Abstract
Objective
Psychological constructs are associated with cardiovascular health, but the biological mechanisms mediating these relationships are unknown. We examined relationships between psychological constructs and markers of inflammation, endothelial function, and myocardial strain in a cohort of post-acute coronary syndrome (ACS) patients.
Methods
Participants (N=164) attended study visits 2 weeks and 6 months post-ACS. During these visits, they completed self-report measures of depressive symptoms, anxiety, optimism, and gratitude, and blood samples were collected for measurement of biomarkers reflecting inflammation, endothelial function, and myocardial strain. Generalized estimating equations and linear regression analyses were performed to examine concurrent and prospective relationships between psychological constructs and biomarkers.
Results
In concurrent analyses, depressive symptoms were associated with elevated markers of inflammation (interleukin-17: β=.047, 95% confidence interval [.010, .083]), endothelial dysfunction (endothelin-1: β=.020, [.004, .037]), and myocardial strain (N-terminal pro-B-type natriuretic peptide: β=.045, [.008, .083]), independent of age, sex, medical variables, and anxiety, while anxiety was not associated with these markers in multivariable adjusted models. Optimism and gratitude were associated with lower levels of markers of endothelial dysfunction (endothelin-1: gratitude: β=−.009, [−.017, −.001]; optimism: β=−.009, [−.016, −.001]; soluble intercellular adhesion molecule-1: gratitude: β=−.007, [−.014, −.000]), independent of depressive and anxiety symptoms. Psychological constructs at 2 weeks were not prospectively associated with biomarkers at 6 months.
Conclusions
Depressive symptoms were associated with more inflammation, myocardial strain, and endothelial dysfunction in the 6 months post-ACS, while positive psychological constructs were linked to better endothelial function. Larger, prospective studies may clarify the directionality of these relationships.
Keywords: depression, anxiety, optimism, gratitude, inflammation, endothelial function
Introduction
Acute coronary syndromes (ACS; myocardial infarction [MI] or unstable angina [UA]) affect 1.3 million Americans each year (1) and are associated with substantial rates of readmission and death in the following year (2). Inflammation, endothelial dysfunction, and myocardial strain, three physiologic processes underlying CAD, have been linked to poor outcomes post-ACS (3, 4). Inflammatory markers, such as high sensitivity C-reactive protein (hsCRP), various interleukins, and tumor necrosis factor-alpha (TNF-α), have been implicated in the development and progression of CAD (5, 6). Similarly, markers of endothelial dysfunction (e.g., soluble intercellular adhesion molecule-1 [sICAM-1]) and myocardial strain (e.g., N-terminal pro-B-type natriuretic peptide [NT-proBNP], cardiac troponin-I [cTnI]) have been linked to poor health outcomes and reduced survival in patients with ACS (7–11). Better understanding what factors are associated with these markers of heart health may help clinicians to identify those patients at highest risk for complications in the post-ACS period.
Psychological factors—both positive and negative—have also been associated with cardiac prognosis (12–14), but the mechanisms mediating these relationships are unclear. Prior studies have found that depression is associated with abnormal levels of inflammatory cytokines and other established markers of poor cardiac prognosis (15–18), and anxiety has been linked to elevated levels of inflammatory markers in some studies (19, 20) but not others (21–23). On the other hand, smaller numbers of studies have found that positive constructs, such as optimism, may be associated with improved levels of cardiac biomarkers (14, 24), and gratitude has been associated with increased parasympathetic activity (25).
However, there are numerous gaps in the literature related to psychological factors and cardiac biomarkers. First, relatively few studies have examined patients with existing heart disease (14, 15, 26–28), especially in the high-risk post-ACS period. Second, there has been minimal study of the association of anxiety with prognostic biomarkers, despite links between anxiety and post-ACS mortality (19, 21, 29). Third, despite being a commonly experienced psychological state following ACS, the effects of gratitude (“a disposition toward appreciating and being thankful for people, events, and experiences in one’s life”) (30) on biomarkers have, to our knowledge, never been studied post-ACS. Finally, very few studies (and no studies in ACS patients) have concurrently examined the effects of multiple positive and negative constructs on key biomarkers to assess which psychological factors most strongly correlate with prognosis.
