Associations of diet and metabolic profile with risk of cardiometabolic disease and neurodegenerative disease
Aims: To investigate the associations of different dietary patterns with risk of cardiometabolic diseases (cardiovascular disease [CVD], type 2 diabetes [T2D], chronic kidney disease [CKD]) and neurodegenerative disease (Alzheimer's disease and Parkinson's disease). In addition, to test whether the associations could be modified/mediated through the circulating multi-omics level outcomes. Moreover, to investigate whether adding the selected multi-omics level outcomes could improve the prediction of the development of cardiometabolic diseases and neurodegenerative disease.
Rationale: Cardiometabolic disease is a serious public health crisis including a group of chronic diseases including coronary heart disease, stroke, diabetes, and CKD. In addition, neurodegenerative disease, e.g. Alzheimer's disease and Parkinson's disease is the leading cause for disability of the older adults. Diet is one of the most important contributors and modifiable risk factors of cardiometabolic diseases and neurodegenerative disease. Dietary pattern approach that considers the complexity of diet has emerged as a useful tool, represent a broader picture of food and nutrient consumption, and may thus be more predictive of disease risk than individual foods or nutrients. Although growing evidence has linked different dietary patterns with risk of cardiometabolic diseases, the association of dietary patterns with risk of neurodegenerative disease is relatively under-investigated. Furthermore, whether the associations of dietary patterns with cardiometabolic diseases and neurodegenerative disease could be modified or mediated by multi-omics level outcomes have not been well scrutinized, which are critical for understanding the causes of disease and making strategies for prevention/treatment.
In addition, nuclear magnetic resonance (NMR) emerging as a promising tool for detecting metabolites and providing absolute quantitation with relatively lower costs and better reproducibility, has been increasingly applied to assess the associations of biomarkers and various of common diseases such as CVD and T2D. However, whether adding the selected multi-omics level outcomes could improve the prediction of the cardiometabolic diseases and neurodegenerative disease is limited.