Retrospective analysis of dual-energy x-ray absorptiometry data demonstrates body composition changes with age in dogs and cats

Allison P. McGrath Hill's Pet Nutrition, Topeka, KS

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Leslie Hancock Hill's Pet Nutrition, Topeka, KS

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Cheryl A. Stiers Hill's Pet Nutrition, Topeka, KS

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John Brejda Alpha Statistical Consulting, Lincoln, NE

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Elizabeth M. Morris Hill's Pet Nutrition, Topeka, KS

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Abstract

OBJECTIVE

Use 18 years of dual-energy x-ray absorptiometry (DEXA) scan data to characterize how body composition changes with age in dogs and cats.

METHODS

This was a retrospective observational study using data obtained from DEXA scans performed between 2006 and 2023. A total of 6,973 observations from 1,273 colony-housed dogs ≤ 1 to 16.1 years old and 6,593 observations from 1,096 colony-housed cats ≤ 1 to 16.9 years old were obtained. Animal ages were rounded to the nearest 1/10-year intervals. Means for each interval were calculated and quadratic, cubic, and quartic polynomial models were fit to assess trends over age.

RESULTS

Age had an effect on all DEXA measurements. In dogs, lean mass increased early in life before slowing to a peak at age 6.3 and then declined gradually. Fat mass also increased until slowing to a peak at age 9.3 and then decreased. In cats, lean mass increased before slowing to a peak at age 4.5, decreased gradually until age 12.5, and then sharply declined. Fat mass increased until slowing to a peak at age 7.5 and then decreased gradually.

CONCLUSIONS

This retrospective study provides a baseline for how body composition changes with age. Results suggest that lean mass loss may begin earlier than previously reported in dogs and cats.

CLINICAL RELEVANCE

Sarcopenia and obesity are common conditions in aging pets. Results can be used to improve body composition assessment of patients and investigate the efficacy of nutritional interventions.

Abstract

OBJECTIVE

Use 18 years of dual-energy x-ray absorptiometry (DEXA) scan data to characterize how body composition changes with age in dogs and cats.

METHODS

This was a retrospective observational study using data obtained from DEXA scans performed between 2006 and 2023. A total of 6,973 observations from 1,273 colony-housed dogs ≤ 1 to 16.1 years old and 6,593 observations from 1,096 colony-housed cats ≤ 1 to 16.9 years old were obtained. Animal ages were rounded to the nearest 1/10-year intervals. Means for each interval were calculated and quadratic, cubic, and quartic polynomial models were fit to assess trends over age.

RESULTS

Age had an effect on all DEXA measurements. In dogs, lean mass increased early in life before slowing to a peak at age 6.3 and then declined gradually. Fat mass also increased until slowing to a peak at age 9.3 and then decreased. In cats, lean mass increased before slowing to a peak at age 4.5, decreased gradually until age 12.5, and then sharply declined. Fat mass increased until slowing to a peak at age 7.5 and then decreased gradually.

CONCLUSIONS

This retrospective study provides a baseline for how body composition changes with age. Results suggest that lean mass loss may begin earlier than previously reported in dogs and cats.

CLINICAL RELEVANCE

Sarcopenia and obesity are common conditions in aging pets. Results can be used to improve body composition assessment of patients and investigate the efficacy of nutritional interventions.

Dual-energy x-ray absorptiometry (DEXA) is a noninvasive method for estimating body composition.1,2 In a DEXA scan, 2 x-rays of different energy levels are emitted from the DEXA machine, which are absorbed by the body. Whole body mass can be classified into bone mineral and soft tissue components based on their differing levels of absorption, or attenuation, to the x-rays. Soft tissue can be further separated into fat and lean soft tissue components based on the ratio of attenuation at the energy levels of the 2 x-rays.3 The use of DEXA to estimate body composition has been validated in both dogs and cats1,2 and is considered to provide an accurate estimation of these measures when compared to chemical analysis of deceased dogs.

The mass of lean tissue in the body, or lean body mass (LBM), is a valuable endpoint obtained from DEXA scans. Lean body mass mainly consists of muscle and organ tissue, and the maintenance of LBM is traditionally considered a marker of protein adequacy.4 Lean body mass loss has been associated with reduced strength, immune function, and healing of wounds, as well as shortened lifespan.5 As a result, a loss of LBM is a major predictor of death in both dogs and cats.68 Dual-energy x-ray absorptiometry scans also quantify the mass of body tissue existing as fat. The majority of fat in the body exists as adipose tissue and excess body fat has been found to increase the risk of several health conditions and a reduced lifespan.911 Bone mineral content (BMC) and bone mineral density (BMD) are additional measures obtained from DEXA scans.1 Bone mineral content refers to the mass of bone, while BMD is the ratio of BMC to bone size, and may be considered a measure of areal density.1 Bone loss can be identified through a reduction in BMC and/or BMD, and a low bone content in terms of total mass and/or density may be associated with a greater risk of bone fracture and bone disease.12

Previous work investigating body composition changes with age has been limited in terms of their duration, sample size, and/or population diversity. Most notably, while previous studies58,13 have identified a decrease in LBM in the senior life stage, previous work has not quantified lean mass changes in conjunction with other body composition measures across a wide range of ages using a large population of colony dogs and cats. Therefore, the objective of this present study was to use body composition data obtained through DEXA scans performed from 2006 to 2023 to characterize how lean mass, fat mass, total mass, BMC, and BMD change with age in a population of dogs and cats being fed a representation of globally available foods. The underlying hypothesis was that body composition would change with age as follows in dogs and cats: lean mass would exhibit a quadratic effect, increasing up to a peak and then declining, fat mass would increase linearly, and BMC and BMD would both remain unchanged with an increase in age.

