Habitual Ultra-Processed Food Intake Is Associated with Gut Dysbiosis and Pro- Inflammatory Metabolite Profiles in Korean Patients with IBD

Habitual Ultra-Processed Food Intake Is Associated with Gut Dysbiosis and Pro- Inflammatory Metabolite Profiles in Korean Patients with IBD | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Habitual Ultra-Processed Food Intake Is Associated with Gut Dysbiosis and Pro- Inflammatory Metabolite Profiles in Korean Patients with IBD Woo-Jeong Shon, Kyung A Kim, Joo Sung Kim, Byeong Gwan Kim, Jong Pil Im, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9445079/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background and Aims Ultra-processed food (UPF) is increasingly consumed worldwide and may influence gut microbial ecology relevant to inflammatory bowel disease (IBD). However, patient-level multi-omics data remain scarce. We investigated whether habitual UPF intake is associated with specific microbiota and metabolite profiles in Korean patients with IBD. Methods Dietary intake was assessed using a validated food frequency questionnaire, and foods was categorized by the NOVA system. UPF intake was expressed as percent of energy, and patients were stratified into UPF low (Q1–Q2) and UPF high (Q3–Q4). Fecal samples underwent 16S rRNA sequencing and untargeted metabolomics. Microbiome differences were tested using PERMANOVA for beta-diversity and Mann–Whitney U tests for taxa. Differential metabolites were defined by p < 0.05 and |fold change|≥1.5, followed by Reactome enrichment with FDR correction. Correlations among microbiota, metabolites, and UPF subgroups were examined using Spearman tests with Benjamini–Hochberg adjustment. Associations between UPF intake and clinical characteristics were analyzed using Spearman tests, η² from ANOVA and point-biserial correlation. Results Microbial beta-diversity differed significantly between UPF low and UPF high participants. UPF high participants showed expansion of pro-inflammatory pathobionts ( Escherichia–Shigella, Proteus, Parasutterella, Enterococcus, Fusobacterium, and Clostridium innocuum group ) and depletion of anti-inflammatory commensals ( Faecalibacterium, Butyricicoccus, Lachnospiraceae ND3007 group, and Bifidobacterium ). Metabolomic profiling revealed enrichment of inflammatory pathways (phospholipid metabolism, eNOS/NO signaling, mitochondrial β-oxidation, FMO3-mediated TMA to TMAO, tryptophan catabolism) and reduction of anti-inflammatory metabolites (AHR ligands, BAAT-conjugated bile acids). Integrated analyses demonstrated significant correlations between dysbiotic taxa and inflammatory metabolites. Among NOVA-defined UPF subgroups, sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries showed the strongest associations with these adverse signatures. Analysis of clinical characteristics showed trends between total UPF intake and inflammatory markers (WBC, CRP, fecal calprotectin), and a meaningful association with upper gastrointestinal tract involvement in CD patients. Subgroup analysis showed that sugar-sweetened beverage intake was significantly associated with CRP elevation and upper gastrointestinal involvement in CD patients. Conclusions In IBD, higher UPF intake, particularly from specific NOVA-defined subgroups, is associated with gut dysbiosis and a pro-inflammatory metabolome, which in turn correlates with unfavorable clinical characteristics. These findings provide patient-based multi-omics evidence and underscore clinically relevant dietary targets for IBD management. ultra-processed foods inflammatory bowel disease gut microbiota metabolomics multi-omics NOVA classification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Brief summary of the article We integrated dietary assessment with fecal microbiome and metabolome profiling in Korean IBD patients to test how habitual ultra-processed food (UPF) intake relates to gut ecology. Higher UPF intake was associated with gut microbial dysbiosis characterized by expansion of pathobionts and enrichment of pro-inflammatory metabolites. Among UPF subgroups, sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries showed the strongest associations with these adverse signatures. UPF intake, especially sugar-sweetened beverages, also showed meaningful association with adverse clinical characteristics, providing further evidence for clinically relevant dietary targets for IBD management. Key Message What is already known? UPF consumption shifts the gut environment into a pro-inflammatory state, aggravating IBD status. What is new here? This study provides the first patient-based multi-omics evidence from a Korean IBD cohort demonstrating that habitual UPF intake is associated with gut dysbiosis and a pro-inflammatory metabolic profile. How can this study help patient care? Our study provides practical and realistic goals for diet management in IBD patients. 1. Introduction The global rise in ultra-processed food (UPF) intake has emerged as a significant public health concern, contributing to the increasing prevalence of non-communicable diseases such as obesity, type 2 diabetes, and cardiovascular conditions [ 1 , 2 ]. UPFs are industrial formulations that include ingredients not commonly used in home cooking, such as preservatives, emulsifiers, and artificial flavorings, and are typically high in added sugars and saturated fats while being low in fiber and micronutrients [ 3 ]. Beyond their systemic metabolic effects, accumulating evidence suggests that UPFs may adversely affect gut health by altering the composition and function of the gut microbiota [ 4 , 5 ]. Given the central role of the gut in immune regulation and host–microbe interactions, such diet-induced shifts in the intestinal ecosystem may have important implications for chronic inflammatory diseases. Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract, driven by complex interactions among genetic, environmental, microbial, and dietary factors. Among these, dietary composition is increasingly recognized as a critical modulator of the gut microbiota and mucosal immune homeostasis [ 6 , 7 ]. Diets characterized by high intake of UPF, such as the Western diet, have been associated with reduced microbial diversity, increased abundance of pro-inflammatory taxa, and compromised intestinal barrier function, as demonstrated in both animal models and human observational studies [ 8 , 9 ]. While recent prospective cohorts have reported an association between higher UPF intake and increased risk of IBD onset or relapse [ 10 , 11 ], few studies have investigated whether habitual UPF intake is linked to differences in the gut microbial or gut metabolic profiles of patients already diagnosed with IBD. In particular, data from East Asian populations remain scarce, despite distinct regional dietary patterns and gut microbial configurations compared to Western populations, underscoring the need for population-specific evidence [ 12 ]. Although dietary influences on the gut microbiota are well documented, most studies have evaluated microbial or metabolite profiles in isolation, without integrating both layers. In the context of IBD, multi-omics analyses that link quantitative dietary exposures, such as UPF intake, with both microbial and metabolic features remain rare. This represents a critical gap, as converging evidence suggests that diet-driven alterations in gut ecology can modulate mucosal immunity, disease severity, and therapeutic response [ 13 – 15 ]. Moreover, current dietary guidelines for IBD remain largely empirical and nonspecific, offering limited guidance on the role of UPF [ 16 , 17 ]. Well-designed patient-based studies are therefore needed to clarify how habitual UPF intake shapes the functional gut environment in individuals with IBD. To build a more integrated understanding of diet-microbiome interactions in IBD, we conducted a combined dietary, gut microbiome, and gut metabolome analysis in Korean patients with IBD. Using a validated food frequency questionnaire and the NOVA classification system, we stratified patients according to habitual UPF intake. Fecal samples underwent 16S rRNA gene sequencing and untargeted metabolomic profiling to characterize both compositional and functional features of the gut environment. By directly linking quantitative dietary exposures with multi-omics profiles in a clinically well-characterized cohort, our study provides patient-level evidence clarifying how habitual UPF intake relates to gut ecological states in IBD. These insights may help inform more targeted, mechanism-based dietary strategies for clinical management. 2. Materials and Methods 2.1 Study population Patients were drawn from the SNU-PREDICT (NCT07166588) cohort, a multicenter prospective study designed to advance precision medicine in inflammatory bowel disease. Participants were recruited from three tertiary care centers in Korea: Seoul National University Hospital, SMG-SNU Boramae Medical Center, and Kyungpook National University Hospital. The cohort included individuals aged ≥10 years with a confirmed diagnosis of IBD, either newly diagnosed or under follow-up care. Exclusion criteria were: (1) indeterminate colitis diagnosed by IBD specialists, (2) current or prior malignancy, (3) history of cancer treatment, and (4) any condition judged by the investigator to interfere with study participation. 2.2. Dietary assessment and UPF classification Habitual dietary intake was assessed using a validated semi-quantitative food frequency questionnaire (FFQ) derived from the Korea National Health and Nutrition Examination Survey (KNHANES), which includes 112 food items representative of the Korean diet. For each item, participants reported both the average frequency of intake over the previous year and the typical portion size, enabling estimation of mean daily intake. The validity and reproducibility of this semi-quantitative FFQ have been previously established in Korean adults [18]. Each FFQ item was classified according to the NOVA food classification system, which categorizes foods based on the extent and purpose of industrial processing [3]. In line with NOVA criteria, a total of 23 items were identified as ultra-processed foods (UPFs; NOVA Group 4) (Table 1). To ensure cultural relevance, the classification was guided by previous Korean studies that applied NOVA to national dietary data and demonstrated significant associations between UPF intake and health outcomes such as obesity, mortality, and long-term dietary trends [19–21]. For further analysis, the 23 UPF items were grouped into eight sub‑categories: (1) packaged snacks and confectioneries, (2) ultra‑processed breads and cereals, (3) processed meats, (4) ready‑to‑eat mixed dishes, (5) sugar‑sweetened beverages, (6) dairy‑ and nondairy‑based desserts, (7) condiments, and (8) alcoholic beverages. Total UPF intake was calculated as the percentage of total daily energy intake contributed by NOVA Group 4 items. Participants were stratified into quartiles (Q1–Q4) based on this percentage for primary analyses. Additionally, associations between each UPF sub-category and gut microbial and metabolic features were explored as part of the multi-omics analysis. 2.3. Data collection Medical history, laboratory results and endoscopy of enrolled subjects were reviewed in detail. Medical history included IBD subtype, sex, age, body mass index (BMI), and current use of biologics. BMI was calculated as weight (kilogram, kg) divided by height (meter, m) squared (kg/m2). Laboratory markers included the following inflammatory markers: White blood cell count (WBC), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR). Presence of anti-Saccharomyces cerevisiae antibody (ASCA) was defined as either ASCA IgA or IgG >5 U/mL and presence of Antineutrophil Cytoplasmic Antibody (ANCA) was defined as MPO or Pr III titer >5 IU/mL. The same fecal sample was used for fecal calprotectin measurement, C. difficile toxin testing, and microbiome and metabolome profiling. Presence of C. difficile toxin was measured in two ways, using toxin assay and toxin gene PCR. Initial IBD endoscopic severity was evaluated with the Mayo Endoscopic Score (MES) for ulcerative colitis and the Simple Endoscopic Score for Crohn’s disease (SES-CD). Severity was categorized as inactive (MES 0 or SES-CD 0 to 2), mild (MES 1 or SES-CD 3 to 6), moderate (MES 2 or SES-CD 7 to 9), and severe (MES 3 or SES-CD 10 or more). Disease extent, behavior, upper gastrointestinal tract involvement and perianal involvement were categorized by reviewing initial endoscopy findings. Disease location and behavior (stricturing or penetrating for CD) was based on the Montreal classification. 2.4. Ethics approval and consent to participate This study protocol was approved by the Institutional Review Board of the three referral medical centers (IRB numbers: H-2108-175-1248 at Seoul National University Hospital, 30-2021-22 at Seoul Metropolitan Government-Seoul National University Boramae Medical Center, 2020-11-063 at Kyungpook National University Hospital). All materials were obtained with informed consent under institutional review board-approved protocols, and the study was conducted in compliance with the tenets of the Declaration of Helsinki. Written informed consent was obtained from all enrolled subjects. 