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Effects of six carbohydrate sources on cat diet digestibility and postprandial glucose and insulin response

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The effects of diets with different starch sources on the total tract apparent digestibility and glucose and insulin responses in cats were investigated. Six experimental diets consisting of 35% starch were extruded, each containing one of the following ingredients: cassava flour, brewers rice, corn, sorghum, peas, or lentils. The experiment was carried out on 36 cats with 6 replications per diet in a completely randomized block design. The brewers rice diet offered greater DM, OM, and GE digestibility than the sorghum, corn, lentil, and pea diets (P < 0.05). For starch digestibility, the brewers rice diet had greater values (98.6%) than the sorghum (93.9%), lentil (95.2%), and pea (96.3%) diets (P < 0.05); however, starch digestibility was >93% for all the diets, proving that despite the low carbohydrate content of carnivorous diets, cats can efficiently digest this nutrient when it is properly processed into kibble. Mean and maximum glucose concentration and area under the glucose curve were greater for the corn-based diet than the cassava flour, sorghum, lentil, and pea diets (P < 0.05). The corn-based diets led to greater values for the mean glucose incremental concentration (10.2 mg/dL), maximum glucose incremental concentration (24.8 mg/dL), and area under the incremental glucose curve (185.5 mg.dL(-1).h(-1)) than the lentil diet (2.9 mg/dL, 3.1 mg/dL, and -40.4 mg.dL(-1).h(-1), respectively; P < 0.05). When compared with baseline values, only the corn diet stimulated an increase in the glucose response, occurring at 4 and 10 h postmeal (P < 0.05). The corn-based diet resulted in greater values for maximum incremental insulin concentration and area under the incremental insulin curve than the lentil-based diet (P < 0.05). However, plasma insulin concentrations rose in relation to the basal values for cats fed corn, sorghum, pea, and brewers rice diets (P < 0.05). Variations in diet digestibility and postprandial response can be explained by differences in the chemical composition of the starch source, including fiber content and granule structure, and also differences in the chemical compositions of the diets. The data suggest that starch has less of an effect on the cat postprandial glucose and insulin responses than on those of dogs and humans. This can be explained by the metabolic peculiarities of felines, which may slow and prolong starch digestion and absorption, leading to the delayed, less pronounced effects on their blood responses.
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Pereira and F. Prada
L. D. de-Oliveira, A. C. Carciofi, M. C. C. Oliveira, R. S. Vasconcellos, R. S. Bazolli, G. T.
and insulin response
Effects of six carbohydrate sources on cat diet digestibility and postprandial glucose
published online May 9, 2008J ANIM SCI
http://jas.fass.org/content/early/2008/05/09/jas.2007-0354.citation
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Running head: Evaluation of carbohydrate sources for cats1
2
3
4
Effects of six carbohydrate sources on cat diet digestibility and postprandial glucose and 5
insulin response
1
6
7
L. D. de-Oliveira
*
, A. C. Carciofi
*2
, M. C. C. Oliveira
*
, R. S. Vasconcellos
*
, R. S. Bazolli
*
, G. T. 8
Pereira
*
, and F. Prada
§
9
10
*
Sao Paulo State University. Faculty of Agrarian and Veterinary Sciences, Jaboticabal, SP 14884-11
900, Brazil.
§
University of Sao Paulo. Faculty of Veterinary Medicine and Animal Sciences, Sao 12
Paulo, SP 05508-270, Brazil13
1
The authors acknowledge the financial support of Fundação de Amparo à Pesquisa do
Estado de São Paulo (process 03/07496-0).
2
Corresponding author: aulus.carciofi@gmail.com
Page 1 of 33 Journal of Animal Science
Published Online First on May 9, 2008 as doi:10.2527/jas.2007-0354
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ABSTRACT: The effects of diets with different starch sources on the total tract apparent 14
digestibility and glucose and insulin responses in cats were investigated. Six experimental diets 15
consisting of 35% starch were extruded, each containing 1 of the following ingredients: cassava 16
flour, brewer’s rice, corn, sorghum, peas or lentils. The experiment was carried out on 36 cats 17
with 6 replications per diet in a completely randomized block design. The brewer’s rice diet 18
offered greater DM, OM, and GE digestibility than the sorghum, corn, lentil, and pea diets (P < 19
0.05). For starch digestibility, the brewer’s rice diet had greater values (98.6%) than the sorghum 20
(93.9%), lentil (95.2%), and pea (96.3%) diets (P < 0.05); however, starch digestibility was > 21
93% for all the diets, proving that despite the low carbohydrate content of carnivorous diets, cats 22
can efficiently digest this nutrient when it is properly processed into kibbles. Mean and maximum 23
glucose concentration, and area under the glucose curve were greater for the corn-based diet than 24
the cassava flour, sorghum, lentil, and pea diets (P < 0.05). The corn-based diets led to greater 25
values for the mean glucose incremental concentration (10.2 mg/dl), maximum glucose 26
incremental concentration (24.8 mg/dL), and area under the incremental glucose curve (185.5 mg 27
• dL
-1
• h
-1
) than the lentil diet (2.9 mg/dL, 3.1 mg/dL, and –40.4 mg • dL
-1
• h
-1
, respectively; P < 28
0.05). When compared to baseline values, only the corn diet stimulated an increase in the glucose 29
response, occurring at 4 and 10 h post-meal (P < 0.05). The corn-based diet resulted in greater 30
values for maximum incremental insulin concentration and area under the incremental insulin 31
curve than the lentil-based diet (P < 0.05). However, plasma insulin concentrations rose in 32
relation to the basal values for cats fed corn, sorghum, pea, and brewer’s rice diets (P < 0.05). 33
Variations in diet digestibility and postprandial response can be explained by differences in the 34
chemical composition of the starch source, including fiber content and granule structure, and also 35
differences in the diets’ chemical compositions. The data suggest that starch has less of an impact 36
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on the cat postprandial glucose and insulin responses than on those of dogs and humans. This can 37
be explained by the metabolic peculiarities of felines, which may slow and prolong starch 38
digestion and absorption, leading to the delayed, less pronounced effects on their blood 39
responses.40
