Risk prediction for renal dysfunction in people with type 2 diabetes
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Molecular. Genetics & Pop Health
Abstract
Background/Aims
We are currently reaching an epidemic of type 2 diabetes, with patients living longer and developing complications which affect longevity and quality of life. There is an urgent need to develop accurate methods of predicting who will and who won't develop complications, so that targeted preventive measures can be instituted without over-intervention in healthy individuals. Such complications include macrovascular disease (heart attacks and strokes), kidney dysfunction, eye disease/retinopathy and cognitive impairments such as memory loss. As an emerging approach for disease prediction and prevention, as well as treatment, the application of the research techniques employed within Precision Medicine in well-phenotyped, longitudinal cohorts, is ideal for addressing this research need.
I am given unrestricted access to the Edinburgh Type 2 Diabetes Study (ET2DS), which includes serial data (4 time-points over 10 years) on a wide range of clinical, environmental, genetic and -omic variables plus stored biological samples and images, linked to routine healthcare data, for over 1000 men and women with type 2 diabetes. The ET2DS is also a member of global collaborations which opens up the possibility of replicating findings in additional cohorts, and, for the student, of experiencing research undertaken in a variety of different locations.
Research question: During my project, I will focus on developing prognostic models with renal dysfunction as the primary outcome, in order to identify a clinically-useful panel of biomarkers which best predicts the onset of renal dysfunction, or its progression, in people with type 2 diabetes. A similar approach has been used previously to predict risk of cardiovascular disease in the ET2DS2. In addition to using the wide range of clinical, environmental, genetic and circulating biomarkers already measured in the ET2DS, there will be the opportunity to use stored samples (and images) to collect de novo data on biomarkers identified as novel candidate renal risk factors3, and to combine these into prognostic models.
We are currently reaching an epidemic of type 2 diabetes, with patients living longer and developing complications which affect longevity and quality of life. There is an urgent need to develop accurate methods of predicting who will and who won't develop complications, so that targeted preventive measures can be instituted without over-intervention in healthy individuals. Such complications include macrovascular disease (heart attacks and strokes), kidney dysfunction, eye disease/retinopathy and cognitive impairments such as memory loss. As an emerging approach for disease prediction and prevention, as well as treatment, the application of the research techniques employed within Precision Medicine in well-phenotyped, longitudinal cohorts, is ideal for addressing this research need.
I am given unrestricted access to the Edinburgh Type 2 Diabetes Study (ET2DS), which includes serial data (4 time-points over 10 years) on a wide range of clinical, environmental, genetic and -omic variables plus stored biological samples and images, linked to routine healthcare data, for over 1000 men and women with type 2 diabetes. The ET2DS is also a member of global collaborations which opens up the possibility of replicating findings in additional cohorts, and, for the student, of experiencing research undertaken in a variety of different locations.
Research question: During my project, I will focus on developing prognostic models with renal dysfunction as the primary outcome, in order to identify a clinically-useful panel of biomarkers which best predicts the onset of renal dysfunction, or its progression, in people with type 2 diabetes. A similar approach has been used previously to predict risk of cardiovascular disease in the ET2DS2. In addition to using the wide range of clinical, environmental, genetic and circulating biomarkers already measured in the ET2DS, there will be the opportunity to use stored samples (and images) to collect de novo data on biomarkers identified as novel candidate renal risk factors3, and to combine these into prognostic models.
Organisations
People |
ORCID iD |
| Jacqueline Price (Primary Supervisor) | |
| Justina Krasauskaite (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/N013166/1 | 01/10/2016 | 30/09/2025 | |||
| 2261250 | Studentship | MR/N013166/1 | 01/09/2019 | 30/11/2023 | Justina Krasauskaite |