Longitudinal Alzheimer's Disease Research Articles

Pathway enrichment in genome-wide analysis of longitudinal Alzheimer's disease biomarker endophenotypes.

The genetic pathways that influence longitudinal heterogeneous changes in Alzheimer's disease (AD) may provide insight into disease mechanisms and potential therapeutic targets. Longitudinal endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) representing amyloid, tau, neurodegeneration (A/T/N), and cognition were selected. Genome-wide association analysis was performed using a linear mixed model (LMM) approach, followed by gene and pathway enrichment with significant and functionally relevant SNPs. A total of 33 and 19 statistically significant pathways were identified associating with the intercept and longitudinal trajectory, respectively. The longitudinal intercept pathways represent eight groups: immune, metabolic, cell growth and survival, DNA maintenance, neuronal signaling, RAS/MAPK/ERKsignalingpathways, vesicle and lysosomal transport, and transcription modification. Longitudinal trajectory pathways represented six groups: Immune, metabolic, cell signaling, cytoskeleton, and glycosylation. Longitudinal enrichment identified pathways that uniquely associate with trajectories of key AD biomarkers and cognition, providing new insight into AD course-related mechanisms and potential new therapeutic targets. A systematic genome-wide analysis with longitudinal AD biomarker endophenotypes was performed. Enriched pathways were identified with functionally derived SNP to gene analysis. Fifty-two pathways were associated with longitudinal trajectory and intercept. Many of the identified pathways are specific steps in larger pathways implicated in AD. The identified pathways may provide therapeutic targets and areas for further study.

Association of CSF α-synuclein seed amplification assay positivity with disease progression and cognitive decline: A longitudinal Alzheimer's Disease Neuroimaging Initiative study.

Cerebrospinal fluid (CSF) α-synuclein (α-syn) seed amplification assay (SAA) is a sensitive and specific tool for detecting Lewy body co-pathology in Alzheimer's disease. A total of 1637 cross-sectional and 407 longitudinal CSF samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were tested with SAA. We examined longitudinal dynamics of amyloid beta (Aβ), α-syn seeds, and phosphorylated tau181 (p-tau181), along with global and domain-specific cognition in stable SAA+, stable SAA-, and those who converted to SAA+ from SAA-. SAA+ individuals had faster cognitive decline than SAA-, notably in mild cognitive impairment, and presented with earlier symptom onset. SAA+ conversion was associated with CSF Aβ42 positivity but did not impact the progression of either CSF Aβ42 or CSF p-tau181 status. CSF Aβ42, p-tau181, and α-syn SAA were all strong predictors of clinical progression, particularly CSF Aβ42. In vitro, CSF α-syn SAA kinetic parameters were associated with participant demographics, clinical profiles, and cognitive decline. These results highlight the interplay between amyloid and α-syn and their association with disease progression. Seed amplification assay (SAA) positivity was associated with greater cognitive decline and earlier symptom onset. Thirty-four Alzheimer's Disease Neuroimaging Initiative (ADNI) individuals progressed from SAA- to SAA+, that is, ≈ 5% conversion. SAA conversion was associated with amyloid beta (Aβ) pathology and greater cognitive decline. SAA status did not impact the progression of either CSF Aβ42 or phosphorylated tau181 biomarkers. Change in clinical diagnosis was associated with both Alzheimer's disease biomarkers and SAA. SAA kinetic parameters were associated with clinical features and progression.

A Comparison of an Australian Observational Longitudinal Alzheimer's Disease Cohort to Community-Based Australian Data.

