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Lopera-Maya EA, Li S, de Brouwer R, Nolte IM, van Breen J, Jongbloed JDH, Swertz MA, Snieder H, Franke L, Wijmenga C, de Boer RA, Deelen P, van der Zwaag PA, Sanna S. Phenotypic and Genetic Factors Associated with Absence of Cardiomyopathy Symptoms in PLN:c.40_42delAGA Carriers. J Cardiovasc Transl Res 2023; 16:1251-1266. [PMID: 36622581 PMCID: PMC10721704 DOI: 10.1007/s12265-022-10347-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 12/14/2022] [Indexed: 01/10/2023]
Abstract
The c.40_42delAGA variant in the phospholamban gene (PLN) has been associated with dilated and arrhythmogenic cardiomyopathy, with up to 70% of carriers experiencing a major cardiac event by age 70. However, there are carriers who remain asymptomatic at older ages. To understand the mechanisms behind this incomplete penetrance, we evaluated potential phenotypic and genetic modifiers in 74 PLN:c.40_42delAGA carriers identified in 36,339 participants of the Lifelines population cohort. Asymptomatic carriers (N = 48) showed shorter QRS duration (- 5.73 ms, q value = 0.001) compared to asymptomatic non-carriers, an effect we could replicate in two different independent cohorts. Furthermore, symptomatic carriers showed a higher correlation (rPearson = 0.17) between polygenic predisposition to higher QRS (PGSQRS) and QRS (p value = 1.98 × 10-8), suggesting that the effect of the genetic variation on cardiac rhythm might be increased in symptomatic carriers. Our results allow for improved clinical interpretation for asymptomatic carriers, while our approach could guide future studies on genetic diseases with incomplete penetrance.
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Affiliation(s)
- Esteban A Lopera-Maya
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Shuang Li
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Remco de Brouwer
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Justin van Breen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Jan D H Jongbloed
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Morris A Swertz
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Harold Snieder
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Cisca Wijmenga
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Rudolf A de Boer
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Patrick Deelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Oncode Institute, Utrecht, Netherlands
| | - Paul A van der Zwaag
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
| | - Serena Sanna
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
- Institute for Genetic and Biomedical Research (IRGB), National Research Council (CNR), Cagliari, Italy.
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Tardo DT, Peck M, Subbiah R, Vandenberg JI, Hill AP. The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review. Ann Noninvasive Electrocardiol 2023; 28:e13015. [PMID: 36345173 PMCID: PMC9833360 DOI: 10.1111/anec.13015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 10/12/2022] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION QTc prolongation is key in diagnosing long QT syndrome (LQTS), however 25%-50% with congenital LQTS (cLQTS) demonstrate a normal resting QTc. T wave morphology (TWM) can distinguish cLQTS subtypes but its role in acquired LQTS (aLQTS) is unclear. METHODS Electronic databases were searched using the terms "LQTS," "long QT syndrome," "QTc prolongation," "prolonged QT," and "T wave," "T wave morphology," "T wave pattern," "T wave biomarkers." Whole text articles assessing TWM, independent of QTc, were included. RESULTS Seventeen studies met criteria. TWM measurements included T-wave amplitude, duration, magnitude, Tpeak-Tend, QTpeak, left and right slope, center of gravity (COG), sigmoidal and polynomial classifiers, repolarizing integral, morphology combination score (MCS) and principal component analysis (PCA); and vectorcardiographic biomarkers. cLQTS were distinguished from controls by sigmoidal and polynomial classifiers, MCS, QTpeak, Tpeak-Tend, left slope; and COG x axis. MCS detected aLQTS more significantly than QTc. Flatness, asymmetry and notching, J-Tpeak; and Tpeak-Tend correlated with QTc in aLQTS. Multichannel block in aLQTS was identified by early repolarization (ERD30% ) and late repolarization (LRD30% ), with ERD reflecting hERG-specific blockade. Cardiac events were predicted in cLQTS by T wave flatness, notching, and inversion in leads II and V5 , left slope in lead V6 ; and COG last 25% in lead I. T wave right slope in lead I and T-roundness achieved this in aLQTS. CONCLUSION Numerous TWM biomarkers which supplement QTc assessment were identified. Their diagnostic capabilities include differentiation of genotypes, identification of concealed LQTS, differentiating aLQTS from cLQTS; and determining multichannel versus hERG channel blockade.
