Other times, dietary fibers and prebiotics are fermented by gut microbes into multiple small compounds, including short chain fatty acids that can decrease blood glucose and improve insulin sensitivity, leading to better control of glucose metabolism.

The gut microbiota of people with type 2 diabetes is altered, as beneficial bacteria are missing and potential problematic bacteria with pro-inflammatory functions are found in excess.

That has led scientists to explore individualized dietetic management of patients with type 2 diabetes based on personal characteristics, including their gut microbiota. Gut microbiota composition can also be used as a means of providing an early diagnosis of type 2 diabetes.

While interventions that target the altered gut microbiome, such as a complex mix of dietary fibers , appear promising for improving metabolic diseases, it is too early to recommend specific probiotics alone or combined with prebiotic fibers for managing type 2 diabetes.

Nevertheless, one take-home piece of advice that is effective and safe for controlling blood sugar is increasing the amount and variety of vegetables, fruits, legumes and nuts in your diet. In this interview on the occasion of the 10th anniversary of GMFH , Dr.

Have you seen our previous interviews with experts? Most research on the role of gut microbiota in the gut-brain axis has focused on bacteria, while fungi living inside the gut have been overlooked.

What do we know about the role of gut fungi in the communication between the gut and the brain? The low amount of bacteria from the gut microbiota able to process bilirubin, a product of heme degradation, during the neonatal period of life suggests a strong connection between the microbiome composition and development of jaundice in infants.

In other words, the lack of certain bacteria in the gut of infants seems to be linked to the risk of developing jaundice. In this interview, Dr. Núria Malats from the Spanish National Cancer Research Centre CNIO shares promising advances regarding the relationship between gut microbiota and pancreatic cancer, unveiling exciting possibilities for early detection and personalized treatment.

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When you eat, you are feeding the huge community of microbes living in your gut that can influence blood sugar Lifestyle changes involving both diet and physical activity are the first go-to advice for managing type 2 diabetes.

The study, published in the peer-reviewed journal Diabetes , found people with higher levels of a bacterium called Coprococcus 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, PhD , the 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.

Investigators involved in MILES have been collecting information from participating Black and non-Hispanic white adults between 40 and 80 years of age since An earlier cohort study from the MILES trial found that birth by cesarean section is associated with a higher risk for developing prediabetes and diabetes.

For the most recent study to come out of this ongoing trial, investigators analyzed data from people without known diabetes who were recruited from the Wake Forest Baptist Health System in Winston-Salem, North Carolina.

Study participants were asked to attend three clinic visits and collect stool samples prior to the visits. Investigators analyzed data collected at the first visit. Each participant also filled out a diet questionnaire and took an oral glucose tolerance test, which was used to determine ability to process glucose.

Investigators found 28 people had oral glucose tolerance results that met the criteria for diabetes. Coprococcus and related bacteria formed a network of bacteria with beneficial effects on insulin sensitivity.

Despite being a producer of butyrate, Flavonifractor was associated with insulin resistance; prior work by others have found higher levels of Flavonifractor in the stool of people with diabetes. Investigators are continuing to study samples from patients who participated in this study to learn how insulin production and the composition of the microbiome change over time.

They also plan to study how diet may affect the bacterial balance of the microbiome. Goodarzi emphasized, however, that it is too early to know how people can change their microbiome to reduce their diabetes risk.

Goodarzi said. The researchers found that while most bacteria that produce butyrate were associated with better insulin sensitivity, a few were associated with insulin resistance.

Goodarzi explained. For the study, investigators analyzed data from people who had not previously been diagnosed with diabetes. Of the participants, were non-Hispanic whites, and were African-American. None of the participants had recently experienced severe gastrointestinal illness or used medicines like antibiotics that could impact the microbiome.

Researchers found 28 of the participants had diabetes, and an additional were classified as having prediabetes. Participants with diabetes and prediabetes were combined into a single group and were compared with the participants with healthy glucose tolerance.

Participants were asked to collect a stool sample 1—2 days before coming to the clinic. Researchers found that participants with abnormalities in blood glucose levels were older, more often male, and had higher BMI.

They discovered that Coprococcus and related bacteria had beneficial effects on insulin sensitivity. But Flavonifractor , despite producing butyrate, was associated with insulin resistance. The analyses found 10 bacteria associated with a lower rate of blood sugar levels fluctuating abnormally and two bacteria associated with adverse associations on blood sugar levels.

Goodarzi told MNT. If so proven, clinical trials will be the next step to determine whether modulating these bacteria via prebiotics, probiotics, or antibiotics, depending on the bacterial targets are a viable option to prevent or treat diabetes. For individuals looking to promote their gut health in general, Dr.

Kristin Kirkpatrick , R. Sources of prebiotics include:. Roxana Ehsani , R. Ehsani suggested kefir for people looking to improve their gut health.

