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. 2026 Mar 11;32:100619. doi: 10.1016/j.vas.2026.100619

Starch Overload and Cecal Alkalinization: Impacts on the Intestinal Microbiota and Health of Horses

Caio Carvalho Bustamante a,⁎,1, Pâmela AMaldaner Pereira a,b,1, Camila Cesáreo Fernandes b, Paulo Alescio Canola a, Renata GS Doria c, Marcio C Costa d, Carlos Augusto A Valadão a
PMCID: PMC13000721  PMID: 41868108

Abstract

Starch overload induces dysbiosis primarily through a reduction in cecal pH. This study aimed to evaluate the cecal microbiota following excessive starch intake, with or without the administration of an intracecal buffering solution. We hypothesized that the buffering solution would mitigate changes in the intestinal microbiota and associated clinical signs. Ten horses were randomly assigned to two groups, each receiving distinct treatments (Group I: saline solution [WSa] and starch-buffer [SB]; Group II: buffer-water [WB] and starch-saline [SSa]). Following starch overload (17.6 g/kg body weight via nasogastric tube), clinical assessments and cecal content sampling were conducted at six time points (T0, T08, T12, T24, T48, T72), corresponding to 0, 8-, 12-, 24-, 48-, and 72-hours period. DNA was extracted, and the V4 region of the 16S rRNA gene was amplified and sequenced using the Illumina platform. Sequence data were processed using QIIME2. Statistical analysis was performed using the ExpDes package in R, with means compared via Tukey’s test. Diversity analysis revealed decreased richness and evenness post-treatment in Group I, with reductions observed as early as T08 in the SB treatment. The relative abundance of Bacillota significantly increased in the SB and SSa treatments between T08 and T24, whereas Bacteroidota showed a concurrent decline. All horses receiving these treatments exhibited at least one clinical sign following starch overload, with the SB treatment associated with more severe and prolonged clinical manifestations. The administration of the buffering solution in combination with starch overload exacerbated alterations in the intestinal microbiota and clinical outcomes.

Keywords: Equine, Cecum, Microbiota, Overload, Buffer

1. Introduction

The intestinal microbiota plays an important role in metabolic and immunological homeostasis, prompting extensive investigation in both humans and animals over the past decade (Costa & Weese, 2018; Jandhyala et al., 2015; Shreiner et al., 2015). 16S rRNA metabarcoding has been consolidated as high-resolution approach for the characterization of complex microbial communities (Quince et al., 2017; Weinroth et al., 2022).

In horses, fermentation of forage within the cecum and colon supplies a substantial proportion of daily energy requirements. Because this hind-gut ecosystem is highly responsive to dietary inputs, abrupt changes in feed composition can disrupt microbial activity (Daly et al., 2012; Hansen et al., 2015; Julliand & Grimm, 2017). Diets rich in starch are particularly problematic: they alter the relative abundance of bacterial taxa (Bulmer et al., 2019) and can injure the gastric mucosa (Colombino et al., 2022). Starch overload is almost invariably linked to dysbiosis, manifesting clinically as diarrhea (Rodriguez et al., 2015), abdominal pain Hudson et al. (2001), colitis (Costa et al., 2012) and, indirectly, laminitis via lipopolysaccharide (LPS) release from dying Gram-negative bacteria (Tuniyazi et al., 2021). The underlying trigger is a fall in cecal pH associated with lactic acidosis: excess soluble carbohydrate promotes the proliferation of lactic-acid-producing bacteria such as Streptococcus sp. and Lactobacillus sp., driving acidification and selective loss of less acid-tolerant taxa (Biddle et al., 2013; Garber et al., 2020).

Alkalinising agents offer a logical countermeasure. Sodium bicarbonate administered orally elevates equine cecal pH (Taylor et al., 2014), while aluminum and magnesium hydroxide preparations have demonstrated similar effects (Ahmed et al., 2002; Clark et al., 1996; Maia et al., 2017). These agents are routinely used to manage gastric ulceration and large-colon impaction in horses (Clark et al., 1996; Tillotson & Traub-Dargatz, 2003).

Given that the pH decrease induced by corn-starch overload selects for acidophilic bacteria and precipitates dysbiosis, we investigated whether intracecal buffering could mitigate these disturbances. The objective was not only to assess microbiota composition under these conditions but also to evaluate the potential of a targeted intervention to prevent microbial imbalance and its associated clinical manifestations. Therefore, the hypothesis proposed that buffering would preserve microbial diversity and reduce clinical signs. By exploring a practical intervention for diet-induced dysbiosis, this study seeks to inform preventive and therapeutic strategies in veterinary practice and, by extension, to provide insights relevant to other monogastric herbivores and potentially to human medicine.

