Effects of a novel dental chew on oral health outcomes, halitosis, and microbiota of adult dogs

Abstract

Halitosis in dogs is an initial indication of periodontitis, highlighting its significance as a vital marker for underlying problems. Moreover, the oral microbial population has a significant influence on periodontal disease. Measuring the oral microbiota may be used in addition to breath odor, dental plaque, and gingivitis scoring to assess the impact of dental chews on oral health. In this study, we aimed to determine the differences in breath odor, oral health outcomes, and oral microbiota of adult dogs consuming a novel dental chew compared with control dogs consuming only a diet. Twelve healthy adult female beagle dogs were used in a crossover design study. Treatments (n = 12/group) included: diet only (control) or the diet + a novel dental chew. Each day, one chew was provided 4 h after mealtime. On days 1, 7, 14, 21, and 27, breath samples were analyzed for total volatile sulfur compound concentrations using a halimeter. On day 0 of each period, teeth were cleaned by a veterinary dentist blinded to treatments. Teeth were scored for plaque, calculus, and gingivitis by the same veterinary dentist on day 28 of each period. After scoring, subgingival and supragingival plaque samples were collected for microbiota analysis using Illumina MiSeq. All data were analyzed using SAS (version 9.4) using the Mixed Models procedure, with P < 0.05 being significant. Overall, the dental chews were well accepted. Dogs consuming the dental chews had lower calculus coverage, thickness, and scores, lower gingivitis scores, and less pocket bleeding than control dogs. Breath volatile sulfur compounds were lower in dogs consuming the dental chews. Bacterial alpha-diversity analysis demonstrated that control dogs had higher bacterial richness than dogs fed dental chews. Bacterial beta-diversity analysis demonstrated that samples clustered based on treatment. In subgingival and supragingival plaque, control dogs had higher relative abundances of potentially pathogenic bacteria (Pelistega, Desulfovibrio, Desulfomicrobium, Fretibacterium, Helcococcus, and Treponema) and lower relative abundances of genera associated with oral health (Neisseria, Actinomyces, and Corynebacterium). Our results suggest that the dental chew tested in this study may aid in reducing periodontal disease risk in dogs by beneficially shifting the microbiota population and inhabiting plaque buildup.

In this study, we aimed to determine the effects of a novel dental chew on the breath odor, oral health outcomes, and oral microbiota of dogs. Dogs consuming chews had lower calculus coverage, thickness, and scores, lower gingivitis scores, less pocket bleeding, lower bacterial alpha diversity, and improved changes to the oral microbiota population.

Introduction

Oral malodor (halitosis) is one of the first signs of periodontitis in dogs and therefore represents a crucial indicator of underlying issues. Oral malodor is mainly derived from the byproducts resulting from the microbial fermentation of proteins, peptides, and mucins present in saliva, blood, gingival crevicular fluid, lysed neutrophils, desquamated epithelial cells, and any residual food particles that remain on the surfaces of the mouth (Coli and Tonzetich, 1992; Doran et al., 2004). The concentration of volatile sulfur compounds (VSC) is strongly linked to the degree of oral malodor, which is often perceived as a problem by dog owners. Even low concentrations of VSC are highly toxic to tissues and associated with periodontal disease (Ratcliff and Johnson, 1999; Makino et al., 2012; Hampelska et al., 2020), one of the most common diseases observed in small animal practices (Lund et al., 1999; Robinson et al., 2015).

VSC can increase oral mucosa permeability, allowing toxic substances to penetrate the tissue barrier, and ultimately causing inflammation and tissue damage (Yaegaki, 2008). The most common microbes producing VSC are gram-negative anaerobes, including Campylobacter, Fusobacterium, Treponema, Porphyromonas, Tannerella, Prevotella, Aggregatibacter, Actinobacillus, and Bacteroides, with gram-positive Peptostreptococcus, Gemella, and Eubacterium being linked with the severity of mouth odor (Persson et al., 1990; de Boever and Loesche, 1995; Awano et al., 2002; Krespi et al., 2006; Allaker, 2010; Salako and Philip, 2011; Aylikci and Çolak, 2013; Ruparell et al., 2020). Some of the bacterial taxa have also been linked to periodontal disease in dogs (Davis et al., 2013). Accurate measurements of VSC can be obtained by using scientific devices such as a halimeter (Salako and Philip, 2011). Additionally, the periodontal disease process is believed to be more severe in dogs due to an alkaline oral environment, which promotes the growth of bacteria linked with periodontal disease (Lavy et al., 2012; Ruparell et al., 2020) such as Actinomyces and Porphyromonas (Thompson et al., 2015).

Regular brushing and professional cleaning by veterinary professionals are crucial in managing malodor in dogs. Neglecting oral care increases the risk of periodontal disease, as the accumulation of dental plaque on the teeth has been linked with the severity of gingivitis and periodontitis (Davies et al., 1997; Culham and Rawlings, 1998; Riggio et al., 2011; Davis et al., 2013; Wallis et al., 2015; Oba et al., 2021). Dental plaque initially forms above the gum line (supragingivally) and can later extend below the gum line (subgingivally; Fine, 1988; Bernimoulin, 2003). Both supragingival (SUP) and subgingival (SUB) plaque can form a hard and mineralized mass called calculus, and calculus formation can occur within three days of initial plaque formation (Mandel, 1974; Bernimoulin, 2003). Additionally, if the SUP plaque is allowed to grow without interference by any oral hygiene practice, it will result in the establishment of gingivitis after 2 to 3 wk (Löe et al., 1965; Theilade et al., 1966). This condition is especially significant because it is considered a precursor to periodontitis, a disease that is characterized by inflammation of the gums along with loss of connective tissue attachment and bone (DePaola et al., 1989).

