Quantifying differences in the canine gut microbiome between fresh and processed diets: A home-based, cross- sectional study with demographic diversity

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We aimed to conduct the largest and most demographically diverse home-based exploratory cross-sectional study to date on the effects of different diet types on the canine microbiome. Although a range of diets were fed to the dogs in our study, the major focus was on comparing the impact of a gently cooked (fresh) diet with a conventional dry processed (kibble) diet. Methods: A total of 103 dogs were recruited for this study. Each participant provided a single faecal microbiome sample along with a completed questionnaire on their dog’s health and nutritional history. Microbial DNA was extracted and sequenced using 16S rRNA gene analysis to characterise the bacterial composition of the faecal sample, which is representative of the gut microbiome. Results: The study revealed several significant trends, providing a deeper and more complete understanding of the effects of feeding methods on the canine microbiome. Microbiome metrics, such as alpha diversity and richness, were lower in fresh-fed dogs, while levels of opportunistic and potentially pathogenic bacteria, such as the Sutterella genus, were higher in kibble-fed dogs. Biochemical pathway analysis using the Picrust2 toolkit identified several pathways that were more abundant in fresh-fed compared with kibble-fed dogs, such as the combined protein degradation pathway and the synthesis of butyrate from amino acids. We subsequently developed a simple classifier model which differentiated microbiome samples from fresh-fed and kibble-fed dogs, with pathways such as POLYAMSYN, HISDEG and PWY0.1296 emerging as strong predictors for distinguishing between the two dietary cohorts. Conclusions: This study provides a robust and statistically significant investigation into the effects of fresh and kibble diets on the canine gut microbiome. To strengthen the findings and robustness of this preliminary research, we recommend that future studies incorporate metabolomic analysis, shotgun sequencing, and stringent control of the brand or quality of kibble diet. faecal microbiome dog food companion animal nutrition fresh diet processed diet Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Companion animal nutrition has been an area of growing commercial interest since the early 1860s when the first commercial pet food was introduced in the United Kingdom. James Spratt’s “Patented Meat Fibrine Dog Cakes” were the precursor to modern processed dog foods, which have since evolved to claim that they provide complete nutrition for all stages of a dog’s life. This marked a significant shift from previous feeding practices, where domesticated animals were predominantly given leftover food from their human family meals. Throughout the 20th century, large-scale production and the rise of highly processed foods, including dry/kibble and wet/canned varieties, quickly established these products as the standard in companion animal nutrition. During this period, due to the lower status of companion animals and the overriding trends in human nutrition that favoured commercial, efficient, and highly processed foods, little attention was given to potential health concerns related to feeding animals similar diets. In the 1990s, Dr. Ian Billinghurst’s publication, Give Your Dog a Bone [ 1 ], ignited a movement encouraging a return to fresh and raw diets for companion animals. In the 1990s and early 2000s, interest in human nutrition grew significantly, with several leading researchers exploring topics, such as the role of dietary fatty acids [ 2 ], trans fats in industrially processed foods [ 3 ], the benefits of fibres and prebiotics [ 4 ], and the positive effects of fresh, unprocessed foods [ 5 , 6 ]. Many studies in human nutrition followed protocols similar to those in human clinical trials, using large sample sizes and a scientific, rigorous, methodical approach that included a collection of various interventional biomarkers. It is now understood that many nutritional processes are modulated by the complex network and ecosystem of bacteria inhabiting various parts of the body, particularly throughout the digestive system. Both the oral and gut microbiome are closely linked to health and nutrition, supporting both the degradation of macronutrients [ 7 ] and the synthesis of key molecules, such as vitamin K [ 8 ] and short-chain fatty acids (SCFAs) [ 9 ], which are well-documented for their health-promoting effects [ 10 , 11 ]. Recent advances in computational capabilities, next-generation sequencing (NGS) technology and genetic sequencing methods have enabled a more detailed characterisation of the complex gut microbiome. In humans, these technologies allow exploration of personalised nutrition [ 12 ], treatment of aggressive Clostridium difficile infections [ 13 ], obesity [ 14 ], and cancer [ 15 ], among numerous other pathologies. Following this trend, there has been renewed interest in studying microbiota in companion animals, particularly with the growing attention to pet nutrition. Similar to humans, it is likely that the canine immune response is impacted by the composition of the gut microbiota. With the increasing incidence of skin allergies and gastrointestinal sensitivities in pets, the gut microbiome has become a promising therapeutic target [ 16 ]. Recognising diet as an important indicator of the overall microbiome, a number of studies have focused on assessing how different diets impact the gut microbiota of dogs. These have studied the impact on the canine gut microbiome of fresh compared to extruded kibble diets [ 17 – 20 ]. Others have determined whether the gut microbiota are impacted by raw food [ 17 , 21 – 23 ]. Broadly, these studies showed differences in both the richness and diversity of the canine microbiome and metabolic pathways associated with the different diets. However, variability was observed between studies which may reflect limitations including small sample sizes ( i.e. , fewer than 30 dogs), limited breed diversity, the use of generalised kennel-based facilities, which may inaccurately represent domestic living conditions, and a lack of standardisation in the specific diet(s) provided to the pets. A systematic review of the impact of diet on gut microbiota showed that the quality of protein and the processing of feed had a significant impact [ 24 ]. However, this did not include data from studies of the dog microbiome. To fully characterise the effect of a well-formulated fresh diet on the canine microbiome, there is a need to conduct a large, breed-diverse, home-based study with a standardised approach to fresh food intervention across all participants. In this study, we therefore aimed to conduct an exploratory, large, multi-breed, home-based cross-sectional investigation into the effects of a human-grade, fresh, canine-specific, gently cooked, fresh diet on the faecal microbiome of a healthy canine cohort. Materials and Methods Animals and study design All participating dogs were home-based canines ( Canis familiaris ) located in the United Kingdom, aged 1–8 years old at recruitment and with no reported chronic health conditions. Participants were selected under the criterion that their dogs had not been administered antibiotics within three months prior to the start of the study, ensuring no additional microbiome modulation from recent antibiotic use that could impact the results. As a baseline cross-sectional study, pet parents disclosed their dogs’ current diet. All participants on the fresh diet were consuming food from Butternut Box (BB), a UK-based manufacturer of fresh, human-grade, gently cooked dog food. The rest of the cohort consisted of kibble (i.e. processed, dry food), a combination of kibble and fresh diets, canned (i.e. processed, wet food) or raw diets. Although we explicitly recruited participants on a BB diet or Kibble, certain participants indicated different diets upon study commencement (canned and raw) and, due to the logistics of the study, were allowed to continue their participation in the study. Participants were recruited from the general population of home-based dogs in the UK through various methods, such as social media outreach, word-of-mouth, and in-person sign-ups leveraging Butternut Box ambassador and staff networks. All owners were required to sign a consent form for the use of their dogs’ microbiome samples in the study and to approve the use of the data collected. As part of the study process, participants were asked to provide user-reported results on the general well-being of their dogs. A variety of user-reported health outcome categories were assessed during the study, including stomach sensitivity, skin health, activity levels and appetite. Sampling Each participant received a Zymo Research R1101 faecal collection tube (Zymo Research, USA) to collect fresh, naturally obtained faecal samples from their dogs. These tubes were pre-filled with a DNA/RNA shield and a lysis matrix to ensure the stability of bacterial matter during transportation to the laboratory. Using the spoon provided in the tube, participants collected a small pea-sized sample from the surface of the faecal pile and sealed it immediately in the collection tube. Sample tubes were then placed in a plastic bag and mailed directly to the laboratory using a pre-labelled mailer. At the time of sample collection, participants completed a survey to provide validating information on their dogs’ profile and health. Once received at the laboratory, all samples were stored at -80°C until extraction. Extraction and library preparation DNA was extracted from faecal suspension using a QIAmp PowerFecal DNA kit (Qiagen, USA). The DNA library for targeting the V3-V4 region on the 16S rRNA gene was prepared using the Quick-16S Plus™ NGS Library Prep Kit (Zymo Research, USA). DNA sequencing was performed on an Illumina MiSeq system (Illumina, USA). All samples were extracted and sequenced as a single batch to avoid batch effects. Data analysis All analyses were conducted in R (version 4.0.2). An in-house bioinformatic workflow was set up to perform quality control on the sequenced files and assign taxonomy to sequence variants. Microbiome analysis was performed using the Phyloseq (1.32.0) and Vegan (2.6-4) libraries. The diversity scores were adjusted for age. Functional pathway prediction was conducted using PICRUSt2 in conjunction with the Metacyc database, with raw pathway abundances converted to relative abundances and scaled to a percentile score between 1 and 10. A paired t-test was performed on normally distributed diversity and pathway scores. Results Study participants In total, 103 dogs were successfully recruited and completed the study, representing 44 different breeds, including pedigree breeds, crossbreed combinations, and mixed breeds (see Supplementary Table 1 for a full list). Of the dogs, 41% (n = 43) were fed the BB diet and 33% (n = 34) were fed a kibble/processed diet. These diets had been fed to the dogs in the study for at least three months before the initial enrollment survey. The remaining 26 dogs were fed a different diet or a combination of diets: 21% (n = 22) of the dogs received a combination of kibble and BB diets, 2% (n = 2) were fed a raw diet, and 2% (n = 2) were fed a canned diet. To ensure completeness, all samples were included in the analysis, but we primarily focused on the comparison between fresh and kibble diets. Regarding dog characteristics, 51% of the dogs were male (n = 52), and 49% were female (n = 51), with a median age of three years. The most common were “mixed breeds” (a mix of more than two known or unknown breeds) (n = 17), Cockapoos (Cocker Spaniel crossed with Poodle) (n = 8) and Labrador Retrievers (n = 7). As this study focused on exploratory endpoints rather than breed-related outcomes, breeds were user-reported with no further genetic testing or validation. Self-reported outcomes As part of the study, participants were asked to provide user-reported assessments of the general well-being of their dogs. Four key health outcome categories were evaluated: stomach sensitivity, skin concerns, activity levels and appetite. As a proportion of the diet types, BB-fed dogs indicated fewer negative symptoms overall than those on kibble diets. More specifically, dogs on the BB diet had fewer skin, appetite and gastrointestinal issues, suggesting that dogs fed a fresh diet may be less prone to these complications (Fig. 1 ). Interestingly, dogs on combination diets reported higher appetite (12.5%), activity (33.3%), and skin concerns (16.7%) than the other two diet types. This, however, may be self-selecting as dogs on a combination diet may be fed this to compensate for various health concerns (e.g., poor appetite). Furthermore, dogs on a combination diet reported less stomach sensitivity issues than those on kibble diets (20.8% vs 40.6%), potentially suggesting that the introduction of fresh food may lower stomach sensitivity compared to a fully kibble diet. Across the categories, more dogs on the BB diet reported lower activity levels (36.2%), however, this was categorically based on the number of hours of activity per day, so it may not be truly reflective of