2. Materials and methods

2.1. Animals, management, and procedures

The study was conducted using ten mixed-breed horses (four geldings and six mares) with a mean age of 13 ± 5.6 years and an average body weight of 353 ± 28 kg. Prior to the experiment, all animals were dewormed (Eqvalan®, Boehringer Ingelheim, Paulínia, SP), vaccinated against rabies (Rabmune®, Ceva, Paulínia, SP), and subjected to thorough clinical and laboratory evaluations, including hematological and biochemical profiling, to confirm the absence of systemic or gastrointestinal disorders.

The horses were housed in individual stalls with free access to water and mineral salt and were allowed to graze in a paddock for three hours daily. Their baseline diet consisted of 2 kg of commercial concentrate feed (Selvagem®, Agromix, Jaboticabal, SP), split into two daily 1 kg feedings, and 4 kg of Coast cross hay (Cynodon dactylon) offered once daily via suspended haynets.

All horses underwent right paracostal typhlopexy (Uribe Diaz et al., 2010), followed by surgical placement of a cecal catheter using an 18-gauge Levine tube (Bustamante et al., 2022). A 15-day postoperative adaptation period was provided. During this phase, the horses maintained under the same management conditions, with the exception of paddock access being restricted. Hay intake was increased and evenly divided into two daily meals, totaling 8 kg/day.

2.2. Design, experimental evaluations, and sample collection

The ten horses were randomly assigned to two groups (Group I and Group II), each consisting of five animals. Both groups underwent two distinct treatments in a crossover design.

Initially, Group I received the water-saline treatment (WSa). At time point T0 (baseline), 10 L of water was administered via nasogastric tube, followed eight hours later (T08) administration of 5 L of 0.9 % sodium chloride solution intracecally via the previously implanted a Levine catheter. Simultaneously, Group II received the starch-saline treatment (SSa), which involved nasogastric administration of corn starch (17.6 g/kg body weight; Maisena Duryea®, Unilever Brazil, Garanhuns, PE) diluted in 10 L of water at T0, followed eight hours later (T08) by intracecal infusion of 5 L of 0.9 % sodium chloride solution.

Experimental evaluations and cecal content sampling were conducted at six time points: T0 (baseline), and at eight, 12, 24, 48, and 72 h post-nasogastric administration (T08, T12, T24, T48, and T72, respectively).

Fifteen days after completion of the first treatment series (following T72), the groups underwent crossover treatments. Group I received starch-buffer treatment (SB), involving nasogastric administration of corn starch (17.6 g/kg body weight) diluted in 10 L of water at T0, followed by intracecal administration of a buffer solution (20 g/100 kg body weight of aluminum hydroxide [Al(OH)3] + 20 g/100 kg body weight of magnesium hydroxide [Mg(OH)2]; Maia et al., 2017) diluted in 5 L of 0.9 % sodium chloride solution at T08. Concurrently, Group II underwent the water-buffer treatment (WB), consisting of nasogastric administration of 10 L of water at T0, followed by the same buffer solution, as in Group I, intracecally at T08.

All clinical evaluations and sampling procedures were conducted in a blinded manner at the predefined six time points. Clinical assessments included a full physical examination based on equine-specific parameters (Feitosa, 2008), comprising heart rate, rectal temperature (using a veterinary thermometer for large animals), and hydration status via capillary refill time (CRT) and oral mucosa moisture (categorized as moist, slightly moist, or dry). Lameness was assessed using the Obel grading system (Obel, 1948) across a six-meter observation track, where the number of strides was recorded. Abdominal pain was classified as absent, mild, or severe. Additional observations included feed intake (normal or anorexic) and fecal consistency (normal or soft). All these evaluation are outlined in Supplementary Table S1.

Horses exhibiting an Obel grade ≥2 were treated immediately following clinical evaluation and sample collection at T72. Treatment included intravenous administration of phenylbutazone (Equipalazone®; Ceva Saúde Animal Ltda., Paulínia, SP) at 2–4 mg/kg twice daily for five days, followed by oral administration of 1 g phenylbutazone sachets (2 mg/kg) once daily for 10 days. Concurrently, omeprazole (4 mg/kg) was administered orally once daily for 21 days as gastric protection, and cryotherapy was implemented during the first 72 h post-experiment.

Cecal content was collected at each time point using a 60 mL syringe attached to the Levine catheter, with a minimum of 20 mL obtained per sampling. Samples were transferred to sterile Falcon tubes and stored at −20 °C until further analysis.

2.3. Data processing

2.3.1. DNA extraction and analysis

The cecal content samples were first thawed for 24 h at 4 °C (Stewart et al., 2018). DNA was extracted using the QIAamp PowerFecal Pro DNA Kit (Qiagen, Solana Beach, CA) according to the manufacturer’s instructions. After extraction, DNA integrity was assessed using 1 % (w/v) agarose gel electrophoresis, and DNA concentration was measured with a Qubit® 2.0 Fluorometer (Thermo Fisher Scientific, MA, USA) using the Qubit dsDNA HS Assay Kit (Invitrogen®), following the manufacturer’s recommendations.

