Integrated modeLs for Early Risk-prediction in Africa (ILERA) study

非洲早期风险预测综合模型 (ILERA) 研究

• 批准号: 10712951
• 负责人: Ananyo Choudhury
• 金额: $ 25万
• 依托单位: WITS HEALTH CONSORTIUM (PTY), LTD
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712951
• 关键词: Address; Adipose tissue; Africa; African; Age; Blood; Blood Pressure; Body mass index; Burkina Faso; Cardiometabolic Disease; Categories; Cessation of life; Cholesterol; Collaborations; Communicable Diseases; Complex; Country; Data; Data Collection; Data Science; Data Set; Diastolic blood pressure; Diet; Disease; Disease Outcome; Drug Targeting; Drug usage; Environment; European; Evaluation; Exercise; Gene Expression; Generations; Genetic Risk; Genomics; Genotype; Genotype-Tissue Expression Project; Ghana; Grant; Health; Health Priorities; Healthcare; High Density Lipoproteins; Hypertension; Individual; Industrialization; Kenya; Kidney Diseases; Life Style; Lipids; Longevity; Low-Density Lipoproteins; Medical center; Methods; Modeling; Obesity; Participant; Population; Pulse Rates; Quality of life; Renal function; Research; Resource-limited setting; Risk; Science; Smoking; Socioeconomic Status; South Africa; South African; System; Technology; Testing; Time; Triglycerides; Universities; Variant; Visceral; Waist-Hip Ratio; Whole Blood; Wit; Zimbabwe; age related; biomarker identification; cohort; deep learning; disorder risk; experience; functional genomics; genome resource; genome wide association study; genome-wide; genomic data; high risk; improved; industry partner; insight; multiple omics; non-genetic; novel; polygenic risk score; population based; population stratification; predictive modeling; premature; public health intervention; risk prediction; risk prediction model; statistics; subcutaneous; trait; transcriptome; transcriptome sequencing; transcriptomics; translational potential; whole genome

Project Summary Cardiometabolic diseases (CMDs) claim millions of lives in Africa every year and a sizable portion of these deaths are premature. Despite the availability of simple and affordable approaches such as lifestyle adjustment and the use of drugs (e.g. lipid lowering statins) that could increase lifespan and improve the quality of life, this is becoming a more serious health burden in Africa with time. The ability to prioritize healthcare to the populations that are at highest risk could be especially relevant in resource constrained environments. One of the major challenges to accurately stratifying a population by risk is the low predictivity of current polygenic risk scoring models (PRSs) in African populations. The Integrated modeLs for Early Risk-prediction in Africa (ILERA) study (Ilera in Yourba means health) aims to investigate the potential for improving the prediction of 13 cardiometabolic disease indicator levels (and thereby of CMDs) by integrating diverse types of data (genomic, transcriptomic, lifestyle-related data) into risk prediction models. Starting with currently best performing PRSs, we plan to progressively add layers of data such as predicted transcriptomes, environment and lifestyle information to assess whether this additional data, either independently or in combination with others, could improve prediction. To allow for complex and non-linear interactions between these factors, data-driven approaches will be employed to integrate these variables with the genomic data. In-depth evaluation of the predictivity of these models will be performed in independent cohorts from South, East and West Africa and also in longitudinal data from the same cohort. The potential for an early warning system aimed at public health intervention will be investigated using a combination of the best predictive models and traits. The project will be led from the University of the Witwatersrand (Wits), collaborating with the Wits Donald Gordon Medical Center, the African Institute of Biomedical Science and Technology (ABiST) Zimbabwe and an US based industry partner, Variant Bio. The predicted transcriptome will be based on 750 South African participants with whole genome sequence and blood transcriptome RNA-Seq. The primary target dataset of ~5000 participants was generated through the H3Africa AWI-Gen study and the models will be tested in two Southern African datasets (~1200 participants from South Africa and Zimbabwe) as well as ~6000 participants from Ghana, Burkina Faso and Kenya. Longitudinal data, captured 5 years after baseline data collection, will be used to understand the impact of age on the predictive models. The study will build on years of existing successful collaboration and will tap into the Wits experience in genomics research, Variant Bio’s expertise in multi-omics research and leverage partnership with other projects in the DSI-Africa consortium for data science capacity.

项目摘要 心脏代谢性疾病(CMD)每年在非洲夺走数百万人的生命，其中相当大一部分 死亡还为时过早。尽管有简单和负担得起的方法，如调整生活方式 以及使用可以延长寿命和改善生活质量的药物(例如降脂药物)，这 随着时间的推移，它正在成为非洲更严重的健康负担。能够优先为民众提供医疗保健服务 在资源受限的环境中，风险最高的风险可能尤其相关。其中一个主要的 根据风险准确地对人群进行分层的挑战是当前多基因风险评分的低预测性 非洲人口中的模型(PRS)。 非洲早期风险预测综合模型(ILERA)研究(尤尔巴的Ilera意为健康)旨在 探讨改善13个心脏代谢性疾病指标水平预测的可能性(和 从而通过将不同类型的数据(基因组、转录组、与生活方式相关的数据)集成到风险中 预测模型。从当前性能最佳的PRS开始，我们计划逐步添加数据层 例如预测转录本、环境和生活方式信息，以评估该附加数据是否， 无论是独立的，还是与其他人联合使用，都可以提高预测能力。考虑到复杂和非线性 这些因素之间的相互作用，将采用数据驱动的方法将这些变量与 基因组数据。将在独立的队列中对这些模型的预测性进行深入评估 来自南非、东非和西非的数据，以及来自同一队列的纵向数据。提早出现的可能性 针对公共卫生干预的预警系统将使用最好的预测性组合进行调查 模型和特征。 该项目将由威特沃特斯兰德大学(WITS)领导，与Witts Donald Gordon合作 医疗中心、非洲生物医学科学和技术研究所(ABiST)津巴布韦和美国 基于行业合作伙伴的Variant Bio。预测的转录组将基于750名南非参与者 与全基因组序列和血液转录组RNA-Seq.~5000的主要目标数据集 参与者是通过H3Africa AWI-Gen研究产生的，模型将在两个南部地区进行测试 非洲数据集(来自南非和津巴布韦的约1200名参与者)以及来自 加纳、布基纳法索和肯尼亚。将使用基线数据收集后5年捕获的纵向数据 了解年龄对预测模型的影响。这项研究将建立在多年现有成功的基础上 并将利用Wits在基因组学研究方面的经验，Variant Bio在多组学方面的专业知识 研究和利用与数据科学非洲联盟中其他项目的伙伴关系，以提高数据科学能力。