FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / J Vet Intern Med / Metabolic perturbations in Welsh Ponies with insulin dysregu...
Journal of veterinary internal medicine2018; 32(3); 1215-1233; doi: 10.1111/jvim.15095

Metabolic perturbations in Welsh Ponies with insulin dysregulation, obesity, and laminitis.

Abstract: Metabolomics, the study of small-molecule metabolites, has increased understanding of human metabolic diseases, but has not been used to study equine metabolic syndrome (EMS). Objective: (1) To examine the serum metabolome of Welsh Ponies with and without insulin dysregulation before and during an oral sugar test (OST). (2) To identify differences in metabolites in ponies with insulin dysregulation, obesity, or history of laminitis. Methods: Twenty Welsh Ponies (mean ± SD; 13.8 ± 9.0 years) classified as non-insulin dysregulated [CON] (n = 10, insulin  60 mU/L) at 75 minutes after administration of Karo syrup, obese (n = 6) or nonobese (n = 14), and history of laminitis (n = 9) or no history of laminitis (n = 11). Methods: Case-control study. Metabolomic analysis was performed on serum obtained at 0 minutes (baseline) and 75 minutes during the OST. Data were analyzed with multivariable mixed linear models with significance set at P ≤ .05. Results: Metabolomic analysis of 646 metabolites (506 known) detected significant metabolite differences. At baseline, 55 metabolites (insulin response), 91 metabolites (obesity status), and 136 metabolites (laminitis history) were different. At 75 minutes, 51 metabolites (insulin response), 102 metabolites (obesity status), and 124 metabolites (laminitis history) were different. Conclusions: Use of metabolomics could have diagnostic utility for early detection of EMS and provide new knowledge regarding the pathophysiology of metabolic perturbations associated with this condition that might lead to improved clinical management.
Copyright © 2018 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.
Get Full Text
Publication Date: 2018-03-23 PubMed ID: 29572947PubMed Central: PMC5980341DOI: 10.1111/jvim.15095Google 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.

The research studies the presence and variations of small-molecule metabolites in Welsh Ponies to understand insulin dysregulation, obesity, and laminitis. It aims to identify any distinct metabolic markers that can help diagnose equine metabolic syndrome (EMS) earlier and improve its clinical management.

Objective

The key objective of the study was twofold:

  • The researchers wanted to explore the serum metabolome of Welsh Ponies with and without insulin dysregulation, both before and while under an oral sugar test (OST).
  • The aim was also to identify differences in metabolites in Welsh Ponies with insulin dysregulation, obesity, or has had a history of laminitis.

Methods

The study was performed as a case-control experiment with the following key aspects:

  • The research sample included twenty Welsh Ponies with a mean of 13.8±9.0 years. The ponies were categorised based on whether they were non-insulin dysregulated or insulin-dysregulated, obese or non-obese, and if they had a history of laminitis or not.
  • The serum was subjected to metabolomic analysis at 0 minutes (baseline) and 75 minutes during the OST.
  • Metabolic data was then processed using multivariable mixed linear models with significance set at P ≤ .05.

Results

The results from the metabolomic analysis of 646 metabolites (of which, 506 were known) showed significant differences:

  • At the baseline, distinct differences in 55 metabolites (relating to insulin response), 91 metabolites (relating to obesity status), and 136 metabolites (relating to laminitis history) were observed.
  • At 75 minutes into the OST, the study noticed variations in 51 metabolites (in relation to insulin response), 102 metabolites (with respect to obesity status), and 124 metabolites (in regards to laminitis history).

Conclusions

This study concludes that the use of metabolomics could:

  • Potentially be a useful diagnostic tool for early detection of equine metabolic syndrome (EMS).
  • Provide new knowledge about the metabolic perturbations linked to EMS, thereby guiding improved clinical management of this condition.

