Hydroxypropyl starch enhances gut health in geese

Abstract

This study aimed to investigate the effects of hydroxypropyl starch (HPS) on intestinal morphology, serum biochemical parameters, gut microbiota, and ileal content metabolome in Yangzhou geese. A total of 240 30-day-old Yangzhou geese with similar body weight were randomly assigned to three groups: control group (CG), hydroxypropyl starch group (HS), and sodium urate group (SU). Each group had four replicates with 20 geese per replicate. After a 21-day feeding period, serum, kidney, and terminal ileum samples were collected for analysis. The results showed that HPS reduced serum uric acid levels and improved intestinal structural integrity, although the changes in both parameters did not reach statistical significance (P > 0.05), but showed a certain trend of improvement. Histological examination revealed that the HS group maintained intact cell morphology with no inflammatory cell infiltration, cell infiltration, or glomerular epithelial cell shedding. In contrast, the CG group exhibited nearly complete shedding of glomerular epithelial cells, enlarged intercellular spaces, and some cell infiltration, while the SU group showed glomerular epithelial cell shedding, blurred cell boundaries, and presence of inflammatory cells. HPS supplementation exhibited a tendency to increase the operational taxonomic unit (OTU) index, and significantly increased the abundance of Rothia (P < 0.05). Ileal content metabolomics analysis revealed that in the HS group, differentially expressed metabolites were mainly enriched in multiple amino acid biosynthesis pathways, with cysteine identified as a key metabolite (P < 0.05); whereas in the SU group, differentially expressed metabolites were mainly enriched in the primary bile acid biosynthesis pathway, with bile acids as key metabolites (P < 0.05).

Introduction

At present, the scale of goose farming in China has reached 90% of the world's total, and it has shown a steady upward trend in recent years (Dumlu, 2024). With the continuous development of the goose breeding industry, in order to improve production efficiency, breeding enterprises have reduced the use of premium cellulose (Kjeller et al., 2024). The premium cellulose has the effect of enriching the intestinal flora (Xu et al., 2017), and will be fermented into short-chain fatty acids (SCFAs) by microorganisms in the intestine (Barszcz et al., 2024). SCFAs has the function of maintaining the stability of intestinal microorganisms and uric acid metabolism and transport (Martínez-Nava et al., 2023; Tong et al., 2022; Vieira et al., 2017). Geese are herbivorous animals, and the lack of intake of premium cellulose will lead to an imbalance of intestinal microorganisms in geese and cause the occurrence of gout (Heitel et al., 2018; Ho et al., 2015). Therefore, it is necessary to find a premium cellulose that can be added to the diet to meet the physiological needs of geese.

Currently, gout is an important factor causing the death of geese, which has brought great damage to the husbandry industry (Ni et al., 2021). Etiologically, dietary factors play a crucial role, pharmacological interventions such as the use of drugs promoting uric acid excretion or anti-inflammatory agents, have shown some efficacy in clinical trials (Liu et al., 2022). Dietary adjustments remain fundamental. Reducing the protein level and optimizing the diet formulation to correct nutrient imbalances can alleviate symptoms (Chi et al., 2020; Crane et al., 2013). Studies have shown that resistant starch and prebiotics can also inhibit the occurrence of inflammation by changing the structure and microbial composition of the intestine and enhancing the gut immunity to prevent the occurrence of gout (Javad et al., 2017). In this study, the effects of hydroxypropyl starch on intestinal microbes and metabolites in geese were investigated from the perspective of nutrition.

As a type of resistant starch, HPS exhibits various biological activities and can be completely degraded. It is one of the important additives to improve animal health, prevent diseases and promote growth (Jiang et al., 2024). HPS is not digested and absorbed in the small intestine (Bojarczuk et al., 2022; Kraithong et al., 2022), but can be degraded by zymosis in the large intestine. It can improve the intestinal microflora of livestock and poultry, regulate the proportion of Bacteroides and Firmicutes in the intestine, increase the production of short-chain fatty acids SCFAs, inhibit the occurrence of inflammation, and thus enhance the body 's immunity (Le Leu et al., 2007; Metzler-Zebeli et al., 2019; Qian et al., 2013).

