Introduction

Depressive disorders, categorized under mood disorders, feature primary symptoms of depressed mood and the loss of interest or pleasure.1,2 The Global Burden of Disease Study has highlighted the contribution of depressive disorders to the global burden by revealing an increase in disability-adjusted life years (DALYs) from 19th in 1990 to 13th in 2019.3 The World Health Organization (WHO) predicts that depressive disorders will become the most prevalent disabling condition worldwide by 2030. Within the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) depressive disorders group, major depressive disorder (MDD) stands out as a typical condition.1,4 Recent national survey data in China indicates an estimated lifetime prevalence of depressive disorders at 6.8%, with a 12-month prevalence of 2.1% and a lifetime prevalence of 3.4% for MDD.5 This high prevalence imposes a significant burden for a country with nearly one-fifth of the global population.

Metabolic syndrome (MetS) is a pathological condition characterized by several factors, including insulin resistance, atherogenic dyslipidemia, central obesity, and hypertension.6,7 It is closely linked to an elevated risk of developing diabetes and cardiovascular disease (CVD).6 With advancements in global healthcare, MetS has appeared as a major health threat in the modern era, surpassing infectious diseases.7 Statistics show that approximately a quarter of the global population is affected by MetS,8 and epidemiological studies indicate a continuous increase in its prevalence over the years.9,10 Two nationwide population-based surveys conducted in China revealed a gradual rise in MetS prevalence from 13.7% in 2000–2001 to 31.1% in 2015−2017.11,12 As of 2021, hypertension, dyslipidemia, and type 2 diabetes mellitus have posed a threat to the lives and health of hundreds of millions of people in China, causing significant socioeconomic burdens.13,14

The intrinsic association between MDD and MetS has been a key area of academic interest. Researchers have discovered shared genetic pathways between MDD and MetS,15–17 and there is often an assumption that metabolic disorders are present at the beginning of MDD onset, even before the administration of drugs.18–20 Studies have found that the prevalence of MetS and the level of its components are significantly higher in the primary MDD population compared to healthy controls.21,22 However, the metabolic disturbances in MDD patients who are exposed to antidepressant and antipsychotic medications are even more surprising and noteworthy than the inherent metabolic disturbances in MDD patients.23–25 A meta-analysis showed that the use of antidepressants increased the risk of new-onset diabetes by 1.25 times in people with MDD.26 Meanwhile, another meta-analysis similarly found that the combined use of antipsychotics was an important moderator of metabolic disorders in people with MDD.27

Based on the previous studies mentioned above, it is reasonable to hypothesize that antidepressant medications may have a broader influence on the development of MetS, resulting in an increased prevalence of MetS in patients with MDD. The aim of our paper is to assess the impact of antidepressant exposure on MetS in first hospitalized MDD patients by comparing differences in clinical characteristics related to MetS between patients with and without a history of antidepressant exposure, and to provide feasible references and insights for clinical interventions.

Subjects and MethodsSubjects

A total of 636 patients admitted to the Wuhan Mental Health Center between July 2017 and August 2022 with a history of antidepressant exposure and first hospitalization for MDD were included in this study.

The inclusion criteria of the patients were as follows: 1) meeting the diagnostic criteria of MDD in the International Classification of Diseases, 10th edition (ICD-10); 2) no history of previous hospitalization; 3) aged between 18 and 60 years old and of Chinese Han ethnicity; 4) no restriction on the type of antidepressant medication and the type of antipsychotic medication to be used in the outpatient clinic; and 5) a total score equal to or higher than 24 on the 17-item Hamilton Depression Scale (HAMD-17).

Patients who met any of the following criteria were excluded: 1) lactating or pregnant women; 2) those with a history of drug dependence; 3) those with severe physical illness or personality disorders; 4) those with a previous diagnosis of diabetes mellitus; and 5) those who were unable to cooperate with psychiatric evaluation due to severe behavioral disorders or other reasons, such as agitation, reticence, impulsivity, etc.

Meanwhile, we included 345 first-hospitalized and drug-naïve MDD patients as a control group, who were matched to the study group in terms of age, duration of disease, and sex ratio. The inclusion and exclusion criteria of the control group were the same as those of the study group, except that they were drug-naïve.

