Integrative analysis of plasma small-molecule and gut-microbiome markers of sarcopenia in a pilot study within an Indian cohort.

Sarcopenia, the age-associated decline in muscle mass and strength, is influenced by metabolic, inflammatory, and microbiome-related factors. However, integrative analyses combining these dimensions remain limited. This study applies a multi-omics workflow to identify plasma metabolite, lipid, and microbiome signatures linked to sarcopenia in older adults. Forty community-dwelling adults aged 60–87 years were classified as sarcopenic (n = 15) or non-sarcopenic (n = 25) using EWGSOP2 criteria, incorporating dominant hand grip strength (DHGS), chair rise time, psoas muscle cross-sectional area (CT), and SARC-F score. Plasma metabolomics (308 metabolites) and lipidomics (295 lipids) were performed using LC-MS/MS. A support vector machine (SVM) model with recursive feature elimination identified discriminative metabolites. Gut microbiome profiles were generated using 16 S rRNA sequencing and correlated with metabolite patterns. DHGS was the strongest clinical predictor of sarcopenia (AUROC = 0.93). Sarcopenic subjects exhibited higher systemic inflammation (neutrophil-to-lymphocyte ratio, p = 0.011) and elevated plasma arachidonic acid (p = 0.013). Thirteen lipid species—primarily lysophosphatidylcholines, lysophosphatidylethanolamines, hexosylceramides, and acylcarnitines—were significantly associated with sarcopenia. Twenty-four metabolites, including spermidine, lysine, homoarginine, and karanjin, were correlated with sarcopenia. A 16-metabolite panel derived from SVM modeling classified sarcopenic status with 89% accuracy. Microbiome analysis identified 54 taxa linked to sarcopenia, including a subgroup with a dysbiotic, pro-inflammatory microbiome. This integrative multi-omics study identifies exploratory candidate markers—13 lipids, 16 metabolites, and 54 microbial taxa—associated with sarcopenia, highlighting host–microbiome metabolic interactions and providing a framework for early biomarker discovery. Using this pilot study a validation in a larger independent cohort can be designed.