2.3.2. Amplification and sequencing

The extracted DNA was used to amplify the V4 region of the 16S rRNA gene through PCR (Reaction: 95 °C (3 min) + [98 °C (30 sec), 55 °C (30 sec), 72 °C (45 sec)] x 35 cycles + 72 °C (5 min)), using the primers 515F (5′ GTGYCAGCMGCCGCGGTAA-3′) (Parada et al., 2016) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) (Caporaso et al., 2011). The integrity of the amplified fragments was evaluated using a 2 % agarose gel and visualized on a Dual LED Blue/White Light Transilluminator (KASVI). The fragments were then purified from the 2 % (w/v) agarose gel using the Zymoclean™ Gel DNA Recovery Kit (Zymo Research, Irvine, CA), following the manufacturer’s guidelines. The library was prepared using the Nextera XT Index v2 Kit (Illumina). Library quantification was performed via PCR using the Qubit dsDNA HS Assay Kit (Invitrogen®), and the libraries were pooled equimolarly and sequenced (2 × 150 bp) using the MiSeq® Reagent Kit v2 (300 cycles) on the Illumina platform.

2.4. Statistical and bioinformatics analysis

Sequences obtained from the V4 16S rRNA region sequencing were processed using QIIME2 version 2023.7 (Bolyen et al., 2019). Primer sequences were first verified with USEARCH11 (Edgar et al., 2010) and subsequently removed using the ‘cutadapt’ tool in QIIME2. The DADA2 plugin was used for quality filtering, chimera removal, and effective merging of paired-end reads. Taxonomy was assigned to ASVs (Amplicon Sequence Variants) using the Silva 138.1 database (99 % identity) (Bokulich et al., 2018; Quast et al., 2013). The feature table was filtered to improve data quality, removing ASVs present in only one sample and those identified as chloroplast or mitochondrial sequences. The McirobiomeAnalyst web server (Dhariwal et al., 2017; Lu et al., 2023) was used for diversity analyses. Alpha diversity was assessed through observed ASVs and the Shannon and Chao1 indices, in which significant differences in ASVs were evaluated using the Kuskal-Wallis test. Beta diversity was evaluated using the Jaccard and Bray-Curtis indices (PERMANOVA). Comparative analysis at the genus level was performed using LefSe (Linear discriminant analysis Effect Size), applying a threshold of p < 0.05 and LDA score>2.0. Samples were rarefied to the minimum library size. The R package ExpDes was used to conduct ANOVA to evaluate interactions between treatments and experimental times for relative abundance data. When significant differences were detected via the F-test, means were compared using Tukey’s test. A p-value threshold of <0.05 was applied for all statistical tests.

3. Results

3.1. Sequencing analysis and alpha and beta diversities

Sequencing yielded 5672,268 reads, averaging 47,268 ± 6335 per sample. Following quality control, 4063 ASVs were identified and classified into 22 phyla, 121 families, and 288 bacterial genera. Among these, 98.70 % of ASVs were taxonomically classified at the family level and 80.75 % at the genus level.

Alpha diversity analysis revealed lower richness and evenness following treatments in Group I (p < 0.0001), which included animals subjected to starch-buffer (SB) and water-saline (WSa) treatments, respectively (Fig. 1A and B). The SB treatment showed a marked reduction in alpha diversity starting at eight hours (T08) and persisting at lower levels through 72 h post-treatment. This trend is illustrated in Fig. 1C and D, which depict a sustained decrease over time. Furthermore, significant differences were observed between baseline (T0) and final (T72) diversity levels after the starch-saline (SSa) treatment (Fig. 1C and D). Although not statistically significant (p > 0.05), SSa treatment also showed a decrease in richness and diversity starting at eight hours (SSa_T08) following nasogastric starch administration. Alpha diversity remained unchanged over time for the WB and WSa treatments (p > 0.05) (Fig. 1C and D).

Fig. 1.

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Box plots of alpha diversity (Shannon index and observed ASVs) across groups (A and B) and over time (C and D). Boxes represent the interquartile range; the internal line indicates the median, and the black diamond represents the mean. Treatments: SB = starch-buffer; SSa = starch-saline; WB = water-buffer; WSa = water-saline. Evaluation times: T0 (baseline), T08 (8 h post-nasogastric administration), T12 (12 h post-nasogastric administration), T24 (24 h post-nasogastric administration), T48 (48 h post-nasogastric administration), T72 (72 h post-nasogastric administration). Significant differences (p < 0.05) were determined using the Kruskal-Wallis test with FDR correction. Different letters above boxes indicate statistically different values. Graphs were generated using R and the ggplot2 package.

