Dataset Information

Four genetic loci influencing electrocardiographic indices of left ventricular hypertrophy.

ABSTRACT: BACKGROUND:Presence of left ventricular hypertrophy on an ECG (ECG-LVH) is widely assessed clinically and provides prognostic information in some settings. There is evidence for significant heritability of ECG-LVH. We conducted a large-scale gene-centric association analysis of 4 commonly measured indices of ECG-LVH. METHODS AND RESULTS:We calculated the Sokolow-Lyon index, Cornell product, 12-lead QRS voltage sum, and 12-lead QRS voltage product in 10 256 individuals from 3 population-based cohorts and typed their DNA using a customized gene array (the Illumina HumanCVD BeadChip 50K array), containing 49 094 genetic variants in ?2100 genes of cardiovascular relevance. We followed-up promising associations in 11 777 additional individuals. We identified and replicated 4 loci associated with ECG-LVH indices: 3p22.2 (SCN5A, rs6797133, P=1.22 × 10(-7)) with Cornell product and 12q13.3 (PTGES3, rs2290893, P=3.74 × 10(-8)), 15q25.2 (NMB, rs2292462, P=3.23 × 10(-9)), and 15q26.3 (IGF1R, rs4966014, P=1.26 × 10(-7)) with the 12-lead QRS voltage sum. The odds ratio of being in the top decile for the 12-lead QRS voltage sum for those carrying 6 trait-raising alleles at the 12q13.3, 15q25.2, and 15q26.3 loci versus those carrying 0 to 1 alleles was 1.60 (95% CI: 1.20 to 2.29). Lead single-nucleotide polymorphisms at the 12q13.3 and 15q25.2 loci showed significant expression quantitative trait loci effects in monocytes. CONCLUSIONS:These findings provide novel insights into the genetic determination of ECG-LVH. The findings could help to improve our understanding of the mechanisms determining this prognostically important trait.

SUBMITTER: Shah S

PROVIDER: S-EPMC5291170 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Four genetic loci influencing electrocardiographic indices of left ventricular hypertrophy.

Shah Sonia S Nelson Christopher P CP Gaunt Tom R TR van der Harst Pim P Barnes Timothy T Braund Peter S PS Lawlor Debbie A DA Casas Juan-Pablo JP Padmanabhan Sandosh S Drenos Fotios F Kivimaki Mika M Talmud Philippa J PJ Humphries Steve E SE Whittaker John J Morris Richard W RW Whincup Peter H PH Dominiczak Anna A Munroe Patricia B PB Johnson Toby T Goodall Alison H AH Cambien Francois F Diemert Patrick P Hengstenberg Christian C Ouwehand Willem H WH Felix Janine F JF Glazer Nicole L NL Tomaszewski Maciej M Burton Paul R PR Tobin Martin D MD van Veldhuisen Dirk J DJ de Boer Rudolf A RA Navis Gerjan G van Gilst Wiek H WH Mayosi Bongani M BM Thompson John R JR Kumari Meena M MacFarlane Peter W PW Day Ian N M IN Hingorani Aroon D AD Samani Nilesh J NJ

Circulation. Cardiovascular genetics 20110930 6

<h4>Background</h4>Presence of left ventricular hypertrophy on an ECG (ECG-LVH) is widely assessed clinically and provides prognostic information in some settings. There is evidence for significant heritability of ECG-LVH. We conducted a large-scale gene-centric association analysis of 4 commonly measured indices of ECG-LVH.<h4>Methods and results</h4>We calculated the Sokolow-Lyon index, Cornell product, 12-lead QRS voltage sum, and 12-lead QRS voltage product in 10 256 individuals from 3 popul ...[more]

