Use of a novel morphometric method and body fat index system for estimation of body composition in overweight and obese dogs

Angela L. Witzel Department of Small Animal Clinical Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN 37996

Search for other papers by Angela L. Witzel in
Current site
Google Scholar
PubMed
Close
 DVM, PhD
,
Claudia A. Kirk Department of Small Animal Clinical Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN 37996

Search for other papers by Claudia A. Kirk in
Current site
Google Scholar
PubMed
Close
 DVM, PhD
,
George A. Henry Department of Small Animal Clinical Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN 37996

Search for other papers by George A. Henry in
Current site
Google Scholar
PubMed
Close
 DVM
,
Philip W. Toll Hill's Pet Nutrition Inc, 400 SW 8th St, Topeka, KS 66601

Search for other papers by Philip W. Toll in
Current site
Google Scholar
PubMed
Close
 DVM, MS
,
John J. Brejda Alpha Statistical Consulting, 4501 S 54th, Lincoln, NE 68516

Search for other papers by John J. Brejda in
Current site
Google Scholar
PubMed
Close
 PhD
, and
Inke Paetau-Robinson Hill's Pet Nutrition Inc, 400 SW 8th St, Topeka, KS 66601

Search for other papers by Inke Paetau-Robinson in
Current site
Google Scholar
PubMed
Close
 PhD

Abstract

Objective—To develop morphometric equations for prediction of body composition and create a body fat index (BFI) to estimate body fat percentage in overweight and obese dogs.

Design—Prospective evaluation study.

Animals—83 overweight or obese dogs ≥ 1 year of age.

Procedures—Body condition score (BCS) was assessed on a 5-point scale, morphometric measurements were made, and visual and palpation-based assessments and dual-energy x-ray absorptiometry (DEXA) were performed. Equations for predicting lean body mass, fat mass, and body fat as a percentage of total body weight (ie, body fat percentage) on the basis of morphometric measurements were generated with best-fit statistical models. Visual and palpation-based descriptors were used to develop a BFI. Predicted values for body composition components were compared with DEXA-measured values.

Results—For the study population, the developed morphometric equations accounted for 98% of the variation in lean body mass and fat mass and 82% of the variation in body fat percentage. The proportion of dogs with predicted values within 10% of the DEXA values was 66 of 83 (80%) for lean body mass, 56 of 83 (68%) for fat mass, and 56 of 83 (67%) for body fat percentage. The BFI accurately predicted body fat percentage in 25 of 47 (53%) dogs, whereas the value predicted with BCS was accurate in 6 of 47 (13%) dogs.

Conclusions and Clinical Relevance—Morphometric measurements and the BFI appeared to be more accurate than the 5-point BCS method for estimation of body fat percentage in overweight and obese dogs. Further research is needed to assess the applicability of these findings to other populations of dogs. (J Am Vet Med Assoc 2014;244:1279–1284)

Abstract

Objective—To develop morphometric equations for prediction of body composition and create a body fat index (BFI) to estimate body fat percentage in overweight and obese dogs.

Design—Prospective evaluation study.

Animals—83 overweight or obese dogs ≥ 1 year of age.

Procedures—Body condition score (BCS) was assessed on a 5-point scale, morphometric measurements were made, and visual and palpation-based assessments and dual-energy x-ray absorptiometry (DEXA) were performed. Equations for predicting lean body mass, fat mass, and body fat as a percentage of total body weight (ie, body fat percentage) on the basis of morphometric measurements were generated with best-fit statistical models. Visual and palpation-based descriptors were used to develop a BFI. Predicted values for body composition components were compared with DEXA-measured values.

Results—For the study population, the developed morphometric equations accounted for 98% of the variation in lean body mass and fat mass and 82% of the variation in body fat percentage. The proportion of dogs with predicted values within 10% of the DEXA values was 66 of 83 (80%) for lean body mass, 56 of 83 (68%) for fat mass, and 56 of 83 (67%) for body fat percentage. The BFI accurately predicted body fat percentage in 25 of 47 (53%) dogs, whereas the value predicted with BCS was accurate in 6 of 47 (13%) dogs.