Beta diversity analysis based on the Bray-Curtis index and PERMANOVA demonstrated microbial community dissimilarities among groups and over time (p = 0.001) (Fig. 2A). In the left plot (Fig. 2A), most SB samples (orange) tended to cluster in the upper-right quadrant, while SSa (green) and WB (blue) samples showed some overlap, particularly in the upper-left quadrant. The WSa group (purple) exhibited greater dispersion, with some samples distant from the others.

Fig. 2.

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Principal Coordinate Analysis (PCoA) using the Bray-Curtis index for bacterial communities across treatments (A) and time points for the SB treatment (B). The percentages in the axis titles represent sample variability. Treatments: WSa = water-saline; WB = water-buffer; SSa = starch-saline; SB = starch-buffer. Evaluation times: T0 (baseline), T08 (8 h post-nasogastric administration), T12 (12 h post-nasogastric administration), T24 (24 h post-nasogastric administration), T48 (48 h post-nasogastric administration), and T72 (72 h post-nasogastric administration). Significant differences (p < 0.05) were determined using PERMANOVA with FDR adjustment. The graphs were generated using R and the ggplot2 package.

The right panel (Fig. 2B) focuses on beta diversity within the SB group over time, revealing significant differences between sampling times (p = 0.001), particularly at T08, T12, T24, and T48. Conversely, samples collected at T0 and T72 in this group displayed tighter clustering (p = 0.001) (Fig. 2B). For the SSa, WB, and WSa treatments, no significant differences in beta diversity were observed over time (p > 0.05; data not shown).

3.2. Taxonomic composition and relative abundance

The bacterial communities in the cecal samples were characterized at the phylum, family, and genus levels for each treatment and sampling time. Among the evaluated treatments, two phyla predominated: Bacillota, with an overall mean of approximately 67 %, and Bacteroidota, with 17.6 %. These were followed by less abundant phyla, including Verrucomicrobiota (7.8 %), Pseudomonadota (2 %), Actinobacteriota (1.4 %), Desulfobacterota (1.2 %), and Spirochaetota (1.2 %).

The phylum Bacillota increased sharply in both the SB and SSa treatments during the first eight hours after starch overload (p = 0.003), peaked at T24, and declined from T48 onward (Fig. 3) (Table S2). In contrast, Bacteroidota in the SB treatment fell within T08, reached 0 % at 24 h, and stabilized after 48 h. Actinobacteriota and Desulfobacterota rose at T72 in the SB and WSa treatments, respectively (Fig. 3) (Table S2). Spirochaetota remained more abundant in the WB and SSa treatments than WSa and SB (p = 0.01), irrespective of sampling time (Fig. 3) (Table S2). No other phylum displayed significant temporal changes (p > 0.05).

Fig. 3.

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Representative graph of the relative abundance of the most abundant Phyla (top 20) in cecal samples of equines across treatments (WSa = water-saline; WB = water-buffer; SSa = starch-saline; SB = starch-buffer) and evaluation times: T0 (baseline), T08 (eight hours post-nasogastric administration), T12 (12 h post-nasogastric administration), T24 (24 h post-nasogastric administration), T48 (48 h post-nasogastric administration), and T72 (72 h post-nasogastric administration).

At the family level, shifts in the SB treatment began eight hours after nasogastric starch administration. Lactobacillaceae increased from T08 and declined after T48 (p = 0.0001), whereas Lachnospiraceae showed the opposite pattern over the same interval (p < 0.05) (Fig. 4) (Table S3). Bacteroidaceae expanded progressively from T48 onward (p < 0.0001) (Fig. 4) (Table S3). In WSa treatment, the only family displaying a significant rise was Desulfovibrionaceae, observed at 72 h (p < 0.05) (Fig. 4) (Table S3), corroborating the LefSe analysis for this time point (data not shown).

Fig. 4.

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Representative graph of the relative abundance of the most abundant Family (top 20) in cecal samples of equines across treatments (WSa = water-saline; WB = water-buffer; SSa = starch-saline; SB = starch-buffer) and evaluation times: T0 (baseline), T08 (eight hours post-nasogastric administration), T12 (12 h post-nasogastric administration), T24 (24 h post-nasogastric administration), T48 (48 h post-nasogastric administration), and T72 (72 h post-nasogastric administration).

Twenty-one genera exceeded 1 % mean relative abundance. Streptococcus was predominant (19.4 %), followed by an uncultivable genus from Lactobacillaceae (6.9 %), WCHB1_41 from the Kiritimatiellae class (4.8 %), Rikenellaceae_RC9_gut_group (4.6 %), and the p-251-o5 group from the Bacteroidia class (4.4 %).

