Spicy Food Intake and Dietary Factors Shape the Gut Microbiome and Metabolism of Mucin and Short-Chain Fatty Acids in Healthy Adults

Spicy Food Intake and Dietary Factors Shape the Gut Microbiome and Metabolism of Mucin and Short-Chain Fatty Acids in Healthy Adults | 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 Article Spicy Food Intake and Dietary Factors Shape the Gut Microbiome and Metabolism of Mucin and Short-Chain Fatty Acids in Healthy Adults Uigi Min, Jihyun Kim, Jinwook Kim, Hyunmi Jin, Hyunji Oh, Soyeon Ahn, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8419849/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract The intestinal mucus layer provides a primary barrier between the gut microbiota and the epithelium. Capsaicin, a major component of spicy foods, has been reported to enhance muc2 expression and mucus secretion; however, evidence from preclinical and clinical studies remains inconsistent. How spicy food interacts with external factors such as alcohol to influence mucus-layer homeostasis also remains insufficiently understood. Using shotgun metagenomics, we investigated the associations between spicy food intake, alcohol consumption, intestinal fatty acid–binding protein (I-FABP), liver fatty acid–binding protein (L-FABP), gut microbial composition, and functional pathways in 229 healthy Korean adults. Alcohol intake was positively correlated with urinary I-FABP (ρ = 0.26), indicating mild epithelial stress, whereas spicy food intake was not associated with either FABP biomarker. Individuals who consumed highly spicy food exhibited an increased abundance of short-chain fatty acids (SCFA)-producing and mucin-metabolizing taxa, including Blautia , Coprococcus , and Ruminococcus , along with higher activity of mucin-degradation and SCFA-production pathways. Individuals who consumed a high level of alcohol showed stronger enrichment of mucin-degrading taxa, with reduced SCFA flux and increased abundance of Proteobacteria and Fusobacteriota. Cross-classified dietary groups revealed distinct mucin and SCFA gene activity patterns. Notably, the DHSH group displayed concurrent elevation of mucin turnover and SCFA production with indications of dysbiosis. These findings suggest that spicy food may modulate mucus layer metabolism in a context-dependent manner, whereas alcohol more consistently perturbs mucin–SCFA networks and epithelial integrity. Biological sciences/Biochemistry Health sciences/Gastroenterology Biological sciences/Microbiology Gut microbiome Mucin metabolism Spicy food intake Alcohol consumption Shotgun metagenomics Short-chain fatty acids Intestinal epithelial integrity Intestinal-fatty acid–binding protein Liver-fatty acid–binding protein Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The mucus layer, constituting the primary interface between the gut microbiota and host epithelium, performs multiple physiological functions beyond acting as a simple physical barrier, including providing a habitat for microorganisms, regulating the exchange of metabolic products, and coordinating immune responses 1 , 2 . This layer is a key determinant of the stability of the gut ecosystem, and structural or chemical perturbations of the mucus layer have been reported as early indicators of increased intestinal permeability, chronic inflammation, and metabolic disorders 2 . Therefore, understanding the factors that regulate mucus layer homeostasis is essential for characterizing the health of the gut environment. Dietary factors are recognized as major external variables that modify the composition and function of the mucus layer. Capsaicin, the principal bioactive compound in spicy foods, has emerged as a modulator capable of directly influencing mucus layer ecology. Experimental studies evaluating the physiological effects of spicy foods have shown that capsaicin increases muc2 expression in intestinal epithelial cells and promotes the expansion of mucin-utilizing microbial taxa, thereby supporting mucus-layer regeneration and stability 3 , 4 . Capsaicin has also been reported to enhance short-chain fatty acid (SCFA) biosynthesis pathways and regulate host energy metabolism 5 , suggesting a role as a metabolic mediator that strengthens interactions between the host and gut microbiota. However, the findings across various studies are inconsistent 6 – 8 . In rodents given high doses of capsaicin (40 mg/kg), mucosal irritation and barrier disruption were evident 9 , whereas human intervention studies have shown discomfort at low doses (0.75 mg/day) and metabolic improvements at higher doses (135 mg/day) 10,11 , indicating that the physiological response to capsaicin is not a linear dose-dependent relationship. Similarly, epidemiological studies have reported associations between spicy food intake and reduced all-cause mortality and metabolic disease risk 12 – 14 , although these associations remain inconsistent across populations. This inconsistency is likely attributable to the co-consumption of spicy foods with other dietary factors, such as high-fat meals or alcohol, which may modify or obscure the intrinsic effects of capsaicin 15 . Thus, the influence of spicy food consumption on the mucus layer and microbial function requires evaluation within the broader context of real-world dietary patterns, emphasizing the need for analytical approaches that can quantitatively capture such contextual variation. Most prior studies of the gut microbiota have been based on 16S rRNA sequencing, which provides insights into microbial composition and diversity but offers limited resolution for interpreting functional regulation at the metabolic pathway level, particularly for mucus-related gene clusters or SCFA biosynthetic pathways that are directly linked to gut barrier homeostasis 16 . Consequently, these approaches are insufficient for evaluating the ecological significance of dietary exposure. In contrast, shotgun metagenomics enables the direct profiling of microbial genes and functional characterization of metabolic networks modulated by dietary factors, including spicy food intake 17 , 18 . In this study, we performed shotgun metagenomic profiling of a cohort of more than 200 healthy Korean adults to investigate the mucus-associated metabolic pathways and functional features of the gut microbiota in relation to the frequency of consumption of spicy food. The goal was to characterize how food intake influences the microbial networks involved in mucus utilization, SCFA production, and intestinal barrier maintenance. By interpreting the relationship between spicy food consumption and the gut ecosystem at both structural and functional levels, this study provides a basis for a more refined understanding of the physiological mechanisms through which dietary habits contribute to gut barrier homeostasis. Results Sample Collection and Baseline Characteristics A total of 240 healthy adults were initially recruited for this study. However, 11 participants withdrew consent or were lost to follow-up, resulting in a final cohort of 229 individuals. All participants completed a structured questionnaire on dietary and lifestyle habits and provided fecal, urine, and oral epithelial samples. Urine samples were used to quantify inflammation- and epithelial injury-related biomarkers, including TNF-α, IL-6, I-FABP, and L-FABP. Oral samples were used to measure TRPV1 expression, and fecal samples were subjected to shotgun metagenomic sequencing (Fig. 1 A). The demographic and lifestyle characteristics of the participants are summarized in Supplementary Table 1. The study group consisted of 87 male (38%) and 142 female participants (62%). The mean age was 38.3 ± 11.1 years, and the mean body mass index (BMI) was 22.9 ± 3.4 kg/m², indicating that most individuals were within the normal weight range. The average frequency of spicy food consumption was 1.9 ± 0.7 points, corresponding to approximately “2–4 times per month.” Consumption of high-fat foods (1.9 ± 0.8), instant foods (1.7 ± 0.8), and fast foods (1.4 ± 0.7) fell within the “rarely to < 1 time per week” range, indicating generally healthy dietary habits. Vegetable preference relative to meat was moderate (4.2 ± 2.0 on a 1–10 scale), suggesting a tendency toward vegetable-based diets. Vitamin (1.3 ± 0.5) and probiotic intake scores (1.6 ± 0.5) indicated infrequent use. Alcohol drinking frequency (1.7 ± 1.0) and drinking volume per occasion (1.6 ± 1.0) corresponded to “2–4 times per month, 1–3 drinks per occasion.” Smoking scores were low (0.2 ± 0.5), reflecting a predominantly non-smoking population. Correlations Between Biomarkers and Metadata Spicy food is known to stimulate gastrointestinal physiological responses and mucus secretion, producing both beneficial and adverse health effects 6 – 8 , whereas alcohol consumption has been reported as a representative disruptor of the intestinal barrier, inducing epithelial injury and suppressing mucus secretion 19 , 20 . Considering these prior findings, we designed the present study with the aim of evaluating how these two dietary factors are associated with markers of epithelial injury and the composition of the gut microbiota. We first assessed, through analysis of Spearman’s correlation, the relationships between survey-based demographic, dietary, and lifestyle variables and a number of biomarkers: I-FABP and L-FABP, reflecting intestinal barrier damage; inflammatory markers (TNF-α and IL-6); and the sensory receptor for pungency (TRPV1). In the biomarker assessment, TNF-α, IL-6, and TRPV1 were detected at low concentrations and were therefore excluded from subsequent analyses (Supplementary Fig. 1). Analysis of the Spearman’s correlation between demographic variables and epithelial injury markers showed that I-FABP exhibited weak positive correlations with age (ρ = 0.22), BMI (ρ = 0.19), and stool frequency (ρ = 0.24), and a negative correlation with sex (ρ = − 0.27) (Fig. 1 B). L-FABP showed a positive correlation with I-FABP (ρ = 0.31), suggesting that the two markers reflect similar epithelial injury responses. Examination of correlations between dietary factors and biomarkers revealed that alcohol intake (g/day) was positively correlated with I-FABP (ρ = 0.26), whereas no correlation was observed with L-FABP (ρ = 0.05). Spicy food intake showed little association with I-FABP (ρ = 0.01) or L-FABP (ρ = 0.05), and the same was true for other dietary variables. These patterns suggest that alcohol consumption represents a dietary factor exerting a more direct and sensitive influence on intestinal epithelial injury. Accordingly, the interpretation of gut microbial composition and functional pathway changes requires analytical strategies that incorporate alcohol intake as a principal independent variable while simultaneously adjusting for sex, age, BMI, and stool frequency as confounders. This approach enables a clearer separation of the effects of spicy food versus alcohol consumption and provides a more accurate framework for explaining the physiological variations reflected by epithelial injury markers. Besides, alcohol exhibited some correlation (although weak) with high-fat and instant food intake, indicating that it may be part of a broader cluster of co-occurring dietary behaviors. Alcohol intake also showed a weak positive correlation with consumption of instant foods (ρ = 0.22) and preference for high-fat foods (ρ = 0.20). Lifestyle variables such as smoking, physical activity, and sleep quality were not correlated with either I-FABP or L-FABP. Microbiome Composition and Diversity Gut microbiome profiles were generated by shotgun metagenomic sequencing of fecal samples collected from 229 participants (Supplementary Fig. 2A). A total of 1,527 species were detected across all samples, and the two phyla commonly reported to be dominant in the human gut microbiome, Bacillota and Bacteroidota, were the most abundant 21 . When evaluating the extent to which environmental and dietary factors contributed to the taxonomic variation, sex, stool frequency, and age showed the highest explanatory power, followed by BMI, I-FABP levels, smoking, and alcohol consumption (Fig. 2 A). In contrast, other factors, including meat–vegetable preference, vitamin intake, and sleep quality, explained less than 1% of the variation in microbial community composition. Comparison of SCFA-Targeted Gene Family Distributions among Groups To assess the activation status of short-chain fatty acid (SCFA)-producing pathways, species-level stratified KEGG ortholog (KO) profiles were compared between the groups (Fig. 5 A). SCFA-related genes were analyzed based on a previously established list of SCFA-producing-associated KOs 39 . Group-wise comparison revealed that the DHSH group exhibited the highest overall abundance of gene families associated with acetate, propionate, and butyrate production (K00925, K01738, K16363, K01646, K19697, K01034, K01443, and K02535). The major contributing species were Anaerostipes hadrus , Blautia hansenii , Escherichia coli , and Fusobacterium mortiferum . Similarly, the DLSH group showed elevated abundance of acetate-, propionate-, and butyrate-producing gene families (K01026, K00929, and K00138, respectively), with Blautia obeum , Blautia producta , and Klebsiella pneumoniae identified as key contributors. In contrast, the DLSL group displayed a high abundance of the acetate-producing KO K16363 associated with Bacteroides nordii , whereas the propionate- and butyrate-producing pathways were comparatively reduced. The DHSL group exhibited only a limited presence of SCFA production–related genes, suggesting relatively weak activation of these metabolic pathways. Comparison of Mucin-Degrading Gene Family Distributions among Groups To investigate the mucin degradation–related gene families, both KEGG ortholog (KO) and Glycoside Hydrolase (GH) family annotations were analyzed based on the glycan utilization gene set proposed by Labarthe et al. (2023) 40 . Distinct combinations of mucin-derived glycan degradation and downstream metabolic pathways were observed in relation to the level of consumption of spicy food and alcohol (Fig. 4 B). The DHSH group was enriched in gene families related to starch degradation (GH13), cellulose/β-glucan degradation (GH5), sulfate reduction (K00394, K00395, K00956), and galactose metabolism (K00965, K00849). Moreover, characteristic enrichment of acetyl-CoA-derived SCFA-producing flux genes (K00625, K00925, K00656, K01034, and K00626) was observed, along with glycolytic enzymes (K01689, K01810, and K00927) and C1 metabolism–associated enzymes (K01491 and K00297). The major associated species were Anaerostipes hadrus and Fusobacterium mortiferum . In the DLSH group, numerous glycoside hydrolase families were enriched, including those involved in xylan degradation (GH10, GH120, GH30, and GH39), cellulose degradation (GH16 and GH26), and broad glycan hydrolysis (GH3), indicating a strong capacity for complex polysaccharide degradation. Additional enrichment in the galactose-containing glycan degradation (GH35) and β-galactosidase/β-mannosidase families (GH2) was observed. Metabolic pathways associated with fructose, mannose, and fucose (K01628, K01813, and K01818), sulfate assimilation (K00957), and butyrate production (K00929) were also identified. The dominant contributing species was Blautia producta , with additional contributions from Coprococcus eutactus and Fusobacterium mortiferum . The DHSL group exhibited characteristic enrichment of glycolytic pathways (K00134 and K00927), acetate formation (K00925), and sulfate assimilation (K00957). Within glycoside hydrolases, α-fucosidase families (GH29, GH95), broad glycosidase activity (GH3), and glucuronoxylan xylanohydrolase (GH30) were predominant. Furthermore, genes associated with the uronate pathway (xylulokinase, K00854) and C1 metabolism (methylenetetrahydrofolate dehydrogenase, K01491) were detected and were mainly attributed to Parabacteroides distasonis . In contrast, the DLSL group showed enrichment of glycolytic enzymes such as enolase (K01689) and pyruvate kinase (K01810) related to Bacteroides nordii , but lacked mucin- or diet-derived glycan–degrading enzymes and downstream SCFA-producing genes. Network Analysis Integrated network analysis revealed that host-related factors (alcohol, spicy food, I-FABP, L-FABP, and TRPV1) exhibited multiple correlations with key microbial species and metabolic gene clusters (Fig. 6 ). Consumption of spicy food was positively correlated with abundance of Blautia producta and Fusobacterium mortiferum , which in turn was associated with genes related to SCFA synthesis (K00925, K00929, K01026, and K01034). The abundance of these species also showed positive correlations with mucin-degrading genes (K00965 and K00626), clustering with Blautia obeum , Coprococcus eutactus , Anaerostipes hadrus , Klebsiella pneumoniae , and Escherichia coli . Conversely, alcohol consumption was positively correlated with abundance of Parabacteroides distasonis and Bacteroides nordii and was associated with mucin degradation–related KOs (K00854, K00134) and GH families (GH2, GH3, GH5, GH30, and GH95). These taxa formed a cluster with Blautia hansenii but showed relatively weak associations with SCFA-related gene clusters. At the metadata level, I-FABP was positively correlated with L-FABP and alcohol intake but not with spicy food intake. Spicy food intake was positively correlated with alcohol consumption, and the two factors were jointly associated with abundance of Fusobacterium mortiferum . Discussion In this study, we evaluated how spicy food consumption and alcohol intake differentially influence intestinal epithelial injury, dietary and lifestyle factors, and gut microbial composition in healthy adults. Among the epithelial injury markers, I-FABP exhibited significant correlations with age, BMI, stool frequency, and sex, indicating that subtle epithelial damage signals are strongly influenced by demographic and physiological factors (Fig. 1 B). In contrast, spicy food consumption frequency showed no association with either I-FABP or L-FABP, and