First posted online on 17 May 2018 as 10.1242/jeb.173450 Access the most recent version at http://jeb.biologists.org/lookup/doi/10.1242/jeb.173450
When Fed Foods with Similar Palatability, Healthy Adult Dogs and Cats Choose Different Macronutrient Compositions
Jean A. Halla*; Jodi C. Vondranb; Melissa A. Vanchinab; Dennis E. Jewellb a
Department of Biomedical Sciences, Dryden Hall 206, College of Veterinary Medicine, Oregon
State University, Corvallis, Oregon, 97331-4802, United States of America b
Pet Nutrition Center, Hill's Pet Nutrition, Inc, 1035 NE 43rd Street, Topeka, Kansas, 66617-
1587, United States of America
*Corresponding author, E-mail:
[email protected] (Jean A. Hall) E-mail address:
[email protected] (Jodi C. Vondran). E-mail address:
[email protected] (Melissa A. Vanchina). E-mail address:
[email protected] (Dennis E. Jewell).
Summary Statement Dogs and cats offered foods with different macronutrient composition, but equivalent
of circulating metabolites reflect food choices.
© 2018. Published by The Company of Biologists Ltd.
Journal of Experimental Biology • Accepted manuscript
palatability, make food choices based on species, age, and body composition, and concentrations
Abstract Dogs and cats make short-term food choices based on palatability. We hypothesized that if palatability were masked, long-term food choices would be based on physiologic requirements, and circulating metabolite concentrations would reflect those choices. Four experimental foods with similar palatability, but varying in macronutrient composition, were prepared for healthy adult dogs (n=17) and cats (n=27). Food 1 was high protein; Food 2 was high fat; Food 3 was high carbohydrates; and Food 4 was balanced for macronutrients. By choosing any combination of foods, dogs and cats could individually set their macronutrient intake. Plasma metabolomic profiles were determined at baseline and after animals had consumed their food intake of choice for 28 days. Based on food intake calculations over 28 days, dogs on average chose to consume most of their calories from fat (41.1±4.3%) and then carbohydrate (35.8±3.7%), whereas cats on average chose to consume most of their calories from carbohydrate (43.1±4.0%) and then protein (30.3±3.9%; all P0.05).
Journal of Experimental Biology • Accepted manuscript
interfere with intra-day sample variability.
For macronutrient preferences studies, analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC). To investigate the relationship between food macronutrients, Pearson correlation coefficients were measured between the response variables using PROC GLM. Statistical significance was declared at P ≤ 0.05 and a tendency at 0.05 < P ≤ 0.10. Because the total amount of calories consumed is divided among calories from protein, carbohydrate, and fat, caloric intake data is presented as compositional. Aitchison’s centered log ratio transformation was performed on a composition consisting of protein, carbohydrate and fat and used in the subsequent statistical analysis, as described by Pawlowsky-Glahn et al. (Pawlowsky-Glahn et al., 2015). In brief, the centered log ratio of each macronutrient intake over 28 days, expressed per calories consumed over 28 days, was calculated by taking the ratio of each macronutrient intake to the geometric mean of the three-component composition, and then calculating the natural log of each ratio. Centered log ratio transformed data were subsequently used to calculate r values. For visual purposes, data are presented in figures as untransformed data for percent calories from protein, fat, and carbohydrate. Macronutrient intake comparisons between species were completed using the centered log transformed data and the PROC GLM program. To investigate the relationship between protein intake from calories (the outcome variable) and other variables: age, fat (g), lean (g), bone mineral (g), and gender, a regression
The five-fold cross validation stopping rule was used to terminate stepping when improvement in the cross validation r2 was minimal. The groups defined by the regression tree analysis for protein intake, based on age and lean body mass (cats), and fat body mass and age (dogs), divided cats into four groups and dogs into three groups. We used these groups to determine the effect of consuming macronutrients of choice for 28 days on selected metabolites of the major macronutrient classes (essential fatty acids and long chain elongation and desaturation products, essential and nonessential amino acids, glucose and tricarboxylic acid cycle intermediates, urea cycle intermediates, and sulfated microbial catabolic products), using MULTTEST. Animal was considered the experiment unit. For metabolites, an adjusted P ≤ 0.05 for type 1 error was used.
