The effects of age and dietary resistant starch on digestibility, fermentation end products in faeces and postprandial glucose and insulin responses of dogs

Ribeiro, Érico de Mello; Peixoto, Mayara Corrêa; Putarov, Thaila Cristina; Monti, Mariana; Pacheco, Peterson Dante Gavasso; Loureiro, Bruna Agy; Pereira, Gener Tadeu; Carciofí, Aulus Cavalieri

doi:10.1080/1745039x.2019.1652516

Cited by 23 publications

(34 citation statements)

References 57 publications

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“…Sex [22], genetic performances [23], phase of growth, other than diet [24] have been reported to influence gut microbiota in pigs. In healthy dogs, variations of microbiome with age, from weaning to adulthood, were reported [25,26], with a stabilization of the core gut microbiota at maturity. Since only adult dogs were recruited in the present study, the factor age was not considered.…”

Section: Discussionmentioning

confidence: 99%

Learning machine approach reveals microbial signatures of diet and sex in dog

et al. 2020

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The characterization of the microbial population of many niches of the organism, as the gastrointestinal tract, is now possible thanks to the use of high-throughput DNA sequencing technique. Several studies in the companion animals field already investigated faecal microbiome in healthy or affected subjects, although the methodologies used in the different laboratories and the limited number of animals recruited in each experiment does not allow a straight comparison among published results. In the present study, we report data collected from several in house researches carried out in healthy dogs, with the aim to seek for a variability of microbial taxa in the faeces, caused by factors such as diet and sex. The database contains 340 samples from 132 dogs, collected serially during dietary intervention studies. The procedure of samples collection, storage, DNA extraction and sequencing, bioinformatic and statistical analysis followed a standardized pipeline. Microbial profiles of faecal samples have been analyzed applying dimensional reduction discriminant analysis followed by random forest analysis to the relative abundances of genera in the feces as variables. The results supported the responsiveness of microbiota at a genera taxonomic level to dietary factor and allowed to cluster dogs according this factor with high accuracy. Also sex factor clustered dogs, with castrated males and spayed females forming a separated group in comparison to intact dogs, strengthening the hypothesis of a bidirectional interaction between microbiota and endocrine status of the host. The findings of the present analysis are promising for a better comprehension of the mechanisms that regulate the connection of the microorganisms living the gastrointestinal tract with the diet and the host. This preliminary study deserves further investigation for the identification of the factors affecting faecal microbiome in dogs.

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Section: Discussionmentioning

confidence: 99%

Learning machine approach reveals microbial signatures of diet and sex in dog

et al. 2020

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“…In humans, geographical variations, ethnicity, host genetic, immunity, lifestyle and dietary habits have been reported to affect gut microbiota [28]. In healthy dogs, variations of microbiome with age, from weaning to adulthood, were reported [27,29], with a stabilization of the core gut microbiota at maturity. A strong similarity between human and dog microbiota has been reported, suggesting that the same factors affecting gut microbial community in the former can act also in the latter [30].…”

Section: Discussionmentioning

confidence: 99%

“…Vazquez-Baeza et al (2016) [32] reported that the diversity and structure of microbial community, more than the variation of single taxon, could be used as a signature of the fecal microbiota to separate dogs with IBD from healthy dogs. The better characterization of the gut microbiome has been obtained, in the past, thanks to studies with subjects that lived in the same controlled environment, such as a shelter, kennel or university facilities, where the dogs received the same diet under strict control, either for a short or long period of time [5,29,31]. In this study, although the potential bias due to the effect of the different environments and of the owners can be claimed, it was still possible to separate dogs based on their diet.…”

Section: Discussionmentioning

confidence: 99%

Blood Microbiome: A New Marker of Gut Microbial Population in Dogs?

Scarsella

Sandri

Monego

et al. 2020

Veterinary Sciences

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The characterization of the microbial population in different compartments of the organism, such as the gastrointestinal tract, is now possible thanks to the use of high-throughput DNA sequencing technique. Several studies in the companion animals field have already investigated the fecal microbiome in healthy or sick subjects; however, the methodologies used in the different laboratories and the limited number of animals recruited in each experiment do not allow a straight comparison among published results. Previously, our research focused on the characterization of the microbial taxa variability in 340 fecal samples from 132 healthy dogs, collected serially from several in-house experiments. The results supported the responsiveness of microbiota to dietary and sex factors and allowed us to cluster dogs with high accuracy. For the present study, intestinal and blood microbiota of healthy dogs from different breeds, genders, ages and food habits were collected, with three principal aims: firstly, to confirm the results of our previous study regarding the fecal microbiome affected by the different type of diet; secondly, to investigate the existence of a blood microbial population, even in heathy subjects; and thirdly, to seek for a possible connection between the fecal and the blood microbiota. Limited researches have been published on blood microbiota in humans, and this is the first evidence of the presence of a bacterial population in the blood of dogs. Moreover, gut and blood microbiota can discriminate the animals by factors such as diet, suggesting some relationship between them. These preliminary results make us believe in the use of the blood microbiome for diagnostic purposes, such as researching and preventing gut inflammatory diseases.

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“…The extrusion process conditions such as water and steam additions combined with mechanical energy promote starch gelatinization (3). Past studies have demonstrated that decreasing the amount of mechanical energy (4)(5)(6), increasing process water content addition (3), and increasing the starch ingredient particle size (4,5) minimize starch gelatinization, which yields some resistant starches (RS). Resistant starches represent the starch fraction that escapes small intestinal digestion and undergoes fermentation by saccharolytic bacteria within the colon to produce short chain fatty acids (SCFA).…”

Section: Introductionmentioning

confidence: 99%

A model that predicts resistant starch in a dog kibble using a small-scale twin-screw extruder

Alvarenga

Keller

Waldy³

et al. 2021

Preprint

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The objective of this work was to modify extrusion parameters to yield greater resistant starch (RS) in a kibble and create a model to predict its concentration. A dog food was extruded through a small-scale twin-screw extruder as a central composite design with 6 central points (replicates) and 14 single replicates. There were three factors tested at three levels: corn particle size, extruder shaft speed, and in-barrel moisture (IBM). The remaining processing inputs were kept constant. Chemical and physical starch analyses were performed. A model to predict RS was created using the REG procedure from SAS. Pearson correlations between extrusion parameters and starch analyses were conducted. A model to predict RS was created (R2adj= 0.834; P < .0001). Both SME and extrudate temperature had a high negative correlation with RS and RVA raw starch. Results suggest that low mechanical energy and high IBM increase kibble RS.

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The effects of age and dietary resistant starch on digestibility, fermentation end products in faeces and postprandial glucose and insulin responses of dogs

Cited by 23 publications

References 57 publications

Learning machine approach reveals microbial signatures of diet and sex in dog

Learning machine approach reveals microbial signatures of diet and sex in dog

Blood Microbiome: A New Marker of Gut Microbial Population in Dogs?

A model that predicts resistant starch in a dog kibble using a small-scale twin-screw extruder

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