Abstract
Sarcopenia is a significant geriatric syndrome that involves the loss of skeletal muscle mass and strength. Due to its substantial endocrine role, the metabolic microenvironment of skeletal muscle undergoes changes with age. Examining the pathogenesis of sarcopenia through focusing on metabolic dysregulation could offer insights for developing more effective intervention strategies. In this study, we analyzed the transcriptomics data to identify specific genes involved in the regulation of metabolism in skeletal muscle during the development of sarcopenia. Three machine learning algorithms were employed to screen key target genes exhibiting strong correlations with metabolism, which were further validated using RNA-sequencing data and publicly accessible datasets. Among them, the metabolic enzyme nicotinamide N-methyltransferase (NNMT) was elevated in sarcopenia, and predicted sarcopenia with an area under the curve exceeding 0.7, suggesting it as a potential therapeutic target for sarcopenia. As expected, inhibition of NNMT improved the grip strength in aging mice and alleviated age-related decline in the mass index of the quadriceps femoris muscles and whole-body lean mass index. Additionally, the NNMTi treatment increased the levels of nicotinamide adenine dinucleotide (NAD+) content, as well as PGC1α and p-AMPK expression in the muscles of both the D-galactose-treated mouse model and naturally aging mouse model. Overall, this work demonstrates NNMT as a promising target for preventing age-related decline in muscle mass and strength.
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