Cardiovascular disease (CVD) is still the most frequent cause of mortality in developed countries (27). Yusuf et al. (40) conclude that smoking and unfavorable blood lipid profiles are the most important risk factors for premature myocardial infarction worldwide, and the World Health Organization currently ranks physical inactivity as the fourth most important global risk factor for future mortality (38). The American Heart Association states that ideal cardiovascular health consists of three biomarkers of cardiometabolic health (low cholesterol, blood pressure, and fasting glucose levels) and four health behaviors (nonsmoking, body mass index [BMI] < 25 kg·m−2, healthy eating, and regular physical activity [PA]) (25). In addition, strong epidemiological evidence exists, in which psychosocial stress plays an important part in the development of CVD and strongly affects the prognosis of cardiac patients (5,23).
Recently, Hamer (16) presented a conceptual framework for possible pathophysiological pathways linking psychosocial stress with CVD. Therein, he suggests that psychosocial stress might affect psychobiological processes, including hypothalamic–pituitary–adrenal and sympathetic nervous system function, endothelial dysfunction, inflammation, and autonomic control. In addition, the framework suggests that psychosocial stress negatively affects people’s health behaviors (e.g., by contributing to physical inactivity), which in turn affects the aforementioned biological processes, thereby increasing the risk of developing a manifest CVD. Various factors (such as cardiorespiratory fitness [CRF]) may influence the pathways in this framework and, thus, mitigate the detrimental effects associated with psychosocial stress on CVD (Fig. 1).
The notion that high levels of physical fitness might act as a protective buffer against the negative effects of chronic stress on both physical and mental health has been propounded since the early 1980s (35). A systematic review of the literature (encompassing 27 studies) shows that the stress-buffering potential of CRF was supported at least partly in approximately half of the investigations (15). Although the potential of CRF and PA to buffer the negative effects of psychosocial stress has been supported in prior research, studies focusing on cardiometabolic risk factors and cardiovascular health outcomes are scarce (34). Given the global disease burden associated with CVD (32), it is clear that more research is warranted. Therefore, the present study examined whether CRF moderates the relationship between perceived stress and several cardiometabolic risk factors in a population of employed Swedish adults.
Three hypotheses were formulated. First, we expected that high perceived stress would be associated with a less favorable cardiometabolic risk profile with increased systolic blood pressure (SBP) and diastolic blood pressure (DBP), BMI, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), glycated hemoglobin (HbA1c), and decreased high-density lipoprotein cholesterol (HDL-C) (19). Second, we expected that higher levels of CRF would be associated with a less unfavorable cardiometabolic risk profile (24). Third, we expected significant two-way interaction effects between perceived stress and CRF. Thus, we hypothesized that the differences between CRF groups in the previously mentioned outcomes would be relatively small among participants with low stress levels. By contrast, we assumed that among highly stressed participants, those with high CRF would have a less unfavorable cardiometabolic risk profile than counterparts with low CRF (15).
Participants were recruited from an ongoing longitudinal cohort study with a random sample of 6000 individuals, mainly health care workers, conducted by the Institute of Stress Medicine, Gothenburg, Sweden (data assessment between 2004 and 2007). Inclusion criteria for the present study were as follows: 1) age between 25 and 50 yr, 2) BMI between 18.5 and 30 kg·m−2, and 3) no medication. Anthropometric measurements and blood samples were collected for initial screening for this particular study to exclude participants with anemia, current infections, diabetes, thyroid illness, vitamin B12 deficiency, or excessive alcohol consumption. In total, 100 men and 100 women were assessed.
Inclusion criteria for stratified sample and assessment of stress
The sample was stratified to ensure that the participants reported different levels of stress. For this purpose, a single-item question of the Nordic Questionnaire for Psychological and Social Factors at Work (QPS Nordic) (11) was used to measure stress. It reads, “Stress means a situation in which a person feels tense, restless, nervous, or anxious, or is unable to sleep at night because his/her mind is troubled all the time. Do you currently feel this kind of stress?” Five response options are offered: “not at all,” “just a little,” “to a certain extent,” “quite a lot,” and “very much.” In the main analyses, participants with low perceived stress (scoring 1–3 [not at all–to a certain extent]) were compared with individuals with high stress (scoring 4–5 [quite a lot–very much]). In a previous study, high stress assessed with this measure proved to be associated with increased psychological and physical symptoms (11). Twenty men and women were selected from each of the five stress categories to ensure a gender-matched sample, which varied in terms of perceived stress.