2.5. Statistical analysis of clinical variables Continuous variables were presented as means with standard deviations, and categorical variables as counts with percentages. Group comparisons based on UPF intake quartiles were performed using one-way χ2 tests or Kruskal-Wallis tests, or Analysis of Variance (ANOVA), as appropriate. Further analysis was done to investigate the association between UPF intake and clinical variables. Spearman’s rank correlation was used for continuous variables (WBC, Hb, ESR, albumin, CRP, fecal calprotectin). Associations with categorical variables were shown by calculating effect sizes, whereby η² from ANOVA was used for multi-category predictors (CD behavior, CD location, UC location, initial endoscopic severity) and point-biserial correlation was used for binary predictors (upper gastrointestinal tract involvement, perianal involvement, C. difficile toxin, ASCA, ANCA, and use of biologics). All analyses were performed using R software (version 4.5.0; R Foundation for Statistical Computing, Vienna, Austria), and statistical significance was defined as a two-sided p-value of < 0.05. 2.7. Fecal sample collection and 16s rRNA sequencing Fecal samples were collected and promptly stored at −80 °C until further processing. For DNA extraction, approximately 200 mg of frozen fecal material was placed into a 2 mL cryotube and extracted using the E.Z.N.A.® DNA Stool Kit (Omega Bio-tek) according to the manufacturer’s protocol. Genomic DNA was eluted in 100 µL of the kit-provided elution buffer, and its concentration and purity were evaluated using a NanoDrop™ spectrophotometer (Thermo Fisher Scientific™). The V3–V4 hypervariable regions of the bacterial 16S rRNA gene were amplified with primers 341F (CCTACGGGNGGCWGCAG) and 805R (GACTACHVGGGTATCTAATCC) (Cosmogenetech) using Phusion™ Plus PCR Master Mixes (Thermo Scientific™). Amplicon libraries were quantified with the GenNext NGS Library Quantification Kit (Toyobo) and subsequently sequenced on the Illumina MiSeq platform using the MiSeq Reagent Kit v2 (500 cycles) to generate 2 x 250 bp paired-end reads. 2.8. Metabolite extraction for metabolite profiling For metabolite extraction, approximately 200 mg of frozen fecal samples were mixed with extraction buffer (HPLC-grade 100% ethanol/20 mM phosphate buffer, 7:3, v/v) at a 3:1 ratio (Extraction buffer: Feces sample). The mixture was homogenized using an ultrasonic sonicator (KBT, Korea) at 100 W for 2 minutes at 4 °C to remove residual particulates. The homogenate was first centrifuged at 3,000 rpm for 10 minutes at 4 °C, and the resulting supernatant was carefully transferred to a new tube. A second centrifugation was performed at 14,000 rpm for 10 minutes at 4 °C to remove residual particulates. The clarified supernatant was then collected and used for metabolomic profiling. 2.9. Statistical analysis of fecal samples For microbiome analyses, alpha-diversity indices (Observed features, Shannon, Simpson) were compared using two-sided Mann–Whitney U tests. Beta-diversity was calculated with Bray–Curtis dissimilarity and tested between groups using PERMANOVA with 999 permutations. Taxonomic differences in relative abundance were evaluated using two-sided Mann–Whitney U tests, with exact p values reported. Differentially abundant taxa were further assessed with LEfSe, using a Kruskal–Wallis test and linear discriminant analysis to estimate effect sizes For metabolomics, intensities were compared between groups using two-sided Mann–Whitney U tests. Metabolites were considered differentially abundant if they met thresholds of p<0.05 and |fold change|≥1.5. Functional enrichment was performed against the Reactome database using a hypergeometric framework, with multiple testing correction by the Benjamini–Hochberg false discovery rate (FDR). Microbiota–metabolite correlations were assessed using two-sided Spearman rank correlations, and results were adjusted for multiple testing using the Benjamini–Hochberg method. UPF subgroup analyses were performed by calculating Spearman correlations between each of the eight NOVA-defined UPF subgroups and (i) the ten most perturbed bacterial genera and (ii) representative metabolites from enriched pathways. Associations with p<0.05 after FDR correction were considered significant. All statistical tests were two-sided, with significance defined at p<0.05. Data are presented as mean ± SEM unless otherwise specified. 3. Results 3.1. General characteristics of the study population according to UPF intake General characteristics of participants across quartiles of UPF intake are summarized in Table 2. Demographic and clinical variables including sex, body mass index (BMI), CRP, ESR, fecal calprotectin, endoscopic severity, and the proportion receiving biologic therapy did not differ significantly across quartiles. The only variable showing a significant difference was age (P = 0.009), with individuals in Q1 being older than those in Q3 and Q4 (Q1 vs Q3, P = 0.011; Q1 vs Q4, P = 0.002). This inverse association between age and UPF consumption is consistent with prior population-based reports showing that younger adults tend to consume more UPFs [22, 23]. 3.2. UPF intake reshapes the gut microbiota in IBD patients We examined the gut microbiota composition of IBD patients according to UPF intake. Patients were divided into quartiles based on UPF intake, and subgroup analyses compared the UPF low group, defined as Q1 to Q2, with the UPF high group, defined as Q3 to Q4. Alpha diversity indices including observed features, Shannon index, and Simpson index did not differ significantly between groups (Fig. 1a). In contrast, beta diversity based on the Bray-Curtis dissimilarity index showed a significant separation between UPF low and UPF high groups (PERMANOVA, P = 0.011; Fig. 1b). At the phylum level, UPF high participants exhibited a significant enrichment of Proteobacteria (Fig. 1c), a hallmark phylum frequently associated with intestinal inflammation. Genus level analyses revealed that several Proteobacteria related genera, including Escherichia-Shigella , Proteus , and Parasutterella , were significantly increased in the UPF high group (Fig. 1d). Additional pathobiont taxa, such as Enterococcus , Fusobacterium , and the Clostridium innocuum group , were also enriched in the UPF high group (Fig. 1e). In contrast, several commensal taxa with anti-inflammatory or barrier supportive functions, including Faecalibacterium , Butyricicoccus , Lachnospiraceae ND3007 group , and Bifidobacterium , were significantly depleted in the UPF high group (Fig. 1f). Taken together, these findings indicate that higher UPF intake is associated with a shift toward a dysbiotic and pro-inflammatory microbial profile characterized by expansion of pathobionts and loss of beneficial commensals. 3.3. UPF intake shifts the intestinal metabolic milieu toward pro-inflammatory signaling in IBD To determine whether UPF associated shifts in the microbiome translate into an altered intestinal biochemical milieu, we profiled the fecal metabolome from the same cohort. Using a two-sided Wilcoxon rank sum test with thresholds of p < 0.05 and |fold change| ≥ 1.5, 155 metabolites differed between UPF low and UPF high groups (Fig. 2a), with 76 increased and 79 decreased in participants with higher UPF intake. Reactome enrichment of these differentially abundant metabolites identified eight significant pathways with FDR ≤ 0.05 (Fig. 2b): Phospholipid metabolism (R-HSA-1483257), eNOS activation and NO signaling (R-HSA-203615), Mitochondrial fatty acid β-oxidation (R-HSA-77289), FMO3-mediated TMA to TMAO conversion (R-HSA-139970), Tryptophan catabolism (R-HSA-71240), Bile acid and bile salt metabolism (R-HSA-194068), Aryl hydrocarbon receptor (AHR) signaling (R-HSA-8937144), and BAAT-mediated bile acid conjugation (R-HSA-192312). To mechanistically link these pathways to taxa perturbed by UPF intake, we next analyzed genus–metabolite Spearman correlations at the pathway level (Fig. 2c). Genera enriched in the UPF high group ( Escherichia–Shigella , Proteus , Parasutterella , Enterococcus, Fusobacterium, and Clostridium innocuum group ) showed positive correlations with metabolites related to phospholipid metabolism, eNOS and NO signaling, mitochondrial β-oxidation, FMO3 related TMA to TMAO activity, tryptophan catabolism, and bile acid metabolism. Genera depleted in the UPF high group ( Faecalibacterium , Butyricicoccus , Lachnospiraceae ND3007 group , and Bifidobacterium ) showed inverse correlations with these same pathways. In contrast, aryl hydrocarbon receptor signaling and BAAT mediated bile acid conjugation correlated positively with genera enriched in the UPF low group. We then examined representative metabolites within each pathway (Fig. 2d). Genera enriched in the UPF high group correlated positively with TMAO and choline, nitro-tyrosine and asymmetric dimethylarginine, long chain and hydroxy acylcarnitines, and ceramide and lysophosphatidylcholine (lysoPC) species. They also showed positive correlations with kynurenine and the kynurenine to tryptophan ratio and with primary bile acids including chenodeoxycholic acid and deoxycholic acid. By contrast, genera enriched in the UPF low group correlated positively with indole type aryl hydrocarbon receptor ligands such as 3-indolepropionic acid and with glycine/taurine-conjugated bile acids indicative of BAAT activity, including glycoursodeoxycholic acid, and showed inverse associations with the pro-inflammatory metabolite set. Collectively, these findings indicate that higher UPF intake is associated with a coordinated microbe-metabolite pattern that shifts the intestinal environment toward pro-inflammatory signaling, marked by increases in TMAO, NO-derived products, acylcarnitines, ceramides, and primary bile acids, accompanied by reductions in metabolites linked to AHR- and BAAT-related pathways. 3.4. UPF intake stratifies dysbiosis and pro-inflammatory metabolite signatures in IBD To identify which components of UPF account for the microbiome and metabolome patterns described above, we related intake of the eight NOVA defined UPF subgroups (packaged snacks and confectioneries, ultra-processed breads and cereals, processed meats, ready-to-eat dishes, sugar-sweetened beverages, dairy- and nondairy-based desserts, condiments, beverages) to two intestinal readouts. We computed two-sided Spearman correlations between each subgroup and (i) the relative abundances of the ten genera most perturbed by higher UPF intake and (ii) representative metabolites from the Reactome pathways. Across genera, sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries showed the strongest positive correlations with pathobiont taxa ( Escherichia–Shigella, Proteus, Parasutterella, Enterococcus, Fusobacterium, and Clostridium innocuum group ), with reciprocal negative correlations for beneficial commensals ( Faecalibacterium, Butyricicoccus, Lachnospiraceae ND3007 group , and Bifidobacterium ) (Fig. 3a). Ultra-processed breads and cereals and beverages displayed concordant patterns with smaller effect sizes. By contrast, processed meats and dairy- and nondairy-based desserts showed weaker and less consistent associations, and condiments contributed minimal signal. Metabolite correlations recapitulated this hierarchy (Fig. 3b). Intake of sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries correlated positively with pro-inflammatory metabolites across multiple pathways, including trimethylamine N-oxide and choline (FMO3-mediated TMA to TMAO), nitro-tyrosine and asymmetric dimethylarginine (eNOS and NO signaling), long-chain and hydroxy-acylcarnitines (mitochondrial β-oxidation), and ceramide and lysophosphatidylcholine species (phospholipid metabolism). These same subgroups correlated inversely with metabolites linked to protective pathways, including the aryl hydrocarbon receptor ligand 3-indolepropionic acid and BAAT-related conjugated bile acids such as glycoursodeoxycholic acid. Ultra-processed breads and cereals and beverages showed similar but smaller associations, while processed meats, dairy- and nondairy-based desserts, and condiments showed minimal or isolated correlations. Taken together, these analyses indicate that within the UPF category, sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries are the predominant dietary exposures that track with a dysbiotic microbiota and a pro-inflammatory intestinal biochemical milieu in IBD. These subgroup specific signals strengthen the inference that higher UPF intake is linked to pathobiont expansion and increased inflammatory metabolites and identify sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries as potential dietary targets for clinical intervention. 3.5. UPF intake shows meaningful association with adverse clinical characteristics We further examined whether UPF related alterations in the gut environment were reflected in clinical characteristics. A meaningful association was observed between total UPF intake and upper gastrointestinal involvement in CD patients (r_pb=0.169, p=0.076). Positive trends were also noted between total UPF intake and inflammatory markers, including fecal calprotectin (𝜌=0.070, p=0.244), WBC (𝜌=0.030, p=0.561), and CRP (𝜌=0.010, p=0.869). Similar trends were found for other adverse clinical characteristics, including stricturing or penetrating behavior in CD patients (η²=0.045, p=0.156) and use of biologic therapy (r_pb=0.035, p=0.217) (Fig. 4). We next examined UPF subgroups that showed the strongest associations with pro-inflammatory microbiome and metabolome patterns, including sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries. Intake of sugar-sweetened beverages showed a significant association with upper gastrointestinal involvement in CD patients (r_pb=0.360, p=0.000159) and a positive association with higher CRP levels (𝜌=0.123, p=0.035). Ready-to-eat dishes also demonstrated a positive trend with CRP levels (𝜌=0.105, p=0.071). Intake of packaged snacks and confectioneries did not show meaningful associations with clinical characteristics (Fig. 5). 