Conclusions and Clinical Relevance—Morphometric measurements and the BFI appeared to be more accurate than the 5-point BCS method for estimation of body fat percentage in overweight and obese dogs. Further research is needed to assess the applicability of these findings to other populations of dogs. (J Am Vet Med Assoc 2014;244:1279–1284)

Excess weight and obesity have become more common in pet dogs in recent years.1 Results of studies2–4 in the past 10 years have indicated that approximately 30% to 40% of pet dogs are overweight and approximately 5% to 20% are obese. As in humans, obesity in dogs is associated with a variety of health problems, such as insulin resistance, orthopedic disorders, cardiorespiratory disease, urogenital dysfunction, neoplasia, and several functional alterations (eg, decreased ability to exercise).4–6 Therefore, dietary and lifestyle management is needed for many pet dogs to help them reach and maintain a healthy weight.

Unfortunately, owners are often poor judges of their pet's body condition, especially when their pet is obese or overweight.7,8 Veterinarians need to be able to judge body composition and ideal weight to develop feeding plans, but no accurate, noninvasive measures of body condition are available to veterinarians in the clinic. A precise measure of body composition can be obtained by means of DEXA,9 but the equipment is expensive, often unavailable to veterinarians in clinical practices, and impractical for routine use. Portable bioimpedance monitors are noninvasive devices that can be used to assess body fat content in dogs, but these are inaccurate for measurements in very obese dogs.10

Although a BCS can be a useful tool in management of body weight in pets, BCS systems are subjective and group all obese dogs together, irrespective of the extent of obesity. These systems rely on palpation and visual assessment to assign a score to patients where low and high scores equate to low and high amounts of body fat, respectively. The 2 BCS systems commonly used in practice involve use of a 5-point scale and a 9-point scale.11,12 Both of these scales were validated in animals with < 45% body fat, and on the basis of the authors' clinical experience, the number of morbidly obese animals with > 45% body fat appears to be increasing.

Equations based on morphometric measurements have been used to predict body fat percentage in dogs.13,14 However, these equations have been tested and developed only for dogs with body fat comprising approximately 40% or less of body weight. The purpose of the study reported here was to develop equations for accurately predicting lean body mass, fat mass, and body fat as a percentage of weight (ie, body fat percentage) on the basis of morphometric measurements and to develop a BFI system that combines results of palpation and visual assessment to accurately predict body fat percentages in overweight or obese dogs, with DEXA used as a reference standard. We also aimed to evaluate use of the 5-point BCS for estimation of body fat percentage and compare its accuracy with that of the newly developed methods.

Materials and Methods

Animals—Pet dogs ≥ 1 year of age and considered overweight or obese in the opinion of the investigator were recruited during regular visits to the Veterinary Clinic at the University of Tennessee College of Veterinary Medicine, Knoxville, Tenn, between November 8, 2008, and September 31, 2010. Dogs with known severe concurrent systemic or organ disease that might have interfered with accurate assessment of body composition or increased the health risks of general anesthesia and those with an injury or condition resulting in severe swelling or edema were excluded. Additionally, dogs that were pregnant, had aggressive behavior, or were, in the opinion of the investigator, poor candidates for anesthesia were also excluded. Informed written consent was obtained from owners prior to participation of their dogs in the study. The study was approved by the institutional animal care and use committees at the University of Tennessee and Hill's Pet Nutrition Inc and performed in compliance with the Hill's Pet Nutrition Inc Global Animal Welfare Policy.

Study design—The prospective study was conducted in 2 phases. The first phase was used to evaluate feasibility of morphometric measurements for estimation of lean body mass, fat mass, and body fat percentage in a clinical setting and to develop a new BFI system to estimate body fat percentage. In the second phase of the study, an additional group of dogs was enrolled; accuracy of the BCS and BFI systems was evaluated in this new population, and the results were subjectively compared. Morphometric measurements from dogs in both phases of the study were used to develop equations to predict body composition.

During a single visit, 1 of the 2 lead investigators (ALW or CAK) recorded the dog's medical and dietary history, age, sex, and reproductive status; performed a physical examination; determined body weight and BCS; and collected a blood sample for CBC and serum biochemical analysis. Radiographs of the chest and abdomen were also obtained immediately prior to DEXA measurements. From a pool of 6 licensed veterinary technicians and veterinary nutritionists, 4 individuals performed morphometric measurements and a visual and palpation-based assessment of each dog for BFI development (phase 1) and validation (phase 2). All measurements and assessments were performed in duplicate, with ≥ 1 hour between repetitions. Assessments were made independently, but blinding to other investigators' subjective evaluations was not formally assured during this stage. Dogs were typically awake and in a standing position; in some instances, dogs would not allow all measurements of the foot pads while awake and were sedated for some measurements. Dual-energy x-ray absorptiometry was subsequently used to determine lean body mass, fat mass, and body fat percentage in all dogs.

