Chronic Low Back Pain (CLBP) is a complex multi-factorial condition, as well as the most prevalent painful musculoskeletal disorder worldwide. Its causes and mechanisms are numerous and still poorly understood, which leads to common failure in its clinical treatment (e.g. physical therapy - PT). Systemic inflammation has been gaining attention in the literature as a likely contributing aspect to CLBP, but its causes are diverse and still lack thorough insight. One of these causes seems to be the presence of specific SNPs (Single Nucleotide Polymorphism, the simplest and most common variation in human DNA) in certain genes that are known to be related to both inflammation and pain. It is unclear how these variations may affect the outcomes of different types of PT treatment for CLBP, which is an innovative aspect of the proposed research project. Overall, the main objective is to be able to better tailor specific PT treatment to each patient. More specifically, this research aims to understand if there is an association between gene variations and outcomes of PT treatment; it also intends to better understand the mechanisms behind how gene variations affect inflammation, to support the formation of novel research questions in the area of pain. Furthermore, it seeks to determine clinical sub- phenotypes of CLBP using machine learning (Network Phenotyping Strategy - NPS) based on patients? responsiveness to PT treatment. From a career standpoint, it aims at supporting my professional development for a future in pain research and academia. The project will use genetic and clinical data from 200-250 patients, gathered from the 1000 patients? cohort that the Pitt LB3P Mechanistic Research Center will collect; it will include all the patients with CLBP and no diagnosis of inflammatory or auto-immune disease who will undergo PT treatment as part of their standard of care within the University of Pittsburgh Medical Center (UPMC). The data that will be used for this study includes the variations for selected genes, PT treatment (to be collected as a novel aspect facilitated by this proposed supplement), the outcomes variables (disability and pain scores, pain interference, the impression of change), as well as other clinically relevant information (e.g. age, sex, comorbidities). The analysis will be performed with two different approaches: first, it will be analyzed with the traditional statistical testing for genetic variations, to look for associations between SNPs and PT treatment outcomes. The analysis will include Logistic regression analysis for the association between genetic variations and outcome measures, Fisher's exact test for proper group determination, Cochran?s Q test for multiple groups if a non-paired assumption will be found to be more clinically relevant. Subsequently, the dataset will be analyzed using the NPS approach, which will help delineate clinical outcome-based phenotypes based on clinical response to PT treatment. By adding this new layer of information to the clinical approach, this research seeks to improve the assessment and treatment of CLBP in a PT setting and to make it more resource-efficient and tailored to each patient?s needs.
Physical Therapy (PT) is one of the initial standards of care for the treatment of chronic low back pain (CLBP, a challenging condition that affects a large portion of the world population) but provides variable and overall unsatisfactory results. There are many possible contributing causes for CLBP, and one of them is systemic inflammation, which is partly regulated by genes, but to an unknown extent. This research intends to look into how variations in specific genes that are known to influence pain and inflammation may impact the outcomes of PT, to better inform the assessment and treatment patients with CLBP, and reduce the financial burden for the treatment of this condition.