no linkage was observed between FABP-based injury markers and spicy sensitivity or pain responses. These findings suggest that typical levels of spicy food consumption in healthy adults do not induce noticeable structural intestinal damage. However, alcohol intake showed a clear positive correlation with I-FABP, consistent with previous reports linking alcohol to epithelial cytotoxicity, reduced mucus secretion, and weakened barrier function 41,42 . In contrast, the L-FABP levels did not correlate with alcohol consumption. This pattern suggests that alcohol consumption may directly trigger the release of I-FABP, which is exclusively expressed in small intestinal enterocytes, thereby providing a more specific indication of alcohol-induced enterocyte disruption. As L-FABP is expressed across multiple organs, its association with alcohol may be less readily captured in a cross-sectional setting, potentially limiting its sensitivity to alcohol-specific epithelial disturbances 19 ,43 . Urinary inflammatory cytokine and oral epithelial TRPV1 expression were assessed to characterize systemic inflammation and receptor expression 44,45 , but both were detected at low concentrations across samples (Supplementary Fig. 1A). This is consistent with previous reports that healthy adults have low basal cytokine levels and limited detectability 45,46 , reinforcing I-FABP as a relatively stable epithelial injury marker under the conditions of this study. Based on shotgun metagenomic analyses, distinct species-level differences were observed when participants were grouped separately by spicy food or alcohol intake (Fig. 2 A–B). In the high-spicy food group (SH), increases were observed in Blautia , Limosima , Ruminococcus , Butyribacter , and Butyricicoccus , taxa associated with SCFA production and mucin metabolism 4 ,22–25 . These findings suggest that spicy food intake may selectively influence the mucus layer-associated metabolic pathways, supporting hypotheses from studies showing capsaicin-induced stimulation of mucin production 4 . Conversely, increases in Fusobacterium _A mortiferum , Dialister , and species of Dorea , taxa with context-dependent or bidirectional health associations 26–31 , demonstrate that spicy food may act as a complex stimulus capable of eliciting both beneficial and adverse signals, in line with the mixed findings of previous research 6 – 8 . Changes observed in the high-alcohol-intake group (DH) differed markedly. Although some taxa typically considered beneficial, including Butyricicoccus , Lawsonibacter , and Weissella , were enriched 32,34,35 , this was in contrast to the DL group, which showed a greater abundance of SCFA-producing and mucin-degrading taxa such as Alistipes and Blautia 38 . Notably, the epithelial injury marker I-FABP was not different between spicy food groups, but was significantly elevated only in the high-alcohol-intake group ( p = 0.004), supporting the interpretation that alcohol exerts a more direct effect on epithelial injury than spicy food. Thus, the presence of beneficial taxa in the DH group may reflect ecological persistence under alcohol-derived stress, rather than improved gut function 36,37 . Taken together, our results indicate that the intake of spicy food induces variable and mixed shifts within metabolic and mucus-related microbial networks, whereas alcohol intake produces distinct alterations, particularly in pathways linked to mucin metabolism. These findings indicated that spicy foods and alcohol may influence the gut ecosystem through mechanistically divergent routes. When participants were categorized into four combined spicy food–alcohol groups (DLSL, DLSH, DHSL, and DHSH), the overall microbial diversity did not differ significantly across groups, and Bray–Curtis PCoA analyses showed no distinct clustering (Supplementary Fig. 1B–C). Nonetheless, functional pathways related to mucin metabolism and SCFA production were clearly differentiated according to spicy food intake, suggesting that although the global taxonomy remains stable, metabolic network configurations diverge more strongly when spicy food and alcohol exposure intersect (Fig. 3 , Fig. 4 , Fig. 5 ). The high-spicy groups (DHSH and DLSH) were enriched in the genera Blautia , Roseburia , Coprococcus , and Limisoma , taxa known to support combined fiber–mucin utilization and SCFA production 4 ,22–24 . Their presence aligns with the previously defined fiber–mucin coupling metabotypes 1–2, which represent homeostatic states sustaining both mucin turnover and epithelial energy supply 40 . This interpretation was reinforced by functional metagenomic analyses, which showed differential activation of mucin-degrading pathways and SCFA-related gene families in high-spicy groups (Figs. 4 and 5 A–B). These results support the hypothesis that spicy foods exert compensatory or modulatory effects on gut metabolic networks that are otherwise perturbed by alcohol exposure. Given that mucin turnover critically relies on cross-feeding among SCFA-producing bacteria 47 , the observed patterns suggest that spicy food intake, regardless of alcohol consumption, modulates the abundance of genes related to butyrate production, including buk (K00929) and bup (K01034) (Fig. 5 B). In particular, the Blautia producta -associated butyrate kinase gene buk (K00929) was enriched in the DLSH group, whereas the Fusobacterium mortiferum -associated butyrate phosphotransferase gene bup (K01034) was specific to the DHSH group. In the DHSH group, enhanced carbohydrate metabolism (PWY-5676, P461-PWY), sulfate reduction (K00394, K00395, K00956), and glycosidase pathways (GH13–GH5 families), along with β-galactosidase (K01491) and galactokinase (K00965), suggested an active mucin degradation–SCFA production flux. Blautia hansenii and Anaerostipes hadrus appeared to sustain the acetate–propionate flux (Fig. 5 A–B), indicating the active turnover of sulfo-mucin-derived sulfate, mucin glycan degradation, and SCFA production 40 . In contrast, the DHSL group was characterized by enrichment of Clostridium _Q species, known H₂S producers, whereas the DHSH group contained H₂S consumers and SCFA producers such as Anaerostipes hadrus (Fig. 3 A, Fig. 5 B). This pattern implies a disrupted cross-feeding flux in DHSL, marked by H₂S accumulation and a reduction in H₂S-consuming taxa (Faecalibacterium, Roseburia, Anaerostipes), a condition linked to proinflammatory metabolic processes 48 . Network analysis consistently showed that alcohol-associated taxa, such as Parabacteroides and Bacteroides, formed mucin-degrading-dominant networks with weak connectivity to the mucin–SCFA module (Fig. 6 ). These contrasting network structures (Fig. 6 ) indicate that spicy food may support mucin homeostasis via mucin–SCFA coupling, whereas alcohol promotes mucin degradation under reduced SCFA flux through cross-feeding loss and H₂S activation 20 ,36,49 . Furthermore, the negative correlation between TRPV1 and α-fucosidase gene families (GH29, GH95)—which were disconnected from the SCFA gene cluster—aligns with previous reports suggesting that TRPV1 activation indirectly promotes mucin synthesis 4 ,50 . Collectively, these findings imply that the maintenance of mucin homeostasis by spicy food may serve as a determinant of resilience or disruption of gut metabolic health in the presence of alcohol exposure. Nevertheless, the enrichment of Proteobacteria and Fusobacteriota taxa in the SCFA- and mucin-targeted gene families of the DHSH and DLSH groups (Fig. 5 A, B) warrants cautious interpretation. Fusobacterium mortiferum , Escherichia coli , and Klebsiella pneumoniae are well-known dysbiosis-associated taxa that can induce epithelial injury via endotoxin production, and are linked to metabolic and inflammatory disorders 26,51,52 . Genes such as galK (K00849) and galT (K00965) have been suggested to contribute indirectly to LPS biosynthesis 53,54 , and incomplete reduction of H₂S-generating pathways (K00394, K00395, K00956, and K00957) by consumers in the human digestive tract may represent an additional risk factor 55,56 . Notably, Proteobacteria are key constituents of mucin profile 3, reflecting disrupted mucin homeostasis 40 . In the DHSH group, the mucin- and SCFA-producing pathways were relatively upregulated (Fig. 5 B). Although an appropriate level of mucin turnover can strengthen the protective barrier, excessive degradation has been reported to induce inflammatory injury, indicating the need for interpretation based on prior reviews 47 . The amino acid metabolic pathways enriched in the DLSH group, including methionine, arginine, ornithine, and alanine metabolism (Fig. 4 ), may represent terminal metabolic routes associated with mucin degradation 40 . However, hyperactivation of these pathways has also been linked to disturbances in host amino acid homeostasis, excessive production of H₂S and polyamines, and dysregulation of the serotonin–glucose axis, all of which may contribute to gut and metabolic diseases such as inflammatory bowel disease and insulin resistance 57,58 . Furthermore, acetate-generating and stratified Klebsiella pneumoniae , a species associated with fatty liver risk, may reflect a metabolically inflammatory transitional state 59,60 . These findings are consistent with previous reports that high concentrations of capsaicin can negatively impact the gut environment 9 ; however, because this study did not include systematic quantification of capsaicin content in spicy foods, additional evidence is required. Nevertheless, given that the study population comprised healthy adults, epithelial injury markers showed alcohol-specific patterns, and inflammatory markers were detected at low levels, the spicy food intake observed here is likely within a range capable of maintaining mucin homeostasis. However, spicy food consumption may exert bidirectional modulation on mucus metabolism and microbial ecology, acting in either protective or detrimental directions, depending on consumption intensity. This study had several limitations. The participants were healthy adults without clinical diseases; thus, I-FABP fluctuations should be interpreted as physiological variations rather than acute inflammatory responses. The cross-sectional design precludes causal inference, and classification based on consumption frequency rather than on quantitative intake limits the estimation of actual exposure. Additionally, because this was a remote study, blood and tissue analyses were not performed, and mechanistic interpretations related to mucin homeostasis relied on information in the literature rather than direct measurements. An imbalance in group sample sizes may also introduce statistical constraints; however, permutation-based tests and FDR corrections were applied to minimize potential bias. Future studies should incorporate quantitative dietary assessments and longitudinal designs to more clearly define the dose-response relationships between spicy food or alcohol intake and mucus layer function. With respect to spicy foods in particular, integrating quantitative intake data with refined assessments of individual sensitivity and sensory responses may reveal subtle physiological changes not captured by frequency-based classification alone. Moreover, integrated metabolomic and transcriptomic analyses using blood or tissue samples are required to directly verify whether the microbial functional changes observed in this study translate into epithelial injury signals or mucin regulatory pathways. Such approaches would provide a more precise understanding of the mechanisms by which dietary factors modulate gut barrier homeostasis. Conclusions With this study, we offer an analysis of the effects of alcohol and spicy food consumption on intestinal epithelial injury, gut microbial composition, and metabolic pathways in healthy adults. Alcohol intake was associated with elevated I-FABP levels, reflecting microepithelial injury, whereas spicy food intake promoted mucin metabolism and SCFA-linked compensatory pathways, supporting mucus layer stability. However, enriched amino acid metabolism in the DLSH group represents terminal mucin degradation processes that may predispose individuals to metabolic inflammation. In addition, the concurrent enrichment of Proteobacteria and Fusobacteriota in the DHSH group suggests a potential risk of dysbiosis through mucin degradation and endotoxin activation. Together, these findings suggest that spicy food exerts bidirectional regulation on gut mucus metabolism, maintaining mucin homeostasis at moderate levels but potentially aggravating mucus degradation and metabolic imbalance when combined with excessive alcohol exposure. Therefore, the intensity of spicy food intake and the level of alcohol exposure should be considered key determinants of mucin homeostasis and gut metabolic health. Methods Study Population and Design This cross-sectional observational study was conducted between 2024 and 2025 and included 240 healthy Korean adults aged 19 years or older, recruited from Seoul and the surrounding metropolitan area. The study protocol was approved by the Institutional Review Board of Korea National Institute for Bioethics Policy (KoNIBP) (IRB No. P01-202411-02-004) and registered with the Clinical Research Information Service of Korea ( trial number KCT0010676). All participants were provided with a detailed explanation of the study objectives and procedures, and gave written informed consent prior to participation. All methods were performed in accordance with the relevant guidelines and regulations. Healthy adults were specifically recruited to exclude potential disturbances in the gut microbiome caused by diseases or medication and to clearly evaluate functional variations associated with dietary factors in a physiologically stable population. The exclusion criteria included a history of clinically diagnosed disease, current medication use, and no prior consumption of Buldak hot chicken flavor ramen (Samyang Foods Inc., Seoul, Korea), as this particular product was used as the benchmark for perception of spiciness. Eligibility screening was performed using self-report questionnaires and verification steps. To minimize sample degradation, recruitment was limited to participants residing within one–two hours of the study site. Questionnaire Assessment To control for individual factors that could influence the gut microbiome composition, participants completed a structured questionnaire comprising 24 items grouped into three categories: demographic information (six items), health information (five items), and dietary lifestyle factors (13 items). The questionnaire was expressly developed for this study and was partly based on published survey instruments used in previous microbiome and dietary studies involving Korean populations 61,62 . Demographic information included sex (male or female), age group (20s–60s and older), height (cm), weight (kg), and contact information. Health information covered disease diagnosis and medication status within the past three years (including gastrointestinal, hepatic, endocrine, cardiovascular, musculoskeletal, and neurological disorders) using a four-tier scale (no diagnosis, past diagnosis, under treatment, and currently medicated). Participants were also asked about recent antibiotic use (within the past month) and the intake of dietary supplements such as vitamins or probiotics. Stool-related questions included bowel movement frequency (≤ 2 times per week to > 1 time per day), stool form (Bristol Stool Scale, types 1–7), and the presence or absence of recurrent abdominal pain. Dietary and lifestyle items encompassed spicy food preference (10-point extended Likert scale), frequency of spicy food consumption (from never to ≥ 5 times/week), intensity of post-consumption abdominal discomfort (visual analog scale, 0–10), onset of abdominal pain after consumption (from none to > 1 d), and pain duration (from none to ≥ 2–3 days). Additional items assessed dietary tendencies such as meat/vegetable preference (1–10, lower scores indicating meat preference), consumption frequencies of high-fat, instant, or fast foods (from never to ≥ 5 times/week), alcohol consumption (from never to ≥ 5 times/week; drinking amount: 1 to ≥ 7 glasses per occasion), smoking (none to ≥ 1 pack/day), physical activity (0 to ≥ 5 times/week), and self-perceived health, mood, sleep quality, lifestyle regularity, and hygiene (5-point Likert scales). To standardize the perception of “spicy food” across participants, Buldak hot chicken flavor ramen (Samyang Foods Inc., Seoul, Korea) was explicitly referenced in all related questions as the benchmark of spiciness. In accordance with IRB recommendations, individuals with no prior consumption experience of this product were excluded from participation. Alcohol Intake Assessment Daily alcohol intake (g/day) was calculated based on the self-reported drinking frequency and the average amount of alcohol consumed per occasion obtained from the questionnaire. The calculation was standardized using soju, the most commonly consumed alcoholic beverage in Korea (16% alcohol by volume), as a reference. The amount of pure ethanol contained in one glass (50 mL) was calculated as (50 mL × 0.16 × 0.789 g/mL [ethanol density]), equivalent to 6.3 g of alcohol. Accordingly, the daily alcohol intake of each participant was calculated as: 6.3 g × (the average number of glasses consumed per day). Drinking frequency reported on a weekly or monthly basis was converted to daily frequency by dividing by 7 or 30, respectively, and multiplying by the average number of drinks per occasion to yield g/day. For nondrinkers, pseudo-counts were applied to prevent distortion by zero values during statistical analysis. This calculation method was adapted from the standardized procedures used in the Korea National Health and Nutrition Examination Survey (KNHANES) 63 . Group Classification For spicy food consumption, participants whose intake frequency exceeded the median value (once per week) were classified into the high engagement group (SH), whereas those at or