Improvement and validation of comprehensive predictive and precise diagnostic model of diabetes
Approved Research ID: 87286
Approval date: April 13th 2022
Diabetes is deemed as a chronic disease mainly caused by genetic factors, unhealthy lifestyle and leads to disability even death. Various subtypes of diabetes might cause confusion in clinical practice. Meanwhile, there are a portion of diabetes which have not been classified yet. Hence, based on our previous diabetes and prediabetes cohort study, we found some items which could help us to diagnose diabetes patients precisely, to predict the occurrence of diabetes or complications as well. The aims of this study are to: (1) improve our predictive and diagnostic models by genotype and phenotype data from different ethnicities in UK Biobank; (2) verify the diagnostic efficacy of our diagnostic models; (3) explore the association of our predictive model with metabolic traits in UK Biobank; (4) acquire new type of diabetes and the prevalence. Scientific Rationale: Diabetes could be classified by phenotype profile and predicted the outcomes by complications and comorbidities. Our previous study proved a novel type of diabetes through clinical, genetic testing and functional assay. And established the diagnostic and predictive model of diabetes by genetic data, metabolic markers, complications or co-morbidities, and anthropometric metrics. Project Duration: The project will be completed in three years, depending on availability of appropriate data. Public Health Impact: This study could help to characterize new type of diabetes and acquire the comprehensive predictive and precise diagnostic model, the frequency, and efficacy of models. It will be beneficial to clinical physicians to figure out whether the patients with specific phenotypes will need genetic testing in the future. Also, it could help to predict the occurrence and complications of different type of diabetes and to give the instruction on precise treatment.