Abstract
Age-related sarcopenia is a growing global health challenge with no approved pharmacotherapies. Here, we integrate network-based drug repurposing and Mendelian randomization to identify rosiglitazone, a PPARγ agonist used in diabetes, as a potential therapeutic candidate for sarcopenia. In aged male C57BL/6JRj murine models, rosiglitazone administration significantly improved muscle strength, mass, and endurance. Multi-omics profiling revealed its mechanism involves gut microbiota remodeling, activation of skeletal muscle Igf1 signaling, suppression of atrophy-related ubiquitin ligases (Atrogin-1/MuRF1), and modulation of protein metabolism, suggesting a coordinated “gut-muscle-metabolism” axis. Genetic analyses further support the causal role of Clostridiaceae/Clostridium in grip strength. Our findings nominate rosiglitazone as a promising intervention for sarcopenia, warranting further clinical investigation.
Data availability
The GWAS summary statistics of the left (ukb-b-7478) and right grip (ukb-b-10215) strength were downloaded from the MRC Integrative Epidemiology Unit. The GWAS summary statistics of appendicular lean mass (ebi-a-GCST90000025) were downloaded from the GWAS Catalog (https://www.ebi.ac.uk/gwas/). Raw and processed RNA-seq data for mouse skeletal muscle can be accessed via GEO accession GSE256241. Metabolomics data for mouse skeletal muscle can be downloaded from https://www.ebi.ac.uk/metabolights/ (MTBLS9582), while 16S sequencing data for mouse intestinal flora can be downloaded from https://www.ncbi.nlm.nih.gov/ (PRJNA1077899). Source data for the graphs can be found in Supplementary Data 2.
References
Beard, J. R., Officer, A. M. & Cassels, A. K. The world report on ageing and health. Gerontologist 56, S163–S166 (2016).
Campisi, J. et al. From discoveries in ageing research to therapeutics for healthy ageing. Nature 571, 183–192 (2019).
Mayhew, A. J. et al. The prevalence of sarcopenia in community-dwelling older adults, an exploration of differences between studies and within definitions: a systematic review and meta-analyses. Age Ageing 48, 48–56 (2019).
Cruz-Jentoft, A. J. & Sayer, A. A. Sarcopenia. Lancet 393, 2636–2646 (2019).
Tan, L. J. et al. Molecular genetic studies of gene identification for sarcopenia. Hum. Genet. 131, 1–31 (2012).
Liu, J. C. et al. Multi-omics research in sarcopenia: current progress and future prospects. Ageing Res. Rev. 76, 101576 (2022).
Jahchan, N. S. et al. A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors. Cancer Discov. 3, 1364–1377 (2013).
Dudley, J. T. et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl. Med. 3, 96ra76 (2011).
Pushpakom, S. et al. Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 18, 41–58 (2019).
Morselli Gysi, D. et al. Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proc. Natl. Acad. Sci. USA. 118, e2025581118 (2021).
Finan, C. et al. The druggable genome and support for target identification and validation in drug development. Sci. Transl. Med. 9, eaag1166 (2017).
Cook, D. et al. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat. Rev. Drug Discov. 13, 419–431 (2014).
Gill, D. et al. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome Open Res 6, 16 (2021).
Lo, J. H. et al. Sarcopenia: current treatments and new regenerative therapeutic approaches. J. Orthop. Transl. 23, 38–52 (2020).
Bunout, D. et al. The impact of nutritional supplementation and resistance training on the health functioning of free-living Chilean elders: results of 18 months of follow-up. J. Nutr. 131, 2441s–2446s (2001).
Liguori, I. et al. Sarcopenia: assessment of disease burden and strategies to improve outcomes. Clin. Interv. Aging 13, 913–927 (2018).
Dillon, E. L. et al. Amino acid supplementation increases lean body mass, basal muscle protein synthesis, and insulin-like growth factor-I expression in older women. J. Clin. Endocrinol. Metab. 94, 1630–1637 (2009).
Sayer, A. A. & Cruz-Jentoft, A. Sarcopenia definition, diagnosis and treatment: consensus is growing. Age Ageing 51, afac220 (2022).