In both SSa and SB treatments, an unidentified genus from the Muribaculaceae family and Blautia declined from T08 and plateaued after 48 h. In SB and WB treatments, Prevotellaceae_UCG-001 decreased significantly, beginning at T08 in SB and at T12 in WB. Within SB treatment, Bacteroides and an uncultured genus from the Lactobacillaceae family mirrored their family-level trends, rising from T48 and T08, respectively (Fig. 5) (Table S4). Although Streptococcus did not reach statistical significance over time (p > 0,05), its mean relative abundance was higher in SB (0,36 %) and WSa (0,31 %) than in SSa (0.07 %) or WB (0.04 %) irrespective of sampling point (p = 0,00) (Table S5).

Fig. 5.

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Representative graph of the relative abundance of the most abundant Genera (top 20) in cecal samples of equines across treatments (WSa = water-saline; WB = water-buffer; SSa = starch-saline; SB = starch-buffer) and evaluation times: T0 (baseline), T08 (eight hours post-nasogastric administration), T12 (12 h post-nasogastric administration), T24 (24 h post-nasogastric administration), T48 (48 h post-nasogastric administration), and T72 (72 h post-nasogastric administration). *Unidentified genera were classified at the higher taxonomic level.

Other less abundant bacterial genera identified in the study did not show significant variations in relative abundance over time within the experimental treatments (p > 0.05). These included Bacillus (3.3 %), Akkermansia (3 %), Oscillospiraceae_UCG-005 (2.4 %), Clostridium_sensu_stricto_1 (2.2 %), Ligilactobacillus (2.1 %), Solibacillus (2 %), Christensenellaceae_R7_group (1.7 %), Enterococcus (1.6 %), Prevotellaceae_UCG-003 (1.6 %), Succinivibrionaceae_uncultured (1.3 %), Marvinbryantia (1.3 %), and Treponema (1.1 %).

3.3. Differential abundance analysis (LefSe) and intersection analysis

LefSe analysis revealed the most discriminative genera among treatments. Within the 30 most abundant taxa, Streptococcus, Bacteroides, Ligilactobacillus, Lactobacillus, Bifidobacterium, and Veillonella were enriched in the SB treatment, with Streptococcus displaying the highest LDA score and therefore serving as the most distinctive biomarker. In SSa treatment, prominent taxa included Oscillospiraceae_UCG-005, Christensenellaceae_R7_group, Rummellibacillus, Marvinbryantia, Acinetobacter, Eubacterium and members of Lachnospiraceae. The WSa treatment was characterised by enrichment of Enterococcus, Bacillus, Akkermansia, and Paenibacillus, whereas WCHB1_41, Bacteroidales_p-251-o5, Rikenellaceae_RC9_gut_group, and Prevotellaceae_UCG-003 were the principal biomarkers in WB treatment (LDA>4.0) (Fig. 6). These patterns underscore pronounced treatment-specific shifts in microbial composition.

Fig. 6.

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LefSe analysis of the equine cecal microbiota, representing genera with significant differences among treatments SB (starch-buffer), SSa (starch-saline), WB (water-buffer), and WSa (water-saline). LDA>2, p < 0.05.

The intersection plot (Fig. 7) summarises taxa shared among treatments. The largest intersection (1093 genera) was common to all four treatments, indicating a substantial core microbiome. Notable pairwise overlap occurred between SSa and WB (678 shared taxa) and among WSa, SSa, and WB (443 taxa), suggesting ecological or functional similarity between these treatments. Smaller intersections, ranging from 240 to 89 elements, highlight treatment-specific communities, pointing to unique microbial signatures that may reflect adaptation to the distinct physicochemical conditions imposed by each intervention. These data help clarify inter-treatment relationships and identify bacterial groups warranting further investigation.

Fig. 7.

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Intersection analysis of shared ASVs among SB, WSa, SSa, and WB treatments. SB = starch-buffer; SSa = starch-saline; WB = water-buffer; WSa = water-saline.

3.4. Clinical evaluation

All animals were clinically evaluated under the same conditions at each experimental time point (T0, T08, T12, T24, T48, T72). No behavioral and systemic alterations were observed in the WSa and WB treatments. In contrast, all horses (100 %) receiving the SB and SSa treatments exhibited elevated heart rates starting eight hours after starch overload. During this same period, two horses (40 %) in the SB treatment developed hyperthermia (Table S6).

In addition to cardiovascular and thermoregulatory changes, soft feces represented the primary gastrointestinal manifestation. All horses (100 %) in the SB treatment exhibited pasty feces, with two horses (40 %) affected at T12 and three (60 %) at T24. Fecal consistency normalized by T48. In the SSa treatment, all horses (100 %) displayed pasty feces exclusively at T24, with resolution within the same time frame. Additional clinical signs, including abdominal discomfort (recumbency), dehydration, and inappetence, were observed solely in the SB treatment. Two horses (50 %) showed abdominal pain through intermittent recumbency, while all five animals (100 %) experienced dehydration (5–10 %) and reduced appetite between T08 and T72 (Table S6).

