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Prospective Study of the Association Between Dispositional Optimism and Incident Heart Failure

Originally publishedhttps://doi.org/10.1161/CIRCHEARTFAILURE.113.000644Circulation: Heart Failure. 2014;7:394–400

Abstract

Background—

Although higher optimism has been linked with an array of positive health behaviors, biological processes, and cardiovascular outcomes, the relationship between optimism and heart failure has not been examined. In the United States, 80% of heart failures occur in adults aged 65+ years. Therefore, we examined whether higher optimism was linked with a reduced incidence of heart failure among older adults.

Methods and Results—

Prospective data were from the Health and Retirement Study, a nationally representative study of older US adults. Our sample included 6808 participants who were followed for 4 years. Multiple logistic regression models were used to assess whether optimism was independently associated with incident heart failure. We adjusted for sociodemographic, behavioral, biological, and psychological covariates. Higher optimism was associated with a lower risk of incident heart failure during the follow-up period. In a model that adjusted for sociodemographic factors, each SD increase in optimism had an odds ratio of 0.74 (95% confidence interval, 0.63–0.85) for heart failure. Effects of optimism persisted even after adjusting for a wide range of covariates. There was also evidence of a dose–response relationship. As optimism increased, risk of developing heart failure decreased monotonically, with a 48% reduced odds among people with the highest versus lowest optimism.

Conclusions—

This is the first study to suggest that optimism is associated with a lower risk of heart failure. If future studies confirm these findings, they may be used to inform new strategies for preventing or delaying the onset of heart failure.

Introduction

Heart failure is an emerging epidemic.1 In the United States alone, >5.8 million people have the condition and it costs the nation $39 billion annually.2 Because of, in part, an aging population, recent reports have projected that the increasing prevalence of heart failure will translate into significantly rising healthcare costs.3 Given that the risk of heart failure rapidly increases with age, and the number of adults aged >65 years is estimated to double by 2050,4 identifying new targets for prevention of heart failure is increasingly urgent.

Editorial see p 385

Clinical Perspective on p 400

Despite robust literature examining the clinical, socioeconomic, and lifestyle risk factors for heart failure,5,6 links between psychological factors and heart failure have rarely been examined. However, psychological factors may play an important role in the development of heart failure. For example, dispositional optimism—the generalized expectation that good things will happen—has been linked to an array of cardiovascular benefits which range from lower risk of cardiovascular disease to lowered risk of stroke and lower risk of hospitalization after bypass surgery.711 Moreover, optimism is associated with important health behaviors, which in turn are strongly linked with a decreased risk of developing heart failure. For example, optimists are more likely to engage in health-promoting behaviors such as eating healthier diets, exercising more, managing stress better, and abstaining from smoking.1114 Optimism is an individual attribute that is not only ≈25% heritable,15 but can also be shaped by social influences and learned.1619 Thus, it may provide a point of intervention for improving health outcomes. To date, however, no research has examined the relationship between optimism and heart failure.

To fill this gap, we investigated the association between dispositional optimism and incident heart failure. Considering that 80% of heart failures in the United States occur in adults aged >65 years,20 we used data from the Health and Retirement Study, a longitudinal and nationally representative sample of older adults in the United States.

Based on prior research examining the relationship between optimism and other cardiovascular outcomes, we hypothesized that higher optimism would be associated with a lower risk of developing heart failure. To test the hypothesis, we examined optimism’s association with incident heart failure while controlling for a wide range of covariates (eg, sociodemographic, biological, and behavioral factors) that are related to cardiovascular risk. We also considered whether some of these factors (eg, smoking, physical activity) might be on the pathway as potential variables linking higher optimism to a lower risk of heart failure. Although formal tests of mediation were not possible because of data limitations, we assessed whether findings might be consistent with this interpretation. In addition, because several studies have found a link between psychological ill-being and increased risk of cardiovascular events,21 we controlled for anxiety, cynical hostility, and depression. Evidence that optimism is associated with heart failure even after adjusting for these factors would reduce concerns that a relationship between optimism and heart failure was primarily attributable to the mere absence of psychological ill-being.

Methods

Participants

The Health and Retirement Study (HRS) is a nationally representative panel study that has surveyed >22 000 Americans aged ≥50 years biannually since 1992.22,23 In 2006, the HRS added a detailed module that assessed several psychological factors for the first time. Thus, we considered 2006 (the eighth wave) as the baseline for the present study and used psychological and covariate data collected in that wave. Incident heart failure was assessed in follow-up waves: the ninth (2008), tenth (2010), and exit interviews. For respondents who died during the follow-up period, exit interviews were completed by knowledgeable informants (see Methods in the Data Supplement for more detail). The University of Michigan’s Institute for Social Research is responsible for the study and provides extensive documentation about the protocol, instrumentation, sampling strategy, and statistical weighting procedures.22 Because the present study used deidentified, publicly available data, the Institutional Review Board at the University of Michigan exempted it from review.

