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The associations of the Five Factor Model of personality with inflammatory biomarkers: A four-year prospective study
Personality and Individual Differences 54 (2013) 750–755
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The associations of the Five Factor Model of personality with inflammatory biomarkers: A four-year prospective study Galit Armon a,⇑, Samuel Melamed b, Arie Shirom c,1, Shlomo Berliner d, Itzhak Shapira d a
Department of Psychology, University of Haifa, Haifa 31905, Israel Tel Aviv University and Academic College of Tel Aviv–Yaffo, Israel c Tel Aviv University, Tel Aviv, Israel d Tel Aviv Sourasky Medical Center, Israel b
a r t i c l e
i n f o
Article history: Received 14 June 2012 Received in revised form 22 November 2012 Accepted 30 November 2012 Available online 27 December 2012 Keywords: Inflammation C-reactive protein (hsCRP) Fibrinogen Five Factor Model Big Five Personality Longitudinal study Cardiovascular disease Health behaviors
a b s t r a c t This study evaluated the associations of the Five Factor Model of personality with two inflammation biomarkers, C-reactive protein and fibrinogen, and the possible moderating effects of common healthrelated behaviors (physical activity and smoking) concurrently and over four years, while adjusting for socio-demographic and health status. Participants were individuals who underwent a health examination at two points of time, T1 (n = 1709) and T2 (n = 923), about four years apart. Regression analyses uncovered positive associations between Neuroticism and Extraversion with two inflammatory biomarkers at baseline (T1) and over time (T2) and increases in their levels over time. Additionally, a synergistic interaction of neuroticism and physical inactivity was associated with higher levels of inflammation biomarkers at both time periods. Openness was negatively associated with inflammation biomarkers at T1 and T2, but not with changes in their levels. No significant associations were found for Agreeableness and Conscientiousness. The results suggest that personality traits might be involved in the inflammatory process both concurrently and over time, and thus indicate a possible mechanism by which personality traits might influence health, especially cardiovascular disease risk. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Recent evidence underscores the key role played by inflammatory processes in the etiology and prognosis of cardiovascular disease (CVD; Casas, Shah, Hingorani, Danesh, & Pepys, 2008; Danesh et al., 2005). Inflammation biomarkers reflect long-term physiological, cognitive, emotional, and behavioral mechanisms (Black, 2003), some of which are likely to include enduring personality traits (Costa & McCrae, 1992). The present study addresses the links between personality traits and the inflammatory process by focusing on two inflammation biomarkers: high-sensitivity Creactive protein (hsCRP) and fibrinogen. hsCRP is a complex set of proteins produced during the acute phase response when the body is faced with a major infection or trauma (Casas et al., 2008). Fibrinogen is a circulating glycoprotein that acts at the final step in the coagulation response to vascular and tissue injury, where it controls blood loss (Herrick, Blanc-Brude, Gray, & Laurent, 1999). Personality was assessed by the Five Factor Model (FFM) of personality, a widely adopted framework for studying associations
⇑ Corresponding author. Tel.: +972 54 5661782. 1
E-mail address: [email protected] (G. Armon). Deceased.
0191-8869/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.paid.2012.11.035
between personality and health (Chapman, Roberts, & Duberstein, 2011). Possible mechanisms linking personality and inflammatory biomarkers include psycho-physiological and behavioral factors. Psycho-physiological pathways suggest that personality traits influence individuals’ stress appraisals and coping mechanisms (Segerstrom, 2000). The repeated experience of psychological stress leads to continuous activation of the hypothalamic-pituitary-adrenal (HPA) axis, which subsequently results in a chronic inflammatory process (Black, 2003). Behavioral pathways suggest personality traits are a strong predictor of unhealthy behaviors such as smoking and inactivity (see Chapman et al., 2011, for a review) that increase the risk of an inflammatory response (Casas et al., 2008; Danesh et al., 2005). 1.1. The present study Longitudinal studies on the FFM and inflammation biomarkers are relatively sparse (e.g., Chapman et al., 2011). Few reports (e.g., Chapman et al., 2009; Sutin et al., 2010) have looked at personality comprehensively, using the FFM or a large, representative. In this study, the relationship between the FFM and inflammatory biomarkers and the possible moderating effect of common healthrelated behaviors (physical activity and smoking) were examined
G. Armon et al. / Personality and Individual Differences 54 (2013) 750–755
concurrently and over four years. The study was conducted on a group of multi-occupational employed adults, while adjusting for socio-demographic and health status. Neuroticism, predisposing individuals to experience stresses more intensely (Costa & McCrae, 1992), and to engage in risky health behaviors (see review of Chapman et al., 2011), was positively linked to inflammatory markers in an Italian population (Sutin et al., 2010), but this was not found in primary care patients or older adults (Chapman et al., 2009, 2011). In the current study, we expected Neuroticism to be positively associated with inflammatory biomarkers (hypothesis 1a). Additionally, the possibility of synergistic effects of neuroticism and unhealthy behaviors in CVD has been noted with regard to smoking (e.g., Marusic & Eysenck, 2001). Therefore, we expected a synergistic interaction of neuroticism and health-related behaviors (high smoking and low physical activity) in predicting levels of inflammatory biomarkers (hypothesis 1b). Extraverts are predisposed to experience positive emotions (Costa & McCrae, 1992), which have been found to be consistently associated with good physical health, including reduced CVD risk (see recent review of Steptoe, Dockray, & Wardle, 2009). Additionally, Extraversion has been found to act as a buffer against stresses (Wayne, Musica, & Fleeson, 2004) and therefore expected to be negatively associated with inflammation biomarkers (hypothesis 2). Conscientiousness, predisposing individuals to cope effectively with stresses (e.g., Murphy, Miller, & Wrosch, 2012) and leading to patterns of healthy behaviors (see Chapman, Roberts et al., 2011), has been negatively linked to inflammatory markers (Chapman et al., 2011; Sutin et al., 2010). Accordingly, we expected conscientiousness to be negatively associated with inflammation biomarkers (hypothesis 3a) and anticipated a synergistic interaction of conscientiousness and healthy behaviors (low smoking and high physical activity) in predicting lower inflammatory biomarker levels (hypothesis 3b). Openness encompasses cognitive and behavioral flexibility and attunement to internal and external events and experiences (Costa & McCrae, 1992). These factors probably facilitate prevention of avoidable health problems and the capacity to manage stressful situations (e.g., Ironson, O’Cleirigh, Weiss, Schneiderman, & Costa, 2008; Jonassaint et al., 2007). Higher levels of Openness have been associated with decreased risk for CVD and all-cause mortality (see Chapman, Roberts et al., 2011, for a recent review) and with lower levels of inflammation biomarkers (Chapman et al., 2011; Jonassaint et al., 2010). Thus, we expected Openness to be negatively associated with inflammation biomarkers (hypothesis 4). Agreeableness involves compliance, trust, acquiescence, and interpersonal deference (Costa & McCrae, 1992). A high score on Agreeableness may predispose individuals to follow suggested health guidelines and avoid health problems. Furthermore, traits related to low Agreeableness (antagonism), such as anger and hostility, are well-established risk factors for CVD morbidity and mortality (e.g., Tindle et al., 2009). Antagonism-related traits have been directly associated with inflammatory biomarkers (e.g., Marsland, Prather, Petersen, Cohen, & Manuck, 2008; Sutin et al., 2010), but not in all cases (Chapman et al., 2009, 2011). However, Agreeableness can cause individuals to be more vulnerable and reactive to interpersonal stressors or threats (e.g., Suls, Martin, & David, 1998) that might promote the inflammatory process (Black, 2003). In light of this opposing evidence, we offer no a priori hypotheses regarding the association between Agreeableness and the inflammation biomarkers. Following the typological approach for personality (Costa & McCrae, 1992), we also evaluated the interactive effects between the five factors to investigate whether combinations of traits affect inflammation biomarkers. Based on previous evidence
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(e.g., Dembroski & Costa, 1987; Terracciano & Costa, 2004), we examined the possible interactive effects between high neuroticism and low extraversion (poor well-being); high neuroticism and low agreeableness (poor anger control), and high neuroticism and low conscientiousness (poor impulse control). Since such studies are scarce, we tested these interactive effects on an exploratory basis.
