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Abstract: TH-PO404

Biomarkers and CKD Progression: Avoiding Pitfalls in Methodology

Session Information

Category: CKD (Non-Dialysis)

  • 2101 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

  • Ali, Ibrahim, Salford Royal Hospital NHS Foundation Trust, Manchester, United Kingdom
  • Ibrahim, Sara T., Alexandria University, Alexandria, Egypt
  • Chinnadurai, Rajkumar, Salford Royal Hospital NHS Foundation Trust, Manchester, United Kingdom
  • Taal, Maarten W., University of Nottingham, Nottingham, United Kingdom
  • Kalra, Philip A., Salford Royal Hospital NHS Foundation Trust, Manchester, United Kingdom
Background

Many studies that report biomarkers predictive of CKD progression share a key pitfall: poor characterisation of patients’ progression, due to a lack of eGFR values and a short follow-up time. We apply a 2-stage method to robustly categorise patients with linear CKD progression for an upcoming biomarker study.

Methods

We included 2038 CKD patients in the Salford Kidney Study, with a total of 66455 outpatient eGFR values.
Stage 1: Delta (Δ) eGFR (±ml/min/1.73m2/yr) for each patient was calculated by linear regression. ΔeGFR ≤-3ml/min/1.73m2/yr defined rapid progressors (RP); -0.5 to +1m/min/1.73m2/yr defined stable progressors (SP).
Stage 2: We assessed the eGFR-time graphs of RP and SP using our hospital e-records. Patients were selected if they had a clear, linear pattern of progression.
To validate our method, we analysed plasma levels of KIM-1 and NGAL, biomarkers shown to be predictive of ESRD, in RP and SP, expecting higher levels in RP at stages 1 and 2.

Results

By linear regression alone, there were 388 RP and 458 SP. Stage 2 refined this group by unmasking non-linear progression, excluding 22% of RP and 33% of SP in the process (see figure). Median ΔeGFR in the final RP group was -4.69ml/min/1.73m2/yr (IQR -5.86 to -3.65) and 0.09ml/min/1.73m2/yr (IQR -0.53 to 0.93) in SP; p<0.001. Median number of eGFR values per patient was 23 in both groups (IQR 15-35 in RP; 15-39 in SP). Median follow-up was 52months (IQR 36-73) in RP and 78months (IQR 56-112) in SP. KIM-1 and NGAL levels were higher in RP than SP in stage 1 (p<0.001), and also higher in the refined stage 2 group, but significant for only NGAL, p=0.02.

Conclusion

We believe we are the first to combine linear regression and eGFR-time graphs to precisely stratify patients based on their ΔeGFR. Our method, aided by a cohort with multiple eGFR values and long follow-up, should help yield improved insights between measured biomarkers and specific, linear patterns of CKD progression.

Illustrative graphs of the stage 2 process