{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T01:25:54Z","timestamp":1793496354946,"version":"4.5.0"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007771","name":"Medical University of Silesia in Katowice","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007771","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Polycystic ovary syndrome (PCOS) is a common endocrine disorder often associated with disturbances in glucose metabolism and insulin resistance (IR), increasing the risk of type 2 diabetes (T2DM). Standard assessment of glucose dysregulations and IR requires laboratory tests, but simple anthropometric indices, including BMI, WHtR, WHR, VAI, LAP, BAI, BRI and ABSI, may provide non-invasive tools for early risk screening. Their predictive value and optimal cut-off points for detecting glucose dysregulation and IR in PCOS remain unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aims to evaluate the clinical utility of anthropometric indices in identifying glucose dysregulation in women with PCOS, and to provide prognostic insight with potential cut-off points for these indices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This cross-sectional study included 49 women with PCOS according to Rotterdam criteria. Anthropometric measurements (BMI, WC, WHR, WHtR, VAI, BAI, LAP, BRI, ABSI) and fasting biochemical parameters (glucose, insulin) were collected. Correlations between indices and carbohydrate disturbances were assessed using Pearson or Spearman coefficients. The predictive ability of anthropometric indices for glucose dysregulations and IR were evaluated using ROC curve analysis, including AUC, sensitivity, specificity, and optimal cut-off points, while logistic regression quantified the strength of associations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      BMI, WHtR, BAI, VAI, LAP, and BRI were significantly correlated with fasting glucose and insulin levels, indicating a strong link between adiposity and IR in women with PCOS. Among these indices, VAI showed the highest predictive performance for elevated HOMA-IR (AUC = 0.933, cut-off point 0.99; sensitivity 85.7, specificity 90.5%), followed by LAP (AUC = 0.883, cut-off point 27.9) and BMI (AUC = 0.852, cut off point 27 kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ). WHtR, WC, and BRI also demonstrated significant predictive value (AUCs 0.821-0.831). Logistic regression revealed the strongest associations for BMI \u226527.25 kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      and VAI \u22651.07 (OR = 57.0; 95% CI 9.41-345.15; p&amp;lt;0.001), with WC, WHtR, LAP, BAI, and BRI also showed significant predictive value for IR.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Anthropometric indices, particularly VAI, LAP, and BMI, reliably predicts glucose dysregulations and IR in women with PCOS. These simple, non-invasive measurements may serve as useful screening tools for early identifications of glucose dysregulation, aiding risk stratification and guiding further metabolic assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2025.1756679","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T05:41:13Z","timestamp":1769406073000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Clinical utility of anthropometric parameters in identifying glucose dysregulation in women with polycystic ovary syndrome"],"prefix":"10.3389","volume":"16","author":[{"given":"Justyna","family":"Nowak","sequence":"first","affiliation":[{"name":"Department of Metabolic Disease Prevention, Medical University of Silesia","place":["Bytom, Poland"]},{"name":"Department of Cardiovascular Disease Prevention, Medical University of Silesia","place":["Bytom, 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Poland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Zubelewicz-Szkodzi\u0144ska","sequence":"additional","affiliation":[{"name":"Department of Metabolic Disease Prevention, Medical University of Silesia","place":["Bytom, Poland"]},{"name":"Department of Nutrition-Related Disease Prevention, Medical University of Silesia","place":["Bytom, Poland"]},{"name":"Department of Endocrinology, District Hospital","place":["Piekary \u015al\u0105skie, Poland"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,26]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1093\/humrep\/des232","article-title":"Prevalence, phenotype and cardiometabolic risk of polycystic ovary syndrome under different diagnostic criteria","volume":"27","author":"Yildiz","year":"2012","journal-title":"Hum 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