Scientific reports2024; 14(1); 7571; doi: 10.1038/s41598-024-58323-0

Follicular metabolic alterations are associated with obesity in mares and can be mitigated by dietary supplementation.

Abstract: Obesity is a growing concern in human and equine populations, predisposing to metabolic pathologies and reproductive disturbances. Cellular lipid accumulation and mitochondrial dysfunction play an important role in the pathologic consequences of obesity, which may be mitigated by dietary interventions targeting these processes. We hypothesized that obesity in the mare promotes follicular lipid accumulation and altered mitochondrial function of oocytes and granulosa cells, potentially contributing to impaired fertility in this population. We also predicted that these effects could be mitigated by dietary supplementation with a combination of targeted nutrients to improve follicular cell metabolism. Twenty mares were grouped as: Normal Weight [NW, n = 6, body condition score (BCS) 5.7 ± 0.3], Obese (OB, n = 7, BCS 7.7 ± 0.2), and Obese Diet Supplemented (OBD, n = 7, BCS 7.7 ± 0.2), and fed specific feed regimens for ≥ 6 weeks before sampling. Granulosa cells, follicular fluid, and cumulus-oocyte complexes were collected from follicles ≥ 35 mm during estrus and after induction of maturation. Obesity promoted several mitochondrial metabolic disturbances in granulosa cells, reduced L-carnitine availability in the follicle, promoted lipid accumulation in cumulus cells and oocytes, and increased basal oocyte metabolism. Diet supplementation of a complex nutrient mixture mitigated most of the metabolic changes in the follicles of obese mares, resulting in parameters similar to NW mares. In conclusion, obesity disturbs the equine ovarian follicle by promoting lipid accumulation and altering mitochondrial function. These effects may be partially mitigated with targeted nutritional intervention, thereby potentially improving fertility outcomes in the obese female.
Publication Date: 2024-03-30 PubMed ID: 38555310PubMed Central: PMC10981747DOI: 10.1038/s41598-024-58323-0Google 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.
  • 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 study explores the negative effects of obesity on the reproductive capabilities of mares and finds that these effects can be mitigated through nutritional intervention. The researchers discovered that obesity can cause disruptions in mitochondrial metabolic activity in these mares, while a diet supplement restores these parameters to similar levels of non-obese horses, potentially improving fertility.

Objective of the Research

  • The research aimed to understand how obesity affects follicular metabolic alterations in mares and if the condition could be reversed with targeted nutritional interventions.

Hypothesis and Predictions

  • The researchers hypothesized that obesity might boost follicular lipid accumulation and disrupt mitochondrial activity in granulosa cells and oocytes, leading to fertility issues in mares.
  • They further proposed that these effects could be reversed by supplementing the mare’s diet with a mix of specific nutrients aimed at improving the metabolism of follicular cells.

Research Method

  • Twenty mares were separated into groups of Normal Weight (NW), Obese (OB), and Obese Diet Supplemented (OBD), and were fed specific diets for over six weeks before sampling.
  • Granulosa cells, follicular fluid, and cumulus-oocyte complexes were collected from follicles that were 35 mm or larger during estrus and after the induction of maturation.

Findings

  • Obesity caused several mitochondrial metabolic disturbances in granulosa cells, led to decreased L-carnitine availability in the follicle, increased lipid accumulation in cumulus cells and oocytes, and elevated oocyte metabolism.
  • The diet supplement, a complex nutrient mixture, could mitigate most metabolic changes in the follicles of obese mares, resulting in parameters nearly equivalent to those of NW mares.

Conclusion

  • The study concluded that obesity disrupts the equine ovarian follicle by promoting lipid accumulation and impairing mitochondrial function.
  • The findings suggest that these negative effects can be mitigated, at least partly, through targeted nutritional intervention, potentially enhancing fertility outcomes in obese females.

Cite This Article

APA
Catandi GD, Fresa KJ, Cheng MH, Whitcomb LA, Broeckling CD, Chen TW, Chicco AJ, Carnevale EM. (2024). Follicular metabolic alterations are associated with obesity in mares and can be mitigated by dietary supplementation. Sci Rep, 14(1), 7571. https://doi.org/10.1038/s41598-024-58323-0

Publication

ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 14
Issue: 1
Pages: 7571
PII: 7571

