Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2018 Jan 1.
Published in final edited form as: Psychoneuroendocrinology. 2016 Oct 19;75:222–227. doi: 10.1016/j.psyneuen.2016.10.014

Trait Hostility and Cortisol Sensitivity Following a Stressor: The Moderating Role of Stress-Induced Heart Rate Variability

Kyle W Murdock 1, Angie S LeRoy 1,2, Christopher P Fagundes 1,3,4
PMCID: PMC5135566  NIHMSID: NIHMS829509  PMID: 27838516

Abstract

Hostility and adverse health outcomes are inconsistently associated in the literature. Self-regulation and cortisol secretion may play important roles in differentiating those hostile individuals who are at greater risk of negative health outcomes from those who are not. In the present study, we sought to examine if having high self-regulatory strength, as indexed by high stress-induced high-frequency heart rate variability (HF-HRV), buffered the effects of hostility on cortisol secretion. Participants (N = 213) completed a self-report measure of hostility and measurement of HF-HRV at rest and during a social stress task. Saliva samples were collected immediately before (one sample), and over a 50 minute period after (six samples), the stress task to evaluate cortisol secretion over time. Hostile individuals were less likely to demonstrate cortisol sensitivity (i.e., high change in cortisol over time) when they had high stress-induced HF-HRV. Such findings are important given that cortisol sensitivity increases risk of metabolic and inflammatory disorders via glucocorticoid resistance and inflammation. Therefore, interventions that increase stress-induced HF-HRV may reduce the impact of hostility on health outcomes.

Keywords: hostility, heart rate variability, cortisol, coronary heart disease

1. Introduction

There is wealth of evidence in the health psychology and behavioral medicine literatures indicating that chronic anger and hostility contributes to the etiology of coronary heart disease (CHD) and other life-threatening illnesses. Indeed, episodes of anger and hostility precipitate coronary events (Dembroski et al., 1983). Hostility is a personality trait that includes cynicism/mistrust of others, anger, and overt and repressed aggression (Cook & Medley, 1954). Importantly, hostility is an independent risk factor for coronary heart disease and all-cause mortality after accounting for traditional health behaviors (Chida & Steptoe, 2009; Miller et al., 1996). Exaggerated stress reactivity partially explains these findings (Kiecolt-Glaser, et al., 2005). Although hostility is a significant independent predictor of cardiac events, the findings relating hostility with adverse clinical outcomes are inconsistent (Hemingway, 1999). For example, hostility was not associated with CHD among large samples of patients undergoing angiography (Dembroski et al., 1985; Helmer et al., 1991). Moreover, a non-significant association between hostility and CHD was identified in a 30-year longitudinal study (Leon et al., 1988). Accordingly there are likely third variables that influence the association between hostility and health. In the present study, we sought to examine under what conditions hostility is associated with negative physiological outcomes.

Hostility is associated with substantial physiological repercussions for some hostile individuals, but importantly, not for others (e.g., Weidner et al., 1989). The mechanisms driving this finding are not fully understood. One possibility is that there are alterations in physiological systems that regulate hormonal activity, such as the hypothalamic–pituitary–adrenal (HPA) axis, for some hostile individuals. Widely referred to as the “stress hormone,” cortisol is a glucocorticoid that is released by the adrenal glands when the hypothalamic pituitary adrenal (HPA) axis is activated by stress. Cortisol is important for establishing homeostasis after exposure to a stressor. In two studies, cortisol production was high among hostile individuals who developed coronary heart disease and type 2 diabetes (Brydon et al., 2010; Hackett et al., 2015). Such findings suggest that cortisol is important for distinguishing between hostile individuals who experience negative health outcomes and those who do not. Importantly, recent work suggests that cortisol sensitivity (i.e., change in cortisol concentrations over time), as opposed to total cortisol output, is associated with glucocorticoid resistance and negative health outcomes such as inflammatory and metabolic diseases (e.g., coronary heart disease, rheumatoid arthritis, sepsis, type 2 diabetes, metabolic syndrome; for a review, see Quax et al., 2013). Cortisol sensitivity is determined by multiple factors including functional polymorphisms of the gene that encodes glucocorticoid receptors, chronic stress, and waking hours (Quax et al., 2013). It is unclear why some hostile individuals would demonstrate more cortisol sensitivity than others; however, self-regulation likely plays an important role given the association between stress and cortisol sensitivity.

1.1 Self-regulation, heart rate variability, and health

Individuals with poor self-regulatory skills (i.e., the ability to regulate behavior, emotion, and cognition; Gross, 2013) are at risk of poor physical health. Indeed, poor self-regulation is associated with increased risk of cardiovascular disease (Thayer et al., 2010) and mortality in comparison with better self-regulation (Mauss & Gross, 2004). Given that hostility is characterized as the experience of cynicism, anger, and either overt or repressed aggression, hostile individuals need to regulate internally generated thoughts, behaviors, and emotions in order to avoid negative mental and physical health outcomes (Andrews-Hanna, Smallwood, & Spreng, 2014).