Three participants were excluded because gender and age-adjusted CRF scores were not available. In the final sample (n = 197), 51% were men, and the mean age was 39.2 yr (SD = 8.1). Seventy percent reported being married to or living with someone, and 26% reported living alone. Sixty-three percent had an occupation that required higher education (college/university degree), 25% had an occupation not requiring higher education, and 10% were students or unemployed (7% missing). Six percent were smokers, and 87% nonsmokers (5% missing). The regional ethical review board approved the study, and all participants gave written informed consent.
Assessment of CRF
The Åstrand indirect test of maximal oxygen uptake (V˙O2max) was performed in the morning (starting at 7:00 a.m., 7:30 a.m., or 8:00 a.m.) on a bicycle ergometer (Monark Ergomedic 828E). This is a well-validated test for the purposes of measuring submaximal fitness (1). In the present study, the pedaling frequency during the Åstrand test was 50 rpm. The workload was adjusted so that the heart rate was kept between 130 and 160 bpm in participants younger than 40 yr and between 120 and 150 bpm in participants older than 40 yr. Participants were advised to maintain their exercise intensity level at 13 or 14 (slightly strenuous) on the Borg rating of perceived exertion scale (3). A steady state was reached when the heart rate remained stable after 5 or 6 min. The mean steady-state value of heart rate was used to estimate peak oxygen uptake (L·min−1) using a nomogram based on sex and workload (1). A correction factor for age was used, and the value of oxygen uptake was corrected for body weight and then expressed as peak V˙O2max (mL·kg−1·min−1). Gender and age-adjusted cutoffs to categorize participants into groups of low, moderate, and high CRF have been described elsewhere (14).
Assessment of cardiovascular risk factors
A trained research nurse measured SBP and DBP as the mean of two measurements after the participants had rested, while seated, for approximately 5 min, and height and weight for BMI calculations. All subjects had blood drawn from an antecubital vein between 7:30 and 10:00 a.m. after fasting since 10:00 p.m. the day before.
All biochemical analyses were performed at the Laboratory for Clinical Chemistry, Sahlgrenska University Hospital. Blood samples were assayed for TC in serum, HDL-C in serum, LDL-C in serum, TG in serum, and HbA1c in blood using standard laboratory protocols. Detection limit (DL) and coefficient of variation (CV) for each analysis were as follows: TC, DL¼ = 0.08 mmol·L−1, CV = 3%; HDL-C, DL¼ = 0.08 mmol·L−1, CV = 5%; LDL-C, DL¼ = 0.08 mmol·L−1, CV = 4%; TG, DL¼ = 0.02 mmol·L−1, CV = 4%; and HbA1c, DL¼ = 0.4%, CV = 2%.
A series of two-factorial ANCOVA was calculated to test main and interaction effects of CRF and stress. The ANCOVA compared three groups of participants with low, moderate, and high CRF levels. As described earlier, participants were categorized into two groups with low and high perceived stress to ensure a sufficient number of participants per cell. In the ANCOVA, cardiometabolic risk factors were treated as continuous variables. Smoking, education, and marital status were not associated with any of the outcome variables and, therefore, not included as covariates. Main and interaction effects were tested against the 5% level of significance, and eta square (η 2) coefficients were used to evaluate the strength of the relationships. To detect a medium effect in the ANCOVA (power = 0.90, α error = 0.05, df = 2, number of groups = 3), the recommended sample size was 200.
In addition to the independent effects of stress and CRF on single cardiometabolic risk factors, we also examined clustered metabolic risk, expressed by a continuously distributed score based on the sum of the z-standardized residuals of the following components: (SBP/DBP)/2, BMI, TC, HDL-C (*-1), LDL-C, TG, and HbA1c. A continuous variable (with higher scores reflecting increased total cardiometabolic risk) was used in place of risk categories to maximize statistical power (10).
Descriptive statistics for all cardiometabolic risk factors are shown in Table 1. The scores for CRF ranged from 22 to 68 mL·kg−1·min−1 with a mean of 40 (SD = 8.9).
Stress, CRF, and cardiometabolic risk
Table 2 shows that participants with high perceived stress had higher LDL-C, TG, and higher total cardiometabolic risk. The level of explained variance ranged from 3.3% to 7.5%. Furthermore, a nonsignificant trend was found for TC and SBP.
