Glycemic load and gut microbiota -
c Graph displaying the spearman correlation between levels of serum TNF-α and Duodenal bacterial count. d Boxplot comparing the duodenal mucosal oxygen saturation in hyperglycemics and normoglycemics group.
e Graph displaying the spearman correlation between Duodenal oxygen saturation and BMI. Further, to understand the relationship of inflammation and duodenal bacterial burden contributing to the microenvironment changes, we quantified duodenal mucosal oxygenation and permeability.
The tissue oxygenation was assessed using T-Stat and demonstrated a significantly lower mucosal oxygen saturation in the duodenum of hyperglycemic subjects Next, to determine whether the small intestinal permeability was associated with glycemia, we measured the levels of serum zonulin 21 , We observed subjects with normoglycemia and those with hyperglycemia did not differ in terms of zonulin levels To gain a deeper insight into the roles played by the duodenal microbiome, we employed the obtained 16S duodenal bacterial profile data of hyperglycemics and normoglycemics to predict the metabolic pathways using Tax4Fun.
There were eleven metabolic KEGG pathways that were differentially abundant between the two groups Supplementary Fig. However, abundance of pathways belonging to aromatic amino acid biosynthesis phenylalanine, tyrosine, and tryptophan , and of TCA cycle pathway were found to be significantly reduced in hyperglycemia Fig.
Correlation between glycemic markers and altered metabolic pathways predicted using duodenal 16S data. a Box-plot showing the comparison of important metabolic pathways between hyperglycemic and normoglycemic.
The x-axis displays the glycemic status of samples and y-axis shows the relative abundance of metabolic pathways. The diagonal depicting the distribution of the variables and the lower panel shows the scatter graphs with detailed illustration of the relation between the variables.
Next, we examined the relationship between the predicted duodenal metabolic pathways and the glycemic markers using the spearman correlation analysis Supplementary Fig.
Over the last two decades, easy accessibility and high bacterial mass have made stool to be the sample of interest in gut microbiome studies.
As the field is evolving, limitations of using stool as a surrogate for the entire gut have become clear. Stool fails to capture the less abundant yet metabolically active microbes, residing in the upper small bowel.
These microbes are involved in nutrient absorption system, and are very relevant to understanding metabolic health risks. In the developing world, where modifiable adverse environmental factors such as sanitation and hygiene may play bigger roles than genetic risks, it is important to explicitly study upper gut flora and potential dysbiosis.
Yet, there has been no systematic study of the upper gut microbiome in the Indian population. We also provided analysis of the differences between paired duodenal and stool microbiome. As expected, there was a clear separation between the microbial profiles of stool and duodenum as seen previously The duodenal biopsy had major phyla as Proteobacteria, even in normoglycemic group 24 and Bacteroidetes with Firmicutes constitute a dominant fraction of stool microbiota.
While firmicutes form a common link, the composition of Firmicutes in duodenum was different from that in stool and was mainly characterized by families Carnobacteriaceae, Bacilliceae , and Acidaminococcaceae Supplementary Fig.
The genera-level classification of duodenum samples was represented by Delftia sp. whereas stool samples had abundance of Megasphaera sp. The data suggested that stool samples did not provide any approximation of the proximal upper gut microbiota.
Many of our findings are consistent with the results of Li et al 25 , where duodenal luminal and mucosal bacteria were analysed and mucosal diversity was shown to be greater with a unique microbial signature. We found that duodenal microbiota may be clinically more relevant in understanding host metabolic health than stool microbiota.
Despite the small significant difference of 1. In duodenum, families Coriobacteriaceae and Akkermansiaceae were found significantly more abundant in normoglycemic compared to hyperglycemic. These bacterial families have a role in the transformation of bile acid 26 and maintaining the acid pool 27 respectively, both of which are critical part of a microbially driven mechanism of regulating glucose metabolism.
Furthermore, disproportion in the abundance of beneficial micro-organisms is linked with diabetes. On the contrary, previous report by Pellegrini et al.
This may indicate that the pathophysiology of T1DM and T2DM are different. This is further substantiated by the significant enrichment of genera that were associated with gut and metabolic improvement in normoglycemic, such as Lactococcus , which is shown to reduce several potential opportunistic microbes 32 , Akkermansia muciniphilia, which confers anti-inflammatory benefits and preserves the mucosal architecture as well as integrity 33 , Another genus is Atopobium , a Hydrogen-sulphide H 2 S producer 35 , that contributes to small intestinal motility 36 , which is an important determinant of blood glucose The stool profile was consistent with the already available data of an Indian gut Families Prevotellaceae, Succinivibrionceae, Lachospiraceae, Ruminococcaceae dominate in both normoglycemic and hyperglycemic.
