Cardiovascular disease risk factors and depression in Korean women: Results from the fourth Korean National Health and Nutrition Examination Survey

Psychiatry Research 190 (2011) 232–239

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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

Cardiovascular disease risk factors and depression in Korean women: Results from the fourth Korean National Health and Nutrition Examination Survey Jong Eun Park, Jung Eun Lee ⁎ Department of Food and Nutrition, Sookmyung Women's University, 52 Hyochangwon gil, Yongsan gu, Seoul 140-742, Republic of Korea

a r t i c l e

i n f o

Article history: Received 3 November 2010 Received in revised form 8 May 2011 Accepted 24 May 2011 Keywords: Depression Body mass index Hyperlipidemia Cardiovascular disease

a b s t r a c t Depression is the fourth leading factor of disease burden for the global female population, but while increasing evidence has supported a contributing role of depression in cardiovascular disease, little is known about this association within the female population of Korea. We examined the association in a study of 5658 Korean women who participated in the fourth Korean National Health and Nutrition Examination Survey. A logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs). A total of 279 cases of depression were included. Cardiovascular disease risk factors were associated with higher odds of depression: ORs (95% CIs) were 3.99 (2.25–7.05) for current smokers with b 5 pack-years vs. never-smokers, 1.97 (1.18–3.30) for ≥28 vs. b 20 kg/ m2 of body mass index, 1.42 (1.03–1.95) for 100–125 vs. b 100 mg/dL of fasting serum glucose levels, and 2.10 (1.46–3.03) for a history of hyperlipidemia. Women with a history of two or three comorbid disorders (diabetes, hypertension, and cardiovascular disease) had a 1.63-fold higher OR for depression than women without any of these diseases. Korean women with depression had a greater prevalence of major risk factors for cardiovascular disease than women without depression. © 2011 Elsevier Ireland Ltd. All rights reserved.

1. Introduction The Global Burden of Diseases Study by the World Health Organization (WHO) found that depression was the fourth leading contributor to the global burden of disease in 2000 (Ustun et al., 2004). By 2020, depression is expected to become the second highest cause of disease burden worldwide behind only heart disease (Lopez and Murray, 1998). In Korea, the number of people suffering from depression has rapidly increased, and the nationwide Korean Health Insurance Review and Assessment Service estimated that medical treatment for depression exceeded 508,000 patients in 2009, an increase of 16.8% from 2005. As a result, the rate of depression has averaged annual increases of 4% between 2005 and 2009 (Korean Health Insurance Review and Assessment, 2010). Depression has been categorized as a purely psychiatric or mental disorder for an extensive period of time, but recent studies show that depression and depressive symptoms are associated with obesity (de Wit et al., 2010; Luppino et al., 2010), metabolic disorders such as diabetes (Mezuk et al., 2008), metabolic syndrome (Goldbacher et al., 2009), and cardiovascular disease (van Melle et al., 2004; van der

Kooy et al., 2007). Several studies also demonstrated that depression and its associated symptoms are major risk factors for the development of cardiovascular disease in otherwise healthy individuals as well as for recurrent cardiac events, and even death in patients with preexisting cardiovascular disease (Carney and Freedland, 2003; Lett et al., 2004; Whooley et al., 2008). In epidemiologic studies that examined risk factors for cardiovascular disease in relation to depression or depressive symptoms, smoking status (Klungsoyr et al., 2006), physical activity (Whooley et al., 2008), menstrual status and hormone replacement therapy (HRT) (Zweifel and O'Brien, 1997), and hypertension (Scalco et al., 2005) have been shown to be associated with prevalence or incidence of depression. The treatment of depression in patients with cardiovascular disease improves dysphoria and other symptoms of depression, and may increase longevity (Musselman et al., 1998). We investigated whether behavioral, hormonal, and metabolic factors leading to cardiovascular disease were associated with the prevalence of depression among women, who are more likely to experience depression than men (Piccinelli and Wilkinson, 2000; Kuehner, 2003), in a large nationally representative sample of Korean women. 2. Methods

Abbreviations: KNHANES, Korean National Health and Nutrition Examination Survey; HRT, hormone replacement therapy; BMI, body mass index; HDL, high-density lipoprotein; SBP, systolic blood pressure; DBP, diastolic blood pressure; FFQ, food frequency questionnaire; MET, metabolic equivalent of task; OR, odds ratio; CI, confidence interval. ⁎ Corresponding author. E-mail address: [email protected] (J.E. Lee). 0165-1781/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.psychres.2011.05.040

2.1. Study population This study is based on data from the fourth Korean National Health and Nutrition Examination Survey (KNHANES IV) conducted in 2007 and 2008 by the Korea Centers for Disease Control and Prevention (Oh et al., 2007). The KNHANES carried out a crosssectional study of a nationally representative sample of the non-institutionalized

