Effects of Imipramine, Fluvoxamine and Depressive Mood on Autonomic Cardiac Functioning in Major Depressive Disorder

Objective: Diminished HR variability is considered to be associated with depression and the increased risk of cardiovascular disease. The pharmacological effects of antidepressants and depressive mood itself may contribute to alterations in autonomic cardiac functioning, but a limited amount of data is available. We studied the effects of two different types of antidepressant treatments (imipramine and fluvoxamine), in addition to the effect of depressive mood, on the cardiovascular system in depressed patients.

Methods: Depressed inpatients were studied during a drug-free period and after 4 weeks of adequate treatment with imipramine (n = 17) or fluvoxamine (n = 24). Heart rate variability, blood pressure variability, and a baroreflex sensitivity index during supine rest and orthostatic challenge were analyzed by means of spectral techniques to obtain noninvasive parameters of sympathetic and parasympathetic activity.

Results: Both imipramine and fluvoxamine reduced sympathetic and parasympathetic activity, although the effects of imipramine were much more pronounced. Severity of depression was positively related to mean levels of heart rate and blood pressure in the total patient group. There was no convincing evidence that these relationships differed between depressed patients treated with imipramine and those treated with fluvoxamine.

Conclusion: Our findings suggest that alterations in mean heart rate and blood pressure in depressed patients after antidepressant treatment are the result of a combined effect of pharmacological actions of antidepressants and improvement of depressive mood state. The present study did not confirm the relationship between clinical state and cardiovascular variability or baroreflex sensitivity.

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1 Random regression models for continuous data estimate individual effects instead of effects for the total study population [3] [5]. The random regression approach uses data from individuals augmented by information from the total study population to estimate for each subject the personal trend across time. In RRMs, the intercept and time effects are allowed to differ for individuals and therefore are called random terms. In this study the intercept was a random term. The advantage of this analysis technique over the usually applied MANOVAs for repeated measurements is a more efficient use of data, as RRMs can cope with missing values. The estimation of an individual time trend is based on non-missing data for that individual. This implies that missing values are assumed to be missing at random [30], and subjects may have data for different time points. RRMs allow more general and realistic error structures. In this study we assumed an ”unstructured” error structure, which allows both correlations between measurements and variances of measurements to differ [5] [30]. In other words, variances and covariances may differ between repeated measurements.