Publications by authors named "Mark Eijgelsheim"

48 Publications

Acute kidney injury in patients treated with immune checkpoint inhibitors.

J Immunother Cancer 2021 10;9(10)

Division of Hematology-Oncology, VAGLAHS, Department of Medicine, David Geffen School of Medicine at University of California-Los Angeles, Los Angeles, California, USA.

Background: Immune checkpoint inhibitor-associated acute kidney injury (ICPi-AKI) has emerged as an important toxicity among patients with cancer.

Methods: We collected data on 429 patients with ICPi-AKI and 429 control patients who received ICPis contemporaneously but who did not develop ICPi-AKI from 30 sites in 10 countries. Multivariable logistic regression was used to identify predictors of ICPi-AKI and its recovery. A multivariable Cox model was used to estimate the effect of ICPi rechallenge versus no rechallenge on survival following ICPi-AKI.

Results: ICPi-AKI occurred at a median of 16 weeks (IQR 8-32) following ICPi initiation. Lower baseline estimated glomerular filtration rate, proton pump inhibitor (PPI) use, and extrarenal immune-related adverse events (irAEs) were each associated with a higher risk of ICPi-AKI. Acute tubulointerstitial nephritis was the most common lesion on kidney biopsy (125/151 biopsied patients [82.7%]). Renal recovery occurred in 276 patients (64.3%) at a median of 7 weeks (IQR 3-10) following ICPi-AKI. Treatment with corticosteroids within 14 days following ICPi-AKI diagnosis was associated with higher odds of renal recovery (adjusted OR 2.64; 95% CI 1.58 to 4.41). Among patients treated with corticosteroids, early initiation of corticosteroids (within 3 days of ICPi-AKI) was associated with a higher odds of renal recovery compared with later initiation (more than 3 days following ICPi-AKI) (adjusted OR 2.09; 95% CI 1.16 to 3.79). Of 121 patients rechallenged, 20 (16.5%) developed recurrent ICPi-AKI. There was no difference in survival among patients rechallenged versus those not rechallenged following ICPi-AKI.

Conclusions: Patients who developed ICPi-AKI were more likely to have impaired renal function at baseline, use a PPI, and have extrarenal irAEs. Two-thirds of patients had renal recovery following ICPi-AKI. Treatment with corticosteroids was associated with improved renal recovery.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/jitc-2021-003467DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496384PMC
October 2021

Diagnostic Yield of Next-Generation Sequencing in Patients With Chronic Kidney Disease of Unknown Etiology.

Front Genet 2019 13;10:1264. Epub 2019 Dec 13.

Department of Internal Medicine, Division of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.

Advances in next-generation sequencing (NGS) techniques, including whole exome sequencing, have facilitated cost-effective sequencing of large regions of the genome, enabling the implementation of NGS in clinical practice. Chronic kidney disease (CKD) is a major contributor to global burden of disease and is associated with an increased risk of morbidity and mortality. CKD can be caused by a wide variety of primary renal disorders. In about one in five CKD patients, no primary renal disease diagnosis can be established. Moreover, recent studies indicate that the clinical diagnosis may be incorrect in a substantial number of patients. Both the absence of a diagnosis or an incorrect diagnosis can have therapeutic implications. Genetic testing might increase the diagnostic accuracy in patients with CKD, especially in patients with unknown etiology. The diagnostic utility of NGS has been shown mainly in pediatric CKD cohorts, while emerging data suggest that genetic testing can also be a valuable diagnostic tool in adults with CKD. In addition to its implications for unexplained CKD, NGS can contribute to the diagnostic process in kidney diseases with an atypical presentation, where it may lead to reclassification of the primary renal disease diagnosis. So far, only a few studies have reported on the diagnostic yield of NGS-based techniques in patients with unexplained CKD. Here, we will discuss the potential diagnostic role of gene panels and whole exome sequencing in pediatric and adult patients with unexplained and atypical CKD.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fgene.2019.01264DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923268PMC
December 2019

Corrigendum: Normal Values of Corrected Heart-Rate Variability in 10-Second Electrocardiograms for All Ages.

Front Physiol 2019;10:1373. Epub 2019 Nov 1.

Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.

[This corrects the article DOI: 10.3389/fphys.2018.00424.].
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fphys.2019.01373DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839701PMC
November 2019

Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits.

Eur J Hum Genet 2019 06 24;27(6):952-962. Epub 2019 Jan 24.

Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital-The Capital Region, Copenhagen, Denmark.

Genome-wide association studies (GWAS) of quantitative electrocardiographic (ECG) traits in large consortia have identified more than 130 loci associated with QT interval, QRS duration, PR interval, and heart rate (RR interval). In the current study, we meta-analyzed genome-wide association results from 30,000 mostly Dutch samples on four ECG traits: PR interval, QRS duration, QT interval, and RR interval. SNP genotype data was imputed using the Genome of the Netherlands reference panel encompassing 19 million SNPs, including millions of rare SNPs (minor allele frequency < 5%). In addition to many known loci, we identified seven novel locus-trait associations: KCND3, NR3C1, and PLN for PR interval, KCNE1, SGIP1, and NFKB1 for QT interval, and ATP2A2 for QRS duration, of which six were successfully replicated. At these seven loci, we performed conditional analyses and annotated significant SNPs (in exons and regulatory regions), demonstrating involvement of cardiac-related pathways and regulation of nearby genes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41431-018-0295-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6777533PMC
June 2019

A comprehensive evaluation of the genetic architecture of sudden cardiac arrest.

Eur Heart J 2018 11;39(44):3961-3969

Department of Statistics, University of Auckland, Private Bag 92014, Auckland, New Zealand.

Aims: Sudden cardiac arrest (SCA) accounts for 10% of adult mortality in Western populations. We aim to identify potential loci associated with SCA and to identify risk factors causally associated with SCA.

Methods And Results: We carried out a large genome-wide association study (GWAS) for SCA (n = 3939 cases, 25 989 non-cases) to examine common variation genome-wide and in candidate arrhythmia genes. We also exploited Mendelian randomization (MR) methods using cross-trait multi-variant genetic risk score associations (GRSA) to assess causal relationships of 18 risk factors with SCA. No variants were associated with SCA at genome-wide significance, nor were common variants in candidate arrhythmia genes associated with SCA at nominal significance. Using cross-trait GRSA, we established genetic correlation between SCA and (i) coronary artery disease (CAD) and traditional CAD risk factors (blood pressure, lipids, and diabetes), (ii) height and BMI, and (iii) electrical instability traits (QT and atrial fibrillation), suggesting aetiologic roles for these traits in SCA risk.

Conclusions: Our findings show that a comprehensive approach to the genetic architecture of SCA can shed light on the determinants of a complex life-threatening condition with multiple influencing factors in the general population. The results of this genetic analysis, both positive and negative findings, have implications for evaluating the genetic architecture of patients with a family history of SCA, and for efforts to prevent SCA in high-risk populations and the general community.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/eurheartj/ehy474DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6247663PMC
November 2018

PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity.

Nat Commun 2018 07 25;9(1):2904. Epub 2018 Jul 25.

