Dysbiosis Index Research Articles

SMDI: An Index for Measuring Subgingival Microbial Dysbiosis

An intuitive, clinically relevant index of microbial dysbiosis as a summary statistic of subgingival microbiome profiles is needed. Here, we describe a subgingival microbial dysbiosis index (SMDI) based on machine learning analysis of published periodontitis/health 16S microbiome data. The raw sequencing data, split into training and test sets, were quality filtered, taxonomically assigned to the species level, and centered log-ratio transformed. The training data set was subject to random forest analysis to identify discriminating species (DS) between periodontitis and health. DS lists, compiled by various “Gini” importance score cutoffs, were used to compute the SMDI for samples in the training and test data sets as the mean centered log-ratio abundance of periodontitis-associated species subtracted by that of health-associated ones. Diagnostic accuracy was assessed with receiver operating characteristic analysis. An SMDI based on 49 DS provided the highest accuracy with areas under the curve of 0.96 and 0.92 in the training and test data sets, respectively, and ranged from −6 (most normobiotic) to 5 (most dysbiotic) with a value around zero discriminating most of the periodontitis and healthy samples. The top periodontitis-associated DS were Treponema denticola, Mogibacterium timidum, Fretibacterium spp., and Tannerella forsythia, while Actinomyces naeslundii and Streptococcus sanguinis were the top health-associated DS. The index was highly reproducible by hypervariable region. Applying the index to additional test data sets in which nitrate had been used to modulate the microbiome demonstrated that nitrate has dysbiosis-lowering properties in vitro and in vivo. Finally, 3 genera (Treponema, Fretibacterium, and Actinomyces) were identified that could be used for calculation of a simplified SMDI with comparable accuracy. In conclusion, we have developed a nonbiased, reproducible, and easy-to-interpret index that can be used to identify patients/sites at risk of periodontitis, to assess the microbial response to treatment, and, importantly, as a quantitative tool in microbiome modulation studies.

Effect of the Interaction between Dietary Patterns and the Gastric Microbiome on the Risk of Gastric Cancer.

We aimed to observe the combined effects of Gaussian graphical model (GGM)-derived dietary patterns and the gastric microbiome on the risk of gastric cancer (GC) in a Korean population. The study included 268 patients with GC and 288 healthy controls. Food intake was assessed using a 106-item semiquantitative food frequency questionnaire. GGMs were applied to derive dietary pattern networks. 16S rRNA gene sequencing was performed using DNA extracted from gastric biopsy samples. The fruit pattern network was inversely associated with the risk of GC for the highest vs. lowest tertiles in the total population (odds ratio (OR): 0.47; 95% confidence interval (CI): 0.28–0.77; p for trend = 0.003) and in females (OR: 0.38; 95% CI: 0.17–0.83; p for trend = 0.021). Males who had a low microbial dysbiosis index (MDI) and high vegetable and seafood pattern score showed a significantly reduced risk of GC (OR: 0.44; 95% CI: 0.22–0.91; p-interaction = 0.021). Females who had a low MDI and high dairy pattern score showed a significantly reduced risk of GC (OR: 0.23; 95% CI: 0.07–0.76; p-interaction = 0.018). Our novel findings revealed that vegetable and seafood pattern might interact with dysbiosis to attenuate the risk of GC in males, whereas the dairy pattern might interact with dysbiosis to reduce the GC risk in females.

Dysbiosis of Gut Microbiota Is an Independent Risk Factor of Stroke-Associated Pneumonia: A Chinese Pilot Study.

Background and PurposeIdentifying risks of stroke-associated pneumonia (SAP) is important for clinical management. We aimed to evaluate the association between gut microbiome composition and SAP in patients with acute ischemic stroke (AIS).MethodsA prospective observational study was conducted, and 188 AIS patients were enrolled as the training cohort. Fecal and serum samples were collected at admission. SAP was diagnosed by specialized physicians, and disease severity scores were recorded. Fecal samples were subjected to 16S rRNA V4 tag sequencing and analysed with QIIME and LEfSe. Associations between the most relevant taxa and SAP were analysed and validated with an independent cohort. Fecal short-chain fatty acid (SCFA), serum D-lactate (D-LA), intestinal fatty acid-binding protein (iFABP) and lipopolysaccharide binding protein (LBP) levels were measured.ResultsOverall, 52 patients (27.7%) had SAP in the training cohort. The gut microbiome differed between SAP and non-SAP patients; specifically, Roseburia depletion and opportunistic pathogen enrichment were noted in SAP patients, as confirmed in the validation cohort (n=144, 28 SAP [19.4%]). Based on multivariate analysis, Roseburia was identified as a protective factor against SAP in both cohorts (training, aOR 0.52; 95% CI, 0.30-0.90; validation, aOR 0.44; 95% CI, 0.23-0.85). The combination of these taxa into a microbial dysbiosis index (MDI) revealed that dysbiosis increased nearly 2 times risk of SAP (training, aOR 1.95; 95% CI, 1.19-3.20; validation, aOR 2.22; 95% CI, 1.15-4.26). Lower fecal SCFA levels and higher serum D-LA levels were observed in SAP patients. Furthermore, SAP was an independent risk factor of 30-day death and 90-day unfavorable outcome.ConclusionWe demonstrate that a microbial community with depleted Roseburia and enriched opportunistic pathogens is associated with increased risk of SAP among AIS patients. Gut microbiota screening might be useful for identifying patients at high risk for SAP and provide clues for stroke treatment.