Methods

Study design and measurements

A search of historical data from DEXA scans conducted on colony-housed dogs and cats between January 1, 2006, and December 31, 2023, was completed. Animals used in this analysis were housed individually, in pairs in indoor runs, or in groups in spacious rooms with natural light. All dogs and cats had daily opportunities for enrichment through access to care personnel, group socialization, and toys, and dogs also had access to outdoor yards for activity as desired. Dogs and cats were fed once or twice a day based on their maintenance energy requirement for their ideal body weight and had ad libitum access to water. Foods consumed by the animals were selected based on veterinarian discretion or for nutritional study. The diets provided were characterized by a wide variety of forms, flavors, and brands, and animals were fed many different foods throughout their lifetimes. Dogs and cats were up to date on all vaccinations and were under constant veterinary care and supervision. All trial protocols had been approved by the Hill's IACUC and complied with Hill's Global Animal Welfare Policy.

Whole-body DEXA scans were performed using an x-ray bone densitometer, the Hologic QDR4500 (Hologic Inc), as described in the literature.14,15 Total mass was obtained, and LBM, fat mass, BMC, and BMD were calculated using commercially available software provided by the manufacturer. Dual-energy x-ray absorptiometry scans were performed for several reasons, including as a baseline scan at the time of spaying/castrating, for use in clinical study endpoints, as part of an annual checkup, for general monitoring purposes, and/or upon death of the animal. Dual-energy x-ray absorptiometry data were included from both healthy and diseased dogs and cats, and all animals were weaned at the time of the scan. Data from large-breed dogs, or dogs weighing more than 55 lbs, were omitted from this study due to small population size (n = 96). The age at which the DEXA scan was conducted was determined by calculating the amount of time between the animals’ date of birth and date of the scan.

Statistical analysis

Animal ages were rounded to the nearest 0.1-year intervals. Means for each interval were calculated, and quadratic, cubic, and quartic polynomial models were fit to assess trends over age, using PROC REG in SAS (SAS Institute Inc). Performing the regression of DEXA variables based on the means eliminated the need to adjust for multiple random effects in the model and guaranteed the data were normally distributed. Effects were considered significant when P ≤ .05. The final model for each variable was selected based on the statistical significance of the coefficients, the distribution of the residuals showing no lack of fit, and parsimony. In statistics, the parsimony principle states that a simpler model with fewer parameters is favored over more complex models, provided both models describe the phenomenon equally well. In contrast, overfitting occurs when a model includes more terms than are necessary or uses more complicated approaches than necessary to describe the phenomenon under investigation.16,17

Results

Characteristics of animals used in retrospective data analysis

A total of 6,973 observations from 1,273 dogs and 6,593 observations from 1,096 cats were obtained from Hill's medical records database (Table 1). Dogs ranged from < 1 year to 16.1 years of age, with a mean ± SD age of 6.80 ± 4.02. The sex distribution consisted of 634 females (including both spayed and intact) and 639 males (including both castrated and intact). A total of 1,168 dogs were classified as medium sized, consistently weighing 26 to 55 lbs at adulthood, and 105 were classified small sized, consistently weighing less than 25 lbs at adulthood. Breeds of dogs in this dataset included the following: Alaskan Malamute, Beagle, Brussels Griffon, Maltese, Miniature Poodle, Golden Retriever, Labrador Retriever, English Setter, Shetland Sheepdog, German Shepherd, Shih Tzu, Yorkshire Terrier, and a variety of mixed breeds. Dogs had a mean of 5.47 ± 5.35 DEXA scans per animal, with a range of 1 to 27. Cats ranged from < 1 year to 16.9 years of age, with a mean ± SD age of 7.19 ± 4.22. The sex distribution consisted of 604 females (including both spayed and intact) and 492 males (including both castrated and intact). Breeds of cats included the following: 1,085 domestic shorthair, 8 domestic longhair, and 3 mixed breed. Cats had a mean of 6.02 ± 5.28 DEXA scans per animal, with a range of 1 to 34.

Table 1

Characteristics of dogs and cats included in this retrospective analysis.

Characteristic Dogs Cats
No. observations 6,973 6,593
No. animals 1,273 1,096
Mean age ± SD 6.80 ± 4.02 7.19 ± 4.22
Minimum age 0.2 0.1
Maximum age 16.1 16.9
Sex*
 Female or spayed female 634 604
 Male or castrated male 639 492
Breed N/A
 Domestic shorthair 1,085
 Domestic longhair 8
 Mixed 3
Size  N/A
 Small (< 25 lbs) 105
 Medium (26 to 55 lbs) 1,168
DEXAs per animal, mean ± SD 5.47 ± 5.35 6.02 ± 5.28
DEXAs per animal, median (range) 4 (1–27) 5 (1–34)
*

Some animals were intact when some dual-energy x-ray absorptiometry (DEXA) measurements were taken and castrated or spayed at the time of later measurements. Therefore, the female/spayed female and the male/castrated male categories were combined.

N/A = Not applicable.

Change in body composition with age in dogs

Age had a significant effect on all DEXA variables in dogs (P < .001; Table 2), and the mean of each variable with age is presented (Supplementary Table S1). The cubic trends for lean, fat, and total mass are provided (Figure 1). Although quartic trends were also statistically significant, the higher order model did not provide appreciable improvement in terms of modeling trends over age compared to a cubic model, and therefore, the simpler model was selected. Lean body mass increased early in life and then increased at a slowing rate before reaching a peak at age 6.3, where it then declined gradually until age 16.1. Fat mass also increased during the younger ages, before slowing its rate of increase and peaking at age 9.3. Then, fat mass decreased slowly until age 16.1. Total mass increased gradually before slowing to a peak at age 6.9, declined at an increasingly rapid rate until age 12, and continued to decline at a slower rate until age 16.1.

Table 2

Regression coefficients (β0, β1, β2, β3, β4, and R2), associated SEs, and P-values for quadratic, cubic, and quartic models of body composition measures for dogs.