Researcher Affiliations

Catandi, Giovana D
  • Equine Reproduction Laboratory, Department of Biomedical Sciences, Colorado State University, 3101 Rampart Road, Fort Collins, CO, 80521, USA.
  • Department of Biomedical Sciences, Colorado State University, Fort Collins, CO, 80523, USA.
  • Department of Veterinary Clinical Sciences, Oklahoma State University, Stillwater, OK, 74078, USA.
Fresa, Kyle J
  • Equine Reproduction Laboratory, Department of Biomedical Sciences, Colorado State University, 3101 Rampart Road, Fort Collins, CO, 80521, USA.
  • Department of Biomedical Sciences, Colorado State University, Fort Collins, CO, 80523, USA.
Cheng, Ming-Hao
  • Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, 80523, USA.
Whitcomb, Luke A
  • Department of Biomedical Sciences, Colorado State University, Fort Collins, CO, 80523, USA.
Broeckling, Corey D
  • Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, 80523, USA.
Chen, Thomas W
  • Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, 80523, USA.
  • School of Biomedical Engineering, Colorado State University, Fort Collins, CO, 80523, USA.
Chicco, Adam J
  • Department of Biomedical Sciences, Colorado State University, Fort Collins, CO, 80523, USA.
Carnevale, Elaine M
  • Equine Reproduction Laboratory, Department of Biomedical Sciences, Colorado State University, 3101 Rampart Road, Fort Collins, CO, 80521, USA. elaine.carnevale@colostate.edu.
  • Department of Biomedical Sciences, Colorado State University, Fort Collins, CO, 80523, USA. elaine.carnevale@colostate.edu.

MeSH Terms

  • Humans
  • Horses
  • Animals
  • Female
  • Ovarian Follicle / metabolism
  • Oocytes / metabolism
  • Follicular Fluid
  • Obesity / metabolism
  • Lipids
  • Dietary Supplements

Grant Funding

  • Grant No. COLV 2021-09 / Project Accession No. 1026913 / USDA National Institute of Food and Agriculture Animal Health and Disease

Conflict of Interest Statement

The authors declare no competing interests.