Key words: digestion, feline, ingredient, meal response test, starch41
42
INTRODUCTION43
Starch is not naturally found in the diet of the carnivorous cat. Nevertheless, pet foods 44
contain considerable amounts of the ingredient. Dry foods can contain 30 to 60% carbohydrates 45
and canned foods up to 30%, and the greater part of the carbohydrates found in these products is 46
starch. Total apparent digestibility of starch in adult cats was reported as varying from 40 to 47
100% (Pencovic and Morris, 1975; Morris et al., 1977; Wilde and Jansen, 1989; Kienzle, 1994b).48
Whereas both cats and dogs belong to the order Carnivora, significant nutritional and 49
metabolic differences have been shown between the 2 species (Kienzle, 1993; Morris, 2001). 50
Cats are considered to exhibit decreased apparent nutrient digestibility than their canine 51
counterparts (Kendall et al., 1982). Some investigations showed that cats are unable to 52
metabolize sugars in large quantities (Kienzle, 1993, 1994b; Washizu et al., 1999). However, few 53
studies have looked at the digestibility of cat foods containing different starch sources. 54
It is accepted that carbohydrates, primarily starches, are the principal nutrients that 55
determine and modify the postprandial glucose and insulin curves in dogs and humans (Wolever 56
and Bolognesi, 1996; Nguyen et al., 1994). For cats, however, the few studies carried out indicate 57
comparatively small serum glucose and insulin variations after the consumption of different 58
starch and sugar types (Kienzle, 1994a, Bouchard and Sunvold, 2000; Appleton et al., 2004). 59
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Therefore, the objective of the present research was to investigate the effects of cassava flour, 60
brewer’s rice, corn, sorghum, peas, and lentils on the digestibility and postprandial glucose and 61
insulin responses of healthy cats.62
63
EXPERIMENTAL PROCEDURES64
Animals65
Thirty six mixed-breed, neutered, male and female cats, aged 4.9 ± 0.7 years, and 66
weighing 4.34 ± 0.76 kg, i.e., not obese (body condition score between 4 and 6; Laflamme, 67
1997), were used in the digestibility and meal response tests. The cats were kept in the 68
Laboratory of Nutrition and Nutritional Diseases at Sao Paulo State University (Jaboticabal, 69
Brazil). During the digestibility and postprandial response experiments the cats were individually 70
housed in 0.9 × 0.8 × 0.9 m stainless steel metabolic cages. Water was available ad libitum 71
throughout the duration of the experiment. The Ethics Committee for Animal Well-Being at the 72
Faculty of Agrarian and Veterinary Sciences, Sao Paulo State University, approved all 73
experimental procedures.74
75
Diets76
In total, 6 diets were tested and the ingredient composition of each is reported in Table 1. 77
Each diet incorporated 1 of 6 carbohydrates as its exclusive source of starch: corn, brewer’s rice, 78
sorghum, peas, lentils, or cassava flour. Due to the chemical composition of the carbohydrate 79
sources being evaluated, additional ingredients were incorporated to obtain diets that contained 80
percentages (DM basis) of starch, fat, calcium, and phosphorus as balanced as possible. Isolated 81
soybean protein, poultry by-product meal, and poultry fat were used to equalize the formulations. 82
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All diets contained the same amount of added salt, vitamins, and trace minerals. The amount of 83
total dietary fiber (TDF) varied according to the concentration of these nutrients found in the 84
carbohydrate sources (Table 2). Diets were formulated in accordance with the AAFCO (2003) 85
nutrient guide for cats and balanced to meet maintenance requirements before being extruded and 86
kibbled under identical processing conditions in a single-screen extruder at the College of 87
Agricultural and Veterinarian Sciences, Sao Paulo State University (Mab 400S, Extrucenter, 88
Monte Alto, Brazil). The food manufacturing quality was controlled every 20 min by adjusting 89
the density (g /L) of each food preparation in order to achieve the same kibble parameters (i.e.,90
size, expansion) and, indirectly, the same gelatinization (Table 2).91
Digestibility Protocol92
The 36 cats were divided into 3 groups of 12 animals each, allowing 2 cats to be fed the 93
same diet within each group. The experiment was conducted in three periods with one group 94
evaluated during each period. The digestibility assay was carried out through quantitative 95
collection of feces, according to AAFCO (2003) guidelines. A 10-d test-diet adaptation phase 96
preceded a 10-d collection of feces in each experimental period. The quantity of diet provided 97
was calculated using standard equations that determine proper energy requirements for cat 98
maintenance (ME, kcal = 70 × kg BW), in accordance with NRC (1986) data. Each day, food was 99
weighed and divided into 2 equal portions, placed in stainless steel bowls, and left out at 0900 100
and 1700. Bowls were removed before the next meal and any remaining food was weighed and 101
recorded. On the first day of fecal collection, all feces were removed from the cages and 102
discarded before 0800. Fecal output was collected from this point on for the next 10 d at each 103
mealtime. Samples were frozen (−15º C) as they were collected, and pooled by cat.104
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Fecal samples were scored according to the following system: 1 = watery – liquid that can 105
be poured; 2 = soft, unformed – stool assumes shape of container; 3 = soft, formed, moist – softer 106
stool that retains shape; 4 = hard, formed, dry stool – remains firm and soft; 5 = hard, dry pellets 107
– small, hard mass. Fecal pH was determined by mixing 10 mL of distilled water with 5 g of 108
feces and measuring the result with a pH meter (model Q-400-Bd, Quimis, Brazil).109
Postprandial Response Tests110
After the digestibility trials, cat postprandial glucose and insulin responses were measured 111
using a sampling protocol based on Appleton et al. (2004), minus the samples at 14, 16 and 18 h 112
after the meal. As previously, the 36 cats were divided into 3 groups of 12 cats each, with 2 cats 113
receiving the same diet within each group. One group was evaluated per period. The cats were 114
allowed to adapt to their diets for 7 d before samples were taken, and during this period the 115
animals were fed once per day and conditioned to ingest their food within 15 min. Subsequently,116
the cats were deprived of food for 24 h before the initiation of the glucose/insulin response. Two 117
