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Circulating metabolites associate with the QT interval in individuals without prevalent cardiovascular disease

医学 QT间期 疾病 内科学 心脏病学 重症监护医学
作者
William J. Young,Mihir M. Sanghvi,Julia Ramírez,Michele Orini,Andrew Tinker,Helen R. Warren,Pier D. Lambiase,Patricia B. Munroe
出处
期刊:European Heart Journal [Oxford University Press]
卷期号:45 (Supplement_1)
标识
DOI:10.1093/eurheartj/ehae666.2742
摘要

Abstract Background Patients with metabolic syndrome have a higher prevalence of heart-rate corrected QT (QTc) interval prolongation. Despite this the effect of circulating metabolites on the QTc is largely unknown and limited to small studies. Purpose To test for association of circulating metabolites with the QTc interval in the UK Biobank study. Methods Participants were identified from the UK Biobank with same day plasma metabolite profiling and electrocardiograms. All 249 metabolites measured underwent quality control and log transformation to account for rightward skew and zero values. 149 metabolites with inter-correlations >0.7 were excluded. Participants taking QTc-prolonging medication or with a history of ischaemic heart disease or heart failure up to 6 months after the visit, were excluded. Participants were allocated to a training group (N=21,610) or an independent test group (N=5,304), if samples were obtained at recruitment or at a second visit, respectively. In the training group, each metabolite was tested separately for association with the QTc in linear regression analyses, adjusted for clinical covariates (age, sex, fasting time, body mass index (BMI), systolic blood pressure (SBP), smoking status, diabetes, lipid-lowering drug use and dietary factors). Significant metabolites (P<5x10-4) underwent validation in the test group. Sex-stratified analyses were also performed. In the training group, variables in two models underwent regularisation by LASSO regression (10-fold cross-validation) to reduce multicollinearity and downweigh less important variables; 1) clinical variables, 2) clinical variables and validated metabolites. Coefficients were used for QTc prediction in the test group. Significance between the models was evaluated by fisher’s transformation of R-squared statistics. Results In the per metabolite multivariate analysis, 56 metabolites were associated with the QTc interval. 19 metabolites validated in the test group. For 14 of these, absolute effect sizes were >5ms when comparing individuals in the top decile of the metabolite distribution verses the bottom (Figure 1), including the ketone body 3-hydroxybutyrate (9ms), and omega-6 fatty acid to total fatty acid ratio (7.2ms). For associations in males and females separately, significant effect size differences (P<0.05) were observed for 9 metabolites (Figure 2). In the training group, the combined LASSO model selected 17 (of 19) metabolites along with age, sex, SBP and BMI. In the test group, this model R-squared was 0.115 compared with 0.081 for clinical covariates alone, representing a significant 41.9% increase in QTc variation explained (P=0.002). Conclusions This study demonstrates clinically relevant metabolite associations with the QTc in individuals without cardiovascular disease and explain a significant proportion of QTc variation. Further investigation is warranted to test for direct effects on cardiac electrophysiology and proarrhythmic potential.Validated metabolitesSignificant sex differences

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