Quadratic Cubic Quartic
Measurement/coefficient Estimate SE P-value Estimate SE P-value Estimate SE P-value
BMD (g/cm2)
 β0 0.5307 0.0091 < .001 0.481 0.011 < .001 0.463 0.014 < .001
 β1 0.0428 0.0026 < .001 0.0776 0.0059 < .001 0.099 0.012 < .001
 β2 −0.00281 0.00015 < .001 −0.0081 0.00083 < .001 −0.0139 0.0029 < .001
 β3 0.00021654 0.000033 < .001 0.00076 0.00027 0.0054
 β4 −0.0000167 0.0000082 0.0434
R2 0.694 0.757 0.762
BMC (g)
 β0 309.58 7.99 < .001 265.43 9.77 < .001 223.27 11.64 < .001
 β1 35.57 2.26 < .001 66.67 5.14 < .001 114.54 9.70 < .001
 β2 −2.00 0.13 < .001 −6.73 0.73 < .001 −19.67 2.39 < .001
 β3 0.193 0.029 < .001 1.42 0.22 < .001
 β4 −0.0377 0.0067 < .001
R2 0.609 0.692 0.7430
Fat (g)
 β0 2,167 109 < .001 1,797 145 < .001 1,327 180 < .001
 β1 487 31 < .001 748 76 < .001 1,281 150 < .001
 β2 −23.15 1.84 < .001 −63 11 < .001 −207 37 < .001
 β3 1.62 0.44 < .001 15.31 3.39 < .001
 β4 −0.42 0.10 < .001
R2 0.697 0.720 0.745
Lean (g)
 β0 6,124 139 < .001 5,328 168 < .001 4,963 215 < .001
 β1 727 39 < .001 1,288 88 < .001 1,702 179 < .001
 β2 −49.22 2.33 < .001 −134 13 < .001 −247 44 < .001
 β3 3.49 0.51 < .001 14.12 4.04 < .001
 β4 −0.33 0.12 0.009
R2 0.760 0.815 0.822
Total mass (g)
 β0 8,594 219 < .001 7,404 269 < .001 6,510 334 < .001
 β1 1,251 62 < .001 2,090 142 < .001 3,104 278 < .001
 β2 −74.44 3.69 < .001 −202 20 < .001 −476 68 < .001
 β3 5.21 0.81 < .001 31.22 6.29 < .001
 β4 −0.80 0.19 < .001
R2 0.721 0.778 0.800
Percent fat
 β0 25.70 0.51 < .001 24.36 0.68 < .001 21.91 0.84 < .001
 β1 0.85 0.14 < .001 1.79 0.36 < .001 4.57 0.70 < .001
 β2 0.0044 0.0085 0.605 −0.139 0.051 0.007 −0.89 0.17 < .001
 β3 0.0059 0.0021 0.005 0.077 0.016 < .001
 β4 −0.00219 0.00048 < .001
R2 0.810 0.818 0.838
Percent lean
 β0 70.78 0.50 < .001 72.22 0.67 < .001 74.82 0.82 < .001
 β1 −0.81 0.14 < .001 −1.82 0.35 < .001 −4.77 0.68 < .001
 β2 −0.0079 0.0084 0.352 0.146 0.050 0.004 0.94 0.17 < .001
 β3 −0.0063 0.0020 0.002 −0.082 0.015 < .001
 β4 0.00232 0.00047 < .001
R2 0.820 0.829 0.852

BMC = Bone mineral content. BMD = Bone mineral density.

Figure 1
Figure 1

Selected models for mean total, lean, and fat mass for each age in dogs included in this retrospective analysis.

Citation: American Journal of Veterinary Research 2024; 10.2460/ajvr.24.05.0132

On the basis of percent total mass, mean percent lean mass declined from age 0 to 6, continued to decline more slowly from age 6 to 11.5, and once again declined from age 11.5 to 16.1 (Supplementary Figure S1). Mean percent fat mass increased gradually until age 10, whereafter fat mass increased at a more rapid rate.

Bone mineral content increased early in life, before slowing to its peak at age 7.1 (Supplementary Figure S2). Then, BMC decreased before plateauing at age 16.1. Bone mineral density increased before slowing to a peak at age 5.1 and then declined gradually until age 16.1.

Change in body composition with age in cats

Age had a significant effect on all DEXA variables in cats (P < .001; Table 3), and the mean of each variable with age is presented (Supplementary Table S2). The quartic trends for lean and total mass, as well as the cubic trend for fat mass, are modeled (Figure 2). Lean body mass increased until reaching a peak at age 4.5, where it then declined gradually until age 12.5, before sharply declining until age 16.9. Fat mass also increased gradually in the younger ages, reaching a peak at age 7.5 before declining until age 16.9. Total mass increased until peaking at age 5.5, where it declined gradually until age 12, before sharply declining until age 16.9.

Table 3

Regression coefficients, associated SEs, and P-values for quadratic, cubic, and quartic models of body composition measures for cats.

