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. Author manuscript; available in PMC: 2020 Aug 1.
Published in final edited form as: Int J Psychophysiol. 2019 Jun 10;142:57–65. doi: 10.1016/j.ijpsycho.2019.06.005

Associations of depression severity with heart rate and heart rate variability in young adults across normative and clinical populations

Laura M Lesnewich a, Fiona N Conway b, Jennifer F Buckman a,c, Christopher J Brush c, Peter J Ehmann c, David Eddie d, Ryan L Olson e, Brandon L Alderman c, Marsha E Bates a,c
PMCID: PMC6690725  NIHMSID: NIHMS1533172  PMID: 31195066

Abstract

Limitations of current depression treatments may arise from a lack of knowledge about unique psychophysiological processes that contribute to depression across the full range of presentations. This study examined how individual variations in heart rate (HR) and heart rate variability (HRV) are related to depressive symptoms across normative and clinical populations in152 young adults (aged 18–35 years). Moderating effects of sex and antidepressant medication status were considered. Electrocardiogram data were collected during “vanilla” baseline and in response to positive and negative emotional cues. Linear regressions and repeated-measures mixed models were used to assess the relationships between Beck Depression Inventory-II (BDI-II) scores, sex, antidepressant use, and cardiovascular outcomes. Baseline models yielded significant main effects of BDI-II and sex on HR and significant interactions between antidepressant medication status and BDI-II on HRV outcomes. The main effects of BDI-II and sex on HR were no longer significant after controlling for cardiorespiratory fitness. Participants who denied current antidepressant use (n=137) exhibited a negative association and participants who endorsed current antidepressant (n=15) use exhibited a positive association between BDI-II scores and HRV. Emotional reactivity models were largely non-significant with the exception of a significant main effect of antidepressant medication status on high-frequency HRV reactivity. Results indicated antidepressant medication use may moderate the relationship between depression severity and cardiovascular functioning, but this requires replication given the modest proportion of medicated individuals in this study. Overall, findings suggest cardiovascular processes and cardiorespiratory fitness are linked to depression symptomatology and may be important to consider in depression treatment.

Keywords: cardiovascular, mood, sex differences, antidepressant medications, emotional reactivity

Depression is a highly prevalent and disabling disease that is a leading cause of disability worldwide (Ferrari et al., 2013; Ustun et al., 2004) and accounts for a growing economic burden estimated to be over $200 billion annually in the United States (U.S., Greenberg et al., 2015). Despite available evidence-based treatments for depression (Task Force, 1996), up to one third of patients do not achieve remission after multiple treatment attempts (Holtzheimer and Mayberg, 2011). This limitation could be due in part to lack of knowledge and consideration in current treatment practices for unique psychophysiological processes that contribute to depression. To address this issue, this study examined how individual variations in cardiovascular processes, both at rest and in response to emotional challenge, may play a role in maintaining depressive symptoms in a sample of young adults.

Sub-optimal treatment outcomes may also arise from limited consideration for the spectrum of depression severity, as patients may meet full criteria for a depressive disorder at varying levels of severity or exhibit sub-diagnostic clinical presentations (Joo et al., 2016; Judd et al., 1994; Kessler et al., 1997; Sigstrom et al., 2018). Relatedly, a large body of research supports a conceptualization of depression as a continuum of severity rather than as a dichotomous diagnosis (e.g., Hankin et al., 2005; Kessler et al., 1997; Liu, 2016). This conceptualization is also in line with the National Institute of Mental Health’s Research Domain Criteria initiative, which suggests that a categorical, symptom-based diagnostic classification system may obscure a full understanding of the biological basis of psychological conditions (Insel et al., 2010). Despite these recommendations, relatively few studies have examined cardiovascular processes associated with the full spectrum of depression severity across normative, subclinical, and clinical populations, particularly in regard to HRV and emotional reactivity. This study contributes to this small literature by examining cardiovascular indices associated with depression severity, as well as factors that may moderate the relationship between depression severity and the cardiovascular system.

Depression is characterized by a constellation of symptoms (American Psychiatric Association, 2013), some of which are independently associated with maladaptive cardiovascular functioning, such as changes in appetite (Shin et al., 2010; Valentova et al., 2016), disrupted sleep patterns (Bonnet and Arand, 2010; Spiegelhalder et al., 2011; Vgontzas et al., 2009) and attenuated energy levels (Barrett, 2018; Sun et al., 2015). Depressed individuals also tend to engage in limited physical activity (Azevedo Da Silva et al., 2012; Sabiston et al., 2013), a behavioral pattern associated with poor cardiovascular health (Winzer et al., 2018). Indeed, depressed individuals, compared to non-depressed controls, tend to exhibit higher resting heart rate (HR, e.g., Agelink et al., 2002; Davydov et al., 2007) and are at a greater risk for cardiovascular disease (Bylsma et al., 2015; Grippo and Johnson, 2009; Hare et al., 2014; Rottenberg et al., 2014; Rugulies, 2002; Van der Kooy et al., 2007; Wulsin and Singal, 2003). These associations have led to a growing body of research that aims to clarify the role of the cardiovascular system in depression.