activity levels nor an area of concern for this diet type. Biodiversity metrics: alpha diversity, evenness and richness In our study, dogs on a BB diet had a significantly lower level of alpha diversity compared to dogs on kibble and combination diets (p = 0.002) (Fig. 2 ). Diversity scores increased in dogs closer to an exclusively kibble diet, with dogs on a combination diet having diversity scores between those on kibble and BB diets. This suggests a potential link between diet composition and a shift in microbiome diversity in the studied dogs. Results further show that both richness, defined as the number of species present, and evenness, defined as the balance in abundance across species, were higher in dogs on kibble diets than the BB diet, with richness being the main factor driving alpha diversity in the kibble-fed dogs. Phyla and genus level analysis Looking at the higher-level phyla distribution plots, no broad changes were observed across phyla groups (Fig. 3 ). However, one finding was the lower abundance of the Proteobacteria phylum in dogs on the BB diet compared to those on kibble diets, however, this result was not statistically significant (p = 0.288). Bacteroidota levels were statistically significant (p = 0.006), with Kibble diets displaying a higher median score. Obtaining insights from phylum-level analysis, however, does not confer significant conclusions in the absence of a deeper assessment of other metrics. To gain deeper insights, we assessed changes in the major genera observed in the microbiome of the participating dogs (Fig. 4 ). One notable trend was the significantly higher (p = 0.05) abundance of the Sutterella genus in dogs fed a kibble diet compared to those on the BB diets (Fig. 5 ). Biochemical pathway analysis Using the PiCrust2 toolkit, we inferred the function of the microbial taxa identified in the samples by cross-referencing 16S rRNA gene data with a database of reference genomes and associated enzymatic reactions. This approach enabled the characterisation of the functional traits of the microbiome without the need for whole genome sequencing of each bacterium. Although the gut microbiome modulates hundreds of biochemical pathways in a host, we primarily focused on the pathways explicitly related to the canine diet, such as the degradation of proteins, carbohydrates, fats, and chondroitin, as well as the production of SCFAs, particularly butyrate. In our analysis, protein degradation showed the most significant difference between BB/fresh-fed and kibble-fed dogs (Fig. 6 ). BB-fed dogs showed a significantly higher number of protein-degrading pathways detected in their microbiome samples, suggesting that their gut bacteria may be more adapted to breaking down proteins from the host's diet. Pathways associated with carbohydrate and fat degradation showed no significant difference between different diets (Fig. 6 ), indicating that these pathways were less impacted by baseline diet across the cohort. Similarly, there was no significant difference in the average abundance between the different diet cohorts, although dogs on a kibble diet had a wider spread of chondroitin-associated pathways. Results further showed that total SCFA, acetate, and propionate levels were not significantly different between diets, suggesting that the baseline diet had minimal effect on the propagation of bacteria that support these biochemical pathways (Fig. 6 ). However, butyrate production was significantly elevated in fresh-fed dogs, particularly the pathways associated with butyrate synthesis from amino acids rather than carbohydrates (p = 0.01). Pathogenic bacteria In our study, we assessed the presence and abundance of several known genera containing pathogenic bacteria, such as Escherichia, Shigella , Streptococcus , Campylobacter , Helicobacter , Peptoclostridium , and C. difficile , using mapped 16S rRNA gene data. The proportion of samples from each diet with detectable levels of each bacterial species was distributed relatively evenly across the three main types of diet (BB, combination, and processed) (Table 1 ). A higher proportion of BB-diet dogs exhibited detectable levels of Escherichia Shigella , Campylobacter , and C. difficile , while Streptococcus was more frequently detected in kibble-fed dogs. Combination diets accounted for higher proportions of dogs with Helicobacter and Peptoclostridium present. Among these groups, Peptoclostridium was the most prevalent across all diets, with 98% of BB-fed dogs, 100% of combination-fed dogs, and 93% of kibble-fed dogs displaying detectable levels (Table 1 ). Although the percentage of samples with detectable levels of pathogens was similar across diets, the maximum bacterial load was substantially higher in the kibble-fed dogs. Specifically, the maximum observed levels of Streptococcus , Campylobacter , Helicobacter , and C. difficile were orders of magnitude higher in dogs fed a kibble diet compared to BB diets (2.89x, 4.67x, 1.70x, and 4.5x, respectively) (Fig. 7 & Table 2 ). Table 1 The proportion of dogs in each cohort with detectable levels of specific pathogenic bacteria, with bold indicating the specific diet groups showing the highest levels of each pathogen. BB (n = 43) Combination (n = 22) Kibble (n = 34) Escherichia Shigella 48% 45% 37% Streptococcus 67% 73% 76% Campylobacter 29% 9% 24% Helicobacter 50% 73% 41% Peptoclostridium 98% 100% 93% C. difficile 2% (n = 1) 0% 2% (n = 1) Table 2 Statistical analyses of the various reported pathogens, median results of each pathogen count along with the ranges (Minimum - Maximum) observed for each diet type. Across all tagged pathogens except for Escherichia, Shigella , kibble-fed dogs had a significantly higher threshold. C. difficile was largely absent from most dogs in the study. BB (n = 43) Combination (n = 22) Kibble (n = 34) Escherichia.Shigella 0.5 (0.1–26.0) 0.1 (0.1–0.2) 0.3 (0.1–9.5) Streptococcus 1.2 (0.1–19.1) 2.0 (0.1–12.0) 0.2 (0.1–55.2) Campylobacter 0.2 (0.1–0.6) 0.3 (0.3–0.3) 0.8 (0.1–2.8) Helicobacter 0.3 (0.1–2.7) 0.2 (0.1–0.5) 0.2 (0.1–4.6) Peptoclostridium 6.4 (0.1–41.9) 7.9 (3.3–34) 8.6 (3.8–42.4) C.difficile 0.6 0 2.7 Classifying fresh-fed and kibble-fed dogs based on biochemical pathways of the microbiome To identify standout biochemical pathways upregulated in fresh-fed versus kibble-fed dogs, we built a generalised linear model with regularisation applied specifically to pathway abundance data. This approach identified pathways best at differentiating dogs on a fresh diet from those on a kibble diet. The model's Receiver Operating Characteristic (ROC) curve yielded an Area Under the Curve (AUC) of 0.71, indicating good discriminative ability with a 71% likelihood of correctly distinguishing between fresh and kibble diets based on microbiome data. Figure 8 presents the ranking of the top 19 pathways based on their contribution to distinguishing between fresh (BB) and kibble diets. Positive values indicate pathways more strongly associated with fresh-fed dogs, whereas negative values indicate pathways associated with kibble-fed dogs. The top three pathways predictive of fresh diet, identified by the MetaCyc metabolic pathway database [ 25 ] were HISDEG (L-histidine degradation I), POLYAMSYN (superpathway of polyamine biosynthesis I) and PWY0.1296 (purine ribonucleosides degradation). The top pathways associated with kibble diets were P125 (superpathway (R,R)-butanediol biosynthesis), CODH (reductive acetyl coenzyme A pathway) and PWY6545 (pyrimidine deoxyribonucleotides de novo biosynthesis III) (Fig. 8 ). These pathways showed larger weights (or ‘scores’) than those associated with a BB diet, suggesting that these pathways were more commonly distributed in the microbiome of kibble-fed dogs compared to fresh-fed dogs. The P125 pathway had the strongest predictive strength, with a feature importance score of -0.98. Discussion In this study, we sought to explore the association between common canine diets and the gut microbiome of home-based dogs in the United Kingdom. To our knowledge, this is the largest home-based, cross-sectional, multi-breed study of this type, paving the way for future research in this area. Although there is ongoing debate about alternative methods for studying the microbiome and some concerns that faecal samples are insufficient to fully capture the complexity of the gastrointestinal microbiome [ 26 ], faecal sampling remains the most reliable non-invasive and stable method for characterising the bacteria present in the gut. Therefore, in this study, faecal sampling was deemed both appropriate and sufficient to achieve its objectives. Sample collection was smooth, with participants mailing their samples directly to our laboratories via regular postage mail. The plastic Zymo faecal collection tubes proved to be robust both structurally and chemically, with the RNA/DNA shield effectively preserving sample stability during transit. A notable limitation of the study was the six-week delay between initial enrolment and the study commencement, particularly regarding the diets disclosed by pet owners. Although we preselected participants based on feeding their dogs exclusively BB (fresh) or kibble diets, 26 dogs were later identified as being fed raw or a combination of fresh and processed diets, therefore not fulfilling the eligibility criteria. However, to gain additional insight, we sequenced the microbiomes of these dogs, yet refrained from drawing distinctive conclusions about these diet types. Ineligibility is a common issue in similar studies. For example, Tanprasertsuk et al. (2021) reported that 4,978 dog owners were contacted for a large-scale, home-based study, but only 28 dogs met all criteria to provide two microbiome/faecal samples [ 27 ]. In comparison, our method of recruitment, particularly in the UK, showed promise in maintaining high participation rates for potential future studies. To further increase the number of participants, we suggest over-recruiting by 25–30%, as is common in human clinical trials, while sequencing a pre-determined number of samples to manage budgetary constraints. In addition to collecting faecal samples, participants completed a supplementary survey that characterised their dog’s well-being and health. Results showed that fresh-fed dogs had fewer skin, mobility, and gastrointestinal issues. However, it is important to note that these findings are based on a small sample size with user-reported outcomes and limited quantitative measures. While these results provide valuable insight for larger cohort analyses, this study was designed to prioritise participant adherence over methodological stringency. To minimise participant burden, we intentionally limited the number of survey questions while also recognising the uncertainty of specific outcomes due to the exploratory nature of this study. This approach is in line with established clinical trial retention strategies, which suggest that reducing participant burden may significantly improve adherence [ 28 ]. For future studies, we recommend using veterinary-grade characterisation surveys, such as the Canine Atopic Dermatitis Extent and Severity Index (CADESI) [ 29 ] and the Canine Behavioural Assessment and Research Questionnaire (C-BARQ) [ 30 ]. Quantitative data collection methods such as continuous activity monitors are also tools that could be employed in future studies. However, it is important to define study objectives in advance ( e.g. , to characterise skin or gut conditions) to avoid overwhelming participants with lengthy surveys. To better characterise the gut microbiome, various biodiversity metrics have been applied to quantify the complexity of this ecosystem. One commonly used metric is alpha diversity, or Shannon diversity, which was developed in the 1970s by R.H. Whittaker to quantify the variety in a specific ecosystem [ 31 ]. Considering parallels between the microbiome and other ecosystems, this method has been adapted to study the diversity of bacteria in the gut. Alpha diversity, evenness and richness are often used as indicators of host health, as they provide a method to quantify dysbiosis in the gut microbiome, which can promote the proliferation of opportunistic and pathogenic bacterial strains. In our study, dogs on a kibble diet showed a higher level of alpha diversity than those on a fresh diet. Other studies which examined differences in the gut microbiota between fresh and commercial diets (predominantly extruded kibble) also showed increased abundance and diversity of the intestinal microbiota [ 17 – 20 ], although some differences in the specific types and differences in abundance of bacteria were noted between studies. Large differences in beta-diversity (the differences in microbial community composition between two or more samples/groups) were also reported in comparative analyses of canine gut microbiomes in dogs fed fresh compared to kibble diets [ 19 , 20 ]. Although we did not examine this in our study due to low sample sizes, other studies have reported that the gut microbiota of dogs fed raw diets show increased diversity and abundance of bacteria [ 17 , 21 – 23 , 32 ], although increases in opportunistic pathogens were also noted in those fed raw diets [ 21 , 23 ]. While overall biodiversity scores provide a broad view of bacterial colonies in a specific environment, a