Original Research, Human Studies, Basic Neurobiology, Resource

The multiple comorbidities & dimensions of chronic pain present a formidable challenge in disentangling its aetiology. Here, we performed genome-wide association studies of eight chronic pain types using UK Biobank data (N=4,037-79,089 cases; N=239,125 controls), followed by bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine (respectively) their genetic correlations and genetic causal proportion (GCP) parameters with 1,492 other complex traits. We report evidence of a shared genetic signature across chronic pain types as their genetic correlations and GCP directions were broadly consistent across an array of biopsychosocial traits. Across 5,942 significant genetic correlations, 570 trait pairs could be explained by a causal association (|GCP| > 0.6; 5% false discovery rate), including 82 traits affected by pain while 410 contributed to an increased risk of chronic pain (cf. 78 with a decreased risk) such as certain somatic pathologies (e.g., musculoskeletal), psychiatric traits (e.g., depression), socioeconomic factors (e.g., occupation) and medical comorbidities (e.g., cardiovascular disease). This data-driven phenome-wide association analysis has demonstrated a novel and efficient strategy for identifying genetically supported risk & protective traits to enhance the design of interventional trials targeting underlying causal factors and accelerate the development of more effective treatments with broader clinical utility. PERSPECTIVE: Through large-scale phenome-wide association analyses of >1,400 biopsychosocial traits, this article provides evidence for a shared genetic signature across eight common chronic pain types. It lays the foundation for further translational studies focused on identifying causal genetic variants and pathophysiological pathways to develop novel diagnostic & therapeutic technologies and strategies.

Knee osteoarthritis (OA) is the most common form of OA and a leading cause of disability worldwide. Chronic pain and functional loss secondary to knee OA put patients at risk of developing depression, which can also impair their treatment response. However, no tools exist to assist clinicians in identifying patients at risk. Machine learning (ML) predictive models may offer a solution. We investigated whether ML models could predict the development of depression in patients with knee OA and examined which features are the most predictive.

Chronic overlapping pain conditions (COPCs) are a collection of chronic pain syndromes that often co-occur and are thought to share underlying nociplastic pathophysiology. Since they can manifest as seemingly unrelated syndromes they have historically been studied in isolation. Use of International Classification of Diseases (ICD) codes in medical records has been proposed as a means to identify and study trends in COPCs at the population level, however validated code sets are needed. Recently, a code set comprising ICD-10 codes as proxies for 11 COPCs was validated. The goal of this project was to validate a code set composed of ICD-9 codes for the identification of COPCs in administrative datasets. Data was extracted using the Electronic Medical Record Search Engine at the University of Michigan Health System from January 1st, 2011 to January 1st, 2015. The source population were patients with one of the candidate ICD-9 codes corresponding to various COPCs. Natural language searches were used as a reference standard. If code sets met a pre-specified threshold of agreement between ICD-9 codes and natural language searches (≥ 70%), they were retained and diagnostic accuracy statistics were calculated for each code set. Validated ICD-9 code sets were generated for 10 of the 11 COPCs evaluated. The majority had high levels of diagnostic accuracy, with all but one code set achieving ≥ 80% specificity, sensitivity, and predictive values. This code set may be used by pain researchers to identify COPCs using ICD-9 codes in administrative datasets.

Osteoarthritis (OA) and chronic low back pain (CLBP) are common musculoskeletal disorders with substantial patient and societal burden. Nordic administrative registers offer a unique opportunity to study the impact of these conditions in the real-world setting. The Burden of Disease and Management of Osteoarthritis and Chronic Low Back Pain: Health Care Utilization and Sick Leave in Sweden, Norway, Finland and Denmark (BISCUITS) study was designed to study disease prevalence and the societal and economic burden in broad OA and CLBP populations.

To investigate the use of multi-data analysis based on an artificial neural network (ANN) to predict long-term pain outcomes after microvascular decompression (MVD) in patients with trigeminal neuralgia (TN), and to explore key predictors.