Biosignatures from Multimodal Datasets to Deeply Phenotype Patients and Predict Chronic Pain

Session Title: Biosignatures from Multimodal Datasets to Deeply Phenotype Patients and Predict Chronic Pain

Topic: CLINICAL SCIENCE

Description of Workshop: Chronic pain is a complex disease that affects one in five people across the lifespan, resulting in tremendous individual suffering and socioeconomic burden worldwide. Although the designation of “chronic pain” applies arbitrarily to pain lasting longer than three months, research suggests that there may be a period of transition from acute to chronic pain influenced by biological and psychosocial factors. In the face of injury, disease, or environmental challenges, some individuals develop chronic pain whereas others resist it, recover, or return to health. Despite decades of research, the determinants of risk and resilience to chronic pain remain elusive.
No single biomarker or research modality can capture chronic pain’s complexity to predict who will develop chronic pain. Meaningful predictive models will require the integration of multi-modal data from large samples of participants to create biosignatures of risk and resilience. Such signatures will include biological outputs such as omics measures from blood and other tissues and brain imaging, as well as patient-reported outcomes for psychosocial measures, functional and sensory testing, and health systems data from electronic health records (EHR).
This workshop brings together international researchers whose expertise and resources are uniquely complementary. From Japan, senior researcher Takahiro Ushida presents one of the world’s largest multidimensional chronic pain datasets, spanning clinical features, functional outcomes, and psychosocial dimensions. From Esther Pogatzki-Zahn's group in Germany, researcher Daniel Segelcke offers two datasets—published and unpublished—derived from smaller experimental and clinical cohorts that contain tremendous phenotypic depth from multi-omics-based biomarkers together with patient-reported outcomes to model pain resilience versus susceptibility. From the United States, Giovanni Berardi, an early-career researcher in Kathleen Sluka’s group, will highlight data collected by the Acute to Chronic Pain Signatures (A2CPS) Consortium, a landmark NIH-funded initiative building one of the largest integrated biomarker, imaging, psychosocial, and EHR databases ever assembled. A2CPS Communications Director Stephani Sutherland (U.S.) will moderate the session, using Slido to engage the audience with live polls and Q&A opportunities.
The strength of this workshop lies in combining scale with depth. By bridging large-scale patient datasets made up of multimodal, integrative data, we aim to identify multidimensional signatures that will predict the persistence of chronic pain in at-risk populations and reveal biological mechanisms underlying the transition to chronic pain. The discussion will not only highlight the assets and limitations of each approach but also propose pathways to harmonize and align data internationally.

This session will be highly interactive, challenging participants to envision a new global framework that unites diverse data sources, which will be crucial to maximally leverage these invaluable resources. By synergizing large datasets and biomarkers, the workshop seeks to accelerate discovery of the “keys” to chronic pain and chart a path toward earlier detection, prevention, and individualized prediction and treatment.

Speakers