Emerging evidence underscores a close association between the gut microbiota and the pathogenesis of gout. Significant differences in intestinal microbial composition have been observed between geese with gout and healthy controls (Xi et al., 2022). Dysbiosis of the gut flora can disrupt uric acid (UA) excretion and activate inflammatory signaling pathways, thereby elevating systemic inflammation and impairing immunity, which may contribute to gout development (Ma et al., 2021). The intestine accounts for approximately one-third of UA excretion. Upon secretion into the gut, UA is metabolized by certain bacteria, such as Escherichia coli, Clostridium, and Pseudomonas (Karlsson and Barker, 1949). Elevated serum UA can alter gut microbial composition, while modulating the microbiota may in turn help normalize UA levels and alleviate gout. Short-chain fatty acid-producing bacteria, including Lactobacillus and Akkermansia muciniphila, not only participate in purine metabolism via microbial enzymes but also regulate UA transporter expression through metabolites like acetate, propionate, and butyrate (Cui et al., 2026; Qi et al., 2025), thereby influencing intestinal UA excretion. Given that HPS has been shown to modulate gut microbiota and SCFAs production, this study aimed to investigate whether HPS could ameliorate gout in geese via microbial regulation.

To date, the application of HPS in animal studies has been limited. In this experiment, dietary HPS was supplemented to observe its effects on uric acid metabolism in geese. By integrating 16S rDNA gene sequencing and metabolomic approaches, we further elucidated how HPS influences the gut microbiota and metabolic profile of goslings, offering novel insights into the prevention and management of gout.

Materials and methods

Birds and diets

For this experiment, 240 Yangzhou geese aged 25 days with similar body weights were obtained from Bengbu Huaxin Poultry Co., Ltd. After a 5-day adaptive feeding period, the trial commenced. The 240 geese were randomly divided into 3 groups, each containing 4 replicates with 20 geese per replicate. The experiment lasted for 21 days. The dietary regimens for the three experimental groups were as follows: the control group (CG) was fed a basal diet supplemented with 5% corn starch; the hydroxypropyl starch group (HS) received a diet supplemented with 5% HPS; and the sodium urate group (SU) followed the same diet as the HS group (supplemented with 5% HPS) but was administered 30 mg/d sodium urate via gavage during the final 4 days to observe the improved intestine's resistance to UA with HPS supplementation. After the addition of supplements, the dietary crude protein level was adjusted to 21%. The geese were housed in raised wire-floor cages, which had been sanitized prior to the experiment. Throughout the trial, all geese had ad libitum access to feed and water. The composition and nutrient levels of the basal diet were formulated to meet the nutrient requirements of geese recommended by the National Research Council (National Research Council, 1994). The composition and nutritional components of the experimental diet are shown in Table 1.

Table 1.

Composition and nutrient levels of the basal diet.

Item		CG	HPS	SU
Ingredient (%)
Corn		45	45	45
Soybean meal		23	23	23
Wheat grain		9	9	9
Fish meal		7.5	7.5	7.5
Wheat bran		7	7	7
Corn Starch		5	0	0
HPS Hydroxypropyl starch		0	5	5
Limestone		0.7	0.7	0.7
DL-Methionine		0.3	0.3	0.3
NaCl		1.5	1.5	1.5
Premix1		1	1	1
Total		100	100	100
Nutrient levels (%)
ME (MJ/kg)	11.38		11.16	11.16
Crude Protein	21.13		21.12	21.12
Ether extract	2.82		2.81	2.81
Crude fibre	3.30		3.30	3.30

Note: ¹ The premix provides the following per kilogram of diet: Vitamin A (VA) 4500 IU, Vitamin D (VD) 1000 IU, Vitamin E (VE) 30 IU, Vitamin K₃ (VK₃) 1.3 mg, Vitamin B₁ (VB₁) 12.2 mg, Vitamin B₂ (VB₂) 10 mg, Vitamin B₆ (VB₆) 4 mg, Vitamin B₁₂ (VB₁₂) 1.013 mg, Niacin 20 mg, Folic acid 0.5 mg, Biotin 0.04 mg, Calcium (Ca) 7.5 mg, Copper (Cu) 7.5 mg, Iron (Fe) 60 mg, Zinc (Zn) 65 mg, Manganese (Mn) 110 mg, Iodine (I) 1.1 mg, Selenium (Se) 0.15 mg.

Sample collection

After the feeding trial, eight geese were randomly selected from each replicate for carotid artery blood collection. Blood samples were allowed to stand at room temperature for 20 minutes and then centrifuged at 4,000 × g for 10 minutes at 4°C to separate the serum. The serum was stored at −20°C. The experimental geese were euthanized by cervical dislocation, after which a portion of the left kidney and the terminal ileum was collected and fixed in 4% paraformaldehyde. In addition, two samples of the left kidney, two of the terminal ileum, and two of the terminal ileal contents were immediately placed into liquid nitrogen for preservation. After all samples were collected, they were transferred to a − 80°C freezer for long‑term storage.

Histomorphological observation

Ileal tissue samples underwent hematoxylin and eosin (HE) staining. Histopathological images were acquired using an Eclipse Ci-LL microscope (Nikon, Japan). Villus height and crypt depth were measured from the captured images with Image-Pro Plus 6.0 software (Media Cybernetics, USA), and the villus height to crypt depth (V/C) ratio was subsequently calculated.

