Developing symptom clusters: linking inflammatory biomarkers to depressive symptom profiles

Considering the burden of depression and the lack of efficacy of available treatments, there is a need for biomarkers to predict tailored or personalized treatments. However, identifying reliable biomarkers for depression has been challenging, likely owing to the vast symptom heterogeneity and high rates of comorbidity that exists. Examining biomarkers that map onto dimensions of depression as well as shared symptoms/constructs that cut across disorders could be most effective for informing personalized treatment approaches. With a sample of 539 young adults, we conducted a principal component analysis (PCA) followed by hierarchical cluster analysis to develop transdiagnostic clusters of depression and anxiety symptoms. We collected blood to assess whether neuroendocrine (cortisol) and inflammatory profiles (C-reactive protein (CRP), Interleukin (IL)-6, and tumor necrosis factor (TNF) - alpha) could be used to differentiate symptom clusters. Six distinct clusters were identified that differed significantly on symptom dimensions including somatic anxiety, general anxiety, anhedonia, and neurovegetative depression. Moreover, the neurovegetative depression cluster displayed significantly elevated CRP levels compared to other clusters. In fact, inflammation was not strongly associated with overall depression scores or severity, but rather related to specific features of depression marked by eating, appetite, and tiredness. This study emphasizes the importance of characterizing the biological underpinnings of symptom dimensions and subtypes to better understand the etiology of complex mental health disorders such as depression.

Automated summary

Identifying reliable biomarkers for depression has been challenging due to the heterogeneity of symptoms and high rates of comorbidity. This study used principal component analysis and hierarchical cluster analysis to identify six distinct clusters of depression symptoms in a sample of young adults. The study also found that the cluster characterized by neurovegetative depression had significantly elevated levels of inflammation. These findings highlight the importance of understanding the biological underpinnings of symptom dimensions and subtypes in complex mental health disorders like depression.