Exploring the Causes of Chronic Back Pain

Strong association of depression and anxiety with the presence of back pain, while the impact of spinal imaging findings is limited: analysis of an MRI cohort study

The development of back pain is multifactorial and it is not well understood which factors are the main drivers of the disease. Therefore, we applied a machine learning approach to a large data set of existing cohort studies and sought to identify and classify the most important contributors to the presence of back pain among the documented cohort parameters.

Data from 399 participants in the KORA-MRI (Cooperative Health Research in the Augsburg Region) study were analyzed. The data set included whole-body MRI images, including the spine, and metabolic, sociodemographic, anthropometric, and cardiovascular data. The presence of back pain was one of the elements documented in this data set.

Applying a machine learning approach to this pre-existing data set, we sought to identify the variables that were most strongly associated with back pain. Mediation analysis was performed to evaluate the underlying mechanisms of the identified associations.

We found that depression and anxiety were the two most selected predictors of back pain in our model. Furthermore, body mass index (BMI), spinal canal width and disc generation, medium and heavy physical work, as well as cardiovascular factors were among the top 10 most selected predictors.

Using mediation analysis, we found that the effects of anxiety and depression on the presence of back pain were primarily direct effects that were not mediated by spinal imaging .

In summary, we found that psychological factors were the most important predictors of back pain in our cohort. This supports the idea that back pain should be treated in a personalized multidimensional framework.