Quadratic Cubic Quartic
Measurement/coefficient Estimate SE P-value Estimate SE P-value Estimate SE P-value
BMD (g/cm2)
 β0 0.307 0.014 < .001 0.230 0.017 < .001 0.213 0.022 < .001
 β1 0.0198 0.0039 < .001 0.0731 0.0088 < .001 0.093 0.018 < .001
 β2 −0.00155 0.00022 < .001 −0.0094 0.0012 < .001 −0.0145 0.0042 < .001
 β3 0.000307 0.000046 < .001 0.00077 0.00037 0.039
 β4 −0.000014 0.000011 0.207
R2 0.354 0.487 0.488
BMC (g)
 β0 99.85 3.41 < .001 75.31 3.62 < .001 58.06 4.04 < .001
 β1 10.62 0.93 < .001 27.69 1.84 < .001 47.46 3.27 < .001
 β2 −0.640 0.053 < .001 −3.14 0.25 < .001 −8.34 0.78 < .001
 β3 0.098 0.010 < .001 0.573 0.069 < .001
 β4 −0.0140 0.0020 < .001
R2 0.466 0.669 0.743
Fat (g)
 β0 505 46 < .001 282 56 < .001 226 71 0.002
 β1 240 12 < .001 395 29 < .001 459 57 < .001
 β2 −13.44 0.71 < .001 −36.18 3.89 < .001 −53 14 < .001
 β3 0.89 0.15 < .001 2.42 1.21 0.047
 β4 −0.05 0.04 0.203
R2 0.690 0.743 0.743
Lean (g)
 β0 2926 78 < .001 2547 96 < .001 2,091 107 < .001
 β1 220 21 < .001 483 49 < .001 1,006 87 < .001
 β2 −15.18 1.21 < .001 −53.75 6.63 < .001 −191 21 < .001
 β3 1.51 0.26 < .001 14.06 1.82 < .001
 β4 −0.369 0.053 < .001
R2 0.553 0.629 0.712
Total mass (g)
 β0 3531 114 < .001 2,904 136 < .001 2,374 159 < .001
 β1 471 31 < .001 907 69 < .001 1,514 128 < .001
 β2 −29.23 1.76 < .001 −93.22 9.40 < .001 −253 31 < .001
 β3 2.51 0.36 < .001 17.08 2.70 < .001
 β4 −0.429 0.079 < .001
R2 0.629 0.710 0.753
Percent fat
 β0 13.20 0.65 < .001 10.07 0.80 < .001 8.93 1.01 < .001
 β1 2.99 0.18 < .001 5.16 0.41 < .001 6.48 0.82 < .001
 β2 −0.146 0.010 < .001 −0.465 0.056 < .001 −0.81 0.20 < .001
 β3 0.0125 0.0022 < .001 0.044 0.017 0.011
 β4 −0.00093 0.00050 0.067
R2 0.668 0.723 0.727
Percent lean
 β0 84.01 0.65 < .001 87.40 0.79 < .001 88.66 0.99 < .001
 β1 −2.95 0.18 < .001 −5.31 0.40 < .001 −6.75 0.80 < .001
 β2 0.143 0.010 < .001 0.489 0.055 < .001 0.87 0.19 < .001
 β3 −0.0136 0.0021 < .001 −0.048 0.017 0.005
 β4 0.00102 0.00049 0.041
R2 0.667 0.732 0.737
Figure 2
Figure 2

Selected models for mean total, lean, and fat mass for each age in cats included in this retrospective analysis.

Citation: American Journal of Veterinary Research 2024; 10.2460/ajvr.24.05.0132

On a percentage of total mass basis, mean percent lean mass declined sharply before plateauing at age 8.2, increased gradually until age 15.6, and plateaued until age 16.9 (Supplementary Figure S3). Mean percent fat mass increased sharply before peaking at age 7.7, declined gradually until age 16, and plateaued until age 16.9.

Bone mineral content increased dramatically early in life, before slowing to a peak at age 5 (Supplementary Figure S4). Then, BMC decreased until age 10.5, when the rate of decline slowed until age 13, and then declined sharply until age 16.9. Bone mineral density increased gradually before peaking at age 5.1, declined until age 14.7, and increased until age 16.9.

Discussion

This retrospective study is one of the most extensive reports of body composition over the lifetime of dogs and cats; it provides data that may be considered a baseline for how body composition changes with age and life stage in these companion animals.

Change in lean mass with age

This study found that a decline in LBM may start as early as age 6 in dogs and age 4.5 in cats, which is considered the later part of the adult life stage in cats and many small and medium dog breeds. Previous work58,18 shows that a substantial decline in LBM begins upon entry into the mature adult life stage in healthy dogs and cats, although the exact age at which this decline starts and potential genetic-associated differences have not been fully elucidated. The mature adult life stage in dogs is traditionally defined as 50–75% of life expectancy for their breed,19 while the American Animal Hospital Association currently defines this period for cats as between the ages of 7 and 10 years old.20 Progressive lean body mass loss that is not attributed to a disease condition, known as sarcopenia, is a common concern for dogs and cats as they age.18,21 In the gradual process of sarcopenia, the rate of body protein degradation exceeds the rate of protein synthesis, leading to a net loss of LBM.18,21 Frequently, this is accompanied by an increase in fat mass, causing total body weight to remain relatively stable despite a change in body composition.5 The etiology of sarcopenia is multifaceted, but in humans it is thought to involve reduced physical activity, lower quality and quantity of muscle fibers, reduced synthesis and increased degradation of protein, the whole-body influence of chronic inflammation, and/or an impaired ability to repair muscle fiber deterioration.22 There is a negative association between LBM loss and health span, or the period of time an animal is considered to have a good quality of life,23 and the lifespan of dogs and cats, with a loss of lean mass being associated with several health conditions.68 The results of this study reveal that a decline in LBM may begin before entering the mature adult life stage in both dogs and cats. Therefore, it may be advisable to implement strategies to reduce the risk of lean mass loss on the basis of nutrition and activity level before the dog or cat reaches this point of their life.

Similar to humans, physical activity is a main behavioral strategy to help maintain LBM in dogs and cats, although a combination of nutritional and behavioral methods is most effective in limiting LBM loss in the body.5,13,18,24 Since the switch from lean mass accrual to loss observed in the current study occurred before the mature adult life stage in both dogs and cats, the aforementioned nutritional and behavioral strategies should be implemented before this time. More research is needed to determine the most effective nutritional strategy in limiting LBM loss in dogs and cats, as well as insight into what age it is most effective to begin these interventions.

Change in lean and total mass during growth

While the greatest year increase in total mass occurred in the first year of life for both dogs and cats, total mass continued to increase until age 7 in dogs and age 5 in cats before stabilizing. This was accompanied by a continued accumulation of LBM until age 6.3 in dogs and age 4.5 in cats, where peak LBM was reached. Although the National Research Council defines that cats and dogs reach adulthood at 12 months of age, this study in accordance with previous work2529 emphasizes that significant growth may continue beyond an animal's first year of life. There is not a consensus in the literature regarding an exact age in months in which dogs and cats reach ideal adult body weight, largely because the growth phase is influenced by a number of factors including breed, sex, and spay/castration status.26,3032 Notably, large-breed puppies have been found to exhibit more extended periods of growth, generally reaching maturity months later than small-breed puppies.31,32 This study is the first to determine the average age of peak LBM in dogs and cats and may serve as guidance for veterinary practitioners when assessing body composition changes during an animal's lifetime. Future analyses should evaluate the efficacy of dietary and behavioral interventions in supporting the LBM gain that occurs beyond the puppy life stage and in helping to maintain peak LBM for a greater proportion of adulthood, the results of which may assist veterinary practitioners in making recommendations to patients. Furthermore, only small- and medium-sized dogs (55 lbs and below) were included in the study due to the limited data we had on dogs over 55 lbs, so future work is needed to investigate how the age in which LBM peaks differs in dogs of different sizes.