Knowledge of the relationships of depression, anxiety, optimism, and gratitude with biomarkers of cardiac health in the post-ACS period is important for several reasons. Such knowledge would allow us to confirm that these relationships are consistent across clinical populations, as prior studies have focused primarily on patients without acute manifestations of CAD. By examining these four psychological constructs that frequently are experienced in the post-ACS period, it also would allow us to determine which relationships occur independent of the others (e.g., it would allow us to confirm that the relationships between anxiety and biomarkers are not simply due to associations between depression and biomarkers). Finally, confirmation of prospective associations between psychological states and cardiac biomarkers in the post-ACS period would provide support for the notion that developing treatments to reduce depression and anxiety or promote optimism and gratitude may lead to beneficial cardiovascular outcomes.
Accordingly, in a series of ACS patients we examined: (1) concurrent relationships between psychological constructs (depressive symptoms, anxiety symptoms, gratitude, and optimism) and markers of inflammation, endothelial function, and myocardial strain/cardiac prognosis over the course of 6 months, and (2) prospective relationships between psychological states 2 weeks post-ACS and relevant cardiac biomarkers at 6 months. We hypothesized that positive psychological constructs would be associated with less inflammation, improved endothelial function, and reduced cardiac strain, while negative constructs would be associated with the reverse.
Methods
This is an analysis from the Gratitude Research in Acute Coronary Events (GRACE) study (ClinicalTrials.gov identifier NCT01709669), a prospective, observational study to determine the associations between psychological states and health behaviors, biomarkers, and cardiac outcomes in post-ACS patients. The study enrolled participants between September 2012 and January 2014, and full study methods and results have been published elsewhere (30, 31). In our main analyses, optimism, measured 2 weeks following ACS, was prospectively associated with increased physical activity and lower rates of rehospitalization—but not cardiac biomarkers—at 6 months. In contrast, baseline gratitude was not associated with physical activity, biomarkers, or cardiac outcomes over the same time period (30). Prior to the initiation of study procedures, approval was obtained from the Partners Healthcare Institutional Review Board.
Participants
Participants were recruited from one of three inpatient cardiac units at an urban academic medical center. Patients were eligible for enrollment if they were admitted for an ACS (MI or UA), diagnosed using standardized criteria (32, 33). Patients were excluded if they: (1) suffered a periprocedural ACS, (2) had a non-cardiac condition that might alter cardiac biomarkers (e.g., inflammatory diseases) or would lead to death during the subsequent 6 months, (3) were unable to perform physical activity, or (4) were unable to complete self-report questionnaires due to cognitive impairment or language barriers.
Procedures
After eligibility was confirmed, participants provided informed consent and enrolled in the study. During their hospitalization, sociodemographic and medical information was obtained from the medical record. This included body mass index, comorbid cardiac-related diagnoses (hypertension, hyperlipidemia, diabetes mellitus), history of acute coronary syndrome, and current smoking status. Information from the medical record was used to calculate an age-adjusted Charlson Comorbidity Index score. After discharge, participants returned to MGH at 2 weeks and 6 months for study visits, at which time self-report measures were administered to assess psychological, behavioral, and functional status.
Blood Collection
During study visits, blood samples were collected into tubes containing ethylenediaminotetraacetic acid or no anticoagulant for biomarker analysis. The tubes were centrifuged for 15–30 minutes, after which plasma or serum was decanted and aliquoted into 500 μl tubes; processing was performed on ice. Next, samples were immediately frozen at −80 degrees centigrade and not thawed until biomarker measurements were performed.
