Modeling pain variability in fibromyalgia

PROJECT SUMMARY Fibromyalgia (FM) is a common and debilitating chronic pain condition. Given the unknown pathophysiology and the lack of biomarkers in FM, accurate and clinically meaningful assessment of symptoms is critical. Pain and other FM symptoms are typically studied as if they were static with any intra-individual variability either not captured by measurement approaches, averaged out, or treated as noise/error. Yet, when asked, most FM patients will report substantial fluctuations in their symptoms over time. Across chronic pain conditions, higher pain variability is associated with a host of negative outcomes, and has been identified as a promising predictor of treatment response. Yet, methodological tools that could characterize this variability have not been leveraged to their full potential. The proposed project will use ecological momentary assessment (EMA) data (3X/daily ratings of pain, mood, and stress for 8 days) from 72 FM patients collected as part of a NIAMS funded clinical trial (R21AR082574) evaluating metformin as a treatment for FM alongside an added measure of short-term variability in response to experimentally induced pain. Long-term variability in clinical pain has been highlighted as a promising potential predictor of clinically relevant outcomes in chronic pain patients, but it has yet to be fully evaluated in FM patients, and using short-term variability as a predictor is completely novel. While metrics of pain variability likely have clinical utility on their own, decomposing factors (e.g., emotional state or endogenous pain modulatory (EPM) functioning) that contribute to variable pain experiences could have even more utility. The proposed research will apply and develop advanced models to characterize pain variability in fibromyalgia patients. Aims 1 and 2 will determine the potential of both long-term variability of clinical pain (Aim 1) and short- term variability in response to experimental pain (Aim 2) as potential behavioral phenotypes to predict clinically relevant outcomes (symptom severity, functional ability, and treatment response). Aim 3 will craft a theoretically informed model that will index the relative contributions of incoming pain signals, emotional state, and EPM functioning for a given pain state in a given patient with the goal of outputting individualized pain profiles that can be used in precision medicine contexts to target fibromyalgia treatments more effectively. This project is in line with the "precision medicine for arthritis, musculoskeletal, and skin diseases" scientific theme in the 2020-2024 NIAMS strategic plan. This fellowship application describes detailed research and training plans that draw upon the expertise of the Sponsor, Co-Sponsors, and Consultants to prepare the candidate to sucessfully launch a career as an academic researcher at an R1 institution.