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. Author manuscript; available in PMC: 2020 Aug 1.
Published in final edited form as: J Abnorm Child Psychol. 2019 Aug;47(8):1391–1399. doi: 10.1007/s10802-019-00527-4

Adversity and Depression: The Moderating Role of Stress Reactivity Among High and Low Risk Youth

Shimrit Daches 1,2,*, Vera Vine 2, Charles J George 3, Maria Kovacs 2
PMCID: PMC6620119  NIHMSID: NIHMS1523065  PMID: 30828776

Abstract

Adverse life events have been causally linked to depression among youth at high risk for depression. But given that not all high-risk youth develop depression following adversity, individual differences in various processes, including physiological reactivity to stress, are likely to be at play. This longitudinal prospective study tested the hypothesis that, among high-risk youth exposed to adversities, extent of physiological reactivity to laboratory stress (indexed as respiratory sinus arrhythmia; RSA) would predict subsequent depression symptoms. Subjects were youth at high (n = 80) and low (n = 74) familial risk for depression. At Time 1 (T1), RSA was assessed during a cognitive stress task. At Time 2 (T2) about 2 years later, parents reported on adversities experienced by their offspring during the interim. At T1 and T2, youth received a diagnostic evaluation, which included assessment of their depressive symptoms. The three-way interaction of group-X-adversities-X-RSA predicted T2 depressive symptoms (controlling for T1 depressive symptoms). This interaction was mostly driven by the moderating effect of RSA among high-risk youth, such that adversities predicted higher depressive symptoms for those who displayed greater RSA reactivity to stress. Among low-risk youth, an inverse marginal moderating effect of RSA was found, such that adversities tended to predict depressive symptoms for those who displayed blunted RSA reactivity to stress. Thus, high physiological stress reactivity appears to be an additional risk factor for depressive symptoms only among youth at elevated risk for such outcomes, and should be taken into consideration in efforts to prevent depression in these populations.

Keywords: stress reactivity, adversity, depression, respiratory sinus arrhythmia, high-risk offspring

Children of depressed parents are at an increased risk for depression (for reviews and meta-analyses, see Beardslee, Versage & Giadstone, 1998; Downey & Coyne, 1990; Goodman et al., 2011). However, not all high-risk children ultimately develop depression. Factors thought to play a role include the extent of exposure to stressful life events and the ways in which offspring physiologically react to such events (Goodman & Gotlib, 1999; Steinberg & Avenevoli, 2000). The primary goal of the current study was to examine the interaction of adversities and physiological reactivity to stress to prospectively predict depressive symptoms among high- and low-risk youth.

The terms “adversities” and “stressful” or “negative life events” are used interchangeably in the present article and refer to the occurrence of negative events that have been considered harmful in the developmental literature. For example, separation from parental figures, death of close relatives, family financial problems, abuse, or being bullied represent adversities in the lives of children and youth (Green et al., 2010; Mayer et al., 2009; McLaughlin et al., 2015; Yeung, Linver & Brooks–Gunn, 2002). The association between adversities and mental health outcomes (specifically depressive symptoms) has been shown across the life span (e.g., Chapman et al., 2004; Hankin, 2015). Importantly, the reported associations are robust regardless of how adversities are defined (e.g., specific negative events, total negative event counts, weighted negative event counts).

The diathesis-stress model of psychopathology (Heim & Nemeroff, 1999) suggests that interactions between external factors and internal processes yield a more nuanced understanding of the etiology of disorders. Thus, the association of adversities and depression should be subject to various intervening processes. Physiological processes, particularly those associated with the parasympathetic branch of the autonomic nervous system (Porges, 1995), represent a major component of a person’s internal response to stress. More specifically, release of parasympathetic control over the heart via the vagal nerve in response to external events results in increased heart rate, which allows the individual to respond to the demands of the situation. Respiratory sinus arrhythmia (RSA), or variability in heart rate as a function of slowed breathing, is a primary index of cardiac parasympathetic control. At rest, high RSA is desirable and indicates energy preservation (Porges, 2007). However, RSA is expected to decrease in response to stress, which signals relative release of cardiac parasympathetic control in order to facilitate survival and coping behaviors (Porges, 2007). Indeed, RSA decrease (greater RSA reactivity) to a laboratory stressor is associated with better functional outcomes (Gentzler, Santucci, Kovacs & Fox, 2009; for a meta-analysis see Graziano & Derefinko, 2013). On the other hand, atypical RSA reactivity (significantly greater or smaller decrease as compared to normative reactions) is associated with depression (for a review see Hamilton & Alloy, 2016).

