FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Acta veterinaria Scandinavica2016; 58(Suppl 1); 62; doi: 10.1186/s13028-016-0243-2

Relationships between body condition score and ultrasound skin-associated subcutaneous fat depth in equids.

Abstract: In equids, health and welfare depend on body composition. A growing number of equids are now used as leisure and companion animals, and often found overfeed. The need for a close monitoring of body fatness led to the search for tools allowing a rapid and non-invasive estimation of fatness. This study intends to assess real-time ultrasonography (RTU) usefulness in establishing a relationship between ultrasound measures of subcutaneous fat-plus-skin thickness (SF-Skin) and body condition score (BCS) in horses and donkeys. Forty-three healthy animals (16 donkeys and 27 horses) were used in this study to generate 95 records (RTU and BCS pairs), in multiple RTU sessions for 2 years. Using visual appraisal and palpation, BCS was graded in a 1-9 points scale. Real-time ultrasonography images were taken using a 7.5 MHz linear transducer, placed perpendicular to the backbone, over the 3rd lumbar vertebra. ImageJ was used to measure the SF-Skin on RTU images. The relation between BCS and SF-Skin measurements was tested by linear and polynomial regression analysis. Results: The BCS values were similar in horses (5.50; from 3 to 8 points) and donkeys (5.14; from 3 to 7 points). The SF-Skin measures show a similar trend (a mean of 7.1 and 7.7 mm in horses and donkeys, respectively). A polynomial regression among BCS and SF-Skin explained 92 and 77 % of the variation in donkeys and horses respectively. The coefficient of determination was considerably higher for the regression developed for donkeys compared with that of horses (R2 = 0.92 vs. 0.77, respectively), which reduced the accuracy of the method in horses. Both the linear and polynomial models tested show a strong relationship among BCS and SF-Skin for donkeys (R2 > 0.91; P < 0.01) and horses (R2 > 0.74; P < 0.01), despite that the extremes for BCS did not existed in our sample. Conclusions: Our results showed the potential RTU usefulness to monitor body fat in equids. Using a high-frequency transducer and RTU together with image analysis allowed the identification of small SF-skin variations. This report will support further studies on the relationships between SF-Skin and BCS, particularly in extreme BCS scores.
Get Full Text
Publication Date: 2016-10-20 PubMed ID: 27766985PubMed Central: PMC5073852DOI: 10.1186/s13028-016-0243-2Google 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 article investigates the potential use of real-time ultrasonography (RTU) in estimating the body fat of equids (horses and donkeys), by establishing a relationship between ultrasound measurements of subcutaneous fat and skin thickness, and the traditional body condition score.

Study Goal and Methodology

  • The main objective of this study was to evaluate the usefulness of real-time ultrasonography (RTU) in determining the relation between ultrasound measurements of subcutaneous fat-plus-skin thickness (SF-Skin) and the body condition score (BCS) in horses and donkeys.
  • 43 healthy animals, consisting of 16 donkeys and 27 horses, were used to gather 95 records in multiple RTU sessions over a period of 2 years.
  • The BCS was evaluated based on visual assessment and palpation, on a scale of 1 to 9. Ultrasound images were captured with a 7.5 MHz linear transducer, positioned perpendicular to the backbone, over the 3rd lumbar vertebra.
  • A software called ImageJ was used to measure SF-Skin from the ultrasound images, with the results assessed through linear and polynomial regression analysis.

Study Results

  • The study found that BCS values were fairly similar among both animas – with an average of 5.50 among horses and 5.14 among donkeys.
  • Measurements of SF-Skin exhibited a similar trend, with averages of 7.1 and 7.7 mm in horses and donkeys respectively.
  • The regression analysis showed a strong relationship between BCS and SF-Skin, explaining 92% of the variation in donkeys and 77% in horses. The accuracy of this method was found to be higher in donkeys compared to horses.
  • Despite the absence of extreme BCS scores in the sample, both linear and polynomial models showed a significant relationship between BCS and SF-Skin in both horses (R2 > 0.74; P < 0.01) and donkeys (R2 > 0.91; P < 0.01).

Study Conclusion

  • The researchers concluded that real-time ultrasonography (RTU), when used in conjunction with high-frequency transducer and image analysis, has the potential to effectively monitor body fat in equids through tracking small changes in SF-Skin.
  • The findings of this study will support ongoing research into the relationships between SF-Skin and BCS, particularly in equids with extreme BCS scores.

