Central adiposity outperforms disordered eating behavior in predicting type 2 diabetes risk in young adults: an explainable machine learning approach

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dc.contributor.authorAlatas, Hacer
dc.contributor.authorArslan, Nurgul
dc.contributor.authorKurt, Harun
dc.contributor.authorBentli, Serkan
dc.date.accessioned2026-06-19T06:37:54Z
dc.date.available2026-06-19T06:37:54Z
dc.date.issued2026
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractBackground: 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. Methods: 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. Results: 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 (r = 0.01, p = 0.755). In multivariable regression, waist-to-height ratio (beta = 1.42 per 0.05 increase, p < 0.001) and body mass index (beta = 0.31, p < 0.001) were the strongest predictors of diabetes risk. Machine learning models, particularly XGBoost (AUROC = 0.87), highlighted waist-to-height ratio as the most influential predictor. Conclusion: 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.
dc.description.sponsorshipScientific Research Projects Coordination Unit of Malatya Turgut zal University [26G21]
dc.description.sponsorshipThe author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Scientific Research Projects Coordination Unit of Malatya Turgut Ozal University (Project No: 26G21).
dc.identifier.doi10.3389/fpubh.2026.1778156
dc.identifier.issn2296-2565
dc.identifier.orcid0000-0002-8312-1232
dc.identifier.pmid42052024
dc.identifier.scopus2-s2.0-105037426243
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3389/fpubh.2026.1778156
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5277
dc.identifier.volume14
dc.identifier.wosWOS:001750906600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers in Public Health
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectDisordered Eating
dc.subjectFindrisk
dc.subjectMachine Learning
dc.subjectType 2 Diabetes
dc.subjectUniversity Students
dc.titleCentral adiposity outperforms disordered eating behavior in predicting type 2 diabetes risk in young adults: an explainable machine learning approach
dc.typeArticle

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