Study Outcomes and Measures
Negative Psychological Constructs
Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), a nine item scale that queries participants about the frequency with which they experience the nine DSM-IV criteria for depression. Scores range from 0 to 27, with higher scores indicating more frequent depressive symptoms. For anxiety, the Hospital Anxiety and Depression Scale, anxiety subscale (HADS-A) (34) was used. Responses are scored from 0–3, with the total score ranging from 0–27. Both scales have been used in numerous studies of cardiac patients (32, 35, 36), are well-validated (37, 38), and had high internal consistency in our sample (PHQ-9: α=0.82, HADS-A: α=0.85).
Positive Psychological Constructs
We measured gratitude via the Gratitude Questionnaire-6 (GQ-6) (39) and optimism via the Life Orientation Test-Revised (LOT-R) (40). Both the GQ-6 and the LOT-R are well-validated, 6-item scales that have been used in studies involving patients with cardiovascular disease (41–43) and had high internal consistency in our participants (GQ-6: α=0.84, LOT-R: α=0.85). Consistent with the literature, a total optimism score was calculated for the LOT-R, with a higher score representing greater optimism and lower pessimism (40).
Cardiac biomarkers
We measured markers of inflammation, endothelial function, and myocardial strain/injury. Inflammation was assessed using measurement of high sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), interleukin-17 (IL-17), and tumor necrosis factor-alpha (TNF-α); each biomarker has been implicated in the development or progression of CAD (5, 6). Endothelial function was measured through endothelin-1 (ET-1) and soluble intercellular adhesion molecule-1 (sICAM-1). ET-1 is known to be elevated in patients with CAD, especially in the post-MI period (44), and higher levels of sICAM-1 have been associated with cardiac events and mortality in patients with heart disease (7, 8). Finally, cardiac strain was measured through N-terminal pro-B-type natriuretic peptide (NT-proBNP) and cardiac troponin-I (cTnI), which have been linked to subsequent survival in patients with ACS (9–11).
Samples were analyzed in batch by immunoassay kits, as per published methods (and per the team’s prior experience analyzing these biomarkers) via the MGH Research Core Laboratory (hsCRP, sICAM-1) and the Singulex Corporation (IL-6, IL-17, TNF-α, ET-1, cTnI, NT-proBNP). Levels of detection for each assay were as follows: 0.5mg/dL for hsCRP, 0.096ng/mL for sICAM-1, 0.004pg/mL for IL-6, 0.01pg/mL for IL-17, 0.016pg/mL for TNF-α, 0.1pg/mL for ET-1, 0.05pg/mL for cTnI, and 5pg/mL for NT-proBNP.
Statistical Analyses
Relationships between psychological variables and biomarkers were examined in a concurrent manner (examining relationships at the same timepoint and with respect to concurrent change between timepoints within the same model) and in a prospective manner (examining whether psychological variables at baseline predicted biomarker levels at 6 months).
To assess the concurrent relationships between psychological constructs and cardiac biomarkers at baseline and 6 months, a series of generalized estimating equation models with an exchangeable working correlation structure and robust standard errors were used. Three separate models were created, each one adjusted for progressively more covariates. In the first step (Model 1), age and sex were added as covariates. Next (Model 2), medical variables (peak troponin T level [representing severity of current ACS], age-adjusted Charlson Comorbidity Index score (45) [measure of overall medical comorbidity], body mass index, hypertension, hyperlipidemia, current smoking status, admission diagnosis, length of stay, and history of prior ACS) were added to those already adjusted for in Model 1. In the final model (Model 3), measures of depressive (PHQ-9) and anxiety (HADS-A) symptoms were included, in addition to those covariates from Models 1 and 2. Within each model the cross-sectional and longitudinal effects were assumed to be equal.
To assess the prospective relationships between baseline psychological constructs and 6-month biomarker levels, linear regression models were created, adjusting for baseline biomarker levels and with hierarchical adjustment over three successive models as above. Because biomarker concentrations were not normally distributed, all biomarker levels were natural log-transformed, and robust standard errors were used to account for any residual non-normality. All results are presented as regression coefficients with natural log-transformed biomarkers as the outcome; exponentiating the regression coefficient provides an estimate of the impact of the predictor on the geometric mean (46). All tests of significance were two-tailed, with significance set at p<.05, given the exploratory nature of the analyses. However, since four different psychological constructs were examined in our analyses, findings with p≥.0125 (threshold for significance after Bonferroni correction (47)) should be interpreted with caution. Analyses were performed using Stata statistical software (version 11.2, StataCorp, College Station, TX).
