Development of an index based on ultrasonographic measurements for the objective appraisal of body condition in Andalusian horses

Tamara Martin-Gimenez

Universidad de Zaragoza, Faculty of Veterinary Sciences, Dept. Animal Pathology, c/ Miguel Servet 177, 50013 Zaragoza, Spain

Carla N. Aguirre-Pascasio

University of Murcia, Teaching Veterinary Hospital, Campus Universitario de Espinardo, 30100 Espinardo, Murcia, Spain

Ignacio de Blas

Universidad de Zaragoza, Faculty of Veterinary Sciences, Dept. Animal Pathology, c/ Miguel Servet 177, 50013 Zaragoza, Spain

Universidad de Zaragoza-CITA, Instituto Agroalimentario de Aragón (IA2), c/ Miguel Servet 177, 50013 Zaragoza, Spain



Body condition scoring (BCS) is an indirect measure of the level of subcutaneous fat; however, by measuring the subcutaneous fat thicknesses (SFT), the precision of the degree of fatness assessment is improved. The aims were: 1) to develop an alternative body fat scoring index (BFSI) based on ultrasonographic measurements; 2) to assess the agreement between BCS and the new index applied to Andalusian horses; 3) to adjust the BCS cut-off values (if necessary) for overweight and obesity in this breed. One hundred and sixty-six Andalusian horses were included in this cross sectional study. On each horse, BCS, body fat percentage (BF%) and ultrasonography of SFT at localized deposits were evaluated. According to BFSI five possible body categories were established. Only one horse (0.6%) was classified as emaciated, 9.0% as thin, 74.7% as normal, 11.4% as overweight and 4.2% as obese. Despite higher BCS and SFT values were observed compared to other breeds, most of the horses evaluated presented a normal body condition under the new BFSI. BCS and BFSI were significantly associated (p<0.001), however, the concordance was low (weighted Cohen’s kappa coefficient, 0.262 ± 0.071; p=0.004). Using BFSI, obese horses had significantly greater BF% than the rest of categories (p<0.001). BCS showed a good diagnostic accuracy for detection overweight (AUC = 0.759 ± 0.055; p<0.001) and obese (AUC = 0.878 ± 0.050; p=0.001) horses; redefining the cut-off values for overweight and obesity condition as 7.5/9 and 8.5/9 respectively in Andalusian horses.

Additional key words: ultrasonography; subcutaneous fat deposits; objective score; adiposity; obesity; index.

Abbreviations used: AUC (area under the curve); BMI (body mass index); BCS (body condition score); BFSI (body fat scoring index); ROC (receiver operating characteristic curve); SFT (subcutaneous fat thickness); SFT-N25% (SFT over the first third of the neck-length); SFT-N50% (SFT over the second third of the neck-length); SFT-N75% (SFT over the last third of the neck-length); SFT-S (SFT behind the shoulder); SFT-Rb (SFT over the ribs); SFT-R (SFT over the rump); SFT-TH (SFT over the tailhead); BF% (body fat percentage).

Authors' contributions: TM contributed to the conception and study design, execution, data analysis and interpretation, and drafting of the manuscript. CNAP contributed to the conception and study design, revising it critically and preparation of the article. IB contributed to the study design, data analysis and interpretation, and drafting of the manuscript.

Citation: Martin-Gimenez, T.; Aguirre-Pascasio, C. N.; de Blas, I. (2017). Development of an index based on ultrasonographic measurements for the objective appraisal of body condition in Andalusian horses. Spanish Journal of Agricultural Research, Volume 15, Issue 4, e0609.

Received: 18 May 2017. Accepted: 15 Dec 2017.