2. Materials and methods 2.1. Study participants We analyzed data collected from the Tel Aviv Medical Center Inflammation Survey (TAMCIS), a registered data bank of the Israeli Ministry of Justice. This includes a relatively large cohort of individuals who attended the medical center for a routine annual check-up and gave written informed consent for participation in the study, according to the instructions of the local ethics committee. Participants were examined on two occasions, Time 1 (T1) (n = 1709) and Time 2 (T2) (n = 923), on average 45 months apart (range = 21–91, SD = 15.72). These examinations were provided by employers as a subsidized fringe benefit. Thus, attrition between T1 and T2 could be due to change of employer, residence, or work location; totally unrelated to participation in the current study. At T1, participants represented 92% of the Center’s examinees. A systematic check for non-response bias at T1, found that participants and non-participants did not differ on any of the socio-demographic or biomedical variables. The personality traits of those examined at T1 who did not return for a follow-up examination (786 subjects, 46%) did not differ from those who returned at T2. However, they were more likely to be male, older (near retirement age), and less educated; consistent with previous reports on attrition bias in health survey studies (e.g., Korkeila et al., 2001; Ployhart & Vandenberg, 2010). The mean age at T1 of the 923 employees with data at both study moments was 45.56 years (SD = 9.64), 46.43 (SD = 8.99) for the men (69%) and 45.82 (SD = 9.02) for the women (31%).
2.2. Measures High sensitivity C-Reactive Protein (hsCRP) concentrations in serum were determined with the BN II Nephelometer g12 analyzer (DADE Behring, Marburg, Germany) (Rifai, Tracy, & Ridker, 1999). This assay is based on particle-enhanced immunonephelometry, which enables measurement of extremely low hsCRP concentrations (0.175–11 mg/L (initial dilution) according to the manufacturer (Roberts, Moulton, Law, et al., 2001). Fibrinogen plasma concentration was measured by the Clauss method (Clauss, 1957) with an ST-A compact coagulometer (Stago, Asnières, France). The FFM: Personality dimensions were assessed at T1 using the Big Five Mini-Marker scale (Saucier, 1994) while participants were waiting for the physical exam. The scale consists of 40 adjectives that measure five personality factors (eight for each factor): Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to experience. Respondents were asked to indicate how well these adjectives described them on a 9-point Likert scale ranging from 1 (extremely inaccurate) to 9 (extremely accurate). The scores and the Cronbach internal consistency reliabilities of the FFM were largely on par with those reported in the original version (Saucier, 1994), with those reported in a meta-analysis (Viswesvaran & Ones, 2000), and with other studies using a multinational sample (Thompson, 2008) and the Hebrew version of the Mini-Marker scale (e.g., Ein-Gar, Goldenberg, & Sagiv, 2008).
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2.3. Possible moderators Number of cigarettes smoked daily and number of weekly hours customarily engaged in physical activity were documented by selfreport. 2.4. Control variables Analyses were controlled for several demographic and biomedical variables associated with hsCRP and fibrinogen concentrations, including age, gender, education, obesity (waist-to-hip ratio), and having a chronic medical illness (CMI) or taking a medication (e.g., statins) that could impact biomarker levels (Casas et al., 2008; Danesh et al., 2005). Waist-to-hip ratio (WHR), in centimeters was assessed by the medical staff, and used as a continuous variable. CMI was coded, as in other epidemiological studies (Chapman et al., 2011; Gunn et al., 2012), based on self-reported physician diagnosis of at least one of eight medical conditions: diabetes, cancer, cardiovascular disease, respiratory disease, neurological disease, musculoskeletal complaints, rheumatic disease, or gastro-intestinal disease. 2.5. Analytic methods Hypotheses were tested on hsCRP and fibrinogen values at T1 and T2 using ordinary least squares (OLS) regressions (with SPSS software). In the first step, control variables of age, gender, education, and CMI were entered. Length of follow-up in days was included when predicting criteria at T2. When we used the T1 level of each criterion at T2 as the first covariate, results reflected the effects of the predictors on the change in each criterion (Twisk, 2003). In the second step, the main effect variables of the FFM traits were entered simultaneously to isolate the unique effect of each domain. In the third step, common health behaviors (smoking and physical activity) and their interactions with neuroticism and conscientiousness were added. In the fourth step, the interactive terms of neuroticism with extraversion, conscientiousness and agreeableness were added. Only significant interactions are shown in the results table (Cortina, 1993). In order to complement the inferential statistics of p-values, the effect size of the main effects of the FFM was also calculated as the square of the Pearson correlation r (R2) to reflect the proportion of variance shared by the FFM and the inflammation biomarkers (Kelley & Preacher, 2012). 3. Results The unadjusted intercorrelation matrix of the variables included in our analysis and their means and standard deviations are depicted in Table 1. All criteria and predictors were examined to detect outliers or non-normal distributions (skewness >2.0 and kurtosis >5.0); none was detected. Results of the OLS regression analyses testing the associations between the FFM and hsCRP and fibrinogen levels at base line (T1), over time (T2), and with changes in their levels from T1 to T2, are reported in Tables 2 and 3, respectively. Supporting hypothesis 1a, Neuroticism was positively associated with levels of hsCRP at T1 and T2 (p < .01), with fibrinogen at T2 (p < .01) and with increased fibrinogen levels from T1 to T2 (p < .01). Neuroticism was also positively associated with fibrinogen at T1 and with increased hsCRP levels from T1 to T2 (p < .05), but these associations were not significant after Bonferroni correction to the alpha level of .05 (a = 0.016) (Vesey, Vesey, Stroter, & Middleton, 2011). Additionally, the interactive term of neuroticism and physical activity was negatively associated with hsCRP at T1 (p < .01) and with fibrinogen at T1 and T2 (p < .01) such
that high levels of these biomarkers were related to high levels of neuroticism and low levels of physical activity. The interactive term of neuroticism and physical activity was also negatively associated with hsCRP at T1 and with increased hsCRP levels from T1 to T2, but these associations were no longer significant after Bonferroni correction. The interactive term of neuroticism and smoking was not significantly associated with the criteria, thus hypothesis 1b was partially supported. Contrary to our expectation (hypothesis 2), Extraversion was positively associated with T1 and T2 hsCRP levels (p < .01 at both times), and with fibrinogen at T2 (p < .01). Extraversion was also positively associated with fibrinogen at T1 and with increased hsCRP and fibrinogen levels from T1 to T2 (p < .05), but these associations were not significant after Bonferroni correction. The associations between Conscientiousness and the interactive terms of Conscientiousness with physical activity and smoking were not significantly associated with the criteria, thus not supporting hypotheses 3a and 3b. In support of hypothesis 4, Openness was negatively associated with fibrinogen at T1 and T2 (p < .01 at both times). Openness was also negatively associated with hsCRP at T1 (p < .05), but this association was not significant after Bonferroni correction. The associations between Agreeableness with hsCRP and fibrinogen with the criteria were not significant, thus not supporting hypothesis 5. The interactive terms of Neuroticism with Extraversion, Agreeableness and Conscientiousness were not significant. The associations of the FFM with the criteria were not attenuated when health status, socio-demographic status and health behaviors were added to the regression model. Based on the R2 of the main effects of the FFM, the effect sizes were small, but significant (R2 = .02, p < .01) for hsCRP and fibrinogen levels at T1 and T2. The effect sizes based on the R2 of the FFM for changes in hsCRP and fibrinogen levels were also small and significant (R2 = .01, p < .05).
