Main Research Findings

1) Sarcosine was found to reduce sarcopenia while improving adipose thermogenesis and muscle regeneration through the activation of anti-inflammatory macrophages.

 

Selected Data

2) This study completed by the research team of Liu et al employed a multi-faceted methodological approach to investigate the role of sarcosine in sarcopenia, utilizing human cohorts, advanced omics analyses, and various animal models. The research began with the recruitment of 1,052 Han Chinese participants from western China, divided into three cohorts: 503 from the WCHAT study (cohort 1), 510 from the West China Elderly Study (cohort2), and 39 from the Wenjiang Sarcopenia Study. Cohort 1 served as the discovery set, cohort 2 as the validation set, and the 39-individual cohort was used for comparing body composition measurement techniques. Participants were carefully screened to be free of cancer or severe illness, have a life expectancy exceeding six months, and complete comprehensive questionnaires and physical examinations. Fasting peripheral blood samples were collected in EDTA tubes, snap-frozen in liquid nitrogen, and stored at -80°C. Sample collection timelines varied: cohort 1 samples were collected over 60 days in 2018, cohort 2 over 30 days in 2019, and the Wenjiang samples within one week in 2024 [1].

Sarcopenia-related indicators were meticulously measured across all cohorts. Skeletal muscle index (SMI), a key diagnostic criterion for sarcopenia, was primarily assessed using bioelectrical impedance analysis (BIA), with dual-energy X-ray absorptiometry (DXA) additionally used in the 39-person subset for comparative analysis. Isometric handgrip strength (HGS) was quantified using an electronic grip meter, and gait speed (GS) was calculated from a timed 4-meter walk. Sarcopenia was defined according to the 2019 Asian Working Group for Sarcopenia (AWGS) criteria, requiring low SMI combined with either low HGS or low GS. Specific cutoffs for low SMI were established for men (<7.0 kg/m for both BIA and DXA) and women (<5.7 kg/m for BIA, <5.4 kg/m for DXA). Low HGS was defined as <28 kg for men and <18 kg for women, and low GS as <1 m/s. Severe sarcopenia was characterized by the presence of low SMI, HGS, and GS.

For plasma sample preparation, distinct protocols were followed for metabolomics and lipidomics. Polar metabolites were extracted using methanol, incubated at -80°C for one hour, followed by centrifugation to isolate supernatants, which were then dried. Quality control (QC) samples were prepared by pooling extracted metabolites from all plasma samples. For metabolomic analysis via liquid chromatography with tandem mass spectrometry (LC–MS/MS), QC samples were re-dissolved in different solvent volumes relative to plasma for cohort 1 and cohort 2. Internal standards, including Gln-N15, inosine-4N15, and Trp-D5 for positive mode, and cholic acid-D4, inosine-4N15, stearic acid-D35, succinate-D4, and Trp-D5 for negative mode, were incorporated. Lipids were extracted using a dichloromethane/methanol mixture, followed by centrifugation to collect the lower dichloromethane layer. This layer was further purified by mixing with water and re-centrifuging, with the final dichloromethane layer collected and dried. QC samples for lipidomics were prepared similarly and re-dissolved in an equal solvent volume. A panel of internal standards, including D31-Cer, DG, PC, PE, D31-PE, and PS, was used [1].

Metabolomic and lipidomic analyses were performed using high-resolution mass spectrometry. Polar metabolites were analyzed on a Q Exactive coupled with reversed-phase high-performance liquid chromatography (HPLC)–MS/MS in both positive and negative ion modes. Specific columns (BEH amide for positive mode, BEH C18 for negative mode) and non-linear gradients were employed. Full-scan MS was conducted at resolutions of 70,000 positive and 60,000 negative, with MS/MS spectra acquired at resolutions of 17,500 positive and 15,000 negative following stepped collision energies. Raw mass spectra were processed using TraceFinder software. Lipid profiling utilized a CORTECS C18 column with a 35-minute gradient, and full-scan MS was performed at 60,000 resolution in positive mode. MS/MS spectra were acquired at 15,000 resolution using stepped normalized collision energy, with raw data processed via LipidSearch software. Targeted analysis of sarcosine and other amino acids involved extracting plasma or BMDM metabolites and analyzing them on a 6500+ mass spectrometer in positive ion mode, employing multiple reaction monitoring (MRM) with electrospray ionization (ESI) and a BEH Amide column. Parent ions, product ions, and retention times were determined using corresponding reference standards [1].