Chen, L. K. et al. Asian Working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J. Am. Med. Dir. Assoc. 21, 300–307.e302 (2020).
Gerring, Z. F., Mina-Vargas, A., Gamazon, E. R. & Derks, E. M. E-MAGMA: an eQTL-informed method to identify risk genes using genome-wide association study summary statistics. Bioinformatics 37, 2245–2249 (2021).
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).
Rani, J., Shah, A. B. & Ramachandran, S. pubmed.mineR: an R package with text-mining algorithms to analyse PubMed abstracts. J. Biosci. 40, 671–682 (2015).
Barabási, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).
Gustafsson, M. et al. Modules, networks and systems medicine for understanding disease and aiding diagnosis. Genome Med. 6, 82 (2014).
Menche, J. et al. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).
Sasako, T. et al. Deletion of skeletal muscle Akt1/2 causes osteosarcopenia and reduces lifespan in mice. Nat. Commun. 13, 5655 (2022).
Zeng, Z. et al. Exercise-induced autophagy suppresses sarcopenia through Akt/mTOR and Akt/FoxO3a signal pathways and AMPK-mediated mitochondrial quality control. Front. Physiol. 11, 583478 (2020).
Lutz, C. T. & Quinn, L. S. Sarcopenia, obesity, and natural killer cell immune senescence in aging: altered cytokine levels as a common mechanism. Aging 4, 535–546 (2012).
Bian, A.-L. et al. A study on relationship between elderly sarcopenia and inflammatory factors IL-6 and TNF-α. Eur. J. Med. Res. 22, 25 (2017).
Priego, T., et al. Role of hormones in sarcopenia. In: G. Litwack (ed.) Vitamins and Hormones 535–570 (Academic Press, 2021).
Misselbeck, K. et al. A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome. Nat. Commun. 10, 5215 (2019).
Geraci, A. et al. Sarcopenia and menopause: the role of estradiol. Front. Endocrinol. 12, 682012 (2021).
Salamone, I. M. et al. Intermittent glucocorticoid treatment enhances skeletal muscle performance through sexually dimorphic mechanisms. J. Clin. Investig. 132, e149828 (2022).
Feng, X. et al. Angiotensin II receptor blocker telmisartan enhances running endurance of skeletal muscle through activation of the PPAR-δ/AMPK pathway. J. Cell. Mol. Med. 15, 1572–1581 (2011).
Huang, F. et al. Rosiglitazone binds to RXRα to induce RXRα tetramerization and NB4 cell differentiation. Biochem. Biophys. Res. Commun. 530, 160–166 (2020).
Börsch, A. et al. Molecular and phenotypic analysis of rodent models reveals conserved and species-specific modulators of human sarcopenia. Commun. Biol. 4, 194 (2021).
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).
Yoshida, T. & Delafontaine, P. Mechanisms of IGF-1-mediated regulation of skeletal muscle hypertrophy and atrophy. Cells 9, 1970 (2020).
Stitt, T. N. et al. The IGF-1/PI3K/Akt pathway prevents expression of muscle atrophy-induced ubiquitin ligases by inhibiting FOXO transcription factors. Mol. Cell. 14, 395–403 (2004).
Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at: https://doi.org/10.1101/060012 (2021).
Sartori, R., Romanello, V. & Sandri, M. Mechanisms of muscle atrophy and hypertrophy: implications in health and disease. Nat. Commun. 12, 330 (2021).
Pang, Z. et al. Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat. Protoc. 17, 1735–1761 (2022).
Canfield, C.-A. & Bradshaw, P. C. Amino acids in the regulation of aging and aging-related diseases. Transl. Med. Aging 3, 70–89 (2019).
Landi, F. et al. Protein intake and muscle health in old age: from biological plausibility to clinical evidence. Nutrients 8, 295 (2016).
Murton, A. J. Muscle protein turnover in the elderly and its potential contribution to the development of sarcopenia. Proc. Nutr. Soc. 74, 387–396 (2015).
Lu, Y. et al. Systemic and metabolic signature of sarcopenia in community-dwelling older adults. J. Gerontol. A. Biol. Sci. Med. Sci. 75, 309–317 (2019).
