{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:23:33Z","timestamp":1776101013716,"version":"3.50.1"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Disordered eating behaviors are often associated with adverse metabolic outcomes, yet their relationship with type 2 diabetes mellitus(T2DM) risk in young adults is less clear. This study aimed to evaluate the impact of disordered eating behaviors on diabetes risk among university students, using both traditional statistical methods and machine learning approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 1,302 university students participated in this cross-sectional study. Disordered eating behavior was assessed using the Eating Attitudes Test-40 (EAT-40), a validated screening tool for abnormal eating attitudes, while type 2 diabetes risk was estimated using the Finnish Diabetes Risk Score (FINDRISK), a widely used non-invasive instrument designed to estimate the 10-year risk of developing T2DM. Anthropometric measures were recorded according to standardized protocols. Bivariate associations were examined using correlation analysis, while multivariable regression and machine learning models (XGBoost) were applied to determine predictors of diabetes risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of the 1,302 participants, 90.8% were classified as low\/mild risk, 6.2% as moderate risk, and 3.0% as high\/very high risk according to FINDRISK. No significant correlation was found between EAT-40 scores and FINDRISK (\n                      <jats:italic>r<\/jats:italic>\n                      \u202f=\u202f0.01,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.755). In multivariable regression, waist-to-height ratio (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f1.42 per 0.05 increase,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001) and body mass index (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f0.31,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001) were the strongest predictors of diabetes risk. Machine learning models, particularly XGBoost (AUROC\u202f=\u202f0.87), highlighted waist-to-height ratio as the most influential predictor.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In young adults, central adiposity specifically waist-to-height ratio was the most significant predictor of T2DM risk, while disordered eating behavior had minimal independent impact. These findings suggest that simple anthropometric measures could be prioritized for early diabetes risk assessment over eating attitude screening.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1778156","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T16:29:30Z","timestamp":1776097770000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Central adiposity outperforms disordered eating behavior in predicting type 2 diabetes risk in young adults: an explainable machine learning approach"],"prefix":"10.3389","volume":"14","author":[{"given":"Hacer","family":"Alatas","sequence":"first","affiliation":[{"name":"Department of Nutrition and Dietetics, Faculty of Health Sciences, Malatya Turgut Ozal University","place":["Malatya, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nurgul","family":"Arslan","sequence":"additional","affiliation":[{"name":"Department of Nutrition and Dietetics, Atat\u00fcrk Faculty of Health Sciences, Dicle University","place":["Diyarbak\u0131r, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harun","family":"Kurt","sequence":"additional","affiliation":[{"name":"Malatya Provincial Health Directorate, Personnel Services Department","place":["Malatya, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Serkan","family":"Bentli","sequence":"additional","affiliation":[{"name":"Department of Nutrition and Dietetics, Faculty of Health Sciences, Malatya Turgut Ozal University","place":["Malatya, 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