FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / PeerJ / Metabolic impact of weight variations in Icelandic horses.
PeerJ2021; 9; e10764; doi: 10.7717/peerj.10764

Metabolic impact of weight variations in Icelandic horses.

Abstract: Insulin dysregulation (ID) is an equine endocrine disorder, which is often accompanied by obesity and various metabolic perturbations. The relationship between weight variations and fluctuations of the insulin response to oral glucose tests (OGT) as well as the metabolic impact of ID have been described previously. The present study seeks to characterize the concomitant metabolic impact of variations in the insulin response and bodyweight during repeated OGTs using a metabolomics approach. Methods: Nineteen Icelandic horses were subjected to five OGTs over one year and their bodyweight, insulin and metabolic response were monitored. Analysis of metabolite concentrations depending on time (during the OGT), relative bodyweight (rWeight; defined as the bodyweight at one OGT divided by the mean bodyweight across all OGTs) and relative insulin response (rAUCins; defined accordingly from the area under the insulin curve during OGT) was performed using linear models. Additionally, the pathways significantly associated with time, rWeight and rAUCins were identified by rotation set testing. Results: The results suggested that weight gain and worsening of ID activate distinct metabolic pathways. The metabolic profile associated with weight gain indicated an increased activation of arginase, while the pathways associated with time and rAUCins were consistent with the expected effect of glucose and insulin, respectively. Overall, more metabolites were significantly associated with rWeight than with rAUCins.
©2021 Delarocque et al.
Get Full Text
Publication Date: 2021-01-28 PubMed ID: 33575132PubMed Central: PMC7847705DOI: 10.7717/peerj.10764Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This study examines the relationship between weight changes and metabolic impacts in Icelandic horses, particularly those suffering from insulin dysregulation (ID), an endocrine disorder commonly linked with obesity. Through oral glucose tests (OGT), the research characterizes how variations in weight and insulin response influence metabolic patterns.

Study Design

  • The research was conducted on nineteen Icelandic horses over a period of one year.
  • Known for their susceptibility to metabolism related diseases, these horses were subjected to five Oral Glucose Tests (OGTs), which are used to examine how the body responds to sugar.
  • The researchers tracked their bodyweight and monitored their insulin and metabolic responses.
  • The meticulous statistical analysis relied on linear models to examine the relationship between metabolite concentrations, time during the OGT, relative body weight, and relative insulin response.

Key Findings

  • The results of the study suggest that both weight gain and worsening ID stimulate different metabolic pathways.
  • The metabolic profiles detected in weight gain suggest increased activation of arginase, an enzyme that plays a key role in the urea cycle for detoxifying and removing ammonia from the body.
  • The pathways related to time and relative insulin response were found to align with the expected effects of glucose and insulin.
  • Importantly, the study found that more metabolites were significantly associated with adjustments in body weight than with changes in insulin response.

Conclusions

  • The study concludes that variations in weight and insulin response lead to distinct metabolic impacts in Icelandic horses.
  • This novel insight is crucial in understanding the metabolic consequences associated with variations in body weight and insulin response, providing a foundation for expanded research on ID and the metabolic welfare of horses.

Cite This Article

APA
Delarocque J, Frers F, Huber K, Jung K, Feige K, Warnken T. (2021). Metabolic impact of weight variations in Icelandic horses. PeerJ, 9, e10764. https://doi.org/10.7717/peerj.10764

Publication

PeerJ
ISSN: 2167-8359
NlmUniqueID: 101603425
Country: United States
Language: English
Volume: 9
Pages: e10764

Researcher Affiliations

Delarocque, Julien
  • Clinic for Horses, University of Veterinary Medicine Hannover, Foundation, Hannover, Germany.
Frers, Florian
  • Clinic for Horses, University of Veterinary Medicine Hannover, Foundation, Hannover, Germany.
Huber, Korinna
  • Institute of Animal Science, Faculty of Agricultural Sciences, Universität Hohenheim, Stuttgart, Germany.
Jung, Klaus
  • Institute for Animal Breeding and Genetics, Tierärztliche Hochschule Hannover, Hannover, Germany.
Feige, Karsten
  • Clinic for Horses, University of Veterinary Medicine Hannover, Foundation, Hannover, Germany.
Warnken, Tobias
  • Clinic for Horses, University of Veterinary Medicine Hannover, Foundation, Hannover, Germany.

Conflict of Interest Statement

The authors declare there are no competing interests.

