Intra-individual variability of eGFR trajectories in early diabetic kidney disease and lack of performance of prognostic biomarkers

Studies reporting on biomarkers aiming to predict adverse renal outcomes in patients with type 2 diabetes and kidney disease (DKD) conventionally define a surrogate endpoint either as a percentage of decrease of eGFR (e.g. ≥ 30%) or an absolute decline (e.g. ≥ 5 ml/min/year). The application of those study results in clinical practise however relies on the assumption of a linear and intra-individually stable progression of DKD. We studied 860 patients of the PROVALID study and 178 of an independent population with a relatively preserved eGFR at baseline and at least 5 years of follow up. Individuals with a detrimental prognosis were identified using various thresholds of a percentage or absolute decline of eGFR after each year of follow up. Next, we determined how many of the patients met the same criteria at other points in time. Interindividual eGFR decline was highly variable but in addition intra-individual eGFR trajectories also were frequently non-linear. For example, of all subjects reaching an endpoint defined as a decrease of eGFR by ≥ 30% between baseline and 3 years of follow up, only 60.3 and 45.2% lost at least the same amount between baseline and year 4 or 5. The results were similar when only patients on stable medication or subpopulations based on baseline eGFR or albuminuria status were analyzed or an eGFR decline of ≥ 5 ml/min/1.73m2/year was used. Identification of reliable biomarkers predicting adverse prognosis is a strong clinical need given the large interindividual variability of DKD progression. However, it is conceptually challenging in early DKD because of non-linear intra-individual eGFR trajectories. As a result, the performance of a prognostic biomarker may be accurate after a specific time of follow-up in a single population only.

Media Type:

Electronic Article

Year of Publication:

2020

Contained In:

Scientific reports - Vol. 10, No. 1 (2020), p. 19743

Language:

English

Contributors:

Kerschbaum, Julia
Rudnicki, Michael
Dzien, Alexander
Dzien-Bischinger, Christine
Winner, Hannes
Heerspink, Hiddo Lambers
Rosivall, László
Wiecek, Andrzej
Mark, Patrick B
Eder, Susanne
Denicolò, Sara
Mayer, Gert

Links:

Volltext

Keywords:

Journal Article
Research Support, Non-U.S. Gov't

Notes:

Date Revised 19.11.2020

published: Electronic

Citation Status In-Process

Copyright: From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Physical Description:

Online-Ressource

doi:

10.1038/s41598-020-76773-0

PMID:

33184434

PPN (Catalogue-ID):

NLM318508966