Data processing for omics data involved retaining metabolites detected in over 90% of samples, normalizing by total intensity, and imputing missing values with the k-nearest neighbor (KNN) algorithm. For lipidomics, species with identical carbon and double bond numbers were combined per sample and normalized by total intensity. Only metabolites and lipids with a coefficient of variation (CV) less than 0.25 in QC samples were included in downstream analyses. To account for sex-specific influences, initial univariate linear regressions were performed separately for males and females against age, SMI, GS, and HGS. Since sex influenced the magnitude but not the direction of associations, it was subsequently included as a covariate in a combined linear regression model for all participants.

For human skeletal muscle analysis, samples were obtained from 30 female participants (19 healthy, 11 sarcopenic) aged 15-82 years during surgeries for benign tumors, ensuring samples were collected ≥2 cm from tumor margins. Metabolites were extracted with 80% methanol and analyzed by LC–MS. Consensus clustering was applied to sarcopenic individuals using SMI-correlated metabolites and lipids to identify molecular subtypes, employing k-means clustering with Euclidean distance and selecting k=3. Predictive models for SMI and diagnostic models for sarcopenia were constructed using 51 validated metabolites and lipids, along with sex, as independent variables. Cohort 1 was split into training and internal test sets, with cohort 2 serving as external validation. The LASSO algorithm was used for feature selection, identifying 12 non-zero coefficient features, including sex. Model performance was assessed using Spearman’s correlation for SMI prediction and AUC for sarcopenia diagnosis. The DE-SWAN approach was utilized to measure nonlinear changes in 11 feature molecules associated with SMI, with analyses performed separately for males and females due to sex-specific SMI cutoffs. This involved combining cohort 1 and cohort 2 data, scaling, centering, and comparing metabolite levels across SMI bins using a linear model, with results averaged over various bucket sizes to minimize bias [1].

The animal study employed C57BL/6 mice housed under specific pathogen-free conditions. Long-term sarcosine treatment was investigated in young, 2-month-old, and aged, 18-month-old and 22-month-old, male mice, with sarcosine added to maintenance chow at varying doses of 90 or 150 mg/kg/d. Body composition was assessed via DXA every 2-4 months, and at study endpoint, in situ contractile force of leg muscle, muscle weights, and plasma biochemistry were measured. A linear mixed-effects model was used to evaluate sarcosine’s effects on body composition over time. A muscle injury model involved pretreating mice with sarcosine before injecting cardiotoxin into the tibialis anterior (TA) muscle, followed by daily sarcosine administration. Contractile force was measured, and muscle samples were collected for histological analysis. A cell transplantation experiment involved injecting various bone marrow-derived macrophage (BMDM) types into injured muscle, followed by contractile force and histological analysis.

Further detailed methods included BMDM cell culture, immunofluorescence, immunohistochemistry, immunoblotting, and real-time qPCR for gene expression analysis. Statistical analyses predominantly used multiple linear regression, empirical Bayes moderated t-test, Mann–Whitney U-test, Kruskal–Wallis test, LASSO, ridge regression, and Satterthwaite-adjusted F-test, with pathway enrichment performed using HMDB, MetaboAnalyst, Metascape, and GSEA [1].

Discussion

1) The study by Liu et al examined metabolic fluctuations related to aging and sarcopenia, utilizing two large cohorts, cohort 1 with 503 individuals, and cohort 2 with 510 individuals, from western China, aged 50–103 years. Sarcopenia diagnosis adhered to the AWGS criteria, incorporating SMI, HGS, and GS. Cohort 1 served as the discovery set, with 351 sarcopenic individuals, while cohort 2, comprising 136 sarcopenic individuals, functioned as the validation set. Initial QC and t-distributed stochastic neighbor embedding t-SNE analyses confirmed the reliability and stability of the comprehensive plasma metabolomic and lipidomic profiling data. A crucial methodological validation involved a small 39-person cohort, where SMI measurements by BIA showed strong correlation with DXA, and SMI-related metabolite/lipid beta coefficients were highly consistent across both methods, affirming BIA’s reliability for identifying SMI-associated molecules. Furthermore, it was established that sex influenced the magnitude, but not the direction, of metabolic changes related to age, SMI, GS, and HGS, leading to sex-adjusted multivariate linear regression analyses for subsequent investigations [1].

