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Article

Effects of Dietary Non-Fibrous Carbohydrate to Neutral Detergent Fiber Ratio on Apparent Digestibility, Fecal Microbiota, and Plasma Metabolomics in Yili Horses

by
Mengfei Li
,
Zihao Xu
,
Long Sun
,
Zhiqiang Cheng
,
Yingying Yu
,
Yong Chen
,
Fengming Li
and
Changjiang Zang
*
Xinjiang Herbivore Nutrition Laboratory for Meat & Milk, College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China
*
Author to whom correspondence should be addressed.
Animals 2026, 16(5), 844; https://doi.org/10.3390/ani16050844
Submission received: 4 February 2026 / Revised: 26 February 2026 / Accepted: 5 March 2026 / Published: 7 March 2026
(This article belongs to the Special Issue Dietary Regulation of the Rumen Microbiome and Fermentation)
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Simple Summary

Dietary carbohydrates are the main energy source for horses, and the ratio of fiber to non-fibrous carbohydrates (NFC) is crucial for maintaining intestinal health. Furthermore, the nutritional quality of natural pasture is often limited by environmental and climatic factors, making it difficult to meet all of a horse’s nutritional needs. Therefore, supplementing the diet with concentrates has become a necessary means of optimizing dietary structure and improving nutrient digestion and absorption in horses. This study selected 24 Yili horses and divided them into four groups, each fed diets with different ratios of non-fibrous carbohydrates to neutral detergent fiber (NFC/NDF) (NFC/NDF ratios of 0.23, 0.39, 0.52, and 0.69). In addition to measuring apparent nutrient digestibility and fecal fermentation parameters, 16S rRNA sequencing and plasma metabolomics techniques were used to deeply analyze changes in gut microbiota and blood metabolites. The results showed that appropriately increasing the NFC/NDF ratio significantly improved crude protein digestibility and shifted the intestinal fermentation pattern toward propionate production, thereby providing the host with more energy. This indicates that controlling the NFC/NDF ratio at around 0.52 can effectively improve nutrient absorption and energy metabolism efficiency in Yili horses.

Abstract

This study aimed to investigate the effects of dietary NFC/NDF ratio on nutrient apparent digestibility, fecal fermentation parameters, microbial diversity, and plasma metabolomics in Yili horses. Twenty-four healthy Yili horses with similar body weights (406 ± 22.73 kg) were divided into four groups, each with six replicates: the Control Group (CG), Low-NFC Group (LG), Medium-NFC Group (MG), and High-NFC Group (HG). The experiment lasted 52 d, comprising a 7-day adaptation period and a 45-day experimental period. Total fecal collection was conducted from days 41 to 45 to calculate nutrient apparent digestibility. On the final day, rectal fecal samples and blood samples were collected for full-length 16S rRNA gene sequencing and plasma metabolomics analysis. The results revealed the following findings: (1) The apparent digestibility of crude protein (CP) in the MG and HG groups was significantly higher than in the CG (p < 0.01), and significantly higher in the LG group compared to the CG (p < 0.05). (2) Significant differences were observed in fecal pH, propionate concentration, and the acetate-to-propionate ratio between the CG and the experimental groups (p < 0.05). (3) At the phylum level, Firmicutes, Bacteroidota, and Verrucomicrobiota were dominant in the fecal microbiota of all groups. PICRUSt2 prediction indicated that the MG and HG groups primarily enhanced energy conversion efficiency through amino acid metabolism and pantothenate and CoA biosynthesis metabolic pathways. (4) A total of 204 differential metabolites were identified between the CG and MG groups, with 98 upregulated and 106 downregulated in the MG group compared to the CG. These metabolites were mainly enriched in pantothenate and CoA biosynthesis, fructose and mannose metabolism, pyruvate metabolism, and starch and sucrose metabolism pathways. In summary, appropriately increasing NFC/NDF content influences the gut microbiota composition and energy metabolism of Yili horses, thereby effectively improving their digestion and absorption of dietary nutrients.

1. Introduction

The Yili horse is a representative indigenous breed in China, primarily distributed in the Yili Kazakh Autonomous Prefecture of Xinjiang. It has distinct advantages, including tolerance to coarse feed, cold resistance, excellent lactation performance, and outstanding athletic ability [1]. In recent years, with the specialized development of the modern horse industry, the utility of horses has gradually expanded from traditional draft purposes to racing, milk, and meat production. This shift imposes higher demands on feeding management and nutritional regulation [2]. The nutritional quality of natural pasture is significantly influenced by seasons, pasture types, and climatic conditions, making it difficult for energy and protein supplies to meet the nutritional requirements of horses with different production uses at certain times [3]. Therefore, supplementing feed based on grazing and optimising the dietary structure to improve nutrient digestion and absorption efficiency, maintain intestinal microecological stability, and thus improve production performance and health has become an important research direction.
The ratio of dietary Non-Fibrous Carbohydrate (NFC) to Neutral Detergent Fiber (NDF) is a critical factor influencing the host’s energy supply mode, gut fermentation patterns, and microbial composition. NFC primarily consists of rapidly fermentable carbohydrates such as starch, soluble sugars, and pectin. These provide substrates for microbial fermentation and promote the production of Volatile Fatty Acids (VFAs), thereby enhancing the host’s energy supply [4,5]. However, equids are relatively sensitive to high-starch diets. When dietary starch intake exceeds the small intestine’s digestive capacity, undigested starch enters the hindgut and ferments rapidly. This may lead to lactic acid accumulation, decreased intestinal pH, and microbial dysbiosis, thereby increasing the risk of digestive and metabolic diseases. Studies have indicated that when dietary starch intake exceeds 1 g/kg BW per meal, a portion of the starch can ferment rapidly in the hindgut, causing lactic acid accumulation and a reduction in intestinal pH [6]. Furthermore, Willard et al. [7] observed that cecal pH dropped to 4–5 six hours after feeding Quarter Horses a high-concentrate diet, which inhibited the activity of hindgut cellulolytic bacteria and increased the risk of colic and laminitis. NDF is mainly composed of cellulose, hemicellulose, and lignin; among these, cellulose and hemicellulose can be fermented by microorganisms to produce VFAs, such as acetate, which provide energy for the host [8]. Raspa et al. [9] indicated that appropriate structural fiber intake helps maintain the hindgut fermentation environment and microbial stability in equids. Similarly, Liang et al. [10] found that increasing the proportion of roughage in the diet of lactating donkeys could promote energy metabolism and lactation performance. The aforementioned studies suggest that appropriate NFC/NDF levels can modulate fermentation phenotypes by regulating the gut microbiota and are associated with changes in intestinal metabolites.
Currently, the effects of the dietary NFC/NDF ratio have been extensively studied in dairy cows [11] and goats [12], yet research on equids remains relatively scarce. Therefore, using Yili horses as the experimental model, this study employed 16S rRNA gene sequencing and plasma metabolomics to systematically evaluate the effects of dietary NFC/NDF levels on nutritional metabolism and gut microbiota. This research aims to provide new insights into determining the optimal dietary NFC/NDF ratio for Yili horses.