Journal of Experimental Biology • Accepted manuscript
tree analysis was conducted using the predictive modeling platform in JMP 12 (SAS Institute).
RESULTS Palatability equivalency testing Initially, there was a significant effect of macronutrient content on food intake preference. In the dog, there was a preference for high fat food, whereas in the cat, there was a preference for high protein food. Food preference was calculated using a consumption ratio, which was calculated by dividing the amount of test food consumed by total food consumed (values are grams of test food/grams of total food consumed). In dogs, consumption of high fat food (defined as A) was compared individually to consumption of each of the other foods (defined as B) such that the ratio of A/(A+B) was 0.74 for B=high protein food, 0.60 for B=high carbohydrate food, and 0.46 for B=balanced food. A t-test was significant at P0.05) ratios. Because cats initially preferred high protein food, this food was defined as A, and a consumption ratio was subsequently calculated to individually compare consumption of food A
for B=high fat food, 0.60 for B=high carbohydrate food, and 0.67 for B=balanced food. A t-test was significant at P0.05) ratios.
Journal of Experimental Biology • Accepted manuscript
to consumption of each of the other foods (defined as B) such that the ratio of A/(A+B) was 0.72
Macronutrient preference studies Healthy adult cats on average chose to consume most of their calories from carbohydrate (43%) and protein (30%), whereas healthy adult dogs on average chose to consume most of their calories from fat (41%) and carbohydrate (36%). Macronutrient percentages were based on calculations of composite macronutrient intake over the 28-day feeding period (Table 4). Overall, when fed foods with similar palatability, healthy adult dogs and cats chose different macronutrient intakes (P < 0.001). Cats choose a broader concentration of protein intake compared with dogs, as evidenced by the standard deviations for protein intake. No dog or cat, by choice, maximized protein intake by feeding solely from the high protein feeding station. There was not an equal response to change in protein intake between cats and dogs (Figs 1A, 2A). In cats, when protein intake increased, carbohydrate intake decreased (r = -0.77; P < 0.001; Fig. 1B), and fat intake decreased as well (r = -0.47; P = 0.01). In dogs, variation in protein intake was less than observed for cats and was not offset by carbohydrate intake (r < 0.03; P = 0.87; Fig. 2B), although increased protein intake was offset by decreased fat intake similar to cats (r = -0.52; P = 0.03). In dogs, unlike cats, there was an inverse relationship between fat and carbohydrate intake; as fat intake increased, carbohydrate intake decreased (r = 0.87; P < 0.001; Fig. 2C). In cats, as fat intake increased, there was no change in carbohydrate intake (r = -0.20; P = 0.32).
