Case-control study combined with machine learning techniques to identify key genetic variations in GSK3B that affect susceptibility to diabetic kidney diseases

• Published in: BMC endocrine disorders Vol. 25; no. 1; pp. 138 - 17
• Main Authors: Song, Jinfang, Xu, Yi, Xu, Liu, Yang, Tingting, Chen, Ya, Ying, Changjiang, Lu, Qian, Wang, Tao, Yin, Xiaoxing
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 05.06.2025; BioMed Central; BMC
• Subjects: Adult; Aged; Biomarkers; Case-Control Studies; Creatinine; Diabetes; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - complications; Diabetes Mellitus, Type 2 - genetics; Diabetic Nephropathies - diagnosis; Diabetic Nephropathies - etiology; Diabetic Nephropathies - genetics; Disease; Disease susceptibility; Exosomes; Female; Gene polymorphism; Genes; Genetic analysis; Genetic diversity; Genetic Predisposition to Disease; Genomics; Genotype; Genotyping; Glycogen Synthase Kinase 3 beta - genetics; Humans; Kidney diseases; Kinases; Learning algorithms; Machine Learning; Male; Medical screening; Methods; Middle Aged; Patients; Polymorphism; Polymorphism, Single Nucleotide; Prognosis; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Statistical analysis; Susceptibility; Type 2 diabetes; Urine; North Carolina; China; United States > US

The role of genetic susceptibility in early warning and precise treatment of diabetic kidney disease (DKD) requires further investigation. A case-control study was conducted to evaluate the predictive effect of GSK3B genetic polymorphisms on the susceptibility to DKD, with the aim of providing a the...