Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis

Nutrition Journal

Songming Du¹  Guansheng Ma²  Yanping Li³  Haiquan Xu⁴  Qian Zhang⁵  Ailing Liu⁵  Xianwen Shang⁶  Hongwei Guo⁷ 

[1] Chinese Nutrition Society, Beijing, China;Department of Nutrition and Food Hygiene, School of Public Health, Peking University, 38 Xue Yuan Road, 100191, Beijing, China;Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA;Institute of food and nutrition development, Ministry of Agriculture and Rural Affairs, Beijing, China;National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China;National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China;School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, Australia;Department of Medicine (Royal Melbourne Hospital), University of Melbourne, Melbourne, Australia;School of Public Health, Fudan University, Shanghai, China;

关键词: Cardiometabolic risk factors;    Leading dietary determinants;    Healthy diet score;    Machine learning;    Children;   

DOI  :  10.1186/s12937-020-00611-2

来源: Springer

【 摘 要 】

BackgroundIdentifying leading dietary determinants for cardiometabolic risk (CMR) factors is urgent for prioritizing interventions in children. We aimed to identify leading dietary determinants for the change in CMR and create a healthy diet score (HDS) to predict CMR in children.MethodsWe included 5676 children aged 6–13 years in the final analysis with physical examinations, blood tests, and diets assessed at baseline and one year later. CMR score (CMRS) was computed by summing Z-scores of waist circumference, an average of systolic and diastolic blood pressure (SBP and DBP), fasting glucose, high-density lipoprotein cholesterol (HDL-C, multiplying by − 1), and triglycerides. Machine learning was used to identify leading dietary determinants for CMR and an HDS was then computed.ResultsThe nine leading predictors for CMRS were refined grains, seafood, fried foods, sugar-sweetened beverages, wheat, red meat other than pork, rice, fungi and algae, and roots and tubers with the contribution ranging from 3.9 to 19.6% of the total variance. Diets high in seafood, rice, and red meat other than pork but low in other six food groups were associated with a favorable change in CMRS. The HDS was computed based on these nine dietary factors. Children with HDS ≥8 had a higher decrease in CMRS (β (95% CI): − 1.02 (− 1.31, − 0.73)), BMI (− 0.08 (− 0.16, − 0.00)), SBP (− 0.46 (− 0.58, − 0.34)), DBP (− 0.46 (− 0.58, − 0.34)), mean arterial pressure (− 0.50 (− 0.62, − 0.38)), fasting glucose (− 0.22 (− 0.32, − 0.11)), insulin (− 0.52 (− 0.71, − 0.32)), and HOMA-IR (− 0.55 (− 0.73, − 0.36)) compared to those with HDS ≦3. Improved HDS during follow-up was associated with favorable changes in CMRS, BMI, percent body fat, SBP, DBP, mean arterial pressure, HDL-C, fasting glucose, insulin, and HOMA-IR.ConclusionDiets high in seafood, rice, and red meat other than pork and low in refined grains, fried foods, sugar-sweetened beverages, and wheat are leading healthy dietary factors for metabolic health in children. HDS is strongly predictive of CMR factors.

【 授权许可】

CC BY