Framingham cohort links longitudinal brain atrophy patterns to cognitive decline and AD biomarkers in community-based participantsBrain shrinkage patterns predict Alzheimer's risk in older adults

medRxiv Published April 30, 2026 Study authors: Rehman, H.; Tao, Q.; Nolan, J.; Kurniansyah, N.; Ang, T. F. A.; Crane, P. K.; Mukherjee, S.; Saykin,… DOI ↗ Editorial oversight: Dr. Ji-eun Park, MD · Brain, Mind & Pain

AI-generated summary of the cited source, checked by automated accuracy review. How we work

Key Takeaway

Note that PC1 brain atrophy patterns associate with worse cognition and higher AD biomarkers in this cohort.

This observational cohort study included 300 participants from the Framingham Heart Study. The population comprised individuals with Alzheimer's disease, mild cognitive impairment, dementia, or who were cognitively normal. Researchers examined longitudinal brain atrophy patterns using MRI features and compared findings to normative aging patterns. The primary outcome measured longitudinal brain atrophy components using principal components analysis.

Principal component 1 (PC1) explained 75.8% of the variance. PC2 explained 13.8% of the variance, and PC3 explained 5.4% of the variance. PC1 associations were linked to worse cognition and higher plasma AD biomarkers. PC2 showed distinct associations with biomarkers and cognition compared to PC1. PC3 showed no consistent associations with the measured outcomes.

Neuropathological analysis indicated stronger associations with AD-related tau pathology in the absence of concomitant TDP43 pathology for PC1. Secondary outcomes included plasma AD biomarkers such as p-Tau181, total Tau, GFAP, NfL, A{beta}40, and A{beta}42, as well as cognitive outcomes, neuroethological measures, tau pathology, and TDP43 pathology. Safety data, including adverse events, serious adverse events, discontinuations, and tolerability, were not reported.

Key limitations regarding follow-up duration were not reported in the study data. The practice relevance supports multimodal approaches for disease characterization and risk stratification. These findings apply to community-based cohorts investigating Alzheimer's disease and related cognitive conditions.

Imagine watching your brain slowly change over time. For many older adults, this change is normal aging. But sometimes, the pattern of shrinkage signals something more serious. A new look at data from the Framingham Heart Study helps explain these differences. Researchers tracked 300 community-based participants to see how their brains changed. They used MRI scans to measure brain atrophy patterns over time. They compared these patterns to what is expected in normal aging. The goal was to find clear signs of Alzheimer's disease before symptoms become severe. This matters because early detection can change how doctors plan care for patients with mild cognitive impairment or dementia. The team looked at many types of brain changes. They also checked blood markers that show disease activity. Their analysis grouped these changes into three main patterns. The first pattern explained most of the brain changes seen in the group. This first pattern linked to worse thinking skills and higher levels of disease markers in the blood. It also matched strongly with tau protein buildup in the brain. This protein is a key sign of Alzheimer's. The second pattern showed different links to health markers. The third pattern did not show consistent connections to disease. These findings support using many tools to understand brain health. Doctors can use this knowledge to better identify who is at risk. It helps them move away from guessing and toward clearer answers. This approach brings hope to families worried about memory loss.

What this means for you:

Specific brain shrinkage patterns help predict Alzheimer's risk and disease activity in older adults.

Study Details

Study typeCohort

Sample sizen = 211

EvidenceLevel 3

PublishedApr 2026

View Original Abstract ↓

Abstract Background: Characterizing longitudinal patterns of brain atrophy that distinguish Alzheimers disease (AD) and related neurodegeneration along with normative aging remains a major challenge. We aimed to identify data-driven longitudinal brain atrophy components and evaluate their associations with plasma AD biomarkers and cognitive outcomes in a community-based cohort. Methods: We analyzed 756 MRI scans from 300 participants in the Framingham Heart Study (mean 2.52 scans per participant; range 2 to 4). Linear mixed effects models were used to identify MRI features associated with diagnostic group (cognitively normal [CN], mild cognitive impairment [MCI], and dementia). Significant features (n=211) were entered into a longitudinal multivariate decomposition framework (ANOVA Simultaneous Component Analysis with Assorted Linear functions; ALASCA) to derive principal components (PCs) capturing patterns of structural change over time. Associations between PCs and plasma AD biomarkers (p-Tau181, total Tau(tTau), glial fibrillary acidic protein [GFAP], neurofilament light chain [NfL], amyloid-{beta}40 [A{beta}40], and amyloid-{beta}42 [A{beta}42]) were evaluated using multivariable mixed-effects models adjusted for age, sex, education, and APOE {varepsilon}4 status. Cognitive measures and neuroethological measures in a subset were used to assess the functional relevance and biological associations, respectively. Results: The first three PCs explained ~95% of the variance within the modeled MRI feature (n=211) set (PC1: 75.8%, PC2: 13.8%, PC3: 5.4%). PC1 captured medial temporal atrophy involving hippocampal subfields and basolateral amygdala and was associated with worse cognition and higher plasma AD biomarkers. Neuropathological analyses showed stronger associations of PC1-related atrophy with AD-related tau pathology in the absence of concomitant TDP43 pathology. In contrast, PC2 reflected diffuse cortical gray white matter contrast alterations across association cortices and showed distinct associations with biomarkers and cognition compared to PC1, consistent with overlapping aging- and neurodegeneration-related processes. PC3 showed limited variance and no consistent associations. Conclusion: Longitudinal MRI derived components capture distinct patterns of brain structural change associated with neurodegeneration. Medial temporal trajectories are closely associated with AD and related dementia, whereas cortical alterations likely reflect mixed aging- and disease-related processes. Integration of structural MRI with plasma biomarkers provides complementary information on disease expression and heterogeneity, supporting multimodal approaches for disease characterization and risk stratification.

Framingham cohort links longitudinal brain atrophy patterns to cognitive decline and AD biomarkers in community-based participantsBrain shrinkage patterns predict Alzheimer's risk in older adults

Study Details

Intravenous tenecteplase improves functional outcomes but increases hemorrhage risk in non-large vessel occlusion acute ischemic stroke

Tenecteplase improves stroke recovery but increases bleeding risk in late treatment window

Clinical research that matters. Delivered to your inbox.

Framingham cohort links longitudinal brain atrophy patterns to cognitive decline and AD biomarkers in community-based participantsBrain shrinkage patterns predict Alzheimer's risk in older adults

More on Dementia

Study Details

Intravenous tenecteplase improves functional outcomes but increases hemorrhage risk in non-large vessel occlusion acute ischemic stroke

Tenecteplase improves stroke recovery but increases bleeding risk in late treatment window

Clinical research that matters. Delivered to your inbox.

Related in Neurology

From Other Specialties