Journal of Psychosomatic Research 79 (2015) 207–213

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Journal of Psychosomatic Research

Somatic concerns, depressive traits, atherosclerosis and the incidence of cardiovascular disease in ageing Finnish men Tommi Tolmunen a,b,⁎, Soili M. Lehto a,b, Jari Laukkanen a,c, Kimmo Ronkainen a, Juhani Julkunen d, Jussi Kauhanen a a

Department of Psychiatry, Institute of Clinical Medicine, University of Eastern Finland, P.O. Box 1627, FI-70211 Kuopio, Finland Department of Psychiatry, Kuopio University Hospital, P.O. Box 100, FI-70211 Kuopio, Finland Department of Medicine, Lapland Central Hospital, P.O. Box 8041, FI-96101 Rovaniemi, Finland d Institute of Behavioural Sciences (Psychology), University of Helsinki, P.O. Box 9, FI-00014 Helsinki, Finland b c

a r t i c l e

i n f o

Article history: Received 10 December 2014 Received in revised form 6 May 2015 Accepted 11 May 2015 Keywords: Cardiovascular Carotid artery Inflammation MMPI Morbidity Personality

a b s t r a c t Objective: To examine the impact of somatic concerns and depressive traits on carotid artery intima–media thickness (IMT) and on the incidence of cardiovascular disease (CVD). Methods: In the cross-sectional study of 2682 Finnish men aged 42 to 61 years, a subsample of 1333 men had their carotid artery IMT recorded. In the prospective part of the study participants (n = 1453) were followed up for an average of 20 years. Data on incident CVD (n = 766) were obtained from the National Population Register. For both subsamples, the Minnesota Multiphasic Personality Inventory (MMPI) was used to measure depressive traits and somatic concerns. In addition, composite scales of general ill-being, sleep and pain problems, psychological well-being, energy level, and cheerfulness were formed. Results: In the final corrected models there were no significant associations between somatic concerns or depressive traits and IMT. Both somatic concerns and depressive traits predicted a higher CVD incidence after adjustments for age and risk factors. The association between depressive traits and CVD incidence was highest among the subgroup of men with the highest levels of hsCRP, but this result did not reach statistical significance. Of the composite scales, general ill-being predicted a higher CVD incidence in the fully adjusted model, even when all the composite scales were entered into the model simultaneously. Conclusion: Somatic concerns and depressive traits predict a higher CVD incidence. In particular, general ill-being contributes to this association. The association between depressive traits and CVD may be moderated by lowgrade inflammation. © 2015 Elsevier Inc. All rights reserved.

Introduction Cardiovascular diseases (CVD) are among the leading causes of mortality worldwide. The intima–media thickness (IMT) of the carotid artery has been found to reflect the global cardiovascular risk, providing a comprehensive indication of the alterations caused by multiple risk factors over time in the arterial walls [1]. Depression is suggested to have a bidirectional association with CVD, whereby depression increases the risk of incident CVD, while a history of CVD increases the risk of depression [2]. On the biological level, several mechanisms have been suggested to underlie these associations, such as hyperactivity of Abbreviations: BMI, body mass index; CI, confidence interval; Df, degrees of freedom; CVD, cardiovascular disease; hs-CRP, high-sensitivity C-reactive protein; LDL-C, lowdensity lipoprotein cholesterol; MMPI, Minnesota Multiphasic Personality Inventory; RR, risk ratio; SES, socioeconomic status. ⁎ Corresponding author at: Department of Psychiatry, Kuopio University Hospital, P.O. Box 100, FIN-70211 Kuopio, Finland. Tel.: +358 44 717 2988, +358 17 173 599. E-mail address: tommi.tolmunen@kuh.fi (T. Tolmunen).

http://dx.doi.org/10.1016/j.jpsychores.2015.05.006 0022-3999/© 2015 Elsevier Inc. All rights reserved.

the hypothalamic–pituitary–adrenal (HPA) axis, arterial stiffness and endothelial dysfunction. In addition, increased low-grade inflammation is observable in both depression and atherosclerosis [3]. Factors such as marital status, education and income have been found to moderate the relationship between depression and CVD [4]. Depression also associates with several CVD risk factors, such as smoking, obesity and diabetes [5,6]. Recently, it has been observed that unfavourable lifestyle factors, such as smoking, a lack of exercise, an unhealthy diet and excessive alcohol consumption, are important mediating factors linking negative affectivity and vascular risk factors such as hypertension or elevated levels of low-density lipoprotein cholesterol (LDL-C) [7]. However, depression has also been suggested to have an independent association with CVD [8]. Although there is a strong body of evidence supporting the association between depression and CVD [3], the connection between CVD and depressive traits as a personality feature is not well known. Depressive traits are often categorized as a part of neuroticism, a personality feature strongly linked with depression. Those with a high level of neuroticism

