Use of proxies and reference quintiles obtained from minimal model analysis for determination of insulin sensitivity and pancreatic beta-cell responsiveness in horses.
Abstract: To develop proxies calculated from basal plasma glucose and insulin concentrations that predict insulin sensitivity (SI; L.min(-1) x mU(-1)) and beta-cell responsiveness (ie, acute insulin response to glucose [AIRg]; mU/L x min(-1)) and to determine reference quintiles for these and minimal model variables. Methods: 1 laminitic pony and 46 healthy horses. Methods: Basal plasma glucose (mg/dL) and insulin (mU/L) concentrations were determined from blood samples obtained between 8:00 AM and 9:00 AM. Minimal model results for 46 horses were compared by equivalence testing with proxies for screening SI and pancreatic beta-cell responsiveness in humans and with 2 new proxies for screening in horses (ie, reciprocal of the square root of insulin [RISQI] and modified insulin-to-glucose ratio [MIRG]). Results: Best predictors of SI and AIRg were RISQI (r = 0.77) and MIRG (r = 0.75) as follows: SI = 7.93(RISQI) - 1.03 and AIRg = 70.1(MIRG) - 13.8, where RISQI equals plasma insulin concentration(-0.5) and MIRG equals [800 - 0.30(plasma insulin concentration 50)(2)]/(plasma glucose concentration - 30). Total predictive powers were 78% and 80% for RISQI and MIRG, respectively. Reference ranges and quintiles for a population of healthy horses were calculated nonparametrically. Conclusions: Proxies for screening SI and pancreatic beta-cell responsiveness in horses from this study compared favorably with proxies used effectively for humans. Combined use of RISQI and MIRG will enable differentiation between compensated and uncompensated insulin resistance. The sample size of our study allowed for determination of sound reference range values and quintiles for healthy horses.
Publication Date: 2005-12-29 PubMed ID: 16379656DOI: 10.2460/ajvr.2005.66.2114Google Scholar: Lookup
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- Comparative Study
- Journal Article
- Research Support
- Non-U.S. Gov't
Summary
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The research paper investigates and identifies the use of proxies to predict insulin sensitivity and beta-cell responsiveness in horses. Using plasma glucose and insulin concentrations from blood samples of healthy horses and a laminitic pony, the researchers established possible predictors and reference ranges.
Objective and Approach
- The focus here is on developing proxies based on basal or baseline plasma glucose and insulin concentrations. These proxies were designed to predict insulin sensitivity (SI) and beta-cell responsiveness (measured as the acute insulin response to glucose [AIRg]).
- The researchers also aimed to determine reference quintiles for minimal model variables. Proxies and quintiles of these kinds can serve as useful tools in the study and diagnosis of insulin-associated conditions in equine medicine.
- In addition to this, the study’s approach involved comparing minimal model results for 46 horses to human-equivalent proxies for SI and beta-cell responsiveness.
- Two new proxies were developed specifically for horses – Reciprocal of the Square Root of Insulin (RISQI) and Modified Insulin-to-Glucose Ratio (MIRG).
Results
- The study found that RISQI and MIRG were the best predictors of SI and AIRg. The relationships were mathematically expressed, associating the reciprocal of the square root of insulin concentration with SI, and a complex function of insulin and glucose concentrations with AIRg.
- In terms of predictive power, RISQI and MIRG scored 78% and 80%, respectively. This indicates a high level of predictive ability and suggests these proxies could be used effectively to assess the insulin sensitivity and beta-cell responsiveness in horses.
- Also, the research produced reference ranges and quintiles for a healthy horse population, calculated using nonparametric methods.
Conclusions
- In conclusion, the study suggests that the newly-developed proxies compared favorably with those used for humans, indicating a potential for broader application in equine health studies and diagnostics.
- The simultaneous use of RISQI and MIRG could possibly enable differentiation between compensated and uncompensated insulin resistance in horses.
