Integrated Multi-Omics Convergence Scoring Reveals Modular Network Architecture with Age-Associated Mitochondrial Signatures in Sarcopenia

Sarcopenia arises from convergent genetic, epigenetic, transcriptomic, metabolic, and microbiome-associated perturbations, yet conventional multi-omics analyses often yield fragmented, layer-specific feature lists. We developed an integrated multi-omics convergence scoring framework that calculates an Integrated Convergence Score (ICS) by combining standardised layer-level statistical evidence, layer-specific pathway context, network features and sarcopenia-specific disease-relevance evidence. We applied it to skeletal muscle transcriptomes from five independent cohorts (208 participants), ageing-associated muscle DNA methylation, and genome-wide association studies of muscle mass and strength, each analysed against its own null distribution. Gene-level changes were few and did not replicate, whereas mitochondrial respiration, mitochondrial translation and contraction pathway scores were lower in sarcopenia in the pooled analysis. These mitochondrial signatures were also lower in ageing-, frailty-, and pre-sarcopenia-related comparisons, indicating a component shared with muscle ageing. The three layers implicated largely distinct mechanisms. Compared with rank-based combined statistics, the ICS top-ranked genes formed a more connected network, which partly reflects the network component of the score. A context map placed the modules within mitochondrial respiration, creatine-based energy buffering, and NAD–PGC-1α signalling. This framework organises fragmented multi-omics evidence into a systems-level gene network associated with sarcopenia and prioritises candidate genes and mechanisms for future functional studies.