Despite the importance of regular oral care procedures, many dog owners fail to brush their dog’s teeth or arrange for routine cleaning by a veterinary professional (Miller and Harvey, 1994; Enlund et al., 2020). However, because most owners give treats to their pets (Morelli et al., 2020), dental chews may serve as an alternative and play a significant role in promoting periodontal health and reducing oral malodor (Gorrel and Bierer, 1999; Brown and McGenity, 2005; Clarke et al., 2011; Quest, 2013; Jeusette et al., 2016; Carroll et al., 2020; Ruparell et al., 2020; Oba et al., 2021). Studies have shown that incorporating dental chews into a dog’s feeding regime results in significant reduction in plaque and calculus accumulation, as well as gingivitis, and promotes a positive shift in the oral microbiota (Carroll et al., 2020; Ruparell et al., 2020; Oba et al., 2021).

The current study aimed to determine the differences in breath odor, oral health outcomes, and oral microbiota of adult dogs consuming a novel dental chew compared with control dogs consuming only a diet. We hypothesized that dental chew consumption would reduce plaque and calculus accumulation, reduce gingivitis scores, reduce breath odor, and beneficially modify oral microbiota populations.

Materials and Methods

All procedures were approved by the University of Illinois Institutional Animal Care and Use Committee before experimentation (IACUC #20168).

Animals, treatments, and experimental design

Twelve healthy adult female beagle dogs (mean age = 3.50 yr; mean BW = 8.77 kg) were used in a crossover design study. In the first period, half of the dogs were randomly allotted to each treatment. In the second period, dogs were allotted to the opposite treatment. Before the start of the study, all dogs underwent a physical examination, serum chemistry values were evaluated, and a dental evaluation was conducted by a veterinary dentist to confirm eligibility. Dogs were housed individually in pens (1.0 m wide by 1.8 m long) in a humidity- and temperature-controlled animal facility on the University of Illinois campus. The experiment consisted of two 28-d periods. On day 0 of each period, teeth were cleaned by a veterinary dentist. Teeth were then scored by the same veterinary dentist on day 28 of each period. Breath samples were measured for malodor on days 1, 7, 14, 21, and 27 of each period.

All dogs were fed a commercial diet (Simply Nourish Chicken & Brown Rice Recipe Adult Dry Dog Food; Simply Nourish Pet Food Company, Phoenix, AZ) throughout the study. Dogs were allotted to the diet only (control, CT) or the diet + a novel dental chew (treatment, TRT). The dietary guaranteed analysis and ingredient profile are presented in Supplementary Table S1. The novel dental chew tested was a long slender chew with ridges, weighing 26.3 g, and approximately 104.2 mm long × 28.7 mm wide × 10.8 mm high (Supplementary Figure S1).

No additional treats, chew toys, or other dental interventions were permitted for the duration of the study. When allotted to dental chew treatments, food intake was adjusted to compensate for the energy provided by the chews. Dogs were weighed once weekly before feeding, with food offerings adjusted so that the BW of all dogs remained constant throughout the study. Dogs were fed at 0800 hours each morning and were given 1 h to consume their food. Leftover food was weighed each day to calculate intake. Dogs had access to fresh water at all times.

Four hours after eating their diet, dogs receiving a dental chew were given the chews and monitored to ensure consumption and to prevent swallowing of large pieces and/or choking. They were given 1 h to consume their dental chew. Any remaining treats and treat pieces were collected and weighed.

Dental chew hardness and breaking strength analysis

Texture profile analysis was conducted as described by He et al. (2020) using a texture analyzer (TA.HD Plus; Texture Technologies Corp., Scarsdale, NY/Stable Microsystems, Godalming, UK) equipped with heavy-duty platform (HDP/90) with a blank plate. The settings of the instrument included a 2.0 mm/s pretest speed, 1.0 mm/s test speed, 10 mm/s post-test speed, and a load cell capacity of 30 kg. Samples were removed from packages just before testing to avoid moisture loss or uptake from the atmosphere.

Resistance to penetration (hardness) was measured using a 2 mm probe (P/2) accessory attached to the texture analyzer. This probe was used to mimic the biting and chewing behavior of pets. The probe approached the sample and once a 5 g force was attained, a rapid rise in force was observed. The probe returns to its original starting position when a penetration distance of 3 mm from the trigger point is reached. Breaking strength was measured using a three-point bend rig (A/3PB) accessory (Texture Technologies Corp., Scarsdale, NY/Stable Microsystems, Godalming, UK). Ten replicates of the chew were analyzed, with maximum force data being recorded, averaged, and reported as mean maximum force.

Halitosis measurement

On days 1, 7, 14, 21, and 27, breath samples were analyzed for VSC using a halimeter (Interscan Corp, Simi Valley, CA). Halimeter measurements were conducted 3 h after dental chew administration. Halitosis measurements were obtained for each dog using a clean plastic straw as an extension of the halimeter air drawing hose. A clean straw was used for each measurement. The tube was placed over the dog’s tongue and approximately even with the maxillary fourth premolar. The mouth was held gently shut while ensuring that the straw was not bent by the teeth or blocked by the tongue of the dog. The greatest reading of VSC over a period of approximately 30 s was displayed by the halimeter and recorded. The machine was allowed to return to 0 (about 60 to 120 s) before the next measurement was taken. Each dog was measured three times and a mean score was calculated.

Anesthesia for dental scoring

To prepare dogs for dental scoring and plaque collection, the dorsal pedal artery was clipped and the skin was treated with 4% lidocaine cream, which was left in place for 20 min under a nonabsorbent dressing. The cephalic vein was also clipped and the area was prepared aseptically. A 20-gauge intravenous (IV) catheter was placed in the vein to administer anesthetic agents and IV fluids. Dogs were then premedicated with butorphanol (0.4 mg/kg) IV. Once the dorsal pedal artery site was desensitized, a 22 gauge catheter was inserted into the artery. Dogs were pre-oxygenated and anesthesia was induced with propofol (2 to 5 mg/kg) IV. They were then intubated and maintained on isoflurane, with IV fluids administered at 5 mL/kg/h and active heating to maintain normothermia. Cardiovascular and respiratory function were monitored continuously using an anesthetic multiparameter monitor, with recordings taken every 5 min. Additional anesthetic agents and cardiovascular support were administered as needed. After anesthesia, the catheters were removed and a mildly compressive bandage was applied for 15 min. The dogs were closely monitored during the recovery phase and returned to their cages with padded bedding.

