Does Combining the STarT Back Tool With a Polygenic Risk Score for Chronic Low Back Pain Improve Prediction of Work Disability Over 2 Years?

INTRODUCTION

Chronic back pain (CBP) is a leading cause of work disability worldwide, yet identifying individuals at risk remains difficult due to its multifactorial aetiology. This population-based cohort study investigated whether integrating a polygenic risk score (PRS) for CBP with the STarT Back Tool (SBT)-a widely used psychosocial screening instrument-could improve the prediction of work disability, measured as disability leave days over a 2-year follow-up.

METHODS

We analysed data from 1938 participants in the Northern Finland Birth Cohort 1966 with complete genotyping, SBT responses and registry-linked disability records. A zero-inflated negative binomial regression model was applied to account for the highly skewed distribution of work disability days.

RESULTS

Results showed that both SBT and CBP genetic risk independently predicted the cumulative number of disability leave days. While SBT was also associated with the likelihood of having no disability leave, CBP genetic risk was not, suggesting that polygenic risk contributes specifically to the burden of disability among affected individuals. When participants split into 4 risk groups, those in the highest CBP genetic risk quartile experienced significantly more work disability days.

CONCLUSION

The two tools captured complementary domains: SBT reflected modifiable biopsychosocial risks, while the PRS represented fixed genetic liability. This distinction supports the value of integrating a CBP PRS into existing screening frameworks, with potential in early CBP management.

SIGNIFICANCE STATEMENT: This study is the first to combine a polygenic risk score for chronic back pain with a clinical screening tool to assess work disability outcomes. It demonstrates that genetic and psychosocial risk capture distinct aspects of vulnerability, and that their integration improves risk stratification. These findings add to the growing evidence supporting personalized approaches in pain management and highlight the potential utility of genetic data in early assessment of disabling back pain.