- Email: [email protected]
Association between subclinical hypothyroidism and metabolic syndrome among individuals with depression
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Association between subclinical hypothyroidism and metabolic syndrome among individuals with depression Moon-Doo Kim MD, PhD , Hyun-Ju Yang MD, PhD , Na Ri Kang MD , Joon Hyuk Park MD, PhD , Young-Eun Jung MD, PhD PII: DOI: Reference:
S0165-0327(19)31805-1 https://doi.org/10.1016/j.jad.2019.11.080 JAD 11327
To appear in:
Journal of Affective Disorders
Received date: Revised date: Accepted date:
10 July 2019 30 September 2019 12 November 2019
Please cite this article as: Moon-Doo Kim MD, PhD , Hyun-Ju Yang MD, PhD , Na Ri Kang MD , Joon Hyuk Park MD, PhD , Young-Eun Jung MD, PhD , Association between subclinical hypothyroidism and metabolic syndrome among individuals with depression, Journal of Affective Disorders (2019), doi: https://doi.org/10.1016/j.jad.2019.11.080
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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. © 2019 Published by Elsevier B.V.
1
Highlights
The prevalence of subclinical hypothyroidism was 9.4% in individuals with depression.
The odds of having metabolic syndrome were seven times greater among depressed individuals with subclinical hypothyroidism than those without subclinical hypothyroidism, and the number of metabolic syndrome components was positively related to the prevalence of subclinical hypothyroidism.
2
Short Communication
Association between subclinical hypothyroidism and metabolic syndrome among individuals with depression
Moon-Doo Kim, MD, PhD a, Hyun-Ju Yang, MD, PhD a, Na Ri Kang, MD a, Joon Hyuk Park, MD, PhD a, Young-Eun Jung, MD, PhD a*
a
Department of Psychiatry, School of Medicine, Jeju National University, Jeju, Republic of Korea
*Correspondence author: Young-Eun Jung, MD, PhD
Department of Psychiatry, School of Medicine, Jeju National University, 15 Aran 13-gil, Jeju 63241, Korea Tel: +82-64-717-1234, Fax: +82-64-717-1849, E-mail: [email protected]
Running head: Subclinical hypothyroidism and metabolic syndrome in depression
3
ABSTRACTS Background: Although the connection among low thyroid function, metabolic abnormalities, and depression is well documented, the relationship between subclinical hypothyroidism (SCH) and metabolic syndrome (MetS) in depression remains unclear. This study examined the association between SCH and MetS in a large sample with depression. Methods: The study analyzed 370 individuals with depression who participated in the 2014 Korea National Health and Nutrition Examination Survey. Associations between the presence of SCH and MetS were estimated after adjusting for related factors using multivariate logistic regression analysis. Results: In the 370 individuals with depression, the prevalence of SCH was 9.4% (SE = 1.6%). The prevalence of MetS was significantly higher in depressed individuals with than in those without SCH (56.3 ± 9.5% vs. 22.8 ± 2.6%; p = 0.001). After adjusting for covariates, the odds of having MetS were 7.127 times greater among depressed individuals with SCH than among those without SCH (95% confidence interval, 2.077–24.458). Limitations: The cross-sectional study design prevented inferences regarding causality and the effects of changes in variables. Conclusions: Depressed individuals with SCH are more likely to meet the criteria for MetS. These results highlight the significance of low thyroid function and the metabolic burden of individuals with depression.
Key Words: Depression; Subclinical hypothyroidism; Metabolic syndrome
4
1. Introduction Subclinical hypothyroidism (SCH), which is characterized by a mildly elevated serum thyroid-stimulating hormone (TSH) level with a normal serum free thyroxine (fT4) level (Biondi and Cooper, 2008), is an independent risk factor for cardiovascular disease (CVD) (Singh et al., 2008). Recently, several reports have focused on the relationship between SCH and depression. Depression is reportedly observed more frequently among patients with SCH than among patients with overt hypothyroidism. And, patients with SCH have a twofold higher prevalence of depressive symptoms relative to healthy individuals (Chueire et al., 2007; Demartini et al., 2014). Therefore, the guidelines recommended that the diagnosis of SCH should be considered in patients with depression (Garber et al., 2012). Thyroid dysfunction, such as SCH, accompanying depression may also be related to important clinical characteristics that may affect treatment selection and response. For example, there is a bidirectional relationship between depression and metabolic syndrome (MetS), as the presence of depression predicts the future incidence of MetS, and conversely, current MetS is associated with future onset of depression (Marazziti et al., 2014). Thyroid function may underlie the link between depression and MetS, and SCH in depression may be associated with MetS. The association between SCH and MetS, which is an increased risk for CVD (Biondi and Cooper, 2008), among patients with depression is unclear, and the management of SCH in depression remains controversial. This paper determined the rate of SCH among those with depression using a representative sample of Korean adults. Furthermore, we describe the clinical characteristics, including the presence of MetS, of those with depression and SCH.
