Related Experiment Video
Updated: Jul 5, 2025
Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
Published on: April 14, 2016
Big data, big consortia, and pain: UK Biobank, PAINSTORM, and DOLORisk
Harry L Hébert1, Mathilde M V Pascal2, Blair H Smith1
1Chronic Pain Research Group, Division of Population Health and Genomics, Ninewells Hospital & Medical School, University of Dundee, Dundee, United Kingdom.
Insights
Large consortia and biorepositories are crucial for chronic pain (CP) research. Standardized phenotyping in large cohorts enhances study power for understanding CP risk factors and pathogenesis.
Area of Science:
- Pain Medicine
- Genetics
- Epidemiology
Background:
- Chronic pain (CP) is a prevalent, debilitating condition with significant socioeconomic consequences.
- Managing refractory CP and understanding its heterogeneous nature, including neuropathic pain, presents challenges due to limited research data.
- Existing studies are hampered by inconsistent phenotyping, data collection, and small sample sizes, limiting analytical power, especially for genome-wide association studies.
Purpose of the Study:
- To review the approach used in the DOLORisk study for investigating neuropathic pain.
- To explain how DOLORisk has informed ongoing projects like PAINSTORM and UK Biobank rephenotyping.
- To provide an overview of study outputs and lessons learned for future chronic pain research endeavors.
Main Methods:
- Formation of large research consortia (e.g., DOLORisk, PAINSTORM) and utilization of biorepositories (e.g., UK Biobank).
- Implementation of a common approach for CP phenotyping to enable data harmonization across cohorts.
- Leveraging large-scale data to increase statistical power for genetic and etiological studies of CP.
Main Results:
- The development of standardized phenotyping protocols has facilitated data harmonization.
- Increased study power through large consortia and biorepositories enables more robust analyses of CP risk factors and pathogenesis.
- Outputs from DOLORisk and related projects provide insights into neuropathic pain and inform future research strategies.
Conclusions:
- Large-scale, harmonized data collection through consortia and biorepositories is essential for advancing chronic pain research.
- Standardized phenotyping is key to overcoming previous limitations in CP studies, particularly for understanding genetic and environmental risk factors.
- Future research should build upon these collaborative models to enhance understanding and management of chronic pain, including neuropathic pain.
Abstract:
Chronic pain (CP) is a common and often debilitating disorder that has major social and economic impacts. A subset of patients develop CP that significantly interferes with their activities of daily living and requires a high level of healthcare support. The challenge for treating physicians is in preventing the onset of refractory CP or effectively managing existing pain. To be able to do this, it is necessary to understand the risk factors, both genetic and environmental, for the onset of CP and response to treatment, as well as the pathogenesis of the disorder, which is highly heterogenous. However, studies of CP, particularly pain with neuropathic characteristics, have been hindered by a lack of consensus on phenotyping and data collection, making comparisons difficult. Furthermore, existing cohorts have suffered from small sample sizes meaning that analyses, especially genome-wide association studies, are insufficiently powered. The key to overcoming these issues is through the creation of large consortia such as DOLORisk and PAINSTORM and biorepositories, such as UK Biobank, where a common approach can be taken to CP phenotyping, which allows harmonisation across different cohorts and in turn increased study power. This review describes the approach that was used for studying neuropathic pain in DOLORisk and how this has informed current projects such as PAINSTORM, the rephenotyping of UK Biobank, and other endeavours. Moreover, an overview is provided of the outputs from these studies and the lessons learnt for future projects.