Gingivitis, plaque, and calculus scoring

On day 28 of each period, gingivitis, plaque, and calculus scoring was conducted according to Gorrel et al. (1999), but modified to omit measurements on the maxillary and mandibular premolar 2 (Harvey et al., 2012; Supplementary Table S2), and bleeding on probing (BOP) was measured according to the study by Loesche (1979). The same veterinary dentist conducted all gingivitis, plaque, and calculus scoring and BOP, and was blinded to all treatment regimens. For each measurement, the maxillary incisor three, canine, premolar three, premolar four, and molar one and the mandibular canine, premolar three, premolar four, and molar one teeth were scored. This selection allowed for scoring of various types of teeth, including those used to nip and tear food (incisors and canines) as well as teeth used to shear and crush food (premolars and molars).

To assess gingivitis, after an initial visual evaluation of the gingiva a periodontal probe was placed subgingivally on the buccal side of each tooth and values were assigned via visual assessment of inflammation and bleeding, if present, upon probing. The scores for each measure within dog were combined to obtain a mean score. Plaque levels were evaluated by using Trace Disclosing Solution (Young Dental, Earth City, MO) to cover the teeth, followed by a gentle rinse of water to remove the excess. Plaque was hence revealed and subsequently scored for coverage and thickness according to the study by Gorrel et al. (1999) using the anatomical landmarks described by Hennet et al. (2006) to divide the teeth into gingival and occlusal portions (Supplementary Table S2). The average plaque coverage was multiplied by the average of plaque thickness to obtain a whole-mouth mean plaque score for each animal. Calculus scores were based on visual assessment of coverage and thickness on the mesial, buccal, and distal portions of the tooth. The tooth score was the average of the scores for each of the three tooth surfaces. The average of calculus coverage was multiplied by the average of calculus thickness to obtain a whole mouth mean calculus score for each animal. A BOP value of 0 was given to healthy gingival tissue, one for red tissue with no bleeding, two for bleeding without flow along the gingival margin, three for bleeding with flow along the gingival margin, four for copious amounts of bleeding, and five for severely inflamed tissue that has a tendency to spontaneously bleed. The scores for each measure within the dog were combined to obtain a mean score.

When all scoring was complete, SUB and SUP scaling and SUP polishing were done on all teeth with a fine-grade prophy paste to reestablish a clean mouth model for the next study period. Although quantitative light-induced fluorescence has recently been validated to quantify canine plaque (Wallis et al., 2016) and calculus (Wallis et al., 2018), the use of trained, blinded scorers is recommended by the Veterinary Oral Health Council.

Plaque sample collection

Once scored, plaque samples were collected for microbiota analysis and the teeth surfaces were cleaned. Teeth were assessed using a sterile periodontal probe on the gingival margin and sweeping along the base of the crown. SUB and SUP plaque samples were collected from the premolar four and molar one mandibular teeth and the premolar four and molar one maxillary teeth. Plaque samples were placed into sterile 2.0 mL cryovials (CryoELITE, Wheaton, Millville, NJ) and immediately placed on dry ice and then stored at −80 °C until analysis.

Microbiota analysis

Total DNA from plaque samples were extracted using Mo-Bio PowerSoil kits (MO-BIO Laboratories, Inc., Carlsbad, CA). Concentrations of extracted DNA were quantified using a Qubit 3.0 Fluorometer (Life Technologies, Grand Island, NY). 16S rRNA gene amplicons were generated using a Fluidigm Access Array (Fluidigm Corporation, South San Francisco, CA) in combination with Roche High Fidelity Fast Start Kit (Roche, Indianapolis, IN). The primers 515F (5ʹ-GTGCCAGCMGCCGCGGTAA-3ʹ) and 806R (5ʹ-GGACTACHVGGGTWTCTAAT-3ʹ) that target a 252 bp-fragment of the V4 region of the 16S rRNA gene were used for amplification (primers synthesized by IDT Corp., Coralville, IA; Caporaso et al., 2012). CS1 forward tag and CS2 reverse tag were added according to the Fluidigm protocol. Quality of the amplicons was assessed using a Fragment Analyzer (Advanced Analytics, Ames, IA) to confirm amplicon regions and sizes. A DNA pool was generated by combining equimolar amounts of the amplicons from each sample. The pooled samples were then size-selected on a 2% agarose E-gel (Life Technologies, Grand Island, NY) and extracted using a Qiagen gel purification kit (Qiagen, Valencia, CA). Cleaned size-selected pooled products were run on an Agilent Bioanalyzer to confirm appropriate profile and average size. Illumina sequencing was performed on a MiSeq using v3 reagents (Illumina Inc., San Diego, CA) at the Roy J. Carver Biotechnology Center at the University of Illinois.

16S rRNA microbial data analysis

Forward reads were trimmed using the FASTX-Toolkit (version 0.0.13) and QIIME 2 (version 2022.8; Caporaso et al., 2011) was used to process the resulting sequence data. Raw sequenced amplicons were imported into the QIIME2 package and analyzed by the DADA2 pipeline for quality control (QC value ≥ 20; Callahan et al., 2016). Samples were then rarefied to 18,300 reads. Subsequently, samples were assigned to taxonomic groups with the Silva database (Silva 138 99% operational taxonomic units from 515F/806R region of sequences, with the QIIME 2 classifier trained on the 515F/806R V4 region of 16S; Bokulich et al., 2018; Robeson et al., 2021). Rarefied samples were used to analyze the measure of microbiome diversity applicable to a single community (alpha-diversity) and the similarity or dissimilarity of communities (beta-diversity). Principal coordinates analysis was performed using both weighted and unweighted unique fraction metric distances (Lozupone and Knight, 2005). The resulting feature table, rooted tree from reconstructed phylogeny, and taxonomy classification were imported from QIIME2 to R v4.1.2 environment for further data analysis using Microbiome v1.16.0 and Phyloseq v1.38.0 R packages (McMurdie and Holmes, 2013), and MDS ordination was applied to beta-diversity chosen metrics using plot ordination function from Phyloseq package in R, and correlations between the microbiome and oral parameters (oral health scores, salivary pH, and VSC) associations were examined using the Spearman correlation method with an adjusted P-value threshold of 0.01. Furthermore, a heatmap was generated using the Microbiome package in R. Subsequently, linear discriminant analysis effect size (LEfSe) was conducted on the QIIME-generated taxonomic tables to identify the genera enriched in the treatment groups and sample sources (Segata et al., 2011). Significance was set at P < 0.05 and the threshold on the logarithmic linear discriminant analysis score for discriminative features was set at 3.5.

