Chad M. Brummett

Background: Intervertebral disc degeneration (IVDD) encompasses structural changes of the spine that are commonly studied using clinically defined diagnoses or symptom-based phenotypes, which reflect a mixture of anatomical degeneration and pain-related processes. Magnetic resonance imaging (MRI) provides direct assessment of spinal structure, but genetic studies using imaging-derived traits have been limited by challenges in large-scale phenotyping. Methods: We applied SpineNet, a deep learning framework for automated MRI grading, to derive lumbar spine degeneration phenotypes in 7,414 individuals from 3 cohorts. GWAS was conducted within each cohort and results meta-analysed. Phenotypic and genetic relationships were assessed using exploratory factor analysis and LD score regression. genomicSEM modelled shared genetic liability across MRI traits and with GWAS of chronic pain and spinal disorders, followed by a common factor GWAS. Results: GenomicSEM identified a latent genetic dimension representing structural spinal degeneration, with strong contributions from disc narrowing, endplate defect, herniation, foraminal stenosis, and marrow signal change. When integrated with pain and clinical phenotypes, structural MRI traits and chronic pain had distinct genetic dimensions, while clinically defined spinal diagnoses showed contributions from both. The common factor GWAS identified several suggestive loci enriched in regulatory genomic regions, mapping to genes previously implicated in musculoskeletal traits. Phenotypic and genetic analyses revealed a consistent factor structure in which MRI-derived structural traits clustered together and were distinct from alignment-related abnormalities. Conclusions: Automated MRI phenotyping combined with multivariate genetic modelling identified a genetic dimension of structural spinal degeneration that is partially distinct from pain-related liability, providing a scalable framework for future spine studies.

Why some surgical patients develop pain that extends beyond the original site of injury while others do not remains poorly understood. The recently proposed Risk of Pain Spread (ROPS) framework integrates multiple psychosocial risk factors associated with widespread pain, but its generalizability to surgical populations has not been established. Using 2 large presurgical cohorts of participants undergoing total knee arthroplasty or thoracic surgery, we evaluated the generalizability of the ROPS framework and examined relationships among psychosocial and sociodemographic factors, surgical-site pain, nonsurgical-site pain, and widespread pain using complementary latent-variable, regression, Bayesian network, and mediation analyses. Across cohorts and analytical approaches, fatigue emerged as the most consistent correlate of pain outcomes, independent of other measured risk factors. Fatigue also occupied a central position within the conditional dependency structure of psychosocial and pain variables and accounted for substantial shared variance linking sleep disturbance, depressive symptoms, neuroticism, adverse childhood experiences, and socioeconomic disadvantage with pain intensity. Nonsurgical-site pain consistently occupied a position linking pain intensity with widespread pain across complementary analyses. Together, these findings externally validate the ROPS framework in clinically relevant surgical populations and identify fatigue as a central correlate linking biopsychosocial vulnerability with pain burden.

BACKGROUND: Trauma patients frequently have comorbid substance use disorders (SUD), which may complicate pain management and increase the risk of opioid-related adverse outcomes after discharge. We hypothesized that trauma patients with SUD would be prescribed more opioids at discharge than those without SUD. METHODS: We retrospectively analyzed a cohort of adult trauma patients across the state from 2022 to 2025 using a statewide collaborative database. Primary outcomes were opioid prescription at discharge (yes/no) and amount prescribed standardized to oxycodone 5 mg pills. A multivariable approach assessed risk-adjusted differences in outcomes among patients with SUD versus without SUD. RESULTS: Among 29,805 total patients, 8,672 (29.1%) had a diagnosis of SUD. 71.2% of patients with SUD received a discharge opioid prescription, compared with 59.6% of patients without SUD ( p p <0.001) compared with those without. Prescribing patterns also differed by race and ethnicity: Hispanic or Latino patients were 5.4-percentage points more likely to receive an opioid prescription than non-Hispanic or Latino patients, while, among prescription recipients, Black patients received 3.56 fewer pills than White patients. CONCLUSIONS: Our findings demonstrate that patients with SUD received opioids more frequently and in greater amounts at discharge, yet absolute differences were small in magnitude and may not represent clinically meaningful differences. Differences in prescribing by race and ethnicity were larger in magnitude and warrant further investigation. LEVEL OF EVIDENCE: Prognostic and epidemiological; Level III.

