Sample collection and nutritional intervention studies were conducted at the First Affiliated Hospital of Fujian Medical University, involving patients diagnosed with CD. The recruitment process adhered to predefined inclusion and exclusion criteria. Inclusion criteria: The case group consisted of patients with active CD who were presenting for the first time and met the World Health Organization (WHO) diagnostic criteria (Guarner et al., 2024): CDAI >150 and <400; age ≥14 years. Exclusion criteria: (i) CD with intestinal obstruction, as it alters gut transit time and microbiota; (ii) CD with high—flow fistula, which affects gut continuity and function; (iii) patients treated with glucocorticoids, immunosuppressants, biologics, antimicrobials, or probiotics within 3 months before enrolment, as these can affect the gut microbiota. Healthy controls were age—sex matched individuals without digestive diseases or severe organ insufficiency. The healthy control group was selected from age- and sex-matched individuals without digestive system diseases or severe organ insufficiency. All procedures followed the ethical guidelines approved by the Ethics Committee of Fujian Medical University [Approval number: MRCTA, ECFAH of FMU (2022) 317]. All patients provided written informed consent.

The CD group was administered EEN therapy (Peptisorb Liquid, Nutricia) via nasal feeding, according to the EEN requirement of 30–35 kcal/day during the active phase of the disease. Patients in the CD group received all their caloric and nutritional needs solely through EEN and were prohibited from consuming any other sustenance during the feeding period. The duration of the EEN course was 8 weeks in total. Clinical data, along with stool and blood specimens, were collected from the healthy control group, the CD group before EEN treatment, and the CD group after EEN treatment. The collected clinical indicators included BMI, biochemical and hematological parameters. All samples were immediately stored in a standard −80°C freezer for long-term preservation until further analysis.

Fecal samples were stored at 4°C and transferred to −80°C within 15 min. DNA was extracted using the QIAamp DNA Stool Mini Kit (QIAGEN, Germany). PCR amplified the 16S rRNA gene V3-V4 region with primers: forward 5′-CCTACGGGNGGCWGCAG-3′ and reverse 5′-GACTACHVGGGTATCTAATCC-3′. The 25 μL PCR mix contained 12.5 μL 2x KAPA HiFi HotStart ReadyMix, 0.5 μL primers (10 μM), 10 μL DNA, and 1.5 μL ddH2O. Products were confirmed by agarose gel electrophoresis and purified with AMPure XP Beads.

The raw microbial sequencing data were imported into QIIME2-2022.08. Specifically, the QIIME2 import tool was utilized to process the 16S rRNA data, resulting in the generation of the demux.qza file. Subsequent parameter optimization was performed based on the demux.qzv file, which was visualized from the demux.qza file. The DADA2 algorithm was then employed for denoising to eliminate low-quality reads from the sequencing data. We filtered out ASV reads that constituted less than 5% of the sample reads. For the retained high-quality ASVs, the Naïve Bayes classifier¹ was applied to annotate and classify the filtered amplicon sequence variants (ASVs). These annotated ASVs were subsequently used to construct phylogenetic trees.

Metabolites were extracted from frozen blood samples using LC–MS. The extraction protocol was as follows: 200 μL of blood was combined with 800 μL of pre-chilled methanol:acetonitrile (1:1) and vortexed for 1 min. The mixture was then incubated at −20°C for 1 h, followed by centrifugation at 12,000×g for 15 min. The supernatant was carefully collected, dried under a vacuum concentrator, and reconstituted in 100 μL of a 50% methanol solution for LC–MS analysis. For the LC–MS analysis, an Agilent 1290 Infinity II liquid chromatograph coupled to an Agilent 6545 Q-TOF mass spectrometer was used. Chromatographic separation was performed on an Agilent Poroshell 120 EC-C18 column (2.1 × 100 mm, 2.7 μm) maintained at 40°C. The mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). The gradient elution program was set as follows: 0–2 min, 95% A; 2–8 min, 95–5% A; 8–10 min, 5% A; 10–12 min, 5–95% A; 12–15 min, 95% A. The flow rate was maintained at 0.4 mL/min. The mass spectrometer was operated in positive and negative ion modes with the following parameters: gas temperature, 300°C; gas flow, 11 L/min; nebulizer pressure, 45 psi; sheath gas temperature, 350°C; sheath gas flow, 12 L/min; capillary voltage, 3,500 V; fragmentor voltage, 130 V; skimmer voltage, 60 V; octopole RF voltage, 750 V. The mass range was set from m/z 100 to 1700. Data acquisition and analysis were performed using Agilent MassHunter Workstation Software. Four LC–MS methods were employed to measure the gut metabolome in fecal samples, covering polar metabolites, lipids, free fatty acids, and bile acids. MS data acquisition involved metabolite library construction with the NEBNext Ultra Metabolite Library Prep Kit (NEB, USA), following the kit’s protocol for extraction, enrichment, and purification. The quality of the library was assessed using the Agilent 2100 Bioanalyzer prior to sequencing on an LC–MS platform for high-resolution metabolite profiles.

