Biomarkers associated with future suicide risk enhance predictive performance in psychiatric inpatients - Report - MDSpire
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Biomarkers Linked to Future Suicide Risk Improve Predictive Accuracy in Psychiatric Hospitalized Patients

  • By

  • Zheya Cai

  • Enzhao Zhu

  • Jianmeng Dai

  • Xu Zhang

  • Jiayi Wang

  • Xiuake Bahuojia

  • Ruyi Shui

  • Qiuyi Lu

  • Duoduo Bai

  • Shengbei Liu

  • Ruichen Tang

  • Xin Wang

  • Qianyi Yu

  • Han Yang

  • Guoquan Zhou

  • Siqi Liu

  • Zhihao Chen

  • Yuqin Weng

  • Xinyi Tang

  • Huan Wang

  • Huiqing Pan

  • Tongxing Ou

  • Yue Liu

  • Weiwei Xu

  • Kexin Chen

  • Xunuo Lu

  • Wenjing Wang

  • Xuqi Song

  • Zongyuan Wang

  • Feng Wang

  • Dong Wang

  • Kang Ju

  • Liangliang Chen

  • Yichao Yin

  • Chunbo Li

  • Yanping Zhang

  • Pu Ai

  • Tianyu Ji

  • Weizhong Shi

  • Jiaojiao Hou

  • Fazhan Chen

  • Hui Li

  • Zisheng Ai

  • March 27, 2026

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Clinical Report: Biomarkers Linked to Future Suicide Risk Improve Predictive Accuracy

Overview

This study evaluates the association between biomarkers and suicide risk in psychiatric inpatients, demonstrating that integrating these biomarkers with demographic and clinical data significantly enhances predictive accuracy using machine learning. The findings suggest a potential for objective assessments in suicide risk evaluation.

Background

Suicide is a leading cause of death globally, particularly among individuals with psychiatric disorders. Traditional risk assessments often rely on subjective clinical judgment, which can lead to inconsistencies and missed opportunities for timely intervention. The exploration of biomarkers as objective indicators of suicide risk could improve the accuracy of assessments and support better clinical decision-making.

Data Highlights

ParameterAssociation with Suicide Risk
Biomarkers Identified9 associated with elevated risk, 6 with protective effects
Model PerformanceAUC of 0.808 in external testing cohort

Key Findings

  • Nine biomarkers were identified as associated with elevated suicide risk.
  • Six biomarkers showed potential protective effects against suicide risk.
  • Time-trend analyses indicated significant changes in nine biomarkers following risk escalation.
  • Combining biomarkers with demographic and clinical data improved machine learning model performance.
  • The study included data from 2785 first-admission psychiatric inpatients.

Clinical Implications

The integration of biomarkers into suicide risk assessments may provide a more objective approach, potentially leading to improved identification of high-risk patients. This could facilitate timely interventions and enhance overall patient care in psychiatric settings.

Conclusion

The study underscores the potential of biomarkers combined with machine learning to enhance the predictive accuracy of suicide risk assessments, supporting the need for objective measures in clinical practice.

Related Resources & Content

  1. BMC Psychiatry, 2025 -- Multi-system biomarkers of suicide risk in major depressive disorder: integrating erythroid parameters, composite inflammatory indices, and metabolic dysregulation
  2. DIGITAL HEALTH, 2025 -- Real-world implementation and predictive performance of suicide prediction models: A systematic review and meta-analysis
  3. BMC Psychiatry, 2025 -- Modeling Predictive Factors for Suicidal Thoughts in Individuals Experiencing Cognitive Decline
  4. Frontiers in Psychiatry, 2026 -- A hierarchical machine learning model for predicting self-harm and suicidal behavior in hospitalized patients with schizophrenia using clinical history and nursing observations
  5. VA/DoD Clinical Practice Guideline for Assessment and Management of Patients at Risk for Suicide, 2024
  6. The REACH VET Program and Mortality Outcomes Among Veterans at High Risk of Suicide - PMC
  7. National Patient Safety Goals®
  8. Recommendations | Self-harm: assessment, management and preventing recurrence | Guidance | NICE
  9. VA/DoD Clinical Practice Guideline for Assessment and Management of Patients at Risk for Suicide
  10. Machine learning algorithms and their predictive accuracy for suicide and self-harm: Systematic review and meta-analysis - PMC
  11. Inflammatory markers and suicidal behavior: A comprehensive review of emerging evidence - PubMed
  12. C-reactive protein (CRP) level in depressed patients with suicidal behavior: A systematic review and meta-analysis - ScienceDirect
  13. The dexamethasone suppression test as a biomarker for suicidal behavior: A systematic review and meta-analysis - PubMed
  14. Biomarkers associated with future suicide risk enhance predictive performance in psychiatric inpatients | BMJ Health & Care Informatics

Original Source(s)

Biomarkers associated with future suicide risk enhance predictive performance in psychiatric inpatients

  • by Zheya Cai, Enzhao Zhu, Jianmeng Dai, Xu Zhang, Jiayi Wang, Xiuake Bahuojia, Ruyi Shui, Qiuyi Lu, Duoduo Bai, Shengbei Liu, Ruichen Tang, Xin Wang, Qianyi Yu, Han Yang, Guoquan Zhou, Siqi Liu, Zhihao Chen, Yuqin Weng, Xinyi Tang, Huan Wang, Huiqing Pan, Tongxing Ou, Yue Liu, Weiwei Xu, Kexin Chen, Xunuo Lu, Wenjing Wang, Xuqi Song, Zongyuan Wang, Feng Wang, Dong Wang, Kang Ju, Liangliang Chen, Yichao Yin, Chunbo Li, Yanping Zhang, Pu Ai, Tianyu Ji, Weizhong Shi, Jiaojiao Hou, Fazhan Chen, Hui Li, Zisheng Ai

  • March 27, 2026

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