Neuroendocrine Abnormalities as Predictors of Suicidal Ideation in Bipolar Disorder Patients During Depressive Episodes: A Cross-sectional Study

Neuroendocrine Abnormalities as Predictors of Suicidal Ideation in Bipolar Disorder Patients During Depressive Episodes: A Cross-sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Neuroendocrine Abnormalities as Predictors of Suicidal Ideation in Bipolar Disorder Patients During Depressive Episodes: A Cross-sectional Study Xiaoxuan Fan, Xian Shi, Fangyi Deng, Yixian Cai, Yaxi Liu, Yang Zhao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6043432/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Oct, 2025 Read the published version in BMC Psychiatry → Version 1 posted 10 You are reading this latest preprint version Abstract Bipolar disorder (BD) is a psychiatric disorder with a high prevalence of suicidal ideation (SI). While the relationship between BD and the endocrine system is well established, the influence of neuroendocrine hormones on SI during depressive episodes remains poorly understood. This cross-sectional study aimed to identify high-risk neuroendocrine factors for SI and evaluate their predictive efficacy using machine learning techniques. Data were obtained from the electronic medical records of patients hospitalized for BD depressive episodes at The First Affiliated Hospital of Jinan University in Guangzhou, Guangdong Province, China between January 1, 2017, and October 31, 2022. Of 635 eligible patients, 380 exhibited SI. In the multivariate analysis, lower levels of FT4 (OR = 0.925; 95% CI = 0.869–0.986; P = 0.017) and testosterone (OR = 0.799; 95% CI = 0.642–0.993; P = 0.043) were significantly associated with an increased risk of SI. Additionally, an earlier age of onset (OR = 0.936; 95% CI = 0.897–0.977; P = 0.002) and the presence of psychotic symptoms contributed to a higher risk of SI. Furthermore, we developed a supervised learning model—Naive Bayes classifier. The model identified age of onset as a key predictor of SI during depressive episodes of BD, highlighting its importance as the most relevant predictive variable for distinguishing patients at risk of SI. These findings could help clinicians more accurately identify BD patients with higher suicide risk, thereby improving diagnostic sensitivity for this disorder. Bipolar disorder Neuroendocrine Suicidal ideation Machine learning FT4 testosterone Figures Figure 1 Figure 2 1. Introduction Bipolar disorder (BD) is a common psychiatric disorder characterized by manic or hypomanic episodes and depressive episodes, with the latter being a typical feature. According to the WHO’s Mental Health Survey, BD has a global prevalence of 2.4% 1 , making it one of the leading causes of disability worldwide and a substantial social and economic burden. The onset of BD typically occurs between 15 and 25 years of age, with depression often being the initial manifestation 2 . BD is a lifelong episodic disorder marked by frequent relapses 3 , significantly impairing functional capacity, occupational performance, and cognitive abilities 4 , 5 . Globally, suicide ranks among the top 20 causes of death, claiming approximately 700,000 lives annually 6 . BD patients face a significantly higher suicide risk compared to the general population 7 . Approximately one-third of BD patients experience SI 8 , and around 10% die by suicide 9 . BD has a slightly higher lifetime prevalence in men (2.5%) than in women (2.3%) 10 . Moreover, studies show that men with BD are twice as likely to die by suicide as women 11 . Previous research has demonstrated that depressive symptoms can predict SI 12 , highlighting the importance of identifying suicide risk factors during depressive episodes in BD. Understanding the role of neuroendocrine hormone abnormalities on suicide risk is crucial for developing effective interventions and improving patient outcomes. Mood disorders are associated with dysfunction of the neuroendocrine system 13 . The relationship between thyroid dysfunction and BD has been well studied, with thyroid hormones playing a crucial role in neurocognitive development. BD patients are reportedly more prone to thyroid abnormalities than healthy individuals 14 . Hypothalamic-pituitary-thyroid (HPT) axis dysfunction may be related to the pathophysiology and clinical course of BD 15 . In observational studies, both hypothyroidism 16 and hyperthyroidism 17 have been associated with fluctuations in BD symptoms. Although thyroid function alone does not consistently account for mood fluctuations in BD patients 18 , thyroid hormone replacement therapy has been shown to improve mood 19 and extend the duration of euthymia 14 in those with normal thyroid function. This suggests that thyroid hormone abnormalities may play an important role in the development of BD. Abnormalities in the hypothalamic-pituitary-adrenal (HPA) axis may contribute to the pathophysiology of BD 20 . The HPA axis plays a crucial role in mood regulation 21 and can predict the risk of suicide in patients with mood disorders 22 . Cortisol, the end product of the HPA axis, may be linked to an increased suicide risk when its signaling is altered 23 . Specifically, impaired cortisol suppression, an indicator of reduced glucocorticoid receptor feedback, has been identified as a potential predictor of suicide 22 . Additionally, during the depressive, manic, and remission phases, BD patients exhibit elevated levels of cortisol 24 and adrenocorticotropic hormone (ACTH) 20 , suggesting that HPA dysfunction is a characteristic marker in some BD patients. Therefore, a prospective examination of the HPA axis function and its relationship with mood episodes is crucial for the development of effective treatment strategies for BD 25 . The HPA axis is known to mediate stress sensitivity and vulnerability to depressive episodes 26 , leading us to hypothesize that HPA axis dysfunction is closely linked to depressive episodes in BD patients. The HPA, HPT, and hypothalamus-pituitary-gonadal (HPG) axes can interact, with the HPA axis potentially exerting a negative impact on the HPT axis 27 . Increased HPA axis activity can reduce HPG axis activity, and hormones from both the HPT and HPA axes can influence HPG axis function 28 . Testosterone levels may be associated with the course of BD and suicidal behavior, potentially serving as a predictor of suicidal behavior in women with BD 29 , 30 . Given these findings, it is reasonable to hypothesize that BD is related to alterations in the thyroid, adrenal, and gonadal functions. Therefore, a comprehensive assessment is crucial when evaluating clinical and subclinical imbalances associated with different mood states. In recent years, machine learning methods have shown potential in predicting suicide risk and improving diagnostic accuracy with the aim of enhancing the identification of high-risk factors and diagnostic precision through new data analysis techniques 31 . In machine learning, variable importance analysis helps clarify how models use features for prediction. Therefore, in this study, machine learning analysis was employed to build predictive models, aiding in clinical differentiation and diagnosis. The suicide rate among BD patients is 20–30 times higher than in the general population, with BD-II patients facing the highest risk of completed suicide 32 . Existing research has a limited understanding of the relationship between neuroendocrine factors and suicidal ideation (SI) in BD patients. Meta-analyses have shown a lifetime prevalence of 33.9% for suicide attempts in BD patients 33 . Additionally, about 90% of suicide deaths are linked to mental illness, with mood disorders being the most common, and BD patients being particularly affected 34 . Therefore, detecting and intervening in SI is more critical than addressing suicide behavior itself 35 . Identifying additional predictors of SI and enhancing diagnostic sensitivity will help develop effective methods for early detection and prevention, enabling clinicians to adjust hospitalization duration and treatment strategies accordingly. Consequently, our study aimed to explore the relationship between neuroendocrine abnormalities and the risk of SI during depressive episodes in BD patients. We hypothesized that abnormalities in thyroid, adrenal, and reproductive neuroendocrine functions are risk factors for SI. The study sought to identify specific neuroendocrine hormone indicators, recognize high-risk factors for SI, and use machine learning to explore the utility of these factors for clinical outcomes. 2. Methods 2.1. Study design and population This study employed a cross-sectional design. Our sample included individuals who were hospitalized in the Psychiatry Department of the First Affiliated Hospital of Jinan University (FAHJU) in Guangzhou, Guangdong Province, China, between January 1, 2017, and October 31, 2022, and who had received at least one treatment for BD (n = 1127). All patients were diagnosed by four experienced senior psychiatrists using the International Classification of Diseases, 10th Edition (ICD-10), and calibration discussions were conducted. We included patients with diagnoses corresponding to codes F31.4 and F31.5, i.e., patients included in this study had at least one previous episode of mania, hypomania, or mixed affective episodes, with the current episode being classified as a major depressive episode. We confirmed the presence of SI by reviewing each patient’s medical records at admission (Day 0) and during psychiatric evaluation. The collection of biological indicators was strictly limited to all patients who underwent laboratory examinations after fasting between 6:00 and 8:00 a.m. on the day after admission (day 1), ensuring that all samples were obtained within 24 hours after SI assessment. We excluded patients with rapid turnover within 24 hours who did not undergo neuroendocrine hormone testing (n = 200) and those with missing neuroendocrine hormone indicators of interest in this study (n = 292). Following these critical exclusion procedures, 635 patients were included in the final analysis. 2.2. Data resource We obtained data from the FAHJU Hospital Information System (HIS), which compiles the clinical records of both outpatient and inpatient care. As a major tertiary medical institution and key provider of mental health services in South China, the FAHJU maintains enrollment records, demographic information, and medical, laboratory, and prescription data for all patients. Any sensitive personal information was removed; therefore, the responsible Institutional Review Board approved the use of the de-identified data, including patient names, identification numbers, hospital numbers, and addresses, without the need for informed consent. This study was approved by the Medical Ethics Committee of First Affiliated Hospital of Jinan University, China. 2.3. Variables 2.3.1. Socio-demographics and clinical characteristicsCovariates included in this study were demographic information and clinical characteristics. Demographic information included participants' age, sex, and marital status. The clinical characteristics comprised age of onset (self-reported by patients), family history of mental illness, and comorbid psychiatric symptoms. The presence of psychotic symptoms was confirmed by reviewing the current medical history and psychiatric examinations of each patient's admission records. The psychiatric symptoms included obvious positive psychiatric symptoms, including hallucinations and delusions, etc, occurring after the onset of the current episode. Therefore, this variable was defined as the positive psychiatric symptoms in subsequent analysis, thereby distinguishing it from broader non-specific psychiatric symptomology. 