a Department of Dietetics and Nutrition, Harokopio University, Athens, Greece
b First Cardiology Clinic, School of Medicine, University of Athens, Athens, Greece
c Psychiatric Clinic, School of Medicine, University of Athens, Athens, Greece
Received September 25, 2003;
revised January 15, 2004;
accepted January 22, 2004
* Corresponding author. 46 Paleon Polemiston St., Glyfada, Attica 166 74, Greece. Tel.: +30-2109603116; fax: +30-2109600719
E-mail address: d.b.panagiotakos{at}usa.net
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Abstract |
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Methods A random algorithm was developed and stratified by genderage and multistage sampling was performed during 20012002. In this study, we analysed data from 453 men (1989 years old) and 400 women (1884 years old). Inflammation markers were C-reactive protein (CRP), serum amyloid A, white blood cell (WBC) and total platelet counts; and coagulation factors including homocysteine and fibrinogen. Depression was assessed with the Zung Self-Rating Depression Scale (ZDRS range 0100) after validation for the study population. A ZDRS score of 50 or more classified a person as mildly depressive. Statistical adjustments were made for risk factors (age, gender, smoking, diabetes mellitus, and physical activity level).
Results Women had significantly higher scores on the Zung depression scale than men (47±9 vs. 43±10, ). Thus, 21% of men and 27% of women had mild depression, while 4% of men and 6% of women had severe depressive symptomatology. The depression scale correlated positively with C-reactive protein levels (
), white blood cell count (
), and fibrinogen (
) in both genders after adjustment for control variables.
Conclusion This study revealed that depression was associated with inflammation and coagulation factors in cardiovascular disease-free people, suggesting a possible pathway leading to an increased frequency of events of coronary heart disease in depressive individuals.
Key Words: Depression Atherosclerosis Inflammation Coagulation
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Introduction |
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In this study, we sought to evaluate the relationships between depressive symptoms and coagulation and inflammation factors (such as C-reactive protein, serum amyloid A, white blood cell and total platelet counts, as well as homocysteine and fibrinogen) related to cardiovascular risk in healthy men and women from the general population.
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Methods |
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Assessment of depression
Depressive symptomatology was assessed using a translated and validated version of the Zung Self-Rating Depression Scale (ZDRS).13 The ZDRS is a well-known self-rating scale used worldwide for the measurement of depression. It is a self-reporting instrument and was originally developed to assess depression symptoms without the bias of an administrator affecting the results. Higher scores on this scale are indicative of more severe depression.13 The ZDRS consists of 20 items covering affective, psychological, and somatic symptoms. The patient specifies the frequency with which the symptom is experienced (a little=1, some=2, a good part of the time=3, or most of the time=4). A subject with a ZDRS score below 50 is considered normal, a score of 5059 indicates mild depression, a score of 6069 indicates moderate depression, and a score of 70 or more is considered to be indicative of severe depression.13
Previous investigations indicate that the ZDRS is a valid and sensitive measure of clinical severity in depressed patients and support its continued use as a research instrument.14 Moreover, the sensitivity of the ZDRS has been found to be adequate14 and the scale was able to significantly differentiate four severity groups classified on the basis of the global rating.
Biochemical measurements
Blood samples were drawn from the antecubital vein between 8:00 and 10:00 a.m., with the subject in a sitting position and after a 12-h fast and alcohol abstinence. The biochemical evaluation was carried out in the same laboratory, which followed the criteria of the World Health Organisation Lipid Reference Laboratories. High-sensitivity C-reactive protein levels, fibrinogen, and serum amyloid A levels were assessed by BNII Dade Behring automatic nephelometry. White blood cell and total platelet counts were made with automated cell counters. Serum total cholesterol was measured using a chromatographic enzymatic method in a Technicon automatic analyser RA-1000. The intra- and interassay coefficients of variation of cholesterol levels did not exceed 9%.
Demographic, clinical, and life style characteristics
The study questionnaire also included demographic information like age, gender, family status (married, divorced, widowed), financial status (average annual income during the past three years), and educational level. The educational level of the participants (as a proxy of social status) was measured by years of schooling and classified into three groups (Group I: <9 years, Group II: up to high school or technical college (1014 years), and Group III: university).
