brain metabolic features in degenerative forms of dementia and amyloid PET to assess β-amyloid plaque load, in the absence of lacunar vascular lesions with significant memory loss, makes the diagnosis of Alzheimer’s disease more likely, thus reducing errors in differential diagnosis.
On the other hand, when cognitive deficits tend to spare memory and mainly involve language, a positive FDG-PET with a negative amyloid PET allow to suggest the presence of other forms of neurodegenerative dementia, such as FTD.
Our hypothesis to use molecular imaging techniques as complementary rather than redundant information is in accordance with recent data published by Besson et al. (17). They suggested  that MRI and FDG-PET biomarkers should be used in combination with amyloid PET, offering an additive contribution instead of reflecting the same process of neurodegeneration (17). The amyloid PET seems to have a high grade of specificity to discriminate AD from FTD, as confirmed by recent data, showing that the (18)F-florbetapir uptake is significantly different between AD and FTD patients (18). Then, another study found 18F-florbetaben to be useful in distinguishing patients with AD from healthy controls, as well as from patients with other neurodegenerative disorders. In fact, 96% of patient with AD and 60% of patients with MCI displayed broadly distributed cortical 18F-florbetaben retention, compared to 9% of patients with FTD, 25% of patients with VaD, 29% of patients with dementia with Lewy bodies (DLB), and 16% of healthy controls (19).
In conclusion, earlier (20) and more accurate diagnosis of AD (21), by using amyloid PET may help to accurately identify patients with AD, contributing to improve patient’s health status, despite the high costs of this technique. The proposed hypotheses should be confirmed in future studies with adequate sample size and prospective studies to confirm, even post-mortem, diagnosis suggested by integrating neuroimaging and neuropsychological findings.


Acknowledgments: The authors do not have any conflict of interest in the publication of this case series, have all contributed to the conception of the description and in the writing of this case series, and have approved the manuscript in its present form.

Conflict of interest: None.

Ethical standard: We allowed the good clinical practice for detecting different type of dementia in older persons.



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E. Cavedo1, S. Lista2, Z. Khachaturian3, P. Aisen4, P. Amouyel5, K. Herholz6, C.R. Jack7 Jr, R. Sperling8, J. Cummings9, K. Blennow10, S. O’Bryant11, G.B. Frisoni12, A. Khachaturian13, M. Kivipelto14, W. Klunk15, K. Broich16, S. Andrieu17, M. Thiebaut de Schotten18, J.-F. Mangin19, A.A. Lammertsma20, K. Johnson21, S. Teipel22, A. Drzezga23, A. Bokde24, O. Colliot25, H. Bakardjian26, H. Zetterberg27, B. Dubois28, B. Vellas29, L.S. Schneider30, H. Hampel31 


1. Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A) Hôpital de la Pitié-Salpétrière & Institut du Cerveau et de la Moelle épinière (ICM), UMR S 1127, Hôpital de la Pitié-Salpétrière Paris & CATI multicenter neuroimaging platform, France; Laboratory of Epidemiology, Neuroimaging and Telemedicine, IRCCS San Giovanni di Dio Fatebenefratelli Brescia, Italy; 2. AXA Research Fund & UPMC Chair; Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A) Hôpital de la Pitié-Salpétrière & Inserm U1127 Institut du Cerveau et de la Moelle épinière (ICM), Hôpital de la Pitié-Salpétrière Paris, France; 3. The Campaign to Prevent Alzheimer’s Disease by 2020 (PAD2020), Potomac, MD, USA; 4. Department of Neurosciences, University of California San Diego, San Diego, CA, USA; 5. Inserm, U744, Lille, 59000, France; Université Lille 2, Lille, 59000, France; Institut Pasteur de Lille, Lille, 59000, France; Centre Hospitalier Régional Universitaire de Lille, Lille, 59000, France; 6. Institute of Brain, Behaviour and Mental Health, University of Manchester, UK; 7. Department of Radiology, Mayo Clinic, Rochester, MN, USA; 8. Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 9. Cleveland Clinic Lou Ruvo Center for Brain Health, 888 West Bonneville Avenue, Las Vegas, Nevada 89106, USA; 10. Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; 11. Department of Internal Medicine, Institute for Aging & Alzheimer’s Disease Research, University of North Texas Health Science Center, Fort Worth, TX, USA; 12. IRCCS Istituto Centro S. Giovanni di Dio Fatebenefratelli, Brescia, Italy; University Hospitals and University of Geneva, Geneva, Switzerland; 13. Executive Editor, Alzheimer’s & Dementia; 14. Karolinska Institutet Alzheimer Research Center, NVS, Stockholm, Sweden; 15. Department of Psychiatry, University of Pittsburgh School of Medicine, USA; Department of Neurology, University of Pittsburgh School of Medicine, USA; 16. Federal Institute of Drugs and Medical Devices (BfArM), Bonn, Germany; 17. Inserm UMR1027, Université de Toulouse III Paul Sabatier, Toulouse, France; Public health department, CHU de Toulouse; 18. Natbrainlab, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, King’s College London, London, UK; Université Pierre et Marie Curie-Paris 6, Centre de Recherche de l’Institut du Cerveau et de la Moelle épinière (ICM), UMRS 1127 Paris, France; Inserm, U 1127, Paris, France; CNRS, UMR 7225, Paris, France; 19. CEA UNATI, Neurospin, CEA Gif-sur-Yvette, France & CATI multicenter neuroimaging platform; 20. Department of Radiology & Nuclear Medicine, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, The Netherlands; 21. Departments of Radiology and Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 22. Department of Psychosomatic Medicine, University of Rostock, and DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany; 23. Department of Nuclear Medicine, University Hospital of Cologne, Cologne Germany; 24. Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland; 25. Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225 ICM, 75013, Paris, France; Inria, Aramis project-team, Centre de Recherche Paris-Rocquencourt, France; 26. Institute of Memory and Alzheimer’s Disease (IM2A), Pitié-Salpétrière University Hospital, Paris, France; IHU-A-ICM – Paris Institute of Translational Neurosciences, Paris, France; 27. Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; UCL Institute of Neurology, Queen Square, London, UK; 28. Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A) Hôpital de la Pitié-Salpétrière & Inserm U1127 Institut du Cerveau et de la Moelle épinière (ICM), Hôpital de la Pitié-Salpétrière Paris, France; 29. Inserm UMR1027, University of Toulouse, Toulouse, France; 30. Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 31. AXA Research Fund & UPMC Chair; Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A) Hôpital de la Pitié-Salpétrière & Inserm U1127 Institut du Cerveau et de la Moelle épinière (ICM), Hôpital de la Pitié-Salpétrière Paris, France

Corresponding Author: Enrica Cavedo and Harald Hampel, Université Pierre et Marie Curie, Paris 06, Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital de la Salpêtrière, 47 Bd de l’hôpital, 75013 Paris, France – Tel 33 1 42 61925,;;