deeper investigation of specific bacteria is important to determine their impact on the overall ecosystem. Greater microbiome diversity is often regarded as beneficial, but this is not true if this diversity is driven by higher levels of pathogenic or opportunistic bacteria. Our results showed no broad changes across the phyla groups, supporting previous findings that the microbiome response to diet is highly individualised in dogs [ 33 ]. However, one distinction was the lower level of the Proteobacteria phylum in dogs on the BB/fresh diet compared to those on kibble diets in our study. Although not significant (p = 0.288), this finding is in contrast with the results of Geary et al. (2022), and Do et al. (2021), who reported an increase in abundance of the Proteobacteria phyla members E. coli and Helicobacter in dogs consuming mildly cooked human-grade food [ 19 , 20 ]. Algya et al. (2018) also showed higher levels of Proteobacteria in dogs fed a fresh diet [ 17 ]. While the Proteobacteria phylum includes pathogenic bacteria such as E. coli , Salmonella , Pseudomonas , and Helicobacter , some of these can be beneficial within a healthy microbiome, although many are particularly opportunistic. The decreased level of Proteobacteria in fresh-fed dogs in our study may suggest a potential link between diet and the proliferation of opportunistic bacteria, which should be further explored. Multiple microbiome-related studies show that the microbiome response tends to be highly individualised due to numerous environmental and host-specific factors. In our study, this was evident in outliers, which deviated from the observed trends (Fig. 3 ). Further analysis at the genus level is required to fully characterise the impact of canine diet on this phylum. Further assessment of changes in the major bacterial genera revealed a significantly elevated level of the Sutterella genus in dogs on the kibble/processed diets compared to those of the fresh/BB cohort (Figs. 4 and 5 ) . Algya et al. (2018) also reported an altered abundance of Sutterella [ 17 ] when comparing the gut microbiome of dogs fed different diets, with those on raw diets showing the highest levels. Sutterella has been previously associated with obesity, acute diarrhoea, and idiopathic inflammatory bowel disease in dogs [ 34 – 36 ]. While no detailed information on body condition score/obesity was collected during this study, the higher abundance of Sutterella in kibble-fed dogs suggests a potential link between processed diets, obesity susceptibility, and the growth of this genus of bacteria. Sutterella is also associated with various health conditions due to its capacity to degrade immunoglobulin A (IgA) [ 37 ], a crucial antibody for protecting mucosal membranes in the gut, respiratory tract and oral cavity, by neutralising pathogens and toxins and preventing their entry into the body. In particular, IgA prevents the movement of toxins from the gut into the bloodstream, thereby preventing generalised infection and inflammation. The elevated abundance of Sutterella may result in further degradation and depletion of IgA, potentially enabling the growth of other pathogens and leading to a cascade of infection. Studies in humans demonstrated that Sutterella levels decrease with high-protein diets [ 38 ] and increase with lower-quality diets [ 39 ]. Well-balanced, high-quality, fresh diets provide higher-quality protein compared to processed diets and may contribute to the observed reduction in Sutterella levels in BB-fed dogs. With regards to metabolic pathway analysis ( Fig. 6 ) , both protein degradation and butyrate synthesis (from amino acids) were significantly increased in dogs fed a fresh diet. These findings suggest that the higher-quality protein in fresh diets, compared to kibble-based foods, promotes the propagation of bacteria associated with protein degradation and synthesis. This is particularly relevant to a fresh diet, which is often claimed to have higher bioavailability of nutrients due to minimal processing [ 40 ]. The elevated presence of protein-degrading bacteria in fresh-fed dogs indicates a layered interaction within the microbiome. Diets high in bioavailable protein appear to encourage the growth of bacteria specialised in protein degradation, which, in turn, creates a positive feedback loop whereby higher protein bioavailability promotes the growth of protein-degrading bacteria, which further promotes protein breakdown. However, further research is needed to fully understand this interaction and its health benefits for the host. By contrast, dogs fed kibble diets were reported to have increased carbohydrate metabolism compared to those on fresh diets [ 18 – 20 ], and a greater abundance of bacterial species associated with the breakdown of carbohydrates [ 23 ]. While all bacteria contribute to the overall stasis of a healthy microbiome, certain pathogenic bacteria warrant closer investigation due to their ability to disrupt the health of a canine host. In our study, we assessed the presence and abundance of several genera known to contain pathogenic and opportunistic bacteria, such as Escherichia Shigella , Streptococcus , Campylobacter , Helicobacter , Peptoclostridium and C. difficile [ 41 – 47 ], Among them Peptoclostridium was the most prevalent across all diets, present in nearly all dogs regardless of diet (Table 1 ). This aligns with the existing literature, suggesting that the role of Peptoclostridium is less evident compared to other bacterial genera identified. These results suggest that although diet alone may not determine the presence of pathogens in a host, it may impact the prevalence of these bacteria. Specifically, a processed kibble diet could, over time, create an environment that allows the growth of opportunistic and pathogenic bacteria. This highlights the need for further investigation into whether certain diets contribute to microbial imbalance that increases the risk of disease in the host. This study did not quantify the duration of feeding a specific diet, which means that some dogs may have been on a processed diet for only slightly more than the required three-month minimum, while others may have been on the same diet for years. Additionally, some owners may have switched their dogs' diet between enrolment and the study start, as observed with the 26 dogs on combination, canned and raw diets that did not explicitly meet the original recruitment criteria. To address these limitations, future studies should assess whether prolonged exposure to a specific diet increases the abundance of pathogenic bacteria (via a potential “loading effect”), ultimately leading to health deterioration. Our in-house built generalised linear model with regularisation, specifically applied to pathway abundance data, yielded an AUC of 0.71, indicating a moderate discriminative ability with a 71% chance of correctly distinguishing between the fresh- and kibble-fed dogs. While this performance is significantly better than random guessing (AUC = 0.5), it also implies room for improvement. The moderate AUC of 0.71 highlights the model's capability to differentiate diets to a certain extent, but it also suggests the need for larger datasets to reduce potential overfitting and improve predictive accuracy and reliability. Although relatively large for pet-focused research, a sample size of 77 dogs may still be limited for robust machine learning applications, even with the very high levels of individual bacteria and pathways detected. In our study, the top three pathways predictive of the BB diet were HISDEG (L-histidine degradation I), POLYAMSYN (superpathway of polyamine biosynthesis I) and PWY0.1296 (purine ribonucleosides degradation). HISDEG is a vital metabolic pathway observed in healthy animals and plays a fundamental role in breaking down L-histamine into L-glutamate via urocanate. L-glutamate serves as a building block for several proteins in the body and functions as a key neurotransmitter in the brain, where it is associated with improved cognitive functions such as learning and memory. Although L-glutamate is sometimes added as a supplement in commercial dog food, our results suggest that fresh-fed dogs may naturally produce more L-glutamate through this pathway. Furthermore, POLYAMSYN relates to the synthesis of polyamines, such as putrescine, spermidine, and spermine, which support cell growth, function, and maintenance, especially in high-turnover organs like the gastrointestinal tract and skin. Increased polyamine synthesis may contribute to the reduced gastrointestinal and skin issues, as observed in fresh-fed dogs in our study (Fig. 1 ). Additionally, E. coli K-12, the main producer of these polyamines, was detected in a higher proportion of dogs on the BB diet. The PWY0.1296 pathway is also modulated by E. coli and involves the degradation of all four naturally occurring ribonucleosides, which provide energy for bacterial growth and contribute to host metabolism via purine recycling. The L-histidine degradation pathway was a strong predictor of the BB diet in our linear model, reinforcing its relevance to this diet type. While the power of this model could be improved with a larger sample size to mitigate potential overfitting, it does provide valuable insight into the pathways that are predictive of different diet types. Furthermore, our findings are based on pathway inferences derived from a reference database [ 25 ]. To improve the resolution and accuracy of these results, two approaches can be considered. First, applying shotgun metagenomic sequencing would provide a strain-level resolution and functional information on specific bacteria, reducing the reliance on large metabolic pathway databases. Second, measuring the levels of key metabolites of interest through metabolomic analysis, for example, certain SCFAs such as butyrate (both across cohorts and longitudinally), would reveal the actual biochemical products of gut bacteria activity. Certain bacteria are associated with specific metabolic pathways, but their activity within these pathways depends on various factors, including the overall microbiome composition, the source of nutrients and dietary fibres, which also regulate nutrient availability, further complicating analyses. The top pathways predictive of kibble-fed diets, characterised by higher scores (weights), included P125 (superpathway (R,R)-butanediol biosynthesis), CODH (reductive acetyl coenzyme A pathway) and PWY6545 (pyrimidine deoxyribonucleotides de novo biosynthesis III). Among these, the P125 pathway had the strongest predictive strength, with a feature importance score of -0.098. This pathway is involved in the synthesis of butanediol, a chemical compound with industrial uses and unclear biological function in canines. Some studies suggest that it may regulate intracellular pH and NADH/NAD + ratios, but its role in canine health remains unclear [ 48 , 49 ]. Studies in murine models present conflicting data on 2,3-butanediol, a key compound in this pathway. While Veeravalli et al. (2022) reported that it decreased plasma cholesterol levels in mice [ 50 ], Hsieh et al. (2007) found that it may be used by bacteria to evade the host's immune response, potentially causing disease [ 51 ]. Furthermore, the CODH pathway synthesises acetyl-CoA from CO 2 , a precursor of acetate, a part of key SCFA produced in the gut. Excessive acetyl-CoA levels can lead to metabolic imbalances [ 52 ], lipid disorders [ 53 ], glucose metabolism interference [ 54 ], and cancer [ 55 ]. The PWY6545 pathway is involved in the biosynthesis of pyrimidine and purine nucleoside triphosphates for DNA and RNA synthesis. Upregulation of the PWY6545 pathway may indicate errors in bacterial replication. It is important to emphasise that these results represent pathways rather than the actual metabolite production, as pathway activation highly depends on the bacterial environment and available metabolites. While this study focused on the association of dietary changes with differences in the gut microbiome, the ultimate goal for pet owners and nutrition companies is to quantitatively and objectively demonstrate that feeding a certain diet has a significant impact on overall pet health and not merely on the presence and prevalence of gut bacteria. Although the relationship between diet, the microbiome, and health outcomes is highly individualised, it would be valuable to explore other measures of health in participating dogs to better characterise the effects of different diets. Including blood and urine analyses, such as measuring blood glucose levels, could provide a more comprehensive understanding of the effects of specific diets on overall health. Given the growing body of research demonstrating the importance of the oral microbiome and its link to periodontal disease and overall health [ 56 ], future studies should investigate the relationship between dysbiosis in the gut and the oral microbiome, similar to studies already conducted in humans [ 57 ]. Furthermore, recruiting dogs with specific health conditions could provide deeper insights into the therapeutic potential of dietary changes. For example, as our study suggests that a fresh diet positively impacts skin and gut health, a follow-up study focusing on dogs with dermatological or gastrointestinal disorders could