Discussion/conclusion
anxious, anxiety
DISORDER, BRAIN, DISORDERS
This study demonstrated that low-burden, minimal-cost, self-report methods can accurately identify psychological phenotypes based on characteristics related to treatment (mindfulness, interoceptive awareness), mechanism (worry) and outcomes (anxiety) in individuals seeking treatment for anxiety (Study 1), and general populations (Study 2). We found that these phenotypes also constitute a marker of mental illness susceptibility (Studies 1 and 2) and treatment response (Study 1). These results demonstrate the proof-of-concept that rapid, low-cost methods can be employed to match individuals with treatment to optimize outcomes—for example, mindfulness training for anxiety can be tailored to particular subgroups’ baseline psychological phenotype.Our results show high degree of similarity in cluster-based solutions (between cluster correlations of 0.67–0.92 across studies) in both treatment-seeking individuals and at the general population level for psychological attributes with characteristics relevant to a disorder’s symptomatology, mechanistic factors, and type of psychological treatment. In addition, cluster membership significantly interacted (moderate to large effect size) with anxiety symptom improvement from our intervention (Study 1). In essence, clinicians could benefit from this information by recommending this type of mindfulness-based intervention as higher-line treatment options to anxious patients with interoceptive awareness (Cluster 1) or lower anxiety/worry with high awareness (Cluster 3) as they showed large effect sizes of symptom improvement, but opt for a different treatment selection for participants with some anxiety/lower interoceptive awareness (Cluster 2). Future studies optimizing this Cluster 2’s outcome could further guide recommendations for participants with this type of baseline psychological profile.In Study 1, although baseline differences in anxiety were observed between clusters (with highest anxiety scores observed in Cluster 1, followed by Cluster 2, and with Cluster 3 having lowest anxiety scores), Cluster 2’s non-significant outcome improvement from the intervention was unlikely due to baseline GAD score differences. As such, although anxious patients with interoceptive awareness (Cluster1) was higher in baseline anxiety compared to Clusters 2 and 3, clusters 2 and 3 were not significantly different from each other in baseline GAD scores. The cluster that did not show significant responses to the intervention was Cluster 2, which indicates that the latter effect was not explained by differences in baseline GAD levels between clusters because the cluster with lowest baseline anxiety (Cluster 3) showed significant improvement from the intervention.The results of these studies could also guide future research in a number of ways: first by applying the same clustering analysis framework to implement personalized medicine approaches to other mental health disorders by using attributes relevant to the disorder’s symptomatology, mechanistic factors, and characteristics of the intervention. Brain and physiological correlates of psychological phenotypes could be studied as well. Together, this type of framework could enhance the application of personalized medicine into clinical mental health settings, where heterogeneity to treatment is present and empirically-based guidelines are lacking for treatment selection based on individual characteristics