At class level, Bacteroidia, and Clostridia were prominent and to a lesser extent, Gammaproteobacteria, Actinobacteria and Negativicutes classes were also present. The stool microbial community was similar between the groups while the duodenal profile showed important microbial differences associated with glycemia.
This could be caused by the duodenum and pancreas shared embryological, functional, vascular and structural relationship, which could affect the entero-insular signaling, crucial for maintaining glucose homeostasis We found greater inter-individual variability in the mucosal microbial composition similar to the findings of Vaga et al.
To better understand the full potential of upper gut microbes to modulate human health, the factors influencing the interactions between mucosal microbes and their environment are worth exploring.
Indeed, the prominent one is the amount of duodenal mucosal bacteria due to the aggregates of lymphoid follicles residing underneath Excess of bacteria may be related to immune activation that may have an etiological role in diabetes Further, the positive correlation between TNF-α and HbA1c corroborated such an association.
We also noted reduced anti-inflammatory IL levels, which is shown to increase insulin sensitivity and affect peripheral glucose metabolism 44 , Together, our results suggested the duodenal microenvironment to be inflammatory, overpopulated with bacteria, and with reduced anti-inflammatory signals in hyperglycemic condition.
Another important microenvironment parameter influenced by intestinal bacteria is gut permeability. In this study, we measured serum zonulin, a marker of small intestinal permeability. Research by Asmar et al. In our data, we did not find any difference in zonulin levels between the groups, as observed in previous reports 47 or any association with taxa.
Interestingly, higher zonulin levels showed a trend of association with increased mucosal bacterial load. The relatively small size of the dataset for zonulin measurements is likely to underestimate the relationship between permeability and microbial markers.
To our observation, our data was at least sufficient to provide a robust validation to the previously addressed associations between zonulin and anthropometric measurements related to obesity Besides physical barrier, tissue oxygenation maintains the functional balance of mucosa and directly shapes microbial community as exemplified by the increased amount of dissolved oxygen after RYGB surgery that favors the proliferation of Proteobacteria phyla On the other hand, microbes also actively participate in shaping the mucosal environment by degrading mucus or triggering AMP production Our observations showed a lower duodenal tissue oxygenation in hyperglycemia.
This decrease could be due to the reduced perfusion of the gut or due to increased local utilization of oxygen 51 , which could not be distinguished by the data. Thus, the question of whether decreased oxygenation is a cause, or consequence, of the altered microbial profile or metabolism in the duodenum, remains to be explored.
It is interesting that the oxygen saturation values for both groups are lower than those reported in Western studies Since this is the first such study from South Asia, it is conceivable that bacterial overload and lower oxygenation may be frequent in low-middle income tropical countries.
Future multi-centric studies should explore this further. There is not a considerable body of clinical data to link the pathogenesis of metabolic diseases to microbial disturbances in the upper gut or the associated environment. In this study, we have investigated this relationship and predicted the metabolic state of the duodenum in hyperglycemic condition.
In duodenum, the main source of energy is amino acids such that 30 to 50 percent of amino acids do not enter portal circulation To our observation, we found diminished ability of synthesizing aromatic amino acids phenylalanine, tyrosine, and tryptophan in hyperglycemia which could impact host physiology.
Small molecules with potent biological effects are derived from these aromatic amino acids. For example, tryptophan derived metabolites viz. Tryptamine, indole 3-acetic acid are important modulators of epithelial integrity and mucosal immune response Another key pathway implicated in altered β-cell metabolism is the TCA cycle pathway 55 , which was found to be reduced in the duodenum of hyperglycemic.
Such abberant change in local duodenal metabolism under high glycemic condition could be brought on by altered microbial metabolism. The changed metabolic state could possibly also disrupt the core imbued duodenal pathways of nutrition sensing and entero-insular signalling, critical for maintaining glucose homeostasis We acknowledge some important limitations of the study.
First, the age of the normoglycemic group was significantly lower than that of the hyperglycemics. This could be attributed to the increased prevalence of hyperglycemia with aging.
To account for effect of age, we have adjusted the age variable while analyzing data using DESeq2, as previously reported in small bowel microbiome study Secondly, the study was performed in a limited number of subjects and directionality of associations could not be concluded from the data.
Third, zonulin levels represent the small intestinal permeability and are not specific only to duodenum but in the jejunum as well, thus it cannot be directly linked to duodenal microbiome.
Fourth, the study is cross-sectional in nature and captures the intestinal bacterial composition at a single time point. There are multiple factors which define strengths of the study. First, we included microenvironment variables oxygenation, permeability, inflammation alongside the microbiome, unlike most prior duodenal microbiome studies.
