Skip to main content
Download PDF

Elevated thyroid stimulating hormone and metabolic syndrome risk in patients with first-episode and drug-naïve major depressive disorder: a large-scale cross-sectional study

BMC Psychiatry volume 24, Article number: 380 (2024) Cite this article

Abstract

Background

Metabolic syndrome (MetS) is common in major depressive disorder (MDD), but its relationship with thyroid hormones remains unclear. We aimed to examine the association of thyroid hormones and MetS in first-episode drug-naïve (FEDN) MDD patients.

Methods

We recruited 1718 unmedicated MDD patients in this cross-sectional study. MetS was defined based on the 2004 Chinese Diabetes Society Criteria. Serum thyroid hormones including free thyroxine (FT4), free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), thyroid peroxidase antibodies (TPOAb), and anti-thyroglobulin (TGAb) were examined. We used the logistic regression model to determine risk factors for MetS and examined the performance of the regression model by using the Area Under the Curve (AUC). In addition, we performed the trend test to test whether the results were robust.

Results

The prevalence of MetS in unmedicated MDD patients was 34.4%. MDD patients with MetS had higher levels of serum TSH, TGAb, and TPOAb (all P < 0.001). Concurrently, serum TSH levels were independent risk factors for MetS in MDD patients (OR:1.49, 95%CI: 1.40–1.58), which could also distinguish MDD patients with and without MetS (AUC was 0.77). Additionally, in the trend test, the results also indicated a similar trend when TSH was used as a categorical variable (P for trend < 0.001).

Conclusions

This study suggests that TSH levels were independent risk factors for MetS in FEDN MDD patients (OR:1.49). The examination of thyroid function may contribute to the early detection of MetS.

Peer Review reports

Introduction

As the most debilitating disorders worldwide, metabolic syndrome (MetS) and major depressive disorder (MDD) are often reported as comorbid [1]. According to recent findings from the World Health Organization, MDD becomes the leading cause of disability globally, while people with depression in the world are estimated to be 4.4% [2]. MetS refers to a group of disorders including hypertension, insulin resistance, dyslipidemia, and obesity, which have the potential to lead to cardiovascular disease and type 2 diabetes [3]. MetS is steadily increasing and is estimated that will affect more than a billion people worldwide [4].

MDD patients were more likely to develop MetS, based on a meta-analysis, patients with MDD were 1.5 times more likely to have MetS when compared with controls in the general population [5, 6]. The high rates of MetS in MDD patients may be caused by unhealthy lifestyle habits, insulin resistance, central and peripheral inflammation, and hyperactivity of the hypothalamo-pituitary-adrenal axis [7,8,9]. Thyroid hormones are well known to participate in energy metabolism, and several studies conducted in the general population have reported a link between thyroid function and MetS [10,11,12,13]. Thus, a study from 2014 Korea National Health and Nutrition Examination Survey (n = 370) suggested that depressed individuals with subclinical hypothyroidism were more likely to meet the criteria for MetS [14]. However, the association of thyroid hormones and MetS in Chinese MDD patients has been little explored.

Furthermore, although the relationship between antidepressants and MetS remains controversial, it is widely believed that antipsychotics could contribute to MetS [15,16,17,18,19]. Therefore, to exclude confounding effects of medication, disease course, and comorbidities, we selected unmedicated MDD patients to examine the relationship between MetS and thyroid hormones. In this study, we aimed to: (1) compare serum thyroid hormones between MDD patients with and without MetS; and (2) identify the independent association of thyroid function with MetS risk. We hypothesized that there would be significant differences in thyroid function between MDD patients with and without MetS, with thyroid hormones being related to the increased risk of MetS in MDD patients.

Methods

Participants

All participants provided informed consent. The Institutional Review Board of the First Affiliated Hospital of Shanxi Medical University approved the study protocol (No.2016-Y27). After a comprehensive clinical evaluation, we recruited 1718 unmedicated MDD patients during 2015–2017. Inclusion criteria included (1) aged 16–65 years; (2) meeting MDD criteria according to the fouth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV); (3) the overall score of the 17 item-Hamilton Rating Scale for Depression (HAMD-17) ≥ 24; (4) first-onset and no previous treatment. Exclusion criteria included (1) with a substance use disorder except for nicotine; (2) with any serious physical illnesses; and (3) being pregnant or breastfeeding.