Researchers think that gut bacteria may trigger inflammation, which in turn prevents insulin from working correctly, thus causing type 2 diabetes. New research in young mice and zebrafish has uncovered the role of a bacterial protein in the development of diabetes.

This discovery could lead to…. A recent study finds that gut bacteria populations fluctuate throughout the day and that this occurs to a lesser extent in people with type 2 diabetes.

A new review of existing research examines the effect of the gut bacteria composition on the effectiveness of type 2 diabetes medications. A new study involving patients with type 2 diabetes suggests that a low-carbohydrate diet helped over half of the participants achieve remission.

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Medical News Today. Health Conditions Health Products Discover Tools Connect. Type 2 diabetes: Researchers identify gut bacteria linked to insulin sensitivity. By Beth JoJack on January 12, — Fact checked by Harriet Pike, Ph.

Serum insulin was measured by electrochemiluminescence immunoassay technology. In the LLD study, glucose levels were measured by hydrogen 1 nuclear magnetic resonance, and serum insulin was measured on an architect system Abbott Laboratories.

Data on type 2 diabetes status in both cohorts were collected in In the LLD study, information on type 2 diabetes was collected through self-reported questionnaires and fasting glucose measured in the research center. All potential events of type 2 diabetes were independently adjudicated by 2 study physicians.

In case of disagreement, consensus was sought from an endocrinologist. In both cohorts, information on educational level, smoking status, dietary intake, and physical activity was assessed through interviews and questionnaires.

We used natural log-transformed HOMA-IR for all analyses to obtain a normal distribution. For diversity measures, we investigated associations for Shannon index, richness, and Inverse Simpson index and insulin resistance using linear regression and type 2 diabetes using logistic regression.

We analyzed associations of Bray-Curtis dissimilarity matrix with insulin resistance and type 2 diabetes using permutation analysis of variance permutations. For analyses for gut microbial taxa, we first added 1 to all taxa counts to prevent missingness derived from log zero. Subsequently, to reduce the skewness of the distribution of microbial taxa counts, we performed natural log transformation.

The associations between microbial taxa and insulin resistance and type 2 diabetes were assessed by linear regression and logistic regression, respectively.

For all analyses, we adjusted for age, sex, time in mail RS only , and DNA batch effect RS only in model 1. In model 2, we additionally adjusted for alcohol intake, energy intake, smoking, educational level RS only , and physical activity.

In model 3, we additionally adjusted for body mass index BMI; calculated as weight in kilograms divided by height in meters squared. Last, in model 4, we additionally adjusted for use of lipid-lowering drugs and proton pump inhibitors. We conducted 3 sets of sensitivity analyses based on model 4.

In case of statically significant interaction terms, stratified analyses by these factors would be conducted eg, separate for men and women.

Second, we reexamined associations of α diversity, β diversity, and taxa with HOMA-IR and type 2 diabetes by additionally adjusting for diet quality score and blood pressure in the RS.

We did not examine associations between taxa and type 2 diabetes after excluding these patients because excluding these participants resulted in smaller groups and fewer cases, which combined with the multiple tests of taxa would result in extremely low statistical power.

Because some data eg, data on physical activity and energy intake were collected using different questionnaires between 2 cohorts, to better achieve control for confounding for all main analyses, we first conducted analyses in the 2 studies separately and then combined the associations for α diversity and for taxa available in both cohorts using fixed-effects meta-analysis.

Associations of β diversity could not be pooled and were presented for each cohort separately. A total of type 2 diabetes cases were identified among participants, from the Rotterdam Study mean [SD] age, Characteristics of the study population are given in Table 1.

Associations were similar across all 4 models Table 2. For type 2 diabetes, higher richness was associated with a lower prevalence of type 2 diabetes odds ratio [OR], 0. A higher Shannon index was not associated with a lower prevalence of type 2 diabetes OR, 0.

After multiple adjustment model 4 , we observed 7 taxa to be associated with HOMA-IR and 5 taxa with type 2 diabetes in the meta-analysis. eTables 2 and 3 in the Supplement give all results in models 1 to 4 in the separated analyses of the 2 cohorts including overlapping and nonoverlapping taxa and the meta-analysis overlapping taxa.

First, we observed that associations for α diversity or taxa with HOMA-IR or type 2 diabetes did not differ by age, sex, or BMI. We also observed that the effect estimates for the associations between α diversity and type 2 diabetes were similar when excluding patients using metformin OR, 0.

This cross-sectional study of a large, population-based sample found associations between gut microbiome composition and type 2 diabetes prevalence and with insulin resistance among individuals without diabetes, independent of several sociodemographic and lifestyle factors.

Specifically, the study found that higher α diversity was associated with lower insulin resistance and lower prevalence of type 2 diabetes and that variations of gut microbial β diversity were associated with insulin resistance.

