{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T17:00:41Z","timestamp":1793638841766,"version":"4.5.1"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Sarcopenia is common among older adults with type 2 diabetes mellitus (T2DM). Although chronic hyperglycemia is known to contribute to muscle loss, it remains unclear whether glucose fluctuations\u2014independent of average glucose levels\u2014also play a role. This study aimed to investigate the association between glucose fluctuation and pectoralis muscle mass in elderly patients with T2DM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cross-sectional study included 303 elderly patients with T2DM who underwent continuous glucose monitoring and chest CT between October 2022 and December 2025. Glycemic variability was assessed using several metrics: coefficient of variation (CV), standard deviation (SD), mean amplitude of glycemic excursions (MAGE), mean of daily differences, time in range, time above range, time below range (TBR), and mean blood glucose. Pectoralis muscle index (PMI) was measured from CT images at the T4 vertebra level. Multivariable linear regression, subgroup analyses, and sensitivity analyses were performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Four glycemic variability indicators\u2014CV, SD, MAGE, and TBR\u2014were independently associated with lower PMI after adjusting for confounders (all\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.05). Among these, CV explained the largest proportion of PMI variance (adjusted R\u00b2 = 0.22), followed by TBR (0.21), SD (0.20), and MAGE (0.19). A linear dose-response relationship was observed between CV and PMI (\n                      <jats:italic>P<\/jats:italic>\n                      for trend &amp;lt; 0.001). The inverse association was stronger in patients with diabetes duration \u2265 10 years (\u03b2 = \u20130.18,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.001) and those with BMI &amp;lt; 24 kg\/m\u00b2 (\u03b2 = \u20130.18,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.001), with significant interactions for both subgroups (\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0=\u00a00.022 and 0.027, respectively). Sensitivity analyses confirmed the robustness of these findings.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Greater glycemic variability is independently associated with lower pectoralis muscle mass in elderly patients with T2DM, especially among those with longer disease duration or normal body weight. Stabilizing glucose fluctuations may be an important consideration for preserving muscle mass in this population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1863226","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T04:36:12Z","timestamp":1782102972000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Glycemic variability and muscle loss in elderly type 2 diabetes: insights from continuous glucose monitoring and chest CT in 303 patients"],"prefix":"10.3389","volume":"17","author":[{"given":"Xiao-le","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Endocrinology, Affiliated Hospital of Jiangnan University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Affiliated Hospital of Jiangnan University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-pu","family":"Mao","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Affiliated Hospital of Jiangnan University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-yan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Affiliated Hospital of Jiangnan University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1038\/s41556-022-00842-x","article-title":"Telomere dysfunction in ageing and age-related diseases","volume":"24","author":"Rossiello","year":"2022","journal-title":"Nat Cell Biol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"8677","DOI":"10.3390\/ijerph19148677","article-title":"Management 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