Significant main effects associated with CRF were observed for SBP, DBP, BMI, LDL-C, TG, and total cardiometabolic risk score, with levels of explained variance up to 12.4% (Table 2). In addition, a nonsignificant trend was found for TC and SBP. Bonferroni post hoc tests showed that participants with low CRF had higher SBP, DBP, BMI, LDL-C, TG, and total cardiometabolic risk than counterparts with high CRF. Participants with moderate CRF did not differ from the other two groups.
In addition, a significant interaction between stress and CRF was observed for SBP, DBP, LDL-C, TG, and total cardiometabolic risk score (Table 2). Among participants with low stress levels, no substantial group differences were found to be associated with CRF. By contrast, significant group differences emerged among participants who experienced high stress, showing that those with high CRF had lower SBP and DBP than participants with low and moderate CRF. As shown in Figures 2A–2C, a similar moderation effect was found for LDL-C, TG, and total cardiometabolic risk score. Thus, among participants reporting higher perceived stress, those with high CRF had lower LDL-C and TG levels and lower cardiometabolic risk score than counterparts with low or moderate CRF, with levels of variance explained by the interaction ranging from 3.6% to 4.8%. Although not statistically significant, the findings pointed in a similar direction for TC and HbA1c (levels of variance explained by the interaction term of 2.6% and 3.1%). Taken together, high levels of CRF significantly buffered the relationship between stress and cardiometabolic risk in five of the nine outcome measures.
The most important finding of this study is the moderating effect of CRF among highly stressed participants. Thus, participants with high perceived stress do not show increased cardiometabolic risk if they have high CRF levels, indicating that exercise training may be associated with a protective effect during periods of high stress.
The strength of the present study is that it expands on previous research into the role of stress in CVD; few earlier studies have examined CRF as a potential buffer of stress-related cardiometabolic risk (39) or cardiovascular health outcomes (20). In a 30-yr follow-up investigation of 5249 Danish workers, high physical work demands were associated with an increased risk of ischemic heart disease in the least fit and moderately fit group, but not among highly fit men (20); however, these results are limited to physical work demands and may not be applicable to perceived psychosocial stress. In our opinion, the importance of extending the field of research to focus on perceived psychosocial stress, and cardiometabolic risk as an outcome, is warranted on several grounds. First, psychosocial stress has been identified as an important risk factor of CVD (16). Second, the relationship between stress and CVD is mediated via cardiometabolic influences (16). Because high blood pressure is among the most serious public health challenges in Western societies, even small reductions in DBP can have substantial implications for primary prevention (7). Moreover, epidemiological studies show that elevated blood TG concentrations, high levels of TC and LDL-C, and low levels of HDL-C are independent risk factors for CVD (8). Evidence also exists that obesity and increased HbA1c levels are associated with increased risk of nonfatal and fatal CVD in individuals without diabetes (37). Third, it is well known that the risk of developing CVD is strongly related to lifestyle, including physical inactivity, low CRF, high-fat diet, and smoking, as major risk factors (30).
On the basis of previous research (21), we expected that elevated stress would be associated with increased cardiometabolic risk. Researchers have proposed several mechanisms to explain the way in which psychosocial stress affects cardiovascular health, including a blunted baroreflex sensitivity, an elevation of arterial blood pressure, and an increased neurohumoral arousal, HbA1c, lipid responses, and plasma fibrinogen responses during acute mental stress (19,33,36). The latter is in line with a meta-analysis showing that increased cardiovascular responses (e.g., heart rate and blood pressure) to experimentally induced stress are associated with higher risks of future hypertension, subclinical atherosclerosis, and CVD events (6). Although highly stressed participants in the present study had increased overall cardiometabolic risk, significant main effects for stress were only found for LDL-C and TG. However, the absence of a relationship between perceived stress and other risk factors might be attributable to the fact that the present sample was comparatively healthy, according to the standards described in the European guidelines on CVD prevention in clinical practice (9); in the present sample, only 7.6% (n = 15) had high SBP (≥140 mm Hg), only 0.5% (n = 1) had high DBP (≥90 mm Hg), and only 1% (n = 2) had high HbA1c levels (≥6.5%). Thus, it can be hypothesized that perceived stress would have been more closely associated with cardiometabolic risk if more participants had had clinically salient cardiometabolic risk profiles.
