scholarly journals PTU-007 The development of obesity and type 2 diabetes harnessing the gut microbiome and host genetics

2019 ◽  
Author(s):  
Jocelyn Cheuk ◽  
Sylvia Pender ◽  
Felino Cagampang
Keyword(s):  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Host Genetics

Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies

Gut ◽  
2021 ◽  
pp. gutjnl-2021-324053
Author(s):  
Qibin Qi ◽  
Jun Li ◽  
Bing Yu ◽  
Jee-Young Moon ◽  
Jin C Chai ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Probiotic Bacterium ◽  
Gut Bacteria ◽  
Dietary Factors ◽  
Milk Intake ◽  
Tryptophan Metabolism ◽  
Host Genetics ◽  
Tryptophan Metabolites

ObjectiveTryptophan can be catabolised to various metabolites through host kynurenine and microbial indole pathways. We aimed to examine relationships of host and microbial tryptophan metabolites with incident type 2 diabetes (T2D), host genetics, diet and gut microbiota.MethodWe analysed associations between circulating levels of 11 tryptophan metabolites and incident T2D in 9180 participants of diverse racial/ethnic backgrounds from five cohorts. We examined host genome-wide variants, dietary intake and gut microbiome associated with these metabolites.ResultsTryptophan, four kynurenine-pathway metabolites (kynurenine, kynurenate, xanthurenate and quinolinate) and indolelactate were positively associated with T2D risk, while indolepropionate was inversely associated with T2D risk. We identified multiple host genetic variants, dietary factors, gut bacteria and their potential interplay associated with these T2D-relaetd metabolites. Intakes of fibre-rich foods, but not protein/tryptophan-rich foods, were the dietary factors most strongly associated with tryptophan metabolites. The fibre-indolepropionate association was partially explained by indolepropionate-associated gut bacteria, mostly fibre-using Firmicutes. We identified a novel association between a host functional LCT variant (determining lactase persistence) and serum indolepropionate, which might be related to a host gene-diet interaction on gut Bifidobacterium, a probiotic bacterium significantly associated with indolepropionate independent of other fibre-related bacteria. Higher milk intake was associated with higher levels of gut Bifidobacterium and serum indolepropionate only among genetically lactase non-persistent individuals.ConclusionHigher milk intake among lactase non-persistent individuals, and higher fibre intake were associated with a favourable profile of circulating tryptophan metabolites for T2D, potentially through the host–microbial cross-talk shifting tryptophan metabolism toward gut microbial indolepropionate production.

scholarly journals The Relationship between the Gut Microbiome and Metformin as a Key for Treating Type 2 Diabetes Mellitus

2021 ◽  
Vol 22 (7) ◽  
pp. 3566
Author(s):  
Chae Bin Lee ◽  
Soon Uk Chae ◽  
Seong Jun Jo ◽  
Ui Min Jerng ◽  
Soo Kyung Bae
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Glucose Metabolism ◽  
Gut Microbiome ◽  
Metabolic Disorders ◽  
Current Knowledge ◽  
Potential Target ◽  
The Relationship

Metformin is the first-line pharmacotherapy for treating type 2 diabetes mellitus (T2DM); however, its mechanism of modulating glucose metabolism is elusive. Recent advances have identified the gut as a potential target of metformin. As patients with metabolic disorders exhibit dysbiosis, the gut microbiome has garnered interest as a potential target for metabolic disease. Henceforth, studies have focused on unraveling the relationship of metabolic disorders with the human gut microbiome. According to various metagenome studies, gut dysbiosis is evident in T2DM patients. Besides this, alterations in the gut microbiome were also observed in the metformin-treated T2DM patients compared to the non-treated T2DM patients. Thus, several studies on rodents have suggested potential mechanisms interacting with the gut microbiome, including regulation of glucose metabolism, an increase in short-chain fatty acids, strengthening intestinal permeability against lipopolysaccharides, modulating the immune response, and interaction with bile acids. Furthermore, human studies have demonstrated evidence substantiating the hypotheses based on rodent studies. This review discusses the current knowledge of how metformin modulates T2DM with respect to the gut microbiome and discusses the prospect of harnessing this mechanism in treating T2DM.

