scholarly journals Multi-Trait Genomic Risk Stratification for Type 2 Diabetes

2021 ◽  
Vol 8 ◽  
Author(s):  
Palle Duun Rohde ◽  
Mette Nyegaard ◽  
Mads Kjolby ◽  
Peter Sørensen
Keyword(s):  
Type 2 Diabetes ◽  
Risk Assessment ◽  
Prediction Accuracy ◽  
Medication Use ◽  
Summary Statistics ◽  
Uk Biobank ◽  
Current Medication ◽  
Correlated Information ◽  
The Uk

Type 2 diabetes mellitus (T2DM) is continuously rising with more disease cases every year. T2DM is a chronic disease with many severe comorbidities and therefore remains a burden for the patient and the society. Disease prevention, early diagnosis, and stratified treatment are important elements in slowing down the increase in diabetes prevalence. T2DM has a substantial genetic component with an estimated heritability of 40–70%, and more than 500 genetic loci have been associated with T2DM. Because of the intrinsic genetic basis of T2DM, one tool for risk assessment is genome-wide genetic risk scores (GRS). Current GRS only account for a small proportion of the T2DM risk; thus, better methods are warranted for more accurate risk assessment. T2DM is correlated with several other diseases and complex traits, and incorporating this information by adjusting effect size of the included markers could improve risk prediction. The aim of this study was to develop multi-trait (MT)-GRS leveraging correlated information. We used phenotype and genotype information from the UK Biobank, and summary statistics from two independent T2DM studies. Marker effects for T2DM and seven correlated traits, namely, height, body mass index, pulse rate, diastolic and systolic blood pressure, smoking status, and information on current medication use, were estimated (i.e., by logistic and linear regression) within the UK Biobank. These summary statistics, together with the two independent training summary statistics, were incorporated into the MT-GRS prediction in different combinations. The prediction accuracy of the MT-GRS was improved by 12.5% compared to the single-trait GRS. Testing the MT-GRS strategy in two independent T2DM studies resulted in an elevated accuracy by 50–94%. Finally, combining the seven information traits with the two independent T2DM studies further increased the prediction accuracy by 34%. Across comparisons, body mass index and current medication use were the two traits that displayed the largest weights in construction of the MT-GRS. These results explicitly demonstrate the added benefit of leveraging correlated information when constructing genetic scores. In conclusion, constructing GRS not only based on the disease itself but incorporating genomic information from other correlated traits as well is strongly advisable for obtaining improved individual risk stratification.

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 Alcohol use and cardiometabolic risk in the UK Biobank: a Mendelian randomization study

2020 ◽  
Author(s):  
Joanna Lankester ◽  
Daniela Zanetti ◽  
Erik Ingelsson ◽  
Themistocles L. Assimes
Keyword(s):  
Type 2 Diabetes ◽  
Alcohol Consumption ◽  
Alcohol Use ◽  
Mendelian Randomization ◽  
Lower Amount ◽  
Cardiometabolic Health ◽  
Uk Biobank ◽  
The Uk ◽  
Cardiometabolic Traits

