Sex- and Age-Stratified Bioinformatic Analysis of Human Aging: Molecular Mechanisms, Epigenetic Remodeling, and Large-Scale Omics Integration

Abstract

Human aging is a heterogeneous biological process shaped by sex, tissue context, immune state, and cumulative molecular injury. Large-scale bioinformatic studies now indicate that males and females do not age identically at the transcriptomic and epigenetic levels, and that these differences are further modified by chronological age, cellular composition, and disease burden. A major unresolved challenge is how to integrate sex-stratified and age-stratified omics information into a coherent mechanistic framework that explains differential trajectories of inflammation, chromatin remodeling, transcriptional drift, and biological age acceleration. This article presents a journal-style integrative synthesis of human large-data analyses focused on transcriptomics, DNA methylation, single-cell profiling, and multi-tissue age prediction. Across blood, brain, lung, muscle, and frailty-related cohorts, convergent evidence suggests that aging-associated immune activation is not uniform between males and females, and that sex modifies both the amplitude and direction of age-linked molecular change. Transcriptome-scale studies indicate sex-dependent regulation of inflammatory programs, mitochondrial pathways, extracellular matrix remodeling, and cellular stress responses. Epigenetic analyses reveal widespread autosomal DNA methylation differences between men and women, as well as sex-specific age-related methylation dynamics that likely influence gene expression regulation across the lifespan. At the systems level, integrative analyses support a model in which endocrine environment, immune composition, chromatin state, and tissue-specific resilience interact to generate distinct aging phenotypes in males and females. Biological age estimators derived from RNA and DNA methylation further show that chronological age alone is insufficient to explain inter-individual variation, and that sex may be an independent modifier of molecular age trajectories. The most informative analytical designs therefore separate male and female participants, stratify by age group, control for cell-type composition, and integrate transcriptomic with epigenetic data rather than treating them as isolated layers. A high-impact framework for future studies should combine multi-omic harmonization, single-cell deconvolution, longitudinal modeling, and mechanistic interpretation of sex-biased regulatory networks. Such an approach will improve the understanding of healthy aging, inflammaging, frailty, neurodegeneration, and age-related disease susceptibility, while enabling more precise biomarkers and sex-aware translational strategies.