2. Materials and Methods

2.1. Ethical Considerations

All animal care and handling procedures adhered to the Guidance of the Care and Use of Laboratory Animals in China and were approved by the Animal Care Committee of Xinjiang Agricultural University (animal protocol number: 2024019).

2.2. Animals and Dietary Composition

The experiment was conducted at Zhaosu Horse Farm, Zhaosu County, Ili Kazakh Autonomous Prefecture, Xinjiang. Twenty-four healthy Yili horses with similar body weights (406 ± 22.73 kg) were selected and randomly divided into four groups: the Control Group (CG), which served as the reference group and received a basal roughage diet consistent with local horse farm feeding practices, and three experimental groups, classified by dietary NFC levels: the Low-NFC Group (LG, 0.39), Medium-NFC Group (MG, 0.52), and High-NFC Group (HG, 0.69). The experimental diets were formulated according to the nutrient requirements for 400 kg lactating mares, as specified in the NRC Equine Nutrition Guidelines [8], and consisted of concentrate supplements and forage. The corn component was provided within a commercial ground concentrate supplement, processed according to industry standards for equine nutrition to ensure appropriate particle size for optimal pre-cecal digestion. Detailed information on dietary composition and nutrient levels is presented in Table 1.
The total experimental period lasted 52 days, comprising a 7-day adaptation period and a 45-day formal experimental period. Before the start of the experiment, the stables were thoroughly cleaned and disinfected, and strict deworming measures were implemented. Each mare was housed in an individual stall. Roughage was provided in the troughs at 09:30, 14:00, 20:00, and 01:30 the following day. Concentrate supplements were fed using custom-made feed bags after the horses had consumed the roughage. After feeding, the horses were moved to a dedicated paddock for free exercise and provided with ad libitum access to water, but they were prevented from grazing or accessing other feed. A digestion trial was conducted from day 41 to day 45 of the formal experimental period. All mares remained clinically healthy through veterinary examinations during the experiment period.

2.3. Sample Collection

2.3.1. Feed and Fecal Sample Collection

During the experimental period, 100 g of concentrate samples were collected. Forage samples (a mixture of alfalfa, grass hay, and corn stover) were collected using the five-point sampling method. All feed samples were stored in sealed bags for subsequent nutrient analysis.
During the digestion trial, total fecal output was collected daily using custom-made fecal collection harnesses; total fecal output was weighed and recorded. A 200 g aliquot of homogenised feces was collected daily into sterile plastic containers and stored at −20 °C. At the end of the sampling period, the daily fecal samples were pooled proportionally and thoroughly mixed for the determination of total tract apparent digestibility. Upon the conclusion of the digestion trial, rectal fecal samples were collected from each mare. These samples were immediately placed into sterile, nuclease-free cryotubes and stored at −80 °C for subsequent microbial diversity analysis.

2.3.2. Blood Sample Collection and Plasma Preparation

On the final day of the experiment, one hour before morning feeding, jugular venous blood was collected from all mares. Blood samples were collected into heparin sodium anticoagulant tubes and then centrifuged at 1500× g for 15 min. The supernatant plasma was separated and aliquoted into sterile, nuclease-free cryotubes. Samples were immediately snap-frozen in liquid nitrogen and stored at −80 °C for subsequent plasma metabolomics analysis.

2.4. Sample Determination and Analysis

Feed and fecal samples were first oven-dried at 65 °C for 72 h to constant weight, then ground to pass through a 1 mm screen. The contents of dry matter (DM), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), ash, and gross energy (GE) in both feed and fecal samples were determined. DM (no. 930.15), Ash (no. 942.05), and CP (no. 976.05) contents were analyzed according to the standard methods of the Association of Official Analytical Chemists (AOAC) [13]. Gross energy (GE) was measured using a high-precision calorimeter (OR2014, Shanghai Ou Rui Instrument Equipment Co., Ltd., Shanghai, China). Organic matter (OM) was calculated by subtracting ash from dry matter. NDF and ADF concentrations were determined using an automated fiber analyzer (Hanon-2000, Hanon Advanced Technology Group Co., Ltd., Shandong, China) following the procedures described by Van Soest et al. [14]. The total tract apparent digestibility (TTAD) was calculated for CP, OM, and GE, while total tract digestibility (TTD) was determined for NDF and ADF, as follows:
TAAD / TTD   ( % ) = Nutrient   intake - fecal   nutrient   output Nutrient   intake × 100
Fecal pH was determined by diluting 5 g of fresh feces in 15 mL of distilled water (1:3 w/v). The mixture was stirred at room temperature for 3–5 min, and the pH was measured using a portable pH meter [15] (FiveEasy 22, Mettler Toledo, Shanghai, China).
The concentrations of fecal volatile fatty acids (VFAs), including acetate, propionate, butyrate, isobutyrate, and isovalerate, were determined by gas chromatography (GC). Sample pretreatment was performed according to the method described by Huang et al. [16]. VFA quantification was carried out using a gas chromatograph (GC-2010, Shimadzu Corporation, Kyoto, Japan) equipped with a Stabilwax capillary column (30 m × 0.20 mm × 0.33 µm, Shimadzu Corporation, Kyoto, Japan). The chromatographic conditions were as follows: the initial column oven temperature was set at 55 °C, increased to 200 °C at 13 °C/min, and held for 0.5 min. The injector and detector temperatures were maintained at 230 °C and 240 °C, respectively. Nitrogen (N2) was used as the carrier gas at a flow rate of 5.0 mL/min.

2.5. Genomic DNA Extraction, PCR Amplification and Purification, and Sequencing

Total genomic DNA was extracted from fecal samples using the Fecal Genomic DNA Extraction Kit (TIANGEN, Shanghai, China) according to the manufacturer’s instructions. The quality of the extracted DNA was assessed by 1% agarose gel electrophoresis. The full-length 16S rRNA gene was amplified using universal primers 27F (5′-AGRGTTYGATYMTGGCTCAG-3′) and 1492R (5′-RGYTACCTTGTTACGACTT-3′) (synthesized by Biological Engineering Co., Ltd., Shanghai, China). PCR thermocycling conditions were as follows: initial denaturation at 95 °C for 5 min; 27 cycles of denaturation at 98 °C for 30 s, annealing at 57 °C for 30 s, and extension at 72 °C for 60 s; and a final extension at 72 °C for 5 min. PCR amplicons were purified using SMRTbell® Cleanup Beads and quantified using a Qubit fluorometer. Sequencing libraries were constructed using the Kinnex Concatenation Kit. Finally, the libraries were sequenced on the PacBio platform (Pacific Biosciences, Menlo Park, CA, USA).