Statistical analysis

All tooth scoring data were analyzed using the Mixed Models procedure of SAS (version 9.4; SAS Institute, Cary, NC), with animal being considered a random effect. Halimeter and salivary pH data were analyzed using repeated measures using the Mixed Models procedure of SAS, testing for differences due to treatment, time, and treatment × time interactions. QIIME-generated taxonomic tables were analyzed using the Mixed Models procedure of SAS to identify taxa with significant differences in relative abundance, while accounting for the random effect of the animals and fixed effect of the treatment. Data are reported as LS means ± SEM with statistical significance set at P < 0.05.

Results

The overall consumption of dental chews was 92.50% ± 2.57%, with the acceptance of the whole chews being 91.33% ± 3.07%.

Dental chew hardness and breaking strength

The hardness of the dental chew tested was 75.2 ± 3.4 Newtons. The breaking strength of the dental chew tested was 64.0 ± 7.7 Newtons.

Dental scoring and salivary pH

Dogs consuming the dental chews had lower (P < 0.05) calculus coverage, thickness, and scores, lower (P = 0.001) gingivitis scores, and less (P < 0.01) pocket bleeding than control dogs (Table 1). There was no significant difference in plaque scores between the two treatment groups. Breath VSC were lower (P < 0.01) in dogs consuming the dental chews (Figure 1). However, the consumption of dental chews did not affect salivary pH (Figure 1).

Table 1.

Oral plaque, gingivitis, and calculus scores and gum bleeding of healthy adult dogs consuming a commercially available dry food only (control) or dry food + dental chews (treatment) for 28 d

Item	Control (n = 12)	Treatment (n = 12)	SEM1	P-value
Plaque coverage	2.83	2.70	0.07	0.1877
Plaque thickness	2.15	2.00	0.09	0.2475
Plaque score2	6.13	5.49	0.38	0.2333
Calculus coverage	2.26a	1.73b	0.16	0.0223
Calculus thickness	0.99a	0.90b	0.03	0.0485
Calculus score2	2.26a	1.62b	0.19	0.0203
Gingivitis score	1.39a	1.23b	0.03	0.0010
Pocket bleeding	1.84a	1.37b	0.11	0.0056

¹SEM = pooled standard errors of the mean.

²Plaque and calculus scores ranged from 0 (low) to 12 (maximum).

^a-bMeans with different superscripts within a row differ (P < 0.05).

Figure 1.

Salivary pH (A) and volatile sulfur concentrations (B) of healthy adult dogs consuming a commercially available dry food only (Control) or dry food + dental chews (Treatment) for 28 d.

Oral microbiota populations

Illumina 16S rRNA gene amplicon sequencing produced a total of 2,467,880 sequences, with an average of 51,414 sequences per sample. A total of 2,220,923 reads were retained after quality control measures, with an average of 46,269 reads (range = 1,889 to 71,384) per sample.

Alpha-diversity analysis demonstrated that control dogs had higher bacterial richness than dogs fed dental chews (Figure 2). Specifically, observed OTU, which is determined by the presence or absence of different taxa (richness; P < 0.01) and Faith’s phylogenetic distance (P = 0.01) were higher in controls than dogs fed dental chews when all samples were included in the analyses. The Shannon diversity index that takes into consideration the number of species (richness) and their relative abundance (evenness) demonstrated that SUB plaque samples had greater (P < 0.01) diversity than SUP plaque samples (Figure 2). Moreover, Faith’s phylogenetic distance was found to be higher (P < 0.05) in control dogs than those fed dental chews in SUP samples. Beta-diversity analysis demonstrated that samples clustered based on treatment (control vs. dental chew) and sample source (SUB vs. SUP plaque; Figure 3; P = 0.001).

Figure 2.

Bacterial alpha-diversity measures of subgingival (Sub) and supragingival (Sup) plaque samples were collected from dogs consuming a commercially available dry food only (CON) or dry food + dental chews (TRT) for 28 d. Observed OTUs (A) and Faith’s PD (phylogenetic diversity; D) showed that diversity was higher in CON than in TRT. Shannon diversity index showed that samples from Sub were more diverse than samples from Sup (H). Faith’s PD (F) showed that Sup samples of CON dogs were more diverse than Sup samples of TRT dogs.

Figure 3.

Bacterial beta-diversity measures of subgingival (Sub) and supragingival (Sup) plaque samples collected from dogs consuming a commercially available dry food only (CON) or dry food + dental chews (TRT) for 28 d. Principal coordinates analysis plots of unweighted (A–C) and weighted (D–F) unique fraction metric distances of plaque microbial communities were performed on the operational taxonomic unit abundance matrix. Unweighted and weighted unique fraction metric distances demonstrated that CON samples clustered separately from TRT samples (P = 0.001), Sub samples clustered separately from Supr (P = 0.001), and CONSub, ConSup, TRTSub, and TRTSup all clustered separately from each other (P = 0.001).

The three most abundant bacterial phyla in SUB and SUP plaque samples were Proteobacteria, Bacteroidota, and Firmicutes. The six most abundant genera in SUB plaque samples were Moraxella, Porphyromonas, Aquaspirillum, Fusobacterium, Neisseria, and Corynebacterium, with Moraxella, Porphyromonas, Aquaspirillum, Neisseria, Arcobacter, and Alloprevotella being most abundant in SUP plaque samples.

