Association between biological rhythm disturbances and suicidal ideation in mood disorders: A cross-sectional study in China

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We examined whether disturbances across multiple biological rhythm domains are associated with suicidal ideation during depressive episodes. In a cross-sectional study, 287 adults with major depressive disorder or bipolar disorder were recruited from three hospitals in China (2023–2024). Suicidal ideation was defined using Patient Health Questionnaire-9 item 9; biological rhythms were assessed with the Chinese version of the Biological Rhythms Interview of Assessment in Neuropsychiatry; anxiety was measured with the Generalized Anxiety Disorder-7. In multivariable logistic regression, greater depressive symptom severity, more severe sleep and eating rhythm disruption, and earlier age at first depressive episode were independently associated with suicidal ideation. Model performance was acceptable (Nagelkerke R² = 0.585; Hosmer–Lemeshow p = 0.69). Receiver operating characteristic analyses yielded clinically relevant thresholds (area under the curve: PHQ-9, 0.870 with cutoff 15; sleep rhythm, 0.851 with cutoff 11; eating rhythm, 0.814 with cutoff 9; age of onset, 0.719 with cutoff 23 years). Overall, 69.7% of participants endorsed suicidal ideation. These findings suggest that incorporating biological rhythm assessment with depressive symptom evaluation may improve identification of individuals at elevated risk. Longitudinal studies are needed to clarify mechanisms and guide prevention. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Health sciences/Risk factors Suicidal ideation Biological rhythms Major depressive disorder bipolar disorder Depression Risk assessment Figures Figure 1 Background Biological rhythms, regulated by endogenous clocks and external cues such as light, temperature, and social activity, are essential for maintaining physical and psychological health 1 . Disturbances in these rhythms are increasingly recognized as important features of mood disorders 2 , 3 . Clinical studies have reported that patients with major depressive disorder (MDD) or bipolar disorder (BD) often show disrupted patterns of sleep, eating, and daily activity, which are associated with more severe depressive symptoms 4 . Suicidal ideation is a critical concern during depressive episodes, as it substantially increases the risk of suicidal behavior and worsens functional outcomes 5 , 6 . Compared to patients without suicidal ideation, those affected tend to report lower quality of life, more severe psychological and physical symptoms, and impaired social functioning 5 , 7 . Recent evidence suggests that suicidal thoughts may also follow rhythmic patterns, influenced by circadian and seasonal variations 8 , 9 . For instance, individuals with an evening chronotype appear more vulnerable to suicidal ideation, and preliminary studies have linked eating rhythm disturbances to suicidality in MDD 10 , 11 . However, these findings are limited by small sample sizes and single-center designs, which restrict generalizability. Given these limitations, further research is needed to clarify the relationship between biological rhythm disturbances and suicidal ideation. To address this gap, the present cross-sectional study examined associations between multiple rhythm domains (sleep, activity, social, and eating) and suicidal ideation in a relatively large, multi-center clinical sample of patients with mood disorders during depressive episodes. This approach allows for a more comprehensive understanding of rhythm disturbances in suicidality and may inform future strategies for risk assessment and prevention. Methods Participants This cross-sectional study was conducted between January 2023 and December 2024 at three hospitals in China: Linxia Hui Autonomous Prefecture Hospital of Traditional Chinese Medicine, Dongyang People’s Hospital and Wuhan No.1 Hospital. Patients were recruited consecutively from outpatient clinics and inpatient wards using a convenience sampling method. Inclusion and exclusion criteria Eligible participants were adults aged 18–60 years, with at least primary school education, who were diagnosed by psychiatrists with a major depressive episode (in the context of either MDD or BD) according to DSM-5 criteria. All participants were in the acute phase of illness, with stratification based on pharmacological treatment history. Exclusion criteria included severe physical illness or neurological conditions affecting biological rhythms, recent night-shift work or travel across time zones, mania or manic episodes, schizophrenia, schizoaffective disorder, substance use disorders, intellectual disability, or conditions precluding standardized assessment (see Supplementary Table S1 ). Measures Demographic and clinical information was collected using a structured questionnaire. Depressive symptoms and suicidal ideation were assessed with the Patient Health Questionnaire-9 (PHQ-9) 12 – 14 . Item 9 was used to classify suicidal ideation: participants scoring 0 were considered negative, and those scoring ≥ 1 were classified as positive. This approach has been widely used in previous studies 15 , 16 . Biological rhythms were evaluated with the validated Chinese version of the Biological Rhythms Interview of Assessment in Neuropsychiatry (C-BRIAN) 17 , which covers sleep, activity, social, and eating rhythms 4 , 11 . Anxiety symptoms were assessed with the Generalized Anxiety Disorder-7 (GAD-7) scale is commonly used to assess anxiety symptoms and is widely employed in China 18 , 19 . Manic symptoms were screened with the Altman Self-Rating Mania Scale (ASRM) is a tool used to evaluate both the presence and the intensity of manic symptoms in individuals 20 , 21 . We used this tool to exclude patients with manic episodes. Ethical considerations The study protocol was approved by the Ethics Committee of Linxia Hui Autonomous Prefecture Hospital of Traditional Chinese Medicine (Approval No. 20230012). Patients were recruited from Wuhan No.1 Hospital/Wuhan Hospital of Traditional Chinese and Western Medicine and Dongyang People’s Hospital as collaborating centers under this approval. Written informed consent was obtained from all participants (or their legal guardians if under 18 years of age). All methods were performed in accordance with the relevant guidelines and regulations, and the study was conducted in accordance with the Declaration of Helsinki. Statistical analysis Continuous variables were tested for normality using the Kolmogorov–Smirnov test. Non-normally distributed variables were compared with the Mann–Whitney U test and presented as medians (25th–75th percentile). Categorical variables were analyzed with chi-squared tests and expressed as frequencies (%). Independent factors associated with suicidal ideation were examined with binary logistic regression (stepwise method). Model performance was evaluated with Receiver Operating Characteristic (ROC) curves and the area under the curve (AUC). Cut-off values were derived from ROC analyses to provide clinically interpretable thresholds. Full results of sensitivity analyses are provided in the Supplementary Materials. All analyses were performed using SPSS version 27.0, with statistical significance set at p < 0.05. Results The demographic and clinical characteristics of the participants A total of 300 survey forms were distributed. After excluding 13 forms due to evident errors in completion, data from 287 participants were included in the clinical analysis. The demographic and clinical characteristics of the participants