Journal of advanced research · 2026
Mapping sarcopenia's causal proteome reveals a leptin-driven inflammatory-mitochondrial axis for early prediction.
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Sun Jing, Yan Mingchen, Jiang Dan, Yang Chen, Duan Fuyu, Luo Jie, Chen Hao, Zhang John Zh, Lian Qizhou
Аннотация
INTRODUCTION: Sarcopenia, the age-related loss of muscle mass and function, is a major barrier to healthy aging. However, its molecular origins remain obscure, and current clinical tools lack the sensitivity needed to detect risk before significant decline occurs.
OBJECTIVES: We sought to map the causal proteome of sarcopenia to clarify its pathogenesis and derive a blood-based signature capable of predicting disease onset years in advance.
METHODS: Our approach integrated multi-omics with causal inference. We first screened muscle transcriptomes to guide a proteome-wide Mendelian randomization (MR) analysis, leveraging cis-pQTLs and UK Biobank GWAS data to isolate causal proteins. Concurrently, we analyzed 2,920 plasma proteins in the UK Biobank to pinpoint markers associated with future sarcopenia risk. We then combined these causal and prospective datasets to train a machine learning predictor.
RESULTS: We identified 39 circulating proteins with causal effects on muscle mass or strength, implicating specific growth (HBEGF), inflammatory (TLR2), and metabolic (MDH1) pathways. By integrating these causal drivers with prospective biomarkers, our machine learning model predicted incident sarcopenia up to six years prior to diagnosis (AUC = 0.738). Notably, the model distinguished true sarcopenia from simple muscle weakness. Network analysis placed Leptin (LEP) at the core of this signature, linking systemic metabolic signals to downstream inflammatory effectors.
CONCLUSION: Our findings support a mechanism where chronic, LEP-associated inflammation converges with mitochondrial bioenergetic failure to drive muscle decline. This study provides proof-of-concept for a plasma proteomic tool for early risk stratification and establishes a new framework of high-confidence targets for therapeutic development.
OBJECTIVES: We sought to map the causal proteome of sarcopenia to clarify its pathogenesis and derive a blood-based signature capable of predicting disease onset years in advance.
METHODS: Our approach integrated multi-omics with causal inference. We first screened muscle transcriptomes to guide a proteome-wide Mendelian randomization (MR) analysis, leveraging cis-pQTLs and UK Biobank GWAS data to isolate causal proteins. Concurrently, we analyzed 2,920 plasma proteins in the UK Biobank to pinpoint markers associated with future sarcopenia risk. We then combined these causal and prospective datasets to train a machine learning predictor.
RESULTS: We identified 39 circulating proteins with causal effects on muscle mass or strength, implicating specific growth (HBEGF), inflammatory (TLR2), and metabolic (MDH1) pathways. By integrating these causal drivers with prospective biomarkers, our machine learning model predicted incident sarcopenia up to six years prior to diagnosis (AUC = 0.738). Notably, the model distinguished true sarcopenia from simple muscle weakness. Network analysis placed Leptin (LEP) at the core of this signature, linking systemic metabolic signals to downstream inflammatory effectors.
CONCLUSION: Our findings support a mechanism where chronic, LEP-associated inflammation converges with mitochondrial bioenergetic failure to drive muscle decline. This study provides proof-of-concept for a plasma proteomic tool for early risk stratification and establishes a new framework of high-confidence targets for therapeutic development.