Heart rate variability (HRV), or the variation in time between heartbeats, has garnered particular interest in the depression literature for a number of reasons. First, HRV reflects multiple underlying biological mechanisms, most notably those associated with the sympathetic and parasympathetic nervous systems. Second, HRV is associated with both physical (Alderman and Olson, 2014; Tsuji et al., 1996) and mental (Beauchaine and Thayer, 2015; Kemp and Quintana, 2013; Udo et al., 2013) health outcomes. Third, HRV reflects adaptability (Hamilton and Alloy, 2016), emotion regulation (Thayer and Lane, 2009), and behavioral flexibility (Thayer et al., 2012), all of which are compromised by depression (Ehrenthal et al., 2010; Joormann and Gotlib, 2010; Stange et al., 2017). Last, it has been suggested that HRV may be a useful endophenotype for psychological and physiological comorbidities (Sgoifo et al., 2015).

HRV has been quantified by distinct indices that correspond to cardiovascular, respiratory, central nervous, and autonomic nervous system functioning (Shaffer and Ginsberg, 2017; Task Force, 1996). Total HRV, which can be measured as the standard deviation of normal-to-normal intervals (SDNN), captures overall adaptability and system flexibility. High-frequency HRV (HF-HRV) measures parasympathetic nervous system activity and reflects vagal influences on the heart. A growing body of literature suggests that depressed individuals have lower vagally-mediated HRV as measured by HF-HRV and the root mean square of the successive differences (RMSSD) indices at baseline (i.e., in the absence of challenge) compared to their non-depressed counterparts (e.g., Rottenberg, 2007; Sgoifo et al., 2015), although these group-level differences tend to have small-to-medium effect sizes (Kemp et al., 2010; Koenig et al., 2016; Rottenberg, 2007).

Beyond measurements of baseline HRV, changes in HRV during challenge, referred to as HRV reactivity, also may provide important information for understanding the physiological processes associated with depression. Where baseline HRV represents the potential to adapt to challenge (Hamilton and Alloy, 2016), HRV reactivity reflects dynamic processes that are adapting to challenge in real-time. Indeed, research from our laboratory has highlighted the importance of examining HRV both at baseline and in response to a challenge to capture the adaptability of the cardiovascular system (Udo et al., 2013).

Depression researchers have examined cardiovascular reactivity in a number of contexts, including stress and emotional loading (Hamilton and Alloy, 2016). Many (de Rooij et al., 2010; Ehrenthal et al., 2010; Salomon et al., 2013; Salomon et al., 2009; Schiweck et al., 2019; Schwerdtfeger and Rosenkaimer, 2011), but not all (e.g., Liang et al., 2015; Rottenberg et al., 2007) stress reactivity studies using classic laboratory stress paradigms have shown that cardiovascular reactivity was attenuated in depressed compared to non-depressed individuals. Inconsistencies may be due to the type of stressor used (Hamilton and Alloy, 2016; Hu et al., 2016; Schwerdtfeger and Rosenkaimer, 2011). Similarly, a consistent pattern of HRV reactivity to emotional challenge has yet to emerge. Emotional reactivity, however, may be particularly informative to our understanding of the psychophysiology of depression, as depression can be characterized by chronic depressed mood, anhedonia (i.e., reduced experience of pleasure), or both (American Psychiatric Association, 2013). Prospective studies suggest worse depression course is linked to reduced HRV reactivity to sad stimuli (Gentzler et al., 2009; Panaite et al., 2016; Rottenberg et al., 2005). Cross-sectional studies, however, have reported attenuated reactivity to unpleasant stimuli (e.g., Rottenberg et al., 2003), attenuated reactivity to pleasant, but not unpleasant stimuli (e.g., Benvenuti et al., 2015), and increased reactivity to unpleasant stimuli (e.g., Pang and Beauchaine, 2013) in depressed individuals compared to controls. Importantly, most studies on HRV reactivity to emotional challenges characterize depression as a dichotomous variable. It is possible that measurement of a psychological construct, such as depression, on a continuum of severity may better parallel the sensitivity of physiological parameters, such as HRV, which are dimensional biomarkers of flexibility in a biological system (Tapanainen et al., 2002). The goal of the present study was to examine how HRV indices vary across the full spectrum of depression severity in order to uncover individual differences in this relationship that may inform treatment.

