Effects of inulin on growth performance and intestinal health of broilers by modulating intestinal microbiota and metabolome

Abstract

Inulin, a natural dietary fiber, confers multiple physiological benefits. However, the effects of inulin on the liver and intestinal health of broilers remain unclear. The study investigated the effects and mechanisms of inulin supplementation on hepatic and intestinal health of broilers. A total of 192 male broilers (821.15 ± 14.99 g) at 21 d of age were randomly allocated to four treatment groups, comprising eight replicates per group with six broilers each. The dietary treatments included: a control group (CON) receiving a basal diet and three treatment groups fed the basal diet supplemented with 0.5, 1.0, or 2.0 g/kg inulin (designated as INU-0.5, INU-1.0, and INU-2.0, respectively). Results indicated that dietary inulin supplementation elicited a quadratic response in final body weight (FBW, P = 0.032). Inulin supplementation produced linear improvements in average daily gain (ADG, P = 0.044) alongside quadratic reductions in both average daily feed intake (ADFI) and feed-to-gain ratio (F/G ratio) (P < 0.001). Notably, inulin supplementation linearly decreased malondialdehyde (MDA) levels (P < 0.001) while significantly enhancing superoxide dismutase (SOD) activity (P < 0.001). Furthermore, inulin supplementation demonstrated dose-dependent effects on lipid metabolism, including a linear reduction in abdominal fat deposition (P < 0.001), decreased hepatic and serum concentrations of total cholesterol (TCHO, both P < 0.001) and triglycerides (TG, P < 0.001 and P = 0.001, respectively), and reduced serum levels of both high-density lipoprotein (HDL, P = 0.010) and low-density lipoprotein (LDL, P < 0.001) (P < 0.01). Parallel improvements in intestinal barrier function were observed, with linear increases in jejunal expression of tight junction proteins (claudin-5, occludin, and zonula occludens-1 [ZO-1]), mucin 2 (MUC2), and diamine oxidase (DAO) (P < 0.001). Additionally, inulin supplementation selectively increased the abundances of key microbes, including Bacteroides, Lactobacillus, and Akkermansia, while decreased the abundances of Alistipes, Odoribacte, Parabacteroides, Rikenella, and Erysipelatoclostridium (P < 0.001). These microbial shifts were associated with modulation of key metabolic pathways, including taurine and hypotaurine metabolism, purine metabolism, arginine and proline, and phenylalanine metabolism. Collectively, these findings demonstrate that inulin supplementation enhances broiler productivity while improving both intestinal and hepatic health (P < 0.05) through microbiota-mediated metabolic regulation. Therefore, dietary inulin supplementation would be a recommendable nutritional strategy to optimize production efficiency in commercial broiler industry.

1. Introduction

Intensive breeding practices and increasing stocking density have created numerous challenges in broiler production, including suboptimal feed utilization, oxidative damage, frequent intestinal disease outbreaks, and declining performance (Ducatelle et al., 2023; Magnuson et al., 2020; Shakeri et al., 2014). Therefore, a natural feed additive with antioxidant properties that enhances production efficiency and regulates gut health is crucial for the poultry industry.

In recent years, extensive research has demonstrated that prebiotics can enhance intestinal health and performance by regulating intestinal microbes in animals (Ravanal et al., 2025). Inulin, as a functional plant-derived and water-soluble dietary fiber composed of polymerized fructose, exhibits health-promoting functional properties (Shoaib et al., 2016). Furthermore, inulin supplementation demonstrated beneficial effects on lipid metabolism, body weight regulation, colon cancer risk reduction, and enhanced mineral absorption through modulation of gut microbiota composition (Qin et al., 2023). Meanwhile, metabolites generated by gut microbiota fermentation of inulin are captured by the intestinal mucosa and subsequently absorbed and utilized by the host (Makki et al., 2018). These metabolites influence the abundance of specific microbiota by regulating intestinal pH and modifying the homeostasis within the intestinal ecosystem (Sheng et al., 2023). Therefore, inulin exhibits a diverse range of advantageous physiological functions.

Despite these valuable insights, several critical limitations and unresolved questions remain in the current understanding of how inulin modulates broiler health. These studies often lack a comprehensive understanding of the underlying mechanisms, particularly regarding the gut–liver axis. Furthermore, the effects of integrated inulin on the composition of the intestinal microbial community and cecal metabolites in broilers remain underexplored. Therefore, a comprehensive analysis integrating the gut microbiome and metabolome is crucial for elucidating the mechanisms by which inulin reshapes the entire gut ecosystem and its functional metabolic pathway. Moreover, different gut metabolites derived from inulin regulate metabolic pathways affecting hepatic antioxidant capacity, lipid metabolism, and liver health in broilers (Wang et al., 2024a; Yang et al., 2023). The pathways related to the inulin-activated gut–liver axis and their relationships remain largely undefined.

Therefore, to bridge these critical knowledge gaps and clarify the underlying mechanisms linking dietary inulin supplementation to broiler liver and intestinal health by gut microbiota and metabolites, this study implemented an integrated microbiome-metabolome approach. This study systematically investigated the effects of different concentrations of dietary inulin (0.5, 1.0, and 2.0 g/kg) of broilers on growth performance, liver antioxidant capacity and lipid metabolism, intestinal morphology and barrier function, and crucially, on the cecal microbiota composition and the cecal metabolomic profile. This integrated approach provides novel mechanism insights into inulin-mediated cross-organ modulation in broilers. This study hypothesized that dietary inulin supplementation, especially at an optimal concentration, enhances the growth performance of broilers and improves liver and intestinal health by specifically modulating the composition of the cecal microbiota, which subsequently alters the cecal metabolome and leads to the upregulation of critical metabolic pathways.

2. Materials and methods

2.1. Animal ethics statement

The experimental procedures were approved by the Department of Animal Nutrition, Shandong Agricultural University, and followed the Chinese guidelines for animal welfare for animal research (Protocol No. S20210072).

2.2. Preparation of inulin

The inulin was extracted from Jerusalem artichoke and purified following the methods previously described for this study, contained 95% fructan linked by 2,1 glycosidic bonds (degree of polymerization [DP] = 10-60), and the crude ash content was less than 0.1% (Cui et al., 2022; Zhang et al., 2019). Briefly, inulin was extracted through a concentrated water-bath extraction process, followed by protein removal and dialysis. Additionally, small molecules with a molar mass less than 3500 were removed using an ultrafiltration membrane (Beijing Solarbio Science & Technology Co., Ltd., Beijing, China), which included the separation of monosaccharides or some oligosaccharides with degree of polymerization values less than 10. The resulting dialysate was collected, and the inulin sample was obtained by freeze-drying to a constant weight using a vacuum dryer (Biosafer, Nanjing, Jiangsu, China).