Ileal metabolomics sequencing

Metabolomic profiling was performed on the CG, HS, and SU groups. The analytical methodology was supported by Gene Denovo Co., Ltd. (Guangzhou, China). Samples were separated using hydrophilic interaction liquid chromatography (HILIC) on an ACQUITY UPLC BEH Amide column (1.7 μm, 2.1 mm × 100 mm; Waters, Ireland) coupled with an Agilent 1290 Infinity LC system. Mass spectrometry detection was conducted using an AB Triple TOF 6600 spectrometer in both positive and negative ion modes. Differential metabolites were selected based on a variable importance in projection (VIP)>1.0, fold change (FC)>1.0, and a significance threshold of P < 0.05. Metabolites meeting these criteria were considered differentially abundant and were subsequently subjected to pathway enrichment analysis.

16S rDNA sequencing of the cecum contents

The DNA of the sample was extracted. Avari able region of 16S rDNA was amplified and sequenced using a high-throughput sequencer. The methods were provided by the Wekemo Bioincloud Company (Shenzhen, China). The amplified region was 16SV34, and the primers used were CCTA YG GGRBGCASCAG (341, F, 5′–3′) and GGA CTA CNNGGG TAT CTAAT (806, R, 5′–3′). All the raw sequences (input) of the samples were quality controlled (filtered), denoised, merged, and nonchimeric using the Qiime2 software DADA2 plugin. Subsequently, the operational taxonomic units (OTUs) were identified. Furthermore, the species diversity of a sample was evaluated using operational taxonomic unit (OTU) (observed-features, Shannon, Chao1, Simpson, and Faith’s phylogenetic diversity) indices.

Data analysis

Univariate analysis of variance (ANOVA) was performed using the General Linear Model (GLM) procedure in IBM SPSS Statistics 16 (IBM Corporation, Somers, New York, USA). When the ANOVA results indicated significant differences among groups, post hoc pairwise comparisons were conducted using Duncan’s multiple range test. The statistical significance level was set at P < 0.05. Different lowercase letters indicate significant differences(P > 0.05), whereas the same letter or no letter indicates no significant difference(P > 0.05).

Results

Uric acid content in serum

Currently, serum uric acid (UA) concentration is the primary indicator for diagnosing gout in poultry. As shown in Fig. 1, the HPS group exhibited a decreased serum UA level compared with the CG and SU groups, whereas the SU group showed a higher serum UA level than both the CG and HPS groups. Although HPS demonstrated a trend toward lowering UA levels, none of the differences among the three groups reached statistical significance (P > 0.05).

Fig. 1.

Serum uric acid levels in each group. control group (CG), hydroxypropyl starch group (HS), sodium urate group (SU).

Tissue morphology observation

The histological structure of the ileum is shown in Fig. 2. As presented in Table 2, the addition of HPS in the HS group increased both villus height and crypt depth in Yangzhou geese, but the differences were not statistically significant compared with the CG group and the SU group (P > 0.05). The ratio of villus height to crypt depth in the SU group was significantly lower than that in the CG group and the HS group (P < 0.05), suggesting that excessively high uric acid levels in vivo may disrupt intestinal structural integrity.

Fig. 2.

The intestinal morphology after different treatments. The unified ruler is 200 μm. Ileal tissue sections of CG group (A), HS group (B), SU group (C).

Table 2.

Ileal intestinal morphology in different experimental groups.

Items	CG	HS	SU	P-value
Villus height	912.52 ± 216.98	1065.94 ± 85.17	884.56 ± 107.72	0.228
Crypt depth	172.89 ± 39.78	221.78 ± 20.21	217.02 ± 19.15	0.067
Villus height/Crypt depth(V/C)	5.2925 ± 0.59a	4.8375 ± 0.54ab	4.0675 ± 0.26b	0.018

In the HS group, the overall cellular morphology remained intact, with no inflammatory cells, no evidence of cell infiltration, and no detachment of glomerular epithelial cells (Fig. 3A). Compared with the HS group, the CG group exhibited almost complete detachment of glomerular epithelial cells, intercellular spaces forming vacuoles, and some degree of cell infiltration (Fig. 3B). Compared with the HS group, the SU group showed detachment of glomerular epithelial cells, blurred cell boundaries, and the presence of inflammatory cells (Fig. 3C).

Fig. 3.

Kidney sections after different treatments. The unified ruler is 200 μm. Ileal tissue sections of CG group (A), HS group (B), SU group (C).

