Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-18T20:40:02.489Z Has data issue: false hasContentIssue false
  • English
  • Français

Antidepressants and heart-rate variability in older adults: a population-based study

Published online by Cambridge University Press:  18 December 2015

R. Noordam ,
M. E. van den Berg ,
M. N. Niemeijer ,
N. Aarts ,
A. Hofman ,
H. Tiemeier ,
J. A. Kors ,
B. H. Stricker ,
M. Eijgelsheim  and
L. E. Visser
...Show all authors
R. Noordam
Affiliation:
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
M. E. van den Berg
Affiliation:
Department of Medical Informatics, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
M. N. Niemeijer
Affiliation:
Department of Epidemiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
N. Aarts
Affiliation:
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
A. Hofman
Affiliation:
Department of Epidemiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
H. Tiemeier
Affiliation:
Department of Epidemiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
J. A. Kors
Affiliation:
Department of Medical Informatics, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
B. H. Stricker*
Affiliation:
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
M. Eijgelsheim
Affiliation:
Department of Epidemiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Internal Medicine, University Medical Center Groningen, Groningen, The Netherlands
L. E. Visser
Affiliation:
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 Apotheek Haagse Ziekenhuizen – HAGA, The Hague, The Netherlands
P. R. Rijnbeek
Affiliation:
Department of Medical Informatics, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
*
*Address for correspondence: B. H. Stricker, Mmed, Ph.D., Department of Epidemiology, Erasmus MC – University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands (Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Tricyclic antidepressants (TCAs) and selective serotonin reuptake inhibitors (SSRIs) may be associated with lower heart rate variability (HRV), a condition associated with increased mortality risk. We aimed to investigate the association between TCAs, SSRIs and HRV in a population-based study.

Method

In the prospective Rotterdam Study cohort, up to five electrocardiograms (ECGs) per participant were recorded (1991–2012). Two HRV variables were studied based on 10-s ECG recordings: standard deviation of normal-to-normal RR intervals (SDNN) and root mean square of successive RR interval differences (RMSSD). We compared the HRV on ECGs recorded during use of antidepressants with the HRV on ECGs recorded during non-use of any antidepressant. Additionally, we analysed the change in HRV on consecutive ECGs. Those who started or stopped using antidepressants before the second ECG were compared with non-users on two ECGs.

Results

We included 23 647 ECGs from 11 729 participants (59% women, mean age 64.6 years at baseline). Compared to ECGs recorded during non-use of antidepressants (n = 22 971), SDNN and RMSSD were lower in ECGs recorded during use of TCAs (n = 296) and SSRIs (n = 380). Participants who started using TCAs before the second ECG had a decrease in HRV and those who stopped had an increase in HRV compared to consistent non-users (p < 0.001). Starting or stopping SSRIs was not associated with HRV changes.

Conclusion

TCAs were associated with a lower HRV in all analyses, indicating a real drug effect. For SSRIs the results are mixed, indicating a weaker association, possibly due to other factors.

