Improving anxiety research: novel approach to reveal trait anxiety through summary measures of multiple states

The reliability and validity of preclinical anxiety testing is essential for translating animal research into clinical use. However, the most used anxiety tests lack inter-test correlations and have repeatability issues. Translational animal research should be able to capture stable individual traits to aid the development of personalised medicine. However, the current approach is using one type of test one time, therefore we can only measure transient states of animals, which are strongly influenced by experimental conditions. Here, we propose a validated, optimised test battery which can reliably capture trait anxiety in rats and mice of both sexes. Instead of developing novel tests, we combined widely-used tests (elevated plus-maze, open field and light-dark test) to provide instantly applicable adjustments for better predictive validity. We repeated these tests three times to capture multiple anxious states, which we combined together to generate summary measures (SuMs). Our approach resolved between-test correlation issues of anxiety tests and provided better predictions for subsequent outcomes under anxiogenic conditions or fear conditioning. Moreover, SuMs were more sensitive to detect anxiety-differences in an social isolation paradigm. Finally, we tested our method’s efficiency in discovering anxiety-related molecular pathways through RNA sequencing of the medial prefrontal cortex. Using SuMs, we identified four-times more molecular correlates of trait anxiety, which pointed out novel functional gene clusters. Furthermore, 16% of the most robust molecular findings also showed correlation with anxiety in the amygdala. In summary, SuMs are necessary to capture trait anxiety in rodents, providing better predictions for potential therapeutic targets.