Aging significantly impacted metabolism, with 98 metabolites and 61 lipids associated with age in cohort1. Fifty-four metabolites, including acylcarnitines and indoles, showed positive correlations with age, while 44 were negatively associated. Among lipids, phosphatidylcholines and ceramides were positively correlated with age, whereas sphingomyelins exhibited negative associations. Regarding muscle mass, 66 metabolites, predominantly amino acids, peptides, and fatty acids, positively correlated with SMI, while 10 showed negative correlations. Key lipids such as triacylglycerols and diacylglycerols were positively correlated with SMI, contrasting with negative correlations for most Cers and PCs. Fewer metabolites and lipids were linked to GS and HGS, possibly due to higher individual variability in these functional measures. Comparing metabolic profiles across normal, sarcopenic, and severe sarcopenic individuals revealed that lipids were the most significantly altered, with TGs downregulated and PCs upregulated in both sarcopenia and severe sarcopenia [1].

Overlap analysis demonstrated that SMI-associated molecules captured a high proportion of molecules altered in sarcopenia by 50%, and severe sarcopenia by 80.4%, suggesting SMI-based multivariate linear regression analysis is effective in identifying key sarcopenia-related molecules. Pathway enrichment analysis highlighted the involvement of several metabolic pathways, including alanine, aspartate, and glutamate metabolism; arginine and proline metabolism; biosynthesis of unsaturated fatty acids; glycine, serine, and threonine metabolism; and tryptophan metabolism. Most metabolites in these pathways were negatively correlated with age but positively with SMI, except for those in the tryptophan pathway, which showed complex roles, including the age-related accumulation of L-kynurenine contributing to muscle atrophy.

Moving beyond general associations, the study performed a metabolic classification of sarcopenia by categorizing 351 sarcopenic individuals from cohort1 into three distinct subtypes, S1, S2, and S3, using SMI-related metabolites and lipids. While the overall composition of sarcopenia severity did not significantly differ among these subtypes, SMI, GS, and age exhibited significant differences, particularly in female participants. S1 was characterized by a greater fat mass percentage and elevated blood TG levels. S2 displayed reduced levels of amino acids, analogs, and tricarboxylic acid cycle metabolites. In contrast, S3 exhibited elevated levels of these same metabolites, along with higher levels of anti-inflammatory metabolites like bile acids and indoles, correlating with a milder sarcopenic condition. These findings underscore the metabolic heterogeneity of sarcopenia, paving the way for more personalized therapeutic approaches [1].

To translate these metabolic insights into practical applications, the researchers developed metabolic models for SMI prediction and sarcopenia diagnosis. Fifty-three molecules including 19 metabolites and 34 lipids, consistently and significantly associated with SMI across both cohorts were identified. After targeted mass spectrometry validation, 51 of these were used to construct a generalized linear LASSO regression model for SMI prediction. This model identified 12 key feature variables with non-zero coefficients, including creatinine, sarcosine, eicosapentaenoic acid, L-glutamic acid, prasterone sulfate, palmitoleic acid, and sex. This predictive model demonstrated good performance, with Spearman’s correlation coefficients of 0.71 (training), 0.73 (testing), and 0.65 (validation cohort 2). Ridge regression, using these same 12 features, yielded an area under the curve (AUC) of approximately 0.78 across all datasets for sarcopenia diagnosis, indicating strong discriminatory power [1].