We employed the Phylogenetic Investigation of Unobserved States (PICRUSt2) algorithm to predict metabolic pathways using the MetaCyc metabolic pathway database, a comprehensive repository of experimentally validated metabolic pathways across all domains of life.² To examine differences in microbial functional profiles among the healthy control, post-treatment, and pre-treatment groups, we applied the DESeq2 differential expression analysis algorithm. A false discovery rate (FDR) threshold of <0.05 and a log2-fold change (log2FC) threshold were set to identify microbial functions that exhibited statistically significant differences between the groups. This approach enabled a systematic evaluation of the functional shifts in microbial communities associated with treatment effects.

Data processing and analysis were performed using GraphPad Prism software. Results are presented as the mean ± standard error of the mean unless otherwise specified. Comparisons between CD patients before and after EEN therapy were conducted using either a t-test or one-way ANOVA, with p values <0.05 considered statistically significant. The results were then integrated with gut microbiome characteristics and metabolite data to explore potential mechanistic associations.

A total of 18 CD patients after EEN treatment, 25 CD patients before EEN treatment, and 24 healthy controls were included in this study. EEN therapy led to significant improvements in various biochemical and hematological parameters in CD patients (Table 1). Notably, albumin levels increased significantly (p < 0.001), indicating enhanced nutritional status. Inflammatory markers, including erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), showed significant reductions (both p < 0.001), reflecting a decrease in systemic inflammation. Furthermore, the Crohn’s Disease Activity Index (CDAI) score, which assesses disease activity, decreased significantly (p < 0.001), suggesting clinical improvement. These findings underscore the efficacy of EEN in managing CD by improving both nutritional and inflammatory profiles.

EEN therapy induces significant alterations in the gut microbiome structure of CD patients, despite no notable change in overall alpha diversity. Indices such as the Shannon index, Chao 1 index, and observed features revealed that the post-treatment and pre-treatment groups exhibited significantly reduced alpha diversity compared to the healthy control group (Figures 1A–C). Additionally, the Simpson index indicated that the pre-treatment group had substantially lower alpha diversity than the healthy control group (Figure 1D). Principal coordinate analysis (PCoA), based on Bray-Curtis and Jaccard distances, demonstrated significant differences in beta diversity among the healthy control, post-treatment, and pre-treatment groups (Figures 1E,F). These findings underscore the impact of EEN on the gut microbiome structure in CD patients, emphasizing its therapeutic potential in modulating the gut microbiome.

At the phylum level, the dominant microbial phyla across the healthy control, post-treatment, and pre-treatment groups included Firmicutes, Bacteroidota, Proteobacteria, Fusobacteriota, Actinobacteriota, Verrucomicrobiota, Desulfobacterota, Euryarchaeota, Synergistota, and Patescibacteria (Figure 2A). Analysis of microbial community abundance revealed that the top 10 genera across the three groups were Bacteroides, Faecalibacterium, Fusobacterium, Ruminococcus gnavus, Parabacteroides, Veillonella, Ralstonia, Megamonas, Alistipes, Hungatella, Eubacterium, Gordonibacter, Dysgonomonas, and Bifidobacterium (Figure 2B).

LEfSe analysis at the genus level revealed significant differences in microbial composition among the three groups. The healthy control group showed enrichment of 44 microbial taxa, with beneficial genera such as Bifidobacterium, Roseburia, and Levilactobacillus cerevisiae standing out. These beneficial genera are known to enhance immunity and produce short-chain fatty acids, promoting gut health. The post-treatment group exhibited enrichment of 10 genera, which may also have beneficial effects, such as Eubacterium, Gordonibacter, and Barnesiella. These bacteria can potentially contribute to anti-inflammatory effects and gut homeostasis. The pre-treatment group had higher abundances of Fusobacterium, Veillonella, Acidaminococcus, Megasphaera, and Moryella, which are generally considered harmful or pathogenic and are often associated with gastrointestinal disorders and infections (Figure 2C). These findings highlight distinct microbial signatures associated with treatment status and health controls.

At the KO pathway level, the post-treatment group exhibited significant activation of energy metabolism pathways, including ABC transporters, the pentose phosphate pathway, and glycolysis. In contrast, the pre-treatment group was characterized by predominant alterations in butanoate metabolism and glutathione metabolism (Figure 3A). EC enzyme analysis revealed a marked upregulation of 13 enzymes, including L-seryl-tRNA(Sec) selenium transferase, in the post-treatment group, alongside specific modifications in 19 enzymes, such as iron-chelate-transporting ATPase, following treatment (Figure 3B). MetaCyc pathway analysis further demonstrated substantial enhancement of nucleotide biosynthesis pathways, particularly the biosynthesis of adenosine and guanosine deoxyribonucleotides, in the post-treatment group, concomitant with the suppression of biotin and folate biosynthesis pathways (Figure 3C).

