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Genetic and Environmental Influences on the Correlations between Traits of Metabolic Syndrome and CKD.

ABSTRACT: BACKGROUND AND OBJECTIVES:Metabolic syndrome is a cluster of risk factors associated with CKD. By studying the genetic and environmental influences on how traits of metabolic syndrome correlate with CKD, the understanding of the etiological relationships can be improved. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS:From the population-based TwinGene project within the Swedish Twin Registry, 4721 complete twin pairs (9442 European ancestry participants) were included in this cross-sectional twin study. Metabolic syndrome-related continuous traits were measured, and the binary components as well as the status of metabolic syndrome were defined according to the National Cholesterol Education Program-Adult Treatment Panel III. The eGFR was calculated by cystatin C-based equations from the CKD epidemiology collaboration group, and CKD was defined by eGFR<60 ml/min per 1.73 m2. Genetic and environmental contributions to the correlations between traits of metabolic syndrome and CKD were estimated by using twin-based bivariate structural equation models. RESULTS:The correlation between metabolic syndrome and eGFR-defined CKD was 0.16 (95% confidence interval [95% CI], 0.12 to 0.20), out of which 51% (95% CI, 12% to 90%) was explained by genes, whereas 15% (95% CI, 0% to 42%) and 34% (95% CI, 16% to 52%) was explained by the shared and nonshared environment, respectively. The genetic and environmental correlations between metabolic syndrome and CKD were 0.29 (95% CI, 0.07 to 0.51) and 0.27 (95% CI, 0.13 to 0.41), respectively. For the correlation between abdominal obesity and eGFR, 69% (95% CI, 10% to 100%) was explained by genes and 23% (95% CI, 5% to 41%) was explained by environment. The genetic correlation between abdominal obesity and eGFR was -0.30 (95% CI, -0.54 to -0.06), whereas the environmental correlation was -0.14 (95% CI, -0.22 to -0.06). CONCLUSIONS:Both genes and environment contribute to the correlation between metabolic syndrome and eGFR-defined CKD. The genetic contribution is particularly important to the correlation between abdominal obesity and eGFR.

SUBMITTER: Chen X

PROVIDER: S-EPMC6832046 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Genetic and Environmental Influences on the Correlations between Traits of Metabolic Syndrome and CKD.

Chen Xu X Bhuiyan Istiak I Kuja-Halkola Ralf R Magnusson Patrik K E PKE Svensson Per P

Clinical journal of the American Society of Nephrology : CJASN 20190910 11

<h4>Background and objectives</h4>Metabolic syndrome is a cluster of risk factors associated with CKD. By studying the genetic and environmental influences on how traits of metabolic syndrome correlate with CKD, the understanding of the etiological relationships can be improved.<h4>Design, setting, participants, & measurements</h4>From the population-based TwinGene project within the Swedish Twin Registry, 4721 complete twin pairs (9442 European ancestry participants) were included in this cross ...[more]

PMID: 31506278

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Project description:BACKGROUND:The variation and covariation for many cardiometabolic traits have been decomposed into genetic and environmental fractions, by using twin or single-nucleotide polymorphism (SNP) models. However, differences in population, age, sex, and other factors hamper the comparison between twin- and SNP-based estimates. METHODS AND RESULTS:Twenty-four cardiometabolic traits and 700,000 genotyped SNPs were available in the study base of 10 682 twins from TwinGene cohort. For the 27 highly correlated pairs (absolute phenotypic correlation coefficient ?0.40), twin-based bivariate structural equation models were performed in 3870 complete twin pairs, and SNP-based bivariate genomic relatedness matrix restricted maximum likelihood methods were performed in 5779 unrelated individuals. In twin models, the model including additive genetic variance and unique/nonshared environmental variance was the best-fitted model for 7 pairs (5 of them were between blood pressure traits); the model including additive genetic variance, common/shared environmental variance, and unique/nonshared environmental variance components was best fitted for 4 pairs, but estimates of shared environment were close to zero; and the model including additive genetic variance, dominant genetic variance, and unique/nonshared environmental variance was best fitted for 16 pairs, in which significant dominant genetic effects were identified for 13 pairs (including all 9 obesity-related pairs). However, SNP models did not identify significant estimates of dominant genetic effects for any pairs. In the paired t test, twin- and SNP-based estimates of additive genetic correlation were not significantly different (both were 0.67 on average), whereas the nonshared environmental correlations from these 2 models differed slightly from each other (on average, twin-based estimate=0.64 and SNP-based estimate=0.68). CONCLUSIONS:Beside additive genetic effects and nonshared environment, nonadditive genetic effects (dominance) also contribute to the covariation between certain cardiometabolic traits (especially for obesity-related pairs); contributions from the shared environment seem to be weak for their covariation in TwinGene samples.

Dataset Information

Genetic and Environmental Influences on the Correlations between Traits of Metabolic Syndrome and CKD.

Publications

Genetic and Environmental Influences on the Correlations between Traits of Metabolic Syndrome and CKD.

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