Demographics and clinical interview

Demographics and clinical variables were collected. Any self-injurious behavior that could lead to suicide was classified as a suicide attempt. To assess clinical symptoms of mood, the HAMD-17 and the Hamilton Anxiety Scale (HAMA) were used. A cut-off of 24 was used in the HAMD-17 examination to select participants [20]. Furthermore, psychotic symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) positive subscale. Patients scoring more than 15 were considered to have psychotic symptoms [21].

Blood samples

Serum samples were obtained from each participant between 6 and 8 a.m after an overnight fast, and promptly delivered to the hospital’s laboratory center for analysis before 11 a.m on the same day. Lipid profiles including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were detected by the enzymatic colorimetric assays. The fasting blood glucose (FBG) were measured by a Cobas E610 (Roche, Basel, Switzerland). The levels of free thyroxine (FT4), free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), thyroid peroxidase antibodies (TPOAb), and anti-thyroglobulin (TGAb) were assessed using the Roche C6000 Electrochemiluminescence Immunoassay Analyzer (Roche Diagnostics, Indianapolis, IN, USA). As reported in our previous studies, we classified TSH into three groups: <2.5, 2.5–4.2, and ≥ 4.2 mIU/L [22].

Metabolic syndrome

Blood pressure, height, and weight were assessed by trained nurses. Body mass index (BMI) was calculated using the formula: BMI = Weight (kg)/Height (m)2. MetS was defined according to the 2004 Chinese Diabetes Society Criteria [23, 24]. The criteria are that at least 3 of the following cardiometabolic risk factors are met: (1) BMI ≥ 25 kg/m2; (2) serum HDL-C < 0.9 mmol/L in men, or < 1.0 mmol/L in women; (3) TG ≥ 1.7 mmol/L; (4) systolic blood pressure (SBP) ≥ 140 mmHg, or diastolic blood pressure (DBP) ≥ 90 mmHg; and (5) FBG ≥ 6.1 mmol/L.

Statistical analysis

Demographic characteristics of different groups of MetS were analyzed using chi-square test, independent samples t-test, or Mann-Whitney U-test. Bonferroni correction was adopted to adjust multiple tests. We then applied univariate analysis to identify risk factors for MetS in MDD patients. Factors with significant differences were entered into logistic regression (Forward: LR). In order to distinguish MDD patients with and without MetS, we evaluated the discriminative power of TSH using the the area under the receiver operating characteristic curve (AUC-ROC) method. In addition, the independent association between TSH and MetS in MDD patients was examined using multivariate logistic regression. Age, gender, marital status, and education were adjusted in Model 1. And model 2 was adjusted for disease duration, suicide attempts, psychotic symptoms, HAMD score, and HAMA score. To validate the results were robust and to examine the possibility of nonlinearity, we converted TSH into a categorical variable and conducted the trend test. P < 0.05 (two-sided) was considered significant. All analyses were performed with R 4.2.0.

Results

Comparison of MDD patients with or without comorbid MetS

Table 1 shows the proportion of MetS in MDD patients was 34.4% (591/1718). Compared to MDD patients without MetS, those who with MetS were more likely to be older, female, less educated, and married. In addition, MetS group had more suicide attempts, psychotic symptoms, and much higher HAMA, HAMD scores than non-MetS group (all P < 0.001). Serum TSH, TGAb, and TPOAb levels were higher in MDD patients with MetS when compared with those without MetS (all P < 0.001). Nevertheless, no differences were found in FT3 and FT4 levels within the two groups.

Table 1 Socio-demographics and clinical characteristics of subjects
Full size table

Independent risk factors for MetS in MDD patients

Factors with significant differences in univariate analysis were entered into the multivariable logistic regression for MetS. We included eleven variables (age, gender, marital status, illness duration, suicide attempts, psychotic symptoms, HAMA, HAMD, TSH, TgAb, and TPOAb) in the regression model. After controlling for confounding factors, the independent risk factors for MetS in MDD patients were as follows (Table 2): age (B = 0.02, P = 0.006, OR = 1.02), gender (B = 0.56, P < 0.001, OR = 1.75), HAMD score (B = 0.07, P = 0.006, OR = 1.08), and TSH (B = 0.40, P < 0.001, OR = 1.49).

