Background & Aims
Chronic pain is a multifactorial problem that affects approx. 20% of the world’s adult population [1–4]. The current treatments of chronic pain are not satisfactory and an understanding of the fundamental mechanisms underlying chronic pain in humans is lacking. This dramatically hampers the development of new therapies. The current study assessed pain sensitivity, inflammation, microRNA, and psychological factors and combined these in a large-scale multifactorial, network model to understand the factors involved in the complex perception of chronic pain and thereby contribute to a better understanding of the underlying mechanisms.
Methods
The present study involved 75 patients with and without chronic postoperative pain 5-years after total knee arthroplasty. Clinical pain intensity, Oxford Knee Score, and pain catastrophizing were assessed as clinical parameters, and Quantitative Sensory Testing (QST) was assessed for mechanistic evaluation of the pain sensitivity. MicroRNAs and inflammatory biomarkers using next-generation sequencing and the Luminex approach, respectively, were analyzed. Supervised multivariate data analysis with Data Integration Analysis for Biomarker Discovery using Latent cOmponents (DIABLO) in R-studio was utilized to explain clinical pain intensity and to reduce the number of parameters in the model. Finally, the variables included in the final DIABLO model were analyzed in a linear regression to explore how well the DIABLO model explains clinical pain.
Results
The DIABLO model identified 40 significant variables, which included 20 microRNAs, 15 inflammatory markers, 2 clinical outcomes, and 3 QST assessments. The DIABLO model demonstrates multiple correlations between the included variables, indicating that these parameters interact with each other in pain mechanistic networks. The linear regression model demonstrated an explanatory value (R²) of 81% for clinical pain intensity.
Conclusions
This is the first study to demonstrate large-scale mechanistic networks in patients with chronic pain. The model explained 81% of the variability, which is a substantial leap forward when compared to previous studies, indicating that the multifactorial complexity of chronic pain should be studied in networks rather than focusing on single parameters.
References
1Breivik H, Collett B, Ventafridda V, Cohen R, Gallacher D. Survey of chronic pain in Europe: Prevalence, impact on daily life, and treatment. European Journal of Pain 2006; 10: 287–333.
2Goldberg DS, McGee SJ. Pain as a global public health priority. BMC Public Health 2011; 11: 770–4.
3Gureje O, Von Korff M, Kola L, et al. The relation between multiple pains and mental disorders: Results from the World Mental Health Surveys. Pain 2008; 135: 82–91.
4Cohen SP, Vase L, Hooten WM. Chronic pain: an update on burden, best practices, and new advances. Lancet 2021; 397: 2082–97.
Presenting Author
Rocco Giordano
Poster Authors
Rocco Giordano
MSc
Center for Neuroplasticity and Pain, HST, Faculty of Medicine, Aalborg University, Aalborg, DK
Lead Author
Lars Arendt-Nielsen
PhD
Aalborg University
Lead Author
Camilla Capritotti
M.Sc.
Aalborg University
Lead Author
Maria Carla Gerra
Ph.D.
Universitá di Parma
Lead Author
Andreas Kappel
MD
Aalborg University and Aalborg University Hospital
Lead Author
Svend Erik Østgaard
MD
Aalborg University and Aalborg University Hospital
Lead Author
Cristina Dallabona
Ph.D.
Universitá di Parma
Lead Author
Kristian Petersen
PhD
Aalborg University, Aalborg, Denmark
Lead Author
Topics
- Genetics