Information about smoking habits was collected using a standardised questionnaire developed for the study. Current smokers were defined as those who smoked at least one cigarette per day. Never smokers were those who had never tried a cigarette in their lives and former smokers were defined as persons who had stopped smoking more than one year earlier. In all multivariate statistical analyses, cigarette smoking habits were quantified using pack-years (cigarette packs per dayxyears of smoking). However, to correct for the amount of nicotine contained in various types of cigarettes smoked (i.e., light, heavy, very heavy), we assigned a weight to each different type of cigarette pack using 0.8 mg/cigarette as the standard. Physical activity was defined as leisure-time activity of a certain intensity (in kcal/min burned) and duration (minutes per session), at least once a week during the past year, and was graded in qualitative terms such as light (<4 kcal/min burned), moderate (47 kcal/min burned), and vigorous (>7 kcal/min burned). The rest of the subjects were defined as physically inactive. Body mass index was calculated as weight (kg) divided by standing height (m2). Obesity was defined as body mass index>29.9 kg/m2.
Arterial blood pressure was measured three times on the right arm (ELKA anaeroid manometric sphygmometer, Von Schlieben Co, West Germany) at the end of the physical examination, after the subject had been in a sitting position for at least 30 min. The systolic blood pressure level was determined by the first perception of sound (of a tapping quality). The diastolic blood pressure level was determined by phase V, when the repetitive sounds become fully muffled (disappear). Changes in loudness were not considered. Patients whose average blood pressure levels were 140/90 mm Hg or more or who were on antihypertensive medication were classified as hypertensives. In addition, for classification purposes hypercholesterolaemia was defined as total serum cholesterol levels greater than 200 mg/dl or the use of lipid-lowering agents, and diabetes mellitus was defined as blood sugar>125 mg/dl or the use of antidiabetic medication. However, in all statistical analyses continuous measurements of blood pressure and chemistries were used.
Consumption of non-refined cereals and products, vegetables, legumes, fruits, olive oil, dairy products, fish, pulses, nuts, potatoes, eggs, sweets, poultry, red meat, and meat products were measured as an average per week during the past year through a validated food-frequency questionnaire from the Department of Nutritional Epidemiology of our institute.15 The frequency of consumption was then quantified approximately in terms of the number of times a month a food was consumed. Alcohol consumption was measured by daily ethanol intake in wineglasses (100 ml and 12% ethanol concentration). Based on the Mediterranean diet pyramid that has been proposed by a Harvard-led team with substantial input from Greek scientists,16 we calculated a special dietary score that assessed adherence to the Mediterranean diet.17 Higher values of this score indicate adherence to the traditional Mediterranean diet (i.e., which is also characterised by moderate consumption of fat and a high monounsaturated:saturated fat ratio), while lower values indicate adherence to a "Westernised" diet.
Since depression affects men and women differently3,4 and several of the biological markers investigated differ between genders,17 we hypothesised that the effect of gender on the depressioninflammation/coagulation relationship may be significant. Thus, we present all the findings separately for men and women.
Details regarding the aims and the design of the ATTICA study have been presented elsewhere.17,18
Statistical analysis
Based on a statistical power calculation (using East 3, 2003, Cytel Software Corporation, USA), we found that the number of participants was adequate to evaluate greater than 0.5 two-tailed standardised differences in the inflammatory and coagulation markers investigated between depression groups. In particular, we achieved a statistical power 0.80 at
0.05 probability level (
-value).
Continuous variables are presented as mean values±standard deviation, while qualitative variables are presented as absolute and relative frequencies.
Univariate analysis was initially applied to test the associations between inflammation, coagulation markers (outcome), and ZDRS score (main effect), as well as the associations between various characteristics of the participants and both the outcome and main effect of interest. In particular, associations between categorical variables were tested using contingency tables and the calculation of Pearsons test. Comparisons between normally distributed continuous variables and categorical variables were made using the Student t-test and one-way analysis of variance (ANOVA), after testing for the equality of variances (homoscedacity). However, the association between the ZDRS score and other categorical variables was tested through calculations of the non-parametric MannWhitney and KruskalWallis criteria. Correlations between normally distributed continuous variables were evaluated by calculating the Pearson r-coefficient. Correlations between skewed continuous or discrete variables were evaluated using Spearmans
-coefficient. Moreover, the associations between the ZDRS score and the biochemical markers investigated and other continuous variables (age, body mass index, years of education) were performed by calculating the
-coefficient.
