Scientific Reports (May 2024)

Dietary intake, obesity, and metabolic risk factors among children and adolescents in the SEACO-CH20 cross-sectional study

  • Amutha Ramadas,
  • Hussein Rizal,
  • Sutha Rajakumar,
  • Jeevitha Mariapun,
  • Mohamed Shajahan Yasin,
  • Miranda E. G. Armstrong,
  • Tin Tin Su

DOI
https://doi.org/10.1038/s41598-024-61090-7
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 12

Abstract

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Abstract We investigated the association between dietary intake and metabolic risk factors in children and adolescents within a semi-rural Malaysian community. Using an interviewer-led questionnaire, we surveyed 623 participants aged 7–18 from the South East Asia Community Observatory (SEACO). Anthropometric and blood pressure data were collected from all participants, while a subset (n = 162) provided blood samples for biomarker analysis, including fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Metabolic syndrome was determined using the International Diabetes Federation’s Definition of Metabolic Syndrome in Children and Adolescents. Most participants were Malay (66.8%), with a median household income of MYR1,500 and a balanced sex distribution. Cereals, processed foods, beverages, fruits, and vegetables were commonly consumed. Obesity and abdominal obesity were prevalent, affecting more than a third of participants. Adherence to dietary recommendations was generally poor (ranging from 19.9 to 58.1%) and varied across age, sex, and ethnicity. Notably, some food groups displayed unexpected associations with health markers; for instance, fruit consumption was linked to abdominal obesity in children (abdominal obesity vs. normal: 2.4 servings/day vs. 1.6 servings/day). These findings emphasise the necessity of longitudinal studies to explore the complex relationship between diet and long-term health outcomes, including cardiometabolic diseases, while acknowledging the unique challenges posed by the COVID-19 pandemic on data collection and analysis.