2.3.2. Neuroendocrine hormones The study investigated neuroendocrine hormones related to the HPT/HPA/HPG axes using laboratory indicators, including thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4), ACTH, cortisol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), prolactin, testosterone, and estradiol. These laboratory indicators were measured in fasting patients between 6:00 AM and 8:00 AM on the day following admission. Among these hormones, FSH, LH, and estradiol levels are influenced by the menstrual cycle. As this study could not control for the effects of the menstrual cycle, the results may have been impacted by this factor. 2.3.3. Suicidal ideation assessment This study measured SI in BD patients during depressive episodes at the time of admission. The presence of SI was determined through self-reports by patients during initial psychiatric evaluation, focusing specifically on suicidal thoughts at the time of admission. The presence of any SI was defined as “positive”; otherwise, it was recorded as “negative”. The assessment window covered 24 hours prior to admission (first psychiatric interview). Therefore, patients who had previously reported SI outside this time frame were not included in this measure. SI data were recorded using the FAHJU Hospital Information System (HIS). We selected patients with SI present during the psychiatric examination post-admission, as previous studies have shown that SI is closely related to depressive episodes in BD and serves as a key monitoring indicator in clinical practice 36 , This is crucial for improving the prognosis of patients with SI. 2.4. Statistical analysis Statistical analysis was performed using the R software (version R 3.3.1) ( https://www.R-project.org/ ). Descriptive analyses are presented as medians [interquartile range (IQR)] for continuous variables and as absolute and relative frequencies for categorical variables. Patients were divided into two groups: those with and without SI. Chi-square tests or t-tests were used to analyze demographic and clinically relevant variables between the two groups. For the multivariate analysis, we estimated the sample size using the pwr package in R. We employed the pwr.f2.test function for the calculation, setting the number of predictors to 11, effect size squared to 0.15, significance level to 0.05, and statistical power to 0.80. Based on these parameters, the calculated sample size was 110. The final sample size of 635 patients met the minimum requirement. Summary statistics are presented as medians [IQR] for continuous variables and percentages for categorical variables, as appropriate. Initial univariate logistic regression analyses were conducted to investigate the individual associations between all covariates and SI in BD patients. Covariates from the univariate models that were associated with the outcome ( P < 0.2) were included in the final multivariate analysis. Multivariate logistic regression analysis was utilized to assess the independent effects of cortisol, testosterone, FT4, and TSH levels, while adjustment for confounding factors. Results from the logistic regression models are presented as odds ratios (OR) with 95% confidence intervals (CI), and the goodness-of-fit for each multivariate model was confirmed using the Hosmer-Lemeshow test with a P-value > 0.05. Given the hormonal differences between sexes, separate multivariate regression analyses were conducted for male and female participants, and the interaction effects were tested. The results showed that the interaction effects were not statistically significant. 2.5. Machine learning analysis In this study, we developed a supervised machine learning model to predict SI on admission in BD patients during depressive episodes. The predictors included in the machine learning model were the same as the sociodemographic and clinical features used in classical bivariate analyses. Machine learning analysis was conducted using the R software (version R 3.3.1) and R Studio with the CARET package. The CARET package (short for Classification and Regression Training) is a collection of functions designed to simplify the process of creating predictive models. This package includes tools for (1) Leave-One-Out Cross-Validation (LOOCV), (2) preprocessing, (3) resampling to adjust feature selection models, (4) estimating variable importance, and (5) other functions. The CARET package provides various tools for developing predictive models using R’s extensive model library. There are many different modeling functions in R, each with a varying model training and/or prediction syntax. The package was initially designed to provide a unified interface for the functions themselves and to standardize common tasks such as parameter tuning and variable importance. All twelve 12 initially subjected to multivariable logistic regression analysis were systematically incorporated into the machine learning model’s final analysis. There were some dependencies among the hormones, indicating that the variables were not entirely independent. The outcome of interest, SI, is a binary variable. Considering that the Naive Bayes model performs exceptionally well with high-dimensional data, is robust, and offers simple computations with fast training and prediction, we used a Naive Bayes classifier with a high-density kernel to train the model. The Naive Bayes classifier is a series of probabilistic algorithms based on Bayes’ theorem using the Maximum A Posteriori decision rule. We employed LOOCV, receiver operating characteristic (ROC) curves, and area under the curve (AUC) to estimate the model performance, accuracy, and kappa. LOOCV involves training the algorithm using all participants except one, testing the model on the omitted participant, and then repeating this process until each participant has been tested at least once during the evaluation. This approach ensures that our model is tested on "unseen" data to avoid overfitting. 3. Results 3.1. Sample feature In total, 635 patients were included in the final analysis. Of these, 59.8% (n = 380) reported having SI. The median age of the entire sample was 22 years (interquartile range, 14 years), with the majority being female (n = 480; 75.6%). Approximately 62.8% (n = 399) of patients had positive psychotic symptoms. Table 1 displays the sociodemographic and clinical characteristics of BD patients with depressive episodes who either denied or reported experiencing SI, as well as neuroendocrine hormones related to the HPT/HPA/HPG axes. The results indicated statistically significant differences between the two groups for several factors, including age, age at onset, sex, marital status, and levels of various hormones, including cortisol, FT4, FSH, testosterone, TSH, and estradiol. Additionally, we assessed potential differences in non-missing variables between the excluded patients and those ultimately included in the analysis. The findings indicated no significant overall differences (Supplementary Table S1). Table 1. Demographic and clinical characteristics. Characteristic Total Sample (N = 635) Without SI (N = 255) With SI (N = 380) Z/c P -value Age (years), median (IQR) 22.00 (14.00) 29.00 (19.00) 19.00 (9.00) 9.187 <0.001 Age of onset (years), median (IQR) 18.00 (11.50) 23.00 (16.00) 16.00 (8.00) 10.082 <0.001 ACTH (pg/ml), median (IQR) 26.64 (23.34) 28.30 (23.58) 25.09 (23.06) 1.853 0.064 Cortisol (nmol/L), median (IQR) 316.63 (249.57) 347.68 (228.56) 293.87 (243.65) 3.687 <0.001 FT3 (pmol/L), median (IQR) 5.02 (0.86) 5.04 (0.88) 5.00 (0.84) 0.164 0.87 FT4 (pmol/L), median (IQR) 10.79 (3.07) 11.18 (3.28) 10.57 (2.98) 3.667 <0.001 FSH (mIU/L), median (IQR) 5.79 (4.34) 6.21 (4.10) 5.53 (4.28) 3.287 0.001 LH(IU/L), median (IQR) 6.63 (7.10) 5.93 (7.71) 6.94 (6.70) 0.768 0.442 Progesterone(ng/ml), median (IQR) 0.72 (1.01) 0.72 (0.78) 0.72 (1.29) 0.152 0.879 Prolactin (ng/ml), median (IQR) 26.23 (39.15) 25.23 (40.35) 26.44 (38.51) 0.511 0.609 Testosterone (nmol/L), median (IQR) 0.53 (1.09) 0.60 (3.00) 0.51 (0.48) 3.060 0.002 TSH (mIU/L), median (IQR) 1.78 (1.67) 1.66 (1.41) 1.86 (1.83) 3.032 0.002 Estradiol (pg/ml), median (IQR) 47.03 (55.23) 42.15 (45.99) 50.70 (62.97) 3.550 <0.001 Sex, % 19.208 <0.001 Female 480 (75.6) 169 (66.3) 311 (81.8) Male 155 (24.4) 86 (33.7) 69 (18.2) Marital status, % 166.652 <0.001 Single 451 (71.0) 136 (53.3) 315 (82.9) Married 162 (25.5) 104 (40.8) 58 (15.3) Divorced 22 (3.5) 15 (5.9) 7 (1.8) Family history of psychiatric disease, % 4.802 0.028 No 475 (74.8) 203 (79.6) 272 (71.6) Yes 160 (25.2) 52 (20.4) 108 (28.4) Physical comorbidities, % 3.074 0.08 No 499 (78.6) 191 (74.9) 308 (81.1) Yes 136 (21.4) 64 (25.1) 72 (18.9) Substance abuse, % 1.033 0.309 No 628 (98.9) 254 (99.6) 374 (98.4) Yes 7 (1.1) 1 (0.4) 6 (1.6) Positive psychotic symptoms, % 6.942 0.008 No 236 (37.2) 111 (43.5) 125 (32.9) Yes 399 (62.8) 144 (56.5) 255 (67.1) Personality disorder, % 0.022 0.882 No 613 (96.5) 247 (96.9) 366 (96.3) Yes 22 (3.5) 8 (3.1) 14 (3.7) Notes: The table uses t-tests and chi-squared tests to compare patients with and without suicidal ideation. TSH, thyroid-stimulating hormone. T3, triiodothyronine. T4, thyroxine. FT3, free triiodothyronine. FT4, free thyroxine. ACTH, adrenocorticotropic hormone. FSH, follicle-stimulating hormone. LH, luteinizing hormone 3.2. Univariate and multivariate analysis In the univariate analysis, being married ( P = 0.001), having a positive family history ( P = 0.023), and having comorbid psychotic symptoms ( P = 0.007) were identified as factors that increased the risk of SI. Among the neuroendocrine variables, the following were significantly associated with SI during depressive episode in BD patients: Thyroid function variable FT4 (unadjusted OR = 0.902; 95% CI = 0.852–0.954; P < 0.001), HPA axis hormone cortisol (unadjusted OR = 0.998; 95% CI = 0.997–0.999; P < 0.001), sex hormone FSH (unadjusted OR = 0.984; 95% CI = 0.974–0.995; P = 0.006), and testosterone (unadjusted OR = 0.819; 95% CI = 0.750–0.893; P < 0.001). We conducted an interaction analysis between marital status and age, and the results showed a P-value less than 0.05, indicating statistical significance. This suggests that the effect of marital status on SI is influenced by age. In the multivariate logistic regression analysis, the following factors were independently associated with an increased risk of SI during depressive episodes: lower FT4 levels (OR = 0.925; 95% CI = 0.869–0.986; P = 0.017) and lower testosterone levels (OR = 0.799; 95% CI = 0.642–0.993; P = 0.043) levels were significantly associated with a higher risk of SI in BD patients during depressive episodes. Additionally, an earlier age of onset (OR = 0.936; 95% CI = 0.897–0.977; P = 0.002) and the presence of psychotic symptoms also increased the risk of SI (Table 2). Table 2. Univariate and multivariate analyses between neuroendocrine and suicidal ideation. Characteristic Univariate analyses Multivariate analyses OR 95%CI P OR 95%CI P Predictor ACTH (pg/ml) 0.995 0.988-1.002 0.204 Cortisol (nmol/L) 0.998 0.997-0.999 <0.001 0.999 0.998-1.000 0.103 FT3 (pmol/L) 0.949 0.774-1.164 0.616 FT4 (pmol/L) 0.902 0.852-0.954 <0.001 0.925 0.869-0.986 0.017 FSH (mIU/L) 0.984 0.974-0.995 0.006 1.009 0.995-1.023 0.211 LH 0.999 0.985-1.012 0.834 Progesterone 1.012 0.977-1.049 0.508 Prolactin (ng/ml) 0.999 0.995-1.002 0.999 Testosterone (nmol/L) 0.819 0.750-0.893 <0.001 0.799 0.642-0.993 0.043 TSH (mIU/L) 1.023 0.959-1.090 0.491 Estradiol (pg/ml) 1 0.999-1.001 0.633 Confounding factor Age (years) 0.942 0.928-0.956 <0.001 1.005 0.965-1.046 0.818 Age of onset (years) 0.926 0.909-0.943 <0.001 0.936 0.897-0.977 0.002 Sex, % 0.436 0.302-0.630 <0.001 1.006 0.397-2.549 0.99 Marital status, % single reference reference reference reference reference reference married 4.963 1.979-12.447 0.001 1.793 0.613-5.244 0.286 divorced 1.195 0.461-3.099 0.714 Family history of psychiatric disease, % 1.55 1.063-2.261 0.023 1.312 0.852-2.019 0.217 Physical comorbidities, % 0.698 0.476-1.022 0.065 0.992 0.620-1.590 0.975 Substance abuse, % 4.075 0.488-34.050 0.195 7.531 0.768-73.828 0.083 Positive psychotic symptoms, % 1.572 1.134-2.181 0.007 1.559 1.065-2.282 0.022 Personality disorder, % 1.181 0.488-2.857 0.712 Abbreviations: OR, odds ratio. CI, confidence interval. TSH, thyroid-stimulating