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Epigenetic Age as a Predictor of Cardiovascular Risk: Grayscale Features in a Low-Income Longitudinal Cohort: Expansion and Analysis of the Fragile Families and Child Wellbeing Study

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dc.contributor.advisorNotterman, Daniel A.
dc.contributor.authorNdubisi, Angel
dc.date.accessioned2026-07-23T14:56:56Z
dc.date.available2026-07-23T14:56:56Z
dc.date.issued2026-04-19
dc.description.abstractCardiovascular disease (CVD) remains a leading cause of morbidity and mortality worldwide. Novel, non-invasive biomarkers that capture early vascular changes and biological aging may improve risk stratification, particularly in socioeconomically diverse populations. To evaluate associations between grayscale imaging features, cardiovascular health as measured by Life’s Essential 8 (LE8), and DNA methylation (DNAm)- based epigenetic aging. Data were analyzed from 1,421 participants in the Fragile Families and Child Wellbeing Study. Grayscale imaging features, including grayscale median (GSM), entropy, gray level dependence matrix (GLDM), and gray level co-occurrence matrix metrics (SGLD-ASM, SGLD-HOM), were examined in relation to LE8 scores (including sleep) and multiple DNAm clocks. Associations were assessed using linear regression and generalized additive models (GAMs) to capture nonlinear relationships. GSM was the only grayscale feature significantly associated with LE8 (p<0.01), demonstrating a positive relationship in which higher GSM values corresponded to better cardiovascular health. GAM analyses revealed a nonlinear association with a plateau at GSM ~80-90. Other grayscale features were not significantly associated with LE8. Healthmap and regression analyses showed largely non-significant relationships between grayscale features and DNAm clocks; however, select measures of pace of aging and PhenoAge demonstrated modest inverse associations with GSM. Entropy showed weak negative trends with LE8 and certain DNAm measures. Grayscale median is a promising imaging derived marker of cardiovascular health and shows limited but suggestive links to epigenetic aging. These findings support the integration of scalable biomarkers with comprehensive health metrics like LE8 to enhance early detection and prevention of CVD, with applications in global health.
dc.identifier.urihttps://theses-dissertations.princeton.edu/handle/88435/dsp013197xq55k
dc.language.isoen_US
dc.titleEpigenetic Age as a Predictor of Cardiovascular Risk: Grayscale Features in a Low-Income Longitudinal Cohort: Expansion and Analysis of the Fragile Families and Child Wellbeing Study
dc.typePrinceton University Senior Theses
dspace.entity.typePublication
dspace.workflow.startDateTime2026-04-19T20:41:59.531Z
pu.contributor.authorid920315560
pu.date.classyear2026
pu.departmentMolecular Biology

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