Keywords

Antidepressive agentselectrocardiographyepidemiologyheart-rate variabilitypopulation surveillance
Type
Original Articles
Information
Psychological Medicine , Volume 46 , Issue 6 , April 2016 , pp. 1239 - 1247
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aarts, N, Noordam, R, Hofman, A, Tiemeier, H, Stricker, BH, Visser, LE (2014). Utilization patterns of antidepressants between 1991 and 2011 in a population-based cohort of middle-aged and elderly. European Psychiatry 29, 365–370.Google Scholar
American Diabetes Association (2010). Diagnosis and classification of diabetes mellitus. Diabetes Care 33 (Suppl. 1), S62–S69.Google Scholar
Beekman, AT, Deeg, DJ, Van Limbeek, J, Braam, AW, De Vries, MZ, Van Tilburg, W (1997). Criterion validity of the center for epidemiologic studies depression scale (CES-D): results from a community-based sample of older subjects in The Netherlands. Psychological Medicine 27, 231–235.Google Scholar
Bigger, JT Jr, Kleiger, RE, Fleiss, JL, Rolnitzky, LM, Steinman, RC, Miller, JP (1988). Components of heart rate variability measured during healing of acute myocardial infarction. American Journal of Cardiology 61, 208–215.CrossRefGoogle ScholarPubMed
Darpo, B, Agin, M, Kazierad, DJ, Layton, G, Muirhead, G, Gray, P, Jorkasky, DK (2006). Man versus machine: is there an optimal method for QT measurements in thorough QT studies? Journal of Clinical Pharmacology 46, 598–612.Google Scholar
Davidson, J, Watkins, L, Owens, M, Krulewicz, S, Connor, K, Carpenter, D, Krishnan, R, Nemeroff, C (2005). Effects of paroxetine and venlafaxine XR on heart rate variability in depression. Journal of Clinical Psychopharmacology 25, 480–484.CrossRefGoogle ScholarPubMed
de Bruyne, MC, Kors, JA, Hoes, AW, Kruijssen, DA, Deckers, JW, Grosfeld, M, van Herpen, G, Grobbee, DE, van Bemmel, JH (1997). Diagnostic interpretation of electrocardiograms in population-based research: computer program research physicians, or cardiologists? Journal of Clinical Epidemiology 50, 947–952.Google Scholar
de Bruyne, MC, Kors, JA, Hoes, AW, Klootwijk, P, Dekker, JM, Hofman, A, van Bemmel, JH, Grobbee, DE (1999). Both decreased and increased heart rate variability on the standard 10-second electrocardiogram predict cardiac mortality in the elderly: the Rotterdam Study. American Journal of Epidemiology 150, 1282–1288.Google Scholar
Dekker, JM, Schouten, EG, Klootwijk, P, Pool, J, Swenne, CA, Kromhout, D (1997). Heart rate variability from short electrocardiographic recordings predicts mortality from all causes in middle-aged and elderly men. The Zutphen Study. American Journal of Epidemiology 145, 899–908.Google Scholar
Erdogan, A, Coch, M, Bilgin, M, Parahuleva, M, Tillmanns, H, Waldecker, B, Soydan, N (2008). Prognostic value of heart rate variability after acute myocardial infarction in the era of immediate reperfusion. Herzschrittmachertherapie & Elektrophysiologie 19, 161–168.CrossRefGoogle ScholarPubMed
Fei, L, Copie, X, Malik, M, Camm, AJ (1996). Short- and long-term assessment of heart rate variability for risk stratification after acute myocardial infarction. American Journal of Cardiology 77, 681–684.Google Scholar
Hamer, M, Batty, GD, Seldenrijk, A, Kivimaki, M (2011). Antidepressant medication use and future risk of cardiovascular disease: the Scottish Health Survey. European Heart Journal 32, 437–442.CrossRefGoogle ScholarPubMed
Hofman, A, Darwish Murad, S, van Duijn, CM, Franco, OH, Goedegebure, A, Ikram, MA, Klaver, CC, Nijsten, TE, Peeters, RP, Stricker, BH, Tiemeier, HW, Uitterlinden, AG, Vernooij, MW (2013). The Rotterdam Study: 2014 objectives and design update. European Journal of Epidemiology 28, 889–926.Google Scholar
Hofman, A, Grobbee, DE, de Jong, PT, van den Ouweland, FA (1991). Determinants of disease and disability in the elderly: the Rotterdam Elderly Study. European Journal of Epidemiology 7, 403–422.Google Scholar
Huikuri, HV, Raatikainen, MJ, Moerch-Joergensen, R, Hartikainen, J, Virtanen, V, Boland, J, Anttonen, O, Hoest, N, Boersma, LV, Platou, ES, Messier, MD, Bloch-Thomsen, PE (2009). Prediction of fatal or near-fatal cardiac arrhythmia events in patients with depressed left ventricular function after an acute myocardial infarction. European Heart Journal 30, 689–698.Google Scholar
Kemp, AH, Quintana, DS, Gray, MA, Felmingham, KL, Brown, K, Gatt, JM (2010). Impact of depression and antidepressant treatment on heart rate variability: a review and meta-analysis. Biological Psychiatry 67, 1067–1074.Google Scholar
Khaykin, Y, Dorian, P, Baker, B, Shapiro, C, Sandor, P, Mironov, D, Irvine, J, Newman, D (1998). Autonomic correlates of antidepressant treatment using heart-rate variability analysis. Canadian Journal of Psychiatry 43, 183–186.Google Scholar
Kleiger, RE, Miller, JP, Bigger, JT Jr, Moss, AJ (1987). Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. American Journal of Cardiology 59, 256–262.Google Scholar
Kors, JA, van Herpen, G (2009). Methodology of QT-interval measurement in the modular ECG analysis system (MEANS). Annals of Noninvasive Electrocardiology 14 (Suppl. 1), S48–S53.Google Scholar
