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Kidney Int. 2015 Oct;88(4):888-96. doi: 10.1038/ki.2015.199. Epub 2015 Jul 22.

Biomarkers of rapid chronic kidney disease progression in type 2 diabetes.

Author information

1
Population Health Sciences, University of Dundee, Dundee, UK.
2
Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK.
3
R&D Metabolism and PK Germany, Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany.
4
Pfizer Worldwide Research and Development, Pfizer, Groton, Connecticut, USA.
5
WellChild Laboratory, Kings College, London, UK.
6
University Department of Paediatrics, Cambridge University, Cambridge, UK.
7
Medical Research Institute, University of Dundee, Dundee, UK.
8
Cardiovascular and Metabolism Disease Therapeutic Area, F. Hoffmann-La Roche, Basel, Switzerland.
9
Department of Clinical Sciences, Diabetes and Endocrinology, Lund University, Malmo, Sweden.
10
Department of Chronic Disease Prevention, The National Institute for Health and Welfare, Helsinki, Finland.
11
Pharmatics Limited, Edinburgh, UK.
12
Department of Public Health, NHS Fife, UK.

Abstract

Here we evaluated the performance of a large set of serum biomarkers for the prediction of rapid progression of chronic kidney disease (CKD) in patients with type 2 diabetes. We used a case-control design nested within a prospective cohort of patients with baseline eGFR 30-60 ml/min per 1.73 m(2). Within a 3.5-year period of Go-DARTS study patients, 154 had over a 40% eGFR decline and 153 controls maintained over 95% of baseline eGFR. A total of 207 serum biomarkers were measured and logistic regression was used with forward selection to choose a subset that were maximized on top of clinical variables including age, gender, hemoglobin A1c, eGFR, and albuminuria. Nested cross-validation determined the best number of biomarkers to retain and evaluate for predictive performance. Ultimately, 30 biomarkers showed significant associations with rapid progression and adjusted for clinical characteristics. A panel of 14 biomarkers increased the area under the ROC curve from 0.706 (clinical data alone) to 0.868. Biomarkers selected included fibroblast growth factor-21, the symmetric to asymmetric dimethylarginine ratio, β2-microglobulin, C16-acylcarnitine, and kidney injury molecule-1. Use of more extensive clinical data including prebaseline eGFR slope improved prediction but to a lesser extent than biomarkers (area under the ROC curve of 0.793). Thus we identified several novel associations of biomarkers with CKD progression and the utility of a small panel of biomarkers to improve prediction.

PMID:
26200946
DOI:
10.1038/ki.2015.199
[Indexed for MEDLINE]
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