Characterization and comparison of fecal microbiota in horses with pituitary pars intermedia dysfunction and age‐matched controls

Abstract

Background

Altered gut microbiota has been associated with dopaminergic degenerative diseases in people, but studies on horses with pituitary pars intermedia dysfunction (PPID) are lacking.

Hypothesis/Objectives

Investigate the effect of PPID on fecal microbiota in horses.

Animals

Nine horses with PPID and 13 age‐matched control horses.

Methods

Prospective control study. Fecal samples were collected bimonthly. Microbial analysis used 16S rRNA sequencing to determine the relative abundance at genus and phylum levels, assess alpha and beta diversity and identify core microbiota.

Results

Horses with PPID had decreased relative abundances of Christensenellaceae R‐7 group (median; 95% confidence interval [CI]: PPID, 2.04; 1.82‐2.35 vs control, 2.54; 2.37‐2.76; P = .02) and NK4A214 group (PPID, 2.21; 2.02‐2.56 vs control, 2.62; 2.44‐2.85; P = .05), and significant lower abundances of Romboutsia (log2FoldChange = −3.54; P = .04) and Peptococcaceae uncultured (log2FoldChange = −0.89; P = .04) by differential abundance analysis. However, the abundance of Fibrobacter (log2FoldChange = 0.74; P = .04) was significantly higher in the PPID group. A significant effect of PPID on beta diversity was observed (P = .004), whereas alpha diversity varied with months (P = .001). Seven unique genera were identified in horses with PPID and 12 in control horses.

Conclusions and Clinical Importance

The fecal microbial composition is altered in horses with PPID. These findings support the potential role of the microbiota‐gut‐brain axis in the pathogenesis of PPID.

Abbreviations

ASV

amplicon sequence variants

CI

confidence interval

DNA

deoxyribonucleic acid

EDTA

ethylenediaminetetraacetic acid

ENS

enteric nervous system

gDNA

genomic deoxyribonucleic acid

PCoA

principal coordinates analysis

PERMANOVA

permutational multivariate analysis of variance

PPID

pituitary pars intermedia dysfunction

QIIME 2

quantitative insights into microbial ecology 2

ROUT

robust regression and outlier removal

rRNA

ribosomal ribonucleic acid

SCFA

short‐chain fatty acids

TRH

thyrotropin‐releasing hormone

1. INTRODUCTION

Pituitary pars intermedia dysfunction (PPID) is a prevalent endocrine disorder affecting >20% of older horses. ¹ , ² In cases with PPID, excessive secretion of adrenocorticotropic hormone (ACTH) is caused by the loss of dopaminergic inhibition in the pars intermedia, which is thought to result from a loss of functional dopaminergic periventricular neurons. ³ , ⁴ Similarly, Parkinson's disease is characterized by the loss of dopaminergic neurons and widespread misfolded α‐synuclein aggregation in the nervous system. ⁵ Parkinson's disease is a common neurodegenerative disorder of older people, diagnosed in 2% to 3% of the population >65 years of age, and evidence links gut microbiota to the pathophysiology of Parkinson's disease. ⁶

Although the links between gut microbiota and neurodegenerative diseases are unclear, the presence of misfolded α‐synuclein aggregates in the enteric nervous system has been associated with intestinal inflammation and increased gut permeability. ⁷ Such intestinal changes are associated with a shift in gut microbiota with decreased abundance of short‐chain fatty acid (SCFA)‐producing taxa. ⁸ Some evidence suggests that alterations in gut microbiota composition play an important role in the pathogenesis of Parkinson's disease by promoting the characteristic ascending neurodegenerative spread of misfolded α‐synuclein aggregates from the enteric nervous system to the brain. ⁹ Many animal models have further supported the possibility of bidirectional communication between the brain and intestinal tract in Parkinson's disease, suggesting that dopaminergic degeneration in the brain could impact intestinal health, and in turn, intestinal dysbiosis could exacerbate disease progression in the central nervous system. ¹⁰ , ¹¹ Chronic inflammation leading to altered immune function is recognized as a key contributor to neurodegenerative disorders, and intestinal inflammation associated with decreased intestinal immunity is thought to play an important role in the pathogenesis of Parkinson's disease. ¹²