Second, the use of negative controls during DNA extraction provided an additional confidence to the results of the study, as low biomass biopsy samples are prone to contamination. Third, we analyzed hyperglycemia without remaining restricted only to diabetics, to study the early disease associated microbial changes which could be leading to diabetes and most importantly, the paired assessment of duodenal and stool bacterial profile was performed for the enrolled patients.
In conclusion, variability in the duodenal mucosa associated microbiota profile showed association with glycemia. The assessment of small intestinal mucosa associated microbiota, with the inclusion of a quantitative approach, merits deeper investigation as a health determinant.
Understanding the compositional changes in the small intestine bacterial population is an important area of research, especially in the context of human nutrition and to find future link to human health and disease. In this cross-sectional study, we recruited 69 subjects after obtaining informed consent in —19 according to the guidelines of the Declaration of Helsinki, The study was conducted in compliance with the Indian Council for Medical Research guidelines, Good Practices for Clinical Research in India.
Written informed consent was obtained from all study participants. The inclusion criteria included adult subjects 18—64 years who underwent Upper GI endoscopy as part of their standard screening for GI symptoms, anemia as well as to rule out celiac disease or GI bleeding.
Subjects who were pregnant, lactating, had celiac disease, colon cancer and gastrointestinal GI bleeding were excluded. Overall, 33 established hyperglycemic and 21 normoglycemic were included in the study.
As shown in Table 1 , there were statistically significant differences in age and clinical traits of the groups, with unvarying BMI. Duodenal biopsies from the D2 region were obtained endoscopically using the Olympus endoscope GIF-HQ Blood samples were collected in additive free red capped vacutainers to obtain sera.
The extraction of DNA from stool and biopsy samples was performed using Qiagen Stool Mini kit and DNeasy Blood and Tissue kit respectively, with the additional step of bead-beating Negative controls were maintained in each batch of DNA extraction. For library preparation, V3-V4 region of 16S rRNA was selected and paired-end sequencing was performed as per the Illumina approved protocol for 16 s rRNA marker gene survey 60 on MiSeq platform V3, cycles, Illumina.
The final library was reconstituted using the Tris buffer at 4 nM. The amplicon sequence variant ASV tables from each of the two duodenum and stool runs were then combined and taxonomy was assigned using SILVA database Release Subsequently, the phyloseq object containing the phylogenetic tree, constructed using the Neighbor joining method, was decontaminated using the "decontam" package after chimera removal.
In decontam, there are two approaches for decontamination; i. The combined approach was applied for stool, and the recommended prevalence-based method was used for low-biomass duodenum samples. R was used for statistical analysis and ggplot2, ggpubr packages were used for data visualization.
Based on the distribution, clinical parameters were expressed as medians with interquartile range [Q1-Q3] or mean standard deviation. Histogram was constructed to represent the top 10 taxa with the highest proportion at different taxonomic levels in duodenum and stool samples.
To compare paired quantitative samples, non-parametric Wilcoxon Sign Rank test was applied. For beta diversity, the rarefied object was hellinger transformed and Principal Coordinate Analysis PCoA was used to discover grouping patterns.
Variability in the community structure was assessed by permutational analysis of variance PERMANOVA using adonis function in vegan package The inter-sample Bray Curtis measurements were computed using the microbiome package. Using the DESeq2 package, the differential abundance analysis on untransformed species level phyloseq object was performed between stool and biopsy samples.
The differential abundance analysis was also performed between hyperglycemic and normoglycemic to find characteristic features at different taxonomic levels with respect to glycemia in stool and duodenum samples.
A series of tenfold dilutions of recombinant plasmid full length 16S rRNA gene of E. coli cloned in pRS vector were used to create a standard curve. The amplification conditions were 3 min at 95 °C followed by 40 cycles of 95 °C for 15 s and data collection at 60 °C for 20 s.
The software xPONENT 3. was used to examine the data and the levels of inflammatory markers were compared between the two study groups. The relationship between these factors and anthropometric measurements, glycemic markers, and bacterial load was examined by Spearman correlation test.
The FDA-approved T-Stat Spectros , was used to measure duodenal mucosal oxygen saturation D2. The gut permeability assay using serum zonulin was performed using ELISA kit Immundiagnostik AG, Bensheim, Germany Only pre-endoscopic sera samples were used to measure zonulin 16 hyperglycemic and 12 normoglycemic.
The spearman correlation test was used to test association between zonulin and anthropometric measurements and bacterial load. Based on the 16S sequencing data, Tax4Fun t4f. in "themetagenomics" package was used to predict metabolic capabilities of duodenal bacteria.
