Risk factors analysis and prediction models of obesity in college students based on dietary patterns.

Abstract

Background: Overweight and obesity among college students have become significant public health concerns. This study aims to develop a nomogram model for assessing obesity risk in college students.

Methods: A cross-sectional study was conducted among college students in Xuzhou. Demographic, dietary, and lifestyle information was obtained through self-administered questionnaires, while body composition was assessed using the InBody 570 analyzer. Dietary patterns and obesity prevalence were examined through multiple indicators. Principal component analysis (PCA), logistic regression, and a non-invasive risk assessment model based on percentage of body fat (PBF) were applied.

Results: The vegetable meat grain dietary pattern and milk egg dietary pattern were associated with a reduced risk of PBF (P < 0.01), while the snack mode dietary pattern and aquatic meat dietary pattern increased the risk of PBF (P < 0.05). Binary logistic regression identified gender, physical activity, late-night snacking, regular meals, and a healthy diet as key predictors of PBF obesity in college students. The model achieved an area under curve (AUC) of 0.805, with a non-significant Hosmer-Lemeshow (H-L) test (P > 0.05). Decision curve analysis (DCA) showed that the model outperformed extreme curves, indicating its reliability.

Conclusion: This study highlights the high prevalence of overweight and obesity among college students and the importance of using multiple indicators for comprehensive evaluation. The developed PBF-based nomogram model demonstrates potential for obesity screening but requires further validation in diverse populations.