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BackgroundThe 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.MethodsOlder 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).ResultsAt 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.ConclusionsThese 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.
BackgroundThe 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.MethodsOlder 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).ResultsAt 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.ConclusionsThese 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.
BackgroundThis study aimed to explore the association between gut microbiota functional profiles and skeletal muscle mass, focusing on sex‐specific differences in a population under 65 years of age.MethodsStool samples from participants were analysed using metagenomic shotgun sequencing. Skeletal muscle mass and skeletal muscle mass index (SMI) were quantified (SMI [%] = total appendage muscle mass [kg]/body weight [kg] × 100) using bioelectrical impedance analysis. Participants were categorized into SMI quartiles, and associations between gut microbiota, functional profiling and SMI were assessed by sex, adjusting for age, BMI and physical activity.ResultsThe cohort included 1027 participants (651 men, 376 women). In men, Escherichia coli (log2 fold change 3.08, q = 0.001), Ruminococcus_B gnavus (log2 fold change 2.89, q = 0.014) and Enterocloster sp001517625 (log2 fold change 2.47, q = 0.026) were more abundant in the lowest SMI compared to the highest SMI group. In contrast, Bifidobacterium bifidum (log2 fold change 3.13, q = 0.025) showed higher levels in the second lowest SMI group in women. Microbial pathways associated with amino acid synthesis (MET‐SAM‐PWY: log2 fold change 0.42; METSYN‐PWY: log2 fold change 0.44; SER‐GLYSYN‐PWY: log2 fold change 0.20; PWY‐5347: log2 fold change 0.41; P4‐PWY: log2 fold change 0.53), N‐acetylneuraminate degradation (log2 fold change 0.43), isoprene biosynthesis (log2 fold change 0.20) and purine nucleotide degradation and salvage (PWY‐6353: log2 fold change 0.42; PWY‐6608: log2 fold change 0.38; PWY66‐409: log2 fold change 0.52; SALVADEHYPOX‐PWY: log2 fold change 0.43) were enriched in the lowest SMI in men (q < 0.10). In women, the second lowest SMI group showed enrichment in energy‐related pathways, including lactic acid fermentation (ANAEROFRUCAT‐PWY: log2 fold change 0.19), pentose phosphate pathway (PENTOSE‐P‐PWY: log2 fold change 0.30) and carbohydrate degradation (PWY‐5484: log2 fold change 0.31; GLYCOLYSIS: log2 fold change 0.29; PWY‐6901: log2 fold change 0.27) (q < 0.05).ConclusionsThis study highlights sex‐specific differences in gut microbiota and functional pathways associated with SMI. These findings suggest that gut microbiota may play a role in muscle health and point toward microbiota‐targeted strategies for maintaining muscle mass.
MicroRNA-1 (miR-1) is the most abundant miRNA in adult skeletal muscle. To determine the function of miR-1 in adult skeletal muscle, we generated an inducible, skeletal muscle-specific miR-1 knockout (KO) mouse. Integration of RNA-sequencing (RNA-seq) data from miR-1 KO muscle with Argonaute 2 enhanced crosslinking and immunoprecipitation sequencing (AGO2 eCLIP-seq) from human skeletal muscle identified miR-1 target genes involved with glycolysis and pyruvate metabolism. The loss of miR-1 in skeletal muscle induced cancer-like metabolic reprogramming, as shown by higher pyruvate kinase muscle isozyme M2 (PKM2) protein levels, which promoted glycolysis. Comprehensive bioenergetic and metabolic phenotyping combined with skeletal muscle proteomics and metabolomics further demonstrated that miR-1 KO induced metabolic inflexibility as a result of pyruvate oxidation resistance. While the genetic loss of miR-1 reduced endurance exercise performance in mice and inC. elegans,the physiological down-regulation of miR-1 expression in response to a hypertrophic stimulus in both humans and mice causes a similar metabolic reprogramming that supports muscle cell growth. Taken together, these data identify a novel post-translational mechanism of adult skeletal muscle metabolism regulation mediated by miR-1.
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