• View in gallery
    Figure 1

    Percent relative abundance of top phyla for all horses, horses that did not receive sweet feed, and horses that did receive sweet feed.

  • View in gallery
    Figure 2

    Diversity indices for groups based on type of hay fed. Pairwise comparison showed a difference between grass hay and alfalfa + grass hay groups for Taxa_S (P = .001) (A), Shannon_H (P = .002) (B), and Chao-1 (P < .001) (C). Different superscripts (a, b) indicate significant differences among groups.

  • View in gallery
    Figure 3

    Heatmap showing the proportions of the top 50 annotated sequence variants (ASVs) with samples grouped by sweet feed inclusion in the horse’s diet.

  • View in gallery
    Figure 4

    Principal coordinate analysis (PCoA) using Bray-Curtis (A) and Jaccard (B) indices of samples grouped by sweet feed inclusion in the horse’s diet.

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Dietary and management factors influence the equine gastric microbiome

Linda J. PaulLouisiana State University, Veterinary Clinical Sciences, Equine Health Studies Program, Baton Rouge, LA

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Aaron C. EricssonUniversity of Missouri, Metagenomics Center, Equine Gut Group, Columbia, MO

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Frank M. AndrewsLouisiana State University, Veterinary Clinical Sciences, Equine Health Studies Program, Baton Rouge, LA

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Zachary McAdamsUniversity of Missouri, Metagenomics Center, Equine Gut Group, Columbia, MO

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Michael L. KeowenLouisiana State University, Veterinary Clinical Sciences, Equine Health Studies Program, Baton Rouge, LA

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Michael P. St BlancLouisiana State University, Veterinary Clinical Sciences, Equine Health Studies Program, Baton Rouge, LA

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Heidi E. BanseLouisiana State University, Veterinary Clinical Sciences, Equine Health Studies Program, Baton Rouge, LA

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Abstract

OBJECTIVE

The purpose of this study was to characterize the relationship of diet and management factors with the glandular gastric mucosal microbiome. We hypothesize that the gastric mucosal microbial community is influenced by diet and management factors. Our specific objective is to characterize the gastric mucosal microbiome in relation to these factors.

ANIMALS

57 client-owned horses in the southern Louisiana region with and without equine glandular gastric disease.

PROCEDURES

Diet and management data were collected via a questionnaire. Gastroscopy was used for evaluation of equine gastric ulcer syndrome and collection of glandular mucosal pinch biopsies. 16S rRNA amplicon sequencing was used for microbiome analysis. Similarity and diversity indices and sequence read counts of individual taxa were compared between diet and management factors.

RESULTS

Differences were detected in association with offering hay, type of hay, sweet feed, turnout, and stalling. Offering hay and stalling showed differences in similarity indices, whereas hay type, sweet feed, and turnout showed differences in similarity and diversity indices. Offering hay, hay type, and sweet feed were also associated with differences in individual sequence read counts.

CLINICAL RELEVANCE

This study provides preliminary characterization of the complex relationship between the glandular gastric microbiome and diet/management factors. The ideal microbiome to promote a healthy glandular gastric environment remains unknown.

Abstract

OBJECTIVE

The purpose of this study was to characterize the relationship of diet and management factors with the glandular gastric mucosal microbiome. We hypothesize that the gastric mucosal microbial community is influenced by diet and management factors. Our specific objective is to characterize the gastric mucosal microbiome in relation to these factors.

ANIMALS

57 client-owned horses in the southern Louisiana region with and without equine glandular gastric disease.

PROCEDURES

Diet and management data were collected via a questionnaire. Gastroscopy was used for evaluation of equine gastric ulcer syndrome and collection of glandular mucosal pinch biopsies. 16S rRNA amplicon sequencing was used for microbiome analysis. Similarity and diversity indices and sequence read counts of individual taxa were compared between diet and management factors.

RESULTS

Differences were detected in association with offering hay, type of hay, sweet feed, turnout, and stalling. Offering hay and stalling showed differences in similarity indices, whereas hay type, sweet feed, and turnout showed differences in similarity and diversity indices. Offering hay, hay type, and sweet feed were also associated with differences in individual sequence read counts.