2.6. Bioinformatics Analysis

Raw sequencing data were denoised using DADA2 (v1.14.0) to obtain high-quality reads. Taxonomy was assigned using BLAST (v2.10.0) against the NCBI NT database, with a 0.8 similarity threshold. Following quality control, unique sequences were identified as Amplicon Sequence Variants (ASVs). To normalize sequencing depth, samples were rarefied to 95% of the minimum sequence count across all samples. Alpha diversity indices were calculated using Mothur (v3.8.31). Beta diversity was estimated in QIIME 2 using weighted Unifrac distances. Finally, LEfSe was used to identify species showing significant differences between the groups, with the default LDA score threshold set to 3. Functional prediction of the bacterial community was conducted using PICRUSt2 (v2.1.2-b). Differences were considered statistically significant at p < 0.05. All visualizations were generated using the vegan package (v4.4.2) in the R statistical environment.

2.7. Plasma Metabolomics Analysis

Plasma samples were thawed at 4 °C and vortexed thoroughly to ensure homogeneity. A 200 µL aliquot of each sample was mixed with 800 µL of pre-cooled methanol and incubated at −20 °C for 30 min to precipitate proteins. The mixture was centrifuged at 16,000× g for 20 min at 4 °C. The supernatant was carefully collected for subsequent mass spectrometry analysis.
Chromatographic separation was performed using a Waters UPLC I-Class Ultra-High-Performance Liquid Chromatography (UHPLC) system (Waters Corporation, Milford, MA, USA) equipped with a Waters ACQUITY UPLC HSS T3 column (1.7 µm, 2.1 × 100 mm). The instrument parameters were as follows: injection volume, 4 µL; column temperature, 40 °C; and flow rate, 300 µL/min. The mobile phases consisted of 0.1% formic acid in water (Solvent A) and 0.1% formic acid in acetonitrile (Solvent B). The solvent gradient elution program was as follows: 0–1.5 min, 0% B; 1.5–6 min, 0–48% B; 6–10 min, 48–100% B; 10–12 min, 100% B; 12–12.1 min, 100–0% B; and 12.1–15 min, 0% B for re-equilibration.
Mass spectrometric detection was performed using an AB SCIEX TripleTOF® 5600+ system (AB SCIEX, Framingham, MA, USA). The instrument was operated in Information-Dependent Acquisition (IDA) mode with an electrospray ionisation (esi) source, scanning in both positive (ESI+) and negative (ESI−) ion modes.

2.8. Metabolomics Data Processing and Analysis

Raw data processing, including peak alignment, retention time correction, and peak area extraction, was performed using MS-DIAL software (v4.9.221218). Metabolite structure identification was performed by matching exact masses and MS/MS spectra against public databases, such as the Human Metabolome Database (HMDB), MassBank, and GNPS.
For the extracted data, ion peaks with more than 50% missing values within any group were excluded from subsequent statistical analyses. Data from positive and negative ionization modes were normalized separately using the Total Peak Area (TPA) method. The datasets were subsequently merged, and pattern recognition analysis was performed using Python (v3.9). Before multivariate analysis, the data were scaled using Unit Variance (UV) scaling.

2.9. Statistical Analysis

Preliminary data management was conducted using Microsoft Excel 2019. All statistical evaluations regarding total tract apparent digestibility, fecal pH, and VFA concentrations were performed using SPSS software version 26.0 (IBM Corp., Armonk, NY, USA). Data were screened for normality and homogeneity of variances using the Shapiro–Wilk and Levene’s tests, respectively. For variables meeting the assumption of homoscedasticity, differences among dietary treatments were analyzed via one-way analysis of variance (ANOVA) followed by Duncan’s multiple range test for post hoc comparisons. In cases of heteroscedasticity, as observed with fecal pH and VFA profiles, Welch’s ANOVA was employed, and differences were identified using the Games–Howell post hoc test. A post hoc power analysis was also performed to confirm that the sample size (n = 6 per group) provided sufficient statistical power (e.g., 1 − β = 0.956 for CP digestibility) to detect significant biological differences. Data are expressed as means and the standard error of the mean (SEM). Statistical significance was defined as p < 0.05, while p < 0.01 was considered highly significant.

3. Results

3.1. Effect of Dietary NFC/NDF Ratio on Apparent Digestibility of Nutrients in Yili Horses

As shown in Figure 1, the apparent digestibility of CP in the MG and HG groups was significantly higher than in the CG group (p < 0.01), and the LG group was significantly higher than the CG group (p < 0.05). There were no significant differences in other indicators across the groups (p > 0.05).

3.2. Effects of Dietary NFC/NDF Ratio on Fecal pH and VFA Composition

Table 2 presents the effects of the dietary NFC/NDF ratio on fecal VFA composition and pH in Yili horses. Compared with the CG, fecal pH was significantly lower in the HG group (p < 0.05). Fecal propionate concentrations in the MG and HG groups were significantly higher than in the CG (p < 0.05). Furthermore, the acetate/propionate ratio in the CG was significantly higher than in the LG, MG, and HG groups (p < 0.05). No significant differences were observed in other fermentation parameters among the groups (p > 0.05).

3.3. Effects of Dietary NFC/NDF Ratio on Intestinal Microflora Composition

3.3.1. Analysis of α-Diversity of Species

To investigate the impact of dietary NFC/NDF ratios on the composition of the fecal microbiome in Yili horses, the fecal microbiota was profiled using 16S rRNA gene sequencing. As illustrated in Figure 2, alpha diversity metrics showed that the Chao1 index and the number of observed species in the HG group were significantly higher than in the CG (p < 0.05; Figure 2A,B). In contrast, no significant differences were observed in the Shannon and Simpson indices across the groups (p > 0.05; Figure 2C,D).

3.3.2. Analysis of β-Diversity of Species

Figure 3 shows the effect of the dietary NFC/NDF ratio on the beta diversity of the fecal microbiota in Yili horses. As shown in the Venn diagram (Figure 3A), 260 ASVs were shared across the four groups. The numbers of unique ASVs identified in the CG, LG, MG, and HG groups were 550, 793, 647, and 1076, respectively.
Principal Coordinate Analysis (PCoA) (Figure 3B) and Non-metric Multidimensional Scaling (NMDS) (Figure 3C) were used to assess differences in microbial community structure. The PCoA results indicated that Principal Coordinate 1 (PC1) and Principal Coordinate 2 (PC2) explained 12.03% and 8.25% of the total variance, respectively. The NMDS analysis yielded a stress value of 0.18, indicating a reliable representation of the data structure. Notably, the HG group was clearly separated from the other groups, whereas the CG, LG, and MG groups showed greater similarity and overlap.