Copyright © 2017 INIA. This is an open access article distributed under the terms of the Creative Commons Attribution (CC-by) Spain 3.0 License.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Correspondence should be addressed to Tamara Martin-Gimenez:











Body mass index (BMI) is the most common objective measure used to classify excess adiposity in human beings (Lau et al., 2007). Although BMI has also been applied in horses and ponies (Donaldson et al., 2004; Carter et al., 2009a; Thatcher et al., 2012; Banse & McFarlane, 2014), the main system to assess body condition in horses is based on assigning a subjective body condition score (BCS). This method consists in evaluating the deposition of subcutaneous fat in specific body regions and the subsequent assignment of a score considering established criteria through a palpation and visual assessment (Carter & Dugdale, 2013). Even though it has become a universally accepted method to estimate the degree of fatness (Dugdale et al., 2012), BCS possesses well-known limitations at the individual level as occurs with the BMI, including the inability of both systems to directly distinguish between lean and fat tissue (Frankenfield et al., 2001; Geor & Harris, 2013). Therefore, with the same BCS, substantial variation in adiposity can occur (Dugdale et al., 2011a). Furthermore, in the case of BCS, its inherent subjectivity and, thus, the semi-quantitative nature of this evaluation, lead to the belief that these scoring systems are unreliable and are necessary clinically more applicable and useful subdivisions to differentiate horses with higher scores (Burkholder, 2000; Dugdale et al., 2012).

Considering that the limitations are common not only in horses but also in all animal species where BCS has been utilized, several studies have developed alternative methods to differentiate body condition using objective criteria (Bewley et al., 2008; Azzaro et al., 2011; Halachmi et al., 2013; Das & Paksoy, 2015; Pfeifer et al., 2017). However, some of them require specialised equipment, software and personnel to interpret the results making them less applicable in clinical settings. Moreover, most of them have been implemented only for their use in cattle (Bewley et al., 2008; Azzaro et al., 2011; Halachmi et al., 2013; Das & Paksoy, 2015; Pfeifer et al., 2017) and therefore, the necessity to find a suitable method to objectively evaluate the body condition in horses is still lacking. Against this perspective, taking into account that body weight alone is not a good indicator of relative adiposity (Carter & Dugdale, 2013), a good assessment of body fat reserves, minimizing the influence of body dimensions and intestinal contents, can be achieved by evaluating ultrasonographically the amount of subcutaneous fat.

Therefore, the aims of this study were: 1) to develop an alternative body scoring index based on the ultrasonographic evaluation of localized subcutaneous fat deposits; 2) to assess the agreement between BCS and the new index applied to Andalusian horses; 3) to adjust the BCS cut-off values (if necessary) for overweight and obesity in this breed.



From a population of over 1,500 Andalusian horses located in south-eastern Spain (comprising the provinces of Albacete, Alicante and Murcia), 166 (6.7 ± 3.7 years, 78 stallions and 88 females) were utilized in this cross sectional study for the development of a new body fat scoring index (BFSI) built on the application of ultrasonographic measurements. The sample was randomly selected in order to ensure a sufficient number of horses from both genders representing their score characteristics, and hence testing the reliability of this method.

Body and fat measurements

Using the nine point scale described by Henneke et al. (1983), two independent and trained evaluators assigned the scores on each horse and the average value was used as final BCS. Considering previous descriptions (Thatcher et al., 2012) but also the phenotypic characteristics of the Andalusian horses, the following four body categories were established: thin horses (if BCS = 4.5), normal body condition (if BCS 5- 6.5), overweight (if BCS 7-7.5) and obese horses (if BCS = 8).

Afterwards, the amount of fat reserves were measured evaluating the subcutaneous fat thickness (SFT) by real-time ultrasound at seven localized fat deposits which included: three equidistant areas along the neck crest (SFT-N25%, SFT-N50%, SFT-N75%), behind the shoulder (SFT-S), the ribs (SFT-Rb), the rump (SFT-R) and the tailhead (SFT-TH) as previously described (Martin-Gimenez et al., 2016). Ultrasonography was carried out using a portable Honda Electronics HS-1500V (Aichi, Japan) ultrasound device in B-mode with a linear transducer at 7.5 MHz frequency. BCS evaluation and the ultrasonographic measurements were taken for each horse on the same day. Measurements were obtained by freezing the image on the screen and measuring the position of maximal fat thickness. All measurements of SFT were performed in triplicate by the same researcher. To assess the reliability of repeated measurements, the intraclass correlation coefficients were calculated showing a significant repeatability (p<0.001) of 96.8% for SFT-N25%, 95.6% for SFT-N50%, 97.2% for SFT-N75%, 98.6% for SFT-S, 98.6% for SFT-Rb, 99.4% for SFT-R and 99.6% for SFT-TH. Because the agreement between different measurements was good (Fleiss, 1986) mean values of the three measurements were used for statistical analyses.

