Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2014 Dec 1.
Published in final edited form as: J Psychosom Res. 2013 Oct 5;75(6):511–517. doi: 10.1016/j.jpsychores.2013.09.008

Depression, anxiety, and prevalent diabetes in the Chinese population: Findings from the China Kadoorie Biobank of 0.5 million people

Briana Mezuk 1,2, Yiping Chen 3, Canqing Yu 3,4, Yu Guo 5, Zheng Bian 5, Rory Collins 3, Junshi Chen 6, Zengchang Pang 7, Huijun Wang 8, Richard Peto 3, Xiangsan Que 9, Hui Zhang 10, Zhongwen Tan 11, Kenneth S Kendler 2, Liming Li 4,5, Zhengming Chen 3
PMCID: PMC3919064  NIHMSID: NIHMS531343  PMID: 24290039

Abstract

Objective

Despite previous investigation, uncertainty remains about the nature of the associations of major depression (MD) with type 2 diabetes mellitus (T2DM), particularly in adult Chinese, and the relevance of generalized anxiety disorder (GAD) for T2DM.

Methods

Cross-sectional data from the China Kadoorie Biobank Study, a sample of approximately 500,000 adults from 10 geographically defined regions of China, were analyzed. Past year MD and GAD were assessed using the Composite International Diagnostic Inventory. T2DM was defined as either having self-reported physician diagnosis of diabetes at age 30 or later (“clinically-identified” cases) or having a non-fasting blood glucose ≥11.1 mmol/L or fasting blood glucose ≥7.0 mmol/L but no prior diagnosis of diabetes (“screen-detected” cases). Logistic regression was used to assess the relationship between MD and GAD with clinically-identified and screen-detected T2DM, adjusting for demographic characteristics and health behaviors.

Results

The prevalence of T2DM was 5.3% (3.2% clinically-identified and 2.1% screen-detected). MD was significantly associated with clinically-identified T2DM (Odds ratio [OR]: 1.75, 95% Confidence Interval (CI): 1.47 – 2.08), but not with screen-detected T2DM (OR: 1.18, 95% CI: 0.92 – 1.51). GAD was associated with both clinically-identified (OR: 2.14, 95% CI: 1.60 – 2.88) and screen-detected (OR: 1.44, 95% CI: 0.99 – 2.08) T2DM. The relationship between MD and GAD with T2DM was moderated by obesity.

Conclusion

MD is associated with clinically-identified, but not screen-detected T2DM. GAD is associated with both clinically-identified and screen-detected T2DM. The relationship between MD and T2DM is strongest among those who are not obese.

Keywords: anxiety, culture, depression, epidemiology, type 2 diabetes

INTRODUCTION

Major depression (MD) often co-occurs with chronic medical conditions such as type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) [1-3]. Prospective studies indicate that the relationship between MD and T2DM is likely bi-directional, although the mechanisms underlying their relationship are largely unknown. There is recent evidence that undiagnosed T2DM is not strongly associated with MD, suggesting that the risk of MD subsequent to T2DM is likely driven by factors related to clinical treatment and self-management rather than hyperglycemia or related physiologic changes per say [4, 5].

MD often co-occurs with anxiety disorders, particularly generalized anxiety disorder (GAD) [6], and twin studies have indicated that part of the comorbidity between MD and GAD results from shared genetic liability [7]. However, relatively few studies have addressed the relationship between anxiety disorders and medical comorbidity [8]. In several cross-sectional reports and a recent meta-analysis, GAD and other anxiety disorders were modestly associated with T2DM [8-12], but a recent longitudinal study found no association between common anxiety disorders (e.g., GAD, panic disorder, agoraphobia, social phobia) and T2DM incidence [13]. These findings contrast with the fairly robust relationship between MD and T2DM, suggesting that different biological or psychological processes may underlie the co-occurrence of chronic conditions like T2DM with MD versus GAD.

China is undergoing rapid urbanization and modernization, with significant increases over the last few decades in the incidence of major chronic non-communicable diseases such as obesity, T2DM, and CVD [14, 15]. For example, the prevalence of T2DM in China has increased from 2.6% in 2000 to 9.7% in 2010 [16]. Parallel increases in the prevalence of MD have also been reported [17,18], with the lifetime risk for MD projected to be over 20 times greater for young adults compared with previous generations [19]. However, this increase in the prevalence of MD is likely confounded by improved awareness and detection of MD in the Chinese population [17, 20]. In addition, the relationships between psychiatric and medical conditions may be population-specific. There is evidence that people with depressive symptoms tend to have higher body mass index in Western non-Hispanic white populations, but the opposite is generally seen in Asian populations, supporting the so-called ‘jolly fat’ hypothesis (i.e., an inverse relationship between depressive symptoms and weight) [21-25]. Since obesity is a strong risk factor for T2DM, it is therefore possible that the relationship of MD, and related psychiatric conditions, with T2DM may differ between Chinese and Western populations.

To date only a few studies have examined the relationship between mood and anxiety disorders and T2DM in Asian populations, and the findings from these reports are mixed, partly due to limited sample sizes and the use of symptom scales rather than diagnostic instruments to assess psychiatric symptomology [26 – 29]. Moreover, most such studies were based on clinical data and thus have limited generalizability to the general population.

We report a detailed analysis of cross-sectional data from the China Kadoorie Biobank, a population-based study of 0.5 million people enrolled from 10 geographically defined regions of China. The aims of this study were to (1) examine the comorbidity between T2DM with MD and GAD, and further (2) to assess whether these relationships differ between clinically-identified versus undiagnosed diabetes.

