Computational precision nutrition for sarcopenia and associated multimorbidity in ageing

Sarcopenia is the age-related progressive decline in both skeletal muscle mass and function. It acts as an exacerbator of multimorbidity, engaging in bidirectional pathological cycles with multiple chronic diseases, and contributes substantially to increased mortality, disability and healthcare costs. Current nutritional support is often limited by a generic one-size-fits-all approach that fails to account for substantial inter-individual variation across genetics, metabolism, lifestyle, gut microbiome composition and other dimensions. This Review charts the progression from established strategies towards preclinical exploration of computational precision nutrition. We first delineate the multifactorial pathophysiology of sarcopenia-including dysregulated muscle protein turnover, chronic inflammageing, and anorexia of ageing-thereby providing critical targets for nutritional intervention. We then describe a conceptual data-driven framework that integrates multi-omics, digital monitoring, and artificial intelligence (AI). This framework has been proposed to operate as a continuous cycle: multidimensional data acquisition feeds AI-powered simulation via digital twins, enabling tailored dietary recommendations and culminating in ongoing refinement through real-time monitoring. However, it is important to emphasise that most components of this framework remain investigational and have not yet been validated in sarcopenia populations. Finally, we discuss the substantial translational challenges-including evidence gaps, practical implementation barriers, and ethical considerations-that must be addressed to determine whether this approach can achieve the vision of more proactive, pre-emptive, and precise nutritional care for sarcopenia.