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<p>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 (<i>n</i> = 15) or non-sarcopenic (<i>n</i> = 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&#xa0;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, <i>p</i> = 0.011) and elevated plasma arachidonic acid (<i>p</i> = 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月17日

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  • Maroof Athar Hashmi,
  • Shivangi Verma,
  • Raviswamy G. H. Math,
  • Sneha Muralidharan,
  • Gautham Pranesh,
  • M. P. Sahana,
  • Nivedita Hariharan,
  • Madhusudan N. C.,
  • Vijay Kamath,
  • Vishwanath Yaligod,
  • Santosh Angadi Hiremath,
  • Abhijit Jawali,
  • Tatarao Maddipati,
  • Sindhulina Chandrasingh,
  • Asha Thomas,
  • Niranjan Mallnaik,
  • V. C. Shanmuganand,
  • Carolin Elizabeth George,
  • Alexander Thomas,
  • Tarini Shankar Ghosh,
  • Arvind Ramanathan

摘要

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.