Cite This Article

APA
Jacob SI, Murray KJ, Rendahl AK, Geor RJ, Schultz NE, McCue ME. (2018). Metabolic perturbations in Welsh Ponies with insulin dysregulation, obesity, and laminitis. J Vet Intern Med, 32(3), 1215-1233. https://doi.org/10.1111/jvim.15095

Publication

Journal of veterinary internal medicine
ISSN: 1939-1676
NlmUniqueID: 8708660
Country: United States
Language: English
Volume: 32
Issue: 3
Pages: 1215-1233

Researcher Affiliations

Jacob, Sarah I
  • Michigan State University College of Veterinary Medicine, Large Animal Clinical Sciences, East Lansing, Michigan.
Murray, Kevin J
  • University of Minnesota College of Veterinary Medicine, Veterinary Population Medicine, St. Paul, Minnesota.
Rendahl, Aaron K
  • University of Minnesota College of Veterinary Medicine, Veterinary Population Medicine, St. Paul, Minnesota.
Geor, Raymond J
  • Massey University College of Sciences, Palmerston North, New Zealand.
Schultz, Nichol E
  • University of Minnesota College of Veterinary Medicine, Veterinary Population Medicine, St. Paul, Minnesota.
McCue, Molly E
  • University of Minnesota College of Veterinary Medicine, Veterinary Population Medicine, St. Paul, Minnesota.

MeSH Terms

  • Animals
  • Blood Glucose / analysis
  • Female
  • Foot Diseases / metabolism
  • Foot Diseases / veterinary
  • Glucose Tolerance Test / veterinary
  • Hoof and Claw
  • Horse Diseases / metabolism
  • Horses
  • Insulin / blood
  • Insulin / metabolism
  • Male
  • Metabolic Syndrome / metabolism
  • Metabolic Syndrome / veterinary
  • Metabolomics
  • Obesity / metabolism
  • Obesity / veterinary