CLINICAL RELEVANCE

This study provides preliminary characterization of the complex relationship between the glandular gastric microbiome and diet/management factors. The ideal microbiome to promote a healthy glandular gastric environment remains unknown.

Investigation of factors contributing to gastric disease is paramount to optimizing equine gastric health. Of diseases impacting the equine stomach, equine gastric ulcer syndrome (EGUS) is the most common. Equine gastric ulcer syndrome is divided into two separate disease processes based on the anatomic region affected: equine squamous gastric disease (ESGD) or equine glandular gastric disease (EGGD). The EGGD prevalence data reported for breeds, disciplines, and competition status varies but can be as high as 70%.14 Risk factors identified for EGGD include the following: greater than 5 to 6 days of exercise per week, actively competing, higher number of handlers, and racing below expectations.1,5,6 Despite recognition of risk factors associated with EGGD, there remains limited information regarding potential underlying pathophysiologic mechanisms.

In people, the most common causes of gastric disease are Helicobacter pylori infection and nonsteroidal anti-inflammatory drugs (NSAIDs),7 but investigations of these etiologies in horses have shown no associations.810 There is also evidence of gastric microbiome changes associated with gastritis in the absence of H pylori.11,12 Gastrointestinal syndrome (GIS), which includes any gastrointestinal disturbance or dysfunction associated with exercise, is a common occurrence in both elite and recreational athletes.13 Many athletes develop dietary practices in attempts to decrease symptoms; however, the evidence basis of these decisions is lacking.14 The influences of diet and exercise practices on the gastric microbiome have been investigated to help understand these relationships, but varying results indicate the immense complexity of factors involved.1518

A recent study19 from our lab investigated changes in the microbiome associated with EGGD in horses under similar diet and management. Previous studies8,9,20 in small numbers of horses have demonstrated that alterations in management or diet impact the gastric microbiome. Furthermore, changes to the gastrointestinal (gastric or fecal) microbiome have been reported in association with type and duration of colic,21 withholding of feed,22 and management factors such as housing, bedding type, volume of water intake, and frequency of concentrated feed and roughage.8,9 For equine athletes, the knowledge of the interplay of management factors, the microbiome, and gastrointestinal health remains limited. Improved knowledge in this area could lead to strategies that improve gastric health through manipulation of the microbiome or mitigation of risk factors.

The objective of this study was to further characterize the relationship between the glandular gastric mucosal microbiome and diet or management factors by examining a population of horses across different barns with varying management strategies and dietary protocols in horses both with and without EGGD. We hypothesize that there are differences in the gastric mucosal microbial community associated with diet and management factors.

Materials and Methods

These data were collected as part of a case-control study in horses with naturally occurring EGGD. The study protocol was approved by the Louisiana State University Institutional Animal Care and Use Committee (IACUC 19-071). Client-owned horses involved in athletic performance were recruited from the southern Louisiana region. Owner consent was obtained, and a questionnaire was filled out by the owner or farm manager for each horse (Supplementary Table S1). After an overnight fast, horses underwent sedation for gastroscopic evaluation with a 3-m endoscope. Presence of EGGD was recorded using a semiquantitative scale.23 Only horses with no evidence of glandular disease (EGGD score = 0) or horses with glandular mucosal disruption (EGGD score ≥ 2) were enrolled. Horses with hyperemia as the only glandular abnormality (EGGD score = 1) were excluded. Pinch biopsy forceps (2.3-mm oval cupped with spike) were used to collect mucosal biopsies of endoscopically normal (median = 3; range, 1 to 4) and abnormal glandular mucosa (median = 2; range, 1 to 3) in horses with and without EGGD. Biopsies of normal mucosa were collected from the pylorus. All horses with areas of abnormal mucosa had lesions within the pylorus/antrum (n = 31). Three horses also had lesions in the glandular fundus. Biopsies were rinsed with sterile saline and then flash frozen in liquid nitrogen and stored at –80°C until processing. The biopsy channel and biopsy forceps were disinfected between horses with an accelerated hydrogen peroxide product to prevent microbial cross contamination. Sterile water was used to rinse these instruments after disinfection.

