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Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in children and adolescents. The gut microbiota plays an important role in the pathophysiology of NAFLD through the gut–liver axis. Therefore, we aimed to investigate the genus and species of gut microbiota and their functions in children and adolescents with NAFLD. From May 2017 to July 2018, a total of 58 children and adolescents, including 27 abnormal weight (AW) (obese) NAFLD patients, 16 AW non-NAFLD children, and 15 healthy children, were enrolled in this study at Shenzhen Children’s Hospital. All of them underwent magnetic resonance spectroscopy (MRS) to quantify the liver fat fraction. Stool samples were collected and analysed with metagenomics. According to body mass index (BMI) and MRS proton density fat fraction (MRS-PDFF), we divided the participants into BMI groups, including the AW group (n = 43) and the Lean group (n = 15); MRS groups, including the NAFLD group (n = 27) and the Control group (n = 31); and BMI-MRS 3 groups, including NAFLD_AW (AW children with NAFLD) (n = 27), Ctrl_AW (n = 16) (AW children without NAFLD) and Ctrl_Lean (n = 15). There was no difference in sex or age among those groups ( p  > 0.05). In the BMI groups, at the genus level, Dialister , Akkermansia , Odoribacter, and Alistipes exhibited a significant decrease in AW children compared with the Lean group. At the species level, Megamonas hypermegale was increased in the AW group, while Akkermansia muciniphila , Dialister invisus , Alistipes putredinis , Bacteroides massiliensis , Odoribacter splanchnicus, and Bacteroides thetaiotaomicron were decreased in AW children, compared to the Lean group. Compared with the Control group, the genus Megamonas , the species of Megamonas hypermegale and Megamonas rupellensis, increased in the NAFLD group. Furthermore, the genus Megamonas was enriched in the NAFLD_AW group, while Odoribacter , Alistipes , Dialister, and Akkermansia were depleted compared with the Ctrl_Lean or Ctrl_AW group at the genus level. Megamonas hypermegale and Megamonas rupellensis exhibited a significant increase in NAFLD_AW children compared with the Ctrl_Lean or Ctrl_AW group at the species level. Compared with healthy children, the pathways of P461-PWY contributed by the genus Megamonas were significantly increased in NAFLD_AW. We found that compared to healthy children, the genus Megamonas was enriched, while Megamonas hypermegale and Megamonas rupellensis were enriched at the species level in children and adolescents with NAFLD. This indicates that the NAFLD status and/or diet associated with NAFLD patients might lead to the enrichment of the genus Megamonas or Megamonas  species.

BackgroundPrehypertension (pHT) is a well-established risk factor for the development of cardiovascular diseases. Adiposity and imbalances in microbiota play a critical role in the development of prehypertension. However, how adiposity influences prehypertension through gut microbiota remains unclear.MethodsA total of 192 participants from central China were categorized into three groups based on body mass index (BMI) and blood pressure: healthy (HC), adiposity with normal tension (Ad-NT), and adiposity with pHT (Ad-pHT). Adiposity was defined as a BMI ≥ 24, and prehypertension as a systolic blood pressure of 130–139 mmHg or a diastolic blood pressure of 80–89 mmHg. Shotgun metagenomic sequencing was performed on fecal samples. Taxonomic and functional annotations were obtained using the bioBakery3 pipeline (MetaPhlAn and HUMAnN), and STAMP was used to analyze differences in microbial species and metabolic pathways across groups.ResultsFollowing the filtering criteria of prevalence < 10% and average relative abundance < 0.005%, 145 bacterial species and 348 metabolic pathways were obtained. We found thirteen differential species, including Parabacteroides distasonis, Megamonas funiformis, and Megamonas hypermegale. P. distasonis exhibited a reduction during the transition from adiposity to prehypertension (HC vs Ad-pHT, P < 0.05; Ad-NT vs Ad-pHT, P < 0.05) (IDDF2025-ABS-0024 figure 1. Differentially abundant gut microbiota among groups). Additionally, 67 metabolic pathways differed between the HC and Ad-pHT. P. distasonis was involved in 17 pathways, especially the short-chain fatty acid production via the pyruvate fermentation to propanoate I pathway. Moreover, two additional species (M. funiformis and M. hypermegale) might contribute to adiposity through fatty acid synthesis pathways.Abstract IDDF2025-ABS-0024 Figure 1Differentially abundant gut microbiota among groups[Figure omitted. See PDF]ConclusionsP. distasonis is a prehypertension-specific species and may drive the transition from adiposity to prehypertension through the biosynthesis of short-chain fatty acids. These findings highlight the potential of targeting gut microbiota to modulate this transition and mitigate the progression of prehypertension.