assess whether dietary interventions could slow disease progression. In line with this, a recent study showed that a transition from a fresh diet to a dry diet resulted in a reduction in the diversity and abundance of the skin microbiome [ 58 ], which could significantly impact skin health. An improvement for further research would be to conduct an interventional study transitioning dogs from a processed diet to a freshly cooked diet. A further advancement could involve interventional crossover trials, where participants are fed a kibble diet followed by a fresh diet and then returned to the original kibble diet. Such design would allow for the characterisation of microbiome changes as a result of different diets in a single dog, as well as the duration of these effects. However, this approach may introduce ethical considerations, as it involves knowingly giving a dog food that may be perceived as less beneficial by the study coordinator, even for a short period. A key recommendation for future research is the standardisation and control of diets for the kibble cohort. In our study, all dogs on the fresh diet were fed gently cooked, fresh BB food, which resulted in a lower dietary variation in this group. However, dogs on kibble diets were not limited to controlled, pre-selected kibble. As with any diet type, nutritional profile and production method can differ significantly between brands. Conclusions As the largest and most demographically diverse study of its kind, our study makes a significant contribution to characterising the canine microbiome, demonstrating the viability of large-scale, home-based methods in study implementation, and providing evidence of the potential positive effects associated with a fresh diet in canines. Abbreviations SCFAs Short-chain fatty acids NGS Next-generation sequencing BB Butternut Box ROC Receiver Operating Characteristic AUC Area Under the Curve HISDEG L-histidine degradation POLYAMSYN Superpathway of polyamine biosynthesis I PWYO.1296 Purine ribonucleosides degradation P125 Superpathway (R,R)-butanediol biosynthesis CODH Reductive acetyl coenzyme A pathway PWY6545 Pyrimidine deoxyribonucleotides de novo biosynthesis III C-BARQ Canine Behavioural Assessment and Research Questionnaire CADESI Canine Atopic Dermatitis Extent and Severity Index IgA Immunoglobulin A Declarations Ethics approval and consent to participate As an exploratory baseline study with no alterations to participants' routine diet or invasive procedures, this study did not require ethics approval. All pet owners provided informed consent for participation, and it was clearly communicated to them that no specific health outcomes were anticipated from the study results. Consent for publication All pet owners provided informed consent for publication. Availability of data and material Data supporting the results can be found in these publicly available repositories: 1. https://github.com/treattherapeutics/canine-microbiome-diet-study 2. https://zenodo.org/records/15341771 Competing interests The author(s) E.M.M.B. and Y.Y.Y. declare that they have no competing interests. The author, C.C., declares the following, which may be considered as potential competing interests: C.C. is an employee of Dogmates Ltd. Funding Dogmates Ltd (trading as Butternut Box) funded the study, conducted by Treat Therapeutics Ltd. EMMB and Yi Yi Yang are past and current employees of Treat Therapeutics who analysed the data generated by the study. Authors' contributions C.C. conceived the presented idea. C.C. and E.M.M.B. developed the conceptual ideas and proof outline. E.M.M.B. and Y.Y.Y. performed the data analysis and computations of the data set. 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Fresh Food Consumption Increases Microbiome Diversity and Promotes Changes in Bacteria Composition on the Skin of Pet Dogs Compared to Dry Foods. Animals. 2022;12(15):1881. Additional Declarations Competing interest reported. ​​The author(s) E.M.M.B. and Y.Y.Y. declare that they have no competing interests. The author, C.C., declares the following, which may be considered as potential competing interests: C.C. is an employee of Dogmates Ltd and holds shares in the company. Supplementary Files SupplementaryTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6401817","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522662136,"identity":"7c2ddf4f-8b37-4509-9841-b51b9482b63c","order_by":0,"name":"Ciara 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Kibble dogs were more likely to report sensitive stomachs at baseline (40.6%) compared to BB and combination diets. Since this was a cross-sectional study and not a therapeutics trial, this outcome was not self-selecting.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6401817/v1/b38198297998d9fde76f27ee.jpeg"},{"id":92682979,"identity":"a837f766-aaaf-4d2f-93f5-07e08760c0bc","added_by":"auto","created_at":"2025-10-03 01:26:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177700,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent biodiversity metrics observed in the three main diet types: BB (fresh), combination and kibble. A significant difference in alpha diversity was found between the kibble and fresh diets (p=0.002), primarily driven by the higher levels of richness in the gut microbiome of kibble-fed dogs (p=0.04).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6401817/v1/fc97ac0559f5bae1aed65df6.jpeg"},{"id":92681850,"identity":"41257044-d425-4983-98cc-d57ac3f64e63","added_by":"auto","created_at":"2025-10-03 01:10:29","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":435623,"visible":true,"origin":"","legend":"\u003cp\u003ePhyla distribution across different diets. Difference was observed in the abundance of \u003cem\u003eProteobacteria\u003c/em\u003e between BB and kibble-fed dogs, although this was not significant (p=0.288), with \u003cem\u003eProteobacteria\u003c/em\u003e being more common in kibble-fed dogs. Bacteroidota levels were significantly higher in kibble-fed dogs compared to BB dogs (p=0.006).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6401817/v1/48f3a8c76d6e93814288858b.jpeg"},{"id":92682980,"identity":"5a1434e5-db9b-4a82-9a4f-820ade37fa99","added_by":"auto","created_at":"2025-10-03 01:26:29","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":468161,"visible":true,"origin":"","legend":"\u003cp\u003eGenus distribution across different diets. \u003cem\u003eSutterella\u003c/em\u003ewas significantly more prevalent in kibble-fed dogs compared to those on BB (p=0.05) diets.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6401817/v1/0c5f8dbbf6f63d72283d1e11.jpeg"},{"id":92682570,"identity":"4aefd279-79eb-4a50-8a19-389d5a19f57d","added_by":"auto","created_at":"2025-10-03 01:18:29","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181152,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whisker plot of \u003cem\u003eSutterella\u003c/em\u003e levels across diets. \u003cem\u003eSutterella\u003c/em\u003e was more prevalent in BB-fed dogs compared to kibble-fed dogs.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6401817/v1/f3f4a521507479a0379a9123.jpeg"},{"id":92681854,"identity":"a0739ad7-629a-4bb6-8650-3145c39cad49","added_by":"auto","created_at":"2025-10-03 01:10:29","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":476686,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whisker plots of biochemical pathways related to canine health and nutrition. Protein degradation pathways were significantly elevated in fresh-fed dogs compared to other diets, while other nutritional pathways, such as carbohydrates, fat, and chondroitin, showed no significant difference between diets. BB dogs also showed significantly higher levels of SCFA versus kibble-fed dogs (p=0.01). SCFA, short-chain fatty acids.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6401817/v1/0e662b455d91f6780a057263.jpeg"},{"id":92682982,"identity":"409d3288-e93b-470e-a690-0ec2ee540f1a","added_by":"auto","created_at":"2025-10-03 01:26:29","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":339562,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whisker plots of pathogenic bacteria abundance. The plots reveal that the bacterial load in dogs on kibble diets tends to be comparable to those on BB or combination diet(s).\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6401817/v1/3792ed88478f83d9c9703948.jpeg"},{"id":92681870,"identity":"cd8f1406-3439-4ea4-91e9-0e3d9d000c39","added_by":"auto","created_at":"2025-10-03 01:10:30","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":256111,"visible":true,"origin":"","legend":"\u003cp\u003eBar plot of pathway feature strengths identified by linear model differentiating kibble and fresh diets. Pathways with stronger negative values were more prominently associated with kibble-fed dogs, whereas those with stronger positive values were associated with BB-fed dogs.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6401817/v1/45ad54e1146a7f2a11915a56.jpeg"},{"id":95788753,"identity":"4b55a3f5-bfc7-4ccf-a0ea-f9573f968746","added_by":"auto","created_at":"2025-11-13 06:09:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3474278,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6401817/v1/23668bf8-5231-42ff-9260-28e9abcdbb81.pdf"},{"id":92681849,"identity":"e6800c52-5f36-4126-ad41-43005ec1ab71","added_by":"auto","created_at":"2025-10-03 01:10:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16660,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6401817/v1/2680683c8f3996a4163b3326.docx"}],"financialInterests":"Competing interest reported. ​​The author(s) E.M.M.B. and Y.Y.Y. declare that they have no competing interests. The author, C.C., declares the following, which may be considered as potential competing interests: C.C. is an employee of Dogmates Ltd and holds shares in the company.","formattedTitle":"Quantifying differences in the canine gut microbiome between fresh and processed diets: A home-based, cross- sectional study with demographic diversity","fulltext":[{"header":"Background","content":"\u003cp\u003eCompanion animal nutrition has been an area of growing commercial interest since the early 1860s when the first commercial pet food was introduced in the United Kingdom. James Spratt\u0026rsquo;s \u0026ldquo;Patented Meat Fibrine Dog Cakes\u0026rdquo; were the precursor to modern processed dog foods, which have since evolved to claim that they provide complete nutrition for all stages of a dog\u0026rsquo;s life. This marked a significant shift from previous feeding practices, where domesticated animals were predominantly given leftover food from their human family meals. Throughout the 20th century, large-scale production and the rise of highly processed foods, including dry/kibble and wet/canned varieties, quickly established these products as the standard in companion animal nutrition. During this period, due to the lower status of companion animals and the overriding trends in human nutrition that favoured commercial, efficient, and highly processed foods, little attention was given to potential health concerns related to feeding animals similar diets. In the 1990s, Dr. Ian Billinghurst\u0026rsquo;s publication, \u003cem\u003eGive Your Dog a Bone\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], ignited a movement encouraging a return to fresh and raw diets for companion animals.\u003c/p\u003e\u003cp\u003eIn the 1990s and early 2000s, interest in human nutrition grew significantly, with several leading researchers exploring topics, such as the role of dietary fatty acids [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], trans fats in industrially processed foods [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the benefits of fibres and prebiotics [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and the positive effects of fresh, unprocessed foods [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Many studies in human nutrition followed protocols similar to those in human clinical trials, using large sample sizes and a scientific, rigorous, methodical approach that included a collection of various interventional biomarkers.\u003c/p\u003e\u003cp\u003eIt is now understood that many nutritional processes are modulated by the complex network and ecosystem of bacteria inhabiting various parts of the body, particularly throughout the digestive system. Both the oral and gut microbiome are closely linked to health and nutrition, supporting both the degradation of macronutrients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and the synthesis of key molecules, such as vitamin K [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and short-chain fatty acids (SCFAs) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which are well-documented for their health-promoting effects [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent advances in computational capabilities, next-generation sequencing (NGS) technology and genetic sequencing methods have enabled a more detailed characterisation of the complex gut microbiome. In humans, these technologies allow exploration of personalised nutrition [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], treatment of aggressive \u003cem\u003eClostridium difficile\u003c/em\u003e infections [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], obesity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], among numerous other pathologies.