Locomotor activity was assessed by counting the number of steps within a predefined six-meter space, with a baseline range of 8–9 steps per animal. In the SSa treatment, three horses (60 %) demonstrated increased step counts (10 steps) at T48 and T72. One of these horses exhibited an Obel grade 3 lameness score at T72, characterized by head nodding when bearing weight on the left forelimb. In the SB treatment, all animals (100 %) exhibited increased step counts, beginning at T24 for one horse (20 %) and at T48 for the remaining four horses (80 %), with counts ranging from 10 to 14 steps. Of these, three horses displayed Obel grades ranging from 2 to 4 (Table S6). Two horses alternated weight bearing between forelimbs and hindlimbs, consistent with moderate discomfort (grades 2–3), while one horse demonstrated severe impairment, intermittently assuming sternal recumbency and struggling to maintain quadrupedal stance (grade 4).

4. Discussion

This study aimed to characterize the cecal microbiota of horses subjected to dietary corn starch overload, with or without intracecal administration of an alkalinizing solution. The main findings revealed substantial alterations in microbial diversity and composition in response to starch overload and alkalinizing treatment.

A decline in alpha diversity indices, reflecting species richness and evenness, is commonly linked to microbial imbalance or dysbiosis (Boucher et al., 2024). Such reductions typically result from the overgrowth of specific taxa during intestinal disturbances (Costa et al., 2012) or carbohydrate overfeeding (Hesta & Costa, 2021). In this study, a decrease in alpha diversity was expected in the SB (starch + buffer) and SSa (starch + saline) groups. Although diversity declined in both groups eight hours post-starch administration, only the SB group showed a statistically significant reduction, indicating greater microbiota instability. Beta diversity analysis revealed sample clustering according to experimental groups, even among samples collected at different time points. For example, horses in the WSa and SB treatments exhibited similar microbial compositions over time, as did those in the SSa and WB treatments. The reasons behind these patterns remains unclear.

Maintaining an optimal cecal pH is crucial for microbial homeostasis. The alkalinizing solution used in this study aimed to buffer pH fluctuations resulting from starch fermentation, creating conditions favorable for pH-sensitive bacteria. However, in the SB treatment, the solution failed to prevent significant microbiota changes eight hours post-dysbiosis induction. Most horses exhibited microbial alterations lasting up to 48 h, with recovery initiating at 72 h. One animal remained dysbiotic, highlighting interindividual variability in resilience (Dougal et al., 2014). This lack of immediate effect aligns with prior findings on the limited impact of buffer solutions on fecal microbiota (Bustamante et al., 2022). Notably, the detection methods in this study targeted bacterial DNA, not viability, which may explain the persistence of dysbiosis despite pH normalization.

A marked decrease in the relative abundance of Lachnospiraceae family (acid-sensitive fibrolytic bacteria) (Harlow et al., 2016; Park et al., 2021), was observed between T08 and T24 in the SB treatment. This reduction coincided with diarrhea in all treated horses and corroborates previous findings (Onishi et al., 2012; Santos et al., 2009). Additionally, clinical signs such as tachycardia, hyperthermia, dehydration, and lameness were observed, indicating systemic inflammation. The connection between systemic inflammatory response syndrome (SIRS) and laminitis is well established, with SIRS serving as a precursor to laminar injury in carbohydrate overload models (Faleiros & Belknap, 2017). Similar to previous studies (Leise et al., 2011; Lima et al., 2013), hyperthermia and tachycardia were observed prior to the onset of lameness, which began at 24 h post-overload. French and Pollitt (2004) and Lima et al. (2016) also reported SIRS signs, including diarrhea, inappetence, dehydration, fever, and tachycardia, between eight and 48 h following carbohydrate overload. These findings support the hypothesis that microbiota disruption increases intestinal permeability and absorption of bacterial endotoxins such as LPS through a compromised mucosal barrier (Faleiros & Belknap, 2017). The timing of these effects coincides with a decline in fibrolytic bacteria, such as Lachnospiraceae, and an increase in amylolytic taxa, suggesting a direct link between microbial shifts and systemic clinical manifestations. The Lachnospiraceae family plays a vital role in maintaining intestinal metabolic balance (Costa et al., 2012; Li et al., 2022). These bacteria ferment sugars to produce volatile fatty acids (VFAs), particularly butyrate (Biddle et al., 2013; Vacca et al., 2020), a key energy source for intestinal epithelial cells and a potent anti-inflammatory metabolite essential for colonic health (Schoster et al., 2017).

