A comprehensive risk factor analysis using association rules in people with diabetic kidney disease

Area of Science:

• Medical Informatics
• Machine Learning in Healthcare
• Nephrology

Background:

• Chronic kidney disease (CKD) poses a significant risk to diabetic patients.
• Association rule mining offers a transparent method to uncover risk factors for CKD.
• Limited clinical data exists on the application of association rules for diabetic CKD risk assessment.

Purpose of the Study:

• To apply association rule mining to identify and evaluate risks for kidney disease in general and worker diabetic cohorts.
• To examine the association of risk factors' absence with stable and worsening kidney function.
• To stratify risk assessment based on KDIGO (Kidney Disease: Improving Global Outcomes) CKD risk categories.

Main Methods:

• Utilized association rule mining with confidence values and a lift > 1 for significance.
• Analyzed two cohorts: a General cohort (4935 participants, mean age 66.7) and a Worker cohort (2153 participants, mean age 47.8).
• Stratified analyses by KDIGO CKD risk categories.

Main Results:

• Good glycemic control linked to stable kidney function in low-risk General (confidence 0.82) and very-high risk Worker (confidence 0.77) cohorts.
• Poor glycemic control associated with worsening kidney function (HbA1c confidences 0.41 and 0.27).
• Anemia, obesity, and hypertension showed significant associations in low-risk General and very-high risk Worker cohorts.

Conclusions:

• Stratified risk assessment using association rules highlights the importance of specific risk factors in diabetic kidney disease.
• The presence or absence of risk factors like glycemic control, anemia, obesity, and hypertension significantly impacts kidney function.
• Association rules provide valuable insights for targeted interventions in diabetic populations at risk for CKD.