Prevalence and risk factors for depression in somatic symptom disorder patients: a cross-sectional clinical study in China

Abstract

Introduction and objectives

The comorbidity of somatic symptom disorder (SSD) and depressive symptoms is common; however, the demographic and clinical factors correlated with depression among SSD patients remain unclear. The purpose of this study was to explore the prevalence and associated factors of depressive symptoms in Chinese Han patients with SSD.

Methods

In this cross-sectional study, 899 outpatients diagnosed with SSD were included. Demographic data were collected, and clinical assessments were conducted, which included blood pressure measurements and laboratory tests for thyroid-stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), anti-thyroglobulin (TGAb), free triiodothyronine (FT3), free thyroxine (FT4), fasting blood glucose (FBG), and lipid profiles. The participants were evaluated using the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Insomnia Severity Index (ISI), and Perceived Social Support Scale (PSSS). Data were analyzed using descriptive statistics, chi-square tests, nonparametric tests, logistic regression analysis, and receiver operating characteristic (ROC) curve analysis with calculation of the area under the curve (AUC), as appropriate.

Results

The prevalence of depression in SSD patients was 83.6%. Compared with those without depression, depression in SSD patients was associated with age, age of onset, duration of illness, marital status, TSH, FBG, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein (LDL-C), systolic blood pressure, diastolic blood pressure, anxiety symptoms, insomnia and levels of perceived social support (all p < 0.05). Multivariate logistic regression analysis indicated that age, TC, insomnia and levels of perceived social support were correlated with depression in SSD patients (all p < 0.01). Among these factors, insomnia had the highest AUC value of 0.908. However, the combination of insomnia and perceived social support achieved an even higher AUC value of 0.926.

Conclusions

Our findings suggest a high prevalence of depression in SSD patients. Several factors are associated with depression in SSD patients. Insomnia is a robust risk factor for depression in patients with SSD, and its discriminatory capacity is enhanced when combined with the assessment of perceived social support levels.

Introduction

Somatic symptom disorder (SSD) is a psychological condition characterized by excessive health concerns about one or more physical symptoms, which are either unexplained by a medical disease or disproportionate to the underlying cause [1]. The diagnosis was introduced in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). This diagnostic category replaced somatoform disorder, undifferentiated somatoform disorder, hypochondriasis, and pain disorders in the Diagnostic and Statistical Manual of Mental Disorders, fourth edition [2]. Compared with somatoform disorders, SSD focuses on the nature of the response to somatic symptoms rather than a specific number of symptoms across different organ systems [3]. More importantly, the exclusion of actual or identifiable underlying medical disorders was no longer needed. The diagnoses of SSD and comorbidities may be more frequent [4].

The prevalence of SSD varies widely depending on the diagnostic tools and population studied. In general medical settings, SSD is estimated to affect between 3.5% and 45.5% of patients [5–7]. SSD patients have lower quality of life than healthy individuals do, and their symptoms may affect their daily life, career, general functioning and family functioning [8, 9]. The disorder poses significant challenges for both patients and healthcare providers, given the frequent consultations, diagnostic tests, and treatments, which cause high socioeconomic costs [10, 11].

One of the most complex aspects of SSD is its frequent comorbidity with psychiatric disorders, particularly depression, anxiety and alexithymia [12]. The relationship between SSD and depression is likely bidirectional: chronic somatic complaints can exacerbate feelings of hopelessness and despair, whereas depressive symptoms can heighten sensitivity to somatic discomfort, leading to a vicious cycle of physical and emotional distress [13–15]. Individuals with SSD and comorbid depression often experience worse outcomes, including greater functional impairment, reduced quality of life, and prolonged illness duration, than those with SSD alone [9, 16, 17].

However, the risk factors for comorbid depression in patients with SSD have not been explored yet, most studies only investigated some of the complex of features associated with SSD. Multiple factors have been implicated in the development of depressive disorders or symptoms, including sociodemographic characteristics, family history of mental illness, metabolic dysfunctions, thyroid dysfunction, psychological factors and clinical symptoms such as insomnia and anxiety [18–20]. For example, a large-scale prospective cohort study found that several metabolic parameters were independently associated with an increased risk of depression [21]. Panicker et al. found that lower levels of TSH were related to higher depressive symptoms [22]. Furthermore, perceived social support has drawn increasing attention and has been identified as a key determinant of depression [23]. Notably, it has also been shown to correlate strongly with the severity of somatic symptoms in patients with SSD [24]. Although metabolic parameters, thyroid hormones, insomnia, anxiety, and perceived social support have been proposed as risk factors for depression in general population, their specific contributions within the SSD population remain insufficiently examined. This gap is particularly evident in studies involving first-episode and drug- naïve (FEDN) patients with SSD. Studying FEDN patients is particularly advantageous for understanding the prevalence and symptom patterns of depressive symptoms in SSD, as it provides an opportunity to minimize confounding factors such as illness duration, long-term medication effects, and the psychiatric and medical comorbidities associated with chronic SSD.