4. Discussion This study provides the first patient-based multi-omics evidence from a Korean IBD cohort demonstrating that habitual UPF intake is associated with gut microbial dysbiosis and a pro-inflammatory metabolic landscape. Patients with higher UPF intake displayed distinct beta-diversity and compositional shifts characterized by an expansion of pathobionts and depletion of beneficial commensals, accompanied by metabolomic alterations favoring inflammatory pathways and diminishing protective ones. Furthermore, clinical characteristics mirrored these intestinal changes, with meaningful associations between UPF intake, particularly sugar-sweetened beverages, and adverse clinical characteristics. By integrating dietary, microbial, and metabolomic layers, these findings advance understanding of how UPF intake contributes to an adverse intestinal milieu in IBD and influences clinical outcomes. In patients with higher UPF intake, the gut microbiota exhibited a dysbiotic pattern marked by expansion of taxa with pathogenic potential and depletion of commensals essential for mucosal homeostasis. Increases in Escherichia–Shigella, Proteus, Parasutterella, Enterococcus, Fusobacterium, and the Clostridium innocuum group are notable given their documented roles in endotoxin production, mucosal invasion, and amplification of inflammatory responses in IBD [24, 25]. Conversely, reductions in Faecalibacterium, Butyricicoccus, Lachnospiraceae ND3007 group, and Bifidobacterium indicate loss of key taxa involved in short-chain fatty acid production and epithelial barrier maintenance [26, 27]. While Western dietary patterns have been linked to similar perturbations, our findings emphasize that habitual UPF intake, defined by the degree of industrial processing rather than nutrient composition, may represent an independent determinant of microbial imbalance in IBD. These observations underscore the need to consider food processing features, alongside macronutrient profiles, when evaluating diet–microbiome interactions. Metabolomic profiling revealed that patients with higher UPF intake exhibited enrichment of pathways related to phospholipid metabolism, eNOS/NO signaling, mitochondrial β-oxidation, FMO3-mediated conversion of TMA to TMAO, and tryptophan catabolism. Pro-inflammatory metabolites, including TMAO, asymmetric dimethylarginine (ADMA), nitrotyrosine, acylcarnitines, ceramides, and lysophosphatidylcholines, were elevated in UPF high individuals. These metabolites have well-established roles in mucosal inflammation. TMAO augments macrophage activation and vascular inflammation [28], ADMA and nitrotyrosine impair nitric oxide bioavailability and promote oxidative stress [29], and acylcarnitines, ceramides, and lysophosphatidylcholines contribute to mitochondrial dysfunction and epithelial barrier injury [30]. In parallel, levels of protective metabolites were reduced, including the aryl hydrocarbon receptor ligand 3-indolepropionic acid and BAAT-conjugated bile acids, both of which enhance epithelial integrity and temper inflammatory responses [31, 32]. Thus, higher UPF intake appears to increase exposure to pro-inflammatory metabolites while diminishing anti-inflammatory metabolic defenses. Integrated analyses further demonstrated significant correlations between microbial and metabolic alterations, supporting functional coupling between dysbiotic taxa and inflammatory metabolites. Expansion of Enterobacteriaceae was positively associated with TMAO levels, consistent with microbial generation of TMA as a substrate for hepatic oxidation [33, 34]. Fusobacterium abundance correlated with ceramide- and phospholipid-related metabolites, implicating membrane lipid remodeling and epithelial disruption [35, 36]. Parasutterella was linked with altered bile acid profiles, consistent with its reported role in bile acid metabolism [37, 38]. These integrated findings highlight the coherence of UPF associated dysbiosis and metabolomic signatures within inflammatory pathways. Among the NOVA-defined UPF subgroups, sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries showed the strongest associations with adverse microbial and metabolic profiles. Prior studies provide plausible mechanisms for these associations. Sugar-sweetened beverages supply rapidly fermentable carbohydrates, enhancing endotoxin release and mucosal immune activation [39]. Ready-to-eat dishes and packaged snacks contain high levels of emulsifiers, preservatives, and refined fats that disrupt mucus layers, impair barrier function, and drive inflammation [40, 41]. In addition, processed fats and choline-rich additives promote TMA/TMAO generation, while saturated fats and trans fats facilitate ceramide biosynthesis [42]. These mechanistic links reinforce the observed correlations in our study and suggest that reducing overall UPF intake, particularly intake of sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries, may represent a practical and clinically relevant target for dietary management in IBD. Further analysis of clinical characteristics showed a meaningful association between total UPF intake and upper gastrointestinal tract involvement in CD patients. Among NOVA-defined UPF subgroups, upper gastrointestinal tract involvement and CRP emerged as key clinical associations. Multiple studies have reported that upper gastrointestinal tract involvement is associated with poor outcomes, such as relapses and the need for surgery [43-46]. For instance, Sun XW et al., reported that patients with upper gastrointestinal involvement exhibited higher rates of abdominal surgery [44]. CRP is also a widely used biomarker that provides useful information for assessing inflammation in IBD patients, with multiple studies demonstrating its value, including the study by Omer et al. [47]. Thus, our findings provide clinical evidence that higher UPF intake, particularly sugar-sweetened beverages, is linked to heightened inflammation and adverse disease features in CD, reflected by its associations with upper gastrointestinal involvement and CRP elevation. A key strength of this study is its patient-based design integrating dietary, microbial and metabolomic data within a single analytical framework. By quantifying habitual UPF intake with a validated tool and linking it to fecal microbiota and metabolomic signatures, we were able to characterize multidimensional interactions between diet and the intestinal environment. The use of NOVA-defined subgroups further enabled us to delineate heterogeneity within UPF intake, highlighting specific food categories most strongly associated with adverse microbial and metabolic profiles. As an observational study, however, causal inference cannot be established, and residual confounding may persist. Even with this limitation, the convergence of dietary, microbial and metabolomic findings provides robust evidence supporting the impact of UPF intake on the intestinal environment in IBD and reinforces the rationale for future dietary intervention trials. In conclusion, habitual UPF intake in Korean patients with IBD was associated with gut microbial dysbiosis and pro-inflammatory metabolomic profile. These associations were most pronounced for sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries, indicating that reducing intake of these categories may represent a pragmatic and clinically relevant dietary target. These patterns were further reflected in adverse clinical characteristics, particularly in relation to sugar-sweetened beverages. Together, our findings provide patient-based multi-omics evidence linking UPF intake to an inflammatory intestinal milieu and underscore the potential of dietary modification as a strategy to improve outcomes in IBD. Declarations Funding This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (No. NRF-2022R1F1A1076019, No. RS-2023-00227939 and No. RS-2024-00355567); the Seoul National University Hospital Research Fund (No. 26-2021-0060 and No. 04-2024-0370); the Research Supporting Program of the Korean Association for the Study of Intestinal Diseases (2024, No. 2024-5); and the general clinical research grant-in-aid from the Seoul Metropolitan Government Seoul National University (SMG-SNU) Boramae Medical Center (No. 04-2025-0006). The biospecimens and data used in this study were also provided by the Biobank of Korea–Kyungpook National University Hospital (KNUH), a member of the Korea Biobank Network. All materials derived from the National Biobank of Korea-KNUH were obtained (with informed consent) under institutional review board (IRB)-approved protocols. (project No. 2024-ER0506-00) Author contributions W-J.S. conceived and designed the study, performed data analysis, conducted the investigations, and drafted the manuscript. K.A.K. contributed to data analysis, investigation, and drafting of the manuscript. J.S.K., B.G.K., J.P.I., H.J.L., S.H.K., J.W.K., H.W.K., K.W.K., J-W.C., D.H.C., and D.H.K. contributed to sample processing, data acquisition, and investigation. E.S.K. contributed to the study design. S-J.K. contributed to study design and oversaw project administration. Conflict of interest The authors declare no conflicts of interest. Ethical Considerations The requirement for informed consent was waived. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Owing to privacy and ethical restrictions, the datasets cannot be made publicly available. References Monteiro CA, Moubarac JC, Cannon G, Ng SW, Popkin B. 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Clinical characteristics and postoperative outcomes of patients presenting with upper gastrointestinal tract Crohn disease. Ann Coloproctol. 2020. Kim OZ, et al. The clinical characteristics and prognosis of Crohn's disease in Korean patients showing proximal small bowel involvement: results from the CONNECT study. Gut Liver. 2018. Omer N, et al. Diagnostic Value of Inflammatory Markers in Inflammatory Bowel Disease: Clinical and Endoscopic Correlations. Cureus. 2025. Tables Table 1. Classification of 112 individual food frequency questionnaire (FFQ) food items according to the NOVA food systems NOVA food groups Food categorization Food items 23 Food items classified as ultra-processed foods (UPF) Packaged snacks and confectioneries snacks; cookies and crackers; candy/chocolate Ultra-processed breads and cereals loaf breads; steamed buns with red bean paste (or other fillings); other breads/pastries (cream bun, castella); corn flakes (cereals) Processed meats ham; Korean sausage (sundae); fish cake/crab stick/fish paste Ready-to-eat/heat mixed dishes Instant noodles (ramyeon); pizza; hamburger and sandwich Sugar-sweetened beverages carbonated beverages (coke, sprite); other beverages (rice punch, yuja citron tea, rice drink); fruit juice Dairy- and nondairy-based desserts milk (including plain milk); yoghurt (liquid type); yoghurt (curd type); soybean milk; ice cream Condiments coffee creamer (coffee with added sugar or cream); butter; jam Beverages Distilled liquor (soju) Table 2: General characteristics of the study population according to quartiles of UPF intake Characteristic Q1 (N = 78) Q2 (N = 78) Q3 (N = 79) Q4 (N = 78) No. of samples used for analysis of characteristic p-value Post-hoc analysis IBD Subtype, N (%) UC 59 (75.6%) 53 (67.9%) 52 (65.8%) 44 (56.4%) 313 0.087 CD 19 (24.4%) 25 (32.1%) 27 (34.2%) 34 (43.6%) Sex, N (%) Male 58 (74.4%) 59 (75.6%) 55 (69.6%) 54 (69.2%) 313 0.742 Female 20 (25.6%) 19 (24.4%) 24 (30.4%) 24 (30.8%) Age (years), median (IQR) 37.0 (24.0-53.0) 31.0 (23.0-44.8) 29.0 (21.0-43.0) 29.5 (22.0-37.0) 306 0.009 Q1 vs Q2: 0.027 Q1 vs Q3: 0.011 Q1 vs Q4: 0.002 Q2 vs Q3: 0.658 Q2 vs Q4: 0.306 Q3 vs Q4: 0.61 BMI, mean (SD) 22.5 ± 3.4 23.6 ± 4.0 23.4 ± 3.7 23.3 ± 4.2 312 0.287 WBC (10³/ul), mean (SD) 7.4 ± 3.3 7.6 ± 2.9 7.4 ± 3.5 7.5 ± 3.1 298 0.960 Hb (g/dL), mean (SD) 14.0 ± 3.7 13.8 ± 2.2 13.4 ± 2.2 13.0 ± 2.2 299 0.109 Albumin (g/dL), mean (SD) 4.2 ± 0.5 4.3 ± 0.6 4.3 ± 0.6 4.2 ± 0.6 299 0.796 ESR (mm/hr), mean (SD) 25.1 ± 20.8 25.8 ± 25.3 23.5 ± 21.0 27.0 ± 26.8 295 0.836 CRP (mg/L), mean (SD) 1.3 ± 2.9 1.3 ± 2.2 1.7 ± 4.6 1.4 ± 3.5 297 0.852 ASCA IgA/IgG (U/mL), N (%) Negative (0~5) 28 (59.6%) 24 (42.1%) 26 (44.1%) 26 (51.0%) 214 0.28 Positive (>5) 19 (40.4%) 33 (57.9%) 33 (55.9%) 25 (49.0%) 0.28 ANCA MPO/Pr III Ab (IU/mL), N (%) Negative (0~5) 52 (86.7%) 54 (84.4%) 58 (85.3%) 45 (80.4%) 248 0.811 Positive (>5) 8 (13.3%) 10 (15.6%) 10 (14.7%) 11 (19.6%) 0.811 Fecal calprotectin (ug/g), mean (SD) 808.0 ± 1329.8 841.9 ± 1312.1 909.6 ± 1340.4 932.8 ± 1391.6 283 0.942 C. difficile Toxin Assay, N (%) Negative 25 (100.0%) 35 (100.0%) 27 (100.0%) 24 (100.0%) 111 0.342 Positive 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) C. difficile Toxin Gene PCR, N (%) Negative 34 (87.2%) 41 (89.1%) 38 (84.4%) 40 (95.2%) 172 0.433 Positive 5 (12.8%) 5 (10.9%) 7 (15.6%) 2 (4.8%) 0.433 UC Disease Extent, N (%) E1 17 (28.8%) 13 (24.5%) 11 (21.2%) 13 (29.5%) 208 0.747 E2 21 (35.6%) 20 (37.7%) 21 (40.4%) 16 (36.4%) 0.960 E3 20 (33.9%) 19 (35.8%) 20 (38.5%) 15 (34.1%) 0.959 Undeterminate 1 (1.7%) 1 (1.9%) 0 (0.0%) 0 (0.0%) 0.628 CD Disease Extent, N (%) L1 6 (31.6%) 3 (12.0%) 8 (29.6%) 9 (26.5%) 105 0.383 L2 1 (5.3%) 3 (12.0%) 3 (11.1%) 3 (8.8%) 0.879 L3 12 (63.2%) 19 (76.0%) 16 (59.3%) 21 (61.8%) 0.594 Undeterminate 0 (0.0%) 0 (0.0%) 0 (0.0%) 1 (2.9%) 0.550 CD Upper GI involvement, N (%) No 18 (94.7%) 25 (100.0%) 27 (100.0%) 29 (85.3%) 105 0.041 Yes 1 (5.3%) 0 (0.0%) 0 (0.0%) 5 (14.7%) 0.041 CD Behavior, N (%) B1 10 (52.6%) 19 (79.2%) 16 (59.3%) 26 (76.5%) 104 0.137 B2 2 (10.5%) 4 (16.7%) 8 (29.6%) 5 (14.7%) 0.330 B3 7 (36.8%) 1 (4.2%) 3 (11.1%) 3 (8.8%) 0.010 CD Perianal involvement, N (%) No 11 (57.9%) 19 (76.0%) 19 (70.4%) 24 (70.6%) 105 0.629 Yes 8 (42.1%) 6 (24.0%) 8 (29.6%) 10 (29.4%) 0.629 Initial Endoscopic Severity, N (%) Inactive 14 (22.6%) 21 (31.3%) 15 (23.4%) 15 (25.4%) 252 0.659 Mild 15 (24.2%) 14 (20.9%) 16 (25.0%) 16 (27.1%) 0.874 Moderate 23 (37.1%) 19 (28.4%) 22 (34.4%) 19 (32.2%) 0.754 Severe 10 (16.1%) 13 (19.4%) 11 (17.2%) 9 (15.3%) 0.933 Biologics Use, N (%) No 63 (81.8%) 59 (75.6%) 53 (67.1%) 57 (73.1%) 312 0.206 Yes 14 (18.2%) 19 (24.4%) 26 (32.9%) 21 (26.9%) 0.206 Additional Declarations No competing interests reported. 