METHODS

Sample

Data come from the baseline interview of the China Kadoorie Biobank (CKB) Study, a population-based study of 10 geographically defined regions interviewed between 2004 and 2008 [30]. The 10 regions were selected to provide approximately equal coverage of rural (Gansu, Henan, Sichuan, Hunan, and Zhejiang) and urban (Harbin, Qingdao, Suzhou, Liuzhou, and Haikou) provinces. Details of the study design and sample characteristics are described elsewhere [32]. Briefly, potential participants were approached in person by community leaders or health workers, and over 99% of those who consented to the baseline survey completed all assessments. In total, 512,891 adults aged 30 – 79 (representing approximately 30% of the total population of the 10 regions sampled) completed the interviewer-administered laptop-based questionnaire and clinic visit, and 98.4% completed a non-fasting blood spot test for glucose. During the clinic visit, a range of physical measurements were completed, including height and weight (used to calculate body mass index (BMI)), hip and waist circumference, bioimpedance, systolic and diastolic blood pressure (mmHg) and lung function. Participants with a non-fasting blood glucose level between ≥7.8mmol/L and <11.1mmol/L (N=29,636) were invited back to have a fasting blood glucose test, of which 13,289 (44.8%) completed the fasting draw. As shown by Supplemental Table 1, those who were completed the fasting draw differed from those who were invited but did not compete the draw in terms of gender, marital status, education, socioeconomic status, province, smoking status, and physical activity (although in most cases the absolute amount of difference was small, but due to the large sample size still statistically significant). However, importantly, those who completed the fasting draw did not differ in terms of age (F=1.85, p=0.173), MD status (X2=0.86, p=0.354) or GAD status (X2=0.270, p=0.604). This analysis was restricted to participants who had valid non-fasting blood glucose measures (N=504,548).

Measures

Exposures

Past year major depression (MD) and generalized anxiety disorder (GAD) were assessed using the Chinese version of computerized Composite International Diagnostic Inventory –Short form (CIDI-SF) using face-to-face interview by trained health workers at the study clinic. The CIDI is a fully-structured diagnostic instrument based on criteria from the Diagnostic and Statistical Manual of Mental Disorders – IV (DSM-IV) and has moderate concordance with clinical psychiatric interviews [31, 32]; the Chinese version of the CIDI (calibrated as part of the World Mental Health Surveys) produces similar population estimates of MD to the Structured Clinical Interview for DSM (SCID) [33, 34]. MD was indicated by the presence of dysphoria and/or anhedonia accompanied by a clustering of somatic, cognitive, and behavioral disturbances, including appetite or weight change, sleeping problems, feelings of guilt or worthlessness, psychomotor changes, fatigue, concentration problems, and thoughts of suicide that lasted two weeks or more. GAD was indicated by the presence of excessive anxiety or worry for at least six months accompanied by the presence of irritability, muscle tension, sleep disturbances, difficulty concentrating, tiring easily, and feelings of restlessness.

Outcomes

Two diabetes outcomes were examined: clinically-identified T2DM and screen-detected T2DM. Clinically-identified T2DM was defined as self-report of physician diagnosis of diabetes with an onset of age 30 or later; the age criteria was applied in order to exclude most cases of type 1 diabetes which tends to onset in adolescence. Screen-detected T2DM was defined according to the American Diabetes Association guidelines as either: (a) non-fasting blood glucose ≥11.1 mmol/L or (b) fasting blood glucose ≥7.0 mmol/L [35], plus no report of physician diagnosis of diabetes at the baseline survey. Overall, 20,502 individuals completed both a fasting and non-fasting blood draw (Supplemental Table 1). Of those who had a non-fasting blood glucose ≥11.1 mmol/L, 51.9% reported a physician diagnosis of diabetes. Sensitivity analyses restricting the undiagnosed diabetes cases to those with fasting glucose levels were conducted.

Other covariates

Demographic characteristics including age, sex, marital status (categorized as married, widowed/separated/divorced, and never married), and education (categorized as no formal schooling, primary school, middle school, high school, and college/university graduate) were assessed as part of the face-to-face interview. Two indicators of high socioeconomic status (SES) were considered in order to capture the diversity of SES across the rural and urban regions: (1) annual household income of ≥20,000 Yuan, and (2) presence of a flushing toilet in the home. Province was included as a nine-level dummy variable with Gansu as the reference category. Health behaviors were assessed by self-report and included smoking status (categorized as never, former, and current regular smoker), alcohol use (categorized as consuming alcohol at least once per week vs. less because alcohol use is less common in China than in western nations), and total physical activity calculated as metabolic equivalent task hours (MET-hours/day) spent on work, transportation, housework, and non-sedentary recreation and sedentary leisure time was quantified as hours per day.

The CKB study was approved by the Institutional Review Boards at Oxford University and the China National Center for Disease Control (IRB ethics approval number X101222001 issued by the Chinese Academy of Medicine). All participants provided informed consent.

Analysis

Initially, comparisons between respondents with clinically-identified, screen-detected, and no T2DM were made using chi-squared tests for categorical variables and one-way Analysis of Variance (ANOVA) F-tests for continuous variables. The comorbidity between MD, GAD and physician diagnosis of neurasthenia was assessed using tetrachoric correlation coefficients. Next, a series of logistic regression models were fit to assess the relationships between MD and GAD with T2DM. Four nested logistic models were fit: (1) unadjusted, (2) adjusted for demographic and socioeconomic characteristics and region, (3) additionally adjusted for health behaviors smoking and alcohol use, and physical activity, and (4) additionally adjusted for health indicators BMI and systolic blood pressure. In order to assess whether the relationship between these mental health predictors and diabetes status was influenced by whether or not the T2DM had been clinically-identified, these analyses were repeated separately for (a) clinically-identified diabetes and (b) screen-detected T2DM. For the analyses predicting clinically-identified T2DM, those participants with screen-detected diabetes were excluded (N = 10,528, resulting in an analytic sample of 494,020), and similarly for the analyses predicting screen-detected T2DM those participants with clinically-identified T2DM were excluded (N=15,981, resulting in an analytic sample of 488,567). Finally, given the impact that the ‘jolly fat’ hypothesis may have on the relationship between these mental health indictors and T2DM, analyses were repeated within strata of BMI defined according to World Health Organization (WHO) categories: underweight (BMI <18.5 kg/m2), normal (18.5 kg/m2 ≥ BMI < 25 kg/m2), overweight (25 kg/m2 ≥ BMI < 30 kg/m2) and obese (BMI ≥ 30 kg/m2).