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Affiliation(s)
- Daniel T. Tardo
- Cardiac Electrophysiology LaboratoryVictor Chang Cardiac Research InstituteDarlinghurstNew South WalesAustralia
- Department of CardiologySt. Vincent's HospitalDarlinghurstNew South WalesAustralia
- School of MedicineUniversity of Notre Dame AustraliaDarlinghurstNew South WalesAustralia
| | - Matthew Peck
- Cardiac Electrophysiology LaboratoryVictor Chang Cardiac Research InstituteDarlinghurstNew South WalesAustralia
| | - Rajesh N. Subbiah
- Cardiac Electrophysiology LaboratoryVictor Chang Cardiac Research InstituteDarlinghurstNew South WalesAustralia
- Department of CardiologySt. Vincent's HospitalDarlinghurstNew South WalesAustralia
- St. Vincent's Clinical School, Faculty of MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Jamie I. Vandenberg
- Cardiac Electrophysiology LaboratoryVictor Chang Cardiac Research InstituteDarlinghurstNew South WalesAustralia
- St. Vincent's Clinical School, Faculty of MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Adam. P. Hill
- Cardiac Electrophysiology LaboratoryVictor Chang Cardiac Research InstituteDarlinghurstNew South WalesAustralia
- St. Vincent's Clinical School, Faculty of MedicineUniversity of New South WalesSydneyNew South WalesAustralia
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Hoffmann TJ, Lu M, Oni-Orisan A, Lee C, Risch N, Iribarren C. A large genome-wide association study of QT interval length utilizing electronic health records. Genetics 2022; 222:iyac157. [PMID: 36271874 PMCID: PMC9713425 DOI: 10.1093/genetics/iyac157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 09/22/2022] [Indexed: 12/13/2022] Open
Abstract
QT interval length is an important risk factor for adverse cardiovascular outcomes; however, the genetic architecture of QT interval remains incompletely understood. We conducted a genome-wide association study of 76,995 ancestrally diverse Kaiser Permanente Northern California members enrolled in the Genetic Epidemiology Research on Adult Health and Aging cohort using 448,517 longitudinal QT interval measurements, uncovering 9 novel variants, most replicating in 40,537 individuals in the UK Biobank and Population Architecture using Genomics and Epidemiology studies. A meta-analysis of all 3 cohorts (n = 117,532) uncovered an additional 19 novel variants. Conditional analysis identified 15 additional variants, 3 of which were novel. Little, if any, difference was seen when adjusting for putative QT interval lengthening medications genome-wide. Using multiple measurements in Genetic Epidemiology Research on Adult Health and Aging increased variance explained by 163%, and we show that the ≈6 measurements in Genetic Epidemiology Research on Adult Health and Aging was equivalent to a 2.4× increase in sample size of a design with a single measurement. The array heritability was estimated at ≈17%, approximately half of our estimate of 36% from family correlations. Heritability enrichment was estimated highest and most significant in cardiovascular tissue (enrichment 7.2, 95% CI = 5.7-8.7, P = 2.1e-10), and many of the novel variants included expression quantitative trait loci in heart and other relevant tissues. Comparing our results to other cardiac function traits, it appears that QT interval has a multifactorial genetic etiology.