The present study aimed to investigate the prevalence, sociodemographic risk factors, and clinical risk factors for comorbid depression among FEDN patients with SSD in a Chinese Han population. By identifying the risk factors for comorbid depression in FEDN patients with SSD, this research can help clinical practices to more effectively address the needs of this vulnerable population.

Materials and methods

Participants

A cross-sectional design was employed in this study. The ethics committee of the Third People’s Hospital of Ganzhou reviewed and approved this study. Written informed consent forms were obtained from all patients before they participated in this study. Consecutive outpatients in this study were recruited from the Third People’s Hospital of Ganzhou, Ganzhou People’s Hospital and First Affiliated Hospital of Gannan Medical University from January 2023 to April 2024. Inclusion criteria were: (1) self-identified Han Chinese ethnicity; (2) age between 18 and 60 years; (3) a diagnosis of SSD according to DSM-5 criteria; and (4) the ability to provide written informed consent. Diagnoses were made by trained psychiatrists and validated by a senior psychiatrist with extensive clinical experience. It is important to note that the diagnoses of SSD and concurrent conditions such as anxiety disorders or depressive disorders are not mutually exclusive [1]. 98 patients were excluded for the following reasons: pregnancy or lactation (n = 23), substance use disorders as defined by DSM-5 (n = 25), severe personality disorders (n = 13), serious physical illnesses (n = 12), refusal to participate (n = 20), and other unspecified reasons (n = 5). After exclusions, 899 participants were included in the final analysis. The flowchart of the study is shown in Fig. 1.

Fig. 1.

Flowchart of the study

Depressive symptoms

Depressive symptoms were measured via the Patient Health Questionnaire-9 (PHQ-9). The scale includes 9 items that are scored on a 4-point scale ranging from 0 (‘not at all’) to 3 (‘almost every day’) [25]. The overall score of the PHQ-9 ranges from 0 to 27. PHQ-9 scores of 5, 10, 15, and 20 indicate mild, moderate, moderately severe, and severe depressive symptoms, respectively [26]. All patients were divided into two groups, one with depressive symptoms and one without, on the basis of their PHQ-9 scores. A PHQ-9 score of ≧ 5 was used as the threshold for depressive symptoms, based on previous studies of SSD in Chinese populations [27].The Chinese version of the PHQ-9 is a reliable and valid measure for assessing depressive symptoms in numerous studies of the Chinese population [28]. The internal consistency of the scale was 0.97 in the present study.

Anxiety symptoms

The Generalized Anxiety Disorder-7 (GAD-7) was chosen for assessing the presence and severity of anxiety symptoms. The scale includes 7 items, which mainly ask how often the subject was bothered by each symptom over the last 2 weeks, and scores for each item range from 0 (not at all) to 3 (nearly every day) [29]. The GAD-7 total score ranges from 0 to 21, cut-point of ≥ 5 indicating clinical anxiety symptoms [30]. Additionally, the Chinese version of the GAD-7 has been tested and used in general hospital patients and has shown acceptable psychometric properties [31]. The internal consistency of GAD-7 was 0.95 in the study.

Insomnia symptoms

The Insomnia Severity Index (ISI) was selected to assess the perceived severity and impact of insomnia. This instrument is a 7-item self-report questionnaire designed to evaluate symptoms over the previous month [32]. The responses are rated on a 5-point Likert scale ranging from 0 (no problem) to 4 (very severe problem), yielding a total score between 0 and 28. Scores are interpreted as follows: absence of insomnia (0–7), subthreshold insomnia (8–14), moderate insomnia (15–21), and severe insomnia (22–28) [33], score ≥ 8 indicating clinical insomnia. The ISI has demonstrated acceptable internal consistency and satisfactory convergent validity [34]. Additionally, prior studies have confirmed the adequate psychometric properties of the Chinese version [35]. The Cronbach’s alpha coefficient of the ISI was 0.96 in the study.