Body fat percentage (BF%) was also calculated to assess the new BFSI as monitor parameter of body fat stores. This variable was estimated from the equation of Kane et al. (1987) where: BF% = 2.47 + 5.47 * (rump fat in cm). The site to measure rump fat was determined by placing the probe over the rump at approximately 5 cm lateral from the midline at the centre of the pelvic bone (Westervelt et al., 1976).

All the measurements and body condition estimations were collected following informed consent from the owners.

Statistical analysis

Data were expressed as mean ± standard deviation (SD) or percentage, as appropriate. Normality of quantitative variables was checked using the Kolmogorov-Smirnov test.

To test the usefulness of SFT measurement technique, two different approaches were performed: 1) analysis of variance (ANOVA) to determine the relationship among the different scores included in the BCS system and the SFT values and; 2) association between BCS, BF% and SFTs using the Pearson’s and Spearman’s correlation coefficients. Besides, Student’s t test for independent samples was used to evaluate the association among the gender and the SFTs (Daniel, 2000).

Construction of the new body fat scoring index

Mean and SD of each SFT measurement were calculated. Depending on the number of SDs from the average SFT value, a standardized score (equal to the integer part of the standardized residual) was assigned to every SFT on each horse. The difference between an individual SFT value and the mean divided by the SD corresponds to the standardized residual (Daniel, 2000). To simplify these calculations and taking into account the mean and SD, different intervals were established attributing to them the following standardized scores: -2 (- 8, mean – 2*SD], -1 (mean – 2*SD, mean – SD], 0 (mean – SD, mean + SD]), +1 [mean + SD, mean + 2*SD), +2 [mean + 2*SD, mean + 3*SD) and +3 [mean + 3*SD, + 8). The overall objective score of a horse resulted from the sum of all scores obtained at each anatomical area. Differences between genders in overall objective scores were assessed using Student’s t test or Mann-Whitney test depending on normality. Mean and SD of the overall objective scores and their intervals, similarly to what was done with each of the SFT measurements, were estimated defining five possible BFSI categories. To analyse the association between BCS and the BFSI, Pearson’s Chi-square test, Spearman’s correlation and Cohen’s kappa coefficients were calculated (Cohen, 1968; Daniel, 2000; Thrusfield, 2005).

Spearman’s correlations were used to evaluate the association between BF%, BCS and BFSI. To determine changes in BF% across the BCS and BFSI categories, an ANOVA was performed. Duncan post hoc test was used to separate between significant means.

The reliability of the BCS to distinguish between horses that did and did not exhibit an overweight or obesity state defined by the new BFSI, was estimated by calculating the area under the curve (AUC). The coordinates of the receiver operating characteristic curve (ROC) were used to set the cut-off values that maximised the accuracy (proportion of true results) (Greiner et al., 2000). Their confidence intervals (CI) were calculated using the Wilson’s score method (Wilson, 1927).

The analyses were carried out with the statistical software program IBM SPSS for Windows Vers. 19, except for the calculation of weighted Cohen’s kappa coefficient that was used StatsToDo ( Values of p<0.05 were considered significant.


The global mean of BCS was 6.12 ± 1.05 without significant differences between both genders (p=0.695). Agreement between the two body condition evaluators was moderate (Cohen’s kappa weighted =0.493, CI95%: 0.423, 0.564; p<0.001). Mean values ± SD of the seven SFT measurements are described in Table 1. Regard to the gender, males presented significantly higher values at four SFT measurements (SFT-N25%, SFT-N50%, SFT-S and SFT-Rb) than females (Table 1). Subcutaneous fat thicknesses (with the exception of SFT-N25% and SFT-N50%) were significantly correlated with BCS and BF% (Table 1). Likewise, most of SFT measurements were significantly different depending on the BCSs assigned in the sample (Table 1), increasing their values as the BCS does, despite the low correlations (Fig. 1).

Table 1. Global mean ± SD values of subcutaneous fat thickness (SFT) and their relationship with gender, body condition score (BCS) and body fat percentage (BF%).

Figure 1. Variation of subcutaneous fat thickness (SFT) (mean ± standard error) across the body condition scores (BCS). Significance of ANOVA: SFT-N25%, p=0.238; SFT-N50%, p=0.740; SFT-N75%, p <0.001; SFT-S, p=0.002; SFT-Rb, p=0.005; SFT-R, p <0.001; SFT-TH, p<0.001.

