Aims: To investigate the associations of multi-omics, genetic, lifestyle and environmental risk factors with the morbidity and mortality of cardio-metabolic diseases.
Scientific rationale: Cardio-metabolic diseases are the most common cause of morbidity and mortality worldwide, representing a major public health challenge. Some clinical, genetic, and environmental risk factors associated to cardio-metabolic diseases have been well-established, including hypertension, diabetes, obesity, cigarette smoking, air pollution exposures, diet and physical activity. Recent studies suggested that muti-omics (including metabolomics, proteomics, and radiomics) imbalance also played an important role in the development and progression of cardio-metabolic diseases. Recent evidence showed that combining multi-omics data with clinical, genetic, lifestyle, and environmental risk factors might help to improve the evaluation of the risk of cardio-metabolic diseases. Understanding the independent and joint effect of multi-omics, genetic, lifestyle, and environmental risk factors on the morbidity and mortality of cardio-metabolic diseases will help to assess the modifiable risk and take strategies to prevent the cardio-metabolic diseases and reduce the mortality. Machine learning techniques are being increasingly adapted for use in the risk-prediction failed. This study also investigated the predictive values of above risk factors for cardio-metabolic diseases-related long-term outcomes by the machine learning techniques.
Project duration: 36 months
Public health impact: This project is expected to improve understanding of the impact of multi-omics, genetic, lifestyle, and environmental risk factors on the morbidity and mortality of cardio-metabolic diseases, and therefore contribute to improving health status and extending healthy lifespan.