J.E. Park, J.E. Lee / Psychiatry Research 190 (2011) 232–239 Korean civilian population to monitor trends toward the prevalence, awareness, treatment, and control of selected chronic diseases, to assess trends in nutritional status and risk behaviors; and to analyze risk factors for chronic diseases (Oh et al., 2007). The survey used a stratified, multistage probability sampling strategy based on selection of geographical region, gender and age groups (Oh et al., 2007). Extensive data on health and nutritional status were collected using standardized high-quality methods including health interviews, health examinations (physical examinations and clinical measurements), and dietary interviews. Professionally trained staff administered faceto-face interviews to obtain information on sociodemographic characteristics, lifestyle, health, and nutritional status using structured questionnaires designed for both households and individuals. Health examinations were performed in local community health centers and clinics, and anthropometric and laboratory indices were collected by standard protocols. In total, 18,983 individuals (6455 in 2007 and 12,528 in 2008) responded to the KNHANES IV, but 14,338 individuals (71.2% in 2007 and 77.8% in 2008) participated in the health interviews, health examinations, and dietary interviews. Study participants were limited to women at least 20 years of age, and as a result, the final sample for the present study was composed of 5658 women (1721 in 2007 and 3937 in 2008). The study was approved by the Korea Centers for Disease Control and Prevention Institutional Review Board. Written informed consent was obtained from all study participants. 2.2. Case ascertainment and definition To include clinically diagnosed depression cases, we limited cases of depression to women who self-reported ‘being diagnosed with depression by a physician’. Participants were asked, “Over your lifetime, have you ever had depression?” followed by “Have you ever been diagnosed with depression by a physician?” If this was the case, the age of diagnosis was asked. Cases of depression were defined as women who answered “yes” to both questions (n = 279). Non-cases were defined as those who have never had depression (n = 4598) and those who had not been diagnosed as having depression by a physician (n = 781). In total, 279 of 5658 women had physiciandiagnosed depression. 2.3. Anthropometric and clinical measurements Anthropometric measurements of participants including height, body weight, body mass index (BMI), and waist circumference were conducted at local community health centers and clinics as a part of the health examination (Oh et al., 2007). Anthropometric indices were measured while the participants were wearing light clothes and without shoes. Height was measured to the nearest 0.1 cm in an upright position, and weight was measured to the nearest 0.1 kg in a standing position. BMI was calculated from a measurement of weight and height of participants (weight [kg]/height [m2]). Waist circumference was measured to the nearest 0.1 cm at the middle point between the bottom of the rib cage and above the top of the iliac crest. Fasting blood samples were collected from participants who visited local community health centers and clinics. Measurements of fasting serum glucose, triglyceride, total cholesterol, and high-density lipoprotein cholesterol (HDL-cholesterol) concentrations were performed by laboratory staff at Seoul Medical Science Institute (Seoul, Korea) for KNHANES 2007 and Neodin Medical Institute (Seoul, Korea) for KNHANES 2008. Fasting serum glucose, triglyceride, total cholesterol, and HDL-cholesterol levels were measured using enzymatic methods with an autoanalyzer (ADVIA 1650, Siemens, USA, in 2007; Hitachi Automatic Analyzer 7600, Hitachi, Japan, in 2008). Blood pressure was measured by health professionals three times after the participants had rested for a period of 5 min in a stable state and the average of the last two readings for systolic blood pressure (SBP) and diastolic blood pressure (DBP) was used. 2.4. Assessment of other risk factors 2.4.1. Socioeconomic status Participants were asked about their marital status, and household income. Marital status was classified into four categories (married, separated/divorced, widowed, and never married). Household income was divided by equivalised quartiles in each period and equivalised total household income was estimated by dividing the total household income by the square of number of people in household (equivalised household income = total household income (Korean won)/√number of people in household). 2.4.2. Lifestyle factors Participants were also asked about smoking status (never-smoking, past-smoking, or current-smoking) followed by the average number of cigarettes smoked daily, age at which smoking started, and duration of smoking. Pack-years of smoking was calculated by multiplying the number of packs of cigarettes smoked per day by the number of years that the participants had currently smoked and in the past. Questionnaires were used to calculate the servings per week of alcoholic beverages by multiplying the frequency of alcohol consumption by servings of alcoholic beverages consumed on one occasion. Coffee consumption was assessed using the frequencies on the 63-item food frequency questionnaire (FFQ) (Oh et al., 2007). Frequencies on the FFQs were placed into 10 categories (do not drink coffee, drink 6–11 times per year, drink 1 or 2–3 times per month, drink 1, 2–3, or 4–6 times per week, and drink 1, 2, or 3