The study also found that a higher abundance of these 12 taxa may benefit risk of insulin resistance and type 2 diabetes: Christensenellaceae, Clostridiaceae 1, Peptostreptococcaceae, Christensenellaceae R7 group, Marvinbryantia , Ruminococcaceae UCG, Ruminococcaceae UCG, Ruminococcaceae UCG, Ruminococcaceae NK4A group, C sensu stricto 1, Intestinibacter , and Romboutsia.

Several previous studies 2 , 3 , 5 have examined associations with type 2 diabetes but were limited by small sample size, restriction to patient settings, and the lack of adjustment for important confounders, such as energy intake, physical activity, and socioeconomic factors.

The current study is the first, to our knowledge, to comprehensively investigate the associations between gut microbiome composition with type 2 diabetes in a large population-based sample for which we adjusted for a series of key confounders.

Similar to a previous study, 5 the current study found that higher α diversity was associated with lower prevalence of type 2 diabetes. These associations were independent of energy intake, physical activity, educational level, smoking, and medication use.

Furthermore, this evidence was extended to indicate that α and β diversity are linked to insulin resistance, further confirming that variation of gut microbiome composition is also closely associated with earlier stages in the development of type 2 diabetes.

Furthermore, 12 taxa were associated with insulin resistance or type 2 diabetes. All 12 are known to be butyrate-producing bacteria. For instance, Clostridium species and Clostridiales species were inversely associated with insulin resistance.

The current findings were also in line with previous studies 5 , 6 that reported that a higher abundance of the 2 butyrate-producing bacteria, Clostridiaceae 1 and C sensu stricto 1, were associated with lower prevalence of type 2 diabetes.

However, the current study yielded 10 novel associations. These 10, also all butyrate-producing bacteria, were all inversely associated with insulin resistance or type 2 diabetes: Christensenellaceae, Peptostreptococcaceae, Christensenellaceae R7 group, Marvinbryantia , Ruminococcaceae UCG, Ruminococcaceae UCG, Ruminococcaceae UCG, Ruminococcaceae NK4A group, Intestinibacter , and Romboutsia.

These findings further extend the evidence that higher abundance of butyrate-producing bacteria is associated with lower risk of type 2 diabetes. Of interest, some of these newly identified bacteria associated with type 2 diabetes have been previously reported in relation to obesity, which is closely associated with insulin resistance and development of type 2 diabetes.

For example, a previous study by Goodrich et al 26 reported that higher abundance of Christensenellaceae was linked to a lower BMI. In the current analyses, associations with insulin resistance and type 2 diabetes independent of BMI were observed, suggesting a role in the development of type 2 diabetes beyond obesity.

Furthermore, this study found similar effect estimates for α diversity and type 2 diabetes when excluding patients using metformin and associations with HOMA-IR among individuals who did not use metformin, suggesting that the observed associations between gut microbiome and diabetes were not driven by use of metformin.

In addition, although the observed bacteria associated with insulin resistance and type 2 diabetes were all butyrate-producing bacteria, the specific butyrate-producing bacteria that were identified differed between the insulin resistance and type 2 diabetes analysis. This finding may be explained by actual differences of gut microbiome composition among different severities of insulin resistance, by residual confounding of medication or other treatments, or by chance and small differences in effect sizes.

Possible explanations for the observed associations may involve potential beneficial effects of the butyrate that are produced by these bacteria.

Butyrate has been suggested to induce beneficial metabolic effects through enhancement of mitochondrial activity, improvement of energy metabolism, activation of intestinal gluconeogenesis, and prevention of metabolic endotoxemia and inflammation via different routes of gene expression and hormone regulation.

Future research should validate the hypothesis of butyrate-producing bacteria affecting glucose metabolism and diabetes risk via production of butyrate. This study has several strengths.

Second, the study adjusted for various confounders in the analyses, such as alcohol use, physical activity, BMI, educational level, and smoking status. Most previous studies 2 - 4 did not adjust for these important confounders.

Third, although temporality cannot be studied in the cross-sectional design, to minimize reverse bias and potential effects of medication use, associations between gut microbiome not only with type 2 diabetes status but also with insulin resistance were examined among participants without diabetes, and similar results were observed, suggesting that microbiome composition may already play a role in earlier phases of development of type 2 diabetes.

The study also has several limitations. First, it is a cross-sectional study, and thus the ability to assess temporality and causality is limited. Second, data on the concentrations of butyrate in stool or blood samples were not available, which limited conclusions about a role of butyrate in the observed associations.

Third, gut microbiome composition was determined from stool samples. Because gut microbiome composition varies throughout the gut with respect to the anatomical location along the gut and at the given site, a more complete picture of the gut microbiome might be obtained by obtaining samples from different locations along the intestines.