Analyses were conducted using SAS 9.3 statistical software (SAS Institute, Cary NC) and all p-values refer to two-tailed tests. Statistical significance was set of p<0.05.

RESULTS

Baseline participant characteristics stratified by diabetes status are shown in Table 1. The overall prevalence of T2DM was 5.3% (3.2% clinically-identified and 2.1% screen-detected), and T2DM (both clinically-identified and screen-detected) was more common in urban provinces than in rural ones (7.2% versus 3.7%). The proportion of T2DM cases that were clinically-identified ranged from 78% in Zhejiang province to 39% in Gansu province (both rural provinces), with a median of 57%. For those with clinically-identified T2DM the mean (SD) age of diagnosis was 53.4 (9.3) years. Of the 3,198 individuals with past year MD, 471 (14.7%) also met criteria for GAD (terachoric correlation (r2) = 0.75, SE = 0.01), and of the 1,147 individuals with past year GAD, 471 (41.1%) also met criteria for MD. MD was significantly more common among those with clinically-identified T2DM compared with those with screen-detected T2DM (0.9% vs. 0.6%, respectively) or without diabetes (0.6% and 1.1%). However, GAD was only marginally more common among the clinically-identified T2DM group.

Table 1.

Descriptive characteristics of participants by type 2 diabetes status

Type 2 diabetes
Clinically-
identified
(N= 15981)		 Screen-detected
(N= 10528)		 No diabetes
(N=478039)		 F/Chi2, p-value
Socio-demographic characteristics
Age in years (Mean, SD) 59.11 (9.1) 56.25 (9.9) 51.19 (10.6) 5401.70, <.0001
Female 9905 (62.0) 6218 (59.1) 281614 (58.9) 60.25, <.0001
Marital status
 Married 13820 (86.5) 9194 (87.3) 433918 (90.8) 616.45, <.0001
 Widowed/Separated/Divorced 2104 (13.2) 1276 (12.1) 40528 (8.5)
 Never married 57 (0.4) 58 (0.6) 3593 (0.8)
Education
 No formal school 3145 (19.7) 2245 (21.3) 86760 (18.2)
 Primary School 5068 (31.7) 3598 (34.2) 153853 (32.2)
 Middle School 4143 (25.9) 2658 (25.3) 136253 (28.5) 300.60, <.0001
 High School 2353 (14.7) 1467 (13.9) 73209 (15.3)
 College/University 1272 (8.0) 560 (5.3) 27964 (5.9)
Socioeconomic status
 Household income >= 20,000
 Yuan/year		 7424 (46.5) 4077 (38.7) 204167 (42.7) 159.64, <.0001
 Have flushing toilet in home 10182 (63.7) 5494 (52.2) 239555 (50.1) 1155.43, <.0001
Province
 Qingdao 1853 (11.6) 954 (9.1) 32210 (6.7)
 Harbin 3538 (22.1) 1799 (17.1) 52150 (10.9) 5360.29, <.0001
 Haikou 1160 (7.3) 694 (6.6) 27828 (5.8)
 Suzhou 1422 (8.9) 1057 (10.0) 50776 (10.6)
 Liuzhou 2503 (15.7) 1212 (11.5) 46458 (9.7)
 Sichuan 894 (5.6) 1183 (11.2) 49903 (10.4)
 Gansu 620 (3.9) 967 (9.2) 48449 (10.1)
 Henan 1490 (9.3) 1333 (12.7) 60533 (12.7)
 Zhejiang 1367 (8.6) 417 (4.0) 52048 (10.9)
 Hunan 1134 (7.1) 912 (8.7) 57684 (12.1)
Health behaviors
Smoking status
 Never 10388 (65.0) 6414 (60.9) 296178 (62.0)
 Former 2557 (16.0) 1455 (13.8) 54435 (11.4) 704.70, <.0001
 Current regular 3036 (19.0) 2659 (25.3) 127426 (26.7)
Consumes alcohol weekly 1474 (9.2) 1685 (16.0) 71119 (14.9) 407.85, <.0001
Total physical activity (Mean, SD) 18.46 (10.4) 21.62 (12.2) 25.84 (13.6) 2784.32, <.0001
Mental health indicators
Major depression 139 (0.9) 67 (0.6) 2992 (0.6) 14.61, 0.0007
Generalized anxiety disorder 49 (0.3) 29 (0.3) 1069 (0.2) 5.79, 0.0552
Health indicators
Age of diabetes diagnosis (Mean, SD) 53.41 (9.3) - -
BMI in kg/m2 (Mean, SD) 24.93 (3.5) 25.24 (3.7) 23.59 (3.4) 2407.20, <.0001
Waist circumference in cm (Mean, SD) 85.85 (9.7) 86.52 (10.1) 80.00 (9.6) 5059.43, <.0001
Systolic BP in mmHg (Mean, SD) 141.85 (22.6) 141.78 (22.6) 130.55 (21.0) 3580.23, <.0001
Open in a new tab

Values are N (%) unless otherwise specified. P-value refers to comparison across three diabetes categories (clinically-identified, screen-detected, and no diabetes).

Past year MD was significantly associated with prevalent T2DM, but this relationship was largely driven by the association with clinically-identified cases (Table 2). In fully-adjusted models, MD was associated with 1.75 times greater likelihood of having clinically-identified T2DM (95% Confidence Interval (CI): 1.47 – 2.08), but was only modestly associated with screen-detected T2DM (Odds Ratio (OR): 1.18, 95% CI: 0.92 – 1.51). In contrast, GAD was associated with more than two-fold higher likelihood of clinically-identified T2DM (OR: 2.14, 95% CI: 1.60 – 2.88), and a marginally significant 44% greater likelihood of screen-detected T2DM (OR: 1.44, 95% CI: 0.99 – 2.08). Adjustment for health behaviors and indicators (Table 2, Models 3 and 4, respectively) did not substantially alter the strength of the association between MD and GAD with diabetes status from Model 2 (adjusted for demographic characteristics only), indicating that these factors did not mediate the association between these disorders and T2DM. Sensitivity analyses restricting the screen-detected T2DM cases to those identified by fasting blood glucose measures only produced similar results (Supplemental Table 2).