days prior, each cat had been aseptically catheterized under light sedation with levomepromazine 118
(Neozine, Aventis, Brazil), with a central venous catheter (Intracath, 30.5 cm, 1.1 mm, Becton 119
Dickinson Vascular Access, UT) inserted into a jugular vein. Catheters were flushed twice daily 120
with dilute, heparinized saline (20 IU / mL) to maintain patency. Blood samples were taken pre-121
feeding (baseline sample, time 0) and 1, 2, 4, 6, 8, 10 and 12 h post-feeding (the times were 122
counted from the end of the meal). Cats were fed their total daily energy requirement (NRC, 123
1986) and allowed a maximum of 15 min to consume their diets; only those that finished their 124
allotted diet in 15 min were tested. Blood was collected at the same time, starting at 0700. Each 125
1.5-mL blood sample was taken using a syringe and transferred to a Na-heparin tube, centrifuged 126
(2,000 × g for 5 min), and the plasma divided equally into 2 Eppendorf tubes. Plasma samples for 127
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glucose measurement were kept under refrigeration (4
o
C) for a maximum of 2 h before analysis; 128
plasma samples for analysis of insulin were frozen (-70º C) for a maximum of 2 mo before they 129
were analyzed.130
Laboratorial Analyses131
At the end of the collection period, feces were thawed, homogenized, and pooled by cat. 132
Before performing laboratory tests, feces were dried in a forced-air oven at 55º C for 72 h133
(Fanem, Sao Paulo, Brazil) and ground in a cutting mill with a 1-mm sieve. Food samples were 134
ground in a similar fashion. Diet and feces were analyzed for DM, OM, ash, CP (Kjeldahl 135
method), acid-hydrolyzed fat, phosphorus, and calcium using AOAC (1995) methods. 136
Determination of TDF was carried out according to Prosky et al. (1992). Gross energy 137
content of diets and fecal matter was determined using a bomb calorimeter (model 1261, Parr 138
Instrument Company, Moline, IL, USA). The total amount of starch was analyzed according to 139
the guidelines set out by Miller (1959) and Hendrix (1993); whereas, amylose and amylopectin 140
content were determined following Knutson’s (1986) methodology. All analyses were carried out 141
in duplicate, with a coefficient of variation below 5%.142
Plasma glucose concentrations were determined by a glucose oxidase test (GOD-ANA, 143
Labtest Diagnóstica S.A., Lagoa Santa, Brazil) using a semi-automated glucose analyzer 144
(Labquest model BIO-2000, Labtest Diagnóstica S.A., Lagoa Santa, Brazil). Plasma insulin was 145
measured by RIA using a commercially available kit (human insulin as a standard; Diagnostic 146
Products Corporation, Los Angeles, CA; I
125
as tracer) that was validated for cats (Nelson et al., 147
1990). The intra-assay coefficient of variation for insulin was 7.6% and the SE was 0.12 µIU per 148
mL.149
Calculations150
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Nutrient apparent digestibility values were determined for each experimental diet. 151
Changes in plasma glucose and insulin concentrations were calculated for each postprandial 152
period. Responses were compared for the average and maximum increase, the average and 153
maximum incremental increase (the difference between the absolute glucose or insulin 154
concentration of the sample and the baseline concentration), and the time to peak increase. The 155
integrated area under the postprandial glucose and insulin response curves and the integrated area 156
under the incremental postprandial glucose and insulin curves were calculated by the trapezoidal 157
method. The software ORIGIN (Microcal Software, Inc. Northampton, MA) was used for area 158
under curve computing.159
Statistical Analyses160
Data were analyzed in a completely randomized block design using the GLM procedures161
(SAS Inst. Inc., Cary, NC). The individual cat was considered the experimental unit. Model sums 162
of squares were separated into diet, period (blocks), and animal effects. Where significant (P < 163
0.05) differences were detected in ANOVA’s F test for nutrient intakes, feces characteristics, or 164
apparent total tract nutrient digestibilities, multiple comparisons of means were made using 165
Tukey’s test (P < 0.05). Repeated measures analysis of variance with 2 among-animals factors 166
(diet and period) and 1 within-animals factor (time of sampling) was the statistical method 167
chosen to evaluate the effects of diet and time on postprandial plasma changes. Pairwise means 168
comparisons were also made through Tukey’s test (P < 0.05) when the ANOVA F test results 169
were statistically significant. All data complied with the assumptions of ANOVA models. 170
171
RESULTS172
Chemical Composition and Process173
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The chemical compositions of the starch sources and experimental diets are given in Table 174
2. The DM and OM ingredient composition were similar for all sources. Crude protein 175
concentration was greatest in peas and lentils, and lowest in cassava flour. Fat concentration was176
greatest in corn and sorghum. Regarding carbohydrate composition, cassava flour and brewer’s 177
rice had the greatest amounts of starch; lentils and peas had the greatest concentrations of fiber.178
All diets contained comparable concentrations of DM and OM. Mean starch concentration 179
was 35.1% and the sorghum-based diet contained 5% more starch than the pea diet. The 180
concentrations of CP varied due to its usage in completing the formulations, and the fiber varied 181
according to its concentration in the tested ingredients. Regarding food processing, kibble 182
densities were comparable among the experimental diets, especially after drying (Table 2), 183
indicating similar cooking.184
Digestibility185
Daily nutrient intake, apparent total tract digestibility values and cat fecal characteristics 186
are presented in Table 3. Differences in TDF and fat ingestion among diets were verified (P < 187
0.05) and can be explained by differences in food composition. Variations in fat ingestion were 188
low, and likely did not interfere with digestibility determinations. Ingestion of the other nutrients 189
was similar among diets. 190
The brewer’s rice-based diet gave greater digestibility of DM, OM, CP, starch, and GE (P 191
< 0.05) than the sorghum, lentil, and pea diets. The cassava flour-based diet gave intermediate 192
results when compared to brewer’s rice and the other diets for DM, OM, and GE. The corn-based 193
diet presented intermediate results when compared to brewer’s rice and the other diets for CP 194
digestibility. Total dietary fiber digestibility was greater for the lentil diet than the cassava flour 195