2.3. Animal trials, design, and diet

Arbor Acres broilers were obtained from Tai'an New Farm Ark Livestock Co., Ltd., Shandong, China. A total of 192 male broilers at 21 d of age, with similar initial body weight (IBW, 821.15 ± 14.99 g), were assigned to four treatment groups, with eight replicates per group and six broilers per replicate. As shown in Fig. 1, the four treatments were as follows: the control group (CON) fed the basal diet, and three inulin groups (INU-0.5, INU-1.0, and INU-2.0) fed the basal diet supplemented with 0.5, 1.0, and 2.0 g/kg of inulin, respectively. The basal diet (Table 1) was designed to meet the nutritional requirements recommended by the feeding standards for broilers in NY/T 33-2004 (Ministry of Agriculture of the People's Republic of China, 2004). Metabolizable energy was estimated using the data from the China Feed Database (2020). The dry matter (DM) was calculated by subtracting the moisture content from 100%, and the moisture content was determined according to the GB/T 6435-2014 (China National Standard, 2014). The crude ash level was measured using the muffle furnace technique according to GB/T 6438-2007 (China National Standard, 2007). The organic matter (OM) content was calculated from the ash content via the formula: OM (%) = DM% – Ash (%). The crude protein level was analysed according to GB/T 6432-2018 (China National Standard, 2018b). Crude fiber was analysed according to GB/T 6434-2022 (China National Standard, 2022). Calcium (Ca) content was analysed according to GB/T 6436-2018 (China National Standard, 2018a). Total phosphorus (P) content was analysed according to GB/T 6437-2018 (China National Standard, 2018c). Methionine and lysine levels were analysed according to GB/T 15399-2018 (China National Standard, 2018d). The experiment lasted for 21 d, with a 3-d adaptation period. Broilers were raised in three-layer stepped cages (Weifang-Fengmu Livestock and Poultry Machiner, Shandong, China) with three broilers in each cage (the rearing area for each broiler was at least 950 cm²). The temperature of the room was consistently maintained at 24 to 26 °C, with an average relative humidity of 40%. Illumination was provided by incandescent lamps, with a light intensity of 12.56 lx. The lighting schedule followed a program of 18 h of light and 6 h of darkness. One cage as a replicate unit was used to average daily gain (ADG) and average daily feed intake (ADFI). Feed efficiency was calculated by dividing feed intake by body weight gain (F/G ratio).

Fig. 1.

Experimental design. CON = control; INU = inulin.

Table 1.

Composition and nutrient levels of the basal diet (DM basis, %).

Item	Content
Ingredients
Corn	50.75
Soybean meal	32.00
Wheat bran	8.00
Soybean oil	4.50
CaHPO4·2H2O	1.50
CaCO3	1.50
NaCl	0.40
DL-Methionine	0.12
L-Lysine hydrochloride	0.23
Premix1	1.00
Total	100.00
Nutrient levels2
Metabolizable energy, MJ/kg	12.60
Organic mater	95.24
Crude protein	19.93
Crude fiber	2.85
Calcium	1.05
Phosphorus	0.66
Methionine	0.45
Lysine	1.20

¹Provided per kilogram of diet: 10,000 IU, of vitamin A; 3000 IU, of vitamin D₃; 20 IU, of vitamin E; 2 mg of vitamin K₃; 2 mg of vitamin B₁; 8 mg of vitamin B₂; 4 mg of vitamin B₆; 0.06 mg of vitamin B₁₂; 20 mg of D-pantothenic acid; 60 mg of nicotinic acid; 1 mg of folic acid; 0.25 mg of biotin; 100 mg of Fe as ferrous sulfate; 10 mg of Cu as copper sulfate, 100 mg of Mn as manganese sulfate; 100 mg of Zn as zinc sulfate; 0.25 mg of Se as sodium selenite; 0.7 mg of I as calcium iodide.

²The metabolizable energy was calculated based on the nutritional values of the feedstuffs from the China Feed Database (2020) while all the other contents were analysed (n = 8).

2.4. Sample collection and determination

At the end of the feeding trial (42 d of age), ten broilers were fasted for 12 h and weighed to determine the final body weight (FBW), then sacrificed for sample collection. Blood samples were collected from the wing veins of each broiler immediately prior to euthanasia by cervical dislocation. Serum was collected by centrifuging the blood at 1500 × g for 15 min and stored at −80 °C. The serum concentrations of alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglycerides (TG), total cholesterol (TCHO), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) were analysed by the biochemical analyzer (Hitachi Ltd., Tokyo, Japan). Additionally, the serum levels of glutathione peroxidase (GSH-Px; Cat. # MM-34192O1) and superoxide dismutase (SOD; Cat. # MM-2703O1) were measured using ELISA kits (Jiangsu Meimian Industrial Co., Ltd., Yancheng, Jiangsu, China). The zonula occludens-1 (ZO-1; Cat. # MM-60011O1), occludin (Cat. # MM-60055O1), claudin-5 (Cat. # MM-60241O1), mucin2 (MUC2; Cat. # MM-4212O1), and mucosal diamine oxidase (DAO; Cat. # MM-33277O1) in the jejunum were measured using ELISA kits (Jiangsu Meimian Industrial). The malondialdehyde (MDA; Cat. # A003-1-2), liver TG (Cat. #F001-1-1), and TCHO (Cat. #F002-1-1) concentrations were detected by commercial assay kits (Jiancheng Bioengineering Institute, Nanjing, Jiangsu, China). All procedures were conducted according to the manufacturer's instructions. Fresh cecal digesta samples were collected immediately after euthanasia, transferred into sterile 5-mL centrifuge tubes, and snap-frozen in liquid nitrogen. Upon arrival at the laboratory, the samples were stored at −80 °C until further processing for DNA extraction, microbial community profiling, and metabolome analysis.

2.5. Histological assessments of the liver and small intestine

The duodenum, jejunum, ileum, and a portion of the left liver lobe were rinsed with 0.9% saline solution and fixed in 10% formalin for 48 h. The tissues were then dehydrated, embedded in paraffin, sectioned with a microtome (4.5 μm), stained with hematoxylin and eosin (H&E), and examined for hepatic steatosis using a light microscope (Nikon Inc., Tokyo, Japan). The intestinal morphologies were evaluated by measuring (ImageJ software version 6.0, NIH, Bethesda, USA) the villus height, crypt depth, and calculating the ratio of villus height to crypt depth (V/C).

