Effect of Nutrition on Age-Related Metabolic Markers and the Gut Microbiota in Cats.
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ABSTRACT:
SUBMITTER: Ephraim E
PROVIDER: S-EPMC8706506 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The transition of intestinal microbiota with age has been well described in humans. However, the age-related changes in intestinal microbiota of cats have not been well studied. In the present study, we investigated the composition of intestinal microbiota of cats in 5 different age groups (pre-weanling, weanling, young, aged, senile) with a culture-based method. For lactobacilli and bifidobacteria, we also quantified with molecular-based method, real-time PCR. The results suggested that the composition of the feline intestinal microbiota changes with age, while the changes were different from those of humans and dogs. Bifidobacteria which are predominant in human intestine or lactobacilli which are predominant in dog intestine, did not appear to be important in cat intestines. Enterococci, instead, seem to be major lactic acid producing bacteria in cats. We also identified lactobacilli and bifidobacteria at the species level based on 16S rRNA gene sequences and found that the species composition of Lactobacillus also changed with age.
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Project description:Background and aimChronic inflammation is a primary risk factor for chronic metabolic disease and may be triggered by a "leaky gut." Several biomarkers have been recognized to indicate intestinal permeability (i.e., leaky gut) and bacterial translocation. Nonetheless, which of these biomarkers exhibit the highest correlation with metabolic health parameters remains unclear. Hence, this study aimed to explore the correlation between leaky gut-related markers and metabolic health.MethodsBased on waist circumference, plasma fasting glucose, plasma gamma-glutamyl transpeptidase (GGT), and plasma LDL cholesterol, two groups of 40 subjects with the most extreme metabolic health profiles were selected from the NQplus cohort study (n = 2048), which was previously conducted by the Wageningen University's Division of Human Nutrition. Eight potential leaky gut-related markers were selected from the literature and measured in serum or EDTA plasma samples of these selected individuals. These samples were also obtained from the NQplus cohort study.ResultsFrom the leaky gut markers, levels of zonulin, lipopolysaccharide-binding protein, soluble CD14, bactericidal/permeability-increasing protein, and peptidoglycan were significantly higher in individuals with unhealthy metabolic profiles (p<0.05). No differences in EndoCAb IgM, EndoCAb IgA, and EndoCAb IgG were observed between healthy and unhealthy individuals. Stepwise regression analysis revealed that zonulin was substantially associated with metabolic health parameters such as BMI, blood glucose, triglyceride, GGT, and C-reactive protein levels. C-reactive protein, an inflammation marker, showed the most pronounced association with zonulin.ConclusionsBiomarkers that link a leaky gut and subsequent bacterial translocation to metabolic health were identified in this study. Especially zonulin may aid in monitoring a leaky gut and detecting individuals at risk for developing chronic metabolic diseases.
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Project description:BackgroundGut microbial gene richness and specific bacterial species are associated with metabolic risk markers in humans, but the impact of host physiology and dietary habits on the link between the gut microbiota and metabolic markers remain unclear. The objective of this study was to identify gut metagenomic markers associated with estimates of insulin resistance, lipid metabolism and inflammation in obesity, and to explore whether the associations between metagenomic and metabolic markers persisted after adjustment for body fat, age and habitual dietary intake.MethodsFaecal DNA from 53 women with obesity was analysed through quantitative metagenomic sequencing and analysis, and a systematic search was performed for bacterial genes associated with estimates of insulin resistance, inflammation and lipid metabolism. Subsequently, the correlations between metagenomic species and metabolic markers were tested by linear regression models, with and without covariate adjustment.ResultsOne hundred and fourteen metagenomic species correlated with metabolic markers (P<0.001) including Akkermansia muciniphila, Bilophila wadsworthia, Bifidobacterium longum and Faecalibacterium prausnitzii, but also species not previously associated with metabolic markers including Bacteroides faecis and Dorea longicatena. The majority of the identified correlations between bacterial species and metabolic markers persisted after adjustment for differences in body fat, age and dietary macronutrient composition; however, the negative correlation with insulin resistance observed for B. longum and F. prausnitzii appeared to be modified by the intake of dietary fibre and fat, respectively.ConclusionsThis study shows that several gut bacterial species are linked to metabolic risk markers in obesity, also after adjustment for potential confounders, such as long-term diet composition. The study supports the use of gut metagenomic markers for metabolic disease prediction and warrants further investigation of causality.
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Project description:Today, domestic cats are important human companion animals for their appearance and favorable personalities. During the history of their domestication, the morphological and genetic portraits of domestic cats changed significantly from their wild ancestors, and the gut microbial communities of different breeds of cats also apparently differ. In the current study, the gut microbiota of Ragdoll cats and Felinae cats were analyzed and compared. Our data indicated that the diversity and richness of the gut microbiota in the Felinae cats were much higher than in the Ragdoll cats. The taxonomic analyses revealed that the most predominant phyla of the feline gut microbiota were Firmicutes, Bacteroidota, Fusobacteriota, Proteobacteria, Actinobacteriota, Campilobacterota, and others, while the most predominant genera were Anaerococcus, Fusobacterium, Bacteroides, Escherichia-Shigella, Finegoldia, Porphyromonas, Collinsella, Lactobacillus, Ruminococcus_gnavus_group, Prevotella, and others. Different microbial communities between the Ragdoll group and the Felinae group were observed, and the compared results demonstrated that the relative abundances of beneficial microbes (such as Lactobacillus, Enterococcus, Streptococcus, Blautia, Roseburia, and so on) in the Ragdoll group were much higher than in the Felinae group. The co-occurrence network revealed that the number of nodes and links in the Felinae group was significantly higher than the Ragdoll group, which meant that the network of the Felinae group was larger and more complex than that of the Ragdoll group. PICRUSt function analyses indicated that the differences in microbial genes might influence the energy metabolism and immune functions of the host. In all, our data demonstrated that the richness and diversity of beneficial microbes in the Ragdoll group were much higher than the Felinae group. Therefore, it is possible to isolate and identify more candidate probiotics in the gut microbiota of growing Ragdoll cats.
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