Due to significant differences in SFT values were observed according to the gender, firstly different sets of intervals had to be created stratified by sex to define the standardized scores corresponding to each fat deposit (Table 2). Secondly, the overall objective score of a horse was calculated adding up the seven standardized scores obtained. Global mean of the new BFSI was 0.265 ± 2.697 being similar between males and females (p=0.910). Based on the absence of differences between genders after standardizing the scores, it was possible to define the final scores common to both genders. In this instance, five body categories (emaciated, thin, normal, overweight and obese) were proposed so that an animal with an overall standardized score equal to or greater than 6 was considered as obese, while an animal with a score between 3 and 5 (both inclusive) was classified as overweight (Table 3).

Table 2. Standardized scores and corresponding intervals for each subcutaneous fat thickness (SFT) measurement according to the gender

Table 3. Body condition classification using the new body fat scoring index (BFSI)

Table 3. Body condition classification using the new body fat scoring index (BFSI)

The application of the BCS system showed that the majority of the samples were distributed among the interval scores of 5 and 6.5 (63.9%) and 7-7.5 (22.9%). In addition, 8.4% and 4.8% were classified as thin horses (BCS = 4.5) and obese (BCS = 8) respectively. Concerning the BFSI, only one horse was classified as emaciated (0.6%) (because of its low representativeness, it was excluded for further calculation of agreement). Most of the horses (74.7%) had a normal body condition and 11.4% were overweight. Fifteen horses (9.0%) were considered as thin horses while seven (4.2%) were categorized as obese (Table 4 & Fig. 2).

Table 4. Association among the body condition score (BCS) and the new body fat scoring index (BFSI) categories.

Figure 2. Distribution and comparison of body condition categories when body condition score (BCS) and body fat scoring index (BFSI) are applied.

The correlation between BCS and the BFSI was significant and moderate attending to the Spearman’s correlation coefficient value (rho=0.428; p<0.001). In addition, the association among the BCS and the BFSI categories was highly significant (p<0.001), however the concordance between both body scoring methods was very low, evidenced by a weighted Cohen’s kappa coefficient of 0.262 ± 0.071 (p=0.004) (Table 4). In this manner, it could be observed as remarkable results, that half of horses with BCS = 8 were classified as having a normal body condition with the BFSI, while the remaining 50% were equally distributed among overweight and obese. Similarly, in the case of the horses with BCS = 7-7.5, more than half (65.8%) were categorized as normal with the BFSI, 23.7% as overweight and 10.5% as obese (Table 4).

The global BF% was 10.36 ± 2.94 without significant differences between both genders (p=0.126). Otherwise, BF% was significantly correlated with BCS (rho=0.491; p<0.001) and BFSI (rho=0.503; p<0.001). Depending on the BCS categorization, significant differences (p<0.001) were found among thin (7.74 ± 2.89) vs normal horses (9.82 ± 2.18) and, these two categories vs overweight (12.10 ± 2.73) and obese (13.84 ± 5.42). However, no differences were observed between overweight and obese horses. On the contrary when BFSI was applied, obese horses (16.03 ± 4.45) presented higher BF% values (p<0.001) respect to overweight horses (12.50 ± 2.78), as well as these two categories respect to normal (9.93 ± 2.36) and thin horses (8.84 ± 2.71). In this case, BF% was similar between thin and normal horses.

Diagnostic accuracy of BCS to distinguish overweight or obese horses from all other horses was assessed by evaluation of areas under the ROC curves. For the first case, BCS had an AUC =0.759 ± 0.055 (p<0.001). In the second case, BCS presented an AUC = 0.878 ± 0.050 (p=0.001) (Fig. 3). The ROC curve analysis was also employed to determine the BCS cut-off values for detecting overweight (BFSI = [3, 5]) and obese (BFSI =6) horses, and these values were 7.5 with an accuracy of 85.54% (CI95%: 79.39%, 90.09%) and 8.5 with an accuracy of 96.99% (CI95%: 93.14%, 98.71%), respectively.

Figure 3. Operating characteristic curves (ROC) of body condition score for estimation of the overweight and obese states.