Determination of BCS and BFI—The BCS of each dog in the study was determined by investigators. A 5-point scale (where 1 = emaciated and 5 = obese) was used for assignment of BCS as previously described.11

In phase 1, visual and palpation-based assessment data were collected and used to develop a BFI chart for overweight or obese dogs; the BFI descriptions were created on the basis of investigators' evaluations and descriptions of various aspects or regions of each dog's body (body shape as viewed from the side [lateral aspect], from above [dorsal aspect], and from behind [caudal aspect] and prominence, ease of palpation, and fat covering of the ribs and of the tail base). At the time of measurement, investigators were blinded to the body fat percentage determined by DEXA. Each investigator was asked to select the best description for each body region or variable from a prepared lista and to provide additional comments if needed. Dogs were then categorized according to DEXA-measured body fat percentage (26% to 35% [BFI 30], 36% to 45% [BFI 40], 46% to 55% [BFI 50], or 56% to 65% [BFI 60]). For each body region, the most frequently selected description for dogs with a given body fat percentage was entered into the BFI chart for that variable and category. For example, all rib fat covering descriptions for dogs with a body fat percentage of 46% to 55% were noted, and the most frequently used description was incorporated into the chart as the rib fat covering descriptor for a BFI of 50.

When the BFI chart was used for assessment of dogs enrolled in phase 2, investigators selected the best description for each variable for each dog. An overall BFI score was assigned to each dog on the basis of the most frequently selected descriptions for these variables (Appendix 1). If an equal number of descriptions were chosen from different categories, the investigator's overall impression was used for the BFI assignment. The BCS and BFI values for dogs enrolled in phase 2 were used to estimate body fat percentages, and the results were compared with DEXA-measured body fat percentages for the same dogs.

Morphometric measurements—Morphometric measurements of the body, 1 hind limb, 1 forelimb, and the head (Appendix 2) were made for each animal by 4 investigators in duplicate with an infant measuring rod,b a floor height measuring rod,b digital calipers,c and standard tailor's and metal measuring tape. All investigators measured the same limb (left or right) of a given animal. The mean of the duplicate measurements from all investigators was used in the development of prediction equations.

DEXA—Propofol (2 to 4 mg/kg [0.91 to 1.8 mg/lb]) was administered through a cephalic catheter, and DEXA was performed with a bone densitometerd set up for a whole-body scan. Dogs were placed in sternal recumbency on the DEXA table with the head at the end of the table where the scan was initiated and the nose behind the table line at the head of the table. The dog's head and spine were aligned on the center line, and the carpal joints were flexed to create an approximately 90° angle between each foot and the long axis of the forelimb. When possible, the forefeet were positioned caudal to the base of the skull. The scan field included the entire animal. Lean body mass, fat mass, and body fat percentage were calculated with commercially available software.e

Data analysis—Statistical analysis was performed with statistical software.f Values of P < 0.05 were considered significant.

Multiple regression analysis was used to determine which morphometric variables provided the best estimates of lean body mass, fat mass, and body fat percentage as measured by DEXA. All morphometric measurements were initially screened for collinearity with the collinearity optiong in the regression procedure.h Sets of variables with small eigenvalues and a condition index > 30 were considered to be highly correlated, which can result in unstable parameter estimates. Therefore, only 1 variable from each highly correlated set was included in the model selection procedure.

Four selection procedures were used to select the equation that best predicted lean body mass, fat mass, and body fat percentage. These were the stepwise selection procedure, Akaike information criterion, Schwarz Bayesian criterion statistics, and maximum adjusted R2 statistic. Only the best candidate model selected with each procedure was evaluated further. If different procedures selected the same model, that model was evaluated only once. Each candidate model was evaluated for significance of the predictor variables at α = 0.05, violations of the assumptions underlying linear regression, and the presence of collinearity among the predictor variables, which was assessed with the variance inflation factor. Models containing predictor variables with variance inflation factors > 10 were considered to be undesirable. The assumption that residuals were normally distributed was evaluated with the Shapiro-Wilk test. Studentized residuals were plotted as a function of predicted values to determine whether patterns existed for a candidate model. Studentized residuals exceeding ± 2.5 were considered to be unusually large and an indication of a poor fit between the model and the animals associated with the large residuals. The model that best satisfied the evaluation criteria and had the smallest SE was chosen as the best model for each response variable.

