Detection of C-Peptide in Urine as a Measure of Ongoing Beta Cell Function.

C-peptide is a protein secreted by the pancreatic beta cells in equimolar quantities with insulin, following the cleavage of proinsulin into insulin. Measurement of C-peptide is used as a surrogate marker of endogenous insulin secretory capacity. Assessing C-peptide levels can be useful in classifying the subtype of diabetes as well as assessing potential treatment choices in the management of diabetes.Standard measures of C-peptide involve blood samples collected either fasted or, most often, after a fixed stimulus (such as oral glucose, mixed meal, or IV glucagon). Despite the established clinical utility of blood C-peptide measurement, its widespread use is limited. In many instances this is due to perceived practical restrictions associated with sample collection.Urine C-peptide measurement is an attractive noninvasive alternative to blood measures of beta-cell function. Urine C-peptide creatinine ratio measured in a single post stimulated sample has been shown to be a robust, reproducible measure of endogenous C-peptide which is stable for three days at room temperature when collected in boric acid. Modern high sensitivity immunoassay technologies have facilitated measurement of C-peptide down to single picomolar concentrations.Accepted manuscript - 12 month embargo (with set statement

IFCC reference system for measurement of hemoglobin A(1c) in human blood and the National Standardization Schemes in the United States, Japan, and Sweden: A method-comparison study

Background: The national programs for the harmonization of hemoglobin (Hb)A(1c) measurements in the US [National Glycohemoglobin Standardization Program (NGSP)], Japan [Japanese Diabetes Society (JDS)/Japanese Society of Clinical Chemistry (JSCC)], and Sweden are based on different designated comparison methods (DCMs). The future basis for international standardization will be the reference system developed by the IFCC Working Group on HbA(1c) Standardization. The aim of the present study was to determine the relationships between the IFCC Reference Method (RM) and the DCMs. Methods: Four method-comparison studies were performed in 2001-2003. In each study five to eight pooled blood samples were measured by 11 reference laboratories of the IFCC Network of Reference Laboratories, 9 Secondary Reference Laboratories of the NGSP, 3 reference laboratories of the JDS/JSCC program, and a Swedish reference laboratory. Regression equations were determined for the relationship between the IFCC RM and each of the DCMs. Results: Significant differences were observed between the HbA(1c) results of the IFCC RM and those of the DCMs. Significant differences were also demonstrated between the three DCMs. However, in all cases the relationship of the DCMs with the RM were linear. There were no statistically significant differences between the regression equations calculated for each of the four studies; therefore, the results could be combined. The relationship is described by the following regression equations: NGSP-HbA(1c) = 0.915(IFCC-HbA(1c)) + 2.15% (r(2) = 0.998); JDS/JSCC-HbA(1c) = 0.927(IFCC-HbA(1c)) + 1.73% (r(2) = 0.997); Swedish-HbA(1c) = 0.989(IFCC-HbA(1c)) + 0.88% (r(2) = 0.996). Conclusion: There is a firm and reproducible link between the IFCC RM and DCM HbA(1c) values. (C) 2004 American Association for Clinical Chemistr

The majority of patients with long-duration type 1 diabetes are insulin microsecretors and have functioning beta cells.

Classically, type 1 diabetes is thought to proceed to absolute insulin deficiency. Recently developed ultrasensitive assays capable of detecting C-peptide under 5 pmol/l now allow very low levels of C-peptide to be detected in patients with long-standing type 1 diabetes. It is not known whether this low-level endogenous insulin secretion responds to physiological stimuli. We aimed to assess how commonly low-level detectable C-peptide occurs in long-duration type 1 diabetes and whether it responds to a meal stimulus.This article is freely available via Open Access. Click on the 'Additional Link' above to access the full-text via the publisher's site

Numerical Methods for Coupled Population Balance Systems Applied to the Dynamical Simulation of Crystallization Processes

Uni- and bi-variate crystallization processes are considered that are modeled with population balance systems (PBSs). Experimental results for uni-variate processes in a helically coiled flowtube crystallizer are presented.Asurvey on numerical methods for the simulation of uni-variate PBSs is provided with the emphasis on a coupled stochastic-deterministic method. In this method, the equations of the PBS from computational fluid dynamics are solved deterministically and the population balance equation is solved with a stochastic algorithm. With this method, simulations of a crystallization process in a fluidized bed crystallizer are performed that identify appropriate values for two parameters of the model such that considerably improved results are obtained than reported so far in the literature. For bi-variate processes, the identification of agglomeration kernels from experimental data is briefly discussed. Even for multi-variate processes, an efficient algorithm for evaluating the agglomeration term is presented that is based on the fast Fourier transform (FFT). The complexity of this algorithm is discussed as well as the number of moments that can be conserved