Results
Of 212 post-ACS patients who enrolled in the study, 164 completed the 2-week visit, and 156 completed the follow-up visit at 6 months. All participants who provided baseline data (N=164) were included in our analyses. Fifty-four percent of participants were admitted for an MI, and 46% with UA; 42% had experienced an ACS in the past. See Table 1 for participants’ sociodemographic and medical baseline characteristics.
Table 1.
Characteristics of participants who completed the 2-week visit (N=164)
| Characteristics | N (%)* |
|---|---|
| Demographics and psychosocial characteristics | |
| Age (mean [SD]), years | 61.5 (10.6) |
| Male sex | 137 (84) |
| White | 137 (84) |
| Married | 113 (69) |
| Living alone | 38 (23) |
| Medical characteristics | |
| Body mass index (mean [SD]), kg/m2 | 28.9 (5.2) |
| Hypertension | 103 (63) |
| Diabetes mellitus | 34 (21) |
| Hyperlipidemia | 132 (81) |
| Current smoking | 21 (13) |
| Prior acute coronary syndrome | 69 (42) |
| Admission diagnosis of myocardial infarction | 88 (54) |
| Length of stay (days; mean [SD]) | 3 (2.0) |
| Troponin T during index hospitalization (ng/mL) | 1.5 (3.5) |
| Charlson Comorbidity Index (age adjusted) | 3.3 (1.6) |
| Medications prescribed at discharge | |
| Aspirin | 159 (97) |
| Beta blocker | 144 (88) |
| Angiotensin converting enzyme inhibitor | 90 (55) |
| Antiplatelet agent | 127 (77) |
| Statin | 153 (93) |
| Antidepressant | 27 (16) |
| Anxiolytic | 16 (10) |
| Self-report measures at baseline 2-week visit | |
| Patient Health Questionnaire-9 (depressive symptoms; range: 0–27) (mean [SD]) | 4.3 (4.4) |
| Clinically Significant Depressive Symptoms (PHQ-9 ≥ 10) | 17 (10) |
| Hospital Anxiety and Depression Scale-Anxiety subscale (anxiety symptoms; range: 0–21) (mean [SD]) | 4.3 (4.0) |
| Clinically Significant Anxiety Symptoms (HADS-A ≥ 8) | 32 (20) |
| Life Orientation Test-Revised (optimism; range: 0–24) (mean [SD]) | 17.7 (5.6) |
| Gratitude Questionnaire-6 (gratitude; range: 6 – 42) (mean [SD]) | 36.5 (5.8) |
| Biological measures at baseline 2-week visit (mean [SD]) | |
| High sensitivity C-reactive protein (μg/mL) | 5.1 (9.3) |
| Tumor necrosis factor-α (pg/mL) | 4.0 (1.5) |
| Interleukin-17 (pg/mL) | 0.3 (0.7) |
| Interleukin-6 (pg/mL) | 3.7 (4.1) |
| Soluble intercellular adhesion molecule-1 (μg/mL) | 0.5 (0.2) |
| Endothelin-1 (pg/mL) | 4.5 (3.1) |
| Cardiac troponin-I (ng/mL) | 5.8 (15.7) |
| N-terminal pro-B-type natriuretic peptide (pg/mL) | 536.6 (1022.5) |
| Self-report measures at 6-month visit** | |
| Patient Health Questionnaire-9 (depression; range: 0–27) (mean [SD]) | 3.3 (4.0) |
| Clinically Significant Depressive Symptoms (PHQ-9 ≥ 10) | 7 (4) |
| Hospital Anxiety and Depression Scale-Anxiety subscale (anxiety symptoms; range: 0–21) (mean [SD]) | 3.8 (3.8) |
| Clinically Significant Anxiety Symptoms (HADS-A ≥ 8) | 22 (14) |
| Life Orientation Test-Revised (optimism; range: 0–24) (mean [SD]) | 18.0 (5.8) |
| Gratitude Questionnaire-6 (gratitude; range: 6 – 42) (mean [SD]) | 37.2 (5.1) |
