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Data suggest that women exhibit elevated insoluble tau aggregates relative to age-matched men. Whether this sex difference is due to upstream soluble phosphorylated tau(p-tau) is unclear. We examined whether sex and amyloid-β(Aβ) predict baseline plasma p-tau217 and whether baseline plasma p-tau217 and p-tau217/Aβ42 predict longitudinal tau positron emission tomography(PET) in a sex-specific manner. 998 clinically normal individuals (mean-age:71; 429 APOEε4 carriers; 525 Aβ+; Table) from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease trial, the companion Longitudinal Evaluation of Amyloid Risk and Neurodegeneration study(A4/LEARN), the Wisconsin Registry for Alzheimer's Prevention(WRAP), Harvard Aging Brain Study(HABS) and Alzheimer's Disease Neuroimaging Initiative(ADNI) had multiple tau-PET scans and at least one measurement of ptau217. Average tau-PET follow-up time was 3.6 years (range=1.3-8.9years. We used regression models of soluble p-tau217 to examine cross-sectional sex×Aβ interaction. Longitudinally, random-effects models (participant-specific intercepts and slopes) estimated the sex×baseline-p-tau217 time interaction on each of nine apriori tau-PET regions (including rostral middle frontal gyri, fusiform gyrus, inferior temporal and parietal gyri, the superior parietal lobule, precuneus, lateral occipital cortex, parahippocampal and amygdala). All models adjusted age. ADNI used the p-tau217/Aβ42 ratio. In A4/LEARN, women exhibited elevated baseline p-tau217 concentrations relative to men, particularly at higher neocortical-Aβ(β=-0.16, p = 0.008). No significant interactions were observed in the other cohorts. Longitudinal sex×baseline-p-tau217×time interactions were significant in five, six, four, and seven tau-PET regions in A4/LEARN, WRAP, HABS and ADNI cohorts, respectively. Across all significant interactions, women showed worse tau trajectories than men at higher p-tau217(A4/LEARN, WRAP, HABS; Figure 1) and p-tau217/Aβ42(ADNI; Figure 2) concentrations. Covarying for APOEε4-status, baseline Aβ and baseline-tau-SUVR did not attenuate the longitudinal sex effects. These findings suggest that sex differences in tau proliferation may be exacerbated by upstream soluble p-tau levels. Earlier therapeutic approaches reducing soluble p-tau levels might be particularly important in women.

INTRODUCTION Cerebral small vessel disease (CSVD) contributes to the development of Alzheimer's disease (AD) dementia and co‐occurs with AD‐associated proteinopathies. However, how sex modulates the interaction between CSVD and AD‐associated proteinopathies in the medial temporal lobe (MTL) remains unclear. METHODS One hundred fifty‐two autopsy cases from the Massachusetts Alzheimer's Disease Research Center were included. Deep‐learning and semiquantitative scores were applied to MTL histological sections to obtain quantitative measures of proteinopathies and CSVD (cerebral amyloid angiopathy [CAA] and arteriolosclerosis). The effect of sex on AD‐associated proteinopathies and the interaction between sex, CSVD, and apolipoprotein E (APOE) genotype were analyzed using linear mixed‐effect models. RESULTS In women, higher CAA burden was associated with lower amyloid beta (Aβ) plaques but higher tau tangles density. No interaction effect was found for arteriolosclerosis. Women <75 years of age carrying the APOE ε4 allele had higher Aβ plaque burden than ε4 non‐carriers. DISCUSSION Our results highlight the complex effect of sex on microvascular and AD‐associated pathologies in the MTL. Highlights Deep learning was used to obtain quantitative measures of proteinopathies. In the medial temporal lobe, women had fewer amyloid beta (Aβ) plaques and more tau tangles compared to men. Women with more cerebral amyloid angiopathy (CAA) had fewer Aβ plaques but a higher tau tangle density. After age stratification, women younger than 75 years and ε4 carriers had higher Aβ plaque burden.