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T. Tolmunen et al. / Journal of Psychosomatic Research 79 (2015) 207–213

may also be more vulnerable to major depressive episodes [9]. Neuroticism has additionally been found to associate with increased low-grade inflammation [10]. There has been debate concerning whether depression in general or only somatic symptoms of depression such as fatigue associate with CVD. In some studies, the core symptoms of depression, both cognitive and somatic, have predicted CVD [11], whereas in others, only somatic symptoms have associated with CVD incidence [12]. In two recent studies, somatic complaints or somatic concerns measured by subscales of the Minnesota Multiphasic Personality Inventory (MMPI) predicted increased all-cause mortality [13,14]. Based on the earlier studies, we first hypothesized that both depressive traits and somatic concerns would be associated with increased carotid IMT in a cross-sectional setting and with higher CVD incidence in a population-based cohort with a follow-up time of 20 years. Secondly, based on the earlier literature, we assumed that the association between depressive traits and CVD incidence would be moderated by factors such as marital status. As low-grade inflammation is a common feature associating with both atherosclerosis and some subtypes of depression, we assumed that an increased level of hsCRP in particular could be a novel moderating factor between depression and CVD morbidity. Thirdly, in order to more precisely extract specific aspects of somatic concerns and depressive traits associating with increased IMT and CVD risk, we used factor analysis to form composite scales and analysed their effects in multivariate models. Method Study population The Kuopio Ischemic Heart Disease Study (KIHD) was designed to investigate risk predictors for atherosclerotic cardiovascular outcomes in a population-based sample of men. The participants were randomly selected from the general population in two cohorts [15]. Baseline data were collected from the city of Kuopio and surrounding rural communities in eastern Finland. The first cohort included 1166 men aged 54 years (83.3%) from a possible eligible sample of 1399 who were enrolled into the study between March 1984 and August 1986. The second cohort included 1516 men aged 42, 48, 54 and 60 years (82.6%) from an eligible group of 1836 who were enrolled in the study between August 1986 and December 1989. No systematic differences between the two cohorts were observed with respect to baseline demographic or subject characteristics other than the age distribution. Thus, data from the two cohorts were combined for analysis. Sociodemographic and other background characteristics of the sample have earlier been described in detail for the baseline [15,16] and for the follow-up [17,18]. Those with an hsCRP level exceeding 10 were excluded from the analyses in order to avoid bias due to acute infections and inflammatory conditions (n = 94, 3.4% of the whole sample) [19]. For the baseline analyses, those with incomplete data were excluded, leaving a total of 1287 men to be analysed in a cross-sectional setting. For the follow-up study, those with a CVD history at baseline (n = 1016, 37.9% of the whole sample) and those with incomplete data were excluded. In order to further enhance the estimation of causality, we also excluded those who received a CVD diagnosis during the first two years of the follow-up (n = 87), leaving 1453 men for the final analyses. There was some overlap between the above-mentioned exclusions. Each participant provided written informed consent. Outcome Every resident of Finland has a unique personal identifier (PID) that is used in registers. Incident CVD was defined by record linkage from the national computerized hospitalization registry, which covers every hospitalization in Finland. Data on non-fatal and fatal coronary events were obtained by computer linkage to the national hospital discharge

and death certificate registers. Diagnostic information was collected from hospitals and classified using identical diagnostic criteria. If a subject had multiple non-fatal coronary events during the follow-up, the first event after the baseline was defined as an outcome event. CVD diagnoses were classified according to the ninth and tenth International Classification of Diseases (ICD-9 and ICD-10). All new CVD diagnoses between the baseline assessments and 31 December 2011 were included. The average follow-up time for the participants until the first CVD event was 19.8 years (25th–75th percentile 10.8–24.1 y). Ultrasonographic assessment of the intima–media thickness of the common carotid artery The carotid artery IMT was assessed by high-resolution B-mode ultrasonography of the right and left CCAs at the distal end, proximal to the carotid bulb. The ultrasound equipment (Biosound Phase 2; Biosound Inc., Indianapolis) was equipped with a high-resolution probe. Images were focused on the posterior wall of the right and left CCAs and were recorded on videotape for image analysis. The ultrasonographic examinations were carried out by well-trained ultrasound technicians and were performed after the subjects had rested in a supine position for 15 min. IMT measurements were performed through computerized analysis of the videotaped ultrasound images with PROSOUND software (University of Southern California, Los Angeles). The maximum IMT was computed as the average of the points of maximum thickness from the right and left CCAs, which is indicative of the depth of intrusion of the IMT into the lumen in this part of the CCA. The method is explained in more detail elsewhere [20,21]. IMT measurements were made through computerized analysis of the videotaped ultrasound images with PROSOUND software (University of Southern California, Los Angeles). This software uses an edge-detection algorithm, specifically designed for use with ultrasound imaging, that allows automatic detection, tracking and recording of the intima–lumen and media–adventitia interfaces, estimated at approximately 100 points, in both the right and left CCAs in a 1.0–1.5-cm section [21]. The maximum IMT was computed as the average of the points of maximum thickness from the right and left CCAs, which is indicative of the depth of intrusion of the IMT into the lumen in this part of the CCA. A separate study concerning the intra- and interobserver variability in IMT measurements was carried out in 10 randomly chosen middleaged men who had participated in the KIHD study. The betweenobserver CV was 10.5% for the first assessments by 4 observers for both the right and left CCAs. The correlation coefficients ranged from 0.90 to 0.99. The intraobserver variability (reproducibility) was described by the absolute value of difference between the first and the third measurement by each observer. The mean absolute difference was 0.087 mm, which is 8.1% of the mean of all measurements [20]. Minnesota Multiphasic Personality Inventory The Minnesota Multiphasic Personality Inventory (MMPI) is probably the most widely used measure for personality assessment. The original questionnaire consists of 566 true or false items (of which 16 are repeated) covering a large variety of physical and mental symptoms, thoughts, beliefs, attitudes and life experiences. The same item can be used in several subscales, causing item overlap. For example, the subscales Depression and Hypochondriasis share eight questions [22]. When our study was initiated, only the original version, MMPI-I, was available. The revised MMPI-2 was released in 1989 [23]. The subscale names have been capitalized throughout this paper to distinguish them from the general use of the same concepts. We measured depressive traits with the original MMPI subscale Depression. The scale consists of 60 questions related to symptoms and feelings concerning appetite, waking up to noise, fitful and disturbed sleep, a life full of interest, the ability to work, caring for things, preferring to pass by friends, being happy, lacking in self-confidence, having a

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worthwhile life, being capable and smart, experiencing criticism or scolding, feeling useless, being well, gaining or losing weight, crying easily, feeling tension, being cheerful without reason, caring for what is happening to oneself, being full of energy, and brooding over thoughts. In our data, question 58, “everything is turning out just like the prophets of the bible said it would” was omitted. In the present sample, Cronbach's alpha for Depression was 0.73. Somatic concerns were examined with the original subscale Hypochondriasis. It covers features defined as a “high concern for disease, vague and diffuse complaints of physical difficulties, pessimism, and narcissism.” The scale consists of 33 questions related to symptoms such as feeling fresh and rested, hands and feet are warm, physical health, body feelings, bowel movement, muscles twitching, fullness in the head, being well, tender feelings on the head, a tired feeling in the eyes, a tight band around head, headaches, and nausea and vomiting. Cronbach's alpha for Hypochondriasis was 0.86. We use the term somatic concerns in this paper instead of Hypochondriasis, as it better reflects contemporary medical language. Other characteristics Participants completed questionnaires relating to their sociodemographic background, smoking (yes/no), alcohol consumption (grammes per week), education (years) and marital status (married or living with a partner vs. living alone). A variety of indicators of adulthood socioeconomic status were available, including current income, current and previous occupations, the highest level of education, the perception of financial security and housing tenure. In addition, summing the number of material possessions from a list of 12 was used to create an index of material living conditions. The variable “adulthood socioeconomic status” was formed from these indicators [24]. A research nurse measured the weight and height of the participants, and the body mass index (BMI; kg/m2) was calculated. Resting blood pressure was measured between 8:00 and 10:00 AM on the first examination day with a random-zero mercury sphygmomanometer. The measurement protocol included, after 5 min of supine rest, three measurements in the supine, one in the standing and two in the sitting position, with 5-minute intervals. The mean of all six systolic pressure values was used in the analyses. A history of CVD was defined as having a diagnosis of CVD other than hypertension. A detailed description of the laboratory measurements is provided elsewhere [25]. In brief, subjects came for venous blood sampling between 8:00 and 10:00 AM. They were instructed to abstain from alcohol use for three days and from smoking and eating for 12 h before sampling. After a 30-minute rest in the supine position, blood samples were obtained by venipuncture and collected into vacuum tubes (Venoject; Terumo, Leuven, Belgium). No tourniquet was used. Serum low-density lipoprotein cholesterol (LDL-C) was precipitated by using polyvinyl sulphate (Boehringer Mannheim, Mannheim, Germany) and calculated as the difference between total and supernatant cholesterol. Physical activity was assessed using the 12-Month Physical Activity questionnaire [26]. The checklist included the most common physical activities of Finnish middle-aged men, such as walking, jogging, skiing, bicycling, swimming and ball games. For each activity performed, the subjects were asked to record the frequency, average duration and intensity. The energy expenditure from physical activity was expressed as kcal per day.

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assessed with linear regression. The normal distribution of residuals was verified for all continuous variables in each analysis and covariates did not correlate with residuals. The differences in the MMPI subscale scores and the background variables between those who died during the follow-up and the rest of the cohort were examined with the Mann–Whitney U-test due to non-normal distributions. The rate ratios (RRs) with regard to each MMPI subscale for the incidence of CVD, and injury death were examined using a Cox ‘proportional hazards’ model with the following adjustments: age (Model 1); age, alcohol consumption, smoking and physical activity (Model 2); marital status and adulthood socioeconomic status (Model 3); systolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, history of diabetes and body mass index (Model 4); age and hs-CRP (Model 5); and all the above-described variables, i.e., age, smoking, alcohol consumption, physical activity, marital status, socioeconomic status, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, history of diabetes, BMI, systolic blood pressure and hsCRP (Model 6). To enhance the clarity and interpretability of our findings, we used the standard deviations (SD) of the MMPI scales in the analyses. Each of the MMPI scale variables was separately entered into the models as a continuous variable, and the RRs thus indicate the increase in risk for each 1-SD increase in MMPI subscale scores. All the covariates except smoking and marital status were entered into the models as continuous variables. To examine the interactions between the MMPI subscales and other covariates, we created MMPI subscale ∗ variable interactions with age as a covariate. To assess the association between depressive traits and CVD incidence in context of low-grade inflammation, we divided hs-CRP into three classes according to the American Heart Association Scientific Statement levels of CRP: less than 1, 1 to 3, and greater than 3 mg/L (milligrammes per litre). These reference values have been suggested to be able to discriminate between individuals with a low, moderate and high risk of future heart attack and stroke [19]. Finally, we performed a factor analysis utilizing all the questions from the MMPI subscales Depression and Hypochondriasis. We used varimax rotation to make the factors easier to distinguish, and chose the first five factors, after which there was little change in eigenvalues. Only the items with loadings for a particular factor that were more than 0.30 were included in the subscales. Scores of the items with a negative loading were subtracted from the total subscale scores. Five new subscales were created and analysed with linear regression and with Cox ‘proportional hazards’. Correlations between the composite scales were analysed, and Spearman's correlation coefficients were less than 0.53. Finally, all the composite scales were entered into the models with all the other covariates (Model 7). Two-tailed p-values below 0.05 were considered to indicate statistical significance. All analyses were conducted with the SPSS statistical package (version 17.0; SPSS Inc., Chicago, IL). Results IMT and CVD risk In linear regression analyses, somatic concerns associated with increased IMT, but the association lost its statistical significance in the fully adjusted model (Table 1). Men receiving a diagnosis of CVD during the follow-up had higher BMI, systolic blood pressure and LDL cholesterol levels. Furthermore, they had higher scores for somatic concerns and depressive traits (Table 2). Somatic concerns and depressive traits also predicted a higher CVD incidence, even after full adjustments (Table 3). Interaction analysis

Statistics For multivariate analyses, we used log10 transformation for variables with a non-normal distribution, i.e., alcohol consumption, physical activity and hs-CRP. A value of one was added to the original values of alcohol consumption and physical exercise in order to make logarithm transformation possible. The associations between the intima–media thickness of the carotid artery and MMPI scores were

We analysed the interactions between depressive traits, somatic concerns and other variables one by one with age as a covariate in all models. Hs-CRP and marital status significantly interacted with depressive traits (data not shown), but no covariates interacted with somatic concerns. In order to further analyse the possible moderating effect of hs-CRP on the association between depressive traits and CVD incidence in the fully adjusted model (Model 6), we divided the sample into three groups according the hs-CRP level. The RR for CVD was 1.067 (CI 0.952–1.196) in the lowest group, 1.084 (95% CI 0.969–1.212) in the middle group and 1.132 (95% CI 0.937–1.367) in the highest group. Regarding marital status, the RR for CVD was 1.069 (CI 95% 0.982–1.163) for

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Table 1 Associations between the MMPI subscales and intima–media thickness of the carotid artery in linear regression models. Each of the scale variables was entered into the models separately with covariates. Somatic concerns

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Depression

β

t

P

R square

Adjusted R square

β

t

p

R square

Adjusted R square

.084 .064 .059 .077 .064 .040

3.18 2.43 2.12 2.97 2.42 1.49

.001 .02 .03 .003 .02 .14

.17 .19 .18 .21 .19 .23

.17 .19 .17 .21 .18 .23

.020 .009 .006 .021 .014 .005

.76 .35 .22 .81 .53 .19

.45 .73 .83 .42 .60 .85

.16 .19 .18 .21 .18 .23

.16 .18 .17 .20 .18 .23

Model 1: adjusted for age. Model 2: adjusted for age and lifestyle variables (alcohol consumption, smoking and physical activity). Model 3: adjusted for age and psychosocial variables (marital status and adulthood socioeconomic status). Model 4: adjusted for age and somatic variables (systolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, history of diabetes and body mass index). Model 5: adjusted for age and hs-CRP. Model 6: adjusted for all the above-described variables (i.e., age, smoking, alcohol consumption, physical activity, marital status, socioeconomic status, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, history of diabetes, BMI, systolic blood pressure and hs-CRP). those who were co-habiting and 1.386 (CI 95% 1.075–1.052) for those who were living alone. There were no interactions between age, depressive traits and somatic concerns.

Discussion Summary of the main findings

Structure of novel composite scales Factor analysis provided five new composite scales from questions concerning depression and somatic concerns: 1) general ill-being, 2) sleep and pain problems, 3) psychological well-being, 4) the energy level and 5) cheerfulness (Table 4). General ill-being consists of 9 questions related to topics such as being able to work as before, being well in recent years, not tiring quickly, never having felt better than now, and having a satisfactory memory (Cronbach's alpha 0.73). Sleep and pain problems consist of 13 questions related to topics such as feeling a tight band about the head, tender feelings on top of the head, fitful and disturbed sleep, a great deal of stomach trouble, mostly sleeping without thoughts and waking up fresh and rested (Cronbach's alpha 0.79). Psychological wellbeing consists of 7 questions related to feelings such lacking self-confidence, feeling useless, being unable to care for things, brooding a great deal and working under a great deal of tension (Cronbach's alpha 0.67). The energy level consists of 7 questions related to topics such as at times being full of energy, being as capable and smart as others, feeling that life is worthwhile and difficulty in starting to do things (Cronbach's alpha 0.62). Cheerfulness consists of 5 questions related to feelings such as sometimes being cheerful without reason, sometimes feeling on top of the world, feeling that life is full of interests every day, being a good mixer, and being happy most of the time (Cronbach's alpha 0.43). Novel composite scales and IMT In linear regression with fully adjusted Model 6, there were no significant associations between the composite scales and IMT, neither when entered separately nor simultaneously into the model (Table 5). Novel composite scales and CVD risk For CVD risk, in fully adjusted Model 6, general ill-being, but not other composite scales predicted an increased CVD risk. Finally, when all the composite scales were simultaneously entered into Model 6, general ill-being still predicted an increased CVD risk (Table 6).

Somatic concerns and depressive traits predicted a higher incidence of CVD in the Finnish male population. Inflammation measured with hsCRP moderated the relationship between depressive traits and the CVD risk. Of the novel composite scales, general ill-being predicted an increased CVD risk, even when the composite scales were simultaneously entered into the fully adjusted model.

Strengths and limitations The main strength of our study is the large, regionally comprehensive sample with a high sample inclusion rate and long follow-up period. The availability of a wide range of well-validated measures for adjustments also added to the reliability of our findings. For example, in Finland, the nationwide system of registers covers all data on hospital discharge diagnoses, and it has been widely utilized in epidemiological studies [27,28]. Therefore, our follow-up outcome measure can be considered reliable. The main weakness of our study is that the results may not be generalizable to women or to all age groups, but represent the ageing Finnish male population. When the study was initiated in 1984, Finland and eastern Finland in particular was considered to be an area of high CVD incidence, particularly in men [29]. Therefore, the initial focus of the study was on CVD in men, and only men were consequently included. Secondly, the revised version of the Minnesota Multiphasic

Table 2 Baseline characteristics of the study population according to a diagnosis of cardiovascular disease (CVD) during the follow-up.

d

Depressive traits Somatic concernsd Mean of max IMTd Age (years)d Physical activity (kcal/d)d Alcohol/week (grammes)d Smoking n (%) Marital status, living alone (%) Body mass index g/m2d LDL-Cd Systolic blood pressured Hs-CRPd

CVD diagnosis (n = 766)

No CVD (n = 687)

Dfa

Test value

p value

23.0 (20.0–26.0) 7.0 (4.0–14.0) .95 (.82–1.09) 54.3 (48.3–55.0) 84.4 (35.5–215.6) 42.8 (10.7–106.0) 234 (31.4) 95 (12.8) 26.4 (24.3–28.6) 4.0 (3.4–4.7) 133.7 (123.2–144.0) 1.3 (.7–2.5)

22.0 (17.0–26.0) 7.0 (4.0–10.0) .87 (.77–1.01) 48.7 (42.8–54.5) 83.3 (32.2–169.8) 40.0 (7.7–99.4) 191 (28.3) 86 (12.8) 25.8 (24.0–28.1) 3.8 (120.3–138.0) 128.8 (3.2–4.2) 1.13 (.6–2.1)

– – – – – – 1 1 – – – –

2.42 2.70 5.04 6.83 .50 .09 1.59 0.00 3.81 3.00 6.04 3.62

.02b .007b b.001b b.001b .62b .93b .21c 1.00 b.001b .003b b.001b b.001b

Abbreviations: CVD = cardiovascular disease; Df = degrees of freedom; LDL-C = low-density lipoprotein cholesterol. a When applicable. b Mann–Whitney U-test. c Chi-squared test. d Median (25th–75th percentile).

T. Tolmunen et al. / Journal of Psychosomatic Research 79 (2015) 207–213 Table 3 Rate ratios (RRs) (95% confidence intervals) for receiving a diagnosis of cardiovascular disease (n = 766) during an average follow-up period of 25 years using Cox proportional hazards' models The MMPI subscales were entered into the model separately with covariates. Those, who received a cardiovascular disease diagnosis during the first two years of the follow-up, were excluded.

Model 1. Model 2. Model 3. Model 4. Model 5. Model 6.

Somatic concerns RRa (CI 95%)

Depression RRa (CI 95%)

1.144 (1.060–1.236) 1.136 (1.051–1.227) 1.124 (1.039–1.217) 1.119 (1.035–1.210) 1.131 (1.046–1.223) 1.097 (1.011–1.189)

1.105 (1.029–1.187) 1.104 (1.028–1.186) 1.088 (1.012–1.169) 1.097 (1.021–1.178) 1.102 (1.026–1.184) 1.085 (1.008–1.167)

CI = confidence interval, MMPI = Minnesota Multiphasic Personality Inventory, RR = rate ratio, SD = standard deviation. Model 1: adjusted for age. Model 2: adjusted for age and lifestyle variables (alcohol consumption, smoking and physical activity). Model 3: adjusted for age and psychosocial variables (marital status and adulthood socioeconomic status). Model 4: adjusted for age and somatic variables (systolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, history of diabetes and body mass index). Model 5: adjusted for age and hs-CRP. Model 6: adjusted for all the above-described variables (i.e., age, smoking, alcohol consumption, physical activity, marital status, socioeconomic status, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, history of diabetes, BMI, systolic blood pressure and hs-CRP). a RRs show the increase in risk for each 1-SD increase in the MMPI subscale.

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Personality Inventory, MMPI-2, might have been a more valid measure for personality characteristics, but it was not available when the present study was initiated. Furthermore, the items of the subscale of Depression might not be consistent with contemporary theories of depression. We did not have the complete MMPI-I questionnaire and were thus not able to use validity scales, which would have added to the reliability of the measurements. This might have increased the possibility of type 2 errors in our results.

Comparison with the existing literature By definition, the MMPI subscale Hypochondriasis measures perceptions of and the preoccupation with health and health issues. Our study cannot clarify the suggested mechanisms behind the association between somatic concerns and CVD risk. There are several possible explanations, of which some or all may co-exist. First, somatic concerns may be an indicator of the general stress level, which may in turn increase CVD morbidity. Secondly, somatic concerns may reflect an underlying subclinical somatic disease. Nevertheless, general ill-being was a stronger predictor of CVD than specific somatic symptoms such as pain or sleep problems. In our study, the marital status and socioeconomic status, including the educational level, attenuated the association between depressive traits and the CVD incidence more than other variables, which is consistent with earlier literature on depressive disorders and CVD [4].

Table 4 Factor loadings and communalities based on a factor analysis with varimax rotation for 41 items from the MMPI subscales Depression and Hypochondriasis. General ill-being Being well in last years As able to work as before Physical health as good as friends Do not tire quickly Never pains over heart Never felt better than now Never notice heart pounding My memory is all right No problem with muscles twitching Feeling tight band about head Fullness in head most of the time Tender feelings on top of head Fitful and disturbed sleep Great deal of stomach trouble Discomfort in pit of stomach Sleeping without thoughts mostly Easy to keep balance in walking I have few or no pains Body have feelings Hardly ever pain in back of neck Seldom or never dizzy spells Waking up fresh and rested I brood a great deal Lacking in self-confidence Can't care for things Feeling useless at times Criticism or scolding hurts Sweating easily when embarrassed I work under a great deal of tension At times full of energy Capable and smart as others Feeling life worthwhile Afraid of losing mind Feeling weak much of the time Difficulty in starting to do things Not more nervous than others Happy most of the time Sometimes cheerful without reason Sometimes on top of the world Life is full of interests every day I am a good mixer a

Sleep and pain

Psychological well-being

Energy level

Cheerfulness

Communalities

.30a .63 .61 .42 .38

.59 .55 .58 .43 .41 .36 .32 .27 .22 .42 .40 .33 .39 .25 .33 .29 .24 .24 .41 .31 .26 .31 .43 .44 .32 .37 .31 .25 .28 .39 .38 .34 .32 .37 .31 .31 .40 .48 .46 .31 .25

.71 .70 .70 .61 .56 .50 .49 .42 .32

.31 .64 −.49 .39 .35

.60 .56 .54 .48 .47 .45 −.38 −.32 −.33a .38a −.36a −.33a −.31a

.34

−.33 .61 .57 .55 .51 .51 .43 .40

.33 .35

Counted to this particular factor due to content of the question despite of higher loading for other factor.

.56 .53 .52 −.44 −.43 −.42 .39 .42

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Table 5 Associations between the composite scales derived from MMPI subscales Depression and Hypochondriasis and intima–media thickness of the carotid artery in the linear regression model adjusted for age, smoking, alcohol consumption, physical activity, marital status, socioeconomic status, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, history of diabetes, BMI, systolic blood pressure and hs-CRP. Composite scales separately

General ill-being Sleep and pain Psychological well-being Energy Cheerfulness All composite scales

