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Designing interventions guided by digital phenotype and pharmacogenetics in Spain for suicidal behaviour based on
Carmen Artés1, Alejandro Porras-Segovia2,3,4, Miguel Ruiz-Veguilla5,6
1Translational Psychiatry Research Group, Instituto de Investigacion Sanitaria de la Fundacion Jimenez Diaz, Madrid, Community of Madrid, Spain.
BMJ Open
|May 20, 2026
Summary
This study aims to develop a predictive algorithm for suicidal behavior by integrating genomic data, digital phenotypes, and exposomic factors. The goal is to reduce suicide reattempts and their associated emotional and economic burdens.
Area of Science:
- Integrative psychiatry and computational biology.
- Genomic epidemiology and digital phenotyping for mental health.
- Personalized medicine and cost-effectiveness in suicide prevention.
Background:
- Suicide causes approximately 700,000 deaths globally each year, imposing a significant financial burden.
- Integrating genomic, exposomic, and digital data can improve short-term prediction of suicidal behavior.
- Current strategies can be enhanced by personalized, real-time interventions to prevent suicide attempt recurrence.
Purpose of the Study:
- To develop a predictive algorithm for suicidal behavior.
- To integrate psychiatric assessments, genetic risk markers, digital phenotypes, and exposomic data.
- To identify cost-effective, personalized treatment strategies for suicide prevention.
Main Methods:
- Retrospective multicenter study recruiting over 5000 participants with a history of suicide across 25 hospitals in Spain.
- Data collection includes psychiatric assessments, biospecimens (DNA, RNA, plasma, serum), Google Takeout data, and clinical records.
- Genotyping and genome-wide association studies (GWAS) will be performed, with statistical analyses combining regression models and AI algorithms.
Main Results:
- Identification of predictive behavioral, genomic, and digital markers associated with suicidal behavior.
- Development of a robust algorithm for short-term prediction of suicide attempts.
- Cost-effectiveness analysis of pharmacogenomic markers for antidepressant response.
Conclusions:
- The study protocol outlines a comprehensive approach to predicting suicidal behavior by integrating diverse data sources.
- Successful implementation is expected to aid in reducing suicide reattempts and alleviating the burden on individuals and healthcare systems.
- This research aims to advance personalized interventions for suicide prevention through data-driven insights.
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