hormone. T3, triiodothyronine. T4, thyroxine. FT3, free triiodothyronine. FT4, free thyroxine. ACTH, adrenocorticotropic hormone. FSH, follicle stimulating hormone. LH, luteinizing hormone 3.3. Predictive model for SI in patients with bipolar disorder during depressive episodes The Naive Bayes algorithm (Table 3) distinguished between patients with and without SI during depressive episodes in BD with an accuracy of 80.49% (95% CI = 6.66–8.23, P = 0.03, 84.76% sensitivity, 76.22% specificity). Figure 1 shows the variable importance in predicting SI in BD patients during depressive episodes. The most relevant predictors for distinguishing patients with SI were age of onset, marital status, and cortisol levels. Additionally, the thyroid function indicator FT4 and sex hormones, such as testosterone and FSH, played significant roles. The AUC of the model was 0.90, and the ROC curve for this model is shown in Figure 2. The sensitivity of the curve was 84.76%, indicating that the model could correctly identify 84.76% of the positive samples, which is considered a desirable range for medical diagnosis or risk prediction. Table 3. Performance measures of the Naïve Bayes algorithm in differentiating between BD depressive episode patients with and without suicidal ideation. Sensitivity Specificity PPV NPV Balanced accuracy Naive Bayes 84.76% 76.22% 79.20% 82.39% 80.49% Abbreviations: PPV, Positive Predictive Value. NPV, Negative Predictive Value 4. Discussion To the best of our knowledge, this is the first study to explore the impact of neuroendocrine hormone levels on SI in BD patients during depressive episodes and use a Naive Bayes model to predict key indicators among the risk factors for SI. Baseline data were successfully collected from 635 patients experiencing depressive episodes of BD, of whom 380 (59.84%) exhibited SI. These results indicated that lower serum FT4 and testosterone levels, earlier age of onset, and the presence of positive psychotic symptoms were significant risk factors for SI during depressive episodes in BD patients. Notably, we further used the Naive Bayes method to construct a prediction model, and the results showed that age of onset contributed more significantly than other features in the classification task, playing a critical role in predicting the risk of SI. In our study, BD patients experiencing depressive episodes were divided into two groups: those with and without SI. The results showed a significant difference in FT4 levels between the two groups, which aligns with previous research findings 37 . Additionally, the lower the FT4 level, the greater was the likelihood of SI. Thyroid dysfunction is closely associated with the development and progression of psychiatric disorders and is significantly correlated with SI 38 . A meta-analysis indicated that BD patients with a history of suicide attempts are more likely to have hypothyroidism and that patients with suicidal behaviors have lower levels of FT3 and TT4 39 . Because observational studies cannot establish a causal relationship between FT4 levels and BD risk factors, some researchers have conducted Mendelian randomization studies, which revealed that higher FT4 levels are associated with a reduced risk of BD 40,41 , highlighting the importance of FT4 in BD risk assessment. It is well established that BD patients with depressive symptoms are at a higher risk of suicide. A study involving male participants found that the prevalence of thyroid disease in those with a history of SI is 6.5% compared to 1.9% in those without such a history 42 . Therefore, early identification of SI is crucial for the effective treatment of BD patients during depressive episodes. Suicidal behavior during the first episode of major depressive disorder (MDD) is linked to thyroid hormone dysregulation 43 . When MDD patients are in a prolonged depressive state, there is a decline in serotonergic and noradrenergic system function 44 ，along with gradual decompensation of thyroid function, leading to increased TSH levels. This exacerbates anxiety, depression, and psychotic symptoms, thereby increasing the risk of suicide 45 . Therefore, regular monitoring of thyroid function in depressive patients is essential for the early detection of SI 46 . These findings align with the existing literature, demonstrating that FT4 plays an important role in assessing the risk of SI in BD patients during depressive episodes. Our study further emphasizes the significance of these factors in BD. Our study found that testosterone plays an important role in SI during depressive episodes in BD, with higher testosterone levels associated with a reduced likelihood of SI. Testosterone has been linked to suicide risk 47–49 ; it may exert antidepressant effects by influencing the brain’s limbic system 50 . It may also influence suicidal behavior in BD patients by regulating the HPA axis and serotonergic system 51 . While some studies have found a positive correlation between higher testosterone levels and the number of suicide attempts 29 , others have reported no significant difference in testosterone levels between male suicide attempters and healthy controls 52 . The varying impact of testosterone on suicide attempts may be owing to differences in testosterone levels between men and women. To address this, some studies have specifically examined each sex. A prospective study of female BD patients suggested that higher baseline testosterone levels could predict suicide attempts during follow-up 50 . However, another study in men found that both high and low testosterone levels could contribute to suicidal behavior 47 . In BD patients, females tend to have higher SI scores compared to those of males 29 . In our study, we conducted a sex-stratified analysis but found no statistically significant difference between males and females during depressive episodes in BD. The role of testosterone in the neurobiology of mood disorders, suicidal behaviors, and SI remains complex. SI is associated with psychotic symptoms and earlier age of onset 53 . Our study found that psychotic symptoms were independent risk factors for SI during depressive episodes in BD patients. Patients with psychotic symptoms often experience more severe psychological distress, which may increase their risk of developing SI. This finding is consistent with those of previous studies, such as the significant association between delusions and suicidal behavior 54 . Research on schizophrenia patients has shown that psychotic symptoms can predict suicidal activity in this population 55 . However, some studies contradict these conclusions, suggesting that the presence of psychotic symptoms at any point during the course of BD is not associated with an increased risk of suicide attempts or death by suicide 56,57 . In summary, previous studies have not fully elucidated the relationship between psychotic symptoms and suicide outcomes in adults. Therefore, our study incorporated data on psychotic symptoms, laying the foundation for future research on the complex association between psychotic symptoms and suicidal behaviors. This underscores the importance of considering multidimensional factors in suicide risk assessment. Understanding these risk factors can help clinicians identify BD patients at high risk of suicide more accurately, allowing for targeted intervention strategies. The use of machine-learning algorithms to predict suicide-related outcomes (such as SI, suicide attempts, and suicidal behavior) has emerged as a growing research field in recent years, showing promising performance 58,59 . This study obtained a more comprehensive analytical perspective by combining multiple logistic regression analysis with the Naive Bayes model in machine learning. The importance and directional information provided by logistic regression can guide the selection of features for naive Bayes models, thus ensuring a balance between model interpretability and predictive performance. By comparing the prediction results of the two models, we found that the age at onset was the most important predictor of SI during depressive episodes in BD patients. This suggests that age at onset has a significant impact on predicting SI, possibly reflecting differences in vulnerability to mental health issues across different age groups. Additionally, our study revealed that patients with SI were younger and had an earlier age of onset. Childhood abuse may be an important risk factor for SI 60 . It is worth noting the risk pattern of childhood sexual abuse in bisexual groups: even if individuals do not currently report Si, it has become a risk factor for suicidal behavior 61 . This phenomenon may stem from the neurobiological consequences of traumatic stress abuse experiences that lead to cortisol rhythm disturbances and systemic inflammatory responses (such as CRP increase) by activating the HPA axis 62 . These neuroendocrine changes may constitute a biological bridge connecting childhood traumatic abuse and later suicidal behaviors. The timing of disease onset may also reflect the severity of the illness or the cumulative psychological stress over time. . Adolescents are the key population of focus in current clinical research 63 . SI assessment for this population needs to pay special attention to the age of onset. Child abuse mediates the relationship between neuroendocrine abnormalities and suicide. This study provides valuable insights into the prediction of SI in BD patients during depressive episodes using a Naive Bayes model. Future research should validate and expand these findings and further study the mediating role of childhood trauma experience in the pathway of "neuroendocrine abnormalities suicidality" to promote the application of data-driven suicide risk prediction in clinical practice, ultimately improve patient management and treatment. Our study has several limitations. First, our results are applicable to a hospitalised population with complete neuroendocrine hormone examinations. Careful extrapolation is required for patients with a rapid turnover or critical illness. Future research should be combined with rapid bedside rapid detection to reduce the lack of data; because we were unable to trace patients’ specific medication history prior to admission, this study did not record such information. However, previous research has indicated that mood stabilizers and antipsychotic medications have significant effects on neuroendocrine hormones 64 . This may have affected the results of our study. Second, as this study employed a cross-sectional design, it cannot reveal the dynamic processes that change over time. Additionally, the observational nature of the study limits our ability to make definitive causal inferences about the relationship between thyroid function and SI in BD patients during depressive episodes following hospitalization. Therefore, a more detailed longitudinal study design is needed in the future, such as dynamic monitoring of the fluctuation of the SI score and neuroendocrine biomarkers, to further clarify how these biomarkers change with SI severity. Third, given the limited sample size and assumption of variable independence in the model, future research should explore the use of more complex models to validate these findings and investigate the interactions between different variables. Fourth, previous studies have identified several factors linked to suicidal ideation in severe mental illness, including age, sex, BD type, childhood abuse, comorbid substance use disorder, anxiety, mixed mood states, and sleep. However, we did not include some of theseas confounders, such as sleep disorders. Previous studies have shown that reduced total sleep time is significantly associated with current SI 65 , and because of the presence of insomnia, SI is unlikely to be relieved even after antidepressant treatment 66 .Future research should control for them, even though unmeasured confounders are unlikely to explain our findings fully. Therefore, screening for thyroid function risk factors at admission, prior to hospitalization, or post-discharge may overemphasize the importance of monitoring thyroid metabolic risk. Fifth, the definition of SI in this study was somewhat subjective and lacked more detailed inquiries, which has certain limitations. Existing studies have pointed out that about 50% of suicide attempters may not report or deny SI 67 . In addition, MDD patients with higher impulsivity usually have more SI 68 , and after controlling for aggressive factors, SI is still associated with impulsivity 69 . Therefore, patients with impulsive personality traits may directly adopt suicidal behavior rather than merely expressing SI. Based on this, future research should integrate multi-dimensional assessment methods, such as increasing the use of the Impulsivity Scale to improve the accuracy and validity of suicide risk prediction. Finally, we did not collect data on other risk factors related to the hypothalamic-pituitary-target organ system, which may have prevented us from fully assessing the impact of neuroendocrine function on SI in BD. These factors include imaging abnormalities and the female gonadal status. Moreover, the adverse effects of prescription medications resulting from the severity of psychotic symptoms in BD patients may also be linked to an increased risk of SI in these patients. 