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times per day). Participants were also asked about their vitamin or mineral supplement use (yes or no). Women reported their menstrual status and HRT use. We considered women who reported that their menstrual periods had ceased or who had undergone hysterectomy as postmenopausal women. If menstrual status was unknown, women aged 50 years or older were defined as postmenopausal (n = 465). The average age for menopause in Korean women is 50 years of age (49.4 ± 5.1 years) (Korea Centers for Disease Control and Prevention, 2008). Level of physical activity was calculated using Metabolic Equivalent of Task values (MET) based on self-reported frequency and duration of vigorous intensity, moderate intensity, and walking activities during the week. MET hours per week was computed by multiplying the MET value of a particular activity (walking = 3.3 METs, moderate physical activity = 4.0 METs, and vigorous physical activity = 8.0 METs) with hours spent in that particular activity (Ainsworth et al., 2000). Total weekly physical activity was calculated by summing MET hours per week of walking, moderate, and vigorous activity. 2.4.3. Morbidity status We considered participant to have dyslipidemia if they had total cholesterol levels of greater than 240 mg/dL, triglyceride levels of greater than 200 mg/dL, HDLcholesterol levels of less than 40 mg/dL, or had currently used a lipid-lowering drug. Metabolic syndrome was defined according to the criteria established by the National Cholesterol Education Program's Adult Treatment Panel III (Grundy et al., 2005) and the WHO Asian-Pacific values (World Health Organization, 2000). At least three of following criteria must be present to be considered metabolic syndrome: abdominal obesity (waist circumference N80 cm), high fasting triglycerides (≥ 150 mg/dL), low HDL-cholesterol (b50 mg/dL), high blood pressure (SBP ≥ 130 mm Hg or DBP ≥85 mm Hg, or drug treatment for hypertension), or high fasting glucose (≥100 mg/ dL or drug treatment for diabetes). Participants were considered to have a history of hyperlipidemia, diabetes, hypertension, or cardiovascular disease, including stroke, myocardial infarction, or angina pectoris, if at least one of the following was reported: a history of physician diagnosed with these diseases, use of medical drugs and/or treatments for one of these diseases, or outpatient services and hospitalization experience for these diseases. 2.5. Statistical analyses Data from the two surveys (KNHANES 2007 and 2008) were combined. A chisquare test was used to compare the characteristics of case and non-case participants. In addition, mean values and standard errors for age and anthropometric data were compared between depression cases and non-cases using a t-test. We obtained the odds ratio (OR) and two-sided 95% confidence interval (CI) using a multivariateadjusted logistic regression analysis. In the multivariate models, several possible risk factors were accounted for, including age (years; continuous), marital status (married, separated/divorced, widowed, never married), household income (low, lower middle, upper middle, high; quartiles in each period), BMI (kg/m2; continuous), smoking status (never-smoking, past b 3, past ≥3, current b 5, current ≥5 pack-years), alcohol consumption (do not drink alcohol, ≤1, 2–3, ≥4 servings per week), frequencies of coffee consumption (do not drink coffee, ≤1, 2–6 times per week, 1, ≥2 times per day), vitamin or mineral supplement use (yes, no), menstrual status and HRT use (premenopausal, postmenopausal with no HRT use, postmenopausal with HRT use, postmenopausal with HRT use unknown), and physical activity (b 5.0, 5.0–19.9, 20.0– 49.9, ≥ 50.0 MET hours per week). Further adjustments were made for a history of hyperlipidemia (yes, no), diabetes (yes, no), hypertension (yes, no), and cardiovascular diseases (yes, no) in additional analyses. To test for trends across waist circumference (cm), BMI (kg/m2), and physical activity (MET hours per week), we calculated the median values for each category of waist circumference, BMI, and physical activity and then assign participants those median values. This variable was treated as a continuous term in the model. The associations between cardiovascular risk factors and depression were also evaluated according to menstrual status (premenopausal, postmenopausal), smoking status (never-smoking, ever-smoking), BMI (b22.0, 22.0–24.9, ≥ 25.0 kg/m2), history of hyperlipidemia (yes, no), diabetes (yes, no), and hypertension (yes, no) using a logistic regression. For tests for interactions, the model including the cross-product term of cardiovascular risk factors with a potential effect modifier variable was compared to the model without the cross-product term using the likelihood ratio test. All statistical analyses were performed with SAS software version 9.2 (SAS Institute Inc., Cary, North Carolina, USA). P b 0.05 was considered to be statistically significant.

3. Results Characteristics of Korean women in the fourth KNHANES according to depression status are shown in Table 1. Women with depression were more likely to be older, divorced/widowed, and have a lower household income than women without depression. The proportions of smoking (past or current), non-coffee drinking, postmenopausal status, and supplement use were higher in women with depression than those without. Women with depression were more likely to have a greater

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Table 1 Characteristics of physician-diagnosed cases and non-cases among Korean women in the KNHANES 2007 and 2008.

No. (%) of participants 2007 2008 Age (years) Marital status Married Divorced Widowed Never married Household income Low Lower middle Upper middle High Smoking status Never smoking Past, b 3 pack-years Past, ≥ 3 pack-years Current, b 5 pack-years Current, ≥5 pack-years Alcohol consumption during the previous year None 1 serving/week or less 2–3 servings/week 4 servings/week or more Frequencies of coffee consumption None 1 time/week or less 2–6 times/week 1 time/day 2 times/day or more Menstrual status and HRT use Premenopausal Postmenopausal with no HRT use Postmenopausal with HRT use Postmenopausal with HRT use unknown Vitamin or mineral supplement use No Yes Physical activity (MET h/week) b5.0 5.0–19.9 20.0–49.9 ≥50.0 Waist circumference (cm) BMI (kg/m2) Blood pressure levels (mm Hg) SBP b 140 and DBP b90 SBP ≥ 140 or DBP ≥90a Fasting serum glucose levels (mg/dL) b100 100–125 ≥126b Dyslipidemia Absence Presence Metabolic syndrome Absence Presence

Physician-diagnosed depression (n = 279)

Non-depression (n = 5379)