Previous studies have proposed that Andalusian horses have an innate tendency towards obesity. However, previous appraisements of their body condition have been made based on palpation and visual estimation (Bamford et al., 2013; Potter et al., 2013). The main goals of this study have been to estimate objectively the body condition in this breed, and to demonstrate that the assumed BCS cut-off values indicative of overweight and obesity state need to be modified in this breed.

Body scoring systems have been applied across diverse animal species from its use in primates (Clingerman & Summers, 2005), wild animals (Gerhart et al., 1996), cattle (Edmonson et al., 1989) and companion animals including horses (Henneke et al., 1983; Carroll & Huntington, 1988; Laflamme, 1997; Mawby et al., 2004). Otherwise, ultrasonography has demonstrated to be an accepted method for measuring fat reserves in farm species (Silva & Cadavez, 2012) and equids (Gentry et al., 2004) due to its objectivity, repeatability of the technique (Martin-Gimenez et al., 2016), low cost and the possibility of being used in field conditions (Quaresma et al., 2013). Thus, in many species, ultrasonography has also been utilized to validate the condition scoring process (Domecq et al., 1994; Gentry et al., 2004; Alapati et al., 2010; Morfeld et al., 2014) and/or to predict the total fat content using mathematical equations that frequently include some SFT measurement (Westervelt et al., 1976; Kane et al., 1987; Stephenson et al., 1998). Conversely, in this case the ultrasonography was used to create a new objective scoring system. For that, a body assessment method was built considering the objectivity provided by the ultrasonographic evaluation of subcutaneous fat deposits and keeping in mind that none of the SFT measurements by themselves have been able to result in a good prediction of BCS demonstrated by the low correlations observed. In relation with this, it is important to notice that despite the high SFT values registered, it was also observed a considerable heterogeneity in fat deposition patterns between individual animals that could explain these weak relationships. So that horses with high BCS can present certain areas with low amount of fat and vice versa. To the authors’ this may be explained by two ways. Firstly, although subcutaneous adipose tissue is a key determinant of BCS, the subjectivity of this method make that it can be influenced by others factors, such as the conformation inherent to the breed evaluated. Then, higher scores may not be the consequence of high SFT. Secondly it has been shown that even in homogeneous populations the distribution of fat between different deposits is highly variable (Pond, 1998). Besides, although the order in which individual adipose tissues are recruited in the development of obesity, is getting to be understood in genetically modified species (Reed et al., 2006), in horses the knowledge of body composition, control of fat deposition, and mobilisation warrants further investigation (Argo, 2009).

Fat deposits examined previously by ultrasonography in equids differed from study to study (Gentry et al., 2004; Carter et al., 2009b; Argo et al., 2012) probably attributed to breed differences and to the ease of obtaining and reading ultrasonographic measurements. Also, if a specific ultrasound imaging analysis software could be developed to decrease the time-consuming and increase the precision of fat measuring, especially with those kind of protocols that involve taking the measurements in triplicate (to increase the accuracy), maybe less differences would be described in the literature. In the present study, the regional deposits selected were closely related to the anatomical locations on which the BCS system is measured and corresponding with the areas with greater tendency to accumulate fat in this breed. Comparing the results, is observed that the thicknesses of fat in most of the areas evaluated were greater than those reported in previous studies (Cartmill et al., 2006; Dugdale et al., 2011b; Quaresma et al., 2013). In addition, due to the representativeness of the sample, the degree of deviation of each SFT measurement on each animal in relation to the average of the sample was estimated. In this manner, the degree of fatness on each body area could be rated and continue monitoring over time which is important since increased regional adiposity is a health issue in horses because its association with altered metabolic states (Johnson, 2002).

The low concordance observed between the BCS and the BFSI could be explained because, although the proportion of horses at the extremes of both scoring scales were relatively similar, the differences were evident in animals with intermediate scores where the proportion of horses classified as normal vs overweight varied clearly between both methods. Otherwise, it is worth mentioning that albeit the high SFTs registered and the mean BCS was greater compared to previous studies (Pratt-Phillips et al., 2010; Turner et al., 2011; Wagner & Tyler, 2011), the overall BF% was lower than in other breeds (Vick et al., 2007; Adams et al., 2009; Ragnarsson & Jansson, 2011). Considering these data, we determined that Andalusians were not as overweight as it could appear if we only use BCS to evaluate the body condition. This also agree with the fact that applying the BFSI, the majority of Andalusians (75.2%) presented a normal body condition, which should be considered in the average for this breed. These findings suggest that the subjective scoring underestimate the optimal body condition and overestimate the overweight state in this breed.