Table 2.

Relative odds of type 2 diabetes associated with major depression, generalized anxiety disorder, and neurasthenia

Model 1
OR (95% CI)		 Model 2
OR (95% CI)		 Model 3
OR (95% CI)		 Model 4
OR (95% CI)
Exposure: CIDI MD
Any diabetes (N=504,548)
 Never MD 1.0 1.0 1.0 1.0
 Past year MD 1.24 (1.08 – 1.43) 1.47 (1.27 – 1.69) 1.42 (1.23 – 1.65) 1.52 (1.31 – 1.75)
Clinically-identified T2DM (N=494,020)
 Never MD 1.0 1.0 1.0 1.0
 Past year MD 1.40 (1.18 – 1.66) 1.71 (1.44 – 2.04) 1.65 (1.38 – 1.96) 1.75 (1.47 – 2.08)
Screen-detected T2DM (N=488,567)
 Never MD 1.0 1.0 1.0 1.0
 Past year MD 1.02 (0.80 – 1.30) 1.12 (0.88 – 1.43) 1.11 (0.87 – 1.42) 1.18 (0.92 – 1.51)
Exposure: CIDI GAD
Any diabetes (N=504,548)
 Never GAD 1.0 1.0 1.0 1.0
 Past year GAD 1.32 (1.05 – 1.66) 1.78 (1.41 – 2.25) 1.74 (1.38 – 2.21) 1.82 (1.44 – 2.30)
Clinically-identified T2DM (N=494,020)
 Never GAD 1.0 1.0 1.0 1.0
 Past year GAD 1.37 (1.03 – 1.83) 2.11 (1.57 – 2.82) 2.05 (1.53 – 2.75) 2.14 (1.60 – 2.88)
Screen-detected T2DM (N=488,567)
 Never GAD 1.0 1.0 1.0 1.0
 Past year GAD 1.23 (0.85 – 1.78) 1.39 (0.96 – 2.01) 1.38 (0.95 – 2.00) 1.44 (0.99 – 2.08)
Open in a new tab

Values are odds ratio (95% confidence interval). Model 1: unadjusted. Model 2: Adjusted for age, sex, marital status, household income, education, presence of a flushing toilet in the home, and province. Model 3: Model 2 plus adjustment for smoking history, alc ohol use, and physical activity. Model 4: M odel 3 plus adjustment for BMI and systolic blood pressure

There was an expected strong positive association of BMI with prevalence of T2DM; with each one-unit increase in kg/m2 the odds of T2DM increased 13% (95% CI: 1.12 – 1.13). In contrast, there was an inverse association of BMI with prevalence of MD (data not shown). As shown by Table 3, the strength of the relationship between MD and any T2DM became attenuated with increasing BMI, which persisted when restricting the outcome to clinically-identified T2DM. BMI moderated the relationship between GAD and T2DM in a curvilinear manner, depending on whether the outcome was clinically-identified or screen-detected T2DM. The strength of the association between GAD and clinically-identified T2DM tended to increase with higher BMI, while higher BMI attenuated the relationship between GAD and screen-detected T2DM. However, we note that these analyses were based on relatively few cases of GAD.

Table 3.

Relative odds of type 2 diabetes associated with major depression and generalized anxiety disorder stratified by body mass index

BMI categories Model 1
OR (95% CI) 		Model 2
OR (95% CI) 		Model 3
OR (95% CI)
Exposure: CIDI MD
Any type of T2DM
 Underweight 1.89 (1.05 – 3.40) 2.21 (1.22 – 4.01) 2.12 (1.17 – 3.85)
 Normal weight 1.24 (1.02 – 1.51) 1.49 (1.22 – 1.82) 1.49 (1.21 – 1.82)
 Overweight 1.36 (1.07 – 1.72) 1.48 (1.16 – 1.89) 1.50 (1.18 – 1.92)
 Obesity 1.25 (0.73 – 2.16) 1.41 (0.81 – 2.54) 1.41 (0.81 – 2.64)
Clinically-identified T2DM
 Underweight 2.12 (0.99 – 4.53) 2.32 (1.07 – 5.00) 2.15 (0.99 – 4.66)
 Normal weight 1.43 (1.14 – 1.81) 1.77 (1.40 – 2.45) 1.75 (1.38 – 2.14)
 Overweight 1.49 (1.12 – 2.00) 1.72 (1.28 – 2.32) 1.73 (1.28 – 2.33)
 Obesity 1.22 (0.59 – 2.51) 1.55 (0.74 – 3.24) 1.51 (0.72 – 3.18)
Screen-detected T2DM
 Underweight 1.64 (0.67 – 4.01) 2.05 (0.83 – 5.06) 2.01 (0.81 – 4.98)
 Normal weight 0.91 (0.63 – 1.33) 1.03 (0.71 – 1.50) 1.05 (0.72 – 1.58)
 Overweight 1.16 (0.79 – 1.70) 1.18 (0.80 – 1.74) 1.21 (0.82 – 1.79)
 Obesity 1.30 (0.60 – 2.80) 1.33 (0.61 – 2.88) 1.35 (0.62 – 2.94)
Exposure: CIDI GAD
Any type of T2DM
 Underweight 2.97 (1.19 – 7.44) 3.40 (1.34 – 8.64) 3.18 (1.24 – 8.13)
 Normal weight 1.15 (0.81 – 1.62) 1.53 (1.08 – 2.17) 1.56 (1.10 – 2.22)
 Overweight 1.35 (0.93 – 1.97) 1.80 (1.23 – 2.64) 1.82 (1.24 – 2.67)
 Obesity 2.43 (1.15 – 5.13) 3.20 (1.50 – 6.86) 3.21 (1.49 – 6.90)
Clinically-identified T2DM
 Underweight 1.13 (0.16 – 8.20) 1.13 (0.16 – 8.31) 0.96 (0.13 – 7.17)
 Normal weight 1.34 (0.90 – 1.99) 1.98 (1.32 – 2.97) 2.02 (1.34 – 3.03)
 Overweight 1.39 (0.96 – 2.24) 2.18 (1.34 – 3.56) 2.19 (1.34 – 3.56)
 Obesity 2.46 (0.96 – 6.36) 3.89 (1.47 – 10.30) 3.77 (1.41 – 10.07)
Screen-detected T2DM
 Underweight 4.99 (1.80 – 13.86) 6.55 (2.30 – 18.65) 6.29 (2.21 – 17.95)
 Normal weight 0.82 (0.43 – 1.59) 0.93 (0.48 – 1.80) 0.97 (0.50 – 1.87)
 Overweight 1.30 (0.73 – 2.33) 1.41 (0.79 – 2.52) 1.44 (0.80 – 2.57)
 Obesity 2.40 (0.95 – 6.83) 2.63 (0.92 – 7.53) 2.66 (0.92 – 7.66)
Open in a new tab

Values are odds ratio (OR) and 95% Confidence Intervals (95% CI).

