Identifying Bio-signatures of Suicidal Subtypes in Veterans

识别退伍军人自杀亚型的生物特征

• 批准号: 10704723
• 负责人: FATEMEH G HAGHIGHI
• 金额: --
• 依托单位: JAMES J PETERS 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: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10704723
• 关键词: Adult; Age; Autoimmune; Behavior; Biological; Biological Markers; Blood specimen; Brain; Categories; Cause of Death; Characteristics; Classification; Clinical; Code; Collaborations; Complex; Computerized Patient Records; Consensus; Control Groups; DNA Methylation; Data; Data Reporting; Diagnosis; Diagnostic; Disease; Electronic Health Record; Epigenetic Process; Feeling suicidal; Future; Gender; Genes; Genetic; Genetic Markers; Genomics; Genotype; Goals; Health; Heterogeneity; Immune response; Impulsivity; Individual; Inflammation; Inflammatory; International Classification of Disease Codes; Laboratories; Link; Longitudinal Studies; Machine Learning; Measures; Mediating; Mental Health; Mental disorders; Methods; Monitor; Pathway Analysis; Pathway interactions; Patient Selection; Patient Self-Report; Patients; Phenotype; Population; Prevention; Process; Race; Recording of previous events; Reporting; Research; Research Personnel; Risk; Sample Size; Sampling; Series; Serotonergic System; Statistical Models; Suicide; Suicide attempt; System; Testing; Time; Training; Validation; Veterans; biological systems; biomarker identification; biosignature; childhood adversity; cohort; computational platform; computerized tools; cytokine; data mining; falls; genetic association; genome-wide; high risk; hypothalamic-pituitary-adrenal axis; improved; machine learning method; methylation pattern; military veteran; patient subsets; polygenic risk score; predictive modeling; prevent; programs; risk prediction; sex; statistical and machine learning; suicidal; suicidal act; suicidal behavior; suicidal patient; suicidal risk; suicide attempter; suicide model; text searching; tool; trait

Suicidal behavior is a complex phenomenon, ranging from low-lethality, low-intent impulsive acts to high- lethality high-intent suicidal acts, and thus likely to be associated with multiple underlying subtypes. Genetic associates of suicidal behavior have been identified in several studies, yet the effect sizes for are usually modest, possibly because of the heterogeneity in the suicidal population and the behavior. Using the Million Veteran Program Gamma computational platform, we propose a unique combination of statistical and machine learning methods to develop our subtypes based on Electronic Patient Records and self-reports; followed by a careful genomic analysis of the resulting subtypes compared to two control groups chosen from the same cohort, with and without mental health disorders. This project aims to develop sophisticated diagnosis tools for preventing future suicidal behavior in US Veterans at high risk. Moreover, the biomarkers identified from this study will be directly applied for validation in the PI and co-investigators’ longitudinal study of high-risk VA patients, a natural validation sample from the same population; promising a combination of the power of both studies.

自杀行为是一种复杂的现象，范围从低致死性，低脉冲行为到高 致命性高发自杀行为，因此可能与多种潜在亚型相关。遗传 在几项研究中已经确定了自杀行为的同伴，但效果大小通常是 谦虚，可能是由于自杀人群的异质性和行为的异质性。使用百万 资深计划伽马计算平台，我们提出了统计和机器的独特组合 学习基于电子患者记录和自我报告的学习方法；然后是一个 与从相同的两个对照组相比，对所得亚型的仔细基因组分析 队列，患有和没有心理健康障碍。该项目旨在开发复杂的诊断工具 防止美国退伍军人的未来自杀行为。此外，从此确定的生物标志物 研究将直接用于验证PI和共同研究者的高风险VA纵向研究 患者，来自同一人群的自然验证样本；承诺将两者的力量结合在一起 研究。