The electrocardiographically measured QT interval (QT) is heritable and its prolongation is an established risk factor for several cardiovascular diseases. Yet, most QT genetic studies have been ...performed in European ancestral populations, possibly reducing their global relevance.
To leverage diversity and improve biological insight, we fine mapped 16 of the 35 previously identified QT loci (46%) in populations of African American (n = 12,410) and Hispanic/Latino (n = 14,837) ancestry.
Racial/ethnic-specific multiple linear regression analyses adjusted for heart rate and clinical covariates were examined separately and in combination after inverse-variance weighted trans-ethnic meta-analysis.
The 16 fine-mapped QT loci included on the Illumina Metabochip represented 21 independent signals, of which 16 (76%) were significantly (P-value≤9.1×10
) associated with QT. Through sequential conditional analysis we also identified three trans-ethnic novel SNPs at ATP1B1, SCN5A-SCN10A, and KCNQ1 and three Hispanic/Latino-specific novel SNPs at NOS1AP and SCN5A-SCN10A (two novel SNPs) with evidence of associations with QT independent of previous identified GWAS lead SNPs. Linkage disequilibrium patterns helped to narrow the region likely to contain the functional variants at several loci, including NOS1AP, USP50-TRPM7, and PRKCA, although intervals surrounding SLC35F1-PLN and CNOT1 remained broad in size (>100 kb). Finally, bioinformatics-based functional characterization suggested a regulatory function in cardiac tissues for the majority of independent signals that generalized and the novel SNPs.
Our findings suggest that a majority of identified SNPs implicate gene regulatory dysfunction in QT prolongation, that the same loci influence variation in QT across global populations, and that additional, novel, population-specific QT signals exist.
Background: With modernization, cardiometabolic disease risk has increased in low and middle-income countries. To better understand cardiometabolic disease etiology, we evaluated the patterning risk ...factors in a susceptible young adult population.Methods and Results: Participants included 1,621 individuals from the 2005 Cebu Longitudinal Health and Nutrition Survey. Using cluster analysis, we grouped individuals by the following biomarkers: triglycerides, HDL and LDL cholesterol, C-reactive protein, blood pressure, homeostasis model assessment of insulin resistance, and fasting glucose. Using multinomial logistic regression models we assessed how diet, adiposity, and environment predicted cardiometabolic clusters. We identified 5 distinct sex-specific clusters: 1) Healthy/High HDL cholesterol (with the addition of high LDL cholesterol in women); 2) Healthy/Low blood pressure; 3) High blood pressure; 4) Insulin resistant/High triglycerides; and 5) High C-reactive protein. Low HDL cholesterol was the most prevalent risk factor (63%). In men and women, a higher intake of saturated fat increased the likelihood of being in the healthy clusters. In men, poorer environmental hygiene increased the likelihood of being in the High C-reactive protein cluster, compared to the healthy clusters (OR 0.74 95% CI 0.60-0.90 and 0.83 0.70-0.99). Adiposity most strongly associated with membership to the Insulin resistant/high triglyceride cluster.Conclusions: Despite the population's youth and leanness, cluster analysis found patterns of cardiometabolic risk. While adiposity measures predicted clustering, diet and environment also independently predicted clustering, emphasizing the importance of screening lean and overweight individuals for cardiometabolic risk. Finding predictors of risk in early adulthood could help inform prevention efforts for future disease.
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.
With modernization, the Philippines has experienced increasing rates of obesity and related cardiometabolic diseases. Studying how risk factors cluster in individuals may offer insight into ...cardiometabolic disease etiology. We used cluster analysis to group women who share the following cardiometabolic biomarkers: fasting triglycerides, HDL-C and LDL-C, C-reactive protein, systolic and diastolic blood pressure, homeostasis model assessment of insulin resistance, and fasting glucose. Participants included 1,768 women (36-69 years) in the Cebu Longitudinal Health and Nutrition Survey. We identified five distinct clusters characterized by: 1) low levels of all risk factors (except HDL-C and LDL-C) or "healthy"; 2) low HDL-C in the absence of other risk factors; 3) elevated blood pressure; 4) insulin resistance; and 5) high C-reactive protein. We identified predictors of cluster membership using multinomial logistic regression. Clusters differed by age, menopausal status, socioeconomic status, saturated fat intake, and combinations of overweight (BMI >23) and high waist circumference (>80 cm). In comparison to the healthy cluster, overweight women without high waist circumference were more likely to be in the high CRP cluster (OR=2.26, 95% CI=1.24-4.11), while women with high waist circumference and not overweight were more likely to be in the elevated blood pressure (OR=2.56, 95% CI=1.20-5.46) or insulin resistant clusters (OR=4.05, 95% CI=1.39-11.8). In addition, a diet lower in saturated fat uniquely increased the likelihood of membership to the low HDL-C cluster. Cluster analysis identified biologically meaningful groups, predicted by modifiable risk factors; this may have implications for the prevention of cardiometabolic diseases.
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