Perceived social support

The Perceived Social Support Scale (PSSS) was used to evaluate the perceptions and feelings of social support [36]. The Chinese version comprises 12 items divided into three subscales, with each item rated on a 7-point scale [37]. Total scores range from 12 to 84 and are categorized as low support (12–36), moderate support (37–60), and high support (61–84). Studies have shown that the Chinese version has strong factorial validity, moderate construct validity, and good test-retest reliability [38]. Each subscale of the PSSS showed excellent internal consistency (α = 0.98).

Measurement of blood parameters

Blood samples were collected from the cubital vein between 8:00 a.m. and 12:00 noon following a fasting period of more than 12 h in accordance with hospital regulations. Participants were also instructed to rest for at least 10 min prior to blood pressure measurement. The following biochemical indices were measured: total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FBG), thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4), thyroglobulin antibodies (TgAb), and thyroid peroxidase antibodies (TPOAb).

Statistical analysis

Descriptive statistics were calculated for all parameters, with continuous variables depicted as the means ± standard deviations and categorical variables shown as counts and percentages. The normality of variable distributions was assessed via the Kolmogorov‒Smirnov test. Since the continuous data were not normally distributed, we employed the Mann‒Whitney U test for continuous variables to compare demographic and clinical profiles. We employed chi-square tests or Fisher’s exact tests for categorical variables. The biochemical parameters were compared between the depression and non-depression groups with Analysis of covariance that was used to control for age and body mass index (BMI) differences between the two groups. Binary logistic regression analysis was then utilized to assess which factors were most strongly associated with comorbid depressive symptoms, with age and BMI included as covariates to control for their potential confounding effects. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminatory capacity of significant factors in distinguishing between SSD patients with and without comorbid depression, with the area under the curve (AUC) calculated. Primary statistical analyses were conducted using SPSS, version 26.0 (SPSS Inc., Chicago, IL, USA), while ROC curve analyses and AUC calculations were performed with MedCalc software, version 20.022 (MedCalc Software Ltd., Ostend, Belgium). A p value of less than 0.05, derived from two-tailed tests, was considered to indicate statistical significance.

Results

Demographic and clinical characteristics of SSD patients with and without depression

A total of 899 eligible participants (Males/females = 320/579) were enrolled in this study. The proportion of SSD patients with depression was 83.6% (752/899). As shown in Table 1, compared with SSD patients without depression, those with comorbid depression were younger, had an earlier age of illness onset, a longer illness duration, and a higher BMI. They were also more likely to be overweight, single, and to present with comorbid anxiety, insomnia, and reduced perceived social support.

Table 1.

Social-demographics and clinical characteristics between somatic symptom disorder with depression and without depression

Characteristics	Non-depression (n = 147)	Depression (n = 752)	x²/F	Df	p-value
Males/females	59/88	261/491	1.353	1	0.209
Age (years)	36.56 ± 12.94	34.03 ± 12.22	5.203	1	0.023
Body mass index (kg/m²)	23.55 ± 1.75	24.45 ± 1.86	29.337	1	0.000
Education level			1.056	1	0.304
Below undergraduate	103(70.1%)	494(65.7%)
Above undergraduate	44(29.9)	258(34.3%)
Marriage status			4.984	1	0.026
Non-married	36(24.5%)	255(33.9%)
Married	111(75.5%)	497(66.1%)
Age of onset (years)	36.33 ± 12.82	33.81 ± 12.08	5.239	1	0.022
Duration of illness (months)	12.05 ± 5.18	12.51 ± 4.74	1.128	1	0.289
Anxiety symptoms			206.453	1	0.000
Without anxiety	92(62.6%)	84(11.2%)
With anxiety	55(37.4%)	668(88.8%)
Insomnia			654.607	1	0.000
Without insomnia	122(83.0%)	10(1.3%)
With insomnia	25(17.0%)	742(98.7%)
Perceived social support			492.326	2	0.000
High level	89(60.5%%)	4(0.5%)
Middle level	45(30.6%)	302(40.2%)
Low level	13(8.8%)	446(59.3%)

Biochemical parameters of the depression SSD group vs. the non-depression SSD group

Table 2 shows the characteristics of thyroid hormone levels and metabolism biomarkers in the depression and non-depression SSD groups. The levels of TSH (p < 0.001), ATG (p = 0.020), TG (p = 0.001), TC (p < 0.001), LDL-C (p < 0.001) and fasting blood glucose (p < 0.001) were significantly greater in the depression SSD group than in the non-depression SSD group, whereas the levels of HDL-C (p = 0.019) were significantly lower in the depression SSD group than in the non-depression SSD group after controlling for age and BMI.