Inter-rater variability was estimated by use of a random-effects ANOVA model that included technician, animals, the duplicate measurements made by each technician, and all interactions as sources of variation in the analysis. The variance associated with technician divided by total variation for each measurement was used as the estimate of intrarater variability.

As animal size increases, the proportion of the body that is metabolically active decreases.15 This relationship is undetectable in most species; however, the diversity of body size in domestic dogs makes calculation of MBS a necessity. Assuming an ideal body fat percentage as 20%, MBS in grams was calculated as follows15:

article image

where LBM is lean body mass in grams as measured by DEXA.

The calculated MBS was then converted back to lean body mass as follows:

article image

This allowed comparison of the lean body mass as measured by DEXA with a predicted lean body mass.

Calculated variables were also developed on the basis of morphometric measurements to account for differences in body size and conformation among dogs. Variables of limb length raised to the power of 0.8 and 1.2 were created with exponents selected on the basis of known scaling factors for MBS and skeletal mass in mammals.15 Diameter and area variables were calculated from circumference measurements for the head, thorax, and pelvis according to the following formulas:

article image

Additional variables calculated were forelimb length minus hind limb length, thoracic circumference minus head circumference, and pelvic circumference divided by head circumference. These variables were used to account for irregularity of animal limbs and shapes.

Accuracy of the BCS and BFI systems for determination of body fat percentage in dogs was evaluated by assigning a body fat percentage estimate to each score and then dividing the estimate for each animal by the body fat percentage determined with DEXA; results for the 2 methods were subjectively compared. For the 5-point BCS scale, scores of 3, 4, and 5 were assigned body fat estimates of 20%, 30%, and 40%, respectively.11 For the BFI, scores of 30, 40, 50, and 60 were assigned body fat estimates of 30%, 40%, 50%, and 60%, respectively. Accuracy of morphometric equations was assessed by division of the predicted value for a given variable by the actual DEXA-measured value.

Results

Dogs—Eighty-three dogs were included in the study (36 in the first phase and 47 in the second phase). There were 47 females (5 sexually intact and 42 spayed) and 36 males (3 sexually intact and 33 neutered). Breeds included mix (n = 19), Labrador Retriever (10), Dachshund (7), Golden Retriever (6), Beagle (5), Australian Shepherd (4), and Cocker Spaniel (3); Miniature Pinscher, Shetland Sheepdog, Jack Russell Terrier, Rottweiler, Shih Tzu, Border Collie, pit bull type, Pug, and Chihuahua (2 each); and Poodle, Corgi, Bull Mastiff, French Bulldog, Basset Hound, Boston Terrier, Doberman Pinscher, German Shepherd Dog, Flat Coated Retriever, Rat Terrier, and Boxer (1 each). Mean ± SD age of dogs in the study was 5.9 ± 2.7 years (range, 1 to 12 years). Body weight ranged from 5 to 74 kg (11.0 to 162.8 lb), and the body fat percentage determined with DEXA ranged from 20.4% to 65.2% (mean ± SD, 43.7 ± 10.5%). Three (4%) dogs had ≥ 60% body fat, 20 (24%) had 50% to < 60% body fat, 31 (37%) had 40% to < 50% body fat, 24 (29%) had 26% to < 40% body fat, and 5 (6%) had < 26% body fat. Dogs are typically considered to have an ideal body condition between 15% and 25% body fat.11,12 All dogs had a BCS of 3 (n = 5), 4 (12), or 5 (66).

Body weight was measured in pounds, and age was measured in years. All other measurements were in centimeters. Forelimb length was measured from the proximal aspect of the metacarpal pad to the point of the elbow, and hind limb length was measured from the proximal aspect of the metatarsal pad to the dorsal tip of the calcaneal tuber. Head circumference was measured at the widest part of the head between the eyes and ears, and pelvic circumference was measured in the approximate area of the fifth lumbar vertebra.

