Devadas, Vivek

Abstract Background Apolipoprotein E (APOE), the major genetic risk factor for Alzheimer's disease (AD), has three common alleles (2, 3 and 4), giving rise to genotypes that are associated with six levels of AD risk: (2/2<2/3<3/3<2/4<3/4<4/4). In our new Arizona APOE Cohort Study, we have begun to establish a longitudinal cohort of more than 300 cognitively unimpaired persons (50‐90 year‐old) stratified by APOE genotype and age decile, matched by age and sex, and have an extensive battery of cognitive, clinical, amyloid (NAV4694) and tau (MK6240) PET, MRI, cerebrospinal fluid (CSF), plasma, and DNA. The study is intended to clarify the impact of APOE4 and APOE2 allelic dose on the predisposition to and protection from AD and inform the design of secondary and primary prevention therapies. Here, we describe the study and the resource of data and samples that are now available to researchers. Methods Participants were recruited from across the United States using GeneMatch and other sources. Comprehensive clinical assessments, brain images, and lumbar punctures are performed every 2 years, 50mL blood samples are acquired annually, and whole genome sequencing is in progress. Results As of January 2025, baseline assessments were completed in 303 participants, 68±9 years of age, including 205 females and 98 males. Sixteen percent are from underrepresented groups. Baseline CSF samples were obtained in 89% of the participants. The complete cohort consists of 17, 63, 41, 65, 66, and 51 persons with the APOE 2/2, 2/3, 3/3, 2/4, 3/4, and 4/4 genotype, respectively. In the first 287 participants with available amyloid PET scans 6% of 2/2, 5% of 2/3, 14% of 3/3, 26% of 2/4, 29% of 3/4, and 52% of 4/4 participants had a positive amyloid PET (≥20 centiloids). Conclusions Building up on our decades of experience in APOE research, we launched the new Arizona APOE Cohort which promises to provide a valuable resource of data and samples to advance the study of APOE, its variants and interactions with other risk and protective factors, and the development, treatment, and prevention of AD. Baseline data are now available to interested and eligible research collaborators.

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

Background: Besides their other roles, brain imaging and other biomarkers of Alzheimer’s disease (AD) have the potential to inform a cognitively unimpaired (CU) person’s likelihood of progression to mild cognitive impairment (MCI) and benefit subject selection when evaluating promising prevention therapies. We previously described that among baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD, hippocampal volume was the best predictor of incident MCI within 2 years (79%sensitivity/78%specificity), using standard automated MRI volumetric algorithmic programs, binary logistic regression, and leave-one-out procedures. Objective: To improve the same prediction by using different hippocampal features and machine learning methods, cross-validated via two independent and prospective cohorts (Arizona and ADNI). Methods: Patch-based sparse coding algorithms were applied to hippocampal surface features of baseline TI-MRIs from 78 CU adults who subsequently progressed to amnestic MCI in approximately 2 years (“progressors”) and 80 matched adults who remained CU for at least 4 years (“nonprogressors”). Nonprogressors and progressors were matched for age, sex, education, and apolipoprotein E4 allele dose. We did not include amyloid or tau biomarkers in defining MCI. Results: We achieved 92%prediction accuracy in the Arizona cohort, 92%prediction accuracy in the ADNI cohort, and 90%prediction accuracy when combining the two demographically distinct cohorts, as compared to 79%(Arizona) and 72%(ADNI) prediction accuracy using hippocampal volume. Conclusion: Surface multivariate morphometry and sparse coding, applied to individual MRIs, may accurately predict imminent progression to MCI even in the absence of other AD biomarkers.

AbstractIntroductionWe previously characterized associations between brain imaging measurements of amyloid‐β (Aβ) plaque burden and apolipoprotein E (APOE) ε4 gene dose in a small number of cognitively unimpaired late‐middle‐aged APOE ε4 homozygotes (HMs), heterozygotes (HTs), and noncarriers (NCs). We now characterize cross‐sectional Aβ plaque, tau tangle, and cortical atrophy (neurodegeneration) measurements, classifications, and associations with age in a larger number of unimpaired HMs, HTs, and NCs over a wider age range.MethodsWe analyzed 11C Pittsburgh compound B (Aβ) positron emission tomography (PET), flortaucipir (tau) PET, and volumetric magnetic resonance imaging data from 164 study participants of age 47–86 years, including 26 APOE ε4 HMs, 48 HTs, and 90 NCs matched for age and sex.ResultsAβ PET measurements rose, plateaued at the respective ages of 68 and 76, and then declined with age in unimpaired HM and HT groups. Compared with NCs, these two groups began to have significantly higher Aβ PET measurements at ages 62 and 70, respectively, and no longer had significantly higher measurements by ages 71 and 78, respectively. They began to have significantly higher entorhinal cortex tau PET measurements at ages 66 and 70, respectively, and no longer had significantly higher measurements by ages 74 and 78, respectively. Brain atrophy measurements tended to decline slowly with age in all three genetic groups. Their elevated tau PET measurements were attributable to those with positive Aβ PET scans. 41.0%, 18.0%, and 5.0% of the 47‐ to 70‐year‐old HMs, HTs, and NCs and 25.0%, 79.0%, and 38.0% of the 71‐ to 86‐year‐old HMs, HTs, and NCs had positive Aβ PET scans, and the long‐term recall memory scores are significantly higher in the older HMs than in HT and NC groups, suggesting resistance to Aβ deposition in those HMs who remained unimpaired at older ages.ConclusionsThis study provides information about Aβ plaque burden, tau tangle burden, and neurodegeneration in cognitively unimpaired persons at three levels of genetic risk for AD. Unimpaired APOE ε4 HMs can be studied before their 70s to evaluate the understanding of factors, processes, and interventions involved in the predisposition to and prevention of AD, and after their 70s, to discover factors, processes, and interventions involved in the resilience or resistance to and prevention of AD.