Two factors that may affect the relationship between depression and HRV are sex (Chen et al., 2010; Thayer et al., 1998) and antidepressant medication (Rottenberg, 2007; Stapelberg et al., 2012; Terhardt et al., 2013). A recent large-scale longitudinal study of over 2,000 initially non-depressed participants found that higher baseline HRV at the first assessment predicted a lower probability of depressive symptoms endorsed at follow-up in men but not women (Jandackova et al., 2016). Additionally, men, but not women, exhibited a negative relationship between HRV and depressive symptoms at follow-up, irrespective of antidepressant treatment. Regarding antidepressant medication, it is well-established that tricyclic medications reduce HRV, but the effects of the more commonly prescribed selective serotonin reuptake inhibitors (SSRIs) remain unclear (Brunoni et al., 2013; Jandackova et al., 2016; Kemp et al., 2016; Kemp et al., 2010; Licht et al., 2010; O’Regan et al., 2015; van Zyl et al., 2008). This study adds to this growing literature by examining both sex and current antidepressant medication status as potential moderating factors of the association between depression severity and cardiovascular functioning.

The present study examined the associations of depression severity to HR and HRV in a sample of young adults. We hypothesized that baseline HR would be positively associated with depression severity, and baseline HRV indices (i.e., SDNN and HF-HRV) would be negatively associated with depression severity. Based on previous findings by Jandackova and colleagues (2016), males were predicted to exhibit a stronger relationship between HRV and depression severity than females. Analyses of the effects of antidepressant medication were treated as exploratory due to the mixed findings in the literature. Additionally, we expected HR and HRV reactivity to emotional challenge would exhibit associations to depression severity, but we did not speculate the direction of these relationships a priori due to mixed findings in this area.

Material and Methods

2.1. Participants

One hundred and seventy-nine participants were recruited as part of a larger, ongoing investigation of aerobic exercise as a behavioral intervention for depressive symptoms (Alderman et al., 2016; Olson et al., 2017). Participants were recruited at a large, Northeastern U.S. university from the general student population as well as from the psychological services clinic in order to capture a sample of participants with a broad range of depressive symptoms. Recruitment strategies included flyers and advertisements for an 8-week exercise study aimed at individuals who did or did not experience depressive symptoms. Several different recruitment flyers were used to minimize selection bias in the recruitment process. Individuals between the ages of 18 and 35 were eligible for study inclusion. Exclusion criteria included bipolar or psychotic disorders, self-injurious or suicidal behavior, a history of neurological disorders, and head injuries resulting in a loss of consciousness. Modules from the Mini-International Neuropsychiatric Interview were used to assess exclusion diagnoses. Twenty-seven participants were excluded from the present analyses because HRV data were not collected (n = 23) or were of insufficient quality to be analyzed (n = 4). Thus, a total of 152 participants were included in the present study. The mean age of the sample was 21.1±2.9 years. Additional demographic characteristics of the sample are summarized in Table 1.

2.2. Depression Severity

The Beck Depression Inventory-II (BDI-II) was used to measure depressive symptom severity. The BDI-II is an empirically validated, 21-item measure of depression symptom severity (Beck et al., 1996) in which participants respond to four-point Likert-type scales (0–3) to indicate severity of common depressive symptoms (e.g., sleep problems, worthlessness, suicidality) over the past two weeks. A score from 0–13 indicates minimal depression, 14–19 indicates mild depression, 20–28 indicates moderate depression, and 29–63 indicates severe depression. The BDI-II demonstrated high internal reliability in the present sample (Cronbach’s α = 0.93).

2.3. Cardiovascular Assessment

Continuous electrocardiogram (ECG) data were recorded via standard Lead II electrode configurations (Pipberger, 1975) using a MindWare BioNex data acquisition system and were digitized at 500 Hz. ECG was recorded during three tasks in the following order: vanilla baseline, emotional cue exposure, and paced breathing. Data from the baseline and emotional cue exposure tasks were analyzed in the present study.

2.4. Procedure

As part of the larger study, participants completed two pre-intervention assessment sessions. The first session involved a battery of self-report psychosocial measures (including the BDI-II), vanilla baseline cardiovascular assessment, and cardiovascular reactivity to the emotional cues. The second session involved an event-related potential assessment using electroencephalography (EEG) and a cardiorespiratory fitness assessment (i.e., VO2 peak) using a modified Bruce protocol on a motor-driven treadmill (for specific details see Olson et al., 2017). All procedures were approved by the university Institutional Review Board for the protection of human subjects, and all participants provided their written informed consent prior to participating in the study. The present study focused on the pre-intervention ECG and self-reported depression data to characterize relationships between depression symptom severity and cardiovascular functioning during the vanilla baseline task and in response to emotional challenge.

The “vanilla” task is a low-demand cognitive task empirically validated as a method for baseline HRV assessment (Jennings et al., 1992). This task serves to stabilize cognitive activity both within and across participants and has been shown to produce more reliable data compared to data collected during an unstructured “resting” baseline assessment (Jennings et al., 1992). Participants viewed colored rectangles for five minutes at a rate of one rectangle every 10 seconds and were instructed to silently count the number of blue rectangles presented.

