Metabolic syndrome and depression are not correlated: results from a community sample exploring the unique and common correlates for the two diseases

Corresponding Author:

Dr. Hao-Jan Yang
Department of Public Health, Chung Shan Medical University, No. 110, Sec. 1, Jianguo N. Rd., Taichung 40201, Taiwan
Tel: +886-4-24730022, Ext: 12109
Fax: +886-4-23248179
E-mail: [email protected]

Abstract

Aim:

More and more studies are suggesting evidence for the comorbidity of cardiovascular disease and depressive disorders, yet the mechanism is obscure. Our study aimed to identify correlates common and unique to metabolic syndrome and depression, in order to clarify the relationship between the two diseases in terms of their taxonomy and potential overlapping mechanisms.

Methods:

Data from a large-scale community sample of 30-year-old or older residents of a Taiwanese city (N=11,258) were analyzed to compare sociodemographic and lifestyle factors between four groups: metabolic syndrome only, depression only, comorbid condition, and no disease. The metabolic syndrome was defined by using the standards published by National Health Promotion Administration and the depression was assessed by using the Mental Health Inventory-5.

Results:

Results showed that prevalence rates of metabolic syndrome and depression were 14% and 16.5%, respectively, whilst a low, <3%, comorbidity rate was found. Education level and weekly exercise frequency were common factors to both metabolic syndrome and depression individually, but their directionality was different. Personal income was a unique factor to metabolic syndrome, while age, sex, and drinking habits were so for depression.

Conclusion:

Our findings implied that metabolic syndrome and depression may not have direct relationship in terms of diagnostic taxonomy. However, social environment and personal lifestyle habits may be common factors connecting the two diseases. Thus, maintaining preferable lifestyle habits is the key to both physiological and psychological health.

Keywords

Metabolic syndrome, Depression, Comorbidity, Correlates

Introduction

Cardiovascular disease (CVD) and major depressive disorder (MDD) are two major diseases that cause the most severe functional impairment in the twenty-first century [1]. This is especially important for countries with high income: of all deaths due to physical disability, the leading cause was MDD, implicated in 9% of them, and the second-place cause was CVD, at 6% [2]. These statistics indicate that CVD and MDD are currently very important topics in public health.

Individuals with metabolic syndrome (MS) are a high-risk group for CVD because both diseases share a cluster of factors and symptoms, such as central obesity, hypertension, high-density lipoprotein (HDL) deficiency, hyperglycemia, and hypertriglyceridemia [3]. In addition to physical problems, researchers have recently suggested that a proportion of MS patients also suffer from mental illness. This is not surprising because people with diabetes [4] or CVD [5] were reported to have high comorbidity with MDD. Those who with MS may have MDD due to obesity affecting their psychological health [6]. Nevertheless, one recent meta-analysis study showed that MDD and MS have a bidirectional relationship, meaning that MDD may cause MS and vice versa [7]. The relationship of the two diseases appears to be intertwined and complicated. Although depressive symptoms are not equal to, but an important precursor of [8], MDD, the term “depression” is used in this article to represent a general concept that mixes both depressive symptoms and MDD wherever necessary.

In order to further understand the mechanisms behind the comorbidity of MS and depression, it is crucial to distinguish common and unique risk factors for both diseases. However, the risk factors involved in MS and depression are extensive, and encompass physiological, psychological, and socio-environmental factors in various aspects. A massive and representative sample covering all kinds of information is necessary for performing statistical comparisons to ensure sufficient statistical power and unbiased generalizability. Accordingly, our study collected extensive information in terms of both physiological and psychological variables from a large-scale and representative community sample. Using this information, we identify the common and unique factors for MS and depression to gain a preliminary understanding of the possible pathological mechanism behind the comorbidity and to provide a reference for future public health prevention and intervention strategies.

