Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease, and it creates tremendous medical care costs. Accurate prediction of DKD and its potential molecular implications remain incompletely understood. Here, we apply artificial intelligence (AI) algorithms to build up an interaction model that tackles the complex interconnections between diabetes and chronic kidney disease (CKD) and to identify a biomarker signature that predisposes high-risk type 2 diabetes patients to progression to DKD. The cohort in this study contains 618 subjects, and these can be split into training (557 subjects) and testing (61 subjects) cohorts. Their mean age was 63.8 ± 12.9 years, and they included 287 males (46.4%). The median estimated glomerular filtration rate was 83.0 mL/min/1.73 m2. Of the subjects, 338 (54.7%) were control subjects, 112 (18.1%) had type 2 diabetes, 73 (11.8%) had nondiabetic CKD, and 95 (15.4%) had DKD (Fig. 1A). The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Institutional Review Board of Chang Gung Medical Foundation (Institutional Review Board no. 201800802B0, 202000077B0A3, 201800273B0C602, and 202002535B0). Informed consent was obtained from all subjects involved in the study.
