Shafaq Sikandar

Meet E-G-G Winner 2022: Shafaq Sikandar

Research title: Identifying a Composite Biomarker Signature for Chronic Pain in Early Rheumatoid Arthritis

Research abstract:
Joint inflammation in rheumatoid arthritis (RA) can be effectively controlled using targeted immunomodulating agents. However, a majority of patients still report moderate-to-severe chronic pain despite controlled disease activity and absence of inflammation. Neither joint structure nor inflammation are reliable predictors for the development of chronic pain in RA. A biomarker signature for chronic pain at early stages of disease could provide significant benefit to patients by improving pain management.

We have unique cohort of patients with early RA, accompanied with clinical disease data and synovial biopsies with histological and transcriptomic profiles. Our preliminary data suggests that the synovial histopathotype may be a significant determinant for chronic pain. The addition of granular pain assessments in this cohort will produce a composite biomarker signature for chronic pain that can be used to stratify patients to improve pain outcomes.

Objective 1: Produce pain profiles and stratify early RA patients based on pain outcomes
• Quantitative sensory testing, questionnaires and measures of endogenous pain mechanisms to assess biopsychosocial and somatosensory components of pain.

Objective 2: Identify a composite biomarker signature for chronic pain using a combination of components of pain profiles, histological and transcriptomic features of diseased synovium and clinical disease data
• Predictive modelling based on longitudinal patient data.

A biomarker signature for chronic pain in early RA has the potential to be (1) be used as a risk stratification tool to identify patients most likely to develop persistent pain following RA diagnosis; and (2) guide personalised treatments that optimise analgesia at an early stage of disease for those patients at most risk of developing chronic pain.