Compared with controls, dogs fed dental chews had differential relative abundance (P < 0.05) of > 30 bacterial genera in SUB plaque samples (Table 2) and >50 bacterial genera in SUP plaque samples (Table 3). Higher (P < 0.05) relative abundances of Actinobacteriota, Corynebacterium, Euzebyaceae uncultured, Leucobacter, Bergeyella, Proteocatella, Gracilibacteria, Lautropia, Neisseria, Pasteurellaceae unclassified, Pasteurella, Acinetobacter, and Luteimonas have observed in dogs fed dental chews vs. controls in both SUB and SUP samples. In contrast, lower (P < 0.05) relative abundances of Chloroflexi, Spirochaetota, Synergistota, F0058, Rikenellaceae RC9 gut group, Tannerella, Kapabacteriales, Flexilinea, Desulfomicrobium, Desulfovibrio, Roseburia, Fusibacter, Peptostreptococcaceae uncultured, Selenomonas, Treponema, and Fretibacterium were observed in dogs fed dental chews vs. controls in both SUB and SUP samples. When sample sources were compared, it was discovered that the relative abundances of nearly 50 bacterial genera were different (P < 0.05) between SUB and SUP samples (Table 4).

Table 2.

Subgingival plaque bacterial phyla and genera (relative abundance, %) affected by 28 d of dental chew consumption

Phyla	Genus	CON	TRT	SEM	P-value
Actinobacteriota		5.36b	9.04a	0.73	0.0030
	Corynebacterium	1.99b	3.79a	0.56	0.0085
	Euzebyaceae uncultured	0.59b	1.04a	0.11	0.0030
	Leucobacter	0.69b	1.50a	0.21	0.0039
Bacteroidota		23.49	21.93	1.00	0.1655
	F0058	0.58a	0.23b	0.10	0.0285
	Rikenellaceae RC9 gut group	0.42a	0.12b	0.08	0.0145
	Tannerella	0.55a	0.31b	0.10	0.0185
	Bergeyella	1.13b	2.29a	0.24	0.0058
	Kapabacteriales	2.08a	0.76b	0.18	<0.0001
Campilobacterota		5.69	4.51	0.57	0.1241
Chloroflexi		0.16a	0.07b	0.03	0.0282
	Flexilinea	0.16a	0.06b	0.02	0.0075
Desulfobacterota		4.39a	1.48b	0.54	0.0018
	Desulfomicrobium	2.57a	1.03b	0.33	0.0043
	Desulfovibrio	1.27a	0.13b	0.23	0.0040
Firmicutes		17.38	16.65	0.77	0.5160
	Anaerorhabdus furcosa group	0.12b	0.22a	0.03	0.0430
	Defluviitaleaceae UCG-011	0.58b	0.78a	0.07	0.0497
	Roseburia	0.50a	0.25b	0.07	0.0132
	Peptococcus	0.99b	1.62a	0.17	0.0014
	Fusibacter	3.43a	1.94b	0.26	0.0001
	Proteocatella	0.51b	1.07a	0.14	0.0085
	Peptostreptococcaceae uncultured	3.21a	2.01b	0.25	0.0002
	Selenomonas	0.21a	0.08b	0.05	0.0138
Fusobacteriota		4.70b	6.04a	0.64	0.0705
Patescibacteria		2.21	2.51	0.27	0.3960
	Gracilibacteria	0.10b	0.47a	0.07	<0.0001
Proteobacteria		31.99	35.50	2.13	0.0897
	Pelistega	0.14a	0.08b	0.05	0.0195
	Lautropia	0.40b	0.80a	0.11	0.0208
	Conchiformibius	0.40b	0.85a	0.14	0.0277
	Neisseria	3.23b	4.57a	0.44	0.0242
	Cardiobacterium	0.40b	0.74a	0.15	0.0213
	Pasteurellaceae unclassified	1.07b	1.85a	0.33	0.0152
	Pasteurella	0.43b	0.82a	0.11	0.0129
	Acinetobacter	0.31b	0.96a	0.28	0.0247
	Luteimonas	0.32b	0.78a	0.10	0.0075
Spirochaetota		3.52a	1.85b	0.41	0.0035
	Treponema	3.40a	1.76b	0.39	0.0038
Synergistota		1.07a	0.30b	0.17	0.0002
	Fretibacterium	1.05a	0.28b	0.16	0.0002

CON, control diet only; TRT, control diet + dental chew.

^a-bMeans with different superscripts within a row differ (P < 0.05).

Table 3.

Supragingival plaque bacterial phyla and genera (relative abundance, %) affected by 28 d of dental chew consumption