References

This article includes 96 references
  1. Silvestris E, de Pergola G, Rosania R, Loverro G. Obesity as disruptor of the female fertility. Reprod. Biol. Endocrinol. 2018;16:22. doi: 10.1186/s12958-018-0336-z.
    doi: 10.1186/s12958-018-0336-zpmc: PMC5845358pubmed: 29523133google scholar: lookup
  2. Shah DK, Missmer SA, Berry KF, Racowsky C, Ginsburg ES. Effect of obesity on oocyte and embryo quality in women undergoing in vitro fertilization. Obstet. Gynecol. 2011;118:63u201370. doi: 10.1097/AOG.0b013e31821fd360.
    doi: 10.1097/AOG.0b013e31821fd360pubmed: 21691164google scholar: lookup
  3. Andreas E, Winstanley YE, Robker RL. Effect of obesity on the ovarian follicular environment and developmental competence of the oocyte. Curr. Opin. Endocr. Metab. Res. 2021;18:152u2013158. doi: 10.1016/j.coemr.2021.03.013.
  4. Richani D, Dunning KR, Thompson JG, Gilchrist RB. Metabolic co-dependence of the oocyte and cumulus cells: Essential role in determining oocyte developmental competence. Hum. Reprod. Update. 2021;27:27u201347. doi: 10.1093/humupd/dmaa043.
    doi: 10.1093/humupd/dmaa043pubmed: 33020823google scholar: lookup
  5. Van Hoeck V, et al. Oocyte developmental failure in response to elevated nonesterified fatty acid concentrations: Mechanistic insights. Reproduction. 2013;145:33u201344. doi: 10.1530/REP-12-0174.
    doi: 10.1530/REP-12-0174pubmed: 23108110google scholar: lookup
  6. Turner N, Robker RL. Developmental programming of obesity and insulin resistance: Does mitochondrial dysfunction in oocytes play a role? MHR Basic Sci. Reprod. Med. 2015;21:23u201330. doi: 10.1093/molehr/gau042.
    doi: 10.1093/molehr/gau042pubmed: 24923276google scholar: lookup
  7. Pratt-Phillips SE, Owens KM, Dowler LE, Cloninger MT. Assessment of resting insulin and leptin concentrations and their association with managerial and innate factors in horses. J. Equine Vet. Sci. 2010;30:127u2013133. doi: 10.1016/j.jevs.2010.01.060.
  8. Thatcher CD, Pleasant RS, Geor RJ, Elvinger F. Prevalence of overconditioning in mature horses in southwest Virginia during the summer. J. Vet. Intern. Med. 2012;26:1413u20131418. doi: 10.1111/j.1939-1676.2012.00995.x.
  9. Ragno VM, Zello GA, Klein CD, Montgomery JB. From table to stable: A comparative review of selected aspects of human and equine metabolic syndrome. J. Equine Vet. Sci. 2019;79:131u2013138. doi: 10.1016/j.jevs.2019.06.003.
    doi: 10.1016/j.jevs.2019.06.003pubmed: 31405493google scholar: lookup
  10. Harris PA, Bamford NJ, Bailey SR. Equine metabolic syndrome: Evolution of understanding over two decades: A personal perspective. Anim. Prod. Sci. 2020;60:2103. doi: 10.1071/AN19386.
    doi: 10.1071/AN19386google scholar: lookup
  11. Johnson PJ, Wiedmeyer CE, Messer NT, Ganjam VK. Medical implications of obesity in horsesu2014Lessons for human obesity. J. Diabetes Sci. Technol. 2009;3:163u2013174. doi: 10.1177/193229680900300119.
    doi: 10.1177/193229680900300119pmc: PMC2769846pubmed: 20046661google scholar: lookup
  12. Holbrook TC, Tipton T, McFarlane D. Neutrophil and cytokine dysregulation in hyperinsulinemic obese horses. Vet. Immunol. Immunopathol. 2012;145:283u2013289. doi: 10.1016/j.vetimm.2011.11.013.
    doi: 10.1016/j.vetimm.2011.11.013pubmed: 22169327google scholar: lookup
  13. Sessions DR, Reedy SE, Vick MM, Murphy BA, Fitzgerald BP. Development of a model for inducing transient insulin resistance in the mare: Preliminary implications regarding the estrous cycle12. J. Anim. Sci. 2004;82:2321u20132328. doi: 10.2527/2004.8282321x.
    doi: 10.2527/2004.8282321xpubmed: 15318731google scholar: lookup
  14. Vick MM, et al. Obesity is associated with altered metabolic and reproductive activity in the mare: Effects of metformin on insulin sensitivity and reproductive cyclicity. Reprod. Fertil. Dev. 2006;18:609. doi: 10.1071/RD06016.
    doi: 10.1071/RD06016pubmed: 16930507google scholar: lookup
  15. Sessions-Bresnahan DR, Schauer KL, Heuberger AL, Carnevale EM. Effect of obesity on the preovulatory follicle and lipid fingerprint of equine oocytes1. Biol. Reprod. 2016 doi: 10.1095/biolreprod.115.130187.
    doi: 10.1095/biolreprod.115.130187pubmed: 26632608google scholar: lookup
