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      Evaluation of Glucose Response to 3 Types of Insulin Using a Continuous Glucose Monitoring System in Healthy Alpacas

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          Persistent hyperglycemia is common in alpacas and typically requires insulin administration for resolution; however, little is known about alpacas’ response to different insulin formulations.


          To evaluate the effects of 3 insulin formulations on blood glucose concentrations and the use of a continuous glucose monitoring ( CGM) system in alpacas.


          Six healthy alpacas.


          The CGM was installed in the left paralumbar fossa at the start of this crossover study and recorded data every 5 minutes. Regular insulin, NPH insulin, insulin glargine, and dextrose were administered to each alpaca over a 2‐week period. Blood samples were collected for glucose testing at 0, 1, 2, 4, 6, 8, and 12 hours, and then every 6 hours after each administration of insulin or dextrose. Data were compared by using method comparison techniques, error grid plots, and ANOVA.


          Blood glucose concentrations decreased most rapidly after regular insulin administration when administered IV or SC as compared to the other formulations. The NPH insulin produced the longest suppression of blood glucose. The mean CGM interstitial compartment glucose concentrations were typically lower than the intravascular compartment glucose concentrations. The alpacas had no adverse reactions to the different insulin formulations.

          Conclusions and Clinical Importance

          The NPH insulin might be more appropriate for long‐term use in hyperglycemic alpacas because of its extended duration of action. A CGM is useful in monitoring glucose trends and reducing blood collection events, but it should not be the sole method for determining treatment protocols.

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          Most cited references 30

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          Continuous glucose monitoring and intensive treatment of type 1 diabetes.

          The value of continuous glucose monitoring in the management of type 1 diabetes mellitus has not been determined. In a multicenter clinical trial, we randomly assigned 322 adults and children who were already receiving intensive therapy for type 1 diabetes to a group with continuous glucose monitoring or to a control group performing home monitoring with a blood glucose meter. All the patients were stratified into three groups according to age and had a glycated hemoglobin level of 7.0 to 10.0%. The primary outcome was the change in the glycated hemoglobin level at 26 weeks. The changes in glycated hemoglobin levels in the two study groups varied markedly according to age group (P=0.003), with a significant difference among patients 25 years of age or older that favored the continuous-monitoring group (mean difference in change, -0.53%; 95% confidence interval [CI], -0.71 to -0.35; P<0.001). The between-group difference was not significant among those who were 15 to 24 years of age (mean difference, 0.08; 95% CI, -0.17 to 0.33; P=0.52) or among those who were 8 to 14 years of age (mean difference, -0.13; 95% CI, -0.38 to 0.11; P=0.29). Secondary glycated hemoglobin outcomes were better in the continuous-monitoring group than in the control group among the oldest and youngest patients but not among those who were 15 to 24 years of age. The use of continuous glucose monitoring averaged 6.0 or more days per week for 83% of patients 25 years of age or older, 30% of those 15 to 24 years of age, and 50% of those 8 to 14 years of age. The rate of severe hypoglycemia was low and did not differ between the two study groups; however, the trial was not powered to detect such a difference. Continuous glucose monitoring can be associated with improved glycemic control in adults with type 1 diabetes. Further work is needed to identify barriers to effectiveness of continuous monitoring in children and adolescents. ( number, NCT00406133.) 2008 Massachusetts Medical Society
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            Agreement between methods of measurement with multiple observations per individual.

            Limits of agreement provide a straightforward and intuitive approach to agreement between different methods for measuring the same quantity. When pairs of observations using the two methods are independent, i.e., on different subjects, the calculations are very simple and straightforward. Some authors collect repeated data, either as repeated pairs of measurements on the same subject, whose true value of the measured quantity may be changing, or more than one measurement by one or both methods of an unchanging underlying quantity. In this paper we describe methods for analysing such clustered observations, both when the underlying quantity is assumed to be changing and when it is not.
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              Evaluating clinical accuracy of systems for self-monitoring of blood glucose.

              Although the scientific literature contains numerous reports of the statistical accuracy of systems for self-monitoring of blood glucose (SMBG), most of these studies determine accuracy in ways that may not be clinically useful. We have developed an error grid analysis (EGA), which describes the clinical accuracy of SMBG systems over the entire range of blood glucose values, taking into account 1) the absolute value of the system-generated glucose value, 2) the absolute value of the reference blood glucose value, 3) the relative difference between these two values, and 4) the clinical significance of this difference. The EGA of accuracy of five different reflectance meters (Eyetone, Dextrometer, Glucometer I, Glucometer II, Memory Glucometer II), a visually interpretable glucose reagent strip (Glucostix), and filter-paper spot glucose determinations is presented. In addition, reanalyses of a laboratory comparison of three reflectance meters (Accucheck II, Glucometer II, Glucoscan 9000) and of two previously published studies comparing the accuracy of five different reflectance meters with EGA is described. EGA provides the practitioner and the researcher with a clinically meaningful method for evaluating the accuracy of blood glucose values generated with various monitoring systems and for analyzing the clinical implications of previously published data.

                Author and article information

                [ 1 ] Department of Clinical SciencesCollege of Veterinary Medicine and Biomedical Sciences Fort Collins CO
                Author notes
                [* ]Corresponding author: S.R. Byers, Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Fort Collins, CO 80523‐1620; e‐mail: stacey.byers@
                J Vet Intern Med
                J. Vet. Intern. Med
                Journal of Veterinary Internal Medicine
                John Wiley and Sons Inc. (Hoboken )
                25 June 2014
                Sep-Oct 2014
                : 28
                : 5 ( doiID: 10.1111/jvim.2014.28.issue-5 )
                : 1613-1620
                Copyright © 2014 by the American College of Veterinary Internal Medicine
                Pages: 8
                Funded by: Colorado State University Center for Companion Animal Studies
                Funded by: PVM Student Research Grant Program
                Standard Article
                Standard Articles
                Custom metadata
                September/October 2014
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.8.9 mode:remove_FC converted:06.05.2016

                Veterinary medicine

                regular insulin, nph insulin, insulin glargine, hyperglycemia


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