According to the neurovisceral integration model (Thayer & Lane, 2009), high frequency heart rate variability (HF-HRV) reflects people’s ability to flexibly respond their environment, including the regulation of stressful thoughts. Stress-induced HF-HRV has been described as representing one’s regulatory strength, or the ability to exert enough self-regulatory effort to overcome demanding circumstances (e.g., Beauchaine, 2001; Segerstrom & Nes, 2007). Hostile individuals may benefit from high HF-HRV when confronted with hostile thoughts and feelings. Indeed, hostile individuals who were exposed to a biofeedback training program that improved HF-HRV demonstrated lower hostility one month after the treatment (Lin et al., 2015). However, it is unclear whether stress-induced HF-HRV changes the association between hostility and biological outcomes such as cortisol output. Given that stress-induced HF-HRV represents regulatory strength, high stress-induced HF-HRV may buffer the negative physiological effects of hostility. That is, when confronted with hostile thoughts or feelings, those with high stress-induced HF-HRV may have the regulatory strength necessary to adaptively regulate their thoughts or feelings so they do not lead to maladaptive physiological stress responses such as cortisol sensitivity. Those with low stress-induced HF-HRV may be more likely to act on, or ineffectively repress, hostile thoughts or feelings, and experience poor physiological outcomes as a result.

In the present study, we sought to examine the potential interactive effects between hostility and stress-induced HF-HRV in predicting cortisol output following a stressor. Consistent with the available literature, it was hypothesized that higher hostility would be associated with greater cortisol sensitivity (i.e., change in cortisol concentration over time following a stressor); however, we hypothesized that individual’s stress-induced HF-HRV would change the association between hostility and cortisol sensitivity such that the association would be significant among those with low stress-induced HF-HRV, but not among those with high stress-induced HF-HRV.

2. Materials and Methods

Data from the Pittsburgh Cold Study 3 were utilized in the present study. The data are publicly available and have been described previously (e.g., Cohen et al., 2013; Janicki-Deverts et al., 2014). Briefly, healthy individuals from the Pittsburgh, Pennsylvania area were recruited to participate between 2008 and 2011. The study was approved by the Carnegie Mellon University and University of Pittsburgh institutional review boards. All participants provided informed consent and were compensated with $1,000 for completing the full study protocol.

For the present study, participants (N = 213) completed a self-report measure of trait hostility and HF-HRV was measured at rest and during the well validated Trier Social Stress Test (TSST; Kirschbaum et al., 1993). In the present study, participants were told that they would be delivering a five-minute speech. The speech involved defending against an alleged transgression (i.e., shoplifting or a traffic violation which were counterbalanced). Following a five minute preparation period, participants delivered the video recorded speech during which HF-HRV was recorded. After the speech, participants completed a mental arithmetic task, which is standard for the TSST. Participant saliva was collected before and after the TSST as described below.

2.1 Measures

2.1.1Trait hostility

Trait hostility was measured with a modified version of the Cook-Medley Hostility Scale (Cook & Medley, 1954) in which the 20 items comprising the cynicism, hostile affect, and aggressive responding subscales were utilized as recommended by Barefoot et al. (1989). For each item, participants were asked to indicate whether a statement about how an individual may view others was true or false for them. The number of responses that were consistent with hostility (two items were reverse coded) for each participant were summed to form an overall score in which higher scores represent greater hostility. Internal consistency for the 20 item scale in the present was acceptable (α = .71).

2.1.2 Heart rate variability

HF-HRV was recorded using a respiration band and three electrocardiogram leads (Vernier Software & Technology, Beaverton, OR). The baseline period lasted 20 minutes. Participants were instructed to sit upright in a chair and rest quietly. Interbeat interval (IBI) sequences were recorded using an automated algorithm (Mindware Version 2.51, Mindware Technologies, LTD) and a 250 Hz sampling frequency (Malik, 1996). Measurement of HF-HRV during baseline was separated into five minute epochs. IBIs were inspected for abnormalities and edited manually. Spectral analysis of IBIs was conducting using a Fast Fourier transformation algorithm (Duhamel & Vetterli, 1990). High frequency (HF) band power was calculated as the sum of the powers associated with any peaks in the range of 0.12 Hz to 0.40 Hz. The five baseline epochs were averaged to form an overall indicator of baseline HF-HRV. The same procedure was utilized to measure stress-induced HF-HRV during the TSST, which consisted of a single five minute epoch.