Change in fat mass with age

In accordance with previous work,7,13 dogs in this study exhibited an increase in percent fat with age. Obesity, an excessive accumulation of adipose tissue in the body, appears to be most common in the later years of life in dogs.11,33 A previous study33 at the University of California-Davis Veterinary Teaching Hospital found that 47.1% of middle-aged dogs (defined as ages 4 to 10, varied with breed size) and 45.6% of senior dogs (defined as ages 10+, varied with breed size) were overweight or obese. However, some dogs do experience weight loss in the most advanced ages of life, which may be due to age-related diseases, loss of appetite, reduced ability to eat due to mobility or dental issues, or altered sensation and perception.13,34 For cats in this study, percent fat increased from ages 0 to 7.7, before slowly declining until age 16. In cats, body fat mass tends to increase over time up until later in life, when they then start to experience a loss of body fat,35,36 which is supported by the data reported in this study. University of California-Davis found that 48.9% of mature cats (ages 7 to 10.9 years) and 47.6% of senior cats (ages 11 to 14.9 years) were overweight or obese, with this percentage declining to 39.7% in the geriatric life stage (ages 15 years and older).37 This study builds on previous research by identifying that average fat mass may begin to decline in cats at around age 8. Excess body fat has been directly linked to earlier morbidity and increased risk of various diseases, including orthopedic disease, diabetes mellitus, hepatic lipidosis, neoplasia, and others,9,11 while an inadequate body fat mass represents an energy deficit and can be a cause or effect of chronic and acute illnesses.38 It is important to note that the feeding strategy for both dogs and cats in this colony was altered within the last 5 years with the intent to bring animals closer to their ideal body weight. At the time of this change, many animals were overweight, resulting in a need to increase their lean mass-to-fat mass ratio. However, with 18 years of DEXA data included in this dataset, we do not have a strong representation of data from animals under this new feeding strategy, and the old feeding strategy may be overrepresented, which is a limitation of the current study. Nevertheless, given that up to 59% of pet dogs and 63% of pet cats are overweight or obese,39 an overrepresentation of animals that were slightly overweight may make the results of this study more applicable to the general pet population.

In dogs, a decline in LBM percentage was accompanied by an increase in fat mass percentage with age. Meanwhile, in cats, a decrease in LBM percentage was accompanied by a relatively constant increase in fat percentage up until roughly age 7, where percent lean mass increased slightly and percent fat mass decreased slightly until age 16. Then, the LBM percentage increased and fat mass percentage decreased slightly until age 16.9. The peak fat mass percentage of dogs was roughly 44% and occurred at age 16, whereas the peak fat mass percentage in cats occurred at age 8.2 and was 31%. This further emphasizes the significant concern of excess fat gain with age in senior dogs that differs from that of senior cats.35 While DEXA scans are considered the “gold standard” in terms of precision and accuracy in determining body fat percentage, other methods of estimating body composition are more widely used in clinical practice. For example, body condition scoring (BCS) is a frequently used method for assessing body composition and estimating body fat percentage in cats, existing in the forms of a 5-, 6-, and 9-point scoring system.11 However, the BCS system has only been validated in dogs and cats with less than 45% body fat, limiting its use in morbidly obese animals. The body fat index (BFI) system is a validated method to estimate body composition, particularly body fat percentage, in overweight and obese dogs and cats.14,15 Like the BCS system, the BFI system utilizes a veterinary assessment of fat deposits present throughout several locations of the body, which is then used to determine an approximate BFI that is associated with a body fat percentage range. The pet's ideal body weight can be calculated based on the determined BFI/body fat percentage. The BFI system has been shown to be a more accurate determination of body fat percentage than the 5-point BCS system when compared to results of DEXA scans and is validated in dogs with up to 65% body fat.14,15 Muscle mass can be evaluated via muscle condition score, in which the amount of muscle wasting is characterized through visualization and palpation of the body by a veterinarian.40 Research suggests that a muscle condition scoring system has high repeatability and moderate reproducibility for dogs and cats, including those experiencing varying degrees of muscle loss.41 However, this system does not give a corresponding estimate of LBM percentage and is highly subjective.

While this section describes multiple alternative methods of evaluating body composition in dogs and cats, these methods lack precision and are subjective in nature, limiting their potential to detect small changes in muscle and fat mass and reliably assess body composition changes over time. Nevertheless, they have been shown to be acceptable alternatives when DEXA scanning is not possible.

Change in BMC and BMD with age

To our knowledge, the present study is one of the most extensive reports investigating changes in BMC and BMD with age in dogs and cats. This study found that in dogs BMC peaks at age 7.1 and BMD peaks at age 5.1. In cats, we observed a peak in BMC at age 5, while BMD peaks at age 5.1. The BMC and BMD decline in cats was much more dramatic than seen in dogs. Limited research exists that quantifies changes in bone mass and bone density with age in dogs and cats, yet some age-related changes have been identified.21 Decreased bone density, or osteopenia, describes a “softening” of bones that makes them susceptible to fracture and an inability to withstand force absorption. Osteopenia may result from reduced bone mineralization and can be due to nutritional, genetic, and/or environmental factors.12 Historical studies have shown that older dogs have a lower volume of cortical and cancellous bone volume than younger dogs42 and that there are changes in calcium homeostasis indicative of skeletal mass loss with age.43 In cats, previous research44 has exhibited a lower trabecular BMD in cats 6 years and older when compared to cats ages 2–5.