Abstract Background Neonatal diarrhea accounts for more than 50% of total deaths in dairy calves. Few population-based studies of cattle have investigated how the microbiota is impacted during diarrhea. Objectives To characterize the fecal microbiota and predict the functional potential of the microbial communities in healthy and diarrheic calves. Methods Fifteen diarrheic calves between the ages of 1 and 30 days and 15 age-matched healthy control calves were enrolled from 2 dairy farms. The Illumina MiSeq sequencer was used for high-throughput sequencing of the V4 region of the 16S rRNA gene (Illumina, San Diego, CA). Results Significant differences in community membership and structure were identified among healthy calves from different farms. Differences in community membership and structure also were identified between healthy and diarrheic calves within each farm. Based on linear discriminant analysis effect size (LEfSe), the genera Bifidobacterium, Megamonas, and a genus of the family Bifidobacteriaceae were associated with health at farm 1, whereas Lachnospiraceae incertae sedis, Dietzia and an unclassified genus of the family Veillonellaceae were significantly associated with health at farm 2. The Phylogenetic Investigation of Communities Reconstruction of Unobserved States (PICRUSt) analysis indicated that diarrheic calves had decreased abundances of genes responsible for metabolism of various vitamins, amino acids, and carbohydrate. Clinical Relevance The fecal microbiota of healthy dairy calves appeared to be farm specific as were the changes observed during diarrhea. The differences in microbiota structure and membership between healthy and diarrheic calves suggest that dysbiosis can occur in diarrheic calves and it is associated with changes in predictive metagenomic function.

Background Helicobacter pylori (H. pylori) is a gram-negative bacterium and dominant microorganism in gastric. In this study, we aimed to analyze the infection rate of H.pylori and risk factors, as well as the gut microbiota composition in young individuals.MethodsRegular dietary habits were ascertained through the administration of a food-frequency questionnaire. To scremming the positive, all participants were tested by using the H.pylori Antigen Detection Kit. And fecal samples were collected from two groups after a 1:1 height, weight, sex and age match. The composition of gut microbiota was assessed by 16S rRNA sequencing. To investigate the relationships between dietary factors and α- and β-diversity indices, as well as relative taxa abundances, we employed statistical analyses including Spearman correlations, permutational ANOVAs, and multivariate linear models.ResultsParticipants included 26.23% (214/816) males and 73.77% (602/816) females, with an average age of 22 ± 8 years, and an average BMI of 20.27±2.67 kg/m2. The fecal antigen test showed that the infection rate of H.pylori was 7.23% (59/816). On the dimension of abdomen pain, esophageal reflux, indigestion and diarrhea were different between groups of H. pylori positive (HP) and H. pylori negative (HN). However, there was no significant between the two groups based on the questionnaires. 16S rRNA gene amplicon sequencing shows no significant differences between the HP and HN groups in terms of alpha diversity, including the Ace and Shannon indices, but the HP group has no statistical decrease. We identified that the Holdemanella, Megamonas and Catenibacterium were associated with the severity of gastrointestinal symptoms. Furthermore, our results indicated that the consumption of pickled foods was negatively correlated with the diversity of gut microbiota.Abstract IDDF2024-ABS-0242 Figure 1Comparison of relative taxa abundance between HP and HN.Abstract IDDF2024-ABS-0242 Figure 2Compositional diversity of the gut microbiome.Abstract IDDF2024-ABS-0242 Figure 3Differences in each dimension of GSRS score between Helicobacter pylori infection group.Abstract IDDF2024-ABS-0242 Figure 4Significant microbial associations of demographics individual diet and gastrointestinal score.ConclusionsThe infection rate of H.pylori was 7.23%. There were no significant differences between the two groups in lifestyle, eating preferences, and taste habits. But the HP group apparently had more severe gastric symptoms. Moreover, the microbial diversity was similar between the two groups. We further explored that Holdemanella, Megamonas and Catenibacterium were associated with the severity of gastrointestinal symptoms and the consumption of pickled foods was negatively correlated with the diversity of gut microbiota.

