scholarly journals Associations between chronotype, MTNR1B genotype and risk of type 2 diabetes in UK Biobank

2019 ◽  
Vol 287 (2) ◽  
pp. 189-196 ◽  
Author(s):  
X. Tan ◽  
D.‐M. Ciuculete ◽  
H.B. Schiöth ◽  
C. Benedict
Keyword(s):  
Type 2 Diabetes ◽  
Uk Biobank

scholarly journals Gallstone disease, diabetes, calcium, triglycerides, smoking and alcohol consumption and pancreatitis risk: Mendelian randomization study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Shuai Yuan ◽  
Edward L. Giovannucci ◽  
Susanna C. Larsson
Keyword(s):  
Type 2 Diabetes ◽  
Chronic Pancreatitis ◽  
Alcohol Consumption ◽  
Mendelian Randomization ◽  
Gallstone Disease ◽  
Smoking Initiation ◽  
Disease Type ◽  
Uk Biobank ◽  
Increased Risk

AbstractWe conducted a Mendelian randomization study to determine the potential causal associations of gallstone disease, diabetes, serum calcium, triglyceride levels, smoking and alcohol consumption with acute and chronic pancreatitis. Genetic variants associated with the exposures at p < 5 × 10−8 were selected from corresponding genome-wide association studies. Summary-level data for pancreatitis were obtained from the FinnGen consortium and UK Biobank. Univariable and multivariable Mendelian randomization analyses were performed and results from FinnGen and UK Biobank were combined using the fixed-effects meta-analysis method. Genetic predisposition to gallstone disease, type 2 diabetes and smoking initiation was associated with an increased risk of acute pancreatitis. The combined odds ratios (ORs) were 1.74 (95% confidence interval (CI), 1.57, 1.93) for gallstone disease, 1.14 (95% CI, 1.06, 1.21) for type 2 diabetes and 1.56 (95% CI, 1.32, 1.83) for smoking initiation. The association for type 2 diabetes attenuated after adjustment for gallstone disease. Genetic predisposition to gallstone disease and smoking initiation as well as higher genetically predicted serum calcium and triglyceride levels were associated with an increased risk of chronic pancreatitis. The combined ORs of chronic pancreatitis were 1.27 (95% CI, 1.08, 1.50) for gallstone disease, 1.86 (95% CI, 1.43, 2.43) for smoking initiation, 2.20 (95% CI, 1.30, 3.72) for calcium and 1.47 (95% CI, 1.23, 1.76) for triglycerides. This study provides evidence in support that gallstone disease, type 2 diabetes, smoking and elevated calcium and triglyceride levels are causally associated with the risk of acute or chronic pancreatitis.

Association of a Healthy Lifestyle With All-Cause and Cause-Specific Mortality Among Individuals With Type 2 Diabetes: A Prospective Study in UK Biobank

2021 ◽  
Author(s):  
Han Han ◽  
Yaying Cao ◽  
Chengwu Feng ◽  
Yan Zheng ◽  
Klodian Dhana ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Respiratory Disease ◽  
Digestive Disease ◽  
Diabetes Management ◽  
Healthy Lifestyle ◽  
Population Attributable Risk ◽  
Uk Biobank ◽  
All Cause Mortality ◽  
Specific Mortality

<a>Objective: </a><a></a><a></a><a></a><a></a><a>To evaluate the association of a healthy lifestyle, involving seven low-risk factors mentioned in diabetes management guidelines (no current smoking, moderate alcohol consumption, regular physical activity, healthy diet, less sedentary behavior, adequate sleep duration, and appropriate social connection), with all-cause and cause-specific mortality among individuals with type 2 diabetes.</a> <p>Research Design and Methods: This study included 13,366 participants with baseline type 2 diabetes from the UK Biobank free of CVD or cancer. Lifestyle information was collected through a baseline questionnaire.</p> <p><a>Results: During a median follow-up of 11.7 years, 1,561 deaths were documented, with 625 from cancer, 370 from CVD, 115 from respiratory disease, 81 from digestive disease, and 74 from neurodegenerative disease.</a><a> In multivariate-adjusted model, each lifestyle factor was significantly associated with all-cause mortality and hazard ratios (95% CIs) associated with the lifestyle score (scoring 6-7 vs. 0-2 unless specified) were 0.42 (0.34, 0.52) for all-cause mortality, 0.57 (0.41, 0.80) for cancer mortality, 0.35 (0.22, 0.56) for CVD mortality, 0.26 (0.10, 0.63) for respiratory mortality, and 0.28 (0.14, 0.53) for digestive mortality (scoring 5-7 vs. 0-2). In the population-attributable-risk analysis, 27.1% (95% CI: 16.1, 38.0%) death was attributable to a poor lifestyle (scoring 0-5). </a><a>The association between a healthy lifestyle and all-cause mortality was consistent, irrespective of factors reflecting diabetes severity (diabetes duration, glycemic control, diabetes-related microvascular disease, and diabetes medication)</a>.</p> <p>Conclusions: <a></a><a></a>A healthy lifestyle was associated with a lower risk of mortality due to all-cause, CVD, cancer, respiratory disease, and digestive disease among individuals with type 2 diabetes. <b></b></p>

