Integrated analysis of chronic pain phenotypes and predictive biomarkers in SLE

Living with lupus presents many challenges, and one of the most difficult is managing chronic pain. More than half of people with lupus experience chronic overlapping pain conditions (COPCs), such as fibromyalgia, migraines, and chronic low back pain. Additionally, a significant number of people with lupus are prescribed long-term opioid medications, and they are at a higher risk of opioid-related hospitalizations. Despite these challenges, there is still a lack of detailed understanding of the underlying processes that cause and maintain chronic pain in lupus. This is partly due to the lack of a multifaceted approach that considers the complex interplay of biological, psychological, and social factors.

Dr. Falasinnu aims to improve chronic pain management in lupus by identifying specific patterns of pain and biomarkers that can predict the onset and progression of COPCs. Using large-scale health data from the U.S. and Denmark, Dr. Falasinnu will first examine the burden and progression of chronic pain in lupus patients, comparing it to people without lupus, assessing how different pain conditions, such as chronic low back pain, contribute to disability in lupus patients. Next, she will use data from LRA’s Lupus Landmark Study, which tracks several patient- and clinician-reported outcomes, including pain and other conditions, over time, to explore how both biological factors (like immune markers) and psychosocial factors (such as stress and depression) work together to predict chronic pain. Dr. Falasinnu aims to develop a more personalized approach to chronic pain treatment by understanding how these combined factors influence pain in lupus.

What this means for people with lupus:

Findings from Dr. Falasinnu’s study could lead to better ways to manage chronic pain in lupus by identifying patterns and markers that predict pain before it worsens. By integrating both biological and emotional factors, these results could enable more accurate, individualized treatments to improve pain relief and quality of life for people living with lupus.

Chronic pain is a predominant and debilitating feature of SLE, affecting over half of patients and frequently overlapping with other pain conditions, such as fibromyalgia and chronic low back pain. Pain in SLE is multifaceted, influenced by biological, psychosocial, and environmental factors, yet it remains under-researched. This project addresses critical gaps in understanding chronic pain trajectories and their underlying mechanisms in SLE, with the ultimate goal of advancing precision pain management and improving patient outcomes. The proposed research focuses on two aims: Characterizing Chronic Pain Trajectories and Disability in SLE: Using large administrative datasets from the United States (Marketscan) and Denmark (Danish National Patient Registry), we will examine the burden, timing, and progression of chronic overlapping pain conditions (COPCs) in SLE. We hypothesize that COPCs occur earlier, progress more rapidly, and mediate the relationship between SLE and disability. Employing robust statistical methods, including conditional negative binomial regression and causal mediation analysis, this aim will identify pain phenotypes and their impact on disability outcomes across diverse healthcare settings. Examining Synergy Between Biomarkers and Psychosocial Drivers in Chronic Pain: Leveraging data from the Lupus Landmark Study, a biobank with over 3,500 patients, this aim will investigate how biomarkers (e.g., immune and inflammatory pathways) and psychosocial factors (e.g., depression, stress) jointly predict chronic pain risk. We will utilize machine learning models to evaluate the combined and individual predictive power of these variables and explore their synergistic interactions. The results will lay the groundwork for personalized pain management strategies, integrating biological and psychosocial domains. Long-term Objectives: This study seeks to develop comprehensive frameworks for chronic pain prediction and management in SLE, addressing both biological and psychosocial contributors. Insights gained will directly inform patient-specific therapeutic strategies, reducing the reliance on opioids, improving quality of life, and minimizing disability. Relevance to Lupus: Chronic pain in SLE is a leading driver of disability, yet its mechanisms and effective treatments are poorly understood. By combining multinational datasets, advanced machine learning techniques, and a biopsychosocial framework, this project aims to unravel the complexity of pain in SLE. The findings will contribute to precision medicine approaches for lupus, providing critical evidence to improve outcomes for this underserved population. This research aligns with the overarching goal of reducing the burden of chronic pain in lupus and enhancing the quality of care for affected individuals.