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: 9925062
• 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 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9925062
• 关键词: Adult; Age; Autoimmune Process; 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; Lead; Link; Longitudinal Studies; Machine Learning; Measures; Mediating; Medical Genetics; Mental Health; Mental disorders; Methods; Modeling; Monitor; Pathway Analysis; Pathway interactions; 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; base; biological systems; childhood adversity; cohort; computational platform; computerized tools; cytokine; data mining; falls; genome-wide; high risk; hypothalamic-pituitary-adrenal axis; improved; individualized prevention; machine learning method; methylation pattern; patient subsets; polygenic risk score; predictive modeling; prevent; programs; sex; statistical and machine learning; suicidal; suicidal act; suicidal behavior; suicidal patient; suicidal risk; suicide attempter; 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，从低致命性、低意图的冲动行为到高致命性的自杀行为。 致命性高意图自杀行为，因此很可能与多种潜在的亚型相关。遗传 自杀行为的关联性已经在几项研究中被确定，但其影响大小通常是 适度，可能是因为自杀人群和行为的异质性。用百万美元 资深程序伽马计算平台，我们提出了一种独特的统计和机器相结合的方法 基于电子病历和自我报告开发我们的子类型的学习方法；随后是 对产生的亚型与从相同基因中选择的两个对照组进行仔细的基因组分析 队列，有或没有精神健康障碍。该项目旨在开发复杂的诊断工具 预防高危美国退伍军人未来的自杀行为。此外，由此确定的生物标志物 这项研究将直接应用于PI和协查人员对高危VA的纵向研究中的验证 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起来 学习。