are presented in Table 1 . In our study, 69.7% of patients were found to have suicidal ideation. The results indicated significant differences between patients with and without suicidal ideation in terms of age, marital status, age at first onset, current use of medication, current engagement in psychotherapy, total and subscale scores on the C-BRIAN scale, as well as scores on the PHQ-9 and GAD-7. Specifically, patients with suicidal ideation were younger (median age: 25.0 [20.0, 32.0] vs. 33.0 [24.0, 48.0], p < 0.001) and had a lower proportion of married individuals (31.00% vs. 57.47%, p < 0.001). They also experienced depression onset at a younger age (median: 16.5 [13.0, 24.8] vs. 26.0 [18.0, 40.0], p < 0.001), were less likely to be receiving medication (80.50% vs. 90.80%, p = 0.03), and less likely to engage in psychotherapy (14.50% vs. 26.44%, p = 0.02). Table 1 Demographic and clinical profiles of participants grouped by suicidal ideation presence no suicidal ideation suicidal ideation z/ χ ² p n = 87 n = 200 Gender, female, n (%) 55(63.22) 146(73.00) 2.76 0.10 Age, Median (IQR) 33.0(24.0,48.0) 25.0(20.0,32.0) -4.94 < 0.001 Years of education, Median (IQR) 12.0(9.0,15.0) 11.0(9.0,15.0) -0.67 0.51 Ethnicity, Han, n (%) 49(56.32) 109(54.50) 0.08 0.78 Married, n (%) 50(57.47) 62(31.00) 17.85 < 0.001 Employment status, n (%) Active 27(31.03) 52(26.00) 0.77 0.38 Disability 60(68.97) 148(74.00) Excessive screen time, n (%) 31(35.63) 82(41.00) 0.73 0.39 Diagnosis, n (%) BD I 31(35.63) 63(31.50) 0.48 0.79 BD II 23(26.44) 55(27.50) MDD 33(37.93) 82(41.00) Age at onset, years, Median (IQR) 26.0(18.0,40.0) 16.5(13.0,24.8) -5.91 < 0.001 Currently on Medication, n (%) 79(90.80) 161(80.50) 4.7 0.03 Mood Stabilizers Usage, n (%) 46(52.87) 92(46.00) 1.15 0.28 Antipsychotic Usage, n (%) 49(56.32) 113(56.50) 0.001 0.98 Antidepressant Usage, n (%) 51(58.62) 124(62.00) 0.29 0.59 Sedative/Anxiolytic Usage, n (%) 41(47.13) 81(40.50) 1.09 0.30 Psychotherapy, n (%) 23(26.44) 29(14.50) 5.82 0.02 Physical Therapy, n (%) 11(12.64) 37(18.50) 1.49 0.22 C-BRIAN total scores, Median (IQR) 36.0(31.0,41.0) 49.0(43.0,55.8) -9.37 < 0.001 Sleep, Median (IQR) 10.0(8.0,12.0) 14.0(12.0,16.0) -9.49 < 0.001 Activity, Median (IQR) 10.0(8.0,13.0) 14.0(11.0,16.0) -5.82 < 0.001 Social, Median (IQR) 9.0(6.0,10.0) 11.0(8.0,13.0) -5.51 < 0.001 Eating, Median (IQR) 7.0(6.0,9.0) 11.0(8.3,13.0) -8.49 < 0.001 PHQ-9, Median (IQR) 10.0(7.0,15.0) 18.0(15.0,22.0) -9.98 < 0.001 ASRM, Median (IQR) 2.0(1.0,5.0) 2.0(1.0,5.0) -0.45 0.648 GAD-7, Median (IQR) 6.0(2.0,11.0) 14.0(10.0,18.0) -7.7 < 0.001 Patients with suicidal ideation also had higher total C-BRIAN scores (median: 49.0 [43.0, 55.8] vs. 36.0 [31.0, 41.0], p < 0.001), including higher subscores for Sleep (14.0 [12.0, 16.0] vs. 10.0 [8.0, 12.0], p < 0.001), Activity (14.0 [11.0, 16.0] vs. 10.0 [8.0, 13.0], p < 0.001), Social (11.0 [8.0, 13.0] vs. 9.0 [6.0, 10.0], p < 0.001), and Eating (11.0 [8.3, 13.0] vs. 7.0 [6.0, 9.0], p < 0.001). Similar patterns were observed for PHQ-9 scores (median: 18.0 [15.0, 22.0] vs. 10.0 [7.0, 15.0], p < 0.001) and GAD-7 scores (median: 14.0 [10.0, 18.0] vs. 6.0 [2.0, 11.0], p < 0.001). There were no statistically significant differences in gender, years of education, ethnicity, employment status, excessive screen time, diagnosis, use of mood stabilizers, antipsychotics, antidepressants, sedatives/anxiolytics, physical therapy, or ASRM scores. Binary logistic regression analysis The results of the binary logistic regression analysis of factors influencing suicidal ideation were summarized in Table 2 . Higher PHQ-9 scores were significantly associated with an increased likelihood of suicidal ideation (B = 0.198, OR = 1.219,95%CI:1.103–1.347, p < 0.001). Disruptions in sleep rhythm (B = 0.211, OR = 1.235, 95%CI:1.078–1.414, p = 0.002) and eating rhythm (B = 0.169, OR = 1.185, 95%CI:1.014–1.383, p = 0.032) were also significantly associated with higher odds of suicidal ideation. Additionally, earlier age of depressive episode onset was associated with higher odds of suicidal ideation (B = − 0.044, OR = 0.957, 95%CI:0.929–0.986, p = 0.004). The logistic regression model’s fit was assessed using McFaddenR 2 = 0.435, Cox & Snell R 2 = 0.414, Nagelkerke R 2 = 0.585. The Hosmer-Lemeshow test yielded a p-value of 0.69. Table 2 Binary Logistic Regression Analysis of Factors Associated with Suicidal Ideation Item Coefficient SE z Wald χ 2 OR OR 95% CI p PHQ-9 0.198 0.051 3.868 14.963 1.219 1.103 ~ 1.347 < 0.001 sleep 0.211 0.069 3.049 9.299 1.235 1.078 ~ 1.414 0.002 eating 0.169 0.079 2.142 4.587 1.185 1.014 ~ 1.383 0.032 Age of onset -0.044 0.015 -2.868 8.225 0.957 0.929 ~ 0.986 0.004 Intercept -5.009 0.948 -5.283 27.914 0.007 0.001 ~ 0.043 < 0.001 Dependent Variable: Presence or Absence of Suicidal Ideation McFadden R 2 = 0.435 Cox & Snell R 2 = 0.414 Nagelkerke R 2 = 0.585 SE: Standard Error; OR: Odds Ratio; CI: Confidence Interval. ROC curve analysis and cutoff values The results of the ROC curve analysis, including the AUC values and the optimal cutoff values for each variable associated with suicidal ideation, are presented in Table 3 and Fig. 1 . The AUC for the PHQ-9 was 0.870 (95% CI: 0.827–0.913, p < 0.001), with an optimal cutoff value of 15. Sleep rhythm disruption had an AUC of 0.851 (95% CI: 0.804–0.899, p < 0.001) and a cutoff value of 11, while eating rhythm disruption showed an AUC of 0.814 (95% CI: 0.764–0.864, p < 0.001) with a cutoff value of 9. Age of onset had an AUC of 0.719 (95% CI: 0.656–0.783, p < 0.001) and an optimal cutoff value of 23 years. Table 3 ROC Results AUC Summary and Cutoff Values for Each Variable in Suicidal Ideation Variable AUC SE 95% CI Cut-off p PHQ-9 0.87 0.022 0.827 ~ 0.913 15 < 0.001 Sleep 0.851 0.024 0.804 ~ 0.899 11 < 0.001 Eating 0.814 0.025 0.764 ~ 0.864 9 < 0.001 Age of onset 0.719 0.033 0.656 ~ 0.783 23 < 0.001 AUC: Area Under the Curve; SE: Standard Error; CI: Confidence Interval. Sensitivity analyses Re-estimating the models after replacing the PHQ-9 total score with the PHQ-8 (sum of items 1–8) yielded directionally consistent associations for sleep rhythm disturbance (OR ≈ 1.3), eating rhythm disturbance (OR ≈ 1.2–1.3), and earlier age at onset (OR ≈ 0.96), with effect sizes within ± 20% of the main estimates. Entering a residualized depression score obtained by regressing the PHQ-9 total on item 9 produced similar estimates for the rhythm measures and age at onset, while the residualized depression term was attenuated and nonsignificant. These findings indicate that the principal results were robust to alternative operationalizations of depressive symptom burden ( Supplementary Tables S2–S4, Supplementary Figure S1 ). Discussion This study examined the association between biological rhythm disturbances and suicidal ideation in patients with mood disorders during depressive episodes. We found that greater severity of depressive symptoms, sleep rhythm disruption, eating rhythm disruption, and earlier age of onset were independently associated with suicidal ideation. These findings highlight the multifactorial nature of suicide risk in mood disorders. In our study, 69.7% of patients reported suicidal ideation. This prevalence falls within the range of previous findings. For instance, Lalthankimi et al. reported a higher prevalence of 83.0% among hospitalized patients, while a large multicenter study by Teismann et al. found a lower prevalence of 36.7% among outpatients 22 , 23 . Since our study included both outpatient and inpatient populations from two hospitals, the observed prevalence is higher than that reported in purely outpatient studies but