Palladini Mariagrazia, Mazza Mario Gennaro, De Lorenzo Rebecca, Spadini Sara, Aggio Veronica, Bessi Margherita, Calesella Federico, Bravi Beatrice, Rovere-Querini Patrizia, Benedetti Francesco
Vita-Salute San Raffaele University, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy.
Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy.
Cytokine. 2025 Feb;186:156839. doi: 10.1016/j.cyto.2024.156839. Epub 2024 Dec 18.
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 post-COVID-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 one-month 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.
越来越多的证据表明，支撑新冠后抑郁症的神经生物学机制主要与免疫反应及随后未解决的低度炎症有关。在此，我们利用在新冠病毒感染者感染后1个月和3个月时检测的一系列广泛的细胞因子血清水平，来预测新冠后抑郁症。在圣拉斐尔医院，通过贝克抑郁量表（BDI-13）和zung自评抑郁量表（ZSDS），对87名新冠康复者在出院后1个月和3个月时的抑郁症状进行了筛查。在两个时间点采集血样并通过Luminex进行分析。我们将1个月时的42种炎症化合物纳入两个单独的惩罚逻辑回归模型，以评估它们在两个时间点识别患有临床抑郁症的新冠康复者的可靠性，该模型应用于机器学习程序中。两个时间点之间分析物降低的差值输入到第三个预测长期抑郁症存在的模型中。计算了5000次自抽样以确定预测因子的显著性。横断面模型的平衡准确率（BA）达到76%，敏感性为70%。高水平的CCL17、CCL22可预测新冠后抑郁症。另一方面，CXCL10、CCL2、CCL3、CCL8、CXCL5、CCL15、CCL23、CXCL13和GM-CSF显示出保护作用。纵向模型也取得了良好的性能（BA = 74%，敏感性 = 68%），揭示CXCL16和CCL25是临床抑郁症的额外驱动因素。此外，分析物随时间变化的动态变化准确地预测了长期抑郁症（BA = 76%，敏感性 = 75%）。我们的研究结果揭示了一种支撑新冠后抑郁症的假定免疫特征，从而强化了深入研究分子机制以适当针对抑郁症的必要性。