2. Methods 2.1 Data source and study sample This study was based on cross-sectional data from the Korea National Health and Nutrition Examination Survey (KNHANES), a nationally representative survey conducted by the Korea Centers
5
for Disease Control and Prevention (KCDC). KNHANES, which began in 1998 and was designed to target the non-institutionalized Korean civilian population, consists of a health interview, nutrition survey, and health examination (Kweon et al., 2014). This study used data from the second year of the sixth KNHANES (VI-2, 2014). KNHANES VI-2 used two-stage stratified clustering. In the first stage, from the 303,180 geographically defined sampling units, 192 primary sampling units (PSUs) were sampled based on administrative districts and housing types. Each PSU contained an average of 60 households. With intra-stratification by residential area, age, and gender, the systematic sampling system was used to extract 20 households from each PSU. Within households, individuals aged 1 year and older participated in the survey. In this dataset, approximately one-third of the total sample, selected using stratified subsampling, underwent measurements of thyroid function [TSH, fT4, and anti-thyroid peroxidase antibody (TPOAb)]. Of 9,701 targeted individuals in KNHANES 2014, 7,550 participated in the survey. Our analysis included those aged 19 years and over who had valid thyroid function data (n = 2,018). Among 2,018 participants, 1,825 completed the Patient Health Questionnaire-9 (PHQ-9). We excluded those participants with overt hypothyroidism or hyperthyroidism or a TPOAb level higher than 34.0 IU/mL (n = 188). Of the remaining 1637 participants, 370 (22.6%) had PHQ-9 scores ≥ 5. Ultimately, we conducted a cross-sectional analysis of 370 participants with depression and 1267 without depression. All study protocols were approved by the Institutional Review Board of KCDC, and written informed consent was secured from all participants before the survey began. This study did not require additional Institutional Review Board approval because KNHANES data are publicly available.
2.2. Measures The presence of depression was identified using the PHQ-9, which is a reliable, valid diagnostic tool for measuring depression severity over the previous 2 weeks (Kroenke et al., 2001). The PHQ-9 is composed of nine items rated from 0 (not at all) to 3 (having the symptoms nearly every day), and the scores for each item are summed to produce a total depression severity score
6
(range 0–27). The Korean version of the PHQ-9 has high internal consistency (Cronbach’s α = 0.86), and the optimal cut-off total score for the presence of depression is 5 (Han et al., 2008). Blood samples were collected after at least 8 h of fasting. Blood samples were collected in 3ml EDTA-coated tubes (BD Vacutainer, Franklin Lakes, NJ). Serum samples used for the analysis of laboratory data were stored at 2–8 °C in refrigerated containers. All laboratory analyses were performed within 24 hours of sample collection. TSH, fT4, and TPOAb were determined by electrochemiluminescence immunoassays (E-602; Roche, Germany). The fasting glucose, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels were measured using enzymatic methods (Hitachi 7600; Hitachi, Japan). SCH was defined as a serum TSH concentration >4.5 mIU/L with a normal fT4 concentration (0.93–1.7 ng/dL) (Blum et al., 2016). We used the National Cholesterol Education Program-Adult Treatment Panel III criteria to determine whether MetS was present, using the cutoffs for the Asia–Pacific region (Grundy et al., 2005). MetS was considered present if three or more of the following five CVD risk factors were present: (i) systolic/diastolic blood pressure ≥130/85 mmHg or antihypertensive drug treatment; (ii) fasting serum triglycerides ≥150 mg/dL; (iii) low HDL-C (<40 mg/dL in men, ≤50 mg/dL in women); (iv) waist circumference ≥90 cm in men and ≥80 cm in women; and (v) fasting serum glucose ≥100 mg/dL or use of antidiabetic medication.
2.3. Covariates Lifestyle factors included current smoking, alcohol use problem, and physical activity. In terms of smoking status, participants were categorized as current smokers or non-smokers. To obtain information on the severity of alcohol use problems, we administered the Alcohol Use Disorder Identification Test-Alcohol Consumption (AUDIT-C) instrument (Gordon et al., 2001). We used a cutoff score of 8 to identify significant alcohol use problems. Physical activity was determined according to metabolic equivalent of task (MET) values, based on the self-reported frequency and duration of vigorous and moderate activity and walking during the previous week. The MET value of a particular activity (vigorous activity = 8.0 MET; moderate activity = 4.0 MET; walking = 3.3 MET) was multiplied by the mean time (hours/week) spent performing that particular activity to calculate
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the MET-hours per week, and the total weekly physical activity was the sum of the weekly METhours for each activity (Ainsworth et al., 2000). Weight and height were collected using standardized procedures, and the body mass index (BMI) was calculated as the weight in kilograms divided by the square of height in meters. The presence of chronic illness was assessed by self-report of a clinical diagnosis made by a physician, including the following diseases: diabetes mellitus, stroke, ischemic heart disease, renal failure, chronic hepatitis, and cancer.
2.4. Statistical analysis As the KNHANES data were acquired using stratified, clustered, systematic sampling, complex sample analyses were performed based on an analysis plan specifying weights, stratification variables, and primary sampling units. Missing data were included in the complex sample analyses to produce results in line with the specified survey-related procedure for data analysis, to ensure nationally representative estimates with accurate variance data. General linear models and chi-square tests were performed to determine the significance of differences in variables after dividing the participants into two groups based on the presence of SCH. Linear trend analysis was used to evaluate the dose–response relationship between SCH and the number of MetS components that had been identified. Finally, to determine the association between SCH and MetS and its components, multiple logistic regression analysis was used to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) with adjustment for age, gender, alcohol use problems, smoking status, physical activity, and BMI. The statistical analyses were performed using SPSS ver. 25.0 (IBM, Armonk, NY, USA) and p < 0.05 was taken to indicate statistical significance.
3. Results The study sample comprised 370 individuals with depression (60.9% women; average age ± standard error (SE) 39.9 ± 0.73 years) and 1267 individual without depression (45.9% women; average age ± SE 42.5 ± 0.41 years). The prevalence of SCH was significant higher in individuals with depression than in individual without depression (9.4 ± 1.6% vs. 5.5 ± 0.8%; p = 0.016).