Model 1: unadjusted. Model 2: Adjusted for age, sex, marital status, household income, education, presence of a flushing toilet in the home, and province. Model 3: Model 2 plus adjustment for smoking history, alcohol use, and physical activity and systolic blood pressure.

Several other factors were also positively associated with prevalence of clinically-identified T2DM, including age (OR(per year): 1.04, 95% CI: 1.04 – 1.05), female gender (OR: 1.48, 95% CI: 1.40 – 1.56), and systolic blood pressure (OR(per mmHg): 1.01, 95% CI: 1.01 – 1.01). On the other hand, clinically-identified T2DM was inversely associated with household income (OR: 0.96, 95% CI: 0.92 – 0.99), having a flushing toilet in the home (OR: 0.84, 95% CI: 0.80 – 0.88), regular consumption of alcohol (OR: 0.54, 95% CI: 0.51 – 0.58), and higher levels of physical activity (OR: 0.98, 95% CI: 0.98 – 0.98). These relationships were similar in magnitude, but not always statistically significant, for screen-detected T2DM. Lower levels of education were significantly associated with increased likelihood of screen-detected T2DM but had no apparent association with clinically-identified T2DM.

DISCUSSION

In the largest study to date of the relationship between common mental disorders and prevalent T2DM in the Chinese population, we found that MD was significantly associated with increased likelihood of clinically-identified, but not screen-detected, T2DM. GAD was also significantly associated with higher prevalence of clinically-identified T2DM, and marginally associated with screen-detected T2DM. MD was inversely related to BMI, and the relationship between MD and T2DM was attenuated by increasing BMI. Our findings add to the growing body of research indicating that the relationship between MD, GAD, and T2DM is bi-directional, and suggest that the relationship between T2DM and mental health is not uniform across mood and anxiety disorders in this population.

Several reasons may help explain why the relationship with T2DM may differ between MD and GAD. The present analysis is based on cross-sectional data, and the information on MD and GAD pertain only to past-year, not lifetime, diagnoses. Therefore, it is not possible to establish whether the onset of MD or GAD occurred before or after T2DM; however, comparing the results from the clinically-identified and screen-detected T2DM cases provides some leverage for understanding these relationships. MD was only associated with clinically-identified T2DM, which is consistent with previous research that the relationship between T2DM and subsequent depressive symptoms is largely driven by diabetes-related distress and the behavior changes needed to effectively self-manage this condition [4, 5]. In contrast, GAD was associated with greater likelihood of both clinically-identified and screen-detected T2DM, although the magnitude of this relationship was stronger for the former. This indicates that processes other than diabetes self-management may contribute to this relationship, including possibly biological mechanisms not shared with MD. For example, a recent study of relaxation training showed changes in expression of genes related to energy metabolism and insulin function post-intervention, suggesting that anxiety symptoms may have direct effects on these systems [36]; more research is needed to understand the potential biological mechanism linking anxiety to metabolic disorders. It is also worth noting that because of the diagnostic criteria for GAD require symptom duration of at least six months, whereas those for MD specify only a period of two weeks, the cases of GAD identified in this study are necessarily more chronic in nature than the MD cases, which may contribute to their disparate relationship with screen-detected T2DM.

Consistent with so-called ‘jolly fat’ hypothesis, we found that the prevalence of MD was inversely associated with BMI and that the relationship between MD and T2DM is particularly strong among individuals who are not obese. These results are inconsistent with the hypothesis that BMI mediates the relationship between MD and T2DM (e.g., appetite and weight gain are symptoms of MD, which would in turn increase risk of T2DM), at least in the Chinese population. Other symptoms of MD unrelated to weight (e.g., sleeping disturbances, fatigue) may be more relevant to T2DM risk [37]. Alternatively, T2DM may only be a risk factor for MD among individuals at otherwise low risk of this disease (e.g., individuals at normal weight), although due to the cross-sectional nature of this data we are not able to explore this hypothesis directly.

These findings have clinical implications regarding screening and diagnosis of T2DM. Consistent with previous research [4, 5] our results suggest that the relationship between T2DM and subsequent MD and GAD is related to the process of clinical diagnosis and self-management rather than hyperglycemia as a disease state per say. Self-management of T2DM can be a challenging, particularly for individuals with limited social and economic resources. Some factors that influence T2DM prognosis are under an individual’s control (e.g., health-related behaviors) while others are not (e.g., access to affordable healthcare, genetic liability), and the process of self-management in and of itself can become a chronic stressor as individuals try, on a daily basis, to meet clinical recommendations for dietary intake, physical activity, and glycemic control [38]. A recent international study found that about 40% of individuals with clinically-identified diabetes had significant diabetes-related distress (in China specifically this estimate was approximately 50%) [38]. However, only one-third of individuals reported that they had been asked about feelings of depression or anxiety by their healthcare provider in the past year. Although there are various recommendations for screening for depression in the context of diabetes, there is no consensus as to which tools would be most appropriate nor how frequently such screening should occur for individuals with T2DM [39].