Regardless of the significant association among the BCS and BFSI established categories, it should be noted that some striking misjudgements were shown. Notable was that, among the horses with BCS 5-6.5, the BCS was not sensitive enough to detect those horses that would better fit in the overweight category under the new BFSI. Likewise, most of the horses with BCS = 7 would present a normal body condition using the BFSI, suggesting again that the subjective scoring method overestimates the overweight and obesity states in Andalusian horses and supporting the need to adjust the BCS ranges in accordance with breed specific criteria.

Among the available methodologies to quantify objectively the body fat content in live horses (Kearns et al., 2002a), estimation of BF% using the method developed by Westervelt et al. (1976) and Kane et al. (1987) suppose the most feasible, cost-effective and prevalent reported method (Kearns et al., 2001, 2002b, 2006; Vick et al., 2007; Adams et al., 2009; Ragnarsson & Jansson, 2011). For this reason, BF% was estimated in this study as a quantitative method of total fat mass assessment and hence, as validation variable to corroborate de adiposity level of each body condition category. The degree of correlation between the BF% and BCS was lower than previously described (Henneke et al., 1983; Vick et al., 2007). These discrepancies could be explained because unlike other studies, in this case both genders have been considered and the number of animals included was much higher. Nevertheless, the use of BFSI improved the degree of association with the BF% and showed a greater sensitivity to distinguish between overweight and obese individuals which support the reliability and potential application of this system by clinicians to detect the subgroups at greater risk of metabolic disturbances.

Undoubtedly, modern nutritional and management practices are contributing to the increase in equine obesity prevalence across most of breeds (Scheibe & Streich, 2003), however many times the scales applied (Henneke et al., 1983; Kohnke, 1992; Kienzle & Schramme, 2004) and terminology to classify the body condition vary making not comparable the results among studies. In relation with this and the importance of establishing specific criteria to define the overweight and obesity, the AUCs showed that the BCS has a good diagnostic accuracy (Greiner et al., 2000). However, considering the faithful fulfilment of the original scale described by Henneke et al. (1983), the results confirm that the scores to designate these two body categories (obese and overweight) should be raised at least in Andalusian horses. Previous studies in which the conventional body scoring system has been utilized, obese horses have been described using different cut-off values (Gentry et al., 2002; Hoffman et al., 2003; Gentry et al., 2004; Buff et al., 2006; Frank et al., 2006; Vick et al., 2006; Waller et al., 2006; Ungru et al., 2012). This lack of consensus among different researches to define obesity stands out the relevance of these results where based strictly on an objective appraisal of the body condition (SFT ultrasonography) and in accordance with a quantitative corroborated obesity variable (%BF) it has been possible to fix concrete cut-off values adjusted to a specific breed.

The new body scoring method presents as main advantages its objectivity, non-invasive nature, quickness, safety, easiness to perform and applicability on a variety of subject populations. Additionally, body assessments by this method can be repeated over an unlimited period of time, making longitudinal studies realizable. However, it should be taken into account that in Spain the castration of horses from this breed is not frequent. This was reflected in the sample studied where all selected males were ungelded. This could be considered as a limitation since the usefulness of BFSI over gelding horses has not been possible to verify, being necessary further investigations to test its application in these horses as well as its repeatability across different breeds.

In conclusion, the majority of Andalusian horses evaluated in this study presented a body condition, which could be considered in the average for this breed. The developed BFSI suggests that the subjective assessment of body condition by conventional BCS systems overestimates the degree of fatness in these horses. In addition, this system discusses the cut-off values traditionally established in BCS scale to define the overweight and obesity and, indicates that it would be necessary to increase them by at least 0.5 points in Andalusians to detect correctly those horses with excess of adiposity.


The authors gratefully acknowledge the help and assistance of Francisco Marin Montoya in conducting the BCS assessment as second evaluator. We also appreciate the collaboration of grooms and horse owners.


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