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BMC veterinary research2021; 17(1); 56; doi: 10.1186/s12917-021-02763-7

Metabolic profile distinguishes laminitis-susceptible and -resistant ponies before and after feeding a high sugar diet.

Abstract: Insulin dysregulation (ID) is a key risk factor for equine endocrinopathic laminitis, but in many cases ID can only be assessed accurately using dynamic tests. The identification of other biomarkers could provide an alternative or adjunct diagnostic method, to allow early intervention before laminitis develops. The present study characterised the metabolome of ponies with varying degrees of ID using basal and postprandial plasma samples obtained during a previous study, which examined the predictive power of blood insulin levels for the development of laminitis, in ponies fed a high-sugar diet. Samples from 10 pre-laminitic (PL - subsequently developed laminitis) and 10 non-laminitic (NL - did not develop laminitis) ponies were used in a targeted metabolomic assay. Differential concentration and pathway analysis were performed using linear models and global tests. Results: Significant changes in the concentration of six glycerophospholipids (adj. P ≤ 0.024) and a global enrichment of the glucose-alanine cycle (adj. P = 0.048) were found to characterise the response of PL ponies to the high-sugar diet. In contrast, the metabolites showed no significant association with the presence or absence of pituitary pars intermedia dysfunction in all ponies. Conclusions: The present results suggest that ID and laminitis risk are associated with alterations in the glycerophospholipid and glucose metabolism, which may help understand and explain some molecular processes causing or resulting from these conditions. The prognostic value of the identified biomarkers for laminitis remains to be investigated in further metabolomic trials in horses and ponies.
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Publication Date: 2021-01-28 PubMed ID: 33509165PubMed Central: PMC7841998DOI: 10.1186/s12917-021-02763-7Google Scholar: Lookup
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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 investigates distinct metabolic characteristics in ponies which are either susceptible or resistant to laminitis, a painful hoof disease. The research identifies potential biomarkers that could help diagnose insulin dysregulation and laminitis risk early, specifically focusing on changes in certain lipids and glucose metabolism in ponies fed a high-sugar diet.

Study Overview

  • The research aims to find alternative or supporting diagnostic methods for detecting insulin dysregulation (ID), which is a major risk factor for equine laminitis, a painful hoof condition in horses.
  • It further seeks to identify metabolic differences in ponies that either develop laminitis (pre-laminitic or PL) or do not develop laminitis (non-laminitic or NL) when fed a high-sugar diet.

Methods

  • The study utilised basal and postprandial (after a meal) plasma samples from a previous study, which investigated whether blood insulin levels could predict laminitis development in ponies given a high-sugar diet.
  • Plasma samples were obtained from 10 PL ponies and 10 NL ponies and underwent a targeted metabolomics assay to characterise the metabolome, the complete set of metabolites in a biological sample.
  • The researchers performed differential concentration and pathway analysis using linear models and global tests.

Results and Conclusion

  • The researchers identified significant changes in the concentration of six types of glycerophospholipids, a class of lipids, in PL ponies’ response to the high-sugar diet.
  • An enrichment, or heightened activity, in glucose-alanine cycle was also found in PL ponies — a metabolic process involving the transfer of energy from muscle tissue to the liver.
  • These metabolites revealed no significant association with pituitary pars intermedia dysfunction, a common endocrine disorder in horses, across all ponies.
  • The authors conclude that the risk of ID and laminitis are associated with changes in glycerophospholipid and glucose metabolism, shedding light on the origin or consequence of these conditions.
  • However, further research needs to be conducted to confirm the predictive value of these identified biomarkers for laminitis.

Cite This Article

APA
Delarocque J, Reiche DB, Meier AD, Warnken T, Feige K, Sillence MN. (2021). Metabolic profile distinguishes laminitis-susceptible and -resistant ponies before and after feeding a high sugar diet. BMC Vet Res, 17(1), 56. https://doi.org/10.1186/s12917-021-02763-7

Publication

BMC veterinary research
ISSN: 1746-6148
NlmUniqueID: 101249759
Country: England
Language: English
Volume: 17
Issue: 1
Pages: 56
PII: 56

Researcher Affiliations

Delarocque, Julien
  • Clinic for Horses, University of Veterinary Medicine Hannover, Foundation, 30559, Hannover, Germany. julien.delarocque@tiho-hannover.de.
Reiche, Dania B
  • Boehringer Ingelheim Vetmedica GmbH, 55216, Ingelheim am Rhein, Germany.
Meier, Alexandra D
  • Biology and Environmental Science School, Queensland University of Technology, Brisbane, Queensland, 4000, Australia.
Warnken, Tobias
  • Clinic for Horses, University of Veterinary Medicine Hannover, Foundation, 30559, Hannover, Germany.
Feige, Karsten
  • Clinic for Horses, University of Veterinary Medicine Hannover, Foundation, 30559, Hannover, Germany.
Sillence, Martin N
  • Biology and Environmental Science School, Queensland University of Technology, Brisbane, Queensland, 4000, Australia.