lower than that in purely inpatient studies, aligning with existing research trends. As expected, depressive symptom severity was strongly associated with suicidal ideation, in line with previous studies emphasizing the link between depression, hopelessness, and suicidality 24 – 26 . Beyond depressive symptom severity, our study further demonstrates that biological rhythm disturbances—particularly disruptions in sleep and eating rhythms—are independent predictors of suicidal ideation. These findings extend previous research, including Liu et al., who reported an association between eating rhythm disruptions and suicidal ideation 11 . By employing a larger sample size (287 vs. 50) and a more comprehensive assessment of biological rhythms, our study provides stronger evidence that rhythm disturbances are not merely secondary to mood symptoms but may contribute directly to suicidality. While Liu et al. provided preliminary evidence of this relationship, our study strengthens their findings by confirming this association in a larger and more diverse clinical population. The mechanisms underlying this link remain unclear but may involve disruptions in energy metabolism, lipid regulation, and neurotransmitter balance, particularly within the serotonin and dopamine systems 27 – 30 . Milaneschi et al. proposed that irregular eating patterns may contribute to depressive symptoms by altering metabolic processes, which in turn affect the hypothalamic–pituitary–adrenal axis and neuroendocrine regulation 31 . Additionally, irregular eating patterns may reflect broader impairments in emotional regulation and stress response, further contributing to suicidal vulnerability 32 . Future studies should further investigate the biological and behavioral mechanisms underlying this association and explore whether targeted interventions aimed at stabilizing eating rhythms could help reduce suicide risk. Sleep rhythm disturbances emerged as a significant predictor of suicidal ideation, aligning with previous findings by Kim et al., who reported an association between night shift work and increased suicidal ideation 33 . Existing literature suggests that sleep dysregulation may contribute to suicidality by disrupting melatonin secretion and promoting neuroinflammation 8 . The present study further supports this hypothesis and suggests that stabilizing sleep patterns may be a crucial target for suicide prevention interventions in patients with mood disorders. Notably, the predictive effect of the age of first depressive onset is one of the key findings of this study. This result aligns with the findings of Olgiati et al., who demonstrated that an earlier age of onset is significantly associated with greater suicidal ideation severity, even after controlling for depressive symptom severity 34 . This association may be attributed to long-term disruptions in neurodevelopment, dysregulation of the stress response system, and impairments in normal psychological functioning during adolescence, all of which may contribute to increased vulnerability to suicidal ideation 35 – 37 . Robustness checks Because the dependent variable was derived from PHQ-9 item 9 (suicidal ideation), a potential concern was measurement overlap when including the PHQ-9 total score as a predictor. To address this, we conducted sensitivity analyses by (i) replacing PHQ-9 with PHQ-8, and (ii) entering a residualized depression score after regressing PHQ-9 total on item 9. Both approaches yielded directionally consistent associations for sleep and eating rhythm disturbances as well as age at onset, with effect sizes remaining within ± 20% of the main estimates 38 . Importantly, the residualized depression term itself was attenuated and nonsignificant. These results suggest that the associations of biological rhythm disturbances and age at onset with suicidal ideation are not artifacts of overlapping measurement, but rather reflect robust and independent effects (see Supplementary Tables S2–S4 and Supplementary Figure S1 ). This study has several limitations. First, we acknowledge that the PHQ-9 was not specifically designed to assess suicidal ideation, and structured scales, such as the Beck Scale for Suicide Ideation, are more effective in evaluating suicide risk 39 , 40 . However, the PHQ-9 is widely used in certain medical institutions. Despite its limitations, item 9 serves as a strong predictor of suicide attempts 41 – 43 . Although sensitivity analyses support the robustness of our findings, residual confounding cannot be completely excluded. Second, its cross-sectional design prevents us from establishing causal relationships between biological rhythm disturbances and suicidal ideation. Third, the reliance on self-reported data may introduce recall bias, and the lack of objective assessments (e.g., actigraphy, circadian biomarkers) limits the physiological validity of our findings 44 – 46 . Fourth, potential confounding factors such as genetic predisposition, medication effects, and socioeconomic influences were not fully accounted for. Additionally, while our sample included both inpatients and outpatients, its generalizability to broader and non-clinical populations remains uncertain. Lastly, the study lacks longitudinal and interventional data, making it unclear whether stabilizing biological rhythms could directly reduce suicide risk. Future research should employ longitudinal designs, objective biological measures, and targeted interventions to validate and extend these findings. Conclusions In summary, suicidal ideation in patients with mood disorders is closely linked to depressive symptom severity, biological rhythm disturbances—particularly in sleep and eating—and earlier onset of depression. Importantly, these associations remained robust in sensitivity analyses that accounted for potential measurement overlap between depressive symptoms and suicidal ideation. These findings suggest that systematic assessment of biological rhythms, alongside depressive symptoms, may enhance suicide risk identification in clinical practice. Incorporating rhythm-related interventions, such as sleep stabilization and structured eating schedules, into routine care could represent a promising strategy for suicide prevention. Future longitudinal studies using objective measures and interventional designs are warranted to clarify these relationships and evaluate targeted preventive approaches. Declarations Competing interests The authors declare that there is no conflict of interest regarding the publication of this paper. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution J.Y. and L.D. conceived the idea for the article. J.Y. wrote the main manuscript. J.Y., L.D. and X.S. recruited and assessed participants. All authors critically appraised and edited the manuscript. All authors contributed to revision and finalization of the manuscript. All authors have approved the final article. Data Availability The de-identified dataset used for the statistical analyses has been provided as Supplementary Data 1 . 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From Social Stress and Isolation to Autonomic Nervous System Dysregulation in Suicidal Behavior. Curr. Psychiatry Rep. 26 , 312–322. 10.1007/s11920-024-01503-6 (2024). Thabane, L. et al. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Med. Res. Methodol. 13 , 92. 10.1186/1471-2288-13-92 (2013). Zou, Y. et al. Efficacy of psychological pain theory-based cognitive therapy in suicidal patients with major depressive disorder: A pilot study. Psychiatry Res. 249 , 23–29. 