BackgroundPsychological stress, which is associated with poor sleep quality, has a profound effect on the gut microbiome. To better elucidate the gut microbiome changes associated with psychological stress, we measured levels of stress and sleep quality among the healthy stool donors recruited through a national campaign, and further assessed their relationship with the gut microbiome.MethodsThe gut microbial composition of 272 adults residing in Singapore was determined by 16S rRNA V3-V4 amplicon sequencing. Stress and distress levels of participants were assessed using the Perceived Stress Scale (PSS) and Kessler Psychological Distress Scale (K10) respectively, while sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Multivariate statistical data analyses were conducted to explore the link between the gut microbiome, stress, distress and sleep quality.ResultsA significant proportion of stool donors exhibited low distress (84.2%) levels. However, nearly one-third of participants reported high-stress levels (33.5%) and poor sleep quality (44.8%) (IDDF2021-ABS-0165 Figure A. Stacked bar plot showing the number of participants under each category of (i) K10 , (ii) PSS and (iii) PSQI). The overall variation in the gut microbiome composition amongst stool donors was predominantly dependent on age and body mass index (BMI), rather than psychosocial stress and sleep quality (IDDF2021-ABS-0165 Figure B. PERMANOVA results of main factors in this study). However, four microbial features (i.e., Streptococcus, Erysipelotrichaceae UCG-003, Sutterella and Parabacteroides) were found to be positively associated with severe distress, of which Erysipelotrichaceae UCG-003 was more abundant in both distressed and stressed donors (DESeq2, p<0.05; (IDDF2021-ABS-0165 Figure C. Bar plot showing differentially abundant genera identified between individuals falling under (i) severe (red) vs. well (blue) category of distress and (ii) high (pink) vs. low (light blue - not detected) stress)). Besides, the abundance of Megamonas in the gut was negatively correlated with PSQI after covariates (i.e., age and BMI) adjustment (IDDF2021-ABS-0165 Figure D. Correlation matrix showing the association between differentially abundant bacterial genera and K10, PSS and PSQI categories after covariates adjustment (based on Pearson’s correlation; p<0.05)), suggesting the lowered abundance of Megamonas observed in distressed individuals may be related to their sleep quality.Abstract IDDF2021-ABS-0165 Figure 1ConclusionsOne-third of the participants exhibited high stress/distress levels and almost half poor sleep. In this study, we identified several bacterial genera that are significantly altered in the gut microbiome of stressed individuals, with Erysipelotrichaceae UCG-003 being consistently increased across people with high stress and severe distress levels. Erysipelotrichaceae UCG-003 was previously reported to be elevated in individuals with sleep deprivation. Our findings also suggest the need to factor for stress and sleep quality when studying the gut microbiome of healthy individuals.

Accumulated evidence supports the beneficial role of inulin in alleviating metabolic dysfunction-associated fatty liver disease (MAFLD) by modulating gut microbiota. However, the underlying mechanisms are not fully understood. Here we used high-fat diet (HFD)-induced laying hen model of MAFLD to investigate the effect of inulin on ameliorating MAFLD and found that the inulin-enriched Megamonas genus was inversely correlated with hepatic steatosis-related parameters. Oral administration of a newly isolated commensal bacterium by culturomics, M. funiformis CML154, to HFD-fed hens and mice ameliorated MAFLD, changed liver gene expression profiles, and increased intestinal propionate concentration. Further evidence demonstrated that the anti-MAFLD effect of M. funiformis CML154 is attributed to propionate-mediated activation of the APN - AMPK - PPARα signaling pathway, thereby inhibiting fatty acid de novo synthesis and promoting β-oxidation. These findings establish the causal relationships among inulin, M. funiformis , and MAFLD, and suggest that M. funiformis CML154 is a probiotic candidate for preventative or therapeutic intervention of MAFLD.