  16. Morley SA, Murray J-A. Effects of body condition score on the reproductive physiology of the broodmare: A review. J. Equine Vet. Sci. 2014;34:842u2013853. doi: 10.1016/j.jevs.2014.04.001.
  17. Robles M, et al. Maternal obesity increases insulin resistance, low-grade inflammation and osteochondrosis lesions in foals and yearlings until 18 months of age. PLOS ONE. 2018;13:e0190309. doi: 10.1371/journal.pone.0190309.
  18. Gastal EL, de Oliveira Gastal M, Wischral u00c1, Davis J. The equine model to study the influence of obesity and insulin resistance in human ovarian function. Acta Sci. Vet. 2011;39(1):s57u201370.
  19. Carnevale EM. The mare as an animal model for reproductive aging in the women. In: Shatten E, Constantinescu H, editors. Animal Models and Human Reproduction. Wiley; 2017. pp. 235u2013242.
  20. Lazzari G. Laboratory production of equine embryos. J. Equine Vet. Sci. 2020 doi: 10.1016/j.jevs.2020.103097.
    doi: 10.1016/j.jevs.2020.103097pubmed: 32563445google scholar: lookup
  21. Benammar A, et al. The mare: A pertinent model for human assisted reproductive technologies? Animals. 2021;11:2304. doi: 10.3390/ani11082304.
    doi: 10.3390/ani11082304pmc: PMC8388489pubmed: 34438761google scholar: lookup
  22. Carnevale EM, Catandi GD, Fresa K. Equine aging and the oocyte: A potential model for reproductive aging in women. J. Equine Vet. Sci. 2020;89:103022. doi: 10.1016/j.jevs.2020.103022.
    doi: 10.1016/j.jevs.2020.103022pubmed: 32563447google scholar: lookup
  23. Catandi G, Obeidat Y, Chicco A, Chen T, Carnevale E. 167 Basal and maximal oxygen consumption of oocytes from young and old mares. Reprod. Fertil. Dev. 2019;31:208u2013208. doi: 10.1071/RDv31n1Ab167.
    doi: 10.1071/RDv31n1Ab167google scholar: lookup
  24. Catandi G, et al. 98 Effects of maternal age on oxygen consumption of oocytes and in vitro-produced equine embryos. Reprod. Fertil. Dev. 2020;32:175u2013175. doi: 10.1071/RDv32n2Ab98.
    doi: 10.1071/RDv32n2Ab98google scholar: lookup
  25. Catandi GD, et al. Equine maternal aging affects oocyte lipid content, metabolic function and developmental potential. Reproduction. 2021;161:399u2013409. doi: 10.1530/REP-20-0494.
    doi: 10.1530/REP-20-0494pmc: PMC7969451pubmed: 33539317google scholar: lookup
  26. Obeidat YM, et al. Monitoring oocyte/embryo respiration using electrochemical-based oxygen sensors. Sens. Actuators B Chem. 2018;276:72u201381. doi: 10.1016/j.snb.2018.07.157.
    doi: 10.1016/j.snb.2018.07.157google scholar: lookup
  27. Obeidat YM, et al. Design of a multi-sensor platform for integrating extracellular acidification rate with multi-metabolite flux measurement for small biological samples. Biosens. Bioelectron. 2019;133:39u201347. doi: 10.1016/j.bios.2019.02.069.
    doi: 10.1016/j.bios.2019.02.069pmc: PMC6660976pubmed: 30909011google scholar: lookup
  28. Catandi GD, et al. Diet affects oocyte metabolism and developmental capacity in the older mare. Am. Assoc. Equine Pract. 2019;65:51u201352.
  29. Catandi G, et al. Maternal diet can alter oocyte mitochondrial number and function. J. Equine Vet. Sci. 2020;89:103030. doi: 10.1016/j.jevs.2020.103030.
  30. Catandi GD, et al. Oocyte metabolic function, lipid composition, and developmental potential are altered by diet in older mares. Reproduction. 2022;163:183u2013198. doi: 10.1530/REP-21-0351.
    doi: 10.1530/REP-21-0351pmc: PMC8942336pubmed: 37379450google scholar: lookup
  31. Gonzalez MB, Robker RL, Rose RD. Obesity and oocyte quality: Significant implications for ART and emerging mechanistic insights. Biol. Reprod. 2022;106:338u2013350. doi: 10.1093/biolre/ioab228.
    doi: 10.1093/biolre/ioab228pubmed: 34918035google scholar: lookup
  32. Noland RC, et al. Carnitine insufficiency caused by aging and overnutrition compromises mitochondrial performance and metabolic control. J. Biol. Chem. 2009;284:22840u201322852. doi: 10.1074/jbc.M109.032888.
    doi: 10.1074/jbc.M109.032888pmc: PMC2755692pubmed: 19553674google scholar: lookup
  33. Muoio DM, et al. Muscle-specific deletion of carnitine acetyltransferase compromises glucose tolerance and metabolic flexibility. Cell Metab. 2012;15:764u2013777. doi: 10.1016/j.cmet.2012.04.005.