5. Conclusion In summary, this study is the first to explore the effect of neuroendocrine hormone levels on SI in BD patients experiencing depressive episodes. The findings provide compelling evidence of a relationship between neuroendocrine function and SI in this population. Specifically, the study found that lower levels of FT4 and testosterone were significantly associated with an increased risk of SI, alongside earlier age of onset and the presence of positive psychotic symptoms as key contributing factors. These findings may assist clinicians in more accurately identifying BD patients at elevated risk of suicide, thereby improving the diagnostic sensitivity and informing early intervention strategies in clinical practice. Abbreviations BD Bipolar disorder SI suicidal ideation HPA hypothalamic-pituitary-adrenal ACTH adrenocorticotropic hormone HPG hypothalamus-pituitary-gonadal TSH thyroid-stimulating hormone FT3 free triiodothyronine FT4 free thyroxine FSH follicle-stimulating hormone LH luteinizing hormone MDD major depressive disorder Declarations Ethics approval and consent to participate This study was approved by the Ethics Review Committee of the First Affiliated Hospital of Jinan University (IRB Approval No. KY-2023-215). All methods were performed in accordance with the guidelines of the Declaration of Helsinki. As the research involved secondary analysis of fully anonymized and de-identified clinical data, the committee waived the requirement for individual informed consent in accordance with national regulations on ethical research. Consent for publication Not applicable. Availability of data and materials The data sets generated and analyzed during the study are available from the corresponding author on reasonable request. Competing interest The authors declare that they have no competing interests. Funding This work was funded by National Natural Science Foundation of China (Grant No.: 81871036) and Science and Technology Projects in Guangzhou (Grant No.: 2025A03J4239). Authors’ contributions X-X.F. and X.S. wrote and edited the manuscript. X-X.F. , X.S. and F-Y.D. collected and analyzed the data. Y-X.C. and Y-X.L. drew the figures. Y.Z. and H.W. collected the data. J-W.L. and J-Y.P. designed the study. J-Y.P. had full access to all the data in the study and had final responsibility for the decision to submit for publication. All authors read and approved the final manuscript. References Merikangas KR, Jin R, He JP, et al. Prevalence and Correlates of Bipolar Spectrum Disorder in the World Mental Health Survey Initiative. 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1","display":"","copyAsset":false,"role":"figure","size":43473,"visible":true,"origin":"","legend":"\u003cp\u003eVariable importance for predicting suicidal ideation during BD depressive episodes based on the Naïve Bayes algorithm.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6043432/v1/cb80ea9e1f0ad17465ba78ad.png"},{"id":81691981,"identity":"85743cdd-b7cd-4d6d-8e6b-ce5869de22a9","added_by":"auto","created_at":"2025-04-30 11:38:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44950,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for predicting suicidal ideation during BD depressive episodes based on the Naïve Bayes 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11:30:45","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12897,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6043432/v1/3ba9362174aba706eace6806.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neuroendocrine Abnormalities as Predictors of Suicidal Ideation in Bipolar Disorder Patients During Depressive Episodes: A Cross-sectional Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBipolar disorder (BD) is a common psychiatric disorder characterized by manic or hypomanic episodes and depressive episodes, with the latter being a typical feature. According to the WHO\u0026rsquo;s Mental Health Survey, BD has a global prevalence of 2.4% \u003csup\u003e1\u003c/sup\u003e, making it one of the leading causes of disability worldwide and a substantial social and economic burden. The onset of BD typically occurs between 15 and 25 years of age, with depression often being the initial manifestation\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. BD is a lifelong episodic disorder marked by frequent relapses\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, significantly impairing functional capacity, occupational performance, and cognitive abilities\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Globally, suicide ranks among the top 20 causes of death, claiming approximately 700,000 lives annually\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. BD patients face a significantly higher suicide risk compared to the general population\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Approximately one-third of BD patients experience SI\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, and around 10% die by suicide\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. BD has a slightly higher lifetime prevalence in men (2.5%) than in women (2.3%)\u003csup\u003e10\u003c/sup\u003e. Moreover, studies show that men with BD are twice as likely to die by suicide as women\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Previous research has demonstrated that depressive symptoms can predict SI\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, highlighting the importance of identifying suicide risk factors during depressive episodes in BD. Understanding the role of neuroendocrine hormone abnormalities on suicide risk is crucial for developing effective interventions and improving patient outcomes.\u003c/p\u003e \u003cp\u003eMood disorders are associated with dysfunction of the neuroendocrine system\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The relationship between thyroid dysfunction and BD has been well studied, with thyroid hormones playing a crucial role in neurocognitive development. BD patients are reportedly more prone to thyroid abnormalities than healthy individuals\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Hypothalamic-pituitary-thyroid (HPT) axis dysfunction may be related to the pathophysiology and clinical course of BD\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In observational studies, both hypothyroidism\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and hyperthyroidism\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e have been associated with fluctuations in BD symptoms. Although thyroid function alone does not consistently account for mood fluctuations in BD patients\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, thyroid hormone replacement therapy has been shown to improve mood\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and extend the duration of euthymia\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e in those with normal thyroid function. This suggests that thyroid hormone abnormalities may play an important role in the development of BD.\u003c/p\u003e \u003cp\u003eAbnormalities in the hypothalamic-pituitary-adrenal (HPA) axis may contribute to the pathophysiology of BD\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The HPA axis plays a crucial role in mood regulation\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and can predict the risk of suicide in patients with mood disorders\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Cortisol, the end product of the HPA axis, may be linked to an increased suicide risk when its signaling is altered\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Specifically, impaired cortisol suppression, an indicator of reduced glucocorticoid receptor feedback, has been identified as a potential predictor of suicide\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Additionally, during the depressive, manic, and remission phases, BD patients exhibit elevated levels of cortisol\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and adrenocorticotropic hormone (ACTH) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, suggesting that HPA dysfunction is a characteristic marker in some BD patients. Therefore, a prospective examination of the HPA axis function and its relationship with mood episodes is crucial for the development of effective treatment strategies for BD\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The HPA axis is known to mediate stress sensitivity and vulnerability to depressive episodes\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, leading us to hypothesize that HPA axis dysfunction is closely linked to depressive episodes in BD patients.\u003c/p\u003e \u003cp\u003eThe HPA, HPT, and hypothalamus-pituitary-gonadal (HPG) axes can interact, with the HPA axis potentially exerting a negative impact on the HPT axis\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Increased HPA axis activity can reduce HPG axis activity, and hormones from both the HPT and HPA axes can influence HPG axis function\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Testosterone levels may be associated with the course of BD and suicidal behavior, potentially serving as a predictor of suicidal behavior in women with BD\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Given these findings, it is reasonable to hypothesize that BD is related to alterations in the thyroid, adrenal, and gonadal functions. Therefore, a comprehensive assessment is crucial when evaluating clinical and subclinical imbalances associated with different mood states.