The study was conducted after obtaining approval from the Ethics Committee of Wuhan Mental Health Center, and written informed consent was obtained from all participants. Participants had the right to withdraw from the study at any time.

Research Design

This research was conducted using a case-control study design. The aim was to compare the metabolic parameters and common clinical indicators between a first hospitalized MDD population with a history of outpatient drug therapy and drug-naïve, as well as to analyze the differences in risk factors for MetS and its severity across two clinical subgroups.

Upon enrollment, we gathered relevant clinical information from individuals diagnosed with MDD who met the inclusion criteria. This included important variables such as age, gender, age of onset, duration of illness, marital status, history of outpatient treatment, and presence or absence of suicidal behavior. To assess the severity of depressive symptoms, we employed the 17-item Hamilton Depression Scale (HAMD-17), while anxiety symptoms were evaluated using the Hamilton Anxiety Scale (HAMA-14). Psychotic symptoms were measured using the Positive Symptom Subscale (PSS) of items P1-P7 in the Positive and Negative Symptom Scale (PANSS). We also used the Clinical Global Impression Scale (CGI) to assess pre-treatment disease severity.

Routine clinical and biochemical indicators obtained using patient fasting venous blood tests provided by the hospital biochemistry laboratory were extracted from the electronic medical record system. These indicators included the measurement of blood lipid profile, namely total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol (HDL-c). Additionally, fasting blood glucose (FBG) levels, body mass index (BMI), blood pressure (specifically systolic blood pressure [SBP] and diastolic blood pressure [DBP]), and thyroid function (specifically thyroid-stimulating hormone [TSH], free triiodothyronine [FT3], and free tetraiodothyronine [FT4]) levels were assessed.

The diagnostic criteria in China for MetS require the presence of at least three of the following five indicators:28 1) abdominal obesity, defined as a waist circumference ≥ 90 cm in men and ≥ 85 cm in women; 2) hyperglycemia, defined as fasting blood glucose ≥ 6.1 mmol/L or a diagnosis of diabetes mellitus; 3) hypertension, defined as systolic blood pressure ≥ 130/85 mmHg or diastolic blood pressure ≥ 85 mmHg or a confirmed diagnosis of hypertension; 4) elevated triglycerides (TG) ≥ 1.70 mmol/L; and 5) low high-density lipoprotein cholesterol (HDL-c) < 1.04 mmol/L.

To evaluate the severity of MetS, scoring rules were established based on prior studies.29,30 These rules were applied to calculate the MetS score for the participants. Firstly, the reciprocal of high-density lipoprotein cholesterol (HDL-c) and mean arterial pressure (MAP) were calculated using the equation MAP = 1/3 × SBP + 2/3 × DBP. Subsequently, the five MetS parameters - waist circumference (WC), triglycerides (TG), reciprocal of HDL-C, fasting blood glucose (FBG), and MAP - were normalized. A principal component analysis with varimax rotation was then conducted on these normalized variables to derive principal components (PCs) that explained a significant portion of the observed variation, having an eigenvalue of 1.0 or higher. In this study, PC1 and PC2 accounted for 25.23% and 20.85% of the variance, respectively. The loadings of the variables on the PC1 (PC2) were as follows: WC 0.26 (−0.63), TG 0.28 (0.50), HDL-C 0.17 (0.61), MAP 0.73 (0.04), and FBG 0.75 (−0.15). Weighted PC scores were then calculated based on the relative weights of PC1 and PC2 in the explained variance. Finally, the MetS score was obtained by summing the individual weighted PC scores.

The psychological scales were administered by two psychiatrists with the title of “attending” or higher, who were uniformly trained and affiliated with the medical institution where the study was conducted.