AbstractObservational studies suggest alcohol use promotes the development of some adverse cardiometabolic traits but protects against others including outcomes related to coronary artery disease. We used Mendelian randomization to explore causal relationships between the degree of alcohol consumption and several cardiometabolic traits in the UK Biobank. We found carriers of the ADH1B Arg47His variant (rs1229984) reported a 26% lower amount of alcohol consumption compared to non-carriers. In our one-sample, two-stage least squares analyses of the UK Biobank using rs1229984 as an instrument, one additional drink/day was associated with statistically significant elevated level of systolic blood pressure (3.0 mmHg), body mass index (0.87 kg/m^2), waist circumference (1.3 cm), body fat percentage (1.7%), low-density lipoprotein levels in blood (0.16 mmol/L), and the risk of myocardial infarction (OR=1.50), stroke (OR=1.52), any cardiovascular disease (OR=1.43), and all-cause mortality (OR=1.41). Conversely, increasing use of alcohol was associated with reduced levels of triglycerides (−0.059 mmol/L) and HbA1C (−0.42 mmol/mol) in the blood, the latter possibly a consequence of a statistically elevated mean corpuscular volume among ADH1B Arg47His carriers. Stratifications by sex and smoking revealed a pattern of more harm of alcohol use among men compared to women, but no consistent difference by smoking status. Men had an increased risk of heart failure (OR = 1.76), atrial fibrillation (OR = 1.35), and type 2 diabetes (OR = 1.31) per additional drink/day. Using summary statistics from external datasets in 2-sample analyses for replication, we found causal associations between alcohol and obesity, stroke, ischemic stroke, and type 2 diabetes. Our results are consistent with an overall harmful effect of alcohol on cardiometabolic health at all levels of use and suggest that even moderate alcohol use should not be promoted as a part of a healthy diet and lifestyle.

scholarly journals Homogeneity in the association of body mass index with type 2 diabetes across the UK Biobank: A Mendelian randomization study

PLoS Medicine ◽  
2019 ◽  
Vol 16 (12) ◽  
pp. e1002982 ◽  
Author(s):  
Michael Wainberg ◽  
Anubha Mahajan ◽  
Anshul Kundaje ◽  
Mark I. McCarthy ◽  
Erik Ingelsson ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Body Mass Index ◽  
Body Mass ◽  
Mendelian Randomization ◽  
Uk Biobank ◽  
The Uk

1563-P: Multimorbidity and Its Associations with All-Cause Mortality in People with Type 2 Diabetes: A Prospective Analysis of the UK Biobank

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1563-P
Author(s):  
JASON I. CHIANG ◽  
PETER HANLON ◽  
BHAUTESH D. JANI ◽  
JO-ANNE E. MANSKI-NANKERVIS ◽  
JOHN FURLER ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Prospective Analysis ◽  
Uk Biobank ◽  
All Cause Mortality ◽  
The Uk

scholarly journals Machine learning reveals time-varying microbial predictors with complex effects on glucose regulation

2020 ◽  
Author(s):  
Oliver Aasmets ◽  
Kreete Lüll ◽  
Jennifer M. Lang ◽  
Calvin Pan ◽  
Johanna Kuusisto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Risk Assessment ◽  
Gut Microbiome ◽  
Prediction Accuracy ◽  
Glycosylated Hemoglobin ◽  
Additional Parameter ◽  
Interpretable Machine Learning ◽  
Microbial Biomarkers

AbstractThe incidence of type 2 diabetes (T2D) has been increasing globally and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long pre-diabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and pre-diabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome In Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4.5 year) period. Our results show that the inclusion of gut microbiome improves prediction accuracy for modelling T2D associated parameters such as glycosylated hemoglobin and insulin measures. We identified novel microbial biomarkers and described their effects on the predictions using interpretable machine learning techniques, which revealed complex linear and non-linear associations. Additionally, the modelling strategy carried out allowed us to compare the stability of model performances and biomarker selection, also revealing differences in short-term and long-term predictions. The identified microbiome biomarkers provide a predictive measure for various metabolic traits related to T2D, thus providing an additional parameter for personal risk assessment. Our work also highlights the need for robust modelling strategies and the value of interpretable machine learning.ImportanceRecent studies have shown a clear link between gut microbiota and type 2 diabetes. However, current results are based on cross-sectional studies that aim to determine the microbial dysbiosis when the disease is already prevalent. In order to consider microbiome as a factor in disease risk assessment, prospective studies are needed. Our study is the first study that assesses the gut microbiome as a predictive measure for several type 2 diabetes associated parameters in a longitudinal study setting. Our results revealed a number of novel microbial biomarkers that can improve the prediction accuracy for continuous insulin measures and glycosylated hemoglobin levels. These results make the prospect of using microbiome in personalized medicine promising.

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