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The differences between depressed individuals with and without SCH were not significant in terms of age, gender, alcohol use problems, smoking status, physical activity, chronic medical illness, and BMI. The TSH level was significantly higher in depressed individuals with SCH than in those without SCH (7.02 ± 0.58 vs. 2.10 ± 0.05 mIU/L, respectively), and the prevalence of MetS was significantly higher in depressed individuals with SCH than in those without SCH (56.3 ± 9.5% vs. 22.8 ± 2.6%; p = 0.001). The prevalence of SCH increased significantly with the number of MetS components (for trend, p = 0.006) in depressed individuals. The prevalence of SCH was highest (23.0%) in depressed individuals with three MetS components (Fig. 1). After adjusting for age, gender, alcohol use problem, smoking status, physical activity, and BMI, the odds of having MetS were 7.127 times greater among depressed individuals with SCH than in those without SCH (95% CI, 2.077–24.458). Of the MetS components, the OR was significantly elevated for the association between SCH and high triglycerides (OR, 3.724; 95% CI, 1.461–7.305) (Table 1). In contrast, no significant association between SCH and MetS was observed for individuals without depression after adjusting for age, gender, alcohol use problem, smoking status, physical activity, and BMI (OR,1.263; 95% CI, 0.560– 2.850).
4. Discussion Our results highlight the prevalence of SCH among individuals with depression. The prevalence of SCH was 9.4% in those with depression, while estimates of the prevalence of SCH in the Korean general population range from 0.1 to 5.6% based on data from healthcare examinations (Ha et al., 2018). More importantly, the odds of having MetS were seven times greater among depressed individuals with SCH than those without SCH, and the number of MetS components was positively related to the prevalence of SCH. Of the MetS components, a high triglyceride level was associated with SCH independent of other factors. Various studies have investigated the associations between SCH and MetS in adults with inconsistent results (Eftekharzadeh et al., 2016; Yang et al., 2016). Our results from depressed sample
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have shown a positive association between SCH and MetS, but there was no association between SCH and MetS in adults without depression. Health behavior and biological mechanisms may underlie the relationship of MetS and SCH with depression. Depression is known to increase the adverse health behaviors such as alcohol consumption, smoking, and decreased physical activity, which may affect the relationship between MetS and SCH. Another possible mechanism is related to hypothalamicpituitary-adrenocortical (HPA), hypothalamic-pituitary-thyroid system, and neurotransmission. Recent studies reported the dysfunction of HPA system in depressed patients with SCH, which may induce MetS (Xu et al., 2017). In the context of the higher cardiovascular risk associated with SCH and MetS (Biondi and Cooper, 2008), low thyroid function may act as an intermediate factor between MetS and CVD. The implications of the prevalence of SCH and MetS are greater in those with depression than in the general population. These factors help to explain the higher risk of medical comorbidities and poorer health outcomes in patients with depression (Marazziti et al., 2014). Furthermore, the presence of MetS, particular components of MetS, or low thyroid function is associated with poorer responses to antidepressants (Gitlin et al., 2004; McIntyre et al., 2016; Pae et al., 2009). The cross-sectional nature of the study limits the interpretation of the results. Although our findings support the hypothesized link between subclinical thyroid dysfunction and metabolic risk factors in depressed adults, confirmation in longitudinal studies is needed. Investigation of a larger cohort may also reveal a more consistent association of MetS with SCH in depression. In addition, the definition of depression using the PHQ-9 is open to question. The PHQ-9 has been validated as a screening tool with acceptable sensitivity and specificity (Han et al., 2008); however, it is not a diagnostic tool. Despite these limitations, to our knowledge, this is the first study to evaluate the association between SCH and MetS in a depressed sample. We used a representative nationwide sample and included a detailed list of possible confounding factors, such as various lifestyle factors.
5. Conclusion Our results highlight the prevalence of SCH in a depressed sample along with the links
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among thyroid dysfunction, lipid abnormalities, and MetS. Although it is difficult to establish a causal relationship, SCH may be association with an increased risk of MetS in individuals with depression. Future studies need to explore the mechanisms and clinical consequences of the observed relationships among thyroid dysfunction, metabolic risk factors, and depression.
Author disclosure - Acknowledgements: None. - Author's contributions : Author YEJ designed the study and wrote the protocol. Author MDK and YEJ managed the literature searches and analyses. Authors YEJ undertook the statistical analysis, and wrote the first draft of the manuscript. Author HJY, JHP and NRK contributed to and have approved the final manuscript. - Role of the Funding source: None. Conflict of interest statement: No conflicts declared.
6. References Ainsworth, B. E., Haskell, W. L., Whitt, M. C., Irwin, M. L., Swartz, A. M., Strath, S. J., O'Brien, L., Bassett Jr., D. R., Schmitz, K. H., Emplaincourt, P. O., Jacobs Jr., D. R., Leon,
W.
A. S., 2000.
Compendium of physical activities: an update of activity codes and MET intensities. Med. Sci. Sports Exerc. 32, S498–S504.
Biondi, B., Cooper, D.S., 2008. The clinical significance of subclinical thyroid dysfunction. Endocr. Rev. 29, 76–131.
Blum, M.R., Wijsman, L.W., Virgini, V.S., Bauer, D.C., den Elzen, W.P., Jukema, J.W., Buckley, B.M.,
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de Craen, A.J., Kearney, P.M., Stott, D.J., Gussekloo, J., Westendorp, R.G., Mooijaart, S.P., Rodondi, N., PROSPER Study Group., 2016. Subclinical thyroid dysfunction and depressive symptoms among the elderly: a prospective cohort study. Neuroendocrinology 103, 291–299.
Chueire, V.B., Romaldini, J.H., Ward, L.S., 2007. Subclinical hypothyroidism increases the risk for depression in the elderly. Arch. Gerontol. Geriatr. 44, 21–28.