Procedure

In 2006, approximately half of the HRS respondents were visited for an enhanced face-to-face interview. At that time, respondents were also asked to complete a leave-behind self-report psychological questionnaire, which they then returned by mail. Among people who were interviewed, the response rate for the leave-behind questionnaire was 90%. Although HRS interviewed all couples in a household, only data for respondents aged ≥50 years are made available through HRS. Therefore, among those who were interviewed face-to-face, 7168 respondents were eligible for HRS. We excluded 360 participants who self-reported a history of heart failure at the 2006 baseline, resulting in a final sample of 6808 respondents.

Measures

Heart Failure Outcome Measurement

Using data from the 2008, 2010, and exit surveys, we defined heart failure incidence as a first fatal or nonfatal heart failure based on self-report or proxy report of a physician’s diagnosis. HRS did not obtain information about subtypes of heart failure, so we could not consider effects separately by subtype. All health conditions in HRS are assessed via self-report of a doctor’s diagnosis. Researchers have rigorously assessed these self-reported health measures, demonstrating their validity and reliability.22 Furthermore, concordance studies comparing self-reports of heart failure with physical measures and medical records have been conducted across diverse populations.2429 Across these studies, agreement between self-reported heart failure and medical records ranged from 87.7% to 96.3%, sensitivity ranged from 47% to 68.6%, and specificity ranged from 95% to 97.7% (see Methods in the Data Supplement for more details).2429

Optimism

Optimism was assessed using the 6-item Life Orientation Test-Revised.30 Studies have demonstrated that the Life Orientation Test-Revised has good reliability.8 The measure has also been demonstrated to have good discriminant and convergent validity.30 Respondents were asked to rate each item on a 6-point Likert scale indicating the degree to which they endorsed such items as, In uncertain times, I usually expect the best. Three negatively worded items were reverse scored. Then, all 6 items were averaged together, with higher scores reflecting higher optimism (Cronbach α=0.78). The overall scores were then standardized (μ=0; σ=1) to facilitate interpretation and comparisons of effect size across optimism studies. In our study, all results can be interpreted as the change in odds of developing heart failure as a function of a 1 SD increase in optimism. In addition, we created quartiles of optimism based on the score distribution in this sample to consider the possibility of threshold or discontinuous effects. Quartiles of optimism were created because naturally occurring or clinically meaningful thresholds have not yet been established for this construct. The mean optimism scores by quartile were 3.21 (low), 4.07 (low-moderate), 4.81 (moderate-high), and 5.68 (high).

Researchers sometimes split the Life Orientation Test-Revised into 2 subscales—one consisting of only positively valenced items and the other consisting of only negatively valenced items. We chose not to create subscales for theoretical and methodological reasons.31,32 Optimism is most accurately captured by a scale that combines positively worded items that are endorsed and negatively worded items that are rejected.31 Furthermore, it is increasingly apparent that this separation into subscales may be at odds with the goal of controlling for acquiescence response bias in the measurement of psychological constructs. Thus, following recent theorizing and work in this area, we used the 6-item composite, rather than creating two 3-item subscales.13,32

Covariates Measurement

Potential covariates included sociodemographic, behavioral, biological, and psychological factors that prior work suggests are relevant to heart failure risk.5,6,21 All of the covariates described below were collected at baseline in 2006.

Sociodemographic covariates include age, sex, race/ethnicity (white, black, Hispanic, other) which was dummy coded with white as the reference group, marital status (married/not married), educational attainment (no degree, General Educational Development or high school diploma, college degree or higher), and total wealth (<25 000, 25 000–124 999, 125 000–299 999, 300 000–649 999, >650 000—based on quintiles of the score distribution in this sample).

Psychological covariates were assessed using measures that have been rigorously evaluated and shown good reliability and validity in previous studies. Depression was measured using the Center for Epidemiological Studies Depression Scale33 (in HRS, M=1.59; SD=2.03; Cronbach α=0.88), anxiety was measured using the Beck Anxiety Inventory (in HRS, M=1.60; SD=0.59; Cronbach α=0.80),34 and cynical hostility was measured using the cynicism subscale of the Cook–Medley Hostility Inventory (in HRS, M=3.97; SD=1.15; Cronbach α=0.79).35 The correlations between optimism and the psychological factors were moderate but significant: −0.30 (depression), −0.33 (anxiety), and 0.36 (cynical hostility).

Potential behavioral and biological covariates that might link optimism to heart failure were also considered. Behavioral covariates included smoking status (never, former, current), frequency of moderate (eg, gardening, dancing, walking at a moderate pace) and vigorous exercise (eg, running, swimming, aerobics; reported as never, 1–4 times per month, more than once a week), and frequency of alcohol consumption (abstinent, <1 or 2 days per month, 1–2 days per week, and >3 days per week) which was dummy coded with abstinent as the reference group.

Biological covariates included self-reported weight in pounds, converted into kilograms, and height in inches, converted into meters (used to calculate body mass index according to kilograms per meter squared) and hypertension and diabetes mellitus (each yes/no based on self-report of a doctor’s diagnosis). Body mass index was categorized as <18.5 (underweight), 18.5 to 24.9 (normal), 25 to 29.9 (overweight), and ≥30 (obese). Because the underweight category contained only 1.43% of the sample and was unstable in statistical analyses, it was collapsed with the normal category.

