ارتباط غیرخطی بین استرس مزمن و واکنش پذیری قلبی عروقی و بازیابی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|39069||2010||7 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Psychophysiology, Volume 77, Issue 2, August 2010, Pages 150–156
Abstract A mixed literature on the influence of chronic and acute stress on cardiovascular reactivity (CVR) and recovery suggests a need for improved modeling of these associations. We examined these associations using both linear and nonlinear (quadratic) models. Data were collected on 129 healthy adults [59% female, ages 18–29 years]. Participants completed the Perceived Stress Scale (PSS) after engaging in a mental arithmetic and a stress recall task. Heart rate (HR), systolic and diastolic blood pressure (SBP, DBP) were measured during rest, task, and recovery periods. Hierarchical ordinary least squares regression was used to examine the association of chronic stress to CVR and recovery with initial cardiovascular values and body mass index entered first as covariates. Hierarchical linear modeling (HLM) was also used for recovery. For reactivity, a quadratic relationship between PSS scores and DBP was observed in females such that those scoring at moderate levels of stress displayed lesser reactivity than those scoring either low or high. For recovery, a quadratic model was supported for SBP among females, with moderate levels of stress associated with greater recovery relative to either low or high levels. For females the quadratic model was also supported for SBP and DBP when examined using HLM. Quadratic modeling may better represent current theories of how chronic stress influences CVR and recovery. Our findings further suggest that these associations may be differentially evident by gender, perhaps due to gender differences in reported stress levels or gender-related task relevance.
Introduction The cumulative effects of stress are apparent on many physiological systems and are thought to play a significant role in disease (McEwen and Stellar, 1993). With regard to cardiovascular disease, an extensive literature has identified individual differences in cardiovascular reactivity (CVR) during acute stress as one potential marker of risk that may contribute to atherogenesis through physical and neuroendocrine damage (Krantz and Manuck, 1984 and Manuck, 1994). Poor cardiovascular recovery from acute stress is another increasingly appreciated marker of risk for hypertension and cardiovascular disease more generally (Brosschot and Thayer, 1998, Carroll et al., 2001, Linden et al., 1997 and Steptoe and Marmot, 2005). Stress occurring over a longer duration may also impact cardiovascular risk insofar as it affects the degree of physiological response and recovery to acute stress, and alters basal levels of cardiovascular function (Dienstbier, 1989, Evans and Kim, 2007, Rozanski et al., 1999 and Suarez et al., 1997). Despite progress in this field, the literature is rather mixed. Among the 19 studies available a decade ago (Gump and Matthews, 1999), about half reported potentiating effects of “background stressors” (i.e., chronic stressors) on CVR, with the remaining studies reporting null findings, or even attenuated reactivity to acute stress. More recent findings overall show a dampening effect of chronic stress on CVR. For example, dampened CVR to acute laboratory stress was reported among adolescents with a greater lifetime exposure to violence (Murali and Chen, 2005). Blunted reactivity has also been associated with exposure to poverty among adolescents (Evans and Kim, 2007), and the occurrence of disruptive life events among middle-aged and older adults (Carroll et al., 2005). In contrast, adults reporting a greater degree of past racial discrimination have demonstrated greater reactivity to an anger recall task (Richman et al., 2007). Recovery from acute stress has received comparatively less attention. A handful of laboratory studies report that chronic stress is associated with slower recovery from acute laboratory stress (Lepore et al., 1997). This is consistent with findings