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 Type 2 diabetes-related variants influence the risk of developing multiple myeloma: results from the IMMEnSE consortium

2015 ◽  
Vol 22 (4) ◽  
pp. 545-559 ◽  
Author(s):  
Rafael Ríos ◽  
Carmen Belén Lupiañez ◽  
Daniele Campa ◽  
Alessandro Martino ◽  
Joaquin Martínez-López ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Multiple Myeloma ◽  
Prediction Models ◽  
Disease Risk ◽  
Association Studies ◽  
Area Under The Curve ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Stratified Analysis

Type 2 diabetes (T2D) has been suggested to be a risk factor for multiple myeloma (MM), but the relationship between the two traits is still not well understood. The aims of this study were to evaluate whether 58 genome-wide-association-studies (GWAS)-identified common variants for T2D influence the risk of developing MM and to determine whether predictive models built with these variants might help to predict the disease risk. We conducted a case–control study including 1420 MM patients and 1858 controls ascertained through the International Multiple Myeloma (IMMEnSE) consortium. Subjects carrying the KCNQ1rs2237892T allele or the CDKN2A-2Brs2383208G/G, IGF1rs35767T/T and MADDrs7944584T/T genotypes had a significantly increased risk of MM (odds ratio (OR)=1.32–2.13) whereas those carrying the KCNJ11rs5215C, KCNJ11rs5219T and THADArs7578597C alleles or the FTOrs8050136A/A and LTArs1041981C/C genotypes showed a significantly decreased risk of developing the disease (OR=0.76–0.85). Interestingly, a prediction model including those T2D-related variants associated with the risk of MM showed a significantly improved discriminatory ability to predict the disease when compared to a model without genetic information (area under the curve (AUC)=0.645 vs AUC=0.629; P=4.05×10−06). A gender-stratified analysis also revealed a significant gender effect modification for ADAM30rs2641348 and NOTCH2rs10923931 variants (Pinteraction=0.001 and 0.0004, respectively). Men carrying the ADAM30rs2641348C and NOTCH2rs10923931T alleles had a significantly decreased risk of MM whereas an opposite but not significant effect was observed in women (ORM=0.71 and ORM=0.66 vs ORW=1.22 and ORW=1.15, respectively). These results suggest that TD2-related variants may influence the risk of developing MM and their genotyping might help to improve MM risk prediction models.

scholarly journals Predictive Supervised Learning Model for Classification of Type 2 Diabetes Mellitus

2021 ◽  
Author(s):  
M.S Roobini ◽  
M Lakshmi
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Performance Measures ◽  
Prediction Models ◽  
Random Forest Algorithm ◽  
Ensemble Approach ◽  
To Receive

Abstract There is a tremendous increase in severe cases of type 2 diabetes in the day today's life. Therefore, proper assessment of the disease is critical to saving society. Many prediction models help identify type 2 diabetes. At the same time, every model varies based on the performance measures. Various kinds of algorithms such as Decision Tree, Logistic Regression, KNN, Random Forest algorithm are applied to identify type 2 diabetes. At this juncture, used the implementation of type 2 Classification by AdaBoost algorithms, an ensemble approach. Here, the proposed methodology of the paper is to implement an ensemble approach of machine learning to receive a better efficiency compared to other existing algorithms for the classification of type 2 diabetes. When compared to all different algorithms, this ensemble approach shows an efficiency of 83%. The accuracy is calculated based on various performance measures.

scholarly journals Arrhythmic Gut Microbiome Signatures for Risk Profiling of Type-2 Diabetes

2020 ◽  
Author(s):  
Sandra Reitmeier ◽  
Silke Kiessling ◽  
Thomas Clavel ◽  
Markus List ◽  
Eduardo L. Almeida ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Risk Profiling

scholarly journals Testing the performance of risk prediction models to determine progression to referable diabetic retinopathy in an Irish type 2 diabetes cohort