2.1.3 Cortisol

Saliva samples were collected with Salivette® devices (Sarstedt, Rommelsdorft, Germany), which include a roll of cotton contained within a collection tube. At each collection point, participants were instructed to place the cotton roll in their mouths and chew it until it became saturated before placing the cotton role into the inner vial of the Salivette and capping the outer tube. Saliva was collected immediately before engaging in the TSST, immediately following the TSST, and every ten minutes during a 50-minute period thereafter (7 samples total). Cortisol samples were sent to Dr. Clemens Kirschbaum’s laboratory in Dresden, Germany where they were assayed. Time-resolved fluorescence immunoassay, with a cortisol-biotin conjugate as a tracer1, was utilized to determine cortisol levels in saliva (Dressendörfer et al., 1992).

2.1.4 Covariates

Self-reports of participant age, sex, race, and smoking status were provided. Moreover, participant height and weight were measured to calculate a body mass index (BMI). The Macarthur Scale of Subjective Social Status USA ladder version (Adler et al., 2000) was utilized to measure adult socioeconomic status (SES). Participants were asked to indicate their own social status on an illustration of a nine step ladder in which the top represents those with the most education, money, and respected jobs. Conversely, the bottom step of the ladder represents those with the least education, money, and respected jobs. Participants were asked to place an “X” on the ladder that best represented their current position. Scores range from 1 (lowest status) to 9 (highest status).

2.2 Analytic strategy

Full information maximum likelihood (FIML) was utilized to handle random missing data, which is superior to listwise deletion or mean imputation (e.g., Akaike, 1998). Data were obtained from all participants for trait hostility, age, sex, race, BMI, smoking status, and SES. Valid HF-HRV data was obtained from 199 participants at baseline and 190 participants during the stressor. Furthermore, valid cortisol measurements across all seven time points were obtained from 202 participants. Baseline HF-HRV was included as a covariate in order for our results to reflect stress-induced HF-HRV relative to baseline. As expected when examining HF-HRV, baseline and stress-induced HF-HRV demonstrated high skewness and kurtosis (all values greater than 3.25). Accordingly, a natural log transformation was utilized to normalize the distributions of the variables. Linear regression analyses were utilized to test for moderation (Hayes, 2013). Predictors were mean centered prior to the analyses. Further, the Johnson-Neyman technique was utilized to identify regions of significance in moderation analyses (Johnson & Fay, 1950). We included the area under the curve (AUC) as our dependent variable given that stress-induced cortisol secretions change over time (e.g., Earle, Linden, & Weinberg, 1999; Elzinga, Bakker, & Bremner, 2005). Specifically, we calculated AUC with respect to sensitivity over time to test our primary hypotheses (AUCI; see Fekedulegn et al., 2007). We also utilized AUC with respect to baseline (AUCB), which reflects total cortisol output (Fekedulegn et al., 2007), in ancillary analyses.

3. Results

Descriptive statistics for study variables are presented in Table 1. Zero-order correlations revealed that participant sex (r = − .16, p = .02) and race (r = − .16, p = .02) were significantly associated with trait hostility such that males and non-White participants reported greater hostility. Participant age (r = − .47, p < .01), baseline HF-HRV (r = .72, p < .01), and body mass index (r = − .34, p < .01) were associated with stress-induced HF-HRV. Furthermore, participant age (r = − .18, p = .01) and sex (r = − .26, p < .001) were associated with cortisol AUCI, with greater AUCI among males. Using a linear regression analysis (see Table 2), trait hostility and stress-induced HF-HRV were not significantly associated with cortisol AUCI; however, there was an interaction effect such that trait hostility was statistically significantly associated with cortisol AUCI when stress-induced HF-HRV was low, but not high (see Figure 1). Specifically, trait hostility and cortisol AUCI were significantly associated when the natural log transformation of stress-induced HF-HRV was less than 5.66, which represented 27.23% of the sample. Figure 2 depicts cortisol levels at high (+1 SD) and low (−1 SD) hostility and stress-induced HF-HRV.

Table 1.

Participant characteristics (N = 213)

Variable Mean (SD) or number (%)
Age 30.13 (10.85)
Sex
 Male 123 (57.75)
 Female 90 (42.35)
Ethnicity
 White 71 (33.33)
 Non-white 142 (66.67)
Body mass index 27.46 (6.50)
Smoking status
 Smoker 72 (33.80)
 Non-smoker 141 (66.20)
Socioeconomic status ladder score 4.23 (1.87)
Trait hostility 10.03 (3.68)
Ln transformed baseline HF-HRV 6.22 (1.19)
Ln transformed stress-induced HF-HRV 6.16 (1.14)
Stress-induced cortisol AUCI 259.08 (213.06)
Stress-induced cortisol AUCB 56.18 (3.88)
Open in a new tab

Note. HF-HRV = high frequency heart rate variability; AUCI= area under the curve with respect to increase/decrease; AUCB= area under the curve with respect to baseline. The unit of cortisol measurement was nmol/l.