4. Discussion This four-year prospective study was designed to facilitate our understanding of the biological mechanisms underlying the associations of personality with health, especially CVD risk, by focusing on two inflammation biomarkers, hsCRP and fibrinogen, which represent important targets in clinical preventive practices designed to reduce the risk of CVD (Casas et al., 2008). Overall, three factors of the FFM were associated with the inflammatory process. Most of these associations were observed for the two inflammation biomarkers, thus providing a cross-validation of the findings. In support of our hypothesis and consistent with recent findings (Sutin et al., 2010), Neuroticism was directly associated with elevated inflammatory biomarkers concurrently and over time, probably through greater HPA axis reactivity in response to stress appraisal (Black, 2003; Marsland et al., 2008). Additionally, our results show a synergetic effect between neuroticism and physical inactivity in the inflammatory process; thus, pointing to the potential importance of daily physical activity to one’s health, and further illuminating the modifying role of health behaviors in the stress response. Contrary to our expectation and to the results of previous studies (e.g. Chapman et al., 2011; Sutin et al., 2010), Extraversion had positive, rather than negative associations with the inflammation biomarkers at T1 and T2. A plausible explanation for this result might be that an appetitive positive affect system, often referred to as Extraversion, involves exaggerated activation of the HPA axis, which promotes the inflammatory process (Black, 2003). An alternative explanation is that individuals with high positive mood states are more likely to be reckless, to perceive themselves as less vulnerable to undesirable health conditions, and to
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G. Armon et al. / Personality and Individual Differences 54 (2013) 750–755 Table 1 Means, standard deviations and intercorrelations of the major study variables. Variable a
hsCRP , T2 hsCRPa, T1 Fibrinogena, T2 Fibrinogena, T1 Neuroticism Extraversion Conscientiousness Openness Agreeableness Age Genderb Educationc WHR Smokingd Physical activity CMIe
a M SD
1
2
3
– .44** .46** .33** .06 .08* .02 .02 .03 .10** .04 .01 .14** .05 .16** .05 – 2.04 1.95
– – .17** .46** .04 .05 .03 .01 .02 .08* .06 .01 .10** .03 .04 .02 – 2.28 3.18
– – – .61** .03 .10** .02 .06 .08* .24** .21** .02 .02 .06 .08* .07* – 296.67 54.79
4
5
6
7
8
9
10
– – – – – .17** .18** .09** .25** .07* .05 .02 .01 .13** .02 .08* .70 3.54 1.22
– – – – – – .24** .09** .26** .04 .05 .08* .01 .04 .05 .01 .74 6.10 1.23
– – – – – – – .11** .23** .01 .01 .05 .03 .03 .04 .06 .80 7.61 .97
– – – – – – – –
– – – – – – – – –
– – – – – – – – – –
– – – – .02 .10** .04 .06 .08* .30** .18** .07* .06 .04 .06 .08* – 268.19 48.88
.04 .06 .05 .02 .04 .07* .04 .07* .72 6.15 1.14
.03 .14** .06 .09 .04 .02 .02 .70 7.66 .89
.01 .07* .31** .13** .06 .15** – 45.56 9.64
11
12
13
14
15
16
– – – – – – – – – – –
– – – – – – – – – – – – .06 .07 .02 .04 – .98 .11
– – – – – – – – – – – – – .10** .12** .09** – .96 .11
– – – – – – – – – – – – – – .07* .04 – .40 .49
– – – – – – – – – – – – – – – .08* – 2.35 2.04
– – – – – – – – – – – – – – – – –
.03 .59** .02 .05 .04 – .31 .46
.51 .50
Notes: n = 923; WHR = waist-to-hip ratio.
a = Cronbach’s alpha. a b c d e * **
mg/L. Gender coded 1 = woman, 0 = man. Education coded 1 = college education or higher, 0 = other. Smoking coded 1 = yes, 0 = no. CMI = chronic medical illness, coded 1 = without CMI, 0 = other. p < .05. p < .01.
Table 2 OLS regressions predicting hsCRP levels by the FFM. Measure
hsCRPa, T1b (n = 1709) b (SEB)
Step 1: Control variables Step 2: Predictors Neuroticism Extraversion Conscientiousness Openness Agreeableness Step 3: health behaviors and their interactions Smokinge Physical activity Neuroticism physical activity
B
hsCRPa, T2c (n = 923)
DR2
b (SEB)
B
.02** .02** .12 .12 .02 .07 .01
(.09) (.05) (.06) (.05) (.07)
.19** 19** .04 .10* .01 .09 .11** .07**
DR2
b (SEB)
DR 2
B
.01* .02** .14 .10 .03 .03 .02
(10) (.06) (.07) (.06) (.09)
.22* 17** .04 .05 .04
.06** .02 (.11) .11 (.03) .12 (.02)
hsCRPa, T2d (n = 923)
.20** .01* .12 .07 .01 .02 .04
(.09) (.05) (.06) (.05) (.08)
.19* .10* .01 .03 .08
.05** .03 (.13) .13 (.03) .11 (.03)
.12 13** .06*
.03** .03 (.12) .12 (.03) .10 (.03)
.03 .12** .06*
Notes: B and b represent the non standardized and standardized partial regression coefficients, respectively; SEB stands for the standard error of the former. a mg/L. b Controlled for health, age, gender, education, and WHR. c Controlled for health, age, gender, education, WHR and time lag. d Controlled for health, age, gender, education, WHR, time lag and hsCRP at T1. e Smoking coded 1 = yes, 0 = no. * p < .05. ** p < .01.
have maladaptive health behaviors (for a review, see Grant & Schwartz, 2011), which might result in elevated levels of inflammation biomarkers. Furthermore, this unexpected effect for Extraversion could be because it is closely associated with the trait of dominance (e.g., Traupman et al., 2009), which has been related to increased CVD risk (Smith et al., 2008). Surprisingly, in contrast to our hypothesis and to results of previous studies (Chapman et al., 2011; Sutin et al., 2010), we did not find significant associations between Conscientiousness and inflammatory biomarkers or a moderating effect of health behaviors on this association. This might be due to potential mediating and moderating mechanisms related to health behaviors not included in this study, such as adherence to routine health screening
(e.g., Armon & Toker, 2012). Additionally, Conscientiousness might be more meaningful for health improvement when facing stress (e.g., Murphy et al., 2012). In support of the study hypothesis and the results of previous studies (Chapman et al., 2011; Jonassaint et al., 2010), the inverse associations between Openness and fibrinogen, measured concurrently and overtime were significant. Recent literature suggests that Openness is correlated with the orbitofrontal cortex function, the region of the brain responsible for cognitive and behavioral flexibility and adaptation (Sutin, Beason-Held, Resnick, & Costa, 2009). The orbitofrontal cortex function is associated with reduced HPA-axis response (Costa-Pinto & Palermo-Neto, 2010), which subsequently results in reduced levels of inflammatory biomarkers
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Table 3 OLS regressions predicting fibrinogen levels by the FFM. Measure
Fibrinogena, T1b (n = 1709) b (SEB)
Step 1: Control variables Step 2: Predictors Neuroticism Extraversion Conscientiousness Openness Agreeableness Step 3: health behaviors and their interactions Smokinge Physical activity Neuroticism physical activity
Fibrinogena, T2c (n = 923) 2
B
DR
b (SEB)
B
DR
**
(2.45) (1.34) (3.02) (1.34) (2.03)
4.91* 2.90* 2.85 3.79** 1.78 .30 1.23 .1.59**
DR2
B
.39** .01*
.11 .02** (2.78) (1.54) (3.44) (1.53) (2.33)
8.58** 4.23** 5.50 3.52** 2.51
.04 (3.57) .07 (.86) .13 (.81)
.12 1.82* 1.82**
.19 .10 .09 .08 .04 .02**
.01 (3.12) .06 (.75) .12 (.62)
b (SEB)
**
.13 .02** .13 .08 .06 .09 .04
Fibrinogena, T2d (n = 923) 2
.11 .06 .05 .03 .03
(2.34) (2.03) (2.88) (1.28) (1.94)
4.60** 2.53* 2.54 1.33 1.48
.02**
.01 .03 (.12) .12 (.03) .10 (.03)
.03 .12** .06*
Notes: n = B and b represent the non standardized and standardized partial regression coefficients, respectively; SEB stands for the standard error of the former. a mg/L. b Controlled for health, age, gender, education, and WHR. c Controlled for health, age, gender, education, WHR and time lag. d Controlled for health, age, gender, education, WHR, time lag and Fibrinogen at T1. e Smoking coded 1 = yes, 0 = no. * p < .05. ** p < .01.
(Black, 2003). Thus, we suggest that Openness might improve cardiovascular health through its buffering effect on the inflammatory process. Finally, we did not find a significant association of Agreeableness with the inflammation biomarkers. We speculated that agreeableness led to varying responses among the sample subjects, thus, counteracting or nullifying the effect of Agreeableness on the criteria in our statistical analysis. This study had several limitations. First, caution is advised in interpreting the findings because the sample is of subjects undergoing a periodic health examination. This could limit the utility of the findings because the results might not be applicable to a broad population that does not undergo health examinations. Second, our inability to replicate some of the results of previous studies might be due to different sample compositions (e.g., participants’ age) or the use of different personality instruments (Pace & Brannick, 2010). In the current study, we used a brief measure of the FFM, the Mini-Marker scale, which has moderate equivalence at the aggregate level with the widely used NEO-PI-R (Costa & McCrae, 1992) in personality-health research. However, a brief measure of the FFM might not cover all aspects of the FFM. Moreover, some personality-based variations in health are not accounted for by the FFM (Paunonen & Jackson, 2000). Thus, future studies should replicate our findings by using the NEO-PI-R and other sources of personality variance to increase accuracy in explaining health. Finally, although significant, the beta weights of the effects of FFM traits on the biomarkers are smaller than .20. However, even small effect sizes can have an important practical influence (e.g., Aguinis, Beaty, Boik, & Pierce, 2005).
Acknowledgement This study was supported by Israel Science Foundation Grant 962/02-1.
References Aguinis, H., Beaty, J. C., Boik, R. J., & Pierce, C. A. (2005). Effect size and power in assessing moderating effects of categorical variables using multiple regression: A 30-year review. Journal of Applied Psychology, 90, 94–107. Armon, G., & Toker, S. (2012). The role of personality in predicting repeat participation in periodic health screening. Journal of Personality.