The associations between inflammation, coagulation markers (dependent variables), and ZDRS score (independent variable) were also tested through multiple linear regression analysis. The results obtained from the regression models are presented as -coefficients and the standard error of the coefficient. The explanatory variables entered in each multivariate model were ZDRS score and (a) the variables that showed a significant association with dependent variables in univariate analysis (at 15% significance level) of food items consumed (because an association with inflammation and coagulation markers has been reported), (b) those associated with ZDRS score, and (c) various first-order interactions with the score. However, it should be mentioned that since lipid-lowering agents have been associated with decreased cholesterol levels and elevated cholesterol levels have been associated with increased levels of inflammatory markers, information about the use of lipid-lowering medications was included as a separate variable in statistical analyses. A backward elimination procedure was applied to all multivariate models (using
as the threshold for removing a variable from the models). Normality tests were applied using the KolmogorovSmirnov criterion. C-reactive protein and homocysteine levels were log-transformed due to their skewed distributions. The assumptions of linearity for continuous independent variables and constant variance of the standardised residuals were assessed by plotting the residuals against fitted values. We also calculated R2 to determine how well each fitted model predicts the dependent variables.
All reported -values are based on two-sided tests and compared to a significance level of 5%. However, due to multiple significance tests, we used the Bonferroni correction (because the number of comparisons was less than 10) to account for the increase in the Type I error. SPSS 11.0 software (SPSS Inc. 2002, Illinois, USA) was used for all statistical calculations.
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Results |
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Because several factors may confound the relationships between ZDRS score and outcome we performed multivariate analyses. Variables also included in the models presented in Table 5 were body mass index, physical activity status, total cholesterol levels, food items consumed, use of lipid-lowering agents and anti-hypertensive medication, as well as pack-years of smoking, since we found that they were more or less closely associated with all the inflammation and coagulation markers investigated (, data not presented in text). We also included age, years of education, and blood pressure levels because they correlated with ZDRS score in the univariate analysis (Table 2). We finally tested for the significance of the interaction terms of ZDRS score with age, gender, years of education, and physical activity status. The results of multivariate analyses are presented separately for men and women since the interaction term between ZDRS score and gender was highly significant (
). We found that C-reactive protein, white blood cell counts, and fibrinogen levels were independently associated with ZDRS score even after adjusting for the aforementioned confounders (Table 5).
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Discussion |
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Depression and inflammatory markers
Recent evidence suggests that inflammatory processes favour the development and progression of thrombotic complications of atherosclerosis.7 Elevated levels of inflammatory biomarkers in apparently healthy populations have been shown to have predictive value for future cardiovascular events.8 In past years few studies aimed to assess the relationship between depressive symptomatology and inflammation processes.1921 Miller et al.20 observed a 40% increase in C-reactive protein and a 36% increase in interleukin-6 levels between depressive and non-depressive adults. Furthermore, similar results were observed in elderly individuals.19 We also found a strong relation between depression and levels of C-reactive protein in both men and women. In particular, compared to non-depressive subjects, men with severe depression had 46% higher C-reactive protein levels while women with severe depression had 57% higher C-reactive protein levels. However, several investigators have related depression with increased smoking habits, body fat, and physical inactivity.19,20 Since smoking, body fat, and physical activity also have a relationship with C-reactive protein levels,18,22,23 it could be hypothesised that they may have a confounding role in the link between depression and inflammatory processes. However, the associations observed remained statistically significant even when we adjusted for several potential confounders like age, body mass index, dietary and smoking habits, as well as physical activity status (Table 5). We expanded the association between depression and inflammation markers by involving white blood cell counts. In particular, we observed that people with significant depressive symptoms had, on average, 11% higher white blood cell counts as compared to normal persons (Table 4). This association remained statistically significant even after adjusting for several potential confounders (Table 5).
