Abstract
Sarcopenia is a geriatric disorder characterized by a gradual loss of muscle mass and function. Despite its prevalence, the underlying mechanisms remain unclear, and there are currently no approved treatments. In this study, we conducted a comprehensive analysis of the molecular and metabolic signatures of skeletal muscle in patients with impaired muscle strength and sarcopenia using multi-omics approaches. Across discovery and replication cohorts, we found that disrupted branched-chain amino acid (BCAA) catabolism is a prominent pathway in sarcopenia, which leads to BCAA accumulation and decreased muscle health. Machine learning analysis further supported the causal role of BCAA catabolic dysfunction in sarcopenia. Using mouse models, we validated that defective BCAA catabolism impairs muscle mass and strength through dysregulated mTOR signaling, and enhancing BCAA catabolism by BT2 protects against sarcopenia in aged mice and in mice lacking Ppm1k, a positive regulator of BCAA catabolism in skeletal muscle. This study highlights improving BCAA catabolism as a potential treatment of sarcopenia.
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Data availability
The omics data are available at the GEO (https://www.ncbi.nlm.nih.gov/geo/, accession no. GSE226151), figshare (https://doi.org/10.6084/m9.figshare.27803934)57 and ProteomeXchange (https://www.proteomexchange.org/, accession no. PXD055032) databases. The accessible links for databases are as follows: KEGG (https://www.genome.jp/kegg/), GO (https://www.geneontology.org/), Reactome (https://reactome.org/), HMDB (https://hmdb.ca/), METLIN (http://metlin.scripps.edu), EHMN (https://www.genesetdb.auckland.ac.nz/haeremai.html), HumanCyc (https://humancyc.org/), INOH (http://www.inoh.org), SMPDB (https://smpdb.ca/), Wikipathways (https://www.wikipathways.org/), Human SwissProt (http://www.expasy.ch/sprot) and GTEx v8 (https://www.genome.gov/Funded-Programs-Projects/Genotype-Tissue-Expression-Project). Source data are provided with this paper. All other data supporting the findings of the study are available from the corresponding authors upon reasonable request.
Code availability
The codes for omics analysis and machine learning model are shared via GitHub (https://github.com/scutaoli1981/AnalysisCodes).
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Acknowledgements
This study was supported, in part, by grants from the National Natural Science Foundation of China (82370260, 82270106 and 82200580); the National Key Research and Development Program of China (2020YFC2005601); the China Postdoctoral Science Foundation (2021TQ0227); the Key Research and Development Program of Sichuan Province (2020YJ0187 and 2022YFS0296); Science and Technology Innovation 2030 ‘Brain Science and Brain-Like Intelligence Technology’ Major Project (The China Brain Project)-5 (2021ZD0201905); and the Science & Technology Department Project of Sichuan Province (2022YFH0074).
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Contributions
T.L., X.Z., R.Z. and M.W. designed the experiments. X.Z., R.Z., M.W., Yanyan Wang, S.W., K.T., Yang Wang, J.C. and X.Y. performed the experiments. X.Z., R.Z., M.W., Yanyan Wang and S.W. analyzed raw data. T.L., Y.C. and X.Y. reviewed the data and made substantial contributions to improving the studies. X.Z., R.Z., M.W., T.L. and Y.C. wrote the manuscript, which was reviewed by all authors.
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Nature Aging thanks Gaurav Ahuja, Seung-Hoi Koo, Nicholas Rattray and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Transcriptional differences among HA, PS, and S.
a, Volcano plots of DEGs in PS/HA, S/PS, and PS/HA (fold change > 1.5 or < 0.67, adjusted p-value < 0.1). b, The DEGs are linearly correlated (positively or negatively) with disease severity. c, GO-enriched pathways of the DEGs in PS/HA, S/PS, and PS/HA (adjusted p-value < 0.05). Node size denotes the % of DEGs and the color indicates the specific enriched pathway. d, Heatmap shows the two-way hierarchical clustering of the metabolic genes in HA, PS, and S groups using complete-linkage. e, PCA plot of all genes from HA, PS, and S groups. The ellipses in PCA plots represent a statistical 95% confidence interval for different groups. f, Weighted gene co-expression network analysis (WGCNA) dendrogram indicates the expression of 9 distinct co-expression modules among the three groups. g, Gene expression patterns across three stages can be classified into five clusters by mFuzz, including three downward patterned clusters (clusters 1, 2, and 3) and two upward patterned clusters (clusters 4 and 5). h, Pathway enrichment analysis of genes in clusters 1, 2, and 3 by Human Phenotype Ontology (adjusted p-value < 0.05). The size of the circle represents the proportion of genes enriched in different pathways and the color gradient indicates statistical significance. i, Pathway enrichment analysis of genes in clusters 4 and 5 by KEGG (adjusted p-value < 0.05). The size of the circle represents the proportion of genes enriched in different pathways and the color gradient indicates statistical significance. P value was determined by the DESeq method and adjusted with the Q-value method (a), or a one-sided hypergeometric test and adjusted with the Benjamini-Hochberg method (c, h, i).