Table 2.

Linear regression analyses of trait hostility, stress-induced heart rate variability, and their interaction in predicting cortisol area under the curve with respect to increase and baseline.

DV Predictors b SE p 95% CI
AUCI
Trait hostility 5.06 3.94 .20 − 2.71, 12.84
Stress-induced HF-HRV −15.44 18.65 .41 − 52.21, 21.33
Trait hostility x stress-induced HF-HRV − 7.27 3.39 .03 −13.94, − 0.59
Age − 4.24 1.59 .01 − 7.38, − 1.10
Sex − 111.85 29.46 > .001 − 169.95, − 53.76
Race 31.21 31.22 .32 − 30.36, 92.77
Body mass index 4.19 2.44 .09 − .63, 9.00
Socioeconomic status 4.79 7.70 .54 − 10.39, 19.96
Smoking status 3.27 31.44 .92 − 58.73, 65.27
F 4.61
df (1, 202)
R2 .15
ΔR2 .02
AUCB
Trait hostility .03 .36 .64 − .87, .54
Stress-induced HF-HRV − .17 .36 .65 − .11, .18
Trait hostility x stress-induced HF-HRV − .04 .07 .50 − .17, .08
Age − .06 .03 .07 − .12, .01
Sex − 1.03 .56 .07 − 2.14, .08
Race .83 .60 .17 − .35, 2.00
Body mass index .02 .05 .74 − .08, .11
Socioeconomic status − .12 .15 .41 − .41, .17
Smoking status .40 .60 .51 − .79, 1.58
F .45
df (1, 202)
R2 .06
ΔR2 < .01
Open in a new tab

Note. DV = dependent variable; SE = standard error, HF-HRV = high frequency heart rate variability; AUCI = area under the curve with respect to increase; AUCB = area under the curve with respect to baseline. Sex coded as 0 = male, 1 = female; Race coded as 0 = non-White, 1 = White; Smoking status coded as 0 = non-smoker, 1 = smoker.

Figure 1.

Figure 1

Open in a new tab

Stress-induced cortisol area under the curve with respect to increase/decrease (AUCI) at high (+1 SD) and low (−1 SD) levels of trait hostility and stress-induced high frequency hear rate variability (HF-HRV). * = significant slope.

Figure 2.

Figure 2

Open in a new tab

Cortisol slopes associated with high (+1 SD) and low (−1 SD) hostility and stress-induced high frequency heart rate variability. 1 = Immediately before the stressor; 2 = Immediately after the stressor; 3 = 10 minutes after the stressor; 4 = 20 minutes after the stressor; 5 = 30 minutes after the stressor; 6 = 40 minutes after the stressor; and 7 = 50 minutes after the stressor.

The experimental hypotheses tested above involved cortisol sensitivity. We wanted to compare this with the baseline hypothesis involving total cortisol output (as we argued that sensitivity mattered more than output). To this end, we examined cortisol AUCB as a dependent variable in an analysis that was analogous to the linear regression described above in order to examine if the interaction between hostility and stress-induced HF-HRV was associated with total cortisol output. Results indicated that hostility, stress-induced HF-HRV, and the interaction between hostility and stress-induced HF-HRV were not statistically significantly associated with cortisol AUCB (see Table 2). All primary findings were significant in unadjusted models (see Table 3).

Table 3.

Unadjusted linear regression analyses predicting cortisol area under the curve with respect to increase and baseline.

DV Predictors b SE p 95% CI
AUCI
Trait hostility 52.54 22.27 .02 8.64, 96.44
Stress-induced HF-HRV 64.01 40.34 .11 − 15.52, 143.54
Trait hostility x stress-induced HF-HRV − 7.26 3.51 .04 − 14.18, − .34
F 4.28
df (1, 208)
R2 .04
ΔR2 .02
AUCB
Trait hostility .36 .41 .39 − .46, 1.17
Stress-induced HF-HRV .55 .75 .47 − .93, 2.02
Trait hostility x stress-induced HF-HRV − .05 .07 .45 − .18, .08
F .57
df (1, 208)
R2 .01
ΔR2 < .01
Open in a new tab

Note. DV = dependent variable; SE = standard error, HF-HRV = high frequency heart rate variability; AUCI = area under the curve with respect to increase; AUCB = area under the curve with respect to baseline.

