Optimal Pair Matching Combined with Machine Learning Predicts a Significant Reduction in Myocardial Infarction Risk in African Americans Following Omega-3 Fatty Acid Supplementation.

Journal: Nutrients
PMID:

Abstract

Conflicting clinical trial results on omega-3 highly unsaturated fatty acids (n-3 HUFA) have prompted uncertainty about their cardioprotective effects. While the VITAL trial found no overall cardiovascular benefit from n-3 HUFA supplementation, its substantial African American (AfAm) enrollment provided a unique opportunity to explore racial differences in response to n-3 HUFA supplementation. The current observational study aimed to simulate randomized clinical trial (RCT) conditions by matching 3766 AfAm and 15,553 non-Hispanic White (NHW) individuals from the VITAL trial utilizing propensity score matching to address the limitations related to differences in confounding variables between the two groups. Within matched groups (3766 AfAm and 3766 NHW), n-3 HUFA supplementation's impact on myocardial infarction (MI), stroke, and cardiovascular disease (CVD) mortality was assessed. A weighted decision tree analysis revealed belonging to the n-3 supplementation group as the most significant predictor of MI among AfAm but not NHW. Further logistic regression using the LASSO method and bootstrap estimation of standard errors indicated n-3 supplementation significantly lowered MI risk in AfAm (OR 0.17, 95% CI [0.048, 0.60]), with no such effect in NHW. This study underscores the critical need for future RCT to explore racial disparities in MI risk associated with n-3 HUFA supplementation and highlights potential causal differences between supplementation health outcomes in AfAm versus NHW populations.

Authors

  • Shudong Sun
    Department of Mathematics, University of Arizona, Tucson, AZ 85721, USA.
  • Aki Hara
    School of Nutritional Sciences and Wellness, College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ 85719, USA.
  • Laurel Johnstone
    School of Nutritional Sciences and Wellness, College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ 85719, USA.
  • Brian Hallmark
    BIO5 Institute, University of Arizona, Tucson, AZ 85719, USA.
  • Joseph C Watkins
    Department of Mathematics, University of Arizona, Tucson, AZ 85721, USA.
  • Cynthia A Thomson
    Department of Health Promotion Sciences, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, USA.
  • Susan M Schembre
    Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA.
  • Susan Sergeant
    Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
  • Jason G Umans
    MedStar Health Research Institute, Field Studies Division, Hyattsville, MD.
  • Guang Yao
    Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ 85721, USA.
  • Hao Helen Zhang
    Statistics and Data Science GIDP, University of Arizona, Tucson, AZ, USA.
  • Floyd H Chilton
    University of Arizona, School of Nutritional Sciences and Wellness, Tucson, AZ, United States.