16S rDNA amplicon sequence analysis

The analysis yielded a total of 753, 734, and 544 operational taxonomic units (OTUs) in the CG, HS, and SU groups, respectively. Among these, 247 OTUs were shared across all three groups, while 259, 338, and 127 were unique to the CG, HS, and SU groups, respectively (Fig. 4). These results indicate that HPS supplementation did not substantially alter the total number of OTUs in the ileal content. In contrast, excessive UA intake resulted in a marked reduction in ileal microbial OTUs.

Fig. 4.

Wayne diagram between different treatment groups.

No significant differences were observed among the three groups for alpha-diversity indices reflecting species richness, including the Observed-species, Chao1, ACE, and Simpson indices (P > 0.05; Table S1). However, the Shannon and PD-whole-tree indices in the HS group were significantly higher than those in both the CG and SU groups (P < 0.05; Table S1), suggesting that HPS inclusion increased microbial evenness and phylogenetic diversity.

Beta diversity analysis, reflecting compositional differences between groups, was performed using Canberra distance-based constrained principal coordinate analysis (CPCoA). The first two principal coordinates, CPCoA1 and CPCoA2, explained 46.20% and 30.24% of the total variation, respectively (Fig. 5). The clear separation among the three groups along these axes indicates significant differences in microbial community structure.

Fig. 5.

PCA analysis results under different treatments.

The effect of HPS on the distribution of intestinal microorganisms

At the phylum level, the intestinal microbiota across the three treatment groups was predominantly composed of Bacteroidota and Firmicutes (Fig. 6A). The relative abundances of Bacteroidetes were 1.83%, 3.88%, and 0.68% in the CG, HS, and SU groups, respectively. In contrast, Firmicutes abundances were higher, reaching 64.05%, 72.36%, and 75.37% in the same groups. Notably, the relative abundance of Proteobacteria, a phylum often associated with potential pathogens, was 21.31% in the CG group and 12.52% in the SU group, but was markedly lower at 4.26% in the HS group. These shifts suggest that hydroxypropyl starch supplementation enriched dominant beneficial phyla while reducing the prevalence of potentially harmful bacteria.

Fig. 6.

(A) The top ten microorganisms at the phylum level (B) The top ten microorganisms at the genus level.

At the genus level, Romboutsia was identified as a major constituent in all groups (Fig. 6B), with relative abundances of 17.38%, 32.48%, and 22.23% in the CG, HS, and SU groups, respectively. Compared to the CG and SU groups, the HS group exhibited increased abundances of the beneficial genera Turicibacter (3.32%, 5.65%, and 2.20%, respectively) and Lactobacillus (1.34%, 6.40%, and 1.46%, respectively). Concurrently, the HS group showed a reduction in the relative abundances of the potentially pathogenic genera Escherichia-Shigella (11.04%, 0.29%, and 5.09%, respectively) and Helicobacter (0.53%, 1.22%, and 6.37%, respectively) (Fig. 6B).

Screening of specific bacteria between each group

Fig. 7 presented the phylogenetic clade diagram and illustrated the distribution of Linear Discriminant Analysis (LDA) scores for the intestinal microbiota. At this level, the abundances in the CG group were up-regulated: Bacillales, Aerococcaceae, Planococcaceae, Facklamia, Peptostreptococcaceae bacterium SK031, and Facklamia-tabacinasalis. The abundance of Enterococcus_cecorum, Clostridia_UcG_014, Subdoligranulum and Clostridium_leptum in the HS group was up-regulated. In the SU group, the abundance of Clostridium_sensu_stricto_1 was up-regulated.

Fig. 7.

(A) LEFSE analysis of the significantly different microorganisms (LDA). Each transverse column represents a species. The length of the column corresponds to the LDA value. A higher LDA score indicates greater differences. The color pairs of the columns are the characteristic microorganisms to which the species belongs. The characteristic microorganisms (biomarkers) had relatively high abundance in the corresponding group. (B) LEFSE analysis of the significantly different microorganisms (cladogram). From the inside to the outside, the cladogram corresponds to different classification levels including phylum, order, family, and genus. The lines between the levels represent the relationships. Each circular node represents a species. Green nodes indicating no significant difference between the groups and non-green nodes indicating that the species is a characteristic microorganism in the corresponding color group (in which the abundance is significantly increased). The colored sector regions mark the subordinate taxonomic intervals of the characterized microorganisms.