La Rovere, MT, Bigger, JT Jr, Marcus, FI, Mortara, A, Schwartz, PJ (1998). Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction. ATRAMI (autonomic tone and reflexes after myocardial infarction) investigators. Lancet 351, 478–484.Google Scholar
Lederbogen, F, Gernoth, C, Weber, B, Colla, M, Kniest, A, Heuser, I, Deuschle, M (2001). Antidepressive treatment with amitriptyline and paroxetine: comparable effects on heart rate variability. Journal of Clinical Psychopharmacology 21, 238–239.Google Scholar
Leening, MJ, Kavousi, M, Heeringa, J, van Rooij, FJ, Verkroost-van Heemst, J, Deckers, JW, Mattace-Raso, FU, Ziere, G, Hofman, A, Stricker, BH, Witteman, JC (2012). Methods of data collection and definitions of cardiac outcomes in the Rotterdam Study. European Journal of Epidemiology 27, 173–185.Google Scholar
Licht, CM, de Geus, EJ, van Dyck, R, Penninx, BW (2010). Longitudinal evidence for unfavorable effects of antidepressants on heart rate variability. Biological Psychiatry 68, 861–868.Google Scholar
Licht, CM, de Geus, EJ, Zitman, FG, Hoogendijk, WJ, van Dyck, R, Penninx, BW (2008). Association between major depressive disorder and heart rate variability in the netherlands study of depression and anxiety (NESDA). Archives of General Psychiatry 65, 1358–1367.Google Scholar
Licht, CM, Naarding, P, Penninx, BW, van der Mast, RC, de Geus, EJ, Comijs, H (2015). The association between depressive disorder and cardiac autonomic control in adults 60 years and older. Psychosomatic Medicine 77, 279–291.Google Scholar
Makikallio, TH, Barthel, P, Schneider, R, Bauer, A, Tapanainen, JM, Tulppo, MP, Schmidt, G, Huikuri, HV (2005). Prediction of sudden cardiac death after acute myocardial infarction: role of Holter monitoring in the modern treatment era. European Heart Journal 26, 762–769.Google Scholar
Malik, M (1997). Time-domain measurement of heart rate variability. Cardiac Electrophysiology Review 1, 329–334.Google Scholar
Malik, M, Bigger, JT, Camm, AJ, Kleiger, RE, Malliani, A, Moss, AJ, Schwartz, PJ (1996). Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. European Heart Journal 17, 354–381.Google Scholar
Noordam, R, Aarts, N, Verhamme, KM, Sturkenboom, MC, Stricker, BH, Visser, LE (2015). Prescription and indication trends of antidepressant drugs in the Netherlands between 1996 and 2012: a dynamic population-based study. European Journal of Clinical Pharmacology 71, 369–375.Google Scholar
O'Regan, C, Kenny, RA, Cronin, H, Finucane, C, Kearney, PM (2015). Antidepressants strongly influence the relationship between depression and heart rate variability: findings from the Irish longitudinal study on ageing (TILDA). Psychological Medicine 45, 623–636.CrossRefGoogle ScholarPubMed
Pomeranz, B, Macaulay, RJ, Caudill, MA, Kutz, I, Adam, D, Gordon, D, Kilborn, KM, Barger, AC, Shannon, DC, Cohen, RJ, Benson, H (1985). Assessment of autonomic function in humans by heart rate spectral analysis. American Journal of Physiology 248, H151–H153.Google Scholar
Radloff, LS (1977). The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement 1, 385–401.Google Scholar
Rajendra Acharya, U, Paul Joseph, K, Kannathal, N, Lim, CM, Suri, JS (2006). Heart rate variability: a review. Medical & Biological Engeneering & Computing 44, 1031–1051.Google Scholar
Snyder, SH, Yamamura, HI (1977). Antidepressants and the muscarinic acetylcholine receptor. Archives of General Psychiatry 34, 236–239.Google Scholar
Stein, PK, Domitrovich, PP, Huikuri, HV, Kleiger, RE (2005). Traditional and nonlinear heart rate variability are each independently associated with mortality after myocardial infarction. Journal of Cardiovascular Electrophysiology 16, 13–20.Google Scholar
Szklo, M (1998). Population-based cohort studies. Epidemiologic Reviews 20, 81–90.Google Scholar
UNESCO (1976). International Standard Classification of Education (ISCED): Paris.Google Scholar
van Bemmel, JH, Kors, JA, van Herpen, G (1990). Methodology of the modular ECG analysis system MEANS. Methods of Information in Medicine 29, 346–353.Google Scholar
WHO Collaborating Centre for Drug Statistics Methodology (2015). Guidelines for ATC classification and DDD assignment. (http://www.whocc.no/atcddd/). Accessed 12 November 2015).Google Scholar
Willems, JL, Abreu-Lima, C, Arnaud, P, van Bemmel, JH, Brohet, C, Degani, R, Denis, B, Gehring, J, Graham, I, van Herpen, G, Machado, H, Macfarlane, PW, Michaelis, J, Moulopoulos, SP, Rubel, P, Zywietz, C (1991). The diagnostic performance of computer programs for the interpretation of electrocardiograms. New England Journal of Medicine 325, 1767–1773.Google Scholar
Willems, JL, Arnaud, P, van Bemmel, JH, Bourdillon, PJ, Degani, R, Denis, B, Graham, I, Harms, FM, Macfarlane, PW, Mazzocca, Gl, Meyer, J, Zywietz, C (1987). A reference data base for multilead electrocardiographic computer measurement programs. Journal of the American College of Cardiology 10, 1313–1321.Google Scholar
Supplementary material: File

Noordam supplementary material

Tables S1-S3

Download Noordam supplementary material(File)
File 23.5 KB