Sex Differences in the Alzheimer's Brain Age Gap: APOE ε4 Plays a Major Role

MRI-derived brain aging trajectories across the Alzheimer's continuum: links to cognition, pathology and lifestyle risk factors

BAG is a reliable non-invasive marker of structural brain health sensitive to AD pathology and to modifiable AD risk and supports its relevance for early risk stratification and prevention-oriented research.

Sex-Specific Structural Vulnerability in Alzheimer’s Disease: Insights from APOE ε 4-Negative Patients

Background: The interaction between sex, APOE ε4 status, and clinical progression in Alzheimer’s Disease (AD) remains a subject of debate. While females are often considered at higher risk for AD, the underlying structural neuroanatomical trajectories and how they are modulated by genotype are not fully elucidated. This study aims to evaluate how sex and the APOE ε4 genotype interact to influence longitudinal brain atrophy across three clinical groups. Methods: We analyzed longitudinal data from 2400 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), stratified by clinical group (i.e., cognitively normal, mild cognitive impairment, and AD), sex, and APOE ε4 carrier status. Using Type III Sum of Squares ANCOVA, we modeled the longitudinal variation in brain volume, controlling for baseline brain volume, and baseline severity of neurocognitive impairment and age at entry. Results: While main effects of sex and APOE genotype were not significant, the triple interaction (APOE * Sex * Clinical Group) was marginally significant (p = 0.051). Post hoc analysis revealed a distinct pattern of structural dimorphism within the AD cohort among APOE ε4-negative individuals with females exhibiting significantly greater structural preservation compared to males (Mean difference = 11.32, p = 0.051). Among APOE ε4 carriers, atrophy trajectories for males and females were statistically indistinguishable (p = 0.922), potentially suggesting that the ε4 allele exerts a dominant neurodegenerative influence that overrides sex-specific physiological differences. Conclusions: These emerging findings highlight the importance of jointly considering biological sex and APOE ε4 status to improve the characterization of Alzheimer’s disease heterogeneity and support precision medicine approaches.

Predicting Alzheimer’s Disease Progression from Mild Cognitive Impairment via Automated Brain Age Modeling

The early detection of individuals with Mild Cognitive Impairment (MCI) who are at high risk of developing Alzheimer’s Disease (AD) is important for proper clinical intervention and disease management. Studies show that there is a strong link between brain age, which is estimated from brain scan data, and actual chronological age. This difference is called the Brain Age Gap (BAG), and it acts as a useful biological marker for identifying people at risk of brain degeneration. This study aims to create an automated dual-network framework that predicts MCI subjects’ likelihood of converting to AD based on their structural magnetic resonance imaging (MRI) brain scans. The two networks will consist of (i) a three-dimensional convolutional neural network (CNN) model that predicts an estimated brain age based on the inputted MRI scan of a subject, and (ii) a risk prediction network that predicts probability of MCI-to-AD conversion by incorporating estimated brain age, BAG, and deep feature representations. The framework will be trained in sequential order using a longitudinal neuroimaging dataset. Results from the experiment indicate higher classification performance can be attained with the proposed dual-network architecture over current state-of-the-art single-stage classification methods or models without brain age. Furthermore, these results strongly support the use of brain age-based predictive features for early prediction of AD risk. The proposed method offers a fully automated and clinically interpretable solution for supporting early diagnosis and personalized intervention planning in Alzheimer’s disease.

Brain age gradients as intermediate phenotypes linking plasma p-tau217 to cognition in community-dwelling older adults

Deep learning-based brain age models quantify regional deviations from normative aging and may capture structural changes relevant to dementia risk. Plasma phosphorylated tau-217 (p-tau217) is a scalable Alzheimer’s disease biomarker, but its relationship to brain aging and cognition in cognitively unimpaired adults is unclear. In this cross-sectional study, we tested whether brain age patterns serve as indirect pathways linking plasma p-tau217 to cognition in the Aging Brain Cohort (ABC). Neuroimaging data from 518 adults (mean age = 43.7 years, 70.8% female) were analyzed using a validated deep learning brain age model, and decomposed via exploratory factor analysis into six gradients: frontal, dorsal, ventral, left frontotemporal, right frontotemporoparietal, and bilateral parietal. In a parallel mediation model including all six gradients as simultaneous mediators in adults aged ≥60 years (N = 71), a significant specific indirect effect of plasma p-tau217 on Montreal Cognitive Assessment (MoCA) scores was observed through accelerated right frontotemporoparietal aging (β = −0.111, 95% CI [−0.313, −0.010], p = 0.031). No other indirect pathways were significant, and neither the total nor direct effect was significant. These findings suggest a specific brain aging phenotype as a potential intermediate pathway linking tau-related pathology to cognition prior to clinical impairment.

Blood Biomarkers of Alzheimer's Disease and Patterns of Structural Brain Changes in the Community.

In cognitively unimpaired older adults, elevated blood p-tau217 was linked to faster shrinkage in AD-specific brain regions, whereas NfL and GFAP were associated with more widespread atrophy, with NfL also associated with accelerated WMH accumulation.

The value of brain age as a transdiagnostic biomarker of neurodegeneration