2021 ◽  
pp. bjophthalmol-2020-318570
Author(s):  
John J Smith ◽  
David M Wright ◽  
Irene M Stratton ◽  
Peter Henry Scanlon ◽  
Noemi Lois
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Risk Prediction ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Risk Prediction Models ◽  
Receiver Operating Characteristic Curves ◽  
Risk Of Progression ◽  
Using Data

Background /AimsTo evaluate the performance of existing prediction models to determine risk of progression to referable diabetic retinopathy (RDR) using data from a prospective Irish cohort of people with type 2 diabetes (T2D).MethodsA cohort of 939 people with T2D followed prospectively was used to test the performance of risk prediction models developed in Gloucester, UK, and Iceland. Observed risk of progression to RDR in the Irish cohort was compared with that derived from each of the prediction models evaluated. Receiver operating characteristic curves assessed models’ performance.ResultsThe cohort was followed for a total of 2929 person years during which 2906 screening episodes occurred. Among 939 individuals followed, there were 40 referrals (4%) for diabetic maculopathy, pre-proliferative DR and proliferative DR. The original Gloucester model, which includes results of two consecutive retinal screenings; a model incorporating, in addition, systemic biomarkers (HbA1c and serum cholesterol); and a model including results of one retinopathy screening, HbA1c, total cholesterol and duration of diabetes, had acceptable discriminatory power (area under the curve (AUC) of 0.69, 0.76 and 0.77, respectively). The Icelandic model, which combined retinopathy grading, duration and type of diabetes, HbA1c and systolic blood pressure, performed very similarly (AUC of 0.74).ConclusionIn an Irish cohort of people with T2D, the prediction models tested had an acceptable performance identifying those at risk of progression to RDR. These risk models would be useful in establishing more personalised screening intervals for people with T2D.

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.

Gamma-oryzanol Ameliorates Insulin Resistance and Hyperlipidemia in Rats with Streptozotocin/nicotinamide-induced Type 2 Diabetes

2010 ◽  
Vol 80 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Hsing-Hsien Cheng ◽  
Chien-Ya Ma ◽  
Tsui-Wei Chou ◽  
Ya-Yen Chen ◽  
Ming-Hoang Lai
Keyword(s):  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
Lipid Metabolism ◽  
Rice Bran Oil ◽  
Area Under The Curve ◽  
Density Lipoprotein ◽  
Negative Influence ◽  
Control Group ◽  
Gamma Oryzanol

Gamma-oryzanol is a component of rice bran oil (RBO) with purported health benefits. This study evaluated the effects of gamma-oryzanol on insulin resistance and lipid metabolism in Wistar rats with type 2 diabetes (T2DM). The rats were divided into three groups and consumed one of the following diets for 5 weeks: 15 % soybean oil (control group); 15 % palm oil (PO); and 15 % PO with the addition of 5.25 g gamma-oryzanol (POO). The results showed that PO markedly increased plasma low-density-lipoprotein cholesterol, plasma triglycerides, and hepatic triglyceride levels, but did not reduce the area under the curve for glucose and insulin significantly, compared with the control group. Adding gamma-oryzanol to PO improved the negative influence of PO on lipid metabolism in T2DM rats. In addition, gamma-oryzanol tended to increase insulin sensitivity in T2DM rats compared to control and PO groups. Longer-term studies are needed to evaluate these effects further.

Direct Isolation of Circulating Extracellular Vesicles from Blood for Vascular Risk Profiling in Type 2 Diabetes Mellitus

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Hui Min Tay ◽  
Sheng Yuan Leong ◽  
Xiaohan Xu ◽  
Fang Kong ◽  
Megha Upadya ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Extracellular Vesicles ◽  
Whole Blood ◽  
Vascular Risk ◽  
Risk Profiling

Extracellular vesicles (EVs) are key mediators of communication among cells, and clinical utilities of EVs-based biomarkers remain limited due to difficulties in isolating EVs from whole blood reliably. We report...

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