3.3.3. Relative Abundance of Microbial Species at Phylum and Genus Levels

At the phylum level, seven dominant phyla were identified (Figure 4A), including Firmicutes, Verrucomicrobia, Fibrobacteres, Spirochaetes, Bacteroidetes, and Proteobacteria. Among these, Firmicutes and Verrucomicrobia were the most abundant, together accounting for 80% of the total relative abundance. The relative abundance of Fibrobacteres decreased with increasing dietary NFC/NDF ratios; however, this difference was not statistically significant (p > 0.05). No significant differences in the relative abundance of other phyla were observed among the groups (p > 0.05).
At the genus level, a total of 32 genera were identified. Figure 4B shows the top 10 most abundant genera: Akkermansia, Streptococcus, Eubacterium, Fibrobacter, Lachnoclostridium, Treponema, Ruminococcus, Oscillibacter and Marseillibacter. Consistent with the phylum-level results, the relative abundance of Fibrobacter decreased with increasing dietary NFC/NDF ratios, whereas Streptococcus increased. Nevertheless, no significant differences were observed among the genera across the treatment groups (p > 0.05).

3.3.4. LEfSe Analysis of Fecal Microbiota in Yili Horses

Linear Discriminant Analysis Effect Size (LEfSe) was used to identify high-dimensional biomarkers and to reveal the specific bacterial taxa that characterise differences among groups. As shown in Figure 5, the HG group had the highest number of differentially abundant taxa, whereas the MG group showed the fewest.
Specifically, five taxa were significantly enriched in the CG, identified as s_Treponema brennaborense, f_Pasteurellaceae, o_Pasteurellales, g_Haemophilus, and s_Haemophilus pittmaniae. In contrast, the taxa primarily enriched in the HG group included g_Bacilliculturomica, s_Bacilliculturomica massiliensis, g_Anaerovorax, and s_Anaerovorax odorimutans.

3.3.5. Prediction of Fecal Microbial Community Function

The functional potential of the fecal microbiota in Yili horses under different dietary NFC/NDF ratios was predicted using PICRUSt2. As shown in Figure 6, at KEGG Level 1, six major metabolic pathways were identified as the most representative of the fecal bacterial community. At Level 2, the predominant functional categories included carbohydrate metabolism, amino acid metabolism, metabolism of cofactors and vitamins, and replication and repair.
Furthermore, we screened the top 10 significantly differing pathways at Level 3 for comparison. The results indicated that the relative abundances of these pathways were significantly higher in the experimental groups than in the CG (p < 0.05). Notably, the HG group showed significant enrichment in pathways such as pantothenate and CoA biosynthesis; nicotinate and nicotinamide metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; and valine, leucine, and isoleucine biosynthesis.

3.4. Effects of Dietary NFC/NDF Ratio on Plasma Metabolites in Yili Horses

Based on the comprehensive results for apparent digestibility and fecal fermentation parameters, plasma samples from the CG and MG groups were selected for untargeted metabolomics analysis. A total of 1542 metabolites were identified (Table S1). Principal Component Analysis (PCA) was used to visualize the distribution of the identified metabolites. As shown in Figure 7A, the score plot based on the first two principal components revealed that the CG and MG groups were not clearly separated. To further investigate metabolic alterations, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was conducted. The results (Figure 7B,C) showed that the model had high explanatory and predictive capabilities (R2Y = 0.997, Q2 = 0.637) with no evidence of overfitting. The samples from the two groups clustered on distinct sides of the plot, indicating effective discrimination between the groups and validating the model’s suitability for identifying differential metabolites.
Differential metabolites were selected based on the Variable Importance in Projection (VIP) values from the OPLS-DA model. The selection criteria are as follows: p < 0.05, VIP > 1. Ultimately, 204 differential metabolites were identified (Table S2). The volcano plot and hierarchical clustering heatmap (Top 50) of these differential metabolites are presented in Figure 7C,D. Among them, 98 metabolites were upregulated (higher levels) in the MG group compared with the CG, while 106 metabolites were downregulated (higher levels in the CG).
To gain a comprehensive understanding of the biological significance of the differential metabolites between the CG and MG groups, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed. As shown in Figure 8A, the differential metabolites were primarily enriched in pathways including pantothenate and CoA biosynthesis, pyruvate metabolism, fructose and mannose metabolism, and starch and sucrose metabolism. To further validate the trends in these metabolic pathways, representative metabolites enriched in the key pathways were selected for visualization. As illustrated in Figure 8B–E, the relative abundances of lactic acid, mannose, GDP-mannose, and maltose were significantly lower in the MG group than in the CG (p < 0.05).

3.5. Correlation Analysis

Spearman correlation coefficients were calculated to assess associations among the measured indices (Figure 9). Regarding the relationship between nutrient apparent digestibility and fecal fermentation parameters (Figure 9A), CP digestibility was significantly positively correlated with propionate (p < 0.01) and isobutyrate (p < 0.05) concentrations, whereas it showed significant negative correlations with fecal pH (p < 0.05) and the acetate/propionate ratio (p < 0.01).
Regarding the associations between fecal microbiota, fermentation parameters, and digestibility (Figure 9B,C), fecal pH was positively correlated with Eubacterium (p < 0.05) and negatively correlated with Streptococcus (p < 0.05). Fibrobacter was negatively correlated with propionate concentration (p < 0.05). Furthermore, Fibrobacter was significantly negatively correlated with the digestibility of ADF and NDF (p < 0.01), as well as with DM, OM, CP, and GE digestibility (p < 0.05).
The integrated analysis of plasma metabolites and other indices (Figure 9D–F) revealed that CP digestibility was strongly positively correlated with uracil (p < 0.01) and negatively correlated with GDP-mannose, mannose, and 2-hydroxy-2-ethylsuccinic acid (p < 0.05). Additionally, both pH and the acetate-to-propionate ratio showed significant positive correlations with lactic acid and pantothenic acid (p < 0.05). Regarding interactions between bacterial genera and metabolites, Streptococcus showed a significant negative correlation with GDP-mannose and pantothenic acid (p < 0.05). In contrast, Fibrobacter showed significant positive correlations with maltose, 2-hydroxy-2-ethylsuccinic acid, and mannose (p < 0.05). Meanwhile, the genus Lachnoclostridium was positively correlated with α-ketoisovalerate (p < 0.05).

