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

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

SUBMITTER: Shang X 

PROVIDER: S-EPMC7502204 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature