Vital exhaustion, depressive symptoms and serum triglyceride levels in high-risk middle-aged men

Psychiatry Research 187 (2011) 363–369

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Psychiatry Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / p s yc h r e s

Vital exhaustion, depressive symptoms and serum triglyceride levels in high-risk middle-aged men Cornel Victor Igna a, Juhani Julkunen a,⁎, Hannu Vanhanen b a b

Department of Behavioural Sciences, University of Helsinki, Helsinki, Finland The Finnish Heart Association, Helsinki, Finland

a r t i c l e

i n f o

Article history: Received 8 April 2009 Received in revised form 15 October 2010 Accepted 19 October 2010

Keywords: Vital exhaustion Depression Serum lipids Triglycerides

a b s t r a c t The role of elevated serum triglyceride level as a risk factor of coronary artery disease is well established. Previous results have also indicated that depression or depressive symptoms and vital exhaustion correlate with triglyceride levels. The aim of this study was to investigate the associations of depressive symptoms, vital exhaustion, and health behavior with serum triglyceride levels. The study sample comprised 444 high-risk middle-aged men. Participants completed self-report questionnaires before laboratory tests. Triglyceride concentrations were measured by the enzymatic method. Vital exhaustion and depression were associated with unhealthy lifestyles and triglycerides. Vital exhaustion and depression were closely correlated constructs with comparable relations with known coronary artery disease risk factors. When comparing vital exhaustion (VE) to Beck Depression Inventory (BDI), however, the first one had a stronger correlation with triglycerides (TG), and also, path analyses showed a direct link from vital exhaustion to body mass index but not from depression. Both vital exhaustion and depression are related to triglyceride levels. The relations are partly mediated by unfavorable lifestyles. Although vital exhaustion is not so commonly assessed as depression, results of this study support the importance of vital exhaustion as a health-related psychological risk factor. © 2010 Elsevier Ireland Ltd. All rights reserved.

1. Introduction The role of elevated serum triglyceride level as an indicator of the metabolic syndrome as well as a risk factor of coronary artery disease (CAD) is well established (Sarwar et al., 2007). At the same time growing research evidence suggests that depression (Huang and Chen, 2004; Brunner et al., 2006) or depressive symptoms and vital exhaustion correlate with triglyceride levels (Kop et al., 1998). From this perspective triglycerides – together with other serum lipids – seem to be a probable mediator in the depression/exhaustion–CAD association. In general, elevated triglyceride concentrations are considered as a valid marker of unhealthy lifestyles. There are, however, several contradictive results in this area; depressive symptoms (Olusi and Fido, 1996; Deisenhammer et al., 2004) or vital exhaustion (Koertge et al., 2003) are not consistently found to associate with level of triglycerides (TG). One possible explanation for the contradictive results could be the use of different covariates in different studies. Triglyceride levels are influenced by age (Keltikangas-Jarvinen et al., 1999; Wakabayashi and KobabaWakabayashi, 2002), and also by body mass index (BMI) (Dunbar and

⁎ Corresponding author. Department of Psychology, P.O. Box 9 (Siltavuorenpenger 20 D), 00014 University of Helsinki, Helsinki, Finland. Tel.: + 358 9 191 29503; fax: + 358 9 191 29521. E-mail address: juhani.julkunen@helsinki.fi (J. Julkunen). 0165-1781/$ – see front matter © 2010 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.psychres.2010.10.016

Rader, 2005), smoking (Tirosh et al., 2007), inactivity, alcohol consumption (Dunbar and Rader, 2005) and diet (Silva et al., 1996; Bhargava, 2006; Gorinstein et al., 2006). Furthermore, in one study it was found that two of these variables, age and alcohol consumption, did not have a linear relationship with triglycerides (Wakabayashi and Kobaba-Wakabayashi, 2002). Another unclear aspect is the depression–vital exhaustion relation. While it has been agreed that depression and vital exhaustion are related, there still is an ongoing debate on whether to consider them as overlapping or distinct concepts. Furthermore, vital exhaustion has been found to precede depression or to represent a broader concept than depression (Kopp et al., 1998). Some studies have indicated that depression and vital exhaustion associate differently to coronary disease risk factors (Lahlou-Laforet et al., 2006), but again, in another study (Wojciechowski et al., 2000), no significant differences were found. While the impact of depression on lifestyle factors is satisfactorily understood (Golden et al., 2004; Lett et al., 2004; Wulsin et al., 2005) the role of vital exhaustion is not so clear. There are studies that suggest a positive relation between vital exhaustion and smoking (Kopp et al., 1998; Schwartz et al., 2004), alcohol consumption (Conduit et al., 1998) and inactivity (Brezinka et al., 1998) but in some other studies no relations of vital exhaustion with these variables were found (Bages et al., 1999; Koertge et al., 2003). Moreover, we could not find any article that had explored the relation between vital exhaustion and diet.

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Hypothetical relations between all these previously mentioned groups of variables are summarized in the theoretical model illustrated in Fig. 1. This model was used to test the direct and indirect effects of vital exhaustion and depression on triglyceride levels. Due to the crosssectional design of the study it is inappropriate to infer causality relations between any variables of the model. Consequently, although our theoretical model seems to suggest that depression or vital exhaustion affects BMI, also the opposite direction of effects is possible, i.e., that high BMI affects depression or vital exhaustion. A related theoretical model has been used to test the relation between depression and cholesterol fractions (Igna et al., 2008). Because this model is a theoretical one it does not include any indication that these two concepts would be working in the same way or not. The indirect link assumes that vital exhaustion and/or depression relates to triglycerides through lifestyle factors (Barth et al., 2004; Lett et al., 2004). In this context, smoking, alcohol consumption, diet, and exercise habits are considered as the most important lifestyle mediators. The previously established strong relations between lifestyle variables and body mass index and between body mass index and triglyceride levels suggest that body mass index is an important mediating factor between lifestyle and triglyceride levels. Education and age were controlled for as confounding variables. Education was assumed to confound the relation of vital exhaustion/depression with lifestyle factors. Age may confound the relation of vital exhaustion/ depression with body mass index and triglycerides (Raikkonen et al., 1994; Palinkas et al., 1996). Furthermore, age has been shown to affect smoking behavior (Osler et al., 1998) and diet (Drewnowski et al., 1997). In sum, the aim of the present study was to investigate the possible direct or indirect relationships of depressive symptoms and vital exhaustion with serum triglyceride levels. 2. Methods 2.1. Design and procedures The present study is a sub-study of the Helsinki Metabolic Syndrome Prevention Trial, which was an uncontrolled preventive trial aimed at improving prevention of metabolic syndrome, type 2 diabetes and cardiovascular diseases by developing a practical method for primary health care. The goals were to screen and find men with a cluster of cardiovascular risk factors and to offer them individual counseling. An invitation letter with questionnaires was mailed to 2990 middle-aged, male Helsinki citizens. It was a population sample, all men of the age cohorts of 40, 45, 50, and 55 years living in the North-Eastern district of the city of Helsinki were invited to participate. Of them, 1288 (43.1%) participated in a screening visit. All participants were asked to answer questionnaires dealing with lifestyle factors and psychosocial risk factors. Study nurses from the Helsinki Heart District interviewed all participants and recorded basic medical measurements. Supplementary laboratory tests, including TG, were performed for high-risk participants. Evaluation of risk was based on a risk index that included five factors: body mass index, total serum cholesterol, blood pressure (systolic and diastolic), smoking habits, and physical activity. The risk evaluation method originated from the North Karelia project and has been later widely used in Finland. The method has been further developed and modified by Ketola and Klockars (1999). Based on their previous study a cut-off at 4.5 points or above was used to indicate high risk (see the Appendix). Questionnaires and laboratory tests were repeated six months later for the high-risk group. Counseling about diet, exercise or smoking cessation was based on the individual risk profile.

Data collection was carried out between May 2001 and June 2004. The ethical approval was attained from the Ethical Committee of the Helsinki University Central Hospital (HUS) on the 24th of April 2001. 2.2. Sample A total of 1288 men from age cohorts of 40, 45, 50, and 55 years were screened for the preventive trial. All cases with a risk index below 4.5 were excluded from the present analysis because TG values were not available. From the remaining sample (N = 537) those with missing triglyceride values, missing depression or vital exhaustion scores, or unclear smoking status were excluded. The final study sample was 444 men (82.68% from all high-risk cases); the mean age was 47.83 years (S.D., 5.33). The means and standard deviations of the five factors in the risk index in this sample were body mass index, M = 28.468; S.D. = 4.74, total serum cholesterol, M = 5.64 mmol/L; S.D. = 1.19, blood pressure — systolic, M = 140.08; S.D. = 16.11, blood pressure — diastolic, M = 91.04; S.D. = 10,44, smoking habits, M = 2.61; S.D. = 2.88, and physical activity, M = 3.07; S.D. = 1.28. Our previous report from this project was based on a sample of 893 men including also low risk subjects (Igna et al., 2008). A total of 320 persons were included in both studies. Using the 4-step classification of BDI (cut-off values: 0–9 = no depression, 10– 18 = mild depression, 19–29 = moderate depression, and 30–60 = severe depression) (Beck et al., 1988), 68.7% of the subjects had no depression, 23.2% had mild depression, 6.3% had moderate depression, and only 1.8% had symptoms of severe depression. In the study sample 12 persons had diabetes and nine persons had angina pectoris and/or symptoms of CHD. 2.3. Measures In the present sub-study three types of variables were used: socio-demographic (age and education), self-report measures (smoking behavior, exercise activity, alcohol consumption, healthy diet, depressive symptoms and vital exhaustion), and medical measurements (body mass index and triglycerides). Descriptive data of these variables are presented in Table 1. Education was assessed with a four-step question. In the present sample 24.3% had primary school, 35.7% had secondary school, 27.7% had graduated from high school, and 11.7% had graduated from a university. Smoking was evaluated with one question about the number of cigarettes smoked per day (0 = not at all, 1 = from time to time, 2 = 1–4 cigarettes per day, 3 = 5–9 cigarettes per day, 4 = 10–14 cigarettes per day, 5 = 15–19 cigarettes per day, 6 = 20– 24 cigarettes per day, 7 = 25–29 cigarettes per day, and 8 = 30 or more cigarettes per day). Inactivity was assessed with one question about the frequency of exercising per week (1 = 3 or more times per week, 2 = 1–2 times per week, 3 = 1 times per week, 4 = from time to time, and 5 = not at all). In the item used in this study the intensity of exercise was specified as “exercise continuing at least 30 min and causing sweating and breathlessness”. Alcohol consumption was evaluated with two questions referring to the frequency of alcohol consumption (1 = not at all, 2 = once or less per month, 3 = 2–4 per month, 4 = 2–3 per week, and 5 = over 3 times per week) and quantity of alcohol consumed per drinking episode (1 = not at all, 2 = 1–2 drinks, 3 = 3–4 drinks, 4 = 5–6 drinks, 5 = 7–9 drinks, and 6 = over 9 drinks). Alcohol variable used in these analyses is the product of code numbers from questions of frequency and quantity of alcohol consumption. Inactivity and alcohol measures were taken from the North Karelia project protocol (Matilainen et al., 1994; Pekkanen et al., 1995), (http://www.thl.fi/thl-client/pdfs/ 731beafd-b544-42b2-b853-baa87db6a046). Diet was evaluated with questions about how often a specific aliment was consumed per week (0 = not at all, 1 = 1–2 days, 2 = 3–5 days, and 3 = 6–7 days). Healthy diet variable was calculated as a sum score of these answers for specific aliments consumed (muesli/cereals, rice/pasta, fish, fresh vegetables, and fruits). The questions for diet were based on the Nordic and Finnish dietary recommendations: (http://www.norden.org/en/publications/publications/2004-013/excerpt and http:// wwwb.mmm.fi/ravitsemusneuvottelukunta/Julkaisut_ENG.htm). Depressive symptoms were assessed using the 21-item Beck Depression Inventory (BDI). It has been widely used in assessment of psychological risks in relation with

Fig. 1. Theoretical model of the vital exhaustion/depression–triglycerides relation.

C.V. Igna et al. / Psychiatry Research 187 (2011) 363–369

significant (p N 0.05) χ2-test indicate an acceptable model (Kline, 2005). Parsimony goodness-of-fit index (PGFI) was also used as a parsimonious fit measure. Its value ranges from 0 to 1, with higher values indicating better fit.

Table 1 Descriptive of self-report measures and medical variables.

Inactivity Smoking Alcohol Healthy diet BDI VE BMI TG (mmol/L)

365

M

S.D.

3.07 2.61 12.03 5.49 7.33 11.32 28.46 1.71

1.28 2.88 5.65 2.41 7.59 10.14 4.74 0.93

Note: For categories of diet and inactivity see the section “Measures”. The mean of smoking equals the mean of the 8-step item explained in the Methods section. The mean of alcohol variable represents a product of frequency and quantity of alcohol consumption. coronary artery disease (Richter et al., 1998). In this study the Cronbach's alpha coefficient for internal consistency was 0.90. Vital exhaustion was assessed using the 21-item Maastricht Questionnaire. The scale has been used as a measure of vital exhaustion as predictor of myocardial infarction (Appels, 1990). Appels' vital exhaustion scale comprised three main characteristics: lack of energy, increased irritability and sleep problems. The two common elements of depression, i.e., lowered self-esteem and feelings of guilt, are not included in this scale. The internal consistency (Cronbach's alpha) has been shown to be good or excellent, and in the present study it was 0.92 indicating an excellent reliability of the scale. 2.4. Medical variables Triglyceride level was estimated by an enzymatic method. Blood samples were taken in the morning with at least 12 h of fasting before. The value of 2 mmol/L is generally used as a clinical cut-off point. Body mass index was calculated by the standard formula: kg/m2 (weight measured in kilograms was divided by squared height measured in meters). 2.5. Statistical analyses Associations of depressive symptoms and vital exhaustion with triglyceride levels were analyzed using correlation analyses and path analyses. Statistical analyses were performed using the SPSS 15.0 software. The hypothetical models of the predictors of triglycerides were evaluated by using path analysis of the Lisrel 8.50 software (Jöreskog and Sörbom, 1993). Before evaluation of path models with the Lisrel 8.50 software, all missing data (3 cases for education, 6 cases for exercise, 7 cases for alcohol consumption, and 9 cases for diet) were processed using the EM (expectation–maximization) algorithm (Enders, 2006) available in SPSS 15.0 software. All factors in path analyses were used as continuous variables. Education and age were included in path analyses as possible confounding factors. Due to the complexity of the path analyses, and because relations between alcohol, inactivity, smoking and diet are well established, the correlations between lifestyle factors were not introduced in the model. Because the distributions of depression and vital exhaustion were positively skewed, these variables were transformed for path analysis using the root square procedure. For skewness and kurtosis the transformed variables had values below 1.0. The chi-square test (χ2), the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the goodness-of-fit index (GFI) were the fit indexes used to evaluate the model. The RMSEA value b0.06, CFI N 0.95, GFI N 0.90 and a non-

3. Results The correlation matrix of all variables controlled for age and education as well as the zero-order correlations are depicted in Table 2. Triglycerides correlated with vital exhaustion and depression but depression showed a significant correlation only when controlled for age and education. The strongest correlation was observed between depression and vital exhaustion scores (see Table 2). To investigate the hypotheses of this study, path analyses were used to evaluate the relations between these variables. Separate models for depression (Fig. 3) and vital exhaustion (Fig. 2) as main predictors of lifestyles and triglycerides were evaluated. Finally, a model including both depressive symptoms and vital exhaustion (Fig. 4) was tested. In our theoretical model the effect of vital exhaustion or depressive symptoms on triglycerides was supposed to work through lifestyle risk factors and body mass index as mediating factors. Further, independent direct links from vital exhaustion and depression to body mass index and triglycerides were expected to emerge as illustrated in the theoretical model presented in Fig. 1. Education and age were controlled for as confounding factors. Because the interrelations of diet, alcohol, inactivity and smoking are well established, the correlations between lifestyle factors were not introduced in the models. Also, in the third model, because the strong relation between depressive symptoms and vital exhaustion is well known and because this strong correlation would generate a model with unfavorable fit indexes, their correlation was not introduced in the model. Fit indices for the models are presented in Table 3. In the third model, depression–TG relation was reduced to zero meanwhile the relation between vital exhaustion and TG became non-significant (T-value 1.68, P = 0.31, d.f. = 4) but had the same standardized solution (0.13) as in the first model. One problem with the path analyses is the small value of PGFI, which is an effect of including in the models statistically nonsignificant relations required by the hypothesized theoretical model. The structure of significant relations as well as the fit indexes for all of the models was to a great extent similar. A significant exception was that vital exhaustion had a direct association with BMI while for depression no significant direct impact on BMI was indicated. Taken together, VE as compared to BDI had a stronger correlation with TG, and also in path models it showed a significant direct link to BMI. These results support the importance of vital exhaustion as a health-related psychological risk factor and clarify some of the possible mediating mechanisms in the previously established VE–CAD relation.

Table 2 Correlations of self-report measures and medical variables. TG TG Inactivity Smoking Alcohol Diet BMI BDI VE Age Education

0.016 −0.075 0.009 0.038 0.221⁎⁎ 0.090 0.114⁎ −0.006 0.031

Inactivity

Smoking

0.020

−0.070 0.086

Alcohol 0.012 0.008 0.212⁎⁎

0.111⁎ 0.017 −0.204⁎⁎ 0.027 0.241⁎⁎ 0.242⁎⁎

0.223⁎⁎ −0.261⁎⁎ −0.286⁎⁎ 0.146⁎⁎ 0.129⁎

−0.074 −0.155⁎ 0.139⁎⁎ 0.146⁎⁎

−0.033 −0.105⁎

−0.011 −0.240⁎⁎

−0.008 −0.071

Diet

BMI

BDI

VE

0.033 −0.183⁎⁎ −0.220⁎⁎ −0.059

0.220⁎⁎ 0.042 −0.268⁎⁎ −0.148⁎ −0.042

0.095⁎ 0.240⁎⁎ 0.130⁎ 0.135⁎ −0.184⁎⁎

0.119⁎ 0.240⁎⁎ 0.110⁎ 0.142⁎ −0.174⁎⁎

0.068 −0.190⁎⁎ −0.182⁎⁎ 0.060 0.202⁎⁎

0.047 0.043 0.089 0.054 0.101⁎

Due to missing data n varied between 424 and 444. Note: above diagonal partial correlations controlled for age and education, below diagonal bivariate correlations. ⁎ Correlation is significant at the 0.05 level (2-tailed). ⁎⁎ Correlation is significant at the 0.01 level (2-tailed).

0.832⁎⁎ 0.132⁎⁎ −0.119⁎

Age

0.096 0.827⁎⁎ 0.140⁎⁎ −0.128⁎⁎

−0.176⁎⁎

Education

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Fig. 2. Significant (solid lines) and non-significant (dotted lines) relationships of the path model with vital exhaustion; only significant coefficients are presented.