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No significant differences were observed.\u003c/p\u003e\n\u003cp\u003e(b) Principal coordinate analysis (PCoA) of Bray–Curtis dissimilarity showing significant beta-diversity differences between UPF low and UPF high groups (PERMANOVA, p = 0.011).\u003c/p\u003e\n\u003cp\u003e(c) Relative abundance of gut microbiota at the phylum level. Left: stacked bar plots of individual samples; middle: group means ± SEM; right: LDA effect size analysis identifying taxa significantly enriched in UPF high participants.\u003c/p\u003e\n\u003cp\u003e(d–f) Differentially abundant genera between groups. UPF-high intake was associated with increased abundance of pro-inflammatory genera (\u003cem\u003eEscherichia–Shigella\u003c/em\u003e, \u003cem\u003eProteus\u003c/em\u003e, \u003cem\u003eParasutterella\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, and \u003cem\u003ethe Clostridium innocuum group\u003c/em\u003e), whereas beneficial genera (\u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eLachnospiraceae ND3007 group\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e) were reduced. Boxplots display relative abundance with individual data points.\u003c/p\u003e\n\u003cp\u003eTwo-sided Mann–Whitney U tests were used for between-group comparisons; exact p-values are shown in the plots. All data are presented as mean ± SEM unless otherwise indicated.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9445079/v1/e04eda11a70f7d5a74a79a9e.jpg"},{"id":107897350,"identity":"a4a0810d-f07c-46cc-bf8a-79f23e4d8d2e","added_by":"auto","created_at":"2026-04-27 10:57:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":572865,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUPF intake is associated with distinct metabolomic alterations and microbiota-metabolite correlations in patients with IBD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Volcano plot depicting differential metabolites between UPF high and UPF low groups. Metabolites with p \u0026lt; 0.05 and |fold change| ≥ 1.5 are highlighted; those increased in UPF high are shown in red, and those enriched in UPF low are shown in blue (two-sided Mann–Whitney U tests). Fold-change and significance thresholds are indicated by dotted lines.\u003c/p\u003e\n\u003cp\u003e(b) Reactome pathway enrichment of differential metabolites. Dot size represents the number of mapped metabolites, and the x-axis displays −log₁₀(FDR). Pathways enriched in UPF-high include phospholipid metabolism (R-HSA-1483257), eNOS activation and NO signaling (R-HSA-203615), mitochondrial fatty-acid β-oxidation (R-HSA-77289), FMO3-mediated conversion of trimethylamine to TMAO (R-HSA-139970), tryptophan catabolism (R-HSA-71240), and bile-acid/bile-salt metabolism (R-HSA-194068). UPF-low participants showed enrichment for aryl hydrocarbon receptor signaling (R-HSA-8937144) and BAAT-mediated bile-acid conjugation (R-HSA-192312).\u003c/p\u003e\n\u003cp\u003e(c) Heatmap of Spearman correlations between pathway-level metabolic scores (rows) and bacterial genera (columns). Positive correlations are shown in red and negative correlations in green. Asterisks indicate significance after Benjamini–Hochberg correction (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e(d) Bubble plot depicting correlations between representative metabolites and bacterial genera, grouped by pathway. Metabolites include hydroxy-tetradecenoylcarnitine (C14:1-OH), hydroxy-hexadecenoylcarnitine (C16:1-OH), 3-nitrotyrosine, asymmetric dimethylarginine (ADMA), lysoPC a C16:0, Cer(d18:1/16:0), choline, TMAO, kynurenine/tryptophan ratio, L-kynurenine, chenodeoxycholic acid (CDCA), deoxycholic acid (DCA), 3-indolepropionic acid (3-IPA), and glycoursodeoxycholic acid (GUDCA). Circle color indicates the Spearman correlation coefficient (red = positive; green = negative), size denotes effect magnitude, and filled circles indicate significance (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9445079/v1/d354df812077bf767e23f3b4.jpg"},{"id":107897275,"identity":"de1e1cd3-1603-4541-a00e-d9918016fac2","added_by":"auto","created_at":"2026-04-27 10:57:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":509116,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific NOVA-defined UPF subgroups drive dysbiotic microbiota and pro-inflammatory metabolite signatures in IBD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Bubble-plot correlations between eight NOVA-defined UPF subgroups (packaged snacks and confectioneries; ultra-processed breads and cereals; processed meats; ready-to-eat dishes; sugar-sweetened beverages; dairy- and nondairy-based desserts; condiments; beverages) and the bacterial genera most perturbed by higher UPF intake. Pathobionts (Escherichia–Shigella, Proteus, Parasutterella, Enterococcus, Fusobacterium, and Clostridium innocuum group) showed positive correlations with sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries, whereas beneficial commensals (Faecalibacterium, Butyricicoccus, Lachnospiraceae ND3007 group, and Bifidobacterium) exhibited reciprocal negative correlations. Circle color indicates Spearman correlation coefficients (red = positive; blue = negative), and filled circles denote significance (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(b) Bubble-plot correlations between the same UPF subgroups and representative metabolites from enriched Reactome pathways, including Cer(d18:1/16:0), lysoPC a C16:0, asymmetric dimethylarginine (ADMA), 3-nitrotyrosine, hydroxy-hexadecenoylcarnitine (C16:1-OH), hydroxy-tetradecenoylcarnitine (C14:1-OH), choline, trimethylamine-N-oxide (TMAO), kynurenine/tryptophan ratio, L-kynurenine, chenodeoxycholic acid (CDCA), deoxycholic acid (DCA), 3-indolepropionic acid (3-IPA), and glycoursodeoxycholic acid (GUDCA). Circle color indicates Spearman correlation (red = positive; blue = negative), circle size indicates effect magnitude, and filled circles denote significance (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9445079/v1/10db50dcd4999006f52ec280.jpg"},{"id":107897287,"identity":"9b0ec109-a822-42b9-aac7-dcc332a9985b","added_by":"auto","created_at":"2026-04-27 10:57:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":188035,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUPF intake is associated with adverse clinical characteristics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmap showing associations between total UPF intake and clinical variables. Spearman’s rank correlation was used for continuous variables (WBC, Hb, ESR, albumin, CRP, fecal calprotectin). For categorical variables, effect sizes were derived using η² from ANOVA for multi-category predictors (CD behavior, CD location, UC location, initial endoscopic severity), and point-biserial correlation for binary predictors (upper gastrointestinal involvement, perianal involvement, C. difficile toxin, ASCA, ANCA, biologics). Positive associations appear in red, negative associations in blue. Exact effect size values are displayed in each cell.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9445079/v1/348bd8d56c870c9777b14414.png"},{"id":107897268,"identity":"cf79ca7d-ca82-4158-ba12-ff26b74139f3","added_by":"auto","created_at":"2026-04-27 10:57:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1374081,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific NOVA-defined UPF subgroups have significant association with adverse clinical characteristics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmaps showing associations between intake of UPF subgroups and clinical variables. Spearman’s rank correlation was applied to continuous variables (WBC, Hb, ESR, albumin, CRP, fecal calprotectin). Effect sizes for categorical variables were calculated using η² from ANOVA (multi-category predictors) or point-biserial correlation (binary predictors). Positive associations are shown in red and negative associations in blue. Significance is denoted as p\u0026lt;0.05 (*) and p\u0026lt;0.10 (†).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9445079/v1/65ccaa3526a62361b9cf52be.png"},{"id":107897491,"identity":"d3a4dff8-6ab4-445c-a4e5-ee72357291da","added_by":"auto","created_at":"2026-04-27 10:57:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3945465,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9445079/v1/0356f29b-f649-49e8-b94e-9b9c89230d2c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Habitual Ultra-Processed Food Intake Is Associated with Gut Dysbiosis and Pro- Inflammatory Metabolite Profiles in Korean Patients with IBD","fulltext":[{"header":"Brief summary of the article","content":"\u003cp\u003eWe integrated dietary assessment with fecal microbiome and metabolome profiling in Korean IBD patients to test how habitual ultra-processed food (UPF) intake relates to gut ecology. Higher UPF intake was associated with gut microbial dysbiosis characterized by expansion of pathobionts and enrichment of pro-inflammatory metabolites. Among UPF subgroups, sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries showed the strongest associations with these adverse signatures. UPF intake, especially sugar-sweetened beverages, also showed meaningful association with adverse clinical characteristics, providing further evidence for clinically relevant dietary targets for IBD management.\u003c/p\u003e"},{"header":"Key Message","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eWhat is already known?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eUPF consumption shifts the gut environment into a pro-inflammatory state, aggravating IBD status.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eWhat is new here?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis study provides the first patient-based multi-omics evidence from a Korean IBD cohort demonstrating that habitual UPF intake is associated with gut dysbiosis and a pro-inflammatory metabolic profile.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eHow can this study help patient care?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOur study provides practical and realistic goals for diet management in IBD patients.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe global rise in ultra-processed food (UPF) intake has emerged as a significant public health concern, contributing to the increasing prevalence of non-communicable diseases such as obesity, type 2 diabetes, and cardiovascular conditions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. UPFs are industrial formulations that include ingredients not commonly used in home cooking, such as preservatives, emulsifiers, and artificial flavorings, and are typically high in added sugars and saturated fats while being low in fiber and micronutrients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Beyond their systemic metabolic effects, accumulating evidence suggests that UPFs may adversely affect gut health by altering the composition and function of the gut microbiota [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given the central role of the gut in immune regulation and host\u0026ndash;microbe interactions, such diet-induced shifts in the intestinal ecosystem may have important implications for chronic inflammatory diseases.