Yeung, S. S. Y., Zhu, Z. L. Y., Kwok, T. & Woo, J. Serum amino acids patterns and 4-year sarcopenia risk in community-dwelling Chinese older adults. Gerontology 68, 736–745 (2021).
Venkatesh, M. et al. Symbiotic bacterial metabolites regulate gastrointestinal barrier function via the xenobiotic sensor PXR and toll-like receptor 4. Immunity 41, 296–310 (2014).
Du, L. et al. Indole-3-propionic acid, a functional metabolite of clostridium sporogenes, promotes muscle tissue development and reduces muscle cell inflammation. Int. J. Mol. Sci. 22, 12435 (2021).
Witham, M. D. et al. Repurposing drugs for diabetes mellitus as potential pharmacological treatments for sarcopenia - a narrative review. Drugs Aging 40, 703–719 (2023).
Lyu, Q. et al. The ameliorating effects of metformin on disarrangement ongoing in gastrocnemius muscle of sarcopenic and obese sarcopenic mice. Biochim. Biophys. Acta Mol. Basis Dis. 1868, 166508 (2022).
Naznin, F. et al. Canagliflozin, a sodium glucose cotransporter 2 inhibitor, attenuates obesity-induced inflammation in the nodose ganglion, hypothalamus, and skeletal muscle of mice. Eur. J. Pharmacol. 794, 37–44 (2017).
Meshkani, R. et al. Rosiglitazone, a PPARγ agonist, ameliorates palmitate-induced insulin resistance and apoptosis in skeletal muscle cells. Cell Biochem. Funct. 32, 683–691 (2014).
Remels, A. H. et al. PPARgamma inhibits NF-kappaB-dependent transcriptional activation in skeletal muscle. Am. J. Physiol. Endocrinol. Metab. 297, E174–E183 (2009).
Massimino, E., Izzo, A., Riccardi, G. & Della Pepa, G. The impact of glucose-lowering drugs on sarcopenia in type 2 diabetes: current evidence and underlying mechanisms. Cells 10, 1958 (2021).
Clemente, J. C., Ursell, L. K., Parfrey, L. W. & Knight, R. The impact of the gut microbiota on human health: an integrative view. Cell 148, 1258–1270 (2012).
Zhao, J., Huang, Y. & Yu, X. A narrative review of gut-muscle axis and sarcopenia: the potential role of gut microbiota. Int. J. Gen. Med. 14, 1263–1273 (2021).
Whang, A., Nagpal, R. & Yadav, H. Bi-directional drug-microbiome interactions of anti-diabetics. EBioMedicine 39, 591–602 (2019).
Sheng, L., Jena, P. K., Hu, Y. & Wan, Y.-J. Y. Age-specific microbiota in altering host inflammatory and metabolic signaling as well as metabolome based on the sex. Hepatobiliary Surg. Nutr. 10, 31–48 (2020).
Chen, Z. et al. Association of insulin resistance and Type 2 diabetes with gut microbial diversity: a microbiome-wide analysis from population studies. JAMA Netw. Open. 4, e2118811 (2021).
Blanton, L. V. et al. Gut bacteria that prevent growth impairments transmitted by microbiota from malnourished children. Science 351, aad3311 (2016).
Huang, W. C. et al. Investigation of the effects of microbiota on exercise physiological adaption, performance, and energy utilization using a gnotobiotic animal model. Front. Microbiol. 10, 1906 (2019).
Dou, L. et al. Supplemental Clostridium butyricum modulates skeletal muscle development and meat quality by shaping the gut microbiota of lambs. Meat Sci. 204, 109235 (2023).
de Kovel, C. G. F., Carrión-Castillo, A. & Francks, C. A large-scale population study of early life factors influencing left-handedness. Sci. Rep. 9, 584 (2019).
Barton-Davis, E. R. et al. Viral-mediated expression of insulin-like growth factor I blocks the aging-related loss of skeletal muscle function. Proc. Natl. Acad. Sci. USA 95, 15603–15607 (1998).
Hammers, D. W. et al. Impairment of IGF-I expression and anabolic signaling following ischemia/reperfusion in skeletal muscle of old mice. Exp. Gerontol. 46, 265–272 (2011).
Yan, J. & Charles, J. F. Gut microbiota and IGF-1. Calcif. Tissue Int. 102, 406–414 (2018).
Yan, J. et al. Gut microbiota induce IGF-1 and promote bone formation and growth. Proc. Natl. Acad. Sci. USA 113, E7554–E7563 (2016).
Guo, P., Zhang, K., Ma, X. & He, P. Clostridium species as probiotics: potentials and challenges. J. Anim. Sci. Biotechnol. 11, 24 (2020).
Fujita, S. & Volpi, E. Amino acids and muscle loss with aging. J. Nutr. 136, 277s–280s (2006).
Tang, Q., Tan, P., Ma, N. & Ma, X. Physiological functions of threonine in animals: beyond nutrition metabolism. Nutrients 13, 2592 (2021).
Zhao, Y. et al. Effect of dietary threonine on growth performance and muscle growth, protein synthesis and antioxidant-related signalling pathways of hybrid catfish Pelteobagrus vachelli♀ × Leiocassis longirostris♂. Br. J. Nutr. 123, 121–134 (2020).
Kim, J., Jo, Y., Cho, D. & Ryu, D. L-threonine promotes healthspan by expediting ferritin-dependent ferroptosis inhibition in C. elegans. Nat. Commun. 13, 6554 (2022).
Broady Health Sciences, L. Use of jasmonate for improving skeletal muscle function. US20120172450A1 (2011).
Carter, W. J. & Lynch, M. E. Comparison of the effects of salbutamol and clenbuterol on skeletal muscle mass and carcass composition in senescent rats. Metabolism 43, 1119–1125 (1994).
Hostrup, M. et al. Beta(2) -adrenoceptor agonist salbutamol increases protein turnover rates and alters signalling in skeletal muscle after resistance exercise in young men. J. Physiol. 596, 4121–4139 (2018).
McLennan, I. S. Hormonal regulation of myoblast proliferation and myotube production in vivo: influence of prostaglandins. J. Exp. Zool. 241, 237–245 (1987).
Horsley, V. & Pavlath, G. K. Prostaglandin F2(alpha) stimulates growth of skeletal muscle cells via an NFATC2-dependent pathway. J. Cell Biol. 161, 111–118 (2003).
Ambigaipalan, P., Oh, W. Y. & Shahidi, F. Epigallocatechin (EGC) esters as potential sources of antioxidants. Food Chem. 309, 125609 (2020).
Ramirez-Sanchez, I. et al. Restorative potential of (-)-epicatechin in a rat model of Gulf War illness muscle atrophy and fatigue. Sci. Rep. 11, 21861 (2021).
Cheah, I. K. & Halliwell, B. Ergothioneine, recent developments. Redox Biol. 42, 101868 (2021).
Fovet, T. et al. Ergothioneine improves aerobic performance without any negative effect on early muscle recovery signaling in response to acute exercise. Front. Physiol. 13, 834597 (2022).
Nissen, S. E. & Wolski, K. Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. N. Engl. J. Med. 356, 2457–2471 (2007).
Kahn, S. E. et al. Glycemic durability of rosiglitazone, metformin, or glyburide monotherapy. N. Engl. J. Med. 355, 2427–2443 (2006).
Kahn, S. E. et al. Rosiglitazone-associated fractures in type 2 diabetes: an analysis from A Diabetes Outcome Progression Trial (ADOPT). Diab. Care 31, 845–851 (2008).
Kim, M. K. et al. Long-term effects of rosiglitazone on the progressive decline in renal function in patients with type 2 diabetes. Korean J. Intern. Med. 24, 227–232 (2009).
Lebovitz, H. E., Kreider, M. & Freed, M. I. Evaluation of liver function in type 2 diabetic patients during clinical trials: evidence that rosiglitazone does not cause hepatic dysfunction. Diab. Care 25, 815–821 (2002).
Pei, Y.-F. et al. The genetic architecture of appendicular lean mass characterized by association analysis in the UK Biobank study. Commun. Biol. 3, 608 (2020).