Given its theorized adaptive function, normative RSA reactivity is associated with resilience in the face of negative life events. RSA reactivity to a laboratory stressor served as a protective factor in the concurrent and prospective relationship between exposure to parental marital conflict and internalizing symptoms of children (El-Sheikh & Whitson, 2006; Katz & Gottman, 1997; Whitson & El-Sheikh, 2003). McLaughlin, Alves and Sheridan (2014) reported that a broader range of adversities positively predicted internalizing symptoms among community-based youth with low (but not with high) RSA reactivity to a laboratory social stressor. Among healthy adults, Stange, Hamilton, Olino, Fresco and Alloy (2017) found that increased RSA reactivity to sad stimuli in the laboratory was prospectively associated with lower levels of depressive symptoms in response to life stress. Thus, it seems that individual differences in parasympathetic reactivity to stress may moderate adjustment to adverse life events, which is in line with the buffering effects of other normative physiological responses in the context of adversity (Badanes, Watamura & Hankin, 2011; Ouellet-Morin et al., 2013; von Klitzing et al., 2012).

It is unclear, however, whether the links between adversities, RSA reactivity, and depressive symptoms among youth at high familial risk for depression could be expected to be similar to the documented associations that characterize normal controls. Previous work from our group suggests that children with a familial history of depression show several abnormalities in RSA. For one, they fail to show the developmentally normative increase in resting RSA (Gentzler, Rottenberg, Kovacs, George & Morey, 2012). They also exhibit an atypical RSA pattern (combination of resting RSA and RSA reactivity to sad stimuli) that predicts increased depressive symptoms (Yaroslavsky, Rottenberg & Kovacs, 2014). These findings raise the possibility that individual differences in RSA reactivity might moderate the degree of maladjustment among adversity-exposed youth, depending on depression risk status. Namely, compared to their healthy counterparts, RSA reactivity to stress may fail to buffer youth at high familial risk for depression.

In light of the literature, the present study examined whether the prospective relationship between adversity and depression symptoms is moderated by parasympathetic reactivity to stress among youth at high and low familial risk for depression. Consistent with past work, we hypothesized that adversities will be associated with depressive symptoms. We also expected that RSA reactivity to stress will differentially moderate the association between adversities and depressive symptoms, depending on familial depression risk status. Namely, for the low-risk but not the high-risk group, we expected greater RSA reactivity to stress to function as a protective factor against depressive symptoms in response to adversity.

Method

Participants

We report on 80 high-risk offspring whose parents had lifetime histories of depression and 74 low-risk offspring who had parents free of any lifetime major psychiatric diagnoses. Importantly, both groups were themselves free of lifetime depressive disorders. Five additional youth were excluded due to missing psychosocial or physiological data (3 from the high-risk group). At study entry (T1), the sample averaged 11.38 years old (8–16, SD = 2.47) and was 49% male. Racial background was 66% Caucasian, 24% African American, and 10% multi-racial. At follow-up (T2), on average 2.38 years later (1.72–5.03, SD = 0.61), the youth averaged 13.76 years of age (9–19, SD = 2.55)1. The high- and low-risk groups did not differ in sex, age, racial distributions, but socio-economic status was significantly higher in low-risk youth. The high-risk group showed higher rates of anxiety and behavioral disorders than their low-risk peers2. A summary of all demographic and clinical variables is presented in Table 1.

Table 1.

Demographic characteristics of youth at high- and low-risk for depression.