\u003c/p\u003e\u003cp\u003eFollowing this trend, there has been renewed interest in studying microbiota in companion animals, particularly with the growing attention to pet nutrition. Similar to humans, it is likely that the canine immune response is impacted by the composition of the gut microbiota. With the increasing incidence of skin allergies and gastrointestinal sensitivities in pets, the gut microbiome has become a promising therapeutic target [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecognising diet as an important indicator of the overall microbiome, a number of studies have focused on assessing how different diets impact the gut microbiota of dogs. These have studied the impact on the canine gut microbiome of fresh compared to extruded kibble diets [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Others have determined whether the gut microbiota are impacted by raw food [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Broadly, these studies showed differences in both the richness and diversity of the canine microbiome and metabolic pathways associated with the different diets. However, variability was observed between studies which may reflect limitations including small sample sizes (\u003cem\u003ei.e.\u003c/em\u003e, fewer than 30 dogs), limited breed diversity, the use of generalised kennel-based facilities, which may inaccurately represent domestic living conditions, and a lack of standardisation in the specific diet(s) provided to the pets. A systematic review of the impact of diet on gut microbiota showed that the quality of protein and the processing of feed had a significant impact [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, this did not include data from studies of the dog microbiome. To fully characterise the effect of a well-formulated fresh diet on the canine microbiome, there is a need to conduct a large, breed-diverse, home-based study with a standardised approach to fresh food intervention across all participants.\u003c/p\u003e\u003cp\u003eIn this study, we therefore aimed to conduct an exploratory, large, multi-breed, home-based cross-sectional investigation into the effects of a human-grade, fresh, canine-specific, gently cooked, fresh diet on the faecal microbiome of a healthy canine cohort.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eAnimals and study design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll participating dogs were home-based canines (\u003cem\u003eCanis familiaris\u003c/em\u003e) located in the United Kingdom, aged 1\u0026ndash;8 years old at recruitment and with no reported chronic health conditions. Participants were selected under the criterion that their dogs had not been administered antibiotics within three months prior to the start of the study, ensuring no additional microbiome modulation from recent antibiotic use that could impact the results.\u003c/p\u003e\u003cp\u003eAs a baseline cross-sectional study, pet parents disclosed their dogs\u0026rsquo; current diet. All participants on the fresh diet were consuming food from Butternut Box (BB), a UK-based manufacturer of fresh, human-grade, gently cooked dog food. The rest of the cohort consisted of kibble (i.e. processed, dry food), a combination of kibble and fresh diets, canned (i.e. processed, wet food) or raw diets. Although we explicitly recruited participants on a BB diet or Kibble, certain participants indicated different diets upon study commencement (canned and raw) and, due to the logistics of the study, were allowed to continue their participation in the study. Participants were recruited from the general population of home-based dogs in the UK through various methods, such as social media outreach, word-of-mouth, and in-person sign-ups leveraging Butternut Box ambassador and staff networks. All owners were required to sign a consent form for the use of their dogs\u0026rsquo; microbiome samples in the study and to approve the use of the data collected.\u003c/p\u003e\u003cp\u003eAs part of the study process, participants were asked to provide user-reported results on the general well-being of their dogs. A variety of user-reported health outcome categories were assessed during the study, including stomach sensitivity, skin health, activity levels and appetite.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEach participant received a Zymo Research R1101 faecal collection tube (Zymo Research, USA) to collect fresh, naturally obtained faecal samples from their dogs. These tubes were pre-filled with a DNA/RNA shield and a lysis matrix to ensure the stability of bacterial matter during transportation to the laboratory. Using the spoon provided in the tube, participants collected a small pea-sized sample from the surface of the faecal pile and sealed it immediately in the collection tube. Sample tubes were then placed in a plastic bag and mailed directly to the laboratory using a pre-labelled mailer. At the time of sample collection, participants completed a survey to provide validating information on their dogs\u0026rsquo; profile and health. Once received at the laboratory, all samples were stored at -80\u0026deg;C until extraction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExtraction and library preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDNA was extracted from faecal suspension using a QIAmp PowerFecal DNA kit (Qiagen, USA). The DNA library for targeting the V3-V4 region on the 16S rRNA gene was prepared using the Quick-16S Plus\u0026trade; NGS Library Prep Kit (Zymo Research, USA). DNA sequencing was performed on an Illumina MiSeq system (Illumina, USA). All samples were extracted and sequenced as a single batch to avoid batch effects.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eAll analyses were conducted in R (version 4.0.2). An in-house bioinformatic workflow was set up to perform quality control on the sequenced files and assign taxonomy to sequence variants. Microbiome analysis was performed using the Phyloseq (1.32.0) and Vegan (2.6-4) libraries. The diversity scores were adjusted for age. Functional pathway prediction was conducted using PICRUSt2 in conjunction with the Metacyc database, with raw pathway abundances converted to relative abundances and scaled to a percentile score between 1 and 10. A paired t-test was performed on normally distributed diversity and pathway scores.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eStudy participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn total, 103 dogs were successfully recruited and completed the study, representing 44 different breeds, including pedigree breeds, crossbreed combinations, and mixed breeds (see \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e for a full list). Of the dogs, 41% (n\u0026thinsp;=\u0026thinsp;43) were fed the BB diet and 33% (n\u0026thinsp;=\u0026thinsp;34) were fed a kibble/processed diet. These diets had been fed to the dogs in the study for at least three months before the initial enrollment survey. The remaining 26 dogs were fed a different diet or a combination of diets: 21% (n\u0026thinsp;=\u0026thinsp;22) of the dogs received a combination of kibble and BB diets, 2% (n\u0026thinsp;=\u0026thinsp;2) were fed a raw diet, and 2% (n\u0026thinsp;=\u0026thinsp;2) were fed a canned diet. To ensure completeness, all samples were included in the analysis, but we primarily focused on the comparison between fresh and kibble diets.\u003c/p\u003e\u003cp\u003eRegarding dog characteristics, 51% of the dogs were male (n\u0026thinsp;=\u0026thinsp;52), and 49% were female (n\u0026thinsp;=\u0026thinsp;51), with a median age of three years. The most common were \u0026ldquo;mixed breeds\u0026rdquo; (a mix of more than two known or unknown breeds) (n\u0026thinsp;=\u0026thinsp;17), Cockapoos (Cocker Spaniel crossed with Poodle) (n\u0026thinsp;=\u0026thinsp;8) and Labrador Retrievers (n\u0026thinsp;=\u0026thinsp;7). As this study focused on exploratory endpoints rather than breed-related outcomes, breeds were user-reported with no further genetic testing or validation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSelf-reported outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs part of the study, participants were asked to provide user-reported assessments of the general well-being of their dogs. Four key health outcome categories were evaluated: stomach sensitivity, skin concerns, activity levels and appetite. As a proportion of the diet types, BB-fed dogs indicated fewer negative symptoms overall than those on kibble diets. More specifically, dogs on the BB diet had fewer skin, appetite and gastrointestinal issues, suggesting that dogs fed a fresh diet may be less prone to these complications (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Interestingly, dogs on combination diets reported higher appetite (12.5%), activity (33.3%), and skin concerns (16.7%) than the other two diet types. This, however, may be self-selecting as dogs on a combination diet may be fed this to compensate for various health concerns (e.g., poor appetite). Furthermore, dogs on a combination diet reported less stomach sensitivity issues than those on kibble diets (20.8% vs 40.6%), potentially suggesting that the introduction of fresh food may lower stomach sensitivity compared to a fully kibble diet. Across the categories, more dogs on the BB diet reported lower activity levels (36.2%), however, this was categorically based on the number of hours of activity per day, so it may not be truly reflective of activity levels nor an area of concern for this diet type.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBiodiversity metrics: alpha diversity, evenness and richness\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn our study, dogs on a BB diet had a significantly lower level of alpha diversity compared to dogs on kibble and combination diets (p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Diversity scores increased in dogs closer to an exclusively kibble diet, with dogs on a combination diet having diversity scores between those on kibble and BB diets. This suggests a potential link between diet composition and a shift in microbiome diversity in the studied dogs. Results further show that both richness, defined as the number of species present, and evenness, defined as the balance in abundance across species, were higher in dogs on kibble diets than the BB diet, with richness being the main factor driving alpha diversity in the kibble-fed dogs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhyla and genus level analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLooking at the higher-level phyla distribution plots, no broad changes were observed across phyla groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, one finding was the lower abundance of the \u003cem\u003eProteobacteria\u003c/em\u003e phylum in dogs on the BB diet compared to those on kibble diets, however, this result was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.288). Bacteroidota levels were statistically significant (p\u0026thinsp;=\u0026thinsp;0.006), with Kibble diets displaying a higher median score. Obtaining insights from phylum-level analysis, however, does not confer significant conclusions in the absence of a deeper assessment of other metrics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e To gain deeper insights, we assessed changes in the major genera observed in the microbiome of the participating dogs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). One notable trend was the significantly higher (p\u0026thinsp;=\u0026thinsp;0.05) abundance of the \u003cem\u003eSutterella\u003c/em\u003e genus in dogs fed a kibble diet compared to those on the BB diets (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBiochemical pathway analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing the PiCrust2 toolkit, we inferred the function of the microbial taxa identified in the samples by cross-referencing 16S rRNA gene data with a database of reference genomes and associated enzymatic reactions. This approach enabled the characterisation of the functional traits of the microbiome without the need for whole genome sequencing of each bacterium. Although the gut microbiome modulates hundreds of biochemical pathways in a host, we primarily focused on the pathways explicitly related to the canine diet, such as the degradation of proteins, carbohydrates, fats, and chondroitin, as well as the production of SCFAs, particularly butyrate.\u003c/p\u003e\u003cp\u003eIn our analysis, protein degradation showed the most significant difference between BB/fresh-fed and kibble-fed dogs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). BB-fed dogs showed a significantly higher number of protein-degrading pathways detected in their microbiome samples, suggesting that their gut bacteria may be more adapted to breaking down proteins from the host's diet.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePathways associated with carbohydrate and fat degradation showed no significant difference between different diets (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), indicating that these pathways were less impacted by baseline diet across the cohort. Similarly, there was no significant difference in the average abundance between the different diet cohorts, although dogs on a kibble diet had a wider spread of chondroitin-associated pathways.