| Title: | Exploring the contribution of lifestyle interventions on the risk of Metabolic Diseases, Cardiovascular Disease and Neurodegenerative Disorders |
| Lead Institution: | Guangzhou Medical University |
| Principal investigator: |
Application 220028
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About
(1)Aims: Our research will utilize artificial intelligence techniques to establish a prospective cohort of natural populations by requesting the demographic information, education, lifestyle, health and medical history, physical measurements, death register, cancer register, biochemical and hematological assays, current smoking status, alcohol drinking status, biochemical and hematological assays, long-term drug therapy against chronic disease, follow-up data and genomics data from the UK Biobank. (2) Scientific rationale: Chronic diseases have become one of the leading causes of mortality in the world and brought a huge burden of disease. Early risk assessment and prevention of chronic diseases are important goals of health care. The causes of chronic diseases are complex and multifactorial. Lifestyle habits play an important role in the occurrence and development of chronic diseases including Metabolic Diseases, Cardiovascular Disease and Neurodegenerative Disorders. However, the added value of poly-factor risk score on top of clinical risk factors or validation prediction models of many chronic diseases is not examined and the clinical utility of poly-factor risk score in risk prediction remains unclear. This study proposes to establish a prospective cohort of natural populations by requesting the demographic information, education, lifestyle, health and medical history, physical measurements, death register, cancer register, biochemical and hematological assays, follow-up data and genomics data from the UK Biobank. (3) Project duration: The period of this study is 5 years. (4) Public health impact: The methodologies and experiences learned from the proposed project, would not only pave the way for the efficient management, analysis and decision-making of chronic diseases, but also, from a more general perspective, contribute to meaningful or secondary use of big clinical data for health service management and clinical decision support.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 20529 | Association of the single-point insulin sensitivity estimator (SPISE) index and incident cardiovascular disease in early-stage CKM syndrome: insights from the UK biobank | Jie Zou (+9) | 2026 | Diabetology & Metabolic Syndrome |
Enabling scientific discoveries that improve human health