Grant Funding

  • T32 AR007612 / NIAMS NIH HHS

References

This article includes 45 references
  1. Blanco-Gómez A, Castillo-Lluva S, Del Mar Sáez-Freire M, Hontecillas-Prieto L, Mao JH, Castellanos-Martín A, Pérez-Losada J. Missing heritability of complex diseases: Enlightenment by genetic variants from intermediate phenotypes.. Bioessays 2016 Jul;38(7):664-73.
    pmc: PMC5064854pubmed: 27241833doi: 10.1002/bies.201600084google scholar: lookup
  2. Tóth F, Frank N, Martin-Jiménez T, Elliott SB, Geor RJ, Boston RC. Measurement of C-peptide concentrations and responses to somatostatin, glucose infusion, and insulin resistance in horses.. Equine Vet J 2010 Mar;42(2):149-55.
    pubmed: 20156251doi: 10.2746/042516409x478497google scholar: lookup
  3. de Laat MA, Sillence MN, McGowan CM, Pollitt CC. Continuous intravenous infusion of glucose induces endogenous hyperinsulinaemia and lamellar histopathology in Standardbred horses.. Vet J 2012 Mar;191(3):317-22.
    pubmed: 21873088doi: 10.1016/j.tvjl.2011.07.007google scholar: lookup
  4. Asplin KE, Sillence MN, Pollitt CC, McGowan CM. Induction of laminitis by prolonged hyperinsulinaemia in clinically normal ponies.. Vet J 2007 Nov;174(3):530-5.
    pubmed: 17719811doi: 10.1016/j.tvjl.2007.07.003google scholar: lookup
  5. Waller AP, Kohler K, Burns TA, Mudge MC, Belknap JK, Lacombe VA. Naturally occurring compensated insulin resistance selectively alters glucose transporters in visceral and subcutaneous adipose tissues without change in AS160 activation.. Biochim Biophys Acta 2011 Sep;1812(9):1098-103.
    pmc: PMC3143249pubmed: 21352908doi: 10.1016/j.bbadis.2011.02.007google scholar: lookup
  6. Burns TA, Geor RJ, Mudge MC, McCutcheon LJ, Hinchcliff KW, Belknap JK. Proinflammatory cytokine and chemokine gene expression profiles in subcutaneous and visceral adipose tissue depots of insulin-resistant and insulin-sensitive light breed horses.. J Vet Intern Med 2010 Jul-Aug;24(4):932-9.
    pubmed: 20649750doi: 10.1111/j.1939-1676.2010.0551.xgoogle scholar: lookup
  7. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, Cheng D, Jewell K, Arndt D, Sawhney S, Fung C, Nikolai L, Lewis M, Coutouly MA, Forsythe I, Tang P, Shrivastava S, Jeroncic K, Stothard P, Amegbey G, Block D, Hau DD, Wagner J, Miniaci J, Clements M, Gebremedhin M, Guo N, Zhang Y, Duggan GE, Macinnis GD, Weljie AM, Dowlatabadi R, Bamforth F, Clive D, Greiner R, Li L, Marrie T, Sykes BD, Vogel HJ, Querengesser L. HMDB: the Human Metabolome Database.. Nucleic Acids Res 2007 Jan;35(Database issue):D521-6.
    pmc: PMC1899095pubmed: 17202168doi: 10.1093/nar/gkl923google scholar: lookup
  8. Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, Young N, Xia J, Knox C, Dong E, Huang P, Hollander Z, Pedersen TL, Smith SR, Bamforth F, Greiner R, McManus B, Newman JW, Goodfriend T, Wishart DS. The human serum metabolome.. PLoS One 2011 Feb 16;6(2):e16957.
    pmc: PMC3040193pubmed: 21359215doi: 10.1371/journal.pone.0016957google scholar: lookup
  9. Floegel A, Stefan N, Yu Z, Mühlenbruch K, Drogan D, Joost HG, Fritsche A, Häring HU, Hrabě de Angelis M, Peters A, Roden M, Prehn C, Wang-Sattler R, Illig T, Schulze MB, Adamski J, Boeing H, Pischon T. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach.. Diabetes 2013 Feb;62(2):639-48.
    pmc: PMC3554384pubmed: 23043162doi: 10.2337/db12-0495google scholar: lookup
  10. Menni C, Fauman E, Erte I, Perry JR, Kastenmüller G, Shin SY, Petersen AK, Hyde C, Psatha M, Ward KJ, Yuan W, Milburn M, Palmer CN, Frayling TM, Trimmer J, Bell JT, Gieger C, Mohney RP, Brosnan MJ, Suhre K, Soranzo N, Spector TD. Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach.. Diabetes 2013 Dec;62(12):4270-6.
    pmc: PMC3837024pubmed: 23884885doi: 10.2337/db13-0570google scholar: lookup
  11. Milburn MV, Lawton KA. Application of metabolomics to diagnosis of insulin resistance.. Annu Rev Med 2013;64:291-305.
    pubmed: 23327524doi: 10.1146/annurev-med-061511-134747google scholar: lookup
  12. Suhre K, Meisinger C, Döring A, Altmaier E, Belcredi P, Gieger C, Chang D, Milburn MV, Gall WE, Weinberger KM, Mewes HW, Hrabé de Angelis M, Wichmann HE, Kronenberg F, Adamski J, Illig T. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting.. PLoS One 2010 Nov 11;5(11):e13953.
    pmc: PMC2978704pubmed: 21085649doi: 10.1371/journal.pone.0013953google scholar: lookup