Table 2.

Comparison of biochemical parameters and blood pressure between non-depression and depression SSD patients, adjusted for age and BMI (Analysis of covariance)

	Non-depression (n = 147)	Depression (n = 752)	Partial η²	P-value
FT3, pmol/L	4.87 ± 1.05	4.92 ± 1.01	0.000	0.504
FT4, pmol/L	16.39 ± 4.39	16.58 ± 4.42	0.000	0.515
TSH, µIU/ml	3.26 ± 2.46	5.27 ± 2.46	0.057	0.000
ATG, IU/L	20.72 ± 13.79	21.3 ± 38.22	0.006	0.020
ATPO, IU/L	16.07 ± 17.27	17.45 ± 22.93	0.004	0.066
TG, mmol/L	1.67 ± 1.05	2.02 ± 1.42	0.013	0.001
TC, mmol/L	4.48 ± 1.06	5.31 ± 1.62	0.076	0.000
HDL-C, mmol/L	1.25 ± 0.35	1.23 ± 0.42	0.006	0.019
LDL-C, mmol/L	2.53 ± 0.90	2.98 ± 1.20	0.040	0.000
FBG, mmol/L	5.08 ± 0.86	5.38 ± 0.88	0.021	0.000
SBP, mmHg	118.0 ± 16.0	120.0 ± 16.0	0.035	0.000
DBP, mmHg	75.0 ± 8.0	76.0 ± 9.0	0.017	0.000

SSD Somatic Symptom Disorder, BMI Body Mass Index, FT3 Free Triiodothyronine, FT4 Free Thyroxine, TSH Thyroid Stimulating Hormone, ATG Anti-Thyroglobulin Antibody, ATPO Anti-Thyroid Peroxidase Antibody, TG Triglycerides, TC Total Cholesterol, HDL-C High-Density Lipoprotein Cholesterol, LDL-C Low-Density Lipoprotein Cholesterol, FBG Fasting Blood Glucose, SBP Systolic Blood Pressure, DBP Diastolic Blood Pressure

Risk factors for depression in SSD patients

We then focused on the risk factors for depression symptoms in SSD patients. Significantly different variables from the univariate analysis were included in forward stepwise conditional multivariate logistic regression to verify risk factors for depression in SSD patients. As presented in Table 3, the risk factors for depression symptoms in SSD patients were as follows: age (Wald x²=15.20, df = 1, p < 0.001), TC (Wald x²=6.77, df = 1, p = 0.009), insomnia (Wald x²=78.22, df = 1, p < 0.001), and lower levels of perceived social support (Wald x²=19.03, df = 2, p < 0.001). In addition, the ROC analysis revealed the following AUC values for each risk factor: age, 0.553; TC, 0.720; insomnia, 0.908; and levels of perceived social support, 0.873. We integrated the parameters associated with the two highest AUC values and found an AUC value of 0.926 for the combination of insomnia and levels of perceived social support to distinguish between patients with depression symptoms and patients without depression symptoms (as presented in Fig. 2).

Table 3.

Factors associated with depression in somatic symptom disorder patients

	B	Wald	P	Exp(B)	95%CI
Age	−0.066	15.20	0.000	0.94	0.91–0.97
Body Mass Index	0.067	0.43	0.513	1.069	0.875–1.306
Total Cholesterol	0.49	6.77	0.009	1.64	1.13–2.37
Insomnia	4.52	78.22	0.000	91.66	33.68-249.48
Perceived social support		19.03	0.000
Middle level	2.72	16.07	0.000	15.14	4.01–57.16
Low level	3.27	18.45	0.000	26.41	5.92-117.75

Fig. 2.

The discriminatory capacity of insomnia, levels of perceived social support, and the combination for distinguishing between SSD patients with and without depression

Discussion

To the best of our knowledge, this is the first study to investigate the prevalence, demographic and clinical characteristics of comorbid depressive symptoms in first-episode and drug-naïve patients with SSD in a Chinese Han population. We found that a large majority of FEDN patients were depressed, and the significant correlates of depressive symptoms in this population were age, TC, insomnia, and levels of perceived social support.

