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Prediction of Chronic Kidney Disease Stage 3 by CKD273, a Urinary Proteomic Biomarker.

Articolo
Data di Pubblicazione:
2017
Abstract:
Introduction: CKD273 is a urinary biomarker, which in advanced chronic kidney disease predicts further deterioration. We investigated whether CKD273 can also predict a decline of estimated glomerular filtration rate (eGFR) to <60 ml/min per 1.73 m2. Methods: In analyses of 2087 individuals from 6 cohorts (46.4% women; 73.5% with diabetes; mean age, 46.1 years; eGFR $ 60 ml/min per 1.73 m2, 100%; urinary albumin excretion rate [UAE] $20 mg/min, 6.2%), we accounted for cohort, sex, age, mean arterial pressure, diabetes, and eGFR at baseline and expressed associations per 1-SD increment in urinary biomarkers. Results: Over 5 (median) follow-up visits, eGFR decreased more with higher baseline CKD273 than UAE (1.64 vs. 0.82 ml/min per 1.73 m2; P < 0.0001). Over 4.6 years (median), 390 participants experienced a first renal endpoint (eGFR decrease by $10 to <60 ml/min per 1.73 m2), and 172 experienced an endpoint sustained over follow-up. The risk of a first and sustained renal endpoint increased with UAE (hazard ratio $ 1.23; P # 0.043) and CKD273 ($ 1.20; P # 0.031). UAE ($20 mg/min) and CKD273 ($0.154) thresholds yielded sensitivities of 30% and 33% and specificities of 82% and 83% (P # 0.0001 for difference between UAE and CKD273 in proportion of correctly classified individuals). As continuous markers, CKD273 (P ¼ 0.039), but not UAE (P ¼ 0.065), increased the integrated discrimination improvement, while both UAE and CKD273 improved the net reclassification index (P # 0.0003), except for UAE per threshold (P ¼ 0.086). Discussion: In conclusion, while accounting for baseline eGFR, albuminuria, and covariables, CKD273 adds to the prediction of stage 3 chronic kidney disease, at which point intervention remains an achievable therapeutic target.
Tipologia CRIS:
03A-Articolo su Rivista
Keywords:
biomarker; chronic kidney disease; clinical science; glomerular filtration rate; peptidomics; proteomics
Elenco autori:
Pontillo C, Zhang ZY, Schanstra JP, Jacobs L, Zürbig P, Thijs L, Ramírez-Torres A, Heerspink HJL, Lindhardt M, Klein R, Orchard T, Porta M, Bilous RW, Charturvedi N, Rossing P, Vlahou A, Schepers E, Glorieux G, Mullen W, Delles C, Verhamme P, Vanholder R, Staessen JA, Mischak H, Jankowski J.
Link alla scheda completa:
https://iris.unito.it/handle/2318/1668533
Link al Full Text:
https://iris.unito.it/retrieve/handle/2318/1668533/414480/Prediction%20CKD%20by%20CKD273%20-%20DIRECT.pdf
Pubblicato in:
KIDNEY INTERNATIONAL REPORTS
Journal
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