PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Psychiatr Res. Author manuscript; available in PMC 2010 December 1.
Published in final edited form as:
PMCID: PMC2783820
NIHMSID: NIHMS130247

Does Improving Mood in Depressed Patients Alter Factors That May Affect Cardiovascular Disease Risk?

Abstract

To determine if improvement in mood would ameliorate autonomic dysregulation, HPA dysfunction, and typical risk factors and C-reactive protein in depressed patients with elevated cardiovascular disease risk (CVD), 48 depressed participants with elevated cardiovascular risk factors were randomized to a cognitive behavioral intervention (CBT) or a waiting list control (WLC) condition. Twenty non-depressed age and risk-matched controls were also recruited. Traditional risk factors (e.g., lipids, blood pressure) and C-reactive protein were assessed pre- and post-treatment six months later. Subjects also underwent a psychophysiological stress test while cardiovascular physiology was measured. Salivary cortisol was measured during the day and during the psychological stress test. At post-treatment, the CBT subjects were significantly less depressed than WLC subjects. There was no significant difference in change scores on any of the traditional risk factors or C-reactive protein, cortisol measures, or cardiovascular physiology, except for triglyceride levels and heart rate, which were significantly lower in treatment compared to control subjects. The normal controls exhibited no change in the variables measured during the same time. A significant improvement in mood may have little impact on most traditional or atypical risk factors, cortisol or cardiophysiology.

Keywords: depression, CVD, cognitive behavioral therapy, risk factors

1. Introduction

Depression has a major impact on mortality, morbidity and functional recovery in patients with CVD (Aromaa et al., 1994; Carney et al., 2004; Frasure-Smith et al., 1995). A number of mechanisms have been proposed to explain how depression might increase CVD risk. Most recent attention has focused on HPA axis regulation, autonomic cardiovascular regulation and inflammation although other mechanisms have been proposed (Musselman et al 1998, Howren et al., 2009). In a previous study of older patients with elevated CVD risk and depression we found significantly less respiratory variation in sinus rhythm adjusted for respiratory variables (RSATF), a measure of autonomic control of the CV system, higher plasma levels of C-reactive protein and reduced levels of cortisol in response to psychological stress in depressed patients compared to a non-depressed control group (Taylor et al., 2006).

The argument for focusing on autonomic nervous system dysregulation as a possible explanation for the higher morbidity and mortality in depressed patients begins with observations that patients with greater CVD reactivity and dysregulation are at heightened risk for developing cardiovascular disease and for accelerated disease progression (Strike & Steptoe, 2003). Depression has effects on the autonomic control of the cardiovascular system. The respiratory sinus arrhythmia (RSA) is one measure of this regulation (Katona & Jih, 1975). Peripheral control of RSA is achieved mainly via the parasympathetic cholinergic vagus nerve. A high degree of RSA is observed in normal hearts with good cardiac function, whereas RSA can be significantly decreased in patients with severe CAD or heart failure (Dalack & Roose, 1990). The relative risk of sudden death after acute MI is significantly higher in patients with decreased RSA (Kleiger et al., 1987). Depression is associated with reduced RSA variability (Licht et al., 2008).

Abnormal HPA axis function may contribute to CVD risk through a variety of related risk factors, including elevated BP, high lipid levels, insulin resistance, and abdominal obesity, defined as the metabolic syndrome (Musselman et al., 1998; Brown et al., 2004). Although the conventional wisdom is that depression is associated with abnormal HPA axis function manifest as hypercortisolemia, this may only be the case in severely depressed patients, particularly those with psychotic features and melancholia. Patients with less several depression and atypical features may show a reduced cortisol response to stress and have low or normal ambulatory measures of cortisol (Taylor et al., 2006; Gold & Chrousos., 2002; Stewart et al., 2005). For instance, in a meta-analysis of 7 studies that examined the association between depression and cortisol responses to psychological stressors. Burke et al. (2005a) found that MDD patients had blunted reactivity and impaired recovery to stress reactivity. In a study of 1100 low-income women, Burke et al. (2005b) also found that women with very high levels of depressive symptoms exhibit blunted cortisol responses to a naturalistic psychological stressor.