\u003c/p\u003e \u003cp\u003eIn recent years, machine learning methods have shown potential in predicting suicide risk and improving diagnostic accuracy with the aim of enhancing the identification of high-risk factors and diagnostic precision through new data analysis techniques\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In machine learning, variable importance analysis helps clarify how models use features for prediction. Therefore, in this study, machine learning analysis was employed to build predictive models, aiding in clinical differentiation and diagnosis. The suicide rate among BD patients is 20\u0026ndash;30 times higher than in the general population, with BD-II patients facing the highest risk of completed suicide\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Existing research has a limited understanding of the relationship between neuroendocrine factors and suicidal ideation (SI) in BD patients. Meta-analyses have shown a lifetime prevalence of 33.9% for suicide attempts in BD patients\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Additionally, about 90% of suicide deaths are linked to mental illness, with mood disorders being the most common, and BD patients being particularly affected\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Therefore, detecting and intervening in SI is more critical than addressing suicide behavior itself\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Identifying additional predictors of SI and enhancing diagnostic sensitivity will help develop effective methods for early detection and prevention, enabling clinicians to adjust hospitalization duration and treatment strategies accordingly. Consequently, our study aimed to explore the relationship between neuroendocrine abnormalities and the risk of SI during depressive episodes in BD patients. We hypothesized that abnormalities in thyroid, adrenal, and reproductive neuroendocrine functions are risk factors for SI. The study sought to identify specific neuroendocrine hormone indicators, recognize high-risk factors for SI, and use machine learning to explore the utility of these factors for clinical outcomes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design and population\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional design. Our sample included individuals who were hospitalized in the Psychiatry Department of the First Affiliated Hospital of Jinan University (FAHJU) in Guangzhou, Guangdong Province, China, between January 1, 2017, and October 31, 2022, and who had received at least one treatment for BD (n\u0026thinsp;=\u0026thinsp;1127). All patients were diagnosed by four experienced senior psychiatrists using the International Classification of Diseases, 10th Edition (ICD-10), and calibration discussions were conducted. We included patients with diagnoses corresponding to codes F31.4 and F31.5, i.e., patients included in this study had at least one previous episode of mania, hypomania, or mixed affective episodes, with the current episode being classified as a major depressive episode. We confirmed the presence of SI by reviewing each patient\u0026rsquo;s medical records at admission (Day 0) and during psychiatric evaluation. The collection of biological indicators was strictly limited to all patients who underwent laboratory examinations after fasting between 6:00 and 8:00 a.m. on the day after admission (day 1), ensuring that all samples were obtained within 24 hours after SI assessment. We excluded patients with rapid turnover within 24 hours who did not undergo neuroendocrine hormone testing (n\u0026thinsp;=\u0026thinsp;200) and those with missing neuroendocrine hormone indicators of interest in this study (n\u0026thinsp;=\u0026thinsp;292). Following these critical exclusion procedures, 635 patients were included in the final analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data resource\u003c/h2\u003e \u003cp\u003eWe obtained data from the FAHJU Hospital Information System (HIS), which compiles the clinical records of both outpatient and inpatient care. As a major tertiary medical institution and key provider of mental health services in South China, the FAHJU maintains enrollment records, demographic information, and medical, laboratory, and prescription data for all patients. Any sensitive personal information was removed; therefore, the responsible Institutional Review Board approved the use of the de-identified data, including patient names, identification numbers, hospital numbers, and addresses, without the need for informed consent. This study was approved by the Medical Ethics Committee of First Affiliated Hospital of Jinan University, China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Variables\u003c/h2\u003e \u003cp\u003e2.3.1. Socio-demographics and clinical characteristicsCovariates included in this study were demographic information and clinical characteristics. Demographic information included participants' age, sex, and marital status. The clinical characteristics comprised age of onset (self-reported by patients), family history of mental illness, and comorbid psychiatric symptoms. The presence of psychotic symptoms was confirmed by reviewing the current medical history and psychiatric examinations of each patient's admission records. The psychiatric symptoms included obvious positive psychiatric symptoms, including hallucinations and delusions, etc, occurring after the onset of the current episode. Therefore, this variable was defined as the positive psychiatric symptoms in subsequent analysis, thereby distinguishing it from broader non-specific psychiatric symptomology.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Neuroendocrine hormones\u003c/h2\u003e \u003cp\u003eThe study investigated neuroendocrine hormones related to the HPT/HPA/HPG axes using laboratory indicators, including thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4), ACTH, cortisol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), prolactin, testosterone, and estradiol. These laboratory indicators were measured in fasting patients between 6:00 AM and 8:00 AM on the day following admission. Among these hormones, FSH, LH, and estradiol levels are influenced by the menstrual cycle. As this study could not control for the effects of the menstrual cycle, the results may have been impacted by this factor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Suicidal ideation assessment\u003c/h2\u003e \u003cp\u003eThis study measured SI in BD patients during depressive episodes at the time of admission. The presence of SI was determined through self-reports by patients during initial psychiatric evaluation, focusing specifically on suicidal thoughts at the time of admission. The presence of any SI was defined as \u0026ldquo;positive\u0026rdquo;; otherwise, it was recorded as \u0026ldquo;negative\u0026rdquo;. The assessment window covered 24 hours prior to admission (first psychiatric interview). Therefore, patients who had previously reported SI outside this time frame were not included in this measure. SI data were recorded using the FAHJU Hospital Information System (HIS). We selected patients with SI present during the psychiatric examination post-admission, as previous studies have shown that SI is closely related to depressive episodes in BD and serves as a key monitoring indicator in clinical practice\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, This is crucial for improving the prognosis of patients with SI.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using the R software (version R 3.3.1) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Descriptive analyses are presented as medians [interquartile range (IQR)] for continuous variables and as absolute and relative frequencies for categorical variables. Patients were divided into two groups: those with and without SI. Chi-square tests or t-tests were used to analyze demographic and clinically relevant variables between the two groups.\u003c/p\u003e \u003cp\u003eFor the multivariate analysis, we estimated the sample size using the pwr package in R. We employed the pwr.f2.test function for the calculation, setting the number of predictors to 11, effect size squared to 0.15, significance level to 0.05, and statistical power to 0.80. Based on these parameters, the calculated sample size was 110. The final sample size of 635 patients met the minimum requirement.\u003c/p\u003e \u003cp\u003eSummary statistics are presented as medians [IQR] for continuous variables and percentages for categorical variables, as appropriate. Initial univariate logistic regression analyses were conducted to investigate the individual associations between all covariates and SI in BD patients. Covariates from the univariate models that were associated with the outcome (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.2) were included in the final multivariate analysis. Multivariate logistic regression analysis was utilized to assess the independent effects of cortisol, testosterone, FT4, and TSH levels, while adjustment for confounding factors. Results from the logistic regression models are presented as odds ratios (OR) with 95% confidence intervals (CI), and the goodness-of-fit for each multivariate model was confirmed using the Hosmer-Lemeshow test with a P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05. Given the hormonal differences between sexes, separate multivariate regression analyses were conducted for male and female participants, and the interaction effects were tested. The results showed that the interaction effects were not statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Machine learning analysis\u003c/h2\u003e \u003cp\u003eIn this study, we developed a supervised machine learning model to predict SI on admission in BD patients during depressive episodes. The predictors included in the machine learning model were the same as the sociodemographic and clinical features used in classical bivariate analyses. Machine learning analysis was conducted using the R software (version R 3.3.1) and R Studio with the CARET package. The CARET package (short for Classification and Regression Training) is a collection of functions designed to simplify the process of creating predictive models. This package includes tools for (1) Leave-One-Out Cross-Validation (LOOCV), (2) preprocessing, (3) resampling to adjust feature selection models, (4) estimating variable importance, and (5) other functions. The CARET package provides various tools for developing predictive models using R\u0026rsquo;s extensive model library. There are many different modeling functions in R, each with a varying model training and/or prediction syntax. The package was initially designed to provide a unified interface for the functions themselves and to standardize common tasks such as parameter tuning and variable importance.\u003c/p\u003e \u003cp\u003eAll twelve 12 initially subjected to multivariable logistic regression analysis were systematically incorporated into the machine learning model\u0026rsquo;s final analysis. There were some dependencies among the hormones, indicating that the variables were not entirely independent. The outcome of interest, SI, is a binary variable. Considering that the Naive Bayes model performs exceptionally well with high-dimensional data, is robust, and offers simple computations with fast training and prediction, we used a Naive Bayes classifier with a high-density kernel to train the model. The Naive Bayes classifier is a series of probabilistic algorithms based on Bayes\u0026rsquo; theorem using the Maximum A Posteriori decision rule. We employed LOOCV, receiver operating characteristic (ROC) curves, and area under the curve (AUC) to estimate the model performance, accuracy, and kappa. LOOCV involves training the algorithm using all participants except one, testing the model on the omitted participant, and then repeating this process until each participant has been tested at least once during the evaluation. This approach ensures that our model is tested on \"unseen\" data to avoid overfitting.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003ch3\u003e3.1. Sample feature\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eIn total, 635 patients were included in the final analysis. Of these, 59.8% (n = 380) reported having SI. The median age of the entire sample was 22 years (interquartile range, 14 years), with the majority being female (n = 480; 75.6%). Approximately 62.8% (n = 399) of patients had positive psychotic symptoms. Table 1 displays the sociodemographic and clinical characteristics of BD patients with depressive episodes who either denied or reported experiencing\u0026nbsp;SI, as well as neuroendocrine hormones related to the HPT/HPA/HPG axes. The results indicated\u0026nbsp;statistically significant differences between the two groups for several factors, including age, age at onset, sex, marital status, and levels of various hormones,\u0026nbsp;including cortisol, FT4, FSH, testosterone, TSH, and estradiol.\u0026nbsp;Additionally,\u0026nbsp;we assessed potential differences in non-missing variables between the excluded patients and those ultimately included in the analysis. The findings indicated no significant overall differences (Supplementary Table S1).