2.6. Cecal microbiota determination by 16S rRNA gene sequencing

A QIAquick Gel Extraction Kit (Qiagen, Hilden, Germany; Cat. # 28704) was used to extract DNA from cecal digesta samples, and the purity of the DNA was assessed by agarose gel electrophoresis on 1% gels. The universal primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) were employed to amplify the V3–V4 hypervariable region of the 16S rRNA gene. Amplicon libraries were sequenced on the Illumina NovaSeq platform (Novogene Co., Ltd., Beijing, China), generating 250 bp paired-end reads. FLASH (V1.2.7, Johns Hopkins University, Maryland, USA) was employed to merge the paired-end reads and generate raw tags (Magoč and Salzberg, 2011). Additionally, in accordance with the QIIME (version 1.9.1, University of Colorado Boulder, Colorado, USA) quality control pipeline, raw tags were filtered to obtain high-quality clean tags (Bokulich et al., 2013). Chimeric sequences were identified by comparing the clean tags against the Silva reference database, and subsequently removed, yielding the final set of effective tags (Edgar, 2013; Haas et al., 2011). High-quality reads were selected for bioinformatics analysis after clustering into operational taxonomic units (OTUs) at 97% sequence similarity. Alpha diversity was assessed using the observed-species, Shannon, Simpson, Chao1, abundance-based coverage estimator (ACE), and phylogenetic diversity (PD-whole-tree) indices. Beta diversity analysis was performed to investigate the structural variations in microbial communities among the experimental groups, which was visualized via the constrained principal coordinate analysis (CPCoA). Significant differences in microbiota among treatment groups at each classification level were identified using one-way ANOVA followed by linear discriminant analysis (LDA) effect size (LEfSe) (Chen et al., 2024). To explore the alterations in cecum microbiota, the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) was employed to forecast their functional characteristics using the KEGG database (Douglas et al., 2020).

2.7. Determination of the metabolome for cecal digesta

The cecal metabolome analysis was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) by Novogene Co., Ltd., Beijing, China. Briefly, cecal digesta samples were extracted with ice-cold 80% methanol. The clarified supernatant was subjected to LC-MS/MS analysis. Vanquish UHPLC system coupled to an Orbitrap Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). Chromatographic separation used a Hypesil Gold C18 column (100 nm× 2.1 mm, 1.9 μm, Thermo Fisher Scientific, Waltham, MA, USA) maintained at 40 °C with a 0.2 mL/min flow rate. The eluents for the positive polarity mode were eluent A (0.1% FA in water) and eluent B (methanol). For negative polarity mode, the mobile phase consisted of eluent A (5 mmol/L ammonium acetate, pH 9.0) and eluent B (methanol). The gradient program was as follows: 2% B (0-1.5 min), ramped to 85% B (3 min), then to 100% B (10 min), followed by re-equilibration at 2% B (10.1-12 min).

The Q Exactive HF mass spectrometer was operated in positive/negative polarity mode with spray voltage of 3.2 kV, capillary temperature of 320 °C, sheath gas flow rate of 40 arb, auxiliary gas flow rate of 10 arb, Funnel RF level of 40, and auxiliary gas heater temperature of 350 °C (Cao et al., 2020). Finally, the obtained data were processed to identify metabolites. The identified metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The orthogonal partial least squares discrimination analysis (OPLS-DA) was used to observe the overall distribution trend of metabolites among the four treatment groups.

2.8. Statistical analysis

In the current trial, group differences were tested with Welch's one-way ANOVA; when the omnibus test was significant, Dunnett's T3 post hoc comparison (each treatment vs Control) was applied. All tests were two-sided with α = 0.05. In addition, orthogonal polynomial contrasts were used to assess linear and quadratic trends across increasing dietary inulin levels. Analyses were performed using GraphPad Prism version 8.0 (GraphPad Software, San Diego, California, USA) and IBM SPSS Statistics 27 (IBM Corp., Armonk, New York, USA). Data are presented as the mean and SEM. A general linear model (GLM) was used to assess other data. The mathematical model for the GLM is as follows:

Y_ij = μ + α_i + β_i

where Y_ij denotes the dependent variable, μ represents the overall mean, a_i signifies the fixed treatment effect, and β_i indicates the random error term.

The characteristic metabolites were selected based on the fold change (FC) > 1.2 or FC < 0.833, variable importance of projection (VIP) > 1, and P-value <0.05. Correlations among significantly modified microbiota (relative abundance >0.1%), differential metabolites, and broiler health parameters were analysed using Spearman's correlation in SPSS and visualized with the heatmap (HemI 1.0.3.7, heatmap illustrator, Huazhong University of Science and Technology, Wuhan, Hubei, China). The enrichment of metabolic pathways and analysis of discrepant metabolic pathways were performed via MetaboAnalyst 6.0 online software and the KEGG pathway database (https://www.metaboanalyst.ca/MetaboAnalyst/home.xhtml).

3. Results

3.1. Growth performance of broilers

The effects of dietary inulin supplementation on the growth performance are shown in Table 2. The results indicated that a quadratic increase in FBW in response to increasing concentrations of dietary inulin supplementation (P = 0.032), and FBW was significantly increased in the INU-2.0 group (P < 0.001). Meanwhile, inulin supplementation induced linear increases in ADG (P = 0.044), and quadratic decreases in ADFI (P < 0.001) and F/G ratio (P = 0.021).

Table 2.

Effects of inulin on the growth performance of broilers.

Item	Groups1				SEM	P-values
	CON	INU-0.5	INU-1.0	INU-2.0		ANOVA	Linear	Quadratic
IBW, g	820.25	821.67	822.82	819.83	1.249	0.831	0.986	0.386
FBW, g	2273.89b	2286.83b	2291.32b	2341.84a	4.812	<0.001	<0.001	0.032
ADG, g	69.17	69.77	69.91	72.30	0.526	0.155	0.044	0.378
ADFI, g	134.44a	123.32b	120.70b	120.34b	1.351	<0.001	<0.001	<0.001
F/G ratio, g/g	1.95a	1.77b	1.73bc	1.67c	0.024	<0.001	<0.001	0.021

IBW = initial body weight; FBW = final body weight; ADG = average daily gain; ADFI = average daily feed intake; F/G = feed-to-gain ratio.

Within a row, values with different superscript letters are significantly different (n = 8, P < 0.05). No superscript or the same superscript indicates no significant difference (P > 0.05).

¹Dietary inulin treatment groups: the basal diet with 0, 0.5, 1.0, or 2.0 g/kg inulin.