There is substantial evidence that inflammation plays an important role in atherogenesis (Moreno, 2004). Clinical depression is associated with marked increases in systematic inflammation as evidence by elevations in circulating concentrations of C-reactive protein and other inflammatory markers (Miller et al., 2005; Howren et al., 2009).

One way to examine the relationship between depression and potential CVD risk factors, is to determine if treating depression improves them. Changes in HPA activity have been observed after pharmacologic treatments for depression, although generally in patients with more severe depression and the results have been inconsistent (Linkowaski et al., 1987; Deusckle et al., 2003; Rota et al., 2005). These studies are complicated both by the immediate effects that antidepressants may have on the HPA axis and different HPA effects among antidepressants (Nemeroff & Owen, 2004). Other studies have examined the effects of treating depression on autonomic dysfunction. Tricyclic antidepressants tend to increase heart rate and decrease heart rate variability (HRV), presumably because of their anticholinergic side effects. Selective serotonin reuptake inhibitors (SSRIs) may also affect HRV (Bar et al 2004) but the results have been inconsistent (Carney et al., 2005). There is some evidence that the observed differences in RSA between depressed and non-depressed patients may be related to pharmacotherapy (Licht et al., 2009). To avoid medication confounds, Carney et al. (2000) examined the effects of cognitive behavioral therapy on HRV in fifty patients with stable CHD and co-morbid major depression. Heart rate was significantly lower following treatment and there was also a significant increase in a time-domain index of HRV during the daytime in the severely depressed group but not in the mildly depressed group, and there were no changes in 24-hour measures.

In summary, while some studies have examined the effects of medications on improving mood and in change in factors by which depression might increase CVD risk, little is known about the physiological effect of psychological interventions. Psychological interventions are effective treatments for depression and can avoid the multiple confounding effects of pharmacological therapies.

The purpose of this study was to determine the effects of reducing negative mood/improving depression in depressed, older patients with elevated CVD risk. Our hypotheses were that improved mood would be associated with improvement in two variables found to be different between depressed and non-depressed subjects in our previous study: cortisol dysregulation (as reflected by a hypocortisolemic response to stress testing), parasympathetic cardiovascular function (as reflected in higher RSATF activity). Our secondary analyses compared the effects of treating depression on ameliorating traditional risk factors (blood pressure, heart rate, lipids, triglycerides) and a measure of inflammation (C-reactive protein.)

2. Methods

2.1 Participants

The depressed patient sample consisted of 16 men and 32 women, aged 55 or older, at high risk for coronary artery disease. To be considered high risk, subjects needed to have a history of elevated BP and/or lipids, and to be taking medications to reduce BP and/or lipids as indicated. Subjects were excluded if they (a) currently smoked or (b) were taking any medications that might affect the HPA axis or autonomic nervous system (ANS) activity that could not be stopped during testing. Potential female subjects had to be post-menopausal or to be on stable doses of estrogen. A non-depressed sample consisted of 8 women and 12 men, matched for age and cardiovascular risk with the depressed sample. Patients and controls were recruited through advertisements and cardiovascular risk factor clinics. Most subjects (>75%) were recruited from the mass media. The study was approved by the Stanford IRB, and all subjects provided informed consent. The study was conducted from 6/1/02 through 5/31/05. The sample size (25 subjects/group at post-test) was based on having adequate power to detect an effect size of about 0.4 in the primary measures. A flowchart for subject recruitment, randomization and follow-up can be seen in Figure 1. The non-depressed control subjects were tested at baseline and again at six months. All measures were collected by research assistants blinded to the subjects randomization or depression status.

2.2 Measurements

2.2.1 Sociodemographic, Psychiatric and Medical Variables

Baseline demographics included education, ethnicity, and marital status. A structured clinical interview focusing on depression and modified to include the Hamilton Depression Interview was used (Freedland et al., 2002). All interviews and diagnoses were reviewed by a senior psychologist or psychiatrist. All subjects also completed the Beck Depression Inventory (BDI). Interviewers were blind to the participants randomization status at post-test.

A standard questionnaire assessing coronary risk factors, medications, and medical diagnoses was completed at baseline by all subjects. Subjects also underwent a physical examination by a cardiologist who reviewed and confirmed diagnoses related to CVD and CVD risk (e.g. hypercholesterolemia, hypertension.) A resting 12-lead electrocardiogram (ECG) was recorded. Subjects with significant abnormalities in their resting ECG (e.g. significant arrhythmia that would interfere with the physiological recordings) were excluded.