Association between bacteria other than Helicobacter pylori and the risk of gastric cancer.

The gastric microbiota, including Helicobacter pylori (HP), has a remarkable role in gastric cancer (GC) occurrence. Evidence for the role of non-HP bacteria in GC risk is limited. We aimed to observe the association between bacteria other than HP and risk of GC in a Korean population. In this study, 268 GC cases and 288healthy controls were included. Demographic data and total energy intake data were collected using a general questionnaire and a semiquantitative food frequency questionnaire, respectively. 16S rRNA gene sequencing was performed using DNA extracted from gastric biopsy samples. Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and non-HP Proteobacteria were the five main phyla in the gastric environment. The five phyla were negatively related to the relative abundance of Helicobacter species (all p<0.001). The Shannon index, richness, and Pilou-evenness were negatively correlated with Helicobacter species (all p<0.001), while the microbial dysbiosis index was positively correlated with Helicobacter species (p<0.001). Participants with a higher relative abundance of Actinobacteria species showed a significantly increased risk of GC (OR: 3.16, 95% CI=1.92-5.19, p-trend<0.001). The non-HP microbiota composition among the four groups (HP+cases, HP- cases, HP+controls, and HP- controls) was significantly different (ANOSIM R=0.10, p=0.001). Other than HP, several bacterial species might be associated with GC risk. HP status and GC status could determine the differences in microbial compositions. Further large prospective studies are warranted to confirm our findings.

Identification and engraftment of new bacterial strains by shotgun metagenomic sequence analysis in patients with recurrent Clostridioides difficile infection before and after fecal microbiota transplantation and in healthy human subjects.

BackgroundRecurrent Clostridioides diffícile infection (RCDI) is associated with major bacterial dysbiosis and colitis. Fecal microbiota transplantation (FMT) is a highly effective therapeutic modality for RCDI. While several studies have identified bacterial species associated with resolution of symptoms in patients, characterization of the fecal microbiome at the bacterial strain level in RCDI patients before and after FMT and healthy donors, has been lacking. The aim of this study was to examine the ability of bacterial strains from healthy donors to engraft in the gastrointestinal tract of patients with RCDI following FMT.MethodsFecal samples were collected from 22 patients with RCDI before and after FMT and their corresponding healthy donors. Total DNA was extracted from each sample and analyzed by shotgun metagenomic sequencing. The Cosmos-ID analysis platform was used for taxonomic assignment of sequences and calculation of the relative abundance (RA) of bacterial species and strains. From these data, the total number of bacterial strains (BSI), Shannon diversity index, dysbiosis index (DI), and bacterial engraftment factor, were calculated for each strain.FindingsA marked reduction (p<0·0001) in the RA of total and specific bacterial strains, especially from phylum Firmicutes, was observed in RCDI patients prior to FMT. This change was associated with an increase in the DI (p<0·0001) and in pathobiont bacterial strains from phylum Proteobacteria, such as Escherichia coli O157:H7 and Klebsiella pneumoniae UCI 34. BSI was significantly lower in this group of patients as compared to healthy donors and correlated with the Shannon Index. (p<0·0001). Identification and engraftment of bacterial strains from healthy donors revealed a greater diversity and higher relative abundance of short-chain fatty acid (SCFA)-producing bacterial strains, including Lachnospiraceae bacterium 5_1_63FAA_u_t, Dorea formicigenerans ATCC 27755, Anaerostipes hadrusand others, in RCDI patients after FMT.InterpretationThese observations identify a group of SCFA-producing bacterial strains from healthy donors that engraft well in patients with RCDI following FMT and are associated with complete resolution of clinical symptoms and bacterial dysbiosis.

Long-term effects of fecal microbiota transplantation (FMT) in patients with irritable bowel syndrome.

We recently found fecal microbiota transplantation (FMT) in irritable bowel syndrome (IBS) patients to be an effective and safe treatment after 3months. The present follow-up study investigated the efficacy and safety of FMT at 1year after treatment. This study included 77 of the 91 IBS patients who had responded to FMT in our previous study. Patients provided a fecal sample and completed five questionnaires to assess their symptoms and quality of life at 1year after FMT. The dysbiosis index (DI) and fecal bacterial profile were analyzed using a 16S rRNA gene-based DNA probe hybridization. The levels of fecal short-chain fatty acids (SCFAs) were determined by gas chromatography. There was a persistent response to FMT at 1year after treatment in 32 (86.5%) and 35 (87.5%) patients who received 30-g and 60-g FMT, respectively. In the 30-g FMT group, 12 (32.4%) and 8 (21.6%) patients showed complete remission at 1year and 3months, respectively; the corresponding numbers in the 60-g FMT group were 18 (45%) and 11 (27.5%), respectively. Abdominal symptoms and the quality of life were improved at 1year compared with after 3months. These findings were accompanied by comprehensive changes in the fecal bacterial profile and SCFAs. Most of the IBS patients maintained a response at 1year after FMT. Moreover, the improvements in symptoms and quality of life increased over time. Changes in DI, fecal bacterial profile and SCFAs were more comprehensive at 1year than after 3months. www.clinicaltrials.gov (NCT03822299).

