{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T17:42:37Z","timestamp":1785519757761,"version":"3.56.0"},"reference-count":44,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T00:00:00Z","timestamp":1685059200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Rheumatoid arthritis (RA) is an autoimmune disease that affects the joints, leading to chronic synovial inflammation and local tissue destruction. Extra\u2010articular manifestations may also occur, such as changes in body composition. Skeletal muscle wasting is often observed in patients with RA, but methods for assessing loss of muscle mass are expensive and not widely available. Metabolomic analysis has shown great potential for identifying changes in the metabolite profile of patients with autoimmune diseases. In this setting, urine metabolomic profiling in patients with RA may be a useful tool to identify skeletal muscle wasting.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Patients aged 40\u201370\u00a0years with RA have been recruited according to the 2010 ACR\/EULAR classification criteria. Further, the Disease Activity Score in 28 joints using the C\u2010reactive protein level (DAS28\u2010CRP) determined the disease activity. The muscle mass was measured by Dual X\u2010ray absorptiometry (DXA) to generate the appendicular lean mass index (ALMI) by summing the lean mass measurements for both arms and legs and dividing them by height squared (kg\/height<jats:sup>2<\/jats:sup>). Finally, urine metabolomic analysis by <jats:sup>1<\/jats:sup>H nuclear magnetic resonance (<jats:sup>1<\/jats:sup>H\u2010NMR) spectroscopy was performed and the metabolomics data set analysed using the BAYESIL and MetaboAnalyst software packages. Principal component analysis (PCA) and partial least squares\u2010discriminant analysis (PLS\u2010DA) were applied to the <jats:sup>1<\/jats:sup>H\u2010NMR data, followed by Spearman's correlation analysis. The combined receiver operating characteristic curve (ROC) was calculated, as well as the logistic regression analyses to establish a diagnostic model. The significance level at <jats:italic>P<\/jats:italic>\u00a0&lt;\u00a00.05 was set for all analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The total set of subjects investigated included 90 patients with RA. Most patients were women (86.7%), with a mean age of 56.5\u00a0\u00b1\u00a07.3\u00a0years old and a median DAS28\u2010CRP of 3.0 (IQR 1.0\u20133.0). Fifteen metabolites were identified in the urine samples with high variable importance in projection (VIP scores) by MetaboAnalyst. Of these, dimethylglycine (<jats:italic>r<\/jats:italic>\u00a0=\u00a00.205; <jats:italic>P<\/jats:italic>\u00a0=\u00a00.053), oxoisovalerate (<jats:italic>r<\/jats:italic>\u00a0=\u00a0\u22120.203; <jats:italic>P<\/jats:italic>\u00a0=\u00a00.055), and isobutyric acid (<jats:italic>r<\/jats:italic>\u00a0=\u00a0\u22120.249; <jats:italic>P<\/jats:italic>\u00a0=\u00a00.018) were significantly correlated with ALMI. Based on the low muscle mass (ALMI \u22646.0\u00a0kg\/m<jats:sup>2<\/jats:sup> for women and \u22648.1\u00a0kg\/m<jats:sup>2<\/jats:sup> for men) a diagnostic model have been established with dimethylglycine (area under the curve [AUC]\u00a0=\u00a00.65), oxoisovalerate (AUC\u00a0=\u00a00.49), and isobutyric acid (AUC\u00a0=\u00a00.83) with significant sensitivity and specificity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Isobutyric acid, oxoisovalerate, and dimethylglycine from urine samples were associated with low skeletal muscle mass in patients with RA. These findings suggest that this group of metabolites may be further tested as biomarkers for identification of skeletal muscle wasting.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jcsm.13240","type":"journal-article","created":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T06:40:05Z","timestamp":1685169605000},"page":"1657-1669","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Urinary metabolomic biomarker candidates for skeletal muscle wasting in patients with rheumatoid arthritis"],"prefix":"10.1002","volume":"14","author":[{"given":"Marianne S.","family":"Oliveira","sequence":"first","affiliation":[{"name":"Autoimmune Disease Laboratory Hospital de Cl\u00ednicas de Porto Alegre  Porto Alegre Brazil"},{"name":"Medical School Universidade Federal do Rio Grande do Sul  Porto Alegre Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafaela C.E.","family":"Santo","sequence":"additional","affiliation":[{"name":"Autoimmune Disease Laboratory Hospital de Cl\u00ednicas de Porto Alegre  Porto Alegre Brazil"},{"name":"Medical School Universidade Federal do Rio Grande do Sul  Porto Alegre Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jordana M.S.","family":"Silva","sequence":"additional","affiliation":[{"name":"Autoimmune Disease Laboratory Hospital de Cl\u00ednicas de Porto Alegre  Porto Alegre Brazil"},{"name":"Medical School Universidade Federal do Rio Grande do Sul  Porto Alegre Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paulo V.G.","family":"Alabarse","sequence":"additional","affiliation":[{"name":"University of California  San Diego California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claiton V.","family":"Brenol","sequence":"additional","affiliation":[{"name":"Autoimmune Disease Laboratory Hospital de Cl\u00ednicas de Porto Alegre  Porto 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