2.2.2 Cardiovascular Risk Factors

Baseline resting BP was measured in triplicate by auscultation using a mercury sphygmomanometer. Body weight (in kg) and height (in m) were obtained in a standing position, with shoes removed. These were used to calculate body mass index (BMI) (in kg/m2).

C-reactive protein was measured using a high-sensitivity assay (Dade, Behring, Marburg, Germany). (The final sample for the hsCRP assay was run on only 81% (35/43) of depressed subjects and 70% (14/20) control subjects because of a change to this assay method.) Fasting plasma levels of total cholesterol and triglycerides were measured using standard enzymatic procedures.

2.2.3 Cortisol

At baseline, each participant was scheduled to obtain saliva samples using cotton swabs in “salivette devices” at the time of waking, 30 minutes later, and then at 1200h, 1700h, and 2100h on each of two baseline saliva collection days. Saliva samples were obtained 10 times during the stress task. Salivary cortisols were assayed using luminescence immunoassay (LIA) reagents provided by Immuno-Biological Laboratories, Inc. Hamburg, Germany. Assay sensitivity was 0.015 microg/dl.

2.2.4 Psychological Stress Testing

A modified version of the Trier Social Stress Test (TSST) (Kirschbaum et al., 1993) was used in this study. The TSST is a standardized social and cognitive stressor composed of 5 minutes of anticipatory stress, followed by 5 minutes of public speaking (simulated job interview) and 5 minutes of mental arithmetic, both done before a panel of 2 evaluators. Subjects were sitting in a comfortable chair throughout the entire procedure.

2.2.5 Cardiovascular and Respiratory Physiology Data Recording and Analysis

Placement of electrodes/sensors, data recording, and data reduction followed conventions established for psychophysiological research and published guidelines (Wilhelm et al., 2004). A Finapres 2300 blood pressure monitor (Ohmeda, Inc., Madison, WI) obtained the continuous arterial pulse pressure waveform using the volume clamp method. Respiratory pattern data was measured using thoracic and abdominal bellows (Lafayette Instrument, Inc., Lafayette, IN) connected to pneumographic transducers (James Long Company, Inc., Caroga Lake, NY). In addition to these continuous measurements, an automatic blood pressure monitor (Dinamap 1846SX, Critikon & GE Healthcare, Chalfont St. Giles, UK) measured blood pressure with a cuff around the upper right arm. Inflation was triggered 2 minutes after onset of each test segment.

Physiological signals were analyzed and averaged for each 5-minute period using an integrated suite of biosignal analysis programs written in MATLAB (Mathworks, Inc., Natick, MA). Rate-pressure product was calculated as HR × SBP. The impedance cardiogram dZ/dt-signal was ensemble averaged in alignment with the R-wave time over 5-minute periods. Characteristic points (B, Z, X) of the inverted dZ/dt signal of ensemble-averaged beats were identified automatically after exclusion of abnormal beats and edited when necessary. Pre-ejection period (PEP) (in ml) was calculated as the interval from the ECG Q-point to the ICG B-point. PEP is inversely related to left-ventricular contractility and beta-adrenergic sympathetic influences on the myocardium. Transfer function respiratory sinus arrhythmia (RSATF) was quantified by fast Fourier transform and the averaged periodogram method as the magnitude of the transfer function relating RR interval to lung volume oscillations at the prominent respiratory frequency (in ms/ml) (Wilhelm et a., 2004). Non-respiratory adjusted RSA, the high frequency (HF) power of heart period variability (in ms2), was computed as the summed power spectral density of RR interval between 0.15-0.5 Hz for normative comparison. Similarly, spectral density of RR interval was summed over the low (LF, 0.07-0.15 Hz, in ms2) and very low (VLF, 0.0033-0.07 Hz, in ms2) frequency bands. HF, LF, and VLF power measurements were normalized by natural logarithmic transformation. As reported in the baseline paper, the TSST had a significant impact on HR, SBP, DBP, presystolic ejection period and cardiac output (Taylor et al., 2006). For instance, SBP increased by 32% for depressed patients and 40% for controls; HR increased by 22% and 18%. These results are comparable to what has been reported in other studies with similar populations and design (Strike & Steptoe, 2003).