Variable Group Effect Size
High-risk n = 80 Low-risk n = 74
Sex (n; % female) 39 (49%) 39 (53%) Φ = .04
Age, years at T1 11.19 (8 – 16; 2.48) 11.58 (8 – 16; 2.46) d = .22
Age, years at T2 13.50 (10 – 19; 2.60) 14.05 (10 – 19; 2.47) d = .30
Race (n of C/A/B) 57/14/9 44/23/7 φ = .11
Hollingshead’s Level of Socio-economic status (n of I/II/III/IV/V) 12/19/20/11/18 5/56/8/5/0 φ† = .32*
Anxiety Dx T1 (%) 18 3 χ2 = 11.11*
Behavioral Dx T1 (%) 23 9 χ2 = 6.43*
Events 3.65 (0 – 8; 2.19) 2.23 (0 – 8; 2.26) d = .64*
Sx T1 4.26 (0 – 13; 3.45) 2.09 (0 – 7; 2.04) d = .46*
Sx T2 4.08 (0 – 23; 5.27) 1.27 (0 – 12; 2.18) d = .58*
Depression Dx T2 (%) 9 1 χ2 = 4.27*
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Note. Data are given as means (Range; SD) unless otherwise noted. Race: C = Caucasian, A = African American,B = Biracial;Hollingshead’s Level of Socio-economic status: I = highest, V = lowest of higher scoring parent. Anxiety Dx = history of an anxiety disorder; Behavioral Dx = history of a behavioral disorder (e.g., ADHD, oppositional defiant disorder). Events = adverse life events reported over the follow up; Sx T1 = Depressive symptoms reported at T1; Sx T2 = Depressive symptoms reported at T2; Depression Dx = depression disorder diagnosed at T2.

†

Categories I and II vs. III, IV, and V.

*

p < .01, all effect sizes and p-values adjusted for family clusters.

Parents’ lifetime histories of mood disorder were established in a previous Program Project on risk factors for childhood-onset depression (COD; Miller et al., 2002). As described in detail elsewhere (Forbes, Miller, Cohn, Fox & Kovacs, 2005), parents of the high-risk group had to meet DSM criteria for a depressive disorder episode (n = 37 with unipolar depressive disorder; n = 11 with bipolar disorder) with a first onset during childhood or early adolescence. The final sample represented a total of 89 families; 55% of them contributed more than one offspring to the study.

Procedures

The current study met with institutional review board approval at the University of Pittsburgh. Written informed consent was obtained from each parent and youth at T1 and T2. At T1, all youth completed a psychiatric diagnostic interview along with self-rated questionnaires. They also completed an experimental protocol that entailed viewing a neutral film and completing a stressful cognitive task, along with several other tasks (not included in the present report). The neutral film clip, which served as baseline, displayed fish swimming in an aquarium and was 3 minutes in length; an Emotional Stroop task (described below) served as the experimental stressor. Throughout the experimental protocol, psychophysiological data were collected continuously. Participants were asked to sit as still as possible during the procedures. A research assistant tracked subject compliance, and no collection errors were reported. At T2, youth again completed a psychiatric diagnostic interview along with self-rated questionnaires.

Measures and Tasks

Symptom and diagnostic assessment.

Parents and offspring were interviewed separately about the offspring’s current depressive symptoms and psychiatric disorders using the multi-informant Interview Schedule for Children and Adolescents-Diagnostic Version (ISCA-D), which is an extension and modification of the Interview Schedule for Children and Adolescents (ISCA; Sherrill & Kovacs, 2000) and includes most DSM-IV Axis-I diagnoses. The clinician’s overall rating for each of 17 depression symptoms (based on separate interviews with the offspring about him/herself and the parent about the offspring) were used to compute a continuous depression severity score (range: 0 to 34). Interviewers were trained clinicians with a Masters or Doctoral degree. Final diagnoses were derived by clinical consensus, similar to the process of best-estimate consensus diagnoses (Maziade et al., 1992). In an interrater reliability trial using recordings of randomly selected T1 interviews, the sum of current depression symptom items showed excellent reliability (ICC = .91, n = 30 subjects).