Statistical Analysis

We conducted multiple logistic regression analyses to test whether optimism was associated with a lower risk of heart failure. Logistic regression was used because we did not have detailed information on the date each heart failure occurred. Odds ratios (ORs) provide a good approximation of hazards ratios in this study for 4 reasons: the follow-up time was short, the sample size was large, the risk ratio was moderate in size, and the outcome incidence ratio was low (probability of heart failure was 6.14% in our sample).36 The impact of covariates on the relationship between optimism and heart failure was estimated by adjusting for blocks of covariates.

We first examined a minimally adjusted model and then considered the impact that adding demographic covariates had on the association between optimism and heart failure. We subsequently considered the impact of biological or behavioral covariates in a third and a fourth model. In models 3 and 4, an observed reduction in the association between optimism and heart failure, after adding either biological or behavioral covariates, may be consistent with the possibility that each block of variables represents a potential pathway linking optimism to risk of heart failure. Model 1 adjusted for only age and sex. Model 2, the core model, included age, sex, race/ethnicity, marital status, educational degree, and total wealth. Three additional models were created; model 3—core model+health behaviors (smoking status, exercise, alcohol frequency) and model 4—core model+biological factors (hypertension, diabetes mellitus, body mass index). Although doing so could overfit the model and raise multicollinearity issues, we also created a model 5, which included all covariates.

Several additional analyses were performed. First, we examined whether associations found between optimism and heart failure were maintained even when controlling for depression, anxiety, and cynical hostility. Using the core model, we added each psychological factor one at a time. Second, we examined the data for a potential threshold effect by considering quartiles of optimism. Third, to assess the possibility that the associations found in our study might be because of reverse causality (ie, having undiagnosed heart failure may lead to lower optimism), we re-examined the association between optimism and heart failure after excluding any cases of heart failure that developed within 2 years of baseline. Although this analysis cannot fully rule out the possibility of reverse causality, it may provide evidence to reduce concerns that prodromal disease alters a person’s generalized expectations for the future. In this analysis (n=6549), we had to drop participants who self-identified themselves in a race/ethnicity category other than white, black, or Hispanic because there were not enough cases of heart failure to power the analyses for this group. Fourth, we tested a potential interaction between optimism and sex to assess possible sex differences in the association of interest.

Logits were converted into ORs for ease of interpretation. Given that the probability of heart failure was rare in our sample (6.14%), our reported ORs may be regarded as relative risks.36 All reported results in this study were weighted using HRS sampling weights to account for the complex multistage probability survey design, which includes individual nonresponse, sample clustering, stratification, and further poststratification using Stata (StataCorp 2011, Stata Statistical Software: Release 12, StataCorp LP, College Station, TX).

Missing Data Analysis

For all study variables, the overall item nonresponse rate was only 0.48%. However, the missing data were distributed across variables, resulting in a 4.22% loss of respondents when complete case analyses were attempted. Therefore, to examine the impact of missing data on our results and to obtain less biased estimates, multiple imputation procedures were used to impute missing data. Results were largely the same between the original and imputed data sets. We therefore used the data set with multiple imputation for all analyses reported here because this technique provides a more accurate estimate of association than other methods of handling missing data.37

Results

Descriptive Analyses

The average age of respondents at baseline was 70 years (SD=10.26). Respondents tended to be female (59%) and married (62%). Most had a high school degree (54%) or attended some college (22%). Respondents identified as being white (71%), black (17%), Hispanic (10%), or Other (2%). Among the 6808 participants, 418 respondents developed heart failure during the 4-year follow-up (196 women and 222 men). Table 1 describes the distribution of covariates across quartiles of optimism.

Table 1. Distribution of Respondent Characteristics by Level of Optimism

CharacteristicOptimism
Low (n=1892)Low-Moderate (n=1563)Moderate-High (n=1855)High (n=1498)
Mean age (SD)69.97 (10.63)70.06 (10.04)70.01 (10.44)68.89 (9.71)
Female1084 (57.29)875 (56.00)1066 (57.47)978 (65.30)
Married status1120 (59.21)932 (59.66)1181 (63.69)967 (64.54)
Race/ethnicity
 White1350 (71.37)1090 (69.76)1346 (72.56)1112 (74.24)
 Black317 (16.76)260 (16.62)320 (17.26)244 (16.27)
 Hispanic194 (10.26)178 (11.39)174 (9.36)126 (8.46)
 Other31 (1.62)35 (2.23)15 (0.82)16 (1.04)
Education
 Less than high school660 (34.88)382 (24.42)384 (20.70)220 (14.66)
 High school970 (51.22)897 (57.42)1006 (54.24)794 (53.00)
 College degree or higher262 (13.89)284 (18.16)465 (25.07)484 (32.34)
Total wealth
 First quintile553 (29.25)365 (23.38)301 (16.22)230 (15.33)
 Second quintile459 (24.24)309 (19.77)375 (20.20)201 (13.44)
 Third quintile384 (20.30)320 (20.45)422 (22.73)296 (19.78)
 Fourth quintile291 (15.36)281 (18.00)383 (20.67)382 (25.51)
 Fifth quintile205 (10.86)288 (18.39)374 (20.20)389 (25.94)
Smoking status
 Never785 (41.48)616 (39.40)849 (45.77)690 (46.06)
 Former smoker770 (40.72)715 (45.74)823 (44.36)671 (44.82)
 Current smoker337 (17.80)232 (14.86)183 (9.87)137 (9.12)
Exercise
 Never1431 (75.62)1078 (68.99)1147 (61.86)881 (58.82)
 1–4 times per month196 (10.38)181 (11.57)289 (15.57)215 (14.35)
 >1× per week265 (14.00)304 (19.45)419 (22.57)402 (26.83)
Alcohol frequency
 Never1072 (56.67)825 (52.77)925 (49.85)722 (48.19)
 <1 per week309 (16.35)304 (19.46)350 (18.88)249 (16.62)
 1–2 per week245 (12.95)198 (12.65)290 (15.66)248 (16.54)
 3+ per week266 (14.03)236 (15.12)290 (15.62)279 (18.64)
Hypertension1090 (57.61)968 (61.96)930 (57.67)788 (52.59)
Diabetes mellitus419 (22.13)391 (25.04)268 (19.86)203 (13.54)
BMI, kg/m2
 Underweight (<18.5)510 (26.97)452 (28.90)528 (28.49)481 (32.13)
 Normal (25–29.9)679 (35.86)574 (36.72)695 (37.45)589 (39.34)
 Overweight (≥30)703 (37.17)537 (34.38)632 (34.06)428 (28.53)