References

This article includes 56 references
  1. Carter RA, McCutcheon LJ, George LA, Smith TL, Frank N, Geor RJ. Effects of diet-induced weight gain on insulin sensitivity and plasma hormone and lipid concentrations in horses.. American Journal of Veterinary Research 2009;70:1250–1258.
    doi: 10.2460/ajvr.70.10.1250pubmed: 19795940google scholar: lookup
  2. Allalou A, Nalla A, Prentice KJ, Liu Y, Zhang M, Dai FF, Ning X, Osborne LR, Cox BJ, Gunderson EP, Wheeler MB. A predictive metabolic signature for the transition from gestational diabetes mellitus to type 2 diabetes.. Diabetes 2016;65:2529–2539.
    doi: 10.2337/db15-1720pmc: PMC5001181pubmed: 27338739google scholar: lookup
  3. Bamford NJ, Potter SJ, Baskerville CL, Harris PA, Bailey SR. Effect of increased adiposity on insulin sensitivity and adipokine concentrations in different equine breeds adapted to cereal-rich or fat-rich meals.. The Veterinary Journal 2016;214:14–20.
    doi: 10.1016/j.tvjl.2016.02.002pubmed: 27387720google scholar: lookup
  4. Bamford NJ, Potter SJ, Baskerville CL, Harris PA, Bailey SR. Influence of dietary restriction and low-intensity exercise on weight loss and insulin sensitivity in obese equids.. Journal of Veterinary Internal Medicine 2019;33:280–286.
    doi: 10.1111/jvim.15374pmc: PMC6335535pubmed: 30520164google scholar: lookup
  5. Banse HE, Frank N, Kwong GPS, McFarlane D. Relationship of oxidative stress in skeletal muscle with obesity and obesity-associated hyperinsulinemia in horses.. Canadian Journal of Veterinary Research = Revue Canadienne de Recherche Veterinaire 2015;79:329–338.
    doi: 10.1161/HYPERTENSIONAHA.110.164350pmc: PMC4581679pubmed: 26424915google scholar: lookup
  6. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.. Journal of the Royal Statistical Society. Series B (Methodological) 1995;57:289–300.
    doi: 10.2307/2346101google scholar: lookup
  7. Bertin FR, De Laat MA. The diagnosis of equine insulin dysregulation.. Equine Veterinary Journal 2017;49:570–576.
    doi: 10.1111/evj.12703pubmed: 28543410google scholar: lookup
  8. Blaue D, Schedlbauer C, Starzonek J, Gittel C, Brehm W, Einspanier A, Vervuert I. Effects of body weight gain on insulin and lipid metabolism in equines.. Domestic Animal Endocrinology 2019;68:111–118.
    doi: 10.1016/j.domaniend.2019.01.003pubmed: 31035090google scholar: lookup
  9. Bode-Böger SM. Effect of L-arginine supplementation on NO production in man.. European Journal of Clinical Pharmacology 2006;62:91–99.
    doi: 10.1007/s00228-005-0004-zpubmed: 0google scholar: lookup
  10. Carter RA, McCutcheon LJ, Valle E, Meilahn EN, Geor RJ. Effects of exercise training on adiposity, insulin sensitivity, and plasma hormone and lipid concentrations in overweight or obese, insulin-resistant horses.. American Journal of Veterinary Research 2010;71:314–321.
    doi: 10.2460/ajvr.71.3.314pubmed: 20187833google scholar: lookup
  11. Coleman MC, Whitfield-Cargile CM, Madrigal RG, Cohen ND. Comparison of the microbiome, metabolome, and lipidome of obese and non-obese horses.. PLOS ONE 2019;14:1–17.
    doi: 10.1371/journal.pone.0215918pmc: PMC6478336pubmed: 31013335google scholar: lookup
  12. De Laat MA, McGree JM, Sillence MN. Equine hyperinsulinemia: investigation of the enteroinsular axis during insulin dysregulation.. American Journal of Physiology - Endocrinology And Metabolism 2015;310:E61–E72.
    doi: 10.1152/ajpendo.00362.2015pubmed: 26530154google scholar: lookup
  13. Delarocque J, Frers F, Huber K, Feige K, Warnken T. Weight loss is linearly associated with a reduction of the insulin response to an oral glucose test in Icelandic horses.. BMC Veterinary Research 2020a;16:151.
    doi: 10.1186/s12917-020-02356-wpmc: PMC7245939pubmed: 32448298google scholar: lookup
