| Title: | Identifying mechanisms and risk markers for metabolic dysfunction, diabetes and diabetes related complications: Data science approaches integrating clinical, genetic and environmental factors |
| Lead Institution: | Steno Diabetes Center Copenhagen |
| Principal investigator: | Dr Tarun veer Singh Ahluwalia |
Application 71699
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About
Diabetes prevalence is spiraling globally and with it, so is the risk of complications of the heart, kidneys, eyes, and nerves. Diabetes and associated complications lead to diminished quality of life, and increased mortality and associated health care costs. In 2010, over 634,000 EU citizens died as a result of diabetic complications and co-morbidities (~1 death per minute), totaling 11% of all deaths in EU adults aged 20-79 years. Being complex in nature, diabetes and its complications are modulated by genes, lifestyle and multiple risk factors. The current project seeks to leverage the large number of samples of the UK Biobank (UKBB) data to study novel and existing risk (genetic and clinical) markers, associated lifestyle and clinical factors that impact micro and macrovascular complications in individuals with and without diabetes. Most studies exploring genes, lifestyle and clinical risk factors for vascular complications have either been underpowered or heterogenous due to multiple smaller studies having differences in phenotyping, and data collection methods. With the UK Biobank data, we endeavor to achieve well powered quality data on genes, metabolites and clinical and/or other risk factors relating vascular complications and cardio-metabolic traits measured in a homogenous way reducing overall study related bias. Additionally, the unique imaging data on heart, brain and liver will provide a valuable opportunity to combine similar data from the Steno Diabetes Center Copenhagen and relevant collaborators. Finally, we would be able to answer important scientific questions pertaining effects of genes (and functional variants), environment (comorbid medical conditions like respiratory and psychiatric illnesses, and metabolic disorders, clinical markers and non-invasive imaging markers), and their interaction on cardio-metabolic function; risk of diabetes and complications; and other associated conditions. We will be exploring novel information from genome sequence data which will be released by the UKBB this year and the opportunity to integrate non-invasive imaging data using machine learning methods to improve disease risk prediction for individuals with vascular complications and related outcomes. The project duration would be three years from date of data access. The project will further knowledge in disease prognosis, diagnosis, and risk assessment (using genetic and non-genetic clinical variables) among individuals with and without diabetes, which hopefully can be applied to help people avoid developing diabetes and its complications. It will also help towards identification of novel drug targets and improved clinical practices, impacting patient care, quality of life and eventually health care costs for diabetes complications.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 11138 | Four missense genetic variants in CUBN are associated with higher levels of eGFR in non-diabetes but not in diabetes mellitus or its subtypes: A genetic association study in Europeans | Nicoline Uglebjerg (+22) | 2023 | Frontiers in Endocrinology |
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