| Cardiac biomarkers at 6-month visit (mean [SD]) | |
| High sensitivity C-reactive protein (μg/mL) | 4.0 (6.9) |
| Tumor necrosis factor-α (pg/mL) | 3.9 (1.6) |
| Interleukin-17 (pg/mL) | 0.3 (0.5) |
| Interleukin-6 (pg/mL) | 3.1 (2.5) |
| Soluble intercellular adhesion molecule-1 (μg/mL) | 0.5 (0.1) |
| Endothelin-1 (pg/mL) | 4.4 (3.3) |
| Cardiac troponin-I (ng/mL) | 2.3 (4.2) |
| N-terminal pro-B-type natriuretic peptide (pg/mL) | 368.4 (817.2) |
All numbers are N (%) unless otherwise specified.
N=156 for all 6-month self-report measures
HADS-A = Hospital Anxiety and Depression Scale-Anxiety subscale; PHQ-9 = Patient Health Questionnaire-9
Negative psychological constructs
On concurrent analysis, more depressive symptoms were significantly associated with higher levels of several markers of inflammation (see Table 2). Adjusting for age and sex, depressive symptoms were significantly positively correlated with levels of TNF-α (β = .016, p < .001), IL-17 (β = .030, p = .006), and IL-6 (β = .019, p = .024), but not with hsCRP (p = .18). On adjustment for medical covariates, correlations between depressive symptoms and IL-17 and TNF-α remained significant (TNF-α: β = .012, p = .002; IL-17: β = .029, p = .007), while the correlation between depressive symptoms and IL-6 did not. Finally, when adjusting for anxiety symptoms, depressive symptoms remained significantly positively correlated with IL-17 (β = .047, p = .012), but the relationship between depressive symptoms and TNF-α became marginally significant (β = .010, p = .081).
Table 2.
Concurrent associations between psychological constructs and biomarkers of interest
| Biomarkers (β [95% confidence interval]; biomarkers were natural log-transformed) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictor | Model | hsCRP | TNF-α | IL-17 | IL-6 | sICAM-1 | ET-1 | cTnI | NT-proBNP |
| Depressive Symptoms (PHQ-9) | 1 | .026 (−.012, .064) |
.016*** (.008, .024) |
.030** (.009, .052) |
.019* (.002, .036) |
.004 (−.007, .014) |
.015** (.005, .025) |
.036** (.010, .062) |
.066*** (.035, .097) |
| 2 | −.001 (−.038, .035) |
.012** (.005, .020) |
.029** (.008, .050) |
.007 (−.009, .022) |
−.002 (−.011, .007) |
.011† (−.000, .022) |
.020 (−.008, .048) |
.045** (.017, .074) |
|
| 3 | −.021 (−.064, .021) |
.010† (−.001, .021) |
.047* (.010, .083) |
.001 (−.017, .020) |
−.000 (−.012, .011) |
.020* (.004, .037) |
.029† (−.003, .061) |
.045* (.008, .083) |
|
| Anxiety Symptoms (HADS-A) | 1 | .036† (−.003, .075) |
.012* (.002, .023) |
−.002 (−.026, .021) |
.018† (−.001, .037) |
.001 (−.008, .010) |
−.002 (−.015, .012) |
.018 (−.018, .053) |
.043† (−.001, .086) |
| 2 | .020 (−.017, .058) |
.011* (.001, .020) |
−.003 (−.027, .022) |
.010 (−.007, .027) |
−.002 (−.010, .005) |
−.004 (−.017, .009) |
.003 (−.031, .036) |
.029 (−.007, .065) |
|
| 3 | .034 (−.009, .077) |
.005 (−.009, .018) |
−.032 (−.073, .009) |
.010 (−.010, .030) |
−.002 (−.012, .008) |
−.017† (−.035, .001) |
−.015 (−.054, .023) |
−.000 (−.046, .045) |
|
| Optimism (LOT-R) | 1 | −.025† (−.052, .002) |