below the median were assigned to the low engagement group (SL). Alcohol intake groups were defined using the same approach: participants whose estimated alcohol consumption exceeded the median value (2.91 g/day) were categorized as the high-engagement group (DH) and those at or below the median as the low-engagement group (DL). The classification of spicy food and alcohol intake groups was based on the quartile distribution of each survey variable, with the median used as the cutoff. Participants whose values exceeded the median were placed in the high group, and those with corresponding values at or below the median were placed in the Low group. Accordingly, participants were assigned to one of the following four groups: DLSL (Drink-Low–Spicy-Low), DHSL (Drink-High–Spicy-Low), DLSH (Drink-Low–Spicy-High), and DHSH (Drink-High–Spicy-High). Sample Collection and Storage The participants self-collected fecal (approximately 3 g), urine (approximately 25 mL), and oral epithelial cell (approximately 0.1 mL) samples using sterile collection kits provided by the study team. Sampling was performed at home or in the workplace, and specimens were immediately transported via rapid local delivery to the research facility. All samples were stored at − 80°C until analysis to minimize degradation. DNA Extraction and Shotgun Metagenomic Sequencing Genomic DNA was extracted using the DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany). DNA quality and concentration were verified before shotgun metagenomic sequencing on the Illumina NovaSeq platform with 150-bp paired-end reads. The quality of the raw sequencing reads was initially assessed using FastQC to ensure the overall read quality before downstream processing. All steps for metagenome sequencing and preprocessing followed the bioBakery workflow ( https://github.com/biobakery/biobakery/wiki/biobakery_workflows ). Subsequent preprocessing was performed using KneadData ( https://github.com/biobakery/kneaddata ), which consisted of three main steps: (1) Quality trimming was performed using Trimmomatic, applying a sliding window approach to remove reads when four consecutive bases had Phred scores below 20. Reads shorter than 60 bp were excluded from analysis. (2) Adapter sequences were removed using Trimmomatic. (3) Host-derived and common sequencing components were decontaminated using Bowtie2 by aligning reads against the human reference genome (hg38). Taxonomic and Functional Profiling Microbial taxonomic and functional profiles were generated using MetaPhlAn 4 (v4.1.1) and HUMAnN 3.6, respectively. Functional annotations included MetaCyc pathways and KEGG Orthologs (KOs). In the taxonomic analysis, the species-level genome bin (SGBs) output by MetaPhlAn 4 was converted into a GTDB-based taxonomy using the sgb_to_gtdb_profile.py script provided by the MetaPhlAn 4 suite. Downstream analysis retained only taxa or functional features (MetaCyc pathway or KO) that were present at a minimum relative abundance of 0.005% in all samples. Alpha diversity was calculated using the Shannon index and the phyloseq R package. For beta diversity, Bray–Curtis dissimilarity was calculated using the phyloseq R package based on relative abundance data. Principal coordinate analysis (PCoA) was performed using the Bray–Curtis distance matrix. To test for class-level differences in community composition, a permutational multivariate analysis of variance (PERMANOVA) was conducted using 999 permutations, with sample class (DLSL, DLSH, DHSL, and DHSH) as the main factor and BMI, sex, and age as covariates. Linear Mixed Model Analysis To identify microbial taxa and functional features associated with metadata, linear mixed models were fitted using the MaAsLin2 R package. The fixed effects included the sample class, BMI, sex, and age. Only taxa and functional features (pathways or gene families) with a prevalence of at least 5% across all samples were included. P-values were corrected for multiple testing using the Benjamini-Hochberg method, as implemented in MaAsLin2. Microbial Network and Correlation Analysis To explore the associations between microbial species, functional features, and metadata, Spearman’s rank correlations (for metadata) and Pearson’s correlations (for continuous microbial profiles) were computed. Microbial interaction networks were visualized using Cytoscape (v3.10.3). Measurement of Urinary Biomarkers Urinary samples were analyzed to quantify intestinal epithelial injury markers, including intestinal fatty acid–binding protein (I-FABP) and liver fatty acid–binding protein (L-FABP), as well as inflammatory markers interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α). Intestinal epithelial injury markers were measured using the Human I-FABP and L-FABP ELISA Kits (R&D Systems, MN, USA), and inflammatory markers were quantified using the Human IL-6 and TNF-α ELISA Kits (R&D Systems, MN, USA). Urine samples were thawed gradually at 4°C overnight prior to analysis, and a single freeze–thaw cycle was performed to ensure consistency across all measurements. Before quantification, samples were centrifuged at 500 g for 15 min at 4°C, and the supernatant was collected to remove debris. All measurements and quantification were performed according to the manufacturer’s ELISA protocol. Analysis of TRPV1 Expression in Oral Epithelial Cells TRPV1 expression in oral epithelial cells was measured by collecting tongue epithelial samples and quantifying TRPV1 and GAPDH levels using the Human TRPV1 ELISA Kit (Antibodies.com, Cambridge, UK) and Human GAPDH ELISA Kit (Invitrogen, CA, USA). The relative TRPV1 expression was calculated as the TRPV1-to-GAPDH ratio for intergroup comparisons. The collected epithelial cells were dissolved in 1 mL cold radioimmunoprecipitation assay (RIPA) lysis buffer (Thermo Fisher Scientific, MA, USA) containing protease inhibitors. Cell disruption and protein extraction were performed using a bead-beating grinder (MP Biomedicals, CA, USA) at 4 m/s for 20 s and repeated twice using Lysing Matrix E tubes. Samples were centrifuged at 500 g for 15 min at 4°C to remove debris, and all procedures were conducted on ice. TRPV1 and GAPDH levels were quantified using ELISA, according to the manufacturer’s protocols, and TRPV1 values were normalized to GAPDH levels to account for sample variability. Statistical Analysis All statistical analyses were conducted in R (The R Project for Statistical Computing; v4.4.2; https://www.r-project.org ) using RStudio (v2024.12.1.563). For multiple comparisons involving microbiome and functional gene analyses, false discovery rate (FDR) correction was performed using the Benjamini–Hochberg method. Following the analytical threshold applied by Huang et al. (2024) 64 , an adjusted FDR < 0.25 was considered statistically significant for exploratory multi-omics association analyses. Abbreviations SCFA Short-chain fatty acid I-FABP Intestinal-fatty acid-binding protein L-FABP Liver-fatty acid-binding protein TNF-α Tumor necrosis factor-α IL-6 Interleukin-6 TRPV1 Transient receptor potential vanilloid 1 KO KEGG ortholog GH Glycoside hydrolase BMI Body mass index PERMANOVA Permutational multivariate analysis of variance Declarations Competing Interests This work was supported by internal research funding from Samyang Foods Inc. The funder had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or the decision to submit the article for publication. All authors are employees of Samyang Foods Inc. Funding This work was supported by internal research funding from Samyang Foods Inc. The funder had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or the decision to submit the article for publication. Author Contribution U. M., Jinwook Kim, and W. L. conceptualized the study. Jihyun Kim developed the analytical pipeline, performed the statistical analyses, and prepared all visualizations. U.M., H.J., and Jihyun Kim curated and validated the metagenomic data. Jinwook Kim, H.O., and S.A. conducted the biomarker analyses. U.M. and Jihyun Kim interpreted the results and wrote the manuscript with input from all the authors. H.S. and W.L. supervised the study. All authors reviewed and approved the final version of the manuscript. Acknowledgements This work was supported by the Research Center of the Mitomics Institute, Samyang Foods Inc. Data Availability The metagenomic sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under the accession number PRJNA1366038. During the peer-review process, the data are accessible to editors and reviewers via the following confidential reviewer access link provided by NCBI:https://dataview.ncbi.nlm.nih.gov/object/PRJNA1366038?reviewer=qnor0m05t9ccvactnnah81gcui.Due to the ethical restrictions imposed by the Institutional Review Board of the Korea National Institute for Bioethics Policy (KoNIBP) (IRB No. P01-202411-02-004), the individual-level demographic and questionnaire data cannot be made publicly available. Anonymized data may be obtained from the corresponding author upon reasonable request. References Paone, P. & Cani, P. D. Mucus barrier, mucins and gut microbiota: the expected slimy partners? Gut 69 , 2232–2243 (2020). Luis, A. S. & Hansson, G. C. Intestinal mucus and their glycans: A habitat for thriving microbiota. Cell Host & Microbe 31 , 1087–1100 (2023). Shen, W. et al. Anti-obesity effect of capsaicin in mice fed with high-fat diet is associated with an increase in population of the gut bacterium Akkermansia muciniphila. Front. Microbiol. 8 , 272 (2017). Kumar, V. et al. Mucin secretory action of capsaicin prevents high fat diet-induced gut barrier dysfunction in C57BL/6 mice colon. Biomed. Pharmacother. 145 , 112452 (2022). Wang, Y., Tang, C., Tang, Y., Yin, H. & Liu, X. Capsaicin has an anti-obesity effect through alterations in gut microbiota populations and short-chain fatty acid concentrations. Food Nutr. Res. 64 , 10–29219 (2020). Xiang, Y. et al. Beneficial effects of dietary capsaicin in gastrointestinal health and disease. Exp. Cell Res. 417 , 113227 (2022). Zhang, W. et al. The effects of capsaicin intake on weight loss among overweight and obese subjects: a systematic review and meta-analysis of randomised controlled trials. Br. J. Nutr. 130 , 1645–1656 (2023). Xiang, Q. et al. Capsaicin—the spicy ingredient of chili peppers: A review of the gastrointestinal effects and mechanisms. Trends Food Sci. Technol. 116 , 755–765 (2021). Xiang, Q. et al. Capsaicin, the spicy ingredient of chili peppers: effects on gastrointestinal tract and composition of gut microbiota at various dosages. Foods 11 , 686 (2022). Hammer, J. & Vogelsang, H. Characterization of sensations induced by capsaicin in the upper gastrointestinal tract. Neurogastroenterol. Motil. 19 , 279–287 (2007). Lejeune, M. P., Kovacs, E. M. & Westerterp-Plantenga, M. S. Effect of capsaicin on substrate oxidation and weight maintenance after modest body-weight loss in human subjects. Br. J. Nutr. 90 , 651–659 (2003). Lv, J. et al. Consumption of spicy foods and total and cause specific mortality: population based cohort study. BMJ 351 (2015). Bonaccio, M. et al. Chili pepper consumption and mortality in Italian adults. J. Am. Coll. Cardiol. 74 , 3139–3149 (2019). Alatorre-Cruz, J. M. et al. Traditional Mexican food: Phenolic content and public health relationship. Foods 12 , 1233 (2023). Wang, M., Huang, W. & Xu, Y. Effects of spicy food consumption on overweight/obesity, hypertension and blood lipids in China: a meta-analysis of cross-sectional studies. Nutr. J. 22 , 29 (2023). Knight, R. et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16 , 410–422 (2018). Jovel, J. et al. Characterization of the gut microbiome using 16S or shotgun metagenomics. Front. Microbiol. 7 , 459 (2016). Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15 , 962–968 (2018). Sosnowski, K. & Przybyłkowski, A. Ethanol-induced changes to the gut microbiome compromise the intestinal homeostasis: A review. Gut Microbes 16 , 2393272 (2024). Bishehsari, F. et al. Alcohol and gut-derived inflammation. Alcohol Res. Curr. Rev. 38 , 163 (2017). Structure, function and diversity of the healthy human microbiome. Nature 486 , 207–214 (2012). Parada Venegas, D. et al. Short chain fatty acids (SCFAs)-mediated gut epithelial and immune regulation and its relevance for inflammatory bowel diseases. Front. Immunol. 10 , 277 (2019). Singh, V. et al. Butyrate producers, “the sentinel of gut”: Their intestinal significance with and beyond butyrate, and prospective use as microbial therapeutics. Front. Microbiol. 13 , 1103836 (2023). Zhu, Y. et al. Exploration of the Muribaculaceae family in the gut microbiota: diversity, metabolism, and function. Nutrients 16 , 2660 (2024). Zou, Y. et al. Butyribacter intestini gen. nov., sp. nov., a butyric acid-producing bacterium of the family Lachnospiraceae isolated from human faeces, and reclassification of Acetivibrio ethanolgignens as Acetanaerobacter ethanolgignens gen. nov., comb. nov. Syst. Appl. Microbiol. 44 , 126201 (2021). Deng, J. et al. Fusobacterium mortiferum and its metabolite 5-aminovaleric acid promote the development of colorectal cancer in obese individuals through Wnt/β-catenin pathway by DKK2. Gut Microbes 17 , 2502138 (2025). Rashidi, A. et al. No evidence for colonization of oral bacteria in the distal gut in healthy adults. PNAS 118 , e2114152118 (2021). Shi, H. et al. The gut microbiome as mediator between diet and its impact on immune function. Sci. Rep. 12 , 5149 (2022). Anthamatten, L. et al. Stratification of human gut microbiomes by succinotype is associated with inflammatory bowel disease status. Microbiome 12 , 186 (2024). Petrov, V. et al. Analysis of gut microbiota in patients with Parkinson’s disease. Bull. Exp. Biol. Med. 162 , 734–737 (2017). Liang, X. et al. Gut microbiome, cognitive function and brain structure: a multi-omics integration analysis. Transl. Neurodegener. 11 , 49 (2022). Sakamoto, M. et al. Draft genome sequence of Lawsonibacter asaccharolyticus… Genome Announc. 6 , 10–1128 (2018). Selma, M. V. et al. Description of urolithin production capacity from ellagic acid of two human intestinal Gordonibacter species. Food Funct . 5 , 1779–1784 (2014). Geirnaert, A. et al. Butyricicoccus pullicaecorum, a butyrate producer with probiotic potential, is intrinsically tolerant to stomach and small intestine conditions. Anaerobe 30 , 70–74 (2014). Moon, Y. et al. Intracellular lipid accumulation inhibitory effect of Weissella koreensis OK1-6… J. Appl. Microbiol. 113 , 652–658 (2012). Chen, G. et al. Gut microbiota dysbiosis: The potential mechanisms by which alcohol disrupts gut and brain functions. Front. Microbiol. 13 , 916765 (2022). Gloor, G. B. et al. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8 , 2224 (2017). Glover, J. S., Ticer, T. D. & Engevik, M. A. Characterizing the mucin-degrading capacity of the human gut microbiota. Sci. Rep. 12 , 8456 (2022). Zhang, J. et al. Altered synthesis of genes associated with short-chain fatty acids in the gut of patients with atrial fibrillation. BMC Genomics 22 , 634 (2021). Labarthe, S. et al. Four functional profiles for fibre and mucin metabolism in the human gut microbiome. Microbiome 11 , 231 (2023). de Jong, W.–J. et al. The effect of acute alcohol intoxication on gut wall integrity in healthy male volunteers. Alcohol 49 , 65–70 (2015). Kokesova, A. et al. The intestinal fatty acid-binding protein as a marker for intestinal damage in gastroschisis. PLoS One 14 , e0210797 (2019). Voth, M. et al. I-FABP and L-FABP are early markers for abdominal injury… Clin. Chem. Lab. Med. 53 , 771–780 (2015). Chen, H. et al. Urinary interleukin-6 as a diagnostic and prognostic marker of acute kidney injury in dogs. Vet. Rec. 191 , no–no (2022). Takahashi, N. et al. Epithelial TRPV1 channels: expression, function, and pathogenicity in the oral cavity. J. Oral Biosci. 62 , 235–241 (2020). Biancotto, A. et al. Baseline levels and temporal stability of 27 multiplexed serum cytokine concentrations… PLoS One 8 , e76091 (2013). Yamaguchi, M. & Yamamoto, K. Mucin glycans and their degradation by gut microbiota. Glycoconj. J. 40 , 493–512 (2023). Marcelino, V. R. et al. Disease-specific loss of microbial cross-feeding interactions in the human gut. Nat. Commun. 14 , 6546 (2023). Herrnreiter, C. J. et al. Bacterial dysbiosis and decrease in SCFA correlate with intestinal inflammation following alcohol intoxication and burn injury. eGastroenterology 3 (2025). Kumar, V. et al. Role of TRPV1 in colonic mucin production and gut microbiota profile. Eur. J. Pharmacol. 888 , 173567 (2020). Li, Z. et al. Fusobacterium in the microbiome: from health to disease across the oral–gut axis and beyond. npj Biofilms Microbiomes 11 , 200 (2025). Shin, N.–R., Whon, T. W. & Bae, J.–W. Proteobacteria: microbial signature of dysbiosis in gut microbiota. Trends Biotechnol . 33 , 496–503 (2015). Tang, M. et al. Correlation assessment among clinical phenotypes… Hum. Mutat. 33 , 1107–1115 (2012). Debouck, C. et al. Structure of the galactokinase gene of Escherichia coli… Nucleic Acids Res. 13 , 1841–1853 (1985). Braccia, D. J. et al. Hydrogen sulfide (H2S) via cysteine degradation is ubiquitous in the human gut microbiome. Front. Microbiol. 12 , 705583 (2021). Luo, W. et al. Microbial assimilatory sulfate reduction-mediated H2S: An overlooked role in Crohn’s disease. Microbiome 12 , 152 (2024). Chen, Y. & Fang, J.–Y. Colonic microbiota amino acid metabolism in gut health regulation. Cell Insight 100227 (2025). Li, T.–T. et al. Microbiota metabolism of intestinal amino acids impacts host nutrient homeostasis and physiology. Cell Host Microbe 32 , 661–675 (2024). Samuelson, D. R. et al. Alcohol-associated intestinal dysbiosis impairs pulmonary defense against Klebsiella pneumoniae. PLoS Pathog . 13 , e1006426 (2017). Shen, M. et al. Alcohol-induced gut microbiome dysbiosis enhances the colonization of Klebsiella pneumoniae. mSystems 9 , e00052–24 (2024). Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352 , 560–564 (2016). Shin, J.–H. et al. Differential effects of typical Korean versus American-style diets on gut microbial composition… Nutrients 11 , 2450 (2019). Hong, J. W., Noh, J. H. & Kim, D.