Longitudinal dynamics of gene expression and metabolomics in an aging population cohort
Abstract
Multiomic profiling provides a comprehensive physiological overview at the molecular level, but understanding of its spatiotemporal dynamics remains limited in human populations. We profiled longitudinal whole-blood gene expression and metabolite levels in 335 females over 8 years. Levels of 5061 genes and 181 metabolites changed over time, with individual trajectories often diverging from population-level trends. Longitudinally variable genes showed cell type specificity and enrichment for aging-relevant pathways, including cardiometabolic and neurodegenerative disorders. Longitudinal trajectories were further shaped by genetics, circadian rhythm, seasonality, and environmental pollutant exposures. Integrative analyses revealed extensive static and time-variable cross-omic connectivity. Longitudinal profiling offers insight into the temporal evolution of age-related conditions at the molecular level, and understanding individual variation within these longitudinal patterns will be essential for future precision medicine approaches.
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Authors: Julia S. El-Sayed Moustafa, Anna Ramisch, Yasrab Raza, Gwenaël G. R. Leday, Yunlong Jiao, Dongmeng Wang, Michael Stevens, Amy L. Roberts, Max Tomlinson, Xiaolang Yan, Elizabeth Ing‐Simmons, Samuel Wadge, Moustafa Abdalla, Mario Falchi, Chris Holmes, Cristina Menni, George Nicholson, Mark I. McCarthy, E. T. Dermitzakis, Sylvia Richardson, Tim D. Spector, Kerrin S. Small
Institutions: University of Oxford, University of Geneva, University of Cambridge, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, King's College London, MRC Biostatistics Unit, Medical Research Council, Oxford BioMedica (United Kingdom), Centre for Human Genetics, Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford Fertility