  14. Delarocque J, Frers F, Jung K, Warnken T, Feige K. Metabolic changes induced by oral glucose tests in horses and their diagnostic use.. Journal of Veterinary Internal Medicine 2020b. Epub ahead of print 2020 5 December.
    doi: 10.1111/jvim.15992pmc: PMC7848347pubmed: 33277752google scholar: lookup
  15. Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-Mcintyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN, Nicholls AW, Wilson ID, Kell DB, Goodacre R. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.. Nature Protocols 2011;6:1060–1083.
    doi: 10.1038/nprot.2011.335pubmed: 21720319google scholar: lookup
  16. Durham AE, Frank N, McGowan CM, Menzies-Gow NJ, Roelfsema E, Vervuert I, Feige K, Fey K. ECEIM consensus statement on equine metabolic syndrome.. Journal of Veterinary Internal Medicine 2019;33:335–349.
    doi: 10.1111/jvim.15423pmc: PMC6430910pubmed: 30724412google scholar: lookup
  17. Felig P. Amino acid metabolism in man.. Annual Review of Biochemistry 1975;44:933–955.
    doi: 10.1146/annurev.bi.44.070175.004441pubmed: 1094924google scholar: lookup
  18. Floyd JC, Fajans SS, Conn JW, Knopf RF, Rull J. Stimulation of insulin secretion by amino acids.. Journal of Clinical Investigation 1966;45:1487–1502.
    doi: 10.1172/JCI105456pmc: PMC292828pubmed: 5919350google scholar: lookup
  19. Frank N, Tadros EM. Insulin dysregulation.. Equine Veterinary Journal 2014;46:103–112.
    doi: 10.1111/evj.12169pubmed: 24033478google scholar: lookup
  20. Geor RJ, Harris P. Dietary management of obesity and insulin resistance: countering risk for laminitis.. Veterinary Clinics of North America - Equine Practice 2009;25:51–65.
    doi: 10.1016/j.cveq.2009.02.001pubmed: 19303550google scholar: lookup
  21. Goeman JJ, Bühlmann P. Analyzing gene expression data in terms of gene sets: methodological issues.. Bioinformatics 2007;23:980–987.
    doi: 10.1093/bioinformatics/btm051pubmed: 17303618google scholar: lookup
  22. Hamberg O, Vilstrup H. Effects of insulin and glucose on urea synthesis in normal man, independent of pancreatic hormone secretion.. Journal of Hepatology 1994;21:381–387.
    doi: 10.1016/S0168-8278(05)80317-4pubmed: 7836708google scholar: lookup
  23. Hanamatsu H, Ohnishi S, Sakai S, Yuyama K, Mitsutake S, Takeda H, Hashino S, Igarashi Y. Altered levels of serum sphingomyelin and ceramide containing distinct acyl chains in young obese adults.. Nutrition and Diabetes 2014;4:e141–7.
    doi: 10.1038/nutd.2014.38pmc: PMC4217001pubmed: 25329603google scholar: lookup
  24. Hastie T, Tibshirani R, Sherlock G, Eisen M, Brown P, Botstein D. Imputing missing data for gene expression arrays. 1999.
  25. Holland WL, Knotts TA, Chavez JA, Wang L-P, Hoehn KL, Summers SA. Lipid mediators of insulin resistance.. Nutrition Reviews 2008;65:S39–S46.
    doi: 10.1111/j.1753-4887.2007.tb00327.xpubmed: 17605313google scholar: lookup
  26. Bolstad BM, Irizarry R, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.. Bioinformatics 2003;19:185–193.
    doi: 10.1093/bioinformatics/19.2.185pubmed: 12538238google scholar: lookup
  27. Isherwood CM, Van der Veen DR, Johnston JD, Skene DJ. Twenty-four-hour rhythmicity of circulating metabolites: effect of body mass and type 2 diabetes.. FASEB Journal 2017;31:5557–5567.
    doi: 10.1096/fj.201700323Rpmc: PMC5690388pubmed: 28821636google scholar: lookup
  28. Jacob SI, Murray KJ, Rendahl AK, Geor RJ, Schultz NE, McCue ME. Metabolic perturbations in Welsh Ponies with insulin dysregulation, obesity, and laminitis.. Journal of Veterinary Internal Medicine 2018;32:1215–1233.
    doi: 10.1111/jvim.15095pmc: PMC5980341pubmed: 29572947google scholar: lookup
  29. Jewison T, Su Y, Disfany FM, Liang Y, Knox C, MacIejewski A, Poelzer J, Huynh J, Zhou Y, Arndt D, Djoumbou Y, Liu Y, Deng L, Guo AC, Han B, Pon A, Wilson M, Rafatnia S, Liu P, Wishart DS. SMPDB 2.0: big improvements to the small molecule pathway database.. Nucleic Acids Research 2014;42:478–484.
    doi: 10.1093/nar/gkt1067pmc: PMC3965088pubmed: 24203708google scholar: lookup
  30. Kashyap SR, Lara A, Zhang R, Park YM, DeFronzo RA. Insulin reduces plasma arginase activity in type 2 diabetic patients.. Diabetes Care 2008;31:134–139.
    doi: 10.2337/dc07-1198pmc: PMC3101496pubmed: 17928367google scholar: lookup
  31. Kell DB, Oliver SG. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era.. BioEssays 2004;26:99–105.
    doi: 10.1002/bies.10385pubmed: 14696046google scholar: lookup
  32. Kenéz Warnken T, Feige K, Huber K. Lower plasma trans-4-hydroxyproline and methionine sulfoxide levels are associated with insulin dysregulation in horses.. BMC Veterinary Research 2018;14:146.
    doi: 10.1186/s12917-018-1479-zpmc: PMC5930486pubmed: 29716602google scholar: lookup
  33. Kimball SR, Vary TC, Jefferson LS. Regulation of protein synthesis by insulin.. Annual Review of Physiology 1994;56:321–348.
    doi: 10.1146/annurev.ph.56.030194.001541pubmed: 8010743google scholar: lookup
  34. Kondoh T, Torii K. MSG intake suppresses weight gain, fat deposition, and plasma leptin levels in male Sprague-Dawley rats.. Physiology and Behavior 2008;95:135–144.
    doi: 10.1016/j.physbeh.2008.05.010pubmed: 18559279google scholar: lookup
  35. Kövamees O, Shemyakin A, Pernow J. Amino acid metabolism reflecting arginase activity is increased in patients with type 2 diabetes and associated with endothelial dysfunction.. Diabetes and Vascular Disease Research 2016;13:354–360.
    doi: 10.1177/1479164116643916pubmed: 27190086google scholar: lookup
  36. Lent-Schochet D, McLaughlin M, Ramakrishnan N, Jialal I. Exploratory metabolomics of metabolic syndrome: a status report.. World Journal of Diabetes 2019;10:23–36.
    doi: 10.4239/wjd.v10.i1.23pmc: PMC6347655pubmed: 30697368google scholar: lookup
  37. McKnight JR, Satterfield MC, Jobgen WS, Smith SB, Spencer TE, Meininger CJ, McNeal CJ, Wu G. Beneficial effects of L-arginine on reducing obesity: potential mechanisms and important implications for human health.. Amino Acids 2010;39:349–357.
    doi: 10.1007/s00726-010-0598-zpubmed: 20437186google scholar: lookup
  38. Mong MC, Chao CY, Yin MC. Histidine and carnosine alleviated hepatic steatosis in mice consumed high saturated fat diet.. European Journal of Pharmacology 2011;653:82–88.
    doi: 10.1016/j.ejphar.2010.12.001pubmed: 21167151google scholar: lookup
  39. Moore JL, Siciliano PD, Pratt-Phillips SE. Effects of diet versus exercise on morphometric measurements, blood hormone concentrations, and oral sugar test response in obese horses.. Journal of Equine Veterinary Science 2019;78:38–45.
    doi: 10.1016/j.jevs.2019.03.214pubmed: 31203982google scholar: lookup
  40. Morgan RA, Keen JA, McGowan CM. Treatment of equine metabolic syndrome: a clinical case series.. Equine Veterinary Journal 2016;48:422–426.
    doi: 10.1111/evj.12445pubmed: 25808563google scholar: lookup
  41. Morgan RA, Keen JA, Walker BR, Hadoke PWF. Vascular dysfunction in horses with endocrinopathic laminitis.. PLOS ONE 2016;11:1–14.
    doi: 10.1371/journal.pone.0163815pmc: PMC5042533pubmed: 27684374google scholar: lookup
  42. Morgan RA, McGowan TW, Mcgowan CM. Prevalence and risk factors for hyperinsulinaemia in ponies in Queensland, Australia.. Australian Veterinary Journal 2014;92:101–106.
    doi: 10.1111/avj.12159pubmed: 24673135google scholar: lookup
  43. Morris SM. Arginine metabolism revisited.. Journal of Nutrition 2016;146:2579S–2586S.
    doi: 10.3945/jn.115.226621pubmed: 27934648google scholar: lookup
  44. Newsholme P, Bender K, Kiely A, Brennan L. Amino acid metabolism, insulin secretion and diabetes.. Biochemical Society Transactions 2007;35:1180–1186.
    doi: 10.1042/BST0351180pubmed: 17956307google scholar: lookup
  45. Pallares-Méndez R, Aguilar-Salinas CA, Cruz-Bautista I, Del Bosque-Plata L. Metabolomics in diabetes, a review.. Annals of Medicine 2016;48:89–102.
