Literature DB >> 6373457

C-peptide as a measure of the secretion and hepatic extraction of insulin. Pitfalls and limitations.

K S Polonsky, A H Rubenstein.   

Abstract

The large and variable hepatic extraction of insulin is a major obstacle to our ability to quantitate insulin secretion accurately in human subjects. The evidence that C-peptide is secreted from the beta cell in equimolar concentration with insulin, but not extracted by the liver to any significant degree, has provided a firm scientific basis for the use of peripheral C-peptide concentrations as a semiquantitative marker of beta cell secretory activity in a variety of clinical situations. Thus, plasma C-peptide has proved to be extremely valuable in the study of the natural history of type 1 diabetes, to monitor insulin secretion in patients with insulin antibodies, and as an adjunct in the investigation of patients with hypoglycemic disorders. The use of the peripheral C-peptide concentration to accurately quantitate the rate of insulin secretion is more controversial. This is mainly because understanding of the kinetics and metabolism of C-peptide under different conditions is incomplete. Unfortunately, sufficient quantities of human C-peptide are not available to allow the experimental validation of the mathematical formulae that have been proposed for the calculation of insulin secretion from peripheral C-peptide concentrations. Until it is possible to perform such experiments, the accuracy of studies that have derived insulin secretion rates from peripheral C-peptide levels will remain uncertain. The assumption that the peripheral C-peptide:insulin molar ratio can be used as a reflection of hepatic insulin extraction has not been experimentally validated. The marked difference in the plasma half-lives of insulin and C-peptide complicates the interpretation of changes in their ratios.(ABSTRACT TRUNCATED AT 250 WORDS)

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Year:  1984        PMID: 6373457     DOI: 10.2337/diab.33.5.486

Source DB:  PubMed          Journal:  Diabetes        ISSN: 0012-1797            Impact factor:   9.461


  115 in total

1.  Ethnic differences in glucose disposal, hepatic insulin sensitivity, and endogenous glucose production among African American and European American women.

Authors:  Amy C Ellis; Jessica A Alvarez; Wesley M Granger; Fernando Ovalle; Barbara A Gower
Journal:  Metabolism       Date:  2011-11-08       Impact factor: 8.694

2.  Insulin pens. Is delivery sacrificed to improve patient compliance?

Authors:  D Gordon
Journal:  Clin Pharmacokinet       Date:  1992-10       Impact factor: 6.447

3.  Bayesian functional integral method for inferring continuous data from discrete measurements.

Authors:  William J Heuett; Bernard V Miller; Susan B Racette; John O Holloszy; Carson C Chow; Vipul Periwal
Journal:  Biophys J       Date:  2012-02-07       Impact factor: 4.033

4.  A model of GLP-1 action on insulin secretion in nondiabetic subjects.

Authors:  Chiara Dalla Man; Francesco Micheletto; Airani Sathananthan; Robert A Rizza; Adrian Vella; Claudio Cobelli
Journal:  Am J Physiol Endocrinol Metab       Date:  2010-02-23       Impact factor: 4.310

5.  Studies on the mechanism of action of sulphonylureas in type II diabetic subjects: gliquidone.

Authors:  E Bonora; P Moghetti; M Querena; M Zenere; V Cacciatori; F Tosi; D Travia; G Zoppini; M Muggeo
Journal:  J Endocrinol Invest       Date:  1992-01       Impact factor: 4.256

6.  Functional high-intensity training improves pancreatic β-cell function in adults with type 2 diabetes.

Authors:  Stephan Nieuwoudt; Ciarán E Fealy; Julie A Foucher; Amanda R Scelsi; Steven K Malin; Mangesh Pagadala; Michael Rocco; Bartolome Burguera; John P Kirwan
Journal:  Am J Physiol Endocrinol Metab       Date:  2017-05-16       Impact factor: 4.310

7.  Paradoxical inhibition of insulin secretion by glucose in non-insulin-dependent diabetic patients.

Authors:  M Linstow; K J Mikines; F Dela; H Galbo
Journal:  Acta Diabetol       Date:  1995-03       Impact factor: 4.280

Review 8.  Methods for Measuring Risk for Type 2 Diabetes in Youth: the Oral Glucose Tolerance Test (OGTT).

Authors:  Melinda E Chen; Rebecca S Aguirre; Tamara S Hannon
Journal:  Curr Diab Rep       Date:  2018-06-16       Impact factor: 4.810

9.  Splanchnic insulin metabolism in obesity. Influence of body fat distribution.

Authors:  A N Peiris; R A Mueller; G A Smith; M F Struve; A H Kissebah
Journal:  J Clin Invest       Date:  1986-12       Impact factor: 14.808

10.  Gastric inhibitory polypeptide (GIP) hypersecretion in obesity depends on meal size and is not related to hyperinsulinemia.

Authors:  R Ebert; W Creutzfeldt
Journal:  Acta Diabetol Lat       Date:  1989 Jan-Mar
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