Table 2 Factors associated with metabolic syndrome in MDD patients
Full size table

Association of serum TSH and MetS risk in MDD patients

The results of AUC ROC showed that TSH had the highest AUC value of 0.77, which could distinguish MDD patients with MetS from those without MetS. Furthermore, when we combined age, gender, HAMD score, and TSH, we found an AUC value of 0.78 which was close to the AUC value of TSH itself (Fig. 1).

Fig. 1
figure 1

The discriminatory capacity of related factors for distinguishing between patients with and without MetS in MDD patients. The area under the curve of Age, Gender, HAMD score, and TSH were 0.59, 0.56, 0.66, and 0.77, respectively

Full size image

In the trend test (Table 3), we stratified TSH into three subgroups (< 2.5, 2.5–4.2, and ≥ 4.2, respectively) and used the lowest subgroup (< 2.5) as the reference group. The results showed a similar trend when TSH was used as a categorical variable, with the highest TSH subgroup (≥ 4.2) having a higher risk of MetS than the lowest subgroup (< 2.5) (P < 0.001 for trend).

Table 3 Multivariable logistic regression analysis between TSH and MetS in MDD patients
Full size table

Discussion

The present study found the relationship between thyroid hormones and MetS risk in MDD patients. The main findings were: (1) MDD patients with MetS had higher levels of serum TSH, TPOAb, and TgAb than those who without MetS; (2) Age, gender, TSH levels, and HAMD score were independent risk factors for MetS in MDD patients; and (3) TSH levels had an ability to discriminate between MDD patients with MetS and without MetS (AUC was 0.77).

We found a prevalence of 34.4% for MetS in our MDD patients, which was close to a previous meta-analysis that included 18 studies of depression defined by interview (n = 5,531) and reported a rate of 30.5% [6]. This study also found that older age was related to MetS risk in MDD patients, which is consistent with data from the general population and patients with severe mental illness [25,26,27]. One possible reason for this is the slowing of energy metabolism with age, along with the onset of many diseases, such as obesity, diabetes, and hypertension, which would contribute to a high MetS risk [28]. Furthermore, patients who had higher HAMD scores were at a significantly increased risk of MetS, which may be due to overlapping pathophysiology, socioeconomic factors, and lifestyle [6]. There is clear evidence that most patients with MDD adopt an unhealthy lifestyle with changes in diet and exercise, which increases the incidence of hyperglycemia and hypertriglyceridemia and therefore may further increase the MetS risk. Concurrently, according to this study, women were at a higher risk of developing MetS than men. MetS is believed to increase more rapidly with age in women than in men, mainly due to hormonal changes during and after menopause [29].

Importantly, we found that TSH levels in MDD patients were independently associated with increased MetS risk, but there was no relationship between MetS risk and FT3, FT4, TgAb, and TPOAb. For more than a decade, investigators have extensively investigated thyroid hormones and MetS, but the results have been inconsistent [30,31,32]. Recently, according to a Chinese Mendelian randomization study using 2,903 individuals with normal thyroid function, FT3 and TSH levels are positively related to MetS incidence [11]. The present study strengthens the evidence that TSH levels are associated with the MetS risk. Moreover, in a large Korean study (n = 4,775), TPOAb positivity was associated with MetS in euthyroid subjects (OR:1.39, 95% CI:1.05–1.84), suggesting thyroid autoimmunity plays a role in MetS [12]. However, we did not find an association between TPOAb and MetS risk in MDD patients. Several factors may contribute to the conflicting results described above, including different subjects, ethnicity, design, setting, the definition of MetS, and population iodine intake.

Thyroid hormones play a comprehensive role in each of components of MetS, engaging with diverse pathways and mechanisms. Thyroid dysfunction has a clear influence on body weight, primarily due to edema, and thyroid hormones can also contribute to obesity and dyslipidemia by modulating metabolic rates, lipid synthesis, and degradation [33]. In addition, thyroid dysfunction may lead to insulin resistance, thereby resulting in elevated blood sugar levels [34].