    doi: 10.1016/j.cmet.2012.04.005pmc: PMC3348515pubmed: 22560225google scholar: lookup
  34. Vincent JB. New evidence against chromium as an essential trace element. J. Nutr. 2017;147:2212u20132219. doi: 10.3945/jn.117.255901.
    doi: 10.3945/jn.117.255901pubmed: 29021369google scholar: lookup
  35. Jamilian M, et al. The influences of chromium supplementation on glycemic control, markers of cardio-metabolic risk, and oxidative stress in infertile polycystic ovary syndrome women candidate for in vitro fertilization: A randomized, double-blind, placebo-controlled trial. Biol. Trace Elem. Res. 2018;185:48u201355. doi: 10.1007/s12011-017-1236-3.
    doi: 10.1007/s12011-017-1236-3pubmed: 29307112google scholar: lookup
  36. Jamilian M, et al. Effects of chromium and carnitine co-supplementation on body weight and metabolic profiles in overweight and obese women with polycystic ovary syndrome: A randomized, double-blind, placebo-controlled trial. Biol. Trace Elem. Res. 2020;193:334u2013341. doi: 10.1007/s12011-019-01720-8.
    doi: 10.1007/s12011-019-01720-8pubmed: 30977089google scholar: lookup
  37. Seiler SE, et al. Obesity and lipid stress inhibit carnitine acetyltransferase activity. J. Lipid Res. 2014;55:635u2013644. doi: 10.1194/jlr.M043448.
    doi: 10.1194/jlr.M043448pmc: PMC3966698pubmed: 24395925google scholar: lookup
  38. Gervais A, Battista M-C, Carranza-Mamane B, Lavoie HB, Baillargeon J-P. Follicular fluid concentrations of lipids and their metabolites are associated with intraovarian gonadotropin-stimulated androgen production in women undergoing in vitro fertilization. J. Clin. Endocrinol. Metab. 2015;100:1845u20131854. doi: 10.1210/jc.2014-3649.
    doi: 10.1210/jc.2014-3649pubmed: 25695883google scholar: lookup
  39. Igosheva N, et al. Maternal diet-induced obesity alters mitochondrial activity and redox status in mouse oocytes and zygotes. PLoS ONE. 2010;5:e10074. doi: 10.1371/journal.pone.0010074.
  40. Yang X, et al. Exposure to lipid-rich follicular fluid is associated with endoplasmic reticulum stress and impaired oocyte maturation in cumulus-oocyte complexes. Fertil. Steril. 2012;97:1438u20131443. doi: 10.1016/j.fertnstert.2012.02.034.
  41. Boots CE, Boudoures A, Zhang W, Drury A, Moley KH. Obesity-induced oocyte mitochondrial defects are partially prevented and rescued by supplementation with co-enzyme Q10 in a mouse model. Hum. Reprod. 2016;31:2090u20132097. doi: 10.1093/humrep/dew181.
    doi: 10.1093/humrep/dew181pmc: PMC4991662pubmed: 27432748google scholar: lookup
  42. Sutton-McDowall ML, et al. Nonesterified fatty acid-induced endoplasmic reticulum stress in cattle cumulus oocyte complexes alters cell metabolism and developmental competence1. Biol. Reprod. 2016 doi: 10.1095/biolreprod.115.131862.
    doi: 10.1095/biolreprod.115.131862pubmed: 26658709google scholar: lookup
  43. Carnevale EM. The mare model for follicular maturation and reproductive aging in the woman. Theriogenology. 2008;69:23u201330. doi: 10.1016/j.theriogenology.2007.09.011.
  44. Valckx SD, et al. Fatty acid composition of the follicular fluid of normal weight, overweight and obese women undergoing assisted reproductive treatment: A descriptive cross-sectional study. Reprod. Biol. Endocrinol. 2014;12:13. doi: 10.1186/1477-7827-12-13.
    doi: 10.1186/1477-7827-12-13pmc: PMC3916060pubmed: 24498875google scholar: lookup
  45. Pantasri T, et al. Distinct localisation of lipids in the ovarian follicular environment. Reprod. Fertil. Dev. 2015;27:593. doi: 10.1071/RD14321.
    doi: 10.1071/RD14321pubmed: 25751151google scholar: lookup
  46. Gonzalez MB, Lane M, Knight EJ, Robker RL. Inflammatory markers in human follicular fluid correlate with lipid levels and Body Mass Index. J. Reprod. Immunol. 2018;130:25u201329. doi: 10.1016/j.jri.2018.08.005.
    doi: 10.1016/j.jri.2018.08.005pubmed: 30174020google scholar: lookup
  47. Valckx SDM, et al. BMI-related metabolic composition of the follicular fluid of women undergoing assisted reproductive treatment and the consequences for oocyte and embryo quality. Hum. Reprod. 2012;27:3531u20133539. doi: 10.1093/humrep/des350.
    doi: 10.1093/humrep/des350pubmed: 23019302google scholar: lookup