Adverse life events.

Exposure to major adversities between T1 and T2 was assessed using a fully structured interview with the parent as to whether or not the offspring had been exposed to 31 adversities, including: a) Parental health problems: hospitalization, physical or psychiatric illness of biological or stepparents; b) Death of close relatives: parental, or other death in the family; c) Sociodemographic events: financial problems, moving, parental unemployment, natural disaster, loss of home; d) Intrafamilial events: birth, hospitalization, psychiatric illness of sibling, foster care, family arguments, and divorce of parents; and e) Physical or sexual abuse. Event occurrence was recorded as “1” (yes). The final score was the arithmetic sum of the events that were endorsed. We have previously reported that our event list discriminated emotionally healthy school-based controls from clinically referred youth with major depressive disorder (Mayer et al., 2009).

Measurement of RSA.

At T1, an electrocardiogram (ECG) was recorded via Mindware BioLab software (MindWare Technologies, Ltd., Gahanna, OH). According to published guidelines (Berntson et al., 1997), the ECG signals were acquired at 1000-Hz sampling rate. The electrodes were placed on participants’ lower left and upper right rib cage. Respiratory data were collected via bands placed around the abdomen and calibrated against a fixed volume bag, which was calculated by the Mindware HRV 3.0.21 software (MindWare Technologies, Ltd., Gahanna, OH).

Mindware HRV 3.0.21 software (MindWare Technologies, Ltd., Gahanna, OH) was used to calculate RSA. R-wave markers in the ECG signal were processed with the MAD/MED artifact detection algorithm, and signals were visually inspected, and suspected artifacts manually corrected (Berntson et al., 1997). The interbeat interval (IBI) series was resampled in equal 250 ms intervals, linearly detrended, and tapered using a Hanning window. Heart rate variability (HRV) was calculated using Fast Fourier transformation analysis of the IBI series, and RSA was defined as the log transformed high frequency (HF) power band of HRV (.12-.40 Hz range; see Berntson et al., 1997). Hereafter we refer to HF-HRV as RSA, since HF-HRV is the power band of HRV that occurs in the typical range of respiration.

RSA was calculated for each epoch separately (i.e., baseline and during the stressful task). In addition, an RSA change score (RSA reactivity) was computed by subtracting baseline RSA from RSA during the stressful task. Note that, given the negative mean of RSA reactivity scores, higher/more positive reactivity scores reflect above-average heart rate variability relative to baseline, and in some cases even augmentation of RSA. Lower/more negative reactivity scores reflect lower than average heart rate variability, or more extreme RSA withdrawal or “reactivity,” compared to baseline.

Stress task.

The experimental stressor was a version of the Emotional Stroop task (E-Stroop; Williams, Mathews & MacLeod, 1996), which is an adaptation of the color-word Stroop task (Stroop, 1935). Numerous studies have demonstrated the feasibility of using demanding cognitive tasks, such as mental arithmetic, the Stroop task, problem solving, matching figures to numbers, as laboratory stressors (for a review see Graziano & Derefinko, 2013). The Stroop task has been used previously as a stressor to produce heightened physiological reactivity (e.g., Duan et al., 2015; Gerra et al., 2000; Lutgendorf, Kreder, Rothrock, Rtliff & Zimmerman, 2000; McCann et al., 1993). The task involved viewing facial expressions of emotions (happiness, sadness, or neutral) that were presented over backgrounds of different colors (red, green, or blue). Subjects were asked to label the emotion, or name the color of the background, or switch between emotion- and color-naming based on whether the face was presented inside a frame. The stimulus set was created using 2 male and 2 female models from the NimStim Set of Facial Expressions, a standardized set of emotional faces (Tottenham et al., 2009). Prior to this task, an emotion identification test of 30 randomly selected faces (10 of each valence) was conducted, and all subjects were able to name the emotions successfully.