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA.

Electrocardiographic PR interval measures atrio-ventricular depolarization and conduction, and abnormal PR interval is a risk factor for atrial fibrillation and heart block. Our genome-wide association study of over 92,000 European-descent individuals identifies 44 PR interval loci (34 novel). Examination of these loci reveals known and previously not-yet-reported biological processes involved in cardiac atrial electrical activity. Genes in these loci are over-represented in cardiac disease processes including heart block and atrial fibrillation. Variants in over half of the 44 loci were associated with atrial or blood transcript expression levels, or were in high linkage disequilibrium with missense variants. Six additional loci were identified either by meta-analysis of ~105,000 African and European-descent individuals and/or by pleiotropic analyses combining PR interval with heart rate, QRS interval, and atrial fibrillation. These findings implicate developmental pathways, and identify transcription factors, ion-channel genes, and cell-junction/cell-signaling proteins in atrio-ventricular conduction, identifying potential targets for drug development.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-018-04766-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6060178PMC
July 2018

Exome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6.

Genome Biol 2018 07 17;19(1):87. Epub 2018 Jul 17.

Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.

Background: Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear.

Results: Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction.

Conclusions: Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13059-018-1457-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6048820PMC
July 2018

ExomeChip-Wide Analysis of 95 626 Individuals Identifies 10 Novel Loci Associated With QT and JT Intervals.

Circ Genom Precis Med 2018 01;11(1):e001758

From the Predoctoral Training Program in Human Genetics (N.A.B.) and McKusick-Nathans Institute of Genetic Medicine (N.A.B., D.E.A.), Johns Hopkins School of Medicine, Baltimore, MD; Cardiovascular Health Research Unit, Department of Medicine (J.A.B., J.C.B., T.A., N.S.), Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), and Cardiovascular Health Research Unit, Department of Epidemiology (S.R.H.), University of Washington, Seattle; Icelandic Heart Association, Kopavogur (A.V.S., V.G.); Faculty of Medicine, University of Iceland, Reykavik (A.V.S., V.G.); Clinical Pharmacology Department, William Harvey Research Institute, Barts and London School of Medicine and Dentistry (H.R.W., P.B.M.) and NIHR Barts Cardiovascular Biomedical Research Unit (H.R.W., P.B.M.), Queen Mary University of London, United Kingdom; Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, MA (H.L., Z.X.); School for Cardiovascular Diseases, Maastricht Center for Systems Biology and Department of Biochemistry, Maastricht University, The Netherlands (A.I.); Genetic Epidemiology Unit, Department of Epidemiology (A.I., C.M.v.D.) and Department of Medical Informatics (J.A.K.), Erasmus University Medical Center, Rotterdam, The Netherlands; Biostatistics Department, Boston University School of Public Health, MA (C.-T.L.); Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine (J.M., C.H.), Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine (A.C.), and Usher Institute for Population Health Sciences and Informatics (I.R.), University of Edinburgh, United Kingdom; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA (F.R., P.T.E., S.A.L., J.R.); Center for Human Genetic Research (F.R., J.R.), Cardiovascular Research Center (P.L.H., P.T.E., S.A.L.), and Center for Human Genetic Research and Cardiovascular Research Center (C.H.N.-C.), Harvard Medical School, Massachusetts General Hospital, Boston; Department of Cardiovascular Sciences (L.M.H., C.P.N., N.J.S.) and Genetic Epidemiology Group, Department of Health Sciences (M.D.T.), University of Leicester, United Kingdom; NIHR Leicester Cardiovascular Biomedical Research Unit (L.M.H., C.P.N.) and NIHR Leicester Respiratory Biomedical Research Unit (M.D.T.), Glenfield Hospital, United Kingdom; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences (N.G., J.B.-J., T.H., O.P.), Department of Clinical Medicine, Faculty of Health and Medical Sciences (A.L.), and Laboratory of Experimental Cardiology (J.K.K.), University of Copenhagen, Denmark; Department of Data Science, School of Population Health (H.M.) and Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Institute of Genetic Epidemiology (M.M.-N.), Institute of Epidemiology II (A.P., M.W., S.P.), Research Unit of Molecular Epidemiology (M.W.), and Institute of Human Genetics (T.M.), Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg; Department of Medicine I, University Hospital Munich, Ludwig-Maximilians University, Germany (M.M.-N., M.F.S., S.K.); DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance (M.M.-N., M.F.S., A.P., T.M., S.K.); MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Scotland (J.E.H.); Department of Cardiology (N.V., R.A.d.B., P.v.d.M., P.v.d.H.) and Department of Internal Medicine (M.E.), University Medical Center Groningen, University of Groningen, The Netherlands; Institute for Translational Genomics and Population Sciences and Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance (X.G., J.Y., Y.-D.I.C.); Department of Clinical Epidemiology (R.L.-G., R.d.M.) and University of Split School of Medicine (I.K., O.P.), University of Split, Croatia; Departments of Cardiology (S.T., J.W.J., A.C.M.), Gerontology and Geriatrics (S.T.), and Public Health and Primary Care (D.O.M.-K.), Leiden University Medical Center, The Netherlands; Departments of Medical Informatics (M.v.d.B.), Epidemiology (B.H.C.S.), and Epidemiology (M.E.), Erasmus MC - University Medical Center Rotterdam, The Netherlands; Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University, Greifswald, Germany (S.W., U.V., G.H.); DZHK (German Centre for Cardiovascular Research), partner site Greifswald (S.W., U.V., H.V., S.B.F., M.D.); Cardiogenetics Lab, Genetics and Molecular Cell Sciences Research Centre, Cardiovascular and Cell Sciences Institute, St George's, University of London, United Kingdom (B.P.P., Y.J.); Division Heart and Lungs, Department of Cardiology, (J.v.S., F.W.A.) and Julius Center for Health Sciences and Primary Care (M.L.B.), University Medical Center Utrecht, The Netherlands; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (J.H., C.K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (L.-P.L., N.M., T.L.); Department of Clinical Physiology, Tampere University Hospital, University of Tampere School of Medicine, Finland (M.K.); Division of Nephrology and Hypertension, Internal Medicine, School of Medicine, University of Utah, Salt Lake City (M.L.); Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA (A.A.); Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston-Salem, NC (E.Z.S.); Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD (T.B.H., L.J.L.); Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, United Kingdom (S.P., A.D.); Institute of Medical Sciences, Aberdeen Biomedical Imaging Centre, University of Aberdeen, United Kingdom (A.D.M.); Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen (A.L.); Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark (A.L.); German Center for Diabetes Research, Neuherberg (A.P.); Institute of Human Genetics, Technische Universität München, Germany (T.M.); Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands (J.W.J.); Interuniversity Cardiology Institute of Netherlands, Utrecht (J.W.J.); Inspectorate of Health Care, Utrecht, The Netherlands (B.H.C.S.); Human Genomics Facility (F.R.) and Human Genotyping Facility (A.U.), Erasmus MC - University Medical Center Rotterdam, The Netherlands; Institute for Community Medicine (H.V.) and Department of Internal Medicine B (S.B.F., M.D.), University Medicine Greifswald, Germany; Department of Twin Research and Genetic Epidemiology, King's College London, United Kingdom (M.M., T.D.S.); Stanford School of Medicine, CA (M.P.); Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital and Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Finland (O.T.R.); Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge (C.H.N.-C.); NIHR Leicester Biomedical Research Unit in Cardiovascular Disease, United Kingdom (N.J.S.); Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht (F.W.A.); and Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, United Kingdom (F.W.A.).