  48. Mirabi P, et al. Does different BMI influence oocyte and embryo quality by inducing fatty acid in follicular fluid? Taiwan. J. Obstet. Gynecol. 2017;56:159u2013164. doi: 10.1016/j.tjog.2016.11.005.
    doi: 10.1016/j.tjog.2016.11.005pubmed: 28420500google scholar: lookup
  49. Ribeiro RM, et al. Changes in metabolic and physiological biomarkers in Mangalarga Marchador horses with induced obesity. Vet. J. 2021;270:105627. doi: 10.1016/j.tvjl.2021.105627.
    doi: 10.1016/j.tvjl.2021.105627pubmed: 33641803google scholar: lookup
  50. Wu LL-Y, et al. High-fat diet causes lipotoxicity responses in cumulus-oocyte complexes and decreased fertilization rates. Endocrinology. 2010;151:5438u20135445. doi: 10.1210/en.2010-0551.
    doi: 10.1210/en.2010-0551pubmed: 20861227google scholar: lookup
  51. Lolicato F, et al. The cumulus cell layer protects the bovine maturing oocyte against fatty acid-induced lipotoxicity1. Biol. Reprod. 2015 doi: 10.1095/biolreprod.114.120634.
    doi: 10.1095/biolreprod.114.120634pubmed: 25297544google scholar: lookup
  52. Aardema H, et al. Bovine cumulus cells protect maturing oocytes from increased fatty acid levels by massive intracellular lipid storage. Biol. Reprod. 2013;88:164u2013164. doi: 10.1095/biolreprod.112.106062.
    doi: 10.1095/biolreprod.112.106062pubmed: 23616596google scholar: lookup
  53. Montani DA, et al. The follicular microenviroment as a predictor of pregnancy: MALDI-TOF MS lipid profile in cumulus cells. J. Assist. Reprod. Genet. 2012;29:1289u20131297. doi: 10.1007/s10815-012-9859-y.
    doi: 10.1007/s10815-012-9859-ypmc: PMC3510365pubmed: 22968515google scholar: lookup
  54. Montani DA, et al. Lipid profile of cumulus cells as a predictive tool for pregnancy outcomes. Fertil. Steril. 2013;100:S343u2013S344. doi: 10.1016/j.fertnstert.2013.07.863.
  55. El-Hayek S, Yang Q, Abbassi L, FitzHarris G, Clarke HJ. Mammalian oocytes locally remodel follicular architecture to provide the foundation for Germline-soma communication. Curr. Biol. 2018;28:1124u20131131.e3. doi: 10.1016/j.cub.2018.02.039.
    doi: 10.1016/j.cub.2018.02.039pmc: PMC5882553pubmed: 29576478google scholar: lookup
  56. Altermatt JL, Suh TK, Stokes JE, Carnevale EM. Effects of age and equine follicle-stimulating hormone (eFSH) on collection and viability of equine oocytes assessed by morphology and developmental competency after intracytoplasmic sperm injection (ICSI) Reprod. Fertil. Dev. 2009;21:615u2013623. doi: 10.1071/RD08210.
    doi: 10.1071/RD08210pubmed: 19383268google scholar: lookup
  57. Dunning KR, Russell DL, Robker RL. Lipids and oocyte developmental competence: The role of fatty acids and u03b2-oxidation. Reproduction. 2014;148:R15u2013R27. doi: 10.1530/REP-13-0251.
    doi: 10.1530/REP-13-0251pubmed: 24760880google scholar: lookup
  58. Schooneman MG, Vaz FM, Houten SM, Soeters MR. Acylcarnitines. Diabetes. 2013;62:1u20138. doi: 10.2337/db12-0466.
    doi: 10.2337/db12-0466pmc: PMC3526046pubmed: 23258903google scholar: lookup
  59. Koves TR, et al. Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab. 2008;7:45u201356. doi: 10.1016/j.cmet.2007.10.013.
    doi: 10.1016/j.cmet.2007.10.013pubmed: 18177724google scholar: lookup
  60. Vu00e1rnagy u00c1, et al. Acylcarnitine esters profiling of serum and follicular fluid in patients undergoing in vitro fertilization. Reprod. Biol. Endocrinol. 2013;11:67. doi: 10.1186/1477-7827-11-67.
    doi: 10.1186/1477-7827-11-67pmc: PMC3724743pubmed: 23866102google scholar: lookup
  61. Ginther OJ, et al. Comparative study of the dynamics of follicular waves in mares and women. Biol. Reprod. 2004;71:1195u20131201. doi: 10.1095/biolreprod.104.031054.
  62. Calcaterra V, et al. Polycystic ovary syndrome in insulin-resistant adolescents with obesity: The role of nutrition therapy and food supplements as a strategy to protect fertility. Nutrients. 2021;13:1848. doi: 10.3390/nu13061848.
    doi: 10.3390/nu13061848pmc: PMC8228678pubmed: 34071499google scholar: lookup
  63. Gambineri A, et al. Female infertility: Which role for obesity? Int. J. Obes. Suppl. 2019;9:65u201372. doi: 10.1038/s41367-019-0009-1.
    doi: 10.1038/s41367-019-0009-1pmc: PMC6683114pubmed: 31391925google scholar: lookup
