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.

项目总结