Investigating the medical phenome of speech-language traits: risk, resilience, and opportunities for intervention

调查言语特征的医学现象：风险、恢复力和干预机会

• 批准号: 10799383
• 负责人: Srishti Nayak
• 金额: $ 17.5万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-08 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10799383
• 关键词: Acceleration; Accounting; African American population; All of Us Research Program; Articulation; Awareness; Behavior; Biomedical Research; Clinical; Code; Cognitive; Collection; Communication; Communication impairment; Communities; Complex; Data; Databases; Development; Developmental Stuttering; Diagnosis; Disease; Disease Outcome; Early Intervention; Early treatment; Education; Electronic Health Record; Epidemiology; Gene Expression; Generations; Genetic; Genetic Predisposition to Disease; Genetic Risk; Genomics; Health; Health Status; Human Genetics; ICD-9; Individual; Individual Differences; Intervention; Knowledge; Language; Language Development; Language Development Disorders; Language Disorders; Language Pathology; Laws; Learning Disabilities; Link; Maps; Measures; Medical; Mental Health; Mental disorders; Mission; National Institute on Deafness and Other Communication Disorders; Nature; Neurodegenerative Disorders; Organism; Outcome; Participant; Personal Satisfaction; Persons; Phenotype; Population; Predisposition; Public Health; Research; Rest; Risk; Risk Factors; Risk Marker; Speech; Stuttering; Symptoms; Techniques; Testing; United States National Institutes of Health; Work; autism spectrum disorder; biobank; bioinformatics tool; clinical phenotype; clinical risk; comorbidity; developmental disease; disability; diverse data; genetic risk factor; genetic variant; genome wide association study; genome-wide; genomic data; health equity; health inequalities; improved; insight; language impairment; language outcome; machine learning algorithm; medical examination; multidisciplinary; novel; phenome; physical conditioning; polygenic risk score; population health; portability; precision medicine; resilience; resilience factor; risk prediction; spelling; trait; transcriptome

Project Summary Speech-language impairments have been clinically and genetically linked with risks for mental health disorders, poor physical health, and poor educational outcomes. However, large-scale studies of speech-language deficits primarily remain the purview of research on learning disabilities and developmental disorders (e.g., articulation and fluency disorders; developmental language disorder; autism), underestimating the health impact of speech and language traits for all individuals. This project aims to investigate the overall health impact of speech and language difficulties, disorders, and risk. Specifically, we use unbiased, data-driven, computational approaches to examine the medical "phenome", or entire collection of health and disease outcomes (phenotypes) found in the All of Us Electronic Health Records (EH Rs). These phenome-wide association studies (PheWAS) have great potential to discover comorbidities, shared genetic risk for multiple health conditions, and identify targets for early intervention and therapies. In Aim 1 studies, we characterize associations between speech-language difficulties or disorders, and the entirety of the medical phenome. In Aim 2A and 28 studies, we investigate how genetic predispositions for speech-language abilities that have not been measured in All of Us participants (e.g., spelling; phonemic awareness), are associated with the entire medical phenome. In Aim 2A, we focus on genetic risk markers that are derived directly from common genetic variants associated with speech-language phenotypes; and in Aim 28 we build on this by additionally incorporating aspects of the transcriptome (e.g., gene expression), which improves portability of genetic risk predictions across multiple ancestry, and particularly in African Americans - a community who are highly underrepresented in biomedical research, and both historically and presently subject to systemic health inequity. Our approach, which is agnostic to specific clinical phenotypes, symptoms, diagnoses, or disorders, will reveal population-level health and disease outcomes associated with speech-language traits in populations that are usually underrepresented in biomedical research. This project directly responds to the call to make discoveries using high-quality All of Us data; advances the NIDCD mission to improve the lives of people with communication disorders; and enhances diversity in genomics research through the use of diverse data, and through opportunities for diversifying the workforce. By combining precision medicine techniques with a health equity and community-engaged focus, findings from this project will address critical health needs for subsets of individuals with certain clinical and/or genetic risk factors related to speech and language, as well as entire communities in whom communication traits/disorders have been understudied.

项目概要 言语障碍在临床和遗传上与精神健康障碍、身体健康状况不佳和教育成果不佳的风险有关。然而，对言语缺陷的大规模研究主要仍属于学习障碍和发育障碍（例如，发音和流畅性障碍；发育性语言障碍；自闭症）的研究范围，低估了言语和语言特征对所有个体的健康影响。该项目旨在调查言语和语言困难、障碍和风险对整体健康的影响。具体来说，我们使用公正的、数据驱动的计算方法来检查医学“现象”，或在“所有人电子健康记录”(EH Rs) 中发现的健康和疾病结果（表型）的整个集合。这些全表组关联研究 (PheWAS) 具有巨大的潜力，可以发现合并症、多种健康状况的共同遗传风险，并确定早期干预和治疗的目标。在目标 1 研究中，我们描述了言语困难或障碍与整个医学现象之间的关联。在 Aim 2A 和 28 研究中，我们调查了尚未在 All of Us 参与者中测量的言语语言能力的遗传倾向（例如拼写、音素意识）如何与整个医学现象相关。在目标 2A 中，我们重点关注直接源自与言语表型相关的常见遗传变异的遗传风险标记；在目标 28 中，我们在此基础上额外纳入了转录组的各个方面（例如基因表达），从而提高了跨多个血统的遗传风险预测的可移植性，特别是在非裔美国人中——这是一个在生物医学研究中代表性严重不足的社区，以及无论是历史上还是现在，都受到系统性卫生不平等的影响。我们的方法与特定的临床表型、症状、诊断或疾病无关，将揭示与生物医学研究中通常代表性不足的人群的言语特征相关的人群水平的健康和疾病结果。该项目直接响应了利用高质量的“我们所有人”数据进行发现的号召；推进 NIDCD 改善沟通障碍患者生活的使命；通过使用多样化的数据和实现劳动力多样化的机会，增强基因组学研究的多样性。通过将精准医疗技术与健康公平和社区参与的重点相结合，该项目的研究结果将满足具有与言语和语言相关的某些临床和/或遗传风险因素的个人子集以及整个社区的关键健康需求。沟通特征/障碍尚未得到充分研究。