  64. Laskowski D, et al. Insulin during in vitro oocyte maturation has an impact on development, mitochondria, and cytoskeleton in bovine day 8 blastocysts. Theriogenology. 2017;101:15u201325. doi: 10.1016/j.theriogenology.2017.06.002.
  65. Hauck AK, Bernlohr DA. Oxidative stress and lipotoxicity. J. Lipid Res. 2016;57:1976u20131986. doi: 10.1194/jlr.R066597.
    doi: 10.1194/jlr.R066597pmc: PMC5087875pubmed: 27009116google scholar: lookup
  66. Li X, et al. Targeting mitochondrial reactive oxygen species as novel therapy for inflammatory diseases and cancers. J. Hematol. Oncol. 2013;6:19. doi: 10.1186/1756-8722-6-19.
    doi: 10.1186/1756-8722-6-19pmc: PMC3599349pubmed: 23442817google scholar: lookup
  67. Leroy JLMR, et al. Maternal metabolic health and fertility: We should not only care about but also for the oocyte! Reprod. Fertil. Dev. 2022;35:1u201318. doi: 10.1071/RD22204.
    doi: 10.1071/RD22204pubmed: 36592978google scholar: lookup
  68. Cheng M-H, et al. Novel microsensors revealed the impact of high maternal body weight and advanced maternal aging on individual human oocyte metabolic function. 78th Sci. Congr. Am. Soc. Reprod. Med. 2022;118:e153.
  69. Luzzo KM, et al. High fat diet induced developmental defects in the mouse: Oocyte meiotic aneuploidy and fetal growth retardation/brain defects. PLoS ONE. 2012;7:e49217. doi: 10.1371/journal.pone.0049217.
  70. Wu LL, et al. Mitochondrial dysfunction in oocytes of obese mothers: Transmission to offspring and reversal by pharmacological endoplasmic reticulum stress inhibitors. Development. 2015;142:681u2013691. doi: 10.1242/dev.114850.
    doi: 10.1242/dev.114850pubmed: 25670793google scholar: lookup
  71. Marei WFA, et al. Differential effects of high fat diet-induced obesity on oocyte mitochondrial functions in inbred and outbred mice. Sci. Rep. 2020;10:9806. doi: 10.1038/s41598-020-66702-6.
    doi: 10.1038/s41598-020-66702-6pmc: PMC7299992pubmed: 32555236google scholar: lookup
  72. Taherkhani S, Suzuki K, Ruhee RT. A brief overview of oxidative stress in adipose tissue with a therapeutic approach to taking antioxidant supplements. Antioxidants. 2021;10:594. doi: 10.3390/antiox10040594.
    doi: 10.3390/antiox10040594pmc: PMC8069597pubmed: 33924341google scholar: lookup
  73. Nilsson MI, et al. A multi-ingredient supplement protects against obesity and infertility in western diet-fed mice. Nutrients. 2023;15:611. doi: 10.3390/nu15030611.
    doi: 10.3390/nu15030611pmc: PMC9921271pubmed: 36771318google scholar: lookup
  74. Surai, P. F. Antioxidant Action of Carnitine: Molecular Mechanisms and Practical Applications. EC Veterinary Science2.1, 6-84 (2015).
  75. Raviv S, et al. Lipid droplets in granulosa cells are correlated with reduced pregnancy rates. J. Ovarian Res. 2020;13:4. doi: 10.1186/s13048-019-0606-1.
    doi: 10.1186/s13048-019-0606-1pmc: PMC6945749pubmed: 31907049google scholar: lookup
  76. Su Y-Q, Sugiura K, Eppig J. Mouse oocyte control of granulosa cell development and function: Paracrine regulation of cumulus cell metabolism. Semin. Reprod. Med. 2009;27:032u2013042. doi: 10.1055/s-0028-1108008.
    doi: 10.1055/s-0028-1108008pmc: PMC2742468pubmed: 19197803google scholar: lookup
  77. Robles M, et al. Maternal nutrition during pregnancy affects testicular and bone development, glucose metabolism and response to overnutrition in weaned horses up to two years. PLOS ONE. 2017;12:e0169295. doi: 10.1371/journal.pone.0169295.
  78. Robles M, et al. Placental function and structure at term is altered in broodmares fed with cereals from mid-gestation. Placenta. 2018;64:44u201352. doi: 10.1016/j.placenta.2018.02.003.
  79. Leroy J, Van Soom A, Opsomer G, Goovaerts I, Bols P. Reduced fertility in high-yielding dairy cows: Are the Oocyte and embryo in danger? Part II mechanisms linking nutrition and reduced oocyte and embryo quality in high-yielding dairy cows*. Reprod. Domest. Anim. 2008;43:623u2013632. doi: 10.1111/j.1439-0531.2007.00961.x.
  80. Rooke JA, et al. Dietary carbohydrates and amino acids influence oocyte quality in dairy heifers. Reprod. Fertil. Dev. 2009;21:419. doi: 10.1071/RD08193.
    doi: 10.1071/RD08193pubmed: 19261219google scholar: lookup