The emotional cue exposure task included two 2.5-minute blocks of emotional images (positive and negative valence) selected from the International Affective Picture System (IAPS, Lang et al., 2001). Participants were given instruction to view the images. Each image was displayed for six seconds with no inter-stimulus interval. There were a total of 25 positive and 25 negative cues presented. The order of emotional cue blocks was randomized across participants, and a 60-second rest period was included between blocks. Published valence and arousal norms for the 50 images (Lang et al., 2001) confirmed that mean pleasure ratings were significantly (p < 0.0001) greater for the positive compared to the negative cues and there was no statistical difference in mean arousal ratings between the cue sets.

2.5. Data Analysis

2.5.1. Cardiovascular data processing.

MindWare HRV version 3.0.17 software (MindWare Technologies, Ltd., Gahanna, OH, USA) was used to analyze the ECG waveforms and calculate cardiovascular indices of mean HR, total HRV (SDNN), and high frequency HRV (HF-HRV). HR is reported in beats-per-minute (BPM). SDNN was calculated as the standard deviation in beat-to-beat intervals across the ECG time series, and HF-HRV was calculated as total power of the 0.15–0.40 Hz band of the ECG frequency spectrum. Per standard procedure (Task Force, 1996), SDNN and HF-HRV data underwent a natural logarithmic transformation to satisfy assumptions for parametric statistical analyses. Thus, these variables are reported in natural logarithm units.

2.5.2. Correction for chronotropic state.

A recent paper discussed the complex interplay between HRV and tonic chronotropic state (i.e., mean heart period) and recommended reporting how adjustment for chronotropic state affects HRV outcomes (de Geus et al., 2019). In accordance with these recommendations, a second set of baseline HRV measures were calculated adjusting for mean heart period using the coefficient of variation method (de Geus et al., 2019). Adjusted SDNN (cvSDNN) was defined as 100*(SDNN/IBI), where IBI was the mean inter-beat interval over the course of the vanilla baseline task. Adjusted HF-HRV (cvHF-HRV) was defined as 100*(HF-HRV/IBI2). These adjusted variables underwent a natural logarithmic transformation to satisfy assumptions for parametric statistical tests.

2.5.3. Reactivity score calculation.

Reactivity scores were calculated as the residuals of cardiovascular responses during positive and negative emotional cues each regressed on cardiovascular responses during vanilla baseline. Residual scores, rather than difference scores, were used to operationally define HRV reactivity in the current data set based on established guidelines (Burt and Obradović, 2013).

2.5.4. Statistical analyses.

Linear regressions assessed relationships between depression severity (BDI-II), sex (male, female), antidepressant medication status (yes, no), and cardiovascular responses (HR, SDNN, HF-HRV) during vanilla baseline. BDI-II x sex and BDI-II x antidepressant interactions were also modeled. Identical models were analyzed for IBI-adjusted SDNN and HF-HRV (i.e., cvSDNN, cvHF-HRV). Repeated-measures mixed models were used to examine the effects of depression severity, sex, antidepressant status, and cue valence (positive, negative) on cardiovascular reactivity to emotional cues. BDI-II x sex and BDI-II x antidepressant interaction terms were also included, and each model controlled for baseline HR, SDNN, or HF-HRV, respectively. BDI-II, sex, antidepressant medication status, and baseline cardiovascular covariates were mean centered in all baseline and reactivity models. Additionally, all models were estimated including covariates of body mass index (BMI) and cardiorespiratory fitness (VO2 peak). Analyses were performed using PROC MIXED implemented in SAS 9.4 software (SAS Institute, Cary, NC, USA), and the reported statistics are from the solution for fixed effects of each model.

Results

3.1. Depression and Antidepressant Treatment

The mean BDI-II score across the entire sample was 15.9 (moderate depression) with a standard deviation of 11 points. BDI-II scores ranged from 0 (minimal) to 53 (severe), covering nearly the full range of scores. Fifteen participants (10% of the sample) endorsed current antidepressant medication treatment. Eleven participants reported single antidepressant treatment and four participants reported current use of two antidepressants. Frequencies of antidepressant medications endorsed are summarized in Table 2.

3.2. Baseline Cardiovascular Analyses

There were no missing data points for HR and SDNN data collected during the vanilla baseline task; one participant had missing baseline HF-HRV data. Thus, N = 152 participants were included in baseline HR and SDNN models, and N = 151 participants were included in the baseline HF-HRV model.

3.2.1. HR.

There were significant main effects of depression severity (i.e., BDI-II) and sex on mean HR. Higher BDI-II scores were associated with higher HR, and females had higher HR compared to males (see Figure 1). No significant interactions were observed between BDI-II and sex or current antidepressant use. These results are summarized in Table 3.

Figure 1.

Figure 1

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Significant (p < 0.05) main effect of BDI-II on baseline HR; this effect was no longer significant after controlling for cardiorespiratory fitness.

3.2.2. SDNN.

The main effects of depression severity and sex were not statistically significant. There was a significant main effect of antidepressant medication status on baseline SDNN. Participants who endorsed current antidepressant use had lower SDNN compared to participants who did not endorse antidepressants. As well, a significant interaction between depression severity and antidepressant medication status was observed. Participants currently taking antidepressants exhibited a positive relationship between vanilla baseline SDNN and depression severity, and participants not currently taking antidepressants showed a negative relationship (see Figure 2). The interaction between depression severity and sex was not significant.