The present study has several important clinical implications. Firstly, our study indicate the need for a more comprehensive consideration of metabolic health in depression treatment. Incorporating thyroid function testing into the treatment plans of patients with depression may lead to better clinical outcomes. Secondly, preventive measures may be necessary in patients with depression to reduce the occurrence and progression of metabolic syndrome. This may include regular monitoring of thyroid function, lifestyle modifications, and adjustments to medication therapies. Thirdly, healthcare providers can better assess the overall health status of patients and develop more personalized treatment plans.

There are some limitations to this study that should be acknowledged. First, causality could not be inferred due to the cross-sectional design. Second, in addition to thyroid dysfunction’s impact on metabolic syndrome components, potential confounding factors like iodine intake, physical activity, smoking, and alcohol consumption may influence our study’s outcomes. Their complex interactions with metabolic pathways necessitate cautious interpretation. Third, a potential limitation is that there are no data on patients taking any antihypertensive, antidiabetic, or lipid-lowering drugs that may affect peripheral biomarkers.

Conclusions

Our study showed that serum TSH levels were independent associated with MetS risk in first episode and drug naïve patients MDD patients. Thyroid function tests may be benefit to the prevention and early detection of MetS in MDD. Besides, more longitudinal studies are needed in the future to clarify the cause-and-effect relationship.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AUC:

Area Under the Curve

AUC-ROC:

area under the receiver operating characteristic curve

DSM-IV:

Diagnostic and Statistical Manual of Mental Disorders

FT4:

free thyroxine

FT3:

free triiodothyronine

FEDN:

first-episode drug-naïve major depressive disorder patients

FBG:

fasting blood glucose

HAMD:

Hamilton Rating Scale for Depression

HAMA:

Hamilton Anxiety Scale

HDL:

High-Density Lipoprotein

MetS:

metabolic syndrome

MDD:

major depressive disorder

PANSS:

Positive and Negative Syndrome Scale

TSH:

thyroid-stimulating hormone

TPOAb:

thyroid peroxidase antibodies

TGAb:

anti-thyroglobulin

References

  1. Qiu W, Cai X, Zheng C, Qiu S, Ke H, Huang Y. Update on the relationship between Depression and Neuroendocrine Metabolism. Front NeuroSci. 2021;15:728810.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Friedrich MJ. Depression is the leading cause of disability around the World. JAMA. 2017;317(15):1517.

    PubMed  Google Scholar 

  3. Huang PL. A comprehensive definition for metabolic syndrome. Dis Models Mech. 2009;2(5–6):231–7.

    Article  CAS  Google Scholar 

  4. Saklayen MG. The global epidemic of the metabolic syndrome. Curr Hypertens Rep. 2018;20(2):12.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Zhang M, Chen J, Yin Z, Wang L, Peng L. The association between depression and metabolic syndrome and its components: a bidirectional two-sample mendelian randomization study. Translational Psychiatry. 2021;11(1):633.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Vancampfort D, Correll CU, Wampers M, Sienaert P, Mitchell AJ, De Herdt A, Probst M, Scheewe TW, De Hert M. Metabolic syndrome and metabolic abnormalities in patients with major depressive disorder: a meta-analysis of prevalences and moderating variables. Psychol Med. 2014;44(10):2017–28.

    Article  CAS  PubMed  Google Scholar 

  7. Chan KL, Cathomas F, Russo SJ. Central and peripheral inflammation link metabolic syndrome and major depressive disorder. Physiol (Bethesda Md). 2019;34(2):123–33.

    CAS  Google Scholar 

  8. Moreira FP, Jansen K, Cardoso TA, Mondin TC, Vieira IS, Magalhães P, Kapczinski F, Souza LDM, da Silva RA, Oses JP, et al. Metabolic syndrome, depression and anhedonia among young adults. Psychiatry Res. 2019;271:306–10.

    Article  PubMed  Google Scholar 

  9. Takahashi A, Ohira T, Okazaki K, Yasumura S, Sakai A, Maeda M, Yabe H, Hosoya M, Ohtsuru A, Kawasaki Y, et al. Effects of Psychological and Lifestyle factors on metabolic syndrome following the Fukushima Daiichi Nuclear Power Plant Accident: the Fukushima Health Management Survey. J Atheroscler Thromb. 2020;27(9):1010–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Gutch M, Rungta S, Kumar S, Agarwal A, Bhattacharya A, Razi SM. Thyroid functions and serum lipid profile in metabolic syndrome. Biomedical J. 2017;40(3):147–53.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Liang S, Cai J, Yang Y, Zhang L, Taylor P, Ming J, Yu X, Hu R, Zhou J, Da-Yan CM, et al. Relationship between thyroid hormones and metabolic syndrome in a normal thyroid function population in Western China: a cross-sectional study based on both epidemiological and genetic analysis. Chin Med J. 2021;135(3):350–2.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kim HJ, Park SJ, Park HK, Byun DW, Suh K, Yoo MH. Thyroid autoimmunity and metabolic syndrome: a nationwide population-based study. Eur J Endocrinol. 2021;185(5):707–15.