DNA was extracted using a commercial kit from gastric mucosal biopsies, according to the manufacturer’s recommendations, with minor adaptations as previously described.20 The 16S rRNA amplicon library construction and sequencing were performed at the University of Missouri DNA Core facility. Concentrations of DNA for each sample were determined fluorometrically using quant-iT BR dsDNA reagent kits, and all samples were normalized to a standard concentration for PCR amplification. Bacterial 16S rRNA amplicons were generated via amplification of the V4 hypervariable region of the 16S rRNA gene using single-indexed universal primers (U515F/806R) flanked by Illumina standard adapter sequences and the following parameters: 98°C(3:00) + [98°C(0:15) + 50°C(0:30) + 72°C(0:30)] X 25 or 40 cycles + 72°C(7:00). Amplicons were then pooled for sequencing using the Illumina MiSeq platform and V2 chemistry with 2 X 250-bp paired-end reads, as previously described.24

All informatics analysis was performed at the University of Missouri Informatics Research Core Facility. Primers designed to match the 5′-ends of forward and reverse reads were removed from the forward read using Cutadapt.25 Reverse complements of the primer to any reverse reads present were removed from the forward read. For reverse reads, a similar, but opposite, approach was performed. Read pairs were rejected if either did not match a 5′-primer with an allowable error rate of 0.1. To denoise, dereplicate, and count amplicon sequence variants (ASVs), the QIIME226 DADA2 plug­in27 (version 1.10.0) was used with R version 3.5.1 and Biom version 2.1.7. The Silva.v13228 database was used to assign final taxonomies.

Statistical analysis

Breed was dichotomized to Thoroughbred (TB) and non-Thoroughbred (non-TB) for statistical analysis. For ESGD comparisons, only horses with a score of 0 or ≥ 2 were included. Continuous data (age, years showing, time at current barn, minutes of exercise per week) were assessed for normality using a Shapiro Wilk test. Continuous factors were dichotomized at the median value for comparison of similarity indices.

Biopsy samples returning > 5,000 ASV read counts were included in microbiome comparisons. Repetitive biopsy samples meeting inclusion criteria were averaged for each horse. Overall compositional differences between subjects associated with management factors were assessed using similarity (β-diversity) indices of ASV data rarefied to 5,000 reads. One-way permutational multivariate analysis of variance (PERMANOVA) was used with 9,999 permutations and Bonferroni correction of pairwise comparisons.29 Jaccard (J) and Bray-Curtis (BC) similarity indices were used to assess for compositional differences based on what taxa are present (unweighted) as well as for the effect of taxa present in combination with the abundance of those taxa (weighted).29 Principal coordinate analysis (PCoA) was performed on ¼ root-transformed rarefied data.29 The α-diversity indices (diversity within subjects) Taxa_S (TS), Shannon_H (SH), and Chao-1 (C1) of ASVs were calculated,29 and values were compared among groups via t test, ANOVA, Mann-Whitney U, or Kruskal-Wallis. Significance was set at a P value of ≤ .05.

Management factors with a PERMANOVA P ≤ .05 for either similarity index underwent further analysis. Comparison of individual ASV read counts were compared across these management factor groups via Kruskal-Wallis or Mann-Whitney U followed by Benjamini Hochberg procedure for a 5% false discovery rate.

Initial analysis showed differences between racehorses and show horses, and signalment data (age, breed, and sex) and management factors were compared between discipline categories (racing or show) with Fisher’s Exact, Mann-Whitney U, or Pearson’s chi-square test. Because the show discipline included multiple breeds, comparisons were made between Thoroughbred (n = 18), Warmblood (12), and other (3; including Paint [2] and Connemara pony [1]).

Results

A total of 57 horses from ten barns were enrolled in this study (EGGD ≥ 2, n = 32; EGGD 0, 25). For questionnaire data, horses were only included in the analysis of that factor if an answer was provided (Table 1). The median age of subjects was 5 years (range, 1 to 20; n = 53). There were 33 males and 21 females (n = 54) and 37 TB and 17 non-TB (54). The median number of years competing was 2.5 years (range, 0 to 10; n = 32). The median length of time at the subject’s current barn was 1 year (range, 0 to 11; n = 52).