Unless otherwise noted, values are number of participants (percentage). BMI indicates body mass index.

Optimism and Heart Failure Incidence

Associations between optimism and heart failure were highly consistent across all 5 models. For example, in the core model (model 2), each SD increase in optimism was associated with a multivariate-adjusted OR of 0.74 for heart failure (95% confidence interval [CI], 0.63–0.85), suggesting that people with higher optimism were at lower risk for incident heart failure. When considering each block of potential pathway covariates, the association between optimism and incident heart failure was somewhat attenuated, but remained significant in all the models (models 3–5, Table 2). See Table I in the Data Supplement for more detailed information about these results.

Table 2. Odds Ratios for the Association Between Optimism and Heart Failure

ModelCovariatesAdjusted Logistic Regression (95% CI)
1Age+sex0.68* (0.59–0.77)
2Demographic0.74* (0.63–0.85)
3Demographic+health behaviors0.75* (0.66–0.87)
4Demographic+biological factors§0.76* (0.66–0.89)
5All covariates0.78* (0.68–0.90)

CI indicates confidence interval.

*P<0.05.

Demographic factors: age, sex, race/ethnicity, marital status, education level, total wealth.

Health behaviors: smoking, exercise, alcohol frequency.

§Biological factors: hypertension, diabetes mellitus, body mass index.

All covariates: age, sex, race/ethnicity, marital status, education level, total wealth, smoking, exercise, alcohol frequency, hypertension, diabetes mellitus, body mass index.

Considering Psychological Ill-Being

Each psychological factor when added sequentially to the base model caused only a modest decrease in the association between optimism and heart failure. For example, when anxiety was added to the core model, the multivariate-adjusted OR for optimism was 0.79 (95% CI, 0.66–0.96). Overall, the relationship between optimism and heart failure remained significant in each of the analyses. When all 3 psychological factors were simultaneously added to the base model, the effect of optimism remained significant (OR, 0.83; 95% CI, 0.69–0.99). See Table II in the Data Supplement for more detailed information about these results.

Additional Analyses

When examining quartiles of optimism, the findings suggested a dose–response relationship (Table 3). For example, in the core model (model 2, Table 3) relative to those with the lowest optimism, people with moderately high optimism had a somewhat lower risk of heart failure (OR, 0.61; 95% CI, 0.44–0.86), whereas those with the highest optimism had the lowest risk of heart failure (OR, 0.42; 95% CI, 0.27–0.64). These findings were maintained even after adjusting for biological and behavioral covariates.

Table 3. Odds Ratios for the Association Between Optimism and Heart Failure by Quartiles

ModelQuartile GroupAdjusted Logistic Regression (95% CI)
1Low (reference group)1.00
Low-moderate0.64* (0.44–0.94)
Moderate-high0.52* (0.38–0.72)
High0.33* (0.22–0.51)
2Low (reference group)1.00
Low-moderate0.70* (0.48–1.03)
Moderate-high0.61* (0.44–0.86)
High0.42* (0.27–0.64)
3†‡Low (reference group)1.00
Low-moderate0.71* (0.49–1.02)
Moderate-high0.66* (0.48–0.90)
High0.44* (0.29–0.68)
4§Low (reference group)1.00
Low-moderate0.71* (0.48–1.03)
Moderate-high0.63* (0.45–0.88)
High0.47* (0.30–0.72)
5Low (reference group)1.00
Low-moderate0.75* (0.51–1.08)
Moderate-high0.69* (0.49–0.95)
High0.52* (0.33–0.81)

CI indicates confidence interval.

*P<0.05.

Demographic factors: age, sex, race/ethnicity, marital status, education level, total wealth.

Health behaviors: smoking, exercise, alcohol frequency.

§Biological factors: hypertension, diabetes mellitus, body mass index.

All covariates: age, sex, race/ethnicity, marital status, education level, total wealth, smoking, exercise, alcohol frequency, hypertension, diabetes mellitus, body mass index.

