Aims: We aim to integrate and analyze multi-omics data, including epidemiology, imaging, genomics, and other relevant factors, to construct a multidimensional analysis model for cardio-cerebrovascular diseases. This model will facilitate the identification of high-risk populations and assist in making precise treatment strategies.
Scientific rationale: Cardio-cerebrovascular diseases represent significant global contributors to mortality and disability, impacting a broad demographic spectrum with a high prevalence. Variability in the occurrence, progression, treatment response, and prognosis of these diseases among individuals is markedly influenced by diverse factors such as lifestyle, age, environment, genetic elements, and genetic polymorphisms. Consequently, the early diagnosis and long-term management of cardio-cerebrovascular diseases hinge upon the theoretical underpinnings and clinical expertise of physicians. The flexible adjustment of suitable treatment plans based on individual characteristics of different patients highly depends on the accumulated experience of physicians over time. To address the experiential diversity among physicians, this study aims to comprehensively integrate multidimensional data from cardio-cerebrovascular diseases cohorts, encompassing epidemiological, demographic, environmental, psychological, lifestyle, laboratory findings, electrocardiographic patterns, cardio-cerebrovascular imaging traits, and genomic profiles. A multidimensional risk model for cardio-cerebrovascular diseases will be developed, facilitating phenotype differentiation analysis, disease risk assessment, treatment response prediction, and long-term prognostic evaluation based on multidimensional datasets.
Project duration: The project is expected to span approximately 3 years, during which the associations between data from different dimensions and cardio-cerebrovascular diseases will be analyzed individually and subsequently amalgamated into the multidimensional analytical framework.
Public health impact: By constructing a multidimensional analysis model, we plan to use necessary demographic information and medical examination data to assist physicians in the early identification of high-risk population for cardio-cerebrovascular diseases. The model will also predict the potential differences in treatment response to different treatment options, helping physicians develop more individualized and precise treatment plans. In addition to medical practice, the model will further reveal the significant phenotypic variability within the vast patient population of cardio-cerebrovascular diseases through the analysis of multi-omics data from multiple dimensions.
