Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
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.
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.
Insertions and deletions (indels) are the second most common type of variation in the human genome. However, limited data on their associations with exercise‐related phenotypes have been documented. The aim of the present study was to examine the association between 18,370 indel variants and power athlete status, followed by additional studies in 357,246 individuals. In the discovery phase, the D allele of the MDM4 gene rs35493922 I/D polymorphism was over‐represented in power athletes compared with control subjects (P = 7.8 × 10−9) and endurance athletes (P = 0.0012). These findings were replicated in independent cohorts, showing a higher D allele frequency in power athletes compared with control subjects (P = 0.016) and endurance athletes (P = 0.031). Furthermore, the D allele was positively associated (P = 0.0013) with greater fat‐free mass in the UK Biobank. MDM4 encodes a protein that inhibits the activity of p53, which induces muscle fibre atrophy. Accordingly, we found that MDM4 expression was significantly higher in the vastus lateralis of power athletes compared with endurance athletes (P = 0.0009) and was positively correlated with the percentage of fast‐twitch muscle fibres (P = 0.0062) and the relative area occupied by fast‐twitch muscle fibres (P = 0.0086). The association between MDM4 gene expression and an increased proportion of fast‐twitch muscle fibres was confirmed in two additional cohorts. Finally, we found that the MDM4 DD genotype was associated with increased MDM4 gene expression in vastus lateralis and greater cross‐sectional area of fast‐twitch muscle fibres. In conclusion, MDM4 is suggested to be a potential regulator of muscle fibre specification and size, with its indel variant being associated with power athlete status.Highlights What is the central question of this study?Which indel variants are functional and associated with sport‐ and exercise‐related traits? What is the main finding and its importance?Out of 18,370 tested indels, the MDM4 gene rs35493922 I/D polymorphism was found to be the functional variant (affecting gene expression) and the most significant, with the deletion allele showing associations with power athlete status, fat‐free mass and cross‐sectional area of fast‐twitch muscle fibres. Furthermore, the expression of MDM4 was positively correlated with the percentage of fast‐twitch muscle fibres and the relative area occupied by fast‐twitch muscle fibres.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.