Partial least squares-discriminant analysis (PLS-DA) was conducted to analyze the fecal metabolomic profiles of the healthy control, post-treatment, and pre-treatment groups. The PLS-DA score plot revealed that post-treatment samples were positioned between the pre-treatment and healthy control groups, indicating a partial shift in the metabolic profile toward a healthier state following therapy (Figure 4A). Volcano plot analysis further demonstrated significant alterations in fecal metabolites. Compared to the pre-treatment group, the post-treatment group exhibited downregulation of six metabolites (3,5-Dichloro-2-methylmuconolactone, NCI60_031845, Floionolic acid, PHENAZINE, 4-carboxy-2-hydroxymuconate semialdehyde hemiacetal, and Propyl acetate) and upregulation of one metabolite (ecdysone palmitate) (Figures 5A, 6A). When compared to the healthy control group, the pre-treatment group showed a downregulation of 60 metabolites and an upregulation of 145 metabolites (Figures 5B, 6B). In contrast, the post-treatment group demonstrated a downregulation of 21 metabolites and an upregulation of eight metabolites relative to healthy controls (Figures 5C, 6C). These findings highlight the metabolic shifts associated with EEN therapy and suggest its potential role in modulating the gut metabolome toward a healthier profile.

A comprehensive metabolic pathway analysis of fecal samples from CD patients undergoing EEN therapy revealed distinct metabolic patterns across different phases of treatment. In the comparison between the post-treatment and pre-treatment groups (Figures 7A–C), significant alterations were observed in key microbial metabolic pathways, including phenazine biosynthesis, benzoate degradation, and toluene degradation, along with notable fold changes in cutin, suberin, and wax biosynthesis. The comparison between the pre-treatment group and healthy controls (Figures 7D–F) demonstrated profound baseline disturbances in microbial metabolism, particularly affecting secondary metabolite biosynthesis and specific pathways such as caprolactam degradation. The post-treatment versus healthy control comparison (Figures 7G–I) revealed persistent yet attenuated metabolic dysregulation, with incomplete normalization of phenazine biosynthesis and toluene degradation pathways, as well as residual abnormalities in cutin/suberine biosynthesis. Network analyses (Figures 7C,F,I) illustrated the complex interactions between these metabolic pathways, highlighting the extensive and specific nature of EEN-induced metabolic remodeling.

Consistent patterns were observed in blood samples from the same cohort using PLS-DA analysis (Figure 4B). Volcano plot analysis of fecal metabolites revealed significant alterations: compared to the pre-treatment group, the post-treatment group exhibited 7 downregulated metabolites [Sphingosine 1-phosphate, 2-(5-methylthio) pentylmalate, 1-(1-Propenylthio) propyl propyl disulfide, PC (15:0/20:1(11Z)), Paspalicine, 3-Deoxo-4b-deoxypaxilline, and methyl gibberellin A9] and 22 upregulated metabolites [PC (16:0/18:2(9Z,12Z)), Erythromycin C, omega-Hydroxy-beta-dihydromenaquinone-9, etc.] (Figures 8A, 9A). More substantial differences were observed when comparing the pre-treatment group to healthy controls, with 105 downregulated and 100 upregulated metabolites (Figures 8B, 9B). In contrast, the post-treatment group showed 76 downregulated and 110 upregulated metabolites (Figures 8C, 9C).

Metabolic pathway analysis of blood samples from CD patients undergoing EEN therapy revealed three distinct patterns of metabolic remodeling across different treatment phases. In the post-treatment versus pre-treatment comparison (Figures 10A–C), significant upregulation was observed in specialized metabolic pathways, including indole diterpene alkaloid biosynthesis and porphyrin/chlorophyll metabolism, alongside the activation of the phosphotransferase system (PTS) and sphingolipid signaling pathways. The pre-treatment versus healthy control comparison (Figures 10D–F) demonstrated marked dysregulation in key metabolic processes, particularly linoleic acid metabolism, and the pentose phosphate pathway, accompanied by enhanced biosynthesis of various secondary metabolites. Network analyses (Figures 10C,I) revealed intricate metabolic interactions, underscoring the central role of sphingolipid metabolism in the treatment response and its strong connectivity with neuroactive ligand-receptor interactions and secondary metabolite biosynthesis pathways. The post-treatment versus control comparison (Figures 10G–I) showed partial metabolic normalization, with persistent alterations in ubiquinone/terpenoid-quinone biosynthesis and incomplete restoration of galactose metabolism. Collectively, these findings demonstrate that EEN therapy induces stage-specific metabolic reprogramming in CD patients, characterized by the activation of specialized metabolite production, partial correction of lipid metabolism abnormalities, and persistent modifications in neuroactive compound synthesis while maintaining differential regulation of energy-related pathways.

Although this study provides valuable insights into the effects of EEN on the gut microbiome and metabolome in CD, its cross-sectional design limits the ability to draw definitive conclusions regarding causality. Future research should focus on longitudinal, prospective studies with adequately powered sample sizes to confirm these findings and explore the long-term effects of EEN on microbial and metabolic profiles. Furthermore, mechanistic studies are essential to elucidate how specific microbial taxa and metabolites interact with host immune and metabolic pathways to facilitate disease remission. In addition, functional validation studies can confirm the role of these altered metabolites in the pathogenesis of CD and their association with inflammation. Finally, the development of personalized nutritional therapies tailored to individual microbial and metabolic signatures holds great promise for improving therapeutic outcomes in CD patients. Such approaches could ultimately lead to more precise and effective disease management strategies.