MO137DIFFERENCES IN GUT MICROBIOTA PROFILES AND FUNCTIONS BETWEEN END-STAGE KIDNEY DISEASE AND HEALTHY POPULATIONS

Abstract Background and Aims Patients with end-stage kidney disease (ESKD) are characterized by altered gut microbiota, impaired intestinal barrier function, and experienced gut microbiota-derived metabolites related to systemic complications. However, limited studies evaluated the microbial diversity and function in ESKD patients previously. Method Compared to age- and gender-matched subjects without kidney disease, 82 ESKD patients in the discovery cohort and 58 ESKD patients in the validation cohort were investigated for the microbial richness, biodiversity, gut dysbiosis, microbial composition differences, and the functional changes by gut metabolic module analysis. Bacterial derived free form protein-bound uremic toxins were analyzed by mass spectrometry and their association with microbial richness in ESKD patients was determined. Results Compared to controls, an increased α-diversity and distinct β-diversity were found in ESKD (Figure). The increase in α-diversity was correlated with protein-bound uremic toxins, particularly hippuric acid. A higher microbial dysbiosis index (MDI) was found in ESKD patients with the following enriched genera: Facealibacterium, Ruminococcus, Fusobacterium, Dorea, Anaerovorax, Sarcina, Akkermansia, Streptococcus, and Dysgonomonas. MDI at the genus level successfully differentiated between ESKD and controls in the discovery cohort (area under the curve [AUC] of 81.9%) and the validation cohort (AUC of 83.2%). Regarding functional enrichment analysis with gut metabolic modules, ESKD subjects presented with gut microbial function of increased saccharide and amino acid metabolism compared with matched controls. Conclusion An enriched but dysbiotic gut microbiota was presented in ESKD patients, in which the bacteria that were present increase amino acid metabolism linked to the production of protein-bound uremic toxins.

P009 Disease classifier and microbial dysbiosis index tools cross-predict various pathogenic conditions due to general microbial signal

Abstract Background Microbial dysbiosis is widely described in inflammatory bowel disease (IBD), and has been shown to predict IBD state. However, many other diseases including neuro-psychiatric, metabolic, and malignancies, most of which do not result in gut inflammation, are also linked with gut microbial alteration. Since most studies focus on a single disease, the extent of similarity between different diseases is usually not examined. Methods We reanalyzed raw sequencing data from 12,838 human gut V4 16Sseq samples, spanning 59 case-controls comparisons and 28 unique diseases. Novel statistical approach was applied to reduce the effect of the different cohorts; all samples were processed uniformly, and differentially expressed amplicon sequence variants (ASVs) were identified within each cohort. The resulting behavior (direction of change and effect size) of each ASV were then combined across all studies. We used random forest as our classifier and generated non-specific dysbiosis index (NSDI). Results For the disease prediction, each cohort was randomly subsampled to 23 healthy and 23 disease samples. Random forest classifier was trained on one disease/control cohort, and the trained classifier was then used to predict the status of a different disease/control cohort. Disease classifiers performed well in identifying many sick vs. healthy states but failed to differentiate between different diseases. For example, a classifier trained on IBD cohort classified relatively good also disease/control in lupus, schizophrenia, or Parkinson’s from different cohorts. We show this cross-identification is due to a large number of shared disease-associated bacteria and utilize these bacteria to define a novel non-specific dysbiosis index (NSDI). After, we identified 114 non-disease specific ASVs (86 up and 28 down regulated ASVs across diseases in comparison to controls), we calculate the per-sample NSDI by rank-transforming the bacteria within the sample and computing the normalized log ratio of the sum of the ranks of the 86 down and 28 up regulated ASVs. The resulting NSDI is shown to perform better than the previously published CD dysbiosis index (Gevers et al, 2014; PMID: 24629344) indicating that NSDI can successfully differentiate between most cases and controls across a wide variety of diseases. Conclusion A robust non-specific general response of the gut microbiome is detected across different diseases, some of which is shared with IBD. Classifiers trained on a single disease may identify the general non-specific signal and therefore care should be taken when interpreting the classifier predictions. Finally, our NSDI can be used to prioritize the per-sample degree of dysbiosis.

P683 Patients with newly diagnosed fistulizing perianal Crohn’s disease have a distinct microbial signature

Abstract Background Alterations in gut bacterial microbiota are strongly associated with the pathogenesis of Crohn’s disease (CD). Up to 1/3 of patients with CD have perianal involvement (P-CD), either fistulizing (f-P-CD) or non-fistulizing (nf-P-CD). We hypothesized that alterations in fecal microbiota might drive perianal CD phenotypes. Methods Patients with newly diagnosed treatment naive CD were consecutively recruited. Patients were stratified into f-P-CD and nf-P-CD, and compared with CD without perianal involvement (non-P-CD). Bacterial 16S rRNA gene sequencing was performed. Microbial dysbiosis index (MDI), Shannon diversity score (H) and log ratio between anaerobic and aerobic bacteria (anaerobic index, AI) were calculated. Linear discriminant analysis effect size (LEfSe) was used to identify specific taxa discriminating between different patient groups. Fecal calprotectin (FC) was measured. Results We included 97 CD patients (50 males, median age 28 [IQR 22–41] years). Other than perianal involvement patients in both groups had comparable distribution of CD location and phenotype. The microbial indices MDI, H and AI as well as FC levels, did not differ between the P-CD (n=25) and non-P-CD (n=72) groups. When compared to non-P-CD, the P-CD exhibited significantly lower relative abundance in the Streptococci genera (p=0.01) and in the Ruminiclostridium_5 genera (p=0.02). Within the P-CD group, patients with f-P-CD (n=12) had significantly lower H than those with nf-P-CD (means: 2.0 vs 2.45, p=0.045), while FC levels were comparable between f-P-CD and nf-P-CD. Moreover, proportions of Coprococcus_3 and Lacnoclostridium were reduced in patients with f-P-CD vs nf-P-CD (p= 0.008, p=0.05, respectively), while Streptococcus was elevated (p=0.03). Conclusion Perianal CD is a spectrum with distinct microbial alterations in different phenotypes. Microbial alterations correspond to the severity of perianal involvement. This finding may suggest that the microbiome has a mechanistic role in complicated perianal CD.

