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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Related Experiment Videos

Modeling Longitudinal Relationships Between CKD-MBD Biomarker Trajectories with Interpretable Machine Learning in a

Tolgay Taskapan1, Hulya Taskapan2, Antonio Bellasi3

  • 1Research Department, Kidney Life Sciences Institute, Toronto, ON M5G 2N2, Canada.

Journal of Clinical Medicine
|May 27, 2026
PubMed
Summary

Understanding longitudinal changes in chronic kidney disease-mineral and bone disorder (CKD-MBD) biomarkers reveals interconnected pathways. This study highlights how kidney function, hemoglobin, and vitamin D trajectories influence CKD-MBD progression.

Keywords:
CKD-MBDFGF23SHAPcalciumlongitudinal modelingmachine learningparathyroid hormonephosphate

Related Experiment Videos

Area of Science:

  • Nephrology
  • Endocrinology
  • Biostatistics

Background:

  • Chronic kidney disease-mineral and bone disorder (CKD-MBD) involves complex biomarker interactions (parathyroid hormone, FGF23, phosphate, calcium, vitamin D).
  • Current clinical decisions often rely on static biomarker values, potentially missing dynamic relationships.
  • A trajectory-based approach offers a more integrated understanding of CKD-MBD progression.

Purpose of the Study:

  • To analyze longitudinal changes and interrelationships between CKD-MBD biomarkers.
  • To investigate associations between biomarker trajectories and other clinical features in non-dialysis CKD patients.
  • To establish a framework for understanding CKD-MBD progression through dynamic biomarker analysis.

Main Methods:

  • Utilized mixed-effects models to derive annualized slopes for CKD-MBD biomarkers (PTH, FGF23, phosphate, calcium) and other clinical features in 1968 adults with non-dialysis CKD.
  • Employed Extreme Gradient Boosting regression and SHapley Additive exPlanations (SHAP) to evaluate 36 features, including baseline levels and longitudinal changes.
  • Assessed relationships between biomarker trajectories and factors like eGFR, hemoglobin, vitamin D, and urine albumin creatinine ratio (UACR).

Main Results:

  • Models explained substantial variance in biomarker slopes (R² up to 0.86) without overfitting.
  • Baseline values and eGFR slopes were strongly associated with biomarker trajectories.
  • Positive correlations were found between PTH and FGF23 slopes; declining hemoglobin and increased variability were linked to steeper FGF23 and phosphate slopes; declining 25(OH)D was linked to rising PTH.

Conclusions:

  • Consistent relationships exist between CKD-MBD biomarker trajectories.
  • Longitudinal changes in kidney function, hemoglobin, bicarbonate, UACR, and vitamin D are associated with CKD-MBD biomarker dynamics.
  • Findings confirm the multi-system nature of CKD-MBD and suggest potential modifiable pathways for intervention.