# Diagnosis and Emerging Biomarkers of Cystic Fibrosis-Related Kidney Disease (CFKD)

## TL;DR

This paper discusses new biomarkers and precision medicine approaches for early detection and management of kidney disease in cystic fibrosis patients.

## Contribution

The paper introduces CFKD as a unifying term and highlights emerging biomarkers and machine learning for personalized kidney disease management in cystic fibrosis.

## Key findings

- CF-related kidney disease (CFKD) is increasingly linked to cardiovascular complications and end-stage kidney disease.
- Novel biomarkers and machine learning could enable early detection and personalized treatment of kidney damage in cystic fibrosis patients.

## Abstract

As people with cystic fibrosis (PwCF) live longer, kidney disease is emerging as a significant comorbidity that is increasingly linked to cardiovascular complications and progression to end-stage kidney disease. In our recent review, we proposed the unifying term CF-related kidney disease (CFKD) to encompass the spectrum of kidney dysfunction observed in this population. Early detection of kidney injury is critical for improving long-term outcomes, yet remains challenging due to the limited sensitivity of conventional laboratory tests, particularly in individuals with altered muscle mass and unique CF pathophysiology. Emerging approaches, including novel blood and urinary biomarkers, urinary extracellular vesicles, and genetic risk profiling, offer promising avenues for identifying subclinical kidney damage. When integrated with machine learning algorithms, these tools may enable the development of personalized risk stratification models and targeted therapeutic strategies. This precision medicine approach has the potential to transform kidney disease management in PwCF, shifting care from reactive treatment of late-stage disease to proactive monitoring and early intervention.