Effects of Synbiotics on the Fecal Microbiome and Metabolomic Profiles of Healthy Research Dogs Administered Antibiotics: A Randomized, Controlled Trial.

Background: Antibiotic-associated gastrointestinal signs occurred in 100% of dogs administered enrofloxacin with metronidazole in a previous study, and signs partially were mitigated by synbiotics. The objective of this randomized, double-blinded, placebo-controlled trial was to compare the fecal microbiome and metabolome of dogs administered enrofloxacin and metronidazole, followed by either a placebo or a bacterial/yeast synbiotic combination.Methods: Twenty-two healthy research dogs were randomized to two treatment groups. There were three study periods: baseline, treatment, and washout. Dogs were administered enrofloxacin (10 mg/kg qd) and metronidazole (12.5 mg/kg BID), followed 1 h later by placebo or a commercially-available synbiotic combination (BID), per os for 21 days with reevaluation 56 days thereafter. Fecal samples were collected on days 5–7 (baseline), 26–28, and 82–84. The fecal microbiome was analyzed by qPCR and sequencing of 16S rRNA genes; time-of-flight mass spectrometry was used to determine metabolomic profiles. Split plot repeated measures mixed model ANOVA was used to compare results between treatment groups. P < 0.05 was considered significant, with Benjamini and Hochberg's False Discovery Rate used to adjust for multiple comparisons.Results: Alpha diversity metrics differed significantly over time in both treatment groups, with incomplete recovery by days 82–84. Beta diversity and the dysbiosis index differed significantly over time and between treatment groups, with incomplete recovery at days 82–84 for dogs in the placebo group. Significant group-by-time interactions were noted for 15 genera, including Adlercreutzia, Bifidobacterium, Slackia, Turicibacter, Clostridium (including C. hiranonis) [Ruminococcus], Erysipelotrichaceae_g_, [Eubacterium], and Succinivibrionaceae_g_. Concurrent group and time effects were present for six genera, including Collinsella, Ruminococcaceae_g_, and Prevotella. Metabolite profiles differed significantly by group-by-time, group, and time for 28, 20, and 192 metabolites, respectively. These included short-chain fatty acid, bile acid, tryptophan, sphingolipid, benzoic acid, and cinnaminic acid metabolites, as well as fucose and ethanolamine. Changes in many taxa and metabolites persisted through days 82–84.Conclusion: Antibiotic administration causes sustained dysbiosis and dysmetabolism in dogs. Significant group-by-time interactions were noted for a number of taxa and metabolites, potentially contributing to decreased antibiotic-induced gastrointestinal effects in dogs administered synbiotics.

Comparison of the Therapeutic Effect of Treatment with Antibiotics or Nutraceuticals on Clinical Activity and the Fecal Microbiome of Dogs with Acute Diarrhea.

Simple SummaryAcute diarrhea in dogs is one of the most common reasons for veterinary visits. Although this disorder is generally self-limiting, antibiotics are still frequently used as treatment for acute diarrhea in clinical practice. Antimicrobial resistance represents a major challenge for public health and requires immediate and drastic solutions. To date, the emergence and spread of antimicrobial resistance has been attributed to the misuse or indiscriminate use of antibiotics. The aim of this study is to compare the effects on clinical activity and fecal microbiota of the administration of an antibiotic combination in comparison to a nutraceutical product in dogs with acute non-hemorrhagic diarrhea. The results of the present study suggest that this nutraceutical treatment had a similar clinical effect compared to the antibiotic formulation and may represent an alternative to commonly used antimicrobial therapy.Dogs with acute diarrhea are often presented to clinical practice and, although this generally represents a self-limiting condition, antibiotics are still frequently used as treatment. The aim of this study was to evaluate the effects in dogs with acute non-hemorrhagic diarrhea of the administration of an antibiotic combination in comparison to a nutraceutical product. Thirty dogs were enrolled and randomly assigned to two groups: 15 dogs (group A) received a nutraceutical commercial product while 15 dogs (group B) received an antimicrobial combination of metronidazole and spiramycin. For each dog, the Canine Acute Diarrhea Severity Index, the fecal microbiota and the Dysbiosis Index were assessed. Both stool consistency and frequency decreased on day 2 in the dogs of group A compared to baseline, while in group B, these parameters significantly decreased at days 3 and 4. The global concern for rising antibiotic resistance associated with indiscriminate use of antimicrobials, in both humans and animals, suggests the necessity of avoiding empirical and injudicious use of these molecules in diarrheic dogs. These results suggest that the nutraceutical treatment had a similar clinical effect compared to the antibiotic formulation, representing a valid antibiotic-sparing therapeutic approach in canine acute diarrhea.

