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Biomarkers of rapid chronic kidney disease progression in type 2 diabetes

  • Helen C. Looker (Lead / Corresponding author)
  • , Marco Colombo
  • , Sibylle Hess
  • , Mary J. Brosnan
  • , Bassam Farran
  • , R. Neil Dalton
  • , Max C. Wong
  • , Charles Turner
  • , Colin N. A. Palmer
  • , Everson Nogoceke
  • , Leif Groop
  • , Veikko Salomaa
  • , David B. Dunger
  • , Felix Agakov
  • , Paul M. McKeigue
  • , Helen M. Colhoun
  • , on behalf of the SUMMIT Investigators

    Research output: Contribution to journalArticlepeer-review

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    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 m2. 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.Kidney International advance online publication, 22 July 2015; doi:10.1038/ki.2015.199.

    Original languageEnglish
    Pages (from-to)888-896
    Number of pages9
    JournalKidney International
    Volume88
    Issue number4
    Early online date22 Jul 2015
    DOIs
    Publication statusPublished - Oct 2015

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

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