Table 1

Features of the 57 horses enrolled in this study.

Factor All responses Racehorses Show horses P value (race vs show)
Age (years) < .001*
   Median 5 2 10.5
   Range 1–20 1–6 4–20
    n = 53 n = 21 n = 32
Gender .105
   Male 33 10 23
   Female 21 11 10
n = 54 n = 21 n = 33
Breed .007*
   TB 37 19 18
   Non-TB 17 2 15
n = 54 n = 21 n = 33
Years competing .267
   Median 2.5 2 3.5
   Range 0–10 1–4 0–10
n = 32 n = 10 n = 22
Years at current barn .382
   Median 1 1 1.75
   Range 0–11 0–4 0–11
n = 52 n = 19 n = 33
Exercise frequency (dpw) < .001*
   Median 5 6 4
   Range 1–7 2–7 1–5
   < 5 dpw n = 21 n = 3 n = 18
   ≥ 5 dpw n = 25 n = 14 n = 11
Exercise exposure (mpw) .164
   Median 180 180 180
   Range 20–420 20–420 38–300
   < 180 mpw n = 18 n = 7 n = 11
   ≥ 180 mpw n = 28 n = 10 n = 18
Ulcer suspicion .005*
   Yes 22 3 19
   No 25 14 11
n = 47 n = 17 n = 30
Hay fed .071
   Yes 46 20 26
   No 6 0 6
n = 52 n = 20 n = 32
Type of hay fed < .001*
   Grass hay 21 0 21
   Alfalfa hay 12 10 2
   Both 13 10 3
n = 46 n = 20 n = 26
Sweet feed in diet < .001*
   Yes 23 21 2
   No 30 0 30
n = 53 n = 21 n = 32
Ulcer prevention 1.000
   Yes 9 3 6
   No 40 15 25
n = 49 n = 18 n = 31
Other supplements or medications .231
   Yes 8 1 7
   No 42 17 25
n = 50 n = 18 n = 32
Stall .722
   Yes 42 17 25
   No 10 3 7
n = 52 n = 20 n = 32
Turnout < .001*
   Yes 34 8 26
   No 10 10 0
n = 44 n = 18 n = 26
*

Significant difference between racehorses and show horses (P ≤ .05).

The median exercise frequency in days per week (dpw) was 5 (range, 1 to 7; n = 46) and the median exercise exposure time in minutes per week (mpw) was 180 (range, 20 to 420; 46). These variables were dichotomized at the median for analysis of association with the gastric microbiome. There were 22 respondents that indicated a suspicion of ulcers in the subject (no suspicion, n = 25; total responses, 47). Forty-six horses were fed hay (no hay, n = 6); of these, 21 received only grass hay, 12 only alfalfa hay, and 13 received both hay types (total responses, 46). Grass hay types for the grass hay only group included Bermuda (n = 9), Bahia and Bermuda (3), coastal (6), Alicia (1), round bale (1), and nonspecified (1). Grass hay types for horses receiving alfalfa and grass hay included Bermuda (n = 3), coastal (3), Alicia (3), Alicia and Bahia (1), timothy (2), and nonspecified (1). There was only one horse that did not receive any grain or pelleted feed (grain or pelleted feed fed, n = 53; total responses, 54). Of the horses that received grain or pelleted feed, 30 received a commercial pelleted feed, 17 sweet feed, 5 a combination of oats and sweet feed, and 1 pelleted feed and sweet feed (total responses, n = 53). Due to small sample sizes, for further analysis of grain diet, this factor was dichotomized to sweet feed, yes (n = 23) or no (30).

Current housing was stall (n = 36), pasture, (7), dry lot/paddock (3), stall and pasture (5), and stall and pasture and dry lot/paddock (1). Due to the small sample size, this factor was dichotomized to stall, yes (n = 42) or no (10). Turnout was part of the management routine for 34 horses and 10 horses had no turnout (total responses, n = 44). Nine horses received some type of ulcer prevention such as an omeprazole product (n = 3) or other commercial gastric supplement (6) (no ulcer prevention, 40; total responses, 49). Other supplements or medications such as joint and muscle supplements were received by 8 horses (no other supplements or medications, n = 42; total responses, 50). Due to variability in the type of gastric ulcer prevention, supplements, and medications, these questions were excluded from microbiome comparison.