4. Discussion

4.1. Effects of Dietary NFC/NDF Ratio on Apparent Digestibility and Fecal Fermentation Parameters in Yili Horses

The ability of animals to utilize nutrients is a core indicator for evaluating feed value and the nutritional status of the body [17]. Unlike ruminants, microbial fermentation in equids primarily occurs in the hindgut (cecum and colon) [18]. Therefore, the ratio of NFC/NDF in the diet directly determines the type and intensity of hindgut fermentation, which in turn regulates the efficiency of nutrient digestion by altering intestinal pH and volatile fatty acid (VFA) concentrations [19].
This study showed that increasing dietary NFC levels significantly altered the hindgut fermentation environment in Yili horses. Although VFA concentrations in the cecum are usually higher than in feces, fecal VFAs are still an effective surrogate indicator for assessing hindgut fermentation status [20]. Experimental data showed that fecal pH in the high-NFC groups (MG and HG) showed a decreasing trend (remaining within the physiological range of approximately 7.0), while propionate concentration significantly increased, and the acetate/propionate ratio significantly decreased. This result is consistent with the research of Hintz et al. [21] and Medina et al. [22], indicating that an increased NFC ratio enhanced enzymatic digestion and absorption in the small intestine. Undigested NFC entering the hindgut provided sufficient easily fermentable substrates for microorganisms, stimulating microbial activity and promoting a shift in fermentation patterns from acetate-dominated to propionate-dominated [23]. The primary VFAs in the equine gut are acetate, propionate, and butyrate [24]. Physiologically, acetate enters the tricarboxylic acid (TCA) cycle to generate ATP, whereas propionate can be converted into glucose [25]. This change in the fermentation environment directly affected the apparent digestibility of nutrients. This study found that CP digestibility was significantly higher in the high-NFC groups than in the control group. This is consistent with the conclusions of Jouany et al. [26] and Schaafstra et al. [27] that high starch or supplemental concentrate diets can improve the digestibility of CP and organic matter. Although the high fiber content in roughage usually limits digestibility [18], in this study, NDF and ADF digestibility showed a “first increase then decrease” trend with increasing NFC levels. At appropriate NFC levels (such as the MG group), enhanced fermentation promoted overall digestion; however, when NFC was too high (HG group), propionate-type fermentation led to a decrease in intestinal pH. In the hindgut of horses, starch-degrading bacteria and cellulolytic bacteria compete for substrates [28]. A high-NFC environment promotes the rapid proliferation of starch-degrading bacteria and the production of acidic substances. This change in the microecological environment inhibits the activity of pH-sensitive cellulolytic bacteria, thereby the digestibility of NDF and ADF in the later stages [29].

4.2. Effects of Dietary NFC/NDF Ratio on Fecal Microbial Diversity in Yili Horses

The gut microbiota plays a pivotal role in nutrient utilization, intestinal development, and immune modulation in animals [30,31,32]. In the present study, the sequencing coverage for all samples reached 100%, indicating that the sequencing depth was sufficient to capture the actual microbial profile of the fecal microbiota in Yili horses. Regarding alpha diversity, both the Chao 1 index and the number of observed species in the HG group were significantly higher than those in the CG. While increased diversity is often associated with ecosystem health, in the context of high-NFC diets, it should be interpreted with caution. This phenomenon may represent a state of ‘early dysbiosis’, where the introduction of non-structural carbohydrates promotes the growth of amylolytic and saccharolytic taxa without an immediate loss of the resident fibrolytic community. However, such a shift can reduce the functional stability of the hindgut microbiome, making it more susceptible to pH fluctuations and metabolic disturbances. At the phylum level, Firmicutes is one of the dominant phyla in the equine gut and is primarily involved in the degradation of dietary fiber. In contrast, Bacteroidetes mainly participates in the breakdown of non- fibrous substances, such as starch, and is associated with propionate production [33,34]. Verrucomicrobia is also widely distributed in the equine cecum, colon, and feces. Previous studies have shown a positive correlation between the relative abundance of Verrucomicrobia and the expression of genes involved in gastrointestinal immune regulation, suggesting that variations in its abundance have significant implications for host health [35,36]. In this trial, Firmicutes, Bacteroidetes, and Verrucomicrobia were identified as the predominant phyla, which is consistent with previous findings on the equine gut microbiome [37,38]. Additionally, a higher abundance of Fibrobacteres was observed in the fecal samples of the CG, aligning with the results reported by Dougal et al. [19]. Fibrobacteres facilitates the degradation of fibrous materials, and its abundance is negatively correlated with the dietary NFC ratio; specifically, high-concentrate diets create an environment unfavorable for the proliferation of Fibrobacteres [39,40]. Collectively, these results suggest that an appropriate dietary NFC/NDF ratio can modulate the equine gut microbiota, thereby improving the digestion and absorption of dietary nutrients.
Building on the phylum-level analysis, examining ASV abundance at the genus level provides a more precise insight into how dietary NFC/NDF ratios influence the fecal microbial community. The present study found that the relative abundances of Treponema, Lacrimispora, and Fibrobacter were higher in the CG than in the experimental groups. Previous studies have indicated that Treponema and members of the Lacrimispora family are more abundant in healthy horses and play a crucial role in maintaining gastrointestinal health [41]. Collinet et al. [42], in a study of the fecal microbiota of Warmblood horses, found that the relative abundance of Lacrimispora was positively correlated with the concentration of fibrolytic bacteria. These findings suggest that high-fiber diets promote the proliferation of genera dominated by fibrolytic bacteria, thereby enhancing the capacity for fiber degradation [43]. Additionally, the relative abundance of Akkermansia in the CG was higher than in the experimental groups. This genus is known as a mucin-degrading bacterium that helps maintain the integrity of the mucin layer and reduces intestinal inflammation, thereby exerting a positive effect on host gut health [44]. In contrast, the HG group exhibited a significantly higher relative abundance of Streptococcus. Streptococcus is a typical amylolytic and lactate-producing bacterium. The accumulation of lactate in the gut can lower pH, potentially triggering intestinal disorders such as acidosis [45,46]. Garner et al. [47] reported that feeding horses excessive starch increased the abundance of cecal Streptococcus. Although no abnormal fecal pH values were observed in the present study, the elevated abundance of Streptococcus suggests a potential risk of hindgut acidosis in horses in the HG group.
PICRUSt2 predicts the functional profiles of microbial communities by inferring their gene content from full-length 16S rRNA sequences of characterised bacteria. In the present study, the predicted bacterial functions primarily encompassed seven categories: metabolism, genetic information processing, environmental information processing, cellular processes, human diseases, and organismal systems. Notably, the pathways for pantothenate and CoA biosynthesis, as well as valine, leucine, and isoleucine biosynthesis, showed the highest levels of enrichment. Previous studies have demonstrated that dietary carbohydrates are fermented by the gut microbiota into metabolites, primarily short-chain fatty acids (SCFAs) such as acetate, propionate, and butyrate, which play a key role in regulating host energy metabolism [48]. In our study, increasing dietary NFC levels increased carbohydrate intake, which subsequently altered the relative abundances of g_Massilimaliae, g_Anaerovorax, g_Bacilliculturomica, and s_Bacilliculturomica massiliensis. These microbial shifts appear to contribute to improved energy metabolism in horses.