    Article  CAS  PubMed  Google Scholar 

  13. He J, Lai Y, Yang J, Yao Y, Li Y, Teng W, Shan Z. The relationship between thyroid function and metabolic syndrome and its components: a cross-sectional study in a Chinese Population. Front Endocrinol. 2021;12:661160.

    Article  Google Scholar 

  14. Kim MD, Yang HJ, Kang NR, Park JH, Jung YE. Association between subclinical hypothyroidism and metabolic syndrome among individuals with depression. J Affect Disord. 2020;264:494–7.

    Article  PubMed  Google Scholar 

  15. Mazereel V, Detraux J, Vancampfort D, van Winkel R, De Hert M. Impact of Psychotropic Medication effects on obesity and the metabolic syndrome in people with Serious Mental illness. Front Endocrinol. 2020;11:573479.

    Article  Google Scholar 

  16. Bernardo M, Rico-Villademoros F, García-Rizo C, Rojo R, Gómez-Huelgas R. Real-World Data on the adverse metabolic effects of Second-Generation antipsychotics and their potential determinants in adult patients: a systematic review of Population-Based studies. Adv Therapy. 2021;38(5):2491–512.

    Article  CAS  Google Scholar 

  17. Gramaglia C, Gambaro E, Bartolomei G, Camera P, Chiarelli-Serra M, Lorenzini L, Zeppegno P. Increased risk of metabolic syndrome in antidepressants users: a Mini Review. Front Psychiatry. 2018;9:621.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hiles SA, Révész D, Lamers F, Giltay E, Penninx BW. BIDIRECTIONAL PROSPECTIVE ASSOCIATIONS OF METABOLIC SYNDROME COMPONENTS WITH DEPRESSION, ANXIETY, AND ANTIDEPRESSANT USE. Depress Anxiety. 2016;33(8):754–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Corruble E, El Asmar K, Trabado S, Verstuyft C, Falissard B, Colle R, Petit AC, Gressier F, Brailly-Tabard S, Ferreri F, et al. Treating major depressive episodes with antidepressants can induce or worsen metabolic syndrome: results of the METADAP cohort. World Psychiatry: Official J World Psychiatric Association (WPA). 2015;14(3):366–7.

    Article  Google Scholar 

  20. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23(1):56–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13(2):261–76.

    Article  CAS  PubMed  Google Scholar 

  22. Yang W, Qu M, Jiang R, Lang X, Zhang XY. Association between thyroid function and comorbid anxiety in first-episode and drug naïve patients with major depressive disorder. Eur Arch Psychiatry Clin NeuroSci 2022.

  23. Li XH, Lin HY, Wang SH, Guan LY, Wang YB. Association of Microalbuminuria with Metabolic Syndrome among Aged Population. BioMed research international 2016, 2016:9241278.

  24. EPoMSoCD S. Recommendations provided by Chinese diabetes society on metabolic syndrome. Chin J Diabetes. 2004;12:156–61.

    Google Scholar 

  25. North BJ, Sinclair DA. The intersection between aging and cardiovascular disease. Circul Res. 2012;110(8):1097–108.

    Article  CAS  Google Scholar 

  26. Agaba DC, Migisha R, Namayanja R, Katamba G, Lugobe HM, Aheisibwe H, Twesigomwe G, Ashaba S. Prevalence and Associated Factors of Metabolic Syndrome among Patients with Severe Mental Illness Attending a Tertiary Hospital in Southwest Uganda. BioMed research international 2019, 2019:1096201.