Osteoporosis (OP) is a common metabolic bone disease characterized by low bone mass and microstructural deterioration of bone. Changes in the composition and structure of gut microbiota (GM) are related to changes of bone mass and bone microstructure. However, the relationship between GM and bone mineral density (BMD) is complex, and data are especially scarce for Chinese Han youth. Therefore, 62 Chinese Han youth participants were recruited. Furthermore, according to the T–score evaluation criteria of the World Health Organization (WHO), we divided the BMD levels of participants into three groups: osteoporosis\\BDL, osteopenia\\BDM, normal bone density\\BDH, and the associations between GM community and BMD groups were conducted. According to alpha and beta diversity analysis, significant differences were found in the microbial richness and composition between groups. The dominant phyla of GM in a cohort of Chinese Han youth were Bacteroidota (50.6%) and Firmicutes (41.6%). Anaerobic microorganisms, such as g_Faecalibacterium and g_Megamonas , account for the largest proportion in the gut, which were mainly Firmicutes phylum. The dominant genera and species in the three BMD groups were g_Prevotella , g_Bacteroides , g_Faecalibacterium , g_Megamonas, s_Prevotella copri , s_unclassified_g_Faecalibacterium , s_unclassified_g_Prevotella , s_unclassified_g_Bacteroides and s_Bacteroides plebeius. g_Faecalibacterium , g_Bacteroides and g_Ruminococcus differed between the BDH and BDL groups as well as between the BDH and BDM groups. LEfSe showed three genus communities and eight species communities were enriched in the three BMD groups, respectively. The associations between microbial relative abundance and T–score was not statistically significant by Spearman and regression analysis. In conclusion, the alpha diversity indexes in the BDH group were higher than in the BDL group, and several taxa were identified that may be the targets for diagnosis and therapy of OP.

Background Dietary intervention studies are required to deeper understand the variability of gut microbial ecosystem in healthy dogs under different feeding conditions and to improve diet formulations. The aim of the study was to investigate in dogs the influence of a raw based diet supplemented with vegetable foods on faecal microbiome in comparison with extruded food. Methods Eight healthy adult Boxer dogs were recruited and randomly divided in two experimental blocks of 4 individuals. Dogs were regularly fed a commercial extruded diet (RD) and starting from the beginning of the trial, one group received the raw based diet (MD) and the other group continued to be fed with the RD diet (CD) for a fortnight. After 14 days, the two groups were inverted, the CD group shifted to the MD and the MD shifted to the CD, for the next 14 days. Faeces were collected at the beginning of the study (T0), after 14 days (T14) before the change of diet and at the end of experimental period (T28) for DNA extraction and analysis of metagenome by sequencing 16SrRNA V3 and V4 regions, short chain fatty acids (SCFA), lactate and faecal score. Results A decreased proportion of Lactobacillus , Paralactobacillus ( P  < 0.01) and Prevotella ( P  < 0.05) genera was observed in the MD group while Shannon biodiversity Index significantly increased (3.31 ± 0.15) in comparison to the RD group (2.92 ± 0.31; P  < 0.05). The MD diet significantly ( P  < 0.05) decreased the Faecal Score and increased the lactic acid concentration in the feces in comparison to the RD treatment ( P  < 0.01). Faecal acetate was negatively correlated with Escherichia/Shigella and Megamonas ( P  < 0.01), whilst butyrate was positively correlated with Blautia and Peptococcus ( P  < 0.05). Positive correlations were found between lactate and Megamonas ( P  < 0.05), Escherichia/Shigella ( P  < 0.01) and Lactococcus ( P  < 0.01). Conclusion These results suggest that the diet composition modifies faecal microbial composition and end products of fermentation. The administration of MD diet promoted a more balanced growth of bacterial communities and a positive change in the readouts of healthy gut functions in comparison to RD diet.