Background: QT interval, measured through a standard ECG, captures the time it takes for the cardiac ventricles to depolarize and repolarize. JT interval is the component of the QT interval that reflects ventricular repolarization alone. Prolonged QT interval has been linked to higher risk of sudden cardiac arrest.

Methods And Results: We performed an ExomeChip-wide analysis for both QT and JT intervals, including 209 449 variants, both common and rare, in 17 341 genes from the Illumina Infinium HumanExome BeadChip. We identified 10 loci that modulate QT and JT interval duration that have not been previously reported in the literature using single-variant statistical models in a meta-analysis of 95 626 individuals from 23 cohorts (comprised 83 884 European ancestry individuals, 9610 blacks, 1382 Hispanics, and 750 Asians). This brings the total number of ventricular repolarization associated loci to 45. In addition, our approach of using coding variants has highlighted the role of 17 specific genes for involvement in ventricular repolarization, 7 of which are in novel loci.

Conclusions: Our analyses show a role for myocyte internal structure and interconnections in modulating QT interval duration, adding to previous known roles of potassium, sodium, and calcium ion regulation, as well as autonomic control. We anticipate that these discoveries will open new paths to the goal of making novel remedies for the prevention of lethal ventricular arrhythmias and sudden cardiac arrest.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1161/CIRCGEN.117.001758DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5992491PMC
January 2018

Normal Values of Corrected Heart-Rate Variability in 10-Second Electrocardiograms for All Ages.

Front Physiol 2018 27;9:424. Epub 2018 Apr 27.

Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.

Heart-rate variability (HRV) measured on standard 10-s electrocardiograms (ECGs) has been associated with increased risk of cardiac and all-cause mortality, but age- and sex-dependent normal values have not been established. Since heart rate strongly affects HRV, its effect should be taken into account. We determined a comprehensive set of normal values of heart-rate corrected HRV derived from 10-s ECGs for both children and adults, covering both sexes. Five population studies in the Netherlands (Pediatric Normal ECG Study, Leiden University Einthoven Science Project, Prevention of Renal and Vascular End-stage Disease Study, Utrecht Health Project, Rotterdam Study) provided 10-s, 12-lead ECGs. ECGs were stored digitally and analyzed by well-validated analysis software. We included cardiologically healthy participants, 42% being men. Their ages ranged from 11 days to 91 years. After quality control, 13,943 ECGs were available. Heart-rate correction formulas were derived using an exponential model. Two time-domain HRV markers were analyzed: the corrected standard deviation of the normal-to-normal RR intervals (SDNNc) and corrected root mean square of successive RR-interval differences (RMSSDc). There was a considerable age effect. For both SDNNc and RMSSDc, the median and the lower limit of normal decreased steadily from birth until old age. The upper limit of normal decreased until the age of 60, but increased markedly after that age. Differences of the median were minimal between men and women. We report the first comprehensive set of normal values for heart-rate corrected 10-s HRV, which can be of value in clinical practice and in further research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fphys.2018.00424DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5934689PMC
April 2018

Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval.

Circ Genom Precis Med 2018 05;11(5):e002037

Section of Computational Biomedicine (H.L.) and Section of Cardiovascular Medicine (E.J.B.), Department of Medicine, Boston University School of Medicine, MA. National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, MA (H.L., E.J.B.). Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, University of Utrecht, The Netherlands (J.v.S., F.W.A.). Icelandic Heart Association, Kopavogur (A.V.S., V.G.). Faculty of Medicine, University of Iceland, Reykjavik (A.V.S., V.G.). Predoctoral Training Program in Human Genetics, McKusick-Nathans Institute of Genetic Medicine (N.A.B.) and McKusick-Nathans Institute of Genetic Medicine (D.E.A.), Johns Hopkins University School of Medicine, Baltimore, MD. William Harvey Research Institute (H.R.W., P.B.M.) and NIHR Barts Cardiovascular Research Unit (H.R.W., P.B.M.), Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom. Cardiovascular Health Research Unit, Department of Medicine (J.A.B., J.C.B., C.M.S.), Department of Biostatistics (K.M.R.), Cardiovascular Health Research Unit, Division of Cardiology, Departments of Medicine and Epidemiology (N.S.), Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services (B.M.P.), and Cardiovascular Health Research Unit, Department of Epidemiology (S.R.H.), University of Washington, Seattle. Center for Human Genetic Research (F. Radmanesh, J.R.) and Cardiovascular Research Center (P.L.H., L.-C.W., H.S.J., W.H., A.H., N.R.T., P.T.E., S.A.L.), Massachusetts General Hospital, Boston. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA (L.-C.W., P.T.E., S.A.L.). Department of Cardiovascular Sciences, University of Leicester, United Kingdom (L.H., C.P.N., N.J.S.). NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, United Kingdom (L.H., C.P.N., N.J.S.). The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences (N.G., J.B.-J., O. Pedersen, T.H.), Laboratory of Experimental Cardiology (J.K.K.), and Department of Clinical Medicine, Faculty of Health and Medical Sciences (A.L.), University of Copenhagen, Denmark. Department of Medicine I, University Hospital Munich, Ludwig Maximilian's University Munich, Germany (M.M.-N., M.F.S., S.K.). Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Germany (K.S.). DZHK (German Cardiovascular Research Centre), Partner Site: Munich Heart Alliance, Germany (M.M.-N., M.F.S., A.P., T.M., S.K.). Institute of Genetic Epidemiology (M.M.-N., K.S.), Institute of Epidemiology II (A.P., M.W.), Research Unit of Molecular Epidemiology (M.W.), and Institute of Human Genetics (T.M.), Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine (T.B., J.M., C.H.) and Usher Institute of Population Health Sciences and Informatics (I.R.), University of Edinburgh, United Kingdom. University of Groningen, University Medical Center Groningen, Department of Cardiology, The Netherlands (N.V., R.A.d.B., P.v.d.M., P.v.d.H.). Institute for Translational Genomics and Population Sciences and Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA (H.J.L., Y.-D.I.C., J.Y., X.G., K.D.T., J.I.R.). Department of Clinical Epidemiology (R.L.-G., D.O.M.-K.) and Department of Cardiology (S.T., J.W.J.), Leiden University Medical Center, The Netherlands. Department of Medical Informatics (M.E.v.d.B.), Human Genomics Facility (F. Rivadeneira), Human Genotyping Facility (A.U.), and Department of Epidemiology (M.E., B.H. Stricker), Erasmus MC, University Medical Center Rotterdam, The Netherlands. Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, Germany (S.W., G.H., U.V.). DZHK (German Cardiovascular Research Centre), Partner Site Greifswald, Germany (S.W., H.V., S.B.F., U.V., M.D.). Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (J.H., C.K.). Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences (L.-P.L., T.L.) and Department of Clinical Physiology, Tampere University Hospital and Faculty of Medicine and Life Sciences (M.K.), University of Tampere, Finland. Department of Data Science (H.M.) and Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson. Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD (T.B.H., L.J.L.). Division of Nephrology and Hypertension, Internal Medicine, School of Medicine, University of Utah, Salt Lake City (M.L.). Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA (A.A.). Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston Salem, NC (E.Z.S.). Medical Research Institute (J.M.C.) and Division of Population Health Sciences (B.H. Smith), Ninewells Hospital and Medical School, University of Dundee, United Kingdom. Department of Medical Informatics (J.A.K.) and Genetic Epidemiology Unit, Department of Epidemiology (C.M.v.D.), Erasmus MC, Rotterdam, The Netherlands. TCM Clinical Basis Institute, Zhejiang Chinese Medicine University, Hangzhou, China (Z.X., C.W.). Division of Cardiology, Department of Medicine, UPMC Heart and Vascular Institute, University of Pittsburgh, PA (J.W.M.). German Center for Diabetes Research, Neuherberg, Germany (A.P.). Institute of Human Genetics, Technische Universität München, Germany (T.M.). Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen (A.L.). Department of Clinical Experimental Research, Rigshospitalet, Denmark (A.L.). British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Scotland (S.P.). Institute for Community Medicine (H.V.) and Department of Internal Medicine B (S.B.F., M.D.), University Medicine Greifswald, Germany. Department of Twin Research and Genetic Epidemiology, King's College London, United Kingdom (M.M., T.D.S.). Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands (M.L.B.). Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, CA (M.P.). Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, and Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Finland (O.T.R.). Kaiser Permanente Washington Health Research Institute, Kaiser Foundation Health Plan of Washington, Seattle (B.M.P., S.R.H.). Faculty of Medicine, University of Split, Croatia (O. Polasek). Cardiogenetics Lab, Genetics and Molecular Cell Sciences Research Centre, Cardiovascular and Cell Sciences Institute, St George's, University of London, Cranmer Terrace, United Kingdom (B.P.P., Y.J.). Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands (F.W.A.). Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom (F.W.A.). Farr Institute of Health Informatics Research and Institute of Health Informatics, University College London, London, United Kingdom; CARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio) and Department of Biochemistry, Maastricht University, The Netherlands (A.I.).