Data Analysis

Categorical variables were reported as counts, while continuous variables with normal distribution were depicted using means and standard deviations. Continuous variables that were not normally distributed were expressed using medians (P25, P75). The Shapiro–Wilk test was employed to verify the normality of all continuous variables. For both continuous variables, whether normally or non-normally distributed, and categorical variables, statistical comparisons were made using independent samples t-tests, Mann–Whitney U-tests, and chi-square tests. Pearson’s correlation coefficients were utilized to investigate the relationships between MetS and various clinical variables, excluding components of MetS itself. Binary logistic regression was conducted to pinpoint risk factors for MetS across different clinical groups. In addition, multivariate linear regression models were established to evaluate the risk factors associated with MetS severity within both the study and control cohorts. All statistical analyses were conducted with a two-tailed approach, considering a significant level of 0.05 or lower. Data analysis was performed using SPSS version 27.

ResultsDifferences Between Clinical Parameters in the Study and Control Groups

In the study group, there were 234 patients (36.79%) who were prescribed one antidepressant, 402 patients (62.21%) who were prescribed two antidepressants, and 382 patients (60.06%) who were co-prescribed antipsychotics. The prevalence of MetS in the study group was 8.49% (54/636), while it was 11.59% (40/345) in the control group. However, this difference was not statistically significant (F = 2.49, p = 0.115). Nevertheless, the study group had significantly higher MetS scores (t = −1.10, p = 0.013), and TG levels (t = −2.05, p = 0.041) compared to the controls (as shown in Table 1).

Table 1 The Demographic and General Clinical Data in Different Clinical Groups

Analysis of Factors Associated with MetS by Different Clinical Groups

The results of the correlation analysis were shown in Table 2. In the study group, factors associated with MetS included age, onset age, being married, suicidal behavior, PSS scores, HAMD scores, HAMA scores, CGI-SI scores, TSH levels, TC levels and LDL-C levels, all p-values are less than 0.05. Meanwhile, in the control group, factors associated with MetS were age, onset age, being married, suicidal behavior, PSS scores, HAMD scores, HAMA scores, CGI-SI scores, TSH levels and TC levels, all p-values are less than 0.05.

Table 2 Correlation Between MetS and Demographic and Clinical Variable in Different Clinical Groups

Risk Factor Analysis of MetS in Different Clinical Groups

As shown in Table 3, we constructed binary logistic regression models (Backward: Wald) in each of the two clinical groups with MetS as the outcome variable and the clinical parameters associated with MetS in the previous step as independent variables. In the study group, the identified risk factors for MetS were onset age (B = 0.05, p <0.001, OR = 1.05, 95% CI = 1.02–1.08), TSH level (B = 0.42, p <0.001, OR = 1.53, 95% CI = 1.39–1.68). Meanwhile, in the control group, the identified risk factors for MetS were more extensive and they were, onset age (B = 0.11, p <0.001, OR = 1.12, 95% CI = 1.07–1.16), suicidal behavior (B = 1.54, p = 0.007, OR = 4.65, 95% CI = 1.51–14.33), HAMD scores (B = 0.23, p = 0.008, OR = 1.26, 95% CI = 1.06–1.49) and TSH levels (B = 0.33, p <0.001, OR = 1.39, 95% CI = 1.17–1.65).

Table 3 Binary Logistic Regression Analyses of Determinants of MetS in Different Clinical Groups

Risk Factor Analysis of MetS Scores in Different Clinical Groups

Finally, we constructed multiple linear regression models (Backward) in each of the two clinical groups using MetS scores as the outcome variable and clinical parameters associated with MetS as the independent variables (See Table 4). In the study group, the risk factors identified for higher MetS scores were age (B= 0.01, t = 5.70, p <0.001, 95% CI = 0.01–0.01), and TSH levels (B= 0.06, t = 12.12, p <0.001, 95% CI = 0.05–0.07). Meanwhile, in the control group, the risk factors identified for higher MetS were more extensive and they were, age (B= 0.01, t = 3.11, p = 0.002, 95% CI = 0.00–0.01), being married (B= 0.13, t = 2.63, p = 0.009, 95% CI = 0.03–0.22), HAMD scores (B= 0.01, t = 1.99, p = 0.047, 95% CI = 0.00–0.03) and TSH levels (B= 0.04, t = 5.62, p <0.001, 95% CI = 0.03–0.06), while LDL-c levels (B= −0.06, t = −3.15, p = 0.002, 95% CI = −0.12 - −0.03) was a protective factor.