4. Discussion

Hostility is associated with increased risk of CHD and all-cause mortality (Chida & Steptoe, 2009; Miller et al., 1996). Cortisol sensitivity is a mechanism that may link hostility with inflammatory and metabolic diseases (Quax et al., 2013). However, some hostile individuals do not experience negative health outcomes (e.g., Newman et al., 2011), which may be explained by findings indicating that there was a synergistic relationship between hostility and one’s capacity for self-regulation. In the current study, we demonstrated that hostility was unrelated to cortisol sensitivity if stress-induced HF-HRV was high. When stress-induced HF-HRV was low, higher trait hostility was associated with greater cortisol sensitivity. Accordingly, stress-induced HF-HRV may be important for understanding why some hostile individuals experience negative health outcomes, while others do not.

HF-HRV is associated with one’s ability to react flexibly to their environment and utilize effective self-regulation strategies (Thayer & Lane, 2009). Accordingly, present study data may suggest that those with frequent hostile thoughts may be able to successfully regulate such thoughts if they have the regulatory strength to do so. If successfully regulated over time, hostility may not lead to cortisol sensitivity; however, unsuccessful regulation of hostility may lead to cortisol sensitivity. Such findings may explain why individuals with mood disorders are more likely to demonstrate cortisol sensitivity than those without a mood disorder (Quax et al., 2013; Young et al., 2001), given that successful self-regulation is associated with decreased risk of mental health problems (Gross & Muñoz, 1995).

As mentioned previously, HF-HRV can be improved via biofeedback, and is effective at reducing hostility among some hostile individuals (Lin et al., 2015). Exercise (Iellamo et al., 2000) and mindfulness meditation (Krygier et al., 2013) are also associated with improvements in HF-HRV. Therefore, hostile individuals may benefit from a number of non-invasive treatments by which the strength of the association between hostility and cortisol sensitivity may be reduced. There are a number of other pathophysiological mechanisms (e.g., macrophage migration inhibitory factor, mitogen-activated protein kinases) that may lead to improved cortisol sensitivity if targeted in treatment; however, the clinical effectiveness of these pathways remains unclear and costly side effects can occur (e.g., restricted capacity for bone marrow to generate progenitor cells; Quax et al., 2013).

Cortisol has robust effects on immune cell development, maturation, trafficking, and cytokine production, including production of proinflammatory cytokines (McEwen et al., 1997). For those hostile individuals who are physiologically reactive to stress, inflammation may be one important mechanism underlying the association between hostility and poor health. Indeed, individuals who demonstrated hostile behaviors in a marital conflict discussion had larger increases in plasma IL-6 and TNF-alpha values the morning after the conflict compared with couples who displayed less hostility (Kiecolt-Glaser, et al., 2005). Moreover, cortisol sensitivity promotes glucocorticoid resistance (Quax et al., 2013), which further enhances inflammation (e.g., Miller & Raison, 2016) and increases risk of cardiovascular disease, type 2 diabetes, some cancers, and morbidity and mortality (Kiecolt-Glaser et al., 2010). Therefore, future work would benefit from including measurement of both cortisol sensitivity and proinflammatory cytokines to extend the literature.

The present study is limited by the predominantly white sample. It will be important to investigate whether primary findings exist among racially diverse groups in future work given ethnic variations in HF-HRV (e.g., Hill et al., 2015). In the present study, the TSST was modified such that participants were asked to defend an alleged transgression as opposed to providing a speech as if they were a job applicant (Kirschbaum et al., 1993). Although both situations are arguably stressful, the similarities and/or differences in stress promotion are unclear. The study is also limited by the lack of inclusion of disease outcomes; however, given consistent findings linking cortisol sensitivity with inflammatory and metabolic diseases (Quax et al., 2013), we can be confident that present study findings have important implications for health. Moreover, overt hostile behaviors were not examined in the present study. It would be interesting to examine if high stress-induced HF-HRV reduces the likelihood that hostile individuals engage in overt hostile behaviors in future work. Furthermore, although there is a large literature linking HF-HRV with self-regulation, caution is warranted when inferring associations between psychophysiological markers, such as HF-HRV, and a psychological construct such as self-regulatory strength (Cacioppo & Tassinary, 1990). We cannot say for certain that stress-induced HF-HRV represents self-regulatory strength as physiological responses are multiply determined and sensitive in a number of ways; however, our findings are consistent with the available literature.

5. Conclusions

In the present study, hostility was associated with cortisol sensitivity when stress-induced HF-HRV was low; however, high stress-induced HF-HRV buffered the negative physiological effects of high hostility. Such findings suggest that hostile thoughts may be appropriately regulated if one has the self-regulatory strength to do so, thereby reducing the physiological costs of hostility.

Highlights.

  • High stress-induced heart rate variability buffers the impact of hostility on cortisol sensitivity.

  • This effect was not identified when using total cortisol output as an outcome.

  • Findings may be related to hostility being inconsistently associated with health outcomes.