4. Discussion In this study, depressive symptoms and vital exhaustion had almost similar relations to lifestyle variables, except smoking. Both of them were related to increased alcohol consumption, lack of exercise and to a less healthy diet. Only few studies in this context have tested the mediational role of body mass index (Horsten et al., 1997; Koertge et al., 2003). This is surprising because in numerous studies body mass index is related to depression (Skilton et al., 2007; Toker et al., 2007; Vaccarino et al., 2008) as well as to vital exhaustion (Prescott et al., 2003); and furthermore, considering the fact that body mass index is related to elevated triglyceride levels (Knox et al., 1996; McCaffery et al., 2003). Correlations of depression and vital exhaustion with triglycerides indicate that in our sample the relation between vital exhaustion and triglycerides is stronger than the relation between depression and triglycerides. Moreover, in the path analyses only vital exhaustion had a direct relation with BMI. These results support the argument that these two constructs do share a significant common area but they do not overlap completely, as already some previous studies have suggested (Lahlou-Laforet et al., 2006). Depression has some specific symptoms (e.g. guilt and loss of self-esteem) that differentiate it from vital exhaustion. Therefore, as vital exhaustion is missing the cognitive aspects specific for depression, it could be speculated that

the vital exhaustion scale is more focused on physiologic and mood aspects (fatigue, sleep disturbances, and irritability), and in this way it could be more closely related with BMI and TG levels. In the path analysis that included both vital exhaustion and depression only vital exhaustion retained a significant relationship with inactivity while depression had no significant relationship with lifestyle variables. Also, the depression–TG relation was reduced to zero meanwhile the relation between vital exhaustion and TG remained close to significant suggesting a stronger relation for VE to TG. In sum, it seems that including both vital exhaustion and depression in the same model is not improving the model but actually may disguise the specific associations of these constructs with health behavior. The inverse relation between smoking, alcohol and BMI found in the correlations and path analyses is not a new finding. Smoking can reduce appetite and in this way may lead to a reduced BMI (Chiolero et al., 2008), and also alcohol can have the same effect (Suter, 2005). Unexpectedly, we did not find lifestyle variables with a positive correlation with BMI. The most plausible explanation could be that the sample included only high-risk cases. In our previous report based on a larger subsample of the original trial, a positive correlation between BMI and inactivity was found (Igna et al., 2008). A possible biological mechanism relating vital exhaustion to diet style, body mass index and triglycerides is proposed in a study by

Fig. 3. Significant (solid lines) and non-significant (dotted lines) relationships of the path model with depression; only significant coefficients are presented.

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Fig. 4. Significant (solid lines) and non-significant (dotted lines) relationships of the path model with depression and vital exhaustion; only significant coefficients are presented.

Koertge et al. (2003). They suggest that long-term stress and unhealthy diet can through several links generate hyper-insulinemia, and this may lead to a higher level of triglycerides. In another study, results suggested that the incidence of different mental problems simultaneously (post-traumatic stress disorder together with depression) was related to higher triglyceride levels as compared to those who had only one diagnosis or none (Trief et al., 2006). It was also found that depressive people (with or without other diagnosis) also had a higher body mass index than healthy people. From these results it is difficult to discern how much of the impact from mental problems on triglyceride levels acted through some direct physiological mechanisms or were mediated through lifestyle factors that modified body mass index. Actually, our results from separate models for depression and vital exhaustion as predictors of lifestyles and triglycerides indicate that both links are plausible. In this study a direct but also a mediated relationship of vital exhaustion and depression with triglycerides was found. The significant relationship between vital exhaustion–body mass index– triglycerides supports the argument for the importance of body mass index in relation with triglycerides. Considering that body mass index and vital exhaustion can be managed through specific counseling and health promoting programs gives hope for an alternative approach towards reducing levels of triglyceride and cardiovascular disease incidence.