The observed increase in Bacillota and decrease in Bacteroidota in the SB treatment further support the hypothesis that the magnesium and aluminum hydroxide solution influenced microbiota composition. Bacillota abundance increased within the first eight hours peaking at 24 h post-starch overload, while Bacteroidota dropped sharply at 24 h and stabilized thereafter. This hydroxide solution has been used for treating large colon impactions in horses (Lopes et al., 2004; Tillostson & Traub-Dargatz, 2003), due to its osmotic properties that promote intraluminal water influx and facilitate impaction resolution (McGorum & Pirie, 2009). However, magnesium salts can irritate the intestinal mucosa, causing local inflammation and disrupting microbial homeostasis (Belkaid & Hand, 2014; Milani et al., 2015). While the concentration of salts was not measured in this study, literature suggests that approximately 20 % of magnesium is absorbed in the small intestine when administered orally (Hintz & Cymbaluk, 1994; Schryver et al., 1987). Direct cecal administration, as done here, may allow magnesium to reache the large intestine in full, possibly contributing to both microbial and clinical alterations such as pasty feces, dehydration, and apathy. However, given the limited scope of our observations, these potencial effects cannot be confirmed and should be interpreted with caution. Futher studies, including intestinal pH measurements and/or inflammatory markers assessments, are needed to clarify these findings. Moreover, such responses may also be exacerbated by the abrupt delivery of soluble carbohydrates to the cecum and colon (Lima et al., 2013; Milinovich et al., 2010).

According to LefSe analysis, the SB treatment was associated with an increase in amylolytic bacterial genera including Streptococcus, Bacteroides, Ligilactobacillus, Lactobacillus, Bifidobacterium, and Veillonella, which were prevalent during the experiment, corroborating the findings of Moreau et al. (2014) and Onishi et al. (2012). These taxa belong to Bacillota and Bacteroidota, dominant phyla in the equine hindgut (Kauter et al., 2019). Many of these bacteria rapidly proliferate under conditions of lactic acid accumulation and low pH, using soluble carbohydrates as their primary carbon source (Biddle et al., 2013). Some Bacteroides species also metabolize starch, such as the genus Bacteroides sp. (Flint & Duncan, 2014), producing propionate and releasing by-products from protein degradation that can trigger mucosal inflammation (Zafar & Saier, 2018), explaining their higher abundance during the first 48 h in the microbiota of horses in SB treatment. The resulting barrier disruption could allow systemic entry of toxins, contributing to the clinical findings observed (Faleiros & Belknap, 2017). Although Moreau et al. (2014) did not detect Bacteroides in their model, all starch-overloaded horses in that study developed Obel grade 2 laminitis within 26–29 h, similar to the timing and clinical findings in this study. Veillonella, a known lactate-metabolizing genus found in the gut and oral microbiota (Carlier, 2015), was also strongly associated with the SB treatment, likely due to its ability to utilize elevated lactic acid levels.

These microbial shifts likely resulted from exceeding the small intestine’s starch-digesting capacity, causing undigested starch to reach the cecum, where it was fermented. The equine cecum plays a central role in fiber fermentation, enabling nutrient extraction from forage-rich diets. However, excessive soluble carbohydrates favor fermentative taxa, lower pH, and disrupt the native microbial balance (Collinet et al., 2021).

Genera such as Enterococcus, Bacillus, and Akkermansia, enriched in the WSa treatment by LefSe analysis, are beneficial for equine gut health. Enterococcus, a facultative anaerobic microorganisms and homofermentative lactic acid producers (Švec & Devriese, 2015), can be both pathogenic and probiotic (Krawczyk et al., 2021), with some strains producing bacteriocins (Almeida-Santos et al., 2021) or exhibiting antibiotic resistance (Higuita & Huycke, 2014). Bacillus species, especially B. subtilis PB6, support gastrointestinal integrity and defense across species (Burke & Moore, 2017; Ryan et al., 2023; Word et al., 2022). Notably, these genera decreased between 8 and 48 h in starch-treated horses (SB and SSa), potentially weakening gut barriers and contributing to clinical symptoms.

Akkermansia, from the phylum Verrucomicrobiota, is another beneficial genus, originally isolated from healthy individuals (Derrien et al., 2004, 2015), and present in the equine large intestine, particularly in those consuming oligosaccharides or high-fiber diets (Costa et al., 2015; Lindenberg et al., 2021; Raspa et al., 2024). This genus degrades mucin and contributes to immune modulation and barrier maintenance (Everard et al., 2013; Lindenberg et al., 2019). A decline in Akkermansia was observed only in the SB treatment, suggesting its protective role in SSa-treated horses, which exhibited milder clinical signs, such as changes in fecal consistency (pasty) only at T24, and less intense lameness, with only two animals exhibiting mild Obel grade symptoms. Similar protective effects may apply to Lachnospiraceae and other fiber-degrading taxa enriched in the SSa treatment by LefSe analysis. Additionally, continuous hay feeding likely helped stabilize these microbial populations, further supporting the hypothesis that alkalinizing agents exacerbated starch-induced dysbiosis and associated symptoms in the SB treatment.

