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

Journal article


Shang, Xianwen, Li, Yanping, Xu, Haiquan, Zhang, Qian, Liu, Ailing, Du, Songming, Guo, Hongwei and Ma, Guansheng. (2020). 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. 19(1), p. 105. https://doi.org/10.1186/s12937-020-00611-2
AuthorsShang, Xianwen, Li, Yanping, Xu, Haiquan, Zhang, Qian, Liu, Ailing, Du, Songming, Guo, Hongwei and Ma, Guansheng
Abstract

Background
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 (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.

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.

Keywordscardiometabolic risk factors; leading dietary determinants; healthy diet score; machine learning; children
Year2020
JournalNutrition Journal
Journal citation19 (1), p. 105
PublisherBiomed Central Ltd
ISSN1475-2891
Digital Object Identifier (DOI)https://doi.org/10.1186/s12937-020-00611-2
Scopus EID2-s2.0-85091265761
Open accessPublished as ‘gold’ (paid) open access
Research or scholarlyResearch
Page range1-16
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online19 Sep 2020
Publication process dates
Accepted24 Aug 2020
Deposited08 Jul 2021
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