4.1. Study limitations The main limitations of this study are related to the cross-sectional nature of the survey and reliance on self-reports of depressive symptoms as well as of lifestyle data. Furthermore, in the present study only middle-aged, high-risk men were included. Future studies should include women and larger variation of age and risk factor levels.

4.2. Conclusion

Table 3 Fit indexes of the path analyses.

Model VE Model BDI Model BDI and VE

Among fit indexes used, PGFI had a relatively small value although there is no exact cut-off point for this fit index. Together with the high GFI value, it seems probable that the small value of PGFI is due to the complexity of the path models. This complexity resulted mainly from the numerous relations that were indicated by the theoretical model. That affected the parsimony of the models because not all theoretical relations could be confirmed by path analyses. The level of explained variance of BMI remained rather low in our models. Perhaps adding new variables, such as unhealthy diet, a larger proportion of variance in BMI could be explained. Furthermore, despite the numerous statistically significant paths in our models, the amount of explained variance of TG was only about 6% indicating that some important determinants of TG could not be assessed in this study. In this study symptoms of depression were assessed with BDI because this scale has been the most widely used method also in studies of CVD risk factors offering validated reference values, and it has been shown in numerous studies to be a reliable and valid instrument to assess symptoms of depression. However, the use of this scale contains a potential limitation because it has been originally developed for psychiatric patients. In future studies the use of other scales developed for medical patients should be considered. Furthermore, because this sample included only people who were at high risk for CVD and the mean BDI score is quite low, these findings may not be generalizable to people with clinical depression, or patients with CVD. Assessment of nutrition in this study was based on the frequency of consuming certain aliments. Due to study design it was not possible to control for the general amount of food, and therefore our measure should be considered only as tentative estimate of healthy nutrition. Finally, because this study was based on a cross-sectional database and engaged statistical techniques from which no directionality can be suggested, the causal role of any variables can be only speculated.

Chi-square

RMSEA

NFI

CFI

GFI

PGFI

4.78 (d.f. = 4) P = 0.31 4.48 (d.f. = 4) P = 0.35 4.97 (d.f. = 4) P = 0.29

0.021 0.016 0.023

0.98 0.99 0.99

1.00 1.00 1.00

1.00 1.00 1.00

0.09 0.09 0.07

The root mean square error of approximation (RMSEA); the normed fit index (NFI), the comparative fit index (CFI), the goodness-of-fit index (GFI), the parsimony goodnessof-fit index (PGFI).

In this study of high-risk men, the results indicate both a direct and a mediated association for vital exhaustion and depression with triglyceride concentrations. Vital exhaustion has also a direct association with body mass index. Depressive symptoms and vital exhaustion seem to exhibit quite similar but not identical associations with several key risk factors of CVD. Although vital exhaustion is not so commonly assessed as depression, results of this study strengthen

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the evidence for the role of vital exhaustion as a possibly important risk factor of metabolic syndrome and CAD. Acknowledgments We gratefully acknowledge the contribution of Helsinki Heart District personal in collecting the data. The main study was funded by the Finnish Slot Machine Corporation.

Appendix Risk index components

Risk screening-index Risk index

BMI kg/m2

Smoking

Physical inactivity

BP Sys

BP Dias

Cholesterol tot.

0 0.5 1 1.5 2 2.5 3 3.5 4

−24.9 25–26.9 27–28.9 29–30.9 31–

0 Sometimes 1–4/day 5–9 10–14 15–19 20.24 25–29 30–

≥3×/week 1–2×/week 1×/week Sometimes Never

−129 130–139 140–149 150–159 160–

−79 80–89 90–94 95–99 100–

−4.9 5.0–5.4 5.5–5.9 6.0–6.4 6.5–6.9 7.0–7.4 7.5–7.9 8.0–8.4 8.5–

≥4.5 = high risk; Total:_ _ _ _ _.

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