\u003c/p\u003e\u003cp\u003eResults further showed that total SCFA, acetate, and propionate levels were not significantly different between diets, suggesting that the baseline diet had minimal effect on the propagation of bacteria that support these biochemical pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, butyrate production was significantly elevated in fresh-fed dogs, particularly the pathways associated with butyrate synthesis from amino acids rather than carbohydrates (p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePathogenic bacteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn our study, we assessed the presence and abundance of several known genera containing pathogenic bacteria, such as \u003cem\u003eEscherichia, Shigella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eHelicobacter\u003c/em\u003e, \u003cem\u003ePeptoclostridium\u003c/em\u003e, and \u003cem\u003eC. difficile\u003c/em\u003e, using mapped 16S rRNA gene data. The proportion of samples from each diet with detectable levels of each bacterial species was distributed relatively evenly across the three main types of diet (BB, combination, and processed) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A higher proportion of BB-diet dogs exhibited detectable levels of \u003cem\u003eEscherichia Shigella\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e, and \u003cem\u003eC. difficile\u003c/em\u003e, while \u003cem\u003eStreptococcus\u003c/em\u003e was more frequently detected in kibble-fed dogs. Combination diets accounted for higher proportions of dogs with \u003cem\u003eHelicobacter\u003c/em\u003e and \u003cem\u003ePeptoclostridium\u003c/em\u003e present.\u003c/p\u003e\u003cp\u003eAmong these groups, \u003cem\u003ePeptoclostridium\u003c/em\u003e was the most prevalent across all diets, with 98% of BB-fed dogs, 100% of combination-fed dogs, and 93% of kibble-fed dogs displaying detectable levels (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although the percentage of samples with detectable levels of pathogens was similar across diets, the maximum bacterial load was substantially higher in the kibble-fed dogs. Specifically, the maximum observed levels of \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eHelicobacter\u003c/em\u003e, and \u003cem\u003eC. difficile\u003c/em\u003e were orders of magnitude higher in dogs fed a kibble diet compared to BB diets (2.89x, 4.67x, 1.70x, and 4.5x, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cb\u003e\u0026amp;\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe proportion of dogs in each cohort with detectable levels of specific pathogenic bacteria, with bold indicating the specific diet groups showing the highest levels of each pathogen.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBB (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCombination (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKibble (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEscherichia Shigella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e48%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStreptococcus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e76%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCampylobacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e29%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHelicobacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e73%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePeptoclostridium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eC. difficile\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2% (n\u0026thinsp;=\u0026thinsp;1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2% (n\u0026thinsp;=\u0026thinsp;1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistical analyses of the various reported pathogens, median results of each pathogen count along with the ranges (Minimum - Maximum) observed for each diet type. Across all tagged pathogens except for \u003cem\u003eEscherichia, Shigella\u003c/em\u003e, kibble-fed dogs had a significantly higher threshold. \u003cem\u003eC. difficile\u003c/em\u003e was largely absent from most dogs in the study.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBB (n\u0026thinsp;=\u0026thinsp;43)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCombination (n\u0026thinsp;=\u0026thinsp;22)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eKibble (n\u0026thinsp;=\u0026thinsp;34)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEscherichia.Shigella\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e0.5 (0.1\u0026ndash;26.0)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e0.1 (0.1\u0026ndash;0.2)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.3 (0.1\u0026ndash;9.5)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStreptococcus\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e1.2 (0.1\u0026ndash;19.1)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e2.0 (0.1\u0026ndash;12.0)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.2 (0.1\u0026ndash;55.2)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCampylobacter\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.2 (0.1\u0026ndash;0.6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.3 (0.3\u0026ndash;0.3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.8 (0.1\u0026ndash;2.8)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHelicobacter\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.3 (0.1\u0026ndash;2.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.2 (0.1\u0026ndash;0.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.2 (0.1\u0026ndash;4.6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePeptoclostridium\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e6.4 (0.1\u0026ndash;41.9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e7.9 (3.3\u0026ndash;34)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e8.6 (3.8\u0026ndash;42.4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC.difficile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eClassifying fresh-fed and kibble-fed dogs based on biochemical pathways of the microbiome\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify standout biochemical pathways upregulated in fresh-fed versus kibble-fed dogs, we built a generalised linear model with regularisation applied specifically to pathway abundance data. This approach identified pathways best at differentiating dogs on a fresh diet from those on a kibble diet. The model's Receiver Operating Characteristic (ROC) curve yielded an Area Under the Curve (AUC) of 0.71, indicating good discriminative ability with a 71% likelihood of correctly distinguishing between fresh and kibble diets based on microbiome data.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the ranking of the top 19 pathways based on their contribution to distinguishing between fresh (BB) and kibble diets. Positive values indicate pathways more strongly associated with fresh-fed dogs, whereas negative values indicate pathways associated with kibble-fed dogs. The top three pathways predictive of fresh diet, identified by the MetaCyc metabolic pathway database [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] were HISDEG (L-histidine degradation I), POLYAMSYN (superpathway of polyamine biosynthesis I) and PWY0.1296 (purine ribonucleosides degradation).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe top pathways associated with kibble diets were P125 (superpathway (R,R)-butanediol biosynthesis), CODH (reductive acetyl coenzyme A pathway) and PWY6545 (pyrimidine deoxyribonucleotides de novo biosynthesis III) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These pathways showed larger weights (or \u0026lsquo;scores\u0026rsquo;) than those associated with a BB diet, suggesting that these pathways were more commonly distributed in the microbiome of kibble-fed dogs compared to fresh-fed dogs. The P125 pathway had the strongest predictive strength, with a feature importance score of -0.98.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we sought to explore the association between common canine diets and the gut microbiome of home-based dogs in the United Kingdom. To our knowledge, this is the largest home-based, cross-sectional, multi-breed study of this type, paving the way for future research in this area. Although there is ongoing debate about alternative methods for studying the microbiome and some concerns that faecal samples are insufficient to fully capture the complexity of the gastrointestinal microbiome [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], faecal sampling remains the most reliable non-invasive and stable method for characterising the bacteria present in the gut. Therefore, in this study, faecal sampling was deemed both appropriate and sufficient to achieve its objectives.\u003c/p\u003e\u003cp\u003e Sample collection was smooth, with participants mailing their samples directly to our laboratories via regular postage mail. The plastic Zymo faecal collection tubes proved to be robust both structurally and chemically, with the RNA/DNA shield effectively preserving sample stability during transit.\u003c/p\u003e\u003cp\u003eA notable limitation of the study was the six-week delay between initial enrolment and the study commencement, particularly regarding the diets disclosed by pet owners. Although we preselected participants based on feeding their dogs exclusively BB (fresh) or kibble diets, 26 dogs were later identified as being fed raw or a combination of fresh and processed diets, therefore not fulfilling the eligibility criteria. However, to gain additional insight, we sequenced the microbiomes of these dogs, yet refrained from drawing distinctive conclusions about these diet types. Ineligibility is a common issue in similar studies. For example, Tanprasertsuk et al. (2021) reported that 4,978 dog owners were contacted for a large-scale, home-based study, but only 28 dogs met all criteria to provide two microbiome/faecal samples [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In comparison, our method of recruitment, particularly in the UK, showed promise in maintaining high participation rates for potential future studies. To further increase the number of participants, we suggest over-recruiting by 25\u0026ndash;30%, as is common in human clinical trials, while sequencing a pre-determined number of samples to manage budgetary constraints.\u003c/p\u003e\u003cp\u003e In addition to collecting faecal samples, participants completed a supplementary survey that characterised their dog\u0026rsquo;s well-being and health. Results showed that fresh-fed dogs had fewer skin, mobility, and gastrointestinal issues. However, it is important to note that these findings are based on a small sample size with user-reported outcomes and limited quantitative measures. While these results provide valuable insight for larger cohort analyses, this study was designed to prioritise participant adherence over methodological stringency. To minimise participant burden, we intentionally limited the number of survey questions while also recognising the uncertainty of specific outcomes due to the exploratory nature of this study. This approach is in line with established clinical trial retention strategies, which suggest that reducing participant burden may significantly improve adherence [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For future studies, we recommend using veterinary-grade characterisation surveys, such as the Canine Atopic Dermatitis Extent and Severity Index (CADESI) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and the Canine Behavioural Assessment and Research Questionnaire (C-BARQ) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Quantitative data collection methods such as continuous activity monitors are also tools that could be employed in future studies. However, it is important to define study objectives in advance (\u003cem\u003ee.g.\u003c/em\u003e, to characterise skin or gut conditions) to avoid overwhelming participants with lengthy surveys.\u003c/p\u003e\u003cp\u003eTo better characterise the gut microbiome, various biodiversity metrics have been applied to quantify the complexity of this ecosystem. One commonly used metric is alpha diversity, or Shannon diversity, which was developed in the 1970s by R.H. Whittaker to quantify the variety in a specific ecosystem [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Considering parallels between the microbiome and other ecosystems, this method has been adapted to study the diversity of bacteria in the gut. Alpha diversity, evenness and richness are often used as indicators of host health, as they provide a method to quantify dysbiosis in the gut microbiome, which can promote the proliferation of opportunistic and pathogenic bacterial strains.