POS0237 GUT DYSBIOSIS LINKED TO WORSE DISEASE STATUS IN AXIAL SPONDYLOARTHRITIS

Background:Based on clinical and genetic associations, axial spondyloarthritis (axSpA) and inflammatory bowel disease (IBD) are suspected to have a linked pathogenesis. Gut dysbiosis, intrinsic to IBD, has also been observed in axSpA. It is, however, not established to what degree gut dysbiosis is associated with axSpA disease severity.Objectives:To compare presence and degree of gut dysbiosis between axSpA patients and controls, and to explore whether gut dysbiosis is associated with axSpA disease activity, function and pain.Methods:The GA-map Dysbiosis Test (Genetic Analysis, Oslo, Norway) was used to identify and grade gut dysbiosis based on faecal samples from 44 non-radiographic axSpA (nr-axSpA; ASAS criteria) and 88 ankylosing spondylitis (AS; modified New York criteria) patients without IBD, consecutively enrolled in a population-based cohort study, and from 46 controls without rheumatic disease or IBD (frequency-matched to the patients for age/sex). The GA-map Dysbiosis Test is a validated method grading microbiota aberration on a 1-5 scale (Dysbiosis Index, DI), where ≥3 denotes dysbiosis. Analysis of covariance (ANCOVA) was used to compare DI between axSpA patients (nr-axSpA and AS combined) and controls, adjusted for age, sex, body mass index (BMI) and smoking. Within the axSpA group, disease activity (ASDAS-CRP; BASDAI), function (BASFI) and pain (VAS pain) were compared between patients with various DI levels by One-way analysis of variance (ANOVA) or Kruskal-Wallis test, as appropriate. Finally, axSpA patients were subdivided by presence of dysbiosis (DI ≥3 vs. &lt;3) followed by comparison of ASDAS-CRP, BASDAI, BASFI, VAS pain and Evaluator´s global assessment of disease activity (EvalGlobal; 0-4: remission-maximal) by ANCOVA. Analyses were conducted unadjusted and adjusted for age, sex, BMI, smoking, axSpA subtype, gut inflammation (faecal calprotectin ≥50 mg/kg), irritable bowel syndrome symptoms (ROME III criteria), ASAS 3-month NSAID score and cs/bDMARD treatment.Results:Characteristics of the patients/controls are shown in the Table 1. Gut dysbiosis (DI≥3) was observed in 33% of axSpA patients and 17% of controls. DI was significantly higher among the patients (β-estimate [bootstrapped 95%CI] for the between-group difference: 0.34 [0.04-0.65]; p=0.027). In the axSpA group, higher DI was associated with worse scores in all assessed outcomes (Figure 1, panel A). Moreover, presence of dysbiosis (DI≥3) was associated with worse ASDAS-CRP, BASDAI, BASFI, VAS pain and EvalGlobal (Figure 1, panel B; EvalGlobal not shown in the Figure: unadjusted β [bootstrapped 95%CI]: 0.32 [0.09-0.55], adjusted: 0.28 [0.03-0.52] for patients with DI ≥3 vs. &lt;3), with between-group differences remaining significant after adjustment, except for ASDAS-CRP (p=0.079) and VAS pain (p=0.064).Table 1.All patients n=132Nr-axSpAn=44ASn=88Controlsn=46Male sex, n (%)72 (55)17 (39)55 (63)23 (50)Age, y53 (13)48 (12)55 (13)51 (14)Symptom duration, y26 (14)21 (11)28 (14)Body mass index, kg/m227 (4.3)27 (4.2)27 (4.3)25 (3.3)Smoking ever, n (%)43 (33)9 (20)34 (39)13 (28)CRP, mg/L3.7 (5.3)2.3 (2.4)4.3 (6.1)F-Calprotectin ≥50 mg/kg, n (%)46 (35)12 (27)34 (39)Evaluator´s global, 0-4, median (IQR)1 (0-1)1 (0-1)1 (0-1)ASDAS-CRP1.8 (0.9)1.9 (0.9)1.8 (0.9)BASDAI3.1 (2.2)3.3 (1.9)3.0 (2.4)BASFI2.0 (2.1)2.0 (1.7)2.1 (2.2)VAS pain, cm3.3 (2.5)3.4 (2.2)3.2 (2.7)IBS symptoms, n (%)43 (33)15 (34)28 (32)ASAS 3-month NSAID score37 (44)36 (44)37 (44)Ongoing csDMARD, n (%)24 (18)9 (20)15 (17)Ongoing bDMARD, n (%)56 (42)19 (43)37 (42)Mean (SD) unless otherwise specified. y, years; IBS, irritable bowel syndromeConclusion:Gut dysbiosis, present to a higher degree in axSpA patients than controls, is associated with worse axSpA disease activity and function. These associations appear independent of gut inflammation and both NSAID and immunomodulatory treatment. This provides further evidence for an important link between disturbances in gastrointestinal homeostasis and axSpA manifestations, and implies that gut dysbiosis may be a novel biomarker for severe disease.Disclosure of Interests:Jonas Sagard: None declared, Tor Olofsson Consultant of: Eli Lilly, Merck Sharp &amp; Dohme, Elisabeth Mogard: None declared, Jan Marsal Consultant of: AbbVie, Bristol-Myers Squibb, EuroDiagnostica, Ferring, Hospira, Janssen-Cilag, Merck, Sharp &amp; Dohme (MSD), Otsuka, Pfizer, Sandoz, Takeda, Tillotts, UCB Pharma, Grant/research support from: AbbVie, Ferring, and Pfizer, Kristofer Andréasson: None declared, Mats Geijer Speakers bureau: UCB Pharma, AbbVie, Novartis, Pfizer, Lars Erik Kristensen Speakers bureau: Pfizer, AbbVie, Amgen, UCB, Celegene, BMS, MSD, Novartis, Eli Lilly, Janssen pharmaceuticals, Consultant of: Pfizer, AbbVie, Amgen, UCB, Celegene, BMS, MSD, Novartis, Eli Lilly, Janssen pharmaceuticals, Elisabet Lindqvist: None declared, Johan K Wallman Consultant of: Celgene, Eli Lilly, Novartis