  13. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, Heim K, Campillos M, Holzapfel C, Thorand B, Grallert H, Xu T, Bader E, Huth C, Mittelstrass K, Döring A, Meisinger C, Gieger C, Prehn C, Roemisch-Margl W, Carstensen M, Xie L, Yamanaka-Okumura H, Xing G, Ceglarek U, Thiery J, Giani G, Lickert H, Lin X, Li Y, Boeing H, Joost HG, de Angelis MH, Rathmann W, Suhre K, Prokisch H, Peters A, Meitinger T, Roden M, Wichmann HE, Pischon T, Adamski J, Illig T. Novel biomarkers for pre-diabetes identified by metabolomics.. Mol Syst Biol 2012;8:615.
    pmc: PMC3472689pubmed: 23010998doi: 10.1038/msb.2012.43google scholar: lookup
  14. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, O'Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE. Metabolite profiles and the risk of developing diabetes.. Nat Med 2011 Apr;17(4):448-53.
    pmc: PMC3126616pubmed: 21423183doi: 10.1038/nm.2307google scholar: lookup
  15. Magnusson M, Lewis GD, Ericson U, Orho-Melander M, Hedblad B, Engström G, Ostling G, Clish C, Wang TJ, Gerszten RE, Melander O. A diabetes-predictive amino acid score and future cardiovascular disease.. Eur Heart J 2013 Jul;34(26):1982-9.
    pmc: PMC3703309pubmed: 23242195doi: 10.1093/eurheartj/ehs424google scholar: lookup
  16. McCormack SE, Shaham O, McCarthy MA, Deik AA, Wang TJ, Gerszten RE, Clish CB, Mootha VK, Grinspoon SK, Fleischman A. Circulating branched-chain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents.. Pediatr Obes 2013 Feb;8(1):52-61.
    pmc: PMC3519972pubmed: 22961720doi: 10.1111/j.2047-6310.2012.00087.xgoogle scholar: lookup
  17. Schultz NE. Characterization of equine metabolic syndrome and mapping of candidate genetic loci. .
  18. Henneke DR, Potter GD, Kreider JL, Yeates BF. Relationship between condition score, physical measurements and body fat percentage in mares.. Equine Vet J 1983 Oct;15(4):371-2.
    pubmed: 6641685doi: 10.1111/j.2042-3306.1983.tb01826.xgoogle scholar: lookup
  19. Schuver A, Frank N, Chameroy KA, Elliott SB. Assessment of insulin and glucose dynamics by using an oral sugar test in horses. J Equine Vet Sci 2014;34:465–470.
  20. Borer-Weir KE, Bailey SR, Menzies-Gow NJ, Harris PA, Elliott J. Evaluation of a commercially available radioimmunoassay and species-specific ELISAs for measurement of high concentrations of insulin in equine serum.. Am J Vet Res 2012 Oct;73(10):1596-602.
    pubmed: 23013186doi: 10.2460/ajvr.73.10.1596google scholar: lookup
  21. Harford W, Lanza F, Arora A, Graham D, Haber M, Weissfeld A, Rose P, Siepman N. Double-blind, multicenter evaluation of lansoprazole and amoxicillin dual therapy for the cure of Helicobacter pylori infection.. Helicobacter 1996 Dec;1(4):243-50.
    pubmed: 9398875doi: 10.1111/j.1523-5378.1996.tb00046.xgoogle scholar: lookup
  22. Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems.. Anal Chem 2009 Aug 15;81(16):6656-67.
    pubmed: 19624122doi: 10.1021/ac901536hgoogle scholar: lookup
  23. Bridgewater BREA. High Resolution Mass Spectrometry Improves Data Quantity and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High‐Throughput Profiling Metabolomics. J Postgenomics Drug Biomarker Dev 2014;4.
  24. Sha W, da Costa KA, Fischer LM, Milburn MV, Lawton KA, Berger A, Jia W, Zeisel SH. Metabolomic profiling can predict which humans will develop liver dysfunction when deprived of dietary choline.. FASEB J 2010 Aug;24(8):2962-75.
    pmc: PMC2909293pubmed: 20371621doi: 10.1096/fj.09-154054google scholar: lookup
  25. Dehaven CD, Evans AM, Dai H, Lawton KA. Organization of GC/MS and LC/MS metabolomics data into chemical libraries.. J Cheminform 2010 Oct 18;2(1):9.
    pmc: PMC2984397pubmed: 20955607doi: 10.1186/1758-2946-2-9google scholar: lookup
  26. Gao J, Tarcea VG, Karnovsky A, Mirel BR, Weymouth TE, Beecher CW, Cavalcoli JD, Athey BD, Omenn GS, Burant CF, Jagadish HV. Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks.. Bioinformatics 2010 Apr 1;26(7):971-3.
    pmc: PMC2844990pubmed: 20139469doi: 10.1093/bioinformatics/btq048google scholar: lookup
  27. Shaham O, Wei R, Wang TJ, Ricciardi C, Lewis GD, Vasan RS, Carr SA, Thadhani R, Gerszten RE, Mootha VK. Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity.. Mol Syst Biol 2008;4:214.
    pmc: PMC2538910pubmed: 18682704doi: 10.1038/msb.2008.50google scholar: lookup
  28. Ho JE, Larson MG, Vasan RS, Ghorbani A, Cheng S, Rhee EP, Florez JC, Clish CB, Gerszten RE, Wang TJ. Metabolite profiles during oral glucose challenge.. Diabetes 2013 Aug;62(8):2689-98.
    pmc: PMC3717862pubmed: 23382451doi: 10.2337/db12-0754google scholar: lookup
  29. Manfredi JM. Identifying breed differences in insulin dynamics, skeletal muscle and adipose tissue histology and biology. .