    doi: 10.3109/07853890.2015.1137630pubmed: 26883715google scholar: lookup
  46. Pleasant RSS, Suagee JKK, Thatcher CDD, Elvinger F, Geor RJJ. Adiposity, plasma insulin, leptin, lipids, and oxidative stress in mature light breed horses.. Journal of Veterinary Internal Medicine 2013;27:576–582.
    doi: 10.1111/jvim.12056pubmed: 23517373google scholar: lookup
  47. R Core Team . R Foundation for Statistical Computing; Vienna, Austria: 2020.
  48. Reynolds A, Keen JA, Fordham T, Morgan RA. Adipose tissue dysfunction in obese horses with equine metabolic syndrome.. Equine Veterinary Journal 2019;51:760–766.
    doi: 10.1111/evj.13097pmc: PMC6850304pubmed: 30866087google scholar: lookup
  49. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies.. Nucleic Acids Research 2015;43:e47–e47.
    doi: 10.1093/nar/gkv007pmc: PMC4402510pubmed: 25605792google scholar: lookup
  50. Sourij H, Meinitzer A, Pilz S, Grammer TB, Winkelmann BR, Boehm BO, März W. Arginine bioavailability ratios are associated with cardiovascular mortality in patients referred to coronary angiography.. Atherosclerosis 2011;218:220–225.
    doi: 10.1016/j.atherosclerosis.2011.04.041pubmed: 21632053google scholar: lookup
  51. Treiber K, Carter R, Gay L, Williams C, Geor R. Inflammatory and redox status of ponies with a history of pasture-associated laminitis.. Veterinary Immunology and Immunopathology 2009;129:216–220.
    doi: 10.1016/j.vetimm.2008.11.004pubmed: 19108899google scholar: lookup
  52. Van Weyenberg S, Hesta M, Buyse J, Janssens GPJ. The effect of weight loss by energy restriction on metabolic profile and glucose tolerance in ponies.. Journal of Animal Physiology and Animal Nutrition 2008;92:538–545.
    doi: 10.1111/j.1439-0396.2007.00744.xpubmed: 19012597google scholar: lookup
  53. Wallace M, Morris C, O’Grada CM, Ryan M, Dillon ET, Coleman E, Gibney ER, Gibney MJ, Roche HM, Brennan L. Relationship between the lipidome, inflammatory markers and insulin resistance.. Molecular BioSystems 2014;10:1586–1595.
    doi: 10.1039/C3MB70529Cpubmed: 24714806google scholar: lookup
  54. Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vázquez-Fresno R, Sajed T, Johnson D, Li C, Karu N, Sayeeda Z, Lo E, Assempour N, Berjanskii M, Singhal S, Arndt D, Liang Y, Badran H, Grant J, Serra-Cayuela A, Liu Y, Mandal R, Neveu V, Pon A, Knox C, Wilson M, Manach C, Scalbert A. HMDB 4.0: The human metabolome database for 2018.. Nucleic Acids Research 2018;46:D608–D617.
    doi: 10.1093/nar/gkx1089pmc: PMC5753273pubmed: 29140435google scholar: lookup
  55. Wu D, Lim E, Vaillant F, Asselin-Labat ML, Visvader JE, Smyth GK. ROAST: rotation gene set tests for complex microarray experiments.. Bioinformatics 2010;26:2176–2182.
    doi: 10.1093/bioinformatics/btq401pmc: PMC2922896pubmed: 20610611google scholar: lookup
  56. Zhang G, Zwierzchowski G, Mandal R, Wishart DS, Ametaj BN. Serum metabolomics identifies metabolite panels that differentiate lame dairy cows from healthy ones.. Metabolomics 2020;16:1–22.
    doi: 10.1007/s11306-020-01693-zpubmed: 32535675google scholar: lookup

Citations

This article has been cited 2 times.
  1. Warnken T, Schaub C, Delarocque J, Frers F, Feige K, Sonntag J, Reiche DB. Palatability, glycemic, and insulinemic responses to various carbohydrate formulations: Alternatives for the diagnosis of insulin dysregulation in horses?. J Vet Intern Med 2023 Jan;37(1):282-291.
    doi: 10.1111/jvim.16614pubmed: 36625459google scholar: lookup
  2. Leung YH, Kenéz Á, Grob AJ, Feige K, Warnken T. Associations of plasma sphingolipid profiles with insulin response during oral glucose testing in Icelandic horses. J Vet Intern Med 2021 Jul;35(4):2009-2018.
    doi: 10.1111/jvim.16200pubmed: 34105193google scholar: lookup
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.