Novel metabolic and lipidomic biomarkers of sarcopenia

Abstract

Background: The pathophysiology of sarcopenia is complex and multifactorial and has not been fully elucidated. The impact of resistance training and nutritional support (RTNS) on metabolomics and lipodomics in older adults with sarcopenia remains uncertain. This study aimed to explore potential biomarkers of sarcopenia and clinical indicators of RTNS in older sarcopenic adults.

Methods: Older individuals diagnosed with sarcopenia through routine health checkups at a community hospital were recruited for a 12-week randomized controlled trial focusing on RTNS. Plasma metabolomic and lipidomic profiles of 45 patients with sarcopenia and 47 matched controls were analysed using ¹H-nuclear magnetic resonance (¹H-NMR) and liquid chromatography-mass spectrometer (LC-MS).

Results: At baseline, the patient and control groups had similar age, sex, and height distribution. The patient group had significantly lower weight, BMI, grip strength, gait speed, skeletal muscle index, lean mass of both the upper and lower limbs, and lower limb bone mass. There was a significant difference in 12 metabolites between the control and patient groups. They are isoleucine (patient/control fold change [FC] = 0.86 ± 0.04, P = 0.0005), carnitine (FC = 1.05 ± 0.01, P = 0.0110), 1-methylhistamine/3-methylhistamine (FC = 1.24 ± 0.14, P = 0.0039), creatinine (FC = 0.71 ± 0.04, P < 0.0001), carnosine (FC = 0.71 ± 0.04, P = 0.0007), ureidopropionic acid (FC = 0.61 ± 0.10, P = 0.0107), uric acid (FC = 0.88 ± 0.03, P = 0.0083), PC (18:2/20:0) (FC = 0.69 ± 0.03, P = 0.0010), PC (20:2/18:0) (FC = 0.70 ± 0.06, P = 0.0014), PC (18:1/20:1) (FC = 0.74 ± 0.05, P = 0.0015), PI 32:1 (FC = 4.72 ± 0.17, P = 0.0006), and PI 34:3 (FC = 1.88 ± 0.13, P = 0.0003). Among them, carnitine, 1-methylhistamine/3-methylhistamine, creatinine, ureidopropionic acid, uric acid, PI 32:1, and PI 34:3 were first identified. Notably, PI 32:1 had highest diagnostic accuracy (0.938) for sarcopenia. 1-Methylhistamine/3-methylhistamine, carnosine, PC (18:2/20:0), PI 32:1, and PI 34:3 levels were not different from the control group after RTNS. These metabolites are involved in amino acid metabolism, lipid metabolism, and the PI3K-AKT/mTOR signalling pathway through the ingenuity pathway analysis.

Conclusions: These findings provide information on metabolic changes, lipid perturbations, and the role of RTNS in patients with sarcopenia. They reveal new insights into its pathological mechanisms and potential therapies.

Keywords: Biomarker; Lipidome; Metabolome; Nutrition; Resistance training; Sarcopenia.

Figure 1

ROC curve of PI32:1, creatinine, PI34:3, and carnosine. The red dots denote cut‐off value. AUC, area under the receiver operating characteristic curve.

Figure 2

Summary of pathways related to sarcopenia and metabolite‐metabolite interaction network analysis. (A) Network pathway was identified by MetaboAnalyst. Metabolism was inferred from changes in levels of intermediates during substance metabolism in plasma. (B) Cellular enrichment analysis of interaction between changed metabolites and the enzymes. (C) Network pathways were identified by MetaboAnalyst software. Network analysis of differentially expressed metabolites was performed by ingenuity pathway tools (www.ingenuity.com) on metabolites annotated in the ingenuity database. Significant changes in amino acid metabolism, molecular transport, inflammatory response, free radical scavenging, and lipid metabolism, as well as PI3K‐AKT signalling, mTOR signalling, and ERK/MAPK signalling network were identified.

Figure 3

Levels of RTNS‐responsive metabolites in plasma of sarcopenic patients by LC‐QTOF‐MS and LC–MS/MS. Differential expression of RTNS‐responsive metabolites, 1‐methylhistamine/3‐methylhistamine, carnosine, PC (18:2/20:0), PI 32:1, and PI 34:3, in the plasma of patients with sarcopenia were shown. Data are mean ± SEM. *P < 0.05, **P < 0.01 compared with the control group. ^# P < 0.05, ^## P < 0.01 compared with the pre‐exercise group.