–J. High-risk alcohol consumption in Korean adults. PLoS One 12 , e0175299 (2017). Huang, K. D. et al. Dietary selective effects manifest in the human gut microbiota from species composition to strain genetic makeup. Cell Rep . 43 (2024). Additional Declarations Competing interest reported. This work was supported by internal research funding from Samyang Foods Inc. The funder had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or the decision to submit the article for publication. All authors are employees of Samyang Foods Inc. Supplementary Files SuppleFig1.pdf Supplementary Figure 1. Biomarkers distribution with different four groups. Boxplots showing the expression levels of inflammatory markers (TNF-α, IL-6) and TRPV1 according to spicy-alcohol intake groups. P -values were calculated using the Kruskal-Wallis test. SuppleFig2.pdf Supplementary Figure 2. Overall composition and diversity of the gut microbiota. A) The phylogenetic tree based on GTDB of 1,527 microbial species detected across all 299 samples. Each color represents a distinct phylum-level taxonomy. B)Alpha diversity represented by the Shannon index according to different dietary groups. P -values were calculated using the Kruskal-Wallis test. C)Principal coordinate analysis (PCoA) based on Bray-Curtis distances illustrating -diversity among microbial communities across all samples, colored by the different dietary intake groups. P -value was calculated using the PERMANOVA test. SupplementaryTables.xlsx Supplementary Table 1. Baseline demographic, dietary, and lifestyle characteristics of study participants according to spicy-food and alcohol-intake groups. The variables included sex distribution, age, BMI, stool frequency, Bristol Stool Scale score, spicy-food-related responses, dietary preferences, lifestyle habits, and urinary epithelial injury biomarkers (I-FABP and L-FABP). Group differences were evaluated using appropriate statistical tests, and P -values are presented. Supplementary Table 2. Spearman rank-correlation coefficients between metadata variables and intestinal epithelial-injury biomarkers (I-FABP and L-FABP). The table summarizes pairwise associations (ρ) among demographic factors, stool characteristics, spicy-food responses, dietary habits, and biomarker concentrations, along with corresponding P -values. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Mar, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 21 Jan, 2026 Editor invited by journal 30 Dec, 2025 Submission checks completed at journal 28 Dec, 2025 First submitted to journal 28 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8419849","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":577940512,"identity":"26570fab-30e2-42d9-858f-0a04abba7f7e","order_by":0,"name":"Uigi Min","email":"","orcid":"","institution":"Samyang Foods Inc","correspondingAuthor":false,"prefix":"","firstName":"Uigi","middleName":"","lastName":"Min","suffix":""},{"id":577940513,"identity":"5e22c065-cb82-42e6-99c3-d374dcbbcc30","order_by":1,"name":"Jihyun Kim","email":"","orcid":"","institution":"Samyang Foods Inc","correspondingAuthor":false,"prefix":"","firstName":"Jihyun","middleName":"","lastName":"Kim","suffix":""},{"id":577940514,"identity":"a62535ec-52eb-4b2b-89aa-3f39cfddd531","order_by":2,"name":"Jinwook Kim","email":"","orcid":"","institution":"Samyang Foods Inc","correspondingAuthor":false,"prefix":"","firstName":"Jinwook","middleName":"","lastName":"Kim","suffix":""},{"id":577940515,"identity":"77a51633-74f6-47f6-9c9f-4b18454eb2d3","order_by":3,"name":"Hyunmi Jin","email":"","orcid":"","institution":"Samyang Foods Inc","correspondingAuthor":false,"prefix":"","firstName":"Hyunmi","middleName":"","lastName":"Jin","suffix":""},{"id":577940516,"identity":"9aca7fd6-a940-43fd-b408-61ca9d04e9fb","order_by":4,"name":"Hyunji Oh","email":"","orcid":"","institution":"Samyang Foods Inc","correspondingAuthor":false,"prefix":"","firstName":"Hyunji","middleName":"","lastName":"Oh","suffix":""},{"id":577940517,"identity":"4a9570d0-f822-465b-9136-db7bfd344135","order_by":5,"name":"Soyeon Ahn","email":"","orcid":"","institution":"Samyang Foods Inc","correspondingAuthor":false,"prefix":"","firstName":"Soyeon","middleName":"","lastName":"Ahn","suffix":""},{"id":577940518,"identity":"ed69d7bf-e4a1-4361-803a-5b54899bdae8","order_by":6,"name":"Hyeonseok Shin","email":"","orcid":"","institution":"Samyang Foods Inc","correspondingAuthor":false,"prefix":"","firstName":"Hyeonseok","middleName":"","lastName":"Shin","suffix":""},{"id":577940521,"identity":"3d4a42a4-cbde-4f01-822c-e9fee558a7ec","order_by":7,"name":"Wonhee Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACCeYGBoYKMNMALngAvxZGoJYzJGthbEPTghcY3G5sfPBx3j05g9vNmz+8bbPL52dgfojXFoM7B5sNZ24rNja4c6xMcm5bsuXMBjYDvFrMbiS2SfNuS0jccCPHjJm3jdkAqJ6glvbff+eAtRh/5m2rN7A/wP6BoC3MjA1gLQbSvG2HDQwYePDbYn8jsVmy51iCseSNtDLJOeeOG0gc5inAq0VyRvLBDz9qEuT4biRv/vCmrNqAv7198wd8WlABD4hgJl49TMsoGAWjYBSMAjQAAN1IUJtWfKS8AAAAAElFTkSuQmCC","orcid":"","institution":"Samyang Foods Inc","correspondingAuthor":true,"prefix":"","firstName":"Wonhee","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2025-12-22 00:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8419849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8419849/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100952007,"identity":"41fa54ec-f46b-4257-aea1-569b2f65b61d","added_by":"auto","created_at":"2026-01-23 07:11:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":60927,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript251229.docx","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/273fa77c69f572c50b079083.docx"},{"id":100918079,"identity":"4aeba9f6-5460-4012-a069-159e816d7367","added_by":"auto","created_at":"2026-01-22 19:11:43","extension":"json","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10342,"visible":true,"origin":"","legend":"","description":"","filename":"9217518941e14135aba9e1ad8f310e84.json","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/0cb3e361f17d9f12b605c51e.json"},{"id":100951004,"identity":"e99390fa-82ea-4754-8103-4e7076bbc3f0","added_by":"auto","created_at":"2026-01-23 07:09:49","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142415,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleFig1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/27838d44b79ce34afaaec1a4.pdf"},{"id":100918073,"identity":"3247335b-03d8-47b7-b3f8-9a0ee02b43d3","added_by":"auto","created_at":"2026-01-22 19:11:43","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":401629,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleFig2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/37235bbdb70d3045093e18c7.pdf"},{"id":100951843,"identity":"b68743fc-8f8a-4cc2-aac9-4f5dfc94d4c6","added_by":"auto","created_at":"2026-01-23 07:11:20","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20207,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/66bbd43a0289a84e73b828b1.xlsx"},{"id":100951796,"identity":"931c64d3-893f-4e71-b280-f0a46da09b9e","added_by":"auto","created_at":"2026-01-23 07:11:16","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126656,"visible":true,"origin":"","legend":"","description":"","filename":"9217518941e14135aba9e1ad8f310e841enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/4f800a5136442e5a05a62569.xml"},{"id":100918085,"identity":"37c0f194-2b5f-490c-bccb-528b2f5cfa75","added_by":"auto","created_at":"2026-01-22 19:11:49","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":372003,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/1d4424b61f89c4a5efd5f10f.pdf"},{"id":101297055,"identity":"cace3796-326b-4a5a-9d69-127ecfd4ea74","added_by":"auto","created_at":"2026-01-28 09:24:50","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":512134,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/452bf60966efbd5bc2d5e382.pdf"},{"id":100951121,"identity":"072daf10-c2fc-4957-a11e-9e35428d858a","added_by":"auto","created_at":"2026-01-23 07:10:00","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":553454,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/7e1cd46425790c63f6eeb8df.pdf"},{"id":100918081,"identity":"4bc0f2af-a22f-453e-8930-362493bb97c0","added_by":"auto","created_at":"2026-01-22 19:11:44","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":488899,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/1549e247abee418e76128fb2.pdf"},{"id":100918077,"identity":"a4c75aa5-1360-4ac8-b88e-01c5708567f1","added_by":"auto","created_at":"2026-01-22 19:11:43","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179498,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/2971f09ff63f52366f6a5dde.pdf"},{"id":100950028,"identity":"ffec0b7e-7118-4371-85aa-86c09e0cc1ef","added_by":"auto","created_at":"2026-01-23 07:06:42","extension":"pdf","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":381574,"visible":true,"origin":"","legend":"","description":"","filename":"Figure6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/cc27ae693b92a89febce6cc1.pdf"},{"id":100918083,"identity":"25ef8f1b-b0df-4e13-83c7-1f9fc1576e72","added_by":"auto","created_at":"2026-01-22 19:11:44","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122846,"visible":true,"origin":"","legend":"","description":"","filename":"9217518941e14135aba9e1ad8f310e841structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/fb2baafc1a250a8dd7aaf2a7.xml"},{"id":100918080,"identity":"e918fb2f-14a5-4366-b144-9fca995b4791","added_by":"auto","created_at":"2026-01-22 19:11:44","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146681,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/cf405c2e63c253b65da42753.html"},{"id":100918066,"identity":"af3f2caa-d7ea-422a-9ac0-3584eadc0a25","added_by":"auto","created_at":"2026-01-22 19:11:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":376500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design and metadata correlation. A) \u003c/strong\u003eOverview of the study design. \u003cstrong\u003eB)\u003c/strong\u003e Correlation plot showing Spearman’s correlation coefficients between dietary factors, meta info, and intestinal epithelial injury biomarkers (I-FABP, L-FABP).\u003c/p\u003e","description":"","filename":"Figure1361.png","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/f9f09f2a2ede71ff2f5c0fa7.png"},{"id":100951973,"identity":"e1ad92e4-b3e9-4318-afe5-0a1eec012fa5","added_by":"auto","created_at":"2026-01-23 07:11:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":846736,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut microbiota composition in relation to spicy food and alcohol intake. A)\u003c/strong\u003e PERMANOVA assessment of variation in microbial taxonomy explained by different environmental or dietary variables. The x-axis indicates the percentage of variance explained (R\u003csup\u003e2\u003c/sup\u003e), while point size reflects the significance level as -log\u003csub\u003e10\u003c/sub\u003e(p). Variables with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 are highlighted in deeppink, and non-significant variables are shown in gray. \u003cstrong\u003eB)\u003c/strong\u003e Boxplots showing the expression levels of epithelial injury markers (I-FABP, L-FABP) according to spicy food and alcohol groups. \u003cem\u003eP\u003c/em\u003e-values were calculated using the Wilcoxon rank sum test. \u003cstrong\u003eC–D)\u003c/strong\u003e Differentially abundant microbial taxa associated with spicy food (C) and alcohol intake (D), identified by MaAsLin2. Bubble plots show taxa with significant associations (FDR \u0026lt; 0.25) between the low- and high-intake groups in each dietary category. The x-axis represents the MaAsLin2 coefficient (effect size), where positive values indicate higher relative abundance in the SH or DH groups, and negative values indicate higher abundance in the SL or DL groups. Bubble size corresponds to the absolute value of the coefficient, and colors indicate the direction of association (red: enriched in the high-intake group; blue: enriched in the low-intake group). Taxa are ordered by effect size.\u003c/p\u003e","description":"","filename":"Figure1362.png","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/4cd049580bcf418cb9f4852a.png"},{"id":100951152,"identity":"ef11e9d3-36d6-4acb-a088-7a44dcc08a31","added_by":"auto","created_at":"2026-01-23 07:10:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1073883,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGroup-specific differences in gut microbial species composition.\u003c/strong\u003e \u003cstrong\u003eA)\u003c/strong\u003e Heatmap showing the abundances of 53 microbial species that are significantly different among groups. The top annotated color bars represent metadata variables, with corresponding color scales shown on the right. Species abundances were log\u003csub\u003e10\u003c/sub\u003e-transformed and z-scored across all samples. \u003cstrong\u003eB)\u003c/strong\u003e Phylogenetic distribution of 53 microbial species showing significant group-wise differences identified by MaAsLin2 analysis (FDR \u0026lt; 0.25). Colors represent the regression coefficients from the MaAsLin2 model, with red indicating positive and blue indicating negative associations relative to the other groups. \u003cstrong\u003eC)\u003c/strong\u003e Boxplots showing the expression levels of epithelial injury markers (I-FABP, L-FABP) according to different groups. \u003cem\u003eP\u003c/em\u003e-values were calculated by the Kruskal–Wallis rank sum test.\u003c/p\u003e","description":"","filename":"Figure1363.png","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/d50a3c705676bca0db6ee178.png"},{"id":100918071,"identity":"717e5c17-7fc2-484b-b863-7678e62de45a","added_by":"auto","created_at":"2026-01-22 19:11:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1107827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignificantly enriched functional metabolic pathways of the gut microbiota.\u003c/strong\u003e The bar plot shows 38 MetaCyc pathways that differ significantly among the groups. The x-axis represents -log\u003csub\u003e10\u003c/sub\u003e(P-value) derived from the MaAsLin2 model, and the color of each bar indicates the group with the corresponding enrichment.\u003c/p\u003e","description":"","filename":"Figure1364.png","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/469ae120ae4abf5f0c59a588.png"},{"id":100918067,"identity":"36626fdb-cb12-435d-b25c-76bbd1605f2c","added_by":"auto","created_at":"2026-01-22 19:11:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":187402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of SCFA and mucin-related gene families of the gut microbiota. A) \u003c/strong\u003eHeatmap illustrating the associations among short-chain fatty acid (SCFA)-related KEGG Orthologs (KOs), microbial species, and compounds (especially, acetate, butyrate, and propionate). Significantly associated KO-species obtained from HUMAnN3 were identified using MaAsLin2 analysis (FDR \u0026lt; 0.25). \u003cem\u003eLower panel\u003c/em\u003e: group-specific KO abundances; \u003cem\u003eupper panel\u003c/em\u003e: SCFA compounds associated with each KO and the corresponding microbial species expressing them. \u003cstrong\u003eB) \u003c/strong\u003eHeatmap of mucin degradation-related KO and glycoside hydrolases (GHs), and their associated microbial species.\u003c/p\u003e","description":"","filename":"Figure1365.png","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/91e8b36610e75696e2f89414.png"},{"id":100918075,"identity":"355b4df9-01be-409b-99b8-f22c8b6d38a2","added_by":"auto","created_at":"2026-01-22 19:11:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":971696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated correlation network linking host factors, microbial species, and functional gene families.\u003c/strong\u003e Correlation network plot showing the relationships among metadata, microbial species, and gene family (KO or GH). Each node represents the gene families (KO/GH), microbial species, and metadata variable. The shapes and colors of the nodes indicate these three categories, while KOs associated with three compounds (acetate, butyrate, and propionate) are highlighted in distinct colors. Edge colors represent correlation coefficients; edge widths correspond to -log\u003csub\u003e10\u003c/sub\u003e(P-values) derived from Spearman’s or Pearson correlation analysis.\u003c/p\u003e","description":"","filename":"Figure1366.png","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/8c2d6f9c15d6c0d880c72a52.png"},{"id":101299210,"identity":"ac862732-3135-4ef3-89fe-1bff51614767","added_by":"auto","created_at":"2026-01-28 09:40:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5299909,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/6f3bcd82-7534-4ac5-b6ec-811e49821cdf.pdf"},{"id":100918069,"identity":"049feef5-da7b-4970-82d6-84d690672b81","added_by":"auto","created_at":"2026-01-22 19:11:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":142415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003e \u003cstrong\u003eBiomarkers distribution with different four groups. \u003c/strong\u003eBoxplots showing the expression levels of inflammatory markers (TNF-α, IL-6) and TRPV1 according to spicy-alcohol intake groups. \u003cem\u003eP\u003c/em\u003e-values were calculated using the Kruskal-Wallis test.\u003c/p\u003e","description":"","filename":"SuppleFig1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/b88cc947e981dbb9b4232162.pdf"},{"id":100950070,"identity":"d58e159b-9a69-4e2e-badb-8b7e17c708a1","added_by":"auto","created_at":"2026-01-23 07:06:48","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":401629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2.