Data availability
The mass spectrometry proteomics and phosphoproteomics data have been deposited in the ProteomeXchange Consortium via the iProX partner repository with dataset identifier PXD045962 (refs. 48,49). Metabolomic data have been deposited in the European Molecular Biology Laboratory–European Bioinformatics Institute under accession codes MTBLS12015 and MTBLS12036 (ref. 50). The RNA sequencing data have been deposited in the Sequence Read Archive under accession code PRJNA1220290. The Swiss-Prot mouse database (updated in July 2019; 17,019 sequences) was downloaded from UniProt (https://www.uniprot.org/). All other data are available from the corresponding authors upon reasonable request.
Code availability
The custom code used in this study has been deposited on Code Ocean and is available at https://doi.org/10.24433/CO.1445745.v1.
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Acknowledgements
We thank Y. Jiao at the Facility Center of Metabolomics and Lipidomics at Tsinghua University for assisting with metabolomics and Y. Li from the Research Core Facility of West China Hospital for assisting with imaging. This work was supported by the National Key R&D Program of China (2022YFA1303200, 2018YFC2000305 and 2020YFC2005600); the Noncommunicable Chronic Diseases–National Science and Technology Major Project (2024ZD0531100); the National Natural Science Foundation of China (82073221, 82401845, 32301238 and 31870826); the Science and Technology Project of Sichuan Province (2024YFFK0099, 2021YFS0134, 2024NSFSC1601, 2023NSFSC1525, 2021YFS0136 and 2023ZYD0173); the National Clinical Research Center for Geriatrics of West China Hospital at Sichuan University (Z2024JC002); the Sichuan-Chongqing Science and Technology Innovation Cooperation Plan (2024YFHZ0072); the West China Hospital postdoctoral fund (2023HXBH065); and the West China Hospital 1.3.5 project for disciplines of excellence (ZYYC23013).
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Extended data
Extended Data Fig. 1 Workflow of sarcopenia diagnosis and HGS, GS, and SMI distribution in individuals from cohort1 and cohort2.
a, Workflow of sarcopenia diagnosis in this study, based on the 2019 consensus criteria established by the AWGS. b,c, Scatter plot illustrating the distribution of HGS and GS among normal, sarcopenic, and severe sarcopenic female (left) and male (right) individuals in cohort1 (b) and cohort2 (c). HGS, hand grip strength; GS, gait speed. d,e, Box plot depicting the distribution of SMI among normal, sarcopenic, and severe sarcopenic female (left) and male (right) individuals in cohort1 (d) and cohort2 (e) (median ± quartiles; whiskers extend to minimum and maximum values; ‘N’ represents participant number). SMI, skeletal muscle index. Raw and processed data for drawing are provided in Source Data Extended Data Fig. 1.
Extended Data Fig. 2 Assessments of metabolome and lipidome data quality in cohort1 and cohort2.
a-d, Spearman’s correlation coefficient for instrumental QC samples in the metabolomic (a,c) and lipidomic (b,d) analyses for cohort1 (a,b) and cohort2 (c,d). e-h, Relative standard deviation (RSD)% of all identified metabolites (e,g) and lipids (f,h) in instrumental QC samples for cohort1 (e,f) and cohort2 (g,h). i-l, t-SNE plots of the QCs and the samples using the metabolome (i,k) and lipidome (j,l) data of cohort1 (i,j) and cohort2 (k.l). Raw and processed data for drawing are provided in Source Data Extended Data Fig. 2.
Extended Data Fig. 3 Comparisons between different SMI measurement methods.