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Affiliation(s)
- Thomas J Hoffmann
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94143, USA
| | - Meng Lu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Akinyemi Oni-Orisan
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Clinical Pharmacy, University of California San Francisco, San Francisco, CA 94143, USA
| | - Catherine Lee
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Neil Risch
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94143, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Carlos Iribarren
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
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Kornej J, Magnani JW, Preis SR, Soliman EZ, Trinquart L, Ko D, Benjamin EJ, Lin H. P-wave signal-averaged electrocardiography: Reference values, clinical correlates, and heritability in the Framingham Heart Study. Heart Rhythm 2021; 18:1500-1507. [PMID: 33989782 PMCID: PMC8419007 DOI: 10.1016/j.hrthm.2021.05.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/21/2021] [Accepted: 05/06/2021] [Indexed: 11/18/2022]
Abstract
BACKGROUND P-wave signal-averaged electrocardiography (P-SAECG) quantifies atrial electrical activity. P-SAECG measures and their clinical correlates and heritability have had limited characterization in community-based cohorts. OBJECTIVE The purpose of this study was to (1) establish reference values; (2) identify clinical risk factors associated with P-SAECG; and (3) estimate genetic heritability for P-SAECG traits. METHODS We performed P-SAECG in 2 generations of Framingham Heart Study participants. We performed backward elimination regression models to assess associations of clinical factors with each SAECG trait (P-wave [PW] duration, root mean square voltage in terminal 40 ms [RMS40], terminal 30 ms RMS30, terminal 20 ms RMS20, RMS PW, and PW integral). We estimated the adjusted genetic heritability of P-SAECG measures using the Sequential Oligogenic Linkage Analysis Routines (SOLAR) program. RESULTS We included 4307 participants (age 55 ± 14 years; 56% female). The reference values were derived from 1752 participants without cardiovascular risk factors. Median (2.5th percentile; 97.5th percentile) total PW duration was 118 ms (93; 146) in women and 128 ms (104; 158) in men in the reference sample, and 121 ms (94; 151) in women and 129 ms (103; 159) in the entire study cohort (broad sample). In the broad sample, after adjusting for age and sex, total PW duration was positively associated with height, weight, prevalent heart failure, history of atrial fibrillation (AF), and atrioventricular node blockers, and negatively associated with smoking, waist circumference, heart rate, and diabetes. The estimated heritability of P-SAECG traits was moderate, ranging from 11.9% for RMS30 to 24.9% for PW integral. CONCLUSION P-SAECG traits are associated with multiple AF-related risk factors and are moderately heritable.
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Affiliation(s)
- Jelena Kornej
- National Heart, Lung, and Blood Institute and Boston University's Framingham Heart Study, Framingham, Massachusetts; Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts.
| | - Jared W Magnani
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Sarah R Preis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Elsayed Z Soliman
- Epidemiological Cardiology Research Center, Department of Epidemiology, and Department of Medicine-Section on Cardiology, Wake Forest University School of Medicine, Winston Salem, North Carolina
| | - Ludovic Trinquart
- National Heart, Lung, and Blood Institute and Boston University's Framingham Heart Study, Framingham, Massachusetts; Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Darae Ko
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Emelia J Benjamin
- National Heart, Lung, and Blood Institute and Boston University's Framingham Heart Study, Framingham, Massachusetts; Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts; Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
| | - Honghuang Lin
- National Heart, Lung, and Blood Institute and Boston University's Framingham Heart Study, Framingham, Massachusetts; Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
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Ramírez J, van Duijvenboden S, Young WJ, Orini M, Lambiase PD, Munroe PB, Tinker A. Common Genetic Variants Modulate the Electrocardiographic Tpeak-to-Tend Interval. Am J Hum Genet 2020; 106:764-778. [PMID: 32386560 PMCID: PMC7273524 DOI: 10.1016/j.ajhg.2020.04.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 04/08/2020] [Indexed: 02/06/2023] Open
Abstract
Sudden cardiac death is responsible for half of all deaths from cardiovascular disease. The analysis of the electrophysiological substrate for arrhythmias is crucial for optimal risk stratification. A prolonged T-peak-to-Tend (Tpe) interval on the electrocardiogram is an independent predictor of increased arrhythmic risk, and Tpe changes with heart rate are even stronger predictors. However, our understanding of the electrophysiological mechanisms supporting these risk factors is limited. We conducted genome-wide association studies (GWASs) for resting Tpe and Tpe response to exercise and recovery in ∼30,000 individuals, followed by replication in independent samples (∼42,000 for resting Tpe and ∼22,000 for Tpe response to exercise and recovery), all from UK Biobank. Fifteen and one single-nucleotide variants for resting Tpe and Tpe response to exercise, respectively, were formally replicated. In a full dataset GWAS, 13 further loci for resting Tpe, 1 for Tpe response to exercise and 1 for Tpe response to exercise were genome-wide significant (p ≤ 5 × 10-8). Sex-specific analyses indicated seven additional loci. In total, we identify 32 loci for resting Tpe, 3 for Tpe response to exercise and 3 for Tpe response to recovery modulating ventricular repolarization, as well as cardiac conduction and contraction. Our findings shed light on the genetic basis of resting Tpe and Tpe response to exercise and recovery, unveiling plausible candidate genes and biological mechanisms underlying ventricular excitability.
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Affiliation(s)
- Julia Ramírez
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK; Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - Stefan van Duijvenboden
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK; Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - William J Young
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK; Barts Heart Centre, St Bartholomew's Hospital, London EC1A 7BE, UK
| | - Michele Orini
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK; Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK; Barts Heart Centre, St Bartholomew's Hospital, London EC1A 7BE, UK
| | - Pier D Lambiase
- Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK; Barts Heart Centre, St Bartholomew's Hospital, London EC1A 7BE, UK
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK; NIHR Barts Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK.
| | - Andrew Tinker
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK; NIHR Barts Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK.