Observational Alzheimer's disease (AD) cohorts including the Australian, Biomarkers, Imaging and Lifestyle (AIBL) Study have enhanced our understanding of AD. The generalizability of findings from AIBL to the general population has yet to be studied. We aimed to compare characteristics of people with AD dementia in AIBL to 1) the general population of older Australians using pharmacological treatment for AD dementia, and to 2) the general population of older Australians who self-reported a diagnosis of dementia. Descriptive study comparing people aged 65 years of over (1) in AIBL that had a diagnosis of AD dementia, (2) dispensed with pharmacological treatment for AD in Australia in 2021 linked to the Australian census in 2021 (refer to as PBS/census), (3) self-reported a diagnosis of dementia in the 2021 Australian census (refer to as dementia/census). Baseline characteristics included age, sex, highest education attainment, primary language, and medical co-morbidities. Participants in AIBL were younger, had more years of education, and had a lower culturally and linguistically diverse (CALD) population compared to the PBS/census cohort and dementia/census cohort (mean age±standard deviation - AIBL 79±7 years, PBS/census 81±7, p < 0.001, dementia/census 83±8, p < 0.001; greater than 12 years of education AIBL 40%, PBS/census 35%, p = 0.020, dementia/census 29%, p < 0.001; CALD - AIBL 3%, PBS/census 20%, p < 0.001, dementia/census 22%, p < 0.001). Our findings suggest that care should be taken regarding the generalizability of AIBL in CALD populations and the interpretation of results on the natural history of AD.

Beta amyloid PET scans for dementia diagnoses: Practice and research implications from CARE-IDEAS.

Beta amyloid PET scans are a minimally invasive biomarker that may inform Alzheimer's disease (AD) diagnosis. The Caregiver's Reactions and Experience (CARE) study, an IDEAS supplement, aimed to understand experiences of PET scan recipients and their care partners regarding motivations for scans, reporting and interpreting results, and impact of results. Patients with mild cognitive impairment or dementia who agreed to join the CARE-IDEAS study and their care partners participated in a baseline survey and follow-up survey approximately 18 months later, supplemented by in-depth qualitative interviews with subsets of participants. Patients who received scans and volunteered for follow-up research were more likely to be male, better educated, and have higher income than the general population. Survey information was merged with Medicare data. This article integrates findings from several CARE-IDEAS publications and provides implications for practice and research. Although most participants accurately reported scan results, they were often confused about their meaning for prognosis. Some participants reported distress with results, but there were no significant changes in measured depression, burden, or economic strain over time. Many respondents desired more information about prognosis and supportive resources. Scan results were not differentially associated with changes in service use over time. Findings suggest a need for carefully designed and tested tools for clinicians to discuss risks and benefits of scans and their results, and resources to support patients and care partners in subsequent planning. Learning of scan results provides a point-of-contact that should be leveraged to facilitate shared decision-making and person-centered longitudinal AD care.

Cognitive Trajectories and Alzheimer Disease Biomarkers: From Successful Cognitive Aging to Clinical Impairment.

Cross-sectional definitions of successful cognitive aging have been widely utilized, but longitudinal measurements can identify people who do not decline. We performed this study to contrast maintenance with declining trajectories, including clinical conversion. We included baseline cognitively unimpaired Alzheimer's Disease Neuroimaging Initiative participants with 3 or more cognitive testing sessions (n = 539, follow-up 6.1 ± 3.5 years) and calculated slopes of an episodic memory composite (MEM) to classify them into two groups: maintainers (slope ≥ 0) and decliners (slope < 0). Within decliners, we examined a subgroup of individuals who became clinically impaired during follow-up. These groups were compared on baseline characteristics and cognitive performance, as well as both cross-sectional and longitudinal Alzheimer disease (AD) biomarker measures (beta-amyloid [Aβ], tau, and hippocampal volume). Forty-one percent (n = 221) of the cohort were MEM maintainers, and 33% (n = 105) of decliners converted to clinical impairment during follow-up. Compared to those with superior baseline scores, maintainers had lower education and were more likely to be male. Maintainers and decliners did not differ on baseline MEM scores, but maintainers did have higher non-MEM cognitive scores. Maintainers had lower baseline global Aβ, lower tau pathology, and larger hippocampal volumes than decliners, even after removing converters. There were no differences in rates of change of any AD biomarkers between any cognitive trajectory groups except for a higher rate of hippocampal atrophy in clinical converters compared to maintainers. Using longitudinal data to define cognitive trajectory groups reduces education and sex bias and reveals the prognostic importance of early onset of accumulation of AD pathology. ANN NEUROL 2024;96:378-389.