\u003c/p\u003e\n\u003cp\u003eTable 1. Demographic and clinical characteristics.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"944\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sample\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(N = 635)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWithout SI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(N = 255)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWith SI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 380)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eZ/c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.00 (14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.00 (19.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.00 (9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge of onset (years), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.00 (11.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.00 (16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.00 (8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eACTH (pg/ml), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.64 (23.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.30 (23.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.09 (23.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCortisol (nmol/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e316.63 (249.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e347.68 (228.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e293.87 (243.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT3 (pmol/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.02 (0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.04 (0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00 (0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT4 (pmol/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.79 (3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.18 (3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.57 (2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFSH (mIU/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.79 (4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.21 (4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.53 (4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLH(IU/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.63 (7.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.93 (7.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.94 (6.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProgesterone(ng/ml), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72 (1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72 (0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72 (1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProlactin (ng/ml), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.23 (39.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.23 (40.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.44 (38.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTestosterone (nmol/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.53 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.60 (3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.51 (0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.060\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTSH (mIU/L), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.78 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.66 (1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.86 (1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEstradiol (pg/ml), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.03 (55.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.15 (45.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.70 (62.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.550\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSex, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e480 (75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e169 (66.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e311 (81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e155 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86 (33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e166.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e451 (71.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e136 (53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e315 (82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e162 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e104 (40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history of psychiatric disease, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e475 (74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e203 (79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e272 (71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e160 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical comorbidities, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e499 (78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e191 (74.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e308 (81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e136 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSubstance abuse, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e628 (98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e254 (99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e374 (98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePositive psychotic symptoms, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e236 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e399 (62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e144 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e255 (67.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePersonality disorder, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e613 (96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e247 (96.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e366 (96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: The table uses t-tests and chi-squared tests to compare patients with and without suicidal ideation. TSH, thyroid-stimulating hormone. T3, triiodothyronine. T4, thyroxine. FT3, free triiodothyronine. FT4, free thyroxine. ACTH, adrenocorticotropic hormone. FSH, follicle-stimulating hormone. LH, luteinizing hormone\u003c/p\u003e\n\u003ch3\u003e3.2. Univariate and multivariate analysis\u003c/h3\u003e\n\u003cp\u003eIn the univariate analysis, being married (\u003cem\u003eP\u003c/em\u003e = 0.001), having a positive family history (\u003cem\u003eP\u003c/em\u003e = 0.023), and having comorbid psychotic symptoms (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.007) were identified as factors that increased the risk of SI. Among the neuroendocrine variables, the following were significantly associated with SI during depressive episode in BD patients: Thyroid function variable FT4 (unadjusted OR = 0.902; 95% CI = 0.852\u0026ndash;0.954; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), HPA axis hormone cortisol (unadjusted OR = 0.998; 95% CI = 0.997\u0026ndash;0.999; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), sex hormone FSH (unadjusted OR = 0.984; 95% CI = 0.974\u0026ndash;0.995; \u003cem\u003eP\u003c/em\u003e = 0.006), and testosterone (unadjusted OR = 0.819; 95% CI = 0.750\u0026ndash;0.893; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). We conducted an interaction analysis between marital status and age, and the results showed a P-value less than 0.05, indicating statistical significance. This suggests that the effect of marital status on SI is influenced by age.\u003c/p\u003e\n\u003cp\u003eIn the multivariate logistic regression analysis, the following factors were independently associated with an increased risk of SI during depressive episodes: lower FT4 levels (OR = 0.925; 95% CI = 0.869\u0026ndash;0.986; \u003cem\u003eP\u003c/em\u003e = 0.017) and lower testosterone levels (OR = 0.799; 95% CI = 0.642\u0026ndash;0.993; \u003cem\u003eP\u003c/em\u003e = 0.043) levels were significantly associated with a higher risk of SI in BD patients during depressive episodes. Additionally, an earlier age of onset (OR = 0.936; 95% CI = 0.897\u0026ndash;0.977; \u003cem\u003eP\u003c/em\u003e = 0.002) and the presence of psychotic symptoms also increased the risk of SI (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2. Univariate and multivariate analyses between neuroendocrine and suicidal ideation.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"931\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 29.2935%;\"\u003e\n \u003cp\u003eUnivariate analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 28.6186%;\"\u003e\n \u003cp\u003eMultivariate analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eACTH (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.988-1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCortisol (nmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.997-0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e0.998-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFT3 (pmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.774-1.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFT4 (pmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.852-0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.925\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.869-0.986\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFSH (mIU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.974-0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e0.995-1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.985-1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProgesterone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.977-1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProlactin (ng/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.995-1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTestosterone (nmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.750-0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.799\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.642-0.993\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSH (mIU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.959-1.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstradiol (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.999-1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConfounding factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.928-0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e0.965-1.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of onset (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.909-0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.936\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.897-0.977\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.302-0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e0.397-2.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e4.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e1.979-12.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e1.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e0.613-5.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003edivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e1.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.461-3.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history of psychiatric disease, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e1.063-2.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e1.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e0.852-2.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical comorbidities, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.476-1.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.065\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e0.620-1.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubstance abuse, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e4.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.488-34.