- Finally, the sample size used in the study is asserted to have been adequate for determining robust reference values and quintiles for healthy horses, offering a promising model for future research in this area.
Cite This Article
APA
Treiber KH, Kronfeld DS, Hess TM, Boston RC, Harris PA.
(2005).
Use of proxies and reference quintiles obtained from minimal model analysis for determination of insulin sensitivity and pancreatic beta-cell responsiveness in horses.
Am J Vet Res, 66(12), 2114-2121.
https://doi.org/10.2460/ajvr.2005.66.2114 Publication
Researcher Affiliations
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0306, USA.
MeSH Terms
- Animals
- Biomarkers / blood
- Blood Glucose
- Horses / blood
- Insulin / blood
- Insulin Resistance / physiology
- Insulin-Secreting Cells / physiology
- Models, Biological
- Reference Standards
Citations
This article has been cited 13 times.- Akinniyi OO, Sackey AKB, Ochube GE, Mshelia PW, Musa FA, Elijah MO, Jolayemi KO. Occurrence of equine metabolic syndrome, clinical manifestations, and associated risk factors in Nigeria.. J Equine Sci 2023 Jun;34(2):29-35.
- Weinert-Nelson JR, Biddle AS, Sampath H, Williams CA. Fecal Microbiota, Forage Nutrients, and Metabolic Responses of Horses Grazing Warm- and Cool-Season Grass Pastures.. Animals (Basel) 2023 Feb 22;13(5).
- Ahmadi F, Mirshahi A, Mohri M, Sardari K, Sharifi K. Osteochondrosis dissecans (OCD) in horses: hormonal and biochemical study (19 cases).. Vet Res Forum 2021;12(3):325-331.
- Lindåse S, Nostell K, Bergsten P, Forslund A, Bröjer J. Evaluation of fasting plasma insulin and proxy measurements to assess insulin sensitivity in horses.. BMC Vet Res 2021 Feb 15;17(1):78.
- Carslake HB, Pinchbeck GL, McGowan CM. Equine metabolic syndrome in UK native ponies and cobs is highly prevalent with modifiable risk factors.. Equine Vet J 2021 Sep;53(5):923-934.
- Kellon EM, Gustafson KM. Possible dysmetabolic hyperferritinemia in hyperinsulinemic horses.. Open Vet J 2020 Jan;9(4):287-293.
- Horn R, Bertin FR. Evaluation of combined testing to simultaneously diagnose pituitary pars intermedia dysfunction and insulin dysregulation in horses.. J Vet Intern Med 2019 Sep;33(5):2249-2256.
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- Warnken T, Huber K, Feige K. Comparison of three different methods for the quantification of equine insulin.. BMC Vet Res 2016 Sep 9;12(1):196.
- Dunbar LK, Mielnicki KA, Dembek KA, Toribio RE, Burns TA. Evaluation of Four Diagnostic Tests for Insulin Dysregulation in Adult Light-Breed Horses.. J Vet Intern Med 2016 May;30(3):885-91.
- Selim S, Elo K, Jaakkola S, Karikoski N, Boston R, Reilas T, Särkijärvi S, Saastamoinen M, Kokkonen T. Relationships among Body Condition, Insulin Resistance and Subcutaneous Adipose Tissue Gene Expression during the Grazing Season in Mares.. PLoS One 2015;10(5):e0125968.
- George LA, Uthlaut AB, Long NM, Zhang L, Ma Y, Smith DT, Nathanielsz PW, Ford SP. Different levels of overnutrition and weight gain during pregnancy have differential effects on fetal growth and organ development.. Reprod Biol Endocrinol 2010 Jun 24;8:75.
- Johnson PJ, Wiedmeyer CE, Messer NT, Ganjam VK. Medical implications of obesity in horses--lessons for human obesity.. J Diabetes Sci Technol 2009 Jan;3(1):163-74.
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