Improving Regression Analysis with Imputation in a Longitudinal Study of Alzheimer's Disease.

Missing data is prevalent in the Alzheimer's Disease Neuroimaging Initiative (ADNI). It is common to deal with missingness by removing subjects with missing entries prior to statistical analysis; however, this can lead to significant efficiency loss and sometimes bias. It has yet to be demonstrated that the imputation approach to handling this issue can be valuable in some longitudinal regression settings. The purpose of this study is to demonstrate the importance of imputation and how imputation is correctly done in ADNI by analyzing longitudinal Alzheimer's Disease Assessment Scale -Cognitive Subscale 13 (ADAS-Cog 13) scores and their association with baseline patient characteristics. We studied 1,063 subjects in ADNI with mild cognitive impairment. Longitudinal ADAS-Cog 13 scores were modeled with a linear mixed-effects model with baseline clinical and demographic characteristics as predictors. The model estimates obtained without imputation were compared with those obtained after imputation with Multiple Imputation by Chained Equations (MICE). We justify application of MICE by investigating the missing data mechanism and model assumptions. We also assess robustness of the results to the choice of imputation method. The fixed-effects estimates of the linear mixed-effects model after imputation with MICE yield valid, tighter confidence intervals, thus improving the efficiency of the analysis when compared to the analysis done without imputation. Our study demonstrates the importance of accounting for missing data in ADNI. When deciding to perform imputation, care should be taken in choosing the approach, as an invalid one can compromise the statistical analyses.

Longitudinal Alzheimer's Disease Progression Prediction with Modality Uncertainty and Optimization of Information Flow.

Alzheimer's disease (AD) is a global neurodegenerative disorder that affects millions of individuals worldwide. Actual AD imaging datasets challenge the construction of reliable longitudinal models owing to imaging modality uncertainty. In addition, they are still unable to retain or obtain important information during disease progression from previous to followup time points. For example, the output values of current gates in recurrent models should be close to a specific value that indicates the model is uncertain about retaining or forgetting information. In this study, we propose a model which can extract and constrain each modality into a common representation space to capture intermodality interactions among different modalities associated with modality uncertainty to predict AD progression. In addition, we provide an auxiliary function to enhance the ability of recurrent gate robustly and effectively in controlling the flow of information over time using longitudinal data. We conducted comparative analysis on data from the Alzheimer's Disease Neuroimaging Initiative database. Our model outperformed other methods across all evaluation metrics. Therefore, the proposed model provides a promising solution for addressing modality uncertainty challenges in multimodal longitudinal AD progression prediction.

Developing Topics.

APOE*2 and APOE*4 are respectively the strongest protective and risk-increasing genetic variants for late-onset Alzheimer's disease (AD), making APOE status highly relevant towards clinical trial design and AD research broadly. The associations of APOE genotypes with AD are importantly modulated by age, sex, race, ethnicity, and ancestry, but these associations remain unclear, particularly in historically understudied non-white samples. The study overview is shown in Figure.1. Various case-control, family-based, and longitudinal AD genetic cohorts were available through public repositories and underwent extensive genotype and phenotype quality control. These cohorts contributed data for Non-Hispanic Whites (NHW), Non-Hispanic Blacks (NHB), and Hispanics (HISP). Global ancestry was determined using SNPweights v2.1. Case-control logistic regression models evaluated the AD risk associations of APOE*2 dosage, APOE*4 dosage, and APOE genotype, with regard to APOE*33 as the reference, while adjusting for sex, array/batch/center, and global European, African, and Amerindian ancestry. Different stratified analyses, interaction models, and race/ethnicity heterogeneity tests were evaluated. APOE associations in East-Asians (EAS) were obtained through meta-analyses of published AD genetic studies, given the relative paucity of EAS in publicly available genetic cohorts. Compared to prior literature, we made several novel observations (Figure.2). Notably, the associations of APOE genotypes with AD risk were least pronounced in HISP, which was not explained by global ancestry. Further, not just APOE*4, but also APOE*2 followed a reduced association strength with AD risk according to the pattern of EAS > NHW > NHB > HISP (with the exception of APOE*2 in EAS, which showed no significant association). The sex-by-age-specific association of APOE*34 in NHW, which was shown to be stronger in women, was reproduced here at ages 60-70 years, and additionally replicated in NHB and HISP. There were no other significant sex-by-APOE interactions. Merited by recent advances in AD genetic cohorts, we provide the largest-to-date overview of APOE's associations with AD risk across important biological and demographic strata. We expect these insights to be critical to guide AD research and clinical trial design. With increased diversity and phenotype harmonization efforts in AD genetics, future extensions on this work are expected to provide additional insights.