Demartini, B., Ranieri, R., Masu, A., Selle, V., Scarone, S., Gambini, O., 2014. Depressive symptoms and major depressive disorder in patients affected by subclinical hypothyroidism: a cross-sectional study. J. Nerv. Ment. Dis. 202, 603–607.
Eftekharzadeh, A., Khamseh, M.E., Farshchi, A., Malek, M., 2016. The Association between subclinical hypothyroidism and metabolic syndrome as defined by the ATP III criteria. Metab. Syndr. Relat. Disord. 14, 137–144.
Garber, J.R., Cobin, R.H., Gharib, H., Hennessey, J.V., Klein, I., Mechanick, J.I., Pessah-Pollack, R., Singer, P.A., Woeber, K.A.; American Association Of Clinical Endocrinologists And American Thyroid Association Taskforce On Hypothyroidism In Adults., 2012. Clinical practice guidelines for hypothyroidism in adults: cosponsored by the American Association of Clinical Endocrinologists and the American Thyroid Association. Thyroid 22, 1200–1235.
Gitlin, M., Altshuler, L.L., Frye, M.A., Suri, R., Huynh, E.L., Fairbanks, L., Bauer, M., Korenman, S., 2004. Peripheral thyroid hormones and response to selective serotonin reuptake inhibitors. J. Psychiatry Neurosci. 29, 383–386.
Gordon, A.J., Maisto, S.A., McNeil, M., Kraemer, K.L., Conigliaro, R.L., Kelley, M.E., Conigliaro, J., 2001. Three questions can detect hazardous drinkers. J. Fam. Pract. 50, 313c320.
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Grundy, S.M., Cleeman, J.I., Daniels, S.R., Donato, K.A., Eckel, R.H., Franklin, B.A., Gordon, D.J., Krauss, R.M., Savage, P.J., Smith, S.C., Spertus, J.A., Costa, F., American Heart Association; National Heart, Lung, and Blood Institute., 2005. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112, 2735–2752.
Ha, J., Lee, J., Jo, K., Lim, D.J., Kang, M.I., Cha, B.Y., Kim, M.H., 2018. Sex differences in risk factors for subclinical hypothyroidism. Endocr. Connect. 7, 511–522.
Han, C., Jo, S.A., Kwak, J.H., Pae, C.U., Steffens, D., Jo, I., Park, M.H., 2008. Validation of the Patient Health Questionnaire-9 Korean version in the elderly population: the Ansan Geriatric study. Compr. Psychiatry 49, 218–223.
Kroenke, K., Spitzer, R.L., Williams, J.B., 2001. The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613.
Kweon, S., Kim, Y., Jang, M.J., Kim, Y., Kim, K., Choi, S., Chun, C., Khang, Y.H., Oh, K., 2014. Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES). Int. J. Epidemiol. 43, 69–77.
Marazziti, D., Rutigliano, G., Baroni, S., Landi, P., Dell'Osso, L., 2014. Metabolic syndrome and major depression. CNS Spectr. 19, 293–304.
McIntyre, R.S., Fayyad, R., Mackell, J.A., Boucher, M., 2016. Effect of metabolic syndrome and thyroid hormone on efficacy of desvenlafaxine 50 and 100 mg/d in major depressive disorder. Curr. Med. Res. Opin. 32, 587–599.
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Pae, C.U., Mandelli, L., Han, C., Ham, B.J., Masand, P.S., Patkar, A.A., Steffens, D.C., De Ronchi, D., Serretti, A., 2009. Thyroid hormones affect recovery from depression during antidepressant treatment. Psychiatry Clin. Neurosci. 63, 305–313.
Singh, S., Duggal, J., Molnar, J., Maldonado, F., Barsano, C.P., Arora, R., 2008. Impact of subclinical thyroid disorders on coronary heart disease, cardiovascular and all-cause mortality: a meta-analysis. Int. J. Cardiol. 125, 41–48.
Xu, Y.Y., Liang, J., Cao, Y., Shan, F., Liu, Y., Xia, Q.R., 2017. High levels of Nesfatin-1 in relation to the dysfunction of the hypothalamic-pituitary-adrenal and hypothalamus-pituitary-thyroid axes in depressed patients with subclinical hypothyroidism. Neuropsychiatr. Dis. Treat. 13, 1647–1653.
Yang, L., Lv, X., Yue, F., Wei, D., Liu, W., Zhang, T., 2016. Subclinical hypothyroidism and the risk of metabolic syndrome: a meta-analysis of observational studies. Endocr. Res. 41, 158–165.
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Figure 1. The relationship between the number of metabolic syndrome (MetS) components and the prevalence of subclinical hypothyroidism in individuals with depression
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Table 1. Odds ratios (ORs) and 95% confidence intervals (CI) for the association of subclinical hypothyroidism with metabolic syndrome and its components in individuals with depression. Subclinical hypothyroidism Adjusted OR (95% Cl)a
No n (%)
Yes n (%)
Metabolic syndrome
79 (22.8)
17 (56.3)
7.127 (2.077, 24.458)*
Abdominal obesity
78 (22.5)
11 (37.4)
0.980 (0.908, 1.058)
Hypertension
88 (24.0)
10 (29.7)
0.817 (0.257, 2.601)
High triglycerides
95 (28.0)
19 (58.6)
3.724 (1.461, 7.305)*
Low HDL-C
141 (43.3)
21 (59.3)
1.845 (0.744, 4.571)
Elevated fasting plasma glucose
92 (24.5)
11 (39.0)
1.811 (0.655, 5.006)
* p-value < 0.01 HDL-C, high-density lipoprotein cholesterol The unweighted numbers and weighted percentage distributions are shown. a Adjusted for age, gender, alcohol use problem, smoking status, physical activity, and body mass index.
