Maggie A Stanislawski1, Jessica Shaw2, Elizabeth Litkowski3, Ethan M Lange2, Wei Perng4, Dana Dabelea5, Leslie A Lange6. 1. Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO. Electronic address: maggie.stanislawski@cuanschutz.edu. 2. Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO. 3. Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO. 4. Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO. 5. Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO. 6. Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO.
Abstract
OBJECTIVE: To assess the importance of genetic and nongenetic risk factors contributing to hepatic fat accumulation in a multiethnic population of youth. STUDY DESIGN: We investigated the relationship between genetic factors and hepatic fat fraction (HFF) in 347 children aged 12.5-19.5 years. We examined 5 single nucleotide polymorphisms previously associated with HFF and a weighted genetic risk score (GRS) and examined how these associations varied with ethnicity (Hispanic vs non-Hispanic white) and body mass index (BMI) category. We also compared how much variation in HFF was explained by genetic factors vs cardiometabolic factors (BMI z-score and the Homeostasis Model of Insulin Resistance) or diet. RESULTS: PNPLA3 rs738409 and the GRS were each associated with HFF among Hispanic (β = 0.39; 95% CI, 0.16-0.62; P = .001; and β = 0.20; 95% CI, 0.05-0.34; P = .007, respectively) but not non-Hispanic white (β = 0.04; 95% CI, -0.18 to 0.26; P = .696; and β = 0.03; 95% CI, -0.09 to 0.14; P = .651, respectively) youth. Cardiometabolic risk factors explained more of the variation in HFF than genetic risk factors among non-lean Hispanic individuals (27.2% for cardiometabolic markers vs 6.4% for rs738409 and 4.3% for the GRS), and genetic risk factors were more important among lean individuals (2.7% for cardiometabolic markers vs 12.6% for rs738409 and 4.4% for the GRS). CONCLUSIONS: Poor cardiometabolic health may be more important than genetic factors when predicting HFF in overweight and obese young populations. Genetic risk is an important contributor to pediatric HFF among lean Hispanics, but further studies are necessary to elucidate the strength of the association between genetic risk and HFF in non-Hispanic white youth. Published by Elsevier Inc.
OBJECTIVE: To assess the importance of genetic and nongenetic risk factors contributing to hepatic fat accumulation in a multiethnic population of youth. STUDY DESIGN: We investigated the relationship between genetic factors and hepatic fat fraction (HFF) in 347 children aged 12.5-19.5 years. We examined 5 single nucleotide polymorphisms previously associated with HFF and a weighted genetic risk score (GRS) and examined how these associations varied with ethnicity (Hispanic vs non-Hispanic white) and body mass index (BMI) category. We also compared how much variation in HFF was explained by genetic factors vs cardiometabolic factors (BMI z-score and the Homeostasis Model of Insulin Resistance) or diet. RESULTS:PNPLA3rs738409 and the GRS were each associated with HFF among Hispanic (β = 0.39; 95% CI, 0.16-0.62; P = .001; and β = 0.20; 95% CI, 0.05-0.34; P = .007, respectively) but not non-Hispanic white (β = 0.04; 95% CI, -0.18 to 0.26; P = .696; and β = 0.03; 95% CI, -0.09 to 0.14; P = .651, respectively) youth. Cardiometabolic risk factors explained more of the variation in HFF than genetic risk factors among non-lean Hispanic individuals (27.2% for cardiometabolic markers vs 6.4% for rs738409 and 4.3% for the GRS), and genetic risk factors were more important among lean individuals (2.7% for cardiometabolic markers vs 12.6% for rs738409 and 4.4% for the GRS). CONCLUSIONS: Poor cardiometabolic health may be more important than genetic factors when predicting HFF in overweight and obese young populations. Genetic risk is an important contributor to pediatric HFF among lean Hispanics, but further studies are necessary to elucidate the strength of the association between genetic risk and HFF in non-Hispanic white youth. Published by Elsevier Inc.
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