\u003c/strong\u003e \u003cstrong\u003eOverall composition and diversity of the gut microbiota. A) \u003c/strong\u003eThe phylogenetic tree based on GTDB of 1,527 microbial species detected across all 299 samples. Each color represents a distinct phylum-level taxonomy. \u003cstrong\u003eB)\u003c/strong\u003eAlpha diversity represented by the Shannon index according to different dietary groups. \u003cem\u003eP\u003c/em\u003e-values were calculated using the Kruskal-Wallis test. \u003cstrong\u003eC)\u003c/strong\u003ePrincipal coordinate analysis (PCoA) based on Bray-Curtis distances illustrating -diversity among microbial communities across all samples, colored by the different dietary intake groups. \u003cem\u003eP\u003c/em\u003e-value was calculated using the PERMANOVA test.\u003c/p\u003e","description":"","filename":"SuppleFig2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/36eaa84c4dd69d12a8852884.pdf"},{"id":100918087,"identity":"6c3d2ee8-395e-43af-b974-c220f507f9ff","added_by":"auto","created_at":"2026-01-22 19:11:57","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1.\u003c/strong\u003e \u003cstrong\u003eBaseline demographic, dietary, and lifestyle characteristics of study participants according to spicy-food and alcohol-intake groups.\u003c/strong\u003e The variables included sex distribution, age, BMI, stool frequency, Bristol Stool Scale score, spicy-food-related responses, dietary preferences, lifestyle habits, and urinary epithelial injury biomarkers (I-FABP and L-FABP). Group differences were evaluated using appropriate statistical tests, and \u003cem\u003eP\u003c/em\u003e-values are presented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 2. Spearman rank-correlation coefficients between metadata variables and intestinal epithelial-injury biomarkers (I-FABP and L-FABP)\u003c/strong\u003e. The table summarizes pairwise associations (ρ) among demographic factors, stool characteristics, spicy-food responses, dietary habits, and biomarker concentrations, along with corresponding \u003cem\u003eP\u003c/em\u003e-values.\u003c/p\u003e","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8419849/v1/63a99288390fe9e86b9b53c3.xlsx"}],"financialInterests":"Competing interest reported. This work was supported by internal research funding from Samyang Foods Inc. The funder had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or the decision to submit the article for publication. All authors are employees of Samyang Foods Inc.","formattedTitle":"Spicy Food Intake and Dietary Factors Shape the Gut Microbiome and Metabolism of Mucin and Short-Chain Fatty Acids in Healthy Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe mucus layer, constituting the primary interface between the gut microbiota and host epithelium, performs multiple physiological functions beyond acting as a simple physical barrier, including providing a habitat for microorganisms, regulating the exchange of metabolic products, and coordinating immune responses\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This layer is a key determinant of the stability of the gut ecosystem, and structural or chemical perturbations of the mucus layer have been reported as early indicators of increased intestinal permeability, chronic inflammation, and metabolic disorders\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Therefore, understanding the factors that regulate mucus layer homeostasis is essential for characterizing the health of the gut environment.\u003c/p\u003e \u003cp\u003eDietary factors are recognized as major external variables that modify the composition and function of the mucus layer. Capsaicin, the principal bioactive compound in spicy foods, has emerged as a modulator capable of directly influencing mucus layer ecology. Experimental studies evaluating the physiological effects of spicy foods have shown that capsaicin increases \u003cem\u003emuc2\u003c/em\u003e expression in intestinal epithelial cells and promotes the expansion of mucin-utilizing microbial taxa, thereby supporting mucus-layer regeneration and stability\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Capsaicin has also been reported to enhance short-chain fatty acid (SCFA) biosynthesis pathways and regulate host energy metabolism\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, suggesting a role as a metabolic mediator that strengthens interactions between the host and gut microbiota. However, the findings across various studies are inconsistent\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In rodents given high doses of capsaicin (40 mg/kg), mucosal irritation and barrier disruption were evident\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, whereas human intervention studies have shown discomfort at low doses (0.75 mg/day) and metabolic improvements at higher doses (135 mg/day)\u003csup\u003e10,11\u003c/sup\u003e, indicating that the physiological response to capsaicin is not a linear dose-dependent relationship.\u003c/p\u003e \u003cp\u003eSimilarly, epidemiological studies have reported associations between spicy food intake and reduced all-cause mortality and metabolic disease risk\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, although these associations remain inconsistent across populations. This inconsistency is likely attributable to the co-consumption of spicy foods with other dietary factors, such as high-fat meals or alcohol, which may modify or obscure the intrinsic effects of capsaicin\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Thus, the influence of spicy food consumption on the mucus layer and microbial function requires evaluation within the broader context of real-world dietary patterns, emphasizing the need for analytical approaches that can quantitatively capture such contextual variation.\u003c/p\u003e \u003cp\u003eMost prior studies of the gut microbiota have been based on 16S rRNA sequencing, which provides insights into microbial composition and diversity but offers limited resolution for interpreting functional regulation at the metabolic pathway level, particularly for mucus-related gene clusters or SCFA biosynthetic pathways that are directly linked to gut barrier homeostasis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Consequently, these approaches are insufficient for evaluating the ecological significance of dietary exposure. In contrast, shotgun metagenomics enables the direct profiling of microbial genes and functional characterization of metabolic networks modulated by dietary factors, including spicy food intake\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we performed shotgun metagenomic profiling of a cohort of more than 200 healthy Korean adults to investigate the mucus-associated metabolic pathways and functional features of the gut microbiota in relation to the frequency of consumption of spicy food. The goal was to characterize how food intake influences the microbial networks involved in mucus utilization, SCFA production, and intestinal barrier maintenance. By interpreting the relationship between spicy food consumption and the gut ecosystem at both structural and functional levels, this study provides a basis for a more refined understanding of the physiological mechanisms through which dietary habits contribute to gut barrier homeostasis.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample Collection and Baseline Characteristics\u003c/h2\u003e \u003cp\u003eA total of 240 healthy adults were initially recruited for this study. However, 11 participants withdrew consent or were lost to follow-up, resulting in a final cohort of 229 individuals. All participants completed a structured questionnaire on dietary and lifestyle habits and provided fecal, urine, and oral epithelial samples. Urine samples were used to quantify inflammation- and epithelial injury-related biomarkers, including TNF-α, IL-6, I-FABP, and L-FABP. Oral samples were used to measure TRPV1 expression, and fecal samples were subjected to shotgun metagenomic sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The demographic and lifestyle characteristics of the participants are summarized in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study group consisted of 87 male (38%) and 142 female participants (62%). The mean age was 38.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1 years, and the mean body mass index (BMI) was 22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4 kg/m\u0026sup2;, indicating that most individuals were within the normal weight range. The average frequency of spicy food consumption was 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 points, corresponding to approximately \u0026ldquo;2\u0026ndash;4 times per month.\u0026rdquo; Consumption of high-fat foods (1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8), instant foods (1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8), and fast foods (1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7) fell within the \u0026ldquo;rarely to \u0026lt;\u0026thinsp;1 time per week\u0026rdquo; range, indicating generally healthy dietary habits. Vegetable preference relative to meat was moderate (4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 on a 1\u0026ndash;10 scale), suggesting a tendency toward vegetable-based diets. Vitamin (1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5) and probiotic intake scores (1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5) indicated infrequent use. Alcohol drinking frequency (1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0) and drinking volume per occasion (1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0) corresponded to \u0026ldquo;2\u0026ndash;4 times per month, 1\u0026ndash;3 drinks per occasion.\u0026rdquo; Smoking scores were low (0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5), reflecting a predominantly non-smoking population.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelations Between Biomarkers and Metadata\u003c/h3\u003e\n\u003cp\u003eSpicy food is known to stimulate gastrointestinal physiological responses and mucus secretion, producing both beneficial and adverse health effects\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, whereas alcohol consumption has been reported as a representative disruptor of the intestinal barrier, inducing epithelial injury and suppressing mucus secretion\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Considering these prior findings, we designed the present study with the aim of evaluating how these two dietary factors are associated with markers of epithelial injury and the composition of the gut microbiota.\u003c/p\u003e \u003cp\u003eWe first assessed, through analysis of Spearman\u0026rsquo;s correlation, the relationships between survey-based demographic, dietary, and lifestyle variables and a number of biomarkers: I-FABP and L-FABP, reflecting intestinal barrier damage; inflammatory markers (TNF-α and IL-6); and the sensory receptor for pungency (TRPV1). In the biomarker assessment, TNF-α, IL-6, and TRPV1 were detected at low concentrations and were therefore excluded from subsequent analyses (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eAnalysis of the Spearman\u0026rsquo;s correlation between demographic variables and epithelial injury markers showed that I-FABP exhibited weak positive correlations with age (ρ\u0026thinsp;=\u0026thinsp;0.22), BMI (ρ\u0026thinsp;=\u0026thinsp;0.19), and stool frequency (ρ\u0026thinsp;=\u0026thinsp;0.24), and a negative correlation with sex (ρ = \u0026minus;\u0026thinsp;0.27) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). L-FABP showed a positive correlation with I-FABP (ρ\u0026thinsp;=\u0026thinsp;0.31), suggesting that the two markers reflect similar epithelial injury responses. Examination of correlations between dietary factors and biomarkers revealed that alcohol intake (g/day) was positively correlated with I-FABP (ρ\u0026thinsp;=\u0026thinsp;0.26), whereas no correlation was observed with L-FABP (ρ\u0026thinsp;=\u0026thinsp;0.05). Spicy food intake showed little association with I-FABP (ρ\u0026thinsp;=\u0026thinsp;0.01) or L-FABP (ρ\u0026thinsp;=\u0026thinsp;0.05), and the same was true for other dietary variables.\u003c/p\u003e \u003cp\u003eThese patterns suggest that alcohol consumption represents a dietary factor exerting a more direct and sensitive influence on intestinal epithelial injury. Accordingly, the interpretation of gut microbial composition and functional pathway changes requires analytical strategies that incorporate alcohol intake as a principal independent variable while simultaneously adjusting for sex, age, BMI, and stool frequency as confounders. This approach enables a clearer separation of the effects of spicy food versus alcohol consumption and provides a more accurate framework for explaining the physiological variations reflected by epithelial injury markers.\u003c/p\u003e \u003cp\u003eBesides, alcohol exhibited some correlation (although weak) with high-fat and instant food intake, indicating that it may be part of a broader cluster of co-occurring dietary behaviors. Alcohol intake also showed a weak positive correlation with consumption of instant foods (ρ\u0026thinsp;=\u0026thinsp;0.22) and preference for high-fat foods (ρ\u0026thinsp;=\u0026thinsp;0.20). Lifestyle variables such as smoking, physical activity, and sleep quality were not correlated with either I-FABP or L-FABP.\u003c/p\u003e\n\u003ch3\u003eMicrobiome Composition and Diversity\u003c/h3\u003e\n\u003cp\u003eGut microbiome profiles were generated by shotgun metagenomic sequencing of fecal samples collected from 229 participants (Supplementary Fig.\u0026nbsp;2A). A total of 1,527 species were detected across all samples, and the two phyla commonly reported to be dominant in the human gut microbiome, Bacillota and Bacteroidota, were the most abundant\u003csup\u003e21\u003c/sup\u003e. When evaluating the extent to which environmental and dietary factors contributed to the taxonomic variation, sex, stool frequency, and age showed the highest explanatory power, followed by BMI, I-FABP levels, smoking, and alcohol consumption (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In contrast, other factors, including meat\u0026ndash;vegetable preference, vitamin intake, and sleep quality, explained less than 1% of the variation in microbial community composition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eComparison of SCFA-Targeted Gene Family Distributions among Groups\u003c/h3\u003e\n\u003cp\u003eTo assess the activation status of short-chain fatty acid (SCFA)-producing pathways, species-level stratified KEGG ortholog (KO) profiles were compared between the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). SCFA-related genes were analyzed based on a previously established list of SCFA-producing-associated KOs\u003csup\u003e39\u003c/sup\u003e. Group-wise comparison revealed that the DHSH group exhibited the highest overall abundance of gene families associated with acetate, propionate, and butyrate production (K00925, K01738, K16363, K01646, K19697, K01034, K01443, and K02535). The major contributing species were \u003cem\u003eAnaerostipes hadrus\u003c/em\u003e, \u003cem\u003eBlautia hansenii\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, and \u003cem\u003eFusobacterium mortiferum\u003c/em\u003e. Similarly, the DLSH group showed elevated abundance of acetate-, propionate-, and butyrate-producing gene families (K01026, K00929, and K00138, respectively), with \u003cem\u003eBlautia obeum\u003c/em\u003e, \u003cem\u003eBlautia producta\u003c/em\u003e, and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e identified as key contributors. In contrast, the DLSL group displayed a high abundance of the acetate-producing KO K16363 associated with \u003cem\u003eBacteroides nordii\u003c/em\u003e, whereas the propionate- and butyrate-producing pathways were comparatively reduced. The DHSL group exhibited only a limited presence of SCFA production\u0026ndash;related genes, suggesting relatively weak activation of these metabolic pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Mucin-Degrading Gene Family Distributions among Groups\u003c/h2\u003e \u003cp\u003eTo investigate the mucin degradation\u0026ndash;related gene families, both KEGG ortholog (KO) and Glycoside Hydrolase (GH) family annotations were analyzed based on the glycan utilization gene set proposed by Labarthe et al. (2023)\u003csup\u003e40\u003c/sup\u003e. Distinct combinations of mucin-derived glycan degradation and downstream metabolic pathways were observed in relation to the level of consumption of spicy food and alcohol (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The DHSH group was enriched in gene families related to starch degradation (GH13), cellulose/β-glucan degradation (GH5), sulfate reduction (K00394, K00395, K00956), and galactose metabolism (K00965, K00849). Moreover, characteristic enrichment of acetyl-CoA-derived SCFA-producing flux genes (K00625, K00925, K00656, K01034, and K00626) was observed, along with glycolytic enzymes (K01689, K01810, and K00927) and C1 metabolism\u0026ndash;associated enzymes (K01491 and K00297). The major associated species were \u003cem\u003eAnaerostipes hadrus\u003c/em\u003e and \u003cem\u003eFusobacterium mortiferum\u003c/em\u003e. In the DLSH group, numerous glycoside hydrolase families were enriched, including those involved in xylan degradation (GH10, GH120, GH30, and GH39), cellulose degradation (GH16 and GH26), and broad glycan hydrolysis (GH3), indicating a strong capacity for complex polysaccharide degradation. Additional enrichment in the galactose-containing glycan degradation (GH35) and β-galactosidase/β-mannosidase families (GH2) was observed. Metabolic pathways associated with fructose, mannose, and fucose (K01628, K01813, and K01818), sulfate assimilation (K00957), and butyrate production (K00929) were also identified. The dominant contributing species was \u003cem\u003eBlautia producta\u003c/em\u003e, with additional contributions from \u003cem\u003eCoprococcus eutactus\u003c/em\u003e and \u003cem\u003eFusobacterium mortiferum\u003c/em\u003e. The DHSL group exhibited characteristic enrichment of glycolytic pathways (K00134 and K00927), acetate formation (K00925), and sulfate assimilation (K00957). Within glycoside hydrolases, α-fucosidase families (GH29, GH95), broad glycosidase activity (GH3), and glucuronoxylan xylanohydrolase (GH30) were predominant. Furthermore, genes