Phyla	Genus	CON	TRT	SEM	P-value
Actinobacteriota		4.24b	9.46a	0.57	<0.0001
	Actinomyces	1.85b	2.94a	0.19	0.0001
	Bifidobacterium	0.02b	0.52a	0.19	0.0018
	Corynebacterium	0.73b	2.89a	0.35	<0.0001
	Euzebyaceae uncultured	0.29b	0.46a	0.04	0.0013
	Leucobacter	1.26b	2.42a	0.35	0.0005
Bacteroidota		24.85	24.27	1.13	0.6160
	Odoribacter	0.07a	0.01b	0.01	0.0055
	MgMjR-022	0.74a	0.08b	0.17	0.0209
	F0058	0.13a	0.03b	0.02	0.0258
	Rikenellaceae RC9 gut group	0.22a	0.04b	0.03	0.0002
	Tannerella	0.47a	0.18b	0.04	<0.0001
	Capnocytophaga	0.76b	1.79a	0.16	0.0002
	Flavobacterium	0.39b	1.76a	0.37	0.0007
	Bergeyella	1.77b	3.99a	0.37	0.0003
	Kapabacteriales	2.02a	0.79b	0.22	0.0013
Campilobacterota		7.03a	3.83b	0.51	0.0002
	Arcobacter	4.02a	1.87b	0.45	0.0027
	Wolinella	1.94a	1.08b	0.23	0.0132
Chloroflexi		0.25a	0.06b	0.03	<0.0001
	Flexilinea	0.25a	0.06b	0.03	<0.0001
Desulfobacterota		3.95	0.97	0.33	<0.0001
	Desulfomicrobium	1.94a	0.51b	0.17	<0.0001
	Desulfoplanes	0.11a	0.01b	0.02	0.0399
	Desulfovibrio	1.53a	0.26b	0.20	0.0004
Firmicutes		13.88a	11.05b	0.58	0.0006
	Anaerorhabdus furcosa group	0.08b	0.13a	0.02	0.0219
	Trichococcus	0.89b	1.30a	0.23	0.0496
	Streptococcus	0.28b	1.09a	0.38	0.0405
	Gemella	0.05b	0.12a	0.02	0.0097
	Christensenellaceae R-7 group	0.37a	0.21b	0.05	0.0058
	Roseburia	0.32a	0.09b	0.02	<0.0001
	Colidextribacter	0.26a	0.03b	0.03	<0.0001
	Amnipila	0.33a	0.16b	0.04	0.0065
	Fusibacter	2.86a	1.26b	0.17	<0.0001
	Filifactor	0.44a	0.22b	0.06	0.0050
	Proteocatella	0.31b	0.43a	0.04	0.0206
	Peptostreptococcaceae uncultured	2.89a	1.40b	0.181	<0.0001
	Helcococcus	0.42a	0.14b	0.07	0.0096
	Selenomonas	0.20a	0.06b	0.02	0.0003
Fusobacteriota		2.40	2.44	0.33	0.8628
	Oceanivirga	0.04b	0.18a	0.05	0.0105
Patescibacteria		2.72	3.79	0.43	0.0994
	Gracilibacteria	0.19b	1.00a	0.21	<0.0001
Proteobacteria		37.12b	43.12a	1.27	0.0059
	Aquaspirillum	7.15a	4.18b	0.84	0.0166
	Lautropia	0.80b	1.40a	0.16	0.0035
	Brachymonas	0.53a	0.25b	0.06	0.0009
	Variovorax	0.48b	1.11a	0.18	0.0007
	Neisseria	2.83b	5.35a	0.44	0.0004
	Cardiobacteriaceae unclassified	0.89b	1.23a	0.15	0.0169
	Pasteurellaceae unclassified	0.47b	1.93a	0.24	<0.0001
	Frederiksenia	0.31b	0.88a	0.13	0.0102
	Haemophilus	0.06b	0.25a	0.03	<0.0001
	Pasteurella	0.39b	1.37a	0.16	<0.0001
	Acinetobacter	0.05b	0.09a	0.03	0.0302
	Arenimonas	0.24b	0.70a	0.13	0.0043
	Luteimonas	0.72b	1.71a	0.15	0.0001
Spirochaetota		2.45a	0.84b	0.18	<0.0001
	Treponema	2.33a	0.77b	0.17	<0.0001
Synergistota		1.08a	0.17b	0.08	<0.0001
	Fretibacterium	1.04a	0.16b	0.08	<0.0001

CON, control diet only; TRT, control diet + dental chew.

^a-bMeans with different superscripts within a row differ (P < 0.05).

Table 4.

Bacterial phyla and genera (relative abundance, %) that were different between subgingival and supragingival plaque samples after 28 d of dental chew consumption

Phyla	Genus	Sub	Supr	SEM	P-value
Actinobacteriota		7.20	6.85	0.65	0.4585
	Bifidobacterium	0.07b	0.27a	0.11	0.0075
	Corynebacterium	2.89a	1.81b	0.42	0.0063
	Euzebyaceae uncultured	0.82a	0.37b	0.07	<0.0001
	Leucobacter	1.09b	1.84a	0.26	0.0034
Bacteroidota		22.71b	24.56a	0.90	0.0294
	Bacteroides	1.02b	1.83a	0.20	0.0002
	Odoribacter	0.18a	0.04b	0.03	0.0016
	F0058	0.41a	0.08b	0.06	<0.0001
	Alloprevotella	2.13b	2.91a	0.25	0.0003
	Capnocytophaga	0.72b	1.28a	0.14	0.0021
	Flavobacterium	0.52b	1.07a	0.25	0.0263
	Bergeyella	1.71b	2.88a	0.29	0.0057
	Lentimicrobium	0.24a	0.04b	0.04	<0.0001
Campilobacterota		5.10	5.43	0.48	0.5839
	Campylobacter	1.58a	0.92b	0.11	<0.0001
Chloroflexi		0.12	0.16	0.03	0.4206
Desulfobacterota		2.94	2.46	0.43	0.6353
	Desulfoplanes	0.30a	0.06b	0.06	0.0012
	Acholeplasma	0.28a	0.19b	0.03	0.0130
Firmicutes		17.02a	12.47b	0.56	<0.0001
	Anaerorhabdus furcosa_group	0.17a	0.11b	0.02	0.0208
	Gemella	0.13a	0.09b	0.02	0.0330
	Clostridia UCG-014	0.13a	0.09b	0.03	0.0176
	Roseburia	0.37a	0.20b	0.04	0.0208
	Peptococcus	1.30a	0.37b	0.11	<0.0001
	Fusibacter	2.68a	2.06b	0.22	0.0348
	Peptostreptococcaceae uncultured	0.20a	0.07b	0.04	0.0008
	Filifactor	0.68a	0.33b	0.09	0.0030
	Peptostreptococcus	0.32a	0.11b	0.07	0.0034
	Proteocatella	0.79a	0.37b	0.08	<0.0001
	Parvimonas	0.80a	0.18b	0.07	<0.0001
	Peptostreptococcales-Tissierellales uncultured	0.28a	0.11b	0.03	<0.0001
Fusobacteriota		5.37a	2.42b	0.40	<0.0001
	Fusobacterium	4.26a	2.16b	0.37	<0.0001
	Leptotrichia	0.73a	0.11b	0.11	<0.0001
	Leptotrichiaceae uncultured	0.20a	0.01b	0.06	0.0017
	Absconditabacteriales (SR1)	1.64b	2.15a	0.20	0.0151
Patescibacteria		2.36b	3.25a	0.26	0.0252
	Gracilibacteria	0.29b	0.60a	0.13	0.0392
Proteobacteria		33.75b	40.12a	1.40	0.0008
	Aquaspirillum	4.36b	5.66a	0.77	0.0393
	Lautropia	0.60b	1.10a	0.11	0.0020
	Comamonadaceae unclassified	0.91b	1.44a	0.22	0.0028
	Corticibacter	0.89b	1.96a	0.16	<0.0001
	Variovorax	0.32b	0.80a	0.12	0.0003
	Conchiformibius	0.63a	0.18b	0.09	<0.0001
	Cardiobacterium	0.57a	0.02b	0.08	<0.0001
	Escherichia-Shigella	0.19a	0.01b	0.02	<0.0001
	Haemophilus	0.40a	0.16b	0.06	0.0003
	Acinetobacter	0.63a	0.07b	0.16	<0.0001
	Moraxella	15.43b	18.22a	0.91	0.0025
	Arenimonas	0.24b	0.47a	0.12	0.0054
	Luteimonas	0.55b	1.21a	0.12	0.0001
Spirochaetota		2.68a	1.64b	0.28	0.0098
	Treponema	2.58a	1.55b	0.27	0.0087
Synergistota		0.69	0.62	0.13	0.9179