MeSH Terms

  • Animals
  • Blood Glucose / analysis
  • Diet / veterinary
  • Dietary Carbohydrates / adverse effects
  • Disease Resistance
  • Disease Susceptibility / metabolism
  • Disease Susceptibility / veterinary
  • Female
  • Foot Diseases / etiology
  • Foot Diseases / metabolism
  • Foot Diseases / veterinary
  • Glycerophospholipids / blood
  • Hoof and Claw
  • Horse Diseases / etiology
  • Horse Diseases / metabolism
  • Horses / metabolism
  • Insulin / blood
  • Male
  • Metabolome
  • Risk Factors

Conflict of Interest Statement

Dania Reiche is employed by Boehringer Ingelheim Vetmedica GmbH. All other authors have declared that no competing interests exist.

References

This article includes 42 references
  1. Frank N, Tadros EM. Insulin dysregulation.. Equine Vet J 2014 Jan;46(1):103-12.
    doi: 10.1111/evj.12169pubmed: 24033478google scholar: lookup
  2. 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.
    doi: 10.1016/j.tvjl.2007.07.003pubmed: 17719811google scholar: lookup
  3. de Laat MA, McGowan CM, Sillence MN, Pollitt CC. Equine laminitis: induced by 48 h hyperinsulinaemia in Standardbred horses.. Equine Vet J 2010 Mar;42(2):129-35.
    doi: 10.2746/042516409X475779pubmed: 20156248google scholar: lookup
  4. Wishart DS. Metabolomics for Investigating Physiological and Pathophysiological Processes.. Physiol Rev 2019 Oct 1;99(4):1819-1875.
    doi: 10.1152/physrev.00035.2018pubmed: 31434538google scholar: lookup
  5. Lent-Schochet D, McLaughlin M, Ramakrishnan N, Jialal I. Exploratory metabolomics of metabolic syndrome: A status report.. World J Diabetes 2019 Jan 15;10(1):23-36.
    doi: 10.4239/wjd.v10.i1.23pmc: PMC6347655pubmed: 30697368google scholar: lookup
  6. Goldansaz SA, Guo AC, Sajed T, Steele MA, Plastow GS, Wishart DS. Livestock metabolomics and the livestock metabolome: A systematic review.. PLoS One 2017;12(5):e0177675.
    doi: 10.1371/journal.pone.0177675pmc: PMC5439675pubmed: 28531195google scholar: lookup
  7. Kenéz Á, Warnken T, Feige K, Huber K. Lower plasma trans-4-hydroxyproline and methionine sulfoxide levels are associated with insulin dysregulation in horses.. BMC Vet Res 2018 May 2;14(1):146.
    pmc: PMC5930486pubmed: 29716602doi: 10.1186/s12917-018-1479-zgoogle scholar: lookup
  8. Jacob SI, Murray KJ, Rendahl AK, Geor RJ, Schultz NE, McCue ME. Metabolic perturbations in Welsh Ponies with insulin dysregulation, obesity, and laminitis.. J Vet Intern Med 2018 May;32(3):1215-1233.
    doi: 10.1111/jvim.15095pmc: PMC5980341pubmed: 29572947google scholar: lookup
  9. Garner HE, Moore JN, Johnson JH, Clark L, Amend JF, Tritschler LG, Coffmann JR, Sprouse RF, Hutcheson DP, Salem CA. Changes in the caecal flora associated with the onset of laminitis.. Equine Vet J 1978 Oct;10(4):249-52.
    doi: 10.1111/j.2042-3306.1978.tb02273.xpubmed: 738266google scholar: lookup
  10. Meier AD, de Laat MA, Reiche DB, Pollitt CC, Walsh DM, McGree JM, Sillence MN. The oral glucose test predicts laminitis risk in ponies fed a diet high in nonstructural carbohydrates.. Domest Anim Endocrinol 2018 Apr;63:1-9.
    doi: 10.1016/j.domaniend.2017.10.008pubmed: 29172109google scholar: lookup
  11. 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 Res 2018 Jan 4;46(D1):D608-D617.
    doi: 10.1093/nar/gkx1089pmc: PMC5753273pubmed: 29140435google scholar: lookup
  12. 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 Res 2014 Jan;42(Database issue):D478-84.
