OBJECTIVE: To clarify whether random C-peptide is a valuable test in the classification of diabetes. RESEARCH DESIGN AND METHODS: All C-peptide measurements conducted in the diabetic population of Skaraborg (280,539 inhabitants and 3.2% diabetes) between 1995 and 1998 (3,115 samples) were considered, but only patients with well-defined diabetes type (1,449 samples from 1,093 patients) were analyzed for the correlation between diabetes type and C-peptide concentration. Serum C-peptide was measured after fasting over night (fCP), after glucagon stimulation (gCP), and randomly (rCP) without considering previous meals at an ordinary visit to the diabetic clinic (rCP). Receiver Operating Characteristic (ROC) curves were constructed to illustrate the power of the different C-peptide protocols and to determine the optimal cut-off values. RESULTS: Although all three tests had high discriminative power, the ROC curves demonstrated that rCP was superior to fCP and gCP in discriminating type 1 from type 2 diabetes. The optimal cut-off value for rCP was 0.50 nmol/L, for fCP 0.42 nmol/L, and for gCP 0.60 nmol/L. CONCLUSIONS: rCP is more powerful than fCP and gCP in distinguishing type 1 from type 2 diabetes and can therefore be recommended as a classification tool, particularly in outpatients.
OBJECTIVE: To clarify whether random C-peptide is a valuable test in the classification of diabetes. RESEARCH DESIGN AND METHODS: All C-peptide measurements conducted in the diabetic population of Skaraborg (280,539 inhabitants and 3.2% diabetes) between 1995 and 1998 (3,115 samples) were considered, but only patients with well-defined diabetes type (1,449 samples from 1,093 patients) were analyzed for the correlation between diabetes type and C-peptide concentration. Serum C-peptide was measured after fasting over night (fCP), after glucagon stimulation (gCP), and randomly (rCP) without considering previous meals at an ordinary visit to the diabetic clinic (rCP). Receiver Operating Characteristic (ROC) curves were constructed to illustrate the power of the different C-peptide protocols and to determine the optimal cut-off values. RESULTS: Although all three tests had high discriminative power, the ROC curves demonstrated that rCP was superior to fCP and gCP in discriminating type 1 from type 2 diabetes. The optimal cut-off value for rCP was 0.50 nmol/L, for fCP 0.42 nmol/L, and for gCP 0.60 nmol/L. CONCLUSIONS: rCP is more powerful than fCP and gCP in distinguishing type 1 from type 2 diabetes and can therefore be recommended as a classification tool, particularly in outpatients.
Authors: Rebecca B Lipton; Melinda L Drum; Kirstie K Danielson; Siri Aw Greeley; Graeme I Bell; William A Hagopian Journal: Pediatr Diabetes Date: 2011-03-23 Impact factor: 4.866
Authors: Rebecca B Lipton; Melinda Drum; Siri Atma W Greeley; Kirstie K Danielson; Graeme I Bell; William A Hagopian Journal: Pediatr Diabetes Date: 2011-03-21 Impact factor: 4.866
Authors: Shideh Majidi; Alexandra Fouts; Laura Pyle; Christina Chambers; Taylor Armstrong; Zhenyuan Wang; Sat Dev Batish; Georgeanna Klingensmith; Andrea K Steck Journal: Diabetes Technol Ther Date: 2018-02 Impact factor: 6.118
Authors: Fred K Tabung; Weike Wang; Teresa T Fung; Frank B Hu; Stephanie A Smith-Warner; Jorge E Chavarro; Charles S Fuchs; Walter C Willett; Edward L Giovannucci Journal: Br J Nutr Date: 2016-11-08 Impact factor: 3.718