Figure 2.

Figure 2

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Significant (p < 0.01) BDI-II x antidepressants interaction on baseline SDNN (natural log-transformed)

3.2.3. HF-HRV.

The main effects of sex and depression severity were not statistically significant. There was a significant main effect of current antidepressant medication status on HF-HRV. Participants who endorsed antidepressant use had lower HF-HRV compared to participants who did not endorse antidepressants. There also was a significant interaction between depression severity and antidepressant medication status. Similarly to baseline SDNN, baseline HF-HRV exhibited a positive relationship to depression severity among participants currently taking antidepressants, but a negative relationship was observed between baseline HF-HRV and depression severity among participants not currently taking antidepressants (see Figure 3). The interaction between depression severity and sex was not significant. These results are summarized in Table 3.

Figure 3.

Figure 3

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Significant (p < 0.01) BDI-II x antidepressants interaction on baseline HF-HRV (natural log-transformed)

3.4.2. Additional Model Tests.

BMI was not a significant predictor of baseline HR, SDNN, or HF-HRV, nor did this covariate affect the pattern of results reported above. VO2 peak was a significant predictor of baseline HR (t(136) = −5.26, p < 0.0001), SDNN, t(136) = 3.46, p = 0.0007, and HF-HRV, t(135) = 2.30, p = 0.0230, such that greater cardiorespiratoy fitness was associated with lower baseline HR and higher baseline SDNN and HF-HRV. The main effects of BDI-II and sex on baseline HR were no longer significant after controlling for VO2 peak. The main effect of antidepressant medication status, t(136) = −3.63, p = 0.0004, and the antidepressant x BDI-II interaction, t(136) = 3.31, p = 0.0012, on SDNN and the main effect of antidepressant medication status, t(135) = −4.84, p < 0.0001, and the antidepressant x BDI-II interaction, t(135) = 3.27, p = 0.0014, on HF-HRV remained significant after controlling for VO2 peak.

The pattern and interpretation of results for SDNN and HF-HRV did not change due to adjustment for chronotropic state. There remained a significant main effect of antidepressant medication status, t(146) = −3.58, p = 0.0005, and a significant interaction between depression severity and antidepressant medication status, t(146) = 3.80, p = 0.0002, on cvSDNN. There was a significant main effect of antidepressant medication status, t(145) = −4.86, p < 0.0001, and a significant interaction between depression severity and antidepressant medication status, t(145) = 3.62, p = 0.0004, on cvHF-HRV.

Four participants exhibited substantially greater BDI-II scores (i.e., BDI-II>46) relative to the rest of the sample. The pattern and interpretation of results for baseline HR and HF-HRV did not change after excluding these participants from the analysis. For SDNN, sex emerged as a significant covariate after excluding these participants.

3.3. Cardiovascular Reactivity Analyses

Twenty-four participants were missing HRV data for the positive and/or negative emotional cue-reactivity tasks (N = 18 tested prior to implementation of the emotional reactivity tasks; N = 6 due to data acquisition problems). The HR and SDNN reactivity models included 129 participants, and the HF-HRV model included 128 participants. Those who completed the reactivity tasks did not differ from those who did not on any demographic or baseline cardiovascular indicator (p > .05).

There were no significant main effects or interactions in models predicting emotional reactivity for mean HR or SDNN. There was a significant main effect of current antidepressant medication status on HF-HRV reactivity to emotional cues (see Table 4 and Figure 4). Individuals who endorsed antidepressant use exhibited greater HF-HRV reactivity (i.e., vagal withdrawal) to emotional cues compared to individuals who did not endorse current antidepressant usage, regardless of cue valence (i.e., positive or negative). There were no other significant main effects or interactions in the HF-HRV reactivity model.

Figure 4.

Figure 4

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Significant (p < 0.05) main effect of antidepressant medication status on vagal reactivity to emotional cues. The antidepressant x valence interaction was not significant, thus these data were collapsed across cue valence

Including BMI or VO2 peak in the reactivity models did not change the overall pattern of results. The alpha level for a baseline HF-HRV effect on HF-HRV reactivity dropped below 0.05 with BMI (t(116) = 2.10, p = 0.0375) or VO2 peak (t(112) = 2.07, p = 0.0412) in the model, as did the alpha level of the sex effect on SDNN reactivity (t(112) = 2.13, p = 0.0351) with VO2 peak. Excluding four participants with BDI-II>46 did not affect the pattern or interpretation of results in the cardiovascular reactivity models.