Metabolomic analysis

Analysis of basic metabolites

HPS significantly altered the metabolite profile of the ileum contents of Yangzhou geese. The results of PCA analysis on the preprocessed data in positive and negative ion modes for all samples are shown in (Fig. 8). To further elucidate the metabolic impact of HPS, orthogonal partial least squares-discriminant analysis (OPLS-DA) models were constructed for pairwise group comparisons. The models demonstrated high stability and reliability, as indicated by R² values of 0.88 (CG vs. HS), 0.89 (CG vs. SU), and 0.84 (HS vs. SU) (Fig. 9B, D, F). A permutation test (200 iterations) confirmed model validity, with both R² and Q² intercepts decreasing as the retention of permutation decreased, and the regression line of Q² points ascending, confirming no overfitting (Fig. 9). Collectively, both PCA and OPLS-DA results demonstrate that HPS significantly modulates intestinal metabolism in Yangzhou geese, leading to distinct metabolite profiles among the dietary groups.

Fig. 8.

The abscissa represents the first principal component score, denoted as PC1; and the ordinate is the second principal component score, recorded as PC2. PCA component maps of different treatment groups (A) CG group, (B) HS group (C), US group.

Fig. 9.

OPLS-DA component diagram of different treatment groups (A) CG group, (C) HS group, (E) US group, replacement test diagram of different treatment groups (B) CG group, (D) HS group, (F) US group.

Identification and comparative analysis of differential metabolites

The distribution of differential metabolites across group comparisons is summarized in the multi-group scatter plots (Fig. 10). Compared with the CG group, the HS group exhibited 173 differential metabolites (104 up-regulated, 69 down-regulated). Key up-regulated metabolites included N‑leucine, cysteine, L‑leucine, hippuric acid, and 3‑butyric acid, whereas down-regulated metabolites comprised glycerol phosphorylcholine, glycocholic acid, L‑threonine, indole‑3‑butyric acid, and L‑homocitrulline. In the SU versus CG comparison, 106 metabolites were altered (48 up-regulated, 58 down-regulated). Notable up-regulated compounds were 3‑methyl‑2‑oxovalerate, 4‑pyridyloxy acid, creatinine, and 2‑deoxyribose 5‑phosphate, while down-regulated metabolites included taurine deoxycholate, cholic acid, cysteine‑sulfate, cysteine, and phosphoric acid. Between the SU and HS groups, 148 differential metabolites were identified (78 up-regulated, 70 down-regulated). Up-regulated metabolites featured 2‑oxoadipic acid, glycocholic acid, 3‑indolebutyric acid, L‑homocitrulline, and DL‑serine; down-regulated ones included cysteine‑sulfate, uracil, D‑ornithine, 1‑deoxynojirimycin, and 3‑methylxanthine.

Fig. 10.

Scatter plot of differential metabolites. The red dots indicate up-regulation between groups, and the green dots indicate down-regulation between groups.

KEGG enrichment analysis of differential metabolites

KEGG pathway enrichment analysis revealed distinct metabolic perturbations among the experimental groups (Fig. 11). In the CG vs. HS comparison, 173 differential metabolites were identified and significantly enriched (P < 0.05) in 5 pathways, primarily involving glycine, serine, and threonine metabolism; d-amino acid metabolism; prolactin metabolism; and starch metabolism (Fig. 11A). For the CG vs. SU comparison, 106 differential metabolites were significantly enriched (P < 0.05) in 2 pathways: secondary bile acid biosynthesis and primary bile acid biosynthesis (Fig. 11B). Furthermore, the HS vs. SU comparison yielded 148 differential metabolites, with significant enrichment (P < 0.05) in 4 pathways, including the prolactin signaling pathway, galactose metabolism, and inositol phosphate metabolism (Fig. 11C).

Fig. 11.

The bubbles in the figure represent the effect of HPS on the metabolic pathway of the sample. The larger the bubbles and the redder the color, the greater the effect on the pathway. There are three comparisons: (A) CG VS HS, (B) CG VS SU, and (C) HS VS SU.

Correlation analysis of microbial 16 s omics and metabolomics

At the genus level, Pearson correlation coefficients between differential metabolites and differential microorganisms were calculated, yielding a genus-level correlation coefficient table (Table S2), and the results of this analysis were visualized as a heatmap (Fig. 12). The results indicated a strong positive correlation between the elevated abundance of Turicibacter, Rothia, and Faecalitalea in the HPS group and the SCFA-related metabolite d-erythrose 4-phosphate. Conversely, a high abundance of Peptostreptococcus and Globicatella in the SU group showed a significant correlation with uracil levels.

Fig. 12.

Heat map showing the correlation between microbial species and metabolites. Positive correlation is shown in red; negative correlation is shown in blue. The darker the color, the stronger the correlation, * is used to indicate that “species-metabolite” and significant correlation (P < 0.05).