Table 4 Correlates Affecting MetS Scores in Different Clinical Groups: a Multiple Linear Regression Model

Discussion

The key findings of this study are as follows: 1. The prevalence of MetS was not increased in MDD patients with antidepressant exposure, but the severity of MetS was increased. 2. The risk factors for MetS in the antidepressant-exposed group are different and fewer compared to the drug-naïve group. 3. The risk factors for MetS severity in the antidepressant-exposed group are different and fewer compared to the drug-naïve group.

Exposure to antidepressants and antipsychotics is usually recognized as a major cause of metabolic deterioration in patients with MDD.31,32 Drug-induced metabolic disorders exacerbate the elevated in metabolic markers more than in the drug-naïve state.25,33 However, this is still unproven. Two studies have found that blood glucose levels are not elevated in patients with first-onset MDD34 and that maintenance treatment with antidepressants has no effect on insulin resistance.35 Conjunction with our study, antidepressant exposure did not result in elevated levels of a wide range of metabolic parameters other than MetS scores and TC levels. The prevalence of MetS is another topic of concern. Georgina E Crichton et al reported a 1.22-fold increase in the prevalence of MetS in people with MDD who were exposed to antidepressant medications compared to drug naïve patients.36 While another large meta-analysis reported no association between antidepressants and MetS.27 Our study demonstrates that a history of antidepressant treatment does not adversely affect the prevalence of MetS in first hospitalized MDD patients, which is consistent with the latter finding. In short, our report shows that antidepressant exposure increases the severity of MetS in the MDD population but does not negatively affect its prevalence.

An important finding of our study is that patients with antidepressant exposure have a lower number of risk factors associated with MetS compared to drug-naïve patients with MDD. The relationship between MDD and MetS components, including obesity and others, is complex and heterogeneous. It is important to consider the potential impact of antidepressant exposure on metabolic disorders within this context.37,38 However, it is vital to acknowledge that these conclusions cannot be readily applied to specific subgroups within the MDD population. Among frequently discussed risk factors, the severity of depressive symptoms is a notable contributor to MetS. This association has been extensively documented in various studies.36,39 However, it is important to note that this association primarily applies to initial depressive symptoms during the onset of MDD and lacks generalizability.40 Similar to our study, a large cross-sectional study found that suicidal behavior is a risk factor for worsening metabolic markers in an MDD population without prior drug exposure,41 while no correlation was reported in patients with a history of antidepressant use. In the context of our study, exposure to antidepressants appears to moderately decrease the number of risk factors for MetS among the MDD population, potentially contributing to a more precise management of metabolic disorders in this demographic.

Furthermore, we transformed MetS into a continuous variable to evaluate the risk factors associated with MetS score or severity. Similarly, we observed that the risk factors for MetS severity differed between the study and control groups, with fewer risk factors identified in the study group. However, there is a lack of similar studies in this area. Only a few studies have examined the correlation between depressive symptoms and the severity of MetS. One study focused on the general clergy (without antidepressant exposure) and found a correlation between depressive symptoms and MetS severity.40 Another study among the general population of African Americans (drug-naïve) found a correlation between baseline depression scores in females and MetS severity over an 8-year period.42 Two other studies reported that more severe depressive symptoms corresponded to higher levels of MetS components in individuals with MDD.43,44 Consistent with these findings, our study found that the severity of depressive symptoms in the drug-naïve group with a history of MetS was a risk factor for greater MetS severity. However, among patients with a history of antidepressant exposure, only exposure to tricyclic antidepressants was identified as a risk factor for an increased number of MetS components.43 This finding may be further supported by the fact that we identified fewer risk factors for MetS severity in our study group. In conclusion, our study suggests that antidepressant exposure may act as a potential protective factor in reducing the number of risk factors associated with MetS severity in a first hospitalized MDD population.

However, this study has some limitations. Firstly, the risk factors we identified for MetS and its severity were conducted based on cross-sectional studies, which do not allow for the establishment of a causal relationship. Secondly, the conclusion that the number of factors affecting MetS and its severity was less in the study group than in the control group was based on the clinical variables of interest in this study and did not include other biological indicators that were not included as potentially affecting MetS. Thirdly, in the regression analyses of the study group, antidepressant and antipsychotic exposure were not included to maintain consistency with the control group, which may have overlooked and minimized the risk factors identified in the study group. In future studies, we will conduct more rigorous prospective studies to remedy these deficiencies.