In horses, misfolded α‐synuclein proteins have been identified in the pars intermedia of PPID‐affected horses, suggesting a shared mechanism between PPID and Parkinson's disease. ¹³ , ¹⁴ In horses with PPID, the main clinical signs include hypertrichosis, muscle atrophy, laminitis and opportunistic infections, but previous studies also have reported higher strongyle egg shedding, implying that, similar to Parkinson's disease, PPID might also involve alterations in intestinal health and immunity. ¹⁵ Thus, we aimed to investigate the effect of dopaminergic degeneration on the composition of gut microbiota in horses with PPID.

2. MATERIALS AND METHODS

2.1. Animals

All procedures were approved by the Institutional Animal Ethics Committee. Twenty‐two horses >12 years of age underwent a 4‐week acclimatization period on the same pasture. Recruited horses were dewormed with moxidectin 60 days before the start of the study and received no antimicrobial treatments between deworming and the end of the study. Routine vaccination and foot care for all horses were maintained throughout the study. Horses were kept on the same grass pasture from December 2022 to October 2023. Ad libitum lucerne hay was provided during winter (August to October, Southern hemisphere).

2.2. Diagnosis of pituitary pars intermedia dysfunction

Horses were divided into 2 groups according to clinical signs and results of a thyrotropin‐releasing hormone (TRH) stimulation test. ¹⁶ Briefly, blood samples were collected in ethylenediaminetetraacetic acid (EDTA) tubes (BD Vacutainer, New South Wales, Australia) 30 minutes after IV injections of 1 mg of TRH (Sigma‐Aldrich Pty Ltd, North Ryde BC, Australia). ¹⁷ Concentrations of ACTH were determined by a chemiluminescent immunoassay (Immulite 1000 Chemiluminescent Assay, Siemens, Bayswater, Australia) with an interassay coefficient of variation of 4.8% and intra‐assay coefficient of variation of 5.4%, within 4 hours of sample collection. ¹⁸ The PPID group included horses with at least 1 clinical sign consistent with PPID (hypertrichosis, delayed shedding, or epaxial muscle atrophy) and ACTH concentrations ≥41.6 pg/mL after TRH injection. ¹⁶ The control group included horses without clinical signs consistent with PPID and ACTH concentrations <41.6 pg/mL after TRH injection. ¹⁶ Additional physical examinations were performed and blood samples obtained bimonthly to ensure horses were correctly categorized. Beyond the presence of PPID, all the horses were healthy based on physical examinations and regular CBC and serum biochemistry.

2.3. Fecal sample collection

Fresh fecal samples were collected bimonthly by rectal palpation or immediately after witnessed defecation (1 case). This process was conducted after blood collection to avoid a possible confounding effect of rectal palpation‐associated stress on ACTH concentration. Fecal samples were placed into sterile containers and stored at −80°C until DNA extraction.

2.4. DNA extraction

Once the sample was thawed, total genomic DNA (gDNA) was extracted from 250 mg of feces using commercial kits (QIAGEN DNeasy PowerSoil Pro Kit, QIAGEN, Hilden, Germany) following the manufacturer's instructions. Extracted gDNA was quantified using spectrophotometry (Nanodrop 8000 Spectrophotometer, Thermo Fisher Scientific, Massachusetts, USA). Samples with DNA concentrations >1.88 μg/μL were submitted for 16S ribosomal ribonucleic acid (rRNA) gene amplicon sequencing.

