Genetic Influences on Resting Electrocardiographic Variables in Older Women: A Twin Study |
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Authors: | Sara Mutikainen M.Sc. Alfredo Ortega‐Alonso M.Sc. Markku Alén M.D. Ph.D. Jaakko Kaprio M.D. Ph.D. Jouko Karjalainen M.D. Ph.D. Taina Rantanen Ph.D. Urho M. Kujala M.D. Ph.D. |
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Affiliation: | 1. Department of Health Sciences, University of Jyv?skyl?, Jyv?skyl?, Finland;2. Finnish Centre for Interdisciplinary Gerontology, University of Jyv?skyl?, Jyv?skyl?, Finland;3. Department of Medical Rehabilitation, Oulu University Hospital, Oulu, Finland;4. Institute of Health Sciences, University of Oulu, Oulu, Finland;5. Department of Public Health, University of Helsinki, Helsinki, Finland;6. Department of Mental Health and Alcohol Research, National Public Health Institute, Helsinki, Finland;7. Institute of Molecular Medicine, Helsinki, Finland;8. Unit for Sports and Exercise Medicine, University of Helsinki, Helsinki, Finland |
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Abstract: | Background: Previous studies in young and middle‐aged men and women have shown that resting electrocardiographic (ECG) variables are influenced by genetic factors. However, the extent to which resting ECG variables are influenced by genetic factors in older women is unknown. Thus, the aim of this study was to estimate the relative contribution of genetic and environmental influences to individual differences in resting ECG variables among older female twins without overt cardiac diseases. Methods: Resting ECG recordings were obtained from 186 monozygotic and 203 dizygotic twin individuals, aged 63–76 years. Quantitative genetic modeling was used to decompose the phenotypic variance in each resting ECG variable into additive genetic, dominance genetic, shared environmental, and unique environmental influences. Results: The results showed that individual differences in the majority of the resting ECG variables were moderately to highly explained by additive genetic influences, ranging from 32% for T axis to 72% for TV5. The results also suggested dominance genetic influences on QRS duration, TV1, and Sokolow–Lyon voltage (36%, 53%, and 57%, respectively). Unique environmental influences were important for each resting ECG variable, whereas shared environmental influences were detected only for QT interval and QTc. Conclusion: In older women without overt cardiac diseases, genetic influences explain a moderate to high proportion of individual differences in the majority of the resting ECG variables. Genetic influences are especially strong for T‐wave amplitudes, left ventricular mass, and hypertrophy indices, whereas other variables, including heart rate, intervals, and axes, are more affected by environmental influences. |
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Keywords: | electrocardiography heritability quantitative genetic modeling aging |
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