Background: Electrical conduction from the cardiac sinoatrial node to the ventricles is critical for normal heart function. Genome-wide association studies have identified more than a dozen common genetic loci that are associated with PR interval. However, it is unclear whether rare and low-frequency variants also contribute to PR interval heritability.

Methods: We performed large-scale meta-analyses of the PR interval that included 83 367 participants of European ancestry and 9436 of African ancestry. We examined both common and rare variants associated with the PR interval.

Results: We identified 31 genetic loci that were significantly associated with PR interval after Bonferroni correction (<1.2×10), including 11 novel loci that have not been reported previously. Many of these loci are involved in heart morphogenesis. In gene-based analysis, we found that multiple rare variants at (=5.9×10) and (=1.1×10) were associated with PR interval. locus also was implicated in the common variant analysis, whereas was a novel locus.

Conclusions: We identified common variants at 11 novel loci and rare variants within 2 gene regions that were significantly associated with PR interval. Our findings provide novel insights to the current understanding of atrioventricular conduction, which is critical for cardiac activity and an important determinant of health.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1161/CIRCGEN.117.002037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5951629PMC
May 2018

Discovery of novel heart rate-associated loci using the Exome Chip.

Hum Mol Genet 2017 06;26(12):2346-2363

Genetic Epidemiology Unit, Dept. of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, NL.

Resting heart rate is a heritable trait, and an increase in heart rate is associated with increased mortality risk. Genome-wide association study analyses have found loci associated with resting heart rate, at the time of our study these loci explained 0.9% of the variation. This study aims to discover new genetic loci associated with heart rate from Exome Chip meta-analyses.Heart rate was measured from either elecrtrocardiograms or pulse recordings. We meta-analysed heart rate association results from 104 452 European-ancestry individuals from 30 cohorts, genotyped using the Exome Chip. Twenty-four variants were selected for follow-up in an independent dataset (UK Biobank, N = 134 251). Conditional and gene-based testing was undertaken, and variants were investigated with bioinformatics methods.We discovered five novel heart rate loci, and one new independent low-frequency non-synonymous variant in an established heart rate locus (KIAA1755). Lead variants in four of the novel loci are non-synonymous variants in the genes C10orf71, DALDR3, TESK2 and SEC31B. The variant at SEC31B is significantly associated with SEC31B expression in heart and tibial nerve tissue. Further candidate genes were detected from long-range regulatory chromatin interactions in heart tissue (SCD, SLF2 and MAPK8). We observed significant enrichment in DNase I hypersensitive sites in fetal heart and lung. Moreover, enrichment was seen for the first time in human neuronal progenitor cells (derived from embryonic stem cells) and fetal muscle samples by including our novel variants.Our findings advance the knowledge of the genetic architecture of heart rate, and indicate new candidate genes for follow-up functional studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/hmg/ddx113DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5458336PMC
June 2017

52 Genetic Loci Influencing Myocardial Mass.

J Am Coll Cardiol 2016 09;68(13):1435-1448

Department of Medical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands.

Background: Myocardial mass is a key determinant of cardiac muscle function and hypertrophy. Myocardial depolarization leading to cardiac muscle contraction is reflected by the amplitude and duration of the QRS complex on the electrocardiogram (ECG). Abnormal QRS amplitude or duration reflect changes in myocardial mass and conduction, and are associated with increased risk of heart failure and death.

Objectives: This meta-analysis sought to gain insights into the genetic determinants of myocardial mass.

Methods: We carried out a genome-wide association meta-analysis of 4 QRS traits in up to 73,518 individuals of European ancestry, followed by extensive biological and functional assessment.

Results: We identified 52 genomic loci, of which 32 are novel, that are reliably associated with 1 or more QRS phenotypes at p < 1 × 10(-8). These loci are enriched in regions of open chromatin, histone modifications, and transcription factor binding, suggesting that they represent regions of the genome that are actively transcribed in the human heart. Pathway analyses provided evidence that these loci play a role in cardiac hypertrophy. We further highlighted 67 candidate genes at the identified loci that are preferentially expressed in cardiac tissue and associated with cardiac abnormalities in Drosophila melanogaster and Mus musculus. We validated the regulatory function of a novel variant in the SCN5A/SCN10A locus in vitro and in vivo.

Conclusions: Taken together, our findings provide new insights into genes and biological pathways controlling myocardial mass and may help identify novel therapeutic targets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jacc.2016.07.729DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5478167PMC
September 2016

Thyroid Function and Sudden Cardiac Death: A Prospective Population-Based Cohort Study.