Discussion

HRV is emerging as a transdiagnostic biomarker of adaptability and wellness, and more generally, there is increased interest in the interrelation of cardiovascular processes and mental health. The present study sought to examine relationships of HR and HRV to depression severity, characterized as a continuous construct whose distribution spans both normative and clinical populations. HR and HRV were measured at rest and in response to emotional cues to gauge both the capacity of the cardiovascular system to adapt (baseline) as well as its real-time response to challenge (cue reactivity). Sex and current antidepressant medication status were also explored as moderators of these relationships.

The results revealed a significant main effect of depression severity on baseline HR, although this effect disappeared when cardiorespiratory fitness was included in the model, suggesting that the higher HR values observed among individuals with greater depression severity may be accounted for by lower levels of cardiorespiratory fitness. Although no significant main effects of depression severity were observed for HRV outcomes, baseline models yielded significant interactions between antidepressant medication status and depression severity for SDNN and HF-HRV. These effects were remained significant after accounting for variance shared with BMI and cardiorespiratory fitness. Emotional reactivity models were largely non-significant with the exception of a significant main effect of antidepressant medication status on HF-HRV reactivity, which also remained significant when controlling for individual differences in BMI and cardiorespiratory fitness.

As expected, individuals with higher depression severity had higher baseline HR, a finding which corroborates previous research (Agelink et al., 2002). This relationship appeared to be linear, implying a systematic increase in baseline HR as depression severity increased. The causal directionality of this relationship remains unknown, but collective evidence suggests there may be a bi-directional relationship such that cardiovascular vulnerabilities confer risk for future depression (Jandackova et al., 2016) and behavioral sequelae of depression, such as physical inactivity, may increase risk for cardiac events (Whooley et al., 2008). The present study found that the main effect of depression symptom severity on baseline HR was not moderated by either sex or antidepressant medication status, but the effect was fully accounted for by individual differences in fitness (VO2 peak). This finding suggests cardiorespriatory fitness may be protective against the potentially detrimental effects of depression on baseline cardiovascular state and supports aerobic exercise interventions aimed at improving cardiorespiratory fitness and distal outcomes in depression (Stubbs et al., 2016).

Regarding HRV, baseline SDNN and HF-HRV were expected to exhibit a negative relationship to depression severity. Whereas no direct relationships between these HRV indices and severity were observed, the significant interaction effects between severity and medication status indicated that participants who did not endorse current antidepressant use tended to show the expected negative relationship between HRV and depression severity, whereas those who endorsed antidepressant use tended to show the opposite relationship. Interestingly, the model for baseline HR did not yield the same interaction between depression severity and antidepressant medication status. This suggests that antidepressants may uniquely affect mechanisms involved in cardiovascular adaptability. This interaction was unexpected, and the present study was underpowered to detect explanatory factors underlying the differential relationship between depression severity and HRV between medicated and unmedicated individuals. Nevertheless, data from the present study, considered within the mixed extant literature, suggest that understanding the relationship between HRV and depression may be complicated by antidepressant medication use. Antidepressant medications are known to have variable treatment outcomes and side effect profiles across individuals (Anderson et al., 2012; Rush et al., 2006). The present study, as well as the larger literature, suggest their effects on HRV are no different. Rather than aiming to understand whether antidepressant medications affect cardiovascular mechanisms, future larger studies and meta-analyses should explore how individual characteristics, such as disorder severity, may influence the way antidepressants affect cardiovascular functioning.

Of note, significant main effects of current antidepressant use were observed for both SDNN and HF-HRV; antidepressant use was associated with lower levels of HRV in the present sample of young adults. This finding is consistent with previous literature suggesting that certain antidepressants may lower HRV in some individuals (Kemp et al., 2016; Licht et al., 2010; O’Regan et al., 2015). It is possible, however, that medicated participants had lower HRV prior to starting medication treatment, perhaps due to greater depression severity. Larger prospective studies are needed to assess how and for whom antidepressants alter cardiovascular adaptability.

We hypothesized males would exhibit a stronger relationship between HRV and depression severity compared to females based on findings of larger-scale longitudinal studies that have observed this interaction (Chen et al., 2010; Jandackova et al., 2016). The present findings did not support this hypothesis, as no significant interactions between sex and depression severity were observed. Given only 25% (n = 39) of the present sample identified as male, it is possible the present study was underpowered to detect this interaction. Alternatively, it is possible this sex difference may emerge later in the lifespan, as participants in the previous studies were middle-aged (mean age = 55 years, Jandackova et al., 2016) and elderly (mean age = 78 years, Chen et al., 2010) while participants in the present study were young adults (mean age = 21 years).

Contrary to our hypothesis, no significant main effects or interactions were observed between depression severity and/or the valence of emotional cues on HRV reactivity outcomes. That is, cardiovascular reactivity to emotional challenge did not vary as a function of depression severity or emotional cue type in this sample. Although validated as emotional stimuli (Lang et al., 2001), the static visual cues employed in the present study may not have evoked strong enough emotional responses to detect a significant relationship with depression severity. Indeed, other studies that have reported significant relationships between HRV reactivity and depression elicited emotional response using films (e.g., Pang and Beauchaine, 2013; Rottenberg et al., 2003), which may be more potent emotional stimuli than pictures (Ray, 2007).