Cite This Article

APA
Silva SR, Payan-Carreira R, Quaresma M, Guedes CM, Santos AS. (2016). Relationships between body condition score and ultrasound skin-associated subcutaneous fat depth in equids. Acta Vet Scand, 58(Suppl 1), 62. https://doi.org/10.1186/s13028-016-0243-2

Publication

Acta veterinaria Scandinavica
ISSN: 1751-0147
NlmUniqueID: 0370400
Country: England
Language: English
Volume: 58
Issue: Suppl 1
Pages: 62
PII: 62

Researcher Affiliations

Silva, Severiano R
  • CECAV-Centro de Ciência Animal e Veterinária, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801, Vila Real, Portugal.
Payan-Carreira, Rita
  • CECAV-Centro de Ciência Animal e Veterinária, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801, Vila Real, Portugal. rtpayan@gmail.com.
Quaresma, Miguel
  • CECAV-Centro de Ciência Animal e Veterinária, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801, Vila Real, Portugal.
Guedes, Cristina M
  • CECAV-Centro de Ciência Animal e Veterinária, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801, Vila Real, Portugal.
Santos, Ana Sofia
  • EUVG-Escola Universitária Vasco da Gama, Campus Universitário, Bloco B, Lordemão, 3020-210, Coimbra, Portugal.
  • CITAB, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801, Vila Real, Portugal.

MeSH Terms

  • Adiposity
  • Animals
  • Body Composition / physiology
  • Equidae
  • Horses / anatomy & histology
  • Horses / physiology
  • Regression Analysis
  • Skin / diagnostic imaging
  • Subcutaneous Fat / diagnostic imaging