Black, P. H. (2003). The inflammatory response is an integral part of the stress response: Implications for atherosclerosis, insulin resistance, type II diabetes and metabolic syndrome X. Brain, Behavior, and Immunity, 17, 350–364. Casas, J. P., Shah, T., Hingorani, A. D., Danesh, J., & Pepys, M. B. (2008). C-reactive protein and coronary heart disease: A critical review. Journal of Internal Medicine, 264, 295–314. Chapman, B. P., Khan, A., Harper, M., Stockman, D., Walton, J., Dunerstein, P., et al. (2009). Gender, race/ethnicity, personality, and interleukin-6 in urban primary care patients. Brain, Behavior, and Immunity, 23, 636–642. Chapman, B. P., Roberts, B., & Duberstein, P. (2011). Personality and longevity: Knowns, unknowns, and implications for public health and personalized medicine. Journal of Aging Research. http://dx.doi.org/10.4061/2011/759170. Chapman, B. P., van Wijngaarden, E., Seplaki, C. L., Talbot, N., Duberstein, P., & Moynihan, J. (2011). Openness and conscientiousness predict 34-week patterns of Interleukin-6 in older persons. Brain, Behavior, and Immunity, 25, 667–673. Clauss, A. (1957). Rapid physiological coagulation method in determination of fibrinogen. Acta Haematologica, 17, 237–246. Cortina, J. M. (1993). Interaction, nonlinearity, and multicollinearity: Implications for multiple regressions. Journal of Management, 19, 915–922. Costa, P. T., & McCrae, R. R. (1992). NEO PI-R. Professional manual. Odessa, FL: Psychological Assessment Resources, Inc. Costa-Pinto, F. A., & Palermo-Neto, J. (2010). Neuroimmune interactions in stress. NeuroImmuno Modulation, 17(3), 196–199. Danesh, J., Lewington, S., Thompson, S. G., Lowe, G., Collins, R., Kostis, J. B., et al. (2005). Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: An individual participant meta-analysis. JAMA: The Journal of the American Medical Association, 294, 1799–1809. Dembroski, T. M., & Costa, P. T. Jr., (1987). Coronary prone behavior: components of the type A pattern and hostility. Journal of Personality, 55, 211–235. Ein-Gar, D., Goldenberg, J., & Sagiv, L. (2008). Taking control: An integrated model of dispositional self-control and measure. Advances in Consumer Research, 35, 542–550. Grant, A. M., & Schwartz, B. (2011). Too much of a good thing: The challenge and opportunity of the inverted-U. Perspectives in Psychological Science, 6, 61–76. Gunn, L. M., Ayton, D. R., Densley, K., Pallant, J. F., Chondors, P., et al. (2012). The association between chronic illness, multimorbidity and depressive symptoms in Australian primary care cohort. Social Psychiatry and Psychiatric Epidemiology, 47(2), 175–185. Herrick, S., Blanc-Brude, O., Gray, A., & Laurent, G. (1999). Fibrinogen. The International Journal of Biochemistry & Cell Biology, 31, 741–746. Ironson, G. H., O’Cleirigh, C., Weiss, A., Schneiderman, N., & Costa, P. T. Jr., (2008). Personality and HIV disease progression: Role of NEO-PI-R openness, extraversion, and profiles of engagement. Psychosomatic Medicine, 70, 245–253. Jonassaint, C. R., Boyle, S. H., Kuhn, C. M., Siegler, I. C., Copel, W. E., & Williams, R. (2010). Personality and inflammation: The protective effect of openness to experience. Ethnicity and Disease, 20, 11–14. Jonassaint, C. R., Boyle, S. H., Williams, R. B., Mark, D. B., Siegler, I. C., & Barefoot, J. C. (2007). Facets of openness predict mortality in patients with cardiac disease. Psychosomatic Medicine, 69, 319–322. Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17, 137–152. Korkeila, K., Suominen, S., Ahvenainen, J., Ojanlatva, A., Rautava, P., Helenius, H., et al. (2001). Non-response related factors and in a nation-wide health survey. European Journal of Epidemiology, 17, 991–999. Marsland, A. L., Prather, A. A., Petersen, K. L., Cohen, S., & Manuck, S. B. (2008). Antagonistic characteristics are positively associated with inflammatory markers independently of trait negative emotionality. Brain Behavior and Immunity, 22, 753–761.
G. Armon et al. / Personality and Individual Differences 54 (2013) 750–755 Marusic, A., & Eysenck, H. J. (2001). Synergistic interaction of smoking and neuroticism as a risk factor in ischaemic heart disease: Case-control study. Personality and Individual Differences, 30, 47–57. Murphy, M. L. M., Miller, G. E., & Wrosch, C. (2012). Conscientiousness and stress exposure and reactivity: A prospective study of adolescent females. Journal of Behavioral Medicine, 1–12. Pace, V. L., & Brannick, M. T. (2010). How similar are personality scales of the ‘‘same’’ construct? A meta-analytic investigation. Personality and Individual Difference, 49, 669–676. Paunonen, S. V., & Jackson, D. N. (2000). What is beyond the Big Five? Plenty! Journal of Personality, 68, 821–835. Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research: The theory, design, and analysis of change. Journal of Management, 36(1), 94–120. Rifai, N., Tracy, R. P., & Ridker, P. M. (1999). Clinical efficacy of an automated highsensitivity C-reactive protein assay. Clinical Chemistry, 45, 2136–2141. Roberts, W. L., Moulton, L., Law, T. C., et al. (2001). Evaluation of nine automated high-sensitivity C-reactive protein methods: Implications for clinical and epidemiological applications. Clinical Chemistry, 47, 418–425. Saucier, G. (1994). Mini-markers: A brief version of Goldberg’s unipolar big-five markers. Journal of Personality Assessment, 63, 506–516. Segerstrom, S. C. (2000). Personality and the immune system: Models, methods, and mechanisms. Annals of Behavioral Medicine, 22, 180–190. Smith, T. W., Uchino, B. N., Berg, C. A., Florsheim, P., Pearce, G., Hawkins, M., et al. (2008). Associations of self reports versus spouse ratings of negative affectivity, dominance, and affiliation with coronary artery disease: Where should we look and who should we ask when studying personality and health? Health Psychology, 27, 676–684. Steptoe, A., Dockray, S., & Wardle, J. (2009). Positive affect and psychobiological processes relevant to health. Journal of Personality, 77, 1747–1776.
755
Suls, J., Martin, R., & David, J. P. (1998). Person– environment fit and its limits: Agreeableness, neuroticism, and emotional reactivity to interpersonal conflicts. Personality and Social Psychology Bulletin, 24, 88–98. Sutin, A. R., Beason-Held, L. L., Resnick, S. M., & Costa, P. T. (2009). Sex differences in resting-state neural correlates of openness to experience among older adults. Cerebral Cortex, 192, 797–2802. Sutin, A. R., Terracciano, A., Deiana, B., Naitza, S., Ferrucci, L., Uda, M., et al. (2010). High neuroticism and low conscientiousness are associated with interleukin-6. Psychological Medicine, 40, 1485–1493. Terracciano, A., & Costa, P. T. (2004). Smoking and the five-factor model of personality. Addiction, 99, 472–481. Thompson, E. R. (2008). Development and validation of an international English bigfive mini-markers. Personality and Individual Differences, 45(6), 542–548. Tindle, H. A., Chang, Y. F., Kuller, L. H., Manson, J. E., Robinson, J. G., Rosal, M. C., et al. (2009). Optimism, cynical hostility, and incident coronary heart disease and mortality in the women’s health initiative. Circulation, 120, 656–662. Traupman, E. K., Smith, T. W., Uchino, B. N., Berg, C. A., Trobst, K. K., & Costa, P. T. Jr., (2009). Interpersonal circumplex octant, control, and affiliation scales for the NEO-PI-R. Personality and Individual Differences, 47, 457–463. Twisk, J. W. R. (2003). Applied longitudinal data analysis of epidemiology. Cambridge, UK: Cambridge University Press. Vesey, W. B., Vesey, J. T., Stroter, A. D., & Middleton, K. V. (2011). Multiple linear regression: A return to basics in educational research. Multiple Linear Regression Viewpoints, 37(2), 14–22. Viswesvaran, C., & Ones, D. S. (2000). Measurement error in ‘‘big five factors’’ personality assessment: Reliability generalization across studies and measures. Educational and Psychological Measurement, 60(2), 224–235. Wayne, J. H., Musica, N., & Fleeson, W. (2004). Considering the role of personality in the work-family experience. Journal of Vocational Behavior, 64, 108–130.