Gut Dysbiosis and Its Associations with Gut Microbiota-Derived Metabolites in Dogs with Myxomatous Mitral Valve Disease.

ABSTRACTGut dysbiosis and gut microbiota-derived metabolites, including bile acid (BA), short-chain fatty acid, and trimethylamine N-oxide (TMAO), are associated with cardiovascular disease. Canine myxomatous mitral valve disease (MMVD) is a model for human MMVD. The aim of the study is to evaluate gut microbial dysbiosis and its relationship with gut-produced metabolites in dogs with MMVD. Fecal samples from 92 privately owned dogs, including 17 healthy, 23 and 27 asymptomatic MMVD dogs without (stage B1) and with (stage B2) secondary cardiac enlargement, respectively, and 25 MMVD dogs with history of congestive heart failure (stage C or D), were analyzed by 16S rRNA sequencing. Alpha and beta diversities were different between healthy and MMVD dogs (adjusted P < 0.05). The average dysbiosis indexes were −1.48, −0.6, 0.01, and 1.47 for healthy, B1, B2, and C/D dogs, respectively (P = 0.07). Dysbiosis index was negatively correlated with Clostridium hiranonis (P < 0.0001, r = −0.79). Escherichia coli, capable of trimethylamine production in the gut, had an increased abundance (adjusted P < 0.05) and may be responsible for the increased circulating TMAO levels in stage B2 and C/D MMVD dogs. Primary and secondary BAs showed opposite associations with C. hiranonis, a key BA converter (P < 0.0001 for both, r = −0.94 and 0.95, respectively). Secondary BAs appeared to promote the growth of Fusobacterium and Faecalibacterium but inhibit that of E. coli. Multivariate analysis revealed significant but weak associations between gut microbiota and several circulating metabolites, including short-chain acylcarnitines and TMAO.IMPORTANCE Our study expands the current “gut hypothesis” to include gut dysbiosis at the preclinical stage, prior to the onset of heart failure. Gut dysbiosis index increases in proportion to the severity of myxomatous mitral valve disease (MMVD) and is inversely associated with Clostridium hiranonis, a key bile acid (BA) converter in the gut. Secondary BAs appear to promote the growth of beneficial bacteria but inhibit that of harmful ones. An intricate interplay between gut microbiota, gut microbiota-produced metabolites, and MMVD pathophysiological progression is implicated.

Tumor-Associated Microbiota in Esophageal Squamous Cell Carcinoma.

Important evidence indicates the microbiota plays a key role in esophageal squamous cell carcinoma (ESCC). The esophageal microbiota was prospectively investigated in 18 patients with ESCC and 11 patients with physiological normal (PN) esophagus by 16S rRNA gene profiling, using next-generation sequencing. The microbiota composition in tumor tissues of ESCC patients were significantly different from that of patients with PN tissues. The ESCC microbiota was characterized by reduced microbial diversity, by decreased abundance of Bacteroidetes, Fusobacteria, and Spirochaetes. Employing these taxa into a microbial dysbiosis index demonstrated that dysbiosis microbiota had good capacity to discriminate between ESCC and PN esophagus. Functional analysis characterized that ESCC microbiota had altered nitrate reductase and nitrite reductase functions compared with PN group. These results suggest that specific microbes and the microbiota may drive or mitigate ESCC carcinogenesis, and this study will facilitate assigning causal roles in ESCC development to certain microbes and microbiota.

Anti-Acid Drug Treatment Induces Changes in the Gut Microbiome Composition of Hemodialysis Patients.

Anti-acid drugs, proton pump inhibitor (PPI) and histamine-2 blocker (H2-blocker), are commonly prescribed to treat gastrointestinal disorders. These anti-acid drugs alter gut microbiota in the general population, but their effects are not known in hemodialysis patients. Hence, we investigated the microbiota composition in hemodialysis patients treated with PPIs or H2-blocker. Among 193 hemodialysis patients, we identified 32 H2-blocker users, 23 PPI users, and 138 no anti-acid drug subjects. Fecal samples were obtained to analyze the gut microbiome using 16S RNA amplicon sequencing. Differences in the microbial composition of the H2-blocker users, PPI users, and controls were assessed using linear discriminant analysis effect size and the random forest algorithm. The species richness or evenness (α-diversity) was similar among the three groups, whereas the inter-individual diversity (β-diversity) was different between H2-blocker users, PPI users, and controls. Hemodialysis patients treated with H2-blocker and PPIs had a higher microbial dysbiosis index than the controls, with a significant increase in the genera Provetella 2, Phascolarctobacterium, Christensenellaceae R-7 group, and Eubacterium oxidoreducens group in H2-blocker users, and Streptococcus and Veillonella in PPI users. In addition, compared to the H2-blocker users, there was a significant enrichment of the genera Streptococcus in PPI users, as confirmed by the random forest analysis and the confounder-adjusted regression model. In conclusion, PPIs significantly changed the gut microbiota composition in hemodialysis patients compared to H2-blocker users or controls. Importantly, the Streptococcus genus was significantly increased in PPI treatment. These findings caution against the overuse of PPIs.

