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 Diabetes-associated alterations in the cecal microbiome and metabolome are independent of diet or environment in the UC Davis Type 2 Diabetes Mellitus Rat model

2018 ◽  
Vol 315 (5) ◽  
pp. E961-E972 ◽  
Author(s):  
Brian D. Piccolo ◽  
James L. Graham ◽  
Kimber L. Stanhope ◽  
Intawat Nookaew ◽  
Kelly E. Mercer ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Gut Microbiome ◽  
Bacterial Species ◽  
Dietary Factors ◽  
Metagenomic Sequencing ◽  
Bacterial Gene ◽  
Late Stages

The composition of the gut microbiome is altered in obesity and type 2 diabetes; however, it is not known whether these alterations are mediated by dietary factors or related to declines in metabolic health. To address this, cecal contents were collected from age-matched, chow-fed male University of California, Davis Type 2 Diabetes Mellitus (UCD-T2DM) rats before the onset of diabetes (prediabetic PD; n = 15), 2 wk recently diabetic (RD; n = 10), 3 mo (D3M; n = 11), and 6 mo (D6M; n = 8) postonset of diabetes. Bacterial species and functional gene counts were assessed by shotgun metagenomic sequencing of bacterial DNA in cecal contents, while metabolites were identified by gas chromatography-quadrupole time-off-flight-mass spectrometry. Metagenomic analysis showed a shift from Firmicutes species in early stages of diabetes (PD + RD) toward an enrichment of Bacteroidetes species in later stages of diabetes (D3M + D6M). In total, 45 bacterial species discriminated early and late stages of diabetes with 25 of these belonging to either Bacteroides or Prevotella genera. Furthermore, 61 bacterial gene clusters discriminated early and later stages of diabetes with elevations of enzymes related to stress response (e.g., glutathione and glutaredoxin) and amino acid, carbohydrate, and bacterial cell wall metabolism. Twenty-five cecal metabolites discriminated early vs. late stages of diabetes, with the largest differences observed in abundances of dehydroabietic acid and phosphate. Alterations in the gut microbiota and cecal metabolome track diabetes progression in UCD-T2DM rats when controlling for diet, age, and housing environment. Results suggest that diabetes-specific host signals impact the ecology and end product metabolites of the gut microbiome when diet is held constant.

scholarly journals Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes

2020 ◽  
Author(s):  
Wanglong Gou ◽  
Chu-wen Ling ◽  
Yan He ◽  
Zengliang Jiang ◽  
Yuanqing Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Diabetes Risk ◽  
Dietary Factors ◽  
Blood Metabolites ◽  
Learning Framework ◽  
Interpretable Machine Learning ◽  
Prospective Association

<b>OBJECTIVE </b>To identify the core gut microbial features associated with type 2 diabetes risk, and potential demographic, adiposity and dietary factors associated with these features.<b></b> <p><b>RESEARCH DESIGN AND METHODS </b><a>We used an interpretable machine learning framework to identify the type 2 diabetes-related </a>gut microbiome features in the cross-sectional analyses of three Chinese cohorts: <a></a><a>one discovery cohort </a>(n=1832, 270 cases) and two validation cohorts (cohort 1: n=203, 48 cases; cohort 2: n=7009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 non-T2D participants, and assessed the correlation between the MRS and host blood metabolites (n=1016). We transferred human faecal samples with different MRS levels to <a>germ-free mice </a>to confirm the <a>MRS-</a>type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity and dietary factors with the MRS (n=1832).<b></b></p> <p><b>RESULTS<a> </a></b><a></a><a>The MRS (including 14 </a>microbial features) consistently associated with type 2 diabetes, with risk ratio for per one unit change in MRS 1.28 (95%CI 1.23-1.33), 1.23 (1.13-1.34) and 1.12 (1.06-1.18) across 3 cohorts. The MRS was positively associated with future glucose increment (P<0.05), and was correlated with a variety of gut microbiota-derived blood metabolites. Animal study further <a>confirms the MRS-</a>type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome-type 2 diabetes relationship. <b></b></p> <b>CONCLUSIONS </b>Our results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment.

scholarly journals Interpretable machine learning framework reveals novel gut microbiome features in predicting type 2 diabetes

2020 ◽  
Author(s):  
Wanglong Gou ◽  
Chu-wen Ling ◽  
Yan He ◽  
Zengliang Jiang ◽  
Yuanqing Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Drug Targets ◽  
Fat Distribution ◽  
Analytical Framework ◽  
Dietary Factors ◽  
Interpretable Machine Learning ◽  
Key Factor