Discussion

This study demonstrates that dietary supplementation with hydroxypropyl starch (HPS) modulates the composition of the intestinal microbiota and its metabolite profiles in Yangzhou geese, thereby improving intestinal morphology and barrier function. Given the established association between gut dysbiosis and hyperuricemia in poultry, and considering that gout is a major contributor to gosling mortality, serum uric acid levels and renal inflammation were examined as secondary outcome measures in this study. In this experiment, it was discovered that when the protein content in the diet increased, the content of UA in the serum also increased. This result also emerged in other poultry experiments (Ye et al., 2025). In avian nitrogen metabolism, excessive dietary protein intake leads to the substantial production of urate (Creek and Vasaitis, 1961). Unlike mammals, poultry lack the enzyme arginase and are therefore unable to convert ammonia derived from protein catabolism into urea via a complete ornithine cycle. This physiological constraint necessitates an alternative pathway for nitrogen disposal: ammonia is primarily utilized for the synthesis of purines in the body. These purines are subsequently metabolized to UA in the liver, which is then excreted via the kidneys into the urine. This distinctive metabolic characteristic renders poultry particularly susceptible to urate deposition under conditions of high dietary protein or metabolic dysfunction, thereby impacting their health and production performance (Wang et al., 2025) . This experiment found that the content of UA in serum decreased after adding HPS to the diet. This is because resistant starch reduces the content of UA in the serum by enhancing the transfer of urea nitrogen to the large intestine (Mosenthin et al., 1992). Some scholars have reached the same conclusion as this experiment in mice (Xu et al., 2022). Simultaneously, it was found in the experiment that the excessive UA content in the serum led to the occurrence of renal lesions and inflammation. After adding HPS to the diet, it was observed that the goose kidney tissue was intact and the inflammation was alleviated. Excessive uric acid levels can stimulate the inflammatory pathway of the kidney, which results in kidney inflammation and kidney damage. In this experiment, it was found that the addition of HPS to the diet increased the intestinal villi length, the V/C was higher, and the intestine became healthier. When the concentration of UA in serum increases, the intestinal morphology and structure are damaged. Resistant starch is not digested in the first half of the intestine, which increases intestinal peristalsis and accelerates intestinal development. When the concentration of UA in serum increases, excessive UA destroys the structure of the intestine. Previous investigations have examined the influence of diets with varying protein content—specifically, low-protein and high-protein formulations—on growth performance and plasma metabolite profiles in geese. Findings from these investigations align with the results observed in the current study (Ho et al., 2015).

Emerging evidence has demonstrated a complex link between the occurrence of gout and the gut microbiota. One study has shown significant differences in the composition of intestinal flora between geese afflicted with gout and their healthy counterparts (Xi et al., 2022). In the present experiment, alpha diversity analysis indicated that dietary supplementation with HPS could increase the biological abundance and diversity of ileal microorganisms. With the increase of UA content in serum, the abundance and diversity of ileum microorganisms decreased. This observation is further supported by earlier nutritional research. Specifically, prior experiments manipulating the dietary ratio of amylose to amylopectin demonstrated that this starch composition directly influences both the efficiency of starch digestion and the diversity of the intestinal microflora in geese (Yang et al., 2022). Because the increase of UA content in the body destroys the structure and integrity of the intestine, the living environment of some microorganisms is destroyed and some microorganisms cannot survive in the intestine (Ma et al., 2021). HPS is degraded into glucose in the distal segment of the intestine, thereby creating a more favorable microenvironment for microbial proliferation (Yang et al., 2022). Furthermore, the gut microbiota exerts a significant influence on the onset and progression of gout. A characteristic dysbiosis in hyperuricemia involves an elevated ratio of Bacteroidetes to Firmicutes (B/F ratio), a signature of intestinal flora imbalance (Shirvani-Rad et al., 2023). Importantly, this dysbiotic signature appears modifiable. For instance, intervention with anti-hyperuricemic traditional Chinese medicine has been shown to effectively lower the B/F ratio and restore a healthier gut microbiota structure in hyperuricemic mouse models (Qian and Shen, 2024), suggesting a potential therapeutic avenue through microbial regulation. Similar changes were observed in this study, and the addition of HPS reversed the proportion of Bacteroidetes/Firmicutes caused by high protein. In previous studies, it has been suggested that SCFAs may be a new target for the treatment of gout. In this study, the genera related to SCFAs production such as Romboutsia, Turicibacter and Lactobacillus were the most abundant in the HPS group while the abundance was lower in the high UA group. It may be that the microorganisms that produce SCFAs by HPS fermentation in the ileum provide energy to promote their value-added. In addition, the abundance of Helicobacter decreased in the experimental group with HPS, while increased in the high UA group. These findings suggest a potential mechanistic link: Helicobacter may contribute to systemic inflammation by activating the NLRP3 inflammasome, thereby stimulating the production of pro-inflammatory cytokines and chemokines. In contrast, the HPS starch intervention appears to exert a beneficial regulatory effect by reducing the abundance of such harmful bacteria while concurrently promoting the proliferation of beneficial bacterial taxa. This shift in microbial composition likely contributes to an anti-inflammatory intestinal environment, which may help mitigate the inflammatory cascade associated with hyperuricemia and gout. Further analysis of the annotated microbial species revealed that the relative abundances of several beneficial bacteria—including Enterococcus cecorum, Clostridia UCG-014, Lactobacillus salivarius, Subdoligranulum, and Clostridium leptum—were significantly elevated in the HPS group compared with the high-protein group. These taxa are functionally associated with the production of SCFAs and the suppression of intestinal inflammation, as documented in prior studies (Martínez-Nava et al., 2023). Their enrichment under HPS dietary intervention suggests a shift toward a microbiota profile that supports metabolic and anti-inflammatory homeostasis, potentially counteracting the dysbiotic and pro-inflammatory state linked to hyperuricemia and gout development.

