Using 'Big Data' and Precision Medicine to Assess and Manage Suicide Risk in U.S. Veterans

使用“大数据”和精准医学评估和管理美国退伍军人的自杀风险

• 批准号: 9842275
• 负责人: JEAN C. BECKHAM
• 金额: --
• 依托单位: DURHAM VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9842275
• 关键词: Address; Adoption; Age; Algorithms; Area; Big Data; Biological; Biology; Candidate Disease Gene; Clinical; Code; Data; Diagnostic; Enrollment; Event; Failure; Family; Feeling suicidal; Gene Expression; Genetic; Genetic Markers; Genetic Research; Genetic Risk; Genetic study; Genotype; Goals; Grief reaction; Heritability; Intervention; Lead; Mental disorders; Methylation; Military Personnel; Mission; Not Hispanic or Latino; Pain; Pathway interactions; Phenotype; Population; Prevention approach; Research; Sample Size; Self-Direction; Single Nucleotide Polymorphism; Suicide; Suicide attempt; Suicide prevention; Time; Twin Studies; United States Department of Veterans Affairs; Variant; Veterans; Violence; Work; active duty; administrative database; base; cohort; economic cost; genetic variant; genome wide association study; genome-wide; high risk; improved; innovation; novel; phenotypic data; precision medicine; programs; psychogenetics; reducing suicide; risk variant; screening; sex; statistics; suicidal behavior; suicidal morbidity; suicidal risk

Reducing suicide and suicidal behavior (i.e., self-directed violence) is a top priority for the Department of Veterans Affairs. Recent statistics indicate that, on average, 20 Veterans die by suicide in the U.S. each day. Family, adoption, and twin studies indicate that genetic factors account for 30-50% of the heritability in suicidal behavior. Numerous candidate gene and genome wide association studies (GWAS) have been conducted to identify variants associated with suicidal behavior; however, a major limitation of all prior genetic studies in this area of research is low statistical power due to small sample sizes and the infrequency with which suicidal behavior occurs. Another significant limitation concerns the failure of most prior genetic studies of suicidal behavior to include Veterans, despite the fact that Veterans are at significantly increased risk for suicide and suicidal behavior. The proposed research will address these limitations by leveraging the genetic and phenotypic data available through the Million Veteran Program (MVP) and other key administrative databases to perform the largest and most well-powered GWAS of suicidal behavior to date. The potential impact of identifying novel genetic markers that reliably predict suicidal behavior would be enormous. It could fundamentally shift current understanding of the biology of suicide, lead to new and improved approaches to suicide prevention for Veterans and civilians alike, and significantly improve VA's ongoing efforts to identify and intervene with high risk Veterans before they engage in suicidal behavior. Our long-term goal is to develop effective screening and intervention strategies to reduce the occurrence of suicide and suicidal behavior. The overall objective of this application is to discover novel genetic variants that increase Veterans' risk for suicidal behavior. The rationale for the proposed research is that identification of genetic variants that are reliably associated with suicidal behavior could lead to the discovery of novel, clinically-meaningful biological pathways that could, in turn, lead to new and improved suicide prevention approaches for Veterans. We will accomplish our overall objective by pursuing the following specific aims: In Aim 1, we will refine the phenotypes that we will use to define cases of suicidal behavior within MVP. In Aim 2, we will use GWAS to identify novel genetic variants associated with suicide attempts and suicidal ideation among Veterans in MVP. In Aim 3, we will replicate significant findings obtained from the MVP cohort in the Mid-Atlantic MIRECC and Army STARRS Cohorts. In Aim 4, we will explore whether the genetic findings obtained from MVP can be used to improve VA's ability to identify Veterans at risk for suicidal behavior.

减少自杀和自杀行为（即自我导向的暴力）是卫生部的首要任务

项目成果

Deep sequential neural network models improve stratification of suicide attempt risk among US veterans.

深度序列神经网络模型改善了美国退伍军人自杀未遂风险的分层。

• DOI: 10.1093/jamia/ocad167
• 发表时间: 2023
• 期刊: Journal of the American Medical Informatics Association : JAMIA
• 作者: Martinez,Carianne;Levin,Drew;Jones,Jessica;Finley,PatrickD;McMahon,Benjamin;Dhaubhadel,Sayera;Cohn,Judith;MillionVeteranProgram;MVPSuicideExemplarWorkgroup;Oslin,DavidW;Kimbrel,NathanA;Beckham,JeanC
• 通讯作者: Beckham,JeanC