In our present study, the prevalence of comorbid depressive symptoms of 83.6% in FEDN patients with SSD closely corresponds with a report of 75.1% in chronic patients with somatic symptoms and related disorders evaluated at mental health institutions [39]. However, a previous population-based telephone survey revealed that 33.58% of SSD individuals experienced anxiety or depression [40]. These results suggest that the rate of depression varies between patient groups from different sources. The SSD group diagnosed via telephone interviews likely presented milder psychopathological symptoms, including depression, resulting in a lower prevalence.

We found that there were differences in total cholesterol and lipoprotein fractions between the depression and non-depression SSD groups. However, after multiple regression analysis, only TC was positively related to depressive symptoms. Hypercholesterolemia may increase the risk of depressive mood [41]. A prospective cohort study included teenage participants without a diagnosis of depression at baseline. With an average follow-up of 6 years, they reported that increased total cholesterol levels could indicate an increased risk for depressive symptoms in early adulthood [42]. However, the causal relationship between the two remains unclear. Zhang Z et al. found that depression can causally increase the risk of hypercholesterolemia [43]. Neuroinflammation and metabolic dysregulation may jointly contribute to the bidirectional link between hypercholesterolemia and depression [44, 45]. For instance, hypercholesterolemia is frequently associated with insulin resistance. Impaired insulin function can affect the activity of tryptophan hydroxylase, the rate-limiting enzyme in serotonin synthesis. This leads to a decrease in brain serotonin levels, which is closely linked to the occurrence of depression [46].

Our study revealed that insomnia significantly associates the comorbidity of depressive symptoms in patients with SSD, which is consistent with the literature. A meta-analysis of 34 cohort studies, encompassing 172,077 participants with a mean follow-up of 60.4 months, identified insomnia as a significant predictor of depression, with a pooled risk ratio (RR) of 2.27 [47]. A subsequent systematic review of longitudinal studies further supported this relationship, highlighting insomnia as a precursor to depressive episodes [48]. Insomnia may contribute to the dysregulation of monoamine neurotransmitters—such as serotonin, dopamine, and norepinephrine—which play crucial roles in mood stabilization [49]. Insomnia is associated with an increase in adrenocorticotropic hormone (ACTH) and cortisol secretion [50]. High cortisol levels increase the expression of serotonin transporters, thereby accelerating serotonin reuptake and reducing synaptic serotonin availability [51]. Sleep deprivation, a model of insomnia, has been demonstrated to result in reduced synthesis or abnormal metabolism of serotonin [52]. Insomnia may also impair dopaminergic function. A study using positron emission tomography showed a marked decrease in dopamine D2/D3 receptors binding after sleep deprivation [53]. Reduced binding at the D2/D3 receptors can impair the brain’s response to pleasurable stimuli, leading to anhedonia, a core symptom of depression [54]. Furthermore, insomnia promotes inflammatory cytokine release and C-reactive protein (CRP) elevation. Chronic inflammation can induce “sickness behavior” and neurochemical alterations resembling depression [55].

Additionally, the effects of insomnia on cognitive functions, including attention, memory, and decision-making, as well as emotional regulation, may increase vulnerability to depressive symptoms [56]. Notably, insomnia and depression appear to share a bidirectional relationship, where each can exacerbate the other, potentially creating a self-reinforcing cycle of symptoms [57].

In this study, we found that low levels of perceived social support were associated with the onset of depressive symptoms. Numerous studies conducted in both Western countries and Asia have shown that perceived social support can act as a protective factor against the development of depressive symptoms [23, 58]. For example, a longitudinal study revealed that, even after controlling for prior mental health issues such as depression, perceived social support continued to predict depression and anxiety one year later [59]. Chronic somatic symptoms are a significant source of stress, and patients often experience feelings of isolation [60]. According to social support theory, the perceived social support an individual receives can serve as a buffering mechanism, mitigating the negative impact of stressful events on mental health [61]. Low levels of perceived social support may result in a lack of essential emotional and practical resources for individuals facing life challenges [62]. This state of insufficient support may intensify feelings of loneliness, helplessness, and despair, which are key components of depressive symptoms [63]. Furthermore, evidence suggests that lack of social support correlates with elevated cortisol levels [64] and increased inflammatory markers [65], potentially exacerbating sleep disturbances and mood dysregulation. Conversely, a study involving participants from diverse ethnic backgrounds demonstrated that parental support can buffer the increases in cortisol and CRP associated with depression [66]. The beneficial influence of neural systems subserving social behavior on those implementing stress responses may involve oxytocin [67]. Oxytocin exerts an inhibitory effect on ACTH release, consequently regulating cortisol production [68]. Another mechanism may involve neural responses. Functional neuroimaging study has demonstrated that partner support can modulate neural responses to aversive stimuli, particularly in brain regions associated with pain processing, such as the anterior cingulate cortex [69].