−.007* (−.014, −.001) |
.006 (−.010, .022) |
−.010 (−.024, .004) |
−.004 (−.011, .002) |
−.009** (−.016, −.002) |
.009 (−.014, .032) |
−.010 (−.034, .014) |
| 2 | −.022† (−.047, .002) |
−.008** (−.014, −.002) |
.004 (−.012, .020) |
−.008 (−.020, .004) |
−.002 (−.008, .004) |
−.008* (−.014, −.001) |
.005 (−.017, .027) |
−.014 (−.037, .008) |
|
| 3 | −.026† (−.053, .001) |
−.005 (−.011, .002) |
.012 (−.007, .031) |
−.006 (−.019, .007) |
−.004 (−.011, .002) |
−.009* (−.016, −.001) |
.013 (−.011, .037) |
.001 (−.026, .029) |
|
| Gratitude (GQ-6) | 1 | −.027 (−.060, .007) |
−.006 (−.013, .002) |
.009 (−.008, .025) |
−.007 (−.023, .009) |
−.006† (−.014, .001) |
−.010** (−.017, −.003) |
−.018 (−.046, .010) |
−.015 (−.046, .016) |
| 2 | −.022 (−.052, .008) |
−.007* (−.013, −.001) |
.005 (−.012, .022) |
−.004 (−.018, .010) |
−.005 (−.011, .001) |
−.009* (−.016, −.002) |
−.019† (−.038, .001) |
−.018 (−.041, .005) |
|
| 3 | −.026 (−.060, .007) |
−.003 (−.011, .004) |
.015 (−.005, .034) |
−.001 (−.017, .015) |
−.007* (−.014, −.000) |
−.009* (−.017, −.001) |
−.017 (−.038, .004) |
−.004 (−.030, .022) |
|
Model 1: Adjusting for age and gender.
Model 2: Adjusting for Model 1 covariates plus history of prior ACS, peak troponin T, length of stay, admission diagnosis, body mass index, current smoking status, hypertension, hyperlipidemia, and age-adjusted Charlson Comorbidity Index.
Model 3: Adjusting for Model 2 covariates plus depression and anxiety, if applicable.
Key:
= p<.10
= p<.05
= p<.01
= p<.001
Abbreviations: cTnI = cardiac troponin I; ET-1 = endothelin 1; GQ-6 = Gratitude Questionnaire-6; HADS-A = Hospital Anxiety and Depression Scale, anxiety subscale; hsCRP = C-reactive protein; IL-6 = interleukin-6; IL-17 = interleukin-17; LOT-R= Life Orientation Test-Revised; NT-proBNP = N-terminal pro-brain natriuretic peptide; PHQ-9 = Patient Health Questionnaire-9; sICAM-1 = soluble intercellular adhesion molecule-1; TNF-α = tumor necrosis factor-α
Depressive symptoms also were correlated with higher levels of ET-1, even when adjusting for medical variables and anxiety (β = .020, p = .013); however, they were not significantly correlated with sICAM-1. Finally, more depressive symptoms were associated with higher concentrations of the prognostic marker NT-proBNP in all models (model adjusting for age, sex, medical variables, and anxiety symptoms: β = .045, p = .017). More severe depressive symptoms also were associated with greater concentrations of cTnI when adjusting for age and sex (β = .036, p = .006); however, this relationship became marginally significant when adjusting for medical variables and anxiety symptoms (β = .029, p = .075).
In contrast, there were few relationships between anxiety symptoms and biological measures. When adjusting for age and sex, more anxiety symptoms were associated with higher concentrations of TNF-α (β = .012, p = .018). This relationship persisted when adjusting for medical covariates (β = .011, p = .027) but became nonsignificant when adjusting for depressive symptoms (β = .005, p = .51).