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Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
The associations of the Five Factor Model of personality with inflammatory biomarkers: A four-year prospective study Galit Armon a,⇑, Samuel Melamed b, Arie Shirom c,1, Shlomo Berliner d, Itzhak Shapira d a
Department of Psychology, University of Haifa, Haifa 31905, Israel Tel Aviv University and Academic College of Tel Aviv–Yaffo, Israel c Tel Aviv University, Tel Aviv, Israel d Tel Aviv Sourasky Medical Center, Israel b
a r t i c l e
i n f o
Article history: Received 14 June 2012 Received in revised form 22 November 2012 Accepted 30 November 2012 Available online 27 December 2012 Keywords: Inflammation C-reactive protein (hsCRP) Fibrinogen Five Factor Model Big Five Personality Longitudinal study Cardiovascular disease Health behaviors
a b s t r a c t This study evaluated the associations of the Five Factor Model of personality with two inflammation biomarkers, C-reactive protein and fibrinogen, and the possible moderating effects of common healthrelated behaviors (physical activity and smoking) concurrently and over four years, while adjusting for socio-demographic and health status. Participants were individuals who underwent a health examination at two points of time, T1 (n = 1709) and T2 (n = 923), about four years apart. Regression analyses uncovered positive associations between Neuroticism and Extraversion with two inflammatory biomarkers at baseline (T1) and over time (T2) and increases in their levels over time. Additionally, a synergistic interaction of neuroticism and physical inactivity was associated with higher levels of inflammation biomarkers at both time periods. Openness was negatively associated with inflammation biomarkers at T1 and T2, but not with changes in their levels. No significant associations were found for Agreeableness and Conscientiousness. The results suggest that personality traits might be involved in the inflammatory process both concurrently and over time, and thus indicate a possible mechanism by which personality traits might influence health, especially cardiovascular disease risk. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Recent evidence underscores the key role played by inflammatory processes in the etiology and prognosis of cardiovascular disease (CVD; Casas, Shah, Hingorani, Danesh, & Pepys, 2008; Danesh et al., 2005). Inflammation biomarkers reflect long-term physiological, cognitive, emotional, and behavioral mechanisms (Black, 2003), some of which are likely to include enduring personality traits (Costa & McCrae, 1992). The present study addresses the links between personality traits and the inflammatory process by focusing on two inflammation biomarkers: high-sensitivity Creactive protein (hsCRP) and fibrinogen. hsCRP is a complex set of proteins produced during the acute phase response when the body is faced with a major infection or trauma (Casas et al., 2008). Fibrinogen is a circulating glycoprotein that acts at the final step in the coagulation response to vascular and tissue injury, where it controls blood loss (Herrick, Blanc-Brude, Gray, & Laurent, 1999). Personality was assessed by the Five Factor Model (FFM) of personality, a widely adopted framework for studying associations
⇑ Corresponding author. Tel.: +972 54 5661782. 1
E-mail address: [email protected] (G. Armon). Deceased.
0191-8869/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.paid.2012.11.035
between personality and health (Chapman, Roberts, & Duberstein, 2011). Possible mechanisms linking personality and inflammatory biomarkers include psycho-physiological and behavioral factors. Psycho-physiological pathways suggest that personality traits influence individuals’ stress appraisals and coping mechanisms (Segerstrom, 2000). The repeated experience of psychological stress leads to continuous activation of the hypothalamic-pituitary-adrenal (HPA) axis, which subsequently results in a chronic inflammatory process (Black, 2003). Behavioral pathways suggest personality traits are a strong predictor of unhealthy behaviors such as smoking and inactivity (see Chapman et al., 2011, for a review) that increase the risk of an inflammatory response (Casas et al., 2008; Danesh et al., 2005). 1.1. The present study Longitudinal studies on the FFM and inflammation biomarkers are relatively sparse (e.g., Chapman et al., 2011). Few reports (e.g., Chapman et al., 2009; Sutin et al., 2010) have looked at personality comprehensively, using the FFM or a large, representative. In this study, the relationship between the FFM and inflammatory biomarkers and the possible moderating effect of common healthrelated behaviors (physical activity and smoking) were examined
G. Armon et al. / Personality and Individual Differences 54 (2013) 750–755
concurrently and over four years. The study was conducted on a group of multi-occupational employed adults, while adjusting for socio-demographic and health status. Neuroticism, predisposing individuals to experience stresses more intensely (Costa & McCrae, 1992), and to engage in risky health behaviors (see review of Chapman et al., 2011), was positively linked to inflammatory markers in an Italian population (Sutin et al., 2010), but this was not found in primary care patients or older adults (Chapman et al., 2009, 2011). In the current study, we expected Neuroticism to be positively associated with inflammatory biomarkers (hypothesis 1a). Additionally, the possibility of synergistic effects of neuroticism and unhealthy behaviors in CVD has been noted with regard to smoking (e.g., Marusic & Eysenck, 2001). Therefore, we expected a synergistic interaction of neuroticism and health-related behaviors (high smoking and low physical activity) in predicting levels of inflammatory biomarkers (hypothesis 1b). Extraverts are predisposed to experience positive emotions (Costa & McCrae, 1992), which have been found to be consistently associated with good physical health, including reduced CVD risk (see recent review of Steptoe, Dockray, & Wardle, 2009). Additionally, Extraversion has been found to act as a buffer against stresses (Wayne, Musica, & Fleeson, 2004) and therefore expected to be negatively associated with inflammation biomarkers (hypothesis 2). Conscientiousness, predisposing individuals to cope effectively with stresses (e.g., Murphy, Miller, & Wrosch, 2012) and leading to patterns of healthy behaviors (see Chapman, Roberts et al., 2011), has been negatively linked to inflammatory markers (Chapman et al., 2011; Sutin et al., 2010). Accordingly, we expected conscientiousness to be negatively associated with inflammation biomarkers (hypothesis 3a) and anticipated a synergistic interaction of conscientiousness and healthy behaviors (low smoking and high physical activity) in predicting lower inflammatory biomarker levels (hypothesis 3b). Openness encompasses cognitive and behavioral flexibility and attunement to internal and external events and experiences (Costa & McCrae, 1992). These factors probably facilitate prevention of avoidable health problems and the capacity to manage stressful situations (e.g., Ironson, O’Cleirigh, Weiss, Schneiderman, & Costa, 2008; Jonassaint et al., 2007). Higher levels of Openness have been associated with decreased risk for CVD and all-cause mortality (see Chapman, Roberts et al., 2011, for a recent review) and with lower levels of inflammation biomarkers (Chapman et al., 2011; Jonassaint et al., 2010). Thus, we expected Openness to be negatively associated with inflammation biomarkers (hypothesis 4). Agreeableness involves compliance, trust, acquiescence, and interpersonal deference (Costa & McCrae, 1992). A high score on Agreeableness may predispose individuals to follow suggested health guidelines and avoid health problems. Furthermore, traits related to low Agreeableness (antagonism), such as anger and hostility, are well-established risk factors for CVD morbidity and mortality (e.g., Tindle et al., 2009). Antagonism-related traits have been directly associated with inflammatory biomarkers (e.g., Marsland, Prather, Petersen, Cohen, & Manuck, 2008; Sutin et al., 2010), but not in all cases (Chapman et al., 2009, 2011). However, Agreeableness can cause individuals to be more vulnerable and reactive to interpersonal stressors or threats (e.g., Suls, Martin, & David, 1998) that might promote the inflammatory process (Black, 2003). In light of this opposing evidence, we offer no a priori hypotheses regarding the association between Agreeableness and the inflammation biomarkers. Following the typological approach for personality (Costa & McCrae, 1992), we also evaluated the interactive effects between the five factors to investigate whether combinations of traits affect inflammation biomarkers. Based on previous evidence
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(e.g., Dembroski & Costa, 1987; Terracciano & Costa, 2004), we examined the possible interactive effects between high neuroticism and low extraversion (poor well-being); high neuroticism and low agreeableness (poor anger control), and high neuroticism and low conscientiousness (poor impulse control). Since such studies are scarce, we tested these interactive effects on an exploratory basis.
