Biomarker panels associated with progression of renal disease in type 1 diabetes.

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  • Additional Information
    • Corporate Authors:
      FinnDiane Study Group and the Scottish Diabetes Research Network (SDRN) Type 1 Bioresource Collaboration
    • Source:
      Publisher: Springer Verlag Country of Publication: Germany NLM ID: 0006777 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-0428 (Electronic) Linking ISSN: 0012186X NLM ISO Abbreviation: Diabetologia Subsets: MEDLINE
    • Publication Information:
      Original Publication: Berlin Springer Verlag
    • Subject Terms:
      Biomarkers/*blood ; Diabetes Mellitus, Type 1/*blood ; Diabetes Mellitus, Type 1/*pathology; Adult ; Bayes Theorem ; Chromatography, Liquid ; Diabetic Nephropathies/blood ; Diabetic Nephropathies/pathology ; Disease Progression ; Female ; Glomerular Filtration Rate/physiology ; Humans ; Logistic Models ; Male ; Middle Aged ; Tandem Mass Spectrometry
    • Abstract:
      Aims/hypothesis: We aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes.
      Methods: We considered 859 individuals recruited from the Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) and 315 individuals from the Finnish Diabetic Nephropathy (FinnDiane) study. All had an entry eGFR between 30 and 75 ml min -1 [1.73 m] -2 , with those from FinnDiane being oversampled for albuminuria. A total of 297 circulating biomarkers (30 proteins, 121 metabolites, 146 tryptic peptides) were measured in non-fasting serum samples using the Luminex platform and LC electrospray tandem MS (LC-MS/MS). We investigated associations with final eGFR adjusted for baseline eGFR and with rapid progression (a loss of more than 3 ml min -1 [1.73 m] -2  year -1 ) using linear and logistic regression models. Panels of biomarkers were identified using a penalised Bayesian approach, and their performance was evaluated through 10-fold cross-validation and compared with using clinical record data alone.
      Results: For final eGFR, 16 proteins and 30 metabolites or tryptic peptides showed significant association in SDRNT1BIO, and nine proteins and five metabolites or tryptic peptides in FinnDiane, beyond age, sex, diabetes duration, study day eGFR and length of follow-up (all at p < 10 -4 ). The strongest associations were with CD27 antigen (CD27), kidney injury molecule 1 (KIM-1) and α1-microglobulin. Including the Luminex biomarkers on top of baseline covariates increased the r 2 for prediction of final eGFR from 0.47 to 0.58 in SDRNT1BIO and from 0.33 to 0.48 in FinnDiane. At least 75% of the increment in r 2 was attributable to CD27 and KIM-1. However, using the weighted average of historical eGFR gave similar performance to biomarkers. The LC-MS/MS platform performed less well.
      Conclusions/interpretation: Among a large set of associated biomarkers, a sparse panel of just CD27 and KIM-1 contains most of the predictive information for eGFR progression. The increment in prediction beyond clinical data was modest but potentially useful for oversampling individuals with rapid disease progression into clinical trials, especially where there is little information on prior eGFR trajectories.
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    • Grant Information:
      10/0004010 United Kingdom DUK_ Diabetes UK; ETM/47 United Kingdom CSO_ Chief Scientist Office; G0600717 United Kingdom MRC_ Medical Research Council
    • Contributed Indexing:
      Keywords: Clinical science; Epidemiology; Metabolomics; Nephropathy; Proteomics
    • Accession Number:
      0 (Biomarkers)
    • Publication Date:
      Date Created: 20190622 Date Completed: 20200413 Latest Revision: 20231013
    • Publication Date:
      20231215
    • Accession Number:
      PMC6677704
    • Accession Number:
      10.1007/s00125-019-4915-0
    • Accession Number:
      31222504