10.1016/j.psychres.2016.12.046 (2017). Wang, Y., Guobule, N., Li, M. & Li, J. The correlation of facial emotion recognition in patients with drug-naive depression and suicide ideation. J. Affect. Disord . 295 , 250–254. 10.1016/j.jad.2021.08.051 (2021). Louzon, S. A., Bossarte, R., McCarthy, J. F. & Katz, I. R. Does Suicidal Ideation as Measured by the PHQ-9 Predict Suicide Among VA Patients? Psychiatr Serv. 67 , 517–522. 10.1176/appi.ps.201500149 (2016). Chung, T. H. et al. A validation study of PHQ-9 suicide item with the Columbia Suicide Severity Rating Scale in outpatients with mood disorders at National Network of Depression Centers. J. Affect. Disord . 320 , 590–594. 10.1016/j.jad.2022.09.131 (2023). Simon, G. E. et al. Does response on the PHQ-9 Depression Questionnaire predict subsequent suicide attempt or suicide death? Psychiatr Serv. 64 , 1195–1202. 10.1176/appi.ps.201200587 (2013). Neikrug, A. B. Actigraphy in clinical sleep medicine. Sleep. Med. Rev. 68 , 101767. 10.1016/j.smrv.2023.101767 (2023). Pundir, M. et al. Emerging biotechnologies for evaluating disruption of stress, sleep, and circadian rhythm mechanism using aptamer-based detection of salivary biomarkers. Biotechnol. Adv. 59 , 107961. 10.1016/j.biotechadv.2022.107961 (2022). Won, E., Na, K. S. & Kim, Y. K. Associations between Melatonin, Neuroinflammation, and Brain Alterations in Depression. Int. J. Mol. Sci. 23 10.3390/ijms23010305 (2021). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1.xlsx SupplementaryTablesS2S4SupplementaryFigureS1.docx SupplementaryData1.xlsx Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Sep, 2025 Reviews received at journal 30 Aug, 2025 Reviews received at journal 29 Aug, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviews received at journal 29 Aug, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers invited by journal 29 Aug, 2025 Editor assigned by journal 29 Aug, 2025 Editor invited by journal 29 Aug, 2025 Submission checks completed at journal 28 Aug, 2025 First submitted to journal 28 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7414907","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":508296632,"identity":"e26257da-5daa-4c12-b301-0bbb009bb666","order_by":0,"name":"Jing Yu","email":"","orcid":"","institution":"Wuhan No.1 Hospital/Wuhan Hospital of Traditional Chinese and Western Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Yu","suffix":""},{"id":508296633,"identity":"6d332f68-418c-40ea-93d2-23dd81e69fc8","order_by":1,"name":"Xiaohua Shan","email":"","orcid":"","institution":"Linxia Hui Autonomous Prefecture Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaohua","middleName":"","lastName":"Shan","suffix":""},{"id":508296634,"identity":"850ce084-6a5b-4091-a21f-56ee557d4472","order_by":2,"name":"Luting Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACefbm4z8/GPyr72dvIFKLYc+xBGmJigOMM3sOEGvNDR8DCZ4zBxg3zEggUgfjDB4DA8m2O8wGko833mCosYkmqIVduq0gobDtGZu5dFqxBcOxtNwGgrbMObzhgGQbM4/l7BwzCcaGw4S1MNxIMGzgbWOWMLh5hmgtKcYMPGcOGxjc4CFSCzCQ05glKtISJHuAfkkgxi/AqDzG+MHAJoGf/fDGGx9qbIhwGBIwkEggRTlEC6k6RsEoGAWjYGQAAMOaQpkARh0SAAAAAElFTkSuQmCC","orcid":"","institution":"Dongyang People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Luting","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2025-08-20 08:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7414907/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7414907/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-21303-z","type":"published","date":"2025-10-24T16:16:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90883826,"identity":"c387bb18-227a-4ca5-addd-bb001d47802a","added_by":"auto","created_at":"2025-09-09 09:55:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88765,"visible":true,"origin":"","legend":"\u003cp\u003eThe diagonal is generated from the bound values.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7414907/v1/2f4786d98bbc84c6d4a13ec4.png"},{"id":94490392,"identity":"43b93abb-633f-4494-9617-0d25bc4b716b","added_by":"auto","created_at":"2025-10-27 17:09:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1029030,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7414907/v1/9908e63f-16cd-4c6e-81c1-cef3dbf4020e.pdf"},{"id":90881165,"identity":"92ffb518-2b6a-4307-a1d4-b23323b9d418","added_by":"auto","created_at":"2025-09-09 09:39:58","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11480,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7414907/v1/756c9c6dd56970f8c1d6ff91.xlsx"},{"id":90881171,"identity":"b22e2ab0-a029-49c3-aba0-59d855122e4b","added_by":"auto","created_at":"2025-09-09 09:39:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":212946,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS2S4SupplementaryFigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7414907/v1/2539d34bc4ab4b4e903c5501.docx"},{"id":90885186,"identity":"22c4944d-b47c-487d-9ca0-8fe163a71646","added_by":"auto","created_at":"2025-09-09 10:03:58","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":42675,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7414907/v1/88586a388fa0ba8177cace12.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between biological rhythm disturbances and suicidal ideation in mood disorders: A cross-sectional study in China","fulltext":[{"header":"Background","content":"\u003cp\u003eBiological rhythms, regulated by endogenous clocks and external cues such as light, temperature, and social activity, are essential for maintaining physical and psychological health\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Disturbances in these rhythms are increasingly recognized as important features of mood disorders\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Clinical studies have reported that patients with major depressive disorder (MDD) or bipolar disorder (BD) often show disrupted patterns of sleep, eating, and daily activity, which are associated with more severe depressive symptoms\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSuicidal ideation is a critical concern during depressive episodes, as it substantially increases the risk of suicidal behavior and worsens functional outcomes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Compared to patients without suicidal ideation, those affected tend to report lower quality of life, more severe psychological and physical symptoms, and impaired social functioning\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Recent evidence suggests that suicidal thoughts may also follow rhythmic patterns, influenced by circadian and seasonal variations\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. For instance, individuals with an evening chronotype appear more vulnerable to suicidal ideation, and preliminary studies have linked eating rhythm disturbances to suicidality in MDD\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, these findings are limited by small sample sizes and single-center designs, which restrict generalizability.\u003c/p\u003e\u003cp\u003eGiven these limitations, further research is needed to clarify the relationship between biological rhythm disturbances and suicidal ideation. To address this gap, the present cross-sectional study examined associations between multiple rhythm domains (sleep, activity, social, and eating) and suicidal ideation in a relatively large, multi-center clinical sample of patients with mood disorders during depressive episodes. This approach allows for a more comprehensive understanding of rhythm disturbances in suicidality and may inform future strategies for risk assessment and prevention.