Models for estimation of body composition through morphometric measurements—The best-fit equations for estimating MBS, fat mass, and body fat percentage (as compared with DEXA results) were developed on the basis of morphometric data, with lean body mass calculated on the basis of MBS (Table 1). The best-fit equations had adjusted R2 values of 0.98 for estimation of lean body mass, 0.98 for estimation of fat mass, and 0.82 for body fat percentage, indicating that the morphometric equations accounted for 98% of the variation in lean body mass and fat mass and 82% of the variation in body fat percentage (Figure 1). Intrarater variability was nonsignificant and did not contribute to the total variation. Inter-rater variability accounted for < 1% of the total variation. When applied to the study population, calculated values for the study population were within 10% of the DEXA values for 66 of 83 (80%) dogs for lean body mass and 56 of 83 (67%) dogs for fat mass (Table 2). These estimations corresponded well with the DEXA values for lean body mass and fat mass across the entire range of values.

Table 1—

Best-fit equations for estimation of body composition components on the basis of morphometric measurements in 83 overweight or obese dogs.

VariableEquationAdjusted R2
Lean body mass (g)(8.25 × body weight – 9.02 × age + 8.92 × [head circumference/6]2 + 96.86 × forelimb length – 111.07 × [forelimb length – hind limb length] – 357.18)1.333 × 0.80.980
Fat mass (g)229.04 × body weight – 416.63 × hind limb length1.2 + 157.78 × (thoracic circumference – head circumference) + 908.790.978
Body fat percentage0.71 × thoracic circumference – 0.1 × (pelvic circumference/6)2 – 5.78 × hind limb length0.8 + 26.56 × (pelvic circumference/head circumference) + 2.060.817

Body weight was measured in pounds, and age was measured in years. All other measurements were in centimeters. Forelimb length was measured from the proximal aspect of the metacarpal pad to the point of the elbow, and hind limb length was measured from the proximal aspect of the metatarsal pad to the dorsal tip of the calcaneal tuber. Head circumference was measured at the widest part of the head between the eyes and ears, and pelvic circumference was measured in the approximate area of the fifth lumbar vertebra.

Table 2—

Accuracy of the prediction equations in Table 1 for lean body mass, fat mass, and body fat percentage when applied to the study population (n = 83 dogs).

Variable predicted
Result (comparison with DEXA value)Lean massFat massBody fat percentage
Underestimated by > 20%1 (1)5 (6)0 (0)
Underestimated by > 10%–20%4 (5)8 (10)13 (16)
Within 10%66 (80)56 (67)56 (67)
Overestimated by > 10%–20%11 (13)10 (12)7 (8)
Overestimated by > 20%1 (1)4 (5)7 (8)

Data are number (%) of dogs.

Figure 1—
Figure 1—

Observed body composition values measured by DEXA versus those predicted by best-fit equations on the basis of morphometric measurements applied to 83 pet dogs ≥ 1 year of age that were considered overweight or obese by their veterinarian. A—Lean body mass. B—Fat mass. C—Body fat percentage.

Citation: Journal of the American Veterinary Medical Association 244, 11; 10.2460/javma.244.11.1279

Estimation of body fat percentage with BCS and BFI—When the 5-point BCS system was used for evaluation of dogs enrolled in phase 2, body fat percentage estimated on the basis of BCS was within 10% of that determined with DEXA for 6 of 47 (13%) dogs and within 20% of the DEXA value for 19 of 47 (40%) dogs. Body fat percentage estimated with the BFI system in phase 2 was within 10% of the value determined via DEXA for 25 of 47 (53%) dogs and within 20% of the DEXA value for 43 of 47 (91%) dogs.

Data are number (%) of dogs.

Discussion

Veterinarians need to be able to judge body condition and establish an ideal weight to develop feeding plans for overweight or obese dogs, but no accurate, noninvasive measures of body composition are available to veterinarians for assessment of these patients in the clinic. The results of the present study revealed limitations of a traditional 5-point BCS system when used for assessment of overweight and obese dogs; when BCS scores of 3, 4, and 5 were assigned body fat estimates of 20%, 30%, and 40%, respectively,11 this method accurately predicted body fat percentage (within 10% of the value determined by means of DEXA) in only 6 of 47 (13%) overweight or obese dogs. This low degree of accuracy can be attributed to the fact that the BCS system used was designed for assessment of animals with < 40% to 45% body fat.11 To the best of our knowledge, this is true of all canine and feline BCS systems and represents a major flaw if these are used in pets with > 45% body fat. Pet obesity is mimicking the human obesity epidemic, and in our clinical experience, a growing number of dogs are morbidly obese, with > 45% body fat.

