Chronic Kidney Disease Progression in Diabetic Patients: Risk Factors and Predictive Models

Abstract

Background: Chronic Kidney Disease (CKD) affects approximately 30% of individuals with Type 2 Diabetes Mellitus (T2DM), significantly increasing morbidity and mortality rates. Identifying key risk factors and developing predictive models are essential for early intervention and management.

Methods: A retrospective cohort study was conducted using data from 5,000 T2DM patients across multiple centers in the USA and Europe between 2010 and 2020. Baseline characteristics included a mean age of 60 ± 10 years, 55% male, and a median diabetes duration of 8 years. Multivariate Cox proportional hazards models were utilized to assess risk factors associated with CKD progression, defined as a decline in estimated glomerular filtration rate (eGFR) of ≥30% or initiation of renal replacement therapy.

Results: Over a median follow-up of 5 years, 1,200 patients (24%) experienced CKD progression. Significant risk factors identified included:

• Baseline eGFR: Each 10 mL/min/1.73 m² decrease was associated with a 1.5-fold increase in risk (Hazard Ratio [HR] 1.50; 95% Confidence Interval [CI], 1.35–1.66; p < 0.001).
• Albuminuria: Patients with macroalbuminuria had a 2.2-fold higher risk compared to those without albuminuria (HR 2.20; 95% CI, 1.90–2.55; p < 0.001).
• Glycated Hemoglobin (HbA1c): Each 1% increase was linked to a 1.3-fold rise in risk (HR 1.30; 95% CI, 1.20–1.40; p < 0.001).
• Hypertension: Uncontrolled blood pressure (>140/90 mmHg) was associated with a 1.4-fold increased risk (HR 1.40; 95% CI, 1.25–1.56; p < 0.001).

A predictive model incorporating these variables demonstrated good discrimination (C-statistic = 0.78) and calibration, effectively stratifying patients into low, medium, and high-risk categories for CKD progression.

Conclusion: Lower baseline eGFR, presence of albuminuria, higher HbA1c levels, and uncontrolled hypertension are significant predictors of CKD progression in T2DM patients. The developed predictive model can aid clinicians in identifying high-risk individuals, facilitating targeted interventions to slow disease progression.