In summary, several potential mechanisms may be at play: insomnia activates the hypothalamic–pituitary–adrenal axis and inflammatory responses, and imbalances in neurotransmitters such as serotonin and dopamine; concurrently, diminished perceived social support removes a critical buffer against these effects; together, these processes may contribute to the onset and maintenance of depression.

The identification of insomnia and low level of perceived social support as risk factors of depression in SSD has direct clinical relevance. Clinicians should consider insomnia and its approach in close relationship with the evolution of depression among SSD patients. We recommend that clinicians screen SSD patients routinely for sleep disturbances. A recent clinical trial demonstrated digital cognitive-behavioral therapy for insomnia (CBT-I) significantly reduced the incidence of major depression compared to sleep education therapy [70]. This suggests that CBT-I integrated into treatment of SSD patients with insomnia, could improve sleep quality and potentially interrupt the pathway to comorbid depression. Likewise, enhancing perceived social support may buffer against depression in SSD. Strengthening social support networks—through family therapy, support groups, or community services—could serve as a preventive strategy. Thus, our findings encourage a multimodal clinical approach: addressing sleep and psychosocial factors in addition to somatic symptoms may improve prognosis.

This study has several limitations. First, it focuses on patients with SSD in a specific region of China, which may limit the generalizability of the findings. Second, the study relied on self-report measures, which are susceptible to bias. Reliance solely on a self-report screening tool (PHQ-9) to assess depressive symptoms and interpret results is a clear limitation. Future studies should incorporate structured clinical interviews for more accurate diagnosis. As blood samples were collected at varying times during outpatient visits, we could not control for the diurnal variation in TSH levels, which is another limitation of the study. Finally, the cross-sectional nature of this study prevents us from making clear inferences about the causal relationship between SSD and depression. It is still unclear whether SSD leads to depression, or if depression contributes to the development of SSD, or if there is a bidirectional relationship between them. While the current study acknowledges this limitation, there is a need for future research to address this gap. Prospective cohort studies and longitudinal research designs could help clarify the potential causal relationships between SSD and depression. Furthermore, employing advanced statistical methods such as structural equation modeling or mediation analysis might help to infer causal associations in future studies.

Abbreviations

SSD

Somatic symptom disorder

DSM-5

The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders

FEDN

First-episode and drug-naïve

PHQ-9

Patient Health Questionnaire-9

GAD-7

Generalized Anxiety Disorder-7

ISI

Insomnia Severity Index

PSSS

Perceived Social Support Scale

TC

Total cholesterol

TGs

Triglycerides

HDL-C

High-density lipoprotein cholesterol

LDL-C

Low-density lipoprotein cholesterol

FBG

Fasting blood glucose

TSH

Thyroid-stimulating hormone

FT3

Free triiodothyronine

FT4

Free thyroxine

TgAb

Thyroglobulin antibodies

TPOAb

Thyroid peroxidase antibodies

BMI

Body mass index

ROC

Receiver operating characteristic

AUC

Area under the curve

SPSS

Statistical Package for the Social Sciences

ACTH

Adrenocorticotropic hormone

CRP

C-reactive protein

CBT-I

Cognitive-behavioral therapy for insomnia

Authors’ contributions

A.Q.H. and Y.M.L originally designed the study. A.Q.H. wrote the main manuscript text. J.Y.and T.X.collected data and supervised the project. L.H.D. and T.T.Z. collected data. D.C.L. and R.X.X. did the statistical analysis for the study. Y.H.Z. and Y.M.L. contributed to the amendment of the manuscript. All authors reviewed and approved the final manuscript.

Funding

No funding.

Data availability

The data that support the findings of this study are not publicly available due to their containing information that could compromise the privacy of research participants but are available from Anquan Hu upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki. The study protocol received ethical approval from the Internal Review Board of the Third People’s Hospital of Ganzhou (Approval Code: gzsyy2024044). Written informed consent was obtained from all participants prior to study initiation.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.