\u003c/p\u003e\u003cp\u003eIn our study, dogs on a kibble diet showed a higher level of alpha diversity than those on a fresh diet. Other studies which examined differences in the gut microbiota between fresh and commercial diets (predominantly extruded kibble) also showed increased abundance and diversity of the intestinal microbiota [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], although some differences in the specific types and differences in abundance of bacteria were noted between studies. Large differences in beta-diversity (the differences in microbial community composition between two or more samples/groups) were also reported in comparative analyses of canine gut microbiomes in dogs fed fresh compared to kibble diets [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although we did not examine this in our study due to low sample sizes, other studies have reported that the gut microbiota of dogs fed raw diets show increased diversity and abundance of bacteria [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], although increases in opportunistic pathogens were also noted in those fed raw diets [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile overall biodiversity scores provide a broad view of bacterial colonies in a specific environment, a deeper investigation of specific bacteria is important to determine their impact on the overall ecosystem. Greater microbiome diversity is often regarded as beneficial, but this is not true if this diversity is driven by higher levels of pathogenic or opportunistic bacteria. Our results showed no broad changes across the phyla groups, supporting previous findings that the microbiome response to diet is highly individualised in dogs [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, one distinction was the lower level of the \u003cem\u003eProteobacteria\u003c/em\u003e phylum in dogs on the BB/fresh diet compared to those on kibble diets in our study. Although not significant (p\u0026thinsp;=\u0026thinsp;0.288), this finding is in contrast with the results of Geary et al. (2022), and Do et al. (2021), who reported an increase in abundance of the Proteobacteria phyla members \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eHelicobacter\u003c/em\u003e in dogs consuming mildly cooked human-grade food [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Algya et al. (2018) also showed higher levels of Proteobacteria in dogs fed a fresh diet [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. While the \u003cem\u003eProteobacteria\u003c/em\u003e phylum includes pathogenic bacteria such as \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eHelicobacter\u003c/em\u003e, some of these can be beneficial within a healthy microbiome, although many are particularly opportunistic. The decreased level of \u003cem\u003eProteobacteria\u003c/em\u003e in fresh-fed dogs in our study may suggest a potential link between diet and the proliferation of opportunistic bacteria, which should be further explored. Multiple microbiome-related studies show that the microbiome response tends to be highly individualised due to numerous environmental and host-specific factors. In our study, this was evident in outliers, which deviated from the observed trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Further analysis at the genus level is required to fully characterise the impact of canine diet on this phylum.\u003c/p\u003e\u003cp\u003eFurther assessment of changes in the major bacterial genera revealed a significantly elevated level of the \u003cem\u003eSutterella\u003c/em\u003e genus in dogs on the kibble/processed diets compared to those of the fresh/BB cohort (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Algya et al. (2018) also reported an altered abundance of \u003cem\u003eSutterella\u003c/em\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] when comparing the gut microbiome of dogs fed different diets, with those on raw diets showing the highest levels. \u003cem\u003eSutterella\u003c/em\u003e has been previously associated with obesity, acute diarrhoea, and idiopathic inflammatory bowel disease in dogs [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. While no detailed information on body condition score/obesity was collected during this study, the higher abundance of \u003cem\u003eSutterella\u003c/em\u003e in kibble-fed dogs suggests a potential link between processed diets, obesity susceptibility, and the growth of this genus of bacteria. \u003cem\u003eSutterella\u003c/em\u003e is also associated with various health conditions due to its capacity to degrade immunoglobulin A (IgA) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], a crucial antibody for protecting mucosal membranes in the gut, respiratory tract and oral cavity, by neutralising pathogens and toxins and preventing their entry into the body. In particular, IgA prevents the movement of toxins from the gut into the bloodstream, thereby preventing generalised infection and inflammation. The elevated abundance of \u003cem\u003eSutterella\u003c/em\u003e may result in further degradation and depletion of IgA, potentially enabling the growth of other pathogens and leading to a cascade of infection. Studies in humans demonstrated that \u003cem\u003eSutterella\u003c/em\u003e levels decrease with high-protein diets [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and increase with lower-quality diets [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Well-balanced, high-quality, fresh diets provide higher-quality protein compared to processed diets and may contribute to the observed reduction in \u003cem\u003eSutterella\u003c/em\u003e levels in BB-fed dogs.\u003c/p\u003e\u003cp\u003eWith regards to metabolic pathway analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, both protein degradation and butyrate synthesis (from amino acids) were significantly increased in dogs fed a fresh diet. These findings suggest that the higher-quality protein in fresh diets, compared to kibble-based foods, promotes the propagation of bacteria associated with protein degradation and synthesis. This is particularly relevant to a fresh diet, which is often claimed to have higher bioavailability of nutrients due to minimal processing [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The elevated presence of protein-degrading bacteria in fresh-fed dogs indicates a layered interaction within the microbiome. Diets high in bioavailable protein appear to encourage the growth of bacteria specialised in protein degradation, which, in turn, creates a positive feedback loop whereby higher protein bioavailability promotes the growth of protein-degrading bacteria, which further promotes protein breakdown. However, further research is needed to fully understand this interaction and its health benefits for the host. By contrast, dogs fed kibble diets were reported to have increased carbohydrate metabolism compared to those on fresh diets [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and a greater abundance of bacterial species associated with the breakdown of carbohydrates [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile all bacteria contribute to the overall stasis of a healthy microbiome, certain pathogenic bacteria warrant closer investigation due to their ability to disrupt the health of a canine host. In our study, we assessed the presence and abundance of several genera known to contain pathogenic and opportunistic bacteria, such as \u003cem\u003eEscherichia Shigella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eHelicobacter\u003c/em\u003e, \u003cem\u003ePeptoclostridium\u003c/em\u003e and \u003cem\u003eC. difficile\u003c/em\u003e [\u003cspan additionalcitationids=\"CR42 CR43 CR44 CR45 CR46\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], Among them \u003cem\u003ePeptoclostridium\u003c/em\u003e was the most prevalent across all diets, present in nearly all dogs regardless of diet (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This aligns with the existing literature, suggesting that the role of \u003cem\u003ePeptoclostridium\u003c/em\u003e is less evident compared to other bacterial genera identified. These results suggest that although diet alone may not determine the presence of pathogens in a host, it may impact the prevalence of these bacteria. Specifically, a processed kibble diet could, over time, create an environment that allows the growth of opportunistic and pathogenic bacteria. This highlights the need for further investigation into whether certain diets contribute to microbial imbalance that increases the risk of disease in the host. This study did not quantify the duration of feeding a specific diet, which means that some dogs may have been on a processed diet for only slightly more than the required three-month minimum, while others may have been on the same diet for years. Additionally, some owners may have switched their dogs' diet between enrolment and the study start, as observed with the 26 dogs on combination, canned and raw diets that did not explicitly meet the original recruitment criteria. To address these limitations, future studies should assess whether prolonged exposure to a specific diet increases the abundance of pathogenic bacteria (via a potential \u0026ldquo;loading effect\u0026rdquo;), ultimately leading to health deterioration.\u003c/p\u003e\u003cp\u003eOur in-house built generalised linear model with regularisation, specifically applied to pathway abundance data, yielded an AUC of 0.71, indicating a moderate discriminative ability with a 71% chance of correctly distinguishing between the fresh- and kibble-fed dogs. While this performance is significantly better than random guessing (AUC\u0026thinsp;=\u0026thinsp;0.5), it also implies room for improvement. The moderate AUC of 0.71 highlights the model's capability to differentiate diets to a certain extent, but it also suggests the need for larger datasets to reduce potential overfitting and improve predictive accuracy and reliability. Although relatively large for pet-focused research, a sample size of 77 dogs may still be limited for robust machine learning applications, even with the very high levels of individual bacteria and pathways detected.\u003c/p\u003e\u003cp\u003eIn our study, the top three pathways predictive of the BB diet were HISDEG (L-histidine degradation I), POLYAMSYN (superpathway of polyamine biosynthesis I) and PWY0.1296 (purine ribonucleosides degradation). HISDEG is a vital metabolic pathway observed in healthy animals and plays a fundamental role in breaking down L-histamine into L-glutamate via urocanate. L-glutamate serves as a building block for several proteins in the body and functions as a key neurotransmitter in the brain, where it is associated with improved cognitive functions such as learning and memory. Although L-glutamate is sometimes added as a supplement in commercial dog food, our results suggest that fresh-fed dogs may naturally produce more L-glutamate through this pathway. Furthermore, POLYAMSYN relates to the synthesis of polyamines, such as putrescine, spermidine, and spermine, which support cell growth, function, and maintenance, especially in high-turnover organs like the gastrointestinal tract and skin. Increased polyamine synthesis may contribute to the reduced gastrointestinal and skin issues, as observed in fresh-fed dogs in our study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, \u003cem\u003eE. coli\u003c/em\u003e K-12, the main producer of these polyamines, was detected in a higher proportion of dogs on the BB diet. The PWY0.1296 pathway is also modulated by \u003cem\u003eE. coli\u003c/em\u003e and involves the degradation of all four naturally occurring ribonucleosides, which provide energy for bacterial growth and contribute to host metabolism via purine recycling.\u003c/p\u003e\u003cp\u003eThe L-histidine degradation pathway was a strong predictor of the BB diet in our linear model, reinforcing its relevance to this diet type. While the power of this model could be improved with a larger sample size to mitigate potential overfitting, it does provide valuable insight into the pathways that are predictive of different diet types. Furthermore, our findings are based on pathway inferences derived from a reference database [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To improve the resolution and accuracy of these results, two approaches can be considered. First, applying shotgun metagenomic sequencing would provide a strain-level resolution and functional information on specific bacteria, reducing the reliance on large metabolic pathway databases. Second, measuring the levels of key metabolites of interest through metabolomic analysis, for example, certain SCFAs such as butyrate (both across cohorts and longitudinally), would reveal the actual biochemical products of gut bacteria activity. Certain bacteria are associated with specific metabolic pathways, but their activity within these pathways depends on various factors, including the overall microbiome composition, the source of nutrients and dietary fibres, which also regulate nutrient availability, further complicating analyses.