A Rebuttal to "The CKD Classification System in the Precision Medicine Era"

Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

Measurement of Glomerular Filtration Rate in Patients Undergoing Renal Surgery for Cancer: Estimated Glomerular Filtration Rate versus Measured Glomerular Filtration Rate in the Era of Precision Medicine

BACKGROUND

In the era of precision medicine, determining reliable renal function assessment remains a critical and debatable issue, especially in nephrology and oncology.

SUMMARY

This paper delves into the significance of accurately measured glomerular filtration rate (mGFR) in clinical practice, highlighting its essential role in guiding medical decisions and managing kidney health, particularly in the context of renal cancer (RC) patients undergoing nephrotoxic anti-cancer drugs. The limitations and advantages of traditional glomerular filtration rate (GFR) estimation methods, primarily using serum biomarkers like creatinine and cystatin C, are discussed, emphasizing their possible inadequacy in cancer patients. Specifically, newer formulae designed for GFR estimation in cancer patients may not perform at best in RC patients. The paper explores various methods for direct GFR measurement, including the gold standard inulin clearance and alternatives like iohexol plasma clearance.

KEY MESSAGE

Despite the logistical challenges of these methods, their implementation is crucial for accurate renal function assessment. The paper concludes by emphasizing the need for continued research and innovation in GFR measurement methodologies to improve patient outcomes, particularly in populations with complex medical needs.

Renal histology across the stages of chronic kidney disease

BACKGROUND

The chronic kidney disease (CKD) classification represents a simple tool to evaluate kidney disease. However, it is not based on kidney histology and this might limit the correlation between renal function and histological damage. The aim of this study was to examine the presence and magnitude of the discordance between CKD classification and kidney histology.

MATERIALS AND METHODS

We retrospectively analyzed kidney parenchyma histology in a cohort of 200 patients who underwent radical nephrectomy for a kidney mass to observe its correlation with CKD classification. Kidney tissue of the unaffected part of the removed kidney was analyzed and classified with a chronicity score as described by Sethi et al. Then, all patients were classified according to the respective CKD stage using different equations: CKD-EPI, MDRD, FAS and MCQ.

RESULTS

Median age was 67 (57-75). Diabetes, hypertension and overweight were observed in 23%, 60% and 61%, respectively. The CKD-EPI equation stratified 30.5% (n = 61) of the subjects into CKD stage 1, 41.5% (n = 83) into CKD stage 2, 25.5% into CKD stage 3 (n = 51) and 2.5% into CKD stage 4-5 (n = 5). About 30-40% of the patients with CKD stage 3 had mild or no lesions in the histological evaluation (Chronicity Score = 0-1), whereas 7-10% of those with CKD stage 1 had moderate or severe histological lesions (Chronicity Score ≥ 3). Different patients with the same value of estimated glomerular filtration rate (eGFR) had either severe or no histological damage.

CONCLUSIONS

The variability of kidney histology observed within each CKD stage is not negligible. This may limit the reliability of the current CKD classification. More research is needed to clarify the relationship between CKD stages and kidney damage.