Integrated Proteomic and Metabolomic Profiling for Developing Novel Plasma‐Based Diagnostic Models of Sarcopenia

Journal article

Background: Sarcopenia is a progressive, age‐related condition characterized by a decline in skeletal muscle mass, strength and performance. Diagnosis remains challenging because current consensus criteria are difficult to scale and existing biomarkers lack accuracy. This study aimed to develop high‐performance plasma‐based diagnostic models for sarcopenia by integrating proteomic and metabolomic profiles. Methods: Participants were selected from the West China Health and Aging Trend study. Sarcopenia was defined according to the 2019 Asian Working Group for Sarcopenia (AWGS) criteria. Two independent 1:1 age‐ and sex‐matched cohorts were constructed: a discovery cohort (40 sarcopenic, 40 non‐sarcopenic) and a validation cohort (30 sarcopenic, 30 non‐sarcopenic). Fasting plasma samples were profiled using the Olink Explore 384 Inflammation Panel and liquid chromatography–mass spectrometry‐based untargeted metabolomics. Gaussian naïve Bayes classifiers were trained for single‐omics models, and logistic regression was used to construct combined models in the discovery cohort and evaluate performance in the validation cohort. Results: Baseline age and sex were similar in sarcopenic and non‐sarcopenic groups (discovery: median 72.0 vs. 71.5 years, p = 0.714; validation: 71.0 vs. 71.5 years, p = 0.594; women: 52.5% and 53.3%). The sarcopenic group had lower skeletal muscle index, grip strength and gait speed (all p < 0.05). Sixty‐five proteins and 268 metabolites differed between groups. A 7‐protein Gaussian naïve Bayes model achieved AUCs of 0.743 (95% CI 0.718–0.767) in discovery and 0.698 (0.561–0.834) in validation; the metabolomic model yielded 0.828 (0.808–0.849) and 0.751 (0.617–0.885). Combined Model 1 integrated the probabilistic outputs of the proteomic (7 proteins) and metabolomic (7 metabolites) models and reached AUCs of 0.951 (0.937–0.965) and 0.823 (0.717–0.930), outperforming single‐omics models (discovery: both p < 0.001; validation: vs. proteomic p < 0.05; vs. metabolomic p = 0.147). Combined Model 2 incorporated only the top two biomarkers from each platform (CCL13, FGF2, N‐hexadecanoylpyrrolidine and 1‐(cyclohexylmethyl)proline), achieving AUCs of 0.853 (0.828–0.878) in discovery and 0.911 (0.839–0.983) in validation and remained superior to single‐omics models (discovery: both p < 0.001; validation: both p < 0.05). Its validation performance was comparable to Combined Model 1 (p = 0.124), with sensitivity 86.7%, specificity 80.0%, precision 81.2% and F1‐score 0.839. Conclusions: We have developed high‐performance plasma‐based diagnostic models for sarcopenia by integrating inflammatory proteomic and metabolomic signatures. A four‐biomarker model (Combined Model 2) demonstrated excellent diagnostic performance and may provide a promising clinically scalable approach for the early detection of sarcopenia.

Authors: Xu, D; Jin, H; Yang, J; Zuo, Z; Ou, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Journal of Cachexia, Sarcopenia and Muscle
• Volume: 17
• Issue: 1
• Pages: e70188
• Article number: e70188
• Publication date: 2026-01-16
• Acceptance date: 2025-12-16
• DOI: 10.1002/jcsm.70188
• EISSN: 2190-6009
• ISSN: 2190-5991
• Language: English
• Keywords: combined model; proteomic; machine learning; metabolomic; diagnosis; sarcopenia