Composite scales simultaneously

β

t

p

R square

Adjusted R square

β

t

p

.038 .041 −.025 .003 −.027

1.44 1.60 −1.00 .13 −1.10

.15 .11 .36 .90 .27

.24 .24 .24 .24 .24

.23 .23 .23 .23 .23

.030 .025 −.018 −.003 −.04

0.93 .81 −.66 −.1.06 −1.67

.35 .42 .51 .19 .10

In the context of personality features, depressive and anxious traits and emotional vulnerability are often referred to as neuroticism. Neuroticism has associated with several parameters that directly or indirectly increase the risk of CVD, such as decreased cardiovascular stress reactivity [30]. Neuroticism and the tendency for a dysregulated autonomic nervous system has also been proposed to be influenced by similar types of genetic variance, which would explain their link with CVD [31]. In a recent review, no clear associations were reported between personality features and the metabolic syndrome [32]. In line with this, in our study, adjustments for metabolic factors did not greatly attenuate the associations between IMT and personality features. The relationship between depression and CVD is complex and likely to be bi-directional [33]. It is possible that depressive traits increase the risk of state-like depressive episodes and low mood, which in turn associate with CVD directly or via CVD risk factors [3,7]. Depression associates with increased inflammation [3], and inflammation moderated the association between depressive traits and CVD incidence. When the study sample was divided into three groups according to the hsCRP level, the impact of depressive traits on CVD incidence was highest among those with highest levels of hs-CRP. This may be consistent with assumption that people with particular subtypes of depression might be more vulnerable to CVD [3]. As inflammation is a known risk factor for

R square

Adjusted R square

.24

.23

CVD, it is likely that depression with inflammatory features associates more strongly with CVD [3]. Implications for future research The mechanisms behind the associations between personality features, CVD and CVD risk factors are still unclear. Inflammation is a shared aetiopathogenic factor with CVD, depression and depressive traits, and may be an important moderating factor between these associations. Future studies are needed to address other potential biological mediators of the connection between personality features and CVD risk. Declaration of interest None. Acknowledgments The authors wish to thank the personnel of the former National Public Health Institute of Finland for their valuable contribution to this

Table 6 Rate ratios (RRs) (95% confidence intervals) for receiving a diagnosis of cardiovascular disease (n = 766) during an average follow-up period of 25 years using Cox proportional hazards' models The composite scales derived from MMPI subscales Depression and Hypochondriasis were entered into the model separately with covariates. Those who received a cardiovascular disease diagnosis during the first two years of the follow-up were excluded. RRa (CI 95%) General ill-being

Sleep and pain

Psychological well-being

Model 1. Model 2. Model 3. Model 4. Model 5. Model 6 Model 1. Model 2. Model 3. Model 4. Model 5. Model 6 Model 1. Model 2. Model 3. Model 4. Model 5. Model 6

1.666 (1.076–1.265) 1.155 (1.065–1.253) 1.148 (1.058–1.247) 1.137 (1.048–1.233) 1.142 (1.052–1.238) 1.109 (1.020–1.205) 1.204 (.994–1.459) 1.219 (1.005–1.479) 1.145 (.940–1.394) 1.135 (.936–1.377) 1.195 (.985–1.449) 1.115 (.913–1.361) .990 (.942–1.040) .992 (.944–1.042) .992 (.944–1.042) .988 (.941–1.038) .982 (.935–1.932) .989 (.941–1.039)

RRa (CI 95%) Energy level

Cheerfulness

General ill-being Sleep and pain Psychological well-being Energy level Cheerfulness

Model 1. Model 2. Model 3. Model 4. Model 5. Model 6 Model 1. Model 2. Model 3. Model 4. Model 5. Model 6 Model 7. Model 7. Model 7. Model 7. Model 7.

1.072 (.994–1.156) 1.069 (.991–1.154) 1.053 (.974–1.137) 1.069 (.991–1.153) 1.078 (.999–1.162) 1.063 (.983–1.149) .961 (.895–1.033) .961 (.894–1.032) .950 (.884–1.021) .970 (.903–1.043) .975 (.907–1.047) .968 (.900–1.041) 1.127 (1.016–1.251) .935 (.737–1.188) 1.008 (.956–1.062) 1.066 (.975–1.166) .925 (.853–1.002)

Model 1: adjusted for age. Model 2: adjusted for age and lifestyle variables (alcohol consumption, smoking and physical activity). Model 3: adjusted for age and psychosocial variables (marital status and adulthood socioeconomic status). Model 4: adjusted for age and somatic variables (systolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, history of diabetes and body mass index). Model 5: adjusted for age and hs-CRP. Model 6: adjusted for all the above-described variables (i.e., age, smoking, alcohol consumption, physical activity, marital status, socioeconomic status, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, history of diabetes, BMI, systolic blood pressure and hs-CRP). Model 7: adjusted for all the above-described variables (i.e., age, smoking, alcohol consumption, physical activity, marital status, socioeconomic status, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, history of diabetes, BMI, systolic blood pressure and hs-CRP) and with all the composite scales entered into the model together.

T. Tolmunen et al. / Journal of Psychosomatic Research 79 (2015) 207–213

study in the data collection phase. No financial support was received by any of the authors for conducting this study.

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