2.5. Bioinformatics

The 16S rRNA gene amplicon was sequenced by the Australian Centre for Ecogenomics using the Illumina MiSeq Platform (Illumina, California, USA). Variable region V6‐V8 was targeted using the primers 926F (5′‐AAACTYAAAKGAATTGACGG‐3′) and 1392R (5′‐ACGGGCGGTGTGTRC‐3′). ¹⁹ After sequencing, Quantitative Insights into Microbial Ecology 2 (QIIME 2) was used for the bioinformatics analysis of the generated sequence reads for all samples, with the SILVA rRNA database being used to taxonomically classify representative sequence reads. ²⁰ , ²¹

In a first analysis, raw data were integrated by an all‐in‐1 package “phyloseq” in R (Version 4.4.1; R Foundation for Statistical Computing, Vienna, Austria) for downstream analysis. Relative abundances were calculated by converting sequence counts into percentages of the total sequences per sample at the phylum and genus levels. The 20 most common phyla and genera were stacked as a bar chart using package “ggplot2” for ease of visualization. Indices in alpha diversity (including Observed species, Chao1, Shannon index, Simpson index, and Pielou's evenness) and principal coordinates analysis (PCoA) based on Bray‐Curtis dissimilarity in beta diversity were analyzed by package “vegan.” Core microbiota were identified by package “microbiome” including genera with >1% relative abundance and visualized by package “venn.” Differential abundance analysis was conducted using package “DESeq2” with negative binomial distribution models. All related R packages are listed in the Supporting Information (Data S1).

In a complementary analysis, data were rarefied, and visualization of the sequence reads depth was performed after data normalization by R packages (Supporting Information, Data S1). Reads <10 000 were excluded to decrease the impact on sample diversity. As above, R packages were used for the identification and visualization stacked as a bar chart of the top 20 relative abundances at the phylum and genus levels, measurement of alpha diversity parameters (Observed species, Chao1, Shannon index, Simpson index, and Pielou's evenness), PCoA in beta diversity, and investigation of fecal core microbiota with >1% relative abundance at the genus level.

2.6. Statistical analysis

Normality was assessed using a Shapiro‐Wilk test. If normality was not achieved for relative abundance, outliers were excluded using robust regression and outlier removal (ROUT) with Q = 1%, and data was transformed using the formula log10 (relative abundance +1). Differences between groups in age and ACTH concentrations were investigated using an unpaired t‐test or Mann‐Whitney test depending on data distribution. A linear mixed‐effect model was used to evaluate the effect of status (PPID vs control) and time (month) on the relative abundances and alpha diversity parameters. Post hoc comparisons were performed using a Šídák's multiple comparisons test when appropriate. Statistical differences of fecal microbiota between PPID and control groups in beta diversity were calculated by a permutational multivariate analysis of variance (PERMANOVA) comparing the intragroup distances to the intergroup distances in a permutation scheme. Each PERMANOVA was conducted using the R function adonis2 with 999 permutations. For measuring the significant differential abundance of certain taxa between 2 groups, DESeq2 used negative binomial distribution models to account for the inherent variability and dispersion seen in count data to improve the stability of results and provided Benjamini‐Hochberg correction for controlling the false discovery rate. Data are presented as mean or median, depending on data distribution, and 95% confidence interval (CI). Statistical analyses were performed using R and GraphPad Prism (Version 10.2.0; GraphPad Software, LLC, Boston, USA). For all comparisons, P < .05 was considered significant.

3. RESULTS

3.1. Animals

Based on the clinical signs and the TRH stimulation test results, 13 horses were classified as controls and 9 as PPID (Table 1). There were 5 males and 4 females in the PPID group and 8 males and 5 females in the control group. There were 14 Standardbreds (4 in the PPID group and 10 in the control group), 5 Australian stock horses (4 in the PPID group and 1 in the control group), 2 Thoroughbreds (both in the control group), and 1 Quarter horse in the PPID group (Supporting Information, Data S2). All of the horses tolerated the experiments with no adverse effects.

TABLE 1.

Thyrotropin‐releasing hormone stimulation test for animals in the control and pituitary pars intermedia dysfunction (PPID) groups.