  81. Skoracka K, Ratajczak AE, Rychter AM, Dobrowolska A, Krela-Kau017amierczak I. Female fertility and the nutritional approach: The most essential aspects. Adv. Nutr. 2021;12:2372u20132386. doi: 10.1093/advances/nmab068.
    doi: 10.1093/advances/nmab068pmc: PMC8634384pubmed: 34139003google scholar: lookup
  82. Kaczmarek K, Janicki B, Gu0142owska M. Insulin resistance in the horse: A review. J. Appl. Anim. Res. 2016;44:424u2013430. doi: 10.1080/09712119.2015.1091340.
  83. Henneke DR, Potter GD, Kreider JL, Yeates BF. Relationship between condition score, physical measurements and body fat percentage in mares. Equine Vet. J. 1983;15:371u2013372. doi: 10.1111/j.2042-3306.1983.tb01826.x.
  84. Kane RA, Fisher M, Parrett D, Lawrence LM. [Proceedings of the] 10th Equine Nutrition and Physiology Symposium, June 11u201313, 1987, the Fort Collins Marriott, Colorado State University. Equine Nutrition and Physiology Society; 1987.
  85. Carter RA, Geor RJ, Burton Staniar W, Cubitt TA, Harris PA. Apparent adiposity assessed by standardised scoring systems and morphometric measurements in horses and ponies. Vet. J. 2009;179:204u2013210. doi: 10.1016/j.tvjl.2008.02.029.
    doi: 10.1016/j.tvjl.2008.02.029pubmed: 18440844google scholar: lookup
  86. Gentry LR, et al. The relationship between body condition score and ultrasonic fat measurements in mares of high versus low body condition. J. Equine Vet. Sci. 2004;24:198u2013203. doi: 10.1016/j.jevs.2004.04.009.
  87. Carnevale EM. Advances in collection, transport and maturation of equine oocytes for assisted reproductive techniques. Vet. Clin. North Am. Equine Pract. 2016;32:379u2013399. doi: 10.1016/j.cveq.2016.07.002.
    doi: 10.1016/j.cveq.2016.07.002pubmed: 27726987google scholar: lookup
  88. Larsen S, et al. The best approach: Homogenization or manual permeabilization of human skeletal muscle fibers for respirometry? Anal. Biochem. 2014;446:64u201368. doi: 10.1016/j.ab.2013.10.023.
    doi: 10.1016/j.ab.2013.10.023pubmed: 24161612google scholar: lookup
  89. Li Puma LC, et al. Experimental oxygen concentration influences rates of mitochondrial hydrogen peroxide release from cardiac and skeletal muscle preparations. Am. J. Physiol.-Regul. Integr. Comp. Physiol. 2020;318:R972u2013R980. doi: 10.1152/ajpregu.00227.2019.
    doi: 10.1152/ajpregu.00227.2019pubmed: 32233925google scholar: lookup
  90. Pfaffl MW. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001;29:45eu2013445. doi: 10.1093/nar/29.9.e45.
    doi: 10.1093/nar/29.9.e45pmc: PMC55695pubmed: 11328886google scholar: lookup
  91. Dieckmann-Schuppert A, Schnittler H-J. A simple assay for quantification of protein in tissue sections, cell cultures, and cell homogenates, and of protein immobilized on solid surfaces. Cell Tissue Res. 1997;288:119u2013126. doi: 10.1007/s004410050799.
    doi: 10.1007/s004410050799pubmed: 9042779google scholar: lookup
  92. Reisz JA, Zheng C, Du2019Alessandro A, Nemkov T. Untargeted and semi-targeted lipid analysis of biological samples using mass spectrometry-based metabolomics. In: Du2019Alessandro A, editor. High-Throughput Metabolomics: Methods and Protocols. Springer; 2019. pp. 121u2013135.
    pubmed: 31119660
  93. Smith CA, Want EJ, Ou2019Maille G, Abagyan R, Siuzdak G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 2006;78:779u2013787. doi: 10.1021/ac051437y.
    doi: 10.1021/ac051437ypubmed: 16448051google scholar: lookup
  94. Tautenhahn R, Bu00f6ttcher C, Neumann S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinform. 2008;9:504. doi: 10.1186/1471-2105-9-504.
    doi: 10.1186/1471-2105-9-504pmc: PMC2639432pubmed: 19040729google scholar: lookup
  95. Broeckling CD, Afsar FA, Neumann S, Ben-Hur A, Prenni JE. RAMClust: A novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal. Chem. 2014;86:6812u20136817. doi: 10.1021/ac501530d.
    doi: 10.1021/ac501530dpubmed: 24927477google scholar: lookup
  96. Cheng M-H, Chicco AJ, Ball D, Chen TW. Analysis of mitochondrial oxygen consumption and hydrogen peroxide release from cardiac mitochondria using electrochemical multi-sensors. Sens. Actuators B Chem. 2022;360:131641. doi: 10.1016/j.snb.2022.131641.
    doi: 10.1016/j.snb.2022.131641google scholar: lookup

Citations

This article has been cited 0 times.