2. Materials and methods 2.1. Study participants We analyzed data collected from the Tel Aviv Medical Center Inflammation Survey (TAMCIS), a registered data bank of the Israeli Ministry of Justice. This includes a relatively large cohort of individuals who attended the medical center for a routine annual check-up and gave written informed consent for participation in the study, according to the instructions of the local ethics committee. Participants were examined on two occasions, Time 1 (T1) (n = 1709) and Time 2 (T2) (n = 923), on average 45 months apart (range = 21–91, SD = 15.72). These examinations were provided by employers as a subsidized fringe benefit. Thus, attrition between T1 and T2 could be due to change of employer, residence, or work location; totally unrelated to participation in the current study. At T1, participants represented 92% of the Center’s examinees. A systematic check for non-response bias at T1, found that participants and non-participants did not differ on any of the socio-demographic or biomedical variables. The personality traits of those examined at T1 who did not return for a follow-up examination (786 subjects, 46%) did not differ from those who returned at T2. However, they were more likely to be male, older (near retirement age), and less educated; consistent with previous reports on attrition bias in health survey studies (e.g., Korkeila et al., 2001; Ployhart & Vandenberg, 2010). The mean age at T1 of the 923 employees with data at both study moments was 45.56 years (SD = 9.64), 46.43 (SD = 8.99) for the men (69%) and 45.82 (SD = 9.02) for the women (31%).
2.2. Measures High sensitivity C-Reactive Protein (hsCRP) concentrations in serum were determined with the BN II Nephelometer g12 analyzer (DADE Behring, Marburg, Germany) (Rifai, Tracy, & Ridker, 1999). This assay is based on particle-enhanced immunonephelometry, which enables measurement of extremely low hsCRP concentrations (0.175–11 mg/L (initial dilution) according to the manufacturer (Roberts, Moulton, Law, et al., 2001). Fibrinogen plasma concentration was measured by the Clauss method (Clauss, 1957) with an ST-A compact coagulometer (Stago, Asnières, France). The FFM: Personality dimensions were assessed at T1 using the Big Five Mini-Marker scale (Saucier, 1994) while participants were waiting for the physical exam. The scale consists of 40 adjectives that measure five personality factors (eight for each factor): Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to experience. Respondents were asked to indicate how well these adjectives described them on a 9-point Likert scale ranging from 1 (extremely inaccurate) to 9 (extremely accurate). The scores and the Cronbach internal consistency reliabilities of the FFM were largely on par with those reported in the original version (Saucier, 1994), with those reported in a meta-analysis (Viswesvaran & Ones, 2000), and with other studies using a multinational sample (Thompson, 2008) and the Hebrew version of the Mini-Marker scale (e.g., Ein-Gar, Goldenberg, & Sagiv, 2008).
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2.3. Possible moderators Number of cigarettes smoked daily and number of weekly hours customarily engaged in physical activity were documented by selfreport. 2.4. Control variables Analyses were controlled for several demographic and biomedical variables associated with hsCRP and fibrinogen concentrations, including age, gender, education, obesity (waist-to-hip ratio), and having a chronic medical illness (CMI) or taking a medication (e.g., statins) that could impact biomarker levels (Casas et al., 2008; Danesh et al., 2005). Waist-to-hip ratio (WHR), in centimeters was assessed by the medical staff, and used as a continuous variable. CMI was coded, as in other epidemiological studies (Chapman et al., 2011; Gunn et al., 2012), based on self-reported physician diagnosis of at least one of eight medical conditions: diabetes, cancer, cardiovascular disease, respiratory disease, neurological disease, musculoskeletal complaints, rheumatic disease, or gastro-intestinal disease. 2.5. Analytic methods Hypotheses were tested on hsCRP and fibrinogen values at T1 and T2 using ordinary least squares (OLS) regressions (with SPSS software). In the first step, control variables of age, gender, education, and CMI were entered. Length of follow-up in days was included when predicting criteria at T2. When we used the T1 level of each criterion at T2 as the first covariate, results reflected the effects of the predictors on the change in each criterion (Twisk, 2003). In the second step, the main effect variables of the FFM traits were entered simultaneously to isolate the unique effect of each domain. In the third step, common health behaviors (smoking and physical activity) and their interactions with neuroticism and conscientiousness were added. In the fourth step, the interactive terms of neuroticism with extraversion, conscientiousness and agreeableness were added. Only significant interactions are shown in the results table (Cortina, 1993). In order to complement the inferential statistics of p-values, the effect size of the main effects of the FFM was also calculated as the square of the Pearson correlation r (R2) to reflect the proportion of variance shared by the FFM and the inflammation biomarkers (Kelley & Preacher, 2012). 3. Results The unadjusted intercorrelation matrix of the variables included in our analysis and their means and standard deviations are depicted in Table 1. All criteria and predictors were examined to detect outliers or non-normal distributions (skewness >2.0 and kurtosis >5.0); none was detected. Results of the OLS regression analyses testing the associations between the FFM and hsCRP and fibrinogen levels at base line (T1), over time (T2), and with changes in their levels from T1 to T2, are reported in Tables 2 and 3, respectively. Supporting hypothesis 1a, Neuroticism was positively associated with levels of hsCRP at T1 and T2 (p < .01), with fibrinogen at T2 (p < .01) and with increased fibrinogen levels from T1 to T2 (p < .01). Neuroticism was also positively associated with fibrinogen at T1 and with increased hsCRP levels from T1 to T2 (p < .05), but these associations were not significant after Bonferroni correction to the alpha level of .05 (a = 0.016) (Vesey, Vesey, Stroter, & Middleton, 2011). Additionally, the interactive term of neuroticism and physical activity was negatively associated with hsCRP at T1 (p < .01) and with fibrinogen at T1 and T2 (p < .01) such
that high levels of these biomarkers were related to high levels of neuroticism and low levels of physical activity. The interactive term of neuroticism and physical activity was also negatively associated with hsCRP at T1 and with increased hsCRP levels from T1 to T2, but these associations were no longer significant after Bonferroni correction. The interactive term of neuroticism and smoking was not significantly associated with the criteria, thus hypothesis 1b was partially supported. Contrary to our expectation (hypothesis 2), Extraversion was positively associated with T1 and T2 hsCRP levels (p < .01 at both times), and with fibrinogen at T2 (p < .01). Extraversion was also positively associated with fibrinogen at T1 and with increased hsCRP and fibrinogen levels from T1 to T2 (p < .05), but these associations were not significant after Bonferroni correction. The associations between Conscientiousness and the interactive terms of Conscientiousness with physical activity and smoking were not significantly associated with the criteria, thus not supporting hypotheses 3a and 3b. In support of hypothesis 4, Openness was negatively associated with fibrinogen at T1 and T2 (p < .01 at both times). Openness was also negatively associated with hsCRP at T1 (p < .05), but this association was not significant after Bonferroni correction. The associations between Agreeableness with hsCRP and fibrinogen with the criteria were not significant, thus not supporting hypothesis 5. The interactive terms of Neuroticism with Extraversion, Agreeableness and Conscientiousness were not significant. The associations of the FFM with the criteria were not attenuated when health status, socio-demographic status and health behaviors were added to the regression model. Based on the R2 of the main effects of the FFM, the effect sizes were small, but significant (R2 = .02, p < .01) for hsCRP and fibrinogen levels at T1 and T2. The effect sizes based on the R2 of the FFM for changes in hsCRP and fibrinogen levels were also small and significant (R2 = .01, p < .05).
