Muhammad Arif

Aging is a universal biological process that impacts all tissues, leading to functional decline and increased susceptibility to age-related diseases, particularly cardiometabolic disorders. While aging is characterized by hallmarks such as mitochondrial dysfunction, chronic inflammation, and dysregulated metabolism, the molecular mechanisms driving these processes remain incompletely understood, particularly in a tissue-specific context. To address this gap, we conducted a comprehensive transcriptomic analysis across 40 human tissues using data from the Genotype-Tissue Expression (GTEx) project, comparing individuals younger than 40 years with those older than 65 years. We identified over 17,000 differentially expressed genes (DEGs) across tissues, with distinct patterns of up- and down-regulation. Enrichment analyses revealed that up-regulated DEGs were associated with inflammation, immune responses, and apoptosis, while down-regulated DEGs were linked to mitochondrial function, oxidative phosphorylation, and metabolic processes. Using gene co-expression network (GCN) analyses, we identified 1,099 genes as dysregulated nodes (DNs) shared across tissues, reflecting global aging-associated transcriptional shifts. Integrating machine learning approaches, we pinpointed key aging biomarkers, including GDF15 and EDA2R, which demonstrated strong predictive power for aging and were particularly relevant in cardiometabolic tissues such as the heart, liver, skeletal muscle, and adipose tissue. These genes were also validated in plasma proteomics studies and exhibited significant correlations with clinical cardiometabolic health indicators. This study provides a multi-tissue, integrative perspective on aging, uncovering both systemic and tissue-specific molecular signatures. Our findings advance understanding of the molecular underpinnings of aging and identify novel biomarkers that may serve as therapeutic targets for promoting healthy aging and mitigating age-related diseases.