Developmental orthopedic diseases describe a diverse group of musculoskeletal conditions characterized by skeletal abnormalities in growing animals .45 Large-breed puppies are at a higher risk of developmental orthopedic diseases than small and medium breeds.46 Overnutrition may result in rapid growth of low-density bone and abnormal bone formation, potentially predisposing these animals to osteoarthritis and bone fracture.46 In humans, healthy bone growth in children is vital to maximize peak bone density and mass in adults, so adequate nutrition is emphasized from a young age to promote bone health.47 In the context of this study, cats and dogs were fed a variety of complete and balanced foods throughout their lives in amounts based on their maintenance energy requirement; therefore, neither overnutrition nor nutritional imbalances are expected to be the reason for the observed changes in BMC and BMD with age. More research is needed to determine how bone mass and density at a young age impacts these measures later in life and if nutritional guidelines should be shifted to address an age-related decline in bone mass and density observed in dogs and cats.

Limitations and future directions

The animals included in this analysis were all colony housed, which was a limitation of this study. Colony populations differ from the owned-pet population due to a multitude of confounding factors, including but not limited to different feeding habits, exercise levels, enrichment opportunities, and intentional and controlled weight management programs in a portion of the dogs and cats. Additionally, the populations were limited in terms of breed diversity, with the canine population consisting of predominantly, but not exclusively, Beagles, and the feline population consisting of predominantly, but not exclusively, domestic shorthairs, meaning these breeds were overrepresented in this analysis. Despite this limitation, one strength of our dataset is that it is more representative of the general pet dog and cat population than if one individual breed or one breed size was used. For example, although domestic shorthairs dominate our study population, they are also highly prevalent in the US.48 Future work is needed to investigate the effect of a diverse set of breed types and body sizes on body composition measures and their trends with age. Furthermore, this analysis included animals in both a healthy and diseased state as well as at the time of euthanasia, and the proportion of animals falling into each category is unknown. In several instances, an unhealthy state may have warranted the participation in a clinical study where DEXA scan data was an endpoint (eg, testing the efficacy of food formulated for animals with chronic kidney disease), and this could overrepresent diseased animals in the dataset. Additionally, overweight or obese status, which may constitute a disease state, was not recorded at the time of scan. Future analysis should assess healthy and diseased populations, as well as animals with a healthy versus unhealthy body condition, separately to see if and how body composition changes in response to disease onset and in comparison to healthy populations. Furthermore, because these colony animals were fed a variety of different diets throughout their lifetime, including foods from many of the leading pet food brands, the dietary history for each animal is not reported. While the diverse dietary history for each animal included in this analysis makes the results more translatable to the entire dog and cat population, where pet owner food switching is common, future research should investigate the impacts of various nutritional strategies on body composition at different ages and over the duration of each life stage. In the context of LBM specifically, the results of this study can be used as a starting point for comparison of how the periods of lean mass accretion and loss differ as a result of nutritional intervention. It is suggested that feeding diets formulated with increased levels of dietary protein, utilizing high-quality protein sources, and choosing diets with greater inclusion of muscle-building branched-chain amino acids can limit the loss of LBM seen with age or age-associated diseases like chronic kidney disease.18,49 This proposition can be further examined by comparing the body composition with age in populations fed a high-protein diet with this baseline data. Finally, the number of DEXA scans conducted on each animal was highly variable; several animals had just 1 scan, while 1 dog had 27 scans and 1 cat had 34 scans.

There are some limitations to the use of DEXA in clinical practice, including the need for patient sedation; high upfront cost of equipment for clinics, which subsequently affects cost per scan for the pet owner; and difficulty in the interpretation of results due to a lack of validated reference values.1,50 However, the capability of DEXA scans to provide accurate assessments of LBM, fat mass, total mass, BMC, and BMD is currently unrivaled. Given the health implications associated with a suboptimal body composition and major body composition changes as previously described, it is advisable to monitor these measures from a young age. Conducting DEXA scans early and often allows these measures to be tracked over time and may allow for earlier identification of any major body composition changes in an animal. This is especially important in the context of sarcopenia, where changes in LBM may be masked by a subsequent increase in fat mass.6 Despite the clear advantages of DEXA, the aforementioned scoring systems may be a more practical solution in cases where DEXA scanning is not feasible.11,14,15,40,41

This present study may serve as a baseline for dogs and cats in terms of the average or expected LBM, fat mass, BMC, and BMD at each age and can be used to quantify how the body composition shifts throughout dogs’ and cats’ lifetimes. Thus, an individual animal's DEXA results may be evaluated in the context of this analysis to determine if body composition measures are similar to the baseline value for a specific age and assess if these measures are changing as expected.

In summary, this study identifies a baseline for how body composition changes with age in dogs and cats. LBM begins to decrease before the mature adult life stage in both dogs and cats and generally corresponds to an increase in fat mass, with the exception of cats older than 8 years of age. This study further suggests that nutritional and behavioral strategies for healthy aging should begin earlier than the mature adult stage and animals may benefit from these strategies as soon as growth is complete. Furthermore, life stage designations do not fully define how lean mass and fat mass change over the lifetime of dogs and cats, with growth continuing beyond the puppy and kitten life stages and age-related decline in lean mass beginning as soon as growth ceases in the adult life stage. Differences in body composition at each age and nuances present in the trends over the lifetime of dogs and cats exhibited in this study further emphasize metabolic and anatomical differences between these 2 species. Future work should address how body composition differs along animals’ lifespan based on breed size, body condition, and health status, explore how various nutritional strategies impact body composition compared to baseline, and investigate body composition changes with age specifically in client-owned pets living in a home environment.

Supplementary Materials

Supplementary materials are posted online at the journal website: avmajournals.avma.org.

Acknowledgments

The authors acknowledge Ontario Nutri Lab for providing animal housing and DEXA assessments used in this analysis.

Disclosures

Allison P. McGrath, Dr. Hancock, Cheryl A. Stiers, and Dr. Morris are current employees of Hill's Pet Nutrition, Inc.

No AI-assisted technologies were used in the generation of this manuscript.

