Pain Chronicity and Relief: From Molecular Basis to Exercise-Based Rehabilitation
Abstract
Chronic pain is a significant and complex health condition characterized by persistent or recurrent pain lasting more than three months. Exercise-based rehabilitation is an effective non-pharmacological intervention, yet its underlying mechanisms have not been fully elucidated. This review systematically maps the molecular pathways of exercise-induced analgesia onto the pathophysiological cascades of chronic pain, aiming to fill a key gap in the current literature. It explores the molecular and cellular mechanisms underpinning the pathophysiology of chronic pain, indicating that the persistence of chronic pain stems from peripheral sensitization driven by inflammatory mediators and central sensitization involving glial cell activation and N-methyl-D-aspartate (NMDA) receptor-mediated neuroplasticity. Exercise can interrupt these pathological cascades through multi-system adaptations, including activation of the endogenous opioid and serotonergic systems activation and anti-inflammation. However, a significant gap remains in translating this mechanistic understanding of chronic pain into optimized, personalized exercise prescriptions, requiring future research into different exercise modalities, sex-specific responses, and the impact of comorbidities.
Read the paper
Related studies
- Noninvasive and Point-of-Care Surface-Enhanced Raman Scattering (SERS)-Based Breathalyzer for Mass Screening of Coronavirus Disease 2019 (COVID-19) under 5 min
- De Novo Transcriptome Analysis and Detection of Antimicrobial Peptides of the American Cockroach Periplaneta americana (Linnaeus)
- Molecular Characterization and Expression Profiling of Tomato GRF Transcription Factor Family Genes in Response to Abiotic Stresses and Phytohormones
- Assembly of Tight Junction Strands: Claudin-10b and Claudin-3 Form Homo Tetrameric Building Blocks that Polymerise in a Channel-Independent Manner
- Update of the Pompe variant database for the prediction of clinical phenotypes: Novel disease‐associated variants, common sequence variants, and results from newborn screening
- Machine Learning-Based SERS Chemical Space for Two-Way Prediction of Structures and Spectra of Untrained Molecules
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.