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diet (P < 0.05). Starch digestibility was > 93% for all diets, with the brewer’s rice-, cassava flour-196
and corn-based diets presenting greater digestibility than the lentil and sorghum diets (P < 0.05).197
Regarding fecal evaluation, no differences were observed in fecal scores, pH, feces 198
excretion (DM basis) or fecal output. Fecal DM was greater for cats fed the brewer’s rice diet 199
than the cassava flour, sorghum, lentil, and pea diets (P < 0.05). Wet fecal production was lower 200
for cats fed the brewer’s rice diet than the lentil, cassava, sorghum, and pea diets (P < 0.05). 201
Postprandial Responses of Glucose and Insulin202
There were no differences in ingestion of starch, DM, OM, CP and ME (Table 4). Fat 203
ingestion was lower for cats fed the lentil diet compared to the brewer’s rice diet (P < 0.05). Cats 204
fed the sorghum, lentil, and pea diets showed greater TDF ingestion than those fed the brewer’s 205
rice and cassava flour diets (P < 0.05).206
According to Table 5, the mean glucose concentration, the maximum glucose 207
concentration, and the area under the glucose curve were greater for the corn-based than for the 208
cassava flour, sorghum, lentil, and pea diets (P < 0.05). For the mean incremental glucose 209
concentration, the maximum incremental glucose concentration, and the area under the 210
incremental glucose curve, the corn-based diet was greater than the lentil diet only (P < 0.05). 211
When compared to baseline values, only the corn diet stimulated a significant increase in the 212
glucose response, occurring at 4 and 10 h post-meal (P < 0.05), as shown in Figure 1. 213
Similarly, cats fed the corn-based diet showed a greater maximum incremental insulin 214
concentration and greater area under the incremental insulin curve than cats fed the lentil-based 215
diet (P < 0.05). Cats fed the lentil-based diet also presented a lower area under the incremental 216
insulin curve than cats fed the sorghum diet (P < 0.05). Plasma insulin concentrations increased217
above the basal values in cats fed the corn (at 1, 2, 4, 6, and 8 hours post-meal), sorghum (at 4, 6, 218
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8; and 12 hours post-meal), pea (at 4 hours post-meal), and brewer’s rice (at 1, 2, 4, and 6 hours 219
post-meal) diets (P < 0.05), as shown in Figure 1. 220
221
DISCUSSION222
The observed differences in TDF, CP, fat and starch among carbohydrate sources were 223
expected. According to Hoseney (1994) and Svihus et al. (2005), these differences occur because 224
the proportions of hull, pericarp, aleurone and germ in the whole grains differ, making their 225
individual chemical compositions unique. Brewer’s rice is hulled and polished; cassava flour is 226
the residue obtained after hulling, grinding, and removing the soluble starch of the cassava root: 227
this explains the reduced TDF concentrations of these ingredients. The proportion of amylose to 228
amylopectin is also unique to each starch source. Usually, legume starches have greater amylose 229
concentrations than cereal starches (Biliaderis, 1991). 230
Measurements of digestibility indicate that, despite the low carbohydrate content of the 231
carnivorous diets characteristic of the Felidae, cats can efficiently digest starch-based diets which 232
are adequately ground and cooked. The digestibility values of these diets compare to the starch 233
digestibilities of other species such as dogs and rats (Morris et al., 1977; Holm et al., 1988; 234
Murray et al., 1999).235
Most commercial dry, extruded cat foods are composed of rice or corn. Previous studies 236
in cats using cooked, finely ground corn indicated starch digestibilities greater than 90% 237
(Pencovic and Morris, 1975; Morris et al., 1977; Wilde and Jansen, 1989). Studies using dogs 238
also demonstrated that diets based on corn or rice have high digestibilities (Walker et al., 1994; 239
Murray et al., 1999), and that brewer’s rice-based diets have a similar or greater nutrient 240
digestibility than corn diets (Belay et al., 1997; Twomey et al., 2002). In the current study, 241
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brewer’s rice and corn-based diets showed similar starch digestibilities in cats.242
Although no previous research detailing the effects of cassava flour-based diets on cats 243
was encountered, in dogs it was shown that the apparent digestibilities of DM, CP, and fat for 244
brewer’s rice- and cassava flour-based diets do not differ (Kamalu, 1991). These findings were 245
supported in the current study, which characterized these ingredients as being highly digestible 246
by cats.247
Lower digestibility values for the lentil- and pea-based diets compared to those for the 248
other starch sources were also described by other authors working with dogs, who attributed the 249
differences between cereals and legumes to the starch and TDF compositions of the ingredients 250
(Bednar et al., 2001). The elevated amounts of resistant and slowly digestible starches found in 251
lentils (Cummings and Englyst, 1995) results in the slower digestion and absorption rates of this 252
legume in humans (Lin et al., 1992). 253
Among the cereals, including rice and corn, sorghum is considered to have decreased254
starch digestibility (Svihus et al., 2005), a finding confirmed in our study. According to Rooney 255
and Pflugfelder (1986), the protein matrix of the hard outer endosperm closely surrounds 256
sorghum starch; this complex interaction between protein and starch restricts digestibility. 257
Additionally, antinutritional substances such as tannin can contribute to enzymatic inhibition, 258
which may delay sorghum starch digestion.259
Studies on starch usually separate ileal from total tract digestibility because the fraction of 260
starch that escapes enzymatic digestion in the small intestine can be fermented by the microbiota 261
in the large intestine, leading to overestimates of digestibility. However, Kienzle (1993) showed 262
that amylase activity in the gastrointestinal tract of cats is mainly of bodily origin, which led to 263
the conclusion that there is little interference with total apparent starch digestibility. 264
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Another concern is the diet adaptation period. Kienzle (1993) observed that cats fed 265
starch-rich diets early in life show greater amylase activity than cats first fed the same diets as 266
adults. In the current study, all cats were given commercial dry-extruded foods rich in starch 267