When examining the prospective relationships between depressive/anxiety symptoms at 2 weeks and biomarkers at 6 months, no significant associations were found on fully adjusted analyses, though greater depressive symptoms at baseline did predict higher levels of sICAM-1 when sex for age and sex only (β = .015, p = .010; see Table S1, Supplemental Digital Content 1).
Positive psychological constructs
On concurrent analysis (see Table 2), optimism was significantly associated with lower concentrations of TNF-α when adjusting for age and sex (β = −.007, p = .026) and when additionally adjusting for medical covariates (β = −.008, p = .009), but this relationship became non-significant when adding depressive and anxiety symptoms to the analyses (p = .20). When adjusting for age, sex, and medical covariates, gratitude was associated with lower levels of TNF-α (β = −.007, p = .026); however, this relationship became non-significant when adjusting for depressive and anxiety symptoms (p = .36). Neither optimism nor gratitude were associated with concentrations of hsCRP, IL-6, or IL-17.
In contrast, both optimism and gratitude were associated with lower concentrations of ET-1, even when adjusting for age, sex, medical covariates, depressive symptoms, and anxiety symptoms (optimism: β = −.009, p = .028; gratitude: β = −.009, p = .033). Furthermore, gratitude was associated with lower concentrations of sICAM-1 (model adjusting for age, sex, medical factors, depressive symptoms, and anxiety symptoms: β = −.007, p = .037). These positive psychological constructs were not significantly associated with NT-proBNP or cTnI.
Finally, on prospective analysis (Table S1), optimism at 2 weeks was associated with lower levels of TNF-α at 6 months, when adjusting for age and sex (β = −.006, p = .042). However, this association became marginally significant when adjusting for age, sex, and medical covariates (β = −.005, p = .091) and non-significant when adjusting for depressive and anxiety symptoms (p = .26). There were no other significant relationships between optimism/gratitude at 2 weeks and cardiac biomarkers at 6 months.
Discussion
Overall, we found that both positive and negative psychological states were associated with alterations in cardiac biomarkers in the post-ACS period. Specifically, depressive symptoms were linked to markers of inflammation (IL-17), endothelial functioning (ET-1), and myocardial stress/cardiac prognosis (NT-proBNP, cTnI), independent of sociodemographic variables, medical variables, and anxiety. Anxiety also had mild associations with inflammatory markers and prognosis, but these relationships became non-significant when accounting for depressive symptoms. Finally, both gratitude and optimism were associated with improved endothelial function, but not with inflammatory markers or myocardial stress, on maximally adjusted analyses.
The finding that depressive symptoms were linked to inflammation is consistent with the literature, which has frequently found associations between depression and inflammatory markers, including IL-6, TNF-α, and hsCRP, in patients with or without a history of cardiovascular disease (15, 16, 27, 28). Our study suggested possible links between depressive symptoms and TNF-α in patients with CAD and also found a relationship between depressive symptoms and IL-17, a less-studied inflammatory marker (48–50). Depressive symptoms also were significantly associated with elevated levels of ET-1, cTnI, and NT-proBNP. These findings are consistent with prior studies that link depression with impaired endothelial function (26, 51–54) and poor cardiac prognosis (12, 13). Our findings suggest that depression’s relationship to poor cardiac health may in part be explained by its relationships to inflammation and endothelial dysfunction.
Unlike depression, anxiety symptoms were not associated with cardiac biomarkers when adjusting for depressive symptoms. To date, the literature examining the relationships between anxiety and inflammation has been mixed (19–23), and the relationship between anxiety and cardiac prognosis (29, 55) appears weaker than that of depression (12, 55, 56). Our findings suggest that anxiety’s relationship to cardiovascular health may be explained in part by depressive symptoms, which have been more clearly associated with both inflammatory markers and cardiac prognosis in patients with heart disease (12, 56).