  27. Lan Y, Mai Z, Zhou S, Liu Y, Li S, Zhao Z, Duan X, Cai C, Deng T, Zhu W, et al. Prevalence of metabolic syndrome in China: an up-dated cross-sectional study. PLoS ONE. 2018;13(4):e0196012.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Dominguez LJ, Barbagallo M. The biology of the metabolic syndrome and aging. Curr Opin Clin Nutr Metab Care. 2016;19(1):5–11.

    Article  CAS  PubMed  Google Scholar 

  29. Pucci G, Alcidi R, Tap L, Battista F, Mattace-Raso F, Schillaci G. Sex- and gender-related prevalence, cardiovascular risk and therapeutic approach in metabolic syndrome: a review of the literature. Pharmacol Res. 2017;120:34–42.

    Article  PubMed  Google Scholar 

  30. Kim HJ, Bae JC, Park HK, Byun DW, Suh K, Yoo MH, Jae Hwan J, Kim JH, Min YK, Kim SW, et al. Association of triiodothyronine levels with future development of metabolic syndrome in euthyroid middle-aged subjects: a 6-year retrospective longitudinal study. Eur J Endocrinol. 2017;176(4):443–52.

    Article  CAS  PubMed  Google Scholar 

  31. Mehran L, Amouzegar A, Tohidi M, Moayedi M, Azizi F. Serum free thyroxine concentration is associated with metabolic syndrome in euthyroid subjects. Thyroid: Official J Am Thyroid Association. 2014;24(11):1566–74.

    Article  CAS  Google Scholar 

  32. Mehran L, Amouzegar A, Bakhtiyari M, Mansournia MA, Rahimabad PK, Tohidi M, Azizi F. Variations in serum free thyroxine concentration within the reference range predicts the incidence of metabolic syndrome in non-obese adults: a Cohort Study. Thyroid: Official J Am Thyroid Association. 2017;27(7):886–93.

    Article  CAS  Google Scholar 

  33. Iwen KA, Schröder E, Brabant G. Thyroid hormones and the metabolic syndrome. Eur Thyroid J. 2013;2(2):83–92.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Ferrannini E, Iervasi G, Cobb J, Ndreu R, Nannipieri M. Insulin resistance and normal thyroid hormone levels: prospective study and metabolomic analysis. Am J Physiol Endocrinol Metabolism. 2017;312(5):E429–36.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

The Science and Technology Development Program of Nanjing Medical University (NMUB20220190); the Young Talent Development Plan of Changzhou Health Commission (CZQM2023012); the Science and Technology Development Project of Changzhou Health Commission (QN202369); This study was supported by the Elderly Health Research Project of Jiangsu Provincial Health Commission (LKZ2022016); the Project of Changzhou Medical Center, Nanjing Medical University (CMCC202216); the Science and Technology Development Project of Traditional Chinese Medicine in Jiangsu Province (MS2023087).

Author information

Authors and Affiliations

  1. Department of Psychology, The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China

    Qiaoyang Zhang, Guanzhong Dong, Xuanyan Zhu & Yin Cao

  2. Changzhou Medical Center, Nanjing Medical University, Nanjing, Jiangsu Province, China

    Yin Cao

  3. Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing, 100101, China

    Xiangyang Zhang

  4. Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

    Xiangyang Zhang

Authors
  1. Qiaoyang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guanzhong Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuanyan Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yin Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiangyang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the design of the study. QY Z and C Y participated in the design of the study and drafted the manuscript. GZ D and XY Z participated in the design of the study, collected the data, and helped to draft the manuscript. All authors have reviewed and approved the manuscript.

Corresponding authors

Correspondence to Yin Cao or Xiangyang Zhang.

Ethics declarations

Ethics approval and consent to participate

The Institutional Review Board of the First Affiliated Hospital of Shanxi Medical University approved the study protocol (No.2016-Y27). All methods were carried out in accordance with relevant institutional guidelines and regulations. All participants signed informed consent after receiving a full description of the study, an explanation of its purpose, and information about the confidentiality of the data.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Q., Dong, G., Zhu, X. et al. Elevated thyroid stimulating hormone and metabolic syndrome risk in patients with first-episode and drug-naïve major depressive disorder: a large-scale cross-sectional study. BMC Psychiatry 24, 380 (2024). https://doi.org/10.1186/s12888-024-05847-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12888-024-05847-4

Keywords

BMC Psychiatry

ISSN: 1471-244X

Contact us