\u003c/p\u003e\u003cp\u003eThe top pathways predictive of kibble-fed diets, characterised by higher scores (weights), included P125 (superpathway (R,R)-butanediol biosynthesis), CODH (reductive acetyl coenzyme A pathway) and PWY6545 (pyrimidine deoxyribonucleotides de novo biosynthesis III). Among these, the P125 pathway had the strongest predictive strength, with a feature importance score of -0.098. This pathway is involved in the synthesis of butanediol, a chemical compound with industrial uses and unclear biological function in canines. Some studies suggest that it may regulate intracellular pH and NADH/NAD\u0026thinsp;+\u0026thinsp;ratios, but its role in canine health remains unclear [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Studies in murine models present conflicting data on 2,3-butanediol, a key compound in this pathway. While Veeravalli et al. (2022) reported that it decreased plasma cholesterol levels in mice [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], Hsieh et al. (2007) found that it may be used by bacteria to evade the host's immune response, potentially causing disease [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Furthermore, the CODH pathway synthesises acetyl-CoA from CO\u003csub\u003e2\u003c/sub\u003e, a precursor of acetate, a part of key SCFA produced in the gut. Excessive acetyl-CoA levels can lead to metabolic imbalances [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], lipid disorders [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], glucose metabolism interference [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and cancer [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The PWY6545 pathway is involved in the biosynthesis of pyrimidine and purine nucleoside triphosphates for DNA and RNA synthesis. Upregulation of the PWY6545 pathway may indicate errors in bacterial replication. It is important to emphasise that these results represent pathways rather than the actual metabolite production, as pathway activation highly depends on the bacterial environment and available metabolites.\u003c/p\u003e\u003cp\u003eWhile this study focused on the association of dietary changes with differences in the gut microbiome, the ultimate goal for pet owners and nutrition companies is to quantitatively and objectively demonstrate that feeding a certain diet has a significant impact on overall pet health and not merely on the presence and prevalence of gut bacteria. Although the relationship between diet, the microbiome, and health outcomes is highly individualised, it would be valuable to explore other measures of health in participating dogs to better characterise the effects of different diets. Including blood and urine analyses, such as measuring blood glucose levels, could provide a more comprehensive understanding of the effects of specific diets on overall health.\u003c/p\u003e\u003cp\u003eGiven the growing body of research demonstrating the importance of the oral microbiome and its link to periodontal disease and overall health [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], future studies should investigate the relationship between dysbiosis in the gut and the oral microbiome, similar to studies already conducted in humans [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Furthermore, recruiting dogs with specific health conditions could provide deeper insights into the therapeutic potential of dietary changes. For example, as our study suggests that a fresh diet positively impacts skin and gut health, a follow-up study focusing on dogs with dermatological or gastrointestinal disorders could assess whether dietary interventions could slow disease progression. In line with this, a recent study showed that a transition from a fresh diet to a dry diet resulted in a reduction in the diversity and abundance of the skin microbiome [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], which could significantly impact skin health.\u003c/p\u003e\u003cp\u003eAn improvement for further research would be to conduct an interventional study transitioning dogs from a processed diet to a freshly cooked diet. A further advancement could involve interventional crossover trials, where participants are fed a kibble diet followed by a fresh diet and then returned to the original kibble diet. Such design would allow for the characterisation of microbiome changes as a result of different diets in a single dog, as well as the duration of these effects. However, this approach may introduce ethical considerations, as it involves knowingly giving a dog food that may be perceived as less beneficial by the study coordinator, even for a short period. A key recommendation for future research is the standardisation and control of diets for the kibble cohort. In our study, all dogs on the fresh diet were fed gently cooked, fresh BB food, which resulted in a lower dietary variation in this group. However, dogs on kibble diets were not limited to controlled, pre-selected kibble. As with any diet type, nutritional profile and production method can differ significantly between brands.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAs the largest and most demographically diverse study of its kind, our study makes a significant contribution to characterising the canine microbiome, demonstrating the viability of large-scale, home-based methods in study implementation, and providing evidence of the potential positive effects associated with a fresh diet in canines.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSCFAs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Short-chain fatty acids\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNGS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Next-generation sequencing\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Butternut Box\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Receiver Operating Characteristic\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Area Under the Curve\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHISDEG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;L-histidine degradation\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePOLYAMSYN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Superpathway of polyamine biosynthesis I\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePWYO.1296\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Purine ribonucleosides degradation\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eP125\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Superpathway (R,R)-butanediol biosynthesis\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCODH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Reductive acetyl coenzyme A pathway\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePWY6545\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Pyrimidine deoxyribonucleotides de novo biosynthesis III\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC-BARQ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Canine Behavioural Assessment and Research Questionnaire\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCADESI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Canine Atopic Dermatitis Extent and Severity Index\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIgA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Immunoglobulin A \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs an exploratory baseline study with no alterations to participants\u0026apos; routine diet or invasive procedures, this study did not require ethics approval. All pet owners provided informed consent for participation, and it was clearly communicated to them that no specific health outcomes were anticipated from the study results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll pet owners provided informed consent for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the results can be found in these publicly available repositories:\u003c/p\u003e\n\u003cp\u003e1. https://github.com/treattherapeutics/canine-microbiome-diet-study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. https://zenodo.org/records/15341771\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) E.M.M.B. and Y.Y.Y. declare that they have no competing interests.\u0026nbsp;The author, C.C., declares the following,\u0026nbsp;which may be considered as potential competing interests: C.C. is an employee of Dogmates Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDogmates Ltd (trading as Butternut Box) funded the study, conducted by Treat Therapeutics Ltd. EMMB and Yi Yi Yang are past and current employees of Treat Therapeutics who analysed the data generated by the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.C. conceived the presented idea. C.C. and E.M.M.B. developed the conceptual ideas and proof outline. E.M.M.B. and Y.Y.Y. performed the data analysis and computations of the data set. All authors discussed the results and contributed to the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to thank Scientific Writers Ltd., UK, for professional editing support in the preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBillinghurst I. Give Your Dog a Bone. Dogwise Publishing; 1993.\u003c/li\u003e\n\u003cli\u003eWilliams CM. Dietary fatty acids and human health. 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Bone, Joint, and Necrotizing Soft Tissue Infections. In: Baron S, ed. Medical Microbiology. Galveston (TX): University of Texas Medical Branch at Galveston; 1996. Chapter 100. \u003c/li\u003e\n\u003cli\u003eZheng Y, Hao X, Lin X, Zheng Q, Zhang W, Zhou P, et al. Bacterial diversity in the feces of dogs with CPV infection. Microb Pathog. 2018;121:70-6.\u003c/li\u003e\n\u003cli\u003eBerry AP, Levett PN. Chronic diarrhoea in dogs associated with Clostridium difficile infection. Vet Rec. 1986;118:102-3.\u003c/li\u003e\n\u003cli\u003eBooth IR, Kroll RG. Regulation of cytoplasmic pH (pH1) in bacteria and its relationship to metabolism. Biochem Soc Trans. 1983;11(1):70-2.\u003c/li\u003e\n\u003cli\u003eBlomqvist K, Nikkola M, Lehtovaara P, Suihko ML, Airaksinen U, Straby KB, et al. Characterization of the genes of the 2,3-butanediol operons from Klebsiella terrigena and Enterobacter aerogenes. 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Nutrients. 2023;15(15):3377.\u003c/li\u003e\n\u003cli\u003eLeverett K, Manjar\u0026iacute;n R, Laird\u003csup\u003e \u003c/sup\u003eE, Valtierra D, Santiago-Rodriguez TM, Donadelli R, et al. Fresh Food Consumption Increases Microbiome Diversity and Promotes Changes in Bacteria Composition on the Skin of Pet Dogs Compared to Dry Foods. Animals. 2022;12(15):1881.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"faecal microbiome, dog food, companion animal nutrition, fresh diet, processed diet","lastPublishedDoi":"10.21203/rs.3.rs-6401817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6401817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e A number of studies have demonstrated a significant impact of diet on the gut microbiome. We aimed to conduct the largest and most demographically diverse home-based exploratory cross-sectional study to date on the effects of different diet types on the canine microbiome. Although a range of diets were fed to the dogs in our study, the major focus was on comparing the impact of a gently cooked (fresh) diet with a conventional dry processed (kibble) diet.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 103 dogs were recruited for this study. Each participant provided a single faecal microbiome sample along with a completed questionnaire on their dog’s health and nutritional history. Microbial DNA was extracted and sequenced using 16S rRNA gene analysis to characterise the bacterial composition of the faecal sample, which is representative of the gut microbiome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study revealed several significant trends, providing a deeper and more complete understanding of the effects of feeding methods on the canine microbiome. Microbiome metrics, such as alpha diversity and richness, were lower in fresh-fed dogs, while levels of opportunistic and potentially pathogenic bacteria, such as the \u003cem\u003eSutterella\u003c/em\u003egenus, were higher in kibble-fed dogs. Biochemical pathway analysis using the Picrust2 toolkit identified several pathways that were more abundant in fresh-fed compared with kibble-fed dogs, such as the combined protein degradation pathway and the synthesis of butyrate from amino acids. We subsequently developed a simple classifier model which differentiated microbiome samples from fresh-fed and kibble-fed dogs, with pathways such as POLYAMSYN, HISDEG and PWY0.1296 emerging as strong predictors for distinguishing between the two dietary cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study provides a robust and statistically significant investigation into the effects of fresh and kibble diets on the canine gut microbiome. To strengthen the findings and robustness of this preliminary research, we recommend that future studies incorporate metabolomic analysis, shotgun sequencing, and stringent control of the brand or quality of kibble diet.\u003c/p\u003e","manuscriptTitle":"Quantifying differences in the canine gut microbiome between fresh and processed diets: A home-based, cross- sectional study with demographic diversity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 01:10:24","doi":"10.21203/rs.3.rs-6401817/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15f37e70-3c75-40cf-b466-8f909c282fc9","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-13T06:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 01:10:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6401817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6401817","identity":"rs-6401817","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}