The task was presented using E-Prime® experimental software (Psychology Software Tools, Inc., Pittsburgh, PA) equipped with a microphone connected to a serial response box. Following standard Stroop administration, subjects were given 3 practice trials of the task (1 of each condition). In the task, stimuli were presented on the screen until the subject verbally responded, which triggered a 2s inter-trial interval before the next trial. There were 180 trials. Participants were instructed to respond as quickly and accurately as possible. Average time to complete the task was 9.27 minutes (SD = 1.30). There was no difference between groups in completion time (F = .499, p = .48).

Data Processing and Analysis

Descriptive analyses were conducted in SPSS (version 22, IBM Corp., Armonk, NY) and other analyses in SAS (version 9.3, SAS Institute, Inc., Cary, North Carolina). Variables were compared across groups using analyses of variance (ANOVAs) or χ2 tests. We also computed correlations between our main variables. To examine our hypotheses regarding the effect of group (high vs. low risk), adverse life events between T1 and T2, and RSA change score on T2 depressive symptoms, we conducted an analysis of covariance (ANCOVA) with sex, T1 age, respiration rate at stress induction, RSA at baseline, and T1 depressive symptoms as covariates. For group comparisons, ANOVA and χ2 were estimated using Taylor Series linearization (Woodruff, 1971) to correct for population mean variance attributable to family clusters (i.e., offspring who were siblings). For ANCOVA main effects and interactions, a linear mixed effects model was used to account for a family intercept random effect while fixed effect tests were corrected via empirically based (a.k.a., “sandwich”) variance estimators. Effect sizes of group differences are reported in terms of φ for Rao-Scott--corrected χ2, and d for F statistics. Effect sizes of ANCOVA fixed effects are reported as partial R2β.

The three-way interaction of depression risk status by adversities by RSA change score was explored using post-hoc ANCOVAs for each risk group where, as before, depressive symptoms at T2 was the outcome, adversities between T1 and T2 and RSA change score were predictor variables, and sex, T1 age, respiration rate at stress induction, RSA at baseline and T1 depressive symptoms were covariates. To visualize the interaction, model-based estimates of least-squares means were plotted using Tumble graph for the range of ± 1 SD group levels of adversity for high (i.e., +1 SD) and low (−1 SD) RSA change score for each group, assuming mean values of co-variates (Bodner, 2016).

Results

Descriptive Characteristics

Mean heart rate was 84.98 (SD = 12.30) during baseline and 88.03 (SD = 11.33) during the stress task. The low- and high-risk offspring groups did not differ in mean RSA at baseline (log(ms2) = 6.84 (SD = 1.00) vs 6.92 (SD = 1.29); F(1,88) = 0.15, p = .70, d = .08) or during the stress task (log(ms2) = 6.32 (SD = 1.02) vs 6.45 (SD = 1.13); F(1,88) = 0.50, p = .48, d = .15). Likewise, respiration rates for the low- and high-risk offspring were similar during baseline (breaths per minute: 14.78 (SD = 4.86) vs. 14.41 (SD = 4.75); F(1,88) = 0.21, p = .65, d = .10) and the stress task (breaths per minute: 18.19 (SD = 3.33) vs. 17.71 (SD = 3.49), F(1,88) = 0.66, p = .42, d =.17). As shown in Table 2, RSA response to stress induction (M = 6.38, SD = 1.08) correlated with T1 age, length of the task and respiration rate. Also, females tended to report more depressive symptoms than males at T1 (Females: M = 3.74, SD = 3.42; Males: M = 2.68, SD = 2.52, F(1,88) = 4.62, p = .03, d = .46) and at T2 (Females: M = 3.58, SD = 4.96; Males: M = 1.86, SD = 3.33, F(1,88) = 7.43, p = .01, d = .58).

Table 2.

Correlations among key study variables (N = 154).