\u003c/p\u003e \u003cp\u003eInflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract, driven by complex interactions among genetic, environmental, microbial, and dietary factors. Among these, dietary composition is increasingly recognized as a critical modulator of the gut microbiota and mucosal immune homeostasis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Diets characterized by high intake of UPF, such as the Western diet, have been associated with reduced microbial diversity, increased abundance of pro-inflammatory taxa, and compromised intestinal barrier function, as demonstrated in both animal models and human observational studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While recent prospective cohorts have reported an association between higher UPF intake and increased risk of IBD onset or relapse [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], few studies have investigated whether habitual UPF intake is linked to differences in the gut microbial or gut metabolic profiles of patients already diagnosed with IBD. In particular, data from East Asian populations remain scarce, despite distinct regional dietary patterns and gut microbial configurations compared to Western populations, underscoring the need for population-specific evidence [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough dietary influences on the gut microbiota are well documented, most studies have evaluated microbial or metabolite profiles in isolation, without integrating both layers. In the context of IBD, multi-omics analyses that link quantitative dietary exposures, such as UPF intake, with both microbial and metabolic features remain rare. This represents a critical gap, as converging evidence suggests that diet-driven alterations in gut ecology can modulate mucosal immunity, disease severity, and therapeutic response [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, current dietary guidelines for IBD remain largely empirical and nonspecific, offering limited guidance on the role of UPF [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Well-designed patient-based studies are therefore needed to clarify how habitual UPF intake shapes the functional gut environment in individuals with IBD.\u003c/p\u003e \u003cp\u003eTo build a more integrated understanding of diet-microbiome interactions in IBD, we conducted a combined dietary, gut microbiome, and gut metabolome analysis in Korean patients with IBD. Using a validated food frequency questionnaire and the NOVA classification system, we stratified patients according to habitual UPF intake. Fecal samples underwent 16S rRNA gene sequencing and untargeted metabolomic profiling to characterize both compositional and functional features of the gut environment. By directly linking quantitative dietary exposures with multi-omics profiles in a clinically well-characterized cohort, our study provides patient-level evidence clarifying how habitual UPF intake relates to gut ecological states in IBD. These insights may help inform more targeted, mechanism-based dietary strategies for clinical management.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were drawn from the SNU-PREDICT (NCT07166588) cohort, a multicenter prospective study designed to advance precision medicine in inflammatory bowel disease. Participants were recruited from three tertiary care centers in Korea: Seoul National University Hospital, SMG-SNU Boramae Medical Center, and Kyungpook National University Hospital. The cohort included individuals aged \u0026ge;10 years with a confirmed diagnosis of IBD, either newly diagnosed or under follow-up care. Exclusion criteria were: (1) indeterminate colitis diagnosed by IBD specialists, (2) current or prior malignancy, (3) history of cancer treatment, and (4) any condition judged by the investigator to interfere with study participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Dietary assessment and UPF classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHabitual dietary intake was assessed using a validated semi-quantitative food frequency questionnaire (FFQ) derived from the Korea National Health and Nutrition Examination Survey (KNHANES), which includes 112 food items representative of the Korean diet. For each item, participants reported both the average frequency of intake over the previous year and the typical portion size, enabling estimation of mean daily intake. The validity and reproducibility of this semi-quantitative FFQ have been previously established in Korean adults [18].\u003c/p\u003e\n\u003cp\u003eEach FFQ item was classified according to the NOVA food classification system, which categorizes foods based on the extent and purpose of industrial processing [3]. In line with NOVA criteria, a total of 23 items were identified as ultra-processed foods (UPFs; NOVA Group 4) (Table 1). To ensure cultural relevance, the classification was guided by previous Korean studies that applied NOVA to national dietary data and demonstrated significant associations between UPF intake and health outcomes such as obesity, mortality, and long-term dietary trends [19\u0026ndash;21]. For further analysis, the 23 UPF items were grouped into eight sub‑categories: (1) packaged snacks and confectioneries, (2) ultra‑processed breads and cereals, (3) processed meats, (4) ready‑to‑eat mixed dishes, (5) sugar‑sweetened beverages, (6) dairy‑ and nondairy‑based desserts, (7) condiments, and (8) alcoholic beverages. Total UPF intake was calculated as the percentage of total daily energy intake contributed by NOVA Group 4 items. Participants were stratified into quartiles (Q1\u0026ndash;Q4) based on this percentage for primary analyses. Additionally, associations between each UPF sub-category and gut microbial and metabolic features were explored as part of the multi-omics analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMedical history, laboratory results and endoscopy of enrolled subjects were reviewed in detail. Medical history included IBD subtype, sex, age, body mass index (BMI), and current use of biologics. BMI was calculated as weight (kilogram, kg) divided by height (meter, m) squared (kg/m2). Laboratory markers included the following inflammatory markers: White blood cell count (WBC), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR). Presence of anti-Saccharomyces cerevisiae antibody (ASCA) was defined as either ASCA IgA or IgG \u0026gt;5 U/mL and presence of Antineutrophil Cytoplasmic Antibody (ANCA) was defined as MPO or Pr III titer \u0026gt;5 IU/mL. The same fecal sample was used for fecal calprotectin measurement, C. difficile toxin testing, and microbiome and metabolome profiling. Presence of C. difficile toxin was measured in two ways, using toxin assay and toxin gene PCR. Initial IBD endoscopic severity was evaluated with the Mayo Endoscopic Score (MES) for ulcerative colitis and the Simple Endoscopic Score for Crohn\u0026rsquo;s disease (SES-CD). Severity was categorized as inactive (MES 0 or SES-CD 0 to 2), mild (MES 1 or SES-CD 3 to 6), moderate (MES 2 or SES-CD 7 to 9), and severe (MES 3 or SES-CD 10 or more). Disease extent, behavior, upper gastrointestinal tract involvement and perianal involvement were categorized by reviewing initial endoscopy findings. Disease location and behavior (stricturing or penetrating for CD) was based on the Montreal classification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was approved by the Institutional Review Board of the three referral medical centers (IRB numbers: H-2108-175-1248 at Seoul National University Hospital, 30-2021-22 at Seoul Metropolitan Government-Seoul National University Boramae Medical Center, 2020-11-063 at Kyungpook National University Hospital). All materials were obtained with informed consent under institutional review board-approved protocols, and the study was conducted in compliance with the tenets of the Declaration of Helsinki. Written informed consent was obtained from all enrolled subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Statistical analysis of clinical variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables were presented as means with standard deviations, and categorical variables as counts with percentages. Group comparisons based on UPF intake quartiles were performed using one-way \u0026chi;2 tests or Kruskal-Wallis tests, or Analysis of Variance (ANOVA), as appropriate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther analysis was done to investigate the association between UPF intake and clinical variables. Spearman\u0026rsquo;s rank correlation was used for continuous variables (WBC, Hb, ESR, albumin, CRP, fecal calprotectin). Associations with categorical variables were shown by calculating effect sizes, whereby \u0026eta;\u0026sup2; from ANOVA was used for multi-category predictors (CD behavior, CD location, UC location, initial endoscopic severity) and point-biserial correlation was used for binary predictors (upper gastrointestinal tract involvement, perianal involvement, C. difficile toxin, ASCA, ANCA, and use of biologics).\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using R software (version 4.5.0; R Foundation for Statistical Computing, Vienna, Austria), and statistical significance was defined as a two-sided p-value of \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7. Fecal sample collection and 16s rRNA sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFecal samples were collected and promptly stored at \u0026minus;80 \u0026deg;C until further processing. For DNA extraction, approximately 200 mg of frozen fecal material was placed into a 2 mL cryotube and extracted using the E.Z.N.A.\u0026reg; DNA Stool Kit (Omega Bio-tek) according to the manufacturer\u0026rsquo;s protocol. Genomic DNA was eluted in 100 \u0026micro;L of the kit-provided elution buffer, and its concentration and purity were evaluated using a NanoDrop\u0026trade; spectrophotometer (Thermo Fisher Scientific\u0026trade;). The V3\u0026ndash;V4 hypervariable regions of the bacterial 16S rRNA gene were amplified with primers 341F (CCTACGGGNGGCWGCAG) and 805R (GACTACHVGGGTATCTAATCC) (Cosmogenetech) using Phusion\u0026trade; Plus PCR Master Mixes (Thermo Scientific\u0026trade;). Amplicon libraries were quantified with the GenNext NGS Library Quantification Kit (Toyobo) and subsequently sequenced on the Illumina MiSeq platform using the MiSeq Reagent Kit v2 (500 cycles) to generate 2 x 250 bp paired-end reads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8. Metabolite extraction for metabolite profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor metabolite extraction, approximately 200 mg of frozen fecal samples were mixed with extraction buffer (HPLC-grade 100% ethanol/20 mM phosphate buffer, 7:3, v/v) at a 3:1 ratio (Extraction buffer: Feces sample). The mixture was homogenized using an ultrasonic sonicator (KBT, Korea) at 100 W for 2 minutes at 4 \u0026deg;C to remove residual particulates. The homogenate was first centrifuged at 3,000 rpm for 10 minutes at 4 \u0026deg;C, and the resulting supernatant was carefully transferred to a new tube. A second centrifugation was performed at 14,000 rpm for 10 minutes at 4 \u0026deg;C to remove residual particulates. The clarified supernatant was then collected and used for metabolomic profiling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9. Statistical analysis of fecal samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor microbiome analyses, alpha-diversity indices (Observed features, Shannon, Simpson) were compared using two-sided Mann\u0026ndash;Whitney U tests. Beta-diversity was calculated with Bray\u0026ndash;Curtis dissimilarity and tested between groups using PERMANOVA with 999 permutations. Taxonomic differences in relative abundance were evaluated using two-sided Mann\u0026ndash;Whitney U tests, with exact p values reported. Differentially abundant taxa were further assessed with LEfSe, using a Kruskal\u0026ndash;Wallis test and linear discriminant analysis to estimate effect sizes\u003c/p\u003e\n\u003cp\u003eFor metabolomics, intensities were compared between groups using two-sided Mann\u0026ndash;Whitney U tests. Metabolites were considered differentially abundant if they met thresholds of p\u0026lt;0.05 and |fold change|\u0026ge;1.5. Functional enrichment was performed against the Reactome database using a hypergeometric framework, with multiple testing correction by the Benjamini\u0026ndash;Hochberg false discovery rate (FDR).\u003c/p\u003e\n\u003cp\u003eMicrobiota\u0026ndash;metabolite correlations were assessed using two-sided Spearman rank correlations, and results were adjusted for multiple testing using the Benjamini\u0026ndash;Hochberg method.\u003c/p\u003e\n\u003cp\u003eUPF subgroup analyses were performed by calculating Spearman correlations between each of the eight NOVA-defined UPF subgroups and (i) the ten most perturbed bacterial genera and (ii) representative metabolites from enriched pathways. Associations with p\u0026lt;0.05 after FDR correction were considered significant.\u003c/p\u003e\n\u003cp\u003eAll statistical tests were two-sided, with significance defined at p\u0026lt;0.05. Data are presented as mean \u0026plusmn; SEM unless otherwise specified.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. General characteristics of the study population according to UPF intake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneral characteristics of participants across quartiles of UPF intake are summarized in Table 2. Demographic and clinical variables including sex, body mass index (BMI), CRP, ESR, fecal calprotectin, endoscopic severity, and the proportion receiving biologic therapy did not differ significantly across quartiles. The only variable showing a significant difference was age (P = 0.009), with individuals in Q1 being older than those in Q3 and Q4 (Q1 \u003cem\u003evs\u0026nbsp;\u003c/em\u003eQ3, P = 0.011; Q1 \u003cem\u003evs\u003c/em\u003e Q4, P = 0.002). This inverse association between age and UPF consumption is consistent with prior population-based reports showing that younger adults tend to consume more UPFs [22, 23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. UPF intake reshapes the gut microbiota in IBD patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the gut microbiota composition of IBD patients according to UPF intake. Patients were divided into quartiles based on UPF intake, and subgroup analyses compared the UPF low group, defined as Q1 to Q2, with the UPF high group, defined as Q3 to Q4. Alpha diversity indices including observed features, Shannon index, and Simpson index did not differ significantly between groups (Fig. 1a). In contrast, beta diversity based on the Bray-Curtis dissimilarity index showed a significant separation between UPF low and UPF high groups (PERMANOVA, P = 0.011; Fig. 1b).