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.195\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e7.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e0.768-73.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive psychotic symptoms, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e1.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e1.134-2.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.559\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.065-2.282\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.7981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePersonality disorder, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.7746%;\"\u003e\n \u003cp\u003e1.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4194%;\"\u003e\n \u003cp\u003e0.488-2.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0996%;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.8794%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.3696%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: OR, odds ratio. CI, confidence interval. TSH, thyroid-stimulating hormone. T3, triiodothyronine. T4, thyroxine. FT3, free triiodothyronine. FT4, free thyroxine. ACTH, adrenocorticotropic hormone. FSH, follicle stimulating hormone. LH, luteinizing hormone\u003c/p\u003e\n\u003ch3\u003e3.3. Predictive model for SI in patients with bipolar disorder during depressive episodes\u003c/h3\u003e\n\u003cp\u003eThe Naive Bayes algorithm (Table 3) distinguished between patients with and without SI during depressive episodes in BD with an accuracy of 80.49% (95% CI = 6.66\u0026ndash;8.23, \u003cem\u003eP\u003c/em\u003e = 0.03, 84.76% sensitivity, 76.22% specificity). Figure 1 shows the variable importance in predicting SI in BD patients during depressive episodes. The most relevant predictors for distinguishing patients with SI were age of onset, marital status, and cortisol levels. Additionally, the thyroid function indicator FT4 and sex hormones, such as testosterone and FSH, played significant roles. The AUC of the model was 0.90, and the ROC curve for this model is shown in Figure 2. The sensitivity of the curve was 84.76%, indicating that the model could correctly identify 84.76% of the positive samples, which is considered a desirable range for medical diagnosis or risk prediction.\u003c/p\u003e\n\u003cp\u003eTable 3. Performance measures of\u0026nbsp;the Na\u0026iuml;ve Bayes algorithm in differentiating between BD depressive episode patients with and without suicidal ideation.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"563\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\u003ctd\u003e\u003cp\u003e\u003c/p\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBalanced accuracy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNaive Bayes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: PPV, Positive Predictive Value. NPV, Negative Predictive Value\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study to explore the impact of neuroendocrine hormone levels on SI in BD patients during depressive episodes and use a Naive Bayes model to predict key indicators among the risk factors for SI. Baseline data were successfully collected from 635 patients experiencing depressive episodes of BD, of whom 380 (59.84%) exhibited SI. These results indicated that lower serum FT4 and testosterone levels, earlier age of onset, and the presence of positive psychotic symptoms were significant risk factors for SI during depressive episodes in BD patients. Notably, we further used the Naive Bayes method to construct a prediction model, and the results showed that age of onset contributed more significantly than other features in the classification task, playing a critical role in predicting the risk of SI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, BD patients experiencing depressive episodes were divided into two groups: those with and without SI. The results showed a significant difference in FT4 levels between the two groups, which aligns with previous research findings\u003csup\u003e37\u003c/sup\u003e. Additionally, the lower the FT4 level, the greater\u0026nbsp;was the likelihood of SI.\u0026nbsp;Thyroid dysfunction is closely associated with the development and progression of psychiatric disorders and is significantly correlated with SI\u003csup\u003e38\u003c/sup\u003e. A meta-analysis indicated that BD patients with a history of suicide attempts are more likely to have hypothyroidism and\u0026nbsp;that patients with suicidal behaviors have lower levels of FT3 and TT4\u003csup\u003e39\u003c/sup\u003e. Because observational studies cannot establish a causal relationship between FT4 levels and BD risk factors, some researchers\u0026nbsp;have conducted Mendelian randomization studies, which revealed that higher FT4 levels are associated with a reduced risk of BD\u003csup\u003e40,41\u003c/sup\u003e, highlighting the importance of FT4 in BD risk assessment. It is well established that BD patients with depressive symptoms are at a higher risk of suicide. A study involving male participants found that the prevalence of thyroid disease in those with a history of SI is 6.5% compared to 1.9% in those without such a history\u003csup\u003e42\u003c/sup\u003e. Therefore, early identification of SI is crucial for the effective treatment of BD patients\u0026nbsp;during depressive episodes.\u0026nbsp;Suicidal behavior during the first episode of major depressive disorder (MDD) is linked to thyroid hormone dysregulation\u003csup\u003e43\u003c/sup\u003e. When MDD patients\u0026nbsp;are in a prolonged depressive state, there is a decline in serotonergic and noradrenergic system function\u003csup\u003e44\u003c/sup\u003e，along with gradual decompensation of\u0026nbsp;thyroid function, leading to increased TSH levels. This exacerbates anxiety, depression, and psychotic symptoms, thereby increasing the risk of suicide\u003csup\u003e45\u003c/sup\u003e. Therefore, regular monitoring of thyroid function in depressive patients\u0026nbsp;is essential for the early detection of\u0026nbsp;SI\u003csup\u003e46\u003c/sup\u003e. These findings align with\u0026nbsp;the existing literature, demonstrating that FT4 plays an important role in assessing the risk of\u0026nbsp;SI in BD patients\u0026nbsp;during depressive episodes. Our study further emphasizes the significance of these factors in BD.\u003c/p\u003e\n\u003cp\u003eOur study found that testosterone plays an important role in\u0026nbsp;SI during depressive episodes in BD, with higher testosterone levels associated with a reduced likelihood of SI. Testosterone has been linked to suicide risk\u003csup\u003e47–49\u003c/sup\u003e; it may exert antidepressant effects by influencing the brain’s limbic system\u003csup\u003e50\u003c/sup\u003e.\u0026nbsp;It may also influence suicidal behavior in BD patients by regulating the HPA axis and\u0026nbsp;serotonergic system\u003csup\u003e51\u003c/sup\u003e.\u0026nbsp;While some studies have found a positive correlation between higher testosterone levels and the number of suicide attempts\u003csup\u003e29\u003c/sup\u003e, others\u0026nbsp;have reported no significant difference in testosterone levels between male suicide attempters and healthy controls\u003csup\u003e52\u003c/sup\u003e. The varying impact of testosterone on suicide attempts may be owing to differences in testosterone levels between men and women. To address this, some studies have specifically examined each sex. A prospective study of female BD patients suggested that higher baseline testosterone levels could predict suicide attempts during follow-up\u003csup\u003e50\u003c/sup\u003e.\u0026nbsp;However, another study in men found that both high and low testosterone levels could contribute to suicidal behavior\u003csup\u003e47\u003c/sup\u003e.\u0026nbsp;In BD patients, females tend to have higher SI scores compared to those of males\u003csup\u003e29\u003c/sup\u003e.\u0026nbsp;In our study, we conducted a sex-stratified analysis but found no statistically significant difference between males and females during depressive episodes in BD. The role of testosterone in the neurobiology of mood disorders, suicidal behaviors, and\u0026nbsp;SI remains complex.\u003c/p\u003e\n\u003cp\u003eSI is associated with psychotic symptoms and earlier age of onset\u003csup\u003e53\u003c/sup\u003e. Our study found that psychotic symptoms were independent risk factors for\u0026nbsp;SI during depressive episodes in BD patients.\u0026nbsp;Patients with psychotic symptoms often experience more severe psychological distress, which may increase their risk of developing SI. This finding is consistent with those of previous studies, such as the significant association between delusions and suicidal behavior\u003csup\u003e54\u003c/sup\u003e.\u0026nbsp;Research on schizophrenia\u0026nbsp;patients has shown that psychotic symptoms can predict suicidal activity in this population\u003csup\u003e55\u003c/sup\u003e.\u0026nbsp;However, some studies contradict these conclusions, suggesting that the presence of psychotic symptoms at any point during the course of BD is not associated with an increased risk of suicide attempts or death by suicide\u003csup\u003e56,57\u003c/sup\u003e.\u0026nbsp;In summary, previous studies have not fully elucidated the relationship between psychotic symptoms and suicide outcomes\u0026nbsp;in adults. Therefore, our study incorporated data on psychotic symptoms, laying the foundation for future research on the complex association between psychotic symptoms and suicidal behaviors. This underscores the importance of considering multidimensional factors in suicide risk assessment. Understanding these risk factors can help clinicians\u0026nbsp;identify BD patients\u0026nbsp;at high risk of suicide more accurately, allowing for targeted intervention strategies.\u003c/p\u003e\n\u003cp\u003eThe use of machine-learning algorithms to predict suicide-related outcomes (such as\u0026nbsp;SI, suicide attempts, and suicidal behavior) has emerged as a growing research field in recent years, showing promising performance\u003csup\u003e58,59\u003c/sup\u003e.\u0026nbsp;This study obtained a more comprehensive analytical perspective by combining multiple logistic regression analysis with the Naive Bayes model in machine learning. The importance and directional information provided by logistic regression can guide the selection of features for naive Bayes models, thus ensuring a balance between model interpretability and predictive performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy comparing the prediction results of the two models, we found that the age at onset was the most important predictor of SI during depressive episodes in BD patients. This suggests that age at onset has a significant impact on predicting SI, possibly reflecting differences in vulnerability to mental health issues across different age groups. Additionally, our study revealed that patients with\u0026nbsp;SI\u0026nbsp;were younger and had an earlier age of onset. Childhood abuse may be an important risk factor for\u0026nbsp;SI\u003csup\u003e60\u003c/sup\u003e. It is worth noting the risk pattern of childhood sexual abuse in bisexual groups: even if individuals do not currently report Si, it has become a risk factor for suicidal behavior\u003csup\u003e61\u003c/sup\u003e. This phenomenon may stem from the neurobiological consequences of traumatic stress abuse experiences that lead to cortisol rhythm disturbances and systemic inflammatory responses (such as CRP increase) by activating the HPA axis\u003csup\u003e62\u003c/sup\u003e. These neuroendocrine changes may constitute a biological bridge connecting childhood traumatic abuse and later suicidal behaviors. The timing of disease onset may also reflect the severity of the illness or the \u0026nbsp;cumulative psychological stress over time. . Adolescents are the key population of focus in current clinical research\u003csup\u003e63\u003c/sup\u003e. SI assessment for this population needs to pay special attention to the age of onset. Child abuse mediates the relationship between neuroendocrine abnormalities and suicide. This study provides valuable insights into\u0026nbsp;the\u0026nbsp;prediction\u0026nbsp;of\u0026nbsp;SI in BD patients\u0026nbsp;during depressive episodes using\u0026nbsp;a Naive Bayes model. Future research should validate and expand these findings and further study the mediating role of childhood trauma experience in the pathway of \"neuroendocrine abnormalities suicidality\" to promote the application of data-driven suicide risk prediction in clinical practice,\u0026nbsp;ultimately improve patient management and treatment.