PMID: 21965548

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Project description:The electrocardiogram (ECG) is the most common tool used to predict left ventricular hypertrophy (LVH). However, it is limited by its low accuracy (<60%) and sensitivity (30%). We set forth the hypothesis that the Machine Learning (ML) C5.0 algorithm could optimize the ECG in the prediction of LVH by echocardiography (Echo) while also establishing ECG-LVH phenotypes. We used Echo as the standard diagnostic tool to detect LVH and measured the ECG abnormalities found in Echo-LVH. We included 432 patients (power = 99%). Of these, 202 patients (46.7%) had Echo-LVH and 240 (55.6%) were males. We included a wide range of ventricular masses and Echo-LVH severities which were classified as mild (n = 77, 38.1%), moderate (n = 50, 24.7%) and severe (n = 75, 37.1%). Data was divided into a training/testing set (80%/20%) and we applied logistic regression analysis on the ECG measurements. The logistic regression model with the best ability to identify Echo-LVH was introduced into the C5.0 ML algorithm. We created multiple decision trees and selected the tree with the highest performance. The resultant five-level binary decision tree used only six predictive variables and had an accuracy of 71.4% (95%CI, 65.5-80.2), a sensitivity of 79.6%, specificity of 53%, positive predictive value of 66.6% and a negative predictive value of 69.3%. Internal validation reached a mean accuracy of 71.4% (64.4-78.5). Our results were reproduced in a second validation group and a similar diagnostic accuracy was obtained, 73.3% (95%CI, 65.5-80.2), sensitivity (81.6%), specificity (69.3%), positive predictive value (56.3%) and negative predictive value (88.6%). We calculated the Romhilt-Estes multilevel score and compared it to our model. The accuracy of the Romhilt-Estes system had an accuracy of 61.3% (CI95%, 56.5-65.9), a sensitivity of 23.2% and a specificity of 94.8% with similar results in the external validation group. In conclusion, the C5.0 ML algorithm surpassed the accuracy of current ECG criteria in the detection of Echo-LVH. Our new criteria hinge on ECG abnormalities that identify high-risk patients and provide some insight on electrogenesis in Echo-LVH.

Project description:ImportanceCompared with white individuals, black individuals have increased electrocardiographic voltage and an increased prevalence of concentric left ventricular (LV) hypertrophy. Whether environmental or genetic factors lead to these racial differences is unknown.ObjectiveTo determine whether proportion of genetically determined African ancestry among self-reported black individuals is associated with increased electrocardiographic voltage and concentric LV hypertrophy (LVH).Design, setting, and participantsThe Dallas Heart Study is a probability-based cohort study of English- or Spanish-speaking Dallas County, Texas, residents, with deliberate oversampling of black individuals. Participants underwent extensive phenotyping, which included electrocardiography (ECG), cardiac magnetic resonance imaging (CMR), and dual-energy radiography absorptiometry (DEXA) at a single center. Participants aged 18 to 65 years who enrolled in the Dallas Heart Study between July 2000 and December 2002, self-identified as black (n = 1251) or white (n = 826), and had ECG, CMR, and DEXA data were included in this analysis. Data were analyzed from June 2017 to September 2018.ExposuresProportion of African ancestry.Main outcomes and measuresElectrocardiographic voltage (12-lead and 9-lead) and markers of concentric LVH as assessed by CMR (LV concentricity0.67 [LV mass/end-diastolic volume0.67], LV wall thickness [LVWT], and prevalent LVH [defined by LV mass/height2.7]).ResultsOf the 2077 participants included in the study, 1138 (54.8%) were women, and the mean (SD) age was 45.2 (9.9) years. Black race and African ancestry were individually associated with increased ECG voltage, LV concentricity0.67, LVWT, and prevalent LVH in multivariable analyses adjusting for age, sex, systolic blood pressure, antihypertensive medication use, and body composition. When African ancestry and black race were entered together into multivariable models, African ancestry but not black race remained associated with ECG voltage, LVWT, LV concentricity0.67, and prevalent LVH. Among black participants, African ancestry remained associated with these 4 phenotypes (12-lead voltage: β, 0.05; P = .04; LVWT: β, 0.05; P = .02; LV concentricty0.67: β, 0.05; P = .045; prevalent LVH: odds ratio, 1.2; 95% CI, 1.03-1.4; P = .02).Conclusions and relevanceGenetically determined African ancestry was associated with electrocardiographic voltage, measures of concentric LV remodeling, and prevalent LVH. These data support a genetic basis related to African ancestry for the increased prevalence of these cardiovascular traits in black individuals.

Dataset Information

Four genetic loci influencing electrocardiographic indices of left ventricular hypertrophy.

Publications

Four genetic loci influencing electrocardiographic indices of left ventricular hypertrophy.

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