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    Central adiposity outperforms disordered eating behavior in predicting type 2 diabetes risk in young adults: an explainable machine learning approach
    (Frontiers Media Sa, 2026) Alatas, Hacer; Arslan, Nurgul; Kurt, Harun; Bentli, Serkan
    Background: 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.
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    Stigmatization of healthcare professionals during the COVID-19 pandemic: their psychosocial states and the factors affecting them
    (Kare Publ, 2023) Tekin, Cigdem; Karakas, Nese; Akbulut, Sami; Kurt, Harun; Bentli, Recep
    Objectives: It is assumed that healthcare professionals are directly or indirectly subjected to stigma during the COVID-19 pandemic, impacting their psychosocial health. This study aimed to evaluate the psychosocial status of healthcare professionals during the COVID-19 pandemic and examine the factors affecting their exposure to stigma. Methods: This cross-sectional study included all healthcare professionals (n=1132) working in primary and secondary healthcare institutions in Malatya Province. Descriptive questions were asked to measure the stigma experienced by healthcare professionals during the COVID-19 outbreak. The Zung Self-Rating Depression Scale and Insomnia Severity Index were used to evaluate psychosocial health status. Results: Of the participants, 68.7% stated that they were exposed to stigma because they are healthcare professionals. The findings indicated that 72.1% of those who felt stigmatized for being a healthcare professional suffered from mod-erate or severe depression, and 66.9% suffered from subthreshold or moderate insomnia. When their current health state was compared with that before the pandemic, 25.0% said that it became worse\much worse. Conclusion: The results of this study indicated that most participants had been exposed to stigmatization because they are healthcare professionals. The participants who were exposed to stigma were found to suffer more from de-pression and insomnia. When their current health state was compared with that before the pandemic, one of every four participants stated that it became worse/much worse.