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 心脏代谢疾病（CMD）每年在非洲夺取数百万的生命，其中很大一部分 死亡还为时过早。尽管有简单且负担得起的方法，例如生活方式调整 以及使用可能会增加寿命并改善生活质量的药物（例如脂质降低他汀类药物）的使用，这 随着时间的流逝，在非洲变得更加严重的健康燃烧。将医疗保健优先为人口的能力 在资源约束环境中，处于最高风险可能尤其重要。专业之一 通过风险准确地对人口进行准确分层的挑战是当前多基因风险评分的预测性低 非洲人口中的模型（PRS）。 非洲早期风险预测的综合模型（ILERA）研究（Yourba中的ILERA）目标 调查提高13个心脏代谢疾病指标水平的预测的潜力（和 因此，通过将各种类型的数据（基因组，转录组，与生活方式相关的数据）整合到风险中） 预测模型。从目前最佳性能的PRS开始，我们计划逐步添加数据层 例如预测的转录组，环境和生活方式信息，以评估这些附加数据是否 独立或与他人结合使用，可以改善预测。允许复杂和非线性 这些因素之间的相互作用，数据驱动的方法将被雇用以将这些变量与 基因组数据。对这些模型的预测性的深入评估将在独立队列中进行 来自南非和西非以及同一队列的纵向数据。早期的潜力 针对公共卫生干预措施的警告系统将使用最佳预测的组合进行研究 模型和特征。 该项目将由Witwatersrand大学（WITS）领导，与Wits Donald Gordon合作 医疗中心，非洲生物医学科学技术研究所（ABIST）津巴布韦和美国 基于行业合作伙伴，变体生物。预测的转录组将基于750名南非参与者 具有整个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）以及约6000名参与者 加纳，布基纳法索和肯尼亚。基线数据收集5年后捕获的纵向数据将被使用 了解年龄对预测模型的影响。这项研究将基于现有成功的多年 合作，并将利用基因组学研究的WITS经验，变体生物的多摩学专业知识 与DSI-AFRICA联盟中数据科学能力的其他项目的研究和利用合作伙伴关系。