Díaz-Soler Alejandro, Reche-García Cristina, Hernández-Morante Juan José
Eating Disorders Research Unit, Universidad Católica de Murcia, Guadalupe, 30107 Murcia, Spain.
Multidisciplinary Research Group on Health Psychology, Universidad Católica de Murcia, Guadalupe, 30107 Murcia, Spain.
Diseases. 2025 Jul 24;13(8):236. doi: 10.3390/diseases13080236.
Food addiction (FA) is an emerging psychiatric condition that presents behavioral and neurobiological similarities with other addictions, and its early identification is essential to prevent the development of more severe disorders. The aim of the present study was to determine the ability of anthropometric measures, eating habits, symptoms related to eating disorders (ED), and lifestyle features to predict the symptoms of food addiction. Methodology: A cross-sectional study was conducted in a sample of 702 university students (77.3% women; age: 22 ± 6 years). The Food Frequency Questionnaire (FFQ), the Yale Food Addiction Scale 2.0 (YFAS 2.0), the Eating Attitudes Test (EAT-26), anthropometric measurements, and a set of self-report questions on substance use, physical activity level, and other questions were administered. A total of 6.4% of participants presented symptoms compatible with food addiction, and 8.1% were at risk for ED. Additionally, 26.5% reported daily smoking, 70.6% consumed alcohol, 2.9% used illicit drugs, and 29.4% took medication; 35.3% did not engage in physical activity. Individuals with food addiction had higher BMI ( = 0.010), waist circumference ( = 0.001), and body fat ( < 0.001) values, and a higher risk of eating disorders ( = 0.010) compared to those without this condition. In the multivariate logistic model, non-dairy beverage consumption (such as coffee or alcohol), vitamin D deficiency, and waist circumference predicted food addiction symptoms (R = 0.349). Indeed, the machine learning approaches confirmed the influence of these variables. Conclusions: The prediction models allowed an accurate prediction of FA in the university students; moreover, the individualized approach improved the identification of people with FA, involving complex dimensions of eating behavior, body composition, and potential nutritional deficits not previously studied.
食物成瘾（FA）是一种新出现的精神疾病，在行为和神经生物学方面与其他成瘾行为存在相似之处，其早期识别对于预防更严重疾病的发展至关重要。本研究的目的是确定人体测量指标、饮食习惯、与饮食失调（ED）相关的症状以及生活方式特征预测食物成瘾症状的能力。方法：对702名大学生（77.3%为女性；年龄：22±6岁）进行了横断面研究。采用了食物频率问卷（FFQ）、耶鲁食物成瘾量表2.0（YFAS 2.0）、饮食态度测试（EAT - 26）、人体测量指标，以及一组关于物质使用、身体活动水平的自我报告问题和其他问题。共有6.4%的参与者表现出与食物成瘾相符的症状，8.1%有饮食失调风险。此外，26.5%报告每日吸烟，70.6%饮酒，2.9%使用非法药物，29.4%服药；35.3%不进行体育活动。与没有食物成瘾的人相比，食物成瘾者的体重指数（ = 0.010）、腰围（ = 0.001）和体脂（ < 0.001）值更高，且饮食失调风险更高（ = 0.010）。在多变量逻辑模型中，非乳类饮料消费（如咖啡或酒精）、维生素D缺乏和腰围可预测食物成瘾症状（R = 0.349）。事实上，机器学习方法证实了这些变量的影响。结论：预测模型能够准确预测大学生中的食物成瘾情况；此外，个体化方法改进了对食物成瘾者的识别，涉及到以前未研究过的饮食行为、身体成分和潜在营养缺乏等复杂维度。