Microbial community features

There were 6,236 ASVs detected in the mucosal biopsies meeting inclusion criteria. When the data were stratified to taxonomic levels, there were 39 phyla, 1,328 genera, and 2,475 species identified. The dominant phyla were Proteobacteria (39.2%), Firmicutes (29.1%), and Actinobacteriota (21.5%), and Bacteroidota (6.7%) (Figure 1). The dominant genera were Actinobacillus (18.3%), Cutibacterium (7.4%), Staphylococcus (7.4%), and Streptococcus (5.2%).

Figure 1
Figure 1

Percent relative abundance of top phyla for all horses, horses that did not receive sweet feed, and horses that did receive sweet feed.

Citation: Journal of the American Veterinary Medical Association 260, S3; 10.2460/javma.22.07.0277

There were differences in community structure detected for the factors of hay included in the diet (yes or no), the type of hay offered (grass hay, alfalfa hay, or both), sweet feed included in the diet (yes or no), if the horse received turnout (yes or no), and if the horse was stalled (yes or no).

For hay included in the diet, similarity indices differed (BC, P = .005, F = 2.16; J, P = .002, F = 1.48), but the diversity indices showed no differences (TS, P = .900; SH, P = .905; C1, P = .654). When assessing community structure in horses that received hay by the type of hay offered (grass hay, n = 21; alfalfa hay, 12; both, 13), similarity and diversity indices differed (BC, P = .001, F = 2.05; J, P < .001, F = 1.97; TS, P < .001; SH, P = .003; C1, P = .001). Horses receiving only grass hay exhibited the highest diversity (TS, median = 610, range, 298 to 1,182; SH, mean ± SD = 4.17 ± 0.56; C1, mean ± SD = 648.38 ± 235.73) followed by horses receiving only alfalfa hay (TS, median = 430.5, range, 160 to 856; SH, mean ± SD = 3.75 ± 0.73; C1, mean ± SD = 470.85 ± 228.77), and horses receiving both (TS, median = 289, range, 154 to 508; SH, mean ± SD = 3.38 ± 0.58; C1, mean ± SD = 337.75 ± 148.02). Pairwise comparison showed a difference between grass hay and alfalfa plus grass hay groups for all indices (TS, P = .001; SH, P = .002; C1, P < .001) (Figure 2).

Figure 2
Figure 2

Diversity indices for groups based on type of hay fed. Pairwise comparison showed a difference between grass hay and alfalfa + grass hay groups for Taxa_S (P = .001) (A), Shannon_H (P = .002) (B), and Chao-1 (P < .001) (C). Different superscripts (a, b) indicate significant differences among groups.

Citation: Journal of the American Veterinary Medical Association 260, S3; 10.2460/javma.22.07.0277

Horses that received sweet feed compared to horses that received other pelleted feed exhibited differences in similarity and diversity indices (BC, P < .001, F = 2.77; J, P < .001, F = 2.61; TS, P < .001; SH, P < .001; C1, P < .001). Horses that did not receive sweet feed exhibited higher diversity (TS, median = 524, range, 224 to 1,182; SH, mean ± SD = 4.16 ± 0.54; C1, mean ± SD = 606.18 ± 226.02) than horses that received sweet feed (TS, median = 327, range, 154 to 683; SH, mean ± SD = 3.5 ± 0.66; C1, mean ± SD = 386.72 ± 188.28). Principal coordinate analysis (PCoA) and a heatmap with samples grouped by sweet feed offered in the diet are presented (Figures 3 and 4).

Figure 3
Figure 3

Heatmap showing the proportions of the top 50 annotated sequence variants (ASVs) with samples grouped by sweet feed inclusion in the horse’s diet.