Remuneration and Recruitment of Study Participants for AD Cohort Studies From the General Public and From Minority Communities.

Offering remuneration for participation in studies of aging and Alzheimer Disease (AD) may improve recruitment, particularly among minoritized and low-income groups. But remuneration may also raise ethical problems and reduce altruistic motivations for participation. A nationally representative sample of Americans (N=2030) with large (N=500) Black and Hispanic oversamples was asked about willingness to participate in a longitudinal AD cohort study after random assignment of remuneration ($0, $50/visit, $100/visit). Respondents were then asked about their perceived burden, risks, and societal contribution from participation. An offer of remuneration increased willingness to participate, with no difference between $50 and $100. The increase was similar across racial, ethnic, and income groups. Remuneration did not affect perceived risks or altruistic benefits. Compensation caused Whites and Hispanics, but not Blacks, to lower the perceived burden. Modest levels of remuneration are likely to improve recruitment to AD research studies without causing collateral ethical or motivation problems. Remuneration does not differentially enhance minority recruitment.

Association of African Ancestry–Specific APOE Missense Variant R145C With Risk of Alzheimer Disease

Numerous studies have established the association of the common APOE ε2 and APOE ε4 alleles with Alzheimer disease (AD) risk across ancestries. Studies of the interaction of these alleles with other amino acid changes on APOE in non-European ancestries are lacking and may improve ancestry-specific risk prediction. To determine whether APOE amino acid changes specific to individuals of African ancestry modulate AD risk. Case-control study including 31 929 participants and using a sequenced discovery sample (Alzheimer Disease Sequencing Project; stage 1) followed by 2 microarray imputed data sets derived from the Alzheimer Disease Genetic Consortium (stage 2, internal replication) and the Million Veteran Program (stage 3, external validation). This study combined case-control, family-based, population-based, and longitudinal AD cohorts, which recruited participants (1991-2022) in primarily US-based studies with 1 US/Nigerian study. Across all stages, individuals included in this study were of African ancestry. Two APOE missense variants (R145C and R150H) were assessed, stratified by APOE genotype. The primary outcome was AD case-control status, and secondary outcomes included age at AD onset. Stage 1 included 2888 cases (median age, 77 [IQR, 71-83] years; 31.3% male) and 4957 controls (median age, 77 [IQR, 71-83] years; 28.0% male). In stage 2, across multiple cohorts, 1201 cases (median age, 75 [IQR, 69-81] years; 30.8% male) and 2744 controls (median age, 80 [IQR, 75-84] years; 31.4% male) were included. In stage 3, 733 cases (median age, 79.4 [IQR, 73.8-86.5] years; 97.0% male) and 19 406 controls (median age, 71.9 [IQR, 68.4-75.8] years; 94.5% male) were included. In ε3/ε4-stratified analyses of stage 1, R145C was present in 52 individuals with AD (4.8%) and 19 controls (1.5%); R145C was associated with an increased risk of AD (odds ratio [OR], 3.01; 95% CI, 1.87-4.85; P = 6.0 × 10-6) and was associated with a reported younger age at AD onset (β, -5.87 years; 95% CI, -8.35 to -3.4 years; P = 3.4 × 10-6). Association with increased AD risk was replicated in stage 2 (R145C was present in 23 individuals with AD [4.7%] and 21 controls [2.7%]; OR, 2.20; 95% CI, 1.04-4.65; P = .04) and was concordant in stage 3 (R145C was present in 11 individuals with AD [3.8%] and 149 controls [2.7%]; OR, 1.90; 95% CI, 0.99-3.64; P = .051). Association with earlier AD onset was replicated in stage 2 (β, -5.23 years; 95% CI, -9.58 to -0.87 years; P = .02) and stage 3 (β, -10.15 years; 95% CI, -15.66 to -4.64 years; P = 4.0 × 10-4). No significant associations were observed in other APOE strata for R145C or in any APOE strata for R150H. In this exploratory analysis, the APOE ε3[R145C] missense variant was associated with an increased risk of AD among individuals of African ancestry with the ε3/ε4 genotype. With additional external validation, these findings may inform AD genetic risk assessment in individuals of African ancestry.

