Justina Krasauskaite
Thesis title: Risk prediction modelling for renal decline in patients with Type 2 Diabetes
MRC DTP Precision Medicine
Year of study: 3
- Usher Institute
Contact details
- Email: j.krasauskaite@sms.ed.ac.uk
PhD supervisors:
Address
- Street
-
Old Medical School
Teviot Place - City
- Edinburgh
- Post code
- EH8 9AG
Background
Justina is MRC DTP Precision Medicine PhD student at the Usher Institute (University of Edinburgh). Her research focuses 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. To address her research question, Justina undertook a systematic review as well as statistical analysis of data collected for a population based cohort – Edinburgh Type 2 Diabetes Study.
Previously, Justina competitively achieved funding to study for MSc in Genomic Medicine, where she developed skills and understanding in genomics and informatics of rare and common diseases, cancer and infectious diseases. Justina’s MSc dissertation focused on improving the interpretation of rare genetic variants in developmental disorders by combining information from various genomic databases and in-silico protein structure tools. In turn, Justina discovered her passion for application of more novel technological tools to answer questions relevant in clinical practice and/or medical research.
Prior to the postgraduate studies, Justina has also worked as a technician in automated laboratories delivering services for both, NHS as clinical research. Here, she developed skills in good laboratory practice, such as following standard operating procedures, quality assurance and management of biological samples.
Qualifications
BSc Biological Sciences, University of Plymouth, 2013-2015
MSc Genomic Medicine, University of Exeter, 2017-2018
Research summary
The main purpose of this PhD thesis is to derive a biomarker panel comprising of circulating markers, lifestyle and environmental factors that best predict the onset of significantly reduced kidney function. Ultimately, this research will aid the efforts 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.
Current research interests
• Type 2 diabetes and kidney function decline • Metabolomic markers of disease • Risk prediction modelling • Systematic reviewsPast research interests
• Applications of genomics in common-complex and rare disease • Bioinformatics – pipelines for identifying causal variants of disease • Automated diagnostic toolsCurrent project grants
Medical Research Council (MRC) Grant for the DTP in Precision Medicine by the University of Edinburgh, 2019
Past project grants
Higher Education England (HEE) Education programme (funded MSc fees)
University of Edinburgh Training:
Introductory Probability and Statistics (2019)
Introduction to systematic reviews (2019)
Data analysis for epidemiology (2020)
Statistical Modeling for Epidemiology (2020)
Literature reviewing, IAD (2021)
External Training:
Hands-on data analysis for metabolomic profiling, Imperial College London, 2020
Metabolomics Data Processing and Data Analysis, University of Birmingham, Future learn 2021
SysMIC: Online Course in Coding, Modelling & Data Analysis for Bioscience Researchers-Module 1 (Oct 2020-Current)
Data Ethics Pilot School, Eurolife, Medical University of Innsbruck, Austria, 2021