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:BackgroundLeft ventricular hypertrophy (LVH) strongly predicts cardiovascular diseases (CVD) and death. One-fourth of Thai adults suffer from hypertension. Nevertheless, the information on LVH among Thai patients with hypertension is not well characterized. We aimed to identify the prevalence and factors associated with electrocardiographic LVH (ECG-LVH) among patients with hypertension in Thailand.MethodsThe present study obtained the dataset from the Thailand Diabetes Mellitus/Hypertension study, which included hypertension patients aged 20 years and older receiving continuous care at outpatient clinics in hospitals nationwide in 2011-2015 and 2018. Meanwhile, those without a record of 12-lead electrocardiography (ECG) were excluded from the analysis. ECG-LVH was defined as the LVH noted regarding ECG interpretation in the medical records. Multivariable logistic regression analysis was utilized for determining factors associated with ECG-LVH and presented as the adjusted odds ratio (AOR) and 95% confidence interval (CI).ResultsFrom 226,420 hypertensive patients in the Thailand Diabetes Mellitus/Hypertension study, 38,807 individuals (17.1%) with ECG data recorded were included in the analysis. The mean age was 64.8 ± 11.5 years, and 62.2% were women. Overall, 1,557 study participants had ECG-LVH, with an estimated prevalence of 4.0% (95% CI, 3.8-4.2%). Age-adjusted ECG-LVH prevalence among women and men was 3.4 and 5.1%, respectively (P < 0.001). Multivariable analysis determined factors associated with ECG-LVH, including being men (AOR, 1.49; 95% CI, 1.31-1.69), individuals aged 70 to 79 years (AOR, 1.56; 95% CI, 1.20-2.02) and ≥ 80 years (AOR, 2.10; 95% CI, 1.58-2.78) compared to individuals aged less than 50 years, current smokers (AOR, 1.26; 95% CI, 1.09-1.46) compared to those who never smoked, systolic blood pressure ≥ 140 mmHg and diastolic blood pressure ≥ 90 mmHg (AOR, 1.58; 95% CI, 1.30-1.92) compared to systolic blood pressure < 140 mmHg and diastolic blood pressure < 90 mmHg.ConclusionsThe current study illustrated the prevalence of ECG-LVH among Thai patients with hypertension who had ECG recorded and identified high-risk groups who tended to have ECG-LVH. The findings underscore the need for targeted interventions, particularly among high-risk groups such as older individuals, men, and current smokers, to address modifiable factors associated with ECG-LVH.

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.

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Four genetic loci influencing electrocardiographic indices of left ventricular hypertrophy.

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Four genetic loci influencing electrocardiographic indices of left ventricular hypertrophy.

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