References

This article includes 33 references
  1. Hemsworth LM, Jongman E, Coleman GJ. Recreational horse welfare: the relationships between recreational horse owner attributes and recreational horse welfare.. Appl Anim Behav Sci 2015;165:1–16.
    doi: 10.1016/j.applanim.2014.11.019google scholar: lookup
  2. Quaresma M, Payan-Carreira R, Silva SR. Relationship between ultrasound measurements of body fat reserves and body condition score in female donkeys.. Vet J 2013 Aug;197(2):329-34.
    doi: 10.1016/j.tvjl.2012.12.031pubmed: 23395347google scholar: lookup
  3. Argo CM, Dugdale AHA, Curtis GC, Morrison PK. Evaluating body composition in living horses: where are we up to?. In: Maltin CA, Craigie C, Bunger L, editors. Farm animal imaging. Copenhagen, Ingliston: Quality Meat Scotland; 2014. pp. 12–17.
  4. Dugdale AH, Curtis GC, Harris PA, Argo CM. Assessment of body fat in the pony: part I. Relationships between the anatomical distribution of adipose tissue, body composition and body condition.. Equine Vet J 2011 Sep;43(5):552-61.
    doi: 10.1111/j.2042-3306.2010.00330.xpubmed: 21496091google scholar: lookup
  5. Mottet R, Onan G, Hiney K. Revisiting the Henneke body condition scoring system: 25 years later.. J Equine Vet Sci 2009;29:417–418.
    doi: 10.1016/j.jevs.2009.04.116google scholar: lookup
  6. Scholz AM, Bünger L, Kongsro J, Baulain U, Mitchell AD. Non-invasive methods for the determination of body and carcass composition in livestock: dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: invited review.. Animal 2015 Jul;9(7):1250-64.
    doi: 10.1017/S1751731115000336pmc: PMC4492221pubmed: 25743562google scholar: lookup
  7. Superchi P, Vecchi I, Beretti V, Sabbioni A. Relationship among BCS and fat thickness in horses of different breed, gender and age.. Ann Res Rev Biol 2014;4:354–365.
    doi: 10.9734/ARRB/2014/6947google scholar: lookup
  8. Schröder UJ, Staufenbiel R. Invited review: Methods to determine body fat reserves in the dairy cow with special regard to ultrasonographic measurement of backfat thickness.. J Dairy Sci 2006 Jan;89(1):1-14.
    doi: 10.3168/jds.S0022-0302(06)72064-1pubmed: 16357263google scholar: lookup
  9. Bewley JM, Schutz MM. Potential of using new technology for estimating body condition scores.. In: Proceedings of the 18th annual tri-state dairy nutrition conference, Fort Wayne, Indiana, USA; 2009. p. 23–38.
  10. Silva SR, Cadavez VP. Real-time ultrasound (RTU) imaging methods for quality control of meats.. In: Sun DW, editor. Computer vision technology in the food and beverage industries. Cambridge: Woodhead Publishing; 2012. pp. 277–329.
  11. Allen P. Measuring body composition in live meat animal.. In: Wood JD, Fisher AV, editors. Reducing fat in meat animals. London: Elsevier; 1990. pp. 201–254.
  12. Thwaites CJ. Ultrasonic estimation of carcass composition—a review.. Aust Meat Res Comm 1984;47:1–32.
  13. Westervelt R, Stouffer JR, Hintz HF, Schryver HF. Estimating fatness in horses and ponies.. J Anim Sci 1976;43:781–785.
    doi: 10.2527/jas1976.434781xgoogle scholar: lookup
  14. Silva SR, Afonso JJ, Santos VA, Monteiro A, Guedes CM, Azevedo JM, Dias-da-Silva A. In vivo estimation of sheep carcass composition using real-time ultrasound with two probes of 5 and 7.5 MHz and image analysis.. J Anim Sci 2006 Dec;84(12):3433-9.
    doi: 10.2527/jas.2006-154pubmed: 17093238google scholar: lookup
  15. Szabo TL. Diagnostic ultrasound imaging: inside out.. Hartford: Academic Press Series in Biomedical Engineering; 2004.
  16. Stouffer JR. History of ultrasound in animal science.. J Ultrasound Med 2004 May;23(5):577-84.
    pubmed: 15154523doi: 10.7863/jum.2004.23.5.577google scholar: lookup
  17. Goddard PR. General principles.. In: Goddard PJ, editor. Veterinary ultrasonography. Wallingford: CAB International; 1995. pp. 1–19.
  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.
    doi: 10.1111/j.2042-3306.1983.tb01826.xpubmed: 6641685google scholar: lookup
  19. MacNeil MD. Choice of a prediction equation and the use of the selected equation in subsequent experimentation.. J Anim Sci 1983;57:1328–1336.
    doi: 10.2527/jas1983.5751328xgoogle scholar: lookup
  20. Gentry LR, Thompson DL, Gentry GT. The relationship between body condition score and ultrasonic fat measurements in mares of high versus low body condition.. J Equine Vet Sci 2004;24:198–203.
    doi: 10.1016/j.jevs.2004.04.009google scholar: lookup
  21. Christie JL, Hewson CJ, Riley CB, McNiven MA, Dohoo IR, Bate LA. Management factors affecting stereotypies and body condition score in nonracing horses in Prince Edward Island.. Can Vet J 2006 Feb;47(2):136-43.
    pmc: PMC1345728pubmed: 16579039
  22. 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
  23. Argo CM. Feeding thin and starved horses.. In: Geor RJ, Harris PA, Coenen M, editors. Equine applied and clinical nutrition. Amsterdam: Elsevier; 2013. pp. 503–511.
  24. Gee EK, Fennessy PF, Morel PC, Grace ND, Firth EC, Mogg TD. Chemical body composition of 20 Thoroughbred foals at 160 days of age, and preliminary investigation of techniques used to predict body fatness.. N Z Vet J 2003 Jun;51(3):125-31.
    doi: 10.1080/00480169.2003.36351pubmed: 16032311google scholar: lookup
  25. McLaren DG, Novakofski J, Parrett DF, Lo LL, Singh SD, Neumann KR, McKeith FK. A study of operator effects on ultrasonic measures of fat depth and longissimus muscle area in cattle, sheep and pigs.. J Anim Sci 1991 Jan;69(1):54-66.
    doi: 10.2527/1991.69154xpubmed: 2005038google scholar: lookup
  26. Kane RA, Fisher M, Parrett D, Lawrence LM. Estimating fatness in horses.. In: Proceedings of the 10th equine nutrition and physiology symposium. Urbana; 1987. p. 127–31.
  27. Kempster AJ, Arnall D, Alliston JC, Barker JD. An evaluation of two ultrasonic machines (Scanogram and Danscanner) for predicting the body composition of live sheep.. Anim Prod 1982;34:249–255.
    doi: 10.1017/S0003356100010187google scholar: lookup
  28. Silva SR, Gomes MJ, Dias-da-Silva A, Gil LF, Azevedo JM. Estimation in vivo of the body and carcass chemical composition of growing lambs by real-time ultrasonography.. J Anim Sci 2005 Feb;83(2):350-7.
    doi: 10.2527/2005.832350xpubmed: 15644507google scholar: lookup
  29. Thériault M, Pomar C, Castonguay FW. Accuracy of real-time ultrasound measurements of total tissue, fat, and muscle depths at different measuring sites in lamb.. J Anim Sci 2009 May;87(5):1801-13.
    doi: 10.2527/jas.2008-1002pubmed: 19151153google scholar: lookup
  30. Martin-Rosset W, Vernet J, Dubroeucq H, Arnaud G, Picard A, Vermorel M. Variation of fatness and energy content of the body with body condition score in sport horses and its prediction.. In: Saastamoinen MT, Martin-Rosset W, editors. Nutrition of the exercising horse. Wageningen: Wageningen Academic Publishers; 2008. pp. 167–176.
  31. Gregory NG, Robins JK, Thomas DG, Purchas W. Relationship between body condition score and body composition in dairy cows.. New Zeal J Agric Res 1998;41:527–532.
    doi: 10.1080/00288233.1998.9513335google scholar: lookup
  32. Ripoll G, Joy M, Sanz A. Estimation of carcass composition by ultrasound measurements in 4 anatomical locations of 3 commercial categories of lamb.. J Anim Sci 2010 Oct;88(10):3409-18.
    doi: 10.2527/jas.2009-2632pubmed: 20562368google scholar: lookup
  33. Carter RA, Dugdale AHA. Assessment of body condition and bodyweight.. In: Geor RJ, Harris PA, Coenen M, editors. Equine applied and clinical nutrition. Amsterdam: Elsevier; 2013. pp. 393–404.

