{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T11:06:48Z","timestamp":1779188808093,"version":"3.51.4"},"reference-count":38,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Science Progress"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To estimate the prevalence, temporal trends, and sociodemographic disparities of diagnosed diabetes and prediabetes among U.S. adults with low muscle mass using 2011\u20132018 National Health and Nutrition Examination Survey (NHANES) data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This cross-sectional analysis leveraged nationally representative NHANES data (2011\u20132018). A total of 12,753 adults aged \u226518 years with complete appendicular skeletal muscle mass, body mass index, and glycemic status data were included. Crude and age-standardized prevalence rates were analyzed across sociodemographic strata. Multivariate logistic regression analysis was employed to assess the association between low muscle mass and diabetes and prediabetes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>From 2011 to 2018, age-standardized low muscle mass prevalence increased from 10.3% (95% confidence interval (CI): 8.6\u201312.0) to 18.4% (16.1\u201320.7). Among adults with low muscle mass, 14.4% (12.2\u201316.6) had diabetes and 26.5% (23.5\u201329.6) had prediabetes. Striking age-related disparities emerged: adults aged 40\u201359 years exhibited a 3.1-fold higher diabetes prevalence (22.6% [19.1\u201326.5] vs. 7.4% [5.1\u201310.5] in those aged 18\u201339) and a 1.7-fold higher prediabetes prevalence (34.2% [29.4\u201339.2] vs. 20.0% [16.4\u201324.3]). Mexican Americans had 1.6 times higher diabetes prevalence than non-Hispanic Whites, while non-Hispanic Blacks showed 1.3 times higher prevalence than non-Hispanic Whites. Multivariate logistic regression revealed that low muscle mass was significantly associated with a higher risk of diabetes (odds ratio: 1.38, 95% CI: 1.07\u20131.78).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Low muscle mass is an independent predictor of diabetes risk. Over 40% of U.S. adults with low muscle mass exhibit dysglycemia, with rising prevalence and marked sociodemographic gradients. The prolonged diabetes duration in individuals with low muscle mass highlights accelerated metabolic deterioration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00368504261430046","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T18:14:35Z","timestamp":1773252875000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prevalence of diagnosed diabetes and prediabetes among U.S. adults with low muscle mass: A cross-sectional study, 2011\u20132018"],"prefix":"10.1177","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1489-5226","authenticated-orcid":false,"given":"Mengjie","family":"Zhao","sequence":"first","affiliation":[{"name":"China Academy of Chinese Medicine Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Cao","sequence":"additional","affiliation":[{"name":"Beijing University of Chinese Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zirong","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Chinese Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linli","family":"Chen","sequence":"additional","affiliation":[{"name":"University Hospital Bonn"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wantong","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Academy of Chinese Medicine Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuyan","family":"Li","sequence":"additional","affiliation":[{"name":"Xiyuan Hospital, China Academy of Chinese Medicine Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1152\/physrev.00061.2017"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/jcsm.12147"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12603-011-0108-3"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12877-016-0386-z"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00198-013-2550-z"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00198-016-3823-0"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.maturitas.2018.11.003"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1532-5415.2004.52014.x"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.puhe.2022.10.021"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmet.2021.03.020"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-025-21734-3"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/jcsm.12043"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.2147\/DMSO.S186600"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clnesp.2025.02.026"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1111\/jdi.12658"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.27.10.2382"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cellsig.2022.110355"},{"key":"e_1_3_3_19_2","unstructured":"National Center for Health Statistics NHANES Response Rates and Population Totals 2022 https:\/\/wwwn.cdc.gov\/nchs\/nhanes\/ (accessed 1 April 2025)."},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-147-8-200710160-00010"},{"key":"e_1_3_3_21_2","article-title":"Classification and diagnosis of diabetes: standards of medical care in diabetes\u20142022","volume":"45","author":"American Diabetes Association Professional Practice Committee. 2","year":"2022","unstructured":"American Diabetes Association Professional Practice Committee. 2. 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