Age Events Sx T1 Sx T2 RSA Baseline RSA Reactive Respiration Baseline Respiration Reactivity Length of task
Sex −.02 −.02 .17* .20* −.07 −.10 .17* .09 −.15
Age .02 −.02 .09 −.13 −.32** −.09 .42* −.42**
Events . .39** .36** −.04 −.03 −.04 .03 .05
Sx T1 .54** .09 .04 −.02 −.02 .11
Sx T2 . −.07 −.06 −.02 −.01 −.04
RSA Baseline .80** −.22** −.20* −.01
RSA Reactivity −.14 −.44** .29**
Respiration Baseline .20* −.01
Respiration Reactivity −.46**
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Note. Sex = high value represents females; Age = years at T1; Events = number of adverse life events reported over the follow up; Sx T1 = Depressive symptoms reported at T1; Sx T2 = Depressive symptoms reported at T2; RSA = Respiratory Sinus Arrhythmia. Length of task = time to complete the Stroop task in seconds.

*

p < .05,

**

p < .01

Predicting symptoms of depressions

Using the ANCOVA framework, we modeled T2 depressive symptoms using subject’s group (low- or high-risk), adversity score, and RSA change score, with sex, T1 age, respiration rate at stress induction, RSA at baseline and T1 depressive symptoms as covariates. While there were no significant main effects (p > .1), the three way interaction of group by adversities by RSA reactivity was significant (F(1,54) = 7.19, p < .01, R2β = .12).

To probe this interaction, we conducted two sets of post-hoc ANCOVAs, one for each risk group. In these post-hoc analyses, we again predicted T2 depressive symptoms using adversity scores and RSA change score (sex, T1 age, respiration rate at stress induction, RSA at baseline and depressive symptoms at T1 were covariates). For the low-risk group, the main effect of RSA reactivity was not significant (F = 0.01, p = .93, R2β < .01), while the main effect of adversity was marginally significant (F = 3.58, p = .07, R2β = .11). Additionally, a marginally significant interaction was found between adversities and RSA change score (F = 3.52, p = .07, R2β = .11). As illustrated in Figure 1, among low-risk youth, more adversities tended to predict higher levels of T2 depressive symptoms among those with low RSA reactivity to stress (less withdrawal; β = .26, p = .07), but there was no significant association between adversities and depressive symptoms among youth with elevated RSA reactivity to stress (exaggerated withdrawal; β = .09, p = .63). For the high-risk group, there was no main effect for adversities (F = 0.20, p = .66, R2β = .01), but a marginal effect of RSA change score emerged (F = 4.04, p = .06, R2β = .16). The interaction between adversities and RSA change score was significant (F = 7.88, p = .01, R2β = .27). As illustrated by the dark lines in Figure 1, among high-risk youth, more adversities were associated with higher levels of T2 depressive symptoms among those with elevated RSA reactivity to stress (exaggerated withdrawal; β = .74, p < .01), but not among those with decreased RSA reactivity (β < .01, p = .99).

Figure 1.

Figure 1.

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Depressive Symptoms at T2 Predicted by Adversity Score, Risk Group, and RSA Stress-Reactivity.

Discussion

We investigated the role of adverse life events in the progression of depressive symptoms among youth at high and low familial risk for depression and the moderating role of physiological reactivity to stress in that association. Specifically, we hypothesized that adversities will predict depressive symptoms. We also expected that physiological (RSA) reactivity to stress will differentially moderate the association between adversities and depressive symptoms, depending on familial depression risk status. While adversities prospectively correlated with T2 depressive symptoms, this result was no longer evident in a multivariate model, which controlled for T1 depressive symptoms. In other words, adversities between T1 and T2 provided scant explanatory power above and beyond the effects of T1 depressive symptoms. However, we found evidence supporting our second hypothesis: RSA reactivity to stress moderated the association between adversities and depressive symptoms differently among youth at high versus low familial risk for depression. Among high-risk youth, adversities predicted higher depressive symptoms for those with greater RSA reactivity to stress. By contrast, among low-risk youth, adversities showed only a trend toward predicting depressive symptoms for those with blunted RSA reactivity to stress.