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThis cross-sectional study was conducted between January 2023 and December 2024 at three hospitals in China: Linxia Hui Autonomous Prefecture Hospital of Traditional Chinese Medicine, Dongyang People\u0026rsquo;s Hospital and Wuhan No.1 Hospital. Patients were recruited consecutively from outpatient clinics and inpatient wards using a convenience sampling method.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eEligible participants were adults aged 18\u0026ndash;60 years, with at least primary school education, who were diagnosed by psychiatrists with a major depressive episode (in the context of either MDD or BD) according to DSM-5 criteria. All participants were in the acute phase of illness, with stratification based on pharmacological treatment history.\u003c/p\u003e\u003cp\u003eExclusion criteria included severe physical illness or neurological conditions affecting biological rhythms, recent night-shift work or travel across time zones, mania or manic episodes, schizophrenia, schizoaffective disorder, substance use disorders, intellectual disability, or conditions precluding standardized assessment (see \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eDemographic and clinical information was collected using a structured questionnaire.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDepressive symptoms and suicidal ideation\u003c/b\u003e were assessed with the Patient Health Questionnaire-9 (PHQ-9) \u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Item 9 was used to classify suicidal ideation: participants scoring 0 were considered negative, and those scoring\u0026thinsp;\u0026ge;\u0026thinsp;1 were classified as positive. This approach has been widely used in previous studies\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBiological rhythms\u003c/b\u003e were evaluated with the validated Chinese version of the Biological Rhythms Interview of Assessment in Neuropsychiatry (C-BRIAN)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, which covers sleep, activity, social, and eating rhythms\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnxiety symptoms\u003c/b\u003e were assessed with the Generalized Anxiety Disorder-7 (GAD-7) scale is commonly used to assess anxiety symptoms and is widely employed in China \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eManic symptoms\u003c/b\u003e were screened with the Altman Self-Rating Mania Scale (ASRM) is a tool used to evaluate both the presence and the intensity of manic symptoms in individuals \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. We used this tool to exclude patients with manic episodes.\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e The study protocol was approved by the Ethics Committee of Linxia Hui Autonomous Prefecture Hospital of Traditional Chinese Medicine (Approval No. 20230012). Patients were recruited from Wuhan No.1 Hospital/Wuhan Hospital of Traditional Chinese and Western Medicine and Dongyang People\u0026rsquo;s Hospital as collaborating centers under this approval. Written informed consent was obtained from all participants (or their legal guardians if under 18 years of age). All methods were performed in accordance with the relevant guidelines and regulations, and the study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were tested for normality using the Kolmogorov\u0026ndash;Smirnov test. Non-normally distributed variables were compared with the Mann\u0026ndash;Whitney U test and presented as medians (25th\u0026ndash;75th percentile). Categorical variables were analyzed with chi-squared tests and expressed as frequencies (%).\u003c/p\u003e\u003cp\u003eIndependent factors associated with suicidal ideation were examined with binary logistic regression (stepwise method). Model performance was evaluated with Receiver Operating Characteristic (ROC) curves and the area under the curve (AUC). Cut-off values were derived from ROC analyses to provide clinically interpretable thresholds.\u003c/p\u003e\u003cp\u003eFull results of sensitivity analyses are provided in the Supplementary Materials. All analyses were performed using SPSS version 27.0, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eThe demographic and clinical characteristics of the participants\u003c/h2\u003e\u003cp\u003eA total of 300 survey forms were distributed. After excluding 13 forms due to evident errors in completion, data from 287 participants were included in the clinical analysis. The demographic and clinical characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIn our study, 69.7% of patients were found to have suicidal ideation. The results indicated significant differences between patients with and without suicidal ideation in terms of age, marital status, age at first onset, current use of medication, current engagement in psychotherapy, total and subscale scores on the C-BRIAN scale, as well as scores on the PHQ-9 and GAD-7. Specifically, patients with suicidal ideation were younger (median age: 25.0 [20.0, 32.0] vs. 33.0 [24.0, 48.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had a lower proportion of married individuals (31.00% vs. 57.47%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). They also experienced depression onset at a younger age (median: 16.5 [13.0, 24.8] vs. 26.0 [18.0, 40.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), were less likely to be receiving medication (80.50% vs. 90.80%, p\u0026thinsp;=\u0026thinsp;0.03), and less likely to engage in psychotherapy (14.50% vs. 26.44%, p\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and clinical profiles of participants grouped by suicidal ideation presence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eno suicidal ideation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003esuicidal ideation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ez/\u003cem\u003eχ\u003c/em\u003e\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;87\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, female, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55(63.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e146(73.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.0(24.0,48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.0(20.0,32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of education, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.0(9.0,15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.0(9.0,15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity, Han, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49(56.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109(54.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50(57.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62(31.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27(31.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52(26.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60(68.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e148(74.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExcessive screen time, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31(35.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82(41.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31(35.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63(31.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23(26.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55(27.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33(37.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82(41.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at onset, years, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.0(18.0,40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.5(13.0,24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrently on Medication, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79(90.