Acknowledgments

Role of the Funding Source

The data used for this article were collected by the Laboratory for the Study of Stress, Immunity, and Disease at Carnegie Mellon University under the directorship of Sheldon Cohen, PhD; and were accessed via the Common Cold Project (CCP) website (www.commoncoldproject.com). CCP data are made publically available through a grant from the National Center for Complementary and Integrative Health (AT006694); the conduct of the study was supported by a grant from the National Institute of Allergy and Infectious Diseases (R01 AI066367); secondary support was provided by a grant from the National Institutes of Health to the University of Pittsburgh Clinical and Translational Science Institute (UL1 RR024153); and supplemental support was provided by John D. and Catherine T. MacArthur Foundation Research Network on Socioeconomic Status & Health. Preparation of the manuscript was supported by grants from the National Heart, Lung, and Blood Institute (1R01HL127260-01; 1F32HL131353).

The statistical assistance and critical feedback provided by Dr. Fred Oswald on an earlier draft of this manuscript is much appreciated.

Footnotes

Contributors

K.W.M was involved in data analysis and manuscript writing for the present study. A.S.L and C.P.F. provided critical feedback and edited the manuscript.

Conflicts of interest: None

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.

References

  1. Adler NE, Epel E, Castellazzo G, Ickovics J. Relationship of subjective and objective social status with psychological and physical health: preliminary data in healthy white women. Health Psychology. 2000;19:586–592. doi: 10.1037/0278-6133.19.6.586. [DOI] [PubMed] [Google Scholar]
  2. Akaike H. Selected Papers of Hirotugu Akaike. Springer; New York: 1998. Information theory and an extension of the maximum likelihood principle; pp. 199–213. [Google Scholar]
  3. Andrews-Hanna JR, Smallwood J, Spreng RN. The default network and self-generated thought: Component processes, dynamic control, and clinical relevance. Annals of the New York Academy of Sciences. 2014;1316:29–52. doi: 10.1111/nyas.12360. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Barefoot JC, Dodge KA, Peterson BL, Dahlstrom WG, Williams RB. The Cook-Medley hostility scale: Item content and ability to predict survival. Psychosomatic Medicine. 1989;51(1):46–57. doi: 10.1097/00006842-198901000-00005. [DOI] [PubMed] [Google Scholar]
  5. Beauchaine TP. Vagal tone, development, and Gray’s motivational theory: Toward an integrated model of autonomic nervous system functioning in psychopathology. Development and Psychopathology. 2001;13:183–214. doi: 10.1017/S0954579401002012. [DOI] [PubMed] [Google Scholar]
  6. Brydon L, Strike PC, Bhattacharyya MR, Whitehead DL, McEwan J, Zachary I, Steptoe A. Hostile and physiological responses to laboratory stress in acute coronary syndrome patients. Journal of Psychosomatic Research. 2010;68(2):109–116. doi: 10.1016/j.jpsychores.2009.06.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cacioppo JT, Tassinary LG. Inferring psychological significance from physiological signals. American Psychologist. 1990;45(1):16–28. doi: 10.1037/0003-066X.45.1.16. [DOI] [PubMed] [Google Scholar]
  8. Chida Y, Steptoe A. The association of anger and hostility with future coronary heart disease: A meta-analytic review of prospective evidence. Journal of the American College of Cardiology. 2009;53(11):936–946. doi: 10.1016/j.jacc.2008.11.044. [DOI] [PubMed] [Google Scholar]
  9. Cohen S, Janicki-Deverts D, Turner RB, Casselbrant ML, Li-Korotky HS, Epel ES, Doyle WJ. Association between telomere length and experimentally induced upper respiratory viral infection in healthy adults. JAMA. 2013;309(7):699–705. doi: 10.1001/jama.2013.613. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cook WW, Medley DM. Proposed hostility and pharisaic-virtue scales for the MMPI. Journal of Applied Psychology. 1954;38:414–418. doi: 10.1037/h0060667. [DOI] [Google Scholar]
  11. Dembroski TM, MacDougall JM, Herd JA, Shields JL. Perspectives on coronary-prone behavior. In: Krantz DS, Baum A, Singer JE, editors. Handbook of psychology and health. Vol. 3. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc; 1983. pp. 57–83. [Google Scholar]