Methods

▪ Study population

Our study performed analysis based on data from the Landseed Cohort established by Taiwan Landseed Hospital from 2005. Participants were selected from residents over 30 year’s old living in Pingzhen City, Taiwan. According to Household Registration Office, the total population of Pingzhen City over 30 years old numbered about 128,659 in 2013, accounting for 0.85% of the same age population in Taiwan. Through probability-proportional-tosize sampling, Landseed Hospital invited 15,000 residents over 30 years old in Pingzhen City to participate free physical health examinations every two to three years. Meanwhile, participants also completed a questionnaire regarding their family medical history, personal medical history, medication history, mental status, quality of life, diet, exercise habits, etc. We combined the three time points of examination data to focus on factors related to MS and depression, thus including a total of 11,258 individuals in our analysis. Our study design was examined and approved by the Institutional Review Board of Landseed Hospital.

Measurements

▪ Metabolic syndrome

The diagnostic standard for MS used in our study was based on WHO standards and modified by the Taiwan Health Promotion Administration for Taiwanese population. Briefly, individuals having three or more items of the following five are diagnosed with MS: (1) Central obesity (waist circumference ≥ 90 cm (male) or ≥ 80 cm (female)); (2) High blood pressure (SBP ≥ 130 mmHg or DBP ≥ 85 mmHg); (3) Fasting glucose ≥ 100 mg/dl; (4) Low HDL-C (<40 mg/dl (male) or <50 mg/dl (female)); and (5) Triglycerides ≥ 150 mg/dl.

▪ Depressive symptoms

The presence of depressive symptoms in the participants was assessed using the 5-item Mental Health Inventory, MHI-5, extracted from the SF-36 Health Survey. The inventory requires respondents to reply each question (e.g., “How much of the time have you felt downhearted and blue?”) on six-point Likert scale of the past month. Questions 3 and 5 were reverse scored, giving a total score of between 5 and 30, with higher scores representing more depressive symptoms. Recent studies have shown that the MHI-5 is a recommended screening tool for mood disorders [9] and has good reliability [10]. The Cronbach’s alpha of MHI-5 in the present study was 0.83. To increase the specificity of MHI-5, our study transformed the original score to a standardized score distributed between 0 and 100; individuals with a score higher than the threshold of 90 were determined to have a high risk of depression and therefore categorized as depressed group.

▪ Control variables and lifestyle factors

The basic demographic variables and lifestyle factors included in our study were: age, education level, marital status, personal monthly income, smoking, alcohol use, betel nut chewing and other habits, current work load, and weekly exercise time.

▪ Statistical Analyses

We estimate the point prevalence and comorbidity rates of MS and depression. The comorbidity rate was estimated in terms of bidirectional comorbidity rate [11]. In addition, we identified common factors between MS and depression and unique factors to each through logistic regression modeling. By comparing patients with MS only, patients with depression only, and patients with both diseases with patients with neither disease, a given factors was considered a common factor if the odds ratio (OR) significantly deviated from 1 in both MS only and depression only groups, or in the comorbid group. Otherwise, the factor was considered a unique factor if its OR significantly deviated from 1 only in MS only group or only in depression only group. The Hosmer-Lemeshow test was used to examine how well model fit the data for each logistic regression model. The variance inflation factor (VIF) was calculated as a diagnostic index to test multicollinearity in multiple regression models. A mean VIF lower than 10 suggested multicollinearity did not substantially affect model estimates.

Results

Among the 11,258 community residents over 30 years old included in our study, age was normally distributed, with about one third (32.65%) of the residents aged between 50 and 59 years old (Table 1). More females (55.62%) received the health examination than males. Approximately 20% of participants had an education level of elementary school, which is lower than the average of the Taiwanese population. The majority of the participants (87.06%) were married. More than half (57.01%) of the residents had a monthly personal income less than 30,000 NTD (approximately 950 USD). Among the five MS criteria, central obesity was met by the most individuals (39.03%), while HDL deficiency was met by the fewest (16.41%). In total, 14.01% of the population fit the criteria for a diagnosis of MS.

Table 1: Distribution of socio-demographic characteristics and metabolic syndrome-related factors (N=11258).