J Prev Alz Dis 2014;1(3):181-202

Published online November 28, 2014,



Alzheimer’s disease (AD) is a slowly progressing non-linear dynamic brain disease in which pathophysiological abnormalities, detectable in vivo by biological markers, precede overt clinical symptoms by many years to decades. Use of these biomarkers for the detection of early and preclinical AD has become of central importance following publication of two international expert working group’s revised criteria for the diagnosis of AD dementia, mild cognitive impairment (MCI) due to AD, prodromal AD and preclinical AD. As a consequence of matured research evidence six AD biomarkers are sufficiently validated and partly qualified to be incorporated into operationalized clinical diagnostic criteria and use in primary and secondary prevention trials. These biomarkers fall into two molecular categories: biomarkers of amyloid-beta (Aβ) deposition and plaque formation as well as of tau-protein related hyperphosphorylation and neurodegeneration. Three of the six gold-standard (« core feasible) biomarkers are neuroimaging measures and three are cerebrospinal fluid (CSF) analytes. CSF Aβ 1-42 (Aβ1-42), also expressed as Aβ1-42 : Aβ1- 40 ratio, T-tau, and P-tau Thr181 & Thr231 proteins have proven diagnostic accuracy and risk enhancement in prodromal MCI and AD dementia. Conversely, having all three biomarkers in the normal range rules out AD. Intermediate conditions require further patient follow-up. Magnetic resonance imaging (MRI) at increasing field strength and resolution allows detecting the evolution of distinct types of structural and functional abnormality pattern throughout early to late AD stages. Anatomical or volumetric MRI is the most widely used technique and provides local and global measures of atrophy. The revised diagnostic criteria for “prodromal AD” and « mild cognitive impairment due to AD » include hippocampal atrophy (as the fourth validated biomarker), which is considered an indicator of regional neuronal injury. Advanced image analysis techniques generate automatic and reproducible measures both in regions of interest, such as the hippocampus and in an exploratory fashion, observer and hypothesis-indedendent, throughout the entire brain. Evolving modalities such as diffusion-tensor imaging (DTI) and advanced tractography as well as resting-state functional MRI provide useful additionally useful measures indicating the degree of fiber tract and neural network disintegration (structural, effective and functional connectivity) that may substantially contribute to early detection and the mapping of progression. These modalities require further standardization and validation. The use of molecular in vivo amyloid imaging agents (the fifth validated biomarker), such as the Pittsburgh Compound-B and markers of neurodegeneration, such as fluoro-2-deoxy-D-glucose (FDG) (as the sixth validated biomarker) support the detection of early AD pathological processes and associated neurodegeneration. How to use, interpret, and disclose biomarker results drives the need for optimized standardization. Multimodal AD biomarkers do not evolve in an identical manner but rather in a sequential but temporally overlapping fashion. Models of the temporal evolution of AD biomarkers can take the form of plots of biomarker severity (degree of abnormality) versus time. AD biomarkers can be combined to increase accuracy or risk. A list of genetic risk factors is increasingly included in secondary prevention trials to stratify and select individuals at genetic risk of AD. Although most of these biomarker candidates are not yet qualified and approved by regulatory authorities for their intended use in drug trials, they are nonetheless applied in ongoing clinical studies for the following functions: (i) inclusion/exclusion criteria, (ii) patient stratification, (iii) evaluation of treatment effect, (iv) drug target engagement, and (v) safety. Moreover, novel promising hypothesis-driven, as well as exploratory biochemical, genetic, electrophysiological, and neuroimaging markers for use in clinical trials are being developed. The current state-of-the-art and future perspectives on both biological and neuroimaging derived biomarker discovery and development as well as the intended application in prevention trials is outlined in the present publication.


Key words: Alzheimer’s disease, prevention trials, biomarkers, molecular imaging, neuroimaging




A first wave of disease-modifying candidate treatments for Alzheimer disease (AD) has so far failed to demonstrate efficacy in systematic clinical trials and therefore have not gained regulatory approval. Part of the reason is considered to be due to an intervention in a too late stage of AD when pathophysiological mechanisms and irreversible neuropathological lesions of AD have largely spread through the brain (1). Therefore, prevention at earlier preclinical stages seems a promising way to decrease the incidence of this age-associated neurodegenerative disease, and its associated burden for society (2). Further roadblocks to successful development are due to shortcomings and challenges in appropriate trial design (3-5).

A biomarker (biological marker) is defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” (6). Biological and neuroimaging markers of AD are assumed to present central tools for prevention trials and most of them are applied in prevention trials for AD (for an overview, see Table 1). They can be divided into: (i) diagnostic markers, used to enrich, select, and stratify individuals at risk of AD; (ii) endpoint biomarkers, used as outcome measures to monitor the rate of disease progression and detect treatment effects (7), and finally (iii) markers of target engagement, used to target directly the pathophysiology of AD during the preclinical stages (8, 9). Owing to the advances in discovery, development, and validation of AD related neuroimaging and biological markers, it has now become possible to significantly improve the detection and diagnosis of AD by using a combined « multimodal » approach (10, 11). In particular, biomarkers derived from structural/functional/metabolic/molecular neuroimaging and/or neurophysiology (12, 13), and/or neurobiochemistry of cerebrospinal fluid (CSF) (14-16), blood (plasma/serum) and/or (17-19) neurogenetic markers (18, 20, 21) have been introduced. Moreover, the combination of different source biomarkers (22) is believed to make the selection of asymptomatic individuals at risk of AD possible who are a particularly attractive target population for prevention trials. The development of this scenario requires the involvement of regulatory bodies and industry stakeholders providing critical guidance in the area of AD biomarker discovery and application in prevention trials (18, 23).

Here, we review the current and future role of multimodal gold-standard (« core, feasible ») biomarkers –derived from structural, functional, metabolic and molecular neuroimaging, from neurochemistry and genetics – in AD prevention trials, adding some perspectives on biomarker discovery, development, and application in the future prevention trials. In addition, regulatory issues and perspectives related to biomarkers applications in clinical trials will be discussed.


The meaning of prevention in the context of Alzheimer clinical trials

From a public health perspective, treatments as well as clinical trials of therapeutics are classified in terms of primary, secondary, and tertiary prevention interventions (24). Primary prevention aims at reducing the incidence of illness across the broad population by treating the subjects before disease onset, thus promoting the maintenance of good health or eliminating potential causes of disease. Two paradigms of primary prevention approaches are reducing population risk of illness (1) by altering environmental and cardiovascular risk factors, and (2) by using disease-specific mechanistic approaches such as polio vaccination (Figure 1).  Secondary prevention aims at preventing disease at preclinical phases of illness, from progressing to clearly diagnosed disease, while tertiary prevention is focused on treating the disease when it has been clinically diagnosed and its consequences.

Figure 1. Prevention approaches. The range of prevention approaches include one targeting highly specific populations (biomarker evidence for AD pathology) with specific targeted interventions (e.g. anti-amyloid). Another approach is broad, multi-factorial, populationbased, and non-specific. Both approaches are needed and we should probably work more in the ‘area between’ these approaches, combining potential treatments and interventions and to various at-risk populations. (With permission, Solomon et al 2014) (24)


The above definitions are conceptually direct but they do not practically work well with the developing concepts of AD therapeutics. The traditional diagnosis of AD refers to “Alzheimer disease dementia”, that is when the illness is at the late dementia stage (25). Under these considerations, primary and secondary prevention involve delaying or impeding the onset of dementia, while tertiary prevention involves subjects already diagnosed and treated by cognitive enhancers, psychotherapeutic drugs, as well as psychosocial and environmental approaches.

In this perspective, the difference between primary and secondary prevention is whether individuals to be treated have or not signs of cognitive impairment. The recent use of biomarkers or bioscales to establish population risk or to enrich a treatment sample for those more likely than others to develop AD, together with the related evolution of clinical diagnostic constructs of ‘prodromal Alzheimer disease’ or ‘MCI due to AD (26, 27) has created a milieu in which the meaning of ‘prevention of AD’ becomes more nuanced and complex.

Indeed, there is a shared clinical presentation and underlying pathobiology with both prodromal AD and AD (dementia) such that ‘prevention’ might be better considered as delaying the onset of prodromal AD or AD (27).

Secondary prevention may then focus on people who may be at particular, specific risk, have early signs of the illness, or evidence of AD neuropathology that, if further expressed, would lead to the illness. Here, the illness would be represented by the earliest stage of AD that can be accurately diagnosed, and which, currently, is represented by ‘prodromal AD’ or ‘MCI due to AD’ (any attempt to diagnose illness earlier, e.g., ‘pre-clinical’ AD would be far less certain and must rely mainly on the presence of biomarkers of AD neuropathology).

An illustrative exception is the example of the recent Dominantly Inherited Alzheimer Network Trial (DIAN- TU), involving dominantly-inherited AD neuropathology and disease caused by single gene mutations that have nearly 100% penetrance such that it appears that all people with the mutation will sooner or later develop a dementia syndrome (28). In this scenario, the consideration with respect to describing a primary or secondary prevention effort is whether or not the mutation itself without clinical signs can be considered the disease and therefore ‘preclinical AD’.

The concept of ‘primary prevention’ can be taken further by including in clinical trials subjects who are considered to have no evidence of AD pathology based on the absence of clinical signs and negative amyloid biomarker status, assuming that these individuals have a lower risk for AD than the overall population. The complementary approach, however, is selecting a sample with no clinical evidence of AD pathology but that is biomarker positive. This latter sample would have a somewhat higher actuarial risk for illness; and here treatment could be considered either primary or secondary prevention depending on whether the biomarker itself is considered as defining the pathology of AD and diagnosis of the illness (Figure 2) (24). For instance, the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) trial ( (29,

30) selects participants with or without a memory complaint and who are PET amyloid positive for randomized treatment with an antibody targeting Aβ or with placebo. This study may be considered either as primary or secondary prevention trial depending on one’s interpretation of the sample selected for treatment (30-32).

Several current prevention trials focus on individuals who are cognitively within the normal range but are at increased risk for AD due to a mutation (28, 33), amyloid deposition in the brain (A4 trial) (30), an apolipoprotein E and TOMM40 (ApoE/TOMM40) genotype combination (TOMMORROW trial) (34), or ApoE ε4 homozygous status (Alzheimer Prevention Initiative (API), Phoenix) (35). These studies have been developed to prevent the progression from normal or slightly impaired cognition to clear cognitive impairment or, in the TOMMORROW trial, to ‘MCI due to AD’ or AD. Other trials begin with patients in prodromal AD or MCI due to AD and aim at delaying the progression to AD dementia. The majority of these studies are include neuroimaging and biological markers to select target population or as secondary outcome measures. Although biomarkers are potentially useful to select clinical trials sample likely to develop AD, they are not validated as primary surrogate outcomes yet. Thus, clinical outcomes should continue to remain the primary outcomes used in preventive trials.

Figure 2. How disease definition affects prevention. The figure illustrates how two alternative definitions of AD (i.e., definition 1, disease defined as starting with neuropathological changes, and, definition 2, disease starting with clinical symptoms) lead to different definitions of primary, secondary and tertiary prevention. The differences between the definitions may blur distinctions between prevention and treatment strategies. For example, if Abeta-PET positivity is considered and accepted as diagnostic for AD (i.e., preclinical AD) then treating such a sample would be an example of secondary prevention rather than primary (237). Alternatively, if Abeta-PET positivity is considered a risk for the future development of cognitive impairment and Alzheimer pathology then treatment would be considered as primary prevention (238, 239). These frameworks show that it is difficult to define pure primary vs secondary prevention. (With permission, Solomon et al 2014) (24)

Finally, preventive interventions should be targeted for those most at risk by determining each individual’s or group’s risk for cognitive impairment and dementia. It may be possible to identify individuals who are relatively more likely than others to benefit from intensive lifestyle or risk-reduction changes and/or pharmacological interventions. Given the heterogeneous and multifactorial etiology of AD, preventive strategies targeting several risk factors simultaneously may be needed for an optimal preventive effect. Many modifiable risk factors (e.g. high blood pressure, obesity, physical inactivity, cigarette- smoking, and unhealthy diet) are shared among dementia/AD and other late-life chronic conditions (36). Thus, prevention agendas linking dementia and other non-communicable diseases should be developed. Because AD develops over decades, an overall life-course approach to prevention is needed. Different preventive interventions may be needed at different ages and in different contexts (37).


Structural, functional and diffusion Magnetic Resonance Imaging (MRI) markers: current applications ad future methods


Structural MRI markers

Magnetic resonance imaging (MRI) is highly versatile and, thus, multi-modality information can be acquired in a single patient examination, including those discussed in the present section. The most widely studied MRI modality is structural MRI (sMRI). In AD, cerebral atrophy – detected by sMRI – occurs in a characteristic topographic distribution (38, 39) which mirrors the Braak (40) and Delacourte (41) neurofibrillary tangles (NFT) staging. Here, atrophy begins in the medial temporal lobe and spreads to the temporal pole, basal and lateral temporal areas, and medial and lateral parietal areas (42). The primary proteinopathies associated with atrophy in AD are tau and TDP43 (43-45). Atrophy, however, does not follow the topography of Aβ nor is atrophy particularly well correlated with plaque counts Aβ or immunostaining in imaging-autopsy correlations (46, 47). Thus, sMRI is correctly viewed as a direct measure of neurodegeneration.

The location and severity of atrophy can be extracted from grey scale images by qualitative visual grading (48), by quantification of the volume of specific structures, or by measuring volume/thickness from multiple regions of interest to form AD-signature composite measures (49, 50). The most common sMRI measure employed in AD is the atrophy of the hippocampus, recently recommended by the revised criteria for AD as one of AD core biomarkers (25-27, 32, 51, 52). For this reason, international efforts to harmonize the definition of the hippocampus were carried out (53-55). Fully automated MR-based hippocampal volumetry seems to fulfill the requirements for a relevant core feasible biomarker for detection of AD associated neurodegeneration in everyday patient care, such as in a secondary care memory clinic for outpatients. Software used is partly freely available, e.g. as an SPM8 toolbox. These methods seem robust and fast and may be easily integrated into routine workflow (56).

In clinical trials, sMRI is or can be used in a variety of capacities. T2-weighted and FLAIR scans can be used to exclude patients with extensive white matter changes, where cognitive impairment might be significantly contributed by or solely due to microvascular disease (57, 58). Hippocampal atrophy has been approved by the European Medicine Agency (EMA) as a means of enriching trials in prodromal AD populations based on the observation in natural history studies that greater hippocampal atrophy predicts more rapid cognitive decline (59-64). Measures of the rate of brain atrophy have been used as endpoints based on the observation in natural history studies that atrophy rates correlate highly with the rate of concurrent clinical decline (65, 66). Of all known outcome measures (including clinical, psychometric, neuroimaging, and biofluid biomarkers), sMRI seems to have the highest measurement precision and thus has been viewed as an attractive outcome measure for clinical trials (67). However, unexpected or counter intuitive results (i.e. more rapid rates of brain shrinkage in treated subjects) in several disease modifying trials (68) have dampened the enthusiasm of some in the pharmaceutical industry for sMRI as an outcome measure. The most rational explanation for such findings, however, is that there may be first wave of short term volume losses associated with amyloid removal perhaps due to a reduction in activated microglia that were associated with plaques. If and when interventions effective on neurodegeneration will be available, sMRI may be able to map a second wave of volume loss sparing that will map onto AD-specific regions of neurodegeneration. Moreover, if/when interventions that target other aspects of the AD pathophysiological pathway (e.g. tau stabilization, or neuroprotection) will be entered into clinical trials, interest in sMRI as an outcome measure might experience a rapid resurgence. In light of this, we believe that sMRI will continue to have a role in AD clinical trials as an outcome measure.

In addition to its role as a measure of AD-related neurodegeneration, sMRI is also an important safety monitor in clinical trials. Both micro bleeds and transient cerebral edema (known as ARIAH and ARIAE respectively) have been reported in some subjects treated with active Aβ immunization and administration of anti Aβ monoclonal antibodies (68-70). ARIAH is best captured by T2* imaging and ARIAE by FLAIR imaging.


Functional MRI markers

The blood oxygenation level dependent (BOLD) signal measured with Functional Magnetic resonance imaging (fMRI) reflects primarily the local vascular response to regional neuronal activation and intracortical processing (71). At the moment the main use for the BOLD signal would be in secondary prevention trials where the signal would be used to predict conversion of MCI subjects to AD dementia. One approach is to use a cognitive paradigm that “stresses” the brain or structure that is known to be affected in the preclinical stages of the disease. For example a learning paradigm will activate the hippocampus and it has been shown to vary linearly from high to low from HC to MCI to AD dementia patient groups, respectively (72, 73). Another learning paradigm (encode face & name pairing) leads to a nonlinear response in hippocampus, with higher activation in MCI subjects compared to HC and AD dementia patients (74-77). Not only memory but also attention-related paradigms may be used as a secondary prevention biomarker such as working memory (78-80) and perceptual tasks (81-83).

Another strategy for BOLD-based biomarkers that could be used for secondary prevention trials are the intrinsic coherent networks (ICN) (84, 85). The biomarkers would be based on measures of neural network integrity, which have been shown to differentiate among HC, MCI subjects and AD dementia groups (86, 87) and also between HC groups with different amyloid loads (88, 89). Functional MRI based biomarkers could provide an approach to select patients for secondary prevention trials and to track progression from preclinical to clinical stages of the disease but also further work needs to be done to better understand the relationship between the BOLD signal and clinical changes.

As a primary prevention biomarker it still needs considerable research and development work, one of the primary issues is the potential confound between normal aging and development of AD-related pathology. Normal aging alters the potential fMRI biomarker (a recent review (90)) and alterations that are seen in MCI group (74-77) are similar due to middle aged HC with different ApoE status (91). The fMRI signal is shown to be dynamic and further investigation is required before the normal aging related changes can be separated from those due to pathology.

Based on these preliminary results, fMRI represents a promising approach for the selection and the stratification of individuals at risk of AD in clinical prevention trials.


Diffusion weighted imaging

Magnetic resonance diffusion weighted imaging quantifies the diffusion characteristics of water molecules in any tissue (92). White matter microstructure integrity can be estimated applying the tensor model to diffusion weighted images. In so doing, monocentric studies report an accuracy between 77% and 98% for diffusion tensor imaging (DTI) metrics of limbic white matter and of whole-brain voxel-based pattern classifiers (such as mean diffusivity and fractional anisotropy) in studies aimed to discriminate MCI individuals who progress and convert to AD dementia and those who remain stable over a follow-up of 1 to 3 years (93-96). DTI measures, however, are more prone to multicenter variability than classical volumetric MRI sequences (97). Despite higher multicenter variability, DTI detected predementia stages of AD with a moderately higher accuracy than volumetric MRI in a multicenter setting using machine learning algorithms (98).

Longitudinal DTI studies are still rare, indeed, individuals with MCI and AD dementia showed declining integrity of intracortically projecting fiber tracts (99-101). One study has reported a moderate effect of treatment with a cholinesterase inhibitor on fiber tract integrity in AD dementia patients (102).

According to the currently available scientific evidence, DTI will be mainly used in secondary prevention trials to predict AD dementia in individuals with MCI. Currently, evidence demonstrating the potential use of DTI to predict cognitive decline and dementia in cognitively healthy elderly individuals is not sufficient for primary prevention trials. On theoretical grounds, based on the early involvement of axonal and dendritic integrity in AD pathology, such a use seems possible but requires multicenter DTI studies to be conducted in preclinical AD. The use of DTI metrics as a surrogate of fiber tract integrity for clinical trials seems questionable to date given the high vulnerability of DTI measures to scanner drift effects over time compared to classical volumetric MRI data. Future studies are needed to further clarify this issue.

In addition to DTI metrics, tractography of diffusion- weighted imaging (DWI) represents a challenging method to study white matter organization in AD prevention trials population.

Given the dense axonal organization of white matter tissues, water molecules will be more likely to diffuse along rather than across them. Hence, by sequentially piecing together discrete estimates of the brain’s water diffusion, one might reconstruct continuous trajectory that follows the subjacent axonal organization. Using this approach, recent tractography studies identified an extended Papez circuit interconnecting essential areas dedicated to memory, emotion, and behavior (103). Indeed, axonal damage is associated with pathological behavioral manifestation (104, 105) and lead to drastic changes in the water diffusion properties that will affect the tractography reconstructions (106). Preliminary evidences have already associated discrete damage to these connections with early behavioral markers in AD (107, 108) and other dementia disorders (109). However, whether some of these anatomical changes occurred before the appearance of any behavioral signs is still unknown. It still needs to be shown if diffusion imaging tractography applied to pre-symptomatic populations may reveal exciting new footprints, which have the potential to model and predict the conversion from cognitive normality to the prodromal symptomatic stages of AD.


Utility of imaging platforms for AD prevention trials

Harmonization of image acquisition and analysis protocols is mandatory for increased statistical power and smaller sample sizes in AD prevention trials. Hence, following the seminal ADNI initiative (, multiple regional imaging platforms have been set up (110, 111) either in the context of specific multicenter studies or as a service to any study such as the CATI multicenter neuroimaging platform (, the neuGRID4you (, the CBRAIN (, the LONI ( The service model aims at lowering the cost of imaging technology ( The first objective of these platforms is the harmonization of a network of imaging facilities, data collection, rigorous quality control and standard analysis procedures. ADNI protocols are largely embedded in this kind of activity since they have become a standard (112). The second objective is the emergence of a broader spectrum of potential biomarkers, which can stem from new imaging modalities or from ‘‘head-to-head’’ evaluations of new analytic methods. Finally, these platforms generate normative values for determining trial sample size and for the future clinical use of biomarkers. With regard to the challenges ahead, it is eagerly required to create a superarching organization in charge of globally synchronizing this network of platforms to proceed further with the advent of standard protocols and data sharing. It is all the more crucial that a big data perspective is probably mandatory to generate the ultimate models required for the acceptance of imaging biomarkers as surrogate endpoints.


Molecular Imaging Markers: PET FDG, Amyloid, Tau, Neuroinflammation

Positron emission tomography (PET) provides specific imaging biomarkers for early detection and diagnosis and longitudinal assessment of molecular and functional changes associated with disease progression and therapeutic interventions. An increasing number of 18F- labeled tracers are now available for use at clinical sites, not requiring an on-site cyclotron and thus turning brain PET scans into a widely applicable routine tool in dementia research. This will provide detailed insight into human pathophysiology and the effects of early interventions that until recently could only be studied in experimental animals. In this section we will address current use of molecular markers for amyloid and tau, provide an update on FDG as a functional marker, and provide an outlook on new markers for neuroinflammation and transmitters.

Several tracers with similar properties (113), including 18F-florbetapir, 18F-florbetaben, and 18F-flutemetamol, are now being included into observational studies and intervention trials. Their visual analysis in a binary fashion as amyloid positive or negative has been thoroughly validated by post-mortem pathological assessment in AD (Figure 3 shows an example of PET amyloid uptake in controls and AD) (114). Although results are promising, methods for quantitative analysis have not yet reached the same degree of standardization, and more research is needed to understand inter- individual and longitudinal changes.

Figure 3. Positron Emission Tomography Staging of AD pathology. Coronal Positron Emission Tomography images (overlaid with structural Magnetic Resonance) of PiB Aβ (left column) and T807 Tau (right column) acquired from 3 normal individuals (top 3 rows) and a patient with AD dementia (bottom row). Low levels of amyloid are seen in the top 2 cases and high levels in the bottom 2. T807 binding is particularly striking in medial temporal lobe in the middle 2 normal cases, possibly corresponding to Braak Stage III/IV, but is more intense and widespread in the AD dementia case, which is consistent with Braak V/VI

Several important prevention trials on autosomal- dominant AD (ADAD) and late-onset AD (LOAD) incorporating PET amyloid are currently on going (Table 1). The role of PET amyloid in the studies investigating the effect of monoclonal anti-amyloid antibodies varies from that of a primary outcome measure (one arm of DIAN-TU), to secondary outcome measure (API), to screening tool necessary to meet inclusion criteria (A4). In the A4 study, eligible participants must show evidence of elevated amyloid on both a semi-quantitative SUVr measurement and a qualitative binary visual read of a florbetapir PET scan. Amyloid PET is also being utilized as an exploratory outcome measure in A4, along with Tau PET (T807) in a subset of participants in the A4 study. A4 will also include an observational cohort with a group of participants who fell just below the threshold for amyloid eligibility for A4 to determine the factors that predict rapid amyloid accumulation, as these individuals may be ideal candidates for future secondary prevention trials aimed at slowing the production of amyloid-beta.


Tau-PET imaging

In addition to amyloid-beta, deposits of hyperphosphorylated tau are the other main defining neuropathologic feature of AD. Until recently measurement of brain tau deposition has not been possible during life. Several PET ligands highly selective for tau deposits have now been applied to imaging of individuals along the AD spectrum, from cognitively normal to AD dementia. Initial experience with these ligands at a small number of centers (115, 116) indicates that binding is detected in the anatomic areas expected from AD pathology according to the ordinal Braak staging scheme (Figure 3). Thus, binding is observed in medial temporal areas in most cognitively normal older individuals, in additional limbic and neocortical regions among individuals with established AD-like cognitive impairment, and in more widespread neocortical regions among those with AD dementia. While within-subject longitudinal change in tau ligand binding has not yet been reported, the initial experience at the Massachusetts General Hospital in over 200 subjects using 18F-T807 PET suggests that the characteristics of this PET measure are potentially well suited for use in AD prevention trials. This new technology could potentially be used in clinical trials both to stage AD pathology and as a therapeutic endpoint.


FDG-PET imaging

While tracers for amyloid-beta and tau provide images of key pathological protein deposits, 18F-2-fluoro-2-deoxy-D-glucose (FDG) has already been used over many years as a functional marker of cortical synaptic dysfunction for diagnosis (117) and in clinical trials (118). Considerable progress has been made in recent years to derive quantitative biomarkers from FDG scans (119), while further standardization of analysis methods and longitudinal characterization of reference samples is still ongoing.

When applied to Mild Cognitive Impairment (MCI), FDG PET provides a good predictor of progression within the next 2 years (120), while markers of amyloid- beta and tau tend to become positive up to 20 years before actual onset of dementia. Recent studies comparing FDG and amyloid PET have revealed a substantial proportion of patients with amnestic MCI who have impaired FDG uptake while amyloid scans are negative (121). Contrary to the uniform sequential model of disease progression they show a relatively high rate of progression to dementia, and further research is required to clarify which type of dementia they actually suffer from. Considerable heterogeneity of AD subtypes and progression rates is well known from retrospective pathological studies (122), and longitudinal multimodal imaging studies including FDG are expected to provide better predictors and thus improve the efficacy of early intervention studies.


Inflammation- and receptor-PET imaging

Neurodegenerative diseases, including AD, are associated with activation of microglia. This leads to increased mitochondrial expression of the 18-kDa translocator protein (TSPO), which can be imaged using (R)-[11C]PK11195. Recent studies (123, 124) have partially confirmed earlier findings of increased cortical binding potential in AD, but this increase could not be detected in individual patients and was much weaker than the signal on amyloid PET (125). In addition, (R)-[11C]PK11195 was not able to separate clinically stable prodromal AD patients from those who progressed to dementia, and there was no correlation with cognitive function.

More recently, many new TSPO ligands have been developed (126), and TSPO has also been identified as a potential drug target (127). In particular, studies using [11C]PBR28 have shown a signal that correlates with cognitive performance (128), providing a means for detecting changes early in the disease process. However, a major disadvantage of many new TSPO ligands is that, due to genetic polymorphism (129), a subpopulation of subjects will not show binding. There is a need for TSPO ligands that provide high signal, but are insensitive to this polymorphism. In addition, PET ligands for other molecular targets related to neuroinflammation, e.g. monoamine oxidase B located in astrocytes (130), are being investigated. AD is associated with failure of cholinergic neurotransmission, but its relation to clinical symptoms and disease progression is still poorly understood. Thus, ongoing research into development of suitable PET tracers (131) may allow future studies on the relation between pathological protein deposition and their functional interactions and consequences.


Value of multimodal imaging in prevention trials


With regard to preventive strategies of AD, in vivo multi-modal neuroimaging biomarkers may play an important role with regard to early and reliable detection of subjects at risk and to allow measuring of success/improve understanding of failure of therapeutic concepts. In this context, multimodal neuroimaging approaches are expected to be advocated on the basis of several important facts: (i) neurodegeneration in AD cannot be reduced to a singular pathological process in the brain. A number of different neuropathologies are known to be crucially involved in the development of this disorder and the causal interaction between these pathologies is not yet fully understood; (ii) it is well accepted that the onset of development/appearance of the mentioned pathologies in the brain may occur subsequently not simultaneously. Consequently, the presence/detectability of these pathologies depends on the stage of disease; (iii) it has been demonstrated that the temporal development of these different pathologies over time is neither linear, nor parallel to each other (132-134).

These facts explain the potential of multimodal imaging approaches. Several of the characteristic forms of neuropathology known to be involved in AD such as protein aggregation (Aβ and tau), synaptic dysfunction, inflammation and neuronal loss/brain atrophy can be captured using in vivo imaging procedures. However, not a single one out of these pathologies is fully specific for AD (i.e. they can be found in other forms of neurodegeneration as well). Thus, in recent guidelines on the diagnosis of AD, improved diagnostic certainty or increased risk for underlying AD has been proposed for a combination of different disease biomarkers (32). These guidelines divide between markers of Aβ peptides aggregation pathology (including amyloid PET imaging) and markers of neuronal injury (including structural/volumetric MRI and FDG-PET imaging). The authors suggest that cumulative evidence obtained by biomarkers out of these two categories increases the probability for ongoing AD even in preclinical stages. This directly applies to the detection of subjects at risk for AD, e.g. in prevention trials.

It is well accepted that amyloid-pathology may be detectable in the brain of subjects suffering from AD long before clinical symptoms occur and, possibly, also ahead of detectable neuronal injury. However, little is known so far about the time to symptomatic onset in amyloid- positive subjects without cognitive deficits. Furthermore, it has been demonstrated that amyloid-deposition seems to reach a plateau in later stages of AD, whereas markers of neuronal injury seem to better mirror the continued progression of cognitive decline. Consequently, only a multimodal combination of information on amyloid-pathology and neuronal injury may allow a reliable in vivo disease staging, particularly ahead of clinical disease onset.

Figure 4. Multimodal work-up of neurodegeneration, opportunities for combined Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI)

Generally, the classification of disease biomarkers into only 2 categories may represent an oversimplification (135). Depending on the type of prevention approach, higher resolutions of disease stages may be possible and the spectrum may be completed with other available imaging biomarkers, e.g. of tau-aggregation, inflammation, connectivity or receptor status (136-139).

With regard to therapy monitoring or measuring success of any prevention methods, any one-dimensional biomarker assessment may fall short. With regard to the dynamic non-linear and non-parallel natural courses of the different neurodegenerative pathologies over time, relevant changes may be overlooked and inter-patient differences may be interpreted incorrectly. Furthermore, interventions may influence single parameters without effect on other pathologies, e.g. inhibit amyloid- aggregation pathology without slowing down the ongoing cascade of neuronal injury.

The recent introduction of PET/MR technology may represent the ideal tool for multimodal imaging approaches, particularly in longitudinal prevention trials. The systematic combination of complementary MRI and PET-methods may offer a number of advantages leading to the optimal diagnostic assessment and disease quantification with the least possible burden for the patient (Figure 4). Suitable PET/MR examination work- flow protocols have already been published for the assessment of neurodegenerative disorders (140). In short, such protocols may allow for acquisition of data in high quality (motion and partial volume corrected), providing information on neuronal dysfunction, protein aggregation pathology and atrophy and at the same time exclude non-neurodegenerative diseases in a single patient visit.

In summary, multimodal imaging assessment of different types of neuropathology might be designated as the method of choice for a reliable and specific detection and quantification of AD in vivo, and, thus, represent the approach of choice for prevention strategies.


Established and potential CSF biomarkers

At present, there are three gold standard (« core feasible ») CSF biomarkers for AD molecular pathology: total tau protein (T-tau) that reflects the intensity of neuronal/axonal degeneration, hyperphosphorylated tau protein (P-tau) that probably reflects neurofibrillary tangle pathology and the 42 amino-acid-long form of amyloid β (Aβ1-42) that is inversely correlated with Aβ pathology in the brain (low lumbar CSF levels reflect sequestration of the peptide in the brain parenchyma) (141). The biomarkers detect AD with an overall accuracy of 85-95% in both dementia and MCI stages of AD and appear to switch to pathological levels 10-20 years before the first symptoms become recognizable (142). Recently revised diagnostic criteria for AD suggest that biomarkers for both tau and Aβ pathology should be positive if an AD diagnosis is to be made (27). Here, CSF provides a biomarker source covering both these aspects and the assays for T-tau, P-tau and Aβ 1-42 are currently undergoing standardization for such use; the most important international standardization efforts being the Alzheimer’s Association Quality Control program for CSF biomarkers (143, 144), the Alzheimer’s Association Global Biomarkers Standardization Consortium (GBSC) (145) and the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Working Group for CSF Proteins (WG-CSF) (145). Standard operating procedures (SOPs) for CSF sampling and storage have been published (141). As an outcome from the IFCC WG-CSF and the GBSC, the Single-Reaction Monitoring (SRM) mass spectrometry candidate Reference Measurement Procedures (RMP) for Aβ1-42 has been published (146), and certified reference material is being developed. These will be used to harmonize measurements between assay formats and to assure longitudinal stability and minimize batch-to-batch variations, and thereby serve as the basis for the introduction of uniforms cut-off values and a more general use of CSF biomarkers in clinical routine and trials. Updates on the work within the GBSC are available at: biomarker_consortium.asp.

Recent data show that it is possible to identify longitudinal changes in CSF Aβ1-42, T-tau and P-tau in cognitively healthy controls followed with multiple lumbar punctures over several years (147-149), but most studies (with exceptions (147)) show that CSF AD biomarkers are essentially stable in symptomatic AD (150-152). This biomarker stability may be useful in clinical trials to help identify effects of interventions, both on the intended biological target, such as altered Aβ metabolism in response to an anti-Aβ treatment (18). One of the truly longitudinal studies of cognitively normal individuals with repeated CSF samples suggests that Aβ1-42 and T-tau changes occur in parallel and predict upcoming cognitive symptoms better than absolute baseline levels (149). CSF measurements may track trajectories of specific Aβ and APP metabolites (153-156), and down-stream effects on secondary phenomena, such as reduced axonal degeneration in response to a disease- modifying drug as measured by CSF tau levels (157, 158). So far, unfortunately, these changes have not predicted clinical benefit of any anti-AD drug (159).

In addition to T-tau, some CSF biomarkers reflecting neuronal and axonal damage, including visinin-like protein 1 (160) and heart-type fatty acid-binding protein (H-FABP) (161) show a clear increase in AD and correlates with CSF t-tau. Further, a number of novel biomarkers that should be relevant to the disease process in AD are under development. These include markers of synaptic degeneration (e.g. the dendritic protein neurogranin (162)), microglial activation (e.g. chitinase-3- like protein 1, CHI3L1, also called YKL-40 (163)) and protein homeostasis/lysosomal dysfunction (e.g. lysosomal-associated membrane proteins 1 and 2, LAMP- 1 and LAMP-2 (164)). An overview of CSF biomarkers and their interpretation in the scenario of AD prevention trials is reported in Table 2.

There is also a critical need for biomarkers to identify co-morbidities, including blood-brain barrier dysfunction, cerebrovascular disease, and Lewy body and TDP-43 pathologies, that could resemble or aggravate AD.

Table 1. Biological and imaging markers currently used in prevention trials

Table 2. Cerebrospinal fluid biomarkers in prevention trials

Abbreviations: Ab, amyloid-b AD, Alzheimer disease BACE1, b-site APP cleaving enzyme 1 CSF, cerebrospinal fluid H-FABP, Heart fatty acid-binding protein MRI, magnetic resonance imaging PET, positron emission tomography P-tau, phosphorylated tau sAPP, soluble amyloid precursor protein extracellular domain T-tau, total tau VLP-1, visinin-like protein-1


Evolving blood biomarkers

The identification of blood-based biomarkers that have utility in clinical trials for AD is of great importance (165), as they have been recently included as secondary outcome measures in many ongoing trials (Table 1). Blood-based biomarkers and biomarker profiles have been shown to be highly accurate in detecting and discriminating amongst neurodegenerative diseases (19, 166-169) and may serve as a cost-effective first step in a multi-stage screening process for clinical trials (17). As an example, Kiddle (166) and colleagues recently cross-validated the link between 9 markers from previously published studies and AD-related phenotypes across independent cohorts using an independent assay platform (SOMAscan proteomic technology). Recently, O’Bryant and colleagues (168) also cross-validated a serum-based biomarker profile using an independent assay platform (Meso Scale Discovery; 21-protein profile AUC=0.96; 8-protein profile AUC=0.95), across species (mice and humans) and tissues (serum and brain tissue). The proteomic profile approach was also able to extend further and accurately discriminate AD from Parkinson’s disease (168). If demonstrated effective in primary care settings, these blood-based profiles for detection of AD could provide access to clinical trials far beyond what is currently available through specialty clinic settings (168). Additionally, blood-based approaches have been shown capable of detecting Aβ burden (170, 171). Using data from the Australian Imaging, Biomarkers and Lifestyle (AIBL) cohort, a plasma proteomic signature consisting of chemokine 13, IgM-1, PPY, VCAM-1, IL-17, Aβ42, age, ApoE genotype and CDR sum of boxes yielded an AUC=0.88 in AIBL and an AUC=0.85 when applied to the ADNI cohort. The existence of a blood-based screener for Aβ positivity would provide a cost-effective means of screening patients into trials requiring Aβ positivity on PET scans (17, 170). 

Preliminary work also suggests that blood-based profiles can identify patients at risk for progression from MCI to AD (172,173) as well as from cognitively normal towards some level of cognitive impairment (174, 175). Along these lines, recent work identified a 10-protein (plasma) algorithm (TTR, clusterin, cystatinC, A1AcidG, ICAM1, CC4, pigment epithelium-derived factor, A1At, RANTES, ApoC3) that when combined with ApoE genotype predicted progression from MCI to AD with an optimal accuracy of 87% (sensitivity = 0.85, specificity = 0.88) (172). Mapstone and colleagues (174) also provided preliminary data suggesting that a set of 10 lipids can predict progression from control to MCI/AD over a 2-3 year period. Kivipelto and colleagues (37) generated a risk score from the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) study consisting of ApoE genotype, total cholesterol, systolic and diastolic blood pressure, demographics (age, education, gender), and lifestyle (smoking status, Body Mass Index [BMI], physical inactivity) factors that predicted increased risk for dementia over a 20-year period. Each of these methods has potential use in the identification and selection of patients into novel preventative and therapeutic clinical trials. Blood-based biomarkers can be also employed for patient stratification in trials. For example ApoE ε4 which is the strongest risk factor for AD and correlates well with CSF Aβ1-42 levels and increased amyloid burden and has been used for patient stratification into clinical trials (e.g.; identifiers: NCT00574132 and NCT00575055). Recent data also suggests serum/plasma ApoE protein levels are lower among ApoE carriers (169) and that plasma ApoE levels correlate with amyloid PET (176). Therefore, serum/plasma ApoE protein and ApoE genotype may be useful in patient stratification for trials (165). Crenshaw and colleagues (177) generated a patient stratification algorithm based on ApoE ε4 genotype and the TOMM40 gene. Risk stratification per this algorithm assigns all ApoE ε2/ε2 and ε2/ε3 carriers to the low risk group with all ApoE ε4 carriers then assigned to the high risk group. Next, for all non-ApoE ε2 carriers, risk stratification varied by TOMM40 genotype and age. This risk stratification scheme was designed for a preventative trial targeting Pioglitazone for the prevention of cognitive loss (177). Moreover, prior work has suggested that blood- based biomarkers can be utilized for the identification of AD-based endophenotypes (17, 167, 178) with additional work needed to determine if these endophenotypes can predict which groups of patients are more likely to respond to specific interventions (165). Recent findings presented at the Alzheimer’s Association International Conference (AAIC) suggest this is a promising line of investigation. As has been pointed out previously, additional work is needed regarding harmonization of methods for this work to progress (17, 179) with the first guidelines for pre-analytical methods now available (180).


Genetic tests and risk factors for Alzheimer’s disease

AD occurrence and evolution, as for most complex chronic diseases, result from the interactions between environmental factors and an individual susceptibility. The very first genetic determinants have been described for rare hereditary early onset clinical forms almost 25 years ago: the Aβ precursor protein gene (APP), the presenilin 1 (PSEN1) and the presenilin 2 (PSEN2). These three loci were rapidly followed by the discovery of strong and consistent associations of the apolipoprotein E (ApoE) isoforms with late-onset AD. Then, it is only during the last five years, and thanks to large-scale international collaborations such as the AlzGene database ( (181) and high throughput genotyping progresses, that the deciphering of the genetic susceptibility to sporadic AD has rapidly progressed, leading to the identification of 20 confirmed loci, and of 16 putative others (182). The population attributable risk/preventive fractions of each of these loci vary from 27.1% for the ApoE ε4 allele to less than 2% (Table 3). This allows for the establishment of a more precise picture of the genetic susceptibility background associated with the occurrence of late-onset AD, adding to the list of biomarkers a new tool, useful for AD diagnosis and prognosis.

However, the use of this information in current clinical practice still remains limited. In the dominant early onset hereditary forms, when a causal mutation can be identified (in half of these early onset forms), presymptomatic genetic testing could be performed following the protocols issued from the Huntington disease experience by the World Federation of Neurology (183). In late-onset AD, despite a high attributable fraction, the ApoE ε4 allele is not recommended for diagnosis because of its low sensitivity and specificity. Conversely, in clinical and translational studies, genomic biomarkers are of the utmost interest. For instance, when studying AD cases, ApoE ε4 allele is now a common risk factor to systematically register, adjust and stratify on, as age, gender and educational level. Today, it is a major requirement to collect DNA in any clinical study or drug trial and the decreased costs of sequencing offer a unique opportunity to access the genetic susceptibility information of each enrolled individual.

The characterization of the 40 known susceptibility locus genotypes constitutes a major biomarker that can be usefully added to CSF biological measurements and PET imaging. This information helps to stratify the heterogeneity of AD clinical forms and identify specific subgroups with different disease evolution and therapeutical answers. This pharmacogenomics stratification based on the potential biological pathways underpinned by the specific genetic background of each patient, helps to better understand the possible mechanism of action of drugs. In primary and secondary AD prevention trials including asymptomatic patients, the identification of this genetic susceptibility allows to select individuals with the highest risk and the very best chances to benefit from these preventive approaches, improving the statistical power of such studies.

The access to genomics information plays also a major role in the discussions about the efficiency of active and passive anti-Aβ immunotherapies in AD treatment (184). Genomics offer the best opportunity to identify presymptomatic individuals with AD causal mutations or at very high risk of developing AD to better appreciate the potential curative interest of these drugs at a stage where the resilience of cognitive functions is still possible. Thus, the DIAN-TU consortium has initiated a phase II/III randomized, double-blind, placebo-controlled multi-center study of two potential disease modifying therapies in presymptomatic mutation carriers and their non-carrier siblings; a prevention trial is also conducted in 300 symptom-free individuals 30 years of age and older from a large Colombian family with a mutant gene (PSEN1 E280A) and another one in volunteers aged 60 to 75, homozygous for the ApoE ε4, without cognitive impairment is in preparation (35). Considering the increasing knowledge and dissemination of these biomarkers based on genetic information, ethical concerns must be carefully taken into account, especially as direct-to-consumer tests develop for diseases as AD where no therapeutic solution is available yet.


Novel Advances and Research Frontiers : High-field MRI, and neurophysiological EEG- MEG markers

High-field of MRI such as (3T and higher) and ultra- high fields (7T and higher) as well as EEG-MEG techniques push further the possibilities of developing new biomarkers able to select and to monitor the disease in primary prevention trials.

High-fields of MRI: 3T MRI is widely available for clinical trials and the number of ultra-highfield 7T scanners is increasing rapidly as well, with about 40 7T scanners for humans currently installed worldwide (185).

An important contribution of high-field MRI to AD biomarkers is the possibility to measure hippocampal subregions. Indeed, hippocampal subparts show distinct vulnerability to the AD pathological process, as demonstrated by neuropathological studies (186). Such measurements are usually based on T2-, T2*- or proton- density-weighted sequences with high in-plane resolution (about 200µm-500µm). At 3T/4T, it is possible to detect atrophy in different hippocampal subfields, such as CA1 and the subiculum (187, 188). 7T MRI provides higher contrasts, increased signal-to-noise ratio and higher spatial resolution, which dramatically improve the visualization of hippocampal subregions. This makes it possible to quantify the atrophy of distinct hippocampal layers associated with AD, such as the stratum pyramidale and the strata radiatum, lacunosum and moleculare (SRLM), and not only subfields (189-191). These measures have the potential to provide more sensitive and specific biomarkers than global hippocampal volumetry but require further validation in larger samples.

Another important area of research is the detection of amyloid plaques using high-fields MRI. Such detection has been demonstrated in transgenic mouse models of AD (192, 193), as well as in non-transgenic mouse lemur primates in which plaques are more similar to those formed in humans (194). In vivo detection in humans of amyloid plaques by high-fields MRI is an important challenge for the upcoming years and might open promising scenario in prevention AD trials.

Ultra-high-field MRI also improves the assessment of vascular burden associated with AD. Cerebral microbleeds are often found in patients with AD and are likely to be due to frequent association between AD and cerebral amyloid angiopathy. 7T MRI, using T2*- weighted sequences or susceptibility weighted imaging (SWI), provides increased sensitivity to detect cerebral microbleeds (195, 196). 7T can also improve in vivo detection of microinfarcts. A recent 7T study reported an increased number of microinfarcts in AD patients compared to controls 197 while another study reported no difference (198).

Electroencephalography (EEG) and magnetoen cephalography (MEG) modalities (199, 200) are complementary techniques to high-field MRI due to their ability to detect the dynamic behavior of neuronal assembly circuits in the brain and to provide non- invasive time-dependent capabilities with sub- millisecond precision, especially in regard to cortical structures. Two main EEG/MEG biomarker approaches have emerged in using these techniques in AD research – evaluation of localized measures and inter-area connectivity indices (201). Localized neurodynamics biomarkers, such as band power or signal strength/phase, can characterize the change of the dynamic state of a brain area either through spontaneous brain oscillations or event-related activity (202). Evidence points to abnormal slowing of faster alpha and beta cortical rhythms especially in posterior regions and increase of slower delta- and theta-band activity in AD (203). Short- and long-range connectivity estimates, on the other hand, offer high sensitivity to evaluate the integrity of brain pathways or reduction of central cholinergic inputs, if employed properly (204). EEG/MEG connectivity biomarkers have revealed the existence of an entire new class of approaches able to manifest, for example, impaired functional synchrony in the upper alpha and beta bands in AD (205), and declining global synchronization in all frequency bands (206). While the full potential of EEG (207) and MEG (208) biomarkers to characterize degenerative brain changes for primary AD prevention has yet to be realized, a substantial number of studies have demonstrated results compatible with secondary prevention trial strategy. Although numerous studies have investigated the feasibility of EEG/MEG biomarkers in varying degrees, they still could be considered an emerging approach in AD trials, and especially in prevention trials, due to the complexity and multidimensionality of the observed dynamic signals, as well as the need to achieve a converging consensus among studies for better understanding of the disease pathology and its time- dependent aspects.


Regulatory Requirements and evolving challenges

As there is now consensus that effective therapies for AD have to start very early in the disease process after the many failures of development programs, European Medicines Agency (EMA) and food and drug administration (FDA) are reacting to these changes. FDA and EMA suggest potential approaches to clinical trial design and execution that allow for regulatory flexibility and innovation (209, 210). It is outlined that clinical diagnosis of early cognitive impairment might be coupled with specific appropriate biomarkers reflecting in vivo evidence of AD pathology. New diagnostic criteria addressing these issues have been established and are under validation by various working groups (18, 26, 27, 211, 212). Most biomarkers include brain Aβ and Tau load, as measured by PET and CSF levels of Aβ and tau proteins (22, 213), however, there is a clear move to update the amyloid hypothesis and to look for new biomarkers for the different disease stages (214, 215).

However, adequate standardization and validation of these biomarkers for regulatory purposes is still lacking as described by Noel-Storr and colleagues (2013) (216). As far as the CSF biomarkers are concerned, it was recently reported that the overall variability of data coming from a total of 84 laboratories remains too high to allow the validation of universal biomarker cut-off values for its intended use (217), which underpins the urgent need for better harmonization and standardization of these methods.

The use of biomarkers as endpoints in earlier stages of drug development is well established for regulators, and there are examples to approve medicinal products on the basis of their effects on validated surrogate markers, eg, anti-hypertensives, or cholesterol-lowering products. However, these examples have been considered as validated surrogate markers as they allow substitution for a clinically relevant endpoint. In their validation a link between a treatment-induced change in the biomarker and long-term outcome of the relevant clinical measure was undoubtedly established. Therefore the regulatory requirements on biomarkers used as endpoints in clinical trials are high as outlined earlier (210). In consequence EU regulators help applicants in their research and development by issuing opinions on the acceptability of using such biomarkers or a distinct methodology in clinical trials. Since 2011, EMA’s Committee for Medicinal Products for Human Use (CHMP) has adopted and published several qualification opinions for use in the development of medicines for AD. In these qualification opinions biomarkers are accepted for identification and selection of patients at the pre-dementia stage of the disease as well as for selection of patients for clinical trials in mild and moderate AD. In September 2013, a qualification opinion for a novel model of disease progression and trial evaluation in mild and moderate AD was adopted by CHMP. The simulation tool is intended to provide a quantitative rationale for the selection of study design and inclusion criteria for the recruitment of patients.

The EMA guideline on the clinical investigation of medicines for the treatment of AD will be updated on the basis of new knowledge obtained from the validation of the new diagnostic criteria, the use of biomarkers in clinical evaluation and other recent trends in research and development. A first draft will be available soon, in a 2- day workshop later this year the draft will be presented and discussed with the involved stakeholders. The final guidance should help regulators and industry to decide on the most appropriate study design for the distinct stages of AD, particularly in its early preclinical/prodromal stage.


Conclusions & perspective on a decade-long initiative on prevention

The discovery-validation of a broad spectrum of interventions, including pharmacologic, behavioral and life-style treatments, remains a crucial global public policy objective (218-222). Although a series of clinical trials for treating AD dementia have failed during the last two decades, these setbacks have not deterred the confidence of investigators in pursuing the strategic goal of acquiring disease-modifying treatments, which would ameliorate the progression of neurodegeneration with the eventual aim of preventing the onset of symptoms. The optimism of the scientific community, regarding the technical feasibility of discovering strategies to slow or halt neurodegenerative process is conditional, predicated by the availability of adequate resources and our capabilities to surmount the major barriers that are hindering progress of research on prevention. In this scenario, as emerged from the current review, the role of neuroimaging and biological markers is crucial. In particular, they are involved in the future development of technologies algorithms identifying the better combination able to detect accurately the early stages of disease or the prognosis in asymptomatic people at elevated risk. Moreover, they could be essential to select sample of prevention trials and, ultimately, they might be employed as surrogate measure to assess drugs treatment efficacy.

Some of the critical challenges need to be addressed in order to accelerate the pace of Research and Development (R&D) of interventions for prevention.

The first challenge refers to the development of new paradigms and conceptual models for R&D on therapies. The sequential failure of clinical trials based on prevailing theories on dementia along with emerging new knowledge about the complexity of the biology underlying the disease has created the need to re-assess our assumptions about its etiology and the adoption of new paradigms for therapy development. At the present, there is growing consensus that AD is a heterogeneous disorder, a syndrome rather than a disease, with polygenic origins where multiple putative risk factors influence the prolonged progression of neurodegenerative processes. These biological features will require radically different thinking and new approaches to therapy development. In particular, the adoption of concepts from ‘systems theory’ might be well suited for guiding the formulation of new conceptual models for teasing out the complexities of this disease.

The second challenge addresses the issue of developing technologies to accurately detect individuals at elevated risk – among asymptomatic populations. Indeed emerging knowledge showed that the cellular and molecular mechanisms leading to neurodegeneration start decades prior the onset of clinical symptoms of AD. For this reason, prospective prevention trials in the future will require the employment of treatments in the earlier asymptomatic or prodromal phases of neurodegeneration. Presently, crucial rate-limiting factors, which hinder the launch of true prevention trials are: (i) the lack of well-validated technologies for identification of asymptomatic people at elevated risk for the disease; (ii) the need for a reliable measure of disease progression – i.e. a surrogate marker allowing for precise tracking of one or more biological indices of the neurodegenerative process.

The third critical challenge to consider is the need for novel original therapeutic targets, new molecules and paradigms for efficacy validation. In this context, the strategic goal is to enrich the drug discovery pipeline by investigating a wide array of options for therapy development. Notably, this issue may have been exasperated by the limitations of current theories, conceptual models, or even ideas about the pathogenesis of AD and dementia disorders, which have provided a dominant framework and paradigm for drug discovery-development efforts thus far.

Finally, taking into account all the above issues, novel/different regulatory requirements for demonstrating efficacy based on revised guidelines or definitions of outcomes measurements are also required.

In conclusion, the major challenge to contend with will be the development of R&D resources for a multi-national prevention initiative. The convergence of several unique features of AD (e.g. heterogeneity, complex polygenic etiology, and prolonged asymptomatic pre-clinical phase of neurodegeneration) highlights the need for very large cohorts of well-characterized cohorts from various genetic/cultural backgrounds as potential volunteers for both: a) longitudinal epidemiological studies to discover and/or validate putative risk factors and b) clinical studies for prospective validation of potential preventive interventions. A massive international longitudinal database on health aging and pre-dementia or at risk populations, as a shared R&D resource, is an essential infrastructure to address the future needs of a major prevention initiative. Along with a ‘Big-Data’, the field of therapy development will require novel computational capabilities to not only sort out the complex interactions among multiple etiologic factors but also to discover and validate technologies for the early and accurate detection of the disease (220-222).

In spite of many great strides in understanding AD, the lack of effective interventions for chronic brain disorders along with the rapid expansion of the aging population at risk for dementia pose an ever-increasing threat to the solvency of healthcare systems worldwide. The scope and magnitude of this global health-economic crisis demands a commensurate response; fortunately, many countries have begun to develop national plans to address the scientific, social, economic, and political challenges posed by dementia. There are several parallel efforts that reflect the global concerns and international efforts to formulate strategies for overcoming these challenges – e.g. the Organization for Economic Co-operation and Development (OECD) Expert Conferences/G-8 Dementia Summit/Post G-8 Legacy Meeting (218-221, 223). However, the open question remains whether these prospective plans for action will convince policy-makers worldwide to make the necessary financial commitments to significantly increase R&D resources for prevention.

The first ‘call to arms’ for a global mobilization of all necessary resources to address the looming crisis due to the exponential increases in the prevalence of dementia was made in a 1992 editorial (224). In 1997, nearly two decades ago, in a Congressional Testimony on the ‘Prospects of Prevention’, the Alzheimer’s Association (available at made the case for a radical shift in therapy development towards a strategy of ‘Prevention’ (225). In 2009, once again, there was a call to launch a major international initiative called The Campaign to Prevent Alzheimer’s Disease by 2020 (PAD2020) (available at Nearly a quarter of a century after the first plea for action, the worldwide scientific community is well poised to make a quantum advance towards the strategic objectives of preventing dementia. The earlier calls for adoption of alternative paradigms to focus for therapies towards prevention were considered untenable goals. To date, however, there is an overwhelming optimism in the field with respects to the prospects of developing disease modifying intervention to delay the onset of disabling symptoms; and eventually to prevent (218, 226). The prevailing consensus is that current symptomatic treatments are woefully inadequate, indicating an urgent need to re-focusing R&D paradigms towards disease-modifying interventions.


Acknowledgements: The work of EC, OC, JFM is supported by CATI project ((, Fondation Plan Alzheimer). The work of SL and HH is supported by the program “Investissements d’avenir” (grant number ANR-10- IAIHU-06), by the AXA Research Fund, the Fondation Université Pierre et Marie Curie and the Fondation pour la Recherche sur Alzheimer, Paris, France. The work of PA is supported by the the LABEX (laboratory of excellence program investment for the future) DISTALZ grant, Inserm, Institut Pasteur de Lille, Université de Lille 2 and the Lille University Hospital ; the International Genomics of Alzheimer’s Project (IGAP) is supported by the French National Foundation on Alzheimer’s disease and related disorders and the Alzheimer’s Association. The work of CRJ is supported by research funding from the National Institutes of Health ((R01- AG011378, U01-HL096917, U01-AG024904, RO1 AG041851, R01 AG37551, R01AG043392, U01-AG06786)), and the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Foundation. The work of KB is supported by the Swedish Research Council. The work of SEO is supported by National Institutes of Health, National Institutes on Aging (AG039389, AG12300). The work of MK is supported by Alzheimer Association, Alzheimer’s Research Prevention Foundation and Sheikha Salama Bint Hamdan Al Nahyan Foundation. The work of WK is supported by The National Institutes of Health (P50 AG005133, R37 AG025516, P01 AG025204). Support to MTS comes from the ‘Agence Nationale de la Recherche’ [grants number ANR-13-JSV4-0001-01]. The research leading to these results has received funding from the program “Investissements d’avenir” ANR-10- IAIHU-06. The work of AD is supported by the German Research Foundation (DFG). The work of ALWB is supported by Science Foundation Ireland (grant 11/RFP.1/NES/3194). The work of OC is supported by ANR (project HM-TC, grant number ANR-09-EMER-006) and by France Alzheimer Association (project IRMA7). The work of HZ is supported by Swedish Research Council and the Knut and Alice Wallenberg Foundation.




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