The evolution of C-peptide's role in diabetes care.
Diabetes mellitus affects one in nine adults worldwide, with timely diagnosis and accurate classification being essential for patient management. C-peptide is an important biomarker in the diagnostic workup. As diabetes sub-typing and treatment options continue to evolve, this review will highlight the important aspects of C-peptide analysis and interpretation and additionally, evaluate its current and emerging clinical role.
Several sample types and testing strategies such as fasting, random and stimulated C-peptide are available which are reviewed here. Random nonfasting C-peptide is convenient to perform in clinic and performs well compared to gold standard testing for classification of severe insulin deficiency and insulin dependence. C-peptide measurement may also be useful for classifying type 2 diabetes subtypes and in predicting response to treatment. Despite ongoing efforts towards standardization of C-peptide, variation still exists between analytical methods.
This review summarizes recent literature relating to preanalytical, analytical and clinical aspects of C-peptide testing. Future research in this area may build on the role of C-peptide in predicting glycaemic control, clinical complications and response to pharmacotherapy.
Several sample types and testing strategies such as fasting, random and stimulated C-peptide are available which are reviewed here. Random nonfasting C-peptide is convenient to perform in clinic and performs well compared to gold standard testing for classification of severe insulin deficiency and insulin dependence. C-peptide measurement may also be useful for classifying type 2 diabetes subtypes and in predicting response to treatment. Despite ongoing efforts towards standardization of C-peptide, variation still exists between analytical methods.
This review summarizes recent literature relating to preanalytical, analytical and clinical aspects of C-peptide testing. Future research in this area may build on the role of C-peptide in predicting glycaemic control, clinical complications and response to pharmacotherapy.
Authors
Briggs Briggs, Read Read, Darch Darch, Williams Williams, Loh Loh, Kenkre Kenkre
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