An altered microbiota pattern precedes Type 2 diabetes mellitus development: From the CORDIOPREV study

Vals-Delgado, Cristina; Alcalá‐Díaz, Juan F.; Molina‐Abril, Helena; Roncero-Ramos, Irene; Caspers, Martien P. M.; Schuren, Frank; Broek, Tim J. van den; Luque, Raúl M; Pérez‐Martínez, Pablo; Katsiki, Niki; Delgado-Lista, Javier; Ordovás, José M.; Ommen, Ben van; Camargo, Antonio

doi:10.1016/j.jare.2021.05.001

Cited by 28 publications

(32 citation statements)

References 43 publications

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“…Several human studies have reported a cross-sectional association of the microbiota with type 2 diabetes [ 3 , 5 , 6 ]. Recently, two European cohorts with relatively moderate sample sizes ( n = 273 and 608, respectively) examined the prospective association of the gut microbiota with type 2 diabetes or glycaemic traits [ 7 , 8 ]. However, the results from these previous studies were inconsistent, and evidence from large prospective cohorts is still lacking.…”

Section: Introductionmentioning

confidence: 99%

“…Gut microbiota-targeted dietary intervention is a promising and cost-efficient method to reduce disease risk [ 12 , 13 ]. However, neither of the prospective studies [ 7 , 8 ] evaluated the association of dietary or lifestyle factors with gut microbial genera that were specifically associated with type 2 diabetes or glycaemic traits.…”

Section: Introductionmentioning

confidence: 99%

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Association of gut microbiota with glycaemic traits and incident type 2 diabetes, and modulation by habitual diet: a population-based longitudinal cohort study in Chinese adults

Wang

Gou

et al. 2022

Diabetologia

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Aims/hypothesis The gut microbiome is mainly shaped by diet, and varies across geographical regions. Little is known about the longitudinal association of gut microbiota with glycaemic control. We aimed to identify gut microbiota prospectively associated with glycaemic traits and type 2 diabetes in a geographically diverse population, and examined the cross-sectional association of dietary or lifestyle factors with the identified gut microbiota. Methods The China Health and Nutrition Survey is a population-based longitudinal cohort covering 15 provinces/megacities across China. Of the participants in that study, 2772 diabetes-free participants with a gut microbiota profile based on 16S rRNA analysis were included in the present study (age 50.8 ± 12.7 years, mean ± SD). Using a multivariable-adjusted linear mixed-effects model, we examined the prospective association of gut microbiota with glycaemic traits (fasting glucose, fasting insulin, HbA1c and HOMA-IR). We constructed a healthy microbiome index (HMI), and used Poisson regression to examine the relationship between the HMI and incident type 2 diabetes. We evaluated the association of dietary or lifestyle factors with the glycaemic trait-related gut microbiota using a multivariable-adjusted linear regression model. Results After follow-up for 3 years, 123 incident type 2 diabetes cases were identified. We identified 25 gut microbial genera positively or inversely associated with glycaemic traits. The newly created HMI (per SD unit) was inversely associated with incident type 2 diabetes (risk ratio 0.69, 95% CI 0.58, 0.84). Furthermore, we found that several microbial genera that were favourable for the glycaemic trait were consistently associated with healthy dietary habits (higher consumption of vegetable, fruit, fish and nuts). Conclusions/interpretation Our results revealed multiple gut microbiota prospectively associated with glycaemic traits and type 2 diabetes in a geographically diverse population, and highlighted the potential of gut microbiota-based diagnosis or therapy for type 2 diabetes. Data availability The code for data analysis associated with the current study is available at https://github.com/wenutrition/Microbiota-T2D-CHNS Graphical abstract

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Association of gut microbiota with glycaemic traits and incident type 2 diabetes, and modulation by habitual diet: a population-based longitudinal cohort study in Chinese adults

Wang

Gou

et al. 2022

Diabetologia

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“…It is known that the type of intervention and individual factors influence the outcome of weight-reductions means [ 2 , 3 ]. Among the individual factors, the intestinal microbiota is a highly interesting data source that might yield such predictive variables, since it has been shown that obesity and metabolic processes and disease are affected by individual microbiota patterns [ 6 , 7 , 8 , 13 , 14 , 15 ]. Here, we show for the first time that defined microbiota patterns can be related to individual weight loss success following a nonsurgical, multidisciplinary weight loss program performed over one year.…”