  30. Zhao X, Peter A, Fritsche J, Elcnerova M, Fritsche A, Häring HU, Schleicher ED, Xu G, Lehmann R. Changes of the plasma metabolome during an oral glucose tolerance test: is there more than glucose to look at?. Am J Physiol Endocrinol Metab 2009 Feb;296(2):E384-93.
    pubmed: 19066319doi: 10.1152/ajpendo.90748.2008google scholar: lookup
  31. Pallares-Méndez R, Aguilar-Salinas CA, Cruz-Bautista I, Del Bosque-Plata L. Metabolomics in diabetes, a review.. Ann Med 2016;48(1-2):89-102.
    pubmed: 26883715doi: 10.3109/07853890.2015.1137630google scholar: lookup
  32. Lu J, Xie G, Jia W, Jia W. Metabolomics in human type 2 diabetes research.. Front Med 2013 Mar;7(1):4-13.
    pubmed: 23377891doi: 10.1007/s11684-013-0248-4google scholar: lookup
  33. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, Haqq AM, Shah SH, Arlotto M, Slentz CA, Rochon J, Gallup D, Ilkayeva O, Wenner BR, Yancy WS Jr, Eisenson H, Musante G, Surwit RS, Millington DS, Butler MD, Svetkey LP. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance.. Cell Metab 2009 Apr;9(4):311-26.
    pmc: PMC3640280pubmed: 19356713doi: 10.1016/j.cmet.2009.02.002google scholar: lookup
  34. Shulman GI. Cellular mechanisms of insulin resistance.. J Clin Invest 2000 Jul;106(2):171-6.
    pmc: PMC314317pubmed: 10903330doi: 10.1172/jci10583google scholar: lookup
  35. Oberbach A, Blüher M, Wirth H, Till H, Kovacs P, Kullnick Y, Schlichting N, Tomm JM, Rolle-Kampczyk U, Murugaiyan J, Binder H, Dietrich A, von Bergen M. Combined proteomic and metabolomic profiling of serum reveals association of the complement system with obesity and identifies novel markers of body fat mass changes.. J Proteome Res 2011 Oct 7;10(10):4769-88.
    pubmed: 21823675doi: 10.1021/pr2005555google scholar: lookup
  36. Kim HJ, Kim JH, Noh S, Hur HJ, Sung MJ, Hwang JT, Park JH, Yang HJ, Kim MS, Kwon DY, Yoon SH. Metabolomic analysis of livers and serum from high-fat diet induced obese mice.. J Proteome Res 2011 Feb 4;10(2):722-31.
    pubmed: 21047143doi: 10.1021/pr100892rgoogle scholar: lookup
  37. Rauschert S, Uhl O, Koletzko B, Hellmuth C. Metabolomic biomarkers for obesity in humans: a short review.. Ann Nutr Metab 2014;64(3-4):314-24.
    pubmed: 25300275doi: 10.1159/000365040google scholar: lookup
  38. Geidenstam N, Spégel P, Mulder H, Filipsson K, Ridderstråle M, Danielsson AP. Metabolite profile deviations in an oral glucose tolerance test-a comparison between lean and obese individuals.. Obesity (Silver Spring) 2014 Nov;22(11):2388-95.
    pubmed: 25132155doi: 10.1002/oby.20868google scholar: lookup
  39. Ho JE, Larson MG, Ghorbani A, Cheng S, Chen MH, Keyes M, Rhee EP, Clish CB, Vasan RS, Gerszten RE, Wang TJ. Metabolomic Profiles of Body Mass Index in the Framingham Heart Study Reveal Distinct Cardiometabolic Phenotypes.. PLoS One 2016;11(2):e0148361.
    pmc: PMC4749349pubmed: 26863521doi: 10.1371/journal.pone.0148361google scholar: lookup
  40. Park S, Sadanala KC, Kim EK. A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes.. Mol Cells 2015 Jul;38(7):587-96.
    pmc: PMC4507023pubmed: 26072981doi: 10.14348/molcells.2015.0126google scholar: lookup
  41. Buse JB, Freeman JL, Edelman SV, Jovanovic L, McGill JB. Serum 1,5-anhydroglucitol (GlycoMark ): a short-term glycemic marker.. Diabetes Technol Ther 2003;5(3):355-63.
    pubmed: 12828817doi: 10.1089/152091503765691839google scholar: lookup
  42. Frank N, Elliott SB, Brandt LE, Keisler DH. Physical characteristics, blood hormone concentrations, and plasma lipid concentrations in obese horses with insulin resistance.. J Am Vet Med Assoc 2006 May 1;228(9):1383-90.
    pubmed: 16649943doi: 10.2460/javma.228.9.1383google scholar: lookup
  43. Suagee JK, Corl BA, Hulver MW, McCutcheon LJ, Geor RJ. Effects of hyperinsulinemia on glucose and lipid transporter expression in insulin-sensitive horses.. Domest Anim Endocrinol 2011 Apr;40(3):173-81.
    pubmed: 21292427doi: 10.1016/j.domaniend.2010.11.002google scholar: lookup
  44. Pilz S, Meinitzer A, Tomaschitz A, Drechsler C, Ritz E, Krane V, Wanner C, Boehm BO, März W. Low homoarginine concentration is a novel risk factor for heart disease.. Heart 2011 Aug;97(15):1222-7.
    pubmed: 21558479doi: 10.1136/hrt.2010.220731google scholar: lookup
  45. Pilz S, Meinitzer A, Gaksch M, Grübler M, Verheyen N, Drechsler C, Hartaigh BÓ, Lang F, Alesutan I, Voelkl J, März W, Tomaschitz A. Homoarginine in the renal and cardiovascular systems.. Amino Acids 2015 Sep;47(9):1703-13.
    pubmed: 25929587doi: 10.1007/s00726-015-1993-2google scholar: lookup