\u003c/p\u003e\n\u003cp\u003eAt the phylum level, UPF high participants exhibited a significant enrichment of Proteobacteria (Fig. 1c), a hallmark phylum frequently associated with intestinal inflammation. Genus level analyses revealed that several Proteobacteria related genera, including \u003cem\u003eEscherichia-Shigella\u003c/em\u003e, \u003cem\u003eProteus\u003c/em\u003e, and \u003cem\u003eParasutterella\u003c/em\u003e, were significantly increased in the UPF high group (Fig. 1d). Additional pathobiont taxa, such as \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, and \u003cem\u003ethe Clostridium innocuum group\u003c/em\u003e, were also enriched in the UPF high group (Fig. 1e). In contrast, several commensal taxa with anti-inflammatory or barrier supportive functions, including \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eLachnospiraceae ND3007 group\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e, were significantly depleted in the UPF high group (Fig. 1f). Taken together, these findings indicate that higher UPF intake is associated with a shift toward a dysbiotic and pro-inflammatory microbial profile characterized by expansion of pathobionts and loss of beneficial commensals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. UPF intake shifts the intestinal metabolic milieu toward pro-inflammatory signaling in IBD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine whether UPF associated shifts in the microbiome translate into an altered intestinal biochemical milieu, we profiled the fecal metabolome from the same cohort. Using a two-sided Wilcoxon rank sum test with thresholds of p \u0026lt; 0.05 and |fold change| ≥ 1.5, 155 metabolites differed between UPF low and UPF high groups (Fig. 2a), with 76 increased and 79 decreased in participants with higher UPF intake. Reactome enrichment of these differentially abundant metabolites identified eight significant pathways with FDR ≤ 0.05 (Fig. 2b): Phospholipid metabolism (R-HSA-1483257), eNOS activation and NO signaling (R-HSA-203615), Mitochondrial fatty acid β-oxidation (R-HSA-77289), FMO3-mediated TMA to TMAO conversion (R-HSA-139970), Tryptophan catabolism (R-HSA-71240), Bile acid and bile salt metabolism (R-HSA-194068), Aryl hydrocarbon receptor (AHR) signaling (R-HSA-8937144), and BAAT-mediated bile acid conjugation (R-HSA-192312).\u003c/p\u003e\n\u003cp\u003eTo mechanistically link these pathways to taxa perturbed by UPF intake, we next analyzed genus–metabolite Spearman correlations at the pathway level (Fig. 2c). Genera enriched in the UPF high group (\u003cem\u003eEscherichia–Shigella\u003c/em\u003e, \u003cem\u003eProteus\u003c/em\u003e, \u003cem\u003eParasutterella\u003c/em\u003e, \u003cem\u003eEnterococcus, Fusobacterium,\u003c/em\u003e and \u003cem\u003eClostridium innocuum group\u003c/em\u003e) showed positive correlations with metabolites related to phospholipid metabolism, eNOS and NO signaling, mitochondrial β-oxidation, FMO3 related TMA to TMAO activity, tryptophan catabolism, and bile acid metabolism. Genera depleted in the UPF high group (\u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eLachnospiraceae ND3007 group\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e) showed inverse correlations with these same pathways. In contrast, aryl hydrocarbon receptor signaling and BAAT mediated bile acid conjugation correlated positively with genera enriched in the UPF low group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe then examined representative metabolites within each pathway (Fig. 2d). Genera enriched in the UPF high group correlated positively with TMAO and choline, nitro-tyrosine and asymmetric dimethylarginine, long chain and hydroxy acylcarnitines, and ceramide and lysophosphatidylcholine (lysoPC) species. They also showed positive correlations with kynurenine and the kynurenine to tryptophan ratio and with primary bile acids including chenodeoxycholic acid and deoxycholic acid. By contrast, genera enriched in the UPF low group correlated positively with indole type aryl hydrocarbon receptor ligands such as 3-indolepropionic acid and with glycine/taurine-conjugated bile acids indicative of BAAT activity, including glycoursodeoxycholic acid, and showed inverse associations with the pro-inflammatory metabolite set.\u003c/p\u003e\n\u003cp\u003eCollectively, these findings indicate that higher UPF intake is associated with a coordinated microbe-metabolite pattern that shifts the intestinal environment toward pro-inflammatory signaling, marked by increases in TMAO, NO-derived products, acylcarnitines, ceramides, and primary bile acids, accompanied by reductions in metabolites linked to AHR- and BAAT-related pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. UPF intake stratifies dysbiosis and pro-inflammatory metabolite signatures in IBD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify which components of UPF account for the microbiome and metabolome patterns described above, we related intake of the eight NOVA defined UPF subgroups (packaged snacks and confectioneries, ultra-processed breads and cereals, processed meats, ready-to-eat dishes, sugar-sweetened beverages, dairy- and nondairy-based desserts, condiments, beverages) to two intestinal readouts. We computed two-sided Spearman correlations between each subgroup and (i) the relative abundances of the ten genera most perturbed by higher UPF intake and (ii) representative metabolites from the Reactome pathways.\u003c/p\u003e\n\u003cp\u003eAcross genera, sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries showed the strongest positive correlations with pathobiont taxa (\u003cem\u003eEscherichia–Shigella, Proteus, Parasutterella, Enterococcus, Fusobacterium,\u003c/em\u003e and \u003cem\u003eClostridium innocuum group\u003c/em\u003e), with reciprocal negative correlations for beneficial commensals (\u003cem\u003eFaecalibacterium, Butyricicoccus, Lachnospiraceae ND3007 group\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e) (Fig. 3a). Ultra-processed breads and cereals and beverages displayed concordant patterns with smaller effect sizes. By contrast, processed meats and dairy- and nondairy-based desserts showed weaker and less consistent associations, and condiments contributed minimal signal.\u003c/p\u003e\n\u003cp\u003eMetabolite correlations recapitulated this hierarchy (Fig. 3b). Intake of sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries correlated positively with pro-inflammatory metabolites across multiple pathways, including trimethylamine N-oxide and choline (FMO3-mediated TMA to TMAO), nitro-tyrosine and asymmetric dimethylarginine (eNOS and NO signaling), long-chain and hydroxy-acylcarnitines (mitochondrial β-oxidation), and ceramide and lysophosphatidylcholine species (phospholipid metabolism). These same subgroups correlated inversely with metabolites linked to protective pathways, including the aryl hydrocarbon receptor ligand 3-indolepropionic acid and BAAT-related conjugated bile acids such as glycoursodeoxycholic acid. Ultra-processed breads and cereals and beverages showed similar but smaller associations, while processed meats, dairy- and nondairy-based desserts, and condiments showed minimal or isolated correlations.\u003c/p\u003e\n\u003cp\u003eTaken together, these analyses indicate that within the UPF category, sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries are the predominant dietary exposures that track with a dysbiotic microbiota and a pro-inflammatory intestinal biochemical milieu in IBD. These subgroup specific signals strengthen the inference that higher UPF intake is linked to pathobiont expansion and increased inflammatory metabolites and identify sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries as potential dietary targets for clinical intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. UPF intake shows meaningful association with adverse clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further examined whether UPF related alterations in the gut environment were reflected in clinical characteristics. A meaningful association was observed between total UPF intake and upper gastrointestinal involvement in CD patients (r_pb=0.169, p=0.076). Positive trends were also noted between total UPF intake and inflammatory markers, including fecal calprotectin (𝜌=0.070, p=0.244), WBC (𝜌=0.030, p=0.561), and CRP (𝜌=0.010, p=0.869). Similar trends were found for other adverse clinical characteristics, including stricturing or penetrating behavior in CD patients (η²=0.045, p=0.156) and use of biologic therapy (r_pb=0.035, p=0.217) (Fig. 4).\u003c/p\u003e\n\u003cp\u003eWe next examined UPF subgroups that showed the strongest associations with pro-inflammatory microbiome and metabolome patterns, including sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries. Intake of sugar-sweetened beverages showed a significant association with upper gastrointestinal involvement in CD patients (r_pb=0.360, p=0.000159) and a positive association with higher CRP levels (𝜌=0.123, p=0.035). Ready-to-eat dishes also demonstrated a positive trend with CRP levels (𝜌=0.105, p=0.071). Intake of packaged snacks and confectioneries did not show meaningful associations with clinical characteristics (Fig. 5).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides the first patient-based multi-omics evidence from a Korean IBD cohort demonstrating that habitual UPF intake is associated with gut microbial dysbiosis and a pro-inflammatory metabolic landscape. Patients with higher UPF intake displayed distinct beta-diversity and compositional shifts characterized by an expansion of pathobionts and depletion of beneficial commensals, accompanied by metabolomic alterations favoring inflammatory pathways and diminishing protective ones. Furthermore, clinical characteristics mirrored these intestinal changes, with meaningful associations between UPF intake, particularly sugar-sweetened beverages, and adverse clinical characteristics. By integrating dietary, microbial, and metabolomic layers, these findings advance understanding of how UPF intake contributes to an adverse intestinal milieu in IBD and influences clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;In patients with higher UPF intake, the gut microbiota exhibited a dysbiotic pattern marked by expansion of taxa with pathogenic potential and depletion of commensals essential for mucosal homeostasis. Increases in \u003cem\u003eEscherichia\u0026ndash;Shigella, Proteus, Parasutterella, Enterococcus, Fusobacterium,\u003c/em\u003e and the \u003cem\u003eClostridium innocuum group\u003c/em\u003e are notable given their documented roles in endotoxin production, mucosal invasion, and amplification of inflammatory responses in IBD [24, 25]. Conversely, reductions in \u003cem\u003eFaecalibacterium, Butyricicoccus, Lachnospiraceae ND3007 group,\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e indicate loss of key taxa involved in short-chain fatty acid production and epithelial barrier maintenance [26, 27]. While Western dietary patterns have been linked to similar perturbations, our findings emphasize that habitual UPF intake, defined by the degree of industrial processing rather than nutrient composition, may represent an independent determinant of microbial imbalance in IBD. These observations underscore the need to consider food processing features, alongside macronutrient profiles, when evaluating diet\u0026ndash;microbiome interactions.\u003c/p\u003e\n\u003cp\u003eMetabolomic profiling revealed that patients with higher UPF intake exhibited enrichment of pathways related to phospholipid metabolism, eNOS/NO signaling, mitochondrial \u0026beta;-oxidation, FMO3-mediated conversion of TMA to TMAO, and tryptophan catabolism. Pro-inflammatory metabolites, including TMAO, asymmetric dimethylarginine (ADMA), nitrotyrosine, acylcarnitines, ceramides, and lysophosphatidylcholines, were elevated in UPF high individuals. These metabolites have well-established roles in mucosal inflammation. TMAO augments macrophage activation and vascular inflammation [28], ADMA and nitrotyrosine impair nitric oxide bioavailability and promote oxidative stress [29], and acylcarnitines, ceramides, and lysophosphatidylcholines contribute to mitochondrial dysfunction and epithelial barrier injury [30]. In parallel, levels of protective metabolites were reduced, including the aryl hydrocarbon receptor ligand 3-indolepropionic acid and BAAT-conjugated bile acids, both of which enhance epithelial integrity and temper inflammatory responses [31, 32]. Thus, higher UPF intake appears to increase exposure to pro-inflammatory metabolites while diminishing anti-inflammatory metabolic defenses.\u003c/p\u003e\n\u003cp\u003eIntegrated analyses further demonstrated significant correlations between microbial and metabolic alterations, supporting functional coupling between dysbiotic taxa and inflammatory metabolites. Expansion of Enterobacteriaceae was positively associated with TMAO levels, consistent with microbial generation of TMA as a substrate for hepatic oxidation [33, 34]. \u003cem\u003eFusobacterium\u003c/em\u003e abundance correlated with ceramide- and phospholipid-related metabolites, implicating membrane lipid remodeling and epithelial disruption [35, 36]. \u003cem\u003eParasutterella\u0026nbsp;\u003c/em\u003ewas linked with altered bile acid profiles, consistent with its reported role in bile acid metabolism [37, 38]. These integrated findings highlight the coherence of UPF associated dysbiosis and metabolomic signatures within inflammatory pathways.