Citations

This article has been cited 6 times.
  1. Harada K, Akioka K, Izu I, Sasaki N. Ultrasonography-based diagnosis of hemorrhage syndrome in adipose tissues in the crest of the neck of heavy horse breeds. J Vet Med Sci 2023 Jun 13;85(6):637-641.
    doi: 10.1292/jvms.23-0084pubmed: 37183015google scholar: lookup
  2. Nocera I, Bonelli F, Turini L, Madrigali A, Aliboni B, Sgorbini M. Evaluation of Ultrasound Measurement of Subcutaneous Fat Thickness in Dairy Jennies during the Periparturient Period. Animals (Basel) 2022 May 26;12(11).
    doi: 10.3390/ani12111359pubmed: 35681823google scholar: lookup
  3. Domino M, Romaszewski M, Jasiński T, Maśko M. Comparison of the Surface Thermal Patterns of Horses and Donkeys in Infrared Thermography Images. Animals (Basel) 2020 Nov 24;10(12).
    doi: 10.3390/ani10122201pubmed: 33255408google scholar: lookup
  4. Daradics Z, Popescu M, Cătoi C, Mircean MV, Macri A, Mîrza O, Szakacs A, Daina S, Fetea F, Tripon MA, Lupșan AF, Bungărdean D, Călugăr A, Bora FD, Crecan CM. Forage Carbohydrate Profiles and Endocrine Morphometric Interactions in Traditionally Managed Horses from Romania. Life (Basel) 2025 Nov 6;15(11).
    doi: 10.3390/life15111721pubmed: 41302145google scholar: lookup
  5. Aboelmaaty AM, Ahdy AM, El-Khodery S, Elgioushy M. Investigations on metabolic diseases of horses in Egypt. Front Vet Sci 2025;12:1591090.
    doi: 10.3389/fvets.2025.1591090pubmed: 40901060google scholar: lookup
  6. Spanner EA, de Graaf SP, Rickard JP. A multivariate model for the prediction of pregnancy following laparoscopic artificial insemination of sheep. Sci Rep 2024 Nov 11;14(1):27556.
    doi: 10.1038/s41598-024-79253-xpubmed: 39528692google scholar: lookup
Subscribe to our newsletter
Join 99,911 horse owners like you who receive our email updates on horse health and nutrition.