Abstract
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.
Data availability
The analysis of faecal samples generated from metagenomics analysis of the microbiome of 40 subjects is available in NCBI-SRA (Accession number PRJNA1311484; Link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1311484?reviewer=ak2jgkc79417t7f2c9kta66o06). The plasma metabolic data of the individual plasma samples from 39 subjects have been deposited and are available through figshare ( [https://figshare.com/s/6085fa1fea3530006dac](https:/figshare.com/s/6085fa1fea3530006dac) ). Lipidomics data generated and analyzed are included in this published article. The lipidomics data is available through the source data along with this article.
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Author ContributionMH, RM, SM performed the metabolomics and lipidomic analysis; SV and TSG performed all aspects of microbiome analysis, and prepared figures, results and discussion involving microbiome analyses; MH, GP, NH outlined and performed SVM analysis on metabolomics and lipidomic data; MPS, TM, CG implemented clinical cohort design and recruitment; SC performed blood analyses; AT (Asha Thomas) performed and analyzed CT imaging, NM, VK, VY, SH, AJ and MNC performed clinical evaluation of subjects; MH, AT (Alexander Thomas), VCS, TSG, CG, AR designed implemented overall study, wrote and reviewed the manuscript.
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Funding from Vayah-Vikas-Foundation, and Department of Biotechnology, Govt. of India.
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Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. The Study was approved by Bangalore Baptist Hospital Institutional Review Board (BBH/IRB/2022/17) on 28/06/2022.
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Hashmi, M.A., Verma, S., Math, R.G.H. et al. Integrative analysis of plasma small-molecule and gut-microbiome markers of sarcopenia in a pilot study within an Indian cohort. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35476-8
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DOI: https://doi.org/10.1038/s41598-026-35476-8