Circulation 2016 Sep;134(10):713-22

From Rotterdam Thyroid Center (L.C., R.P.P.), Department of Internal Medicine (L.C., B.H.C.S., R.P.P.), and Department of Epidemiology (L.C., M.E.v.d.B., M.N.N., O.H.F., A.D., A.H., M.E., B.H.C.S., R.P.P.), Erasmus University Medical Center; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (A.H.); Departments of Medical Informatics (P.R.R.) and Cardiology (J.W.D.), Erasmus University Medical Center, Rotterdam, The Netherlands; and Inspectorate of Health Care, Utrecht, The Netherlands (B.H.C.S.).

Background: The association between thyroid function and cardiovascular disease is well established, but no study to date has assessed whether it is a risk factor for sudden cardiac death (SCD). Therefore, we studied the association of thyroid function with SCD in a prospective population-based cohort.

Methods: Participants from the Rotterdam Study ≥45 years with thyroid-stimulating hormone or free thyroxine (FT4) measurements and clinical follow-up were eligible. We assessed the association of thyroid-stimulating hormone and FT4 with the risk of SCD by using an age- and sex-adjusted Cox proportional-hazards model, in all participants and also after restricting the analysis to euthyroid participants (defined by thyroid-stimulating hormone 0.4-4.0 mIU/L). Additional adjustment included cardiovascular risk factors, notably hypertension, serum cholesterol, and smoking. We stratified by age and sex and performed sensitivity analyses by excluding participants with abnormal FT4 values (reference range of 0.85-1.95 ng/dL) and including only witnessed SCDs as outcome. Absolute risks were calculated in a competing risk model by taking death by other causes into account.

Results: We included 10 318 participants with 261 incident SCDs (median follow-up, 9.1 years). Higher levels of FT4 were associated with an increased SCD risk, even in the normal range of thyroid function (hazard ratio, 2.28 per 1 ng/dL FT4; 95% confidence interval, 1.31-3.97). Stratification by age or sex and sensitivity analyses did not change the risk estimates substantially. The absolute 10-year risk of SCD increased in euthyroid participants from 1% to 4% with increasing FT4 levels.

Conclusions: Higher FT4 levels are associated with an increased risk of SCD, even in euthyroid participants.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1161/CIRCULATIONAHA.115.020789DOI Listing
September 2016

Association of Lipid-Related Genetic Variants with the Incidence of Atrial Fibrillation: The AFGen Consortium.

PLoS One 2016 21;11(3):e0151932. Epub 2016 Mar 21.

Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America.

Background: Several studies have shown associations between blood lipid levels and the risk of atrial fibrillation (AF). To test the potential effect of blood lipids with AF risk, we assessed whether previously developed lipid gene scores, used as instrumental variables, are associated with the incidence of AF in 7 large cohorts.

Methods: We analyzed 64,901 individuals of European ancestry without previous AF at baseline and with lipid gene scores. Lipid-specific gene scores, based on loci significantly associated with lipid levels, were calculated. Additionally, non-pleiotropic gene scores for high-density lipoprotein cholesterol (HDLc) and low-density lipoprotein cholesterol (LDLc) were calculated using SNPs that were only associated with the specific lipid fraction. Cox models were used to estimate the hazard ratio (HR) and 95% confidence intervals (CI) of AF per 1-standard deviation (SD) increase of each lipid gene score.

Results: During a mean follow-up of 12.0 years, 5434 (8.4%) incident AF cases were identified. After meta-analysis, the HDLc, LDLc, total cholesterol, and triglyceride gene scores were not associated with incidence of AF. Multivariable-adjusted HR (95% CI) were 1.01 (0.98-1.03); 0.98 (0.96-1.01); 0.98 (0.95-1.02); 0.99 (0.97-1.02), respectively. Similarly, non-pleiotropic HDLc and LDLc gene scores showed no association with incident AF: HR (95% CI) = 1.00 (0.97-1.03); 1.01 (0.99-1.04).

Conclusions: In this large cohort study of individuals of European ancestry, gene scores for lipid fractions were not associated with incident AF.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0151932PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4801208PMC
July 2016

Twenty-eight genetic loci associated with ST-T-wave amplitudes of the electrocardiogram.

Hum Mol Genet 2016 05 8;25(10):2093-2103. Epub 2016 Mar 8.

Department of Cardiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands Department of Genetics, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, 3511 GC Utrecht, The Netherlands

The ST-segment and adjacent T-wave (ST-T wave) amplitudes of the electrocardiogram are quantitative characteristics of cardiac repolarization. Repolarization abnormalities have been linked to ventricular arrhythmias and sudden cardiac death. We performed the first genome-wide association meta-analysis of ST-T-wave amplitudes in up to 37 977 individuals identifying 71 robust genotype-phenotype associations clustered within 28 independent loci. Fifty-four genes were prioritized as candidates underlying the phenotypes, including genes with established roles in the cardiac repolarization phase (SCN5A/SCN10A, KCND3, KCNB1, NOS1AP and HEY2) and others with as yet undefined cardiac function. These associations may provide insights in the spatiotemporal contribution of genetic variation influencing cardiac repolarization and provide novel leads for future functional follow-up.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/hmg/ddw058DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5062578PMC
May 2016

ABCB1 gene variants, digoxin and risk of sudden cardiac death in a general population.

Heart 2015 Dec 3;101(24):1973-9. Epub 2015 Nov 3.

Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Internal Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands Inspectorate of Health Care, Utrecht, The Netherlands.

Objective: The ATP-binding cassette B1 (ABCB1) gene encodes P-glycoprotein, a transport protein, which plays an important role in the bioavailability of digoxin. We aimed to investigate the interaction between variants within the ABCB1 gene and digoxin on the risk of sudden cardiac death (SCD).

Methods: Within the Rotterdam Study, a population-based cohort study in persons 45 years of age and older, we used Cox regression to analyse the association between three polymorphisms that have been associated with digoxin bioavailability, extracted from 1000-Genomes imputed ABCB1 genotypes and the risk of SCD, stratified by digoxin use.

Results: In a total study population of 10,932 persons, 419 SCDs occurred during a median follow-up of 9.8 years. In non-users of digoxin, the risk of SCD was not different across genotypes. In digoxin users, homozygous T allele carriers of C1236T (HR 1.90; 95% CI 1.09 to 3.30; allele frequency 0.43), G2677T (HR 1.89; 95% CI 1.10 to 3.24; allele frequency 0.44) and C3435T (HR 1.72; 95% CI 1.03 to 2.87; allele frequency 0.53) had a significantly increased risk of SCD in a recessive model. Interaction between the ABCB1 polymorphisms and digoxin use was significant for C1236T and G2677T in the age-adjusted and sex-adjusted model.

Conclusions: In this study, we showed that in digoxin users variant alleles at each of the three loci in the ABCB1 gene were associated with an increased risk of SCD compared with digoxin users with none or one T allele. If replicated, the findings imply that the ABCB1 genotype modifies the risk of cardiac digoxin toxicity.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/heartjnl-2014-307419DOI Listing
December 2015

Drugs and ventricular repolarization in a general population: the Rotterdam Study.

Pharmacoepidemiol Drug Saf 2015 Oct 6;24(10):1036-41. Epub 2015 Aug 6.

Department of Medical Informatics, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands.