3.2. Lipid parameters and antioxidant abilities of serum and liver

As shown in Table 3, serum and liver MDA concentrations indicated a linear decrease in inulin supplementation groups compared to the CON group as the inulin concentration increased (P < 0.001). Moreover, the activities of liver and serum SOD and serum GSH-Px in the inulin supplementation groups indicated a linear increase compared to the CON group (P < 0.001). Meanwhile, dietary inulin supplementation linearly decreased the TCHO and TG concentrations in the liver (both P < 0.001) and serum (P < 0.001 and P = 0.001, respectively) compared with the CON group. The abdominal fat content was linearly lower in the INU groups compared to the CON group (P < 0.001). Additionally, the serum LDL level exhibited a quadratic decrease with inulin supplementation (P = 0.038). Fig. 2 shows that hematoxylin and eosin staining demonstrated a reduction of fat accumulation in the INU supplementation groups compared with the CON group.

Table 3.

Effects of inulin on the antioxidant and lipid parameters of broilers.

Item	Groups1				SEM	P-values
	CON	INU-0.5	INU-1.0	INU-2.0		ANOVA	Linear	Quadratic
Serum antioxidant
Serum-MDA, nmol/mL	4.03a	3.20b	3.14bc	2.70c	0.104	<0.001	<0.001	<0.001
Serum-GSH-Px, U/mL	69.10c	101.50b	105.27b	120.55a	4.021	<0.001	<0.001	<0.001
Serum-SOD, U/mL	164.27c	252.44b	294.72b	374.79a	14.732	<0.001	<0.001	<0.001
Serum lipid parameters
Serum-TG, mmol/L	0.54a	0.43b	0.42b	0.41b	0.015	0.004	0.001	0.080
Serum-TCHO, mmol/L	4.85a	4.06b	3.86b	3.97b	0.101	<0.001	<0.001	0.007
Serum-HDL, mmol/L	1.91b	1.78c	1.86bc	2.05a	0.026	<0.001	0.010	<0.001
Serum-LDL, mmol/L	1.14a	0.96b	0.87b	0.89b	0.029	0.001	<0.001	0.038
Liver antioxidant capacity
Liver-MDA, nmol/mg protein	20.21a	16.21ab	12.01b	1.96c	1.497	<0.001	<0.001	0.109
Liver-SOD, U/L	1.87d	2.63c	3.13b	3.99a	0.151	<0.001	<0.001	0.674
Liver lipid parameters
Liver-TG, mmol/g protein	0.332a	0.28b	0.28b	0.27b	0.004	<0.001	<0.001	0.002
Liver-TCHO, mmol/g protein	0.49a	0.47b	0.46b	0.46b	0.003	<0.001	<0.001	0.263
Abdominal fat content, %	1.78a	1.67a	1.48b	1.28c	0.038	<0.001	<0.001	0.132

MDA = malondialdehyde; GSH-Px = glutathione peroxidase; SOD = superoxide dismutase; TG = triglycerides; TCHO = total cholesterol; HDL = high-density lipoprotein; LDL = low-density lipoprotein; Prot = protein.

Within a row, values with different superscript letters are significantly different (n = 8, P < 0.05).

¹Dietary inulin treatment groups: the basal diet with 0, 0.5, 1.0, or 2.0 g/kg inulin.

Fig. 2.

Effects of dietary inulin supplementation on liver histomorphometry of paraffin slices (magnification 50×). CON = control; INU = inulin.

3.3. Intestinal development, morphology, and barrier of broilers

As shown in Fig. 3A–C, inulin supplementation improved the intestinal morphology. Specifically, in Table 4, the linear increase was observed for villus height with inulin supplementation in the duodenum (P < 0.001), jejunum (P = 0.005), and ileum (P = 0.007). In contrast, crypt depth exhibited linear decreases with inulin supplementation in the duodenum (P = 0.010), jejunum (P = 0.005), and ileum (P < 0.001). Consequently, the V/C also demonstrated a linear increase in the duodenum (P = 0.001), jejunum (P < 0.001), and ileum (P < 0.001) with inulin supplementation . Furthermore, the levels of claudin-5 and occludin in the jejunum demonstrated linear increases with inulin supplementation (Table 5; P < 0.001), and the levels of ZO-1, MUC2, and DAO demonstrated quadratic increases (Table 5; P = 0.005, P = 0.002, and P = 0.039, respectively).

Fig. 3.

Effects of dietary inulin supplementation on intestinal morphology. (A-C) Morphology of the duodenum, jejunum, and ileum. (magnification 4×). CON = control; INU = inulin.

Table 4.

Effects of inulin on intestinal villus quantification.

Item	Groups1				SEM	P-value
	CON	INU-0.5	INU-1.0	INU-2.0		ANOVA	Linear	Quadratic
Duodenum
Villus height, μm	1032.65b	1333.66a	1358.58a	1463.91a	44.879	<0.001	<0.001	0.100
Crypt depth, μm	226.01a	208.61ab	204.62ab	189.24b	6.985	0.063	0.010	0.910
V/C ratio	6.00b	6.35b	6.90ab	7.90a	0.232	0.009	0.001	0.374
Jejunum
Villus height, μm	1025.66b	1155.89ab	1200.06a	1206.77a	24.897	0.020	0.005	0.144
Crypt depth, μm	166.33a	155.46ab	146.05b	144.97b	3.025	0.028	0.005	0.342
V/C ratio	6.28b	7.57a	8.35a	8.43a	0.226	<0.001	<0.001	0.022
Ileum
Villus height, μm	593.61b	631.18ab	640.96ab	711.63a	15.641	0.042	0.007	0.542
Crypt depth, μm	270.75a	198.01b	193.52b	169.71b	10.811	<0.001	<0.001	0.101
V/C ratio	2.60b	3.45a	3.69a	4.01a	0.145	<0.001	<0.001	0.151

V/C ratio = the ratio of villus height to crypt depth.

Within a row, values with different superscript letters are significantly different (n = 8, P < 0.05). No superscript or the same superscript indicates no significant difference (P > 0.05).

¹Dietary inulin treatment groups: the basal diet with 0, 0.5, 1.0, or 2.0 g/kg inulin.

Table 5.

Effects of inulin supplementation on tight junction-related proteins mRNA expression and jejunal mucosa enzyme activity.