Background: A substantial minority (∼20%) of patients fail to achieve meaningful pain reduction following surgery intended to relieve pain. Risk is elevated in patients with nociplastic pain features, but available self-report measures were not designed for pre-surgical screening. We aimed to develop a brief, data-driven screener for poor analgesic response to surgery. Methods: Participants were recruited from tertiary orthopedic and chronic pelvic pain clinics. Total hip arthroplasty participants had Kellgren–Lawrence grades III–IV with hip pain ≥1 year; hysterectomy participants had chronic pelvic pain ≥6 months. The primary outcome was a 50% reduction in worst pain at six months. Items were selected via elastic net regression with k-fold cross-validation from 68 candidates. Results: Of 428 participants (81% female; mean age 51), 35% failed to achieve a 50% pain reduction. The resulting 11-item screener — the GenerAlized sensory sensitivity for sUrGical rEsponsiveness (GAUGE) — comprises pain across seven body regions and four symptom items measuring interoception (nausea, numbness/tingling) and exteroception (sensitivity to sound, sensitivity to odors). GAUGE outperformed the Central Sensitization Inventory, Fibromyalgia Survey Criteria, and PainDETECT for predicting surgical non-response (RR 1.535, 95% CI 1.342–1.55; AUC 0.738; sensitivity 0.741, specificity 0.635) and for predicting Patient Global Impression of Change. In an independent validation cohort of 54 total knee arthroplasty patients, GAUGE outperformed the Fibromyalgia Survey Criteria in predicting pain severity at six-months. Conclusions: GAUGE is a data-driven, theoretically grounded screener for poor analgesic response to surgery, with potential utility for pre-surgical counseling and clinical trial enrichment.

Background: A substantial proportion of patients report no pain after surgery, resulting in an excess of zero values that pose challenges for analysis using traditional statistical models. The current study was designed to test the hypothesis that a two-part model, commonly used in healthcare expenditures research, would demonstrate superior performance in predicting postsurgical pain when compared with traditional models and would secondarily better identify predictors of this clinically important outcome. Methods: This study analyzed a prospectively collected single-center data set (n = 3,925) of chronic postsurgical pain to compare a novel two-part modeling framework with logistic and linear regression. The two-part model first estimated the probability of experiencing postsurgical pain at 3 months (binary outcome), followed by the severity of pain among those affected (on a 1 to 10 numeric rating scale). To obtain an unbiased assessment of model performance, the data were randomly split into training (n = 3,000) and testing (n = 925) data sets. The models were trained on the training data set and evaluated on the testing set. This process was repeated 400 times to compute average performance estimates. As a secondary aim, the study assessed the associations of 15 baseline factors, including validated measures of patient-reported pain, functional and psychological measures, comorbidities, and surgical details, across the different modeling approaches. Results: The two-part model demonstrated superior predictive performance compared with linear regression alone, with a higher mean R ² value (0.075 vs . 0.050; P < 0.0001), lower root mean square error (1.466 vs . 1.485; P < 0.0001), and lower mean absolute error (1.020 vs . 1.030; P < 0.0001). Statistical comparison with logistic regression alone was not possible due to the shared binary component. The two-part model identified seven baseline preoperative covariates as independently associated with postsurgical pain that were missed by either logistic or linear models or both: self-reported race, education level, American Society of Anesthesiologists classification, overall body pain, widespread pain, symptom severity, and anxiety level. Significant baseline factors identified in all three models were patient sex, surgical type, and surgical site pain. Conclusions: A two-part model may offer a superior statistical approach to linear and logistic regression, better identifying patient- and clinical care–related risk factors of chronic postsurgical pain, primarily by distinguishing factors that drive the occurrence of chronic pain from those that are associated with pain severity.