4.3. Metabolic Response of Yili Horses to Varying Dietary NFC/NDF Ratios

In the present study, untargeted metabolomics analysis was performed on plasma samples from horses in the CG and MG groups using liquid chromatography–tandem mass spectrometry (LC-MS/MS), resulting in the identification of 204 differential metabolites. KEGG enrichment analysis revealed that these metabolites were primarily enriched in the following key pathways: pantothenate and CoA biosynthesis, fructose and mannose metabolism, starch and sucrose metabolism, phenylalanine metabolism, and pyruvate metabolism. Notably, the differential metabolite lactic acid is involved in the regulation of multiple aforementioned metabolic pathways.
As a crucial intermediate product of glucose metabolism in equines, lactic acid concentration is widely considered a key indicator of hindgut fermentation homeostasis [18]. During carbohydrate fermentation, glucose is converted to pyruvate via glycolysis. Subsequently, the reversible conversion between pyruvate and lactate is catalyzed by lactate dehydrogenase (LDH), utilizing the NADH/NAD+ redox system as a cofactor to maintain microbial energy metabolism and redox balance. Previous studies have indicated that high-concentrate diets predispose animals to lactate accumulation and a subsequent decrease in pH, thereby increasing the risk of hindgut subclinical acidosis and laminitis [49]. However, contrary to these expectations, the present study observed that lactic acid levels in the MG group were downregulated across the pathways of fructose and mannose metabolism, glycolysis/gluconeogenesis, and pyruvate metabolism. This finding suggests that under the high-NFC conditions established in this trial, no significant lactic acid accumulation occurred in the host. This phenomenon may be attributed to a shift in metabolic flux: although the high-NFC diet provided ample fermentable substrates and promoted pyruvate production via glycolysis, the conversion of pyruvate to lactate was inhibited. Instead, a greater proportion of pyruvate was likely converted to acetyl-CoA, catalyzed by pyruvate dehydrogenase (PDH), to enter the tricarboxylic acid (TCA) cycle for oxidative energy production. Nilsson et al. [50] reported that high-concentrate diets increase the abundance of genes encoding enzymes involved in non-structural carbohydrate metabolism, thereby facilitating the metabolism of starch and sugar. Consistent with this, our plasma metabolomic analysis revealed that Yili horses fed higher-NFC diets exhibited significant alterations in metabolic pathways associated with carbohydrate utilization and oxidative energy metabolism. We postulate that the metabolic profile shifted toward enhanced substrate availability for aerobic oxidation, thereby mitigating a transition toward extensive lactic acid fermentation. Furthermore, the study found a significant downregulation of maltose, a metabolite in the starch and sucrose metabolic pathways, in the MG group. Maltose is a typical disaccharide intermediate in the starch hydrolysis process, mainly produced by the action of α-amylase on starch in the lumen of the small intestine, and subsequently hydrolyzed into glucose for absorption by the body [51]. Under high-NFC diet conditions, the supply of fermentable carbohydrates increases, and the hydrolysis and final digestion of starch may be more complete, causing disaccharides such as maltose to be more likely to be rapidly hydrolyzed and converted into absorbable glucose before entering the bloodstream, thus leading to a decrease in blood maltose levels. This result suggests that a high-NFC diet may affect the carbohydrate composition and energy substrate supply in the body’s circulation by promoting the conversion and absorption of starch digestion products into monosaccharide forms.

4.4. Exploring the Regulatory Mechanisms of the “Diet-Microbiome-Metabolism Axis” Based on Multi-Omics Association Analysis

Uracil is a key pyrimidine base in nucleotide metabolism and an essential precursor for uridine nucleotides, thereby supporting RNA synthesis and cellular proliferation and renewal. The elevated uracil levels observed in this study suggest that high-NFC diets enhance systemic anabolism in horses. This finding aligns with the significant negative correlations observed between CP digestibility and both pH and the acetate/propionate ratio, and with the significant positive correlations with propionate and isobutyrate concentrations. Collectively, these data indicate a shift in fermentation towards a propionate-dominated pattern, which provides a more abundant energy supply for the host.
Fibrobacter is a key cellulolytic genus in the herbivore digestive tract, capable of fermenting substrates such as cellulose and cellobiose to produce acetate and succinate [52]. In the present study, the high-NFC diet increased the breakdown of fermentable carbohydrates into utilizable sugars in the hindgut, thereby enhancing propionate production via the glycolytic and succinate pathways. Furthermore, a significant negative correlation was observed between the genus Streptococcus and pantothenic acid (Vitamin B5). This may be attributed to the rapid proliferation of Streptococcus under high-NFC conditions, which likely leads to substantial consumption of vitamin precursors synthesized within the gut. Interestingly, metabolomics analysis revealed that differential metabolites were enriched in the pantothenate and CoA biosynthesis pathways, a finding that aligns with the functional predictions obtained from PICRUSt2.
Collectively, these results indicate that the dietary NFC/NDF ratio not only alters the physical digestion process in the gut of Yili horses but also precisely regulates the systemic nutritional and metabolic balance through the microbiota–host axis.

4.5. Limitations and Future Perspectives

Despite providing insights into metabolic and hindgut responses of Yili horses to varying NFC/NDF ratios, several limitations should be acknowledged. First, the experimental diets were not strictly isoenergetic or isonitrogenous. Although this design reflects practical feeding transitions in regional horse production, the progressive increase in digestible energy (DE) and crude protein (CP) with increasing concentrate inclusion represents a potential confounder. Therefore, the observed shifts in fermentation patterns and plasma metabolites may reflect the combined effects of nutrient density and carbohydrate structure rather than the NFC/NDF ratio alone. Second, fecal pH and VFA profiles are non-invasive surrogate markers of hindgut fermentation and may not fully capture the immediate and dynamic conditions in the cecum and proximal colon. Third, untargeted metabolomics was restricted to the CG and MG groups, which precludes a full dose–response evaluation across the entire dietary gradient. In addition, dietary starch content was not measured analytically, and starch intake per meal was estimated, which should be interpreted cautiously. Future studies using strictly controlled isoenergetic/isonitrogenous designs, direct DE/ME determination, larger cohorts including all dietary treatments, and flux-based approaches are warranted to disentangle the specific contributions of energy density and carbohydrate composition.

5. Conclusions

This study demonstrates that adjusting the dietary NFC/NDF ratio modulates nutrient digestibility, hindgut fermentation patterns, and systemic metabolic signatures in Yili horses, providing new insights into the “diet–microbiota–metabolome” axis relevant to precision equine nutrition. A lower NFC/NDF ratio (0.23) was associated with a more fiber-oriented microbial profile and enhanced fiber utilization, whereas a moderate increase to 0.52 (MG) improved apparent crude protein digestibility and shifted fermentation toward a propionate-oriented profile. Practically, an NFC/NDF of 0.52 appears to offer a favorable balance, while a higher NFC/NDF (HG) was accompanied by lower fecal pH, suggesting a stronger hindgut perturbation and the need for caution when increasing readily fermentable carbohydrates. However, fecal indices are surrogate markers, and the experimental duration was relatively short; future studies should evaluate longer-term outcomes and incorporate direct hindgut functional measures and functional metagenomics to refine feeding recommendations.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani16050844/s1, Table S1: Chemical composition of individual forage ingredients (DM basis). Table S2: Plasma metabolites identified in the CG and MG groups.; Table S3: Permutation validation statistics (n = 200) for the OPLS-DA model of plasma metabolomics (CG vs. MG). Table S4: Plasma differential metabolites identified in the CG and MG groups.