    doi: 10.1093/nar/gkt1067pmc: PMC3965088pubmed: 24203708google scholar: lookup
  13. Chang W, Hatch GM, Wang Y, Yu F, Wang M. The relationship between phospholipids and insulin resistance: From clinical to experimental studies.. J Cell Mol Med 2019 Feb;23(2):702-710.
    doi: 10.1111/jcmm.13984pmc: PMC6349352pubmed: 30402908google scholar: lookup
  14. 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.. Mol Biosyst 2014 Jun;10(6):1586-95.
    doi: 10.1039/C3MB70529Cpubmed: 24714806google scholar: lookup
  15. Dugdale AH, Grove-White D, Curtis GC, Harris PA, Argo CM. Body condition scoring as a predictor of body fat in horses and ponies.. Vet J 2012 Nov;194(2):173-8.
    doi: 10.1016/j.tvjl.2012.03.024pubmed: 22578691google scholar: lookup
  16. Imhasly S, Naegeli H, Baumann S, von Bergen M, Luch A, Jungnickel H, Potratz S, Gerspach C. Metabolomic biomarkers correlating with hepatic lipidosis in dairy cows.. BMC Vet Res 2014 Jun 2;10:122.
    pmc: PMC4048253pubmed: 24888604doi: 10.1186/1746-6148-10-122google scholar: lookup
  17. Felig P. The glucose-alanine cycle.. Metabolism 1973 Feb;22(2):179-207.
    doi: 10.1016/0026-0495(73)90269-2pubmed: 4567003google scholar: lookup
  18. Consoli A, Nurjhan N, Reilly JJ Jr, Bier DM, Gerich JE. Mechanism of increased gluconeogenesis in noninsulin-dependent diabetes mellitus. Role of alterations in systemic, hepatic, and muscle lactate and alanine metabolism.. J Clin Invest 1990 Dec;86(6):2038-45.
    doi: 10.1172/JCI114940pmc: PMC329842pubmed: 2254458google scholar: lookup
  19. Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, Yang E, Farrell L, Fox CS, O'Donnell CJ, Carr SA, Vasan RS, Florez JC, Clish CB, Wang TJ, Gerszten RE. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans.. J Clin Invest 2011 Apr;121(4):1402-11.
    doi: 10.1172/JCI44442pmc: PMC3069773pubmed: 21403394google scholar: lookup
  20. 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.
    doi: 10.2337/db12-0495pmc: PMC3554384pubmed: 23043162google scholar: lookup
  21. Ding M, Rexrode KM. A Review of Lipidomics of Cardiovascular Disease Highlights the Importance of Isolating Lipoproteins.. Metabolites 2020 Apr 23;10(4).
    doi: 10.3390/metabo10040163pmc: PMC7240942pubmed: 32340170google scholar: lookup
  22. Mangge H, Summers KL, Meinitzer A, Zelzer S, Almer G, Prassl R, Schnedl WJ, Reininghaus E, Paulmichl K, Weghuber D, Fuchs D. Obesity-related dysregulation of the tryptophan-kynurenine metabolism: role of age and parameters of the metabolic syndrome.. Obesity (Silver Spring) 2014 Jan;22(1):195-201.
    doi: 10.1002/oby.20491pubmed: 23625535google scholar: lookup
  23. 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 Jun 13;16(6):73.
    doi: 10.1007/s11306-020-01693-zpubmed: 32535675google scholar: lookup
  24. Biddle AS, Tomb JF, Fan Z. Microbiome and Blood Analyte Differences Point to Community and Metabolic Signatures in Lean and Obese Horses.. Front Vet Sci 2018;5:225.
    doi: 10.3389/fvets.2018.00225pmc: PMC6158370pubmed: 30294603google scholar: lookup
  25. 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.0215918pmc: PMC6478336pubmed: 31013335google scholar: lookup
  26. Durham AE, Frank N, McGowan CM, Menzies-Gow NJ, Roelfsema E, Vervuert I, Feige K, Fey K. ECEIM consensus statement on equine metabolic syndrome.. J Vet Intern Med 2019 Mar;33(2):335-349.
    doi: 10.1111/jvim.15423pmc: PMC6430910pubmed: 30724412google scholar: lookup
