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Keywords:

  • biomarkers;
  • diabetes;
  • fatty liver;
  • prediction

Diabet. Med. 29, 5–13 (2012)

Abstract

An ideal biomarker should refine identification of those at risk of disease occurrence or progression, improve prediction of complications of disease, and/or guide and help tailor responses to different therapies. Biomarkers that give insights into disease pathogenesis are also of interest. With this in mind, this review describes biomarker studies relevant to diabetes, focusing on those conducted by the author, his colleagues and collaborators. The review highlights several points. (1) Novel biomarkers may not improve prediction of new-onset diabetes in a meaningful way beyond what can be achieved with simple measures combined with HbA1c, and a sensible way ahead may be to combine diabetes and cardiovascular disease prediction using HbA1c and such measures. (2) In terms of disease pathogenesis, associations do not necessarily infer causality; potential for residual confounding and reverse causality should always be borne in mind. The potential relevance of such issues to understanding the relationship of some topical variables/pathways, namely adiponectin, inflammation and vitamin D, with diabetes will be highlighted. (3) How baseline and serial data on biomarkers arising from the liver have improved our understanding of the role of hepatic fat in diabetes pathogenesis will be explored. (4) Future goals for diabetes biomarker research should focus on predicting complications and determining subgroups who may respond better to particular therapies. (5) All novel biomarker research (regardless of analytical platforms used) needs to be tested against information available from commonly measured variables in clinical practice. Otherwise, many claims of clinical utility can be exaggerated. In summary, biomarker research in diabetes is continuing apace in a number of areas, but it remains to be seen whether the promise of biomarker research to improve the care of our patients becomes a reality.