\u003c/p\u003e\n\u003cp\u003eAmong the NOVA-defined UPF subgroups, sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries showed the strongest associations with adverse microbial and metabolic profiles. Prior studies provide plausible mechanisms for these associations. Sugar-sweetened beverages supply rapidly fermentable carbohydrates, enhancing endotoxin release and mucosal immune activation [39]. Ready-to-eat dishes and packaged snacks contain high levels of emulsifiers, preservatives, and refined fats that disrupt mucus layers, impair barrier function, and drive inflammation [40, 41]. In addition, processed fats and choline-rich additives promote TMA/TMAO generation, while saturated fats and trans fats facilitate ceramide biosynthesis [42]. These mechanistic links reinforce the observed correlations in our study and suggest that reducing overall UPF intake, particularly intake of sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries, may represent a practical and clinically relevant target for dietary management in IBD.\u003c/p\u003e\n\u003cp\u003eFurther analysis of clinical characteristics showed a meaningful association between total UPF intake and upper gastrointestinal tract involvement in CD patients. Among NOVA-defined UPF subgroups, upper gastrointestinal tract involvement and CRP emerged as key clinical associations. Multiple studies have reported that upper gastrointestinal tract involvement is associated with poor outcomes, such as relapses and the need for surgery [43-46]. For instance, \u003cem\u003eSun XW\u003c/em\u003e et al., reported that patients with upper gastrointestinal involvement exhibited higher rates of abdominal surgery [44]. CRP is also a widely used biomarker that provides useful information for assessing inflammation in IBD patients, with multiple studies demonstrating its value, including the study by \u003cem\u003eOmer\u0026nbsp;\u003c/em\u003eet al. [47]. Thus, our findings provide clinical evidence that higher UPF intake, particularly sugar-sweetened beverages, is linked to heightened inflammation and adverse disease features in CD, reflected by its associations with upper gastrointestinal involvement and CRP elevation.\u003c/p\u003e\n\u003cp\u003eA key strength of this study is its patient-based design integrating dietary, microbial and metabolomic data within a single analytical framework. By quantifying habitual UPF intake with a validated tool and linking it to fecal microbiota and metabolomic signatures, we were able to characterize multidimensional interactions between diet and the intestinal environment. The use of NOVA-defined subgroups further enabled us to delineate heterogeneity within UPF intake, highlighting specific food categories most strongly associated with adverse microbial and metabolic profiles. As an observational study, however, causal inference cannot be established, and residual confounding may persist. Even with this limitation, the convergence of dietary, microbial and metabolomic findings provides robust evidence supporting the impact of UPF intake on the intestinal environment in IBD and reinforces the rationale for future dietary intervention trials.\u003c/p\u003e\n\u003cp\u003eIn conclusion, habitual UPF intake in Korean patients with IBD was associated with gut microbial dysbiosis and pro-inflammatory metabolomic profile. These associations were most pronounced for sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries, indicating that reducing intake of these categories may represent a pragmatic and clinically relevant dietary target. These patterns were further reflected in adverse clinical characteristics, particularly in relation to sugar-sweetened beverages. Together, our findings provide patient-based multi-omics evidence linking UPF intake to an inflammatory intestinal milieu and underscore the potential of dietary modification as a strategy to improve outcomes in IBD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (No. NRF-2022R1F1A1076019, No. RS-2023-00227939 and No.\u0026nbsp;RS-2024-00355567); the Seoul National University Hospital Research Fund (No. 26-2021-0060 and No. 04-2024-0370); the Research Supporting Program of the Korean Association for the Study of Intestinal Diseases (2024, No. 2024-5); and the general clinical research grant-in-aid from the Seoul Metropolitan Government Seoul National University (SMG-SNU) Boramae Medical Center (No. 04-2025-0006).\u0026nbsp;The biospecimens and data used in this study were also provided by the Biobank of Korea\u0026ndash;Kyungpook National University Hospital (KNUH), a member of the Korea Biobank Network. All materials derived from the National Biobank of Korea-KNUH were obtained (with informed consent) under institutional review board (IRB)-approved protocols. (project No. 2024-ER0506-00)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eW-J.S.\u003c/strong\u003e conceived and designed the study, performed data analysis, conducted the investigations, and drafted the manuscript. \u003cstrong\u003eK.A.K.\u003c/strong\u003e contributed to data analysis, investigation, and drafting of the manuscript. \u003cstrong\u003eJ.S.K., B.G.K., J.P.I., H.J.L., S.H.K., J.W.K., H.W.K., K.W.K., J-W.C., D.H.C., and D.H.K.\u003c/strong\u003e contributed to sample processing, data acquisition, and investigation. \u003cstrong\u003eE.S.K.\u003c/strong\u003e contributed to the study design. \u003cstrong\u003eS-J.K.\u003c/strong\u003e contributed to study design and oversaw project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe requirement for informed consent was waived.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. Owing to privacy and ethical restrictions, the datasets cannot be made publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMonteiro CA, Moubarac JC, Cannon G, Ng SW, Popkin B. Ultra-processed products are becoming dominant in the global food system. Obes Rev. 2013;14 Suppl 2:21\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrour B, Fezeu LK, Kesse-Guyot E, All\u0026egrave;s B, M\u0026eacute;jean C, Andrianasolo RM, et al. Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Sant\u0026eacute;). BMJ. 2019;365:l1451.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro CA, Cannon G, Moubarac JC, Levy RB, Louzada ML, Jaime PC. The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. 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Metabolic, epigenetic, and transgenerational effects of gut bacterial choline consumption. Cell Host Microbe. 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKostic AD, et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe. 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrauss J, et al. Invasive potential of gut mucosa-derived Fusobacterium nucleatum positively correlates with IBD status of the host. Inflamm Bowel Dis. 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJu T, et al. Taxonomic profiling and colonization patterns of the gut microbiota in patients with inflammatory bowel disease. Microbiome. 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, et al. Parasutterella, in association with irritable bowel syndrome and metabolic disorders. Front Microbiol. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaier TV, et al. Impact of dietary sugars on gut microbiome composition and metabolic health. Cell Metab. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChassaing B, et al. Dietary emulsifiers impact the mouse gut microbiota promoting colitis and metabolic syndrome. Nature. 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViennois E, et al. Dietary emulsifier-induced low-grade inflammation promotes colon carcinogenesis. Cancer Res. 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolland WL, et al. Inhibition of ceramide synthesis ameliorates glucocorticoid-, saturated-fat-, and obesity-induced insulin resistance. Cell Metab. 2007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrocco S, et al. Upper gastrointestinal involvement in paediatric onset Crohn's disease: prevalence and clinical implications. J Crohns Colitis. 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun XW, et al. Clinical features and prognosis of Crohn's disease with upper gastrointestinal tract phenotype in Chinese patients. Dig Dis Sci. 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon JS, et al. Clinical characteristics and postoperative outcomes of patients presenting with upper gastrointestinal tract Crohn disease. Ann Coloproctol. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim OZ, et al. The clinical characteristics and prognosis of Crohn's disease in Korean patients showing proximal small bowel involvement: results from the CONNECT study. Gut Liver. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmer N, et al. Diagnostic Value of Inflammatory Markers in Inflammatory Bowel Disease: Clinical and Endoscopic Correlations. Cureus. 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Classification of 112 individual food frequency questionnaire (FFQ) food items according to the NOVA food systems\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eNOVA food groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003eFood categorization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eFood items\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e23 Food items\u003c/p\u003e\n \u003cp\u003eclassified as\u003c/p\u003e\n \u003cp\u003eultra-processed foods (UPF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003ePackaged snacks and confectioneries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003esnacks; cookies and crackers; candy/chocolate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003eUltra-processed breads and cereals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eloaf breads; steamed buns with red bean paste (or other fillings); other breads/pastries (cream bun, castella); corn flakes (cereals)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003eProcessed meats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eham; Korean sausage (sundae); fish cake/crab stick/fish paste\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003eReady-to-eat/heat mixed dishes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eInstant noodles (ramyeon); pizza; hamburger and sandwich\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003eSugar-sweetened beverages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003ecarbonated beverages (coke, sprite); other beverages (rice punch, yuja citron tea, rice drink); fruit juice\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003eDairy- and nondairy-based desserts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003emilk (including plain milk); yoghurt (liquid type); yoghurt (curd type); soybean milk; ice cream\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003eCondiments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003ecoffee creamer (coffee with added sugar or cream); butter; jam\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003eBeverages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eDistilled liquor (soju)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: General characteristics of the study population according to quartiles of UPF intake\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 79)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of samples used for analysis of characteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-hoc analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eIBD Subtype,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e59 (75.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e53 (67.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e52 (65.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e44 (56.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19 (24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e25 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27 (34.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e34 (43.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eSex, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e58 (74.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e59 (75.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e55 (69.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e54 (69.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e20 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19 (24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e24 (30.