We observed a main effect of antidepressant medication status on vagal (i.e., HF-HRV) reactivity to emotional cues. Participants taking medication exhibited greater vagal withdrawal compared to participants not taking medication, regardless of the cue valence (i.e., positive or negative). The effect of antidepressant medication on cardiovascular reactivity is a relatively understudied area. Many studies of HRV reactivity in depression have examined unmedicated samples (e.g., Benvenuti et al., 2015; Cyranowski et al., 2011; Rottenberg et al., 2003). Of those few studies that have included participants taking medications, one did not assess the association between medication status and cardiovascular reactivity (Panaite et al., 2016), and two did not find significant associations (Rottenberg et al., 2005; Yaroslavsky et al., 2014). A prospective study found fluoxetine treatment response, as measured by percent reduction in BDI score, was correlated with pre-treatment low-frequency HRV reactivity to sad stimuli and HR reactivity to happy stimuli (Fraguas et al., 2007), but the sample size in that study was very small (N=8). Overall, research with larger samples is needed to understand how antidepressant treatment may affect cardiovascular reactivity to emotional challenge.

The present study had a several limitations. First, the number of participants who endorsed current antidepressant treatment was small. This limited our interpretation of the present findings and precluded analysis of the potentially differential effects of various drug classes or specific drugs on HR and HRV. Thus, the present findings pertaining to the effect of antidepressant medication should be interpreted with caution. While it is encouraging that the medication effects were robust to covariate control, larger studies are needed to corroborate these findings. Second, we did not exclude participants for conditions that are known to be highly co-morbid with depression, such as anxiety and substance use disorders. Here, we sought to examine a representative sample of young adults with a range of depressive symptoms, but future studies should address the potential mediating effects of these co-morbidities on the reported findings. Third, given the cross-sectional study design and because participants were not randomized to medication treatment, causal effects of antidepressants on cardiovascular functioning could not be inferred. Fourth, as described above, cardiovascular reactivity may have been subtle due to the static visual cues employed. Finally, individuals with heightened depression severity (i.e., BDI-II≥29) were underrepresented in this sample relative to individuals with mild and moderate levels of depression symptom severity. Thus, four participants had substantially greater BDI-II scores compared to the rest of the sample. Nonetheless, exclusion of these observations from the analyses led to negligible differences in results.

Despite these limitations, the present study assessed depression on a continuous scale across normative and clinical populations, which led to clues about how antidepressant medication may alter the relationship between depression severity and cardiovascular functioning. The standardized vanilla task reduced sources of variability in baseline cardiovascular assessment. HR and HRV were assessed during emotional challenge in addition to baseline, enhancing the ability to understand how depression affects cardiovascular functioning in various states. Sex and antidepressant medication status were tested as moderators of the relationship between HRV and depression severity, and current antidepressant treatment emerged as a potential moderator. Last, the introduction of BMI and cardiovascular fitness into models revealed little relationship to the former, but a potential mediational role of cardiovascular fitness in the relationship between depression severity and HR.

The results of this study, within the context of the extant literature, suggest that cardiovascular processes are linked to depression symptomatology and thus may be useful to consider as part of depression treatment. It may be worthwhile to assess whether treatment approaches that aim to enhance cardiovascular functioning are able to bolster effects of other depression therapies while simultaneously decreasing risk for future cardiovascular problems. Such treatments could include exercise-based interventions and HRV biofeedback, both of which have empirical support for the treatment of depression (Bailey et al., 2018; Caldwell and Steffen, 2018; Paolucci et al., 2018; Siepmann et al., 2008). Regular aerobic exercise is additionally known to reduce risk for cardiovascular disease (Pate et al., 1995; Shibata et al., 2018; Whelton et al., 2002), and breathing exercises akin to HRV biofeedback have FDA approval for reducing hypertension (Lin et al., 2012; Mahtani et al., 2016), another risk factor for cardiac events. If paired with first-line pharmacological and psychotherapeutic treatments, these additional interventions may help increase effectiveness of depression treatment through the improvement of cardiovascular processes. In addition, our results suggest future studies should continue to examine the moderating effects of antidepressant medication and other depression treatments on the relationships between depression severity and cardiovascular functioning.

  • Depression severity and sex had significant associations with baseline heart rate

  • Antidepressant status moderated association between depression severity and HRV

  • Heart rate associations were no longer significant after accounting for fitness

Acknowledgments

Funding: This work was supported by the National Institutes of Health [K24AA021778 and K02AA025123].

Appendix A

Table 1.

Sample Demographic Characteristics

Characteristic N Percent
Sex
  Female 113 74.7%
  Male 39 25.3%
Race/Ethnicity
  Asian 60 39.5%
  White 44 29.0%
  Other 21 13.8%
  Latino 20 13.2%
  Black/African American 7 4.6%
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Description: Sex and race/ethnicity breakdown of the sample; total n = 152

Appendix B

Table 2.