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Ramírez J, van Duijvenboden S, Aung N, Laguna P, Pueyo E, Tinker A, Lambiase PD, Orini M, Munroe PB. Cardiovascular Predictive Value and Genetic Basis of Ventricular Repolarization Dynamics. Circ Arrhythm Electrophysiol 2019; 12:e007549. [PMID: 31607149 DOI: 10.1161/circep.119.007549] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Early prediction of cardiovascular risk in the general population remains an important issue. The T-wave morphology restitution (TMR), an ECG marker quantifying ventricular repolarization dynamics, is strongly associated with cardiovascular mortality in patients with heart failure. Our aim was to evaluate the cardiovascular prognostic value of TMR in a UK middle-aged population and identify any genetic contribution. METHODS We analyzed ECG recordings from 55 222 individuals from a UK middle-aged population undergoing an exercise stress test in UK Biobank (UKB). TMR was used to measure ventricular repolarization dynamics, exposed in this cohort by exercise (TMR during exercise, TMRex) and recovery from exercise (TMR during recovery, TMRrec). The primary end point was cardiovascular events; secondary end points were all-cause mortality, ventricular arrhythmias, and atrial fibrillation with median follow-up of 7 years. Genome-wide association studies for TMRex and TMRrec were performed, and genetic risk scores were derived and tested for association in independent samples from the full UKB cohort (N=360 631). RESULTS A total of 1743 (3.2%) individuals in UKB who underwent the exercise stress test had a cardiovascular event, and TMRrec was significantly associated with cardiovascular events (hazard ratio, 1.11; P=5×10-7), independent of clinical variables and other ECG markers. TMRrec was also associated with all-cause mortality (hazard ratio, 1.10) and ventricular arrhythmias (hazard ratio, 1.16). We identified 12 genetic loci in total for TMRex and TMRrec, of which 9 are associated with another ECG marker. Individuals in the top 20% of the TMRrec genetic risk score were significantly more likely to have a cardiovascular event in the full UKB cohort (18 997, 5.3%) than individuals in the bottom 20% (hazard ratio, 1.07; P=6×10-3). CONCLUSIONS TMR and TMR genetic risk scores are significantly associated with cardiovascular risk in a UK middle-aged population, supporting the hypothesis that increased spatio-temporal heterogeneity of ventricular repolarization is a substrate for cardiovascular risk and the validity of TMR as a cardiovascular risk predictor.
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Affiliation(s)
- Julia Ramírez
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (J.R., S.v.D., A.T., M.O., P.B.M.), Queen Mary University of London, United Kingdom.,Institute of Cardiovascular Science, University College London, United Kingdom (J.R., S.v.D., P.D.L., M.O.)
| | - Stefan van Duijvenboden
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (J.R., S.v.D., A.T., M.O., P.B.M.), Queen Mary University of London, United Kingdom.,Institute of Cardiovascular Science, University College London, United Kingdom (J.R., S.v.D., P.D.L., M.O.)
| | - Nay Aung
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute (N.A.), Queen Mary University of London, United Kingdom.,Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom (N.A., P.D.L.)
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, Aragón Institute of Engineering Research, IIS Aragón, University of Zaragoza, Spain (P.L., E.P.).,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain (P.L., E.P.)
| | - Esther Pueyo
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, Aragón Institute of Engineering Research, IIS Aragón, University of Zaragoza, Spain (P.L., E.P.).,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain (P.L., E.P.)
| | - Andrew Tinker
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (J.R., S.v.D., A.T., M.O., P.B.M.), Queen Mary University of London, United Kingdom.,National Institute of Health Research Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry (A.T., P.B.M.), Queen Mary University of London, United Kingdom
| | - Pier D Lambiase
- Institute of Cardiovascular Science, University College London, United Kingdom (J.R., S.v.D., P.D.L., M.O.).,Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom (N.A., P.D.L.)
| | - Michele Orini
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (J.R., S.v.D., A.T., M.O., P.B.M.), Queen Mary University of London, United Kingdom.,Institute of Cardiovascular Science, University College London, United Kingdom (J.R., S.v.D., P.D.L., M.O.)
