ArticleActa neuropathologica2025
Brain-derived 5-hydroxymethylcytosine epigenetic scores are related to Alzheimer's disease pathology and cognitive decline.
Article in Acta neuropathologica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCytosine modifications play critical roles in gene regulation and disease pathogenesis. Elucidating novel epigenetic contributions to Alzheimer's disease (AD) could advance diagnostic, prognostic, and therapeutic strategies. 5-hydroxymethylcytosine (5hmC), a stable and dynamic DNA modification, has emerging links to AD pathophysiology and potential uses as a biomarker. Cognitive decline, a hallmark of AD progression, varies across individuals and is not fully explained by classic pathological markers. Here, we aimed to develop and validate brain-derived 5hmC-based epigenetic scores to distinguish AD from non-AD pathology and examine their relationship to individual cognitive trajectories.
methodsGenome-wide 5hmC profiles were generated using 5hmC-Seal and next-generation sequencing on 1016 postmortem human brain prefrontal cortex samples from well-characterized, deceased participants in a longitudinal, clinical-pathologic research study on aging. Samples were processed in independent training and validation sets. Genomic features (e.g., gene bodies, enhancers) were summarized, followed by differential analysis and machine learning-based feature selection to construct classification models distinguishing AD from non-AD.
resultsAfter quality control and batch correction of 5hmC data, 1005 participants were included, with 655 classified as having AD and 350 as non-AD, according to NIA-AA neuropathologic criteria. In the training set (n = 859), 136 candidate gene bodies and 96 enhancers were selected based on variability and relaxed significance thresholds (p < 0.1). Pathway enrichment analyses implicated cardiovascular function, endocytosis, and MAPK signaling pathways. Using these features, we developed machine learning models that distinguished AD from non-AD with high performance in both the training set (AUC = 87.0%; 95% CI 84.2-89.7%) and validation set (n = 146; AUC = 91.4%; 95% CI 86.6-96.2%). Moreover, the resulting AD-score was significantly associated with rates of global and five domain-specific cognitive decline.
conclusionThis study extends prior work by translating brain 5hmC profiles into epigenetic scores that distinguish AD pathology and reflect individual cognitive trajectories. These findings highlight the potential of brain-derived 5hmC modifications as biomarkers for AD and as tools to advance research on disease progression, offering a new direction for epigenetics-informed clinical applications in AD.
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