It has been suggested that depression promotes systemic inflammation and increases plasma levels of inflammatory cytokines like IL-6 and acute-phase proteins like C-reactive protein.1921 Cytokine synthesis and release activated the inflammatory response system, provoking neuroendocrine changes that are interpreted by the brain as stressors and induce the hyperactivity of the hypothalamicpituitaryadrenal axis and contribute to the development of depression.2427 Primary preinflammatory cytokines (like interleukin 1, TNF-) stimulate the production of a secondary preinflammatory cytokine (interleukin-6) that stimulates the production of acute-phase proteins by the liver, such as C-reactive protein and serum amyloid A.26,27 However, in order to claim a causal relationship between inflammatory processes and depression at the population level, much remains to be learned from future investigations in the field of clinical epidemiology.
Depression and coagulation factors
It has also been suggested that depression leads to high levels of acute-phase proteins, like fibrinogen.25 In addition, fibrinogen forms the substrate for thrombin, is essential for platelet aggregation, modulates endothelial function, and promotes smooth-cell proliferation and migration.28 Thus, fibrinogen is involved in atherothrombogenesis and is an important coagulation marker of cardiovascular disease. We observed that both men and women with even mild depressive symptoms had significantly higher fibrinogen levels (Table 4). Men with severe depression had, on the average, 17% higher fibrinogen levels, while women had 15% higher fibrinogen levels compared to non-depressives. However, depression is associated with elevated levels of cardiovascular factors and life style characteristics, like smoking, obesity, sedentary life, and alcohol consumption.14 Thus, the effect of depression on fibrinogen levels may be confounded by these life style risk factors. After taking into account several biological and non-biological factors related with health behaviour, like smoking, physical activity, and dietary habits, we confirmed the independent association between depression and fibrinogen levels (Table 5). Furthermore, we observed that one of the components of depressive symptomatology, feelings of fatigue, was positively related with increased fibrinogen levels in both genders.
Von Känel et al.29 suggests that in healthy individuals, acute mental stress simultaneously activates coagulation within a physiological range, but in patients with atherosclerosis and impaired endothelial anticoagulant function, procoagulant responses to acute stressors may outweigh anticoagulant mechanisms. Thus, based on these hypotheses, associations between psychological factors and several coagulation variables related to atherosclerosis may provide a plausible biobehavioural link to coronary artery disease.
At this point, it should be noted that some investigators have reported that there is no association between measures of depressive symptoms and markers of immune activation or inflammatory response.30 They suggest that there are problems of confounding by co-morbidity and other health-compromising behaviours. In our work the only significant confounder that may alter our findings was educational status. In a recent publication from the same study, we reported an inverse association between educational status and several cardiovascular risk factors, including inflammation and coagulation markers.30 These associations were explained mainly by the adoption of an unhealthy life style (including heavy smoking, physical inactivity, obesity, and non-compliance with medication) by individuals with a low educational level. In this study, we observed that people with mild-to-severe depressive symptoms had a lower educational status compared to non-depressives. Thus, low educational status and its consequences may partially explain the relationships observed between inflammatory and coagulation markers and depressive symptoms. However, our findings were independent of the effect of educational status and other life style-related behaviours.
Furthermore, it has been suggested that self-reported questionnaires, like the Zung depression questionnaire, are supposed to be used as screening tools and not as substitutes for an in-depth interview.13 The assessment of depressive symptomatology using these questionnaires may not be as accurate and valid for detecting people with psychological disorders. Thus, the proportion of men and women that were defined as having mild or severe depression may be under- or over-estimated by our study. Nevertheless, the observed associations between depression and inflammatory response may not be affected by the use of self-reported questionnaires. In addition, although we tried to exclude people with viral infections, a small percentage of participants may have had subclinical infections. The latter may influence our findings, in part, because it is possible that fatigue, loss of appetite, insomnia, and even dysphoric mood may be more common in patients with even minor viral infections. In addition, the presence of an infection may affect the levels of the inflammation and coagulation markers investigated.
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Conclusion |
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Acknowledgments |
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References |
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