Establishment and Validation of a Risk Prediction Model for Early Diabetic Kidney Disease Based on a Systematic Review and Meta-Analysis of 20 Cohorts

Abstract

Background: Identifying patients at high risk of diabetic kidney disease (DKD) helps improve clinical outcome.

Purpose: To establish a model for predicting DKD.

Data sources: The derivation cohort was from a meta-analysis. The validation cohort was from a Chinese cohort.

Study selection: Cohort studies that reported risk factors of DKD with their corresponding risk ratios (RRs) in patients with type 2 diabetes were selected. All patients had estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m² and urinary albumin-to-creatinine ratio (UACR) <30 mg/g at baseline.

Data extraction: Risk factors and their corresponding RRs were extracted. Only risk factors with statistical significance were included in our DKD risk prediction model.

Data synthesis: Twenty cohorts including 41,271 patients with type 2 diabetes were included in our meta-analysis. Age, BMI, smoking, diabetic retinopathy, hemoglobin A_1c, systolic blood pressure, HDL cholesterol, triglycerides, UACR, and eGFR were statistically significant. All these risk factors were included in the model except eGFR because of the significant heterogeneity among studies. All risk factors were scored according to their weightings, and the highest score was 37.0. The model was validated in an external cohort with a median follow-up of 2.9 years. A cutoff value of 16 was selected with a sensitivity of 0.847 and a specificity of 0.677.

Limitations: There was huge heterogeneity among studies involving eGFR. More evidence is needed to power it as a risk factor of DKD.

Conclusions: The DKD risk prediction model consisting of nine risk factors established in this study is a simple tool for detecting patients at high risk of DKD.