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e161(80.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMood Stabilizers Usage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46(52.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92(46.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntipsychotic Usage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49(56.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e113(56.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntidepressant Usage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51(58.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e124(62.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSedative/Anxiolytic Usage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41(47.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81(40.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychotherapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23(26.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29(14.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical Therapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11(12.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37(18.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-BRIAN total scores, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.0(31.0,41.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.0(43.0,55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.0(8.0,12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.0(12.0,16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActivity, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.0(8.0,13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.0(11.0,16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.0(6.0,10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.0(8.0,13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEating, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.0(6.0,9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.0(8.3,13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHQ-9, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.0(7.0,15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.0(15.0,22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASRM, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.0(1.0,5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.0(1.0,5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.648\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGAD-7, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.0(2.0,11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.0(10.0,18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePatients with suicidal ideation also had higher total C-BRIAN scores (median: 49.0 [43.0, 55.8] vs. 36.0 [31.0, 41.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), including higher subscores for Sleep (14.0 [12.0, 16.0] vs. 10.0 [8.0, 12.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Activity (14.0 [11.0, 16.0] vs. 10.0 [8.0, 13.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Social (11.0 [8.0, 13.0] vs. 9.0 [6.0, 10.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Eating (11.0 [8.3, 13.0] vs. 7.0 [6.0, 9.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar patterns were observed for PHQ-9 scores (median: 18.0 [15.0, 22.0] vs. 10.0 [7.0, 15.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and GAD-7 scores (median: 14.0 [10.0, 18.0] vs. 6.0 [2.0, 11.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eThere were no statistically significant differences in gender, years of education, ethnicity, employment status, excessive screen time, diagnosis, use of mood stabilizers, antipsychotics, antidepressants, sedatives/anxiolytics, physical therapy, or ASRM scores.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBinary logistic regression analysis\u003c/h3\u003e\n\u003cp\u003eThe results of the binary logistic regression analysis of factors influencing suicidal ideation were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Higher PHQ-9 scores were significantly associated with an increased likelihood of suicidal ideation (B\u0026thinsp;=\u0026thinsp;0.198, OR\u0026thinsp;=\u0026thinsp;1.219,95%CI:1.103\u0026ndash;1.347, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Disruptions in sleep rhythm (B\u0026thinsp;=\u0026thinsp;0.211, OR\u0026thinsp;=\u0026thinsp;1.235, 95%CI:1.078\u0026ndash;1.414, p\u0026thinsp;=\u0026thinsp;0.002) and eating rhythm (B\u0026thinsp;=\u0026thinsp;0.169, OR\u0026thinsp;=\u0026thinsp;1.185, 95%CI:1.014\u0026ndash;1.383, p\u0026thinsp;=\u0026thinsp;0.032) were also significantly associated with higher odds of suicidal ideation. Additionally, earlier age of depressive episode onset was associated with higher odds of suicidal ideation (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.044, OR\u0026thinsp;=\u0026thinsp;0.957, 95%CI:0.929\u0026ndash;0.986, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003cp\u003eThe logistic regression model\u0026rsquo;s fit was assessed using McFaddenR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.435, Cox \u0026amp; Snell R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.414, Nagelkerke R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.585. The Hosmer-Lemeshow test yielded a p-value of 0.69.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBinary Logistic Regression Analysis of Factors Associated with Suicidal Ideation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWald χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOR 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHQ-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.103\u0026thinsp;~\u0026thinsp;1.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.078\u0026thinsp;~\u0026thinsp;1.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.014\u0026thinsp;~\u0026thinsp;1.383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of onset\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.929\u0026thinsp;~\u0026thinsp;0.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u0026thinsp;~\u0026thinsp;0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eDependent Variable: Presence or Absence of Suicidal Ideation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eMcFadden\u0026nbsp;\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.435\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eCox \u0026amp; Snell \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.414\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eNagelkerke \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.585\u003c/p\u003e\u003cp\u003eSE: Standard Error; OR: Odds Ratio; CI: Confidence Interval.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eROC curve analysis and cutoff values\u003c/h2\u003e\u003cp\u003eThe results of the ROC curve analysis, including the AUC values and the optimal cutoff values for each variable associated with suicidal ideation, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The AUC for the PHQ-9 was 0.870 (95% CI: 0.827\u0026ndash;0.913, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an optimal cutoff value of 15. Sleep rhythm disruption had an AUC of 0.851 (95% CI: 0.804\u0026ndash;0.899, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a cutoff value of 11, while eating rhythm disruption showed an AUC of 0.814 (95% CI: 0.764\u0026ndash;0.864, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with a cutoff value of 9. Age of onset had an AUC of 0.719 (95% CI: 0.656\u0026ndash;0.783, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and an optimal cutoff value of 23 years.