outside of the laboratory that chronic stress is associated with prolonged cardiovascular activation (Pieper and Brosschot, 2005). However, despite the dynamic nature of cardiovascular recovery from acute stress, a majority of investigators analyze recovery data using change scores or repeated-measures analysis of variance (ANOVA). Currently, more advanced statistical models, such as hierarchical linear modeling (HLM) are available that may better capture individual change during recovery (Llabre et al., 2004), and thus may offer greater insight to how recovery from an acute challenge is impacted by chronic stress. Researchers have also invariably adopted linear conceptualizations of how chronic stress relates to both CVR and recovery, such that greater stress produces either enhanced or blunted CVR, and greater stress produces either quicker/better or slower/poorer recovery. Despite some theoretical and empirical support for each view, these alternatives might not be mutually exclusive. Rather, each may describe different points on a single curve representing the relation between chronic stress severity and cardiovascular function. Thus, cardiovascular reactivity may initially increase as the level of chronic stress increases to a moderate degree, and then be more attenuated at the highest levels. A concurrent examination of stress physiology models supports this nonlinear conceptualization. Dienstbier (1989) in his “physiological toughness” model, reviewed studies indicating that animals with moderate amounts of stress relative to animals with low stress displayed a physiological toughening of catecholamine responses to, and recovery from, acute stress. That is, moderately stressed animals demonstrated a greater reactivity response to acute stressors followed by a more rapid recovery relative to low stressed animals. He suggests that moderate levels of life stress in humans may similarly condition or “toughen” individuals to show a heightened but adaptive physiological reaction to acute challenges followed by relatively quick recovery. In contrast, under conditions of severe ongoing stress, Selye's work on adaptation would suggest that individuals become physiologically depleted and fail to react sufficiently to environmental demands (i.e., exhaustion) (Selye, 1973). Likewise, McEwen's conception of allostatic load suggests that individuals under significant long-term stress may demonstrate inadequate physiological responses to stress, and/or exhibit prolonged physiological responses (i.e., poor recovery) (McEwen, 1998). More specifically, Porges (1995) suggested that severe stress is associated with blunted sympathetic reactivity. This is consistent with the idea that patients with generalized anxiety disorder, who arguably experience chronic and severe stress levels, characteristically exhibit lesser sympathetic reactivity to and slower recovery from acute stress compared to nonanxious individuals (Hoehn-Saric and McLeod, 1988). In the present study we measured perceived stress over the past 30 days and related this to indices of cardiovascular function during a mental arithmetic (MA) and a stress recall task using both linear and nonlinear (quadratic curve) analytic approaches. Based on the above models, we expected that heart rate (HR) and blood pressure reactivity would increase from low to moderate levels of perceived chronic stress. In contrast, models highlighting the physiological depletion associated with high levels of chronic stress suggest that reactivity would be blunted across all cardiovascular domains among the highest stress appraisers. For recovery, we expected that moderate levels of stress would be associated with more rapid and complete recovery compared to the lowest or highest levels of stress. Because of known gender differences in CVR and recovery (Light et al., 1993), we examined the associations between stress and CVR and cardiovascular recovery separately for men and women.
نتیجه گیری انگلیسی