Sub, subgingival plaque; Sup, supragingival plaque.

LeFSe analysis was employed to determine the genera that were enriched in dogs fed dental chews vs. controls, and enriched in SUB plaque vs. SUP plaque samples (Figure 4). When both SUB and SUP samples were included, LeFSe analysis revealed that Pasteurella, Luteimonas, Actinomyces, Flavobacterium, Pasteurellaceae unclassified, Leucobacter, Bergeyella, Neisseria, Corynebacterium were enriched in dogs fed dental chews (P < 0.05), and Wolinella, Fretibacterium, Desulfovibrio, Kapabacteriales, Peptostreptococcaceae uncultured, Desulfomicrobium, Fusibacter, Arcobacter, Treponema, and Aquaspirillum were enriched in controls [linear discriminant analysis (LDA) score ≥ 3.5; P < 0.05]. When comparing sample sources, LeFSe analysis revealed that Campylobacter, Parvimonas, Peptococcus, Corynebacterium, Treponema, and Fusobacterium were enriched in SUB samples, while Leucobacter, Alloprevotella, Bacteroides, Corticibacter, Bergeyella, and Moraxella were enriched in SUP samples (LDA ≥ 3.5; P < 0.05).

Figure 4.

Linear discriminant analysis effect size (LEfSe) analysis of dogs consuming a commercially available dry food only (Control) or dry food + dental chews (Treatment) for 28 d. (A) LEfSe identified bacterial genera that were enriched in control and treatment groups (all sample types combined). (B) LEfSe analysis identified bacterial genera enriched in subgingival and supragingival plaque samples. Linear discriminant analysis (LDA) score ≥ 3.5.

Correlation analysis was employed to determine the bacterial genera and phyla that were correlated with oral parameters (Figure 5). Approximately 25 bacterial taxa were positively correlated (P < 0.05) and approximately 13 bacterial taxa were negatively correlated (P < 0.05) with one or more oral health scores (plaque, calculus, or gingivitis score). Dielma and Acholeplasma were positively correlated (P < 0.05) with salivary pH. Lastly, Amnipila and JGI 0000069-P22 were positively correlated (P < 0.05) with VSC, while Frederiksenia was negatively correlated with VSC.

Figure 5.

Heatmap of significant correlation values (r) between oral microbial taxa and oral parameters. Significant correlations (adj. P < 0.05) are indicated by ‘ + ’.

Discussion

Halitosis occurs as a result of anaerobic gram-negative bacteria metabolizing and producing VSC in the oral cavity. Research conducted in humans has identified specific organisms residing in the coating on the tongue and periodontal pockets that are responsible for VSC production. Notably, bacteria including the gram-negative Campylobacter, Fusobacterium, Treponema, Porphyromonas, Tannerella, Prevotella, Aggregatibacter, Actinobacillus, and Bacteroides, and gram-positive Peptostreptococcus, Gemella, and Eubacterium have been linked to the severity of mouth odor (Persson et al., 1990; de Boever and Loesche, 1995; Awano et al., 2002; Krespi et al., 2006; Allaker, 2010; Salako and Philip, 2011; Aylikci and Çolak, 2013; Ruparell et al., 2020). Both human and canine studies have demonstrated that VSC produced by bacteria can have detrimental effects on oral tissues and contribute to the development of periodontitis (Allaker, 2010; Milella, 2015). Additionally, many pet owners express concern regarding oral malodor in their pets. Therefore, understanding if and how the daily use of dental chews may play a role in the prevention of oral malodor and periodontal diseases is of interest.

A recent dog study investigated the effects of various interventions on oral health outcomes and breath VSC concentrations (Croft et al., 2022). The interventions included two different dental chews, tooth brushing, or no intervention (controls). The researchers observed that the dominant bacteria linked with greater VSC concentrations were Fusobacterium and Porphyromonas. Furthermore, all interventions led to a significant reduction in both the total bacterial load and VSC-producing bacterial load in plaque when compared with the control group (Croft et al., 2022). Those data agreed with previous studies showing that dental chew consumption slows the buildup of plaque as well as oral VSC concentrations (Quest, 2013; Jeusette et al., 2016; Mateo et al., 2020).