62 (22.2) 217 (77.8) 54.9 ± 14.6

1659 (30.8) 3720 (69.2) 49.5 ± 16.8

191 (68.7) 18 (6.5) 60 (21.6) 9 (3.2)

3646 (68.1) 245 (4.6) 932 (17.4) 531 (9.9)

78 76 68 47

1155 1364 1317 1338

P values 0.002

b 0.001 b 0.001

0.006 (29.0) (28.3) (25.3) (17.5)

(22.3) (26.4) (25.5) (25.9) b 0.001

229 (82.1) 13 (4.7) 8 (2.9) 17 (6.1) 12 (4.3)

4725 (88.1) 214 (4.0) 119 (2.2) 119 (2.2) 189 (3.5)

128 (46.0) 79 (28.4) 33 (11.9) 38 (13.7)

2143 (39.9) 1706 (31.8) 832 (15.5) 684 (12.8)

71 34 30 59 60

811 (16.7) 557 (11.5) 582 (12.0) 1400 (28.8) 1511 (31.1)

0.12

b 0.001 (28.0) (13.4) (11.8) (23.2) (23.6)

b 0.001 100 (35.8) 123 (44.1) 36 (12.9) 20 (7.2)

2860 (53.2) 1975 (36.7) 307 (5.7) 237 (4.4)

183 (71.8) 72 (28.2)

3736 (77.6) 1077 (22.4)

67 (24.0) 77 (27.6) 75 (26.9) 60 (21.5) 82.1 ± 9.7 24.1 ± 3.4

1231 (23.0) 1538 (28.7) 1210 (22.6) 1384 (25.8) 79.8 ± 10.0 23.4 ± 3.4

178 (63.8) 101 (36.2)

4025 (74.9) 1351 (25.1)

172 (66.9) 60 (23.4) 25 (9.7)

3810 (75.9) 790 (15.7) 423 (8.4)

148 (57.6) 109 (42.4)

3308 (66.3) 1684 (33.7)

157 (61.3) 99 (38.7)

3472 (70.4) 1460 (29.6)

0.03

0.23

b 0.001 0.001 b 0.001

0.003

0.004

0.002

Note: Values are presented as n (%) or mean ± standard deviation (S.D.). Sum of cases and non-cases in subgroup does not equal to total number of participants in this study because information was not available among a few participants. Abbreviation: HRT, hormone replacement therapy; MET, metabolic equivalent of task; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure. a Use of antihypertensive medication was included. b Use of insulin and oral medication for diabetes were included.

waist circumference and higher BMI, blood pressure, and fasting serum glucose levels than those without depression as well as a higher prevalence of dyslipidemia and metabolic syndrome. Current smokers with b5 pack-years of smoking had 3.99 (95% CI= 2.25–7.05) times higher odds of depression than never-smokers (Model 2; Table 2). Postmenopausal women who underwent HRT had

significantly higher odds of depression than premenopausal women. The age-adjusted odds of depression significantly increased in women with a waist circumference of ≥90 cm (OR [95% CI] = 1.53 [1.08–2.19]; Ptrend = 0.03). However, the association between waist circumference and depression was attenuated after adjusting for additional covariates (Model 2; Table 2).

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Table 2 ORs and 95% CIs of depression according to cardiovascular disease risk factors. OR (95% CI) No. of casesa

Model 1b

Model 2c

Model 3d

Smoking status Never smoking Past, b 3 pack-years Past, ≥3 pack-years Current, b5 pack-years Current, ≥5 pack-years

229 13 8 17 12

1.00 1.41 1.18 3.62 1.17

1.00 1.48 1.17 3.99 1.33

1.00 1.50 1.15 4.06 1.37

Menstrual status and HRT use Premenopausal Postmenopausal with no HRT use Postmenopausal with HRT use Postmenopausal with HRT use unknown

100 123 36 20

1.00 1.31 (0.81–2.10) 2.67 (1.62–4.38) 1.79 (0.96–3.35)

1.00 1.20 (0.73–1.96) 2.44 (1.45–4.12) 2.65 (1.17–6.02)

1.00 1.09 (0.66–1.79) 2.00 (1.16–3.43) 2.45 (1.07–5.60)

Physical activity (MET h/week) b 5.0 5.0–19.9 20.0–49.9 ≥ 50.0 ptrend

67 77 75 60

1.00 0.97 (0.69–1.36) 1.18 (0.84–1.67) 0.82 (0.57–1.17) 0.19

1.00 0.95 (0.68–1.34) 1.20 (0.85–1.70) 0.79 (0.55–1.14) 0.15

1.00 0.97 (0.69–1.37) 1.23 (0.86–1.74) 0.81 (0.56–1.17) 0.17

Waist circumference (cm)e b 75.0 75.0–79.9 80.0–84.9 85.0–89.9 ≥ 90 ptrend

75 44 53 39 68

1.00 1.00 1.20 1.05 1.53 0.03

(0.68–1.47) (0.83–1.73) (0.70–1.57) (1.08–2.19)

1.00 0.95 1.10 0.94 1.42 0.09

(0.64–1.40) (0.75–1.60) (0.62–1.43) (0.98–2.05)

1.00 0.92 1.05 0.86 1.25 0.33

(0.63–1.37) (0.72–1.53) (0.56–1.31) (0.86–1.83)