The samples were normalized by overall functional abundance to compare the relative abundance of predicted pathways between samples and the anticipated functional profile was adjusted for 16S copy number variability.
A linear regression model adjusted for sequencing batch effects and fluctuation in the fraction of unused taxonomic units no hit in the reference database , was used to find pathways that were linked to glycemic status. Subset of data was plotted as correlation graphs using psych package and the entire data was represented as heatmap using the pheatmap package.
Raw sequences and associated metadata have been deposited on NCBI public repository [Bio project- PRJNA, Study ID: SRP]. Abdul, M. et al.
Epidemiology of type 2 diabetes—global burden of disease and forecasted trends. Global Health. Google Scholar. Zhou, W. Longitudinal multi-omics of host—microbe dynamics in prediabetes. Nature , — Article ADS CAS PubMed PubMed Central Google Scholar. Vrieze, A. Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome.
e7 Article CAS PubMed Google Scholar. van Deuren, T. Butyrate to combat obesity and obesity-associated metabolic disorders: Current status and future implications for therapeutic use.
PubMed PubMed Central Google Scholar. Fasano, A. All disease begins in the leaky gut: role of zonulin-mediated gut permeability in the pathogenesis of some chronic inflammatory diseases.
FResearch 9 , 69 Article CAS Google Scholar. Cani, P. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 56 , — Demmer, R. Subgingival microbiota and longitudinal glucose change: The oral infections, glucose intolerance and insulin resistance study ORIGINS.
J Dent Res. Article CAS PubMed PubMed Central Google Scholar. Van Baar, A. Endoscopic duodenal mucosal resurfacing for the treatment of type 2 diabetes mellitus: One-year results from the first international, open-label, prospective, multicentre study.
Gut 69 , — Article PubMed Google Scholar. Mingrone, G. Safety and efficacy of hydrothermal duodenal mucosal resurfacing in patients with type 2 diabetes: The randomised, double-blind, sham-controlled, multicentre REVITA-2 feasibility trial.
Gut 71 , 1—11 Theodorakis, M. Human duodenal enteroendocrine cells: Source of both incretin peptides, GLP-1 and GIP. Article Google Scholar. Bauer, P. Metformin alters upper small intestinal microbiota that impact a glucose-SGLT1-sensing glucoregulatory pathway.
Cell Metab. e5 Côté, C. Resveratrol activates duodenal Sirt1 to reverse insulin resistance in rats through a neuronal network. Donaldson, G. Gut biogeography of the bacterial microbiota. Article PubMed PubMed Central Google Scholar.
Jung, H. Role of the duodenum in the pathogenesis of functional dyspepsia: A paradigm shift. Shanahan, E. Influence of cigarette smoking on the human duodenal mucosa-associated microbiota. Shin, A. Characterization of proximal small intestinal microbiota in patients with suspected small intestinal bacterial overgrowth: A cross-sectional study.
Cheng, J. Duodenal microbiota composition and mucosal homeostasis in pediatric celiac disease. BMC Gastroenterol. Kohi, S. Alterations in the duodenal fluid microbiome of patients with pancreatic cancer.
Shulzhenko, N. CVID enteropathy is characterized by exceeding low mucosal IgA levels and interferon-driven inflammation possibly related to the presence of a pathobiont. Chen, R. Duodenal microbiota in stunted undernourished children with enteropathy.
Mörkl, S. Gut microbiota, dietary intakes and intestinal permeability reflected by serum zonulin in women. Kaczmarczyk, M. The gut microbiota is associated with the small intestinal paracellular permeability and the development of the immune system in healthy children during the first two years of life.
Leite, G. Mapping the segmental microbiomes in the human small bowel in comparison with stool: A REIMAGINE Study. Nardelli, C. Characterization of the duodenal mucosal microbiome in obese adult subjects by 16S rRNA sequencing.
Microorganisms 8 , Li, G. Diversity of duodenal and rectal microbiota in biopsy tissues and luminal contents in healthy volunteers.
Zhao, X. Response of gut microbiota to metabolite changes induced by endurance exercise. Juárez-Fernández, M. The synbiotic combination of Akkermansia muciniphila and quercetin ameliorates early obesity and NAFLD through gut microbiota reshaping and bile acid metabolism modulation.
Bai, L. Comparative genomics analysis of Acinetobacter haemolyticus isolates from sputum samples of respiratory patients. Genomics , — Lécuyer, H. Dolosigranulum pigrum causing nosocomial pneumonia and Septicemia. Tetz, G. Type 1 Diabetes: an association between autoimmunity, the dynamics of gut amyloid-producing E.
coli and their phages. Sci Rep. Article ADS PubMed PubMed Central Google Scholar. Pellegrini, S. Duodenal mucosa of patients with type 1 diabetes shows distinctive inflammatory profile and microbiota.