	Control (n = 13)	PPID (n = 9)	P‐value
Age (year)	19 [15‐21]	20 [14‐23]	.47
Post‐TRH ACTH concentrations (pg/mL)	33.8 [25.2‐38.6]	93.4 [62.2‐125.0]	<.001

Note: Data were presented as median [95% confidence intervals].

3.2. Sequence and alpha diversity analysis

Overall, 9 447 132 reads were amplified from 126 fecal samples, with 89.32% (8 438 621/9 447 132 sequences) passing quality control. The minimum reads per sample were 9706, whereas the maximum reads per sample were 143 627. These reads were taxonomically classified across 603 amplicon sequence variants (ASV), with 422 identified and 44 uncultured at the genus level.

During the process of data rarefication, the depth of sequence reads for each fecal sample was rarefied to 26 602 reads after removing 1 sample that generated reads <10 000, which indicated that each sample reached an adequate depth of coverage for microbial diversity. The results after rarefication are presented in Supporting Information (Data S3).

On nonrarified data, no significant effect of PPID status or months was observed in either observed species or the Chao1 index, but a significant effect of month, but not PPID status, was detected in the Shannon index (P < .001), Simpson index (P = .002), and Pielou's evenness (P < .001; Figure 1).

FIGURE 1.

Alpha diversity in the control and pituitary pars intermedia dysfunction (PPID) groups by month. (A) Shannon index; (B) Simpson index; and (C) Pielou's evenness. *P < .05; **P < .01; ***P < .001.

3.3. Bacterial composition

3.3.1. Phylum level

The fecal microbiome was dominated by the phyla Firmicutes and Bacteroidota, followed by Euryarchaeota, Verrucomicrobiota, and Spirochaetota, Fibrobacterota, Halobacterota, and Actinobacteriota (Figure 2).

FIGURE 2.

Relative abundance of top 20 phyla in the control and pituitary pars intermedia dysfunction (PPID) groups by month.

A significant effect of PPID status on the abundance of the phyla Spirochaetota (P = .04) and Mucoromycota (P = .03) was observed with Spirochaetota having a higher relative abundance in PPID horses in June (P = .02) and Mucoromycota having a higher relative abundance in PPID horses in April (P = .003; Table 2).

TABLE 2.

Relative abundances of phyla Bacteroidota, Spirochaetota, Thermoplasmatota, and Mucoromycota showed mean (±SD) by mixed‐effects model analysis in the control and pituitary pars intermedia dysfunction (PPID) groups by month variation.

Phyla	Group	Dec	Feb	Apr	Jun	Aug	Oct	P value
								Group	Month	Group × Month
Bacteroidota	PPID	31.44 (±2.73)	34.29 (±2.19)	34.49 (±1.88)	33.64 (±3.44)	32.02 (±4.21)	33.96 (±2.15)	.71	.01	.07
	Control	30.59a (±2.04)	32.16 (±4.73)	33.76 (±4.03)	31.10a,b (±5.49)	35.11c (±3.36)	35.64b,c (±2.33)
Spirochaetota	PPID	5.41 (±2.13)	4.80 (±2.73)	4.29 (±2.79)	4.38* (±2.67)	4.61 (±2.56)	5.12 (±2.37)	.04	.01	.52
	Control	5.49a (±2.00)	3.47 (±2.61)	3.09b (±1.86)	2.19b, * (±1.38)	3.78 (±1.63)	3.44 (±1.74)
Thermoplasmatota	PPID	0.83a,b (±0.65)	0.32c (±0.20)	0.32c (±0.16)	0.36c (±0.30)	0.35c (±0.23)	0.22c (±0.16)	.77	<.001	.93
	Control	0.77a (±0.83)	0.30b (±0.25)	0.50 (±0.48)	0.40b (±0.34)	0.33b (±0.15)	0.25b (±0.21)
Mucoromycota	PPID	0.003 (±0.005)	0.21 (±0.25)	0.49* (±1.32)	0.06 (±0.09)	0.15 (±0.24)	0.05 (±0.05)	.03	.30	.28
	Control	0.005 (±0.008)	0.009 (±0.02)	0.004* (±0.008)	0.01 (±0.01)	0.01 (±0.02)	0.01 (±0.03)

Note: Different letters in superscript indicate P < .05 between months.