AbstractGut microbiome targets for type 2 diabetes (T2D) prevention among human cohorts have been controversial. Using an interpretable machine learning-based analytic framework, we identified robust human gut microbiome features, with their optimal threshold, in predicting T2D. Based on the results, we constructed a microbiome risk score (MRS), which was consistently associated with T2D across 3 independent Chinese cohorts involving 9111 participants (926 T2D cases). The MRS could also predict future glucose increment, and was correlated with a variety of gut microbiota-derived blood metabolites. Faecal microbiota transplantation from humans to germ-free mice demonstrated a causal role of the identified combination of microbes in the T2D development. We further identified adiposity and dietary factors which could prospectively modulate the MRS, and found that body fat distribution may be the key factor modulating the gut microbiome-T2D relationship. Taken together, we proposed a new analytical framework for the investigation of microbiome-disease relationship. The identified microbiota may serve as potential drug targets for T2D in future.

scholarly journals Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes

2020 ◽  
Author(s):  
Wanglong Gou ◽  
Chu-wen Ling ◽  
Yan He ◽  
Zengliang Jiang ◽  
Yuanqing Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Diabetes Risk ◽  
Dietary Factors ◽  
Blood Metabolites ◽  
Learning Framework ◽  
Interpretable Machine Learning ◽  
Prospective Association

<b>OBJECTIVE </b>To identify the core gut microbial features associated with type 2 diabetes risk, and potential demographic, adiposity and dietary factors associated with these features.<b></b> <p><b>RESEARCH DESIGN AND METHODS </b><a>We used an interpretable machine learning framework to identify the type 2 diabetes-related </a>gut microbiome features in the cross-sectional analyses of three Chinese cohorts: <a></a><a>one discovery cohort </a>(n=1832, 270 cases) and two validation cohorts (cohort 1: n=203, 48 cases; cohort 2: n=7009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 non-T2D participants, and assessed the correlation between the MRS and host blood metabolites (n=1016). We transferred human faecal samples with different MRS levels to <a>germ-free mice </a>to confirm the <a>MRS-</a>type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity and dietary factors with the MRS (n=1832).<b></b></p> <p><b>RESULTS<a> </a></b><a></a><a>The MRS (including 14 </a>microbial features) consistently associated with type 2 diabetes, with risk ratio for per one unit change in MRS 1.28 (95%CI 1.23-1.33), 1.23 (1.13-1.34) and 1.12 (1.06-1.18) across 3 cohorts. The MRS was positively associated with future glucose increment (P<0.05), and was correlated with a variety of gut microbiota-derived blood metabolites. Animal study further <a>confirms the MRS-</a>type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome-type 2 diabetes relationship. <b></b></p> <b>CONCLUSIONS </b>Our results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment.

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.

Association of Pro-inflammatory Diet With Risk of Type 2 Diabetes and Hypertension; Results From RaNCD Cohort Study

2021 ◽  
Author(s):  
Samira Arbabi Jam ◽  
Shahab Rezaeian ◽  
Farid Najafi ◽  
Behroz Hamze ◽  
Ebrahim Shakiba ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 2 Diabetes ◽  
Cohort Study ◽  
Assessment Tool ◽  
Communicable Diseases ◽  
Dietary Factors ◽  
Base Line ◽  
Cross Sectional ◽  
Non Communicable Diseases

Abstract Background: Dietary factors and inflammation are associated with most non-communicable diseases (NCDs). The Dietary Inflammatory Index (DII) is a developed validated assessment tool. This study was conducted to assessed association of DII with the hypertension and type 2 diabetes mellitus (T2DM). Methods: This cross-sectional analysis was performed on 9,811 participants of 35 to 65 years of the base-line phase data of Ravansar Non-Communicable Diseases (RaNCD) cohort study. The DII was calculated using 31 parameters food of food frequency questionnaire (FFQ). The estimates were performed using univariable and multivariable logistic regression.Results: The mean DII scores in healthy participants was -2.32± 1.60, in participant with T2DM, hypertension and both were -2.23± 1.59, -2.45± 1.60 and -2.25± 1.60, respectively (P= 0.011). Pre-inflammatory diet was significantly higher in male compared to female (P<0.001). In the most pro-inflammatory diet was significantly higher BMI (body mass index), triglyceride, energy intake, smokers; and was significantly lower socio-economic status (SES), physical activity and HDL-C compared to the most anti-inflammatory diet. Participants with T2DM, hypertension and comorbidity had a significantly higher mean of anthropometry indices (P<0.001) and lipid profile compared to healthy subjects (P<0.001). After adjustment for age, sex and physical activity, the odds of T2DM in the fourth quartile of DII was 1.48 (95% CI: 1.19, 1.85) times higher compared to the first quartile of DII. Conclusions: Pro-inflammatory diet was weak associations with hypertension. Pro-inflammatory diet was significant associations with increasing T2DM and its related risk factors. Modification of diet and lifestyle is suggested to reduce inflammation.

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