Combining robust urine biomarkers to assess chronic kidney disease progressionResearch in context
Frank Bienaimé,
Mordi Muorah,
Marie Metzger,
Melanie Broeuilh,
Pascal Houiller,
Martin Flamant,
Jean-Philippe Haymann,
Jacky Vonderscher,
Jacques Mizrahi,
Gérard Friedlander,
Bénédicte Stengel,
Fabiola Terzi,
François Vrtovsnik,
Eric Daugas,
Martin Flamant,
Emmanuelle Vidal-Petiot,
Christian Jacquot,
Alexandre Karras,
Stéphane Roueff,
Eric Thervet,
Pascal Houillier,
Marie Courbebaisse,
Dominique Eladari et Gérard Maruani,
Pablo Urena-Torres,
Jean-Jacques Boffa,
Pierre Ronco,
H. Fessi,
Eric Rondeau,
Emmanuel Letavernier,
Nahid Tabibzadeh,
Jean-Philippe Haymann
Affiliations
Frank Bienaimé
Département « Croissance et Signalisation », Institut Necker Enfants Malades, INSERM U1151, CNRS UMR 8253, Université de Paris Cité, Paris, France; Service d’Explorations Fonctionnelles, Hôpital Necker Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France; Corresponding author. Service d’Explorations Fonctionnelles, Hôpital Necker Enfants Malades, 149 Rue de Sèvres, Paris 75015, France.
Mordi Muorah
Département « Croissance et Signalisation », Institut Necker Enfants Malades, INSERM U1151, CNRS UMR 8253, Université de Paris Cité, Paris, France
Marie Metzger
CESP, Centre de Recherche en Epidémiologie et Santé des Populations, INSERM U1018, Université Paris-Saclay, Villejuif, France
Melanie Broeuilh
Département « Croissance et Signalisation », Institut Necker Enfants Malades, INSERM U1151, CNRS UMR 8253, Université de Paris Cité, Paris, France
Pascal Houiller
Service d’Explorations Fonctionnelles, Hôpital Européen George Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, France
Martin Flamant
Service d’Explorations Fonctionnelles, Hôpital Bichat, Assistance Publique-Hôpitaux de Paris, Paris, France
Jean-Philippe Haymann
Service d’Explorations Fonctionnelles, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, Paris, France
Jacky Vonderscher
Pharma Research and Early Development, Hoffmann-La-Roche Ltd, Basel, France
Jacques Mizrahi
Pharma Research and Early Development, Hoffmann-La-Roche Ltd, Basel, France
Gérard Friedlander
Département « Croissance et Signalisation », Institut Necker Enfants Malades, INSERM U1151, CNRS UMR 8253, Université de Paris Cité, Paris, France
Bénédicte Stengel
CESP, Centre de Recherche en Epidémiologie et Santé des Populations, INSERM U1018, Université Paris-Saclay, Villejuif, France
Fabiola Terzi
Département « Croissance et Signalisation », Institut Necker Enfants Malades, INSERM U1151, CNRS UMR 8253, Université de Paris Cité, Paris, France; Corresponding author. Faculté de Médecine Necker, INSERM U1151, CNRS UMR 8253, Institut Necker Enfants Malades, Université de Paris Cite, 156-160 rue de Vaugirard, Paris 75015, France.
Summary: Background: Urinary biomarkers may improve the prediction of chronic kidney disease (CKD) progression. Yet, data reporting the applicability of most commercial biomarker assays to the detection of their target analyte in urine together with an evaluation of their predictive performance are scarce. Methods: 30 commercial assays (ELISA) were tested for their ability to quantify the target analyte in urine using strict (FDA-approved) validation criteria. In an exploratory analysis, LASSO (Least Absolute Shrinkage and Selection Operator) logistic regression analysis was used to identify potentially complementary biomarkers predicting fast CKD progression, determined as the 51CrEDTA clearance-based measured glomerular filtration rate (mGFR) decline (>10% per year) in a subsample of 229 CKD patients (mean age, 61 years; 66% men; baseline mGFR, 38 mL/min) from the NephroTest prospective cohort. Findings: Among the 30 assays, directed against 24 candidate biomarkers, encompassing different pathophysiological mechanisms of CKD progression, 16 assays fulfilled the FDA-approved criteria. LASSO logistic regressions identified a combination of five biomarkers including CCL2, EGF, KIM1, NGAL, and TGF-α that improved the prediction of fast mGFR decline compared to the kidney failure risk equation variables alone: age, gender, mGFR, and albuminuria. Mean area under the curves (AUC) estimated from 100 re-samples was higher in the model with than without these biomarkers, 0.722 (95% confidence interval 0.652–0.795) vs. 0.682 (0.614–0.748), respectively. Fully-adjusted odds-ratios (95% confidence interval) for fast progression were 1.87 (1.22, 2.98), 1.86 (1.23, 2.89), 0.43 (0.25, 0.70), 1.10 (0.71, 1.83), 0.55 (0.33, 0.89), and 2.99 (1.89, 5.01) for albumin, CCL2, EGF, KIM1, NGAL, and TGF-α, respectively. Interpretation: This study provides a rigorous validation of multiple assays for relevant urinary biomarkers of CKD progression which combination may improve the prediction of CKD progression. Funding: This work was supported by Institut National de la Santé et de la Recherche Médicale, Université de Paris, Assistance Publique Hôpitaux de Paris, Agence Nationale de la Recherche, MSDAVENIR, Pharma Research and Early Development Roche Laboratories (Basel, Switzerland), and Institut Roche de Recherche et Médecine Translationnelle (Paris, France).