In a sensitivity analysis, we excluded individuals who developed heart failure in the first 2 years of follow-up (n=6549). Although statistical power was substantially reduced, the association between optimism and heart failure risk remained significant in all the models (Table 4). Finally, a potential interaction between optimism and sex was formally tested and the result was not significant (P=0.303).

Table 4. Odds Ratios for the Association Between Optimism and Heart Failure (Excluding Individuals With Heart Failure in the First 2 Years of Follow-Up)

ModelCovariatesAdjusted Logistic Regression (95% CI)
1Age+sex0.74* (0.62–0.88)
2Demographic0.78* (0.64–0.96)
3Demographic+health behaviors0.78* (0.64–0.95)
4Demographic+biological factors§0.81* (0.67–1.00)
5All covariates0.81* (0.67–0.99)

CI indicates confidence interval.

*P<0.05.

Demographic factors: age, sex, race/ethnicity, marital status, education level, total wealth.

Health behaviors: smoking, exercise, alcohol frequency.

§Biological factors: hypertension, diabetes mellitus, BMI.

All covariates: age, sex, race/ethnicity, marital status, education level, total wealth, smoking, exercise, alcohol frequency, hypertension, diabetes mellitus, BMI.

Discussion

To date, this is the first study to investigate the association between optimism and risk of developing heart failure. During a 4-year follow-up period, optimism was associated with a reduced likelihood of developing heart failure in a nationally representative sample of older adults (aged >50 years). After adjusting for sociodemographic covariates, each SD increase in optimism was associated with a 26% lower risk of developing heart failure during the follow-up. Furthermore, we observed a dose–response relationship. As optimism levels increased, risk of developing heart failure decreased in a monotonic fashion. In addition, secondary analyses helped temper possible concerns that the optimism and heart failure association found in this study might be largely attributable to undiagnosed heart failure leading to reduced optimism, rather than optimism serving as an antecedent to heart failure. In analyses conducted only after excluding individuals who developed heart failure earlier in the follow-up period, results showed that the association between optimism and heart failure was maintained. Although such findings cannot conclusively rule out the possibility that undiagnosed heart failure may influence optimism, these findings suggest that it is less likely.

The relationship between optimism and heart failure persisted even after adjusting for a range of risk factors including sociodemographic, behavioral, biological, and psychological covariates. These results are consistent with past studies that repeatedly show that the relationship between psychological well-being and cardiovascular outcomes is attenuated by risk-related behavior and biological conditions, but is not fully explained by these factors.11 In fact, the magnitude of attenuation in effect estimates after considering biological and behavioral variables was modest. This suggests that other mechanisms may be at play.

A growing number of studies suggest that the protective nature of optimism may be attributable to both indirect and direct pathways. Optimists may engage in healthier lifestyles that minimize health risks and enhance health. For example, in 1 study, having higher optimism at the outset of a cardiac rehabilitation program predicted increased exercise and successful lowering of body fat, saturated fat, and an index of overall coronary risk.38 Other studies have found that optimists are more likely to engage in health-promoting behaviors such as eating healthier diets, exercising more, managing stress better, and abstaining from smoking.1114 Although we took account of some of these factors, our measures may have been somewhat imprecise and therefore inexactly estimated the contribution of these factors to explaining how optimism might improve cardiovascular health. However, other pathways may be worthy of consideration. For example, direct biological effects of optimism have been hypothesized. Optimism has been linked with healthier levels of interleukin-6, C-reactive protein, fibrinogen, carotid intima-medial thickness, lipids, and serum antioxidants.13,3941 Furthermore, social support has been identified as mediating the effect of optimism on stress and has been posited as a possible mediator between optimism and cardiovascular disease.42 Additional studies are necessary to identify the mechanisms that may underlie the observed protective effect of optimism on heart failure risk.

Past studies have shown that psychological ill-being, as measured by anxiety, hostility, or depression, is associated with an increased risk of adverse cardiovascular events.21 However, there was little evidence of confounding by these factors in our study because the association between optimism and incident heart failure was minimally altered after adjusting for these psychological factors. This finding decreases concerns that optimism merely reflects an absence of psychological ill-being and suggests that optimism may uniquely impact risk of heart failure. It also adds to the research that has begun to disentangle whether the biological benefits originating from psychological well-being are distinct from the physiological costs attributable to psychological ill-being.43,44 In fact, prior work has suggested that psychological well-being and psychological ill-being show distinct biological correlates.44

Our study has several limitations and strengths. Limitations include relying on self-report of heart failure. Numerous studies, however, have shown that self-reported heart failure is a reasonable proxy for more objective measures.2429 Despite the limitations of using self-reported heart failure, our findings are consistent with a substantial body of research demonstrating that optimism is linked with healthier behaviors, healthier physiological profiles, and enhanced cardiovascular health.714,38–41,45 This tempers the likelihood that findings from this study are spurious or attributable to misclassification of the heart failure outcome. In addition, some risk factors, such as family history of cardiovascular disease and genetic vulnerability, were not assessed, and as a result, we could not take into account potential confounding attributable to these factors. Our data also lacked information about pathogenetic subtypes of heart failure, so we could not consider effects separately by subtype. Finally, it is possible that people had lower optimism because they had the side effects of undiagnosed heart failure. Although sensitivity analyses did not suggest that this was a significant problem, additional work is needed to confirm the direction of effects.