\u003c/p\u003e\n\u003cp\u003eOur study has several limitations. First, our results are applicable to a hospitalised population with complete neuroendocrine hormone examinations. Careful extrapolation is required for patients with a rapid turnover or critical illness. Future research should be combined with rapid bedside rapid detection to reduce the lack of data; because we were unable to trace patients’ specific medication history prior to admission, this study did not record such information. However, previous research has indicated that mood stabilizers and antipsychotic medications have significant effects on neuroendocrine hormones\u003csup\u003e64\u003c/sup\u003e. This may have affected the results of our study. Second, as this study employed a\u0026nbsp;cross-sectional\u0026nbsp;design, it cannot reveal the dynamic processes that change over time. Additionally, the observational nature of the study limits our ability to make definitive causal inferences about the relationship between thyroid function and SI in BD patients during depressive episodes following hospitalization. Therefore, a more detailed longitudinal study design is needed in the future, such as dynamic monitoring of the fluctuation of the SI score and neuroendocrine biomarkers, to further clarify how these biomarkers change with SI severity. Third, given the limited sample size and assumption of variable independence in the model, future research should explore the use of more complex models to validate these findings and investigate the interactions between different variables. Fourth, previous studies have identified several factors linked to suicidal ideation in severe mental illness, including age, sex, BD type, childhood abuse, comorbid substance use disorder, anxiety, mixed mood states, and sleep. However, we did not include some of theseas confounders, such as sleep disorders. Previous studies have shown that reduced total sleep time is significantly associated with current SI\u003csup\u003e65\u003c/sup\u003e, and because of the presence of insomnia, SI is unlikely to be relieved even after antidepressant treatment\u0026nbsp;\u003csup\u003e66\u003c/sup\u003e.Future research should control for them, even though unmeasured confounders are unlikely to explain our findings fully. Therefore, screening for thyroid function risk factors at admission, prior to hospitalization, or post-discharge may overemphasize the importance of monitoring thyroid metabolic risk. Fifth, the definition of SI in this study was somewhat subjective and lacked more detailed inquiries, which has certain limitations. Existing studies have pointed out that about 50% of suicide attempters may not report or deny SI\u003csup\u003e67\u003c/sup\u003e. In addition, MDD patients with higher impulsivity usually have more SI\u003csup\u003e68\u003c/sup\u003e, and after controlling for aggressive factors, SI is still associated with impulsivity\u003csup\u003e69\u003c/sup\u003e. Therefore, patients with impulsive personality traits may directly adopt suicidal behavior rather than merely expressing SI. Based on this, future research should integrate multi-dimensional assessment methods, such as increasing the use of the Impulsivity Scale to improve the accuracy and validity of suicide risk prediction. Finally, we did not collect data on other risk factors related to the hypothalamic-pituitary-target organ system, which may have prevented us from fully assessing the impact of neuroendocrine function on SI in BD. These factors include imaging abnormalities and the female gonadal status. Moreover, the adverse effects of prescription medications resulting from the severity of psychotic symptoms in BD patients may also be linked to an increased risk of SI in these patients.\u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, this study is the first to explore the effect of neuroendocrine hormone levels on SI in BD patients experiencing depressive episodes. The findings provide compelling evidence of a relationship between neuroendocrine function and SI in this population. Specifically,\u0026nbsp;the study found that lower levels of FT4 and testosterone were significantly associated with an increased risk of SI, alongside earlier age of onset and the presence of positive psychotic symptoms as key contributing factors.\u0026nbsp;These findings may assist clinicians in more accurately identifying BD patients\u0026nbsp;at elevated risk of suicide, thereby improving the diagnostic sensitivity\u0026nbsp;and informing early intervention strategies in clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBD \u0026nbsp; \u0026nbsp; \u0026nbsp;Bipolar disorder\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSI \u0026nbsp; \u0026nbsp; \u0026nbsp; suicidal ideation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHPA \u0026nbsp; \u0026nbsp; hypothalamic-pituitary-adrenal\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eACTH \u0026nbsp; adrenocorticotropic hormone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHPG \u0026nbsp; \u0026nbsp; hypothalamus-pituitary-gonadal\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTSH \u0026nbsp; \u0026nbsp; thyroid-stimulating hormone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFT3 \u0026nbsp; \u0026nbsp; free triiodothyronine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFT4 \u0026nbsp; \u0026nbsp; free thyroxine\u003c/p\u003e\n\u003cp\u003eFSH \u0026nbsp; \u0026nbsp; follicle-stimulating hormone\u003c/p\u003e\n\u003cp\u003eLH \u0026nbsp; \u0026nbsp; \u0026nbsp;luteinizing hormone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMDD \u0026nbsp; \u0026nbsp;major depressive disorder\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Review Committee of the First Affiliated Hospital of Jinan University (IRB Approval No. KY-2023-215). All methods were performed in accordance with the guidelines of the Declaration of Helsinki. As the research involved secondary analysis of fully anonymized and de-identified clinical data, the committee waived the requirement for individual informed consent in accordance with national regulations on ethical research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets generated and analyzed during the study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by National Natural Science Foundation of China (Grant No.: 81871036) and Science and Technology Projects in Guangzhou (Grant No.: 2025A03J4239).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eX-X.F.\u003c/strong\u003e and \u003cstrong\u003eX.S.\u0026nbsp;\u003c/strong\u003ewrote and edited the manuscript. \u003cstrong\u003eX-X.F.\u003c/strong\u003e, \u003cstrong\u003eX.S.\u003c/strong\u003e and \u003cstrong\u003eF-Y.D.\u003c/strong\u003e collected and analyzed the data. \u003cstrong\u003eY-X.C.\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eY-X.L.\u003c/strong\u003e drew the figures. \u003cstrong\u003eY.Z.\u003c/strong\u003e and \u003cstrong\u003eH.W.\u0026nbsp;\u003c/strong\u003ecollected the data. \u003cstrong\u003eJ-W.L.\u003c/strong\u003e and \u003cstrong\u003eJ-Y.P.\u003c/strong\u003e designed the study. \u003cstrong\u003eJ-Y.P.\u003c/strong\u003e had full access to all the data in the study and had final responsibility for the decision to submit for publication. 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Crisis. 1999;20(1):8\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1027//0227-5910.20.1.8\u003c/span\u003e\u003cspan address=\"10.1027//0227-5910.20.1.8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bipolar disorder, Neuroendocrine, Suicidal ideation, Machine learning, FT4, testosterone","lastPublishedDoi":"10.21203/rs.3.rs-6043432/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6043432/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBipolar disorder (BD) is a psychiatric disorder with a high prevalence of suicidal ideation (SI). While the relationship between BD and the endocrine system is well established, the influence of neuroendocrine hormones on SI during depressive episodes remains poorly understood. This cross-sectional study aimed to identify high-risk neuroendocrine factors for SI and evaluate their predictive efficacy using machine learning techniques. Data were obtained from the electronic medical records of patients hospitalized for BD depressive episodes at The First Affiliated Hospital of Jinan University in Guangzhou, Guangdong Province, China between January 1, 2017, and October 31, 2022. Of 635 eligible patients, 380 exhibited SI. In the multivariate analysis, lower levels of FT4 (OR\u0026thinsp;=\u0026thinsp;0.925; 95% CI\u0026thinsp;=\u0026thinsp;0.869\u0026ndash;0.986; P\u0026thinsp;=\u0026thinsp;0.017) and testosterone (OR\u0026thinsp;=\u0026thinsp;0.799; 95% CI\u0026thinsp;=\u0026thinsp;0.642\u0026ndash;0.993; P\u0026thinsp;=\u0026thinsp;0.043) were significantly associated with an increased risk of SI. Additionally, an earlier age of onset (OR\u0026thinsp;=\u0026thinsp;0.936; 95% CI\u0026thinsp;=\u0026thinsp;0.897\u0026ndash;0.977; P\u0026thinsp;=\u0026thinsp;0.002) and the presence of psychotic symptoms contributed to a higher risk of SI. Furthermore, we developed a supervised learning model\u0026mdash;Naive Bayes classifier. The model identified age of onset as a key predictor of SI during depressive episodes of BD, highlighting its importance as the most relevant predictive variable for distinguishing patients at risk of SI. These findings could help clinicians more accurately identify BD patients with higher suicide risk, thereby improving diagnostic sensitivity for this disorder.\u003c/p\u003e","manuscriptTitle":"Neuroendocrine Abnormalities as Predictors of Suicidal Ideation in Bipolar Disorder Patients During Depressive Episodes: A Cross-sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 11:30:40","doi":"10.21203/rs.3.rs-6043432/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-06T06:09:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T09:35:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202352889907371945129702417235152704588","date":"2025-07-07T06:49:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77318172648931580159422680993680629990","date":"2025-07-02T07:30:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223012865295233274444349737066233790025","date":"2025-04-24T15:16:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"340233245369893476296538864634124271605","date":"2025-04-22T09:48:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-22T09:25:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-22T09:24:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-16T11:14:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-04-14T11:11:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"261d0efc-1c72-4586-8a08-94f09c1cf774","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:08:44+00:00","versionOfRecord":{"articleIdentity":"rs-6043432","link":"https://doi.org/10.1186/s12888-025-07384-0","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-10-28 15:57:42","publishedOnDateReadable":"October 28th, 2025"},"versionCreatedAt":"2025-04-30 11:30:40","video":"","vorDoi":"10.1186/s12888-025-07384-0","vorDoiUrl":"https://doi.org/10.1186/s12888-025-07384-0","workflowStages":[]},"version":"v1","identity":"rs-6043432","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6043432","identity":"rs-6043432","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}