These findings should be interpreted in light of study strengths and limitations. The use of both fasting and non-fasting blood samples to determine undiagnosed T2DM status is another limitation; however, our sensitivity analysis among those with screen-detected T2DM determined by fasting glucose measurements was consistent with the results reported here. Finally, without additional clinical information we cannot definitively know that all the cases of diabetes examined here were type 2; however, over 95% of diabetes cases in the general adult population are T2DM. This study also has a number of strengths. The large, population-based sample mitigates the influence of selection bias and enhances the generalizability of the findings. MD and GAD were assessed using laptop-based validated structured diagnostic interviews with very complete data collection. Finally, in our analytic models we were able to adjust for a number of important confounding health behaviors and characteristics, including smoking, alcohol use, and BMI.

In summary, despite potential cultural differences in the expression of depressive and anxiety symptoms, this study demonstrates that MD and GAD are associated with clinically-identified T2DM in the Chinese population and suggests that there may be different biological and behavior pathways linking MD and GAD with T2DM. The role of culture in potentially modulating the relationship between psychiatric conditions with metabolic disorders such as T2DM requires further research. Populations in the midst of rapid social and economic change may provide a unique setting to understand how these factors influence the relationship between mental and physical health, and the regular resurvey and continuing follow up of the CKB participants for a wide of range of health-related outcomes should soon allow for their relationships to be investigated prospectively.

Supplementary Material

01

Acknowledgements

We thank Judith MacKay in Hong Kong; Yu Wang, Gonghuan Yang, Zhengfu Qiang, Lin Feng, Maigen Zhou, Wenhua Zhao, and Yan Zhang at the Chinese Center for Disease Control and Prevention (CDC); Lingzhi Kong, Xiucheng Yu, and Kun Li at the Ministry of Health of China; and, Sarah Clark, Martin Radley, Mike Hill, at the Clinical Trial Service Unit, Oxford, for assisting with the design, planning, organization, conduct of the study. We especially thank the participants in the study and the members of the survey teams in each of the 10 regional centers; the project development and management teams based at Beijing, Oxford; and the 10 regional centers. The Clinical Trial Service Unit acknowledges support from the British Heart Foundation Centre of Research Excellence, Oxford. Briana Mezuk, Canqing Yu, Yiping Chen and Zhengming Chen are the guarantors of this manuscript.

Funding The baseline survey and the first re-survey were supported by a research grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term continuation of the project is supported by program grants from the Wellcome Trust in the UK (088158/Z/09/Z, 2009-14) and the Chinese Ministry of Science and Technology (2011BAI09B01, 2011-15). The UK Medical Research Council, the British Heart Foundation and Cancer Research UK also provide core funding to the Clinical Trial Service Unit and Epidemiological Studies Unit at Oxford University for the project. B. Mezuk is supported by the National Institute of Health (K01-MH093642-A1 and R21-DK8356430-A1). C. Yu is supported by the National Natural Science Foundation of China (No.81202266) and a visiting fellowship from Sino-British Friendship Fellowship scheme. The sponsors had no role in the design, analysis, interpretation or drafting of this manuscript.

(bmezuk@vcu.edu), (yiping.chen@ctsu.ox.ac.uk), (yucanqing@pku.edu.cn), (guoyu@kscdc.net), (bianzheng@kscdc.net), (secretary@ctsu.ox.ac.uk), (c/o bianzheng@kscdc.net), (cdcpang@126.com), (whjcdc@126.com), (richard.peto@ndm.ox.ac.uk), (quexiangsan1957@163.com), (51875980@qq.com), (c/o bianzheng@kscdc.net), (kindler@vcu.edu), (lmlee@vip.163.com), (zhengming.chen@ctsu.ox.ac.uk)

Author contributions B. Mezuk developed the idea for the study and wrote the first draft of the manuscript. Canqing Yu conducted the data analysis and Yiping Chen supervised the data analysis. Kenneth S. Kendler, Zhengming Chen, Yu Guo, Zheng Bian, Rory Collin,Junshi Chen, Zengchang Pang, Huijun Wang, Richard Peto, Xiangsan Que, Hui Zhang, Zhongwen Tan, and Liming Li edited and critiqued drafts of the manuscript. All authors approved the final version of the manuscript for submission.

Competing interest statement The authors have no completing interests to report.

Members of the China Kadoorie Biobank collaborative group

a) International steering committee: Liming Li (PI), Junshi Chen, Fan Wu (ex-member), Rory Collins, Richard Peto, Zhengming Chen (PI)

b) Study coordinating Centres

International Co-ordinating Centre, Oxford: Zhengming Chen, Garry Lancaster, Xiaoming Yang, Alex Williams, Margaret Smith, Ling Yang, Yumei Chang, Iona Millwood, Yiping Chen, Qiuli Zhang, Sarah Lewington, Gary Whitlock

National Co-ordinating Centre, Beijing: Yu Guo, Guoqing Zhao, Zheng Bian, Can Hou, Yunlong Tan

Regional Co-ordinating Centres, 10 areas in China:

Qingdao

Qingdao Centre for Disease Control: Zengchang Pang, Shanpeng Li, Shaojie Wang ,

Licang Centre for Disease Control: Silu lv

Heilongjiang

Provincial Centre for Disease Control: Zhonghou Zhao, Shumei Liu, Zhigang Pang

Nangang Centre for Disease Control: Liqiu Yang, Hui He, Bo Yu

Hainan

Provincial Centre for Disease Control: Shanqing Wang, Hongmei Wang

Meilan Centre for Disease Control: Chunxing Chen, Xiangyang Zheng

Jiangsu

Provincial Centre for Disease Control: Xiaoshu Hu, Minghao Zhou, Ming Wu, Ran Tao,

Suzhou Centre for Disease Control: Yeyuan Wang, Yihe Hu, Liangcai Ma

Wuzhong Centre for Disease Control: Renxian Zhou

Guanxi

Provincial Centre for Disease Control: Zhenzhu Tang,Naying Chen, Ying Huang

Liuzhou Centre for Disease Control: Mingqiang Li, Zhigao Gan, Jinhuai Meng, Jingxin Qin