  27. Kimball SR, Farrell PA, Jefferson LS. Invited Review: Role of insulin in translational control of protein synthesis in skeletal muscle by amino acids or exercise.. J Appl Physiol (1985) 2002 Sep;93(3):1168-80.
    doi: 10.1152/japplphysiol.00221.2002pubmed: 12183515google 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.
    doi: 10.2337/db12-0754pmc: PMC3717862pubmed: 23382451google scholar: lookup
  29. Warnken T, Brehm R, Feige K, Huber K. Insulin signaling in various equine tissues under basal conditions and acute stimulation by intravenously injected insulin.. Domest Anim Endocrinol 2017 Oct;61:17-26.
    doi: 10.1016/j.domaniend.2017.04.003pubmed: 28595108google scholar: lookup
  30. Johansson E, Lubberink M, Heurling K, Eriksson JW, Skrtic S, Ahlström H, Kullberg J. Whole-Body Imaging of Tissue-specific Insulin Sensitivity and Body Composition by Using an Integrated PET/MR System: A Feasibility Study.. Radiology 2018 Jan;286(1):271-278.
    doi: 10.1148/radiol.2017162949pubmed: 28846496google scholar: lookup
  31. Bagheri M, Farzadfar F, Qi L, Yekaninejad MS, Chamari M, Zeleznik OA, Kalantar Z, Ebrahimi Z, Sheidaie A, Koletzko B, Uhl O, Djazayery A. Obesity-Related Metabolomic Profiles and Discrimination of Metabolically Unhealthy Obesity.. J Proteome Res 2018 Apr 6;17(4):1452-1462.
    doi: 10.1021/acs.jproteome.7b00802pubmed: 29493238google scholar: lookup
  32. 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.
    doi: 10.1186/s12917-016-0828-zpmc: PMC5016943pubmed: 27613127google scholar: lookup
  33. McGowan TW, Pinchbeck GP, McGowan CM. Prevalence, risk factors and clinical signs predictive for equine pituitary pars intermedia dysfunction in aged horses.. Equine Vet J 2013 Jan;45(1):74-9.
    doi: 10.1111/j.2042-3306.2012.00578.xpubmed: 22594955google scholar: lookup
  34. Köller G, Bassewitz K, Schusser GF. [Reference ranges of insulin, insulin like growth factor-1 and adrenocorticotropic hormone in ponies].. Tierarztl Prax Ausg G Grosstiere Nutztiere 2016;44(1):19-25.
    doi: 10.15653/TPG-150428pubmed: 26758970google scholar: lookup
  35. Meier A, de Laat M, Pollitt C, Walsh D, McGree J, Reiche DB, von Salis-Soglio M, Wells-Smith L, Mengeler U, Mesa Salas D, Droegemueller S, Sillence MN. A "modified Obel" method for the severity scoring of (endocrinopathic) equine laminitis.. PeerJ 2019;7:e7084.
    doi: 10.7717/peerj.7084pmc: PMC6557244pubmed: 31211020google scholar: lookup
  36. R Core Team. R: A Language and Environment for Statistical Computing. 2020.
  37. 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.. Nat Protoc 2011 Jun 30;6(7):1060-83.
    doi: 10.1038/nprot.2011.335pubmed: 21720319google scholar: lookup
  38. Hastie T, Tibshirani R, Sherlock G, Eisen M, Brown P, Botstein D. Imputing missing data for gene expression arrays. 1999.
  39. Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.. Bioinformatics 2003 Jan 22;19(2):185-93.
    doi: 10.1093/bioinformatics/19.2.185pubmed: 12538238google scholar: lookup
  40. 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 Res 2015 Apr 20;43(7):e47.
    doi: 10.1093/nar/gkv007pmc: PMC4402510pubmed: 25605792google scholar: lookup
  41. Goeman JJ, van de Geer SA, van Houwelingen HC. Testing against a high dimensional alternative. J R Stat Soc Ser B Stat Methodol 2006;68:477–493.
    doi: 10.1111/j.1467-9868.2006.00551.xgoogle scholar: lookup
  42. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 1995;57:289–300.
    doi: 10.2307/2346101google scholar: lookup

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