since birth, likely contributing to the elevated digestibilities encountered. Finally, another factor 268
to be considered is the food processing itself (Riaz, 2003), in which the milling and extrusion of 269
experimental diets may affect the utilization of the starches.270
Measurements of postprandial responses indicate that differences among diets were not as 271
pronounced for cats as those normally observed for dogs and humans. Existing studies are still 272
inconclusive regarding the effect of starch on cat postprandial glucose and insulin responses; 273
large individual variations of these characteristics have been encountered (Appleton et al., 2004). 274
Data obtained in this study, like in other studies with this species (Kienzle, 1994a; Bouchard and 275
Sunvold, 2000), suggest that starch has only a minor effect on the glucose and insulin responses 276
of cats. This may be explained by the metabolic peculiarities of felines, among them the 277
preferential usage of AA as a source of energy and lower enzymatic capabilities to digest starch 278
and to metabolize dietary glucose (Kienzle, 1993, 1994a; Washizu et al., 1999; Morris, 2001), 279
which can slow and prolong starch digestion and absorption. Reinforcing this observation is the 280
time required for glucose elimination after an intravenous or oral glucose tolerance test, which is 281
prolonged in cats compared to dogs and humans (Kienzle, 1994a; Bouchard and Sunvold, 2000; 282
Appleton et al., 2004). 283
Increased concentration of glucose and insulin, as observed in the digestibility 284
experiment, depends on an integrated evaluation of the diet, including consideration of factors 285
intrinsic to starch; such as the digestion rate, the amylose-to-amylopectin ratio, and the amount of 286
resistant starch; and also extrinsic factors such as processing method, diet composition, and the 287
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amount ingested (Brand et al., 1985; Heaton et al., 1988; Wolever and Bolognesi, 1996; Nguyen 288
et al., 1998). In the current study, diets were formulated to have similar chemical compositions. 289
Variations in DM, OM, starch, and CP ingestion were small and not significant. There were 290
differences between the fat and TDF ingestion for different diets. For humans, Wolever and 291
Bolognesi (1996) have suggested that in practical diets, the apparent effects of protein and fat on 292
the glucose response would be negligible. Interestingly, the quantity of starch ingested 293
corresponds to between 46% and 64% of the glucose response variation, being at times even 294
more important than the type of starch consumed. In the present study, ingestion of starch during 295
the meal-response testing ranged from 7.1 to 10.2 g/kg BW
0.67
for all diets. This is not a 296
significant difference; however, it could reduce the glucose and insulin responses of cats fed the 297
lentil and pea diets.298
Regarding food processing, the ingredients were ground in the same milling machine with 299
the same sieve, and the degree of cooking was controlled by means of kibble density, which was 300
similar among diets. However, starch cooking is an extrinsic factor that can influence starch 301
digestibility and the glucose response. For example, if the gelatinization index (not measured 302
here) was different among the diets, it could have affected the experimental results.303
Dietary fiber content is an additional factor that can alter the postprandial glucose and 304
insulin responses (Wolever, 1990; Graham et al., 1994). The sorghum-, lentil-, and pea-based 305
diets resulted in the greatest ingestions of TDF, which may have played a role in delaying and 306
prolonging the glucose absorption period as well as lessening the variation in glucose and insulin 307
concentration. Some fiber types (e.g., soluble fiber) slow gastrointestinal transit and gastric 308
emptying and decrease starch hydrolysis and, consequently, the rate of glucose absorption. 309
However, some studies have demonstrated that for diets with usual levels of fiber, variations in 310
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the intake of this ingredient did not significantly affect postprandial responses (O’Dea et al.,311
1980; Nguyen et al., 1998). Therefore, it is conceivable that factors other than fiber are 312
responsible for the slower, prolonged glucose and insulin responses observed for diets 313
incorporating lentils and peas. 314
Other studies have shown that the amylose content of starch is directly related to starch 315
digestibility (Biliaderis et al., 1991). In contrast, the glucose response is inversely related to 316
starch’s amylose content (Goddard et al., 1984). Normally, legume starches have high 317
concentrations of amylose and, consequently, are more likely to form resistant starch. Therefore, 318
legume starches may provoke lesser glucose and insulin responses than cereal starches in animals 319
and humans (Lee et al., 1985). Moreover, legume starches have only small concentrations of free 320
sugars and rapidly digestible starch. The slow and incomplete digestion of legume starches is 321
probably related to properties of the starch granule (e.g., amylose:amylopectin ratio) and its 322
physical association with the plant cell wall (fiber), which contribute to reducing total starch 323
gelatinization compared to that of the cereal starches (Englyst and Hudson, 1996; Englyst et al., 324
1996). The current study confirmed that cats fed lentil and pea diets (two leguminous plants) 325
showed decreased blood glucose and increased insulin compared to the other diets, with the 326
exception of the sorghum diet.327
Rice is classified as having a high glycemic index (Jenkins et al., 1981; Goddard et al., 328
1984), leading to large and rapid alterations in blood glucose and insulin concentrations in 329
humans. This characteristic is due to its small quantity of amylose (Belay et al., 1997), associated 330
with a low concentration of TDF. Studies using cats that compared brewer’s rice with corn 331
(Bouchard and Sunvold, 2000) or brewer’s rice with a corn/sorghum blend (Appleton et al., 332
2004) also observed that brewer’s rice was capable of stimulating greater blood glucose and 333
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insulin concentrations. In our study, however, the brewer’s rice- and corn-based diets presented 334
similar results for both the glucose and insulin measurements (Table 5), and only corn provoked a 335
rise in glycemia in relation to the baseline values (Figure 1). For insulin, both corn and brewer’s 336
rice caused an increase in blood concentration in relation to the baseline values.337
