Editorial Type:
Physiology

Evaluation of long-term glucose homeostasis in lean and obese cats by use of continuous glucose monitoring

Margarethe Hoenig Department of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois, Urbana-Champaign, IL 61802.

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 Dr med vet, PhD
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Nicole Pach Department of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois, Urbana-Champaign, IL 61802.

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Karl Thomaseth Institute of Biomedical Engineering ISIB CNR – National Research Council of Italy, Corso Stati Uniti 4, 35127 Padova, Italy.

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Frerich DeVries Boehringer Ingelheim Vetmedica GmbH, Binger Strasse 173, 55216 Ingelheim am Rhein, Germany.

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Duncan C. Ferguson Department of Comparative Bioscience, College of Veterinary Medicine, University of Illinois, Urbana-Champaign, IL 61802.

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DOI:
https://doi.org/10.2460/ajvr.73.7.1100
Volume/Issue:
Volume 73: Issue 7
Online Publication Date:
01 Jul 2012
Restricted access
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Abstract

Objective—To evaluate intraday and interday variations in glucose concentrations in cats and to test the utility of a continuous glucose monitoring system (CGMS).

Animals—6 lean and 8 long-term (> 5 years) obese cats.

Procedures—Blood glucose concentrations were measured during the course of 156 hours by use of a laboratory hexokinase-based reference method and a handheld glucometer. Interstitial glucose concentrations were evaluated with a CGMS.

Results—Paired measures of glucose concentrations obtained with the CGMS typically were marginally higher than concentrations for the reference method and less biased than concentrations obtained with the glucometer. This was partially confirmed by the concordance correlation coefficients of the concentration for the CGMS or glucometer versus the concentration for the reference method, although the correlation coefficients were not significantly different. Mean ± SD area under the curve for the glucose concentration (AUCG) did not differ significantly between lean (14.0 ± 0.5 g/dL•h) and obese (15.2 + 0.5 g/dL•h) cats during the 156-hour period, but one of the obese cats had a much higher AUCG. Within-day glucose variability was small in both lean and obese cats.

Conclusions and Clinical Relevance—Glucose homeostasis was maintained, even in long-term obese cats, and intraday glucose fluctuations were small. One obese cat might have been classified as prediabetic on the basis of the AUCG, which was approximately 25% higher than that of the other obese and lean cats. The CGMS can be useful in the evaluation of long-term effects of drugs or diet on glucose homeostasis in cats.

Abstract

Objective—To evaluate intraday and interday variations in glucose concentrations in cats and to test the utility of a continuous glucose monitoring system (CGMS).

Animals—6 lean and 8 long-term (> 5 years) obese cats.

Procedures—Blood glucose concentrations were measured during the course of 156 hours by use of a laboratory hexokinase-based reference method and a handheld glucometer. Interstitial glucose concentrations were evaluated with a CGMS.

Results—Paired measures of glucose concentrations obtained with the CGMS typically were marginally higher than concentrations for the reference method and less biased than concentrations obtained with the glucometer. This was partially confirmed by the concordance correlation coefficients of the concentration for the CGMS or glucometer versus the concentration for the reference method, although the correlation coefficients were not significantly different. Mean ± SD area under the curve for the glucose concentration (AUCG) did not differ significantly between lean (14.0 ± 0.5 g/dL•h) and obese (15.2 + 0.5 g/dL•h) cats during the 156-hour period, but one of the obese cats had a much higher AUCG. Within-day glucose variability was small in both lean and obese cats.

Conclusions and Clinical Relevance—Glucose homeostasis was maintained, even in long-term obese cats, and intraday glucose fluctuations were small. One obese cat might have been classified as prediabetic on the basis of the AUCG, which was approximately 25% higher than that of the other obese and lean cats. The CGMS can be useful in the evaluation of long-term effects of drugs or diet on glucose homeostasis in cats.

Contributor Notes

Supported in part by a grant from Boehringer Ingelheim Vetmedica GmbH.

The authors thank Dr. David Schaeffer for statistical assistance and Alyssa Galligan for technical assistance.