Why some surgical participants experience pain that extends beyond the original site of injury while others do not remains poorly understood. Both pain intensity and widespread pain contribute to recovery and quality of life, yet their psychosocial correlates are often examined separately. Using data from two large pre-surgical cohorts—participants preparing for knee replacement or thoracic surgery—we examined associations between sociodemographic and psychosocial factors, pain intensity at surgical and non-surgical sites, and widespread chronic pain. Across cohorts and outcomes, fatigue showed the strongest and most consistent associations with pain intensity and widespread pain, independent of other measured factors. Fatigue also occupied a central position in statistical association networks and accounted for substantial shared variance among multiple psychosocial variables, including sleep disturbance, depression, stress, and socioeconomic disadvantage. Pain at non-surgical sites was strongly associated with widespread pain and frequently accounted for observed associations between surgical-site pain and widespread pain. Together, these findings highlight robust patterns of association linking fatigue, pain intensity, and widespread pain in pre-surgical populations. One Sentence Summary: Fatigue is the strongest and most consistent factor linked to how pain intensifies and spreads before surgery.

Fibromyalgia is a common and debilitating chronic pain syndrome of poorly understood etiology. Here, we conduct a multi-ancestry genome-wide association study meta-analysis across 2,563,755 individuals (54,629 cases and 2,509,126 controls) from 11 cohorts, identifying the first 26 risk loci for fibromyalgia. The strongest association was with a coding variant in HTT , the causal gene for Huntington’s disease. Gene prioritization implicated the HTT regulator GPR52 , as well as diverse genes with neural roles, including CAMKV , DCC , DRD2 / NCAM1 , MDGA2 , and CELF4 . Fibromyalgia heritability was exclusively enriched within brain tissues and neural cell types. Fibromyalgia showed strong, positive genetic correlation with a wide range of chronic pain, psychiatric, and somatic disorders, including genetic correlations above 0.7 with low back pain, post-traumatic stress disorder and irritable bowel syndrome. Despite large sex differences in fibromyalgia prevalence, the genetic architecture of fibromyalgia was nearly identical between males and females. This work provides the first robust genetic evidence defining fibromyalgia as a central nervous system disorder, thereby establishing a biological framework for its complex pathophysiology and extensive clinical comorbidities.

Background: Misuse of prescription opioids remains a public health problem. Appropriate short-term use of these medications in opioid-naive patients is indicated in selected settings but can result in unintended prolonged opioid use (UPOU), defined as the continuation of opioid therapy beyond the period by which acute pain would have been expected to resolve. Clinical strategies aimed at preventing UPOU are lacking due to the absence of information about how this poorly understood clinical phenomenon actually develops. Objective: In this research project, 3 Clinical and Translational Science Awards (CTSA) programs (Mayo Clinic, University of Michigan, and Yale University) leveraged the conceptual framework for UPOU to investigate how patient characteristics, practice environment characteristics, and opioid prescriber characteristics facilitate or impede UPOU. All data management and analyses were conducted at a fourth CTSA program (University of Minnesota). This work was accomplished by pursuing 3 specific aims. Methods: In aim 1, opioid-naive adults receiving an initial opioid prescription were recruited for study participation. Opioid prescriptions were identified longitudinally, and patterns of use were categorized as short-term, episodic, or long-term use using established criteria. Using a prospective case-control design, patients progressing to UPOU were matched 1:1 with patients who did not develop UPOU, and differences in patient characteristics were assessed. In aim 2, clinicians who prescribed opioids to patients in aim 1 were identified and recruited for prospective assessments. Institutional and individual practice environments were assessed using a validated self-report survey. In aim 3, structural equation modeling was used to evaluate data collected in aims 1 and 2, and identified interactions were further evaluated in a large national administrative claims database. Results: Patient recruitment began on August 1, 2019. However, due to the COVID-19 pandemic, patient recruitment was slowed and intermittently interrupted over the ensuing 3-year period. As a result of regional variations in the impact of the COVID-19 pandemic on research activities, the majority of patient and clinician recruitment occurred at the Mayo Clinic site. Conclusions: Following complete data analyses, it is anticipated that electronic health record systems will be leveraged to help clinicians identify at risk patients and to develop direct-to-patient educational materials to raise awareness of the risk factors for developing UPOU. Trial Registration: ClinicalTrials.gov NCT04024397; https://clinicaltrials.gov/study/NCT04024397 International Registered Report Identifier (IRRID): DERR1-10.2196/72032