Association between subclinical hypothyroidism and metabolic syndrome among individuals with depression Moon-Doo Kim MD, PhD , Hyun-Ju Yang MD, PhD , Na Ri Kang MD , Joon Hyuk Park MD, PhD , Young-Eun Jung MD, PhD PII: DOI: Reference:
S0165-0327(19)31805-1 https://doi.org/10.1016/j.jad.2019.11.080 JAD 11327
To appear in:
Journal of Affective Disorders
Received date: Revised date: Accepted date:
10 July 2019 30 September 2019 12 November 2019
Please cite this article as: Moon-Doo Kim MD, PhD , Hyun-Ju Yang MD, PhD , Na Ri Kang MD , Joon Hyuk Park MD, PhD , Young-Eun Jung MD, PhD , Association between subclinical hypothyroidism and metabolic syndrome among individuals with depression, Journal of Affective Disorders (2019), doi: https://doi.org/10.1016/j.jad.2019.11.080
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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. © 2019 Published by Elsevier B.V.
1
Highlights
The prevalence of subclinical hypothyroidism was 9.4% in individuals with depression.
The odds of having metabolic syndrome were seven times greater among depressed individuals with subclinical hypothyroidism than those without subclinical hypothyroidism, and the number of metabolic syndrome components was positively related to the prevalence of subclinical hypothyroidism.
2
Short Communication
Association between subclinical hypothyroidism and metabolic syndrome among individuals with depression
Moon-Doo Kim, MD, PhD a, Hyun-Ju Yang, MD, PhD a, Na Ri Kang, MD a, Joon Hyuk Park, MD, PhD a, Young-Eun Jung, MD, PhD a*
a
Department of Psychiatry, School of Medicine, Jeju National University, Jeju, Republic of Korea
*Correspondence author: Young-Eun Jung, MD, PhD
Department of Psychiatry, School of Medicine, Jeju National University, 15 Aran 13-gil, Jeju 63241, Korea Tel: +82-64-717-1234, Fax: +82-64-717-1849, E-mail: [email protected]
Running head: Subclinical hypothyroidism and metabolic syndrome in depression
3
ABSTRACTS Background: Although the connection among low thyroid function, metabolic abnormalities, and depression is well documented, the relationship between subclinical hypothyroidism (SCH) and metabolic syndrome (MetS) in depression remains unclear. This study examined the association between SCH and MetS in a large sample with depression. Methods: The study analyzed 370 individuals with depression who participated in the 2014 Korea National Health and Nutrition Examination Survey. Associations between the presence of SCH and MetS were estimated after adjusting for related factors using multivariate logistic regression analysis. Results: In the 370 individuals with depression, the prevalence of SCH was 9.4% (SE = 1.6%). The prevalence of MetS was significantly higher in depressed individuals with than in those without SCH (56.3 ± 9.5% vs. 22.8 ± 2.6%; p = 0.001). After adjusting for covariates, the odds of having MetS were 7.127 times greater among depressed individuals with SCH than among those without SCH (95% confidence interval, 2.077–24.458). Limitations: The cross-sectional study design prevented inferences regarding causality and the effects of changes in variables. Conclusions: Depressed individuals with SCH are more likely to meet the criteria for MetS. These results highlight the significance of low thyroid function and the metabolic burden of individuals with depression.
Key Words: Depression; Subclinical hypothyroidism; Metabolic syndrome
4
1. Introduction Subclinical hypothyroidism (SCH), which is characterized by a mildly elevated serum thyroid-stimulating hormone (TSH) level with a normal serum free thyroxine (fT4) level (Biondi and Cooper, 2008), is an independent risk factor for cardiovascular disease (CVD) (Singh et al., 2008). Recently, several reports have focused on the relationship between SCH and depression. Depression is reportedly observed more frequently among patients with SCH than among patients with overt hypothyroidism. And, patients with SCH have a twofold higher prevalence of depressive symptoms relative to healthy individuals (Chueire et al., 2007; Demartini et al., 2014). Therefore, the guidelines recommended that the diagnosis of SCH should be considered in patients with depression (Garber et al., 2012). Thyroid dysfunction, such as SCH, accompanying depression may also be related to important clinical characteristics that may affect treatment selection and response. For example, there is a bidirectional relationship between depression and metabolic syndrome (MetS), as the presence of depression predicts the future incidence of MetS, and conversely, current MetS is associated with future onset of depression (Marazziti et al., 2014). Thyroid function may underlie the link between depression and MetS, and SCH in depression may be associated with MetS. The association between SCH and MetS, which is an increased risk for CVD (Biondi and Cooper, 2008), among patients with depression is unclear, and the management of SCH in depression remains controversial. This paper determined the rate of SCH among those with depression using a representative sample of Korean adults. Furthermore, we describe the clinical characteristics, including the presence of MetS, of those with depression and SCH.