a, Scatter plot illustrating the distribution of HGS and GS among normal, sarcopenic, and severe sarcopenic female (left) and male (right) individuals in cohort3. HGS, hand grip strength; GS, gait speed. b, Box plot depicting the distribution of SMI among normal, sarcopenic, and severe sarcopenic female (left) and male (right) individuals in cohort3 (median ± quartiles; whiskers extend to minimum and maximum values; ‘N’ represents participant number). SMI, skeletal muscle index. c,d, Scatter plots showing the correlation between SMI measured by BIA and DXA (c), and the beta coefficients for metabolites and lipids associated with SMI from multi-linear regression models for two methods (d). Co, coefficient. e, Bland-Altman analysis of the beta coefficients for metabolites and lipids correlated with SMI measured by BIA and DXA. The x-axis represents the average of the two coefficients ([betaBIA+betaDXA]/2), and the y-axis represents the differences (betaBIA-betaDXA). f, Bar plot showing the percentage of metabolites and lipids with consistent or differing associations with SMI measured by BIA and DXA. Molecules with beta coefficients greater than 0.1 in at least one method were included. g, Sankey diagram showing the distribution of metabolites and lipids correlated with SMI measured by BIA and DXA. Molecules with beta coefficients greater than 0.1 in at least one method were included. h-k, Sankey diagram showing the distribution of molecules associated with age (h), SMI (i), GS (j), and HGS (k) in females, males, and all participants in cohort1. The results indicate that the significantly correlated metabolites and lipids in females or males alone also exhibit significant correlations with the parameters in all samples, suggesting that sex affects the magnitude but not the direction of age-, SMI-, GS-, and HGS-related changes in metabolites and lipids. Raw and processed data for drawing are provided in Source Data Extended Data Fig. 3.
Extended Data Fig. 4 Overlap of molecules associated with age, SMI, HGS, GS, sarcopenia, and severe sarcopenia.
a,b, Overlap analysis of molecules associated with SMI and sarcopenia (a), and severe sarcopenia (b). c,d, Overlap analysis of molecules associated with HGS and sarcopenia (c), or GS and sarcopenia (d). e,f, Overlap analysis of molecules associated with HGS and severe sarcopenia (e), or GS and severe sarcopenia (f). The sarcopenia- and severe sarcopenia-related molecules were analyzed using the Mann-Whitney U test, compared to normal individuals, with an FDR threshold of < 0.05. g, Overview of major metabolic pathways enriched by metabolites correlated with SMI and age. The coefficients of metabolites with SMI and age are represented by colors. Blue represents negative correlations, and red indicates positive correlations.
Extended Data Fig. 5 Unsupervised consensus clustering of 351 participants from cohort1 into 3 subtypes.
a-g, Consensus clustering matrix of 351 individuals, ranging from k = 2 (a) to k = 8 (g), based on the patterns of 161 SMI-correlated metabolites and lipids. Euclidean distance was used as the distance metric, and the clustering algorithm employed was k-means. h, Cumulative distribution function (CDF) of consensus clustering for k = 2 to k = 8. i, Relative changes in the area under the CDF curve of k = 2 to k = 8. j, Cluster-consensus value for each cluster classified with k = 2 to k = 8. k, t-SNE plots of three metabolic subtypes. l,m, Violin plot representing the GS (l) and ratio of fat mass to weight (%) (m) across normal and three metabolic subtypes for both females and males (two-sided unpaired t test; ns, non-significant; median ± quartiles; whiskers extend to minimum and maximum values; ‘N’ represents participant number).
Extended Data Fig. 6 The significantly changed metabolites and lipids among three subtypes.
a-c, Heatmaps showing the significantly changed metabolites among three subtypes, namely cluster1 (a), cluster2 (b) and cluster3 (c), shown in Fig. 2e. d,e, Heatmaps showing the significantly changed lipids among three subtypes in cluster1 (d) and cluster2 (e) depicted in Fig. 2j.
Extended Data Fig. 7 Feature molecules for SMI prediction models and SMI distribution in two cohorts.
a, Changes in regression coefficients between characteristic variables and lambda values from Lasso machine learning for SMI prediction. b, Mean squared error changes between predicted and measured values versus lambda values. The left and right dotted lines represent the Lambda value (minimum mean square error) and the lambda value for the simplest model, respectively. c,d, Nuclear density plots SMI distribution in female (c) and male (d) participants across two cohorts. e,f, Violin plot showing the relative intensity of sarcosine in the normal, sarcopenic, and severe sarcopenic individuals in cohort1 (e) and cohort2 (f) (two-sided Mann‒Whitney U test; median ± quartiles; whiskers extend to minimum and maximum values; ‘N’ represents participant number). g-i, Scatter plot showing the Pearson correlation between sarcosine levels and SMI (g), HGS (h), and GS (i) in cohort1 (left) and cohort2 (right). Co means coefficients of Pearson correlation, and the p value was calculated by multiple linear regression adjusted with sex.