Sichuan

Provincial Centre for Disease Control: Xianping Wu, Ningmei Zhang

Pengzhou Centre for Disease Control: Guojin Luo, Xiangsan Que, Xiaofang Chen

Gansu

Provincial Centre for Disease Control: Pengfei Ge, Xiaolan Ren,Caixia Dong

Maiji Centre for Disease Control: Hui Zhang, Enke Mao, Zhongxiao Li

Henan

Provincial Centre for Disease Control: Gang Zhou, Shixian Feng

Huixian Centre for Disease Control: Yulian Gao,Tianyou He, Li Jiang, Huarong Sun

Zhejiang

Provincial Centre for Disease Control:Min Yu,Danting Su, Feng Lu

Tongxiang Centre for Disease Control: Yijian Qian, Kunxiang Shi,Yabin Han,Lingli Chen

Hunan

Provincial Centre for Disease Control:Guangchun Li, Huilin Liu,LI Yin

Liuyang Centre for Disease Control: Youping Xiong, Zhongwen Tan, Weifang Jia

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

REFERENCES

  • [1].Mezuk B, Eaton WW, Albrecht S, Golden SH. Depression and type 2 diabetes over the lifespan: A meta-analysis. Diabetes Care. 2008;31:2383–2390. doi: 10.2337/dc08-0985. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [2].Wulsin LR, Singal BM. Do depressive symptoms increase the risk for the onset of coronary disease? A systematic quantitative review; psychosomatic medicine. Psychosom Med. 2003;65:201–210. doi: 10.1097/01.psy.0000058371.50240.e3. [DOI] [PubMed] [Google Scholar]
  • [3].Knol NJ, Twisk JW, Beekman AT, Heine RJ, Snoek FJ, Pouwer F. Depression as a risk factor for the onset of type 2 diabetes mellitus: A meta-analysis. Diabetologia. 2006;49:837–845. doi: 10.1007/s00125-006-0159-x. [DOI] [PubMed] [Google Scholar]
  • [4].Mezuk B, Johnson-Lawrence V, Lee H, et al. Is ignorance bliss? depression, antidepressants, and the diagnosis of prediabetes and type 2 diabetes. Health Psychology. 2013;32:254–263. doi: 10.1037/a0029014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [5].Nouwen A, Nefs G, Caramlau I, et al. Prevalence of depression in individuals with impaired glucose metabolism or undiagnosed diabetes: A systematic review and meta-analysis of the european depression in diabetes (EDID) research consortium. Diabetes Care. 2011;34:752–762. doi: 10.2337/dc10-1414. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [6].Stanley MA, Beck JG. Anxiety disorders. Clin Psychol Rev. 2000;20:731–754. doi: 10.1016/s0272-7358(99)00064-1. [DOI] [PubMed] [Google Scholar]
  • [7].Kendler KS, Gardner CO, Gatz M, Pedersen NL. The sources of co-morbidity between major depression and generalized anxiety disorder in a Swedish national twin sample. Psychol Med. 2007;37:453–462. doi: 10.1017/S0033291706009135. [DOI] [PubMed] [Google Scholar]
  • [8].Smith KJ, Beland M, Clyde M, Gariepy G, Page V, Badawi G, Rabasa-Lhoret R, Schmitz N. Association of diabetes with anxiety: a systematic review and meta-analysis. J Psychosomatic Res. 2013;74:89–99. doi: 10.1016/j.jpsychores.2012.11.013. [DOI] [PubMed] [Google Scholar]
  • [9].Grigsby AB, Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. Prevalence of anxiety in adults with diabetes: A systematic review. J Psychosom Res. 2000;53:1053–1060. doi: 10.1016/s0022-3999(02)00417-8. [DOI] [PubMed] [Google Scholar]
  • [10].Engum A. The role of depression and anxiety in onset of diabetes in a large population-based study. J Psychosom Res. 2007;62:31–38. doi: 10.1016/j.jpsychores.2006.07.009. [DOI] [PubMed] [Google Scholar]
  • [11].Hildrum B, Mykletun A, Midthjell K, Ismail K, Dahl AA. No association of depression and anxiety with the metabolic syndrome: The Norwegian HUNT study. Acta Psychiatr Scand. 2009;120:14–22. doi: 10.1111/j.1600-0447.2008.01315.x. [DOI] [PubMed] [Google Scholar]
  • [12].Lin EHB, Korff MV. Mental disorders among persons with diabetes—Results from the world mental health surveys. J Psychosom Res. 2008;65:571–580. doi: 10.1016/j.jpsychores.2008.06.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Edwards LE, Mezuk B. Anxiety and risk of type 2 diabetes: Evidence from the Baltimore epidemiologic catchment area study. J Psychosom Res. 2012;73:418–423. doi: 10.1016/j.jpsychores.2012.09.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Yu Z, Han S, Chu J, Xu Z, Zhu C, Guo X. Trends in overweight and obesity among children and adolescents in china from 1981 to 2010: A meta-analysis. PLoS ONE. 2012;7:e51949. doi: 10.1371/journal.pone.0051949. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [15].Yan S, Li J, Li S, et al. The expanding burden of cardiometabolic risk in china: The china health and nutrition survey. Obes Rev. 2012;13:810–821. doi: 10.1111/j.1467-789X.2012.01016.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Li H, Oldenburg B, Chamberlain C, et al. Diabetes prevalence and determinants in adults in china mainland from 2000 to 2010: A systematic review. Diabetes Res Clin Pract. 2012;98:226–235. doi: 10.1016/j.diabres.2012.05.010. [DOI] [PubMed] [Google Scholar]
  • [17].Guo WJ, Tsang A, Li T, Lee S. Psychiatric epidemiological surveys in china 1960–2010: How real is the increase of mental disorders? Curr Opin Psychiatry. 2011;24:324–330. doi: 10.1097/YCO.0b013e3283477b0e. [DOI] [PubMed] [Google Scholar]
  • [18].Shen YC, Zhang MY, Huang YQ, et al. Twelve-month prevalence, severity and unmet need for treatment of mental disorders in metropolitan china. Psychol Med. 2006;36:257–267. doi: 10.1017/S0033291705006367. [DOI] [PubMed] [Google Scholar]
