Novel Approach to Examine the Interactive Role of Dietary, Lifestyle, and Genetic Factors on Cardiometabolic Risk Public Deposited

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  • March 22, 2019
  • Zubair, Niha
    • Affiliation: Gillings School of Global Public Health, Department of Nutrition
  • With modernization, cardiometabolic (CM) disease risk has increased in low- and middle-income countries. We sought to understand CM risk in these settings, both in young adults, for whom prevention is an important goal, and in an older population, for whom risk is better established. Differences in the prevalence and patterns of co-occurrence of CM risk factors likely reflect variation in diet, lifestyle, and genetics. Innovative methods are needed to better understand the synergistic effects between these modifiable and non-modifiable factors on CM risk. We evaluated the patterning of CM risk factors in a young adult population participating in the 2005 Cebu Longitudinal Health and Nutrition Survey (CLHNS) (n = 1,621). Using cluster analysis, we grouped individuals by CM biomarkers and then assessed how diet, adiposity, and environment predicted these CM clusters. Despite the population's youth and leanness, cluster analysis found patterns of CM risk. While measures of adiposity strongly predicted cluster membership, diet and environment also independently predicted clustering. Next, we aimed to capture the complex relationship between genetics, adiposity, and CM risk. Here we created genetic risk scores for inflammatory and lipid traits; these scores combined the relatively small additive effects of individual SNPs in Filipino women in the 2005 CLHNS (n= 1,649). We found that each genetic risk score explained a greater proportion of variance in the specified CM trait than any given individual SNP. In addition, we observed that the triglyceride genetic risk score interacted with measures of adiposity to influence triglyceride levels. Lastly, we used cluster analysis to identify groups of women from the 2005 CLHNS (n= 1,584), who shared similar patterns of genetic risk across multiple CM phenotypes. Here we found five distinct genetic risk clusters. These genetic risk clusters along with measures of adiposity and dietary factors, predicted CM trait levels and patterns in this population. In conclusion, our results suggest that examining the synergistic influence of modifiable and non-modifiable factors on CM traits and patterns can help provide insight into the etiology of CM diseases, and thus potentially inform targeted prevention efforts.
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  • In Copyright
  • Adair, Linda
  • Doctor of Philosophy
Graduation year
  • 2013

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