One can build a network of genes by function, by interactions between tissues, by association with specific disease, and so forth. Researchers here assemble a gene network considering associations with aging, age-related disease, and function, and attempt to derive some insight into what the shape of the network, its clusters and connectors, might say about processes of aging. They suggest that there are two broad categories of process at work here: firstly, genes that very broadly affect aging throughout the body, such as those regulating immune system or mitochondrial function, and thus tend to be associated with all age-related disease; versus secondly, genes that affect vulnerability to age-related dysfunction in one specific organ or tissue, and thus tend to be associated with a cluster of diseases associated with that organ or tissue.
Ageing-related diseases (ARDs) display diverse phenotypes yet share an age-dependent rise in incidence, suggesting mechanistic links with ageing processes. We examined whether ageing-related genes differ systematically from genes associated with multiple ARD clusters. Across 57 ARDs from UK Biobank, network analyses showed that ageing-related genes, although rarely ARD-associated, lie significantly closer to many ARDs through greater-than-chance proximity in protein-protein interaction and KEGG networks.
Our results demonstrate that the broad disease impact of highly pleiotropic genes does not require network centrality or broad expression. Rather than forming universal ageing-related cores, these genes often act within tissue-specific, weakly connected modules - a pattern consistent with previous reports that pleiotropic disease-related genes span diverse biological processes rather than collapsing onto a single functional axis.
Beyond these structural insights, our machine learning framework successfully predicted novel ageing-related gene candidates based on their connectivity to clusters of ARD-related genes. Many of these top-ranked genes belonged to conserved stress-response and signalling pathways - such as MAPK, TGF-β/SMAD, and phosphorylation cascades - reinforcing their role in systemic adaptation and maintenance during ageing.
Together, these results reveal a dual organization in the genetic architecture of ageing and multimorbidity: ageing-related genes act as cross-system integrators that maintain regulatory balance, whereas pleiotropic genes associated with specific age-related disease clusters operate as localized drivers of age-dependent disease vulnerability. Integrating these complementary perspectives provides a coherent framework for understanding how intrinsic ageing mechanisms and immune-mediated susceptibility jointly shape the landscape of human multimorbidity.
Link: https://doi.org/10.1007/s10522-026-10429-w
View the full article at FightAging