2. Methods 2.1 Data source and study sample This study was based on cross-sectional data from the Korea National Health and Nutrition Examination Survey (KNHANES), a nationally representative survey conducted by the Korea Centers
5
for Disease Control and Prevention (KCDC). KNHANES, which began in 1998 and was designed to target the non-institutionalized Korean civilian population, consists of a health interview, nutrition survey, and health examination (Kweon et al., 2014). This study used data from the second year of the sixth KNHANES (VI-2, 2014). KNHANES VI-2 used two-stage stratified clustering. In the first stage, from the 303,180 geographically defined sampling units, 192 primary sampling units (PSUs) were sampled based on administrative districts and housing types. Each PSU contained an average of 60 households. With intra-stratification by residential area, age, and gender, the systematic sampling system was used to extract 20 households from each PSU. Within households, individuals aged 1 year and older participated in the survey. In this dataset, approximately one-third of the total sample, selected using stratified subsampling, underwent measurements of thyroid function [TSH, fT4, and anti-thyroid peroxidase antibody (TPOAb)]. Of 9,701 targeted individuals in KNHANES 2014, 7,550 participated in the survey. Our analysis included those aged 19 years and over who had valid thyroid function data (n = 2,018). Among 2,018 participants, 1,825 completed the Patient Health Questionnaire-9 (PHQ-9). We excluded those participants with overt hypothyroidism or hyperthyroidism or a TPOAb level higher than 34.0 IU/mL (n = 188). Of the remaining 1637 participants, 370 (22.6%) had PHQ-9 scores ≥ 5. Ultimately, we conducted a cross-sectional analysis of 370 participants with depression and 1267 without depression. All study protocols were approved by the Institutional Review Board of KCDC, and written informed consent was secured from all participants before the survey began. This study did not require additional Institutional Review Board approval because KNHANES data are publicly available.
2.2. Measures The presence of depression was identified using the PHQ-9, which is a reliable, valid diagnostic tool for measuring depression severity over the previous 2 weeks (Kroenke et al., 2001). The PHQ-9 is composed of nine items rated from 0 (not at all) to 3 (having the symptoms nearly every day), and the scores for each item are summed to produce a total depression severity score
6
(range 0–27). The Korean version of the PHQ-9 has high internal consistency (Cronbach’s α = 0.86), and the optimal cut-off total score for the presence of depression is 5 (Han et al., 2008). Blood samples were collected after at least 8 h of fasting. Blood samples were collected in 3ml EDTA-coated tubes (BD Vacutainer, Franklin Lakes, NJ). Serum samples used for the analysis of laboratory data were stored at 2–8 °C in refrigerated containers. All laboratory analyses were performed within 24 hours of sample collection. TSH, fT4, and TPOAb were determined by electrochemiluminescence immunoassays (E-602; Roche, Germany). The fasting glucose, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels were measured using enzymatic methods (Hitachi 7600; Hitachi, Japan). SCH was defined as a serum TSH concentration >4.5 mIU/L with a normal fT4 concentration (0.93–1.7 ng/dL) (Blum et al., 2016). We used the National Cholesterol Education Program-Adult Treatment Panel III criteria to determine whether MetS was present, using the cutoffs for the Asia–Pacific region (Grundy et al., 2005). MetS was considered present if three or more of the following five CVD risk factors were present: (i) systolic/diastolic blood pressure ≥130/85 mmHg or antihypertensive drug treatment; (ii) fasting serum triglycerides ≥150 mg/dL; (iii) low HDL-C (<40 mg/dL in men, ≤50 mg/dL in women); (iv) waist circumference ≥90 cm in men and ≥80 cm in women; and (v) fasting serum glucose ≥100 mg/dL or use of antidiabetic medication.
2.3. Covariates Lifestyle factors included current smoking, alcohol use problem, and physical activity. In terms of smoking status, participants were categorized as current smokers or non-smokers. To obtain information on the severity of alcohol use problems, we administered the Alcohol Use Disorder Identification Test-Alcohol Consumption (AUDIT-C) instrument (Gordon et al., 2001). We used a cutoff score of 8 to identify significant alcohol use problems. Physical activity was determined according to metabolic equivalent of task (MET) values, based on the self-reported frequency and duration of vigorous and moderate activity and walking during the previous week. The MET value of a particular activity (vigorous activity = 8.0 MET; moderate activity = 4.0 MET; walking = 3.3 MET) was multiplied by the mean time (hours/week) spent performing that particular activity to calculate
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the MET-hours per week, and the total weekly physical activity was the sum of the weekly METhours for each activity (Ainsworth et al., 2000). Weight and height were collected using standardized procedures, and the body mass index (BMI) was calculated as the weight in kilograms divided by the square of height in meters. The presence of chronic illness was assessed by self-report of a clinical diagnosis made by a physician, including the following diseases: diabetes mellitus, stroke, ischemic heart disease, renal failure, chronic hepatitis, and cancer.
2.4. Statistical analysis As the KNHANES data were acquired using stratified, clustered, systematic sampling, complex sample analyses were performed based on an analysis plan specifying weights, stratification variables, and primary sampling units. Missing data were included in the complex sample analyses to produce results in line with the specified survey-related procedure for data analysis, to ensure nationally representative estimates with accurate variance data. General linear models and chi-square tests were performed to determine the significance of differences in variables after dividing the participants into two groups based on the presence of SCH. Linear trend analysis was used to evaluate the dose–response relationship between SCH and the number of MetS components that had been identified. Finally, to determine the association between SCH and MetS and its components, multiple logistic regression analysis was used to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) with adjustment for age, gender, alcohol use problems, smoking status, physical activity, and BMI. The statistical analyses were performed using SPSS ver. 25.0 (IBM, Armonk, NY, USA) and p < 0.05 was taken to indicate statistical significance.
3. Results The study sample comprised 370 individuals with depression (60.9% women; average age ± standard error (SE) 39.9 ± 0.73 years) and 1267 individual without depression (45.9% women; average age ± SE 42.5 ± 0.41 years). The prevalence of SCH was significant higher in individuals with depression than in individual without depression (9.4 ± 1.6% vs. 5.5 ± 0.8%; p = 0.016).