1134-P: Polygenic Risk Score of Type 2 Diabetes as a Predictive Factor for Macrovascular Complications: A Prospective Population-Based UK Biobank Study

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 1134-P
Author(s):  
SANGHYUK JUNG ◽  
DOKYOON KIM ◽  
MANU SHIVAKUMAR ◽  
HONG-HEE WON ◽  
JAE-SEUNG YUN
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Predictive Factor ◽  
Population Based ◽  
Macrovascular Complications ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Polygenic Risk

scholarly journals Night Shift Work, Genetic Risk, and Type 2 Diabetes in the UK Biobank

Diabetes Care ◽  
2018 ◽  
Vol 41 (4) ◽  
pp. 762-769 ◽  
Author(s):  
Céline Vetter ◽  
Hassan S. Dashti ◽  
Jacqueline M. Lane ◽  
Simon G. Anderson ◽  
Eva S. Schernhammer ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Shift Work ◽  
Genetic Risk ◽  
Night Shift ◽  
Uk Biobank ◽  
Night Shift Work ◽  
The Uk

Abstract 021: C-reactive Protein Partially Mediates The Inverse Association Between Coffee Consumption And Risk Of Type 2 Diabetes: Findings From The UK Biobank And The Rotterdam Studies

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Carolina Ochoa-Rosales ◽  
Niels van der Schaft ◽  
Kim V Braun ◽  
Frederick Ho ◽  
Fanny Petermann ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Coffee Consumption ◽  
Inverse Association ◽  
Statistical Regression ◽  
Uk Biobank ◽  
C Reactive Protein ◽  
Coffee Intake ◽  
Reactive Protein ◽  
The Uk

Background: Coffee intake has been linked to lower type 2 diabetes (T2D) risk. We hypothesized this may be mediated by coffee’s effects on inflammation. Methods: Using participants from the UK Biobank (UKB n=145370) and Rotterdam Study (RS n=7172) cohorts, we studied associations of coffee intake with incident T2D; longitudinally measured insulin resistance (HOMA IR); serum levels of inflammation markers; and the mediating role of inflammation. Statistical regression models were adjusted for sociodemographic, lifestyle and health factors. Results: The median follow up was 7 (UKB) and 9 (RS) years. An increase of one coffee cup/day was associated with 4-6% lower T2D risk (RS HR=0.94 [95% CI 0.90; 0.98]; UKB HR=0.96 [0.94; 0.98]); lower HOMA IR (RS β=-0.017 [-0.024; -0.010]); with lower C reactive protein (CRP) and higher adiponectin (Figure1). Consumers of filtered coffee had the lowest T2D risk (UKB HR=0.88 [0.83; 0.93]). CRP levels mediated 9.6% (UKB) and 3.4% (RS) of the total effect of coffee on T2D (Figure 1). Conclusions: We suggest that coffee’s beneficial effects on lower T2D risk are partially mediated by improvements in systemic inflammation.Figure 1. a CRP and a adiponectin refer to the effect of coffee intake on CRP and adiponectin levels. a CRP RS : β=-0.014 (-0.022; -0.005); UKBB a CRP UKB : β=-0.011 (-0.012; -0.009) and RS a adiponectin : β=0.025 (0.007; 0.042). b CRP and b adiponectin refer to the effect of coffee related levels in CRP and adiponectin on incident T2D, independent of coffee. RS b CRP : HR=1.17 (1.04; 1.31); UKB b CRP : HR=1.45 (1.37; 1.54); and b adiponectin : HR=0.58 (0.32; 0.83). c′ refers to coffee’ effect on T2D going directly or via others mediators. UKB c′ independent of CRP : HR=0.96 (0.94; 0.99); RS c′ independent of CRP : HR=0.94 (0.90; 0.99); and RS c′ independent of CRP+adiponectin : HR=0.90 (0.80; 1.01). Coffee related changes in CRP may partially explain the beneficial link between coffee and T2D, mediating a 3.4% (0.6; 4.8, RS) and 9.6% (5.7; 24.4, UKB). Evidence of mediation was also found for adiponectin.

scholarly journals 1013 SHIFT WORK, CHRONOTYPE, AND TYPE 2 DIABETES IN THE UK BIOBANK AND TYPE 2 DIABETES IN THE UK BIOBANK