Obesity is a serious global health issue. Emerging evidence indicates that the gut microbiota may contribute to the development of obesity, possibly by instigating inflammatory processes. The objective of this research is to conduct a comparative analysis of the gut microbiota composition in obese and normal-weight Uygur adults, while examining the associations with adipocyte-related factors and dietary variables. According to the inclusion and exclusion criteria, twenty-seven Uygur adults with obesity and twenty Uygur adults with normal-weight were recruited from a local community. Anthropometric measurements and blood samples were collected. Gut microbiota composition was analyzed using 16 S rRNA gene sequencing. Adipocyte-related factors were measured using enzyme-linked immunosorbent assay (ELISA). Statistical analyses were performed to compare the gut microbiota composition between the two groups and to identify correlations between gut microbiota and adipocyte-related factors. Compared with the normal-weight group, the obese group exhibited a marked reduction in both diversity and richness of the gut microbiota, alongside a decrease in Ruminococcaceae_UCG_014 , Coprococcus_2 , and Parabacteroides , and an increase in Megamonas and Lachnoclostridium , implying a potential link to the development of obesity. Individuals with obesity were found to have higher Leptin (LEP), Interleukin-6 (IL-6), and C-reactive protein (CRP) than normal-weight individuals. Obese Uygur adults exhibited a gut microbiota characterized by diminished diversity and richness relative to normal-weight individuals. Parabacteroides , Megamonas , and Lachnoclostridium may play an important role in the development of obesity in Uygur population. Underlying mechanisms need further investigation.

BackgroundThis manuscript has been accepted for publication in the International Journal of Surgery (2023 IF: 12.5).The study of changes in the microbiome in chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) holds significant potential for developing noninvasive diagnostic tools as well as innovative interventions to alter the progression of diseases. This systematic review and meta-analysis aimed to analyze in detail the taxonomic and functional characteristics of the gut microbiome in patients with CP and PDAC.MethodsTwo researchers conducted a systematic search across public databases to gather all published research up to December 2023. Diversity and gut microbiota composition are the main outcomes the authors focus on.ResultsThis meta-analysis included 14 studies, involving a total of 1511 individuals in the PDAC (n = 285), CP (n = 342), and control (n = 649) groups (IDDF2025-ABS-0273 table 1). Our results show a significant difference in the composition of gut microbiota between PDAC/CP patients compared to healthy controls (HC), as evidenced by a slight decrease in α-diversity, including Shannon (SMD = − 0.33; P = 0.002 and SMD = − 0.59; P < 0.001, respectively) and a statistically significant β-diversity (P < 0.05) (IDDF2025-ABS-0273 table 2, IDDF2025-ABS-0273 table 3). The pooled results showed that at the phylum level, the proportion of Firmicutes was lower in PDAC and CP patients than in HC patients (IDDF2025-ABS-0273 figure 1. Forest plot of meta-analysis of the composition of gut microbiota at phylum level). At the genus level, more than two studies demonstrated that four genera were significantly increased in PDAC patients compared to HC (e.g. Escherichia-Shigella and Veillonella). CP patients had an increase in four genera (e.g. Escherichia-Shigella and Klebsiella) and a decrease in eight genera (e.g. Coprococcus and Bifidobacterium) compared to HC (IDDF2025-ABS-0273 table 4). Functional/metabolomics results from various studies also showed differences between PDAC/CP patients and HC (IDDF2025-ABS-0273 table 4). In addition, this study found no significant differences in