In summary, in hospitalized patients with MDD, antidepressant use was linked to increased severity of MetS but did not impact its overall prevalence. Antidepressants seem to alter MetS risk factors, and these effects should be distinguished from those in drug-naïve patients when devising intervention strategies for MetS in the MDD population.

Data Sharing Statement

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

The ethics committees of the Wuhan mental health center reviewed and approved this study. All subject guardians knew about this study and signed informed consent. All procedures carried out in studies conformed to the 1964 helsinki Declaration and its subsequent amendments or similar ethical standards.

Acknowledgments

We are grateful to all the medical staff and patients in our study and to those who contributed to the diagnosis and clinical evaluation of the subjects.

Funding

This work was supported by Key projects of Anhui Humanities and Social Sciences Research Base (SK2019A0226 to YW: PI) and Japanese Mental Health Okamoto Memorial Foundation Research Assistance Project (Heisei 31 Research 3 to YW: PI).

Disclosure

The authors report no conflicts of interest in this work.

References

1. Uher R, Payne JL, Pavlova B, Perlis RH. Major depressive disorder in DSM-5: implications for clinical practice and research of changes from DSM-IV. Depress Anxiety. 2014;31(6):459–471. doi:10.1002/da.22217

2. Otte C, Gold SM, Penninx BWet al. Major depressive disorder. Nat Rev Dis Primers. 2016;2(1):16065. doi:10.1038/nrdp.2016.65

3. Vos T, Lim SS, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020;396(10258):1204–1222. doi:10.1016/s0140-6736(20)30925-9

4. American Psychiatric Association, D. & Association, A. P. Diagnostic and Statistical Manual of Mental Disorders: DSM-5. Vol. 5. American psychiatric association; Washington, DC: 2013.

5. Huang Y, Wang YU, Z Liuet al. Prevalence of mental disorders in China: a cross-sectional epidemiological study. Lancet Psychiatry. 2019;6(3):211–224. doi:10.1016/s2215-0366(18)30511-x

6. Fahed G, Aoun L, Bou Zerdan M, et al. Metabolic syndrome: updates on pathophysiology and management in 2021. Int J Mol Sci. 2022;23(2). doi:10.3390/ijms23020786

7. Saklayen MG. The global epidemic of the metabolic syndrome. Curr Hypertens Rep. 2018;20(2):12. doi:10.1007/s11906-018-0812-z

8. Schneider JG, Tompkins C, Blumenthal RS, Mora S. The metabolic syndrome in women. Cardiol Rev. 2006;14(6):286–291. doi:10.1097/01.crd.0000233757.15181.67

9. Collaborators, G.B.D.O, et al. Health effects of overweight and obesity in 195 Countries over 25 Years. N Engl J Med. 2017;377(1):13–27. doi:10.1056/NEJMoa1614362

10. Ranasinghe P, Mathangasinghe Y, Jayawardena R, Hills AP, Misra A. Prevalence and trends of metabolic syndrome among adults in the Asia-pacific region: a systematic review. BMC Public Health. 2017;17(1):101. doi:10.1186/s12889-017-4041-1

11. Xi B, He D, Hu Y, Zhou D. Prevalence of metabolic syndrome and its influencing factors among the Chinese adults: the China health and nutrition survey in 2009. Prev Med. 2013;57(6):867–871. doi:10.1016/j.ypmed.2013.09.023

12. Yao F, Bo Y, Zhao L, et al. Prevalence and influencing factors of metabolic syndrome among adults in China from 2015 to 2017. Nutrients. 2021;13(12):4475. doi:10.3390/nu13124475

13. China, T. W. C. o. t. R. o. C. H. a. D. i.. Report on cardiovascular health and diseases in China 2021: an updated summary. Chin Med J. 2022;22:20–36+40. doi:10.3969/j.issn.1000-3614.2022.06.001

14. Group, C. E. C. o. S. D. a. T. o. T. H. C.-m. V. E.. Chinese expert consensus on standardized diagnosis and treatment of “three highs” co-management (2023 Edition). Chin Med J Cardiolo. 2023;06:1–11. doi:10.3760/cma.j.cn116031.2023.1000144