Kawakami, R. et al. Fat-free mass index as a surrogate marker of appendicular skeletal muscle mass index for low muscle mass screening in sarcopenia. J. Am. Med. Dir. Assoc. 23, 1955–1961 (2022).
Giles, J. T. et al. Association of body composition with disability in rheumatoid arthritis: impact of appendicular fat and lean tissue mass. Arthritis Rheum. 59, 1407–1415 (2008).
Stelzer, G. et al. The GeneCards Suite: from gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinforma. 54, 1.30.31–31.30.33 (2016).
Amberger, J. S. et al. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789–D798 (2015).
Piñero, J. et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 48, D845–D855 (2020).
Yu, W., Clyne, M., Khoury, M. J. & Gwinn, M. Phenopedia and genopedia: disease-centered and gene-centered views of the evolving knowledge of human genetic associations. Bioinformatics 26, 145–146 (2010).
Liu, Y., Liang, Y. & Wishart, D. PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more. Nucleic Acids Res. 43, W535–W542 (2015).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 289–300 (1995).
Liang, S. et al. Network-based approach to repurpose approved drugs for COVID-19 by integrating GWAS and text mining data. Processes 10, 326 (2022).
Lizio, M. et al. Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol. 16, 22 (2015).
Luck, K. et al. A reference map of the human binary protein interactome. Nature 580, 402–408 (2020).
Wang, R. S. & Loscalzo, J. Network-based disease module discovery by a novel seed connector algorithm with pathobiological implications. J. Mol. Biol. 430, 2939–2950 (2018).
Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).
Subramanian, A. et al. A next generation connectivity map: L1000 pPlatform and the first 1,000,000. Profiles Cell. 171, 1437–1452 e1417 (2017).
Wang, J. Z. et al. A new method to measure the semantic similarity of GO terms. Bioinformatics 23, 1274–1281 (2007).
Guney, E., Menche, J., Vidal, M. & Barábasi, A.-L. Network-based in silico drug efficacy screening. Nat. Commun. 7, 10331 (2016).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Mankhong, S. et al. Experimental models of sarcopenia: bridging molecular mechanism and therapeutic strategy. Cells 9, 1385 (2020).
Tomczyk, M. et al. Rosiglitazone ameliorates cardiac and skeletal muscle dysfunction by correction of energetics in Huntington’s disease. Cells 11, 2662 (2022).
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 9, 559 (2008).
Horai, H. et al. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 45, 703–714 (2010).
Sud, M. et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 35, D527–D532 (2007).
Dunn, W. B. et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060–1083 (2011).
Abdelrazig, S. et al. Metabolic characterisation of Magnetospirillum gryphiswaldense MSR-1 using LC-MS-based metabolite profiling. RSC Adv. 10, 32548–32560 (2020).
Thevenot, E. A. et al. Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and opls statistical analyses. J. Proteome Res. 14, 3322–3335 (2015).
Xia, J. & Wishart, D. S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat. Protoc. 6, 743–760 (2011).
Want, E. J. et al. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat. Protoc. 8, 17–32 (2013).
Boulesteix, A. L. & Strimmer, K. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief. Bioinform. 8, 32–44 (2007).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Lopera-Maya, E. A. et al. Effect of host genetics on the gut microbiome in 7738 participants of the Dutch Microbiome Project. Nat. Genet. 54, 143–151 (2022).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Acknowledgements
This study did not receive any specific funding.
Author information
Authors and Affiliations
Contributions
Hong-Wen Deng conceived, designed, initiated and directed the whole project. Shuang Liang, as the first author, performed data analysis, experimental validations and drafted the manuscript. Hong-Wen Deng, Hong-Mei Xiao revised, rewrote/restructured some sections and finalized the manuscript. Yong Liu contributed to data analysis.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Communications Biology thanks Bruce Aronow, Zhilong Jia and Inemesit Okon-Ben for their contribution to the peer review of this work. Primary Handling Editors: Laura Rodriguez Perez.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Shuang, L., Liu, Y., Xiao, HM. et al. An integrated drug repositioning analysis identifies rosiglitazone as a treatment for sarcopenia. Commun Biol (2026). https://doi.org/10.1038/s42003-026-09595-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s42003-026-09595-x
