Questions:
1. How do CKM and its stages interact with ageing, cognitive decline, and the risk of developing dementia in affected populations?
2. Which metabolic component contributes most to these adverse effects in the same stage or with similar demographic characteristics in affected populations?
3. Is it possible to further optimize the current CKM classification criteria or PREVENT equation based on demographic and health data of CKM populations for further utilizing genomic data (GWAS, PRS, etc.)?
4.Can we develop risk models based on demographic, health and genetic data in CKM populations to identify the occurrence of cognitive decline, dementia, or other cardiovascular events such as atrial fibrillation years later?
5.In addition to genetic and metabolic factors, what is the impact of environmental exposures, such as air pollution and green space access or personal behaviors on ASCVD, aging and cognitive function in individuals with CKM?
Objectives:
To address the above questions, we intend to leverage the relevant data from the UK Biobank (UKB) database. By utilizing baseline information, health-related data, and genetic data from the population affected by CKM, we aim to enhance existing models and develop a new model. This new model will guide clinicians in making more informed decisions.
Scientific rationale for the research:
CKM Syndrome involves the complex interaction of cardiovascular, renal, and metabolic disorders, posing significant public health challenges. Researching CKM is crucial to understanding its interconnected pathways, which drive disease progression. Inflammation, oxidative stress, and endothelial dysfunction play central roles, yet the influence of genetics and lifestyle remains underexplored. Our study aims to investigate these factors through epidemiological and genomic analyses. By uncovering CKM’s underlying mechanisms, we aim to identify novel biomarkers and therapeutic targets, leading to personalized interventions.
