Baker Heart and Diabetes Institute

3

OFHS240120

The aim of this study is to unravel the complex interplay between genetic and environmental factors in the development, progression, and complications of cardiometabolic diseases. The research questions focus on identifying critical genes and regulatory regions influencing disease risk, understanding the causal mechanisms and biomarkers involved, and developing improved predictive models for these conditions. By analysing genome-wide genotype data, environmental variables, and clinical outcomes, the study seeks to elucidate the aetiology of cardiometabolic diseases and their comorbidities. The researchers aim to construct predictive and causal models using advanced techniques such as machine learning and Mendelian randomisation. Ultimately, the study aims to enhance our understanding of the genetic and molecular pathways underlying these diseases, potentially leading to new therapeutic approaches and improved risk assessment tools. This comprehensive approach could contribute significantly to addressing the growing global burden of cardiometabolic diseases and their associated complications. The scientific rationale for this study proposal on the aetiology and prediction of cardiometabolic diseases stems from the significant global health burden posed by cardiovascular and metabolic conditions. These diseases, including heart disease, diabetes, and obesity, are leading causes of mortality and morbidity worldwide. They are considered to arise from complex interactions between genetic predisposition and environmental factors such as diet, lifestyle, and past history of communicable and noncommunicable diseases, however the whole picture of these factors and interactions are still not understood. The study aims to investigate the critical genes, regulatory regions, and their interactions that influence disease risk, as well as the interplay between genetic and environmental factors underpinning both health and disease development. By leveraging genome-wide genotype data, causal inference techniques, and machine learning approaches, the research seeks to improve understanding of disease mechanisms, develop better predictive models, and identify potential therapeutic interventions. This comprehensive approach could ultimately lead to improved prevention, prediction, and treatment strategies for cardiometabolic diseases.

This research is expected to offer significant public benefits and translational relevance by advancing our understanding of the genetic and environmental factors that contribute to cardiometabolic diseases and their complications. By identifying critical genes and regulatory regions, the study aims to develop more accurate predictive models, which can improve early detection and personalised treatment strategies. Understanding the complex interplay between genetic predisposition and environmental influences will help in crafting targeted interventions to mitigate disease risk and progression. Additionally, the research could identify new therapeutic targets, leading to the development of novel treatments. The findings will also inform public health policies by highlighting key risk factors and effective prevention strategies. Ultimately, this research has the potential to reduce the incidence and severity of cardiometabolic diseases, thereby decreasing healthcare costs and improving the quality of life for individuals affected by these conditions.

Public Health Research

02/02/2025

17/11/2024

TRE