Item	Groups1				SEM	P-value
	CON	INU-0.5	INU-1.0	INU-2.0		ANOVA	Linear	Quadratic
Claudin-5	433.58c	537.84b	629.55a	668.32a	19.380	<0.001	<0.001	0.173
Occludin	79.20c	131.59b	127.81b	165.15a	6.242	<0.001	<0.001	0.235
ZO-1	53.27c	74.71b	83.84b	90.81a	2.780	<0.001	<0.001	0.005
MUC2	92.87c	142.19b	158.32b	178.82a	6.043	<0.001	<0.001	0.002
DAO, U/mL	116.43c	165.19b	166.64b	189.57a	5.567	<0.001	<0.001	0.039

MUC2 = mucin2; DAO, diamine oxidase; ZO-1, zonula occludens-1.

Within a row, values with different superscript letters are significantly different (n = 8, P < 0.05). No superscript or the same superscript indicates no significant difference (P > 0.05).

¹Dietary inulin treatment groups: the basal diet with 0, 0.5, 1.0, or 2.0 g/kg inulin.

3.4. Profile and characteristics of the intestinal microbial community of broilers

High-throughput pyrosequencing of the samples generated a total of 3,289,622 raw reads, which were filtered to produce 2,025,283 clean reads. Based on a threshold of 97% sequence similarity, a total of 2161 (CON), 1514 (INU-0.5), 1515 (INU-1.0), and 1263 (INU-2.0) OTUs were detected in the cecal samples. The number of OTUs decreased with increasing concentration of inulin. As shown in Fig. 4A, the Venn diagram revealed that 141 OTUs were common to all groups, with 1015, 124, 141, and 89 unique OTUs identified in the CON, INU-0.5, INU-1.0, and INU-2.0 groups, respectively.

Fig. 4.

The profile and characteristics of the intestinal microbiota in the broiler among four groups. (A) The Venn diagram of operational taxonomic units (OTUs). (B) The constrained principal coordinate analysis (CPCoA). (C) The Firmicutes to Bacteroidetes ratio (F/B ratio). The relative abundances of bacteria at the (D) phylum, (E) family, and (F) genus levels of the cecal microbiota of the broiler. Significance was evaluated by Welch's one-way ANOVA with Dunnett's T3 multiple comparisons (each treatment vs control). Asterisks denote significant differences vs control (∗∗P < 0.01). Non-significant comparisons are not shown. CON = control; INU = inulin.

The alpha diversity of the cecal microbiota is presented in Table 6. The observed-species, Chao1, ACE, and PD-whole-tree indices were significantly lower in the inulin supplementation groups compared to the CON group, showing both linear (P = 0.003, P = 0.002, P = 0.001, and P < 0.001, respectively) and quadratic decreases (P = 0.010, P = 0.009, P = 0.007, and P < 0.001, respectively). The CPCoA of the Canberra distances revealed significant differences in the cecal microbiota among the four groups with 13.5% of the variance and P-value <0.001 (Fig. 4B).

Table 6.

Analysis of alpha diversity indices.

Item	Groups1				SEM	P-value
	CON	INU-0.5	INU-1.0	INU-2.0		ANOVA	Linear	Quadratic
Observed-species	818.88a	662.38b	670.50b	673.88b	18.061	0.001	0.003	0.010
Shannon	5.21	4.97	4.97	5.07	0.669	0.887	0.682	0.473
Simpson	0.89	0.86	0.85	0.85	0.157	0.667	0.338	0.727
Chao1	912.82a	705.90b	716.11b	722.26b	23.876	<0.001	0.002	0.009
ACE	935.53a	714.77b	723.97b	728.90b	25.184	<0.001	0.001	0.007
PD-whole-tree	72.21a	49.54b	51.04b	48.85b	2.253	<0.001	<0.001	<0.001

ACE = abundance-based coverage estimator; PD-whole-tree = phylogenetic diversity.

Within a row, values with different superscript letters are significantly different (n = 8, P < 0.05). No superscript or the same superscript indicates no significant difference (P > 0.05).

¹Dietary inulin treatment groups: the basal diet with 0, 0.5, 1.0, or 2.0 g/kg inulin.

To further investigate the effects of inulin supplementation on cecal microbiota composition, the bacterial communities were compared at the phylum, family, and genus levels in the CON, INU-0.5, INU-1.0, and INU-2.0 groups. The Firmicutes to Bacteroidetes (F/B ratio) consistently decreased with increasing inulin concentration (Fig. 4C), and the F/B ratio was significantly lower in the INU-2.0 group compared to the CON group (P = 0.034). Cecal microbiota with a relative abundance greater than 0.1% at the phylum, family, and genus levels were compared and analyzed (Table S1). At the phylum level (Fig. 4D), Bacteroidetes and Firmicutes were the dominant phyla in all groups, accounting for nearly 90% of the total bacteria. At the family level (Fig. 4E), within the Bacteroidetes, the dominant families included Bacteroidaceae, Rikenellaceae, Marinifilaceae, and Tannerellaceae. Within the Firmicutes, the dominant families were Ruminococcaceae, Lactobacillaceae, Lachnospiraceae, Erysipelotrichaceae, and Acidaminococcaceae. Other phyla were present at low relative abundances. At the genus level (Fig. 4F), Bacteroides, Alistipes, Faecalibacterium, Lactobacillus, Odoribacter, Parabacteroides, unidentified_Erysipelotrichaceae, Phascolarctobacterium, and [Ruminococcus]_torques_group were dominant in the four groups.

3.5. Discrepant bacterial communities in the cecal microbiota of broiler

As shown in Fig. 5A–C, the microbial composition differed between the inulin groups and the CON group at the phylum, family, and genus levels. At the phylum level, the relative abundances of Bacteroidetes and Verrucomicrobiota significantly increased in the INU-2.0 group (P = 0.002 and P < 0.001, respectively), and the relative abundances of Proteobacteria and Actinobacteriota significantly decreased (P < 0.001) in the inulin supplementation groups. At the family level, the relative abundances of Bacteroidaceae and Lactobacillaceae significantly increased (P < 0.001). Meanwhile, the relative abundances of Rikenellaceae, Marinifilaceae, and Tannerellaceae were significantly reduced in the inulin groups than the CON group (P < 0.001). At the genus level, the relative abundances of Alistipes, Odoribacter, Parabacteroides, Rikenella, and Erysipelatoclostridium significantly decreased in inulin groups (P < 0.001). Conversely, the relative abundances of Bacteroides, Lactobacillus and Akkermansia were significantly enriched in the inulin supplemented groups compared to the CON group (P < 0.001).

Fig. 5.