The proportion of total residents who met the criteria for depression was 16.52% in our study. Individuals diagnosable with both MS and depression accounted for 2.9%, and the proportion with neither was 70.96%. Table 2 showed three models with MS only, depression only, and comorbid condition were regressed on 10 potential factors in terms of age, sex, educational level, marital status, personal income, cigarette use, alcohol use, betel nut use, activity, and frequency of exercise. The Hosmer- Lemeshow goodness-of-fit test for MS only (χ² = 9.25, p = 0.352), depression only (χ² = 16.92, p = 0.129), and comorbid condition (χ² = 20.11, p = 0.083) models showed all models fit the data. The VIFs of all independent variables for the three models were all less than 10, indicating that the relationships among the independent variables were not too high to affect model estimates. In Table 2, personal income was a unique factor related to MS: middle income (monthly income of 50-75 thousand NTD) was a protective factor against MS (OR = 0.6, 95% CI = 0.4-0.8). On the other hand, age, gender, and drinking habits were unique factors related to depression. Higher age conferred a relatively higher risk, and females were at a higher risk than males (OR = 1.3, 95% CI = 1.1-1.6); however, individuals who abstained from alcohol exhibited a significant protective effect (OR = 0.6, 95% CI = 0.4-0.9) against depression compared with individuals who had never used alcohol. Notably, education level and weekly exercise time were related to both MS and depression, yet with opposite directional effects. Low education level was a risk factor for MS (OR = 2.4, 95% CI = 1.1-5.4), but a protective factor against depression (OR = 0.5, 95% CI = 0.3-0.9). Regular exercise (4-7 times per week) was a protective factor against MS (OR = 0.7, 95% CI = 0.6-0.9), but a risk factor for depression (OR = 1.2, 95% CI = 1.1-1.4). These two variables did not show significance in the analysis of comorbidity group.

Table 2: Multiple logistic regression analyses.

Discussion

In this over-30-years-old community sample, both MS (14%) and depression (16.5%) were prevalent, yet residents with comorbid condition for the two diseases accounted for less than 3%. Although the prevalence rate of MS was generally higher with age, the estimated prevalence in our sample is not higher than those obtained from general population in Western countries (~33%) [12,13] or in Asian countries (10-30%) [14]. It is also a lower estimate when compared with large-scale surveys from Taiwan (25.5%) [15] or from China (25%) [16]. This variation in results may be caused by differences in diagnostic standards between studies. Another possible reason may be that our study collected information through community health examinations, and those participants able to go outdoors and undergo examinations are likely relatively healthy individuals. For depression, the estimated point prevalence in this study (16.6%) is consistent with a recent American study [17]. This similarity implies that, unlike many past studies used a standardized score of 70 as their cutoff point and obtained a prevalence rate of about 33% [18], using a standardized score of 90 from the MHI-5 may prevent the prevalence estimation from a high false positive rate.

It is interesting to note that a low number of individuals in the comorbid group and no factor significantly linked to the comorbidity condition. This result might have meant insufficient statistical power, but it could simply indicate the lack of a direct relationship between MS and depression. This is in line with one large-scale community study which showed no correlations between depression-related disorders and the mutant allele of the apoE2 gene [19], implying that MS and depression disorders lack a common genetic background. However, non-genetic factors in terms of education level and weekly exercise frequency may affect the two diseases simultaneously. Nevertheless, the direction of the effect of these two factors on MS and depression were opposite. Individuals with higher education might possess comparatively deeper health knowledge and plan for regular health-promoting activities well, which both decrease the risk for MS [20]. On the other hand, individuals with higher education usually encounter more work-related pressures, a phenomenon that increases the risk for depressive disorders [21]. In our study, the group with under-elementary education included mostly elderly individuals, most of who were retired and thus experience less work-related pressure: this is a likely reason why fewer symptoms of depressive disorders were discovered. It is even possible that the elderly individuals were reluctant to express deep emotion due to Chinese cultural tendencies [22], which caused the appearance of a protective effect in our results.