Previous studies suggest that a maternal history of Alzheimer's disease (AD) confers a greater risk for AD than paternal history through elevated Aβ-PET, reduced brain glucose metabolism, and lower grey matter volume though these studies were primarily cross-sectional, limiting causal inferences. Whether parental history of AD dementia impacts AD endophenotypes longitudinally is not fully described. We examine the effects of parental history of dementia on longitudinal measures of cognition and AD biomarkers in a preclinical population. Longitudinal Aβ-PET ( F-florbetapir), tau-PET ( F-flortaucipir), and cognition (PACC-5) data from 1,693 cognitively unimpaired individuals from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) clinical trial and the adjoining LEARN observational study were used for these studies (mean(SD) =71.29(4.67), female=59%, APOEε4+=35%; n=446 with tau-PET). Parental history was self-reported by participants and defined as \"history of cognitive impairment and/or dementia.\" Linear mixed-effects models assessed the association between parental history (i.e., maternal/paternal) and the outcomes of interest covarying for the participant's age, sex, and years of education where applicable. The following interaction terms were also examined: parental history*sex, parental history*APOEε4 allele count, and parental history*Aβ burden. There was no effect of parental history on longitudinal Aβ accumulation or PACC-5 scores. We found that participants with a paternal history of memory impairment or dementia accumulated tau faster than those without paternal history (β=0.01, SE=0.003, p =0.004, Figure 1). This association remained significant when covarying for APOE-ε4 and APOE-ε2 allele count. Tau accumulation was not affected by paternal age at symptom onset (p =0.175). We found no moderating effects. We observed no association between maternal history and tau accumulation. This is one of the first studies of parental history and longitudinal AD endophenotypes in a large sample of cognitively unimpaired individuals. Our findings that participant-reported history of paternal history is associated with faster tau accumulation stands at odds with evidence suggesting greater risk of maternal history of dementia. However, a recent study by Ourry et al., also highlights a relationship between paternal history and tau. These findings could be driven by biological or survival bias differences in the sample and deserves further exploration.

The BrainAge Gap estimates the discrepancy between predicted and chronological brain age based on neuroimaging. Calculating a Polygenic Risk Score (PRS) from a BrainAge Gap estimate quantifies the genetic predisposition to accelerated brain aging. The objective of this study is to examine the association between the genetic propensity for higher or lower BrainAge Gap and plasma biomarkers of AD. Linking genetic predisposition to brain aging with early AD related changes could improve our understanding of the early disease mechanisms and risk factors METHODS: We examined 3014 cognitively normal participants from the A4 and LEARN studies (71.4 ± 4.6 age; 40% male). PRS of BrainAge models were calculated for each subject using the summary GWAS statistics of Wen et. al, Nature Communications 2024 for three types of BrainAge models: Grey Matter (GM), White Matter (WM) and Functional Connectivity (FC). We focused on the following plasma biomarkers gathered at baseline: p-tau (Eli-Lilly, N = 736), GFAP (Roche Diagnostic, N = 1643) and NfL (Roche Diagnostic, N = 1641). We used a general linear model to study the association between each of the 3 plasma biomarkers with each of the 3 PRS measures, and additionally including the interaction between age and PRS for each BrainAge model. None of the BrainAge PRS were found to be significantly different by Aβ-PET status, APOEε4 carriership, age, sex or education. BrainAge GM PRS was positively associated with p-tau levels (p = 0.01), particularly among the older adults (p = 0.007). In sensitivity analyses, covaring sex, APOEε4 status and years of education did not alter the results. None of the BrainAge PRS were associated with GFAP or NFL. Genetic factors associated with increased propensity for accelerated brain aging in the grey matter is associated with p-tau , an early and sensitive marker of AD. These genetic predispositions were more pronounced in older age, highlighting the importance of age as a critical factor that may interact with genetic susceptibility to brain aging, potentially through cumulative lifetime exposures, increasing vascular burden, or age-related declines in cellular repair mechanisms. Associations solely with BrainAge GM PRS implies the specificity of accelerated brain aging in grey matter as a potential early marker of AD risk.

The BrainAge Gap estimates the discrepancy between predicted and chronological brain age based on neuroimaging. Calculating a Polygenic Risk Score (PRS) from a BrainAge Gap estimate quantifies the genetic predisposition to accelerated brain aging. The objective of this study is to examine the association between the genetic propensity for higher or lower BrainAge Gap and plasma biomarkers of AD. Linking genetic predisposition to brain aging with early AD related changes could improve our understanding of the early disease mechanisms and risk factors METHODS: We examined 3014 cognitively normal participants from the A4 and LEARN studies (71.4 ± 4.6 age; 40% male). PRS of BrainAge models were calculated for each subject using the summary GWAS statistics of Wen et. al, Nature Communications 2024 for three types of BrainAge models: Grey Matter (GM), White Matter (WM) and Functional Connectivity (FC). We focused on the following plasma biomarkers gathered at baseline: p-tau (Eli-Lilly, N = 736), GFAP (Roche Diagnostic, N = 1643) and NfL (Roche Diagnostic, N = 1641). We used a general linear model to study the association between each of the 3 plasma biomarkers with each of the 3 PRS measures, and additionally including the interaction between age and PRS for each BrainAge model. None of the BrainAge PRS were found to be significantly different by Aβ-PET status, APOEε4 carriership, age, sex or education. BrainAge GM PRS was positively associated with p-tau levels (p = 0.01), particularly among the older adults (p = 0.007). In sensitivity analyses, covaring sex, APOEε4 status and years of education did not alter the results. None of the BrainAge PRS were associated with GFAP or NFL. Genetic factors associated with increased propensity for accelerated brain aging in the grey matter is associated with p-tau , an early and sensitive marker of AD. These genetic predispositions were more pronounced in older age, highlighting the importance of age as a critical factor that may interact with genetic susceptibility to brain aging, potentially through cumulative lifetime exposures, increasing vascular burden, or age-related declines in cellular repair mechanisms. Associations solely with BrainAge GM PRS implies the specificity of accelerated brain aging in grey matter as a potential early marker of AD risk.

Due to the high prevalence of Alzheimer's disease (AD) in adults with Down syndrome (DS), trisomy 21 is now considered a genetic form of AD (DSAD). A better understanding of factors that can prevent or delay AD is vital to improve outcomes for adults with DS. In this narrative review, we apply AD and cognitive aging research frameworks to study resistance and resilience in DSAD. Given the variability in the timing of pathology and symptoms, we discuss the evidence supporting the role of genetic, biological, socio‐behavioral, lifestyle, and environmental factors in resistance and resilience to DSAD. We also consider how co‐occurring health conditions in DS may influence resistance and resilience, and how methods from AD research can be applied to DSAD. Ultimately, this framework aims to guide future research and translate findings into clinical interventions to improve outcomes in DSAD. Highlights Definitions of resistance and resilience in the genetic form of Alzheimer's disease (DSAD) are proposed for guiding the field. Variability in the timing of AD pathology and symptoms suggests the potential for resistance and resilience mechanisms in DSAD. Genetic, biological, socio‐behavioral, lifestyle, and environmental factors have the potential to build resistance or resilience in DSAD. Future research will require longitudinal and experimental designs, life course approaches, and large cohort studies.