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry (J.R., S.v.D., A.T., M.O., P.B.M.), Queen Mary University of London, United Kingdom.,National Institute of Health Research Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry (A.T., P.B.M.), Queen Mary University of London, United Kingdom
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Orini M, Tinker A, Munroe PB, Lambiase PD. Long-term intra-individual reproducibility of heart rate dynamics during exercise and recovery in the UK Biobank cohort. PLoS One 2017; 12:e0183732. [PMID: 28873397 PMCID: PMC5584807 DOI: 10.1371/journal.pone.0183732] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 08/09/2017] [Indexed: 12/26/2022] Open
Abstract
Background The heart rate (HR) response to exercise provides useful information about the autonomic function and has prognostic value, but its reproducibility over a long period of time, a critical requirement for using it as a clinical biomarker, is undetermined. Aim To determine the intra-individual reproducibility of HR dynamics during sub-maximum exercise and one minute recovery. Methods 1187 individuals from the Cardio physical fitness assessment test of the UK Biobank repeated a standard exercise stress test twice (recall time 34.2 ± 2.8 months) and were prospectively studied. Results 821 individuals complied with inclusion criteria for reproducibility analysis, including peak workload differences between assessments ≤10 W. Intra-individual correlation between HR profile during the first and the second assessment was very high and higher than inter-individual correlation (0.92±0.08 vs 0.87±0.11, p<0.01). Intra-individual correlation of indices describing HR dynamics was: ρ = 0.81 for maximum HR during exercise; ρ = 0.71 for minimum HR during recovery; ρ = 0.70 for HR changes during both exercise and recovery; Intra-individual correlation was higher for these indices of HR dynamics than for resting HR (ρ = 0.64). Bland-Altman plots demonstrated good agreement between HR indices estimated during the first and second assessment. A small but consistent bias was registered for all repeated measurements. The intra-individual consistency of abnormal values was about 60–70%. Conclusions The HR dynamics during exercise and recovery are reproducible over a period of 3 years, with moderate to strong intra-individual reproducibility of abnormal values.
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Affiliation(s)
- Michele Orini
- Institute of Cardiovascular Science, University College London, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, London, United Kingdom
- * E-mail:
| | - Andrew Tinker
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Patricia B. Munroe
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Pier D. Lambiase
- Institute of Cardiovascular Science, University College London, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, London, United Kingdom
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Immanuel SA, Sadrieh A, Baumert M, Couderc JP, Zareba W, Hill AP, Vandenberg JI. T-wave morphology can distinguish healthy controls from LQTS patients. Physiol Meas 2016; 37:1456-73. [PMID: 27510854 DOI: 10.1088/0967-3334/37/9/1456] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Long QT syndrome (LQTS) is an inherited disorder associated with prolongation of the QT/QTc interval on the surface electrocardiogram (ECG) and a markedly increased risk of sudden cardiac death due to cardiac arrhythmias. Up to 25% of genotype-positive LQTS patients have QT/QTc intervals in the normal range. These patients are, however, still at increased risk of life-threatening events compared to their genotype-negative siblings. Previous studies have shown that analysis of T-wave morphology may enhance discrimination between control and LQTS patients. In this study we tested the hypothesis that automated analysis of T-wave morphology from Holter ECG recordings could distinguish between control and LQTS patients with QTc values in the range 400-450 ms. Holter ECGs were obtained from the Telemetric and Holter ECG Warehouse (THEW) database. Frequency binned averaged ECG waveforms were obtained and extracted T-waves were fitted with a combination of 3 sigmoid functions (upslope, downslope and switch) or two 9th order polynomial functions (upslope and downslope). Neural network classifiers, based on parameters obtained from the sigmoid or polynomial fits to the 1 Hz and 1.3 Hz ECG waveforms, were able to achieve up to 92% discrimination between control and LQTS patients and 88% discrimination between LQTS1 and LQTS2 patients. When we analysed a subgroup of subjects with normal QT intervals (400-450 ms, 67 controls and 61 LQTS), T-wave morphology based parameters enabled 90% discrimination between control and LQTS patients, compared to only 71% when the groups were classified based on QTc alone. In summary, our Holter ECG analysis algorithms demonstrate the feasibility of using automated analysis of T-wave morphology to distinguish LQTS patients, even those with normal QTc, from healthy controls.
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Affiliation(s)
- S A Immanuel
- Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, Australia. Department of Electrical and Electronics Engineering, University of Adelaide, Adelaide, Australia
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