In another study aimed at assessing the benefits of daily dental chew consumption, chews with varying hardness levels (140.5, 61.2, and 58.5 Newtons) and shapes were tested (Carroll et al., 2020). The findings of that study demonstrated that all dental chews reduced VSC concentrations and calculus coverage compared with the control group (no chews). Additionally, two out of the three groups consuming dental chews exhibited lower plaque coverage and thickness compared with controls. Those results were similar to the current study that demonstrated that regular dental chew (hardness level of 75.2 Newtons) consumption resulted in lower oral VSC concentrations, gingivitis scores, pocket bleeding, and calculus coverage and thickness. The plaque coverage, thickness, and scores; however, were not affected by regular dental chew consumption in the present study. These data provide further evidence of the positive impact of dental chews in preventing malodor in dogs.

As a follow-up to the oral health measured by Carroll et al. (2020), the oral microbiota from that study were analyzed and reported (Oba et al., 2021). In that study, control dogs had higher relative abundances of five potentially pathogenic bacteria (Porphyromonas, Anaerovorax, Desulfomicrobium, Tannerella, and Treponema) and lower relative abundances of seven genera associated with oral health (Neisseria, Corynebacterium, Capnocytophaga, Actinomyces, Lautropia, Bergeyella, and Moraxella) when compared with dogs fed chews. Furthermore, 13 microbial taxa were negatively correlated with one or more oral health parameters (Bergeyella, Neisseria, Capnocytophaga, Pasteurella, Moraxella, Actinobacteria, TM7, Leucobacter, Actinomyces, Lautropia, Euzebya, GN02, and p-75-a5). In contrast, 19 microbial taxa were positively correlated with one or more oral health parameters (Campylobacter, Filifactor, Propionivibrio, Wolinella, Helcococcus, Firmicutes, Treponema, Tannerella, Desulfomicrobium, Fusibacter, Spirochaetes, Enhydrobacter, Arcobacter, Anaerovorax, Desulfovibrio, Synergistetes, Chlorobi, Peptostreptococcus, and Porphyromonas). Also, salivary pH was negatively correlated with three microbial taxa (GN02, SR1, and Wolinella), but positively correlated with two microbial taxa (Proteocatella and Streptococcus; Oba et al., 2021).

Another dog study demonstrated that dental chews have a positive influence on SUP plaque via changes in microbiota and that bacteria may be employed as biomarkers of dental health rather than solely relying on the quantification of plaque and calculus (Ruparell et al., 2020). Chew intervention resulted in a significant increase in six health-associated (Klebsiella, Propionibacterium, Catonella, Corynebacterium, Prevotella, and TM7) and three disease-associated (Parvimonas, Actinomyces, and Treponema) bacterial taxa compared with dogs not receiving chews. In contrast, eight disease-associated (Fretibacterium, Helcococcus, Clostridium, Desulfomicrobium, Anaerovorax, Bacterodia bacterium, Neisseria, and Pelistega) and one health-associated (Desulfovibrio) bacterial taxa were increased in dogs receiving no chews compared with the chew group (Ruparell et al., 2020). Collectively, the findings of the current study and those of previous studies suggest that Actinomyces, Bergeyella, Lautropia, Moraxella, Corynebacterium, Prevotella, and Neisseria could possibly serve as indicators of oral health, while Arcobacter, Desulfomicrobium, Desulfovibrio, Fretibacterium, Fusibacter, Helcococcus, Tannerella, Pelistega, and Treponema may indicate poor oral health.

Sanguansermsri et al. (2017) showed that Frederiksenia were more prevalent in healthy dogs than in dogs with periodontitis. In children <6 yr old, JGI 0000069-P22 were abundant in plaque samples of those with caries (Crielaard et al., 2011). In the current study, it was observed that dogs consuming dental chews had a higher relative abundance of Frederiksenia than controls. Additionally, the relative abundance of Amnipila was lower in dogs fed dental chews than in controls. The collection of those studies and the present study suggest that Frederiksenia is beneficial, exhibiting a negative correlation with VSC concentrations and higher prevalence in healthy dogs and those consuming dental chews. Conversely, Amnipila and JGI 0000069-P22 appear to be associated with periodontal diseases based on their positive correlations with VSC concentrations, the positive correlation between Amnipila and poor oral health scores, and greater abundance in children with caries.

In conclusion, the findings of the current study provide further evidence that regular dental chew consumption aids in preventing malodor and improving oral health outcomes (reduced calculus, gingivitis, and bleeding) in dogs. Compared with controls, dogs fed dental chews also had higher relative abundances of six genera associated with oral health (Corynebacterium, Leucobacter, Bergeyella, Lautropia, Neisseria, and Pasteurella), and had lower relative abundances of five genera associated with poor oral health (Tannerella, Desulfomicrobium, Desulfovibrio, Fusibacter, and Treponema) in both SUB and SUP samples. Furthermore, this study identified several bacterial taxa positively or negatively correlated with various oral health parameters. Actinomyces, Bergeyella, Lautropia, Moraxella, Corynebacterium, Frederiksenia, Prevotella, and Neisseria may serve as indicators of oral health, while Arcobacter, Amnipila, Desulfomicrobium, Desulfovibrio, Fretibacterium, Fusibacter, Helcococcus, Spirochaetota, Synergistota, Tannerella, Pelistega, Treponema, and JGI 0000069-P22 may be indicative of poor oral health. The microbiota shifts observed with dental chew consumption suggest promising long-term advantages for oral health because they coincided with decreased calculus coverage/thickness, calculus scores, gingivitis scores, and pocket bleeding. Even though extreme periodontal disease does not develop in just 28 d, these data suggest that dental chew consumption would help mitigate oral health issues over a longer period of time. Overall, these findings highlight the potential of dental chews as an effective preventive measure for maintaining oral health in dogs and provide insights into bacterial taxa that may serve as biomarkers of oral health status.

Glossary

Abbreviations

BC

bones and chews dental chew treatment

BOP

bleeding on probing

CT

control treatment

DL

Dr. Lyon’s dental chew treatment

GR

greenies dental chew treatment

LEfSe

linear discriminant analysis effect size

SUB

subgingival

SUP

supragingival

VSC

volatile sulfur compounds

Conflicts of Interest Statement

A.S. is employed by PetSmart. All other authors have no conflicts of interest.