Section: Discussionmentioning

confidence: 99%

Gut Microbiota Patterns Predicting Long-Term Weight Loss Success in Individuals with Obesity Undergoing Nonsurgical Therapy

Bischoff

Nguyen²,

Seethaler

et al. 2022

Nutrients

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Background: The long-term success of nonsurgical weight reduction programs is variable; thus, predictors of outcome are of major interest. We hypothesized that the intestinal microbiota known to be linked with diet and obesity contain such predictive elements. Methods: Metagenome analysis by shotgun sequencing of stool DNA was performed in a cohort of 15 adults with obesity (mean body mass index 43.1 kg/m2) who underwent a one-year multidisciplinary weight loss program and another year of follow-up. Eight individuals were persistently successful (mean relative weight loss 18.2%), and seven individuals were not successful (0.2%). The relationship between relative abundancies of bacterial genera/species and changes in relative weight loss or body mass index was studied using three different statistical modeling methods. Results: When combining the predictor variables selected by the applied statistical modeling, we identified seven bacterial genera and eight bacterial species as candidates for predicting success of weight loss. By classification of relative weight-loss predictions for each patient using 2–5 term models, 13 or 14 out of 15 individuals were predicted correctly. Conclusions: Our data strongly suggest that gut microbiota patterns allow individual prediction of long-term weight loss success. Prediction accuracy seems to be high but needs confirmation by larger prospective trials.

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“…Analogously, gut microbiota dysbiosis has also been shown to be associated with the development of T2DM. , Wu et al discovered that the abundance of certain butyrate-producing bacteria, such as Clostridium , Oscillibacter , and Alistipes , was reduced both in prediabetes and T2DM individuals . Furthermore, people with high abundance of Prevotella and Granulicatella genera were at higher risk of T2DM, and the combination of microbiome data and clinical parameters could more accurately predict the development of T2DM . It is worth noting that a growing number of studies have explored the mechanisms by which the gut microbiota plays a role in DM, especially T1DM and T2DM (Figure ).…”

Section: Effects Of the Gut Microbiota On Dmmentioning

confidence: 99%

“…30 Furthermore, people with high abundance of Prevotella and Granulicatella genera were at higher risk of T2DM, and the combination of microbiome data and clinical parameters could more accurately predict the development of T2DM. 31 It is worth noting that a growing number of studies have explored the mechanisms by which the gut microbiota plays a role in DM, especially T1DM and T2DM (Figure 1). Molecular Mechanisms for T1DM.…”

Section: ■ Effects Of the Gut Microbiota On Dmmentioning

confidence: 99%

Preclinical Studies of Natural Products Targeting the Gut Microbiota: Beneficial Effects on Diabetes

Jiang

Sun

Zhou

2022

J. Agric. Food Chem.

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Diabetes mellitus (DM) is a serious metabolic disease characterized by persistent hyperglycemia, with a continuously increasing morbidity and mortality. Although traditional treatments including insulin and oral hypoglycemic drugs maintain blood glucose levels within the normal range to a certain extent, there is an urgent need to develop new drugs that can effectively improve glucose metabolism and diabetes-related complications. Notably, accumulated evidence implicates that the gut microbiota is unbalanced in DM individuals and is involved in the physiological and pathological processes of this metabolic disease. In this review, we introduce the molecular mechanisms by which the gut microbiota contributes to the development of DM. Furthermore, we summarize the preclinical studies of bioactive natural products that exert antidiabetic effects by modulating the gut microbiota, aiming to expand the novel therapeutic strategies for DM prevention and management.

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An altered microbiota pattern precedes Type 2 diabetes mellitus development: From the CORDIOPREV study

Abstract: Graphical abstract

Cited by 28 publications

References 43 publications

Association of gut microbiota with glycaemic traits and incident type 2 diabetes, and modulation by habitual diet: a population-based longitudinal cohort study in Chinese adults

Association of gut microbiota with glycaemic traits and incident type 2 diabetes, and modulation by habitual diet: a population-based longitudinal cohort study in Chinese adults

Gut Microbiota Patterns Predicting Long-Term Weight Loss Success in Individuals with Obesity Undergoing Nonsurgical Therapy

Preclinical Studies of Natural Products Targeting the Gut Microbiota: Beneficial Effects on Diabetes

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