2.3 Cognitive Behavior Therapy

The cognitive behavior therapy was based on the approach used in the ENRICHD trial (Berkman et al., 2003) with several modifications to make it potentially more effective. First, weekly mood ratings, combined with activity and use of cognitive techniques, were used to determine activities over the ensuing week that might reduce depression. Second, telephone calls were made to patients when they were unable to attend sessions. Third, participants were provided a 50-page workbook with exercises, to be used between sessions. Participants needed to attend at least ten sessions to be considered treatment completers. All participants were instructed in the use of cognitive behavioral skills that include filling out dysfunctional thought records, overview of cognitive errors and the use of Socratic questioning in order to reframe negative thought. (For details of the effects of the intervention see Strachowski et al., 2008).

2.4 Data Analysis

For each dependent variable, a multiple regression analysis was computed with the change score as the dependent variable and the baseline score, group (CBT versus WLC) and interaction as factors in the model. Thus, the model was: Post-baseline = c_baseline + c_Group + (c_baseline × c_Group), where “c” denotes centering. Centering was done to reduce colinearity (Kraemer & Blasey, 2004). The interaction term was included to avoid biased parameter estimates based on the assumption of equal slopes between groups for post-scores regressed on baseline scores. Analyses were done for completers and for intention-to-treat. TSST data was analyzed using an area under the curve analysis (AUC) (Pruessner et al., 2003). The baseline values were carried forward in case of missing post-data. A 5% two-tailed significance level was used for the primary hypotheses. A 1% two-tailed significance level was used for secondary hypotheses to reduce test-wise error.

3. Results

3.1 Participants

The depressed sample was primarily Caucasian (see Table 1). There were no differences in the demographic variables between the CBT and WLC groups. The demographics of the non-depressed control group are provided for purposes of comparison. For the 22 WLC subjects present at both baseline and post, 37% were on lipid lowering agents, 46% on antihypertensive medications, 14% on glucose altering medications and 23% on none of these agents. At post there was a net change in one less subject taking lipid lowering medication and one less taking an antihypertensive agent. For the 21 CBT subjects, at baseline, 19% were on lipid lowering agents 57% on antihypertensive medications, none were taking glucose altering meds and 14% were on none of these agents. At post test, 3 more subjects had started on lipid lowering medications and one on antihypertensive medications. For the non-depressed controls, 35% were on lipid lowering agents at baseline, 65% were on antihypertensive medications and 23% were on none of these. Thirty-six percent of WLC patients were on antidepressants at the beginning as were 43% of CBT patients. There was a net change of 2 patients starting antidepressants in the WLC group and no net change in the CBC group.

Table 1
Demograhics of Participants Randomly Assigned to Treatment Groups

3.2 CBT Treatment Effects

Attendance of the CBT sessions was extremely high, dropping below 90% for only one week. All subjects completed therapy based on our criteria (>10 sessions attended); the mean number of sessions was 15.1 (range: 12-16). Nine sessions in total were conducted by telephone. The therapy was extremely effective (Strachoswki et al 2008). Scores on the Hamilton Depression Inventory and the Beck Depression Inventory were significantly reduced in the CBT group compared to the WLC group, post-treatment (see Table 2). With missing subjects considered non-responders, 57% (13/23) of subjects in the CBT treatment were considered to be in remission compared to only 4% (1/25) in the WLC group (Chi square [df=1]=9.0, p=0.003).

Table 2
Change in depression and risk and other factors, pre- to post-treatment for the CBT and WLC groups (completer sample)

3.3 Cortisol

As seen in Table 3, there were no significant differences in waking, waking plus 30 minutes or daytime cortisol slopes between CBT or WLC with either the completer or intention-to-treat analyses.

Table 3
Change in cortisol variables, pre- to post-treatment for the CBT and WLC groups (completer sample)

3.4 Typical and CVD Risk Factors and C-reactive protein

On the basis of completer analyses there were no differences in atypical risk factors pre- and post-treatment. There was a significant reduction in triglyceride levels in the CBT compared to WLC group (F = 7.9, p = .01, ES = 0.69) and in heart rate (F=10.0, p=.003, ES= 0.32). With the intention-to-treat analyses, there was also a significant reduction in triglycerides in the CBT group compared to the WLC group (F =5.4, p = .02) and also in heart rate (F=6.7, p=0.01). There was no significant differences in C-reactive protein levels.