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eROC Results AUC Summary and Cutoff Values for Each Variable in Suicidal Ideation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCut-off\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHQ-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.827\u0026thinsp;~\u0026thinsp;0.913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.804\u0026thinsp;~\u0026thinsp;0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.764\u0026thinsp;~\u0026thinsp;0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of onset\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.656\u0026thinsp;~\u0026thinsp;0.783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eAUC: Area Under the Curve; SE: Standard Error; CI: Confidence Interval.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity analyses\u003c/h2\u003e\u003cp\u003eRe-estimating the models after replacing the PHQ-9 total score with the PHQ-8 (sum of items 1\u0026ndash;8) yielded directionally consistent associations for sleep rhythm disturbance (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.3), eating rhythm disturbance (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.2\u0026ndash;1.3), and earlier age at onset (OR\u0026thinsp;\u0026asymp;\u0026thinsp;0.96), with effect sizes within \u0026plusmn;\u0026thinsp;20% of the main estimates. Entering a residualized depression score obtained by regressing the PHQ-9 total on item 9 produced similar estimates for the rhythm measures and age at onset, while the residualized depression term was attenuated and nonsignificant. These findings indicate that the principal results were robust to alternative operationalizations of depressive symptom burden (\u003cb\u003eSupplementary Tables S2\u0026ndash;S4, Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the association between biological rhythm disturbances and suicidal ideation in patients with mood disorders during depressive episodes. We found that greater severity of depressive symptoms, sleep rhythm disruption, eating rhythm disruption, and earlier age of onset were independently associated with suicidal ideation. These findings highlight the multifactorial nature of suicide risk in mood disorders.\u003c/p\u003e\u003cp\u003eIn our study, 69.7% of patients reported suicidal ideation. This prevalence falls within the range of previous findings. For instance, Lalthankimi et al. reported a higher prevalence of 83.0% among hospitalized patients, while a large multicenter study by Teismann et al. found a lower prevalence of 36.7% among outpatients\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Since our study included both outpatient and inpatient populations from two hospitals, the observed prevalence is higher than that reported in purely outpatient studies but lower than that in purely inpatient studies, aligning with existing research trends. As expected, depressive symptom severity was strongly associated with suicidal ideation, in line with previous studies emphasizing the link between depression, hopelessness, and suicidality\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBeyond depressive symptom severity, our study further demonstrates that biological rhythm disturbances\u0026mdash;particularly disruptions in sleep and eating rhythms\u0026mdash;are independent predictors of suicidal ideation. These findings extend previous research, including Liu et al., who reported an association between eating rhythm disruptions and suicidal ideation\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. By employing a larger sample size (287 vs. 50) and a more comprehensive assessment of biological rhythms, our study provides stronger evidence that rhythm disturbances are not merely secondary to mood symptoms but may contribute directly to suicidality. While Liu et al. provided preliminary evidence of this relationship, our study strengthens their findings by confirming this association in a larger and more diverse clinical population.\u003c/p\u003e\u003cp\u003eThe mechanisms underlying this link remain unclear but may involve disruptions in energy metabolism, lipid regulation, and neurotransmitter balance, particularly within the serotonin and dopamine systems\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Milaneschi et al. proposed that irregular eating patterns may contribute to depressive symptoms by altering metabolic processes, which in turn affect the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis and neuroendocrine regulation\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Additionally, irregular eating patterns may reflect broader impairments in emotional regulation and stress response, further contributing to suicidal vulnerability\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Future studies should further investigate the biological and behavioral mechanisms underlying this association and explore whether targeted interventions aimed at stabilizing eating rhythms could help reduce suicide risk.\u003c/p\u003e\u003cp\u003eSleep rhythm disturbances emerged as a significant predictor of suicidal ideation, aligning with previous findings by Kim et al., who reported an association between night shift work and increased suicidal ideation\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Existing literature suggests that sleep dysregulation may contribute to suicidality by disrupting melatonin secretion and promoting neuroinflammation\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The present study further supports this hypothesis and suggests that stabilizing sleep patterns may be a crucial target for suicide prevention interventions in patients with mood disorders.\u003c/p\u003e\u003cp\u003eNotably, the predictive effect of the age of first depressive onset is one of the key findings of this study. This result aligns with the findings of Olgiati et al., who demonstrated that an earlier age of onset is significantly associated with greater suicidal ideation severity, even after controlling for depressive symptom severity\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This association may be attributed to long-term disruptions in neurodevelopment, dysregulation of the stress response system, and impairments in normal psychological functioning during adolescence, all of which may contribute to increased vulnerability to suicidal ideation\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eRobustness checks\u003c/h2\u003e\u003cp\u003eBecause the dependent variable was derived from PHQ-9 item 9 (suicidal ideation), a potential concern was measurement overlap when including the PHQ-9 total score as a predictor. To address this, we conducted sensitivity analyses by (i) replacing PHQ-9 with PHQ-8, and (ii) entering a residualized depression score after regressing PHQ-9 total on item 9. Both approaches yielded directionally consistent associations for sleep and eating rhythm disturbances as well as age at onset, with effect sizes remaining within \u0026plusmn;\u0026thinsp;20% of the main estimates\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Importantly, the residualized depression term itself was attenuated and nonsignificant. These results suggest that the associations of biological rhythm disturbances and age at onset with suicidal ideation are not artifacts of overlapping measurement, but rather reflect robust and independent effects (see \u003cb\u003eSupplementary Tables S2\u0026ndash;S4 and Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, we acknowledge that the PHQ-9 was not specifically designed to assess suicidal