Carvalho, R. Gut microbiome modulation during treatment of mucositis with the dairy bacterium Lactococcus lactis and recombinant strain secreting human antimicrobial PAP.
Depommier, C. Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: A proof-of-concept exploratory study. Bian, X. Administration of Akkermansia muciniphila ameliorates dextran sulfate sodium-induced ulcerative colitis in mice. Mottawea, W. Linden, D. Hydrogen sulfide signaling in the gastrointestinal tract.
Redox Signal. Chaikomin, R. Upper gastrointestinal function and glycemic control in diabetes mellitus. World J. Kulkarni, A. Mining the core gut microbiome from a sample indian population.
Indian J. Del Castillo, E. The microbiomes of pancreatic and duodenum tissue overlap and are highly subject specific but differ between pancreatic cancer and noncancer subjects. Cancer Epidemiol.
Vaga, S. Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals.
Magne, F. Nutrients 12 , Wells, J. Epithelial crosstalk at the microbiota—Mucosal interface. Article ADS CAS PubMed Google Scholar. Yan, L. Association between small intestinal bacterial overgrowth and beta-cell function of type 2 diabetes. Hong, E. Interleukin prevents diet-induced insulin resistance by attenuating macrophage and cytokine response in skeletal muscle.
Diabetes 58 , — Straczkowski, M. Plasma interleukin concentration is positively related to insulin sensitivity in young healthy individuals. Diabetes Care 28 , — Asmar, R.
Each person digests food differently based on their gut microbiome. At Viome, we studied how different individuals responded to foods and found that people experienced very different glycemic responses even among the same foods like bananas and bread. Read more here. Your gut microbiome consists of trillions of bacteria that help you digest your food and absorb the nutrients your body needs.
Depending on your diet, your microbiome could either produce beneficial compounds or irritating substances that could impact how much glucose crosses into your bloodstream and how your metabolic rate. And although the Glycemic Index can help you identify foods high in glucose, Viome can help you find out which foods are right for your body to help balance your gut and your blood sugar, while guiding you toward a healthy weight.
Your best advocate for getting just the right food and supplements your body needs is Viome. Our team at Viome goes beyond a Google search or one-size-fits-all answer to give you evidence-based results on which foods you should enjoy, minimize, and avoid for a more controlled glycemic response.
Because of the complex relationship between diet, mood, and the gut microbiome, scientists have found that dietary changes can impact the relationship between dysregulated digestion and metabolism with mental health disorders.
Many studies have also shown that those who suffer from intestinal issues and disruptions to normal digestion note various mental health symptoms like anxiety and depression.
The gut microbiome plays a significant role in the production and interaction of many neurotransmitters necessary for the body and mind to function at its best. In a recent clinical study from the Viome Health Sciences team, Viome recommendations were shown to help relieve issues associated with digestion and even lessened disruptions to mood, such as symptoms of depression and anxiety.
NEUHOUSERKATHERINE M. NEWTON G,ycemic, KARA BREYMEYERMEREDITH Glycdmic. HULLAR; OR: The Gut Microbiome in Adults microbilta Prediabetes Glycemic load and gut microbiota Effect of Dietary Glycemic Index. Compositional and functional differences in gut bacteria exist between adults with normal glucose tolerance, IGT and type 2 diabetes and can change with dietary factors. The study enrolled 53 adults mean±SEM: age HGI diet Glycemic load and gut microbiota Easy carbohydrate counting of bacteria microbipta in the loa may contribute Glycemic load and gut microbiota the development of Type Glyfemic diabetes, while another may protect from the Glyccemic, Glycemic load and gut microbiota micorbiota early results from an ongoing, prospective study led by Respiratory health at Cedars-Sinai. High-potency weight loss pills study, published in the peer-reviewed journal Guhfound microbikta with higher levels of a bacterium called Miccrobiota tended to have higher insulin sensitivity, while those whose microbiomes had higher levels of the bacterium Flavonifractor tended to have lower insulin sensitivity. For years, investigators have sought to understand why people develop diabetes by studying the composition of the microbiome, which is a collection of microorganisms that include fungi, bacteria and viruses that live in the digestive tract. The microbiome is thought to be affected by medications and diet. Mark Goodarzi, MD, PhDthe director of the Endocrine Genetics Laboratory at Cedars-Sinai, is leading an ongoing study that is following and observing people at risk for diabetes to learn whether those with lower levels of these bacteria develop the disease.
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