ABSTRACT Although many people are familiar with the staggering statistic that up to 30% of Americans suffer from chronic pain, a less-known fact is that most individuals with chronic pain suffer from more than one comorbid pain condition. Pain conditions which frequently co-aggregate have come to be known as chronic overlapping pain conditions (COPCs). Co-aggregation of multiple chronic pain conditions frequently results in negative side effects in addition to pain, including fatigue, sleep deficits, cognitive impairment, functional impairment, and mental health conditions such as depression, anxiety, and even suicidal ideation. Given the complexity of these side effects and their impact on patient well-being, COPCs have worse health outcomes compared to patients with a single chronic pain condition. In addition, the majority of individual COPCs such as temporomandibular joint disorders (TMD), fibromyalgia (FM), and migraines have a higher prevalence in biological females than in biological males, and their coaggregation is likewise higher in females. Thus, it is imperative that additional research investigations be undertaken which explores the mechanistic determinants of COPCs in patients with multiple comorbid conditions, and which critically evaluates the underlying mechanisms in biological males and females. We have developed methods that use clinical, biological, and psychological patient variables to group patients with COPCs into distinct patient clusters: (1) an "adaptive" cluster, which is free of hypersensitivity and psychological distress; (2) a "pain-sensitive" cluster, which has increased pain sensitivity to pain stimuli, but lacks pain-related comorbidities; and (3) a "global symptoms" cluster of patients which has increased sensitivity to pain, along with multiple symptoms of depression and anxiety, and other widespread symptoms. By separating phenotypically distinct COPC patients and analyzing these cohorts as separate entities individually in males and females, we believe we can better understand these conditions and create a more personalized approach to patient care, which will ultimately improve our ability to treat patients with multiple chronic pain conditions. Our overall hypothesis is that patients within each cluster will exhibit a greater degree of similarity in their cellular and molecular makeup compared to patients in other clusters, and we can exploit these cluster-related differences to identify biological markers and treatment approaches that are personalized for each patient group. To test this hypothesis, we will employ proteomic, transcriptomic, and preclinical screening approaches to ascertain molecular and cellular mediators of pain in individual patient clusters and in each sex. In doing so, this project will yield new mechanistic insights into the divergent pathophysiological mechanisms that give rise to pain and its associated comorbidities in males and females.