  12. Dembroski TM, MacDougall JM, Williams RB, Haney TL, Blumenthal JA. Components of Type A, hostility, and anger-in: Relationship to angiographic findings. Psychomatic Medicine. 1985;47(3):219–233. doi: 10.1097/00006842-198505000-00001. [DOI] [PubMed] [Google Scholar]
  13. Dressendörfer RA, Kirschbaum C, Rohde W, Stahl F, Strasburger CJ. Synthesis of a cortisol-biotin conjugate and evaluation as a tracer in an immunoassay for salivary cortisol measurement. The Journal of Steroid Biochemistry and Molecular Biology. 1992;43(7):683–692. doi: 10.1016/0960-0760(92)90294-s. [DOI] [PubMed] [Google Scholar]
  14. Duhamel P, Vetterli M. Fast Fourier transforms: A tutorial review and a state of the art. Signal Processing. 1990;19:259–299. doi: 10.1016/0165-1684(90)90158-U. [DOI] [Google Scholar]
  15. Earle TL, Linden W, Weinberg J. Differential effects of harassment on cardiovascular and salivary cortisol stress reactivity and recovery in women and men. Journal of Psychosomatic Research. 1999;46(2):125–141. doi: 10.1016/S0022-3999(98)00075-0. [DOI] [PubMed] [Google Scholar]
  16. Elzinga BM, Bakker A, Bremner JD. Stress-induced cortisol elevations are associated with impaired delayed, but not immediate recall. Psychiatry Research. 2005;134(3):211–223. doi: 10.1016/j.psychres.2004.11.007. [DOI] [PubMed] [Google Scholar]
  17. Fekedulegn DB, Andrew ME, Burchfiel CM, Violanti JM, Hartley TA, Charles LE, Miller DB. Area under the curve and other summary indicators of repeated waking cortisol measurements. Psychosomatic Medicine. 2007;69:651–659. doi: 10.1097/PSY.0b013e31814c405c. [DOI] [PubMed] [Google Scholar]
  18. Gross JJ. Emotion regulation: Taking stock and moving forward. Emotion. 2013;13(3):359–365. doi: 10.1037/a0032135. [DOI] [PubMed] [Google Scholar]
  19. Gross JJ, Muñoz RF. Emotion regulation and mental health. Clinical Psychology: Science and Practice. 1995;2(2):151–164. doi: 10.1111/j.1468-2850.1995.tb00036.x. [DOI] [Google Scholar]
  20. Hacket RA, Lazzarino AI, Carvalho LA, Hamer M, Steptoe A. Hostility and physiological responses to acute stress in people with type 2 diabetes. Psychosomatic Medicine. 2015;77(4):458–466. doi: 10.1097/PSY.0000000000000172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hayes AF. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press; 2013. [Google Scholar]
  22. Helmer DC, Ragland DR, Syme SL. Hostility and coronary artery disease. American Journal of Epidemiology. 1991;133(2):112–122. doi: 10.1093/oxfordjournals.aje.a115850. [DOI] [PubMed] [Google Scholar]
  23. Hemingway H. Psychosocial factors in the aetiology and prognosis of coronary heart disease: Systematic review of prospective cohort studies. British Journal of Medicine. 1999;318(7196):1460–1467. doi: 10.1136/bmj.318.7196.1460. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hill LK, Hu DD, Koenig J, Sollers JJ, Kapuku G, Wang X, Snieder H, Thayer JF. Ethnic differences in resting heart rate variability: A systematic review and meta-analysis. Psychosomatic Medicine. 2015;77(1):16–25. doi: 10.1097/PSY.0000000000000133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Iellamo F, Legramante JM, Massaro M, Raimondi G, Galante A. Effects of a residential exercise training on baroreflex sensitivity and heart rate variability in patients with coronary artery disease: A randomized, controlled study. Circulation. 2000;102:2588–2592. doi: 10.1161/01.CIR.102.21.2588. [DOI] [PubMed] [Google Scholar]
  26. Janicki-Deverts D, Cohen S, Doyle WJ, Marsland AL, Bosch J. Childhood environments and cytomegalovirus serostatus and reactivation in adults. Brain, Behavior, and Immunity. 2014;40:174–181. doi: 10.1016/j.bbi.2014.03.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Johnson PO, Fay LC. The Johnson-Neyman Technique, its theory and application. Psychometrika. 1950;15(4):349–367. doi: 10.1007/BF02288864. [DOI] [PubMed] [Google Scholar]
  28. Kiecolt-Glaser JK, Gouin JP, Hantsoo L. Close relationships, inflammation, and health. Neuroscience & Biobehavioral Reviews. 2010;35(1):33–38. doi: 10.1016/j.neubiorev.2009.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kiecolt-Glaser JK, Loving TJ, Stowell JR, Malarkey WB, Lemeshow S, Dickinson SL, Glaser R. Hostile marital interactions, proinflammatory cytokine production, and wound healing. Archives of General Psychiatry. 2005;62:1377–1384. doi: 10.1001/archpsyc.62.12.1377. [DOI] [PubMed] [Google Scholar]
  30. Kirschbaum C, Pirke KM, Hellhammer DH. The ‘Trier Social Stress Test’--a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology. 1993;28(1–2):76–81. doi: 10.1159/000119004. 119004. [DOI] [PubMed] [Google Scholar]
  31. Krygier JR, Heathers JAJ, Shahrestani S, Abbott M, Gross JJ, Kemp AH. Mindfulness meditation, well-being, and heart rate variability: A preliminary investigation into the impact of intensive Vipassana meditation. International Journal of Psychophysiology. 2013;89:305–313. doi: 10.1016/j.ijpsycho.2013.06.017. [DOI] [PubMed] [Google Scholar]
  32. Leon GR, Finn SE, Murray D, Bailey JM. Inability to predict cardiovascular disease from hostility scores or MMPI items related to Type A behavior. Journal of Consulting and Clinical Psychology. 1988;56(4):597–600. doi: 10.1037/0022-006X.56.4.597. [DOI] [PubMed] [Google Scholar]
  33. Lin IM, Fan SY, Lu HC, Lin TH, Chu CS, Kuo HF, Lee CS, Lu YH. Randomized controlled trial of heart rate variability biofeedback in cardiac autonomic and hostility among patients with coronary artery disease. Behavior Research and Therapy. 2015;70:38–46. doi: 10.1016/j.brat.2015.05.001. [DOI] [PubMed] [Google Scholar]
  34. Mauss IB, Gross JJ. Emotion suppression and cardiovascular disease: Is hiding feelings bad for your heart? In: Nyklicek I, Vingerhoets A, Temoshok L, editors. The expression of emotion and health: Advances in theory, assessment, and clinical applications. New York, New York: Brunner-Routledge; 2004. pp. 62–81. [Google Scholar]
  35. McEwen BS, Biron CA, Brunson KW, Bulloch K, Chambers WH, Dhabhar FS, Goldfarb RH, Kitson RP, Miller AH, Spencer RL, Weiss JM. The role of adrenocorticoids as modulators of immune function in health and disease: Neural, endocrine, and immune interaction. Brain Research Reviews. 1997;23(1–2):79–133. doi: 10.1016/S0165-0173(96)00012-4. [DOI] [PubMed] [Google Scholar]
  36. Malik M. 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. Circulation. 1996;93(5):1043–1065. doi: 10.1161/01.CIR.93.5.1043. [DOI] [PubMed] [Google Scholar]
  37. Miller AH, Raison CL. The role of inflammation in depression: From evolutionary imperative to modern treatment target. Nature Reviews Immunology. 2016;16:22–34. doi: 10.1038/nri.2015.5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Miller TQ, Smith TW, Turner CW, Guijarro ML, Hallett AJ. A meta-analytic review of research on hostility and physical health. Psychological Bulletin. 1996;119(2):322–348. doi: 10.1037/0033-2909.119.2.322. [DOI] [PubMed] [Google Scholar]
  39. Newman JD, Davidson KW, Shaffer JA, Schwartz JE, Chaplin W, Kirkland S, Shimbo D. Observed hostility and the risk of incident ischemic heart disease: A prospective population study from the 1995 Canadian Nova Scotia Health Study. Journal of the American College of Cardiology. 2011;58(12):1222–1228. doi: 10.1016/j.jacc.2011.04.044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Quax RA, Manenschijn L, Koper JW, Hazes JM, Lamberts SWJ, van Rossum EFC, Feelders RA. Glucocorticoid sensitivity in health and disease. Nature Reviews Endocrinology. 2013;9:670–686. doi: 10.1038/nrendo.2013.183. [DOI] [PubMed] [Google Scholar]
  41. Segerstrom SC, Nes LS. Heart rate variability reflects self-regulatory strength, effort, and fatigue. Psychological Science. 2007;18(3):275–281. doi: 10.1111/j.1467-9280.2007.01888.x. [DOI] [PubMed] [Google Scholar]
  42. Thayer JF, Lane RD. Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience and Biobehavioral Reviews. 2009;33(2):81–88. doi: 10.1016/j.neubiorev.2008.08.004. [DOI] [PubMed] [Google Scholar]
  43. Thayer JF, Yamamoto SS, Brosschot JF. The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. International Journal of Cardiology. 2010;141(2):122–131. doi: 10.1016/j.ijcard.2009.09.543. [DOI] [PubMed] [Google Scholar]
  44. Weidner G, Friend R, Ficarrotto TJ, Mendell NR. Hostility and cardiovascular reactivity to stress in women and men. Psychosomatic Medicine. 1989;51(1):36–45. doi: 10.1097/00006842-198901000-00004. [DOI] [PubMed] [Google Scholar]
  45. Young EA, Carlson NE, Brown MB. Twenty-four-hour ACTH and cortisol pulsatility in depressed women. Neuropsychopharmacology. 2001;25(2):264–276. doi: 10.1016/S0893-133X(00)00236-0. [DOI] [PubMed] [Google Scholar]

RESOURCES