Racial differences in longitudinal Alzheimer's disease biomarkers among cognitively normal adults.

Longitudinal changes in Alzheimer's disease (AD) biomarkers, including cerebrospinal fluid (CSF) analytes, amyloid uptakes from positron emission tomography (PET), structural outcomes from magnetic resonance imaging (MRI), and cognition, have not been compared between Blacks and Whites. A total of 179 Blacks and 1180 Whites who were cognitively normal at baseline and had longitudinal data from at least one biomarker modality were analyzed for the annual rates of change. CSF amyloid beta (Aβ)42/Aβ40 declined more slowly (P=.0390), and amyloid (PET) accumulated more slowly (P=.0157), in Blacks than Whites. CSF Aβ42 changed in opposite directions over time between Blacks and Whites (P=.0039). The annual increase in CSF total tau and phosphorylated tau181 for Blacks was about half of that for Whites. Longitudinal racial differences in amyloid biomarkers are observed. It will be important to comprehensively and prospectively examine the effects of apolipoprotein E genotype and sociocultural factors on these differences.

Relationship of fasting glucose and longitudinal Alzheimer's disease imaging markers.

IntroductionFasting glucose increases with age and is linked to modifiable Alzheimer's disease risk factors such as cardiovascular disease and Type 2 diabetes (T2D).MethodsWe leveraged available biospecimens and neuroimaging measures collected during the Alzheimer's Prevention Through Exercise (APEx) trial (n = 105) to examine the longitudinal relationship between change in blood glucose metabolism and change in regional cerebral amyloid deposition and gray and white matter (WM) neurodegeneration in older adults over 1 year of follow‐up.ResultsIndividuals with improving fasting glucose (n = 61) exhibited less atrophy and regional amyloid accumulation compared to those whose fasting glucose worsened over 1 year (n = 44). Specifically, while individuals with increasing fasting glucose did not yet show cognitive decline, they did have regional atrophy in the hippocampus and inferior parietal cortex, and increased amyloid accumulation in the precuneus cortex. Signs of early dementia pathology occurred in the absence of significant group differences in insulin or body composition, and was not modified by apolipoprotein E ε4 carrier status.DiscussionDysregulation of glucose in late life may signal preclinical brain change prior to clinically relevant cognitive decline. Additional work is needed to determine whether treatments specifically targeting fasting glucose levels may impact change in brain structure or cerebral amyloid in older adults.

Dual feature correlation guided multi-task learning for Alzheimer's disease prediction