15. Postolache TT, Del Bosque‐Plata L, Jabbour S, et al. Co-shared genetics and possible risk gene pathway partially explain the comorbidity of schizophrenia, major depressive disorder, type 2 diabetes, and metabolic syndrome. Am J Med Genet B Neuropsychiatr Genet. 2019;180(3):186–203. doi:10.1002/ajmg.b.32712

16. de Melo LGP, Nunes SOV, Anderson G, et al. Shared metabolic and immune-inflammatory, oxidative and nitrosative stress pathways in the metabolic syndrome and mood disorders. Prog Neuropsychopharmacol Biol Psychiatry. 2017;78:34–50. doi:10.1016/j.pnpbp.2017.04.027

17. Liu D, McIntyre RS, Li R, et al. Genetic association between major depressive disorder and type 2 diabetes mellitus: shared pathways and protein networks. Prog Neuropsychopharmacol Biol Psychiatry. 2021;111:110339. doi:10.1016/j.pnpbp.2021.110339

18. Peng P, Wang Q, Lang XE, Liu T, Zhang XY. Association between thyroid dysfunction, metabolic disturbances, and clinical symptoms in first-episode, untreated Chinese patients with major depressive disorder: undirected and Bayesian network analyses. Front Endocrinol. 2023;14:1138233. doi:10.3389/fendo.2023.1138233

19. Si T, Yang K, Lang X, et al. Prevalence and risk factors of overweight and obesity in Chinese patients with first-episode drug-naïve major depressive disorder. J Affect Disord. 2021;286:351–359. doi:10.1016/j.jad.2021.01.037

20. Hu J, Ji Y, Lang X, Zhang XY. Prevalence and clinical correlates of abnormal lipid metabolism in first-episode and drug-naïve patients with major depressive disorder: a large-scale cross-sectional study. J Psychiatr Res. 2023;163:55–62. doi:10.1016/j.jpsychires.2023.05.016

21. Grover S, Nebhinani N, Chakrabarti S, Avasthi A, Kulhara P. Metabolic syndrome in drug-naïve patients with depressive disorders. Indian J Psychol Med. 2013;35(2):167–173. doi:10.4103/0253-7176.116247

22. Hidese S, Asano S, Saito K, Sasayama D, Kunugi H. Association of depression with body mass index classification, metabolic disease, and lifestyle: a web-based survey involving 11,876 Japanese people. J Psychiatr Res. 2018;102:23–28. doi:10.1016/j.jpsychires.2018.02.009

23. Himmerich H, Minkwitz J, Kirkby KC. Weight gain and metabolic changes during treatment with antipsychotics and antidepressants. Endocr Metab Immune Disord Drug Targets. 2015;15(4):252–260. doi:10.2174/1871530315666150623092031

24. Carvalho AF, Sharma MS, Brunoni AR, Vieta E, Fava GA. The safety, tolerability and risks associated with the use of newer generation antidepressant drugs: a critical review of the literature. Psychother Psychosom. 2016;85(5):270–288. doi:10.1159/000447034

25. 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):754–764. doi:10.1002/da.22512

26. Wang Y, Liu D, Li X, Liu Y, Wu Y. Antidepressants use and the risk of type 2 diabetes mellitus: a systematic review and meta-analysis. J Affect Disord. 2021;287:41–53. doi:10.1016/j.jad.2021.03.023

27. Vancampfort D, Correll CU, Wampers M, et al. Metabolic syndrome and metabolic abnormalities in patients with major depressive disorder: a meta-analysis of prevalences and moderating variables. Psychol Med. 2014;44(10):2017–2028. doi:10.1017/s0033291713002778

28. Zhu J. 2016 Chinese guidelines for the management of dyslipidemia in adults. Chin Med J. 2016;31:937–953. doi:10.3969/j.issn.1000-3614.2016.10.001

29. Zeng K, Wang S, Zhang L, Zhang Y, Ma J. Gender differences in prevalence and associated factors of metabolic syndrome in first-treatment and drug-naïve schizophrenia patients. Ann Gen Psychiatry. 2023;22(1):25. doi:10.1186/s12991-023-00455-0