INTRODUCTION Early biological pathways explaining the risk for Alzheimer's disease (AD)–related cognitive decline remain poorly understood. METHODS Using linear mixed‐effects models, we investigated whether whole blood gene expression (RNA sequencing) moderates the relationship between AD biomarkers measured by amyloid beta (Aβ) and tau‐PET (positron emission tomography) imaging and longitudinal cognition in 770 cognitively unimpaired older adults (Agemean = 71.3, 62% female) from Anti‐Amyloid Treatment in Asymptomatic Alzheimer's (A4) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) (A4/LEARN). RESULTS We identified protective and AD risk–related gene expression signatures on the autosome and X chromosome. Six genes (ngenes(%); 2(33%) X‐linked) interacted with Aβ‐PET, whereas 103 genes (3(3%) X‐linked) interacted with neocortical tau‐PET, to influence cognitive decline. A total of 110 genes (17(15%) X‐linked) and 3156 genes (121(4%) X‐linked) were moderated by both sex and Aβ‐ or tau‐PET, respectively. Pathway enrichment analyses reflected immunity, protein synthesis, and lipid metabolism. DISCUSSION These findings underscore the importance of peripheral transcriptomic markers in identifying sex‐differentiated pathways related to risk of and protection from cognitive decline in preclinical AD. Highlights Whole blood gene expression moderates biomarker–cognition associations in preclinical AD Six genes interacted with amyloid beta positron emission tomography (Aβ‐PET) and 103 with tau‐PET to influence cognitive decline Over 3000 gene‐by‐sex interactions reveal sex‐specific transcriptomic vulnerability Pathways implicate immunity, ribosomal biology, and vesicle trafficking processes Findings support blood‐based, sex‐aware biomarkers for precision AD risk stratification

Higher levels of plasma p-tau are closely associated with increased Aβ-PET burden, and subsequent cognitive decline in older adults, supporting it as a sensitive, early marker of AD. Previous studies implicated X-linked gene expression in AD but were limited to gene expression from postmortem tissue resulting in findings less clinically relevant to early stages of disease. To better inform the relationship between X-linked genes and AD during the earliest disease processes, we aimed to identify associations between whole blood X-linked gene expression and plasma p-tau levels in clinically normal older adults from A4/LEARN. We leveraged Aβ-PET( F-Florbetapir), plasma p-tau (immunoassay, Eli Lilly), and whole blood RNAseq data from 724 cognitively unimpaired participants (72.2years(±4.6); 63%Female; 35%APOEε4+; 26%Aβ+) from the A4 clinical trial at baseline (placebo[31%], treatment[30%]) and the LEARN[39%] observational study. We ran linear regressions adjusting for age, BMI, and cohort to determine associations between the following terms and p-tau (pg/mL): gene, gene*APOEε4, gene*sex, gene*Aβ , and gene*sex*Aβ . Though focusing on X-linked genes, results were FDR-corrected for both autosomal and X-linked genes (n = 20,621). No X-linked genes were directly associated with p-tau levels. 119 X-linked genes were moderated by Aβ and 27 genes by Aβ*sex on p-tau . Notably, we identified 4 genes previously implicated in AD: FAM156B, KDM6A, WWC3, and MIDI1IP1, which are involved in chromatin remodeling, hippo pathway signaling, and lipid signaling. In gene*Aβ models, higher FAM156B expression (β=-0.09(0.03), p <0.001, Figure 1A) was associated with lower p-tau levels among individuals with high Aβ-PET burden whereas higher KDM6A expression (β=0.31(0.10), p =0.003, Figure 1B) was associated with higher p-tau levels in both sexes. In females with elevated Aβ-PET, higher WWC3 expression was associated with lower p-tau (β=-0.44(0.15), p =0.004, Figure 1C). In males with high Aβ-PET burden, both greater WWC3 (β=0.37(0.14), p =0.01, Figure 1C) and MID1IP1 (β=0.60(0.15), p <0.001, Figure 1D) expression was associated with higher p-tau . Significant whole-blood X-linked gene expression associations with p-tau levels in clinically normal older adults are largely moderated by Aβ-PET burden and sex. This study identified both protective and risk genes, highlighting novel gene candidates for further validation and supporting the need to study sex chromosomes in AD.