  • [19].Lee S, Tsang A, Zhang MY, et al. Lifetime prevalence and intercohort variation in DSM-IV disorders in metropolitan china. Psychol Med. 2007;37:61–71. doi: 10.1017/S0033291706008993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20].Kleinman A. Neurasthenia and depression: A study of somatization and culture in china. Cult Med Psychiatry. 1982;6:117–190. doi: 10.1007/BF00051427. [DOI] [PubMed] [Google Scholar]
  • [21].Yu NW, Chen CY, Liu CY, Chau YL, Chang CM. Association of body mass index and depressive symptoms in a Chinese community population: Results from the health promotion knowledge, attitudes, and performance survey in Taiwan. Chang Gung Med J. 2022;34:360–367. [PubMed] [Google Scholar]
  • [22].Dong Q, Liu J, Zheng R, et al. Obesity and depressive symptoms in the elderly: A survey in the rural area of Chizhou, Anhui province. Int J Geriatr Psychiatry. 2013;28:227–232. doi: 10.1002/gps.3815. [DOI] [PubMed] [Google Scholar]
  • [23].Niti HR, Ho RC, Niti M, Kua EH, Ng TP. Body mass index, waist circumference, waist-hip ratio and depressive symptoms in Chinese elderly: A population-based study. Int J Geriatr Psychiatry. 2008;23:401–408. doi: 10.1002/gps.1893. [DOI] [PubMed] [Google Scholar]
  • [24].Li ZB, Ho SY, Chan WM, Ho KS, Li MP, Leung GM, Lam TH. Obesity and depressive symptoms in Chinese elderly. Int J Geriatr Psychiatry. 2004;19:68–74. doi: 10.1002/gps.1040. [DOI] [PubMed] [Google Scholar]
  • [25].Luppino FS, de Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BW, Zitman FG. Overweight, obesity, and depression: A systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry. 2010;67:220–229. doi: 10.1001/archgenpsychiatry.2010.2. [DOI] [PubMed] [Google Scholar]
  • [26].Pan A, Ye X, Franco OH, et al. Insulin resistance and depressive symptoms in middle-aged and elderly Chinese: Findings from the nutrition and health of aging population in China study. J Affect Disord. 2008;109:75–82. doi: 10.1016/j.jad.2007.11.002. [DOI] [PubMed] [Google Scholar]
  • [27].Zhang C, Chen Y, Chen W. Association of psychosocial factors with anxiety and depressive symptoms in Chinese patients with type 2 diabetes. Diabetes Res Clin Pract. 2008;79:523–530. doi: 10.1016/j.diabres.2007.10.014. [DOI] [PubMed] [Google Scholar]
  • [28].Lee S, Chiu A, Tsang A, Chow C, Chan W. Treatment-related stresses and anxiety-depressive symptoms among Chinese outpatients with type 2 diabetes mellitus in Hong Kong. Diabetes Res Clin Pract. 2006;74:282–288. doi: 10.1016/j.diabres.2006.03.026. [DOI] [PubMed] [Google Scholar]
  • [29].Chong SA, Subramaniam M, Chan YH, et al. Depressive symptoms and diabetes mellitus in an asian multiracial population. Asian J Psychiatr. 2009;2:66–70. doi: 10.1016/j.ajp.2009.04.012. [DOI] [PubMed] [Google Scholar]
  • [30].Chen Z, Chen J, Collins R, Guo Y, Peto R, Wu F, Li L, China Kadoorie Biobank (CKB), collaborative group. Chen Z, Chen J, et al. China Kadoorie biobank of 0.5 million people: Survey methods, baseline characteristics and long-term follow-up. Int J Epidemiol. 2011;40:1652–1666. doi: 10.1093/ije/dyr120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Eaton WW, Hall ALF, MacDonald R, McKibben J. Case identification in psychiatric epidemiology: A review. Int Rev Psychiatry. 2007;19:497–507. doi: 10.1080/09540260701564906. [DOI] [PubMed] [Google Scholar]
  • [32].Kessler RC, Andrews G, Mroczek DK, Ustun B, Wittchen HU. The World Health Organization Composite International Diagnostic Interview–Short form (CIDI-SF) Int J Methods Psychiatr Res. 2006;7:171–185. [Google Scholar]
  • [33].Kessler RC, Abelson J, Demler O, et al. Clinical calibration of DSM-IV diagnoses in the World Mental Health (WMH) version of the World Health Organization (WHO) Composite International Diagnostic Interview. Int J Methods Psychiatr Res. 2004;13:122–139. doi: 10.1002/mpr.169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Kessler RC, Ustun TB. The World Mental Health (WMH) Survey Initiative Version of the World Mental Health Organization (WHO) Composite International Diagnostic Interview (CIDI) Int J Methods in Psychiatr Res. 2004;13:93–121. doi: 10.1002/mpr.168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].American Diabetes Association Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36:S67–S74. doi: 10.2337/dc13-S067. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Bhasin MK, Dusek JA, Chang BH, et al. Relaxation response induces temporal transcriptome changes in energy metabolism, insulin secretion, and inflammatory pathways. PLoS One. 2013;8:e62817. doi: 10.1371/journal.pone.0062817. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Hung HC, Yang YC, Ou HY, Wu JS, Lu FH, Chang CJ. The relationship between impaired fasting glucose and self-reported sleep quality in a Chinese population. Clin Endocrinol. 2012;78:518–524. doi: 10.1111/j.1365-2265.2012.04423.x. [DOI] [PubMed] [Google Scholar]
  • [38].Nicolucci A, Kovas Burns K, Holt RI, et al. Diabetes attitudes, wishes and needs second study (DAWN2): Cross-national benchmarking of diabetes-related psychosocial outcomes for people with diabetes. Diabet Med. 2013;30:767–777. doi: 10.1111/dme.12245. [DOI] [PubMed] [Google Scholar]
  • [39].Roy T, Lloyd CE, Pouwer F, Holt RI, Sartorius N. Screening tools uses for measuring depression among people with type 1 and type 2 diabetes: A systematic review. Diabet Med. 2012;29:167–175. doi: 10.1111/j.1464-5491.2011.03401.x. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

01
NIHMS531343-supplement-01.docx (27KB, docx)

RESOURCES