In the present study, we built upon the research of other investigators, in which equations developed on the basis of morphometric measures were used to predict body fat percentage in dogs with reasonable accuracy.13,14 In those studies, equations were developed and tested for dogs with up to approximately 40% body fat, whereas most (54/83 [65%]) dogs in our study had > 40% body fat, and 3 of these had > 60% body fat as measured by DEXA. In our study, the results of the best-fit equations for predicting lean body mass and fat mass were accurate to within 10% of the DEXA values across a wide range of values within the same population of dogs that were used for equation development, and the morphometric measurements took investigators approximately 5 minutes to perform. It is important to note that, although our results indicated that the morphometric equations provided an accurate estimate of body fat percentage in most overweight or obese dogs, additional research is needed to validate these methods in other populations of dogs. In addition, the wide range of size and conformation of domestic dogs presents a major challenge for developing standardized equations for assessing body composition. The present study included dogs of > 20 breeds that ranged in weight from 5 to 74 kg; however, body composition may be more difficult to predict on the basis of morphometric measurements in some breeds (eg, chondrodystrophic or large-headed breeds like Staffordshire Terriers) than in others.

The BFI system developed in the present study relies on palpation and visual assessment in a manner similar to the traditional BCS systems but was accurate in overweight dogs with up to approximately 65% body fat. For 25 of 47 (53%) dogs in phase 2 of the study, body fat percentage was underestimated by ≥ 20% with the 5-point BCS, whereas this result was found for only 2 of these 47 (4%) of dogs when the BFI chart was used. Clinically normal and underweight dogs were not included in the population used for development of the BFI in this study, and its intended application is as an adjunct to BCS when an overweight or obese dog is identified. Estimations of ideal weight are a critical step in effective weight loss, and as the severity of obesity increases, it becomes more challenging for veterinarians to determine the caloric needs of their patients on the basis of body weight. Although further research is needed to validate these systems in dogs, the use of morphometric measurements and body fat descriptors (BCS and BFI) developed for dogs with a wide range of body conditions may help veterinarians to better assess body fat percentage in their patients and to develop appropriate weight loss regimens.

ABBREVIATION

BCS

Body condition score

BFI

Body fat index

DEXA

Dual-energy x-ray absorptiometry

MBS

Metabolic body size

a.

A copy of the original form is available from the corresponding author upon request.

b.

Seca, Birmingham, West Midlands, England.

c.

Ultratech, General, New York, NY.

d.

QDR4500 Acclaim Series Elite, Hologic Inc, Danbury, Conn.

e.

Apex, version 2.3, Hologic Inc, Danbury, Conn.

f.

SAS, version 9.13, SAS Institute Inc, Cary, NC.

g.

COLLIN, SAS, version 9.13, SAS Institute Inc, Cary, NC.

h.

PROC REG, SAS, version 9.13, SAS Institute Inc, Cary, NC.

References

  • 1. German AJ. The growing problem of obesity in dogs and cats. J Nutr 2006;136:1940S1946S.

  • 2. Courcier EA, Thomson RM & Mellor DJ, et al. An epidemiological study of environmental factors associated with canine obesity. J Small Anim Pract 2010;51:362367.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3. McGreevy PD, Thomson PC & Pride C, et al. Prevalence of obesity in dogs examined by Australian veterinary practices and the risk factors involved. Vet Rec 2005;156:695702.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4. Lund EM, Armstrong PJ & Kirk CA, et al. Prevalence and risk factors for obesity in adult dogs from private US veterinary practices. Int J Appl Res Vet Med 2006;4:177186.

