| Title: | Chronic disease outcomes across the glycaemic spectrum: harnessing UK Biobank and linked eHealth data to quantify risks, explore mechanisms and determine treatment impacts. |
| Lead Institution: | University College London |
| Principal investigator: | Professor Nishi Chaturvedi |
Application 7661
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
About
Type 2 diabetes is associated with increases in cardiovascular disease (myocardial infarction, stroke and heart failure), cognitive decline and cancer. HbA1c, a blood test which indicates blood glucose levels (glycaemia) over the preceding 3 months, regardless of diabetes status, is available on all Biobank participants at baseline. We will examine risks of cardiovascular disease, cognitive decline and cancer across the glycaemic spectrum, including in people without diabetes. We will investigate how risks differ by gender and ethnicity. Additionally, we will explore the impact of anti-diabetic, anti-hypertensive and lipid-lowering medication on cardiovascular and cognitive outcomes throughout the glycaemic range. Since HbA1c measures chronic hyperglycaemia more reliably than random/ fasting blood glucose testing alone, the availability of this measure in the Biobank cohort provides a new opportunity to investigate the impact of glycaemia on health outcomes. UK Biobank has large numbers of participants with baseline glycaemic status and incident measures of disease, thus providing a unique and powerful instrument to address these questions. By defining the role of sub-clinical hyperglycaemia in cardiovascular disease, cancer and cognitive decline, we aim to improve the prevention and diagnosis of these diseases, in line with UK Biobank?s core goals. To address these aims, we will require access to data only, to include the following health outcomes records: HES, GP data, cancer and death registers. Measures of HbA1c, socio-demographics, lifestyle factors, anthropometry, cardiovascular risk factors (e.g. blood pressure and cholesterol) and medical and drug history from baseline will all be required. We will use statistical models to show how baseline HbA1c relates to the health outcomes of interest, what other factors influence these associations and whether associations are different in population sub-groups. We intend to use the full dataset of approximately 500,000 people. Outcomes are often compared in populations with and without diabetes, but the validity of this binary classification and widely used cut-off points is not established. Including people with no diabetes as well those with treated and untreated diabetes offers an opportunity to disentangle effects of diabetes treatments on disease.3 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 7980 | A Guide for Selection of Genetic Instruments in Mendelian randomisation (MR) studies of Type-2 diabetes and HbA1c: towards an integrated approach | Victoria Garfield (+3) | 2023 | Diabetes |
| 10465 | Are infections associated with cognitive decline and neuroimaging outcomes? A historical cohort study using data from the UK Biobank study linked to electronic health records | Rutendo Muzambi (+8) | 2022 | Translational Psychiatry |
| 8993 | Validity of European-centric cardiometabolic polygenic scores in multi-ancestry populations | Constantin-Cristian Topriceanu (+3) | 2024 | European Journal of Human Genetics |
Enabling scientific discoveries that improve human health