Bouchard and Sunvold (2000) did not encounter differences in the glucose and insulin 338
responses in a comparison of corn with sorghum. In our study, we observed a lesser glucose 339
response for sorghum compared to corn (mean concentration, maximum concentration, and area 340
under curve) and a similar insulin response for both. Studies using dogs also revealed a small 341
postprandial glucose response for sorghum diets (Bouchard and Sunvold, 1999). The decreased342
response to sorghum may be explained by the characteristics of the starch granule of this grain 343
(Rooney and Pflugfelder, 1986; Svihus et al., 2005) and possibly by the increased ingestion of 344
TDF allowed by this diet; however, studies of sorghum fiber and glycemia in cats were not found 345
in the literature.346
The current study showed that all sources of starch showed satisfactory digestibilities, 347
indicating that they can be used in the formulation of cat food. The brewer’s rice and cassava 348
flour diets had the greatest digestibilities; whereas, the lentil and sorghum diets had the least. 349
Changes in blood glucose concentrations were greater for the corn diet compared to the cassava 350
flour, sorghum, pea, and lentil diets. However, only corn increased the blood glucose 351
concentration over the baseline values. However, differences in blood insulin response were not 352
so evident among the diets, occurring only between the corn and pea diets. Nevertheless, in 353
addition to corn, brewer’s rice, pea, and sorghum also raised the postprandial insulin 354
concentration above the baseline value for at least 1 point on the response curve. Therefore, more 355
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studies are necessary to evaluate the effectiveness of using different starch sources for glucose or 356
insulin modulation in the cat. 357
358
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Table 1. Ingredient composition of experimental cat diets.476
As-fed basis, %
Ingredients
Cassava
flour Corn Sorghum
Brewer’s
rice Lentil Pea
Cassava flour 40.64 - - - - -
Corn - 51.64 - - - -
Sorghum - - 57.42 - - -
Brewer's rice - - - 43.81 - -
Lentil - - - - 67.68 -
Pea - - - - - 64.50
Poultry by-product meal 24.00 24.00 23.00 24.00 17.70 19.00
Isolated soybean protein 17.01 9.94 5.16 16.08 0.18 0.09
Poultry fat 7.93 4.00 4.00 5.69 4.02 5.99
Dried whole egg 2.59 2.59 2.59 2.59 2.59 2.59
Soybean hull 2.00 2.00 2.00 2.00 2.00 2.00
Brewer’s dried yeast 1.50 1.50 1.50 1.50 1.50 1.50
Dried hydrolyzed bovine liver 1.50 1.50 1.50 1.50 1.50 1.50
Dicalcium phosphate 0.90 0.90 0.90 0.90 0.90 0.90
Calcium carbonate 0.70 0.70 0.70 0.70 0.70 0.70
Potassium chloride 0.40 0.40 0.40 0.40 0.40 0.40
Sodium chloride 0.40 0.40 0.40 0.40 0.40 0.40
Taurine 0.20 0.20 0.20 0.20 0.20 0.20
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Vitamin/ mineral premix
1
0.10 0.10 0.10 0.10 0.10 0.10
L-lysine 0.06 0.06 0.06 0.06 0.06 0.06
DL-methionine 0.05 0.05 0.05 0.05 0.05 0.05
Antioxidant
2
0.01 0.01 0.01 0.01 0.01 0.01
Mold inhibitor
3
0.01 0.01 0.01 0.01 0.01 0.01
1
Provided per kg of diet: iron, 120 mg; copper, 15 mg; magnesium, 10 mg; zinc, 150 477
mg; iodine, 2 mg; selenium, 0.2 mg; vitamin A, 18,000 IU; vitamin D
3,
1,000 IU; vitamin E, 100 478
IU; thiamin, 8 mg; riboflavin, 10 mg; pantothenic acid, 50 mg; niacin, 75 mg; vitamin B
6
, 6 mg; 479
folic acid, 0.30 mg; vitamin B
12
, 0.1 mg. 480
2
Banox: BHA, BHT, propyl gallate and calcium carbonate – Alltech do Brasil 481
Agroindustrial Ltda, Curitiba, 81170-610 PR, Brazil.482
3
Mold Zap: Ammonium dipropionate, acetic acid, sorbic acid and benzoic acid – Alltech 483
do Brasil Agroindustrial Ltda, Curitiba, 81170-610 PR, Brazil.484
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Table 2. Chemical composition of starch sources and diets.485
Item
Cassava
flour
Corn Sorghum
1
Brewer’s
rice
Lentil Pea
Starch source composition
DM, % 89.4 88.8 88.7 88.9 88.9 89.3
------------------------------------- % of DM ------------------------------------
OM 99.9 98.1 97.9 97.1 97.3 98.1
CP 1.9 10.5 10.9 7.6 23.3 24.5
Acid-hydrolyzed fat 0.2 5.2 6.0 3.8 0.9 1.2
Starch 94.9 78.4 72.4 88.1 55.3 56.7
Total dietary fiber 1.5 4.1 6.9 1.6 14.1 11.0
Diet composition
DM, % 93.1 92.1 93.5 93.3 92.8 92.5
ME
2
, kcal/g 4.0 3.9 3.9 4.0 3.8 3.8
------------------------------------- % of DM ------------------------------------
OM 92.8 92.2 93.6 93.3 92.0 92.5
CP 31.6 31.3 28.0 35.0 29.2 30.5
Acid-hydrolyzed fat 12.6 11.6 9.7 12.3 9.8 11.7
Total dietary fiber 5.9 9.0 11.0 5.0 14.0 11.3
Starch 36.6 34.3 37.4 36.0 34.1 32.1
Amylose, % starch 19.7 23.4 28.3 22.4 27.8 33.9
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Amylopectin, % starch 80.3 76.6 71.6 77.7 72.2 66.1
Process
3
Density after extrusion, g/L 434.1 467.0 406.5 448.8 423.1 441.2
Density after dried, g/L 380.0 382.9 386.5 385.7 373.1 376.1
1
Sorghum containing 0.57% tannin (as-fed basis).486
2
Metabolizable energy was estimated according to NRC (2006).487
3
The extrusion process was controlled every 20 min through kibble density.488
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Table 3. Nutrient intake, apparent total tract digestibility and fecal characteristics of cats fed 489
experimental diets containing different starch sources.490
Diet
Item
Cassava
flour Corn Sorghum
Brewer’s
rice Lentil Pea
SEM
1
Intake, g • kg BW
-1
• d
-1
DM 17.7 16.3 17.9 16.2 16.3 17.7 0.5
OM 16.4 15.0 16.7 15.1 15.0 16.4 0.5
CP 5.6 5.1 5.0 5.7 5.0 5.2 0.2
Starch 6.5 5.6 6.7 5.8 5.6 5.7 0.2
Acid-hydrolyzed fat 2.2
a
1.9
abc
1.7
cd
2.0
abc
1.6
c
2.0
abc
0.1
Total dietary fiber 1.0
cd
1.5
bc
2.0
ab
0.8
d
2.3
a
2.0
ab
0.1
DM intake
2
, g/d 71.0 67.4 73.2 67.6 64.4 70.0 1.7
Apparent digestibility values, %
DM 80.3
ab
78.5
b
76.3
b
83.2
a
76.5
b
75.9
b
0.6
OM 84.3
ab
82.5
bc
80.0
c
87.9
a
79.0
c
79.1
c
0.6
CP 82.0
b
83.2
ab
80.6
b
87.7
a
80.8
b
82.3
b
0.5
Starch 98.0
ab
97.5
ab
93.9
d
98.6
a
95.2
dc
96.3
bc
0.5
Acid-hydrolyzed fat 89.6
a
85.5
bc
83.3
d
87.8
ab
85.3
bc
88.0
ab
0.3
Gross energy 84.2
ab
82.6
bc
79.6
c
87.6
a
80.1
c
80.5
bc
0.6
Total dietary fiber 5.6
b
18.1
ab
29.0
ab
10.6
ab
33.1
a
15.8
ab
2.7
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Fecal characteristics
Fecal score
3
3.3 3.1 3.2 3.4 3.4 3.5 0.1
Fecal DM, % 28.3
b
35.3
ab
31.5
b
39.5
a
29.5
b
28.6
b
0.9
Fecal pH 5.8 5.9 5.7 6.2 5.4 5.4 0.2
Feces, g/d (as is) 50.4
a
41.8
ab
58.8
a
29.2
b
55.5
a
55.8
a
3.15
Feces, g/d (dry) 13.9 14.5 17.3 11.4 15.5 16.9 0.6
Fecal output (as-is)/feed
DM intake, g/g 0.7 0.7 0.8 0.4 0.9 0.8 0.1
a, b, c, d
Means in the same row not sharing common superscript letters differ (P< 0.05).491
1
n = 6 per diet.492
2
Not statistically analyzed.493
