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.

项目概要 心脏代谢疾病 (CMD) 每年夺去非洲数百万人的生命，其中很大一部分 尽管有简单且负担得起的方法，例如生活方式调整，但死亡仍属过早。 以及使用可以延长寿命和改善生活质量的药物（例如降脂他汀类药物），这 随着时间的推移，优先为人民提供医疗保健的能力正在成为非洲更加严重的健康负担。 风险最高的因素在资源有限的环境中尤其重要。 按风险准确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 非洲人群的模型（PRS）。 非洲早期风险预测综合模型 (ILERA) 研究（Ilera 在 Yourba 中的意思是健康）的目标 研究改善 13 种心脏代谢疾病指标水平预测的潜力（以及 CMD）通过将不同类型的数据（基因组、转录组以及生活方式相关数据）整合到风险中 从目前表现最好的 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， 无论是独立的还是与其他的结合，都可以改进预测，以允许复杂和非线性的预测。 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 基因组数据的深入评估将在独立队列中进行。 来自南部非洲、东非和西非以及来自同一队列的纵向数据的早期潜力。 将结合最佳预测方法对旨在公共卫生干预的预警系统进行调查 模型和特征。 该项目将由金山大学 (Wits) 领导，与金山大学唐纳德·戈登 (Donald Gordon) 合作 医疗中心、津巴布韦非洲生物医学科学技术研究所 (ABiST) 和美国 预测的转录组将基于 750 名南非参与者。 具有约 5000 个全基因组序列和血液转录组 RNA-Seq 的主要目标数据集。 参与者是通过 H3Africa AWI-Gen 研究产生的，这些模型将在南部的两个国家进行测试 非洲数据集（约 1200 名来自南非和津巴布韦的参与者）以及约 6000 名来自南非和津巴布韦的参与者 将使用基线数据收集 5 年后采集的加纳、布基纳法索和肯尼亚的纵向数据。 为了了解年龄对预测模型的影响，该研究将建立在多年现有的成功经验的基础上。 合作并将利用 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 研究并利用与 DSI-非洲联盟其他项目的合作伙伴关系来提高数据科学能力。