    • Search Google Scholar
    • Export Citation
  • 5. Glickman LT, Schofer FS & McKee LJ, et al. Epidemiologic study of insecticide exposures, obesity, and risk of bladder cancer in household dogs. J Toxicol Environ Health 1989;28:407414.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6. Perez Alenza MD, Peña L & del Castillo N, et al. Factors influencing the incidence and prognosis of canine mammary tumours. J Small Anim Pract 2000;41:287291.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7. White GA, Hobson-West P & Cobb K, et al. Canine obesity: is there a difference between veterinarian and owner perception? J Small Anim Pract 2011;52:622626.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8. Courcier EA, Mellor DJ & Thomson RM, et al. A cross sectional study of the prevalence and risk factors for owner misperception of canine body shape in first opinion practice in Glasgow. Prev Vet Med 2011;102:6674.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9. Lauten SD, Cox NR & Brawner WR Jr, et al. Use of dual energy x-ray absorptiometry for noninvasive body composition measurements in clinically normal dogs. Am J Vet Res 2001;62:12951301.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10. German AJ, Holden SL & Morris PJ, et al. Comparison of a bioimpedance monitor with dual-energy x-ray absorptiometry for noninvasive estimation of percentage body fat in dogs. Am J Vet Res 2010;71:393398.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11. Toll PW, Yamka RM & Schoenherr WD, et al. Obesity. In: Hand MS, Thatcher CD, Remillard RL, et al., eds.Small animal clinical nutrition. Topeka, Kan: Mark Morris Institute, 2010;501542.

    • Search Google Scholar
    • Export Citation
  • 12. LaFlamme D. Development and validation of a body condition score system for dogs. Canine Pract 1997;22(4):1015.

  • 13. Jeusette I, Greco D & Aquino F, et al. Effect of breed on body composition and comparison between various methods to estimate body composition in dogs. Res Vet Sci 2010;88:227232.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14. Mawby DI, Bartges JW & d'Avignon A, et al. Comparison of various methods for estimating body fat in dogs. J Am Anim Hosp Assoc 2004;40:109114.

  • 15. National Research Council. Energy. In: Nutrient requirements of dogs and cats. Washington, DC: The National Academies Press, 2006;2839.

    • Search Google Scholar
    • Export Citation

Appendix 1

Body fat index chart with descriptors for visual and palpation-based assessment of overweight or obese dogs.

BFI
Variable30405060
Body fat (%)26–3536–4546–5556–65
Ribs
  ProminenceSlightly to not prominentNot prominentNot prominentNot prominent
  PalpabilityCan be feltVery difficult to feelExtremely difficult to feelImpossible to feel
  Fat coverModerateThickVery thickExtremely thick
Body shape
  From aboveDetectable lumbar waistLoss of lumbar waist, broadened backMarkedly broadened backExtremely broadened back
  From the sideSlight abdominal tuckFlat to bulging abdomenMarked abdominal bulgeSevere abdominal bulge
  From behindLosing muscle definition, rounded appearanceRounded to square appearanceSquare appearanceSquare appearance
Tail base bones
  ProminenceSlightly to not prominentNot prominentNot prominentNot prominent
  PalpabilityCan be feltVery difficult to feelExtremely difficult to feelImpossible to feel
Tail base fat
  Fat coverModerateThickVery thickExtremely thick
  Fat dimpleNoneMay have a small fat dimpleFat dimple or fold presentLarge fat dimple or fat fold present

The BFI was developed on the basis of data obtained for 36 dogs in phase 1 of the study and used independently by 4 investigators to estimate body fat percentage in each of 47 dogs newly enrolled in phase 2. Investigators selected only the best description for each variable; the score most frequently assigned for each dog (ie, the score for the column with the highest number of descriptions chosen) was selected as the overall BFI score for that animal. The results were evaluated by comparison with body fat percentage determined by means of DEXA.

Appendix 2

List of morphometric measurements used in regression analysis to determine equations that best predicted body composition components of interest (lean body mass, fat mass, and body fat percentage).