ideation, and structured scales, such as the Beck Scale for Suicide Ideation, are more effective in evaluating suicide risk\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. However, the PHQ-9 is widely used in certain medical institutions. Despite its limitations, item 9 serves as a strong predictor of suicide attempts\u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Although sensitivity analyses support the robustness of our findings, residual confounding cannot be completely excluded. Second, its cross-sectional design prevents us from establishing causal relationships between biological rhythm disturbances and suicidal ideation. Third, the reliance on self-reported data may introduce recall bias, and the lack of objective assessments (e.g., actigraphy, circadian biomarkers) limits the physiological validity of our findings\u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Fourth, potential confounding factors such as genetic predisposition, medication effects, and socioeconomic influences were not fully accounted for. Additionally, while our sample included both inpatients and outpatients, its generalizability to broader and non-clinical populations remains uncertain. Lastly, the study lacks longitudinal and interventional data, making it unclear whether stabilizing biological rhythms could directly reduce suicide risk. Future research should employ longitudinal designs, objective biological measures, and targeted interventions to validate and extend these findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, suicidal ideation in patients with mood disorders is closely linked to depressive symptom severity, biological rhythm disturbances\u0026mdash;particularly in sleep and eating\u0026mdash;and earlier onset of depression. Importantly, these associations remained robust in sensitivity analyses that accounted for potential measurement overlap between depressive symptoms and suicidal ideation. These findings suggest that systematic assessment of biological rhythms, alongside depressive symptoms, may enhance suicide risk identification in clinical practice. Incorporating rhythm-related interventions, such as sleep stabilization and structured eating schedules, into routine care could represent a promising strategy for suicide prevention. Future longitudinal studies using objective measures and interventional designs are warranted to clarify these relationships and evaluate targeted preventive approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.Y. and L.D. conceived the idea for the article. J.Y. wrote the main manuscript. J.Y., L.D. and X.S. recruited and assessed participants. All authors critically appraised and edited the manuscript. All authors contributed to revision and finalization of the manuscript. All authors have approved the final article.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe de-identified dataset used for the statistical analyses has been provided as \u003cb\u003eSupplementary Data 1\u003c/b\u003e. Additional materials, including statistical syntax, are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCable, J. et al. Sleep and circadian rhythms: pillars of health-a Keystone Symposia report. \u003cem\u003eAnn. N Y Acad. 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Sci.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms23010305\u003c/span\u003e\u003cspan address=\"10.3390/ijms23010305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Suicidal ideation, Biological rhythms, Major depressive disorder, bipolar disorder, Depression, Risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-7414907/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7414907/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSuicidal ideation is a major public health concern in mood disorders. We examined whether disturbances across multiple biological rhythm domains are associated with suicidal ideation during depressive episodes. In a cross-sectional study, 287 adults with major depressive disorder or bipolar disorder were recruited from three hospitals in China (2023\u0026ndash;2024). Suicidal ideation was defined using Patient Health Questionnaire-9 item 9; biological rhythms were assessed with the Chinese version of the Biological Rhythms Interview of Assessment in Neuropsychiatry; anxiety was measured with the Generalized Anxiety Disorder-7. In multivariable logistic regression, greater depressive symptom severity, more severe sleep and eating rhythm disruption, and earlier age at first depressive episode were independently associated with suicidal ideation. Model performance was acceptable (Nagelkerke R\u0026sup2; = 0.585; Hosmer\u0026ndash;Lemeshow p\u0026thinsp;=\u0026thinsp;0.69). Receiver operating characteristic analyses yielded clinically relevant thresholds (area under the curve: PHQ-9, 0.870 with cutoff 15; sleep rhythm, 0.851 with cutoff 11; eating rhythm, 0.814 with cutoff 9; age of onset, 0.719 with cutoff 23 years). Overall, 69.7% of participants endorsed suicidal ideation. These findings suggest that incorporating biological rhythm assessment with depressive symptom evaluation may improve identification of individuals at elevated risk. Longitudinal studies are needed to clarify mechanisms and guide prevention.\u003c/p\u003e","manuscriptTitle":"Association between biological rhythm disturbances and suicidal ideation in mood disorders: A cross-sectional study in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 09:39:54","doi":"10.21203/rs.3.rs-7414907/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-01T06:32:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-30T14:07:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T18:57:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295113018379692045709179074898763486621","date":"2025-08-29T17:36:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T17:35:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256377717540002790715837758145930049786","date":"2025-08-29T17:00:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295113018379692045709179074898763486621","date":"2025-08-29T15:17:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339775293401273004651845113927049101518","date":"2025-08-29T15:05:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-29T15:01:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-29T14:13:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-29T13:05:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-28T09:03:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-28T09:00:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc350b16-d8ff-4ee1-89ab-402ed0e8d173","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53978245,"name":"Health sciences/Diseases"},{"id":53978246,"name":"Health sciences/Health care"},{"id":53978247,"name":"Health sciences/Medical research"},{"id":53978248,"name":"Biological sciences/Neuroscience"},{"id":53978249,"name":"Biological sciences/Psychology"},{"id":53978250,"name":"Social science/Psychology"},{"id":53978251,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-27T16:32:11+00:00","versionOfRecord":{"articleIdentity":"rs-7414907","link":"https://doi.org/10.1038/s41598-025-21303-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-24 16:16:25","publishedOnDateReadable":"October 24th, 2025"},"versionCreatedAt":"2025-09-09 09:39:54","video":"","vorDoi":"10.1038/s41598-025-21303-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-21303-z","workflowStages":[]},"version":"v1","identity":"rs-7414907","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7414907","identity":"rs-7414907","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}