Metabolomics signature for the prediction of diabetic kidney disease in Chinese patients with type 2 diabetes

Dr Cheung, Yu Yan   (Principal Investigator (PI))

Professor Sham Pak Chung   (Co-Investigator)

Dr Lee Chi Ho Paul   (Co-Investigator)

Emeritus Professor Lam Karen Siu Ling   (Co-Investigator)

Professor Xu Aimin   (Co-Investigator)

36

2022-01-15

1493820

Metabolomics signature for the prediction of diabetic kidney disease in Chinese patients with type 2 diabetes

Asian Screening Array, Diabetic kidney disease, Metabolite-Genome-wide association study, Metabolomics, Type 2 diabetes

Others - Medicine, Dentistry and Health

08193216

Health and Medical Research Fund - Full Grant

2020

On-going

Background: Diabetic kidney disease (DKD) is a common microvascular complication of diabetes. Whether circulating metabolites can act as biomarkers for prediction of DKD development in patients with type 2 diabetes (T2DM) in our population is unclear. Genetic regulation of the DKD-associated metabolites also remains to be elucidated. Objectives: This study aims to (i) identify circulating metabolites that are associated with DKD development, (ii) assess the predictive value of the identified metabolites, and (iii) examine for genetic associations of the DKD-associated metabolites using the Asian Screening Array (ASA). Design and subjects: A global untargeted metabolomics profiling study for DKD development will be conducted in 350 patients with T2DM, including 175 incident DKD cases and 175 non-DKD controls. An independent sample set of 350 incident DKD cases and 350 non-DKD controls will serve as the replication cohort. Targeted metabolomics analysis on the identified metabolites will be conducted for validation. The predictive values of the metabolites will be assessed. Metabolite-genome-wide association studies (mGWAS) of the DKD-associated metabolites will be performed using the ASA. Main outcome measures: Primary outcome: Associations of circulating metabolites with DKD development Secondary outcome: Associations of genetic variants with DKD-associated metabolites Data analysis and expected results: Associations of circulating metabolites with DKD development, and genetic associations of the identified metabolites will be examined using the multiple logistic and linear regression analyses, respectively, with adjustment for confounding factors. We expect that significant associations of circulating metabolites with incident DKD and associations of genetic variants with the DKD-associated metabolites will be identified.