Author Contributions

Conceptualization, M.L. and C.Z.; methodology, M.L., Z.X. and L.S.; validation, Z.C. and Y.Y.; formal analysis, M.L., Z.X. and L.S.; investigation, Z.X. and L.S.; resources, C.Z.; data curation, M.L., Z.C. and Y.Y.; writing—original draft preparation, M.L.; writing—review and editing, M.L., F.L. and Y.C.; visualization, M.L., Z.C. and Y.Y.; supervision, F.L., Y.C. and C.Z.; project administration, C.Z. and Y.C.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key R&D Program Special Project (2022YFD1600100) and the Autonomous Region “Three Rural” Backbone Talent Training Project (2023SNGG).

Institutional Review Board Statement

All procedures in this study were approved by the Animal Experiment Ethics Committee of Xinjiang Agricultural University (Animal protocol number: 2024019, approval date: 12 January 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding authors.

Acknowledgments

We would like to thank Shanghai Bioprofile Biotechnology Co., Ltd. for providing the plasma metabolomics analysis.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
LC-MS/MSLiquid Chromatography–Tandem Mass Spectrometry
SEMScanning Electron Microscopy
PCOAPrincipal Coordinate Analysis
NMDSNon-Metric Multidimensional Scaling
LefseLinear Discriminant Analysis Effect Size
PCAPrincipal Components Analysis
OPLS-DAOrthogonal Partial Least Squares Discriminant Analysis
KEGGKyoto Encyclopedia of Genes and Genomes