Intersection analysis of microbiota composition revealed considerable overlap between treatments, particularly between SSa and WB, suggesting ecological or functional similarities. In contrast, smaller intersections (240–89 taxa) highlight treatment-specific microbial features potentially shaped by environmental or physiological conditions. These findings may guide future efforts to identify bacterial groups of interest. However, further research is necessary to determine whether these taxa can serve as reliable biomarkers of intestinal health. Investigating the expression of immune-related genes may clarify their influence on host immune responses. Evaluating these bacteria as therapeutic or prophylactic probiotics could improve equine gut health. Moreover, studies assessing different alkalinizing agents, dose-dependent effects, and microbiota restoration strategies are warranted to enhance dysbiosis management and preserve microbial diversity and function in horses.

An important limitation of this study is the high starch dosage administered (17.6 g/kg of body weight), which substantially exceeds the nutritional recommendations for equines. The National Research Council (NRC) limiting starch intake to 0.2–0.4 % of body weight per meal (approximately 1–2 g/kg) to prevent undigested starch from reaching the hindgut, where its fermentation may lead to acidosis and dysbiosis (Bulmer et al., 2015; Hoffman, 2009). Previous studies have demonstrated that starch intakes above 1.5–2 g/kg per meal significantly increase risk of colic, laminitis and microbiota disruption (Halpin et al., 2020; O’Connor, 2025). Although the dose used in this study exceeds typical feeding practices, it was chosen to simulate acute overload scenarios, such as those resulting from feeding errors, unrestricted access to starch-rich feeds, or abrupt dietary changes. This experimental model is consistent with established protocols and effectively induces clinically relevant microbial and metabolic alterations, enabling a robust assessment of starch overload and the modulatory effects of intracecal buffering.

5. Conclusion

Direct intracecal administration of an aluminum- and magnesium-hydroxide solution failed to prevent dysbiosis induced by excessive starch intake in horses. Instead, it exacerbated the loss of microbial richness and evenness, characterized by a marked expansion of lactic-acid-producing genera (Streptococcus and Lactobacillus) and a concurrent depletion of fiber-degrading, barrier-protective taxa from the families Lachnospiraceae and the phylum Verrucomicrobiota. This microbial shift was accompanied by clinical manifestations consistent with systemic inflammatory response syndrome and the early stages of laminitis.

Although neutralising cecal pH may attenuate some consequences of starch fermentation, the complexity of the equine hind-gut microbiome underscores the need for additional studies, considering that DNA from dead bacteria may persist in the intestinal environment, thereby limiting the temporal interpretation of the observed microbial changes. Therefore, future investigations could employ refined methodological approaches, such as the use of viability-based DNA extraction techniques (e.g., propidium monoazide-PCR), or metatranscriptomic and metabolomic analyses, to better distinguish between active and inactive microbial populations and to more dynamically understand the responses of the equine microbiome to dietary and therapeutic interventions.

Funding

This research was funded by the São Paulo Research Foundation (FAPESP), under process numbers 2015/24860–4 and 2020/09633–0; and by the National Council for Scientific and Technological Development (CNPq), under process number 305377/2017–5.

Ethical statement

The protocol code of the Animal Use and Care Committee of São Paulo State University (protocol code 23,391/15 – July 4, 2016).

Data availability

The dataset analyzed in this study is publicly available at https://www.ncbi.nlm.nih.gov/bioproject/509648 (accessed on January 30, 2023).

CRediT authorship contribution statement

Caio Carvalho Bustamante: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. Pâmela A.Maldaner Pereira: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. Camila Cesáreo Fernandes: Visualization, Methodology, Formal analysis. Paulo Alescio Canola: Writing – review & editing, Visualization, Formal analysis, Conceptualization. Renata G.S. Doria: Writing – review & editing, Visualization, Formal analysis, Conceptualization. Marcio C. Costa: Writing – review & editing, Visualization, Methodology, Formal analysis, Conceptualization. Carlos Augusto A. Valadão: Writing – review & editing, Visualization, Supervision, Project administration, Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.vas.2026.100619.

Appendix. Supplementary materials

mmc1.docx (50.9KB, docx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.docx (50.9KB, docx)

Data Availability Statement

The dataset analyzed in this study is publicly available at https://www.ncbi.nlm.nih.gov/bioproject/509648 (accessed on January 30, 2023).

Articles from Veterinary and Animal Science are provided here courtesy of Elsevier

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