Individuals who performed exercise 4-7 times per week reduced their risk of MS by one-third, yet excessive exercise did not confer a protective effect, compared with those who did less than 3 times per week. These results imply that the effect of exercise on MS may not be a linear or dose-response relationship, but rather that both too much and too little exercise are harmful to the metabolism of adults [23]. The relationship between insufficient exercise and MS has been indicated in many studies [24]. However, the exact mechanism that related excessive exercise to MS is still unclear. It is possible that obese individuals forced themselves to exercise heavily after realizing their high risk for various diseases. Interestingly, our study discovered that individuals who exercised 4-7 times per week had a 20% higher risk of having depression compared with those who did less than 3 times per week. Some previous studies have suggested that exercise helps to reduce risk for depressive disorders. This discrepancy may due to the different definition of exercise between studies where many studies used high-intensity exercise or aerobic exercise as a measure [25], which is not a requirement in the present study.

Our study found that moderate personal income was a unique protective factor against having MS. In Taiwan, a certain proportion of the middleclass population is civil servants, who live rather regular lives and do not regularly consume highfat or high-salt foods and therefore have a low risk of MS. Low-income population lack social resources and social capital, which increases their risk for MS. High-income population do benefit from abundant social resources; however, long working hours, lack of time for exercise, and unhealthy lifestyles can lead to the occurrence of MS.

The unique factors for depression in this study were age, gender and drinking habits, which are consistent with the results of previous studies. The heightened risk for depressive disorders that old age and female gender has intercultural consistency [26]. However, unlike previous findings [27], our study found that the risk of depression is lower in individuals who had quit drinking compared with individuals who never drink. Few past studies have commented on the risk of depression in individuals who quit drinking. One potential cause is that people who successfully quit drinking develop strong personal motivation and positive attitude, and enjoy great support from society [28], which are all protective factors against depressive disorders.

When interpreting the results of our study, one needs to consider the following limitations. First, the relationship between factors and diseases may not be causal because they were analyzed cross-sectionally in this study. Second, our study combined data at three time points; some individuals may be duplicated in the overall dataset. Third, the self-reported weekly exercise frequency may retain large variance in the intensity of exercise performed, and a high frequency may not necessarily represent a large amount of exercise. Fourth, our results can only reflect the situation of a subpopulation of residents of Taoyuan County, and may not be generalizable to the populations of other regions.

Conclusions

The low comorbidity rate of and lack common factors between MS and depression imply that the two diseases have no direct relationship. However, education level and weekly exercise frequency had opposite effects on the two diseases, suggesting that social environment and lifestyle may be factors connecting them. In addition, the variable of social economic status had a unique relationship with MS, and those factors uniquely affecting depression were variables related to demography and lifestyle.

Funding

This work was supported in part by the collaborative projects between Chung Shan Medical University and Landseed Hospital (CSMU-LSH-101-02 and CSMU-LSH-102-01) awarded to Dr. Hao-Jan Yang.