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Figure 1. Effects of dietary NFC/NDF on the apparent digestibility of Yili horses (DM basis). (A) Dry matter digestibility; (B) organic matter digestibility; (C) crude protein digestibility; (D) neutral detergent fiber digestibility; (E) acid detergent fiber digestibility; (F) gross energy digestibility. Each bar represents the mean ± standard error of the mean (SEM). ** p < 0.01; * p < 0.05. CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group (n = 6).
Figure 1. Effects of dietary NFC/NDF on the apparent digestibility of Yili horses (DM basis). (A) Dry matter digestibility; (B) organic matter digestibility; (C) crude protein digestibility; (D) neutral detergent fiber digestibility; (E) acid detergent fiber digestibility; (F) gross energy digestibility. Each bar represents the mean ± standard error of the mean (SEM). ** p < 0.01; * p < 0.05. CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group (n = 6).
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Figure 2. Effect of dietary NFC/NDF on alpha diversity of fecal microbiota in Yili horses. (A) Chao1 index; (B) Observed species; (C) Simpson index; (D) Shannon index. Each bar represents the mean ± standard error of the mean (SEM). ** p < 0.01. CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group.
Figure 2. Effect of dietary NFC/NDF on alpha diversity of fecal microbiota in Yili horses. (A) Chao1 index; (B) Observed species; (C) Simpson index; (D) Shannon index. Each bar represents the mean ± standard error of the mean (SEM). ** p < 0.01. CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group.
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Figure 3. Effect of dietary NFC/NDF on the beta diversity of fecal microbiota in Yili horses. (A) Venn diagram. (B) Principal coordinate analysis (PCoA) based on Weighted-unifrac distance was performed to highlight the differences among these four groups of fecal microbial communities. (C) Non-metric multidimensional scaling (NMDS) based on the Bray–Curtis dissimilarity was conducted to evaluate differences in fecal microbial community structure among the four groups. CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group.
Figure 3. Effect of dietary NFC/NDF on the beta diversity of fecal microbiota in Yili horses. (A) Venn diagram. (B) Principal coordinate analysis (PCoA) based on Weighted-unifrac distance was performed to highlight the differences among these four groups of fecal microbial communities. (C) Non-metric multidimensional scaling (NMDS) based on the Bray–Curtis dissimilarity was conducted to evaluate differences in fecal microbial community structure among the four groups. CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group.
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Figure 4. Species relative abundance histogram. (A) Relative abundance of fecal microbial species in Yili horse at the phylum level (top 7); (B) relative abundance of fecal microbial species in Yili horse at the genus level (top 10). CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group.
Figure 4. Species relative abundance histogram. (A) Relative abundance of fecal microbial species in Yili horse at the phylum level (top 7); (B) relative abundance of fecal microbial species in Yili horse at the genus level (top 10). CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group.
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Figure 5. LEfSe analysis of fecal flora. (A) Histogram of linear discriminant analysis (LDA) scores, showing only taxa meeting an LDA significance threshold > 3. (B) Cladogram depicting the relative abundance of the microbial community across the four groups. CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group.
Figure 5. LEfSe analysis of fecal flora. (A) Histogram of linear discriminant analysis (LDA) scores, showing only taxa meeting an LDA significance threshold > 3. (B) Cladogram depicting the relative abundance of the microbial community across the four groups. CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group.
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Figure 6. Functional prediction of fecal microbial communities using PICRUSt2. (A) Relative enrichment of KEGG level 1 pathways. (B) Relative enrichment of KEGG level 2 pathways. (C) Relative enrichment of KEGG level 3 pathways. CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group.
Figure 6. Functional prediction of fecal microbial communities using PICRUSt2. (A) Relative enrichment of KEGG level 1 pathways. (B) Relative enrichment of KEGG level 2 pathways. (C) Relative enrichment of KEGG level 3 pathways. CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group.
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Figure 7. Effects of dietary NFC/NDF ratio on plasma metabolites in Yili horses. (A) Principal Component Analysis (PCA) plot. (B) Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) plot. (C) OPLS-DA permutation test. Dashed lines represent the trend of R2Y and Q2 across permutations (D) Metabolite volcano plot. (E) Hierarchical clustering heatmap of the top 50 differential metabolites. CG: Control group (n = 6), *** p < 0.001; ** p < 0.01; * p < 0.05; MG: Medium-NFC group (n = 6); HG: High-NFC group (n = 6).
Figure 7. Effects of dietary NFC/NDF ratio on plasma metabolites in Yili horses. (A) Principal Component Analysis (PCA) plot. (B) Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) plot. (C) OPLS-DA permutation test. Dashed lines represent the trend of R2Y and Q2 across permutations (D) Metabolite volcano plot. (E) Hierarchical clustering heatmap of the top 50 differential metabolites. CG: Control group (n = 6), *** p < 0.001; ** p < 0.01; * p < 0.05; MG: Medium-NFC group (n = 6); HG: High-NFC group (n = 6).
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Figure 8. KEGG pathway enrichment analysis and visualization of differential metabolites identified between the CG and MG groups (VIP > 1, FDR < 0.05). (A) Bubble plot of the top 10 enriched KEGG pathways. Bubble size represents the enrichment factor (number of metabolites), and color intensity indicates the pathway impact value (significance level). (B) Lactic acid. (C) Mannose. (D) GDP-mannose. (E) Maltose. * p < 0.05. CG: Control group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group (n = 6).
Figure 8. KEGG pathway enrichment analysis and visualization of differential metabolites identified between the CG and MG groups (VIP > 1, FDR < 0.05). (A) Bubble plot of the top 10 enriched KEGG pathways. Bubble size represents the enrichment factor (number of metabolites), and color intensity indicates the pathway impact value (significance level). (B) Lactic acid. (C) Mannose. (D) GDP-mannose. (E) Maltose. * p < 0.05. CG: Control group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group (n = 6).
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Figure 9. Spearman Correlation Analysis Heatmap. (A) Correlation analysis between apparent digestibility and fecal fermentation parameters. (B) Correlation analysis between fecal fermentation parameters and microbial genera. (C) Correlation analysis between apparent digestibility and microbial genera. (D) Correlation analysis between apparent digestibility and plasma differential metabolites. (E) Correlation analysis between fecal fermentation parameters and differential metabolites in plasma. (F) Correlation analysis between microbial genera and differential metabolites in plasma. * p < 0.05, ** p < 0.01.
Figure 9. Spearman Correlation Analysis Heatmap. (A) Correlation analysis between apparent digestibility and fecal fermentation parameters. (B) Correlation analysis between fecal fermentation parameters and microbial genera. (C) Correlation analysis between apparent digestibility and microbial genera. (D) Correlation analysis between apparent digestibility and plasma differential metabolites. (E) Correlation analysis between fecal fermentation parameters and differential metabolites in plasma. (F) Correlation analysis between microbial genera and differential metabolites in plasma. * p < 0.05, ** p < 0.01.
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Table 1. Composition and nutritional components of the experimental diets (DM-based).
Table 1. Composition and nutritional components of the experimental diets (DM-based).
ItemsCGLGMGHG
Ingredients 1 (%)
forage60.0050.0059.0040.00
Corn stalk10.0015.0010.0020.00
Alfalfa30.0025.0011.0010.00
Corn-5.2010.4015.60
Wheat bran-1.202.403.60
Barley-1.002.003.00
Soybean meal-2.004.006.00
CaHPO4-0.200.400.60
NaCl-0.200.400.60
CaCO3-0.100.200.30
Premix 2-0.100.200.30
Nutritional composition (DM%)
GE (MJ/Kg)17.6317.6617.8317.79
OM93.3693.4293.6793.64
CP10.5110.9311.6311.90
NDF65.7862.3059.3555.65
ADF36.7834.0830.4128.20
EE2.502.613.103.03
Ca0.760.740.690.68
P0.190.20.210.23
NFC 314.5817.5819.6023.06
NFC/NDF 40.230.390.520.69
1 Nutrition level, except NFC, was measured value. 2 The premix provided the following amounts per kilogram of concentrate supplement: Co, 0.5 mg; Cu, 100 mg; I, 3.5 mg; Fe, 500 mg; Mn, 400 mg; Zn, 400 mg; Se, 1 mg; vitamin A, 24,000 IU; vitamin D, 2640 IU; vitamin E, 400 IU; vitamin B1, 30 mg; vitamin B2, 20 mg; vitamin B3, 240 mg; choline chloride, 120 mg; and phytase, 50 mg. 3 NFC = (1 − NDF − CP − EE − Ash) × 100%. 4 The dietary NFC/NDF ratios were selected to represent the nutritional transition from maintenance grazing (CG) to moderate supplementation (LG and MG) and high-energy intake (HG) typically required for Yili horses, while ensuring the NFC levels remained within a safe physiological range to maintain hindgut health.
Table 2. Effects of dietary NFC/NDF ratio on fecal pH and VFA (mmol/L) levels.
Table 2. Effects of dietary NFC/NDF ratio on fecal pH and VFA (mmol/L) levels.
IndicatorsCGLGMGHGSEMp Value
pH7.33 a7.24 a7.11 ab6.79 b0.1830.042
Acetate11.2410.3610.149.940.8340.440
Propionate1.56 b2.02 ab2.39 a2.45 a0.3160.040
Butyrate0.320.430.450.450.1120.579
Isobutyrate0.160.210.250.270.0420.072
Isovalerate0.120.160.190.200.0450.274
Acetate/propionate7.67 a5.12 b4.45 b4.56 b1.0500.020
Total VFA13.3913.1713.4313.301.0200.995
Note: Data are the means of six replicates per treatment (n = 6). The values with different letter superscripts mean significant differences (p < 0.05). VFA = volatile fatty acids. CG: Control group (n = 6); LG: Low-NFC group (n = 6); MG: Medium-NFC group (n = 6); HG: High-NFC group (n = 6).
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MDPI and ACS Style

Li, M.; Xu, Z.; Sun, L.; Cheng, Z.; Yu, Y.; Chen, Y.; Li, F.; Zang, C. Effects of Dietary Non-Fibrous Carbohydrate to Neutral Detergent Fiber Ratio on Apparent Digestibility, Fecal Microbiota, and Plasma Metabolomics in Yili Horses. Animals 2026, 16, 844. https://doi.org/10.3390/ani16050844

AMA Style

Li M, Xu Z, Sun L, Cheng Z, Yu Y, Chen Y, Li F, Zang C. Effects of Dietary Non-Fibrous Carbohydrate to Neutral Detergent Fiber Ratio on Apparent Digestibility, Fecal Microbiota, and Plasma Metabolomics in Yili Horses. Animals. 2026; 16(5):844. https://doi.org/10.3390/ani16050844

Chicago/Turabian Style

Li, Mengfei, Zihao Xu, Long Sun, Zhiqiang Cheng, Yingying Yu, Yong Chen, Fengming Li, and Changjiang Zang. 2026. "Effects of Dietary Non-Fibrous Carbohydrate to Neutral Detergent Fiber Ratio on Apparent Digestibility, Fecal Microbiota, and Plasma Metabolomics in Yili Horses" Animals 16, no. 5: 844. https://doi.org/10.3390/ani16050844

APA Style

Li, M., Xu, Z., Sun, L., Cheng, Z., Yu, Y., Chen, Y., Li, F., & Zang, C. (2026). Effects of Dietary Non-Fibrous Carbohydrate to Neutral Detergent Fiber Ratio on Apparent Digestibility, Fecal Microbiota, and Plasma Metabolomics in Yili Horses. Animals, 16(5), 844. https://doi.org/10.3390/ani16050844

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