Isabelle Bos

The frequency and influence of dementia risk factors in prodromal Alzheimer's disease

We investigated whether dementia risk factors were associated with prodromal Alzheimers disease (AD) according to the International Working Group-2 and National Institute of Aging-Alzheimers Association criteria, and with cognitive decline. A total of 1394 subjects with mild cognitive impairment from 14 different studies were classified according to these research criteria, based on cognitive performance and biomarkers. We compared the frequency of 10 risk factors between the subgroups, and used Cox-regression to examine the effect of risk factors on cognitive decline. Depression, obesity, and hypercholesterolemia occurred more often in individuals with low-AD-likelihood, compared with those with a high-AD-likelihood. Only alcohol use increased the risk of cognitive decline, regardless of AD pathology. These results suggest that traditional risk factors for AD are not associated with prodromal AD or with progression to dementia, among subjects with mild cognitive impairment. Future studies should validate these findings and determine whether risk factors might be of influence at an earlier stage (i.e., preclinical) of AD.

Amyloid-β, Tau, and Cognition in Cognitively Normal Older Individuals: Examining the Necessity to Adjust for Biomarker Status in Normative Data

We investigated whether amyloid-β (Aβ) and tau affected cognition in cognitively normal (CN) individuals, and whether norms for neuropsychological tests based on biomarker-negative individuals would improve early detection of dementia. We included 907 CN individuals from 8 European cohorts and from the Alzheimers disease Neuroimaging Initiative. All individuals were aged above 40, had Aβ status and neuropsychological data available. Linear mixed models were used to assess the associations of Aβ and tau with five neuropsychological tests assessing memory (immediate and delayed recall of Auditory Verbal Learning Test, AVLT), verbal fluency (Verbal Fluency Test, VFT), attention and executive functioning (Trail Making Test, TMT, part A and B). All test except the VFT were associated with Aβ status and this influence was augmented by age. We found no influence of tau on any of the cognitive tests. For the AVLT Immediate and Delayed recall and the TMT part A and B, we calculated norms in individuals without Aβ pathology (Aβ- norms), which we validated in an independent memory-clinic cohort by comparing their predictive accuracy to published norms. For memory tests, the Aβ- norms rightfully identified an additional group of individuals at risk of dementia. For non-memory test we found no difference. We confirmed the relationship between Aβ and cognition in cognitively normal individuals. The Aβ- norms for memory tests in combination with published norms improve prognostic accuracy of dementia.

PLASMA PRIMARY FATTY AMIDES ASSOCIATE TO CSF AMYLOID LEVELS AND ALZHEIMER’S DISEASE PROGRESSION IN THE EMIF-AD BIOMARKER DISCOVERY COHORT

with albumin ratio (r1⁄40.28, p1⁄40.006). We observed no independent effects, but a synergistic effect of IL-6 and albumin ratio on Aß (Z1⁄4-2.72, p1⁄40.006). The JN-analyses showed that the association between albumin ratio and Aßwas significant for IL-6 levels 3.48 pg/ml. We found no direct relation between the albumin ratio and p-Tau levels (p1⁄40.91), but mediation analyses indicated that the mediation of Aß,on the association between BBB dysfuction and p-Tau, was contingent on elevated IL-6 levels (indirect effect for IL-6: mean-1s (b1⁄40.026, Z1⁄40.075, p1⁄40.94), mean (b1⁄41.16, Z1⁄41.64, p1⁄40.09), mean+1s (b1⁄43.51, Z1⁄40.197, p1⁄40.049)). There is a negative relationship between Aß and pTau (p<0.001). Conclusions: The results suggest a link between BBB-dysfunction and markers of neurodegeneration via Aßmediated mechanisms in the presence of elevated IL-6 levels, indicating that inflammation and BBB-dysfunction, in addition to Aß are important components in the path to neurodegeneration. These results fit with autopsy studies reporting a common cooccurrence of Aß and vascular pathology in AD-type dementia.

ROBUST INDIVIDUALIZED PREDICTION MODELS WHICH ARE APPLICABLE ACROSS DIFFERENT COHORTS

impairment (Figure 2), but better longitudinal prediction is provided when age is included in the predictive models (p<.018). A/ T/N group did not predict slope of scores on the psychometric, depression, and neuropsychiatric tests over time. Conclusions:The A/T/N system predicts time to onset of cognitive impairment, and its’ predictive ability is improved by consideration of participant age. Cross-sectional results support those previously reported by others.

GENOMICS AND EPIGENOMICS ANALYSES IN THE EMIF-AD MULTIMODAL BIOMARKER DISCOVERY STUDY

These associations were stronger in MCI compared to CN. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.74 6 0.08 in CN (Figure 1) and an AUC of 0.79 6 0.08 in MCI (Figure 2). Conclusions:Amyloid pathology is associated with changes in structural MRI measures in preclinical and prodromal AD. An automated classifier based on clinical and imaging variables can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in trial design to pre-screen subjects for enrolment.

MRI PREDICTORS OF AMYLOID PATHOLOGY: RESULTS FROM THE EMIF-AD BIOMARKER DISCOVERY STUDY

Anders Wallin, Alberto Lle o, Julius Popp, Pablo Martinez-Lage, Stuart G. Snowden, Cristina Legido-Quigley, Lars Bertram, Frederik Barkhof, Henrik Zetterberg, Johannes Streffer, Pieter Jelle Visser, Simon Lovestone, University of Oxford, Oxford, United Kingdom; Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom; Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden; University of Cardiff, Cardiff, United Kingdom; Cardiff University, Cardiff, United Kingdom; Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands; University Hospital of Leuven, Leuven, Belgium; VU University Medical Center, Amsterdam, Netherlands; Reference Center for Biological Markers of Dementia, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium; Istituto di Ricovero e Cura a Carattere Scientifico, Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Instituto Cl ınic de Neurociencias Hospital Clinic i Universitari, Barcelona, Spain; Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Moelndal, Sweden; Biomedical Research Institute Sant Pau, Barcelona, Spain; Geneva University Hospital, Geneva, Switzerland; Centre of Biomedical Investigation Network for Neurodegenerative Diseases, Madrid, Spain; King’s College London, London, United Kingdom; Steno Diabetes Center Copenhagen, Copenhagen, Denmark; Imperial College London, London, United Kingdom; Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, M€olndal, Sweden; Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, Netherlands. Contact e-mail: sarah.westwood@psych.ox.ac.uk

DURATION OF ALZHEIMER’S DISEASE IN THE PRECLINICAL, PRODROMAL AND DEMENTIA STAGE: A MULTI-STATE MODEL ANALYSIS

that assume the trajectory form is the same for everyone in the sample. Including demographic variables as covariates in such studies only allows for the identification of mean differences. In contrast, we used structural equation model (SEM) trees to identify heterogenous subgroups that demonstrate differential trajectories of change in cognition. SEM Trees partitions the dataset into subsets (groups of participants) based on the splitting of covariates, with a latent change growth model fit to each resultant subset. Methods:9 waves from the representative samples of the Health and Retirement Study and Assets and Health Dynamics of the Oldest Old (N1⁄4 20,685), were analyzed. Covariates were years of education, race (white, black, other), Hispanic, and gender. Each identified subset was fit with SEM models on mental status scores. Results: Different trajectories were observed with covariate splits on race, Hispanic ethnicity, and education combinations. The figure shows that formental status,Whitewith 14 years of education,White, 12-13 years, and Nonwhite, 16 years had highest intercept scores, with age declines after the mid-70’s. Nonwhite andHispanics with 12-15 years andWhite, 9-11 years formed themiddle groups for intercepts, showing declines in the late 60’s. Nonwhites 11 years, and whites 6 years had the lowest intercepts and declines just after age 60. Conclusions:SEM trees indicated different patterns of cognitive aging in a population sample. These findings suggest that cognitive disparities are rooted not only by race or ethnicity but education, and that whites are more protected from cognitive decline than nonwhites even with only a few years of high school. We conclude with a discussion of the importance of examining not just mean effects, but also allowing the change parameters to vary, and by allowing interactions between covariates.