associated with the uronate pathway (xylulokinase, K00854) and C1 metabolism (methylenetetrahydrofolate dehydrogenase, K01491) were detected and were mainly attributed to \u003cem\u003eParabacteroides distasonis\u003c/em\u003e. In contrast, the DLSL group showed enrichment of glycolytic enzymes such as enolase (K01689) and pyruvate kinase (K01810) related to \u003cem\u003eBacteroides nordii\u003c/em\u003e, but lacked mucin- or diet-derived glycan\u0026ndash;degrading enzymes and downstream SCFA-producing genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Analysis\u003c/h2\u003e \u003cp\u003eIntegrated network analysis revealed that host-related factors (alcohol, spicy food, I-FABP, L-FABP, and TRPV1) exhibited multiple correlations with key microbial species and metabolic gene clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Consumption of spicy food was positively correlated with abundance of \u003cem\u003eBlautia producta\u003c/em\u003e and \u003cem\u003eFusobacterium mortiferum\u003c/em\u003e, which in turn was associated with genes related to SCFA synthesis (K00925, K00929, K01026, and K01034). The abundance of these species also showed positive correlations with mucin-degrading genes (K00965 and K00626), clustering with \u003cem\u003eBlautia obeum\u003c/em\u003e, \u003cem\u003eCoprococcus eutactus\u003c/em\u003e, \u003cem\u003eAnaerostipes hadrus\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, and \u003cem\u003eEscherichia coli\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConversely, alcohol consumption was positively correlated with abundance of \u003cem\u003eParabacteroides distasonis\u003c/em\u003e and \u003cem\u003eBacteroides nordii\u003c/em\u003e and was associated with mucin degradation\u0026ndash;related KOs (K00854, K00134) and GH families (GH2, GH3, GH5, GH30, and GH95). These taxa formed a cluster with \u003cem\u003eBlautia hansenii\u003c/em\u003e but showed relatively weak associations with SCFA-related gene clusters. At the metadata level, I-FABP was positively correlated with L-FABP and alcohol intake but not with spicy food intake. Spicy food intake was positively correlated with alcohol consumption, and the two factors were jointly associated with abundance of \u003cem\u003eFusobacterium mortiferum\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we evaluated how spicy food consumption and alcohol intake differentially influence intestinal epithelial injury, dietary and lifestyle factors, and gut microbial composition in healthy adults. Among the epithelial injury markers, I-FABP exhibited significant correlations with age, BMI, stool frequency, and sex, indicating that subtle epithelial damage signals are strongly influenced by demographic and physiological factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In contrast, spicy food consumption frequency showed no association with either I-FABP or L-FABP, and no linkage was observed between FABP-based injury markers and spicy sensitivity or pain responses. These findings suggest that typical levels of spicy food consumption in healthy adults do not induce noticeable structural intestinal damage.\u003c/p\u003e \u003cp\u003eHowever, alcohol intake showed a clear positive correlation with I-FABP, consistent with previous reports linking alcohol to epithelial cytotoxicity, reduced mucus secretion, and weakened barrier function\u003csup\u003e41,42\u003c/sup\u003e. In contrast, the L-FABP levels did not correlate with alcohol consumption. This pattern suggests that alcohol consumption may directly trigger the release of I-FABP, which is exclusively expressed in small intestinal enterocytes, thereby providing a more specific indication of alcohol-induced enterocyte disruption. As L-FABP is expressed across multiple organs, its association with alcohol may be less readily captured in a cross-sectional setting, potentially limiting its sensitivity to alcohol-specific epithelial disturbances\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,43\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUrinary inflammatory cytokine and oral epithelial TRPV1 expression were assessed to characterize systemic inflammation and receptor expression\u003csup\u003e44,45\u003c/sup\u003e, but both were detected at low concentrations across samples (Supplementary Fig.\u0026nbsp;1A). This is consistent with previous reports that healthy adults have low basal cytokine levels and limited detectability\u003csup\u003e45,46\u003c/sup\u003e, reinforcing I-FABP as a relatively stable epithelial injury marker under the conditions of this study.\u003c/p\u003e \u003cp\u003e Based on shotgun metagenomic analyses, distinct species-level differences were observed when participants were grouped separately by spicy food or alcohol intake (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;B). In the high-spicy food group (SH), increases were observed in \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eLimosima\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eButyribacter\u003c/em\u003e, and \u003cem\u003eButyricicoccus\u003c/em\u003e, taxa associated with SCFA production and mucin metabolism\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,22\u0026ndash;25\u003c/sup\u003e. These findings suggest that spicy food intake may selectively influence the mucus layer-associated metabolic pathways, supporting hypotheses from studies showing capsaicin-induced stimulation of mucin production\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Conversely, increases in \u003cem\u003eFusobacterium\u003c/em\u003e_A \u003cem\u003emortiferum\u003c/em\u003e, \u003cem\u003eDialister\u003c/em\u003e, and species of \u003cem\u003eDorea\u003c/em\u003e, taxa with context-dependent or bidirectional health associations\u003csup\u003e26\u0026ndash;31\u003c/sup\u003e, demonstrate that spicy food may act as a complex stimulus capable of eliciting both beneficial and adverse signals, in line with the mixed findings of previous research\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChanges observed in the high-alcohol-intake group (DH) differed markedly. Although some taxa typically considered beneficial, including \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eLawsonibacter\u003c/em\u003e, and \u003cem\u003eWeissella\u003c/em\u003e, were enriched\u003csup\u003e32,34,35\u003c/sup\u003e, this was in contrast to the DL group, which showed a greater abundance of SCFA-producing and mucin-degrading taxa such as \u003cem\u003eAlistipes\u003c/em\u003e and \u003cem\u003eBlautia\u003c/em\u003e\u003csup\u003e38\u003c/sup\u003e. Notably, the epithelial injury marker I-FABP was not different between spicy food groups, but was significantly elevated only in the high-alcohol-intake group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), supporting the interpretation that alcohol exerts a more direct effect on epithelial injury than spicy food. Thus, the presence of beneficial taxa in the DH group may reflect ecological persistence under alcohol-derived stress, rather than improved gut function\u003csup\u003e36,37\u003c/sup\u003e. Taken together, our results indicate that the intake of spicy food induces variable and mixed shifts within metabolic and mucus-related microbial networks, whereas alcohol intake produces distinct alterations, particularly in pathways linked to mucin metabolism. These findings indicated that spicy foods and alcohol may influence the gut ecosystem through mechanistically divergent routes.\u003c/p\u003e \u003cp\u003e When participants were categorized into four combined spicy food\u0026ndash;alcohol groups (DLSL, DLSH, DHSL, and DHSH), the overall microbial diversity did not differ significantly across groups, and Bray\u0026ndash;Curtis PCoA analyses showed no distinct clustering (Supplementary Fig.\u0026nbsp;1B\u0026ndash;C). Nonetheless, functional pathways related to mucin metabolism and SCFA production were clearly differentiated according to spicy food intake, suggesting that although the global taxonomy remains stable, metabolic network configurations diverge more strongly when spicy food and alcohol exposure intersect (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The high-spicy groups (DHSH and DLSH) were enriched in the genera \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eCoprococcus\u003c/em\u003e, and \u003cem\u003eLimisoma\u003c/em\u003e, taxa known to support combined fiber\u0026ndash;mucin utilization and SCFA production\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,22\u0026ndash;24\u003c/sup\u003e. Their presence aligns with the previously defined fiber\u0026ndash;mucin coupling metabotypes 1\u0026ndash;2, which represent homeostatic states sustaining both mucin turnover and epithelial energy supply\u003csup\u003e40\u003c/sup\u003e. This interpretation was reinforced by functional metagenomic analyses, which showed differential activation of mucin-degrading pathways and SCFA-related gene families in high-spicy groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;B). These results support the hypothesis that spicy foods exert compensatory or modulatory effects on gut metabolic networks that are otherwise perturbed by alcohol exposure. Given that mucin turnover critically relies on cross-feeding among SCFA-producing bacteria\u003csup\u003e47\u003c/sup\u003e, the observed patterns suggest that spicy food intake, regardless of alcohol consumption, modulates the abundance of genes related to butyrate production, including buk (K00929) and bup (K01034) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In particular, the \u003cem\u003eBlautia producta\u003c/em\u003e-associated butyrate kinase gene \u003cem\u003ebuk\u003c/em\u003e (K00929) was enriched in the DLSH group, whereas the \u003cem\u003eFusobacterium mortiferum\u003c/em\u003e-associated butyrate phosphotransferase gene \u003cem\u003ebup\u003c/em\u003e (K01034) was specific to the DHSH group. In the DHSH group, enhanced carbohydrate metabolism (PWY-5676, P461-PWY), sulfate reduction (K00394, K00395, K00956), and glycosidase pathways (GH13\u0026ndash;GH5 families), along with β-galactosidase (K01491) and galactokinase (K00965), suggested an active mucin degradation\u0026ndash;SCFA production flux. \u003cem\u003eBlautia hansenii\u003c/em\u003e and \u003cem\u003eAnaerostipes hadrus\u003c/em\u003e appeared to sustain the acetate\u0026ndash;propionate flux (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;B), indicating the active turnover of sulfo-mucin-derived sulfate, mucin glycan degradation, and SCFA production\u003csup\u003e40\u003c/sup\u003e. In contrast, the DHSL group was characterized by enrichment of \u003cem\u003eClostridium\u003c/em\u003e_Q species, known H₂S producers, whereas the DHSH group contained H₂S consumers and SCFA producers such as \u003cem\u003eAnaerostipes hadrus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This pattern implies a disrupted cross-feeding flux in DHSL, marked by H₂S accumulation and a reduction in H₂S-consuming taxa (Faecalibacterium, Roseburia, Anaerostipes), a condition linked to proinflammatory metabolic processes\u003csup\u003e48\u003c/sup\u003e. Network analysis consistently showed that alcohol-associated taxa, such as Parabacteroides and Bacteroides, formed mucin-degrading-dominant networks with weak connectivity to the mucin\u0026ndash;SCFA module (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These contrasting network structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) indicate that spicy food may support mucin homeostasis via mucin\u0026ndash;SCFA coupling, whereas alcohol promotes mucin degradation under reduced SCFA flux through cross-feeding loss and H₂S activation\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,36,49\u003c/sup\u003e. Furthermore, the negative correlation between TRPV1 and α-fucosidase gene families (GH29, GH95)\u0026mdash;which were disconnected from the SCFA gene cluster\u0026mdash;aligns with previous reports suggesting that TRPV1 activation indirectly promotes mucin synthesis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,50\u003c/sup\u003e. Collectively, these findings imply that the maintenance of mucin homeostasis by spicy food may serve as a determinant of resilience or disruption of gut metabolic health in the presence of alcohol exposure.\u003c/p\u003e \u003cp\u003eNevertheless, the enrichment of Proteobacteria and Fusobacteriota taxa in the SCFA- and mucin-targeted gene families of the DHSH and DLSH groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B) warrants cautious interpretation. \u003cem\u003eFusobacterium mortiferum\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e are well-known dysbiosis-associated taxa that can induce epithelial injury via endotoxin production, and are linked to metabolic and inflammatory disorders\u003csup\u003e26,51,52\u003c/sup\u003e. Genes such as \u003cem\u003egalK\u003c/em\u003e (K00849) and \u003cem\u003egalT\u003c/em\u003e (K00965) have been suggested to contribute indirectly to LPS biosynthesis\u003csup\u003e53,54\u003c/sup\u003e, and incomplete reduction of H₂S-generating pathways (K00394, K00395, K00956, and K00957) by consumers in the human digestive tract may represent an additional risk factor\u003csup\u003e55,56\u003c/sup\u003e. Notably, Proteobacteria are key constituents of mucin profile 3, reflecting disrupted mucin homeostasis\u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the DHSH group, the mucin- and SCFA-producing pathways were relatively upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Although an appropriate level of mucin turnover can strengthen the protective barrier, excessive degradation has been reported to induce inflammatory injury, indicating the need for interpretation based on prior reviews\u003csup\u003e47\u003c/sup\u003e. The amino acid metabolic pathways enriched in the DLSH group, including methionine, arginine, ornithine, and alanine metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), may represent terminal metabolic routes associated with mucin degradation\u003csup\u003e40\u003c/sup\u003e. However, hyperactivation of these pathways has also been linked to disturbances in host amino acid homeostasis, excessive production of H₂S and polyamines, and dysregulation of the serotonin\u0026ndash;glucose axis, all of which may contribute to gut and metabolic diseases such as inflammatory bowel disease and insulin resistance\u003csup\u003e57,58\u003c/sup\u003e. Furthermore, acetate-generating and stratified \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, a species associated with fatty liver risk, may reflect a metabolically inflammatory transitional state\u003csup\u003e59,60\u003c/sup\u003e. These findings are consistent with previous reports that high concentrations of capsaicin can negatively impact the gut environment\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e; however, because this study did not include systematic quantification of capsaicin content in spicy foods, additional evidence is required. Nevertheless, given that the study population comprised healthy adults, epithelial injury markers showed alcohol-specific patterns, and inflammatory markers were detected at low levels, the spicy food intake observed here is likely within a range capable of maintaining mucin homeostasis. However, spicy food consumption may exert bidirectional modulation on mucus metabolism and microbial ecology, acting in either protective or detrimental directions, depending on consumption intensity.\u003c/p\u003e \u003cp\u003eThis study had several limitations. The participants were healthy adults without clinical diseases; thus, I-FABP fluctuations should be interpreted as physiological variations rather than acute inflammatory responses. The cross-sectional design precludes causal inference, and classification based on consumption frequency rather than on quantitative intake limits the estimation of actual exposure. Additionally, because this was a remote study, blood and tissue analyses were not performed, and mechanistic interpretations related to mucin homeostasis relied on information in the literature rather than direct measurements. An imbalance in group sample sizes may also introduce statistical constraints; however, permutation-based tests and FDR corrections were applied to minimize potential bias. Future studies should incorporate quantitative dietary assessments and longitudinal designs to more clearly define the dose-response relationships between spicy food or alcohol intake and mucus layer function. With respect to spicy foods in particular, integrating quantitative intake data with refined assessments of individual sensitivity and sensory responses may reveal subtle physiological changes not captured by frequency-based classification alone. Moreover, integrated metabolomic and transcriptomic analyses using blood or tissue samples are required to directly verify whether the microbial functional changes observed in this study translate into epithelial injury signals or mucin regulatory pathways. Such approaches would provide a more precise understanding of the mechanisms by which dietary factors modulate gut barrier homeostasis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWith this study, we offer an analysis of the effects of alcohol and spicy food consumption on intestinal epithelial injury, gut microbial composition, and metabolic pathways in healthy adults. Alcohol intake was associated with elevated I-FABP levels, reflecting microepithelial injury, whereas spicy food intake promoted mucin metabolism and SCFA-linked compensatory pathways, supporting mucus layer stability. However, enriched amino acid metabolism in the DLSH group represents terminal mucin degradation processes that may predispose individuals to metabolic inflammation. In addition, the concurrent enrichment of Proteobacteria and Fusobacteriota in the DHSH group suggests a potential risk of dysbiosis through mucin degradation and endotoxin activation. Together, these findings suggest that spicy food exerts bidirectional regulation on gut mucus metabolism, maintaining mucin homeostasis at moderate levels but potentially aggravating mucus degradation and metabolic imbalance when combined with excessive alcohol exposure. Therefore, the intensity of spicy food intake and the level of alcohol exposure should be considered key determinants of mucin homeostasis and gut metabolic health.