3.5 Psychological Stress Test Variables

There was no significant difference in change scores in any of the stress test data, by completer analysis or by intention-to-treat. Cortisol levels remained low for both CBT and WLC groups.

3.6 Non-depressed control group

As an exploratory analysis we compared pre and post differences between the combined CBT and WLC groups and the non-depressed group (NTC) on the variables that had been shown to be significantly different at baseline (Taylor et al., 2006). Differences between the two groups (depressed and controls combined and NTC) remained very similar at post with control subjects having higher AUC RSATF and lower AUC cortisol on the TSST and lower C-reactive protein levels compared to the combined CBT and WLC subjects.

4. Discussion

Contrary to our hypotheses, there were no changes in the primary outcome variables, RSATF, and cortisol response during stress testing following a significant and robust improvement in mood produced by cognitive-behavioral therapy. However, there was a significant reduction in triglycerides and the triglyceride/HDL index in both the completer and intention-to-treat analyses. There was also a significant reduction in heart rate, in the treatment compared to no-treatment group. The magnitude of differences in the three variables, AUC RSATF., AUC cortisol and C-reactive protein found to be significantly different at baseline (Taylor et al., 2006) between non-depressed and depressed subjects were similar at pre and post test.

The examination of our primary hypotheses required a significant improvement in depression. Fortunately, the therapy was effective, both in the continuous measures (with effect sizes of the Hamilton of 1.8 and on the BDI of 0.85) and in percent of subjects improved: 57% in treatment compared to 4% in the wait-list control. The percentage of subjects in remission is similar to that found in other studies of CBT and comparable to the effect sizes of most pharmacological interventions (Gloaguen et al., 1998; DeRubeis et al., 2005).

To the extent that depression might cause changes in RSATF (and other heart rate variability measures) improvement in depression would be expected to be associated with improvement in these variables. In fact, Carney et al., (2000) did find improvement in HRV following CBT, although the study was uncontrolled, the findings were only significant during the day, and HRV was not adjusted for respiratory confounds. Our data also suggests that changing HPA activity through psychological interventions may be difficult.

The overall lack of change in the primary biological and physiological variables, and the sustained difference between WLC or CBT and normal controls at post suggests that these variables are quite stable and not amenable to change through psychological intervention or affected by change in mood, at least in the short run. The reasons for the persistence of a difference in the physiological differences, despite the improvement in mood, remains to be determined. In the ENRICHD trial (Berkman et al., 2003), which had short-term CBT/no-treatment effects similar to those reported here, there were no differences in CVD morbidity and mortality, although this was confounded by continued improvement in the non-treatment control group. Alternatively, it may be that there is an unknown third factor leading to both depression and biological changes, with no direct causal link between depression and the physiological changes. In that case, changing the third factor would produce concomitant changes in depression and physiology.

The nature of this design adds important information on the possible impaired cortisol response to stress in depressed individuals. As cortisol is important to reduce cytokine response to stress, a lower level of cortisol might increase risk (Miller et al., 2005). To the extent that a normal cortisol response is necessary to reduce proinflammatory response to acute stress (Dhabhar et al.,1996), and the cortisol response remained suppressed, it would not be surprising to see no change in C-reactive protein levels, a non-specific measure of inflammation.

The large change in triglycerides was unexpected. The triglyceride/HDL ratio is significantly related to the metabolic syndrome (e.g.McLaughlin et al., 2003), which, in turn, is related to elevated CVD depending on population and criteria. However, these results need to be interpreted with caution as this was an exploratory analysis. We do not have measures of patients' diets but they were not explicitly encouraged to change diet or to lose weight. The spearman correlation between changes in BMI and changes in triglycerides was not significant, suggesting that changes in BMI do not account for reduction in triglycerides. We also do not measure alcohol intake or have measures of serum glucose, both of which can affect TG levels. The observation of the change in triglycerides with improved mood finding to be replicated in larger samples, followed for longer time with more thorough measures of metabolic risk.

