ORIGINAL RESEARCH article
Front. Nutr.
Sec. Nutritional Epidemiology
Key dietary amino acids modulating overweight/obesity risk in Chinese children and adolescents: A Machine Learning Analysis of a National Survey
Qiangqiang Liu 1
Cheng Li 2
Yifan Zhang 3
Changqing Liu 4
Yiya Liu 5
Meina Tian 4
Qianrang Zhu 6
Yao Chen 7
Lianlong Yu 8
Hongwei Wang 9
1. Jinan Maternity and Child Care Hospital Affiliated to Shandong First Medical University, Jinan, China
2. Department of Clinical Nutrition, Beijing Friendship Hospital, Capital Medical University, Beijing, China
3. Southern Medical University School of Public Health, Guangzhou, China
4. Hebei Province Center for Disease Control and Prevention, Shijiazhuang, China
5. Guizhou Center for Disease Control and Prevention, Guiyang, China
6. Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
7. People’s Hospital of Rizhao, Rizhao, China
8. Shandong Center for Disease Control and Prevention, Jinan, China
9. Rizhao People's Hospital, Rizhao, China
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Abstract
Objective: To mitigate current research limitations, this cross-sectional study aimed to systematically evaluate the associations between dietary amino acids and overweight/obesity and to identify critical biomarkers among Chinese children and adolescents. This was achieved by integrating multiple machine learning algorithms with traditional statistical models. Methods: This study utilized data from the 2016-2019 China Children and Lactating Women Nutrition and Health Surveillance, a nationally representative survey. Participants included children and adolescents aged 6-18 years. Dietary intake was assessed using a validated food frequency questionnaire, and amino acid intakes were calculated. Four machine learning algorithms were applied to build prediction models. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to interpret the optimal model and identify important features. Multivariable logistic regression models were additionally used to examine the relationship between amino acids and overweight/obesity risk. Results: A total of 8,664 participants were included. The LightGBM model showed the best predictive effect (AUC = 0.805). Both SHAP analysis and logistic regression results consistently identified leucine (OR 1.13; 95% CI 1.01 ~ 1.27), threonine (OR 1.41; 95% CI 1.22 ~ 1.63), methionine (OR 1.30; 95% CI 1.07 ~ 1.57), and cysteine (OR 0.71; 95% CI 0.59 ~ 0.84) as key amino acids associated with overweight/obesity risk. After multivariable adjustment, the intake of leucine, threonine, and methionine was positively related to the risk of overweight/obesity, whereas cysteine intake was inversely related to the risk. Restricted cubic spline analyses suggested linear relationships for these associations. Conclusion: Higher dietary intakes of leucine, threonine, and methionine are potential risk factors, while cysteine is a potential protective factor against overweight/obesity in Chinese children and adolescents.
Summary
Keywords
adolescents, Children, Dietary amino acids, machine learning, Obesity, Overweight
Received
16 December 2025
Accepted
19 February 2026
Copyright
© 2026 Liu, Li, Zhang, Liu, Liu, Tian, Zhu, Chen, Yu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yao Chen; Lianlong Yu
Disclaimer
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