Despite these limitations, this research has several considerable strengths. HRS is one of the few nationally representative studies to contain extensive information on both heart failure and potential risk factors, especially those that are psychological in nature. Thus, we were able to assess the association between optimism and heart failure after adjusting for a wide array of covariates. In addition, a widely used and validated measure of the primary exposure of interest was available. Furthermore, the prospective nature of our data minimizes concerns that the associations found in this study are attributable to retrospective reporting bias or reverse causality.

Heart failure is a leading cause of hospitalization among older adults in the United States, and the population of older adults is projected to double by 2050.4 Continued research in this domain may not only enhance our knowledge of optimism’s effects on heart health, but also increase the conceptual and physiological understanding of how mental and physical health interact. Additional longitudinal studies are necessary to examine in more detail how optimism might protect against heart failure. Because heart failure is an umbrella term for many varying forms of the disease including systolic heart failure, diastolic heart failure, and left ventricular heart failure, additional research should evaluate whether optimism has similar protective effects on each of these conditions. This knowledge may then contribute to the development of more specific heart failure prevention and intervention programs. Should future research corroborate our findings, supplementing psychological interventions and current heart failure protocol with interventions shown to reliably increase psychological well-being, such as optimism, may be warranted.17,18,4648

Acknowledgments

We thank the editor, associate editors, and the anonymous reviewers for their valuable comments and suggestions. We also acknowledge the Health and Retirement Study, which is conducted by the Institute for Social Research at the University of Michigan, with grants from the National Institute on Aging (U01AG09740) and the Social Security Administration.

Footnotes

The Data Supplement is available at http://circheartfailure.ahajournals.org/lookup/suppl/doi:10.1161/CIRCHEARTFAILURE.113.000644/-/DC1.

Correspondence to Eric S. Kim, MS, Department of Psychology, University of Michigan, 530 Church St, Ann Arbor, MI 48109-1043. E-mail