Alzheimer's disease (AD) is a gradually progressive neurodegenerative disease affecting cognition functions. Predicting the cognitive scores from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of AD. Despite machine learning algorithms having many successful applications, the prediction model suffers from the so-called curse of dimensionality. Multi-task feature learning (MTFL) has helped tackle this problem incorporating the correlations among multiple clinical cognitive scores. However, MTFL neglects the inherent correlation among brain imaging measures. In order to better predict the cognitive scores and identify stable biomarkers, we first propose a generalized multi-task formulation framework that incorporates the task and feature correlation structures simultaneously. Second, we present a novel feature-aware sparsity-inducing norm (FAS-norm) penalty to incorporate a useful correlation between brain regions by exploiting correlations among features. Three multi-task learning models that incorporate the FAS-norm penalty are proposed following our framework. Finally, the algorithm based on the alternating direction method of multipliers (ADMM) is developed to optimize the non-smooth problems. We comprehensively evaluate the proposed models on the cross-sectional and longitudinal Alzheimer's disease neuroimaging initiative datasets. The inputs are the thickness measures and the volume measures of the cortical regions of interest. Compared with MTFL, our methods achieve an average decrease of 4.28% in overall error in the cross-sectional analysis and an average decrease of 7.97% in the Alzheimer's Disease Assessment Scale cognitive total score longitudinal analysis. Moreover, our methods identify sensitive and stable biomarkers to physicians, such as the hippocampus, lateral ventricle, and corpus callosum.

Analysis of rate of change in quantitative traits for identifying biomarker-mediated genetic effects on MCI and AD.

The relationship between genetic variations such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) is integral in the understanding of Alzheimer's Disease (AD). Given AD as a progressive disorder, QT-based rate of change (RoC) measures are important phenotypes to study in AD genetics. Further mediation analysis provides a promising method to help reveal causal relationship from genetic determinants to longitudinal changes in QTs and to clinical diagnosis. We analyzed the QT data from the Quantitative Templates for the Progression of Alzheimer's disease (QT-PAD, see http://www.pi4cs.org/qt-pad-challenge). QT-PAD is a single and shared data freeze from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including 16 imaging, cognitive and fluid AD QTs. RoC between baseline and month-12 visits was calculated for each QT. Genotyping data were obtained from the ADNI database and went through a standard QC procedure. Genome wide association study (GWAS) was performed using PLINK to examine genetic associations between 565,373 SNPs and the RoC for each QT. For each SNP-RoC association with p<1e-6, a subsequent mediation analysis was performed using the "Mediation" R package to examine the mediation relationship from SNP to RoC to five different diagnostic comparisons (see Table 1). Figure 1 shows the GWAS findings on the RoC measures for the studied QTs. The results included 19 SNPs and 14 QTs, where each SNP or each QT had at least one association with p<10-6 . Table 1 shows the mediation analysis results. RoC in FAQ mediates the effects of rs429358-APOE and rs4420638-APOC1 on all five diagnostic comparisons, and mediates the effect of rs769449-APOE on four comparisons. RoC in Ventricle mediates the effects of both TOMM40 SNPs on four comparisons. RoC in CDRSB mediates the effects of rs5011804-KRAS on three comparisons. Mediation analysis of longitudinal AD biomarkers revealed several candidate causal relationships from genetic determinants (i.e., SNPs from the APOE/TOMM40/APOC1 region and KRAS) to RoC measures in QTs (FAQ, Ventricle, CDRSB), and to various diagnostic comparisons in MCI and AD. The identified patterns warrant further investigation in independent cohorts for deconvoluting mechanistic complexity of AD.

Progression of Alzheimer's Disease by Self-Reported Cancer History in the Alzheimer's Disease Neuroimaging Initiative.

Cross-sectional studies suggest self-reported cancer history is associated with decreased risk of Alzheimer's disease (AD). However, little is known about how self-reported cancer affects longitudinal AD progression, the primary outcome in clinical trials and observational studies. To determine self-reported cancer history's effect on longitudinal AD progression in an observational study. We utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate progression to AD by self-reported all-cancer, breast, prostate, colorectal, or non-melanoma skin cancer history. Linear mixed effects models were used to examine baseline differences and rates of progression on the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) by self-reported cancer history. Age at AD onset was examined using consensus clinical diagnoses with Cox proportional hazards regression. Among 1,271 participants, models revealed no significant differences in progression over time but did reveal significantly lower baseline ADAS-Cog score, indicating better cognition at a given age in those with self-reported cancer history. Cox models indicated those with self-reported cancer history had significantly later age of AD onset (HR: 0.67, 95% CI: 0.53-0.85) after adjustment for covariates. Participants with self-reported cancer history entered ADNI with better cognition and later age of AD onset, but progressed similarly to participants without such history, indicating differences in AD between those with and without self-reported cancer history emerge early in the disease course. Such differences in longitudinal progression by self-reported cancer history could affect AD trials and observational studies, given the current focus on early disease course. Further investigation is warranted with detailed longitudinal assessment of cancer and AD.