Diagnostic value of fecal cultures in dogs with chronic diarrhea.

BackgroundCulture‐based assessment of the fecal microbiome using fecal culture profiles frequently is performed in dogs with chronic diarrhea, but the diagnostic value of this approach has not been determined.ObjectivesTo compare the reported results of fecal culture profiles and the polymerase chain reaction‐based dysbiosis index (DI) between dogs with chronic diarrhea and healthy dogs; to assess interlaboratory variability in bacterial and fungal cultures among 3 veterinary diagnostic laboratories (diagnostic laboratory 1 [L1], diagnostic laboratory 2 [L2], diagnostic laboratory 3 [L3]); and to compare the reported interpretation of culture profiles (normobiosis versus dysbiosis) with those of the DI.AnimalsEighteen dogs with chronic diarrhea (CDG) and 18 healthy control dogs (HG).MethodsIn this prospective, case‐control study, fecal samples were submitted to 3 commercial laboratories for fecal culture. The microbiota was assessed using PCR assays. Dogs receiving antimicrobials were excluded.ResultsDysbiosis index was significantly increased in CDG (mean, 0.9; SD, 3.8; 95% confidence interval [CI], −1.0; 2.8) compared to HG (mean, −3.0; SD, 2.8; CI, −4.3; −1.6; P = .0002), whereas cultures from all laboratories failed to detect significant differences (P = .66, .18, and .66, respectively). Hemolytic Escherichia coli was the only potential enteropathogen on culture, but no significant difference was found between CDG and HG. For diagnosis of dysbiosis, culture showed no agreement with DI (L1, κ = −0.21; CI, −0.44; −0.02; L2, κ = −0.33; CI, −0.58; −0.08; L3, κ = −0.25; CI, −0.39; −0.11). Furthermore, variability among the 3 laboratories was high (L1/L2, κ = 0.15; CI, −0.05; 0.35; L1/L3, κ = −0.08; CI, −0.01; −0.16; L2/L3, κ = −0.06; CI, −0.33; −0.20).Conclusions and clinical importanceFecal cultures failed to distinguish between diseased and healthy dogs, and a high level of interlaboratory variation for culture was found.

Prediction of Postoperative Ileus in Patients With Colorectal Cancer by Preoperative Gut Microbiota.

BackgroundIleus and postoperative ileus (POI) are common complications of colorectal cancer (CRC). However, little is known about the gut microbiota associated with ileus.MethodDifferences in gut microbiota were evaluated by 16S rRNA gene sequencing. We characterized the gut microbiota in 85 CRC patients (cohort 1) and detected differences, and an independent cohort composed of 38 CRC patients (cohort 2) was used to evaluate the results.ResultsThe gut microbiota of CRC patients with and without ileus exhibited large differences in alpha- and beta-diversities and bacterial taxa. The Firmicutes-to-Bacteroidetes ratio and microbial dysbiosis index (MDI) showed greater dysbiosis among ileus patients than among those without ileus. According to the location of CRC, the difference in gut microbiota between patients with and without ileus was more obvious in those with distal CRC than in those with proximal CRC. Finally, Faecalibacterium was significantly reduced in the postoperative perioperative period in patients with ileus. Thus, we used Faecalibacterium as a biomarker for predicting perioperative or POI: the AUC value was 0.74 for perioperative ileus and 0.67 for POI that appeared at 6 months after hospital discharge. The predictive power was evaluated in Cohort 2, with an AUC value of 0.79.ConclusionThese findings regarding difference of gut microbiota in postoperative CRC patients may provide a theoretical basis for the use of microbiota as biomarkers for the prediction of POI.