Citations

This article has been cited 9 times.
  1. Zhu Y, Wang X, Liu B, Yi Z, Zhao Y, Deng L, Holyoak R, Li J. The Effect of Ryegrass Silage Feeding on Equine Fecal Microbiota and Blood Metabolite Profile.. Front Microbiol 2021;12:715709.
    doi: 10.3389/fmicb.2021.715709pubmed: 34497595google scholar: lookup
  2. Arnold C, Pilla R, Chaffin K, Lidbury J, Steiner J, Suchodolski J. Alterations in the Fecal Microbiome and Metabolome of Horses with Antimicrobial-Associated Diarrhea Compared to Antibiotic-Treated and Non-Treated Healthy Case Controls.. Animals (Basel) 2021 Jun 17;11(6).
    doi: 10.3390/ani11061807pubmed: 34204371google scholar: lookup
  3. 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
  4. Delarocque J, Frers F, Huber K, Jung K, Feige K, Warnken T. Metabolic impact of weight variations in Icelandic horses.. PeerJ 2021;9:e10764.
    doi: 10.7717/peerj.10764pubmed: 33575132google scholar: lookup
  5. Delarocque J, Reiche DB, Meier AD, Warnken T, Feige K, Sillence MN. Metabolic profile distinguishes laminitis-susceptible and -resistant ponies before and after feeding a high sugar diet.. BMC Vet Res 2021 Jan 28;17(1):56.
    doi: 10.1186/s12917-021-02763-7pubmed: 33509165google scholar: lookup
  6. Delarocque J, Frers F, Feige K, Huber K, Jung K, Warnken T. Metabolic changes induced by oral glucose tests in horses and their diagnostic use.. J Vet Intern Med 2021 Jan;35(1):597-605.
    doi: 10.1111/jvim.15992pubmed: 33277752google scholar: lookup
  7. Tran H, McConville M, Loukopoulos P. Metabolomics in the study of spontaneous animal diseases.. J Vet Diagn Invest 2020 Sep;32(5):635-647.
    doi: 10.1177/1040638720948505pubmed: 32807042google scholar: lookup
  8. Raudsepp T, Finno CJ, Bellone RR, Petersen JL. Ten years of the horse reference genome: insights into equine biology, domestication and population dynamics in the post-genome era.. Anim Genet 2019 Dec;50(6):569-597.
    doi: 10.1111/age.12857pubmed: 31568563google scholar: lookup
  9. 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(4):e0215918.
    doi: 10.1371/journal.pone.0215918pubmed: 31013335google scholar: lookup
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.