^* P < .05 between control horses and horses with PPID.

A significant effect of month was observed in the relative abundance of phyla Bacteroidota (P = .01), Spirochaetota (P = .01), and Thermoplasmatota (P < .001; Table 2). Bacteroidota had a lower relative abundance in December compared with June (P = .02) and October (P = .01) and a lower relative abundance in June compared with October (P = .02) in the controls. Spirochaetota had a higher relative abundance in December compared with the other months (P = .01 in February and October, P = .002 in April, P < .001 in June, P = .03 in August) and a higher relative abundance in August compared with June (P = .04) in the controls. Thermoplasmatota had a higher relative abundance in December compared with the other months (P = .01 from February to August, P = .003 in October) in PPID horses and a higher relative abundance in December compared with February (P = .003), June (P = .02), August (P = .01), and October (P = .002) in control horses.

3.3.2. Genus level

The top 20 genera are presented in Figure 3. A significant effect of PPID status was observed in the relative abundance of genera Christensenellaceae R‐7 group (P = .02) and NK4A214 group (P = .05; Table 3). Christensenellaceae R‐7 group had a lower relative abundance in PPID horses in February (P = .03). The NK4A214 group had a lower relative abundance in PPID horses in December and June (both P = .04).

FIGURE 3.

Relative abundance of top 20 genera in the control and pituitary pars intermedia dysfunction (PPID) groups by month.

TABLE 3.

Relative abundances (%) of genera Rikenellaceae RC9 gut group, Treponema, RF39, NK4A214 group, and Christensenellaceae R‐7 group showed mean (±SD) by mixed‐effects model analysis in the control and pituitary pars intermedia dysfunction (PPID) groups by month variation.

Genera	Group	Dec	Feb	Apr	Jun	Aug	Oct	P value
								Group	Month	Group × Month
Rikenellaceae RC9 gut group	PPID	7.81 (±1.95)	9.14 (±2.41)	10.63 (±1.96)	9.68 (±2.90)	9.08 (±1.73)	10.30 (±2.15)	.06	.003	.74
	Control	7.51a (±1.95)	10.48 (±2.98)	10.83b (±2.56)	10.62 (±3.21)	11.19b (±3.29)	11.66b (±3.45)
Treponema	PPID	3.95 (±1.37)	3.45 (±2.41)	2.90 (±1.40)	3.36 (±2.57)	3.08 (±1.69)	3.71 (±1.47)	.07	.03	.30
	Control	4.15a (±1.47)	2.58 (±2.13)	2.33 (±1.44)	1.46b (±1.10)	3.06 (±1.44)	2.51 (±1.40)
RF39	PPID	1.32 (±0.69)	2.93 (±0.88)	2.97 (±1.62)	2.22 (±1.14)	3.03 (±1.89)	2.74 (±1.29)	.16	<.001	.78
	Control	1.57a (±0.70)	3.87b (±2.08)	2.92 (±1.22)	2.83 (±1.16)	2.91 (±1.43)	3.27b (±1.11)
NK4A214 group	PPID	2.22* (±0.53)	2.18 (±0.72)	2.50 (±0.89)	2.41* (±0.63)	2.44 (±0.74)	2.24 (±0.82)	.05	.70	.39
	Control	2.85* (±0.83)	2.65 (±0.65)	2.44 (±0.54)	3.01* (±0.66)	2.53 (±0.72)	2.65 (±0.45)
Christensenellaceae R‐7 group	PPID	1.54 (±0.33)	2.37* (±0.88)	2.36 (±0.71)	2.31 (±0.85)	2.52 (±1.05)	1.97 (±0.34)	.02	<.001	.67
	Control	1.72a (±0.56)	3.14b, * (±1.30)	2.89b (±0.94)	2.92b (±0.83)	2.67b (±0.44)	2.66b (±0.56)

Note: Different letters in superscript indicate P < .05 between months.