Citation: Journal of the American Veterinary Medical Association 260, S3; 10.2460/javma.22.07.0277

Figure 4
Figure 4

Principal coordinate analysis (PCoA) using Bray-Curtis (A) and Jaccard (B) indices of samples grouped by sweet feed inclusion in the horse’s diet.

Citation: Journal of the American Veterinary Medical Association 260, S3; 10.2460/javma.22.07.0277

Comparison of horses that did and did not receive turnout as part of their management showed differences in similarity indices and most diversity indices (BC, P = .001, F = 2.66; J, P < .001, F = 1.89; TS, P < .001; SH, P = .062; C1, P = .001). Horses that did receive turnout had higher diversity values for TS and C1 (TS, median = 522.50, range, 182 to 1,182; C1, mean ± SD = 588.15 ± 238.65) than horses that did not receive turnout (TS, median = 235.50, range, 154 to 508; C1, mean ± SD = 309.37 ± 149.44).

Comparison between horses stalled (n = 42) and not stalled (10) showed differences in similarity indices (BC, P = .015, F = 1.79; J, P = .041, F = 1.21) and no differences in diversity indices (TS, P = .232; SH, P = .839; C1, P = .275). Exercise frequency dichotomized at the median of 5 dpw (exercise < 5 dpw, n = 21; exercise ≥ 5 dpw, 25) showed no difference in community structure for similarity (BC, P = .361, F = 1.04; J, P = .053, F = 1.18) or diversity indices (TS, P = .175; SH, P = .385; C1, P = .182). Exercise exposure dichotomized at the median of 180 mpw (exercise < 180 mpw, n = 18; exercise ≥ 180 mpw, 28) showed no difference in community structure for similarity (BC, P = .174, F = 1.20; J, P = .193, F = 1.07) or diversity indices (TS, P = .233; SH, P = .383; C1, P = .329).

Individual ASV differences

Assessment of read counts of individual ASVs found associations with inclusion of hay in the diet, type of hay being offered, and inclusion of sweet feed in the diet. Management factors of stalling and turnout had no impact on individual ASV read counts.

There were more read counts of the ASV classified as the genus Asinibacterium (FDR 5%, P < .001) within the phylum Bacteroidota in horses that did not receive hay (n = 6; median = 2.17, range, 0.67 to 7.33) compared to horses receiving hay (46; median = 0, range, 0 to 5.33). There were more read counts of the ASV classified as the genus Kineococcus (FDR 5%, P < .001) within phylum Actinobacteriota in horses receiving only alfalfa hay (median = 1.65, range, 0 to 16.75) compared to horses receiving only grass hay (median = 0, range, 0 to 0.40) or both alfalfa and grass hay (median = 0, range, 0 to 0).

There were higher read counts for 19 ASVs in biopsies from horses that did not receive sweet feed compared to those that did. Three of these ASVs were classified as the genus Alloprevotella, four were classified in the genus Porphyromonas, and one each was classified as genus Fusobacterium, genus Muribaculum, family Pasteurellaceae, species Alysiella crassa, genus Aggregatibacter, genus Oligella, genus Bergeyella, genus Streptococcus, family Lachnospiraceae, genus Moraxella, genus Bacteroides, and genus Prevotella. These genera/families belonged to the following phyla: Bacteroidota (Alloprevotella, Porphyromonas, Muribaculum, Bergeyella, Bacteroides, and Prevotella), Fusobacteria (Fusobacterium), Proteobacteria (Pasteurellaceae, Alysiella, Aggregatibacter, Oligella, and Moraxella), and Firmicutes (Streptococcus and Lachnospiraceae). Median read counts and P values are reported (Table 2).

Table 2

Taxonomy classifications and median read counts of ASVs with significant differences between horses that did and did not receive sweet feed.