gut microbiota between PDAC and CP patients.Abstract IDDF2025-ABS-0273 Table 1Characteristics of all the studies included in the meta-analysisAuthorYearCountryStudy DesignParticipants in experimental groupNumber of patientsGender (male, %)Age (years)PDAC /CPHCPDAC /CPHCPDAC /CPHCChen2023ChinaCase-controlPDAC: pathology; CP: according to the Asia-Pacific consensusPDAC: 40; CP: 1539PDAC: 20 (50.0); CP: 8 (53.3)20 (51.3)PDAC: 56.6±9.7; CP: 53.77±11.956.4±6.9Kartal2022Spain/ GermanyCase-controlPDAC: pathology; CP: clinical symptoms, transsectional imaging and unequivocal evidencePDAC: 57; CP: 2950NDNDNDNDZhou2021ChinaCase-controlPDAC: pathology; Type 1 autoimmune CP: according to the consensus by IAPPDAC: 32; CP: 3232PDAC: 25 (78.1); CP: 26 (81.3)26 (81.3)PDAC: 59.3±9.5; CP: 58.8±9.858.6±10.3Nagata2022JapanCohortPDAC: pathologyPDAC: 47235PDAC: 26 (55.3)130 (55.3)NDNDHashimoto2022JapanCase-controlPDAC: pathologyPDAC: 568PDAC: 2 (40.0)29 (42.6)PDAC: 73.0 (70.0–89.0)54.0 (50.8–57.3)Kohi2020USACase-controlPDAC: pathologyPDAC: 74134PDAC: 35 (47.6)85 (63.5)PDAC: 63.6 (41.6–79.5)65.3 (42.2–85.5)Half2019IsraelCohortPDAC: pathologyPDAC: 3013PDAC: 16 (53.3)6 (46.2)PDAC: 68.9±6.259.0±8.7Xu2023ChinaCross-sectionalCP: according to the Asia-Pacific consensusCP: 4038CP: 25 (62.5)12 (31.6)CP: 44±1249±10McEachron2022USACohortCP being considered for TPIATCP: 2014NDNDNDNDFrost2020GermanyCohortCP: clinical symptoms, transsectional imaging and unequivocal evidenceCP: 51102CP: 40 (78.4)76 (74.5)CP: 54.0 (50.0–60.5)54.0 (43.2–65.0)Wang2020ChinaCase-controlCP in children: according to the INSPPIRECP: 3035CP: 16 (53.3)23 (65.7)CP: 7.2±0.58.3±0.7Zhou2020ChinaCase-controlCP: according to the Asia-Pacific consensusCP: 7169CP: 41 (57.7)29 (42.0)CP: 44±1147±10Ciocan2018FranceCross-sectionalAlcoholic CPCP: 2445CP: 21 (87.5)41 (91.1)CP: 51.5±9.951.1±8.5Jandhyala2017IndiaCase-controlCP with/without diabetesCP: 3010CP: 22 (73.3)7 (70)CP: 32.8±10.642.3±13.9PDAC, Pancreatic Ductal Adenocarcinoma; CP, Chronic Pancreatitis; HC, Healthy Controls; ND, Not Declared; TPIAT, Total pancreatectomy with islet autotransplantation; INSPPIRE, INternational Study Group of Pediatric Pancreatitis: In Search for a CuRE; IAP, International Association of PancreatologyAbstract IDDF2025-ABS-0273 Table 2The meta-analyses of alpha-diversity outcomes of the included studiesComparisonNO. of studySMD95% CIZ valuep valuePDAC vs. HCShannon Index4-0.325[-0.529,-0.121]-3.1220.002Simpson Index2-0.303[-0.609,0.002]-1.9450.052Evenness2-0.326[-0.633,-0.019]-2.0830.037Richness3-0.070[-0.319,0.179]-0.5480.583CP vs. HCShannon Index9-0.593[-0.747,-0.439]-7.544<0.001Simpson Index6-0.113[-0.282,0.056]-1.3150.189Evenness2-0.244[-0.580,0.092]-1.4230.155Richness6-0.426[-0.619,-0.233]-4.331<0.001CP vs. PDACShannon Index20.069[-0.266,0.404]0.4060.685Simpson Index20.037[-0.297,0.371]0.2180.827Evenness20.038[-0.296,0.371]0.2210.825Richness3-0.103[-0.394,0.188]-0.6940.488PDAC, pancreatic ductal adenocarcinoma; CP, chronic pancreatitis; HC, healthy controls; OR, odds ratio; SMD, standardized mean difference; CI, confidence interval.Abstract IDDF2025-ABS-0273 Table 3Summary of beta diversity assessments in the included studiesStudyα diversityFindingsStatistic valueβ diversityFindingsStatistic value1. PDAC vs. HCChen 2023RichnessThere was no significant difference between the groups.P=0.95PCoA of weighted UniFrac distancesThere was a significant difference in gut microbial community composition between the groups.P=0.002Observed speciesThere was no significant difference between the groups.P=0.84Phylogenetic diversityThere was no significant difference between the groups.P=0.92Kartal 2022RichnessThe PDAC group had lower Richness than HC.P=0.05RDA based on Bray-Curtis distanceThere was a mild but significant difference in gut microbial community composition between the groups.P<0.001ShannonThe PDAC group had lower Shannon diversity than