Extended Data Fig. 2 Transcriptional profile of metabolic pathways in HA, PS, and S groups.
a-f, Transcriptomics analysis showing mRNA expression of genes involved in branched-chain amino acids (BCAAs) catabolism (a), oxidative phosphorylation (b), fatty acid metabolism (c), glucose metabolism (d), pyruvate metabolism (e) and tricarboxylic acid (TCA) cycle (f) in HA, PS, and S (n = 20). Violin plots show the median and interquartile ranges. P value was determined by Kruskal-Wallis with Dunn multiple comparisons test (a, b, c, d, e, f). ns, not significant. n represents the number of independent samples.
Extended Data Fig. 3 Metabolic profiles and clinical characteristics in sarcopenia.
a, Pie chart showing the percentage of metabolic classifications of all identified metabolites. b, The 308 differentially abundant metabolites (DAMs) among HA, PS, and S groups using the multiclass method of the samr package with permutation p value adjustment (adjusted p-value < 0.1). P value was determined by the multiclass of samr with a permutation p value adjustment method. c, Volcano plot of DAMs in PS/HA, S/PS, and S/HA (fold change >1.5 or <0.67, adjusted p-value < 0.1). P value was determined by the twoclass of samr with a permutation p value adjustment method. d, The relationship between clinical features and metabolic pathways was evaluated by gene set variation analysis (GSVA) of pathway scores. The heatmap shows the scores for each metabolic pathway. e, The correlation heatmap shows the relationship between the GSVA scores of oxidative phosphorylation or BCAAs catabolism and clinical features. Red and blue colors indicate positive and negative correlations, respectively. **P < 0.01 and ***P < 0.001 as determined by two-sided Pearson correlation analysis.
Extended Data Fig. 4 External data support our machine learning approach for transcriptome features of sarcopenia.
a, Gene Set Enrichment Analysis (GSEA) of external data (GSE111006, GSE111010, GSE111016). Statistical significance by the Benjamini & Hochberg test. Enrichment score (ES) reflects the degree of enrichment of gene set members at both ends of the ranking list. The ES was calculated as a cumulative statistical value starting from the first gene in the gene set. b, The influence of the number of decision trees on the error rate is shown (left). The X-axis represents the number of decision trees, and the Y-axis indicates the error rate. When the number of decision trees is approximately 500, the error rate is relatively stable. Receiver operating characteristic (ROC) of the random forest model in the training cohort (our and external data) is shown (right). c, Network representation of the protein-protein interactions of the top 36 genes using the STRING. Nodes indicate genes, and the size of the line represents the strength of the correlation. d, Manhattan plot of the lean mass genome-wide association study. Plots display -Log10 (p-value) for discovery meta-analysis of genome-wide associations with lean mass for all SNPs analyzed using fixed-effects. P value was obtained from linear regression analyses and adjusted with the Benjamini-Hochberg method. e, Quantile-quantile (QQ) plot of GWAS meta-analysis results. Only markers that passed the imputation quality score R² > 0.8 and MAF > 1% were used for the plot. f, Functional consequences of SNPs on genes. The histogram displays the proportion of all SNPs which have corresponding functional annotation assigned by ANNOVAR. Bars are colored by log2 (enrichment) relative to all SNPs in the selected reference panel. * P < 0.05 for all indicated comparisons. P value was determined by two-sided Fisher’s exact test and adjusted with the Benjamini-Hochberg method.
Extended Data Fig. 5 GWAS and PheWAS for the lean mass.
a, Forest plots of effect sizes (negative) for Lead SNPs for the lean mass. The error bars are the 95% confidence intervals for the effect value OR. P values are obtained from linear regression model and adjusted with the Bonferroni correction method. b, Association of SNPs including rs7767703, rs12209419, rs9443745, rs3792994, rs4801776, and rs71352704 variants with other traits estimated by linkage disequilibrium score regression analysis in the GWAS summary. Only correlations significant after Bonferroni correction were considered. c, Linear correlation of BCAA catabolism with percentage change in SMI and grip strength. P value was determined by the linear regression analysis method.
Extended Data Fig. 6 Analysis of the replication cohort confirms the metabolic alterations in sarcopenia.
a, Linear regression analysis shows the correlation between BCAT2 (left) and DBT (right) expression and clinical features. b, Concentrations of BCAAs in serum of HA and S groups (n = 20). c, The abundance of metabolites related to BCAA catabolism in HA and S samples (n = 6). d, Schematic depicting fatty acid metabolism. CPT1B and HADHB mRNA levels (n = 20). e, The levels of metabolites related to fatty acid metabolism (n = 6). f, The abundance of proteins related to fatty acid metabolism (n = 6). g, Schematic showing glycolysis and key enzymes. The mRNA expression of HK2, PKM2, and LDHA (n = 20). h, The levels of metabolites in glycolysis (n = 6). i, The abundance of proteins in glycolysis (n = 6). j, Schematic depicting the TCA cycle, and the mRNA expression of indicated enzymes measured (n = 20). k, The levels of TCA cycle intermediates (n = 6). l, The abundance of proteins involved in the TCA cycle (n = 6). m, The mRNA expression of indicated genes (n = 20). n-r, The abundance of proteins related to respiratory chain complex I-V (n = 6). s, The mRNA expression of indicated genes (n = 20). Data are presented as mean ± SD (b, d-s), median and interquartile range (c), or individual data points. P value was determined by unpaired two-sided Student’s t-test (b, d-s), Mann-Whitney test (c), or two-sided Pearson correlation analysis (a). ns, not significant. n represents the number of independent samples.
