Approved Research
Identify risk factors, clinical subtypes, treatment and prevention of chronic kidney disease
Approved Research ID: 91377
Approval date: August 25th 2022
Lay summary
The aims of the current project are to investigate the association between the following factors, including smoking, alcohol, blood lipids, body composition, diets, inflammatory and other laboratory examinations, history of medicine and disease, ECG data, and data from MRI scans.
Chronic kidney disease (CKD) is a progressive disease with high morbidity and mortality. The global burden of CKD is substantial. There were 697.5 million CKD patients worldwide in 2017, resulting in 1.2 million deaths each year. It will become the fifth leading cause of death worldwide by 2040. The early stage of DKD is a controllable and preventable critical stage. Early intervention could help to reverse urinary protein and reduce the occurrence of end-stage renal disease. Once the patient reached the proteinuria stage, even if the application of strict control of blood glucose, blood pressure, and adequate RAS blockers is still unable to completely prevent the progression of DN and the occurrence of end-stage renal disease. In summary, for the prevention and control of CKD, it is crucial to find the real causes of ongoing kidney damage. Furthermore, the identification of different subtypes is more relevant to understand the mechanism and for targeted drug development.
The current project will aim to identify novel modifiable risk factors for CKD. For the prevention and control of CKD, it is crucial to find the real causes of ongoing kidney damage. Furthermore, the identification of different subtypes is more relevant to understand the mechanism and for targeted drug development. Given the limited knowledge currently existing with regard to risk factors for CKD, there is a great need for studies on this disease.
Scope extension:
Chronic kidney disease (CKD) is a progressive disease with high morbidity and mortality. There were 697.5 million CKD patients worldwide in 2017, resulting in 1.2 million deaths each year. It remains unknown the real causes of ongoing kidney damage, which is crucial for the prevention and control of CKD. In the current project, we will therefore conduct a comprehensive analysis of risk factors of CKD to identify potential measures for primary prevention. The aims of the current project are to investigate the association between the following factors, including smoking, alcohol, blood lipids, body composition, diets, inflammatory and other laboratory examinations, history of medicine and disease, ECG data, and data from MRI scans.
What's more important, since CKD is only a symptomatic diagnosis (describing a chronic kidney damage status), precise management targeting primary diseases is fundamental for prevention of CKD. Due to the variability of CKD primary diseases, differentiated prevention should be provided. We further try to discover individualized prevention for CKD, encompassing comprehensive analysis and systematic management of related primary diseases.
According to the latest KDIGO guidelines, significant primary causes of CKD include diabetes, cancers, systemic immune, cardiovascular diseases, etc. This research will also specifically discuss different primary diseases, focusing on how protective/risk factors (such as acceleration for exercise, sleep, etc.) affect primary diseases, thereby providing individualized prevention for CKD. Besides, we observe the microstructure of vital organs and biomolecular changes using imaging and biomolecular data to clarify the interaction mechanisms.
In summary, the research includes three key steps:
1.The association between protective/risk factors and primary diseases (especially diabetes, cancer, cardiovascular, and multimorbidity).
2.The association between these factors and CKD.
3.Investigating the interplay mechanisms by imaging and biomolecular science among them.
All these associations and mechanisms will be demonstrated step-by-step, respectively, finally providing individualized prevention and predictive models.