Purpose: Prolonged ventricular repolarization (measured as heart-rate corrected QT (QTc) prolongation or JT-interval prolongation) is a risk factor for ventricular arrhythmias and can be drug-induced. Drugs can be classified as having known or possible QTc-prolonging properties. Regulatory agencies recommend avoiding concomitant use of multiple QTc-prolonging drugs, but evidence is lacking to what degree ventricular repolarization is influenced by concomitant use of these drugs.

Methods: Within a population-based cohort of persons aged 45 years and older, with up to five electrocardiograms recorded per participant between 1991 and 2010, we used generalised estimating equations to study the association between concomitant use of multiple QTc-prolonging drugs and repolarization duration.

Results: The study population consisted of 13 009 participants with 26 908 electrocardiograms. With the addition of a second or third QTc-prolonging drug there was no substantial increase in QTc and JT interval and no increased risk of a prolonged QTc interval, compared to use of one QTc-prolonging drug. There was a large difference between the effect of one known or one possible QTc-prolonging drugs on QTc interval: 15 ms for known, and 3 ms for possible QTc-prolonging drugs.

Conclusions: In this study, the added prolongation in users of two or three QTc-prolonging drugs on QTc was small. There was a large difference in QTc prolongation between known and possible QTc-prolonging drugs. Further research in larger or high-risk populations is needed to establish whether it is safe to use multiple QTc-prolonging drugs concomitantly to prevent that the current advice might unnecessarily withhold beneficial drugs from patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/pds.3853DOI Listing
October 2015

Consistency of heart rate-QTc prolongation consistency and sudden cardiac death: The Rotterdam Study.

Heart Rhythm 2015 Oct 9;12(10):2078-85. Epub 2015 Jul 9.

Department of Epidemiology; Department of Internal Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands.

Background: A prolonged heart rate-corrected QT (QTc) interval is a well-known risk indicator for sudden cardiac death (SCD) and a contraindication for drugs with potentially arrhythmogenic adverse effects.

Objective: We aimed to study the consistency of QTc interval prolongation and whether a consistent QTc interval prolongation correlates differently with SCD than does an inconsistently prolonged QTc interval.

Methods: We used a population-based cohort study of persons 55 years and older. We excluded participants using QTc-prolonging drugs or with bundle branch block. The QT interval was corrected for heart rate using Bazett and Fridericia formulas. Using a Cox regression model, we assessed the association between QTc interval prolongation consistency and the occurrence of SCD.

Results: A total of 3484 participants had electrocardiograms (ECGs) recorded on 2 consecutive visits. In 96%-98% of participants with a normal QTc interval on the first ECG, the QTc interval remained normal, but only in 27%-35% of those with a prolonged QTc interval, the QTc interval was prolonged on the second ECG after a median of 1.8 years. A consistently prolonged QTc interval was associated with an increased risk of SCD as compared with a consistently normal QTc interval (Bazett: hazard ratio 2.23; 95% confidence interval 1.17-4.24, Fridericia: hazard ratio 6.67; 95% confidence interval 2.96-15.06). A prolonged QTc interval preceded or followed by a normal QTc interval was not significantly associated with an increased risk of SCD.

Conclusion: Persons with an inconsistently prolonged QTc interval did not have a higher risk of SCD than those with a consistently normal QTc interval. Persons with a consistently prolonged QTc interval did have a higher risk of SCD. Our results suggest that repeated measurements of the QTc interval could enhance risk stratification.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.hrthm.2015.07.011DOI Listing
October 2015

Proton pump inhibitors and hypomagnesemia in the general population: a population-based cohort study.

Am J Kidney Dis 2015 Nov 26;66(5):775-82. Epub 2015 Jun 26.

Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands.

Background: Proton pump inhibitor (PPI) use has been associated with hypomagnesemia in case reports and hospital-based cohort studies. Our objective was to determine whether PPI use is associated with hypomagnesemia in the general population and whether this is also found in histamine 2 receptor antagonist (H2RA) users.

Study Design: Prospective cohort study.

Setting & Participants: 9,818 individuals from the general population (Rotterdam Study).

Predictor: PPI use and H2RA use compared to no use.

Outcomes & Measurements: Serum magnesium and hypomagnesemia (serum magnesium ≤ 1.44 mEq/L). Analyses were adjusted for age, sex, body mass index, kidney function, comorbid conditions, and alcohol and diuretic use.

Results: Serum magnesium level was 0.022 mEq/L lower in PPI users (n=724; 95% CI, -0.032 to -0.014 mEq/L) versus those with no use. PPI use was associated with increased risk of hypomagnesemia (n=36; OR, 2.00; 95% CI, 1.36-2.93) compared to no use. Effect modification was found between the use of PPIs and loop diuretics; in participants using loop diuretics (n=270), PPI use was associated with a further increased risk of hypomagnesemia (n=5; OR, 7.22; 95% CI, 1.69-30.83) compared to no use. The increased risk with PPIs was only seen after prolonged use (range, 182-2,618 days; OR, 2.99; 95% CI, 1.73-5.15). Including dietary magnesium intake into the model did not alter results (available for 2,504 participants, including 231 PPI users). H2RA users (n=250) also had a lower serum magnesium level (-0.016 [95% CI, -0.032 to -0.002] mEq/L) and increased risk of hypomagnesemia (n=12; OR, 2.00; 95% CI, 1.08-3.72) compared to those with no use, but no interaction with loop diuretics.

Limitations: Cross-sectional analysis with single serum magnesium measurement.

Conclusions: PPI use is associated with hypomagnesemia in the general population. Prolonged PPI use and concomitant loop diuretic use are associated with a stronger risk increase. Similar but weaker associations were found in H2RA users, except for interaction with loop diuretics.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1053/j.ajkd.2015.05.012DOI Listing
November 2015

Pharmacogenetics of Drug-Induced QT Interval Prolongation: An Update.

Drug Saf 2015 Oct;38(10):855-67

Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.

A prolonged QT interval is an important risk factor for ventricular arrhythmias and sudden cardiac death. QT prolongation can be caused by drugs. There are multiple risk factors for drug-induced QT prolongation, including genetic variation. QT prolongation is one of the most common reasons for withdrawal of drugs from the market, despite the fact that these drugs may be beneficial for certain patients and not harmful in every patient. Identifying genetic variants associated with drug-induced QT prolongation might add to tailored pharmacotherapy and prevent beneficial drugs from being withdrawn unnecessarily. In this review, our objective was to provide an overview of the genetic background of drug-induced QT prolongation, distinguishing pharmacokinetic and pharmacodynamic pathways. Pharmacokinetic-mediated genetic susceptibility is mainly characterized by variation in genes encoding drug-metabolizing cytochrome P450 enzymes or drug transporters. For instance, the P-glycoprotein drug transporter plays a role in the pharmacokinetic susceptibility of drug-induced QT prolongation. The pharmacodynamic component of genetic susceptibility is mainly characterized by genes known to be associated with QT interval duration in the general population and genes in which the causal mutations of congenital long QT syndromes are located. Ethnicity influences susceptibility to drug-induced QT interval prolongation, with Caucasians being more sensitive than other ethnicities. Research on the association between pharmacogenetic interactions and clinical endpoints such as sudden cardiac death is still limited. Future studies in this area could enable us to determine the risk of arrhythmias more adequately in clinical practice.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s40264-015-0316-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579250PMC
October 2015

Subclinical Abnormalities in Echocardiographic Parameters and Risk of Sudden Cardiac Death in a General Population: The Rotterdam Study.