Tumor-Associated Microbiota in Esophageal Squamous Cell Carcinoma

Background: Important evidence indicates that the intestinal tract microbiota plays a key role in esophageal squamous cell carcinoma (ESCC). Our objective is to analyze the composition of the ESCC-associated microbiota and characterize its contribution to the development of ESCC. Methods: The esophageal microbiota was prospectively investigated in 18 patients with ESCC and 11 patients with physiological normal (PN) esophagus by 16S rRNA gene profiling, using next-generation sequencing. The linear discriminant analysis effect size was used to assess the differences in microbial composition of PN esophagus and ESCC. Predictive functional profiling of microbial communities were obtained with PICRUSt. Findings: The microbiota composition in tumor tissues of ESCC patients is significantly different from that of patients with PN tissues. The ESCC microbiota was characterized by reduced microbial diversity, by decreased abundance of Bacteroidetes, Fusobacteria and Spirochactes. Employing these taxa into a microbial dysbiosis index demonstrated that dysbiosis microbiota had good capacity to discriminate between ESCC and PN esophagus. Functional analysis of the microbiota characterized that ESCC microbiota had altered nitrate reductase and nitrite reductase functions when compared with PN group. The observations were confirmed in other validation cohorts. Interpretation: Detailed analysis of the microbiota of the ESCC patients revealed that tumor exhibit a dysbiotic microbial community when compared with PN groups. We characterized microbial compositional changes, and further identified significant enrichments of Treponema amylovorum, Streptococcus infantis, Prevotella nigrescens, Porphyromonas endodontalis, Veillonella dispar, Aggregatibacter segnis, Prevotella melaninogenica, Prevotella intermedia, Prevotella tannerae, Prevotella nanceiensis and Streptococcus anginosus in ESCC oncogenic progression. Trial Registration: This GASTO1039 study was registered at http://www.chictr.org.cn/ (the Chinese Clinical Trial Registry: ChiCTR1800018897). Funding Statement: This research was supported by National Key R&D Program of China (2018YFC0910303), the National Natural Science Foundation of China (81572391, 81630072), Shenzhen Municipal Government (KQTD20170810160226082), Guangdong Basic and Applied Basic Research Foundation (2019A1515011874), and Guangzhou Municipal Science and Technology Bureau/Science and Technology program of Guangzhou city (201904010356). Declaration of Interests: Lu Gao is the employee of BGI Genomics, BGI-Shenzhen, China. The remaining authors declare no competing interests. Ethics Approval Statement: This prospective observational study was conducted according to a protocol approved by the respective Institutional Ethics Committees of the First Affiliated Hospital of Sun Yat-sen University and according to the Declaration of Helsinki.

Developmental stages in microbiota, bile acids, and clostridial species in healthy puppies.

BackgroundThe fecal microbiota, fecal bile acid concentrations, and abundance of Clostridium perfringens and Clostridium difficile are altered in acute and chronic gastrointestinal disease in adult dogs. However, less is known in young puppies.Hypothesis/ObjectivesTo determine composition of the fecal microbiota, assess development of fecal bile acid profiles, and determine the abundance of Clostridial species in puppies, young adult dogs, and adult dogs.AnimalsHealthy puppies from a whelping kennel (n = 53) and healthy client‐owned dogs <1 year old (n = 20) were separated into 6 age groups, then compared to client‐owned dogs over 1 year of age (n = 13).MethodsProspective observational study. Naturally voided fecal samples were analyzed by quantitative polymerase chain reaction to measure bacterial abundances. Fecal bile acids were quantified using gas chromatography‐mass spectrometry.ResultsPuppies up to 5 to 6 weeks of age had increased Dysbiosis Index (median [min‐max]: 5.39 [1.32‐8.6], P < .001), increased abundance of C. difficile (4.1 [0.01‐4.85] log DNA, P < .001), decreased secondary bile acid concentrations (0.61 [0.28‐5.06] μg/mg, P = .006), and decreased abundance of C. hiranonis (0.84 [0.01‐6.71], P = .005) compared to adult dogs (−4.62 [−8.36 to −0.61], 0.01 [0.01‐0.01], 4.12 [0.32‐8.94], and 6.02 [5.06‐7.00], respectively). Secondary bile acid concentration positively correlated with C. hiranonis abundance (ρ = 0.77; P < .001).Conclusions and Clinical ImportanceThe increase in secondary bile acids and simultaneous decrease of C. difficile and C. perfringens after 5 to 6 weeks of age warrants further investigation into regulatory impacts that secondary bile acids could have on clostridial species in dogs.

Fecal microbiota in client-owned obese dogs changes after weight loss with a high-fiber-high-protein diet.

BackgroundThe fecal microbiota from obese individuals can induce obesity in animal models. In addition, studies in humans, animal models and dogs have revealed that the fecal microbiota of subjects with obesity is different from that of lean subjects and changes after weight loss. However, the impact of weight loss on the fecal microbiota in dogs with obesity has not been fully characterized.MethodsIn this study, we used 16S rRNA gene sequencing to investigate the differences in the fecal microbiota of 20 pet dogs with obesity that underwent a weight loss program. The endpoint of the weight loss program was individually tailored to the ideal body weight of each dog. In addition, we evaluated the qPCR based Dysbiosis Index before and after weight loss.ResultsAfter weight loss, the fecal microbiota structure of dogs with obesity changed significantly (weightedANOSIM; p = 0.016, R = 0.073), showing an increase in bacterial richness (p = 0.007), evenness (p = 0.007) and the number of bacterial species (p = 0.007). The fecal microbiota composition of obese dogs after weight loss was characterized by a decrease in Firmicutes (92.3% to 78.2%, q = 0.001), and increase in Bacteroidetes (1.4% to 10.1%, q = 0.002) and Fusobacteria (1.6% to 6.2%, q = 0.040). The qPCR results revealed an overall decrease in the Dysbiosis Index, driven mostly due to a significant decrease in E. coli (p = 0.030), and increase in Fusobacterium spp. (p = 0.017).ConclusionThe changes observed in the fecal microbiota of dogs with obesity after weight loss with a weight loss diet rich in fiber and protein were in agreement with previous studies in humans, that reported an increase of bacterial biodiversity and a decrease of the ratio Firmicutes/Bacteroidetes.