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Review
. 2020 Aug 14;21(16):5846.
doi: 10.3390/ijms21165846.

Contribution of Predictive and Prognostic Biomarkers to Clinical Research on Chronic Kidney Disease

Michele Provenzano  1 , Salvatore Rotundo  2 , Paolo Chiodini  3 , Ida Gagliardi  1 , Ashour Michael  1 , Elvira Angotti  4 , Silvio Borrelli  5 , Raffaele Serra  6 , Daniela Foti  2 , Giovambattista De Sarro  7 , Michele Andreucci  1
Affiliations
Review

Contribution of Predictive and Prognostic Biomarkers to Clinical Research on Chronic Kidney Disease

Michele Provenzano et al. Int J Mol Sci. .

Abstract

Chronic kidney disease (CKD), defined as the presence of albuminuria and/or reduction in estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2, is considered a growing public health problem, with its prevalence and incidence having almost doubled in the past three decades. The implementation of novel biomarkers in clinical practice is crucial, since it could allow earlier diagnosis and lead to an improvement in CKD outcomes. Nevertheless, a clear guidance on how to develop biomarkers in the setting of CKD is not yet available. The aim of this review is to report the framework for implementing biomarkers in observational and intervention studies. Biomarkers are classified as either prognostic or predictive; the first type is used to identify the likelihood of a patient to develop an endpoint regardless of treatment, whereas the second type is used to determine whether the patient is likely to benefit from a specific treatment. Many single assays and complex biomarkers were shown to improve the prediction of cardiovascular and kidney outcomes in CKD patients on top of the traditional risk factors. Biomarkers were also shown to improve clinical trial designs. Understanding the correct ways to validate and implement novel biomarkers in CKD will help to mitigate the global burden of CKD and to improve the individual prognosis of these high-risk patients.

Keywords: CKD; biomarkers; cardiovascular disease; end-stage kidney disease (ESKD); epidemiology.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Adjusted risks for end-stage kidney disease (ESKD), death, and cardiovascular (CV) fatal and non-fatal events, by 24-h proteinuria (panel A) or estimated glomerular filtration rate (eGFR) (panel B) levels. Solid lines represent hazard ratios, whereas dashed lines the 95% confidence intervals. Hazard ratios were modeled by means of restricted cubic spline (RCS) due to the non-linear association with the endpoints. Knots are located at the zeroth, 25th, 50th, and 75th percentiles for proteinuria and 15, 30, 45, 60 mL/min/1.73 m2 for eGFR. Risks are adjusted for the four-variable Tangri equation [26]: age, gender, eGFR, and proteinuria. Rug plots on the x-axis at the top (colored green) represent the distribution of observations. Data source: pooled analysis of six cohorts of CKD patients referred to Italian nephrology clinics [27].
Figure 2
Figure 2
Biomarker-based approaches for patient selection in clinical trials.
See this image and copyright information in PMC

References

    1. FDA-NIH Biomarker Working Group . BEST (Biomarkers, Endpoints, and other Tools) Resource. Food and Drug Administration (US); Silver Spring, MD, USA: National Institutes of Health (US); Bethesda, MD, USA: 2020. [(accessed on 25 June 2020)]. Available online: www.ncbi.nlm.nih.gov/books/NBK326791. - PubMed
    1. Pepe M., Janes H. Methods for Evaluating Prediction Performance of Biomarkers and Tests. In: Lee M.L., Gail M., Pfeiffer R., Satten G., Cai T., Gandy A., editors. Risk Assessment and Evaluation of Predictions. Volume 215. Springer; New York, NY, USA: 2013. Lecture Notes in Statistics. - DOI
    1. Devarajan P., Williams L.M. Proteomics for biomarker discovery in acute kidney injury. Semin. Nephrol. 2007;27:637–651. doi: 10.1016/j.semnephrol.2007.09.005. - DOI - PMC - PubMed
    1. Simon R. Development and validation of biomarker classifiers for treatment selection. J. Stat. Plan. Inference. 2008;138:308–320. doi: 10.1016/j.jspi.2007.06.010. - DOI - PMC - PubMed
    1. Provenzano M., Coppolino G., De Nicola L., Serra R., Garofalo C., Andreucci M., Bolignano D. Unraveling cardiovascular risk in renal patients: A new take on old tale. Front. Cell Dev. Biol. 2019;7:314. doi: 10.3389/fcell.2019.00314. - DOI - PMC - PubMed