Circulating inflammatory markers predict depressive symptomatology in COVID-19 survivors

Growing evidence suggests the neurobiological mechanism upholding post-COVID-19 depression mainly relates to immune response and subsequent unresolved low-grade inflammation. Herein we exploit a broad panel of cytokines serum levels measured in COVID-19 survivors at one- and three-month since infection to predict postCOVID-19 depression. 87 COVID survivors were screened for depressive symptomatology at one- and three-month after discharge through the Beck Depression Inventory (BDI-13) and the Zung Self-Rating Depression Scale (ZSDS) at San Raffaele Hospital. Blood samples were collected at both timepoints and analyzed through Luminex. We entered onemonth 42 inflammatory compounds into two separate penalized logistic regression models to evaluate their reliability in identifying COVID-19 survivors suffering from clinical depression at the two timepoints, applied within a machine learning routine. Delta values of analytes lowering between timepoints were entered in a third model predicting presence long-term depression. 5000 bootstraps were computed to determine significance of predictors. The cross-sectional model reached a balance accuracy (BA) of 76 % and a sensitivity of 70 %. Post-COVID-19 depression was predicted by high levels of CCL17, CCL22. On the other hand, CXCL10, CCL2, CCL3, CCL8, CXCL5, CCL15, CCL23, CXCL13, and GM-CSF showed protective effects. The longitudinal model obtained good performance as well (BA = 74 % and sensitivity = 68 %), revealing CXCL16 and CCL25 as additional drivers of clinical depression. Moreover, dynamic changes of analytes over time accurately predicted long-term depression (BA = 76 % and sensitivity = 75 %). Our findings unveil a putative immune profile upholding post-COVID-19 depression, thus reinforcing the need to deepen molecular mechanisms to appropriately target depression.