Median read count (range)
Taxonomy classification Sweet feed in diet (n = 23) No sweet feed (n = 30) P value
Alloprevotella 0 (0–4.00) 3.10 (0–55.67) < .001
0 (0–1.67) 2.00 (0–29.00) < .001
0 (0–0.67) 0.65 (0–22.25) < .001
Porphyromonas 0 (0–2.00) 1.83 (0–76.70) < .001
3.67 (0–201.00) 46.50 (0.67–459.80) < .001
0 (0–7.33) 0.67 (0–15.67) < .001
0 (0–0) 0.40 (0–31.33) < .001
Fusobacterium 0 (0–19.5) 12.1 (0–319.67) < .001
Muribaculum 0 (0–0) 0.37 (0–15.5) < .001
Pasteurellaceae 5 (0–152.50) 52.27 (2.50–215.40) < .001
Alysiella crassa 0 (0–38.00) 4.95 (0–25.67) < .001
Aggregatibacter 0.83 (0–26.33) 14.40 (0–92.67) < .001
Oligella 0 (0–3.60) 3.17 (0–36.17) < .001
Bergeyella 0 (0–0.40) 0.73 (0–42.75) < .001
Streptococcus 0 (0–5.00) 2.38 (0–24.25) < .001
Lachnospiraceae 0 (0–0.20) 0.25 (0–9.00) < .001
Moraxella 0 (0–4.00) 1.58 (0–12.00) < .001
Bacteroides 0 (0–0.50) 0.68 (0–37.83) < .001
Prevotella 0 (0–0.33) 0.33 (0–12.00) < .001

P values listed are significant based on Benjamini-Hochberg procedure for a 5% false discovery rate (FDR) of 6,236 comparisons.

Associations with discipline

It was noted during the initial analysis that there were some factors biased between race and show horses. Further comparison of factors by discipline (racing, n = 22, or show, 35) was performed (Table 1). There was no difference in sex between disciplines (P = .105). Horses used for racing were younger than those used for show (P < .001). There was a higher percentage of TB used for racing than for show (P = .007). The number of years in competition and the length of time being housed at the current barn were not different between disciplines.

There was no association between discipline and proportion of horses with EGGD (P = .197) or ESGD (P = .131). Exercise frequency (dpw) was higher for racehorses than for show horses (P < .001). Exercise exposure (mpw) was not different between race and show disciplines (P = .164).

There was no difference in inclusion of hay in diet between racehorses and show horses (P = .071). However, horses used for racing were more likely to have alfalfa hay included in their diet than show horses (P < .001). Racehorses were also more likely to have sweet feed included in their diet than show horses (P < .001). There was no difference in whether the horse was stalled between racing and show disciplines (P = .722). Horses used for racing were less likely than show horses to have turnout as part of their management (P < .001).

Comparison of community structures between disciplines (racing or show) showed differences in similarity and diversity indices (BC, P = .001, F = 2.44; J, P < .001, F = 2.52; TS, P < .001; SH, P = .004; C1, P = .001). There was higher diversity in horses used for show (TS, median = 511, range, 224 to 1,182; SH, mean ± SD = 4.1 ± 0.59; C1, mean ± SD = 601.35 ± 214.92) than those used for racing (TS, median = 308, range, 154 to 989; SH, mean ± SD = 3.6 ± 0.75; C1, mean ± SD = 397.76 ± 227.43). Because there were multiple breeds within the show discipline, differences in microbiome were examined across breeds within this group. There was no difference among breeds (TB, WB, other) in microbiome composition (BC, P = .194, F = 1.16; J, P = .533, F = .986).

Discussion

In the present study, the influences of diet and management practices on the glandular gastric microbiome have been described for a population of athletic horses living in a specific geographical region. The microbiome profiles in this study are similar to previous studies8,9,20 examining the glandular mucosal microbiome with dominant phyla being Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidota (formerly Bacteroidetes). In studies20,30 characterizing the microbiome across sections of the equine gastrointestinal tract, the stomach is noted to contain more interindividual variation of the microbiome compared to the hindgut. This indicates that while there is great interindividual variance in the gastric bacterial community, the major phyla have some consistency across equine populations. The interindividual variance among horses in previous studies8,9 aligned with differences in diet and management practices. Similarly, in people, the gastric microbiome composition is impacted by multiple factors including H pylori, health status, diet, medication, age, surgery, and inflammation.15

The findings of this study confirm and further characterize the association of diet and management factors with the composition of the gastric microbiome in horses. There were several factors identified that impacted the microbiome, including feeding hay, type of hay, feeding sweet feed, turnout, and stalling. Offering hay and stalling only showed differences in similarity indices, whereas hay type, sweet feed, and turnout showed differences in similarity and diversity indices. Hay, hay type, and sweet feed additionally influenced read counts of individual taxa. The PCoA and heatmap graphs further supported community differences between horses that did and did not receive sweet feed.