BMI (kg/m2) b 20.0 20.0–21.9 22.0–23.9 24.0–25.9 26.0–27.9 ≥ 28 ptrend

28 55 71 44 42 39

1.00 1.37 1.46 1.02 1.69 1.97 0.02

(0.86–2.19) (0.93–2.30) (0.62–1.66) (1.03–2.78) (1.19–3.26)

1.00 1.36 1.41 0.94 1.63 1.97 0.03

(0.85–2.19) (0.89–2.24) (0.57–1.54) (0.98–2.73) (1.18–3.30)

1.00 1.34 1.34 0.87 1.48 1.77 0.12

(0.83–2.15) (0.84–2.12) (0.52–1.43) (0.88–2.50) (1.05–3.00)

(0.79–2.51) (0.57–2.46) (2.11–6.20) (0.64–2.14)

(0.82–2.68) (0.56–2.44) (2.25–7.05) (0.71–2.48)

Blood pressure levels (mm Hg) SBP b140 and DBP b 90 SBP ≥140 or DBP ≥90f

178 101

1.00 1.24 (0.93–1.66)

1.00 1.10 (0.82–1.49)

Fasting serum glucose levels (mg/dL)e b 100 100–125 ≥ 126g

172 60 25

1.00 1.39 (1.02–1.90) 0.96 (0.61–1.51)

1.00 1.42 (1.03–1.95) 0.94 (0.60–1.49)

Dyslipidemiae Absence Presence

148 109

1.00 1.33 (1.02–1.75)

1.00 1.25 (0.95–1.64)

Metabolic syndromee Absence Presence

157 99

1.00 1.15 (0.87–1.54)

1.00 1.12 (0.83–1.49)

(0.83–2.72) (0.55–2.42) (2.30–7.20) (0.73–2.57)

Abbreviation: OR, odds ratio; CI, confidence interval; HRT, hormone replacement therapy; MET, metabolic equivalent of task; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure. a Numbers do not add to the total number of participants, because participants with missing exposure data were excluded from the analysis. b Age-adjusted: adjusted for period (2007, 2008) and age (years; continuous). c Multivariate-adjusted1: adjusted for period (2007, 2008), age (years; continuous), marital status (married, divorced, widowed, never married), household income (low, lower middle, upper middle, high; quartiles in each period), smoking status (never-smoking, past b 3, past ≥3, current b5, current ≥5 pack-years), alcohol consumption (do not drink alcohol, ≤1, 2–3, ≥4 servings per week), frequencies of coffee consumption (do not drink coffee, ≤1, 2–6 times per week, 1, ≥ 2 times per day), vitamin or mineral supplement use (yes, no), menstrual status and HRT use (premenopausal, postmenopausal with no HRT use, postmenopausal with HRT use, postmenopausal with HRT use unknown), physical activity (b5.0, 5.0–19.9, 20.0–49.9, ≥50.0 MET hours per week), BMI (kg/m2; continuous). d Multivariate-adjusted2: adjusted for history of hyperlipidemia (yes, no), diabetes (yes, no), hypertension (yes, no), cardiovascular disease (yes, no) in addition to covariates in Model 2. e Not adjusted for BMI. f Use of antihypertensive medication was included. g Use of insulin and oral medication for diabetes were included.

A higher BMI was also associated with higher odds of depression. Women with a BMI of ≥28 kg/m 2 had 1.97 (95% CI = 1.18–3.30; Ptrend = 0.03) times higher odds of depression compared to women

with a BMI of b20 kg/m2 (Model 2). The association between BMI and depression remained significant after further adjustment for a history of diabetes, hypertension, hyperlipidemia, or cardiovascular disease (stroke,

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myocardial infarction, or angina pectoris). Compared to women with fasting serum glucose levels of b100 mg/dL, women with fasting serum glucose levels of 100–125 mg/dL (impaired glucose tolerance) had 1.42 (95% CI= 1.03–1.95) times higher odds of depression (Model 2). Increased odds of depression were also found for women with dyslipidemia in the age-adjusted model (OR [95% CI]=1.33 [1.02– 1.75]). However, the association between dyslipidemia and depression was attenuated after adjusting for other covariates. No statistically significant association was observed for physical activity, blood pressure, or metabolic syndrome. For these factors, the results in the age-adjusted models were similar to those in the multivariate-adjusted models. The associations for a history of disease in relation to cardiovascular disease were also examined. A history of diabetes, hypertension, and hyperlipidemia was significantly associated with the presence of depression in the age-adjusted analyses (Model 1; Table 3). However, the significant associations were attenuated for a history of diabetes and hypertension in the multivariate-adjusted models, although the association between a history of hyperlipidemia and depression remained significant (OR [95% CI]=2.10 [1.46–3.03]). Women with a history of two or more comorbidities of hypertension, diabetes, and cardiovascular disease (stroke, myocardial infarction, or angina pectoris) had 1.63 (95% CI=1.05–2.52) times higher odds of depression compared to those without a history of these diseases (Model 2). We evaluated whether the associations between cardiovascular risk factors and depression varied by menstrual status (premenopausal, postmenopausal) and smoking status (never-smoking, eversmoking; Table 4). The association between smoking status and depression was more evident in premenopausal women compared to postmenopausal women (Pheterogeneity = 0.007). However, menstrual status did not modify the associations for physical activity, waist circumference, BMI, blood pressure, fasting serum glucose, dyslipidemia, metabolic syndrome, or a history of comorbidity. A history of smoking was also associated with a higher odds of depression among women without a history of hypertension (OR [95% CI] = 2.06 [1.40–