3
1 = watery – liquid that can be poured; 2 = soft, unformed – stool assumes shape of 494
container; 3 = soft, formed, moist – softer stool that retains shape; 4 = hard, formed, dry stool –495
remains firm and soft; 5 = hard, dry pellets – small, hard mass.496
497
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Table 4. Nutrient intake of cats fed experimental diets containing different starch sources before 498
postprandial response testing.499
Diet
Item
Cassava
flour Corn Sorghum
Brewer’s
rice Lentil Pea
SEM
1
Nutrient intake, g/kg BW
DM 15.2 16.4 16.9 16.0 12.8 14.5 0.6
OM 14.1 15.1 15.8 15.0 11.8 13.4 0.5
Starch 5.6 5.6 6.3 5.8 4.4 4.7 0.2
CP 4.8 5.1 4.7 4.5 3.7 4.4 0.2
Acid-hydrolyzed fat 1.9
ab
1.9
ab
1.6
ab
2.0
a
1.3
b
1.7
ab
0.1
Total dietary fiber 0.9
bc
1.5
ab
1.9
a
0.8
c
1.8
a
1.6
a
0.1
Metabolizable energy,
kcal/kg BW 60.9 65.1 65.9 65.5 48.7 56.4 2.4
a, b, c
Means in the same row not sharing common superscript letters differ (P< 0.05).500
1
n = 6 per diet. 501
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Table 5. Medium, maximum, and time to peak plasma glucose and insulin concentrations, 502
medium and maximum incremental glucose and insulin concentrations, and area under the 503
absolute and incremental glucose and insulin response curves of cats fed experimental diets 504
containing different starch sources
1
.505
Diet
Postprandial response tests
Cassava
flour Corn Sorghum
Brewer’s
rice Lentil Pea
SEM
2
Glucose, mg/dL
Mean concentration 63.2
b
78.7
a
65.5
b
71.2
ab
62.0
b
65.0
b
0.7
Maximum concentration 72.7
bc
93.3
a
73.4
bc
83.8
ab
68.0
c
75.4
bc
1.9
Mean incremental concentration
3
4.4
ab
10.2
a
3.2
ab
5.5
ab
2.9
b
3.7
ab
0.6
Maximum incremental
concentration 14.0
ab
24.8
a
11.1
ab
17.3
ab
3.1
b
14.2
ab
1.7
Time to peak, hours 6.0 7.2 6.4 2.5 4.8 5.3 0.8
Area under curve, mg • dL
-1
• h
-1
765.7
b
965.0
a
792.2
b
854.2
ab
738.4
b
782.2
b
18.3
Area under incremental curve,
mg • dL
-1
• h
-1
61.3
ab
185.5
a
62.5
ab
56.0
ab
-40.4
b
49.5
ab
18.2
Insulin, µIU/mL
Mean concentration 10.9 18.4 19.7 20.1 13.9 16.8 0.7
Maximum concentration 24.0 33.1 29.9 33.5 24.3 26.4 1.8
Mean incremental concentration 9.4 17.0 18.0 13.9 6.1 16.0 0.7
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Maximum incremental
concentration 22.9
ab
32.2
a
28.2
ab
24.4
ab
16.5
b
25.6
ab
1.8
Time to peak, hours 8.0 2.4 4.5 5.7 9.2 5.8 0.7
Area under curve, µIU • dL
-1
• h
-1
144.2 240.6 212.0 239.2 177.6 213.8 15.6
Area under incremental curve,
µIU • dL
-1
• h
-1
130.7
ab
231.2
a
240.5
a
181.8
ab
83.6
b
204.6
ab
16.2
a, b, c
Means in the same row not sharing common superscript letters differ (P< 0.05).506
1
Blood samples were taken at 0 (pre-feeding) and 1, 2, 4, 6, 8, 10 and 12 h after intake of 507
all provided diets.508
2
n = 6 per diet.509
3
Incremental concentration = absolute concentration of glucose or insulin - basal 510
concentration.511
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Figure 1. Plasma glucose and insulin responses curves (mean of 6 cats per diet ± SE) of cats fed 512
experimental diets containing different starch sources. *Values greater than baseline for insulin 513
concentrations (P < 0.05).
§
Values greater than baseline for glucose concentrations (P < 0.05)514
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... to be only semi-domesticated and this process had little effect on metabolism except for a slight increase in their ability to process lipid, with no effect on their capacity to handle dietary starch compared to wild felids (Montague et al., 2014). Interestingly, apparent total tract digestibility for starches in cats is in fact high (93. 9%-98.6%;de Oliveira et al., 2008), despite cats having low enzyme activity for starch digestion, suggesting a disconnect between the two which requires further investigation. Additionally, adding to these observations, other studies have suggested cats handle glucose poorly or are insulin resistant compared to dogs (Batchelor et al., 2011;de Godoy and Swanson, 2013). Ho ...
... The trends in glucose postprandial peaks and elimination were different between cats and dogs, consistent with previous studies (Appleton et al., 2004;Carciofi et al., 2008;Church, 1980;de Oliveira et al., 2008;Verbrugghe et al., 2009). Higher canine intestinal SGLT1 was previously suggested to enable more rapid and higher glucose peaks through improved absorption (Batchelor et al., 2011). ...
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Species differences between domestic cats (Felis catus) and dogs (Canis familiaris) has led to differences in their ability to digest, absorb and metabolize carbohydrates through poorly characterized mechanisms. The current study aimed to first examine biopsied small intestine, pancreas, liver and skeletal muscle from laboratory beagles and domestic cats for mRNA expression of key enzymes involved in starch digestion (amylase), glucose transport (sodium-dependent SGLTs and -independent glucose transporters, GLUT) and glucose metabolism (hexokinase and glucokinase). Cats had lower mRNA expression of most genes examined in almost all tissues compared to dogs (p < 0.05). Next, postprandial glucose, insulin, methylglyoxal (a toxic glucose metabolite) and d-lactate (metabolite of methylglyoxal) after single feedings of different starch sources were tested in fasted dogs and cats. After feeding pure glucose, peak postprandial blood glucose and methylglyoxal were surprisingly similar between dogs and cats, except cats had a longer time to peak and a greater area under the curve consistent with lower glycolytic enzyme expression. After feeding starches or whole diets to dogs, postprandial glycemic response, glycemic index, insulin, methylglyoxal and d-lactate followed reported glycemic index trends in humans. In contrast, cats showed very low to negligible postprandial glycemic responses and low insulin after feeding different starch sources, but not whole diets, with no relationship to methylglyoxal or d-lactate. Thus, the concept of glycemic index appears valid in dogs, but not cats. Differences in amylase, glucose transporters, and glycolytic enzymes are consistent with species differences in starch and glucose handling between cats and dogs.
... Also, while carbohydrates are not essential, cats are capable of digesting and utilising dietary starch, as long as it is adequately processed and provided within an adequate range. 21 other nutrients that are exclusively found or more abundant in animal tissue are essential for cats. For example, cats cannot adequately use beta-carotene (plant based) as a source of vitamin A and require dietary retinol. ...