HC.P=0.003SimpsonThe PDAC group had lower Simpson diversity than HC.P=0.004EvenessThe PDAC group had lower Eveness than HC.P=0.002Zhou 2021RichnessThere was no significant difference between the groups.P=0.26PCoA based on Bray-Curtis metricsThere was a significant difference in gut microbial community composition between the groups.P=0.001ShannonThere was no significant difference between the groups.P=0.27SimpsonThere was no significant difference between the groups.P=0.23EvenessThere was no significant difference between the groups.P=0.29NagataShannonThe PDAC group had lower Shannon diversity than HC.P=0.08MDSThere was a significant difference in gut microbial community composition between the groups.P<0.001SimpsonThere was no significant difference between the groups.NDHashimoto 2022ShannonThere was no significant difference between the groups.NDPCAThere was no significant difference in gut microbial community composition between the groups.NDKohi 2020Observed speciesThe PDAC group had lower observed species than HC.P=0.005PCoA of unweighted Unifrac distancesThere was a significant difference in gut microbial community composition between the groups.P=0.001ShannonThe PDAC group had lower Shannon diversity than HC.P=0.03Phylogenetic diversityThe PDAC group had lower Phylogenetic diversity than HC.P=0.006Half 2019ShannonThere was no significant difference between the groups. P=0.29PCoA of weighted UniFrac distancesThere was a weak but significant difference in gut microbial community composition between the groups.P=0.013PCoA of unweighted UniFrac distancesThere was a weak but significant difference in gut microbial community composition between the groups.P=0.042. CP vs. HCChen 2023RichnessThere was no significant difference between the groups.P=0.23PCoA of weighted UniFrac distancesThere was a significant difference in gut microbial community composition between the groups.P=0.002Observed speciesThere was no significant difference between the groups.P=0.19Phylogenetic diversityThere was no significant difference between the groups.P=0.27Kartal 2022RichnessThere was no significant difference between the groups.P=0.75NDNDNDShannonThere was no significant difference between the groups.P=0.035SimpsonThere was no significant difference between the groups.P=0.27EvenessThere was no significant difference between the groups.P=0.22Zhou 2021RichnessThere was no significant difference between the groups.P=0.51PCoA based on Bray-Curtis metricsThere was no significant difference in gut microbial community composition between the groups.P=0.079ShannonThere was no significant difference between the groups.P=0.45SimpsonThere was no significant difference between the groups.P=0.39EvenessThere was no significant difference between the groups.P=0.47Xu 2023Observed speciesThere was no significant difference between the groups.P=0.056PCoA of weighted UniFrac distancesThere was a significant difference in gut microbial community composition between the groups.P=0.047ShannonThere was no significant difference between the groups.P=0.39SimpsonThere was no significant difference between the groups.P=0.69PCoA of unweighted UniFrac distancesThere was no significant difference in gut microbial community composition between the groups.P=0.078ACEThere was no significant difference between the groups.P=0.08RichnessThere was no significant difference between the groups.P=0.07Phylogenetic diversityThere was no significant difference between the groups.P=0.06McEachron 2022ShannonThe CP group had lower Shannon diversity than HC.P<0.001PCoA based on Bray-Curtis metricsThere was a significant difference in gut microbial community composition between the groups.P=0.002Frost 2020ShannonThe CP group had lower Shannon diversity than HC.P<0.05PCoA based on Bray-Curtis metricsThere was a significant difference in gut microbial community composition between the groups.P<0.001SimpsonThe CP group had lower Si