3.02]; Pheterogeneity = 0.08) but not in women with history of hypertension (data not shown). The associations between physical activity, waist circumference, BMI, blood pressure, fasting serum glucose, dyslipidemia, or metabolic syndrome and depression were investigated in strata of BMI (b22.0, 22.0–24.9, ≥25.0 kg/m 2), history of diabetes (yes, no), hypertension (yes, no), and hyperlipidemia (yes, no; data not shown). Increasing odds of depression were observed with an increase in waist circumference among women without a history of hyperlipidemia but not among women with a history of hyperlipidemia (data not shown; Pheterogeneity = 0.05). BMI and a history of these diseases did not modify the associations for other variables (data not shown). 4. Discussion In this large nationally representative cross-sectional study, the associations between the prevalence of depression and major cardiovascular risk factors were evaluated in a total of 5658 Korean women. Cardiovascular disease risk factors including smoking, postmenopausal HRT use, obesity, fasting serum glucose levels, and a history of hyperlipidemia were associated with a higher prevalence of depression in Korean women. Women who had two or three comorbidities of diabetes, hypertension, and cardiovascular disease (stroke, myocardial infarction, or angina pectoris) had a 1.63-fold increased prevalence of depression compared to those without any of these diseases. Notably, increasing odds of depression were also found with increasing BMI. The association between smoking status and depression was limited to premenopausal women. Nonetheless, these findings should be interpreted with caution, particularly in view of the multiple hypotheses tested. Depression and cardiovascular disease rank among the most prevalent causes of death and disability worldwide (Davidson et al., 2006). Consequently, investigating any links between these diseases is important as it is a major public health issue. Depression is common in patients with chronic diseases, especially associated with a worse prognosis in patients

Table 3 ORs and 95% CIs of depression according to history of diseases related to cardiovascular disease. No. of casesa

OR (95% CI) Model 1b

Model 2c

History of hyperlipidemia Absence Presence

235 44

1.00 2.35 (1.66–3.31)

1.00 2.10 (1.46–3.03)

History of diabetes Absence Presence

242 37

1.00 1.57 (1.08–2.28)

1.00 1.41 (0.95–2.07)

History of hypertension Absence Presence

188 91

1.00 1.47 (1.10–1.97)

1.00 1.29 (0.95–1.75)

History of cardiovascular disease Absence Presence

259 20

1.00 1.26 (0.78–2.05)

1.00 1.17 (0.71–1.92)

History of comorbidityd None 1 ≥2

171 73 35

1.00 1.36 (0.99–1.87) 1.89 (1.24–2.87)

1.00 1.18 (0.85–1.63) 1.63 (1.05–2.52)

Abbreviation: OR, odds ratio; CI, confidence interval. a Numbers do not add to the total number of participants, because participants with missing exposure data were excluded from the analysis. b Age-adjusted: adjusted for period (2007, 2008) and age (years; continuous). c Multivariate-adjusted: adjusted for period (2007, 2008), age (years; continuous), marital status (married, divorced, widowed, never married), household income (low, lower middle, upper middle, high; quartiles in each period), smoking status (never-smoking, past b 3, past ≥3, current b5, current ≥5 pack-years), alcohol consumption (do not drink alcohol, ≤1, 2–3, ≥4 servings per week), frequencies of coffee consumption (do not drink coffee, ≤1, 2–6 times per week, 1, ≥ 2 times per day), vitamin or mineral supplement use (yes, no), menstrual status and HRT use (premenopausal, postmenopausal with no HRT use, postmenopausal with HRT use, postmenopausal with HRT use unknown), physical activity (b5.0, 5.0–19.9, 20.0–49.9, ≥50.0 MET hours per week), BMI (kg/m2; continuous). d History of diabetes, hypertension, and cardiovascular disease (stroke, myocardial infarction, or angina pectoris) were included.

J.E. Park, J.E. Lee / Psychiatry Research 190 (2011) 232–239

237

Table 4 ORs and 95% CIs of depression according to cardiovascular disease risk factors by menstrual status and smoking status. Menstrual status

Smoking status

Premenopausal No. of OR (95% CI) casesa Smoking status Never smoking Ever smoking Physical activity (MET h/week) b10.0 10.0–39.9 ≥40 Waist circumference (cm)c b80.0 80.0–89.9 ≥90 BMI (kg/m2) b22.0 22.0–24.9 ≥25.0 Blood pressure levels (mm Hg) SBP b 140 and DBP b90 SBP ≥ 140 or DBP ≥90d Fasting serum glucose levels (mg/dL)c b100 100–125 ≥126e Dyslipidemiac Absence Presence Metabolic syndromec Absence Presence History of comorbidityf None ≥1

Postmenopausal b

No. of OR (95% CI) casesa

Never smoking b

Pheterogeneity

No. of OR (95% CI) casesa

Ever smoking b

No. of OR (95% CI)b casesa

Pheterogeneity

0.007 71 29

1.00 158 2.81 (1.75–4.52) 21

1.54 (0.95–2.50) 1.72 (0.88–3.33)

32 39 29

1.00 1.19 (0.74–1.93) 1.02 (0.61–1.71)

69 63 47

1.63 (0.88–3.02) 1.60 (0.87–2.94) 1.20 (0.66–2.19)

55 31 14

1.00 1.48 (0.94–2.33) 1.86 (1.00–3.44)

64 61 54

1.76 (1.01–3.06) 1.48 (0.85–2.57) 2.19 (1.23–3.89)

45 32 23

1.00 0.92 (0.58–1.47) 1.08 (0.64–1.83)

38 62 79

1.13 (0.59–2.13) 1.32 (0.74–2.36) 1.55 (0.88–2.75)

92 8

1.00 0.91 (0.43–1.97)

86 93

1.30 (0.79–2.13) 1.47 (0.85–2.56)

77 14 3

1.00 1.39 (0.77–2.53) 1.28 (0.39–4.22)

95 46 22

1.34 (0.80–2.27) 1.88 (1.06–3.33) 1.20 (0.61–2.38)

67 27

1.00 1.55 (0.97–2.48)

81 82

1.43 (0.84–2.43) 1.61 (0.93–2.78)

76 18

1.00 1.48 (0.86–2.54)

81 81

1.44 (0.85–2.43) 1.45 (0.83–2.54)

90 10

1.00 1.50 (0.75–3.00)

81 98

1.37 (0.83–2.26) 1.70 (0.98–2.94)

0.33

0.71 81 85 63

1.00 20 1.07 (0.78–1.47) 17 0.82 (0.58–1.15) 13

1.68 (0.99–2.84) 1.68 (0.96–2.94) 1.63 (0.87–3.05)

95 80 54

1.00 24 1.08 (0.79–1.48) 12 1.45 (1.01–2.10) 14

1.85 (1.14–3.01) 1.63 (0.86–3.10) 2.36 (1.28–4.34)

64 80 85

1.00 19 1.07 (0.76–1.51) 14 1.24 (0.88–1.76) 17

1.84 (1.06–3.18) 1.70 (0.92–3.16) 2.18 (1.22–3.87)

141 88

1.00 37 1.21 (0.88–1.66) 13

1.99 (1.34–2.96) 1.49 (0.80–2.80)

140 51 22

1.00 32 1.46 (1.04–2.06) 9 1.06 (0.65–1.72) 3

1.91 (1.25–2.90) 2.17 (1.04–4.54) 0.93 (0.28–3.04)

121 92

1.00 27 1.29 (0.96–1.74) 17

1.87 (1.19–2.94) 1.82 (1.05–3.16)

128 84

1.00 29 1.15 (0.84–1.57) 15

1.82 (1.18–2.82) 1.72 (0.95–3.10)

135 94

1.00 36 1.40 (1.02–1.92) 14

2.05 (1.37–3.07) 1.67 (0.91–3.07)

0.14

0.70

0.47

0.90

0.60

0.19

0.73

0.20

0.27

0.44

0.24

0.59

0.63

0.13

Abbreviation: OR, odds ratio; CI, confidence interval; MET, metabolic equivalent of task; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure. a Numbers do not add to the total number of participants, because participants with missing exposure data were excluded from the analysis. b Multivariate-adjusted: adjusted for period (2007, 2008), age (years; continuous), marital status (married, divorced, widowed, never married), household income (low, lower middle, upper middle, high; quartiles in each period), smoking status (never-smoking, past b 3, past ≥3, current b5, current ≥5 pack-years), alcohol consumption (do not drink alcohol, ≤1, 2–3, ≥4 servings per week), frequencies of coffee consumption (do not drink coffee, ≤1, 2–6 times per week, 1, ≥ 2 times per day), vitamin or mineral supplement use (yes, no), menstrual status and HRT use (premenopausal, postmenopausal with no HRT use, postmenopausal with HRT use, postmenopausal with HRT use unknown), physical activity (b5.0, 5.0–19.9, 20.0–49.9, ≥50.0 MET hours per week), BMI (kg/m2; continuous). c Not adjusted for BMI. d Use of antihypertensive medication was included. e Use of insulin and oral medication for diabetes were included. f History of diabetes, hypertension, and cardiovascular disease (stroke, myocardial infarction, or angina pectoris) were included.