In humans, aside from the presence of H pylori, the implications of alterations in the microbiome on gastric health remain unclear.15 However, changes in specific taxa have been associated more broadly with gastrointestinal diseases.31 Alloprevotella in the fecal microbiome is associated with unsuccessful weight loss in obese people and in duodenal biopsies is associated with hepatocellular carcinoma.32,33 Porphyromonas is found in the human oral and gastrointestinal microbiome and generally associated with various gastrointestinal cancers and chronic inflammation, but some species have been identified as having a potentially beneficial role.31,34 In horses, a prior study identified modest differences in the microbiome in association with EGGD.19 The significance of changes in community structure and individual taxa identified in this study on the gastric environment in horses is unknown.

In contrast to the impact of dietary factors on the microbiome, there was no effect observed with exercise, either days per week or minutes per week. The lack of association with exercise in this study differs from humans where sedentary behaviors, marathon running, and acute exercise have associated changes in the microbiome.16,35,36 Acute exercise decreases blood flow to the gastrointestinal tract and can lead to decreased gastric emptying making these organs more susceptible to dysfunction and disease.16 However, there are often sport-specific dietary strategies as well as variations in training intensities that complicate the interpretation of these results.37 Overall, it is clear that the relationship of the microbiome with diet, exercise, and gastrointestinal health is complex and multifactorial.

In human studies18 assessing the fecal microbiome of athletes in association with sport and sport-specific diet, the energy intake, as well as the breakdown of carbohydrate, protein, and fat in the diet, has been used for analysis. This level of dietary breakdown was beyond the scope of this study, but a study design that allows for this level of detail in future observational or interventional studies may be useful to better understand the influences on the gastrointestinal microbiome in horses. Another limitation of this study was the exclusion of sedentary horses that would have served as a control to better evaluate for an effect of exercise on the gastric microbiome.

When examining differences with discipline type (racing versus showing), there were multiple differences in signalment, diet, and management between populations that confounded our ability to identify discipline-specific differences in the microbiome. The signalment data differences associated with discipline (age and breed) reflect the horse population of the geographic region and the inherent bias of racehorses being younger Thoroughbreds. Factors that had a lower α-diversity (alfalfa hay fed, inclusion of sweet feed in the diet, and no turnout) were associated with racing. Racehorses were more likely to have alfalfa hay and sweet feed in their diet and less likely to have turnout or grass hay only compared to show horses. All racehorses had sweet feed included in their diet. Taken together, this indicates there are many factors that differ between disciplines and impact the gastric microbiome, including some factors that may not have been measured in this study. The differences in the gastric microbiome found in association with diet and management factors could be a type I error and a reflection of other discipline or breed-associated factors. A study designed to look at more discipline-specific factors may further elucidate the source of the microbiome differences detected in this study between racehorses and show horses. An additional limitation of this study is the survey nature of the diet and management information that relies on the accuracy of responses provided by the horse owner or farm manager.

This study provides preliminary evidence demonstrating a role of diet and housing on the gastric glandular microbiome in horses. Differences in community structure and individual taxa were identified in this study. The significance of these differences on gastric health is unknown. The impact of specific microbiome modifications, at the community or individual taxa level, on gastric health requires further study. Identifying the ideal gastric microbiome in conjunction with how diet and management factors influence this environment may allow for the development of recommendations that reduce the incidence of disease.

Supplementary Materials

Supplementary materials are posted online at the journal website: avmajournals.avma.org

Acknowledgments

The authors acknowledge and thank Jennifer Windham, Christian Arias, and Amanda Distefano for their assistance in sample collection.

Financial support was provided by Boehringer Ingelheim Animal Health.

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Supplementary Materials

Contributor Notes

Corresponding author: Dr. Paul (lindapaul@lsu.edu)