Differential microbes and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional profiles among four groups. Relative abundance of cecal microbiota (>0.1%) at (A) phylum, (B) family, and (C) genus levels. Linear discriminant analysis (LDA) between CON and INU-2.0 groups (LDA score >3.5): (D) histogram of LDA values; (E) cladogram showing taxonomic hierarchy and phylogenetic relationships of discriminant microbes. (F to H) Predicted differential KEGG functions at (F) level 1, (G) level 2, and (H) level 3. Asterisks (∗) and daggers (†) denote significant increases and decreases vs. control (CON), respectively, as determined by Welch's ANOVA with Dunnett's T3 post hoc test (∗/†P < 0.05, ∗∗/††P < 0.01, ∗∗∗/†††P < 0.001). Non-significant comparisons are omitted. CON = control; INU = inulin.

LDA combined with LEfSe was used to identify significant microbial differences between CON and INU-2.0 groups (Fig. 5D; LDA score > 3.5). The results showed that the dietary supplementation with 2.0 g/kg inulin significantly increased the relative abundances of Verrucomicrobiales, Muribaculaceae, Lactobacillus salivarius, and Akkermanisia muciniphila. Meanwhile, the relative abundances of Negativicutes, Rikenella_microfusus, Gammaproteobacteria, Veillonellales_Selenomonadales, and Burkholderiales exhibited a decrease compared to the CON group (LDA score > 3.5). Ultimately, the cladograms depicted the phylogenetic distribution of disparate bacterial taxa across distinct experimental groups (Fig. 5E).

PICRUSt2 was used to predict the functional abundance of cecal bacteria. At the KEGG differential functions in functional classification level 1 (Fig. 5F), cellular processes were decreased (P < 0.016) while metabolism was increased (P < 0.045) in the INU-2.0 group. Additionally, genetic information processing was increased in both the INU-1.0 and INU-2.0 groups (P = 0.003 and P < 0.001, respectively). At the functional classification level 2, replication and repair were increased in both the INU-1.0 and INU-2.0 groups (P = 0.014 and P = 0.026, respectively).Nucleotide metabolism were significantly increased (P = 0.001) with the dietary inulin supplementation. At the functional classification level 3, the purine metabolism and pyrimidine metabolism were significantly increased (P = 0.002 and P < 0.001, respectively) due to dietary inulin supplementation. Furthermore, the ribosome was increased (P < 0.018) in the INU-2.0 group. Nevertheless, the DNA replication proteins (P = 0.009) and glycolysis/gluconeogenesis (P = 0.040) were significantly increased (P < 0.009) in the INU-1.0 group.

3.6. Profile of intestinal metabolites and enrichment of metabolic pathways

The differential metabolites were selected based on the fold change (FC) > 1.2 or FC < 0.833, variable importance of projection (VIP) > 1, and P-value <0.05. A total of 490 differential metabolites (negative and positive ions, Table S2-S4) were identified in the intestinal contents of the broiler in the four groups by LC-MS/MS. As shown in Fig. 6A, 346 differential metabolites were identified between the INU-0.5 group and the CON group, with 200 upregulated and 146 downregulated. As shown in Figs. 6B, 203 differential metabolites were identified between the INU-1.0 group and the CON group, with 77 upregulated and 126 downregulated. As shown in Figs. 6C, 223 differential metabolites were identified between the INU-2.0 group and the CON group, with 67 upregulated and 156 downregulated. The OPLS-DA revealed distinct clustering of intestinal metabolite profiles among the four groups (Fig. 6D). As shown in Fig. 6E, the Venn diagram revealed the numbers of unique metabolites for INU-0.5 vs. CON, INU-1.0 vs. CON, and INU-2.0 vs. CON were 184, 70, and 82, respectively. Additionally, a total of 60 common differential metabolites were identified among the comparisons of INU-0.5 vs. CON, INU-1.0 vs. CON, and INU-2.0 vs. CON. Furthermore, KEGG pathway enrichment analysis was performed on the 60 common differential metabolites to identify enriched metabolic pathways (Fig. 6F). There were four significantly different metabolic pathways (P < 0.05), including purine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, biosynthesis of unsaturated fatty acids, and steroid hormone biosynthesis.

Fig. 6.

Identification and categorization of differential metabolites (n = 8). (A-C) Volcano plots illustrating differential metabolites between treatment groups. (D) Scores scatter plot of the Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) model of gut metabolites. (E) Venn diagram of OTUs compared the metabolite amounts of INU groups (INU-0.5, INU-1.0, INU-2.0) to CON groups. (F) Enrichment analysis of common differential metabolites across inulin-treated groups. (G) The metabolic pathways associated with the differential metabolites between the two groups according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database for the G. gallus (chicken) KEGG pathway database. The node colors represent the variations in metabolites. (1) Taurine and hypotaurine metabolism; (2) Arginine biosynthesis; (3) Phenylalanine metabolism; (4) Pyrimidine metabolism; (5) Arginine and proline metabolism; (6) Tyrosine metabolism; (7) Cysteine and methionine metabolism; (8) Tryptophan metabolism; (9) Histidine metabolism; (10) Lysine degradation. CON = control; INU = inulin.

A bubble diagram illustrating the enriched metabolic pathways of differential metabolites in the CON and inulin groups is shown in Fig. 6G and Table S5. The top ten enriched metabolic pathways were as follows: taurine and hypotaurine metabolism, arginine biosynthesis, phenylalanine metabolism, pyrimidine metabolism, arginine and proline metabolism, tyrosine metabolism, cysteine and methionine metabolism, tryptophan metabolism, histidine metabolism, and lysine degradation. The KEGG pathway mapper was utilized to visualize differential metabolic pathways (Fig. 7). The metabolic network diagram shows the relationships between metabolites and metabolic pathways. Specifically, with dietary inulin supplementation, ornithine and citrulline were involved in the metabolic pathways of arginine biosynthesis; L-pipecolate was a constituent of the lysine degradation pathway; taurine played a pivotal role in taurine and hypotaurine metabolism; phenylpyruvic acid was a byproduct of phenylalanine metabolism, while both pyrimidine and purine metabolism were upregulated.

Fig. 7.

Inulin supplementation significantly modified the metabolic pathways' correlations based on the specific metabolites and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Red indicates upregulation, and green indicates downregulation. The rounded rectangles with different colors represent pathways enriched according to KEGG analysis. The black compounds and gray rectangles represent nonsignificant metabolites and pathways. Dotted lines indicate indirect reactions. CON = control; INU = inulin; AICAR = 5-aminoimidazole-4-carboxamide ribonucleotide; PRPP = phosphoribosyl pyrophosphate; dUMP = 2′-deoxyuridine 5′-monophosphate disodium salt.