SLEEP ◽  
2017 ◽  
Vol 40 (suppl_1) ◽  
pp. A377-A377
Author(s):  
C Vetter ◽  
HS Dashti ◽  
JM Lane ◽  
SG Anderson ◽  
ES Schernhammer ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Shift Work ◽  
Uk Biobank ◽  
The Uk

scholarly journals Adiposity-Mortality Relationships in Type 2 Diabetes, Coronary Heart Disease, and Cancer Subgroups in the UK Biobank, and Their Modification by Smoking

Diabetes Care ◽  
2018 ◽  
Vol 41 (9) ◽  
pp. 1878-1886 ◽  
Author(s):  
David A. Jenkins ◽  
Jack Bowden ◽  
Heather A. Robinson ◽  
Naveed Sattar ◽  
Ruth J.F. Loos ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Uk Biobank ◽  
The Uk

scholarly journals Phenotypic and genetic characterization of lower LDL-C and increased type-2 diabetes risk in the UK Biobank

2020 ◽  
Author(s):  
Ada Admin ◽  
Yann C. Klimentidis ◽  
Amit Arora ◽  
Michelle Newell ◽  
Jin Zhou ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Uk Biobank ◽  
Alcoholic Fatty Liver ◽  
Genome Wide ◽  
Using Data ◽  
Underlying Mechanisms ◽  
Phenotypic And Genetic Characterization ◽  
The Uk

Although hyperlipidemia is traditionally considered a risk factor for type-2 diabetes (T2D), evidence has emerged from statin trials and candidate gene investigations suggesting that lower LDL-C increases T2D risk. We thus sought to more comprehensively examine the phenotypic and genotypic relationships of LDL-C with T2D. Using data from the UK Biobank, we found that levels of circulating LDL-C were negatively associated with T2D prevalence (OR=0.41[0.39, 0.43] per mmol/L unit of LDL-C), despite positive associations of circulating LDL-C with HbA1c and BMI. We then performed the first genome-wide exploration of variants simultaneously associated with lower circulating LDL-C and increased T2D risk, using data on LDL-C from the UK Biobank (n=431,167) and the GLGC consortium (n=188,577), and T2D from the DIAGRAM consortium (n=898,130). We identified 31 loci associated with lower circulating LDL-C and increased T2D, capturing several potential mechanisms. Seven of these loci have previously been identified for this dual phenotype, and 9 have previously been implicated in non-alcoholic fatty liver disease. These findings extend our current understanding of the higher T2D risk among individuals with low circulating LDL-C, and of the underlying mechanisms, including those responsible for the diabetogenic effect of LDL-C-lowering medications.

scholarly journals A Deep Learning Model for Screening Type 2 Diabetes from Retinal Photographs

2021 ◽  
Author(s):  
Jae-Seung Yun ◽  
Jaesik Kim ◽  
Sang-Hyuk Jung ◽  
Seon-Ah Cha ◽  
Seung-Hyun Ko ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Model ◽  
Retinal Images ◽  
Uk Biobank ◽  
Non Invasive ◽  
Deep Learning Algorithm ◽  
Deep Learning Model

Objective: We aimed to develop and evaluate a non-invasive deep learning algorithm for screening type 2 diabetes in UK Biobank participants using retinal images. Research Design and Methods: The deep learning model for prediction of type 2 diabetes was trained on retinal images from 50,077 UK Biobank participants and tested on 12,185 participants. We evaluated its performance in terms of predicting traditional risk factors (TRFs) and genetic risk for diabetes. Next, we compared the performance of three models in predicting type 2 diabetes using 1) an image-only deep learning algorithm, 2) TRFs, 3) the combination of the algorithm and TRFs. Assessing net reclassification improvement (NRI) allowed quantification of the improvement afforded by adding the algorithm to the TRF model. Results: When predicting TRFs with the deep learning algorithm, the areas under the curve (AUCs) obtained with the validation set for age, sex, and HbA1c status were 0.931 (0.928-0.934), 0.933 (0.929-0.936), and 0.734 (0.715-0.752), respectively. When predicting type 2 diabetes, the AUC of the composite logistic model using non-invasive TRFs was 0.810 (0.790-0.830), and that for the deep learning model using only fundus images was 0.731 (0.707-0.756). Upon addition of TRFs to the deep learning algorithm, discriminative performance was improved to 0.844 (0.826-0.861). The addition of the algorithm to the TRFs model improved risk stratification with an overall NRI of 50.8%. Conclusions: Our results demonstrate that this deep learning algorithm can be a useful tool for stratifying individuals at high risk of type 2 diabetes in the general population.

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