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e24 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eAge (years), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e37.0 (24.0-53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.0 (23.0-44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e29.0 (21.0-43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e29.5 (22.0-37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eQ1 vs Q2: 0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eQ1 vs Q3: 0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eQ1 vs Q4: 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eQ2 vs Q3: 0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eQ2 vs Q4: 0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003eQ3 vs Q4: 0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eBMI, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e22.5 \u0026plusmn; 3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23.6 \u0026plusmn; 4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23.4 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23.3 \u0026plusmn; 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eWBC (10\u0026sup3;/ul), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7.4 \u0026plusmn; 3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7.6 \u0026plusmn; 2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7.4 \u0026plusmn; 3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7.5 \u0026plusmn; 3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eHb (g/dL),\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14.0 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e13.8 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e13.4 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e13.0 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eAlbumin (g/dL), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.2 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.3 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.3 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.2 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eESR (mm/hr), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e25.1 \u0026plusmn; 20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e25.8 \u0026plusmn; 25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23.5 \u0026plusmn; 21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.0 \u0026plusmn; 26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eCRP (mg/L),\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.3 \u0026plusmn; 2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.3 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.7 \u0026plusmn; 4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.4 \u0026plusmn; 3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eASCA IgA/IgG (U/mL), N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNegative (0~5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e28 (59.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e24 (42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26 (44.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26 (51.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003ePositive (\u0026gt;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e33 (57.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e33 (55.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e25 (49.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eANCA MPO/Pr III Ab (IU/mL),\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNegative (0~5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e52 (86.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e54 (84.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e58 (85.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e45 (80.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003ePositive (\u0026gt;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e11 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFecal calprotectin (ug/g), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e808.0 \u0026plusmn; 1329.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e841.9 \u0026plusmn; 1312.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e909.6 \u0026plusmn; 1340.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e932.8 \u0026plusmn; 1391.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eC. difficile Toxin Assay, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e25 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e35 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e24 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eC. difficile Toxin Gene PCR, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e34 (87.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41 (89.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e38 (84.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40 (95.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eUC Disease Extent, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e17 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e13 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e11 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e13 (29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e20 (37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e16 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e20 (33.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19 (35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e20 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15 (34.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eUndeterminate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eCD Disease Extent, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6 (31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8 (29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12 (63.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19 (76.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e16 (59.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21 (61.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eUndeterminate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eCD Upper GI involvement,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e18 (94.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e25 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e29 (85.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eCD Behavior,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eB1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10 (52.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19 (79.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e16 (59.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26 (76.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eB2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8 (29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eCD Perianal involvement,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e11 (57.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19 (76.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19 (70.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e24 (70.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8 (42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6 (24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8 (29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10 (29.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eInitial Endoscopic Severity, N (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14 (22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15 (24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e16 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e16 (27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19 (28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e22 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19 (32.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e13 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e11 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003eBiologics Use,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e63 (81.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e59 (75.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e53 (67.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e57 (73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 107px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19 (24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 89px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 63px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"digestive-diseases-and-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ddsj","sideBox":"Learn more about [Digestive Diseases and Sciences](http://link.springer.com/journal/10620)","snPcode":"10620","submissionUrl":"https://submission.nature.com/new-submission/10620/3","title":"Digestive Diseases and Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ultra-processed foods, inflammatory bowel disease, gut microbiota, metabolomics, multi-omics, NOVA classification","lastPublishedDoi":"10.21203/rs.3.rs-9445079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9445079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims\u003c/h2\u003e \u003cp\u003eUltra-processed food (UPF) is increasingly consumed worldwide and may influence gut microbial ecology relevant to inflammatory bowel disease (IBD). However, patient-level multi-omics data remain scarce. We investigated whether habitual UPF intake is associated with specific microbiota and metabolite profiles in Korean patients with IBD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDietary intake was assessed using a validated food frequency questionnaire, and foods was categorized by the NOVA system. UPF intake was expressed as percent of energy, and patients were stratified into UPF low (Q1\u0026ndash;Q2) and UPF high (Q3\u0026ndash;Q4). Fecal samples underwent 16S rRNA sequencing and untargeted metabolomics. Microbiome differences were tested using PERMANOVA for beta-diversity and Mann\u0026ndash;Whitney U tests for taxa. Differential metabolites were defined by p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |fold change|\u0026ge;1.5, followed by Reactome enrichment with FDR correction. Correlations among microbiota, metabolites, and UPF subgroups were examined using Spearman tests with Benjamini\u0026ndash;Hochberg adjustment. Associations between UPF intake and clinical characteristics were analyzed using Spearman tests, η\u0026sup2; from ANOVA and point-biserial correlation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMicrobial beta-diversity differed significantly between UPF low and UPF high participants. UPF high participants showed expansion of pro-inflammatory pathobionts (\u003cem\u003eEscherichia\u0026ndash;Shigella, Proteus, Parasutterella, Enterococcus, Fusobacterium, and Clostridium innocuum group\u003c/em\u003e) and depletion of anti-inflammatory commensals (\u003cem\u003eFaecalibacterium, Butyricicoccus, Lachnospiraceae ND3007 group, and Bifidobacterium\u003c/em\u003e). Metabolomic profiling revealed enrichment of inflammatory pathways (phospholipid metabolism, eNOS/NO signaling, mitochondrial β-oxidation, FMO3-mediated TMA to TMAO, tryptophan catabolism) and reduction of anti-inflammatory metabolites (AHR ligands, BAAT-conjugated bile acids). Integrated analyses demonstrated significant correlations between dysbiotic taxa and inflammatory metabolites. Among NOVA-defined UPF subgroups, sugar-sweetened beverages, ready-to-eat dishes, and packaged snacks and confectioneries showed the strongest associations with these adverse signatures. Analysis of clinical characteristics showed trends between total UPF intake and inflammatory markers (WBC, CRP, fecal calprotectin), and a meaningful association with upper gastrointestinal tract involvement in CD patients. Subgroup analysis showed that sugar-sweetened beverage intake was significantly associated with CRP elevation and upper gastrointestinal involvement in CD patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn IBD, higher UPF intake, particularly from specific NOVA-defined subgroups, is associated with gut dysbiosis and a pro-inflammatory metabolome, which in turn correlates with unfavorable clinical characteristics. These findings provide patient-based multi-omics evidence and underscore clinically relevant dietary targets for IBD management.\u003c/p\u003e","manuscriptTitle":"Habitual Ultra-Processed Food Intake Is Associated with Gut Dysbiosis and Pro- Inflammatory Metabolite Profiles in Korean Patients with IBD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 10:56:10","doi":"10.21203/rs.3.rs-9445079/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T18:21:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T18:08:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107935018664362289341565595427413177473","date":"2026-04-22T17:32:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T14:37:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156423415714468057315912556239797930504","date":"2026-04-21T10:22:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-18T23:30:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T21:12:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T08:49:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Digestive Diseases and Sciences","date":"2026-04-17T06:36:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"digestive-diseases-and-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ddsj","sideBox":"Learn more about [Digestive Diseases and Sciences](http://link.springer.com/journal/10620)","snPcode":"10620","submissionUrl":"https://submission.nature.com/new-submission/10620/3","title":"Digestive Diseases and Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b72ac192-4608-4b1d-aada-80ba17d193a5","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T10:56:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 10:56:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9445079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9445079","identity":"rs-9445079","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}