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression.

Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer’s disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clustering that simultaneously: 1) incorporates whole brain data, 2) leverages unequal visits per individual, 3) compares clusters with a control group, 4) allows for study confounding effects, 5) provides cluster visualization, 6) measures clustering uncertainty. We used amyloid-β positive AD and negative healthy subjects, three longitudinal structural magnetic resonance imaging scans (cortical thickness and subcortical volume) over two years. We found three distinct longitudinal AD brain atrophy patterns: one typical diffuse pattern (n=34, 47.2%), and two atypical patterns: minimal atrophy (n=23 31.9%) and hippocampal sparing (n=9, 12.5%). We also identified outliers (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters differed not only in regional distributions of atrophy at baseline, but also longitudinal atrophy progression, age at AD onset, and cognitive decline. A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed. We believe this framework may aid in disentangling distinct subtypes of AD from disease staging.

Association of Klotho-VS Heterozygosity With Risk of Alzheimer Disease in Individuals Who Carry APOE4

Identification of genetic factors that interact with the apolipoprotein e4 (APOE4) allele to reduce risk for Alzheimer disease (AD) would accelerate the search for new AD drug targets. Klotho-VS heterozygosity (KL-VSHET+ status) protects against aging-associated phenotypes and cognitive decline, but whether it protects individuals who carry APOE4 from AD remains unclear. To determine if KL-VSHET+ status is associated with reduced AD risk and β-amyloid (Aβ) pathology in individuals who carry APOE4. This study combined 25 independent case-control, family-based, and longitudinal AD cohorts that recruited referred and volunteer participants and made data available through public repositories. Analyses were stratified by APOE4 status. Three cohorts were used to evaluate conversion risk, 1 provided longitudinal measures of Aβ CSF and PET, and 3 provided cross-sectional measures of Aβ CSF. Genetic data were available from high-density single-nucleotide variant microarrays. All data were collected between September 2015 and September 2019 and analyzed between April 2019 and December 2019. The risk of AD was evaluated through logistic regression analyses under a case-control design. The risk of conversion to mild cognitive impairment (MCI) or AD was evaluated through competing risks regression. Associations with Aβ, measured from cerebrospinal fluid (CSF) or brain positron emission tomography (PET), were evaluated using linear regression and mixed-effects modeling. Of 36 530 eligible participants, 13 782 were excluded for analysis exclusion criteria or refusal to participate. Participants were men and women aged 60 years and older who were non-Hispanic and of Northwestern European ancestry and had been diagnosed as being cognitively normal or having MCI or AD. The sample included 20 928 participants in case-control studies, 3008 in conversion studies, 556 in Aβ CSF regression analyses, and 251 in PET regression analyses. The genotype KL-VSHET+ was associated with reduced risk for AD in individuals carrying APOE4 who were 60 years or older (odds ratio, 0.75 [95% CI, 0.67-0.84]; P = 7.4 × 10-7), and this was more prominent at ages 60 to 80 years (odds ratio, 0.69 [95% CI, 0.61-0.79]; P = 3.6 × 10-8). Additionally, control participants carrying APOE4 with KL-VS heterozygosity were at reduced risk of converting to MCI or AD (hazard ratio, 0.64 [95% CI, 0.44-0.94]; P = .02). Finally, in control participants who carried APOE4 and were aged 60 to 80 years, KL-VS heterozygosity was associated with higher Aβ in CSF (β, 0.06 [95% CI, 0.01-0.10]; P = .03) and lower Aβ on PET scans (β, -0.04 [95% CI, -0.07 to -0.00]; P = .04). The genotype KL-VSHET+ is associated with reduced AD risk and Aβ burden in individuals who are aged 60 to 80 years, cognitively normal, and carrying APOE4. Molecular pathways associated with KL merit exploration for novel AD drug targets. The KL-VS genotype should be considered in conjunction with the APOE genotype to refine AD prediction models used in clinical trial enrichment and personalized genetic counseling.

Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging.

We develop a conditional generative model for longitudinal image datasets based on sequential invertible neural networks. Longitudinal image acquisitions are common in various scientific and biomedical studies where often each image sequence sample may also come together with various secondary (fixed or temporally dependent) measurements. The key goal is not only to estimate the parameters of a deep generative model for the given longitudinal data, but also to enable evaluation of how the temporal course of the generated longitudinal samples are influenced as a function of induced changes in the (secondary) temporal measurements (or events). Our proposed formulation incorporates recurrent subnetworks and temporal context gating, which provide a smooth transition in a temporal sequence of generated data that can be easily informed or modulated by secondary temporal conditioning variables. We show that the formulation works well despite the smaller sample sizes common in these applications. Our model is validated on two video datasets and a longitudinal Alzheimer's disease (AD) dataset for both quantitative and qualitative evaluations of the generated samples. Further, using our generated longitudinal image samples, we show that we can capture the pathological progressions in the brain that turn out to be consistent with the existing literature, and could facilitate various types of downstream statistical analysis.

A semi-automated pipeline for fulfillment of resource requests from a longitudinal Alzheimer's disease registry.

ObjectiveManaging registries with continual data collection poses challenges, such as following reproducible research protocols and guaranteeing data accessibility. The University of Kansas (KU) Alzheimer’s Disease Center (ADC) maintains one such registry: Curated Clinical Cohort Phenotypes and Observations (C3PO). We created an automated and reproducible process by which investigators have access to C3PO data.Materials and MethodsData was input into Research Electronic Data Capture. Monthly, data part of the Uniform Data Set (UDS), that is data also collected at other ADCs, was uploaded to the National Alzheimer’s Coordinating Center (NACC). Quarterly, NACC cleaned, curated, and returned the UDS to the KU Data Management and Statistics (DMS) Core, where it was stored in C3PO with other quarterly curated site-specific data. Investigators seeking to utilize C3PO submitted a research proposal and requested variables via the publicly accessible and searchable data dictionary. The DMS Core used this variable list and an automated SAS program to create a subset of C3PO.ResultsC3PO contained 1913 variables stored in 15 datasets. From 2017 to 2018, 38 data requests were completed for several KU departments and other research institutions. Completing data requests became more efficient; C3PO subsets were produced in under 10 seconds.DiscussionThe data management strategy outlined above facilitated reproducible research practices, which is fundamental to the future of research as it allows replication and verification to occur.ConclusionWe created a transparent, automated, and efficient process of extracting subsets of data from a registry where data was changing daily.

Vascular risk factors are associated with longitudinal changes in cerebrospinal fluid tau markers and cognition in preclinical Alzheimer's disease

IntroductionVascular factors increase the risk of Alzheimer's disease (AD). We investigated the associations between such factors, longitudinal AD cerebrospinal fluid biomarkers, and cognition. Methods433 cognitively normal participants were classified into four biomarker groups using their baseline amyloid (A+/−) and tau status (T+/−). 184 participants had undergone serial cerebrospinal fluid collection. Frequencies of risk factors and the Framingham Risk Score (FRS) were compared, and we tested the influence of risk factors on change in biomarker concentrations and cognition. ResultsThe absence of obesity, presence of hypertension, and a high FRS were associated with an increase in tau levels, particularly in A+T+ individuals. Risk factors were not associated with amyloid. Depression was associated with higher cognitive scores, whereas high FRS was associated with lower scores and a faster decline. DiscussionOur results demonstrate that vascular risk factors may enhance neurodegeneration but not amyloid accumulation in preclinical AD.