Address correspondence to Dr. Hoenig (mhoenig@illinois.edu).
Cover American Journal of Veterinary Research
American Journal of Veterinary Research
Publication Date:
01 Jul 2012

  • 1. Appleton DJ, Rand JS, Sunvold GD. Insulin sensitivity decreases with obesity, and lean cats with low insulin sensitivity are at greatest risk of glucose intolerance with weight gain. J Feline Med Surg 2001; 3:211–228.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 2. Hoenig M, Alexander S, Holson J, et al. Influence of glucose dosage on interpretation of intravenous glucose tolerance tests in lean and obese cats. J Vet Intern Med 2002; 16:529–532.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 3. Kley S, Caffall Z, Tittle E, et al. Development of a feline proinsulin immunoradiometric assay and a feline proinsulin enzyme-linked immunosorbent assay (ELISA): a novel application to examine beta cell function in cats. Domest Anim Endocrinol 2008; 34:311–318.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 4. Nelson RW, Himsel CA, Feldman EC, et al. Glucose tolerance and insulin response in normal-weight and obese cats. Am J Vet Res 1990; 51:1357–1362.

    • Search Google Scholar
    • Export Citation

  • 5. Wilkins C, Long RC Jr, Waldron M, et al. Assessment of the influence of fatty acids on indices of insulin sensitivity and myocellular lipid content by use of magnetic resonance spectroscopy in cats. Am J Vet Res 2004; 65:1090–1099.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 6. Schiaffini R, Brufani C, Russo B, et al. Abnormal glucose tolerance in children with cystic fibrosis: the predictive role of continuous glucose monitoring system. Eur J Endocrinol 2010; 162:705–710.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 7. Zou CC, Liang L, Hong F, et al. Glucose metabolism disorder in obese children assessed by continuous glucose monitoring system. World J Pediatr 2008; 4:26–30.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 8. Ristic JM, Herrtage ME, Walti-Lauger SM, et al. Evaluation of a continuous glucose monitoring system in cats with diabetes mellitus. J Feline Med Surg 2005; 7:153–162.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 9. Wiedmeyer CE, Johnson PJ, Cohn LA, et al. Evaluation of a continuous glucose monitoring system for use in dogs, cats, and horses. J Am Vet Med Assoc 2003; 223:987–992.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 10. Moretti S, Tschuor F, Osto M, et al. Evaluation of a novel real-time continuous glucose-monitoring system for use in cats. J Vet Intern Med 2010; 24:120–126.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 11. USDA, National Agricultural Library. Animal welfare act quick reference guide. Available at: www.nal.usda.gov/awic/pubs/Legislat/awabrief.shtml. Accessed April 29, 2009.

    • Search Google Scholar
    • Export Citation

  • 12. The National Academies Press. Guide for the care and use of laboratory animals. Available at: http://www.nap.edu/readingroom/books/labrats/. Accessed April 29, 2009.

    • Search Google Scholar
    • Export Citation

  • 13. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1:307–310.

    • Search Google Scholar
    • Export Citation

  • 14. I-Kuei Lin L. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989; 45:255–268.

  • 15. I-Kuei Lin L. A Note on the concordance correlation coefficient. Biometrics 2000; 56:324–325.

  • 16. R Development Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2011.

    • Search Google Scholar
    • Export Citation

  • 17. Pinheiro JC, Bates DM. Mixed-effects models in S and S-PLUS. New York: Springer Publishing Co, 2000.

  • 18. Wisniewski N, Moussy F, Reichert WM. Characterization of implantable biosensor membrane biofouling. J Anal Chem 2000; 366:611–621.

  • 19. Massi-Benedetti M, Orsini-Federici M. Postprandial glucose: the nondiabetic state. Diabetes Obes Metab 2000; 2(suppl 1):S1–S6.

  • 20. Kley S, Hoenig M, Glushka J, et al. The impact of obesity, sex, and diet on hepatic glucose production in cats. Am J Physiol Regul Integr Comp Physiol 2009; 296:R936–R943.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 21. Hoenig M, Jordan ET, Glushka J, et al. Effect of macronutrients, age, and obesity on 6-and 24-h postprandial glucose metabolism in cats. Am J Physiol Regul Integr Comp Physiol 2011; 301:R1798–R1807.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 22. Hoenig M, Thomaseth K, Waldron M, et al. Insulin sensitivity, fat distribution, and adipocytokine response to different diets in lean and obese cats before and after weight loss. Am J Physiol Regul Integr Comp Physiol 2007; 292:R227–R234.

    • Crossref
    • Search Google Scholar
    • Export Citation

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