The changes in heart rate were very close to those reported by Carney et al. (2000) who found a difference of about 5 bpm in his study, compared to the change of 6.5 in this one. However, the WLC also had a reduction of 3.8 bpm and had a lower baseline heart rate. While the change in HR between the two groups may reflect a reduction in sympathetic tone, there were no significant changes in other psychophysiological variables affected by sympathetic tone. Thus, this finding needs to be confirmed in a larger sample.

There are a number of limitations of the study. The sample size was small, and there were relatively few men. Changes were examined only over a six-month period. Detailed measures of diet and exercise would have been helpful. Kraemer et al. (2001) have noted that the term “risk factor” is often used imprecisely, sometimes referring to a factor that increases the probability of an outcome or being associated (correlated) with the outcome or both being associated with the outcome and preceding it. It is often assumed that the risk factor is causally related to the outcome but this can only be shown by demonstrating that a change in the risk factor changes the outcome.

In summary, with the exception of changes in triglycerides and heart rate, there were no significant changes with improved mood on a number of other biological and physiological variables measured. Clinicians must ensure that typical and atypical cardiovascular risk factors are adequately treated with lifestyle and pharmacologic therapy, as appropriate. The changes in triglycerides, which may reflect improvement in the metabolic syndrome, are intriguing need to be confirmed in larger samples whose metabolic syndrome status is better characterized. Finally the intervention proved very robust and was extremely well received by the participants.

Table 4
Change in Trier Social Stress Test variables, pre- to post-treatment for the CBT and WLC groups (completer sample)
Table 5
Pre and post-test means in selected variables for the CBT and WLC (combined) and non-depressed groups

Acknowledgments

Role of the funding source: This study was supported by NIA/NCI Program Project AG18784 and in part by the Swiss National Science Foundation (Grant 105311-105850). It was also supported in part by a Grant 5M01RR000070 from the National Center for Research Resources, National Institutes of Health.

We would like to thank research assistants Allyson DeLorenzo and Christine Celio, and the volunteers who participated in this study.

Footnotes

Conflict of interest statement: All authors declare that they have no conflicts of interest.

Contributors: C. Barr Taylor oversaw all aspects of the study and wrote the final manuscript

Ansgar Conrad was responsible for psychophysiological assessment including data analysis and interpretation of data. He also was instrumental in writing the manuscript

Frank H. Wilhelm oversaw psychophysiological assessment and wrote data analyses programs

Diane Strachowski helped design and provide the intervention and helped with the manuscript preparation

Anna Khaylis provided the psychological stress testing, helped with data collection and analysis and with the manuscript preparation

Eric Neri was responsible for data analysis and helped with data interpretation Janine Giese-Davis designed and implemented the stress testing, helped with data analysis and write-up and helped obtain funding

Walton T Roth oversaw all aspects of physiological data collection and analyses

John P. Cooke oversaw the collection and interpretation of all data related to cardiovascular outcomes

Helena Kraemer provided guidance for all statistical analyses

David Spiegel helped obtain funding, manager the overall research project and helped with data analysis and write-up.