4. Discussion This four-year prospective study was designed to facilitate our understanding of the biological mechanisms underlying the associations of personality with health, especially CVD risk, by focusing on two inflammation biomarkers, hsCRP and fibrinogen, which represent important targets in clinical preventive practices designed to reduce the risk of CVD (Casas et al., 2008). Overall, three factors of the FFM were associated with the inflammatory process. Most of these associations were observed for the two inflammation biomarkers, thus providing a cross-validation of the findings. In support of our hypothesis and consistent with recent findings (Sutin et al., 2010), Neuroticism was directly associated with elevated inflammatory biomarkers concurrently and over time, probably through greater HPA axis reactivity in response to stress appraisal (Black, 2003; Marsland et al., 2008). Additionally, our results show a synergetic effect between neuroticism and physical inactivity in the inflammatory process; thus, pointing to the potential importance of daily physical activity to one’s health, and further illuminating the modifying role of health behaviors in the stress response. Contrary to our expectation and to the results of previous studies (e.g. Chapman et al., 2011; Sutin et al., 2010), Extraversion had positive, rather than negative associations with the inflammation biomarkers at T1 and T2. A plausible explanation for this result might be that an appetitive positive affect system, often referred to as Extraversion, involves exaggerated activation of the HPA axis, which promotes the inflammatory process (Black, 2003). An alternative explanation is that individuals with high positive mood states are more likely to be reckless, to perceive themselves as less vulnerable to undesirable health conditions, and to
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G. Armon et al. / Personality and Individual Differences 54 (2013) 750–755 Table 1 Means, standard deviations and intercorrelations of the major study variables. Variable a
hsCRP , T2 hsCRPa, T1 Fibrinogena, T2 Fibrinogena, T1 Neuroticism Extraversion Conscientiousness Openness Agreeableness Age Genderb Educationc WHR Smokingd Physical activity CMIe
a M SD
1
2
3
– .44** .46** .33** .06 .08* .02 .02 .03 .10** .04 .01 .14** .05 .16** .05 – 2.04 1.95
– – .17** .46** .04 .05 .03 .01 .02 .08* .06 .01 .10** .03 .04 .02 – 2.28 3.18
– – – .61** .03 .10** .02 .06 .08* .24** .21** .02 .02 .06 .08* .07* – 296.67 54.79
4
5
6
7
8
9
10
– – – – – .17** .18** .09** .25** .07* .05 .02 .01 .13** .02 .08* .70 3.54 1.22
– – – – – – .24** .09** .26** .04 .05 .08* .01 .04 .05 .01 .74 6.10 1.23
– – – – – – – .11** .23** .01 .01 .05 .03 .03 .04 .06 .80 7.61 .97
– – – – – – – –
– – – – – – – – –
– – – – – – – – – –
– – – – .02 .10** .04 .06 .08* .30** .18** .07* .06 .04 .06 .08* – 268.19 48.88
.04 .06 .05 .02 .04 .07* .04 .07* .72 6.15 1.14
.03 .14** .06 .09 .04 .02 .02 .70 7.66 .89
.01 .07* .31** .13** .06 .15** – 45.56 9.64
11
12
13
14
15
16
– – – – – – – – – – –
– – – – – – – – – – – – .06 .07 .02 .04 – .98 .11
– – – – – – – – – – – – – .10** .12** .09** – .96 .11
– – – – – – – – – – – – – – .07* .04 – .40 .49
– – – – – – – – – – – – – – – .08* – 2.35 2.04
– – – – – – – – – – – – – – – – –
.03 .59** .02 .05 .04 – .31 .46
.51 .50
Notes: n = 923; WHR = waist-to-hip ratio.
a = Cronbach’s alpha. a b c d e * **
mg/L. Gender coded 1 = woman, 0 = man. Education coded 1 = college education or higher, 0 = other. Smoking coded 1 = yes, 0 = no. CMI = chronic medical illness, coded 1 = without CMI, 0 = other. p < .05. p < .01.
Table 2 OLS regressions predicting hsCRP levels by the FFM. Measure
hsCRPa, T1b (n = 1709) b (SEB)
Step 1: Control variables Step 2: Predictors Neuroticism Extraversion Conscientiousness Openness Agreeableness Step 3: health behaviors and their interactions Smokinge Physical activity Neuroticism physical activity
B
hsCRPa, T2c (n = 923)
DR2
b (SEB)
B
.02** .02** .12 .12 .02 .07 .01
(.09) (.05) (.06) (.05) (.07)
.19** 19** .04 .10* .01 .09 .11** .07**
DR2
b (SEB)
DR 2
B
.01* .02** .14 .10 .03 .03 .02
(10) (.06) (.07) (.06) (.09)
.22* 17** .04 .05 .04
.06** .02 (.11) .11 (.03) .12 (.02)
hsCRPa, T2d (n = 923)
.20** .01* .12 .07 .01 .02 .04
(.09) (.05) (.06) (.05) (.08)
.19* .10* .01 .03 .08
.05** .03 (.13) .13 (.03) .11 (.03)
.12 13** .06*
.03** .03 (.12) .12 (.03) .10 (.03)
.03 .12** .06*
Notes: B and b represent the non standardized and standardized partial regression coefficients, respectively; SEB stands for the standard error of the former. a mg/L. b Controlled for health, age, gender, education, and WHR. c Controlled for health, age, gender, education, WHR and time lag. d Controlled for health, age, gender, education, WHR, time lag and hsCRP at T1. e Smoking coded 1 = yes, 0 = no. * p < .05. ** p < .01.
have maladaptive health behaviors (for a review, see Grant & Schwartz, 2011), which might result in elevated levels of inflammation biomarkers. Furthermore, this unexpected effect for Extraversion could be because it is closely associated with the trait of dominance (e.g., Traupman et al., 2009), which has been related to increased CVD risk (Smith et al., 2008). Surprisingly, in contrast to our hypothesis and to results of previous studies (Chapman et al., 2011; Sutin et al., 2010), we did not find significant associations between Conscientiousness and inflammatory biomarkers or a moderating effect of health behaviors on this association. This might be due to potential mediating and moderating mechanisms related to health behaviors not included in this study, such as adherence to routine health screening
(e.g., Armon & Toker, 2012). Additionally, Conscientiousness might be more meaningful for health improvement when facing stress (e.g., Murphy et al., 2012). In support of the study hypothesis and the results of previous studies (Chapman et al., 2011; Jonassaint et al., 2010), the inverse associations between Openness and fibrinogen, measured concurrently and overtime were significant. Recent literature suggests that Openness is correlated with the orbitofrontal cortex function, the region of the brain responsible for cognitive and behavioral flexibility and adaptation (Sutin, Beason-Held, Resnick, & Costa, 2009). The orbitofrontal cortex function is associated with reduced HPA-axis response (Costa-Pinto & Palermo-Neto, 2010), which subsequently results in reduced levels of inflammatory biomarkers
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G. Armon et al. / Personality and Individual Differences 54 (2013) 750–755
Table 3 OLS regressions predicting fibrinogen levels by the FFM. Measure
Fibrinogena, T1b (n = 1709) b (SEB)
Step 1: Control variables Step 2: Predictors Neuroticism Extraversion Conscientiousness Openness Agreeableness Step 3: health behaviors and their interactions Smokinge Physical activity Neuroticism physical activity
Fibrinogena, T2c (n = 923) 2
B
DR
b (SEB)
B
DR
**
(2.45) (1.34) (3.02) (1.34) (2.03)
4.91* 2.90* 2.85 3.79** 1.78 .30 1.23 .1.59**
DR2
B
.39** .01*
.11 .02** (2.78) (1.54) (3.44) (1.53) (2.33)
8.58** 4.23** 5.50 3.52** 2.51
.04 (3.57) .07 (.86) .13 (.81)
.12 1.82* 1.82**
.19 .10 .09 .08 .04 .02**
.01 (3.12) .06 (.75) .12 (.62)
b (SEB)
**
.13 .02** .13 .08 .06 .09 .04
Fibrinogena, T2d (n = 923) 2
.11 .06 .05 .03 .03
(2.34) (2.03) (2.88) (1.28) (1.94)
4.60** 2.53* 2.54 1.33 1.48
.02**
.01 .03 (.12) .12 (.03) .10 (.03)
.03 .12** .06*
Notes: n = B and b represent the non standardized and standardized partial regression coefficients, respectively; SEB stands for the standard error of the former. a mg/L. b Controlled for health, age, gender, education, and WHR. c Controlled for health, age, gender, education, WHR and time lag. d Controlled for health, age, gender, education, WHR, time lag and Fibrinogen at T1. e Smoking coded 1 = yes, 0 = no. * p < .05. ** p < .01.
(Black, 2003). Thus, we suggest that Openness might improve cardiovascular health through its buffering effect on the inflammatory process. Finally, we did not find a significant association of Agreeableness with the inflammation biomarkers. We speculated that agreeableness led to varying responses among the sample subjects, thus, counteracting or nullifying the effect of Agreeableness on the criteria in our statistical analysis. This study had several limitations. First, caution is advised in interpreting the findings because the sample is of subjects undergoing a periodic health examination. This could limit the utility of the findings because the results might not be applicable to a broad population that does not undergo health examinations. Second, our inability to replicate some of the results of previous studies might be due to different sample compositions (e.g., participants’ age) or the use of different personality instruments (Pace & Brannick, 2010). In the current study, we used a brief measure of the FFM, the Mini-Marker scale, which has moderate equivalence at the aggregate level with the widely used NEO-PI-R (Costa & McCrae, 1992) in personality-health research. However, a brief measure of the FFM might not cover all aspects of the FFM. Moreover, some personality-based variations in health are not accounted for by the FFM (Paunonen & Jackson, 2000). Thus, future studies should replicate our findings by using the NEO-PI-R and other sources of personality variance to increase accuracy in explaining health. Finally, although significant, the beta weights of the effects of FFM traits on the biomarkers are smaller than .20. However, even small effect sizes can have an important practical influence (e.g., Aguinis, Beaty, Boik, & Pierce, 2005).
Acknowledgement This study was supported by Israel Science Foundation Grant 962/02-1.
References Aguinis, H., Beaty, J. C., Boik, R. J., & Pierce, C. A. (2005). Effect size and power in assessing moderating effects of categorical variables using multiple regression: A 30-year review. Journal of Applied Psychology, 90, 94–107. Armon, G., & Toker, S. (2012). The role of personality in predicting repeat participation in periodic health screening. Journal of Personality.