In contrast to negative constructs, optimism and gratitude were associated with improved endothelial function, and less so with severity of inflammation. There has been limited study of relationships between positive psychological constructs and endothelial function (24); our results are novel, therefore, and suggest that improvements in endothelial function may be one mechanism by which these constructs are associated with improved cardiac health. More generally, this study supports the notion that while positive and negative psychological constructs are associated with cardiac outcomes, there may be distinct physiologic pathways or mechanisms by which they are linked to cardiac health.
Finally, while our analyses found significant concurrent relationships between psychological states and biomarkers, no prospective relationships were found, suggesting that the relationships between psychological states and heart health may co-vary over time. Further studies are needed to clarify the directionality of these relationships (i.e., whether cardiac health leads to changes in psychological constructs or psychological constructs lead to changes in cardiac health).
This study had several limitations. It was conducted in an urban academic hospital, and participants were predominantly White and male, which limits the generalizability of the findings. Furthermore, our sample size limited our statistical power to detect relationships between psychological states and biomarkers. In addition, moderate to strong correlations between psychological constructs (see Table S2, Supplemental Digital Content 1) may have contributed to the attenuated relationships between constructs and biomarkers in the fully adjusted (Model 3) analyses. Finally, given the concurrent nature of our analyses, we cannot make interpretations regarding causality. More prospective studies will be needed to explore these relationships further.
Despite these limitations, our study is novel, and extends the literature in several ways. First, this study solidifies the knowledge base that depression is significantly associated with elevated inflammatory markers and endothelial dysfunction in the immediate post-ACS period and suggests that the relationship between anxiety and inflammatory markers may be driven largely by the effects of comorbid depression. Furthermore, we show novel results demonstrating that positive psychological states are strongly correlated to better endothelial function, with minimal associations with inflammation. Finally, by examining both concurrent and prospective relationships in this cohort, this study supports the notion that inflammation and endothelial function may change in parallel to psychological states and calls for further research into determining the directionality of these relationships.
Supplementary Material
Acknowledgments
Source of Funding:
This research project was supported by the Expanding the Science and Practice of Gratitude Project run by UC Berkeley’s Greater Good Science Center in partnership with UC Davis with funding from the John Templeton Foundation (grant 15627) to Dr. Huffman. Time for analysis and article preparation was also funded by the National Heart Lung and Blood Institute through grants K23HL123607 (to Dr. Celano) and R01HL113272 (to Dr. Huffman). Dr. Januzzi is supported in part by the Hutter Family Professorship in the Field of Cardiology. Dr. Januzzi has received grant support from Siemens, Singulex and Prevencio, consulting income from Roche Diagnostics, Critical Diagnostics, Sphingotec, Philips, and Novartis, and participates in clinical endpoint committees for Novartis, Amgen, Janssen, and Boehringer Ingelheim. The Harvard Catalyst Program and Singulex provided support for the analysis of selected biomarkers. The content is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health or other funders.
Acronyms used
- ACS
acute coronary syndrome
- CAD
coronary artery disease
- cTnI
cardiac troponin I
- ET-1
endothelin-1
- GQ-6
Gratitude Questionnaire-6
- GRACE
Gratitude Research in Acute Coronary Events
- HADS-A
Hospital Anxiety and Depression Scale, anxiety subscale
- hsCRP
high-sensitivity C-reactive protein
- IL-6
interleukin-6
- IL-17
interleukin-17
- LOT-R
Life Orientation Test – Revised
- MGH
Massachusetts General Hospital
- MI
myocardial infarction
- NT-proBNP
N-terminal pro-brain natriuretic peptide
- PHQ-9
Patient Health Questionnaire-9
- sICAM-1
soluble intercellular adhesion molecule-1
- TNF-α
tumor necrosis factor-α
- UA
unstable angina
Footnotes
Conflicts of Interest:
The authors have no other conflicts of interest to disclose regarding this research to disclose.
Clinical Trial Registration: Clinicaltrials.gov identifier NCT01709669
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