Extended Data Fig. 7 BCAA catabolic deficiency reduces muscle strength, mass, and fibers in mouse models.
a, Experimental plan. b, WGA-stained TA muscles (n = 6). Scale bar, 200 µm. c, The mRNA expression of Bckdhb and Bcat2 (n = 12). d, BCAA concentrations in mouse muscles (n = 6). e, Grip strength, maximum exercise endurance, and muscle weight of mice (n = 6). f, Western blot analysis of BCKDH phosphorylation (n = 6). g, WGA-stained TA muscles (n = 6). Scale bar, 200 µm. h, Murf1 and Atrogin1expression (n = 6). i, H&E-stained TA muscles (n = 6). Scale bar, 200 µm. j, Expression of indicated genes from transcriptome (n = 4). k, H&E-stained images and mean CSA of iWAT adipocytes (n = 6). Scale bar, 200 µm. l, Western blot analysis of BCKDH-E1a phosphorylation (n = 6). m, n, H&E-stained TA muscles and iWAT adipocytes (n = 6). Scale bar, 200 µm. o, The mean CSA in TA muscle fast-twitch and slow-twitch fibers (n = 6). p, Murf1 and Atrogin1 expression (n = 6). q, The mRNA expression of indicated genes (n = 6). r, Western blot analysis of BCKDH-E1a phosphorylation (n = 6). s, WGA-stained TA muscles (n = 6). Scale bar, 200 µm. t, Experimental plan. u, BCAA concentrations in WT and KO muscles (n = 6). v, Grip strength and maximum exercise endurance of WT and KO mice (n = 6). w, The TA muscle weight of WT and KO mice (n = 6). x, WGA-stained TA muscles and mean CSA (n = 6). Scale bar, 200 µm. y, Murf1 and Atrogin1 expression (n = 6). Data are presented as mean ± SD or individual data points. P value was determined by unpaired two-tailed student’s t-test (c, j, l right, n, o, p, q), paired two-sided student’s t-test (v), or one-way ANOVA followed by Tukey’s post hoc test (d, e, h, k, u, w, x right, y). ns, not significant. n represents the number of independent samples.
Extended Data Fig. 8 BCAA catabolic deficiency causes muscle atrophy and weakness via anabolic resistance.
a, Glucose tolerance test in WT or KO mice after 4 months of CD or HBD feeding (n = 6). b, The changes of OCR of mitochondria from WT and KO muscles (n = 6). c, d, Representative transmission electron microscopy (TEM) images (c) and quantification of mitochondria (d) in WT and KO muscles (n = 6). Scale bar, 1 µm. e, Phosphorylation of BCKDH-E1a at S293 in HA and S patient muscles (n = 6). f, The activity of ACLY and ACC in HA and S patient muscles (n = 10). g, Western blot analysis of the phosphorylation of ACLY and ACC (n = 6). h, i, The concentrations of Cardiolipin (h) and GDF15 (i) in WT and KO muscles (n = 6). j, k, Representative TEM images (j) and quantification of mitochondria (k) in KO muscles (n = 6). Scale bar, 1 µm. l, m, The concentrations of Cardiolipin (l) and GDF15 (m) in KO muscles (n = 6). n, Phosphorylation of BCKDH-E1a at S293 in KO muscles (n = 6). o, BCAA concentrations in KO muscles (n = 6). p, WGA-stained TA muscles from KO mice (n = 6). Scale bar, 200 µm. q, r, Representative TEM images (q) and quantification of mitochondria (r) in KO muscles (n = 6). Scale bar, 1 µm. s, Western blot analysis of the phosphorylation of ACLY and ACC (n = 6). t, u, The concentrations of Cardiolipin (t) and GDF15 (u) in KO muscles (n = 6). v, w, Representative TEM images (v) and quantification of mitochondria (w) in skeletal muscle from aged mice (n = 6). Scale bar, 1 µm. Data are presented as mean ± SD or individual data points. P value was determined by unpaired two-tailed Student’s t-test (b, e, f, k, l, m, n right, o, r, s right, t, u, w), or one-way ANOVA followed by Tukey’s post hoc test (a right, d, g right, h, i). ns, not significant. n represents the number of independent samples.
Extended Data Fig. 9 Schematic diagram of the main finding.
Through comprehensive multi-omics analyses, our study identifies BCAA catabolic dysfunction as a key pathway and promising therapeutic target in sarcopenia.
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Zuo, X., Zhao, R., Wu, M. et al. Multi-omic profiling of sarcopenia identifies disrupted branched-chain amino acid catabolism as a causal mechanism and therapeutic target. Nat Aging (2025). https://doi.org/10.1038/s43587-024-00797-8
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DOI: https://doi.org/10.1038/s43587-024-00797-8