J Card Fail 2016 Jan 18;22(1):17-23. Epub 2015 Jun 18.

Department of Epidemiology, Erasmus Medical Center-University Medical Center Rotterdam, Rotterdam, The Netherlands.

Background: Subclinical cardiac dysfunction has been associated with increased mortality, and heart failure increases the risk of sudden cardiac death (SCD). Less well known is whether subclinical cardiac dysfunction is also a risk factor for SCD. Our objective was to assess the association between echocardiographic parameters and SCD in a community-dwelling population free of heart failure.

Methods And Results: We computed hazard ratios (HRs) for left atrium diameter, left ventricular (LV) end-diastolic dimension, LV end-systolic dimension, LV mass, qualitative LV systolic function, LV fractional shortening, and diastolic function. During a median follow-up of 6.3 years in 4,686 participants, 68 participants died because of SCD. Significant associations with SCD were observed for qualitative LV systolic function and LV fractional shortening. For moderate/poor qualitative LV systolic function, the HR for SCD was 2.54 (95% confidence interval [CI] 1.10-5.87). Each standard deviation decrease in LV fractional shortening was associated with an HR of 1.36 (95% CI 1.09-1.70).

Conclusions: Subclinical abnormalities in LV systolic function were associated with SCD risk in this general population. Although prediction of SCD remains difficult and traditional cardiovascular risk factors are of greatest importance, this knowledge might guide future directions to prevent SCD in persons with subclinical cardiac dysfunction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cardfail.2015.06.007DOI Listing
January 2016

Chronic obstructive pulmonary disease and sudden cardiac death: the Rotterdam study.

Eur Heart J 2015 Jul 28;36(27):1754-61. Epub 2015 Apr 28.

Department of Respiratory Medicine, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium Department of Epidemiology, Erasmus Medical Center, PO Box 2040, Rotterdam 3000 CA, The Netherlands Department of Respiratory Medicine, Erasmus Medical Center, PO Box 2040, Rotterdam 3000 CA, The Netherlands.

Aims: Both sudden cardiac death (SCD) and chronic obstructive pulmonary disease (COPD) are common conditions in the elderly. Previous studies have identified an association between COPD and cardiovascular disease, and with SCD in specific patient groups. Our aim was to investigate whether there is an association between COPD and SCD in the general population.

Methods And Results: The Rotterdam study is a population-based cohort study among 14 926 subjects aged 45 years and older with up to 24 years of follow-up. Analyses were performed with a (time dependent) Cox proportional hazard model adjusted for age, sex, and smoking. Of the 13 471 persons included in the analysis; 1615 had a diagnosis of COPD and there were 551 cases of SCD. Chronic obstructive pulmonary disease was associated with an increased risk of SCD (age- and sex-adjusted hazard ratio, HR, 1.34, 95% CI 1.06-1.70). The risk particularly increased in the period 2000 days (5.48 years) after the diagnosis of COPD (age- and sex-adjusted HR 2.12, 95% CI 1.60-2.82) and increased further to a more than three-fold higher risk in COPD subjects with frequent exacerbations during this period (age- and sex-adjusted HR 3.58, 95% CI 2.35-5.44). Analyses restricted to persons without prevalent myocardial infarction or heart failure yielded similar results.

Conclusion: Chronic obstructive pulmonary disease is associated with an increased risk for SCD. The risk especially increases in persons with frequent exacerbations 5 years after the diagnosis of COPD. This risk indicator could provide new directions for better-targeted actions to prevent SCD.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/eurheartj/ehv121DOI Listing
July 2015

Assessing Prolongation of the Heart Rate Corrected QT Interval in Users of Tricyclic Antidepressants: Advice to Use Fridericia Rather Than Bazett's Correction.

J Clin Psychopharmacol 2015 Jun;35(3):260-5

From the Departments of *Internal Medicine, †Epidemiology, ‡Medical Informatics, and §Cardiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam: ∥Inspectorate of Health Care; and ¶Apotheek Haagse Ziekenhuizen-HAGA, The Hague, The Netherlands.

A prolonged heart rate corrected QT interval (QTc) increases the risk of sudden cardiac death. Some methods of heart rate correction (notably Bazett) overestimate QTc in people with high heart rates. Studies suggest that tricyclic antidepressants (TCAs) can prolong the QTc and increase heart rate. Therefore, we aimed to study whether TCA-induced QTc prolongation is a false-positive observation due to overestimation at high heart rates. For this, we included 12,734 participants from the prospective population-based Rotterdam Study, with a total of 27,068 electrocardiograms (ECGs), of which, 331 during TCA use. Associations between use of TCAs, QTc, and heart rate were studied with linear repeated measurement analyses. QT was corrected for heart rate according to Bazett (QTcBazett), Fridericia (QTcFridericia), or a correction based on regression coefficients obtained from the Rotterdam Study data (QTcStatistical). On ECGs recorded during TCA use, QTcBazett was 6.5 milliseconds (95% confidence interval, 4.0-9.0) longer, and heart rate was 5.8 beats per minute (95% confidence interval, 4.7-6.9) faster than during nonuse. QTcFridericia and QTcStatistical were not statistically significantly longer during TCA use than during nonuse. Furthermore, QTcBazett was similar for ECGs recorded during TCA use and nonuse after statistical adjustment for heart rate. According to our results, TCA use does not seem to be associated with QTc prolongation. Therefore, the current advice of regulatory authorities to restrict the use of these drugs and to do regular checkups of the QTc may need to be revised. Other formulas, like Fridericia's, might be preferred.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/JCP.0000000000000321DOI Listing
June 2015

Declining incidence of sudden cardiac death from 1990-2010 in a general middle-aged and elderly population: The Rotterdam Study.

Heart Rhythm 2015 Jan 30;12(1):123-9. Epub 2014 Sep 30.

Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Internal Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands.

Background: Although sudden cardiac death (SCD) is relatively common, contemporary data on its incidence are lacking.

Objective: The purpose of this study was to investigate the current incidence of SCD and its trend over the past 2 decades in a general middle-aged and elderly population.

Methods: This study was performed within the Rotterdam Study, a prospective population-based cohort study of persons aged 45 years and older. Age-standardized incidence rates of SCD were calculated. To study trends in incidence, we compared 2 subcohorts within the total study population, 1 followed from 1990-2000 and the other from 2001-2010.