An alcohol-free beer enriched with isomaltulose and a resistant dextrin modulates gut microbiome in subjects with type 2 diabetes mellitus and overweight or obesity: a pilot study

2021 ◽  
Author(s):  
Rocío Mateo-Gallego ◽  
Isabel Moreno-Indias ◽  
Ana M. Bea ◽  
Lidia Sánchez-Alcoholado ◽  
Antonio J. Fumanal ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Pilot Study ◽  
Gut Microbiota ◽  
Gut Microbiome ◽  
Low Doses ◽  
Resistant Dextrin ◽  
Overweight Or Obesity

An alcohol-free beer including the substitution of regular carbohydrates for low doses of isomaltulose and maltodextrin within meals significantly impacts gut microbiota in diabetic subjects with overweight or obesity.

scholarly journals Arrhythmic gut microbiome signatures for risk profiling of Type-2 Diabetes

2019 ◽  
Author(s):  
Sandra Reitmeier ◽  
Silke Kießling ◽  
Thomas Clavel ◽  
Markus List ◽  
Eduardo L. Almeida ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Factors Affecting ◽  
Risk Profiling ◽  
Initial Sampling ◽  
Risk Pattern

SummaryTo combat the epidemic increase in Type-2-Diabetes (T2D), risk factors need to be identified. Diet, lifestyle and the gut microbiome are among the most important factors affecting metabolic health. We demonstrate in 1,976 subjects of a prospective population cohort that specific gut microbiota members show diurnal oscillations in their relative abundance and we identified 13 taxa with disrupted rhythmicity in T2D. Prediction models based on this signature classified T2D with an area under the curve of 73%. BMI as microbiota-independent risk marker further improved diagnostic classification of T2D. The validity of this arrhythmic risk signature to predict T2D was confirmed in 699 KORA subjects five years after initial sampling. Shotgun metagenomic analysis linked 26 pathways associated with xenobiotic, amino acid, fatty acid, and taurine metabolism to the diurnal oscillation of gut bacteria. In summary, we determined a cohort-specific risk pattern of arrhythmic taxa which significantly contributes to the classification and prediction of T2D, highlighting the importance of circadian rhythmicity of the microbiome in targeting metabolic human diseases.

scholarly journals Qualitative Parameters of the Colonic Flora in Patients with HNF1A-MODY Are Different from Those Observed in Type 2 Diabetes Mellitus

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Sandra Mrozinska ◽  
Piotr Radkowski ◽  
Tomasz Gosiewski ◽  
Magdalena Szopa ◽  
Malgorzata Bulanda ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Gut Microbiome ◽  
Bacterial Flora ◽  
Control Group ◽  
16S Sequencing ◽  
Microbiome Composition ◽  
Colonic Flora

Background. Type 2 diabetes mellitus (T2DM) is determined by genetic and environmental factors. There have been many studies on the relationship between the composition of the gastrointestinal bacterial flora, T2DM, and obesity. There are no data, however, on the gut microbiome structure in monogenic forms of the disease including Maturity Onset Diabetes of the Young (MODY).Methods. The aim of the investigation was to compare the qualitative parameters of the colonic flora in patients with HNF1A-MODY and T2DM and healthy individuals. 16S sequencing of bacterial DNA isolated from the collected fecal samples using the MiSeq platform was performed.Results. There were significant between-group differences in the bacterial profile. At the phylum level, the amount of Proteobacteria was higher (p=0.0006) and the amount of Bacteroidetes was lower (p=0.0005) in T2DM group in comparison to the control group. In HNF1A-MODY group, the frequency of Bacteroidetes was lower than in the control group (p=0.0143). At the order level, Turicibacterales was more abundant in HNF1A-MODY group than in T2DM group.Conclusions. It appears that there are differences in the gut microbiome composition between patients with HNF1A-MODY and type 2 diabetes. Further investigation on this matter should be conducted.

scholarly journals The Gut Microbiome and Type 2 Diabetes Mellitus: Discussing A Complex Relationship

Biomedicines ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Angelos K. Sikalidis ◽  
Adeline Maykish
Keyword(s):  
Diabetes Mellitus ◽  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Gut Microbiome ◽  
Disease Onset ◽  
The United States ◽  
Key Factors ◽  
Fat Diets

Type 2 diabetes mellitus (T2DM) is a disease that affects over 9% of the United States population and is closely linked to obesity. While obesity was once thought to stem from a sedentary lifestyle and diets high in fat, recent evidence supports the idea that there is more complexity pertinent to the issue. The human gut microbiome has recently been the focus in terms of influencing disease onset. Evidence has shown that the microbiome may be more closely related to T2DM than what was originally thought. High fat diets typically result in poor microbiome heath, which then shifts the gut into a state of dysbiosis. Dysbiosis can then lead to metabolic deregulation, including increased insulin resistance and inflammation, two key factors in the development of T2DM. The purpose of this review is to discuss how microbiome relates to T2DM onset, especially considering obesity, insulin resistance, and inflammation.