References

  • 1. McCullough PA, Philbin EF, Spertus JA, Kaatz S, Sandberg KR, Weaver WD. Confirmation of a heart failure epidemic: Findings from the Resource Utilization Among Congestive Heart Failure (REACH) Study.J Am Coll Cardiol. 2002; 39:60–69.CrossrefMedlineGoogle Scholar
  • 2. Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, De Simone G, Ferguson TB, Ford E, Furie K, Gillespie C, Go A, Greenlund K, Haase N, Hailpern S, Ho PM, Howard V, Kissela B, Kittner S, Lackland D, Lisabeth L, Marelli A, McDermott MM, Meigs J, Mozaffarian D, Mussolino M, Nichol G, Roger VL, Rosamond W, Sacco R, Sorlie P, Stafford R, Thom T, Wasserthiel-Smoller S, Wong ND, Wylie-Rosett J; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2010 update: a report from the American Heart Association.Circulation. 2010; 121:e46–e215.LinkGoogle Scholar
  • 3. Bui AL, Horwich TB, Fonarow GC. Epidemiology and risk profile of heart failure.Nat Rev Cardiol. 2011; 8:30–41.CrossrefMedlineGoogle Scholar
  • 4. Vincent G, Velkoff VThe Next Four Decades: The Older Population in the United States 2010 to 2050. Washington, DC: US Census Bureau; 2010.Google Scholar
  • 5. Gottdiener JS, Arnold AM, Aurigemma GP, Polak JF, Tracy RP, Kitzman DW, Gardin JM, Rutledge JE, Boineau RC. Predictors of congestive heart failure in the elderly: the Cardiovascular Health Study.J Am Coll Cardiol. 2000; 35:1628–1637.CrossrefMedlineGoogle Scholar
  • 6. He J, Ogden LG, Bazzano LA, Vupputuri S, Loria C, Whelton PK. Risk factors for congestive heart failure in US men and women: NHANES I epidemiologic follow-up study.Arch Intern Med. 2001; 161:996–1002.CrossrefMedlineGoogle Scholar
  • 7. Giltay EJ, Geleijnse JM, Zitman FG, Hoekstra T, Schouten EG. Dispositional optimism and all-cause and cardiovascular mortality in a prospective cohort of elderly dutch men and women.Arch Gen Psychiatry. 2004; 61:1126–1135.CrossrefMedlineGoogle Scholar
  • 8. Tindle HA, Chang YF, Kuller LH, Manson JE, Robinson JG, Rosal MC, Siegle GJ, Matthews KA. Optimism, cynical hostility, and incident coronary heart disease and mortality in the Women’s Health Initiative.Circulation. 2009; 120:656–662.LinkGoogle Scholar
  • 9. Kim ES, Park N, Peterson C. Dispositional optimism protects older adults from stroke: the Health and Retirement Study.Stroke. 2011; 42:2855–2859.LinkGoogle Scholar
  • 10. Scheier MF, Matthews KA, Owens JF, Magovern GJ, Lefebvre RC, Abbott RA, Carver CS. Dispositional optimism and recovery from coronary artery bypass surgery: the beneficial effects on physical and psychological well-being.J Pers Soc Psychol. 1989; 57:1024–1040.CrossrefMedlineGoogle Scholar
  • 11. Boehm JK, Kubzansky LD. The heart’s content: the association between positive psychological well-being and cardiovascular health.Psychol Bull. 2012; 138:655–691.CrossrefMedlineGoogle Scholar
  • 12. Carver CS, Scheier MF, Segerstrom SC. Optimism.Clin Psychol Rev. 2010; 30:879–889.CrossrefMedlineGoogle Scholar
  • 13. Boehm JK, Williams DR, Rimm EB, Ryff C, Kubzansky LD. Association between optimism and serum antioxidants in the midlife in the United States study.Psychosom Med. 2013; 75:2–10.CrossrefMedlineGoogle Scholar
  • 14. Giltay EJ, Geleijnse JM, Zitman FG, Buijsse B, Kromhout D. Lifestyle and dietary correlates of dispositional optimism in men: The Zutphen Elderly Study.J Psychosom Res. 2007; 63:483–490.CrossrefMedlineGoogle Scholar
  • 15. Plomin R, Scheier MF, Bergeman CS, Pedersen NL, Nesselroade JR, McClearn GE. Optimism, pessimism and mental health: A twin/adoption analysis.Pers Indiv Differ. 1992; 13:921–930.CrossrefGoogle Scholar
  • 16. Heinonen K, Räikkönen K, Matthews KA, Scheier MF, Raitakari OT, Pulkki L, Keltikangas-Järvinen L. Socioeconomic status in childhood and adulthood: associations with dispositional optimism and pessimism over a 21-year follow-up.J Pers. 2006; 74:1111–1126.CrossrefMedlineGoogle Scholar
  • 17. Peters ML, Flink IK, Boersma K, Linton SJ. Manipulating optimism: Can imagining a best possible self be used to increase positive future expectancies?J Posit Psychol. 2010; 5:204–211.CrossrefGoogle Scholar
  • 18. Meevissen YM, Peters ML, Alberts HJ. Become more optimistic by imagining a best possible self: effects of a two week intervention.J Behav Ther Exp Psychiatry. 2011; 42:371–378.CrossrefMedlineGoogle Scholar
  • 19. Seligman MELearned Optimism: How to Change Your Mind and Your Life. New York, NY: Random House; 2011.Google Scholar
  • 20. Rich MW. Heart failure in the 21st century: a cardiogeriatric syndrome.J Gerontol A Biol Sci Med Sci. 2001; 56:M88–M96.CrossrefMedlineGoogle Scholar
  • 21. Rozanski A, Blumenthal JA, Davidson KW, Saab PG, Kubzansky L. The epidemiology, pathophysiology, and management of psychosocial risk factors in cardiac practice: the emerging field of behavioral cardiology.J Am Coll Cardiol. 2005; 45:637–651.CrossrefMedlineGoogle Scholar
  • 22. Wallace RB, Herzog AR. Overview of the health measures in the Health and Retirement Study.J Hum Resour. 1995; 30:S84–S107.CrossrefGoogle Scholar
  • 23. Health and Retirement Study, (HRS Data Files; 2006, 2008, 2010) Public Use Dataset. Produced and Distributed by the University of Michigan With Funding From the National Institute on Aging (Grant Number NIA U01AG009740). Institute for Social Research: Ann Arbor, MI; 2013.Google Scholar
  • 24. Djoussé L DJ. Relation between modifiable lifestyle factors and lifetime risk of heart failure.JAMA. 2009; 302:394–400.CrossrefMedlineGoogle Scholar
  • 25. Dhingra R, Gaziano JM, Djoussé L. Chronic kidney disease and the risk of heart failure in men.Circ Heart Fail. 2011; 4:138–144.LinkGoogle Scholar
  • 26. Heliövaara M, Aromaa A, Klaukka T, Knekt P, Joukamaa M, Impivaara O. Reliability and validity of interview data on chronic diseases. The Mini-Finland Health Survey.J Clin Epidemiol. 1993; 46:181–191.CrossrefMedlineGoogle Scholar
