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  • Looker, Helen C. (author)

Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes

  • Article/chapterEnglish2015

Publisher, publication year, extent ...

  • 2015-03-05
  • Springer Science and Business Media LLC,2015

Numbers

  • LIBRIS-ID:oai:lup.lub.lu.se:9f1d94ff-778d-40c4-a929-c4b63b382bae
  • https://lup.lub.lu.se/record/7425064URI
  • https://doi.org/10.1007/s00125-015-3535-6DOI
  • http://kipublications.ki.se/Default.aspx?queryparsed=id:131217616URI

Supplementary language notes

  • Language:English
  • Summary in:English

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  • Subject category:art swepub-publicationtype
  • Subject category:ref swepub-contenttype

Notes

  • Aims/hypothesis We selected the most informative protein biomarkers for the prediction of incident cardiovascular disease (CVD) in people with type 2 diabetes. Methods In this nested case-control study we measured 42 candidate CVD biomarkers in 1,123 incident CVD cases and 1,187 controls with type 2 diabetes selected from five European centres. Combinations of biomarkers were selected using cross-validated logistic regression models. Model prediction was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sixteen biomarkers showed univariate associations with incident CVD. The most predictive subset selected by forward selection methods contained six biomarkers: N-terminal pro-B-type natriuretic peptide (OR 1.69 per 1 SD, 95% CI 1.47, 1.95), high-sensitivity troponin T (OR 1.29, 95% CI 1.11, 1.51), IL-6 (OR 1.13, 95% CI 1.02, 1.25), IL-15 (OR 1.15, 95% CI 1.01, 1.31), apolipoprotein C-III (OR 0.79, 95% CI 0.70, 0.88) and soluble receptor for AGE (OR 0.84, 95% CI 0.76, 0.94). The prediction of CVD beyond clinical covariates improved from an AUROC of 0.66 to 0.72 (AUROC for Framingham Risk Score covariates 0.59). In addition to the biomarkers, the most important clinical covariates for improving prediction beyond the Framingham covariates were estimated GFR, insulin therapy and HbA(1c). Conclusions/interpretation We identified six protein biomarkers that in combination with clinical covariates improved the prediction of our model beyond the Framingham Score covariates. Biomarkers can contribute to improved prediction of CVD in diabetes but clinical data including measures of renal function and diabetes-specific factors not included in the Framingham Risk Score are also needed.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Colombo, Marco (author)
  • Agakov, Felix (author)
  • Zeller, Tanja (author)
  • Groop, LeifLund University,Lunds universitet,Genomik, diabetes och endokrinologi,Forskargrupper vid Lunds universitet,Genomics, Diabetes and Endocrinology,Lund University Research Groups(Swepub:lu)endo-lgr (author)
  • Thorand, Barbara (author)
  • Palmer, Colin N. (author)
  • Hamsten, AndersKarolinska Institutet (author)
  • de Faire, UlfKarolinska Institutet (author)
  • Nogoceke, Everson (author)
  • Livingstone, Shona J. (author)
  • Salomaa, Veikko (author)
  • Leander, KarinKarolinska Institutet (author)
  • Barbarini, Nicola (author)
  • Bellazzi, Riccardo (author)
  • van Zuydam, Natalie (author)
  • McKeigue, Paul M. (author)
  • Colhoun, Helen M. (author)
  • Genomik, diabetes och endokrinologiForskargrupper vid Lunds universitet (creator_code:org_t)

Related titles

  • In:Diabetologia: Springer Science and Business Media LLC58:6, s. 1363-13711432-04280012-186X

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