scholarly journals Interpretable Machine Learning Algorithm Reveals Novel Gut Microbiome Features in Predicting Type 2 Diabetes

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1559-1559
Author(s):  
Wanglong Gou ◽  
Chu-Wen Ling ◽  
Yan He ◽  
Zengliang Jiang ◽  
Yuanqing Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Large Scale ◽  
Validation Cohort ◽  
Learning Algorithm ◽  
External Validation ◽  
Positive Association ◽  
Interpretable Machine Learning

Abstract Objectives The gut microbiome-type 2 diabetes (T2D) relationship among human cohorts have been controversial. We hypothesized that this limitation could be addressed by integrating the cutting-edge interpretable machine learning framework and large-scale human cohort studies. Methods 3 independent cohorts with >9000 participants were included in this study. We proposed a new machine learning-based analytic framework — using LightGBM to infer the relationship between incorporated features and T2D, and SHapley Additive explanation(SHAP) to identified microbiome features associated with the risk of T2D. We then generated a microbiome risk score (MRS) integrating the threshold and direction of the identified microbiome features to predict T2D risk. Results We finally identified 15 microbiome features (two of them are indicators of microbial diversity, others are taxa-related features) associated with the risk of T2D. The identified T2D-related gut microbiome features showed superior T2D prediction accuracy compared to host genetics or traditional risk factors. Furthermore, we found that the MRS (per unit change in MRS) consistently showed positive association with T2D risk in the discovery cohort (RR 1.28, 95%CI 1.23-1.33), external validation cohort 1 (RR 1.23, 95%CI 1.13-1.34) and external validation cohort 2 (GGMP, RR 1.12, 95%CI 1.06-1.18). The MRS could also predict future glucose increment. We subsequently identified dietary and lifestyle factors which could prospectively modulate the microbiome features, and found that body fat distribution may be the key factor modulating the gut microbiome-T2D relationship. Conclusions Taken together, we proposed a new analytical framework for the investigation of microbiome-disease relationship. The identified microbiome features may serve as potential drug targets for T2D in future. Funding Sources This study was funded by National Natural Science Foundation of China (81903316, 81773416), Westlake University (101396021801) and the 5010 Program for Clinical Researches (2007032) of the Sun Yat-sen University (Guangzhou, China).

scholarly journals Gut microbiome-related effects of berberine and probiotics on type 2 diabetes (the PREMOTE study)

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Yifei Zhang ◽  
Yanyun Gu ◽  
Huahui Ren ◽  
Shujie Wang ◽  
Huanzi Zhong ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Controlled Trial ◽  
Oral Intake ◽  
Glycated Haemoglobin ◽  
Double Blind ◽  
Promising Target ◽  
Bacteriostatic Agent ◽  
Gastrointestinal Side Effects

Abstract Human gut microbiome is a promising target for managing type 2 diabetes (T2D). Measures altering gut microbiota like oral intake of probiotics or berberine (BBR), a bacteriostatic agent, merit metabolic homoeostasis. We hence conducted a randomized, double-blind, placebo-controlled trial with newly diagnosed T2D patients from 20 centres in China. Four-hundred-nine eligible participants were enroled, randomly assigned (1:1:1:1) and completed a 12-week treatment of either BBR-alone, probiotics+BBR, probiotics-alone, or placebo, after a one-week run-in of gentamycin pretreatment. The changes in glycated haemoglobin, as the primary outcome, in the probiotics+BBR (least-squares mean [95% CI], −1.04[−1.19, −0.89]%) and BBR-alone group (−0.99[−1.16, −0.83]%) were significantly greater than that in the placebo and probiotics-alone groups (−0.59[−0.75, −0.44]%, −0.53[−0.68, −0.37]%, P < 0.001). BBR treatment induced more gastrointestinal side effects. Further metagenomics and metabolomic studies found that the hypoglycaemic effect of BBR is mediated by the inhibition of DCA biotransformation by Ruminococcus bromii. Therefore, our study reports a human microbial related mechanism underlying the antidiabetic effect of BBR on T2D. (Clinicaltrial.gov Identifier: NCT02861261).

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