Funding

This work was funded by Hill's Pet Nutrition, Inc.

References

  • 1.

    Lauten SD, Cox NR, Brawner WR, Baker HJ. Use of dual energy x-ray absorptiometry for noninvasive body composition measurements in clinically normal dogs. Am J Vet Res. 2001;62(8):12951301.

    • Search Google Scholar
    • Export Citation
  • 2.

    Speakman JR, Booles D, Butterwick R. Validation of dual energy X-ray absorptiometry (DXA) by comparison with chemical analysis of dogs and cats. Int J Obes. 2001;25(3):439447.

    • Search Google Scholar
    • Export Citation
  • 3.

    Wang Z, Heymsfield SB, Chen Z, Zhu S, Pierson RN. Estimation of percentage body fat by dual-energy x-ray absorptiometry: evaluation by in vivo human elemental composition. Phys Med Biol. 2010;55(9):2619.

    • Search Google Scholar
    • Export Citation
  • 4.

    Wolfe RR, Kim I-Y, Park S, Ferrando A. Tracing metabolic flux to assess optimal dietary protein and amino acid consumption. Exp Mol Med. 2022;54(9):13231331.

    • Search Google Scholar
    • Export Citation
  • 5.

    Freeman L. Cachexia and sarcopenia: emerging syndromes of importance in dogs and cats. J Vet Intern Med. 2012;26(1):317.

  • 6.

    Cupp C, Kerr W. Effect of diet and body composition on life span in aging cats. In: Proceedings of the Nestlé Purina Companion Animal Nutrition Summit: Focus on Gerontology. Nestlé Purina PetCare; 2010.

    • Search Google Scholar
    • Export Citation
  • 7.

    Lawler DF, Larson BT, Ballam JM, et al. Diet restriction and ageing in the dog: major observations over two decades. Br J Nutr. 2008;99(4):793805.

    • Search Google Scholar
    • Export Citation
  • 8.

    Doria-Rose VP, Scarlett JM. Mortality rates and causes of death among emaciated cats. J Am Vet Med Assoc. 2000;216(3):347351.

  • 9.

    Laflamme D. Companion animals symposium: obesity in dogs and cats: what is wrong with being fat? J Anim Sci. 2012;90(5):16531662.

  • 10.

    Laflamme DP. Understanding and managing obesity in dogs and cats. Vet Clin North Am Small Anim Pract 2006;36(6):12831295.

  • 11.

    German AJ. The growing problem of obesity in dogs and cats. J Nutr. 2006;136(7):1940S1946S.

  • 12.

    Aithal H, Singh G, Amarpal KP, Setia H. Fractures secondary to nutritional bone disease in dogs: a review of 38 cases. J Vet Med Series A. 1999;46(8):483487.

    • Search Google Scholar
    • Export Citation
  • 13.

    Harper EJ. Changing perspectives on aging and energy requirements: aging, body weight and body composition in humans, dogs and cats. J Nutr. 1998;128(12):2627S2631S.

    • Search Google Scholar
    • Export Citation
  • 14.

    Witzel AL, Kirk CA, Henry GA, Toll PW, Brejda JJ, Paetau-Robinson I. Use of a morphometric method and body fat index system for estimation of body composition in overweight and obese cats. J Am Vet Med Assoc. 2014;244(11):12851290.

    • Search Google Scholar
    • Export Citation
  • 15.

    Witzel AL, Kirk CA, Henry GA, Toll PW, Brejda JJ, Paetau-Robinson I. Use of a novel morphometric method and body fat index system for estimation of body composition in overweight and obese dogs. J Am Vet Med Assoc. 2014;244(11):12791284.

    • Search Google Scholar
    • Export Citation
  • 16.

    Epstein R. The principle of parsimony and some applications in psychology. J Mind Behav. 1984:5(2):119130.

  • 17.

    Hawkins DM. The problem of overfitting. J Chem Inf Comput Sci. 2004;44(1):112.

  • 18.

    Laflamme D. Effect of diet on loss and preservation of lean body mass in aging dogs and cats. In: Companion Animal Nutrition Summit. Purina Institute; 2018:4146.

    • Search Google Scholar
    • Export Citation
  • 19.

    Bartges J, Boynton B, Vogt AH, et al. AAHA canine life stage guidelines. J Am Anim Hosp Assoc. 2012;48(1):111.

  • 20.

    Quimby J, Gowland S, Carney HC, DePorter T, Plummer P, Westropp J. 2021 AAHA/AAFP feline life stage guidelines. J Feline Med Surg. 2021;23(3):211233.

    • Search Google Scholar
    • Export Citation
  • 21.

    McKenzie BA. Comparative veterinary geroscience: mechanism of molecular, cellular, and tissue aging in humans, laboratory animal models, and companion dogs and cats. Am J Vet Res. 2022;83(6):ajvr.22.02.0027.

    • Search Google Scholar
    • Export Citation
  • 22.

    Walston JD. Sarcopenia in older adults. Curr Opin Rheumatol. 2012;24(6):623627.

  • 23.

    Chen FL, Ullal TV, Graves JL, et al. Evaluating instruments for assessing healthspan: a multi-center cross-sectional study on health-related quality of life (HRQL) and frailty in the companion dog. Geroscience. 2023;45(4):20892108.

    • Search Google Scholar
    • Export Citation
  • 24.

    Vitger AD, Stallknecht BM, Nielsen DH, Bjornvad CR. Integration of a physical training program in a weight loss plan for overweight pet dogs. J Am Vet Med Assoc. 2016;248(2):174182.

    • Search Google Scholar
    • Export Citation
  • 25.

    National Research Council. Nutrient Requirements of Dogs and Cats. National Academies Press; 2006.

  • 26.

    Salt C, German AJ, Henzel KS, Butterwick RF. Growth standard charts for monitoring bodyweight in intact domestic shorthair kittens from the USA. PLoS One. 2022;17(11):e0277531.

    • Search Google Scholar
    • Export Citation
  • 27.