Metabolomics, a core discipline within systems biology, involves the comprehensive quantification of endogenous small-molecule metabolites within a biological system. This methodology provides a direct and dynamic readout of the physiological state, accurately reflecting the real-time alterations in metabolic pathways and biochemical reactions occurring within an organism (Yilmaz and Celik, 2009). In this experiment, original data were obtained through LC-MC technology. These data were annotated and the results of PCA and OPLS-DA were analyzed, obtaining 336 differential metabolites. The differentially up-regulated metabolites in the HPS group were mainly enriched in energy metabolism, lipid metabolism, and various amino acid metabolic pathways, which is consistent with previous research results. Currently, organic acids, lipid metabolism and amino acid metabolism have been confirmed to have anti-inflammatory and antibacterial functions in vivo (Ho et al., 2015). Cysteine is a prerequisite for the synthesis of small molecule antioxidant GSH (Maher et al., 2007). GSH This is a major antioxidant, often at lower levels in gout patients (Patil et al., 2021). This mechanistic insight is further supported by the documented role of glutathione (GSH), which mitigates monosodium urate (MSU)-induced gouty arthritis through suppression of the NF-κB/NLRP3 and NRF2 pathways (Cheng et al., 2023). The close relationship between cysteine—a key precursor for GSH synthesis—and gout pathophysiology thus becomes evident. In the present study, the addition of HPS elevated cysteine abundance, suggesting a potential novel therapeutic target for gout modulation. Concurrently, metabolomic analysis of the high-UA group revealed that differentially upregulated metabolites were primarily enriched in pathways related to secondary bile acid production and the d-ornithine metabolic axis. Notably, indole-3-butyric acid was among these metabolites. Previous research indicates that excessive systemic levels of indole-3-butyric acid, particularly its distal intestinal toxicity, can contribute to immune dysregulation (Yilmaz and Celik, 2009). Ornithine serves as a critical metabolic hub. It is directly involved in the metabolism of arginine, proline, and glutathione (Sivashanmugam et al., 2017). In poultry, the absence of a functional urea cycle makes the metabolic fate of ornithine particularly crucial for regulating nitrogen metabolism and uric acid synthesis, thereby closely linking it to the development of gout (Wang et al., 2025). Its altered regulation in hyperuricemia may therefore reflect a broader disruption in nitrogen metabolism and microbial co-metabolism, further linking gut-derived metabolites to systemic inflammatory outcomes in gout. In this study, it was found that the high concentration of UA in the blood would lead to the down-regulation of ornithine, which implies that the immune level in the body will decrease and affect the body's health. A consistent pattern emerges when comparing these findings with related nutritional interventions. Research on grape seed proanthocyanidins (GSPs) in geese reported analogous effects, demonstrating that GSP supplementation similarly enhanced antioxidant capacity, improved intestinal barrier function, and modulated the cecal microbial community and its associated metabolites (Deng et al., 2023). Glycocholic acid is an important component of bile acids. Bile is secreted by the liver and aids in fat digestion. Bile acids such as glycocholic acid enhance the efficiency of fat digestion and absorption by emulsifying fat and making it more easily decomposed by digestive enzymes (Riabushko, 2020). This experiment discovered that the glycocholic acid in high UA geese was significantly lower than that in HS group geese, indicating that an excessive UA concentration would have a negative impact on protein metabolism and bile acid metabolism in geese (Kieffer et al., 2016). From the above experiments, it can be concluded that the addition of HPS in the diet accelerates the synthesis of amino acids, thereby enhancing the body 's immunity and inhibiting the occurrence of inflammation, which may inhibit the occurrence of gout. Excessive protein in the diet will lead to accelerated decomposition of amino acids and bile acids in the body, which will reduce the body 's immunity and cause inflammation, which may cause gout.