^* P < .05 between control horses and horses with PPID.

A significant effect of month was observed in the relative abundance of genera Rikenellaceae RC9 gut group (P = .004), Treponema (P = .04), RF39 (P < .001), and Christensenellaceae R‐7 group (P < .001; Table 3). Rikenellaceae RC9 gut group had a lower relative abundance in control horses in December compared with April (P = .04), August (P = .02), and October (P = .005). Treponema had a lower relative abundance in control horses in June compared with December (P < .01). RF39 had a lower relative abundance in control horses in December compared with February (P < .001) and October (P = .04). Christensenellaceae R‐7 group had a lower relative abundance in control horses in December compared with the other months (P < .001 in February, P = .002 in April and June, P = .03 in August, and P = .04 in October).

3.4. Variation among samples

The 2 main components of PCoA captured 35.3% of the variance within the microbial community among the 1‐year fecal samples. Although no distinct clustering by PPID status was noted, a significant but weak effect of PPID (R ² = 0.02; P = .02) on the composition of these communities was observed (Figure 4A). A significant effect of month on the bacterial community composition in controls (R ² = 0.13, P = .001; Figure 4B) and horses with PPID (R ² = 0.15, P = .02; Figure 4C) was observed.

FIGURE 4.

Principal coordinate analysis (PCoA) for presenting (A) the comparison between the controls and the horses with pituitary pars intermedia dysfunction (PPID) throughout a whole year; (B) the comparison of monthly variation in the controls; and (C) the comparison of monthly variation in the horses with PPID.

Sixty‐three genera were identified as core microbiota from the 1‐year fecal samples. Of these, 44 genera were shared between the control and PPID groups. Seven genera were exclusively present in the PPID group, including Methanosphaera, Bacteroides, Lactobacillus, Fastidiosipila, Oribacterium, Porphyromonas and Pilobolus, whereas the control group had 12 unique genera, including Methanomicrobium, Muribaculum, Erysipelotrichaceae uncultured, Mycoplasma, Clostridia UCG‐014, Cellulosilyticum, Lachnoclostridium, Marvinbryantia, Family XIII AD3011 group, Mogibacterium, LD1‐PB3, and Akkermansia (Figure 5).

FIGURE 5.

Venn diagram showing the number and ratio of genera unique to the control and pituitary pars intermedia dysfunction (PPID) horses throughout a whole year.

3.5. Differential abundance analysis

DESeq2 analysis identified significant differences in the abundance of several taxa between the control and PPID groups (Figure 6). Notably, Pilobolus (log2FoldChange = 4.48; P < .001) and Fibrobacter (log2FoldChange = 0.74, P = .04) had significantly higher abundances in the PPID group, whereas UCG‐002 (log2FoldChange = −0.73; P = .01), Peptococcaceae uncultured (log2FoldChange = −0.89; P = .04), Family XIII AD3011 group (log2FoldChange = −0.46; P = .04), Mogibacterium (log2FoldChange = −0.66; P = .02), and Romboutsia (log2FoldChange = −3.54; P = .04) had lower abundances in the control group.

FIGURE 6.

Differential abundances of taxonomy by DESeq2 analysis between the control and pituitary pars intermedia dysfunction (PPID) groups throughout a whole year.

4. DISCUSSION

Our study represents the first analysis of the fecal bacterial communities in horses with PPID compared to age‐matched healthy horses across different months. We found significant variations in the abundance at the phylum and genus levels, along with unique microbiota in the PPID group.