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Practical relevance: Inappetence may have many origins and, as a presenting sign or observation in the hospitalised patient, is common in feline practice. Nutritional assessment of every patient is encouraged, to identify the need for, and appropriate type of, intervention indicated. The impact of malnutrition may be significant on the feline patient, perpetuating illness, delaying recovery, slowing wound healing and negatively impacting gut health and immunity. Delayed intervention may result in the cat's deterioration; hence prompt control of contributing factors such as the underlying illness, pain, nausea, ileus and stress is vital to optimise voluntary food intake. Management is multimodal, comprising reduction of stress, medications and assisted nutrition in the form of tube feeding or parenteral nutrition. Use of antiemetic, analgesic, prokinetic and appetite stimulant medications may restore appetite, but placement of feeding tubes should not be delayed. Feeding tubes are generally well tolerated and allow provision of food, water and medication with minimal stress, although clinicians must be aware of complications such as stoma site infections and refeeding syndrome. Clinical challenges: Cats are vulnerable to malnutrition owing to their unique metabolism and specific nutritional requirements. Moreover, their nature as a species means they are susceptible to stress in the hospital environment, which may result in reduced food intake; previous negative experiences may compound the problem. In particular, an inappropriate clinic environment and/or handling may cause or exacerbate inappetence in hospitalised patients, with negative impacts on recovery. Postponing interventions such as feeding tube placement to await improvement, owing to clinician or caregiver apprehension, may hinder recovery and worsen nutritional deficits. Evidence base: The 2022 ISFM Consensus Guidelines on Management of the Inappetent Hospitalised Cat have been created by a panel of experts brought together by the International Society of Feline Medicine (ISFM). Information is based on the available literature, expert opinion and the panel members' experience.
... The calculation procedures used for interpreting IVGTT data were described in the literature by Mattheuws et al. [66]. The data obtained during the test were analyzed as described by De-Oliveira et al. [67] and Brunetto et al. [9]. The area under the curve (AUC) was calculated in Prism software (GraphPad Prism, version 5) by numerical integrations using the trapezoidal method with mean values at each time for all animals. ...
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Background Obesity is one of the most common nutritional disorders in dogs and cats and is related to the development metabolic comorbidities. Weight loss is the recommended treatment, but success is difficult due to the poor satiety control. Yeast beta-glucans are known as biological modifiers because of their innumerable functions reported in studies with mice and humans, but only one study with dogs was found. This study aimed to evaluate the effects of a diet supplemented with 0.1% beta-glucan on glucose, lipid homeostasis, inflammatory cytokines and satiety parameters in obese dogs. Fourteen dogs composed three experimental groups: Obese group (OG) with seven dogs with body condition score (BCS) 8 or 9; Lean group (LG) included seven non-obese dogs with a BCS of 5; and Supplemented Obese group (SOG) was the OG dogs after 90 days of consumption of the experimental diet. Results Compared to OG, SOG had lower plasma basal glycemic values (p = 0.05) and reduced serum cholesterol and triglyceride levels. TNF-α was lower in SOG than in OG (p = 0.05), and GLP-1 was increased in SOG compared to OG and LG (p = 0.02). Conclusion These results are novel and important for recognizing the possibility of using beta-glucan in obesity prevention and treatment.
... During the fecal collection, fecal quality was scored on the following scale: 1 = watery: liquid that can be poured; 2 = soft, unformed: stool assumes shape of container; 3 = soft, formed, moist: softer stool that retains shape; 4 = hard, formed, dry stool: remains firm and soft; 5 = hard, dry pellets: small, hard mass [42]. ...
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Background: This study aimed to evaluate the effects of increasing dosages of a commercial product composed by Saccharomyces cerevisiae yeast (YAM), with active metabolites, which are beta glucans, nucleotides, organic acids, polyphenols, amino acids, vitamins and minerals (Original XPCtm, Diamond V, IOWA, USA) added to a commercially available dry cat food. Apparent digestibility of dietary nutrients, fecal microbiota, fecal fermentation products and immunological parameters were evaluated. Twenty-seven healthy cats of mixed sexes, with a mean body weight of 4.19 ± 0.83 kg and a mean age of 9.44 ± 5.35 years were distributed by age in an unbalanced randomized block design, consisting of three experimental treatments: CD (control diet), YAM 0.3 (control diet with 0.3% yeast with active metabolites) and YAM 0.6 (control diet with 0.6% yeast with active metabolites). Results: The inclusion of the additive elevated the apparent digestibility of crude fiber (p = 0.013) and ash (p < 0.001) without interfering feed consumption, fecal production and fecal characteristics. Regarding fermentation products present in the feces, prebiotic inclusion increased lactic acid concentration (p = 0.004) while reducing isovaleric acid (p = 0.014), only in the treatment YAM 0.3. No differences were noticed on biogenic amines (BA), fecal pH, ammonia concentration, total and individuals short-chain fatty acids (SCFA) and total and individuals branched-chain fatty acids (BCFA) (except isovaleric acid in YAM 0.3). As regards to fecal microbiota, prebiotic inclusion has resulted in the reduction of Clostridium perfringens (p = 0.023). No differences were found in the immunological parameters evaluated. Conclusion: It can be concluded that the additive, at the levels of inclusion assessed shows prebiotic potential and it has effects on fecal fermentation products and microbiota without interfering on crude protein and dry matter digestibility. More studies evaluating grater inclusion levels of the prebiotic are necessary to determine optimal concentration.
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Smarter understanding of diabetes pathophysiology and pharmacology of insulin therapy can lead to better clinical outcomes. Rather than looking for an insulin formulation that is considered “best” for a general population, it could be appropriate to seek the “smart” insulin choice, tailored to the specific clinical situation. Different treatment goals should be considered, with pros and cons to each. Ideally, insulin therapy in most diabetic dogs should mimic a “basal‐bolus” pattern. The “intermediate”‐acting insulin formulations might provide better “bolus” treatment in dogs than the rapid‐acting formulations used in people. In patients with some residual beta cell function such as many diabetic cats, administering only a “basal” insulin might lead to complete normalisation of blood glucose concentrations. Insulin suspensions (neutral protamine Hagedorn, neutral protamine Hagedorn/regular mixes, lente and protamine zinc insulin) as well as insulin glargine U100 and detemir are “intermediate”‐acting formulations that are administered twice daily. For a formulation to be an effective and safe “basal” insulin, its action should be roughly the same every hour of the day. Currently, only insulin glargine U300 and insulin degludec meet this standard in dogs, whereas in cats, insulin glargine U300 is the closest option.
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