{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T04:49:33Z","timestamp":1782103773507,"version":"3.54.5"},"reference-count":49,"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>Introduction<\/jats:title>\n                    <jats:p>Glycemic variability (GV) strongly influences prognosis in critically ill patients; however, the optimal GV metric is unclear. This meta-analysis compares the association of various GV measures with clinical outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>PubMed, Embase, Web of Science, and the Cochrane Library were searched until June 2025 for studies linking GV metrics with prognosis in critically ill patients. The extracted GV metrics included coefficient of variation (CV), standard deviation (SD), and mean amplitude of glycemic excursions (MAGE). The primary outcomes were all-cause mortality (ACM) and major adverse cardiovascular event (MACE). Pooled hazard ratios (HRs) were estimated using a random effects model (REM) or a fixed effects model (FEM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 31 studies on 98,946 patients were included. Elevated CV was significantly associated with increased 30-day, 90-day, and 1-year ACM. MAGE demonstrated the strongest association with 30-day ACM (HR\u00a0=\u00a01.50, 95%CI = 1.27\u20131.78). Elevated SD (HR\u00a0=\u00a02.45) and MAGE (HR\u00a0=\u00a02.12) were also associated with an increased risk of MACE. Subgroup analysis further revealed that the impact of an increased CV level on 30-day ACM was greater in non-diabetic (NDM) patients (HR\u00a0=\u00a01.40) than in diabetic (DM) patients (HR\u00a0=\u00a01.32).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Elevated GV, particularly MAGE and CV, independently predicted both short- and long-term ACM and MACE. MAGE showed a strong association with short-term ACM. However, whether it is superior to other GV metrics warrants further investigation since available studies were limited. Clinicians should therefore place greater emphasis on dynamic GV monitoring and individualized glucose management to improve patient outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic review registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/www.crd.york.ac.uk\/prospero\/display_record.php?RecordID=1071964<\/jats:ext-link>\n                      , identifier CRD420251071964.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1857523","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T04:37:04Z","timestamp":1782103024000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive value of different glycemic variability indicators for prognosis in critically ill patients: a 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