  • 27. Simpson CF, Boyd CM, Carlson MC, Griswold ME, Guralnik JM, Fried LP. Agreement between self-report of disease diagnoses and medical record validation in disabled older women: factors that modify agreement.J Am Geriatr Soc. 2004; 52:123–127.CrossrefMedlineGoogle Scholar
  • 28. Englert H, Müller-Nordhorn J, Seewald S, Sonntag F, Völler H, Meyer-Sabellek W, Wegscheider K, Windler E, Katus H, Willich SN. Is patient self-report an adequate tool for monitoring cardiovascular conditions in patients with hypercholesterolemia?J Public Health (Oxf). 2010; 32:387–394.CrossrefMedlineGoogle Scholar
  • 29. Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ. Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure.J Clin Epidemiol. 2004; 57:1096–1103.CrossrefMedlineGoogle Scholar
  • 30. Scheier MF, Carver CS, Bridges MW. Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the Life Orientation Test.J Pers Soc Psychol. 1994; 67:1063–1078.CrossrefMedlineGoogle Scholar
  • 31. Ryff CD, Singer B. What to do about positive and negative items in studies of psychological well-being and ill-being?Psychother Psychosom. 2007; 76:61–62.CrossrefGoogle Scholar
  • 32. Segerstrom SC, Evans DR, Eisenlohr-Moul TA. Optimism and pessimism dimensions in the Life Orientation Test-Revised: method and meaning.J Res Pers. 2011; 45:126–129.CrossrefGoogle Scholar
  • 33. Radloff LS. The CES-D Scale: A self-report depression scale for research in the general population.Appl Psychol Meas. 1977; 1:385–401.CrossrefGoogle Scholar
  • 34. Beck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: psychometric properties.J Consult Clin Psychol. 1988; 56:893–897.CrossrefMedlineGoogle Scholar
  • 35. Costa PT, Zonderman AB, McCrae RR, Williams RBCynicism and paranoid alienation in the Cook and Medley HO Scale.Psychosom Med. 1986; 48:283–285.CrossrefMedlineGoogle Scholar
  • 36. Van Belle G.Statistical Rules of Thumb. Hoboken, NJ: Wiley; 2008.CrossrefGoogle Scholar
  • 37. Little RJA, Rubin DBStatistical Analysis With Missing Data. Hoboken, NJ: Wiley; 2002.CrossrefGoogle Scholar
  • 38. Shepperd JA, Maroto JJ, Pbert LA. Dispositional optimism as a predictor of health changes among cardiac patients.J Res Pers. 1996; 30:517–534.CrossrefGoogle Scholar
  • 39. Boehm JK, Williams DR, Rimm EB, Ryff C, Kubzansky LD. Relation between optimism and lipids in midlife.Am J Cardiol. 2013; 111:1425–1431.CrossrefMedlineGoogle Scholar
  • 40. Matthews KA, Räikkönen K, Sutton-Tyrrell K, Kuller LH. Optimistic attitudes protect against progression of carotid atherosclerosis in healthy middle-aged women.Psychosom Med. 2004; 66:640–644.CrossrefMedlineGoogle Scholar
  • 41. Roy B, Diez-Roux AV, Seeman T, Ranjit N, Shea S, Cushman M. Association of optimism and pessimism with inflammation and hemostasis in the Multi-Ethnic Study of Atherosclerosis (MESA).Psychosom Med. 2010; 72:134–140.CrossrefMedlineGoogle Scholar
  • 42. Vollmann M, Antoniw K, Hartung F-M, Renner B. Social support as mediator of the stress buffering effect of optimism: the importance of differentiating the recipients’ and providers’ perspective.Eur J Pers. 2011; 25:146–154.CrossrefGoogle Scholar
  • 43. Seligman MEP. Positive health.Appl Psychol Int Rev. 2008; 57:3–18.CrossrefGoogle Scholar
  • 44. Ryff CD, Dienberg Love G, Urry HL, Muller D, Rosenkranz MA, Friedman EM, Davidson RJ, Singer B. Psychological well-being and ill-being: do they have distinct or mirrored biological correlates?Psychother Psychosom. 2006; 75:85–95.CrossrefMedlineGoogle Scholar
  • 45. Peterson C, Park N, Kim ES. Can optimism decrease the risk of illness and disease among the elderly?Aging Health. 2012; 8:5–8.CrossrefGoogle Scholar
  • 46. Brunwasser SM, Gillham JE, Kim ES. A meta-analytic review of the Penn Resiliency Program’s effect on depressive symptoms.J Consult Clin Psychol. 2009; 77:1042–1054.CrossrefMedlineGoogle Scholar
  • 47. Peterson C, Kim ES. Psychological interventions and coronary heart disease.Int J Clin Health Psychol. 2011; 11:563–575.Google Scholar
  • 48. Boehm J, Vie L, Kubzansky L. The promise of well-being interventions for improving health risk behaviors.Curr Cardiovasc Risk Rep. 2012; 6:511–519.CrossrefGoogle Scholar

CLINICAL PERSPECTIVE

We prospectively analyzed data on 6808 older adults (who were free of heart failure at baseline) from the Health and Retirement Study. Higher optimism was associated with a lower risk of incident heart failure during the 4-year follow-up period. The association persisted even after adjusting for a wide range of potential covariates. This study builds on a growing body of literature that has found prospective links between optimism and better cardiovascular health. This literature suggests that the protective nature of optimism is attributable to both indirect and direct pathways. The indirect pathway might work through behavior. For example, optimists are more likely to engage in health-promoting behaviors such as eating healthier diets, exercising more, managing stress better, and abstaining from smoking. Optimism may also have direct biological effects. For example, optimists have healthier levels of interleukin-6, C-reactive protein, fibrinogen, carotid intima-medial thickness, lipids, and serum antioxidants. Optimism is ≈25% heritable, shaped by social influences, and can also be learned. Causal conclusions cannot be drawn from observational studies. However, psychological exercises and simple, inexpensive interventions are being developed that can enhance optimism. If future research replicates our findings, randomized controlled trials that raise optimism in people at risk for cardiovascular events may reveal innovative ways of helping deter cardiovascular decline, and clinical attention to this attribute may be beneficial. To our knowledge, this is the first study to examine the association between optimism and heart failure.