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The differences between depressed individuals with and without SCH were not significant in terms of age, gender, alcohol use problems, smoking status, physical activity, chronic medical illness, and BMI. The TSH level was significantly higher in depressed individuals with SCH than in those without SCH (7.02 ± 0.58 vs. 2.10 ± 0.05 mIU/L, respectively), and the prevalence of MetS was significantly higher in depressed individuals with SCH than in those without SCH (56.3 ± 9.5% vs. 22.8 ± 2.6%; p = 0.001). The prevalence of SCH increased significantly with the number of MetS components (for trend, p = 0.006) in depressed individuals. The prevalence of SCH was highest (23.0%) in depressed individuals with three MetS components (Fig. 1). After adjusting for age, gender, alcohol use problem, smoking status, physical activity, and BMI, the odds of having MetS were 7.127 times greater among depressed individuals with SCH than in those without SCH (95% CI, 2.077–24.458). Of the MetS components, the OR was significantly elevated for the association between SCH and high triglycerides (OR, 3.724; 95% CI, 1.461–7.305) (Table 1). In contrast, no significant association between SCH and MetS was observed for individuals without depression after adjusting for age, gender, alcohol use problem, smoking status, physical activity, and BMI (OR,1.263; 95% CI, 0.560– 2.850).
4. Discussion Our results highlight the prevalence of SCH among individuals with depression. The prevalence of SCH was 9.4% in those with depression, while estimates of the prevalence of SCH in the Korean general population range from 0.1 to 5.6% based on data from healthcare examinations (Ha et al., 2018). More importantly, the odds of having MetS were seven times greater among depressed individuals with SCH than those without SCH, and the number of MetS components was positively related to the prevalence of SCH. Of the MetS components, a high triglyceride level was associated with SCH independent of other factors. Various studies have investigated the associations between SCH and MetS in adults with inconsistent results (Eftekharzadeh et al., 2016; Yang et al., 2016). Our results from depressed sample
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have shown a positive association between SCH and MetS, but there was no association between SCH and MetS in adults without depression. Health behavior and biological mechanisms may underlie the relationship of MetS and SCH with depression. Depression is known to increase the adverse health behaviors such as alcohol consumption, smoking, and decreased physical activity, which may affect the relationship between MetS and SCH. Another possible mechanism is related to hypothalamicpituitary-adrenocortical (HPA), hypothalamic-pituitary-thyroid system, and neurotransmission. Recent studies reported the dysfunction of HPA system in depressed patients with SCH, which may induce MetS (Xu et al., 2017). In the context of the higher cardiovascular risk associated with SCH and MetS (Biondi and Cooper, 2008), low thyroid function may act as an intermediate factor between MetS and CVD. The implications of the prevalence of SCH and MetS are greater in those with depression than in the general population. These factors help to explain the higher risk of medical comorbidities and poorer health outcomes in patients with depression (Marazziti et al., 2014). Furthermore, the presence of MetS, particular components of MetS, or low thyroid function is associated with poorer responses to antidepressants (Gitlin et al., 2004; McIntyre et al., 2016; Pae et al., 2009). The cross-sectional nature of the study limits the interpretation of the results. Although our findings support the hypothesized link between subclinical thyroid dysfunction and metabolic risk factors in depressed adults, confirmation in longitudinal studies is needed. Investigation of a larger cohort may also reveal a more consistent association of MetS with SCH in depression. In addition, the definition of depression using the PHQ-9 is open to question. The PHQ-9 has been validated as a screening tool with acceptable sensitivity and specificity (Han et al., 2008); however, it is not a diagnostic tool. Despite these limitations, to our knowledge, this is the first study to evaluate the association between SCH and MetS in a depressed sample. We used a representative nationwide sample and included a detailed list of possible confounding factors, such as various lifestyle factors.
5. Conclusion Our results highlight the prevalence of SCH in a depressed sample along with the links
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among thyroid dysfunction, lipid abnormalities, and MetS. Although it is difficult to establish a causal relationship, SCH may be association with an increased risk of MetS in individuals with depression. Future studies need to explore the mechanisms and clinical consequences of the observed relationships among thyroid dysfunction, metabolic risk factors, and depression.
Author disclosure - Acknowledgements: None. - Author's contributions : Author YEJ designed the study and wrote the protocol. Author MDK and YEJ managed the literature searches and analyses. Authors YEJ undertook the statistical analysis, and wrote the first draft of the manuscript. Author HJY, JHP and NRK contributed to and have approved the final manuscript. - Role of the Funding source: None. Conflict of interest statement: No conflicts declared.
6. References Ainsworth, B. E., Haskell, W. L., Whitt, M. C., Irwin, M. L., Swartz, A. M., Strath, S. J., O'Brien, L., Bassett Jr., D. R., Schmitz, K. H., Emplaincourt, P. O., Jacobs Jr., D. R., Leon,
W.
A. S., 2000.
Compendium of physical activities: an update of activity codes and MET intensities. Med. Sci. Sports Exerc. 32, S498–S504.
Biondi, B., Cooper, D.S., 2008. The clinical significance of subclinical thyroid dysfunction. Endocr. Rev. 29, 76–131.
Blum, M.R., Wijsman, L.W., Virgini, V.S., Bauer, D.C., den Elzen, W.P., Jukema, J.W., Buckley, B.M.,
11
de Craen, A.J., Kearney, P.M., Stott, D.J., Gussekloo, J., Westendorp, R.G., Mooijaart, S.P., Rodondi, N., PROSPER Study Group., 2016. Subclinical thyroid dysfunction and depressive symptoms among the elderly: a prospective cohort study. Neuroendocrinology 103, 291–299.
Chueire, V.B., Romaldini, J.H., Ward, L.S., 2007. Subclinical hypothyroidism increases the risk for depression in the elderly. Arch. Gerontol. Geriatr. 44, 21–28.