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

  • Aromaa A, Raitasalo R, Reunanen A, Impivaara O, Heliovaara M, Knekt P, Lehtinen V, Joukamaa M, Maatela J. Depression and cardiovascular diseases. Acta Psychiatric Scandinavica. 1994;77:77–8.
  • Bar KJ, Greiner W, Jochum T, Friedrich M, Wagner G, Sauer H. The influence of major depression and its treatment on heart rate variability and pupillary light reflex parameters. Journal of Affective Disorders. 2004;82(2):245–52. 2004. [PubMed]
  • Berkman LF, Blumenthal J, Burg M, Carney RM, Catellier D, Cowan MJ, Czajkowski SM, DeBusk R, Hosking J, Jaffe A, Kaufmann PG, Mitchell P, Norman J, Powell LH, Raczynski JM, Schneiderman N. Enhancing Recovery in Coronary Heart Disease Patients Investigators (ENRICHD). Effects of treating depression and low Recovery in Coronary Heart Disease Patients (ENRICHD) Randomized Trial. JAMA. 2003;289(23):3106–16. [PubMed]
  • Brown ES, Varghese FP, McEwen BS. Association of depression with medical illness: does cortisol play a role? Biological Psychiatry. 2004;55(1):1–9. [PubMed]
  • Burke HM, Davis MC, Ottec C, Mohr DC. Depression and cortisol responses to psychological stress: A meta-analysis. Psychoneuroendocrinology. 2005a;30(9):846–56. [PubMed]
  • Burke HM, Fernald LC, Gertler PJ, Adler NE. Depressive symptoms are associated with blunted cortisol stress responses in very low-income women. Psychosomatic Medicine. 2005b Mar-Apr;67(2):211–6. [PubMed]
  • Carney RM, Freedland KE, Sheps D. Depression is a risk factor for mortality in coronary heart disease. Psychosomatic Medicine. 2004;66(6):799–801. [PubMed]
  • Carney RM, Freedland KE, Stein PK, Skala JA, Hoffman P, Jaffe AS. Change in heart rate and heart rate variability during treatment for depression in patients with coronary heart disease. Psychosomatic Medicine. 2000;62:639–47. [PubMed]
  • Carney RM, Freedland KE, Veith RC. Depression, the autonomic nervous system, and coronary heart disease. Psychosomatic Medicine. 2005;67 1:S29–33. [PubMed]
  • Dalack GW, Roose SP. Perspectives on the relationship between cardiovascular disease and affective disorder. Journal of Clinical Psychiatry. 1990;51(suppl):4–9. [PubMed]
  • DeRubeis RJ, Hollon SD, Amsterdam JD, Shelton RC, Young PR, Salomon RM, O'Reardon JP, Lovett ML, Gladis MM, Brown LL, Gallop R. Cognitive therapy vs medications in the treatment of moderate to severe depression. Archives of General Psychiatry. 2005;62(4):409–16. [PubMed]
  • Deuschle M, Hamann B, Meichel C, Krumm B, Lederbogen F, Kniest A, Colla M, Heuser I. Antidepressive treatment with amitriptyline and paroxetine: effects on saliva cortisol concentrations. Journal of Clinical Psychopharmacology. 2003;23(2):201–5. [PubMed]
  • Dhabhar FS, Miller AH, McEwen BS, Spencer RL. Stress-induced changes in blood leukocyte distribution. Role of adrenal steroid hormones. Journal of Immunology. 1996;157(4):1638–44. [PubMed]
  • Frasure-Smith N, Lesperance F, Talajic M. Depression and 18-month prognosis after myocardial infarction. Circulation. 1995;91(4):999–1005. [PubMed]
  • Freedland KE, Skala JA, Carney RM, Raczynski JM, Taylor CB, Mendes de Leon CF, Ironson G, Youngblood ME, Krishnan KR, Veith RC. The Depression Interview and Structured Hamilton (DISH): rationale, development, characteristics, and clinical validity. Psychosomatic Medicine. 2002;64(6):897–905. [PubMed]
  • Gloaguen V, Cottraux J, Cucherat M, Blackburn I. A meta-analysis of the effects of cognitive therapy in depressed patients. Journal of Affective Disorders. 1998;49:59–72. [PubMed]
  • Gold PW, Chrousos GP. Organization of the stress system and its dysregulation in melancholic and atypical depression: high vs low CRH/NE states. Molecular Psychiatry. 2002;7(3):254–75. [PubMed]
  • Howren MB, Lamkin DM, Suls J. Associations of depression with C-reactive protein, IL-1, and IL-6: a meta-analysis. Psychosomatic Medicine. 2009;71(2):171–86. [PubMed]
  • Howren MB, Lamkin DM, Suls J. Associations of depression with C-reactive protein, IL-1, and IL-6: a meta-analysis. Psychosomatic Medicine. 2009;71(2):171–86. [PubMed]
  • Katona PG, Jih F. Respiratory sinus arrhythmia: Noninvasive measure of the parasympathetic cardiac control. Journal of Applied Physiology. 1975;39:801–805. [PubMed]