    Salt C, Morris PJ, Butterwick RF, Lund EM, Cole TJ, German AJ. Comparison of growth patterns in healthy dogs and dogs in abnormal body condition using growth standards. PLoS One. 2020;15(9):e0238521.

    • Search Google Scholar
    • Export Citation
  • 28.

    Debraekeleer J, Gross K, Zicker S. Feeding growing puppies: postweaning to adulthood. In: Hand MS, Thatcher CD, Remillard RL, Roudebush P, Novotny BJ. Small Animal Clinical Nutrition. 5th ed. Mark Morris Institute; 2010:311319.

    • Search Google Scholar
    • Export Citation
  • 29.

    Gross K, Becvarova I, Debraekeleer J. Feeding growing kittens: postweaning to adulthood. In: Hand MS, Thatcher CD, Remillard RL, Roudebush P, Novotny BJ. Small Animal Clinical Nutrition. 5th ed. Mark Morris Institute; 2010:429436.

    • Search Google Scholar
    • Export Citation
  • 30.

    Cave NJ, Bridges JP, Weidgraaf K, Thomas DG. Nonlinear mixed models of growth curves from domestic shorthair cats in a breeding colony, housed in a seasonal facility to predict obesity. J Anim Physiol Anim Nutr. 2018;102(5):13901400.

    • Search Google Scholar
    • Export Citation
  • 31.

    Salt C, Morris PJ, German AJ, et al. Growth standard charts for monitoring bodyweight in dogs of different sizes. PLoS One. 2017;12(9):e0182064.

    • Search Google Scholar
    • Export Citation
  • 32.

    Hawthorne AJ, Booles D, Nugent PA, Wilkinson J, Gettinby G. Body-weight changes during growth in puppies of different breeds. J Nutr. 2004;134(8):2027S2030S.

    • Search Google Scholar
    • Export Citation
  • 33.

    Chiang C-F, Villaverde C, Chang W-C, Fascetti AJ, Larsen JA. Prevalence, risk factors, and disease associations of overweight and obesity in dogs that visited the veterinary medical teaching hospital at the University of California, Davis from January 2006 to December 2015. Top Comp Anim Med. 2022;48:100640.

    • Search Google Scholar
    • Export Citation
  • 34.

    Churchill JA, Eirmann L. Senior pet nutrition and management. Vet Clin North Am Small Anim Pract. 2021;51(3):635651.

  • 35.

    Pérez-Camargo G. Cat nutrition: what is new in the old? Compend Contin Educ Vet. 2003;26(2):510.

  • 36.

    Smit M, Corner-Thomas RA, Weidgraaf K, Thomas DG. Association of age and body condition with physical activity of domestic cats (Felis catus). Appl Anim Behav Sci. 2022;248:105584.

    • Search Google Scholar
    • Export Citation
  • 37.

    Chiang C-F, Villaverde C, Chang W-C, Fascetti AJ, Larsen JA. Prevalence, risk factors, and disease associations of overweight and obesity in cats that visited the Veterinary Medical Teaching Hospital at the University of California, Davis from January 2006 to December 2015. Top Comp Anim Med. 2022;47:100620.

    • Search Google Scholar
    • Export Citation
  • 38.

    Johnson LN, Freeman LM. Recognizing, describing, and managing reduced food intake in dogs and cats. J Am Vet Med Assoc. 2017;251(11):12601266.

    • Search Google Scholar
    • Export Citation
  • 39.

    Larsen JA, Villaverde C. Scope of the problem and perception by owners and veterinarians. Vet Clin North Am Small Anim Pract. 2016;46(5):761772.

    • Search Google Scholar
    • Export Citation
  • 40.

    Freeman L, Becvarova I, Cave N, et al. WSAVA nutritional assessment guidelines. J Feline Med Surg. 2011;13(7):516525.

  • 41.

    Freeman LM, Michel KE, Zanghi BM, Boler BMV, Fages J. Usefulness of muscle condition score and ultrasonographic measurements for assessment of muscle mass in cats with cachexia and sarcopenia. Am J Vet Res. 2020;81(3):254259.

    • Search Google Scholar
    • Export Citation
  • 42.

    Simonet WT, Bronk JT, Pinto MR, Williams EA, Meadows TH, Kelly PJ. Cortical and Cancellous Bone: Age-Related Changes in Morphologic Features, Fluid Spaces, and Calcium Homeostasis in Dogs. Elsevier; 1988:154160.

    • Search Google Scholar
    • Export Citation
  • 43.

    Williams EA, Kelly PJ. Age-related changes in bone in the dog: calcium homeostasis. J Orthop Res. 1984;2(1):814.

  • 44.

    Cheon H, Choi W, Lee Y, et al. Assessment of trabecular bone mineral density using quantitative computed tomography in normal cats. J Vet Med Sci. 2012;74(11):14611467.

    • Search Google Scholar
    • Export Citation
  • 45.

    Demko J, McLaughlin R. Developmental orthopedic disease. Vet Clin North Am Small Anim Pract. 2005;35(5):11111135.

  • 46.

    Dämmrich K. Relationship between nutrition and bone growth in large and giant dogs. J Nutr. 1991;121:S114S121.

  • 47.

    Gordon CM, Zemel BS, Wren TA, et al. The determinants of peak bone mass. J Pediatr. 2017;180:261269.

  • 48.

    State of Pet Health 2016 Report. Banfield Pet Hospital; 2016.

  • 49.

    Hall JA, Fritsch DA, Jewell DE, Burris PA, Gross KL. Cats with IRIS stage 1 and 2 chronic kidney disease maintain body weight and lean muscle mass when fed food having increased caloric density, and enhanced concentrations of carnitine and essential amino acids. Vet Rec. 2019;184(6):190190.

    • Search Google Scholar
    • Export Citation
  • 50.

    Santarossa A, Parr JM, Verbrugghe A. The importance of assessing body composition of dogs and cats and methods available for use in clinical practice. J Am Vet Med Assoc. 2017;251(5):521529.

    • Search Google Scholar
    • Export Citation
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