High Body Mass Index Disrupts the Homeostatic Effects of Pain Inhibitory Control in the Symptomatology of Patients With Fibromyalgia

This study examines the influence of body mass index (BMI) on the relationship between quantitative sensory testing measures and clinical characteristics in fibromyalgia syndrome (FMS). Utilizing BMI as a categorical covariate (>= 25 or >= 30 kg/m2) in associations between quantitative sensory testing metrics (pain-60, conditioned pain modulation, and temporal summation of pain [TSP]) and FMS clinical features, we explored BMI's role as both a confounder (change-in-estimate criterion-change equal or higher than 10%) and effect modifier (interaction term). Significant interactions revealed overweight/obese BMI as a modifier in the relationship between conditioned pain modification and both depression and symptom impact, with a homeostatic relationship between better clinical profile and pain inhibitory response observed solely in the normal-weight group. Similar results were found for pain-60 and depression. Additionally, BMI >= 30 kg/m2 modified TSP's effect on pain, demonstrating lower pain with increased TSP, exclusively in the nonobese group. This study highlights the significant role of BMI in moderating the relationships of important pain inhibitory control processes and pain intensity, depression, and the overall impact of FMS symptoms. Our results suggest that high BMI states disrupt the homeostatic effects of pain inhibition, reducing its salutogenic response in FMS participants. We discuss the mechanistic and therapeutic implications of targeting BMI in FMS clinical trials and the potential impact of this important relationship. Perspective: This investigation highlights the disruptive influence of high BMI on pain inhibitory control in fibromyalgia, unbalancing clinical symptoms such as pain and depression. It underscores the necessity of integrating BMI considerations into therapeutic approaches to enhance pain management and patient outcomes. (c) 2024 (c) Published by Elsevier Inc. on behalf of United States Association for the Study of Pain, Inc All rights are reserved, including those for text and data mining, AI training, and similar technologies.