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Associations between Dietary Patterns and Cardiometabolic Risk Factors—A Longitudinal Analysis among High-Risk Individuals for Diabetes in Kerala, India

  • Yingting Cao
  • , Quan Huynh
  • , Nitin Kapoor
  • , Panniyammakal Jeemon
  • , Gabrielli Thais de Mello
  • , Brian Oldenburg
  • , Kavumpurathu Raman Thankappan
  • , Thirunavukkarasu Sathish

Research output: Contribution to journalArticleResearchpeer-review

Abstract

The association between dietary patterns and cardiometabolic risk factors is not well un-derstood among adults in India, particularly among those at high risk for diabetes. For this study, we analyzed the data of 1007 participants (age 30–60 years) from baseline and year one and two follow-ups from the Kerala Diabetes Prevention Program using multi-level mixed effects modelling. Dietary intake was measured using a quantitative food frequency questionnaire, and dietary patterns were identified using principal component analysis. Two dietary patterns were identified: a “snack-fruit” pattern (highly loaded with fats and oils, snacks, and fruits) and a “rice-meat-refined wheat” pattern (highly loaded with meat, rice, and refined wheat). The “snack-fruit” pattern was associated with increased triglycerides (mg/dL) (β = 6.76, 95% CI 2.63–10.89), while the “rice-meat-refined wheat” pattern was associated with elevated Hb1Ac (percentage) (β = 0.04, 95% CI 0.01, 0.07) and central obesity (OR 1.16, 95% CI 1.01, 1.34). These findings may help inform designing dietary interventions for the prevention of diabetes and improving cardiometabolic risk factors in high-diabetes-risk individuals in the Indian setting.

Original languageEnglish
Article number662
Number of pages13
JournalNutrients
Volume14
Issue number3
DOIs
Publication statusPublished - 1 Feb 2022
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Central obesity
  • Elevated Hb1Ac
  • Multi-level mixed effects modeling
  • Principal component analysis

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