References

1. World Health Organization. The Global Burden of Disease: 2004 Update. Geneve (2008).
2. Lopez AD, Mathers CD. Measuring the global burden of disease and epidemiological transitions: 2002-2030. Ann. Trop. Med. Parasitol 100(5-6), 481-499 (2006).
3. Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112(17), 2735-2752 (2005).
4. Mezuk B, Eaton WW, Albrecht S, et al. Depression and type 2 diabetes over the lifespan: a meta-analysis. Diabetes. Care 31(12), 2383-2390 (2008).
5. Musselman DL, Evans DL, Nemeroff CB. The relationship of depression to cardiovascular disease: epidemiology, biology, and treatment. Arch. Gen. Psychiatry 55(7), 580-592 (1998).
6. Carpiniello B, Pinna F, Velluzzi F, et al. Mental disorders in patients with metabolic syndrome. The key role of central obesity. Eat. Weight. Disord 17(4), e259-266 (2012).
7. Pan A, Keum N, Okereke OI, et al. Bidirectional association between depression and metabolic syndrome: a systematic review and meta-analysis of epidemiological studies. Diabetes. Care 35(5), 1171-1180 (2012).
8. Yang HJ, Soong WT, Kuo PH, et al. Using the CES-D in a two-phase survey for depressive disorders among nonreferred adolescents in Taipei: a stratum-specific likelihood ratio analysis. J. Affect. Disord 82(3), 419-430 (2004).
9. Rumpf HJ, Meyer C, Hapke U, et al. Screening for mental health: validity of the MHI-5 using DSM-IV Axis I psychiatric disorders as gold standard. Psychiatry. Res 105(3), 243-253 (2001).
10. van den Beukel TO, Siegert CE, van Dijk S, et al. Comparison of the SF-36 five-item Mental Health Inventory and Beck Depression Inventory for the screening of depressive symptoms in chronic dialysis patients. Nephrol. Dial. Transplant 27(12), 4453-4457 (2012).
11. McConaughy SH, Achenbach TM. Comorbidity of empirically based syndromes in matched general population and clinical samples. J. Child. Psychol. Psychiatry 35(6), 1141-1157 (1994).
12. Kolovou GD, Anagnostopoulou KK, Salpea KD, et al. The prevalence of MetSyn in various populations. Am. J. Med. Sci 333(6), 362-371 (2007).
13. European Society of Cardiology, Epidemiology of MetSyn in Europe (2014).
14. Nestel P, Lyu R, Low LP, et al. Metabolic syndrome: recent prevalence in East and Southeast Asian populations. Asia. Pac. J. Clin. Nutr 16(2), 362-367 (2007).
15. Yeh CJ, Chang HY, Pan WH. Time trend of obesity, the metabolic syndrome and related dietary pattern in Taiwan: from NAHSIT 1993-1996 to NAHSIT 2005-2008. Asia. Pac. J. Clin. Nutr 20(2), 292-300 (2011).
16. Xi B, He D, Hu Y, et al. Prevalence of metabolic syndrome and its influencing factors among the Chinese adults: the China Health and Nutrition Survey in 2009. Prev. Med 57(6), 867-871 (2013).
17. Kessler RC, Petukhova M, Sampson NA, et al. Twelve-month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. Int. J. Meth. Psych. Res 21(3), 169-184 (2012).
18. Rukavina TV, Brborovic O, Fazlic H, et al. Prevalence and five-year cumulative incidence of psychological distress: the CroHort study. Coll. Antropol 36(1), 109-112 (2012).
19. Surtees PG, Wainwright NWJ, Bowman R, et al. No association between APOE and major depressive disorder in a community sample of 17,507 adults. J. Psych. Res 43(9), 843-847 (2009).
20. Ngo AD, Paquet C, Howard NJ, et al. Area-level socioeconomic characteristics and incidence of metabolic syndrome: a prospective cohort study. BMC. Public. Health 1(1), 681-692 (2013).
21. Tennant C. Work-related stress and depressive disorders. J. Psychosom. Res 51(5), 697-704 (2001).
22. Kleinman A. Culture and depression. N. Engl. J. Med 351(10), 951-953 (2004).
23. Scheers T, Philippaerts R, Lefevre J. SenseWear-determined physical activity and sedentary behavior and metabolic syndrome. Med. Sci. Sports. Exerc 45(3), 481-489 (2013).
24. Edwardson CL, Gorely T, Davies MJ, et al. Association of sedentary behaviour with metabolic syndrome: a meta-analysis. PLoS. One 7(4), e34916 (2013).
25. Saeed SA, Antonacci DJ, Bloch RM. Exercise, yoga, and meditation for depressive and anxiety disorders. Am. Fam. Physician 81(8), 981-986 (2010).
26. Luppa M, Sikorski C, Luck T, et al. Age- and gender-specific prevalence of depression in latest-life--systematic review and meta-analysis. J. Affect. Disord 136(3), 212-221 (2012).
27. Schuckit MA, Tom L, Smith TL, et al. Relationships among independent major depressions, alcohol use, and other substance use and related problems over 30 years in 397 families. J. Stud .Alcohol. Drugs 74(2), 271-279 (2013).
28. Rus-Makovec M, Cebasek-Travnik Z. Co-occurring mental and somatic diagnoses of alcohol dependent patients in relation to long-term aftercare alcohol abstinence and well-being. Psychiatr. Danub 20(2), 194-207 (2008).