Demartini, B., Ranieri, R., Masu, A., Selle, V., Scarone, S., Gambini, O., 2014. Depressive symptoms and major depressive disorder in patients affected by subclinical hypothyroidism: a cross-sectional study. J. Nerv. Ment. Dis. 202, 603–607.
Eftekharzadeh, A., Khamseh, M.E., Farshchi, A., Malek, M., 2016. The Association between subclinical hypothyroidism and metabolic syndrome as defined by the ATP III criteria. Metab. Syndr. Relat. Disord. 14, 137–144.
Garber, J.R., Cobin, R.H., Gharib, H., Hennessey, J.V., Klein, I., Mechanick, J.I., Pessah-Pollack, R., Singer, P.A., Woeber, K.A.; American Association Of Clinical Endocrinologists And American Thyroid Association Taskforce On Hypothyroidism In Adults., 2012. Clinical practice guidelines for hypothyroidism in adults: cosponsored by the American Association of Clinical Endocrinologists and the American Thyroid Association. Thyroid 22, 1200–1235.
Gitlin, M., Altshuler, L.L., Frye, M.A., Suri, R., Huynh, E.L., Fairbanks, L., Bauer, M., Korenman, S., 2004. Peripheral thyroid hormones and response to selective serotonin reuptake inhibitors. J. Psychiatry Neurosci. 29, 383–386.
Gordon, A.J., Maisto, S.A., McNeil, M., Kraemer, K.L., Conigliaro, R.L., Kelley, M.E., Conigliaro, J., 2001. Three questions can detect hazardous drinkers. J. Fam. Pract. 50, 313c320.
12
Grundy, S.M., Cleeman, J.I., Daniels, S.R., Donato, K.A., Eckel, R.H., Franklin, B.A., Gordon, D.J., Krauss, R.M., Savage, P.J., Smith, S.C., Spertus, J.A., Costa, F., American Heart Association; National Heart, Lung, and Blood Institute., 2005. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112, 2735–2752.
Ha, J., Lee, J., Jo, K., Lim, D.J., Kang, M.I., Cha, B.Y., Kim, M.H., 2018. Sex differences in risk factors for subclinical hypothyroidism. Endocr. Connect. 7, 511–522.
Han, C., Jo, S.A., Kwak, J.H., Pae, C.U., Steffens, D., Jo, I., Park, M.H., 2008. Validation of the Patient Health Questionnaire-9 Korean version in the elderly population: the Ansan Geriatric study. Compr. Psychiatry 49, 218–223.
Kroenke, K., Spitzer, R.L., Williams, J.B., 2001. The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613.
Kweon, S., Kim, Y., Jang, M.J., Kim, Y., Kim, K., Choi, S., Chun, C., Khang, Y.H., Oh, K., 2014. Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES). Int. J. Epidemiol. 43, 69–77.
Marazziti, D., Rutigliano, G., Baroni, S., Landi, P., Dell'Osso, L., 2014. Metabolic syndrome and major depression. CNS Spectr. 19, 293–304.
McIntyre, R.S., Fayyad, R., Mackell, J.A., Boucher, M., 2016. Effect of metabolic syndrome and thyroid hormone on efficacy of desvenlafaxine 50 and 100 mg/d in major depressive disorder. Curr. Med. Res. Opin. 32, 587–599.
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Pae, C.U., Mandelli, L., Han, C., Ham, B.J., Masand, P.S., Patkar, A.A., Steffens, D.C., De Ronchi, D., Serretti, A., 2009. Thyroid hormones affect recovery from depression during antidepressant treatment. Psychiatry Clin. Neurosci. 63, 305–313.
Singh, S., Duggal, J., Molnar, J., Maldonado, F., Barsano, C.P., Arora, R., 2008. Impact of subclinical thyroid disorders on coronary heart disease, cardiovascular and all-cause mortality: a meta-analysis. Int. J. Cardiol. 125, 41–48.
Xu, Y.Y., Liang, J., Cao, Y., Shan, F., Liu, Y., Xia, Q.R., 2017. High levels of Nesfatin-1 in relation to the dysfunction of the hypothalamic-pituitary-adrenal and hypothalamus-pituitary-thyroid axes in depressed patients with subclinical hypothyroidism. Neuropsychiatr. Dis. Treat. 13, 1647–1653.
Yang, L., Lv, X., Yue, F., Wei, D., Liu, W., Zhang, T., 2016. Subclinical hypothyroidism and the risk of metabolic syndrome: a meta-analysis of observational studies. Endocr. Res. 41, 158–165.
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Figure 1. The relationship between the number of metabolic syndrome (MetS) components and the prevalence of subclinical hypothyroidism in individuals with depression
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Table 1. Odds ratios (ORs) and 95% confidence intervals (CI) for the association of subclinical hypothyroidism with metabolic syndrome and its components in individuals with depression. Subclinical hypothyroidism Adjusted OR (95% Cl)a
No n (%)
Yes n (%)
Metabolic syndrome
79 (22.8)
17 (56.3)
7.127 (2.077, 24.458)*
Abdominal obesity
78 (22.5)
11 (37.4)
0.980 (0.908, 1.058)
Hypertension
88 (24.0)
10 (29.7)
0.817 (0.257, 2.601)
High triglycerides
95 (28.0)
19 (58.6)
3.724 (1.461, 7.305)*
Low HDL-C
141 (43.3)
21 (59.3)
1.845 (0.744, 4.571)
Elevated fasting plasma glucose
92 (24.5)
11 (39.0)
1.811 (0.655, 5.006)
* p-value < 0.01 HDL-C, high-density lipoprotein cholesterol The unweighted numbers and weighted percentage distributions are shown. a Adjusted for age, gender, alcohol use problem, smoking status, physical activity, and body mass index.