Our findings suggest that the 3-way interaction of group by adversity by RSA reactivity was driven mostly by the strong moderating effect of RSA among high-risk youths. Ellis and Boyce (2008; 2011) proposed that in stressful, unsupportive environments, children’s physiological reactivity may over-sensitize them to upheavals and threats. Accordingly, it is not surprising that among our high-risk youth, heightened RSA response to stress appeared to have a harmful effect. Available research suggests that maladaptive outcomes of adversity are especially strong for offspring of depressed parents, as compared to controls (Bouma, Ormel, Verhulst & Oldehinkel, 2008; Morris, Ciesla & Garber 2010; but see Malcarne, Hamilton, Ingram & Taylor, 2000). Our findings suggest that the combination of having a parent with an early-depression history and experiencing adversities represents a challenge for youth who are physiologically more reactive to stress. Such a pattern is consistent with accounts that highlight the adverse biological costs of long-term exposure to stress and portray the blunting of stress-related responses as a possible compensatory adaptation in groups at high levels of risk (e.g., Engert et al., 2010; Harkness, Stewart & Wynne-Edwards, 2011).

Although the present study was not designed to explore the etiology of atypical RSA response, considerable work suggests that stressful early environments tend to have lasting, sensitizing effects on physiological response systems (for a review see Kaffman & Meaney, 2007; Lupien, McEwen, Gunnar & Heim, 2009). Thus, it is likely that the atypical RSA response we found in our high-risk youth was shaped by adversities they probably experienced during earlier development. Future research could utilize a longer prospective framework to trace the developmental trajectory of RSA reactivity to stress in vulnerable populations.

Ellis and Boyce (2008; 2011) further theorized that for children reared in generally un-stressful, supportive environments, heightened physiological reactivity may facilitate social skills and capitalize on available supportive resources. While not statistically significant, our marginal findings regarding the low-risk group are consistent with previous reports that blunted RSA reactivity to stress in normal populations has a harmful effect on functioning (El-Sheikh & Whitson, 2006; McLaughlin et al., 2014). Blunted RSA reactivity to stress among presumably healthy individuals may reflect hypo-activity in the parasympathetic branch of the autonomic nervous system, which might constrain an individual’s ability to respond adaptively to adverse life situations.

Our findings should be interpreted in the context of several limitations. First, we assessed physiological reactivity to a cognitive stress task, which may constrain the generalizability of findings. More specifically, it has been reported that the moderating role of physiological stress reactivity in the association between adversity and maladaptive outcomes can vary depending on the nature of the stressor (e.g., Obradović, Bush & Boyce, 2011), although others have shown no difference (e.g., Holterman, Murray-Close & Breslend, 2016). In addition, while our cognitive stress task demanded a verbal response from participants, our baseline task (e.g., watching a neutral film) involved only visual viewing; thus, the reactivity score may partly reflect the effect of verbalization. Future research should consider other baseline tasks. Further, multiple indices of physiological reactivity would better reflect the complexity of autonomic nervous system functioning, along the lines reported by Holterman and colleagues (2016). Finally, our study may be faulted for not incorporating pubertal examination of the subjects; instead, we statistically accounted for it in our models.

Yet, several strengths make our results compelling, including the prospective design and well characterized sample of participants. Because we limited our sample to never-depressed offspring, patterns of responding in the high-risk group were not confounded by current or past experiences of depression and can thus be considered true indicators of risk. As a result, we conclude that RSA reactivity to stress warrants further consideration as a biological mechanism that shapes the effects of adversity on depression. Interestingly, this mechanism appears to function differently among individuals at high and those at low familial risk for depression. Future studies can explore further how a physiological stress response may serve adaptation vs. maladaptation depending on depression risk status (Ellis & Boyce, 2008, 2011).

Acknowledgments

This study was supported by the National Institute of Mental Health Grant number: RO1 MH085722, Rockville, MD.

Footnotes

1

Average age and time interval between T1 and T2 of excluded participants did not differ from participants included in the study.

2

Adding life-time anxiety and behavioral disorders to the statistical models did not change the direction and significance of the results.

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