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eStudy Population and Design\u003c/h2\u003e\u003cp\u003eThis cross-sectional observational study was conducted between 2024 and 2025 and included 240 healthy Korean adults aged 19 years or older, recruited from Seoul and the surrounding metropolitan area. The study protocol was approved by the Institutional Review Board of Korea National Institute for Bioethics Policy (KoNIBP) (IRB No. P01-202411-02-004) and registered with the Clinical Research Information Service of Korea ( trial number KCT0010676). All participants were provided with a detailed explanation of the study objectives and procedures, and gave written informed consent prior to participation. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\u003cp\u003eHealthy adults were specifically recruited to exclude potential disturbances in the gut microbiome caused by diseases or medication and to clearly evaluate functional variations associated with dietary factors in a physiologically stable population. The exclusion criteria included a history of clinically diagnosed disease, current medication use, and no prior consumption of Buldak hot chicken flavor ramen (Samyang Foods Inc., Seoul, Korea), as this particular product was used as the benchmark for perception of spiciness. Eligibility screening was performed using self-report questionnaires and verification steps. To minimize sample degradation, recruitment was limited to participants residing within one–two hours of the study site.\u003c/p\u003e\u003ch2\u003eQuestionnaire Assessment\u003c/h2\u003e\u003cp\u003eTo control for individual factors that could influence the gut microbiome composition, participants completed a structured questionnaire comprising 24 items grouped into three categories: demographic information (six items), health information (five items), and dietary lifestyle factors (13 items). The questionnaire was expressly developed for this study and was partly based on published survey instruments used in previous microbiome and dietary studies involving Korean populations\u003csup\u003e61,62\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDemographic information included sex (male or female), age group (20s–60s and older), height (cm), weight (kg), and contact information. Health information covered disease diagnosis and medication status within the past three years (including gastrointestinal, hepatic, endocrine, cardiovascular, musculoskeletal, and neurological disorders) using a four-tier scale (no diagnosis, past diagnosis, under treatment, and currently medicated). Participants were also asked about recent antibiotic use (within the past month) and the intake of dietary supplements such as vitamins or probiotics. Stool-related questions included bowel movement frequency (≤ 2 times per week to \u0026gt; 1 time per day), stool form (Bristol Stool Scale, types 1–7), and the presence or absence of recurrent abdominal pain.\u003c/p\u003e\u003cp\u003eDietary and lifestyle items encompassed spicy food preference (10-point extended Likert scale), frequency of spicy food consumption (from never to ≥ 5 times/week), intensity of post-consumption abdominal discomfort (visual analog scale, 0–10), onset of abdominal pain after consumption (from none to \u0026gt; 1 d), and pain duration (from none to ≥ 2–3 days). Additional items assessed dietary tendencies such as meat/vegetable preference (1–10, lower scores indicating meat preference), consumption frequencies of high-fat, instant, or fast foods (from never to ≥ 5 times/week), alcohol consumption (from never to ≥ 5 times/week; drinking amount: 1 to ≥ 7 glasses per occasion), smoking (none to ≥ 1 pack/day), physical activity (0 to ≥ 5 times/week), and self-perceived health, mood, sleep quality, lifestyle regularity, and hygiene (5-point Likert scales).\u003c/p\u003e\u003cp\u003e To standardize the perception of “spicy food” across participants, Buldak hot chicken flavor ramen (Samyang Foods Inc., Seoul, Korea) was explicitly referenced in all related questions as the benchmark of spiciness. In accordance with IRB recommendations, individuals with no prior consumption experience of this product were excluded from participation.\u003c/p\u003e\u003ch2\u003eAlcohol Intake Assessment\u003c/h2\u003e\u003cp\u003eDaily alcohol intake (g/day) was calculated based on the self-reported drinking frequency and the average amount of alcohol consumed per occasion obtained from the questionnaire. The calculation was standardized using soju, the most commonly consumed alcoholic beverage in Korea (16% alcohol by volume), as a reference. The amount of pure ethanol contained in one glass (50 mL) was calculated as (50 mL × 0.16 × 0.789 g/mL [ethanol density]), equivalent to 6.3 g of alcohol. Accordingly, the daily alcohol intake of each participant was calculated as: 6.3 g × (the average number of glasses consumed per day).\u003c/p\u003e\u003cp\u003eDrinking frequency reported on a weekly or monthly basis was converted to daily frequency by dividing by 7 or 30, respectively, and multiplying by the average number of drinks per occasion to yield g/day. For nondrinkers, pseudo-counts were applied to prevent distortion by zero values during statistical analysis. This calculation method was adapted from the standardized procedures used in the Korea National Health and Nutrition Examination Survey (KNHANES)\u003csup\u003e63\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eGroup Classification\u003c/h2\u003e\u003cp\u003eFor spicy food consumption, participants whose intake frequency exceeded the median value (once per week) were classified into the high engagement group (SH), whereas those at or below the median were assigned to the low engagement group (SL). Alcohol intake groups were defined using the same approach: participants whose estimated alcohol consumption exceeded the median value (2.91 g/day) were categorized as the high-engagement group (DH) and those at or below the median as the low-engagement group (DL).\u003c/p\u003e\u003cp\u003eThe classification of spicy food and alcohol intake groups was based on the quartile distribution of each survey variable, with the median used as the cutoff. Participants whose values exceeded the median were placed in the high group, and those with corresponding values at or below the median were placed in the Low group. Accordingly, participants were assigned to one of the following four groups: DLSL (Drink-Low–Spicy-Low), DHSL (Drink-High–Spicy-Low), DLSH (Drink-Low–Spicy-High), and DHSH (Drink-High–Spicy-High).\u003c/p\u003e\u003ch2\u003eSample Collection and Storage\u003c/h2\u003e\u003cp\u003eThe participants self-collected fecal (approximately 3 g), urine (approximately 25 mL), and oral epithelial cell (approximately 0.1 mL) samples using sterile collection kits provided by the study team. Sampling was performed at home or in the workplace, and specimens were immediately transported via rapid local delivery to the research facility. All samples were stored at − 80°C until analysis to minimize degradation.\u003c/p\u003e\u003ch2\u003eDNA Extraction and Shotgun Metagenomic Sequencing\u003c/h2\u003e\u003cp\u003eGenomic DNA was extracted using the DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany). DNA quality and concentration were verified before shotgun metagenomic sequencing on the Illumina NovaSeq platform with 150-bp paired-end reads. The quality of the raw sequencing reads was initially assessed using FastQC to ensure the overall read quality before downstream processing.\u003c/p\u003e\u003cp\u003eAll steps for metagenome sequencing and preprocessing followed the bioBakery workflow (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/biobakery/biobakery/wiki/biobakery_workflows\u003c/span\u003e\u003cspan address=\"https://github.com/biobakery/biobakery/wiki/biobakery_workflows\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Subsequent preprocessing was performed using KneadData (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/biobakery/kneaddata\u003c/span\u003e\u003cspan address=\"https://github.com/biobakery/kneaddata\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which consisted of three main steps: (1) Quality trimming was performed using Trimmomatic, applying a sliding window approach to remove reads when four consecutive bases had Phred scores below 20. Reads shorter than 60 bp were excluded from analysis. (2) Adapter sequences were removed using Trimmomatic. (3) Host-derived and common sequencing components were decontaminated using Bowtie2 by aligning reads against the human reference genome (hg38).\u003c/p\u003e\u003ch2\u003eTaxonomic and Functional Profiling\u003c/h2\u003e\u003cp\u003eMicrobial taxonomic and functional profiles were generated using MetaPhlAn 4 (v4.1.1) and HUMAnN 3.6, respectively. Functional annotations included MetaCyc pathways and KEGG Orthologs (KOs). In the taxonomic analysis, the species-level genome bin (SGBs) output by MetaPhlAn 4 was converted into a GTDB-based taxonomy using the sgb_to_gtdb_profile.py script provided by the MetaPhlAn 4 suite. Downstream analysis retained only taxa or functional features (MetaCyc pathway or KO) that were present at a minimum relative abundance of 0.005% in all samples. Alpha diversity was calculated using the Shannon index and the phyloseq R package. For beta diversity, Bray–Curtis dissimilarity was calculated using the phyloseq R package based on relative abundance data. Principal coordinate analysis (PCoA) was performed using the Bray–Curtis distance matrix. To test for class-level differences in community composition, a permutational multivariate analysis of variance (PERMANOVA) was conducted using 999 permutations, with sample class (DLSL, DLSH, DHSL, and DHSH) as the main factor and BMI, sex, and age as covariates.\u003c/p\u003e\u003ch2\u003eLinear Mixed Model Analysis\u003c/h2\u003e\u003cp\u003eTo identify microbial taxa and functional features associated with metadata, linear mixed models were fitted using the MaAsLin2 R package. The fixed effects included the sample class, BMI, sex, and age. Only taxa and functional features (pathways or gene families) with a prevalence of at least 5% across all samples were included. P-values were corrected for multiple testing using the Benjamini-Hochberg method, as implemented in MaAsLin2.\u003c/p\u003e\u003ch2\u003eMicrobial Network and Correlation Analysis\u003c/h2\u003e\u003cp\u003eTo explore the associations between microbial species, functional features, and metadata, Spearman’s rank correlations (for metadata) and Pearson’s correlations (for continuous microbial profiles) were computed. Microbial interaction networks were visualized using Cytoscape (v3.10.3).\u003c/p\u003e\u003ch2\u003eMeasurement of Urinary Biomarkers\u003c/h2\u003e\u003cp\u003eUrinary samples were analyzed to quantify intestinal epithelial injury markers, including intestinal fatty acid–binding protein (I-FABP) and liver fatty acid–binding protein (L-FABP), as well as inflammatory markers interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α). Intestinal epithelial injury markers were measured using the Human I-FABP and L-FABP ELISA Kits (R\u0026amp;D Systems, MN, USA), and inflammatory markers were quantified using the Human IL-6 and TNF-α ELISA Kits (R\u0026amp;D Systems, MN, USA).\u003c/p\u003e\u003cp\u003eUrine samples were thawed gradually at 4°C overnight prior to analysis, and a single freeze–thaw cycle was performed to ensure consistency across all measurements. Before quantification, samples were centrifuged at 500 g for 15 min at 4°C, and the supernatant was collected to remove debris. All measurements and quantification were performed according to the manufacturer’s ELISA protocol.\u003c/p\u003e\u003ch2\u003eAnalysis of TRPV1 Expression in Oral Epithelial Cells\u003c/h2\u003e\u003cp\u003eTRPV1 expression in oral epithelial cells was measured by collecting tongue epithelial samples and quantifying TRPV1 and GAPDH levels using the Human TRPV1 ELISA Kit (Antibodies.com, Cambridge, UK) and Human GAPDH ELISA Kit (Invitrogen, CA, USA). The relative TRPV1 expression was calculated as the TRPV1-to-GAPDH ratio for intergroup comparisons.\u003c/p\u003e\u003cp\u003eThe collected epithelial cells were dissolved in 1 mL cold radioimmunoprecipitation assay (RIPA) lysis buffer (Thermo Fisher Scientific, MA, USA) containing protease inhibitors. Cell disruption and protein extraction were performed using a bead-beating grinder (MP Biomedicals, CA, USA) at 4 m/s for 20 s and repeated twice using Lysing Matrix E tubes. Samples were centrifuged at 500 g for 15 min at 4°C to remove debris, and all procedures were conducted on ice. TRPV1 and GAPDH levels were quantified using ELISA, according to the manufacturer’s protocols, and TRPV1 values were normalized to GAPDH levels to account for sample variability.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted in R (The R Project for Statistical Computing; v4.4.2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using RStudio (v2024.12.1.563). For multiple comparisons involving microbiome and functional gene analyses, false discovery rate (FDR) correction was performed using the Benjamini–Hochberg method. Following the analytical threshold applied by Huang et al. (2024)\u003csup\u003e64\u003c/sup\u003e, an adjusted FDR \u0026lt; 0.25 was considered statistically significant for exploratory multi-omics association analyses.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSCFA\u0026nbsp; \u0026nbsp;\u0026nbsp;Short-chain fatty acid\u003c/p\u003e\n\u003cp\u003eI-FABP\u0026nbsp;\u0026nbsp;Intestinal-fatty acid-binding protein\u003c/p\u003e\n\u003cp\u003eL-FABP Liver-fatty acid-binding protein\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha;\u0026nbsp; \u0026nbsp;Tumor necrosis factor-\u0026alpha;\u003c/p\u003e\n\u003cp\u003eIL-6\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Interleukin-6\u003c/p\u003e\n\u003cp\u003eTRPV1\u0026nbsp;\u0026nbsp;Transient receptor potential vanilloid 1\u003c/p\u003e\n\u003cp\u003eKO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;KEGG ortholog\u003c/p\u003e\n\u003cp\u003eGH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Glycoside hydrolase\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Body mass index\u003c/p\u003e\n\u003cp\u003ePERMANOVA \u0026nbsp; Permutational multivariate analysis of variance\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThis work was supported by internal research funding from Samyang Foods Inc. The funder had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or the decision to submit the article for publication. All authors are employees of Samyang Foods Inc.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by internal research funding from Samyang Foods Inc. The funder had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or the decision to submit the article for publication.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eU. M., Jinwook Kim, and W. L. conceptualized the study. Jihyun Kim developed the analytical pipeline, performed the statistical analyses, and prepared all visualizations. U.M., H.J., and Jihyun Kim curated and validated the metagenomic data. Jinwook Kim, H.O., and S.A. conducted the biomarker analyses. U.M. and Jihyun Kim interpreted the results and wrote the manuscript with input from all the authors. H.S. and W.L. supervised the study. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the Research Center of the Mitomics Institute, Samyang Foods Inc.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe metagenomic sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under the accession number PRJNA1366038. During the peer-review process, the data are accessible to editors and reviewers via the following confidential reviewer access link provided by NCBI:https://dataview.ncbi.nlm.nih.gov/object/PRJNA1366038?reviewer=qnor0m05t9ccvactnnah81gcui.Due to the ethical restrictions imposed by the Institutional Review Board of the Korea National Institute for Bioethics Policy (KoNIBP) (IRB No. P01-202411-02-004), the individual-level demographic and questionnaire data cannot be made publicly available. Anonymized data may be obtained from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePaone, P. \u0026amp; Cani, P. D. Mucus barrier, mucins and gut microbiota: the expected slimy partners? \u003cem\u003eGut\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 2232\u0026ndash;2243 (2020).\u003c/li\u003e\n \u003cli\u003eLuis, A. S. \u0026amp; Hansson, G. C. Intestinal mucus and their glycans: A habitat for thriving microbiota. \u003cem\u003eCell Host \u0026amp; Microbe\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1087\u0026ndash;1100 (2023).\u003c/li\u003e\n \u003cli\u003eShen, W. et al. Anti-obesity effect of capsaicin in mice fed with high-fat diet is associated with an increase in population of the gut bacterium Akkermansia muciniphila. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 272 (2017).\u003c/li\u003e\n \u003cli\u003eKumar, V. et al. Mucin secretory action of capsaicin prevents high fat diet-induced gut barrier dysfunction in C57BL/6 mice colon. \u003cem\u003eBiomed. Pharmacother.\u003c/em\u003e \u003cstrong\u003e145\u003c/strong\u003e, 112452 (2022).\u003c/li\u003e\n \u003cli\u003eWang, Y., Tang, C., Tang, Y., Yin, H. \u0026amp; Liu, X. Capsaicin has an anti-obesity effect through alterations in gut microbiota populations and short-chain fatty acid concentrations. \u003cem\u003eFood Nutr. Res.\u003c/em\u003e \u003cstrong\u003e64\u003c/strong\u003e, 10\u0026ndash;29219 (2020).\u003c/li\u003e\n \u003cli\u003eXiang, Y. et al. Beneficial effects of dietary capsaicin in gastrointestinal health and disease. \u003cem\u003eExp. Cell Res.\u003c/em\u003e \u003cstrong\u003e417\u003c/strong\u003e, 113227 (2022).