At the genus level, Christensenellaceae R‐7 group and NK4A214 group had lower relative abundance in horses with PPID. A previous study investigating the relationship between fecal microbiota and metabolites in horses found that genera Christensenellaceae R‐7 group and NK4A214 group correlated positively with total SCFA production. ²² , ²³ The role of SCFA in gastrointestinal tract health is unclear, but it appears that SCFA production is associated with enhanced local mucosal immunity and antigen tolerance and could play an important role in the microbiota‐gut‐brain axis. ²⁴ In patients with Parkinson's disease, lower fecal SCFA concentrations have been reported suggesting a possible association between SCFA and the disease. ⁸ Although measuring SCFA was beyond the scope of our study, the results suggest that, as described in Parkinson's disease, the decreased relative abundance of Christensenellaceae R‐7 group and NK4A214 group in horses with PPID might be associated with a reduction in SCFA, resulting in gut dysbiosis. This possibility also could be supported by the fact that Fibrobacter had a higher abundance in the PPID group. Fibrobacter is an obligate fibrolytic, acid‐intolerant bacterium the growth of which is strongly inhibited by acidic pH. ²⁵ This finding suggests that horses with PPID might have higher intestinal pH than controls and less SCFA production. In the differential abundance between PPID and control groups, Peptococcaceae uncultured belonging to Peptococcaceae family and Romboutsia belonging to Ruminococcaceae family had lower abundance in the PPID group. Those 2 families are also positively associated with SCFA production and these results could support the findings observed at the genus level. Taken together, these changes in microbial relative abundances could suggest a more alkaline intestinal environment, potentially resulting from decreased SCFA production, similar to what has been described in Parkinson's disease.

Alpha diversity in our study did not show significant differences in the gut microbiota based on PPID status, aligning with other studies involving endocrine disorders of horses such as hyperinsulinemia‐associated laminitis, where no significant differences in gut microbial diversity were detected. ²⁶ This finding is also consistent with most studies on Parkinson's disease suggesting that gut microbial richness and evenness are not easily influenced by nonprimary gastrointestinal diseases. ²⁷

Comparison of core microbiota between the 2 groups showed that the PPID group had fewer unique genera than the control group, indicating a decrease in microbial diversity among horses with PPID. This finding could imply potential limitations in the functional capabilities of the gut flora and might serve as a marker of disease stage or severity. ²⁸ Although Lactobacillus involved in lactic acid production and Bacteroides involved in SCFA production and typically considered beneficial were exclusively present in PPID horses, their relative abundances (with median relative abundance of Lactobacillus at 0.13% in the PPID horses and 0.12% in the control horses, and median relative abundance of Bacteroides at 0.14% in the PPID horses and 0.16% in the control horses) did not yield significant differences between the PPID and control groups. These results might have been skewed by the presence of 1 horse in the PPID group with a high Lactobacillus relative abundance and another horse in the PPID group with a high Bacteroides relative abundance, both exceeding the 1% cut‐off used for the analysis. Increased relative abundance of Lactobacillus has been observed in various intestinal diseases, such as colic, diarrhea and colitis, suggesting that horses with PPID might have intestinal dysbiosis. ²³ , ²⁶ , ²⁹ A hypothesis suggested by others is that the higher Lactobacillus relative abundance might represent a compensatory mechanism aimed at restoring intestinal homeostasis, but only relative abundance can be obtained with 16S studies, and no conclusion on actual overgrowth can be drawn. ³⁰ Again, the decreased relative abundance of Bacteroides in the PPID group could support the possibility of SCFA reduction in the intestine of horses with PPID. Akkermansia was found exclusively in the control group and is known for its anti‐inflammatory properties through a role in maintaining the integrity of the epithelial layer and modulating immune function. ³¹ , ³² , ³³ This finding could suggest that horses with PPID might have decreased epithelial integrity and immune function predisposing them to low‐grade intestinal inflammation. Chronic inflammation and compromised immunity are increasingly recognized as fundamental components of neurodegenerative disorders, with intestinal inflammation being particularly relevant in the pathogenesis of Parkinson's disease. ¹² Several epidemiological studies have reported increased prevalence and relatively higher risk of Parkinson's disease among patients with inflammatory bowel disease. ³⁴ , ³⁵ , ³⁶ , ³⁷