3.7. Correlations among growth performance, intestinal bacteria, and metabolites

Spearman correlation analysis was conducted to assess the relationships between growth performance, intestinal microbiota, and metabolites. The results were visualized in a correlation heatmap (Fig. 8B–D). Inulin supplementation decreased the alpha diversity of the intestinal community, and increased the abundance of specific microbes, including Bacteroides, Lactobacillus, and Akkermansia (Fig. 5C). Those specific microbes positively correlated with differential cecal metabolites, including taurine, citrulline, ornithine, pseudouridine, L-pipecolate, L-Cysteinesulfinic acid, 5′-Deoxy-5'-(methylthio) adenosine, uracil, epinephrine, thymine, thymidine, urocanic acid, 2-deoxyuridine, 4-guanidinobutyric acid, and spermine (P < 0.05, Fig. 8D). Those differential specific cecal microbiota and metabolites positively correlated with serum HDL, SOD, GSH-Px, ADG, MUC2, DAO, tight junction protein (ZO-1, occludin, and claudin-5), and liver SOD (P < 0.05, Fig. 8B–C).

Fig. 8.

Correlational analysis of inulin supplementation effects on broiler growth performance, gut microbiome, and metabolome. (A) Impact of inulin on gut microbiota diversity and specific bacterial abundance. (B) Correlations between health parameters and differential microbes. (C-D) Correlations between differential intestinal metabolites, health parameters, and differential microbes. CON = control; INU = inulin. HDL = high-density lipoprotein; SOD = superoxide dismutase; ZO-1 = zonula occludens-1; GSH-Px = glutathione peroxidase; MUC2 = mucin 2; DAO = diamine oxidase; ADG = average daily gain; LDL = low-density lipoprotein; MDA = malondialdehyde; TG = triglycerides; ADFI = average daily feed intake; F/G ratio = feed-to-gain ratio; TCHO = total cholesterol; dUMP = 2′-deoxyuridine 5′-monophosphate disodium salt.

Conversely, inulin supplementation decreased some microbes, including Alistipes, Parabacteroides, Odoribacter, Rikenella, and Erysipelatoclostridium. Those decreased microbes positively correlated with L-Cysteine, phenylpyruvic acid, dUMP, Methylimidazoleacetic acid, 2-Ketoadipic acid, 5-hydroxytryptophan, and cholesterol (P < 0.05, Fig. 8D). Those decreased microbes and metabolites positively correlated with lipid and antioxidant indexes of liver and serum, ADFI, and F/G ratio (P < 0.05, Fig. 8B–C).

4. Discussion

This study investigated how dietary inulin supplementation enhanced broiler growth performance (including parameters such as FWB, ADG, ADFI, and F/G ratio), liver health (in terms of antioxidant capacity and lipid metabolism), and intestinal health (regarding villus structure and barrier function) by modulating gut microbiota composition. Specifically, inulin increased the abundance of beneficial bacteria such as Bacteroides, Lactobacillus, and Akkermansia, and influenced metabolic pathways related to taurine and hypotaurine, purine, arginine, phenylalanine, tyrosine, and tryptophan. Using an integrated microbiome-metabolome approach, the study demonstrated that inulin optimized productivity through the “inulin-gut microbiota-metabolite-liver and intestinal health” axis, with 2.0 g/kg identified as the optimal concentration for supplementation.

In this study, a notable finding was that a quadratic increase in FBW of broilers with increasing concentrations of dietary inulin supplementation. Meanwhile, dietary inulin supplementation induced linear increases in ADG and quadratic decreases in ADFI and F/G ratio compared to the CON group. Inulin can enhance feed efficiency in piglets by modulating their gut microbiota. However, an intake of 20 g/day is close to the upper limit of tolerable human consumption for adult humans, and excessive inulin-type fibers can lead to diarrhea (Holscher, 2017). In the present study, different concentrations of dietary inulin exhibited a linear or quadratic relationship with physiological responses. A basal diet supplemented with 2.0 g/kg of inulin was determined to be the optimal concentration.

The synergistic interaction between liver antioxidant capacity and lipid metabolism is critical for liver health. Oxidative stress inevitably arises from increasing stocking density of broilers, resulting in alterations in behavior, physiology, and metabolism. Those problems ultimately contribute to oxidative damage (Zhang et al., 2024a). In the present study, inulin supplementation significantly enhanced the serum and liver SOD levels, serum GSH-Px levels, while decreasing the serum and liver MDA levels. Meanwhile, inulin supplementation significantly decreased the levels of TCHO and TG in the liver and serum. Notably, Bacteroides, Lactobacillus, and Akkermansia can suppress hepatic lipid accumulation and downregulate the expression of hepatic lipid synthesis genes by modulating bile acid metabolism (Qiao et al., 2020; Wang et al., 2024b; Zhu et al., 2025). Simultaneously, several studies have indicated that the increased relative abundance of Bacteroides and Lactobacillus has been associated with enhanced body antioxidant capacity (Wang et al., 2022a; Zhang et al., 2024b). Thus, modulating the abundance of Bacteroides and Lactobacillus through inulin supplementation can enhance antioxidant capacity. Following inulin-mediated modulation of gut microbiota, the resultant metabolites enter the liver through the portal vein circulation, thereby modulating hepatic metabolism and antioxidant pathways.

Villus height and the V/C ratio are commonly used to assess intestinal function, as they positively correlate with digestive and absorptive capacities (Hao et al., 2021). Meanwhile, the intestinal barrier plays a pivotal role in regulating hepatic homeostasis (Yang et al., 2023). In this study, dietary inulin increased the V/C ratio of the duodenum, jejunum, and ileum; the DAO and MUC2 levels in the jejunum mucosa. Additionally, it increased the protein concentrations of claudin-5, occludin, and ZO-1. Lactobacillus and Akkermansia have been demonstrated to improve the morphology of intestinal villi and increase the number of goblet cells, indicating a potential enhancement in the integrity and function of the intestinal mucosal barrier (Kwak et al., 2021). Furthermore, Akkermansia can enhance the expression of intestinal tight junction proteins through activation of the adenosine 5′- monophosphate (AMP)-activated protein kinase pathway (Luo et al., 2022). Meanwhile, Akkermansia plays a pivotal role in promoting intestinal stem cell-mediated epithelial development, significantly contributing to the maintenance of intestinal homeostasis (Kim et al., 2021). Overall, this study demonstrates that dietary inulin contributed to increasing the abundance of Lactobacillus and Akkermansia, thereby maintaining intestinal barrier integrity and promoting intestinal health.

Dietary fiber, particularly prebiotics like inulin, can increase probiotic abundance and promote gut homeostasis by modulating the gut microbiota, thereby contributing to overall health (Han et al., 2023). This study found that inulin supplementation significantly decreased the diversity of cecal microbiota. Previous research reported reduced cecal microbiota diversity in high-fat diet-fed leptin knockout mice supplemented with inulin (Feng Y et al., 2021). Furthermore, inulin supplementation increased the abundance of Bacteroides, Lactobacillus, and Akkermansia at the genus level.