with underlying cardiovascular disease, and may increase the risk of cardiovascular disease in healthy people (Lett et al., 2004; Frasure-Smith and Lesperance, 2006). A meta-analysis of 28 studies (n=80,000) supports the hypothesis that depression is associated with a wide range of cardiovascular diseases, including myocardial infarction, coronary heart disease, and stroke (Relative Risk (RR) [95% CI] =1.46 [1.37–1.55]) (van der Kooy et al., 2007). The Women's Health Initiative Observational Study of 93,676 postmenopausal women aged 50–79 years, who were followed up for an average of 4.1 years, found that depressive symptom was significantly associated with cardiovascular disease. The OR for cardiovascular disease death was 1.50 (95% CI=1.10–2.03) among postmenopausal women with no history of cardiovascular disease (WassertheilSmoller et al., 2004). In a prospective survey of 521 Koreans aged 65 years or over, heart disease and lower HDL-cholesterol at baseline predicted an increased risk of incident depression, and depression was associated with occurrences of stroke during the follow-up period (Kim et al., 2006). The association between cardiovascular disease and history of depression is likely to be mediated by behavioral and pathophysiological risk factors for cardiovascular disease, including obesity, physical inactivity, smoking, hypertension, hyperlipidemia, and diabetes (Jacka et al., 2007). An unhealthy lifestyle in depressed

individuals may influence the development of cardiovascular disease. Also, common underlying factors, such as stress, may contribute to both depression and cardiovascular disease. In response to stress, activation of the hypothalamic-pituitary-adrenocortical (HPA) axis can speed the development of cardiovascular disease. Elevated cortisol promotes the development of atherosclerosis and hypertension, and accelerates injury of vascular endothelial cells. HPA axis hyperactivity, in sequence, augments sympathoadrenal hyperactivity via central regulatory pathways. The resulting increase in plasma catecholamine levels leads to vasoconstriction, platelet activation, and elevated heart rate, all of which are damaging to the cardiovascular system (Joynt et al., 2003). Risk factors contributing to cardiovascular morbidity and mortality have been well documented in many studies and include hypertension, dyslipidemia, diabetes, cigarette smoking, obesity, physical inactivity, and age (Chobanian et al., 2003). Several studies demonstrated a strong relationship between smoking and depression. In a 5year longitudinal study of 1007 young adults, a history of major depression at baseline was associated with a threefold increased risk of progression to daily smoking, and participants with a history of daily smoking were more likely to develop major depression during

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follow-up (Breslau et al., 1998). In a cross-sectional study of 1043 Australian women, a prevalence of smoking was greater among participants diagnosed with major depressive disorder than those without depression, and smoking more than 20 cigarettes per day more than doubled the odds of depression compared to nonsmokers (Pasco et al., 2008). These results correspond well with the current findings in which current smokers with b5 pack-years of smoking have higher odds of depression compared to never-smokers. In our study, smoking was associated with the prevalence of depression among young, but not old, women. Previous studies have showed that being overweight or obese was linked to depression. In general, BMI and depression are more strongly associated with women than men. In a meta-analysis that included 15 longitudinal studies (n = 58,745), obesity at baseline was associated with a 67% higher risk of onset of depression in women and a 31% in men (Luppino et al., 2010). Similarly, a meta-analysis of 17 community-based studies (n = 204,507) revealed a significant positive association between depression and obesity, particularly in women (de Wit et al., 2010). The main strength of this study lies in the analysis of a large representative sample of Korean women concerning the public health issues of cardiovascular disease and depression. Moreover, a comprehensive range of potentially confounding variables was accounted for, which is not frequently allowed in other studies. Also, anthropometric measures and blood pressure were directly assessed by trained health professionals and circulating levels of fasting glucose, total cholesterol, triglyceride, and HDL-cholesterol were available for more than 92% of participants. This study has several limitations. A cross-sectional design did not allow us to draw causal inferences from the observed relationships. In addition, depression was defined based on self-reported data. We found 4.9% of physician-diagnosed depression in this population, which was slightly lower than another Korean study (5.6%) that assessed symptom profiles using depression assessment tools (Cho et al., 2009) or similar to the previous Korean studies (Lee et al., 1987; Cho et al., 2004). According to cross-sectional study based on population-based survey from 10 countries, the lifetime prevalence rate of depression in Korea was lower than Western countries (Weissman et al., 1996). Lower prevalence of depression in Korea may be because of low prevalence of risk factors for depression or reluctance to discuss their mental status among Asians compared to other ethnic groups (Abe-Kim et al., 2007). If depression cases had been well-captured, the positive associations that we observed could have been stronger. However, we cannot rule out the possibility that no or weak associations for some exposures could be due to the unidentified cases. Despite these limitations, we achieved statistically significance for obesity, postmenopausal status, smoking, fasting glucose levels and history of comorbidity. Also, given limited evidence about whether cardiovascular disease risk factors are associated with depression in Asian women, our study supports growing evidence on the positive association between cardiovascular disease and depression. Our study may not be generalizable to other ethnic groups, however biological mechanisms underlying the association may apply to others. In conclusion, a positive relationship exists between depression and cardiovascular disease risk factors among Korean women. This finding provides supporting evidence for the prevalence of depression in association with cardiovascular disease in an Asian female population, which tends to be relatively thinner and have a lower risk of cardiovascular disease than females in Western countries. This study warrants prospective observational studies on depression in Korean and other Asian populations as well as further investigation to determine the pathophysiological mechanisms underlying the association between depression and cardiovascular disease. Further studies need to elucidate a causal relationship between these diseases and provide possible predictive factors concerning how one may lead to the other.

Acknowledgments This research was supported by the Sookmyung Women's University Research Grant (2010) and the Brain Korea 21 (BK 21) Project from the Ministry of Education and Human Resources Development, Republic of Korea.

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