Throughout our 1‐year follow‐up study, alpha diversity appeared to be influenced primarily by month‐to‐month variations rather than disease status, with the control group being more susceptible to seasonal changes. Other studies on horse gut microbiota identified that seasonal changes influenced by weather conditions and dietary adjustments have significant effects. ³⁸ , ³⁹ Specifically, genera Rikenellaceae RC9 gut group, Christensenellaceae R‐7 group, RF39, and Treponema along with phyla Bacteroidota, Spirochaetota, and Thermoplasmatota were affected by monthly variations. The relative abundance of Rikenellaceae RC9 gut group and Christensenellaceae R‐7 group have been shown to be significantly greater in warm seasons in Buryatian horses. ³⁸ In our study, however, these 2 genera had lower relative abundance in December (summer) compared with other months. Phyla Firmicutes and Bacteroidota dominate the horse gut community and previous studies suggest that the relative abundance of Firmicutes was higher in the cold season, whereas Bacteroidota relative abundance remained stable throughout seasonal variations, contradicting our findings. ³⁹ Seasonal shifts in gut microbiota are not limited to horses, and similar changes have been reported in people and other animals in response to dietary variation, temperature, and precipitation. ⁴⁰ , ⁴¹ , ⁴² This effect could be either a result of a direct correlation between weather conditions and the feed available to horses or the impact of weather on environmental bacteria composition, such as soil and grass microbiota. Combining the results of alpha and beta diversity analyses, month effects were predominantly observed in the control group. To mitigate the impact of external variables (besides season) such as age, location, diet, exercise and medications on the gut microbiota, we utilized horses matched by age and kept them in the same location under uniform management throughout the year. ⁴³ , ⁴⁴ , ⁴⁵ , ⁴⁶ , ⁴⁷ Although previous studies have shown that the microbiota of healthy horses is affected by season, it remains unclear why the gut microbiota in healthy horses was more sensitive to monthly changes than that of horses with PPID.

The main limitations of our study include the sample size for horses with PPID, which, combined with the variability within groups, decreased our ability to detect some expected differences, and the absence of measurement of fecal SCFA concentrations and metabolomic analyses, limiting our ability to establish a direct association among gut microbiota, intestinal function and horse health status. Nevertheless, the observed alterations in gut microbiota between horses with and without PPID suggest that PPID is associated with intestinal dysbiosis. Further study should focus on intestinal tissue to explore possible pathological changes related to the brain‐gut axis in horses with PPID.

5. CONCLUSION

We found significant differences in gut microbiota between horses with and without PPID, indicating that PPID might be associated with disruption in intestinal homeostasis. Although alpha diversity primarily changed with monthly variations, the observed differences in core microbiota and abundances of key genera in horses with PPID suggest alterations in the gut ecosystem. These findings emphasize the importance of further research on intestinal health to better understand the pathophysiological changes associated with PPID through the brain‐gut axis.

CONFLICT OF INTEREST DECLARATION

Authors declare no conflict of interest.

OFF‐LABEL ANTIMICROBIAL DECLARATION

Authors declare no off‐label use of antimicrobials.

INSTITUTIONAL ANIMAL CARE AND USE COMMITTEE (IACUC) OR OTHER APPROVAL DECLARATION

Approved by The University of Queensland Animal Ethics Unit (2022/AE000462).

HUMAN ETHICS APPROVAL DECLARATION

Authors declare human ethics approval was not needed for this study.

Supporting information

Data S1. Supporting Information.

Data S2. Supporting Information.

Data S3. Supporting Information.

Data S4. Supporting Information.

Data S5. Supporting Information.

Wang W, Gibson J, Horsman S, Mikkelsen D, Bertin F‐R. Characterization and comparison of fecal microbiota in horses with pituitary pars intermedia dysfunction and age‐matched controls. J Vet Intern Med. 2025;39(1):e17288. doi: 10.1111/jvim.17288