Extended Data Fig. 8 The physiological effects of long-term sarcosine treatment on aged mice.
a, Comparison of lean mass percentage, hand grip strength, and fat mass percentage between 3- (n = 9), 18-, and 22-month-old (n = 8) mice (two-sided unpaired t test, ns, non-significant). b,c, Immunoblotting analysis of mTOR-S2448p in control aged mice (n = 7) and those treated with 90 mg/kg/day sarcosine for 4 months (n = 7) (b) and in control aged mice (n = 6) and those treated with 150 mg/kg/day sarcosine for 2 months (n = 6) (c). d, Partial in situ measurement of contractile force in TA from control (n = 7) and sarcosine (90 mg/kg/day)-treated aged mice (n = 6). Representative images (left) show the relationship between muscle contractile force and increasing voltages (0.3 to 4.25 V), with statistical analysis of relative muscle contractile force (right). e, Bar plot showing the plasma TG levels in control (n = 7) and sarcosine-treated (n = 6) aged mice (two-sided unpaired t test). f, Blood chemistry analysis for control (n = 7) and sarcosine (90 mg/kg/day)-treated (n = 6) aged mice (two-sided unpaired t test, ns, non-significant). g, Representative H&E staining images of the heart, spleen, lung, and kidney from control and sarcosine (90 mg/kg/day)-treated aged mice. For all relevant figures, data are represented as the mean ± SD. Raw and processed data and unprocessed images for drawing are provided in Source Data Extended Data Fig. 8 and Source Image Extended Data Fig. 8, respectively.
Extended Data Fig. 9 Proteomic and phosphoproteomic analyses of sarcosine-treated BMDMsIL-4.
a, Strategy for obtaining anti-inflammatory macrophages. b,c, Principal component analysis (PCA) of the proteome (b) and phosphoproteome data (c) distinguishing BMDMs, BMDMs+sarcosine, BMDMsIL-4 and BMDMsIL-4+sarcosine macrophages. d,e, Distribution of relative standard deviation (RSD)% for proteins (d) and phosphorylation sites (e) across three biological replicates for each group. f,g, Protein expression of SLC38A2 (f) and SLC1A5 (g) in BMDMs, BMDMs+sarcosine, BMDMsIL-4 and BMDMsIL-4+sarcosine macrophages (empirical Bayes moderated t test; n = 3 per group). h,i, Protein-protein interaction (PPI) analysis of proteins with 399 phosphosites co-upregulated in BMDMsIL-4, and BMDMsIL-4+sarcosine macrophages (h), and proteins with 348 phosphosites specifically upregulated in BMDMsIL-4+sarcosine macrophages (i). The p value was calculated by hypergeometric test. j, Proposed mechanism by which sarcosine enhances anti-inflammatory macrophage polorizaiton. For all relevant figures, data are presented as mean ± SD. Raw and processed data for drawing are provided in Source Data Extended Data Fig. 9.
Extended Data Fig. 10 Sarcosine supplementation enhances muscle repair post-injury.
a, Representative H&E staining images of the TA from control and sarcosine-treated mice at day0 (n = 6), day2 (control n = 5, sarcosine n = 6), day4 (n = 7), day8 (n = 7), and day12 (n = 7) post-injury. b, Measurements of partial in situ contractile force of TAs from control and sarcosine-treated mice on day8 post-injury. Representative images show the relationship between muscle contractile force and increasing voltages (0.3 to 5 V). c, Schematic of the cell transplantation strategy and muscle contractile force measurement in CTX-induced muscle injury mice. d,e, Representative images of partial in situ contractile force measurement of TAs on day5 post-transplantation with BMDMsIL-4 and sarcosine-treated BMDMsIL-4 (d), and post-transplantation with BMDMsIL-4 treated with sarcosine and treated with both sarcosine and GCN2iB (e). f,g, Immunoblotting of GNMT and SARDH in the livers of young (n = 5) and aged (n = 5) monkeys (f) and mice (g). h, Immunoblotting of EIF2α-S51p and ATF4 in the TAs from control (n = 7) and sarcosine-treated (n = 7) aged mice. i, Immunoblotting of AMPKα-T172p in the TAs from control (n = 7) and sarcosine-treated (n = 7) aged mice. Unprocessed images for drawing are provided in Source Image Extended Data Fig. 10.
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Liu, Y., Ge, M., Xiao, X. et al. Sarcosine decreases in sarcopenia and enhances muscle regeneration and adipose thermogenesis by activating anti-inflammatory macrophages. Nat Aging (2025). https://doi.org/10.1038/s43587-025-00900-7
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DOI: https://doi.org/10.1038/s43587-025-00900-7