Sample Antidepressant Data

Total Sample Endorsement N Percenta
  No 137 90.3%
  Yes 15 9.7%
Antidepressants Endorsed N Percentb
  Prozac/fluoxetinec 4 26.7%
  Celexa/citalopramc 3 20.0%
  Zoloft/sertralinec 3 20.0%
  Wellbutrin/bupropiond 3 20.0%
  Lexapro/escitalopramc 2 13.3%
  Viibryd/vilazodonec 1 6.7%
  Cymbalta/duloxetinee 1 6.7%
  Remeron/mirtazapinef 1 6.7%
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Description: Rates of antidepressant medication endorsement and types (Brand/generic) of antidepressants endorsed

a

Percent of total sample

b

Percent of sub-sample who endorsed antidepressants

c

Selective serotonin reuptake inhibitor (SSRI)

d

Norepinephrine-dopamine reuptake inhibitor (NDRI)

e

Serotonin-norepinephrine reuptake inhibitor (SNRI)

f

Atypical antidepressant

Appendix C

Table 3.

Models Predicting Cardiovascular Measures at Vanilla Baseline

Model Predictors B SE B DFa t-value p-value
HR
BDI-II 0.19 0.08 146 2.38 0.0185*
Sexb −3.81 1.68 146 −2.27 0.0247*
Antidepressantsc 0.27 2.63 146 0.10 0.9180
BDI-II × Sex −0.03 0.16 146 −0.16 0.8751
BDI-II × Antidepressants 0.18 0.20 146 0.88 0.3823
SDNN
BDI-II −0.01 <0.01 146 −1.83 0.0696
Sex 0.12 0.06 146 1.88 0.0619
Antidepressants −0.31 0.20 146 −3.15 0.0020**
BDI-II × Sex −0.01 0.01 146 −1.16 0.2480
BDI-II × Antidepressants 0.02 0.01 146 2.97 0.0035**
HF-HRV
BDI-II −0.01 0.01 145 −1.71 0.0896
Sex 0.01 0.17 145 0.06 0.9490
Antidepressants −1.21 0.27 145 −4.47 <.0001**
BDI-II × Sex −0.02 0.02 145 −1.04 0.2992
BDI-II × Antidepressants 0.06 0.02 145 3.05 0.0027**
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Description: All main effects and interaction terms included in each model of cardiovascular functioning during the vanilla baseline task; un-standardized beta coefficients, standard errors (SE) of the beta coefficients, t-values, and p-values are shown

*

= significant at p < 0.05

**

= significant at p < 0.01

a

N = 152 for HR and SDNN models; N = 151 for HF-HRV model

b

Females coded as 0 and males coded as 1 in all models; 0 = reference group

c

Un-medicated participants coded as 0 and medicated participants coded as 1 in all models; 0 = reference group

Appendix D

Table 4.

Models Predicting Cardiovascular Reactivity

Model Predictors B SE B DFa t-value p-value
HR
Valence −0.01 0.31 128 −0.02 0.9867
Baseline HR <0.01 0.05 122 −0.03 0.9744
BDI-II −0.06 0.04 122 −1.47 0.1452
Sexb −0.31 0.91 122 −0.34 0.7329
Antidepressantsc 1.86 1.33 122 1.40 0.1632
BDI-II × Sex 0.12 0.08 122 1.43 0.1556
BDI-II × Antidepressants 0.06 0.10 122 0.59 0.5577
SDNN
Valence <0.01 0.03 128 0.07 0.9462
Baseline SDNN 0.08 0.07 122 1.13 0.2596
BDI-II <0.01 <0.01 122 0.31 0.7534
Sex 0.09 0.05 122 1.69 0.0933
Antidepressants −0.15 0.08 122 −1.93 0.0554
BDI-II × Sex <0.01 <0.01 122 0.46 0.6478
BDI-II × Antidepressants <0.01 0.01 122 0.57 0.5703
HF-HRV
Valence −0.02 0.06 127 −0.36 0.7177
Baseline HF-HRV 0.12 0.06 121 1.93 0.0561
BDI-II 0.01 0.01 121 0.84 0.4053
Sex 0.01 0.14 121 0.11 0.9139
Antidepressants −0.56 0.22 121 −2.59 0.0108*
BDI-II × Sex 0.01 0.01 121 0.42 0.6772
BDI-II × Antidepressants 0.01 0.02 121 0.65 0.5166
Open in a new tab

Description: All main effects and interaction terms included in each model of cardiovascular reactivity to positive and negative emotional cues; un-standardized beta coefficients, standard errors (SE) of the beta coefficients, t-values, and p-values are shown

*

= significant at p < 0.05

a

N = 129 for HR and SDNN models; N = 128 for HF-HRV models

b

Females coded as 0 and males coded as 1 in all models; 0 = reference group

c

Un-medicated participants coded as 0 and medicated participants coded as 1 in all models; 0 = reference group

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Declarations of interest: none

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