Black, P. H. (2003). The inflammatory response is an integral part of the stress response: Implications for atherosclerosis, insulin resistance, type II diabetes and metabolic syndrome X. Brain, Behavior, and Immunity, 17, 350–364. Casas, J. P., Shah, T., Hingorani, A. D., Danesh, J., & Pepys, M. B. (2008). C-reactive protein and coronary heart disease: A critical review. Journal of Internal Medicine, 264, 295–314. Chapman, B. P., Khan, A., Harper, M., Stockman, D., Walton, J., Dunerstein, P., et al. (2009). Gender, race/ethnicity, personality, and interleukin-6 in urban primary care patients. Brain, Behavior, and Immunity, 23, 636–642. Chapman, B. P., Roberts, B., & Duberstein, P. (2011). Personality and longevity: Knowns, unknowns, and implications for public health and personalized medicine. Journal of Aging Research. http://dx.doi.org/10.4061/2011/759170. Chapman, B. P., van Wijngaarden, E., Seplaki, C. L., Talbot, N., Duberstein, P., & Moynihan, J. (2011). Openness and conscientiousness predict 34-week patterns of Interleukin-6 in older persons. Brain, Behavior, and Immunity, 25, 667–673. Clauss, A. (1957). Rapid physiological coagulation method in determination of fibrinogen. Acta Haematologica, 17, 237–246. Cortina, J. M. (1993). Interaction, nonlinearity, and multicollinearity: Implications for multiple regressions. Journal of Management, 19, 915–922. Costa, P. T., & McCrae, R. R. (1992). NEO PI-R. Professional manual. Odessa, FL: Psychological Assessment Resources, Inc. Costa-Pinto, F. A., & Palermo-Neto, J. (2010). Neuroimmune interactions in stress. NeuroImmuno Modulation, 17(3), 196–199. Danesh, J., Lewington, S., Thompson, S. G., Lowe, G., Collins, R., Kostis, J. B., et al. (2005). Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: An individual participant meta-analysis. JAMA: The Journal of the American Medical Association, 294, 1799–1809. Dembroski, T. M., & Costa, P. T. Jr., (1987). Coronary prone behavior: components of the type A pattern and hostility. Journal of Personality, 55, 211–235. Ein-Gar, D., Goldenberg, J., & Sagiv, L. (2008). Taking control: An integrated model of dispositional self-control and measure. Advances in Consumer Research, 35, 542–550. Grant, A. M., & Schwartz, B. (2011). Too much of a good thing: The challenge and opportunity of the inverted-U. Perspectives in Psychological Science, 6, 61–76. Gunn, L. M., Ayton, D. R., Densley, K., Pallant, J. F., Chondors, P., et al. (2012). The association between chronic illness, multimorbidity and depressive symptoms in Australian primary care cohort. Social Psychiatry and Psychiatric Epidemiology, 47(2), 175–185. Herrick, S., Blanc-Brude, O., Gray, A., & Laurent, G. (1999). Fibrinogen. The International Journal of Biochemistry & Cell Biology, 31, 741–746. Ironson, G. H., O’Cleirigh, C., Weiss, A., Schneiderman, N., & Costa, P. T. Jr., (2008). Personality and HIV disease progression: Role of NEO-PI-R openness, extraversion, and profiles of engagement. Psychosomatic Medicine, 70, 245–253. Jonassaint, C. R., Boyle, S. H., Kuhn, C. M., Siegler, I. C., Copel, W. E., & Williams, R. (2010). Personality and inflammation: The protective effect of openness to experience. Ethnicity and Disease, 20, 11–14. Jonassaint, C. R., Boyle, S. H., Williams, R. B., Mark, D. B., Siegler, I. C., & Barefoot, J. C. (2007). Facets of openness predict mortality in patients with cardiac disease. Psychosomatic Medicine, 69, 319–322. Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17, 137–152. Korkeila, K., Suominen, S., Ahvenainen, J., Ojanlatva, A., Rautava, P., Helenius, H., et al. (2001). Non-response related factors and in a nation-wide health survey. European Journal of Epidemiology, 17, 991–999. Marsland, A. L., Prather, A. A., Petersen, K. L., Cohen, S., & Manuck, S. B. (2008). Antagonistic characteristics are positively associated with inflammatory markers independently of trait negative emotionality. Brain Behavior and Immunity, 22, 753–761.
G. Armon et al. / Personality and Individual Differences 54 (2013) 750–755 Marusic, A., & Eysenck, H. J. (2001). Synergistic interaction of smoking and neuroticism as a risk factor in ischaemic heart disease: Case-control study. Personality and Individual Differences, 30, 47–57. Murphy, M. L. M., Miller, G. E., & Wrosch, C. (2012). Conscientiousness and stress exposure and reactivity: A prospective study of adolescent females. Journal of Behavioral Medicine, 1–12. Pace, V. L., & Brannick, M. T. (2010). How similar are personality scales of the ‘‘same’’ construct? A meta-analytic investigation. Personality and Individual Difference, 49, 669–676. Paunonen, S. V., & Jackson, D. N. (2000). What is beyond the Big Five? Plenty! Journal of Personality, 68, 821–835. Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research: The theory, design, and analysis of change. Journal of Management, 36(1), 94–120. Rifai, N., Tracy, R. P., & Ridker, P. M. (1999). Clinical efficacy of an automated highsensitivity C-reactive protein assay. Clinical Chemistry, 45, 2136–2141. Roberts, W. L., Moulton, L., Law, T. C., et al. (2001). Evaluation of nine automated high-sensitivity C-reactive protein methods: Implications for clinical and epidemiological applications. Clinical Chemistry, 47, 418–425. Saucier, G. (1994). Mini-markers: A brief version of Goldberg’s unipolar big-five markers. Journal of Personality Assessment, 63, 506–516. Segerstrom, S. C. (2000). Personality and the immune system: Models, methods, and mechanisms. Annals of Behavioral Medicine, 22, 180–190. Smith, T. W., Uchino, B. N., Berg, C. A., Florsheim, P., Pearce, G., Hawkins, M., et al. (2008). Associations of self reports versus spouse ratings of negative affectivity, dominance, and affiliation with coronary artery disease: Where should we look and who should we ask when studying personality and health? Health Psychology, 27, 676–684. Steptoe, A., Dockray, S., & Wardle, J. (2009). Positive affect and psychobiological processes relevant to health. Journal of Personality, 77, 1747–1776.
755
Suls, J., Martin, R., & David, J. P. (1998). Person– environment fit and its limits: Agreeableness, neuroticism, and emotional reactivity to interpersonal conflicts. Personality and Social Psychology Bulletin, 24, 88–98. Sutin, A. R., Beason-Held, L. L., Resnick, S. M., & Costa, P. T. (2009). Sex differences in resting-state neural correlates of openness to experience among older adults. Cerebral Cortex, 192, 797–2802. Sutin, A. R., Terracciano, A., Deiana, B., Naitza, S., Ferrucci, L., Uda, M., et al. (2010). High neuroticism and low conscientiousness are associated with interleukin-6. Psychological Medicine, 40, 1485–1493. Terracciano, A., & Costa, P. T. (2004). Smoking and the five-factor model of personality. Addiction, 99, 472–481. Thompson, E. R. (2008). Development and validation of an international English bigfive mini-markers. Personality and Individual Differences, 45(6), 542–548. Tindle, H. A., Chang, Y. F., Kuller, L. H., Manson, J. E., Robinson, J. G., Rosal, M. C., et al. (2009). Optimism, cynical hostility, and incident coronary heart disease and mortality in the women’s health initiative. Circulation, 120, 656–662. Traupman, E. K., Smith, T. W., Uchino, B. N., Berg, C. A., Trobst, K. K., & Costa, P. T. Jr., (2009). Interpersonal circumplex octant, control, and affiliation scales for the NEO-PI-R. Personality and Individual Differences, 47, 457–463. Twisk, J. W. R. (2003). Applied longitudinal data analysis of epidemiology. Cambridge, UK: Cambridge University Press. Vesey, W. B., Vesey, J. T., Stroter, A. D., & Middleton, K. V. (2011). Multiple linear regression: A return to basics in educational research. Multiple Linear Regression Viewpoints, 37(2), 14–22. Viswesvaran, C., & Ones, D. S. (2000). Measurement error in ‘‘big five factors’’ personality assessment: Reliability generalization across studies and measures. Educational and Psychological Measurement, 60(2), 224–235. Wayne, J. H., Musica, N., & Fleeson, W. (2004). Considering the role of personality in the work-family experience. Journal of Vocational Behavior, 64, 108–130.