Prior evidence suggests that neocortical tau in those with higher β-amyloid (Aβ) may be the main driver of Alzheimer's disease (AD)-related neurodegeneration leading to insidious cognitive decline and ultimately a diagnosis of AD dementia. Resistance to the 'spread' of neocortical tau pathology from localized medial temporal (MTL) regions can be defined as individuals having lower neocortical tau than expected given their individual characteristics, such as demographics and Aβ burden. We examined associations between resistance to neocortical tau pathology and various markers of AD pathology, cognitive performance, and brain reserve. We calculated tau resistance using our published inverse learning method (Figure 1B), which estimates the deviation away from a model trained on an expectation sample (278 Aβ-PET+ older adults with high neocortical tau-PET (PVC_SUVR :inferior temporal/inferior parietal/fusiform/middle temporal) burden based on Gaussian Mixture Modeling;Figure 2A). We ran a series of linear regression models on the remaining 1,374 older adults pooled from the Harvard Aging Brain Study (HABS), ADNI, and A4/LEARN (Demographics in Figure 1A). We examined associations between tau resistance and 1) MTL tau-PET (PVC_SUVR ; entorhinal/amygdala/parahippocampal), 2) measures of brain reserve (hippocampal volume and entorhinal cortical thickness), 3) neocortical Aβ-PET burden (Centiloids), 4) an interaction between MTL tau and Aβ-PET, and 5) cognitive performance (PACC). All models adjusted for age, sex, education, cohort, and APOEε4. Lower MTL tau, lower Aβ, and younger age were significantly associated with higher neocortical tau resistance (β =-0.22(0.06), p <0.001, Figure 2B; β =-0.13(0.03), p <0.001Figure 2C;β =-0.62(0.02), p <0.001). Higher PACC, greater hippocampal volume and thicker entorhinal cortices were associated with lower resistance (β =-0.29(0.02), p <0.001, Figure 2D;β =-0.10(0.03), p <0.001, β =-0.14(0.03), p <0.001). Greater Aβ and MTL tau burden interacted to influence lower resistance (Figure 3). In a sample limited to Aβ+ (N = 537), we found only lower PACC and younger age significantly associated with tau resistance. These findings suggest that baseline levels of MTL tau and age play a role in resisting the advancement of tauopathy into neocortical brain regions, and might be mediated by Aβ in early disease stages. The counter-intuitive association with cognition and brain reserve measures implies that tau resistance is most likely represented by those with greater cognitive impairment and lower reserve, as neocortical tau burden is much lower in clinically-normal older adults.

BrainAge models estimate biological brain age based on neuroimaging data, providing a measure of brain health. This metric is particularly relevant in Alzheimer's disease (AD), where accelerated brain aging is exacerbated by β-amyloid (Aβ) and tau accumulation. We investigated the extent to which BrainAge moderates associations between AD biomarkers and longitudinal cognitive decline across two independent cohorts. We examined 1690 participants from A4/LEARN and 349 from HABS (Table 1). Using the Open-Source tool AgeML within each cohort, we built a BrainAge linear regressor model with 5-fold cross validation using MRI-T1 volumetric and FreeSurfer cortical thickness ROIs. We compared predicted ages with chronological age to create a BrainAge . To avoid regressing out sex and APOEε4 variance, separate male/female models were built with data from APOEε4 non-carriers and applied to each cohort. We examined BrainAge as a moderator of global neocortical Aβ-PET burden, temporal lobe Tau PET composite and p-tau associations with longitudinal PACC using linear mixed effects models. We adjusted for random intercepts and slopes, and baseline age, sex, years of education and APOEε4. In A4/LEARN we additionally adjusted for cumulative dose and treatment group using a spline model. Higher levels of Aβ-PET, Tau-PET and p-tau at baseline was significantly correlated with higher BrainAge (worse) (Figure 1). BrainAge was directly associated with PACC trajectories in both cohorts. It also moderated the association between Aβ and Tau-PET and PACC trajectories such that higher BrainAge was associated with faster cognitive decline with increasing levels of each biomarker. We found the same pattern of effects in p-tau limited only to the A4/LEARN sample but was trend-level in HABS (Figure 2). BrainAge is significantly associated with Aβ and tau burden and moderates their association with cognitive decline, supporting previous literature suggesting that BrainAge is a robust marker of brain health. Prioritizing individuals with worse BrainAge for clinical trials could not only effectively reduce screen fails (estimates forthcoming) but is a potentially feasible approach given that it can be calculated from a single T1-weighted MRI scan. These findings also highlight the importance of age-independent neurodegeneration patterns to contribute unique signal in models of brain health and pathological progression.