Fourth, because of the use of 16S ribosomal RNA data, associations at species and lower levels or associations of functional profiles of gut microbiome composition could not be explored. Metagenomics approaches could overcome these limitations. Fifth, although several covariates were adjusted for, the possibility of residual confounding eg, by occupation or annual income could not be excluded.

Moreover, gut microbiome data were not available for many of the participants of the original cohorts, which might have resulted in selection bias if associations of gut microbiome with development of type 2 diabetes differed in those included and those not included in the current analyses.

In addition, to account for potential bias from missing data on covariates, multiple imputations were used, which should improve the precision of the estimated associations. However, all potential events of type 2 diabetes were independently adjudicated by 2 study physicians and disagreements resolved by consensus with an endocrinologist.

Therefore, the possibility of misidentification is extremely limited, and even if it existed, the limited misidentification or missing data should not have largely affected associations. In addition, a small sample of participants with type 2 diabetes in the LLD study limited additional analysis for gut microbiome and type 2 diabetes eg, by metformin use.

These findings suggest that gut microbiome composition may influence the development of type 2 diabetes. An increased gut microbial diversity, along with specifically more butyrate-producing bacteria, may benefit insulin resistance and risk of type 2 diabetes.

These findings may help provide new insight into causes, mechanisms, and prevention of, as well as therapy for, type 2 diabetes. Published: July 29, Correction: This article was corrected on September 3, , to amend 2 sentences in the Key Points.

Open Access: This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. Corresponding Author: Trudy Voortman, PhD trudy.

voortman erasmusmc. nl , and Zhangling Chen, MD, PhD z. nl , Department of Epidemiology, Erasmus Medical Center, Office Na, PO Box , CA Rotterdam, the Netherlands.

Author Contributions: Dr Z. Chen, Mr Radjabzadeh, and Mr L. Chen are co—first authors. Drs Z. Chen and Voortman had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Critical revision of the manuscript for important intellectual content: Z. Chen, Radjabzadeh, L. Chen, Kurilshikov, Kavousi, Ahmadizar, Ikram, Zhernakova, Fu, Voortman.

Administrative, technical, or material support: Z. Chen, Radjabzadeh, Kurilshikov, Ahmadizar, Ikram, Kraaij. Conflict of Interest Disclosures: None reported. Dr Radjabzadeh was funded by an Erasmus Medical Center mRACE grant Profiling of the Human Gut Microbiome.

The generation and management of stool microbiome data for the Rotterdam Study were executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands.

The LifeLines-DEEP study is funded by grant CVON from the Netherlands Heart Foundation Drs Zhernakova and Fu ; Top Institute Food and Nutrition, Wageningen, the Netherlands; grants VIDI Chen was supported by grant from the China Scholarship Council and the University Medical Centre Groningen.

Additional Contributions: Nahid EI Faquir, PhD, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands, and Jolande Verkroost Van Heemst, PhD, Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands, helped with sample collection and registration, and Pelle van der Wal, PhD, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Kamal Arabe, PhD, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Hedayat Razawy, PhD, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; and Karan Singh Asra, PhD, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands, helped with DNA isolation and sequencing.

Jeroen Raes, PhD, and Jun Wang, PhD KU Leuven, Leuven, Belgium provided guidance in 16S ribosomal RNA profiling and data set generation. We thank Lifelines and UMCG staffs for management and technical support. We gratefully acknowledge the dedication, commitment, and contribution of the participants, general practitioners, and pharmacists who took part in the Rotterdam Study and in the LifeLines-DEEP study.

full text icon Full Text. Download PDF Comment. Top of Article Key Points Abstract Introduction Methods Results Discussion Conclusions Article Information References. Table 1. Characteristics of Participants. View Large Download. Table 2. Association of α Diversity and Insulin Resistance.

Table 3. Association of α Diversity and Type 2 Diabetes. Table 4. Statistically Significant Pooled Associations Between Taxa and Insulin Resistance a. Table 5. Statistically Significant Pooled Associations Between Taxa and Type 2 Diabetes a. eFigure 1. Participant Selection eFigure 2. Taxa in the Rotterdam Study and the LifeLines-DEEP Study eTable 1.

Characteristics of Included and Excluded Participants eTable 2. Associations of Taxa and Insulin Resistance eTable 3. Associations of Taxa and Type 2 Diabetes eTable 4.

Associations of α and β Diversity With Insulin Resistance and Type 2 Diabetes After Additionally Adjusting for Diet Quality and Blood Pressure eTable 5. Statistically Significant Associations Between Taxa and Insulin Resistance After Additionally Adjusting for Diet Quality and Blood Pressure eTable 6.

Statistically Significant Associations Between Taxa and Type 2 Diabetes After Additionally Adjusting for Diet Quality and Blood Pressure eMethods. Supplementary Methods. Chatterjee S, Khunti K, Davies MJ.

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