Region and variableDevice used (units)Measurement description
Body
  LengthInfant measuring rod (cm)From sternum to anus along the long axis of the body
  Front heightFloor height rod (cm)From the floor to the dorsal limit of the scapular region
  Rear heightFloor height rod (cm)From the floor to the dorsal limit of the region over the iliac crest
  Thoracic circumference*Tailor's tape (cm)Around the thorax at the level of the fourth through sixth ribs
  Pelvic circumference*Tailor's tape (cm)Around the caudal aspect of the abdomen at the approximate level of the fifth lumbar vertebra
Hind limb
  Length*Metal tape measure (cm)From the proximal aspect of the metatarsal pad to the proximal tip of the calcaneal tuber
  Calcaneal tuber widthDigital caliper (mm)
  Metatarsal pad widthDigital caliper (mm)Micrometer laid flat onto the foot at the base of the pad
  Metatarsal pad lengthDigital caliper (mm)Micrometer laid flat onto the foot at the base of the pad
Forelimb
  Length*Metal measuring tape (cm)From the proximal aspect of the metacarpal pad to the proximal tip of the olecranon
  Metacarpal pad widthDigital caliper (mm)Micrometer laid flat onto the foot at the base of the pad
  Metacarpal pad lengthDigital caliper (mm)Micrometer laid flat onto the foot at the base of the pad
Head
  Cranial lengthTailor's measuring tape (cm)Midline from the external occipital protuberance to the level of the medial canthus of the eye
  Facial lengthTailor's measuring tape (cm)Midline from the level of the medial canthus of the eye to the tip of the nose
  Circumference*Tailor's measuring tape (cm)Between the eyes and ears at the widest part of the head
  WidthInfant measuring rod (cm)Widest point of the skull in the approximate region of the eyes and ears

Measurements used in the best-fit morphometric equations.

= Location was not further specified.

  • Figure 1—

    Observed body composition values measured by DEXA versus those predicted by best-fit equations on the basis of morphometric measurements applied to 83 pet dogs ≥ 1 year of age that were considered overweight or obese by their veterinarian. A—Lean body mass. B—Fat mass. C—Body fat percentage.

  • 1. German AJ. The growing problem of obesity in dogs and cats. J Nutr 2006;136:1940S1946S.

  • 2. Courcier EA, Thomson RM & Mellor DJ, et al. An epidemiological study of environmental factors associated with canine obesity. J Small Anim Pract 2010;51:362367.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3. McGreevy PD, Thomson PC & Pride C, et al. Prevalence of obesity in dogs examined by Australian veterinary practices and the risk factors involved. Vet Rec 2005;156:695702.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4. Lund EM, Armstrong PJ & Kirk CA, et al. Prevalence and risk factors for obesity in adult dogs from private US veterinary practices. Int J Appl Res Vet Med 2006;4:177186.

    • Search Google Scholar
    • Export Citation
  • 5. Glickman LT, Schofer FS & McKee LJ, et al. Epidemiologic study of insecticide exposures, obesity, and risk of bladder cancer in household dogs. J Toxicol Environ Health 1989;28:407414.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6. Perez Alenza MD, Peña L & del Castillo N, et al. Factors influencing the incidence and prognosis of canine mammary tumours. J Small Anim Pract 2000;41:287291.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7. White GA, Hobson-West P & Cobb K, et al. Canine obesity: is there a difference between veterinarian and owner perception? J Small Anim Pract 2011;52:622626.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8. Courcier EA, Mellor DJ & Thomson RM, et al. A cross sectional study of the prevalence and risk factors for owner misperception of canine body shape in first opinion practice in Glasgow. Prev Vet Med 2011;102:6674.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9. Lauten SD, Cox NR & Brawner WR Jr, et al. Use of dual energy x-ray absorptiometry for noninvasive body composition measurements in clinically normal dogs. Am J Vet Res 2001;62:12951301.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10. German AJ, Holden SL & Morris PJ, et al. Comparison of a bioimpedance monitor with dual-energy x-ray absorptiometry for noninvasive estimation of percentage body fat in dogs. Am J Vet Res 2010;71:393398.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11. Toll PW, Yamka RM & Schoenherr WD, et al. Obesity. In: Hand MS, Thatcher CD, Remillard RL, et al., eds.Small animal clinical nutrition. Topeka, Kan: Mark Morris Institute, 2010;501542.

    • Search Google Scholar
    • Export Citation
  • 12. LaFlamme D. Development and validation of a body condition score system for dogs. Canine Pract 1997;22(4):1015.

  • 13. Jeusette I, Greco D & Aquino F, et al. Effect of breed on body composition and comparison between various methods to estimate body composition in dogs. Res Vet Sci 2010;88:227232.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14. Mawby DI, Bartges JW & d'Avignon A, et al. Comparison of various methods for estimating body fat in dogs. J Am Anim Hosp Assoc 2004;40:109114.

  • 15. National Research Council. Energy. In: Nutrient requirements of dogs and cats. Washington, DC: The National Academies Press, 2006;2839.

    • Search Google Scholar
    • Export Citation

Advertisement