30. Wu M, Shu Y, Wang L, et al. Metabolic syndrome severity score and the progression of CKD. Eur J Clin Invest. 2022;52(1):e13646. doi:10.1111/eci.13646

31. Scheen AJ. Metabolic disorders induced by psychotropic drugs. Ann Endocrinol. 2023;84(3):357–363. doi:10.1016/j.ando.2023.03.006

32. Wen FK, Crosby K, Miller BH, et al. Association of first-line antidepressants and incident adverse metabolic effects. Can Fam Physician. 2020;66(12):898–900. doi:10.46747/cfp.6612898

33. Verhoeven JE, Han LKM, Lever-van Milligen BA, et al. Antidepressants or running therapy: comparing effects on mental and physical health in patients with depression and anxiety disorders. J Affect Disord. 2023;329:19–29. doi:10.1016/j.jad.2023.02.064

34. Çakici N, Sutterland AL, Penninx BWJH, et al. Altered peripheral blood compounds in drug-naïve first-episode patients with either schizophrenia or major depressive disorder: a meta-analysis. Brain Behav Immun. 2020;88:547–558. doi:10.1016/j.bbi.2020.04.039

35. Fernandes BS, Salagre E, Enduru N, Grande I, Vieta E, Zhao Z.et al. Insulin resistance in depression: a large meta-analysis of metabolic parameters and variation. Neurosci Biobehav Rev. 2022;139:104758. doi:10.1016/j.neubiorev.2022.104758

36. Crichton GE, Elias MF, Robbins MA. Association between depressive symptoms, use of antidepressant medication and the metabolic syndrome: the Maine-Syracuse study. BMC Public Health. 2016;16(1):502. doi:10.1186/s12889-016-3170-2

37. Lee SH, Paz-Filho G, Mastronardi C, Licinio J, Wong ML. Is increased antidepressant exposure a contributory factor to the obesity pandemic? Transl Psychiatry. 2016;6(3):e759. doi:10.1038/tp.2016.25

38. McIntyre RS, Park KY, Law CWY, et al. The association between conventional antidepressants and the metabolic syndrome: a review of the evidence and clinical implications. CNS Drugs. 2010;24(9):741–753. doi:10.2165/11533280-000000000-00000

39. Moreira FP, Jansen K, Cardoso TDA, et al. Metabolic syndrome in subjects with bipolar disorder and major depressive disorder in a current depressive episode: population-based study: metabolic syndrome in current depressive episode. J Psychiatr Res. 2017;92:119–123. doi:10.1016/j.jpsychires.2017.03.025

40. Smith TW, Eagle DE, Proeschold-Bell RJ. Prospective associations between depressive symptoms and the metabolic syndrome: the spirited life study of methodist pastors in North Carolina. Ann Behav Med. 2017;51(4):610–619. doi:10.1007/s12160-017-9883-3

41. Zhao K, Zhou S, Shi X, et al. Potential metabolic monitoring indicators of suicide attempts in first episode and drug naive young patients with major depressive disorder: a cross-sectional study. BMC Psychiatry. 2020;20(1):387. doi:10.1186/s12888-020-02791-x

42. Gurka MJ, Vishnu A, Okereke OI, et al. Depressive symptoms are associated with worsened severity of the metabolic syndrome in African American women independent of lifestyle factors: a consideration of mechanistic links from the Jackson heart study. Psychoneuroendocrinology. 2016;68:82–90. doi:10.1016/j.psyneuen.2016.02.030

43. van Reedt Dortland AK, Giltay EJ, van Veen T, Zitman FG, Penninx BW. Metabolic syndrome abnormalities are associated with severity of anxiety and depression and with tricyclic antidepressant use. Acta Psychiatr Scand. 2010;122(1):30–39. doi:10.1111/j.1600-0447.2010.01565.x

44. Luppino FS, van Reedt Dortland AKB, Wardenaar KJ, et al. Symptom dimensions of depression and anxiety and the metabolic syndrome. Psychosom Med. 2011;73(3):257–264. doi:10.1097/PSY.0b013e31820a59c0