Sarcopenia's convergence with chronic multimorbidity represents a compounding mortality risk in aging populations, yet current nutritional interventions remain largely generic. This review maps a conceptual computational framework that would integrate multi-omics data, continuous digital monitoring, and AI-driven digital twins to generate individualized dietary strategies — cycling through data acquisition, simulation, personalized recommendations, and real-time refinement — targeting three core sarcopenic mechanisms: dysregulated muscle protein turnover, chronic inflammageing, and anorexia of ageing.
The framework is intellectually compelling but largely pre-validated. The authors explicitly acknowledge that most components remain investigational and untested in actual sarcopenia populations — a candor that elevates the paper's credibility but also underscores its speculative nature. What makes this worth attention is the mechanistic targeting: inflammageing and anabolic resistance are now well-established drivers of muscle decline, and existing evidence already supports leucine-rich protein timing, omega-3 supplementation, and gut microbiome modulation as partial countermeasures. The digital twin concept, borrowed from engineering, has genuine theoretical appeal for capturing the inter-individual variability that generic protein targets miss entirely. Practically, adults over 60 managing metabolic or cardiovascular comorbidities alongside muscle loss represent exactly the population where one-size-fits-all protein recommendations demonstrably fail. The translational gap between this framework and clinical deployment remains vast — requiring validated biomarker panels, regulatory frameworks for AI dietary tools, and equity-conscious implementation. Incremental for now, but directionally important for precision geroscience.