range between the highest and lowest protein food available to cats) compared with dogs (range 20 to 26%; 37% of the range between the highest and lowest protein food available to dogs). Age, lean body mass, and fat body mass influenced the percent of calories chosen from protein (Fig. 3). In cats, there was a negative relationship between percent of calories consumed from protein and age and lean body mass (r2 = 0.57; P < 0.001). Overall, younger cats showed a greater preference for dietary calories from protein. All cat groups over 3.2 years of age consumed less protein than younger cats. Cats aged 3.2 to 3.4 years consumed the least percent of calories from protein. Younger cats with less lean body mass had a greater preference for dietary calories from protein (34.4%) compared with younger cats with more lean body mass (>3.8 kg). In dogs, there was a relationship between percent of calories chosen from protein and age and fat body mass (r2 = 0.55; P < 0.001). Overall, dogs with greater fat body mass (>4.2 kg) had a greater preference for dietary calories from protein (24.5%). Age also had a significant
Journal of Experimental Biology • Accepted manuscript
Cats, on average, had more variation in protein intake (range 23 to 38%; 78% of the
effect on protein intake in that younger dogs (3.4 years) had significantly lower (P = 0.01) plasma concentrations of DHA compared with younger cats (4.2 kg) had a greater preference for dietary calories from protein (24.5%). Age also had a significant effect on protein intake in that younger dogs ( 3.8
Protein intake, %
34.4
31.4
25.6
29.7
Number of cats, N
6
5
5
11
Linoleic acid [18:2 (n-6)]
1.02
1.21
0.97
0.90
0.09
0.20
Linolenic acid [18:3 (n-3)]
0.97
1.08
1.27
1.03
0.13
0.92
Arachidonic acid [20:4 (n-6)]
1.06
1.20
1.05
0.92
0.20
0.27
EPA [20:5 (n-3)]
4.94
5.53
5.03
4.60
0.70
0.96
DHA [22:6 (n-3)]
3.73a
3.94a
2.99a,b
2.07b
0.44
0.01
Threonine
0.93
1.02
0.84
0.90
0.08
0.95
Lysine
0.78
0.68
0.94
1.28
0.18
0.12
Methionine
0.84
0.81
0.79
0.91
0.07
0.98
Tryptophan
0.97
0.74
0.91
0.88
0.06
0.72
Leucine
0.90
1.03
0.99
1.00
0.07
0.95
Isoleucine
0.95
1.04
1.05
1.04
0.07
0.94
Valine
1.02
1.09
1.07
1.05
0.07
1.00
Tyrosine
1.01
0.97
1.06
1.14
0.06
0.37
Phenylalanine
0.98b
0.97b
1.03b
1.16a
0.05
0.02
Histidine
0.92
0.94
0.95
1.04
0.05
0.35
Essential Amino Acids4
Non Essential Amino Acids
Journal of Experimental Biology • Accepted manuscript
Essential Fatty Acids
Alanine
1.14
1.19
1.12
1.04
0.13
0.99
Asparagine
1.09
1.05
1.00
1.23
0.08
0.88
Aspartate
1.26
1.16
0.93
1.24
0.13
0.99
Cysteine
0.97
0.98
1.15
1.08
0.11
0.44
Cystine
0.30
0.46
0.43
0.34
0.13
0.99
Glutamate
1.26
1.33
1.05
1.11
0.08
0.12
Glutamine
0.91
1.00
0.93
0.87
0.05
0.86
Glycine
1.11
1.08
1.11
1.31
0.08
0.30
Proline
0.96
1.08
1.03
1.09
0.05
0.54
Serine
1.03
1.22
0.98
1.21
0.07
0.90
Glucose and Tricarboxylic Acid Cycle Glucose
1.10
1.03
0.99
0.99
0.05
0.22
Lactate
1.34
1.36
1.11
1.18
0.12
0.39
Pyruvate
1.16
0.76
1.17
0.97
0.20
1.00
Citrate
0.99
0.98
0.98
0.91
0.04
0.33
Alpha-ketoglutarate
1.01
0.82
1.04
1.03
Succinate
0.87
0.92
0.92
0.96
0.10
0.95
Fumarate
1.18
1.22
1.28
1.34
0.19
0.94
Malate
1.24
1.27
1.31
1.44
0.18
0.87
Arginine
1.17a
1.03a,b
0.94b
0.99b
0.07
0.05
Homo Arginine
5.48a
6.27a
1.57b
0.26b
0.61
0.01
Ornithine
0.94
0.79
0.80
1.05
0.15
0.94
Citrulline
0.94
0.77
0.74
0.90
0.08
0.96
Urea
1.21
1.07
1.11
1.20
0.15
0.94
0.99
Urea Cycle
Sulfated Microbial Catabolic Products p-Cresol sulfate
0.50c
5.28c
30.0b
58.8a
9.72