\u003c/li\u003e\n \u003cli\u003eZhang, W. et al. The effects of capsaicin intake on weight loss among overweight and obese subjects: a systematic review and meta-analysis of randomised controlled trials. \u003cem\u003eBr. J. Nutr.\u003c/em\u003e \u003cstrong\u003e130\u003c/strong\u003e, 1645\u0026ndash;1656 (2023).\u003c/li\u003e\n \u003cli\u003eXiang, Q. et al. Capsaicin\u0026mdash;the spicy ingredient of chili peppers: A review of the gastrointestinal effects and mechanisms. \u003cem\u003eTrends Food Sci. Technol.\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 755\u0026ndash;765 (2021).\u003c/li\u003e\n \u003cli\u003eXiang, Q. et al. Capsaicin, the spicy ingredient of chili peppers: effects on gastrointestinal tract and composition of gut microbiota at various dosages. \u003cem\u003eFoods\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 686 (2022).\u003c/li\u003e\n \u003cli\u003eHammer, J. \u0026amp; Vogelsang, H. Characterization of sensations induced by capsaicin in the upper gastrointestinal tract. \u003cem\u003eNeurogastroenterol. Motil.\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 279\u0026ndash;287 (2007).\u003c/li\u003e\n \u003cli\u003eLejeune, M. P., Kovacs, E. M. \u0026amp; Westerterp-Plantenga, M. S. Effect of capsaicin on substrate oxidation and weight maintenance after modest body-weight loss in human subjects. \u003cem\u003eBr. J. Nutr.\u003c/em\u003e \u003cstrong\u003e90\u003c/strong\u003e, 651\u0026ndash;659 (2003).\u003c/li\u003e\n \u003cli\u003eLv, J. et al. Consumption of spicy foods and total and cause specific mortality: population based cohort study. \u003cem\u003eBMJ\u003c/em\u003e \u003cstrong\u003e351\u003c/strong\u003e (2015).\u003c/li\u003e\n \u003cli\u003eBonaccio, M. et al. Chili pepper consumption and mortality in Italian adults. \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 3139\u0026ndash;3149 (2019).\u003c/li\u003e\n \u003cli\u003eAlatorre-Cruz, J. M. et al. Traditional Mexican food: Phenolic content and public health relationship. \u003cem\u003eFoods\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1233 (2023).\u003c/li\u003e\n \u003cli\u003eWang, M., Huang, W. \u0026amp; Xu, Y. Effects of spicy food consumption on overweight/obesity, hypertension and blood lipids in China: a meta-analysis of cross-sectional studies. \u003cem\u003eNutr. J.\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 29 (2023).\u003c/li\u003e\n \u003cli\u003eKnight, R. et al. Best practices for analysing microbiomes. \u003cem\u003eNat. Rev. Microbiol.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 410\u0026ndash;422 (2018).\u003c/li\u003e\n \u003cli\u003eJovel, J. et al. Characterization of the gut microbiome using 16S or shotgun metagenomics. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 459 (2016).\u003c/li\u003e\n \u003cli\u003eFranzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. \u003cem\u003eNat. Methods\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 962\u0026ndash;968 (2018).\u003c/li\u003e\n \u003cli\u003eSosnowski, K. \u0026amp; Przybyłkowski, A. Ethanol-induced changes to the gut microbiome compromise the intestinal homeostasis: A review. \u003cem\u003eGut Microbes\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 2393272 (2024).\u003c/li\u003e\n \u003cli\u003eBishehsari, F. et al. Alcohol and gut-derived inflammation. \u003cem\u003eAlcohol Res. Curr. Rev.\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 163 (2017).\u003c/li\u003e\n \u003cli\u003eStructure, function and diversity of the healthy human microbiome. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e486\u003c/strong\u003e, 207\u0026ndash;214 (2012).\u003c/li\u003e\n \u003cli\u003eParada Venegas, D. et al. Short chain fatty acids (SCFAs)-mediated gut epithelial and immune regulation and its relevance for inflammatory bowel diseases. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 277 (2019).\u003c/li\u003e\n \u003cli\u003eSingh, V. et al. Butyrate producers, \u0026ldquo;the sentinel of gut\u0026rdquo;: Their intestinal significance with and beyond butyrate, and prospective use as microbial therapeutics. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1103836 (2023).\u003c/li\u003e\n \u003cli\u003eZhu, Y. et al. Exploration of the Muribaculaceae family in the gut microbiota: diversity, metabolism, and function. \u003cem\u003eNutrients\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 2660 (2024).\u003c/li\u003e\n \u003cli\u003eZou, Y. et al. Butyribacter intestini gen. nov., sp. nov., a butyric acid-producing bacterium of the family Lachnospiraceae isolated from human faeces, and reclassification of Acetivibrio ethanolgignens as Acetanaerobacter ethanolgignens gen. nov., comb. nov. \u003cem\u003eSyst. Appl. Microbiol.\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 126201 (2021).\u003c/li\u003e\n \u003cli\u003eDeng, J. et al. Fusobacterium mortiferum and its metabolite 5-aminovaleric acid promote the development of colorectal cancer in obese individuals through Wnt/\u0026beta;-catenin pathway by DKK2. \u003cem\u003eGut Microbes\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 2502138 (2025).\u003c/li\u003e\n \u003cli\u003eRashidi, A. et al. No evidence for colonization of oral bacteria in the distal gut in healthy adults. \u003cem\u003ePNAS\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e, e2114152118 (2021).\u003c/li\u003e\n \u003cli\u003eShi, H. et al. The gut microbiome as mediator between diet and its impact on immune function. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 5149 (2022).\u003c/li\u003e\n \u003cli\u003eAnthamatten, L. et al. Stratification of human gut microbiomes by succinotype is associated with inflammatory bowel disease status. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 186 (2024).\u003c/li\u003e\n \u003cli\u003ePetrov, V. et al. Analysis of gut microbiota in patients with Parkinson\u0026rsquo;s disease. \u003cem\u003eBull. Exp. Biol. Med.\u003c/em\u003e \u003cstrong\u003e162\u003c/strong\u003e, 734\u0026ndash;737 (2017).\u003c/li\u003e\n \u003cli\u003eLiang, X. et al. Gut microbiome, cognitive function and brain structure: a multi-omics integration analysis. \u003cem\u003eTransl. Neurodegener.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 49 (2022).\u003c/li\u003e\n \u003cli\u003eSakamoto, M. et al. Draft genome sequence of Lawsonibacter asaccharolyticus\u0026hellip; Genome Announc. \u003cstrong\u003e6\u003c/strong\u003e, 10\u0026ndash;1128 (2018).\u003c/li\u003e\n \u003cli\u003eSelma, M. V. et al. Description of urolithin production capacity from ellagic acid of two human intestinal Gordonibacter species. \u003cem\u003eFood Funct\u003c/em\u003e. \u003cstrong\u003e5\u003c/strong\u003e, 1779\u0026ndash;1784 (2014).\u003c/li\u003e\n \u003cli\u003eGeirnaert, A. et al. Butyricicoccus pullicaecorum, a butyrate producer with probiotic potential, is intrinsically tolerant to stomach and small intestine conditions. \u003cem\u003eAnaerobe\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 70\u0026ndash;74 (2014).\u003c/li\u003e\n \u003cli\u003eMoon, Y. et al. Intracellular lipid accumulation inhibitory effect of Weissella koreensis OK1-6\u0026hellip; J. \u003cem\u003eAppl. Microbiol.\u003c/em\u003e \u003cstrong\u003e113\u003c/strong\u003e, 652\u0026ndash;658 (2012).\u003c/li\u003e\n \u003cli\u003eChen, G. et al. Gut microbiota dysbiosis: The potential mechanisms by which alcohol disrupts gut and brain functions. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 916765 (2022).\u003c/li\u003e\n \u003cli\u003eGloor, G. B. et al. Microbiome datasets are compositional: and this is not optional. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 2224 (2017).\u003c/li\u003e\n \u003cli\u003eGlover, J. S., Ticer, T. D. \u0026amp; Engevik, M. A. Characterizing the mucin-degrading capacity of the human gut microbiota. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 8456 (2022).\u003c/li\u003e\n \u003cli\u003eZhang, J. et al. Altered synthesis of genes associated with short-chain fatty acids in the gut of patients with atrial fibrillation. \u003cem\u003eBMC Genomics\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 634 (2021).\u003c/li\u003e\n \u003cli\u003eLabarthe, S. et al. Four functional profiles for fibre and mucin metabolism in the human gut microbiome. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 231 (2023).\u003c/li\u003e\n \u003cli\u003ede Jong, W.\u0026ndash;J. et al. The effect of acute alcohol intoxication on gut wall integrity in healthy male volunteers. \u003cem\u003eAlcohol\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 65\u0026ndash;70 (2015).\u003c/li\u003e\n \u003cli\u003eKokesova, A. et al. The intestinal fatty acid-binding protein as a marker for intestinal damage in gastroschisis. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, e0210797 (2019).\u003c/li\u003e\n \u003cli\u003eVoth, M. et al. I-FABP and L-FABP are early markers for abdominal injury\u0026hellip; Clin. Chem. \u003cem\u003eLab. Med.\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 771\u0026ndash;780 (2015).\u003c/li\u003e\n \u003cli\u003eChen, H. et al. Urinary interleukin-6 as a diagnostic and prognostic marker of acute kidney injury in dogs. \u003cem\u003eVet. Rec.\u003c/em\u003e \u003cstrong\u003e191\u003c/strong\u003e, no\u0026ndash;no (2022).\u003c/li\u003e\n \u003cli\u003eTakahashi, N. et al. Epithelial TRPV1 channels: expression, function, and pathogenicity in the oral cavity. \u003cem\u003eJ. Oral Biosci.\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 235\u0026ndash;241 (2020).\u003c/li\u003e\n \u003cli\u003eBiancotto, A. et al. Baseline levels and temporal stability of 27 multiplexed serum cytokine concentrations\u0026hellip; PLoS One \u003cstrong\u003e8\u003c/strong\u003e, e76091 (2013).\u003c/li\u003e\n \u003cli\u003eYamaguchi, M. \u0026amp; Yamamoto, K. Mucin glycans and their degradation by gut microbiota. \u003cem\u003eGlycoconj. J.\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 493\u0026ndash;512 (2023).\u003c/li\u003e\n \u003cli\u003eMarcelino, V. R. et al. Disease-specific loss of microbial cross-feeding interactions in the human gut. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 6546 (2023).\u003c/li\u003e\n \u003cli\u003eHerrnreiter, C. J. et al. Bacterial dysbiosis and decrease in SCFA correlate with intestinal inflammation following alcohol intoxication and burn injury. eGastroenterology \u003cstrong\u003e3\u003c/strong\u003e (2025).\u003c/li\u003e\n \u003cli\u003eKumar, V. et al. Role of TRPV1 in colonic mucin production and gut microbiota profile. \u003cem\u003eEur. J. Pharmacol.\u003c/em\u003e \u003cstrong\u003e888\u003c/strong\u003e, 173567 (2020).\u003c/li\u003e\n \u003cli\u003eLi, Z. et al. Fusobacterium in the microbiome: from health to disease across the oral\u0026ndash;gut axis and beyond. \u003cem\u003enpj Biofilms Microbiomes\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 200 (2025).\u003c/li\u003e\n \u003cli\u003eShin, N.\u0026ndash;R., Whon, T. W. \u0026amp; Bae, J.\u0026ndash;W. Proteobacteria: microbial signature of dysbiosis in gut microbiota. \u003cem\u003eTrends Biotechnol\u003c/em\u003e. \u003cstrong\u003e33\u003c/strong\u003e, 496\u0026ndash;503 (2015).\u003c/li\u003e\n \u003cli\u003eTang, M. et al. Correlation assessment among clinical phenotypes\u0026hellip; Hum. \u003cem\u003eMutat.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 1107\u0026ndash;1115 (2012).\u003c/li\u003e\n \u003cli\u003eDebouck, C. et al. Structure of the galactokinase gene of Escherichia coli\u0026hellip; Nucleic Acids Res. \u003cstrong\u003e13\u003c/strong\u003e, 1841\u0026ndash;1853 (1985).\u003c/li\u003e\n \u003cli\u003eBraccia, D. J. et al. Hydrogen sulfide (H2S) via cysteine degradation is ubiquitous in the human gut microbiome. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 705583 (2021).\u003c/li\u003e\n \u003cli\u003eLuo, W. et al. Microbial assimilatory sulfate reduction-mediated H2S: An overlooked role in Crohn\u0026rsquo;s disease. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 152 (2024).\u003c/li\u003e\n \u003cli\u003eChen, Y. \u0026amp; Fang, J.\u0026ndash;Y. Colonic microbiota amino acid metabolism in gut health regulation. \u003cem\u003eCell Insight\u003c/em\u003e 100227 (2025).\u003c/li\u003e\n \u003cli\u003eLi, T.\u0026ndash;T. et al. Microbiota metabolism of intestinal amino acids impacts host nutrient homeostasis and physiology. \u003cem\u003eCell Host Microbe\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 661\u0026ndash;675 (2024).\u003c/li\u003e\n \u003cli\u003eSamuelson, D. R. et al. Alcohol-associated intestinal dysbiosis impairs pulmonary defense against Klebsiella pneumoniae. \u003cem\u003ePLoS Pathog\u003c/em\u003e. \u003cstrong\u003e13\u003c/strong\u003e, e1006426 (2017).\u003c/li\u003e\n \u003cli\u003eShen, M. et al. Alcohol-induced gut microbiome dysbiosis enhances the colonization of Klebsiella pneumoniae. \u003cem\u003emSystems\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e00052\u0026ndash;24 (2024).\u003c/li\u003e\n \u003cli\u003eFalony, G. et al. Population-level analysis of gut microbiome variation. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e352\u003c/strong\u003e, 560\u0026ndash;564 (2016).\u003c/li\u003e\n \u003cli\u003eShin, J.\u0026ndash;H. et al. Differential effects of typical Korean versus American-style diets on gut microbial composition\u0026hellip; Nutrients \u003cstrong\u003e11\u003c/strong\u003e, 2450 (2019).\u003c/li\u003e\n \u003cli\u003eHong, J. W., Noh, J. H. \u0026amp; Kim, D.\u0026ndash;J. High-risk alcohol consumption in Korean adults. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, e0175299 (2017).\u003c/li\u003e\n \u003cli\u003eHuang, K. D. et al. Dietary selective effects manifest in the human gut microbiota from species composition to strain genetic makeup. \u003cem\u003eCell Rep\u003c/em\u003e.\u0026nbsp;\u003cstrong\u003e43\u003c/strong\u003e (2024).\u003cbr\u003e \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gut microbiome, Mucin metabolism, Spicy food intake, Alcohol consumption, Shotgun metagenomics, Short-chain fatty acids, Intestinal epithelial integrity, Intestinal-fatty acid–binding protein, Liver-fatty acid–binding protein","lastPublishedDoi":"10.21203/rs.3.rs-8419849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8419849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe intestinal mucus layer provides a primary barrier between the gut microbiota and the epithelium. Capsaicin, a major component of spicy foods, has been reported to enhance \u003cem\u003emuc2\u003c/em\u003e expression and mucus secretion; however, evidence from preclinical and clinical studies remains inconsistent. How spicy food interacts with external factors such as alcohol to influence mucus-layer homeostasis also remains insufficiently understood. Using shotgun metagenomics, we investigated the associations between spicy food intake, alcohol consumption, intestinal fatty acid\u0026ndash;binding protein (I-FABP), liver fatty acid\u0026ndash;binding protein (L-FABP), gut microbial composition, and functional pathways in 229 healthy Korean adults. Alcohol intake was positively correlated with urinary I-FABP (ρ\u0026thinsp;=\u0026thinsp;0.26), indicating mild epithelial stress, whereas spicy food intake was not associated with either FABP biomarker. Individuals who consumed highly spicy food exhibited an increased abundance of short-chain fatty acids (SCFA)-producing and mucin-metabolizing taxa, including \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eCoprococcus\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e, along with higher activity of mucin-degradation and SCFA-production pathways. Individuals who consumed a high level of alcohol showed stronger enrichment of mucin-degrading taxa, with reduced SCFA flux and increased abundance of Proteobacteria and Fusobacteriota. Cross-classified dietary groups revealed distinct mucin and SCFA gene activity patterns. Notably, the DHSH group displayed concurrent elevation of mucin turnover and SCFA production with indications of dysbiosis. These findings suggest that spicy food may modulate mucus layer metabolism in a context-dependent manner, whereas alcohol more consistently perturbs mucin\u0026ndash;SCFA networks and epithelial integrity.\u003c/p\u003e","manuscriptTitle":"Spicy Food Intake and Dietary Factors Shape the Gut Microbiome and Metabolism of Mucin and Short-Chain Fatty Acids in Healthy Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 19:11:38","doi":"10.21203/rs.3.rs-8419849/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-31T08:19:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T14:17:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184108610330577064633156357941883735511","date":"2026-03-09T01:46:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59482930204093848428819674401899726008","date":"2026-03-07T18:04:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T15:20:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328066120523292605961311637179927721215","date":"2026-02-10T15:53:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299872650325501676368624599229177330311","date":"2026-01-28T04:18:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T08:01:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-21T07:35:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-30T15:00:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T01:31:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-29T01:24:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d1914831-f4b1-4e40-982e-9808d99cb007","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61489483,"name":"Biological sciences/Biochemistry"},{"id":61489484,"name":"Health sciences/Gastroenterology"},{"id":61489485,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-05-13T03:55:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 19:11:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8419849","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8419849","identity":"rs-8419849","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}