  • Kirschbaum C, Pirke KM, Hellhammer DH. The ‘Trier Social Stress Test’--a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology. 1993;28:76–81. [PubMed]
  • Kleiger RE, Miller PJ, Bigger TJ, Moss AJ, the Multicenter Post-Infarction Research Group Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. American Journal of Cardiology. 1987;59:256–262. [PubMed]
  • Kraemer HC, Stice E, Kazdin A, Offord D, Kupfer D. How do risk factors work together? Mediators, moderators, and independent, overlapping, and proxy risk factors. American Journal of Psychiatry. 2001;158(6):848–56. [PubMed]
  • Kraemer HC, Blasey CM. Centering in regression analyses: a strategy to prevent errors in statistical inference. International Journal of Methods in Psychiatric Research. 2004;13:141–51. [PubMed]
  • Licht CM, de Geus EJ, Zitman FG, Hoogendijk WJ, van Dyck R, Penninx BW. Association between major depressive disorder and heart rate variability in the Netherlands Study of Depression and Anxiety (NESDA) Archives of General Psychiatry. 2008;65(12):1358–67. [PubMed]
  • Linkowski P, Mendlewicz J, Kerkhofs M, Leclercq R, Golstein J, Brasseur M, Copinschi G, Van Cauter E. 24-hour profiles of adrenocorticotropin, cortisol, and growth hormone in major depressive illness: effect of antidepressant treatment. Journal of Clinical Endocrinology and Metabolism. 1987;65(1):141–52. [PubMed]
  • McLaughlin T, Abbasi F, Cheal K, Chu J, Lamendola C, Reaven GM. Use of metabolic markers to identify overweight individuals who are insulin resistant. Annals of Internal Medicine. 2003;139:802–809. [PubMed]
  • Miller GE, Rohleder N, Stetler C, Kirshbaum C. Clinical depression and regulation of the inflammatory response during acute stress. Psychosomatic Medicine. 2005;67:679–687. [PubMed]
  • Miller GE, Rohleder N, Stetler C, Kirshbaum C. Clinical depression and regulation of the inflammatory response during acute stress. Psychosomomatic Medicine. 2005;67:679–687. [PubMed]
  • Moreno PR. The year in atherothrombosis. JACC. 2004;44:2099–2110. [PubMed]
  • Musselman DL, Evans DL, Nemeroff CB. The relationship of depression to cardiovascular disease: epidemiology, biology, and treatment. Archives of General Psychiatry. 1998;55(7):580–92. [PubMed]
  • Nemeroff CB, Owens MJ. Pharmacologic differences among the SSRIs: focus on monoamine transporters and the HPA axis. CNS Spectrum. 2004;9(6 Suppl 4):23–31. [PubMed]
  • Pruessner JD, Kirschbaum C, Meinlschmid G, Hellhammer DJ. Two formulas for computation of area under the curve represent measures of total hormone concentration versus time-dependent change. Psychoneuroendocrinology. 2003;28:916–931. [PubMed]
  • Rota E, Broda R, Cangemi L, Migliaretti G, Paccotti P, Rosso C, Torre E, Zeppegno P, Portaleone P. Neuroendocrine (HPA axis) and clinical correlates during fluvoxamine and amitriptyline treatment. Psychiatry Research. 2005;133(23):281–4. [PubMed]
  • Stewart JW, Quitkin FM, McGrath PJ, Klein DF. Defining the boundaries of atypical depression: evidence from the HPA axis supports course of illness distinctions. Journal of Affective Disorders. 2005;86(23):161–7. [PubMed]
  • Strachowski D, Khaylis A, Conrad A, Neri E, Spiegel D, Taylor CB. The effects of directed cognitive behavior therapy on depression in older patients with cardiovascular risk. Depression and Anxiety. 2008;25(8):E1–10. 2007. [PubMed]
  • Strike PC, Steptoe A. Systematic review of mental stress-induced myocardial ischaemia. European Heart Journal. 2003;24:690–703. [PubMed]
  • Taylor CB, Conrad A, Wilhelm FH, Neri E, Delorenzo A, Kramer MA, Giese-Davis J, Roth WT, Oka R, Cooke JP, Kraemer H, Spiegel D. Psychophysiological and cortisol responses to psychological stress in depressed and non-depressed older men and women with elevated CVD risk. Psychosomatic Medicine. 2006;68(4):538–46. [PubMed]
  • Wilhelm FH, Grossman P, Coyle MA. Improving estimation of cardiac vagal tone during spontaneous breathing using a paced breathing calibration. Biomedical Sciences Instrumentation. 2004;40:317–324. [PubMed]
  • Wilhelm FH, Grossman P, Roth WT. Analysis of cardiovascular regulation. Biomedical Sciences Instrumentation. 1999;35:135–140. [PubMed]