. Results 3.1. Demographic characteristics and cardiovascular reactivity across conditions Sample demographics, PSS scores, and baseline cardiovascular data are presented in Table 1. Repeated-measures ANCOVAs indicated significant main effects for cardiovascular responses across tasks [HR: F(4, 508) = 220.26, p < .001; SBP: F(5, 620) = 221.33, p < .001; DBP: F(5, 620) = 196.53, p < .001; MAP: F(5, 620) = 273.46, p < .001]. Pair-wise comparisons indicated significant change scores in the expected direction for both tasks compared to baseline. Heart rate was elevated for both tasks relative to baseline. Systolic blood pressure, DBP, and MAP were also elevated for both tasks relative to both baseline and the pre-recall rest period. A statistically significant effect for gender was found for SBP and MAP only [F(1, 124) = 57.50, p < .001; F(1, 124) = 14.92, p < .001 respectively] with women demonstrating lower blood pressure levels than men across all event periods. This is consistent with general medical findings. No significant interaction effects were found. Table 1. Means and standard deviations of demographic, perceived stress, and baseline cardiovascular values. Variable All Male Female d N = 129 N = 53 N = 76 M SD M SD M SD Age 19.34 2.06 19.13 1.84 19.49 2.20 0.17 BMI 23.15 3.14 23.83 2.93 22.67 3.22 0.38⁎ PSS 18.05 5.91 15.49 5.05 19.84 5.83 0.80⁎⁎ Baseline HR 76.78 10.84 75.54 10.13 77.65 11.30 0.20 Baseline Systolic BP 108.22 10.64 115.57 10.38 103.09 7.32 1.39⁎⁎ Baseline Diastolic BP 64.93 6.14 64.67 6.52 65.11 5.90 0.08 Baseline MAP 81.30 6.67 84.19 6.31 79.29 6.19 0.78⁎⁎ BMI = body mass index; PSS = Perceived Stress Scale; HR = heart rate; BP = blood pressure (mmHg); MAP = mean arterial pressure (mm Hg); d = Cohen's d for gender difference. ⁎ p < .05. ⁎⁎ p < .01 (t-test). Table options Because of the distinct physiological and psychological factors associated with cardiovascular recovery relative to reactivity, we conducted separate repeated-measures ANCOVAs for recovery values. As before, all main effects were significant [HR: F(2, 254) = 243.44, p < .001; SBP: F(2, 248) = 274.23, p < .001; DBP: F(2, 248) = 284.50, p < .001; MAP: F(2, 248) = 362.46, p < .001] and women displayed lower SBP and MAP [F(1, 124) = 43.99, p < .001; F(1, 124) = 11.59, p < .001 respectively] than men with no interaction effect. For HR, while no gender difference was found, an interaction effect [F(2, 254) = 4.62, p < .05] indicated more rapid recovery for women, particularly during short recovery. Similarly, repeated-measures ANCOVAs indicated that both the MA and recall tasks were effective in eliciting negative affect relative to baseline as measured by the PANAS [F(3, 381) = 149.05, p < .001]. 1 No statistically significant gender difference or interaction effect was found. The results of hierarchical OLS regressions for CVR indicated that neither the linear or quadratic models were predictive when all participants were included in the analysis. When examining reactivity by gender, it was found that the quadratic model predicted DBP in females during recall (PSS, R2 = .34, p < .001, Δ R2 < .001, p > .05; PSS2, R2 = .39, p < .001, Δ R2 = .06, p < .05). No significant results were found for males or for the MA task. The relation between chronic stress and cardiovascular recovery was more robust. Hierarchical OLS regression with all participants demonstrated that greater PSS was associated with lesser SBP levels at both short and long recovery (Table 2). Similarly, PSS and MAP were inversely related. As with reactivity, quadratic modeling added explanatory utility when examining the recovery relationships by gender. For females, the expected quadratic relation was found between PSS and SBP at both short and long recovery (Table 2). In consideration of space, only the results of the significant linear and quadratic models for recovery are presented in Table 2. Table 2. Hierarchical OLS regression of linear and quadratic models for perceived stress on cardiovascular recovery scores controlling for earlier cardiovascular values and BMI. Variable/time B SE β R2 ΔR2 All participants Recall to short recovery systolic Step 1: PSS − 1.42 .606 −.113⁎ .727⁎⁎⁎ .010⁎ Step 2: PSS2 .609 .420 .070 .732⁎⁎⁎ .005 Recall to long recovery systolic Step 1: PSS − 1.55 .639 −.134⁎ .638⁎⁎⁎ .014⁎ Step 2: PSS2 .611 .450 .075 .643⁎⁎⁎ .005 Recall to long recovery MAP Step 1: PSS − 1.04 .478 −.142⁎ .494⁎⁎⁎ .019⁎ Step 2: PSS2 .109 .338 .021 .495⁎⁎⁎ .000 Female only Baseline to short recovery systolic Step 1: PSS − 1.31 .806 −.131 .588⁎⁎⁎ .002 Step 2: PSS2 1.26 .474 .215⁎⁎ .626⁎⁎⁎ .038⁎⁎ Baseline to long recovery systolic Step 1: PSS −.623 .610 −.074 .659⁎⁎⁎ .000 Step 2: PSS2 .919 .368 .183⁎ .686⁎⁎⁎ .028⁎ Recall to short recovery systolic Step 1: PSS − 1.31 .761 −.132 .636⁎⁎⁎ .003 Step 2: PSS2 1.14 .448 .194⁎ .667⁎⁎⁎ .031⁎ PSS = Perceived Stress Scale; PSS2 = Squared Value of Perceived Stress Scale; MAP = mean arterial pressure (mm Hg). ⁎ p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001. Table options Table 3 presents the HLM estimates of intercepts and change over time (slope) for HR, SBP, DBP, and MAP. Fig. 1 provides a graphical representation of SBP, DBP, and HR recovery to aid in conceptualization of the shape of the recovery curves. In generating the HLM results, three sets of coefficients were estimated, one using the entire sample, and two split by gender. All estimated coefficients were significantly greater than zero. In addition, each estimate had significant random effects variance, suggesting substantial individual variation that might be explained using level 2 predictor variables. Consistent with the OLS regression findings, no HLM quadratic models using the entire participant sample were significant. For female participants, models including PSS2 were a significantly better fit compared to models with only PSS for predicting SBP and DBP. The significant effects of PSS and PSS2 on the cardiovascular variables are presented in Table 4. Table 3. Hierarchical linear modeling level 1 estimates for cardiovascular recovery variables. Variable All (N = 129) Male (N = 53) Female (N = 76) Coef (SE) Var Cm Coef (SE) Var Cm Coef (SE) Var Cm SBP Intercept 136.0 (2.16)⁎⁎⁎ 474.7⁎⁎⁎ 146.0 (3.57)⁎⁎⁎ 506.4⁎⁎⁎ 129.0 (2.37)⁎⁎⁎ 338.3⁎⁎⁎ Linear Slope − 4.14 (.29)⁎⁎⁎ 6.89⁎⁎⁎ − 4.65 (.51)⁎⁎⁎ 8.76⁎⁎⁎ − 3.78 (.33)⁎⁎⁎ 5.46⁎⁎⁎ DBP Intercept 84.0 (1.43)⁎⁎⁎ 167.7⁎⁎⁎ 86.0 (2.52)⁎⁎⁎ 217.5⁎⁎⁎ 82.7 (1.69)⁎⁎⁎ 138.0⁎⁎⁎ Linear Slope − 3.20 (.22)⁎⁎⁎ 3.32⁎⁎⁎ − 3.59 (.38)⁎⁎⁎ 4.01⁎⁎⁎ − 2.94 (.27)⁎⁎⁎ 2.86⁎⁎⁎ MAP Intercept 107.0 (1.67)⁎⁎⁎ 248.0⁎⁎⁎ 111.0 (3.19)⁎⁎⁎ 396.5⁎⁎⁎ 105.0 (1.76)⁎⁎⁎ 148.5⁎⁎⁎ Linear Slope − 3.95 (.25)⁎⁎⁎ 4.30⁎⁎⁎ − 4.12 (.49)⁎⁎⁎ 8.08⁎⁎⁎ − 3.83 (.25)⁎⁎⁎ 2.09⁎⁎⁎ HR 3 s avg. Intercept 77.1 (.96)⁎⁎⁎ 116.1⁎⁎⁎ 76.3 (1.48)⁎⁎⁎ 112.9⁎⁎⁎ 77.6 (1.26)⁎⁎⁎ 119.2⁎⁎⁎ Linear Slope −.056 (.008)⁎⁎⁎ 0.0064⁎⁎⁎ −.067 (.012)⁎⁎⁎ 0.0054⁎⁎⁎ −.049 (.011)⁎⁎⁎ 0.0071⁎⁎⁎ SE = Standard Error. Var Cm = Variance Component. Intercept values estimate the final blood pressure measurement during the stress recall task and the first heart rate measurement during the recovery period. Slopes for blood pressure variables were modeled using a natural log transformation of time in seconds; SBP = systolic blood pressure (mm Hg); DBP = diastolic blood pressure (mm Hg); MAP = mean arterial blood pressure (mm Hg); HR = heart rate. ⁎⁎⁎ p < .001. Table options Systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate ... Fig. 1. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate (HR) recovery data. Figure options Table 4. Hierarchical linear modeling level 2 estimates of perceived stress on cardiovascular recovery controlling for initial cardiovascular values and BMI. Variable/Time Intercept Slope Deviance Δ Deviance Coef (SE) Coef (SE) Female only Systolic Base model 129.5 (2.08)⁎⁎⁎ − 3.79 (.321)⁎⁎⁎ 3576.76 Step 1: PSS −.927 (2.15) 0.010 (.295) 3576.35 0.41 Step 2: PSS2 2.67 (1.83) −.279 (.213) 3567.90 8.45⁎ Final variance comp 236.1⁎⁎⁎ 5.39⁎⁎⁎ Diastolic Base model 81.93 (1.76)⁎⁎⁎ − 2.76 (.284)⁎⁎⁎ 3480.76 Step 1: PSS 2.21 (1.88) −.533 (.297) 3477.43 3.33 Step 2: PSS2 n/a 0.107 (.061) 3473.36 4.07* Final variance comp 123.4⁎⁎⁎ 2.86⁎⁎⁎ SE = Standard Error; PSS = Perceived Stress Scale; PSS2 = Squared Value of Perceived Stress Scale. ⁎ p < .05. ⁎⁎⁎ p < .001.