To further delineate the relationship between intestinal microbes and metabolites, we performed a correlation analysis between the gut flora and differential intestinal metabolites. The analysis revealed a notable positive correlation between Rothia and the SCFA-related metabolite butanoic acid, 4-ethyl ester within the gut environment. It has been shown that Rothia can decompose starch as a substrate to produce SFCAs (Rooks and Garrett, 2016). Species abundance profiling indicated that Rothia reached its highest relative abundance in the HPS group. The dietary inclusion of HPS appears to promote the proliferation of Rothia, leading to increased microbial production of butyrate. As a beneficial short-chain fatty acid, butyrate is known to upregulate the expression of intestinal tight junction proteins, such as occludin-1 and ZO-1, thereby reinforcing epithelial barrier integrity and stability. This enhancement of barrier function contributes to improved host immunity and supports overall physiological development (Wang et al., 2012). Butyrate can also inhibit the production of inflammatory cytokines (such as IL-1β, IL-6 and IL-8), thereby inhibiting the occurrence of inflammation and strengthening the body's immunity. At present, it has been confirmed that inflammation is directly related to the occurrence of gout. From the above experiments, adding HPS to the diet can change the intestinal structure by promoting the proliferation of Rothia, inhibit the occurrence of inflammation, and thus alleviate the occurrence of gout. Provide a new idea for the subsequent treatment of gout.

Conclusions

This study demonstrates that hydroxypropyl starch (HPS) not only reduces serum uric acid levels and suppresses renal inflammatory responses but also improves intestinal structural integrity. Further analyses based on 16S rDNA sequencing and untargeted metabolomics reveal that HPS significantly reshapes the composition of the ileal microbiota, increases OTU diversity, and elevates the relative abundance of the genus Rothia. Ileal metabolomic profiling also showed that the differential metabolites induced by HPS intervention are primarily enriched in amino acid synthesis pathways, with the most notable changes observed in cysteine metabolism. In summary, HPS may exert a key protective role in the onset and progression of gout by modulating gut microbiota structure and its metabolic functions, thereby influencing host uric acid metabolism and inflammatory status. It should be noted, however, that the observed associations between HPS‑mediated microbial alterations and gout remission are correlational in nature. Further studies employing fecal microbiota transplantation or gnotobiotic models are warranted to establish causality. Overall, this study systematically elucidates part of the molecular mechanisms by which HPS alleviates gout, providing important experimental evidence and theoretical support for its future development as a functional additive for the prevention and treatment of gout in poultry.

Ethical approval and ethics

All experimental procedures and sample collection were performed according to the Regulations for the Administration of Affairs Concerning Experimental Animals (Ministry of Science and Technology, China, revised in July 2013) and approved by the Institutional Animal Care and Use Committee of the College of Animal Science and Technology, Anhui Science and Technology University, Chuzhou, China (permit No. AHSTU2023018). All birds were humanely euthanized at the end of the experiment, and all procedures were designed and conducted to minimize pain and distress in the animals. This study adhered to the ARRIVE guidelines for reporting animal research.

Funding

This research was supported by the Youth Project of the Provincial Natural Science Foundation of Anhui (grant nos. 2108085QC132), Anhui Provincial Key Research and Development Project (grant nos. 202204c06020003) and Key Scientific Research Foundation of the Education Department of Province Anhui (grant nos. 2024AH050301), Postdoctoral Research Project of Anhui Province (grant nos. 2024C865).

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) did not use any Al and Al-assisted technologies.

Data availability

The raw 16S rDNA sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1421068. The metabolomics raw data and associated analysis files have been deposited in the OMIX database (Open Archive for Miscellaneous Data) at the National Genomics Data Center (NGDC), China National Center for Bioinformation (CNCB), and are publicly accessible under BioProject accession number PRJCA058367.

CRediT authorship contribution statement

Xiaorong He: Writing – review & editing, Writing – original draft, Visualization, Software, Investigation, Formal analysis. Xiaotong Tang: Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Investigation. Xiaoxue Wang: Supervision, Resources, Conceptualization. Jinfan Han: Supervision, Resources, Conceptualization. Mengxue Liu: Supervision, Resources, Conceptualization. Wenquan Liu: Writing – review & editing, Validation, Methodology. Jie Zhu: Writing – review & editing, Validation, Methodology. Xueqi Zhu: Writing – review & editing, Validation, Methodology. Lei Zhao: Writing – review & editing, Validation, Methodology. Pengfei Ye: Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

Disclosures

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.