TitleLeading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis
AuthorsShang, Xianwen
Li, Yanping
Xu, Haiquan
Zhang, Qian
Liu, Ailing
Du, Songming
Guo, Hongwei
Ma, Guansheng
AffiliationChinese Ctr Dis Control & Prevent, Natl Inst Nutr & Hlth, Beijing, Peoples R China
Australian Catholic Univ, Sch Behav & Hlth Sci, Melbourne, Vic, Australia
Univ Melbourne, Dept Med, Royal Melbourne Hosp, Melbourne, Vic, Australia
Harvard TH Chan Sch Publ Hlth, Dept Nutr, Boston, MA USA
Minist Agr & Rural Affairs, Inst Food & Nutr Dev, Beijing, Peoples R China
Chinese Nutr Soc, Beijing, Peoples R China
Fudan Univ, Sch Publ Hlth, Shanghai, Peoples R China
Peking Univ, Sch Publ Hlth, Dept Nutr & Food Hyg, 38 Xue Yuan Rd, Beijing 100191, Peoples R China
KeywordsCARDIOVASCULAR-DISEASE
METABOLIC SYNDROME
CHILDHOOD OBESITY
BODY-COMPOSITION
INTERVENTION
ADULTHOOD
ADOLESCENTS
INDEXES
Issue Date19-Sep-2020
PublisherNUTRITION JOURNAL
AbstractBackground Identifying 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. Methods We 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. Results The 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 (beta (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. Conclusion Diets 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.
URIhttp://hdl.handle.net/20.500.11897/592302
DOI10.1186/s12937-020-00611-2
IndexedSCI(E)
Appears in Collections:公共卫生学院

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