GDF15, EGF, and Neopterin in Assessing Progression of Pediatric Chronic Kidney Disease Using Artificial Intelligence...

Area of Science:

• Pediatric Nephrology
• Biomarker Discovery
• Artificial Intelligence in Medicine

Background:

• Chronic kidney disease (CKD) in children is characterized by cell-mediated immunity and chronic inflammation.
• Growth Differentiation Factor 15 (GDF15) indicates inflammation and stress, while Epidermal Growth Factor (EGF) aids renal tubule regeneration.
• Neopterin reflects cell-mediated immunity, being a product of activated monocytes and macrophages.

Purpose of the Study:

• To investigate the role of EGF, GDF15, and neopterin in predicting CKD progression in pediatric patients.
• To develop and validate an artificial intelligence (AI) model for assessing CKD advancement using these biomarkers.
• To elucidate the contribution of inflammatory processes to declining renal function in pediatric CKD.

Main Methods:

• A cohort of 151 children with CKD stages 1-5 was studied.
• Serum concentrations of EGF, GDF15, and neopterin were measured using ELISA.
• An artificial neural network (ANN) model was trained using patient data, including anthropometric, biochemical, and biomarker values.

Main Results:

• The most accurate AI model incorporated EGF, GDF15, and neopterin serum levels.
• This model achieved a high accuracy of 96.77% in classifying patients into CKD stages 1-3 or 4-5.
• The AI model demonstrated excellent predictive capability for CKD progression.

Conclusions:

• Serum concentrations of EGF, GDF15, and neopterin, when analyzed by an AI model, can effectively predict CKD progression in children.
• The findings highlight the critical role of inflammation in the decline of renal function in pediatric CKD.
• This AI-driven approach offers a promising tool for monitoring and managing pediatric CKD.