Disentangling Multimorbidity Progression in Rheumatoid Arthritis (DEMPRA) (MACGREGORA_U24FMH)

Primary supervisor - Dr Max Yates 

Background

Chronic inflammation is a well-established driver of multiple long-term conditions (MLTC). Individuals with persistent disease activity, particularly in the context of rheumatic diseases like Rheumatoid Arthritis (RA), tend to accumulate MLTC at a higher rate over time. However, the specific patterns of MLTC progression and the factors influencing these trajectories remain poorly understood. It is also unclear how early interventions, such as the use of immunomodulatory drugs, may impact MLTC progression.

This research project aims to leverage data from the Norfolk Arthritis Register (NOAR), which is an inception cohort comprising over 5,000 individuals with inflammatory arthritis. These participants have been continuously monitored for up to two decades, with extensive clinical characterisation available. The project benefits from recent linkages to hospital records through the Cogstack interface, which enables in-depth exploration of clinical histories, laboratory findings, and radiology data. Furthermore, more than half of the cohort has provided genomewide scan data. The project has full ethical and governance permissions in place.

Research Methodology

The project uses a multifaceted research approach, incorporating epidemiological methods, statistics, as well as artificial intelligence (AI) and machine learning techniques for analysing big data. This interdisciplinary methodology allows for the extraction of meaningful insights from complex datasets. The candidate will receive tailored training in epidemiological methods, data management, ethics and regulation, and machine learning and AI for the analysis of large datasets.

Training

The successful candidate will benefit from guidance and support from a team of experts dedicated to helping them achieve their research objectives. Specific training in various domains, including epidemiological methods, data management, ethics, regulations, and machine learning/AI for Big Data analysis, will be provided to enhance the candidate's capabilities.

Person Specification

Applicants should hold a bachelor's degree in a relevant field aligned with biological science, mathematics, or computer science. While not mandatory, having a master's degree can strengthen the candidate's application and readiness for the research. This research project provides an exciting opportunity to contribute to our understanding of MLTC progression in the context of chronic inflammation and to apply advanced research methodologies.

Entry requirements

The standard minimum entry requirement for the studentship competition is first degree 2:1.

Acceptable first degree subject areas: Mathematics, Biologic Sciences, Computing.

Start date

October 2024