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.

项目摘要 语音语言障碍在临床和遗传上与精神健康障碍，身体健康不良和教育成果不佳的风险联系在一起。但是，对语音语言缺陷的大规模研究主要是学习障碍和发育障碍的研究（例如表达和流利性障碍；发育性语言障碍；自闭症）的范围，低估了言语和语言特征对所有人的健康影响。该项目旨在调查语音和语言困难，疾病和风险的整体健康影响。具体而言，我们使用公正的，数据驱动的计算方法来检查医学“现象”，或者在我们所有电子健康记录（EH RS）中发现的全部健康和疾病结果（表型）的全部收集。这些全面的关联研究（PHEWAS）具有发现合并症，对多种健康状况的遗传风险以及确定早期干预和疗法的目标的巨大潜力。在AIM 1研究中，我们表征了语音语言困难或疾病与整个医学现象之间的关联。在AIM 2A和28研究中，我们研究了我们所有参与者尚未衡量的语音语言能力的遗传倾向（例如，拼写；音素意识）与整个医学现象有关。在AIM 2a中，我们专注于直接来自与语音语言表型相关的常见遗传变异而得出的遗传风险标记。在AIM 28中，我们还通过融合了转录组的各个方面（例如基因表达），从而提高了多个祖先的遗传风险预测的可移植性，尤其是在非洲裔美国人中 - 一个社区（一个在生物医学研究中的人数不足，在历史上都高度不足，并且在历史上且目前受到系统性健康状况的影响。我们的方法对特定的临床表型，症状，诊断或疾病是不可知的，它将揭示人口水平的健康和疾病结果与人群中通常在生物医学研究中所代表性不足的人群中相关的疾病。该项目直接响应了使用我们所有的所有数据使用高质量发现发现的呼吁。促进NIDCD的使命，以改善沟通障碍者的生活；并通过使用不同的数据以及使劳动力多样化的机会来增强基因组学研究的多样性。通过将精确医学技术与健康公平和社区参与的重点相结合，该项目的发现将解决与言语和语言有关的某些临床和/或遗传危险因素的个人的关键健康需求，以及沟通特征/障碍的整个社区都被研究了。