Results: From 1990-2010, 5512 of 14,628 participants died, of whom 583 (4.0%) were classified as SCD. The overall incidence was 4.2 per 1000 person-years. The incidence was higher in men (5.2 per 1000 person-years) than in women (3.6 per 1000 person-years). Age-adjusted hazard ratio (HR) 1.84 (95% confidence [CI] 1.56-2.17) and risk of SCD increased with age (HR 1.10 per year; 95% CI 1.09-1.11). The incidence rate from 1990-2000 was 4.7 per 1000 person-years vs 2.1 per 1000 person-years from 2001-2010 (age- and sex-adjusted HR of SCD 0.60, 95% CI 0.44-0.80). To check for cohort effects, we also analyzed the incidence of total mortality and found an age- and sex-adjusted HR of total mortality of 0.82 (95% CI 0.75-0.90) for the second compared to the first subcohort, which was significantly higher than the decline in SCD incidence.

Conclusion: We found an incidence of SCD of 4.2 per 1000 person-years. The incidence decreased from 1990-2010, a period during which the diagnosis and treatment of heart disease greatly improved.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.hrthm.2014.09.054DOI Listing
January 2015

Short-term QT variability markers for the prediction of ventricular arrhythmias and sudden cardiac death: a systematic review.

Heart 2014 Dec 4;100(23):1831-6. Epub 2014 Aug 4.

Department of Medical Informatics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.

Sudden cardiac death (SCD) is a major health burden and is primarily caused by ventricular arrhythmias. Currently, the most well-known marker for the risk of ventricular arrhythmias is QT/QTc prolongation. Animal studies indicate that QT variability might be a better indicator. Our objective was to give an overview of the literature on QT variability in humans, therefore we performed a free-text search in PubMed and Embase from inception through February 2013. We identified nine QT variability markers in 109 studies reporting on QT variability markers, measured on the surface ECG. QT variability can be distinguished using two characteristics: heart rate normalisation and whether QT interval is measured on consecutive beats. Most study populations were small (median 48 subjects, range 1-805) and different methods, time intervals and leads for measurement were used. QT variability markers were determinants for the risk of ventricular arrhythmias, (sudden) cardiac death and total mortality. Few studies compared the predictive value of QT variability with that of QT/QTc prolongation. A study comparing all different QT variability markers is lacking. In conclusion, QT variability markers are potential determinants of ventricular arrhythmias and cardiac mortality. However, it is unclear which marker and methodology are clinically most useful as well as what reference values are reliable. More studies on larger datasets are needed to find the most accurate marker for the prediction of arrhythmias and SCD to assess its value in addition to QT/QTc duration and its role in drug-induced arrhythmia and sudden death.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/heartjnl-2014-305671DOI Listing
December 2014

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

Nat Genet 2014 Aug 22;46(8):826-36. Epub 2014 Jun 22.

Center for Biomedicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy (affiliated institute of the University of Lübeck, Lübeck, Germany).

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/ng.3014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4124521PMC
August 2014

Common variation in fatty acid metabolic genes and risk of incident sudden cardiac arrest.

Heart Rhythm 2014 Mar 10;11(3):471-7. Epub 2014 Jan 10.

Cardiovascular Genetics Center, Institut Investigació Biomèdica de Girona IDIBGI-Universitat de Girona, Girona, Spain.

Background: There is limited information on genetic factors associated with sudden cardiac arrest (SCA).

Objective: To assess the association of common variation in genes in fatty acid pathways with SCA risk.

Methods: We selected 85 candidate genes and 1155 single nucleotide polymorphisms (SNPs) tagging common variation in each gene. We investigated the SNP associations with SCA in a population-based case-control study. Cases (n = 2160) were from a repository of SCA in the greater Seattle area. Controls (n = 2615), frequency-matched on age and sex, were from the same area. We used linear logistic regression to examine SNP associations with SCA. We performed permutation-based p-min tests to account for multiple comparisons within each gene. The SNP associations with a corrected P value of <.05 were then examined in a meta-analysis of these SNP associations in 9 replication studies totaling 2129 SCA cases and 23,833 noncases.

Results: Eight SNPs in or near 8 genes were associated with SCA risk in the discovery study, one of which was nominally significant in the replication phase (rs7737692, minor allele frequency 36%, near the LPCAT1 gene). For each copy of the minor allele, rs7737692 was associated with 13% lower SCA risk (95% confidence interval -21% to -5%) in the discovery phase and 9% lower SCA risk (95% confidence interval -16% to -1%) in the replication phase.

Conclusions: While none of the associations reached significance with Bonferroni correction, a common genetic variant near LPCAT1, a gene involved in the remodeling of phospholipids, was nominally associated with incident SCA risk. Further study is needed to validate this observation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.hrthm.2014.01.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3966996PMC
March 2014

Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders.

Nat Genet 2013 Jun 14;45(6):621-31. Epub 2013 Apr 14.

Medical Research Council MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK.

Elevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate-increasing and heart rate-decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/ng.2610DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3696959PMC
June 2013

Genome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function.

PLoS Genet 2012 20;8(12):e1003098. Epub 2012 Dec 20.

Behavioral Health Epidemiology Program, Research Triangle Institute International, Research Triangle Park, North Carolina, USA.

Genome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV(1)), and its ratio to forced vital capacity (FEV(1)/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV(1) and FEV(1)/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest P(JMA = )5.00×10(-11)), HLA-DQB1 and HLA-DQA2 (smallest P(JMA = )4.35×10(-9)), and KCNJ2 and SOX9 (smallest P(JMA = )1.28×10(-8)) were associated with FEV(1)/FVC or FEV(1) in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pgen.1003098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3527213PMC
May 2013

CYP1A2 and coffee intake and the modifying effect of sex, age, and smoking.

Am J Clin Nutr 2012 Jul 30;96(1):182-7. Epub 2012 May 30.

Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands.

Background: The enzyme CYP1A2 (cytochrome 1A2) is involved in the metabolism of certain drugs and caffeine, and its activity can be influenced by factors such as sex, age, and smoking. The single nucleotide polymorphism (SNP) rs762551A>C, which has also been studied for its modifying effect on cardiovascular disease, has been reported to alter enzyme activity.

Objective: The objective was to study the effect of CYP1A2, sex, age, and smoking on coffee intake.

Design: Within the Rotterdam Study, a population-based cohort, all coffee drinkers for whom genome-wide association data were available were selected. Because SNP rs762551 was not on the Illumina 550 platform, SNP rs2472299 was used as a proxy, with the A allele of rs762551 linked to the G allele of rs2472299. Linear regression analyses were used to determine the effect and interaction of rs2472299, sex, age, and smoking on coffee intake. Adjusted geometric means of coffee intake were calculated per genotype for the different smoking and sex strata by using multivariable general linear models. A combined analysis, with the use of a "risk score," was performed to determine the contribution of each separate factor.

Results: rs2472299G>A, female sex, and nonsmoking were significantly inversely related to coffee intake. Coffee intake was lowest in nonsmoking women homozygous for rs2472299G>A (3.49 cups/d; ∼436 mL). All factors contributed almost linearly to the intake of coffee, with the highest coffee intake in smoking men without the A allele (5.32 cups/d; ∼665 mL).

Conclusion: rs2472299G>A, linked to rs762551A>C, sex, age, and smoking significantly contribute to coffee intake.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3945/ajcn.111.027102DOI Listing
July 2012
-->