The DE-SWAN algorithm further revealed the highest number of differential molecules at SMI values closely corresponding to established sarcopenia diagnostic thresholds (5.6 for females, 7.2 for males), emphasizing the clinical relevance of these molecules. Notably, sarcosine and creatinine emerged as the most influential features in both models. Plasma sarcosine levels were significantly reduced in sarcopenic individuals, with further pronounced decreases in severe sarcopenia. Sarcosine levels positively correlated with SMI but not HGS or GS. Intriguingly, this age-dependent decrease in sarcosine levels was consistently observed across aged humans, monkeys, and mice, and human muscle sarcosine levels negatively correlated with age while positively correlating with SMI. These collective findings established plasma sarcosine as an age-dependent metabolic biomarker associated with sarcopenia.

To functionally validate sarcosine’s role in sarcopenia, long-term sarcosine treatment was administered to young and aged mice. Aged mice exhibiting sarcopenic traits of reduced lean mass and grip strength, and increased fat mass showed significant benefits from sarcosine treatment at doses of 90 mg/kg/d for 4 months. Aged mice treated with sarcosine experienced reduced body weight, preserved lean mass, increased lean mass percentage, and enhanced myofiber cross-sectional areas and MyHC expression, alongside activation of the mTOR signaling pathway. However, sarcosine did not significantly improve skeletal muscle contractile force [1].

Further analysis revealed that sarcosine primarily reduced fat mass, which contributed to the observed increase in lean mass percentage. In young mice, sarcosine prevented age-related increases in fat mass and preserved lean mass percentage homeostasis over 11 months. Sarcosine also reduced visceral adipose tissue (VAT) adipocyte sizes and hepatic lipid accumulation, and lowered plasma TG levels. Proteomic analysis of VAT from aged mice revealed that sarcosine enhanced the thermogenesis pathway, notably upregulating UCP1 and promoting VAT browning, consistent with an increase in brown adipose tissue (BAT) weight. This fat-reducing effect was linked to sarcosine downregulating pro-inflammatory genes and increasing anti-inflammatory macrophages in VAT, suggesting sarcosine activates anti-inflammatory macrophages to promote adipose browning and thermogenesis.

Additional mechanistic insights revealed that sarcosine boosts anti-inflammatory macrophages via the GCN2 pathway. In IL-4 induced bone marrow-derived macrophages (BMDMsIL-4), sarcosine upregulated anti-inflammatory markers (Arg1, Fizz1) and activated the Eif2ak4 (GCN2) pathway and its downstream targets (ATF3, ASNS). Targeted metabolomics showed that sarcosine treatment reduced various amino acid levels in BMDMsIL-4, decreasing amino acid availability and enhancing GCN2 activation, which phosphorylates EIF2α and promotes protein synthesis. GCN2 activation was also confirmed in resident VAT macrophages and was abrogated by a GCN2 inhibitor, which also reduced anti-inflammatory macrophage markers. Finally, sarcosine significantly enhanced muscle regeneration in a cardiotoxin-induced TA muscle injury model in mice [1].

Sarcosine-treated mice showed faster resolution of muscle necrosis, accelerated muscle regeneration, increased anti-inflammatory F4/80+CD206+ macrophages, higher numbers of proliferating satellite cells, earlier expression of muscle differentiation markers (eMyHC, MyoD, MyoG), larger regenerating myofibers, and greater muscle contractile force. Crucially, transplantation experiments with sarcosine-treated BMDMsIL-4 accelerated muscle repair and improved contractile force in injured muscles, an effect that was compromised by GCN2 inhibition. These findings collectively demonstrate that sarcosine promotes anti-inflammatory macrophage activation and muscle regeneration via a GCN2-dependent mechanism, highlighting its therapeutic potential for sarcopenia [1].

 

Disclaimer

**LAB USE ONLY**
*This information is for educational purposes only and does not constitute medical advice. THE PRODUCTS DESCRIBED HEREIN ARE FOR RESEARCH USE ONLY. All clinical research must be conducted with oversight from the appropriate Institutional Review Board (IRB). All preclinical research must be conducted with oversight from the appropriate Institutional Animal Care and Use Committee (IACUC) following the guidelines of the Animal Welfare Act (AWA).

 

Citations

[1] 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;5(9):1810-1827. doi:10.1038/s43587-025-00900-7

 

Sarcosine is sold for laboratory research use only. Terms of sale apply. Not for human consumption, nor medical, veterinary, or household uses. Please familiarize yourself with our Terms & Conditions prior to ordering.