Hamer (16) suggested that up to 30% of the association between stress and CVD can be explained by the fact that individuals with high stress levels engage in less PA. Accordingly, in a study with 6576 community-dwelling adults, increased risk for CVD events attributable to psychological distress at the 7-yr follow-up decreased by 22% after controlling for PA (17). Because mental stress and exercise elicit similar central and peripheral responses, and both cause substantial cardiovascular responses (16), exposure to exercise-based stress might cause an adaptation process, which could result in a blunted physiological response when individuals face psychosocial stress. This notion is supported by a meta-analysis showing that a single bout of acute exercise before a psychosocial stressor results in reduced cardiovascular response compared with sedentary activities (18). Hamer (16) therefore concludes that “the potential stress-buffering benefits of regular exercise may be largely accounted for by the fact that exercisers are more often in the postexercise window when they encounter daily stressors” and that “the attenuation in blood pressure reactivity immediately after a single bout of acute exercise is thought to be best explained by a reduction in regional vascular resistance mediated by sympathetic nervous inhibition” (p. 899). Moreover, studies have shown that trained individuals show blunted cardiovascular, endocrine, and inflammatory responses to psychosocial stress (22,28,29). Taken together, it can be assumed that the stress-buffering effects of CRF are attributable to several biological processes occurring in the cardiovascular, inflammatory, and neuroendocrine systems, which play an important role in CVD pathology (16).
The present study supports previous research on the stress-buffering potential of PA and CRF (12,13,15), showing that CRF not only prevents subjective health complaints, particularly in more vulnerable individuals demonstrating high stress , but also protects against objectively assessed CVD risk factors (20). Our findings are at odds with those from a study with U.S. law enforcement officers (39), in which the interaction of stress and CRF was not related to cardiometabolic risk. However, that study focused on specific work-related stressors, whereas stress was defined more broadly in the present investigation. In our study, the level of explained variance (3.6%–4.8%) by the interaction terms was of substantial magnitude. Furthermore, the findings are clinically relevant because among the highly stressed participants, the groups with low and moderate CRF exceeded the critical value for LDL-C (≥3.0 mmol·L−1) (9), whereas those with high CRF remained lower than this cutoff point.
The current study must be considered in light of certain limitations. Although the selection procedure ensured that participants varied on perceived stress levels, similarly large variations in cardiometabolic risk factors and CRF were not reached. Nevertheless, the variation in these variables was large enough to detect significant two-way interaction effects. Moreover, the participants were generally healthy and educated, which makes it difficult to generalize our results to groups with lower socioeconomic and poorer health status. Furthermore, the level of stress is based on self-report, which in principle is not a limitation because stress is a subjective experience (35). In addition, although it is assumed that hereditary factors explain between 25% and 40% of variance in CRF, the genetic contribution to CVD is less than 30% (4), leaving scope for a substantial effect of modifiable environmental factors. Specifically, Blair et al. (2) showed that hereditary factors alone are not responsible for the relationship between CRF and cardiovascular mortality because participants who became more fit had a significantly lower mortality risk during follow-up compared with unfit counterparts who did not improve. We also acknowledge that family history or other biomarkers of CVD such as C-reactive protein were not assessed. Moreover, the cross-sectional nature of the data does not allow conclusions regarding cause and effect to be drawn. Thus, stress might not only cause disturbances in pathophysiological markers but also vice versa (16). Finally, CRF was assessed with a submaximal fitness test. Nonetheless, V˙O2max based on work performance during a maximal exercise test is highly correlated with direct measurements of oxygen uptake (r ≥ 0.90), and correlations between maximal and submaximal tests are reasonably high (r = 0.70–0.90) (26).
Conclusions and clinical implications
Only a minority of European citizens engage in sufficient PA to meet recommended health standards (31). Because CRF seems to be an important resilience factor for the individual to prevent health problems due to stress, promoting PA among individuals should be a priority. However, the importance of CRF is still underestimated in clinical practice. Currently, CRF is not routinely assessed in general or clinical settings, despite it being a strong predictor of future morbidity and mortality (40). We therefore suggest that clinicians should evaluate CRF more frequently, as part of individual risk assessment, and try to help patients to integrate regular PA into their daily life.
The authors gratefully acknowledge the valuable help of Anna Rutgersson, Anneli Samuelsson, Sandra Pettersson, and Hans Mandelholm for performing the cycle tests; Karin Nygren and Marie-Louise Norberg for supervising the screening and inclusion procedures and data collection; and Flora Colledge for proofreading the manuscript. They also thank all participants for their valuable time and contributions to the study. Funding for this study was provided by the Swedish government, which had no further role in the study design, collection, analysis, interpretation of data, writing of this report, and decision to submit this paper for publication. The authors alone are responsible for the content and writing of the paper. They declare no conflict of interest. All authors have read and approved the manuscript. Finally, the authors acknowledge that the results of the present study do not constitute endorsement by the American College of Sports Medicine.
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