Inulin supplementation upregulated the taurine and hypotaurine metabolic pathway. Taurine is a highly abundant amino acid in animals and enhances growth performance, antioxidant capacity, lipid metabolism, and intestinal health (Han et al., 2020; Nardelli et al., 2011; Wang et al., 2023). The increased abundance of Bacteroides, Lactobacillus, and Akkermansia was generally positively correlated with enhanced taurine and hypotaurine metabolic pathways, as reflected in metabolite levels. The increase of taurine can enhance the mRNA expression and enzyme activity of cholesterol 7α-hydroxylase, stimulating cholesterol decomposition into bile acid in the liver (Murakami et al., 2016). Furthermore, taurine can conjugate with bile acids to facilitate bile acid metabolism (Wang et al., 2022b). This study observed the downregulation of cholesterol levels and various bile acids (7-dehydrocholic acid, allocholic acid, and lithocholic acid) in the inulin supplemented groups. Additionally, a significant reduction in serum total cholesterol concentration, associated with altered bile acid levels, was potentially attributable to the increased presence of taurine. Therefore, this study demonstrated that inulin supplementation enhanced lipid metabolism, antioxidant activity, and feed efficiency by increasing the abundance of Bacteroides, Lactobacillus, and Akkermansia, thereby elevating taurine metabolism in the broiler.

Inulin supplementation also modulated purine metabolism. This modulation may be attributed to Lactobacillus, which facilitates purine metabolite excretion, enhances the levels of deoxyinosine and inosine, and activates the peroxisome proliferator-activated receptor gamma signaling pathway, thereby improving intestinal barrier function (Han et al., 2025; Li et al., 2021). Lactobacillus enhances energy acquisition efficiency by promoting the turnover of intestinal purine metabolites and optimizing gut microbial metabolism (Fu et al., 2024). Meanwhile, the increased abundance of Akkermansia significantly enhanced the levels of adenosine and guanine in the intestine. Akkermansia plays a crucial role in restoring colonic metabolic homeostasis by modulating purine metabolism (Chen et al., 2025). Additionally, Bacteroides collaborate to regulate the balance of purine metabolism by enhancing lipid metabolism (Zheng et al., 2023).

Meanwhile, inulin supplementation upregulated the levels of citrulline and ornithine within the arginine and proline metabolism pathway. Lactobacillus and Bacteroides play crucial roles in the regulation of arginine synthesis and metabolism (Qi et al., 2019; Zeng et al., 2024). Elevating dietary arginine and citrulline levels can enhance the performance of broilers raised under warm conditions (Dao et al., 2021). Furthermore, citrulline possesses antioxidant and anti-inflammatory properties (Wijnands et al., 2012). Uyanga et al. (2020) demonstrated that citrulline supplementation can reduce lipid peroxidation and enhance the activities of SOD, CAT, and T-AOC. Therefore, the dietary inulin induced a beneficial increase in citrulline levels, potentially enhancing antioxidant function and promoting the fat metabolism of broilers. Additionally, ornithine is enzymatically converted to polyamines in broiler kidneys, which play crucial roles in animal health (Furukawa et al., 2021). Polyamines promote rapid growth, intestinal mucosa development, and recovery after injury in broilers (Bardócz et al., 1995). Therefore, inulin supplementation could augment ornithine levels within the anabolic pathway of arginine, thereby modulating intestinal health and bolstering antioxidant capacity in broilers.

Inulin supplementation resulted in the downregulation of phenylpyruvic acid in the phenylalanine metabolism and phenylalanine, tyrosine and tryptophan biosynthesis. The reduction in pyruvic acid levels may mitigate lipid oxidative stress by decreasing the concentration of its derivatives, such as phenyllactic acid (Hoyles et al., 2018). Meanwhile, the upregulation of phenylalanine, tyrosine, and tryptophan biosynthesis pathways is frequently associated with enhanced lipid accumulation, and decreased phenylpyruvic acid may suppress the activity of the metabolic pathways, consequently mitigating the development of lipid accumulation (Guo et al., 2023; Ji et al., 2024).

The correlation among health parameters, intestinal microbiota, and metabolites was assessed by Spearman's rank analysis, and the results revealed that upregulated differential metabolites after dietary inulin supplementation were positively correlated with health parameters and inulin-promoted microbes, while negatively correlated with inulin-restrained microbes. This indicates that inulin supplementation regulated the abundance of intestinal microbes to modulate differential metabolic pathways, including taurine and hypotaurine metabolism, purine metabolism, arginine and proline metabolism, and phenylalanine metabolism. Those modulations further improved production efficiency, feed efficiency, and health parameters in broilers.

This study presents a comprehensive blueprint for utilizing inulin as a sustainable strategy to enhance broiler productivity and health through microbiota-metabolite methods. Specifically, dietary inulin supplementation enhanced the abundance of Bacteroides, Lactobacillus, and Akkermansia, and those key microbes modulated metabolites in the metabolic pathways of taurine and hypotaurine, purine, arginine and proline, as well as phenylalanine. These regulated metabolites exhibited biological activities, acted on target organs, and ultimately improved the growth performance, liver, and intestinal health of broilers. Dietary supplementation of 2.0 g/kg inulin to the diet had the best effect. However, this study had several limitations. Firstly, the upper limit for the concentration of inulin in the diet remains to be determined. Secondly, further research is required to clarify the molecular mechanisms through which inulin influences microbial and metabolite health.

5. Conclusion

In conclusion, under intensive feeding conditions, the supplementation of inulin in the diet enhanced the growth performance, intestinal and liver health of broilers by modulating intestinal microbiota involving Bacteroides, Lactobacillus, and Akkermansia, and differential metabolites. It influenced metabolic pathways such as taurine and hypotaurine, purine, arginine and proline, as well as phenylalanine. Among the different concentrations, a dietary inulin level of 2.0 g/kg demonstrated the most significant effects.

Credit Author Statement

Yipeng Li: Writing – original draft, Methodology, Investigation. Kayeon Ko: Visualization. Xiangning Liu: Writing – original draft, Methodology, Investigation. Miroslava Kačániová: Supervision. Yunkyoung Lee: Supervision, Funding acquisition, Conceptualization. Guiguo Zhang: Supervision, Funding acquisition, Conceptualization.

Declaration of competing interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the content of this paper.

Appendix A. Supplementary data

The following is the supplementary data related to this article: