Novel clinical and immunological features associated with persistent post-acute sequelae of COVID-19 after six months of follow-up: a pilot study

Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19

The risk factors for post-COVID-19 cognitive impairment have been poorly described. This study aimed to identify the sociodemographic, clinical, and lifestyle characteristics that characterize a group of post-COVID-19 condition (PCC) participants with neuropsychological impairment. The study sample included 426 participants with PCC who underwent a neurobehavioral evaluation. We selected seven mental speed processing and executive function variables to obtain a data-driven partition. Clustering algorithms were applied, including K-means, bisecting K-means, and Gaussian mixture models. Different machine learning algorithms were then used to obtain a classifier able to separate the two clusters according to the demographic, clinical, emotional, and lifestyle variables, including logistic regression with least absolute shrinkage and selection operator (LASSO) (L1) and Ridge (L2) regularization, support vector machines (linear/quadratic/radial basis function kernels), and decision tree ensembles (random forest/gradient boosting trees). All clustering quality measures were in agreement in detecting only two clusters in the data based solely on cognitive performance. A model with four variables (cognitive reserve, depressive symptoms, obesity, and change in work situation) obtained with logistic regression with LASSO regularization was able to classify between good and poor cognitive performers with an accuracy and a weighted averaged precision of 72%, a recall of 73%, and an area under the curve of 0.72. PCC individuals with a lower cognitive reserve, more depressive symptoms, obesity, and a change in employment status were at greater risk for poor performance on tasks requiring mental processing speed and executive function. Study registration: www.ClinicalTrials.gov , identifier NCT05307575.

High Levels of IL-1β, TNF-α and MIP-1α One Month after the Onset of the Acute SARS-CoV-2 Infection, Predictors of Post COVID-19 in Hospitalized Patients

The pandemic caused by SARS-CoV-2 infection has left behind a new symptomatology called post COVID-19, or "long COVID". The pathophysiological mechanisms still remain controversial; however, a link between persistent inflammation and these sequelae has been suggested. Herein, we longitudinally assessed up- and downstream molecules of the NLRP3 inflammasome's pathway in three study groups: healthy donors (HC, n = 14) and donors with a confirmed SARS-CoV-2 infection who had been hospitalized, the latter divided into post COVID-19 (PC, n = 27) and non-post COVID-19 patients (nPC, n = 27) based on the presence or absence of symptomatology at month 6, respectively. Plasma cytokines (IL-1β, IL-3, IL-6, IL-8, IL-18, IP-10, MIG, TNF-α, IFN-γ, MIP-1α and MIP-1β) and total peroxide (TPX) levels were quantified at baseline and at months 1 and 6 after the onset of the infection. Baseline values were the highest for both TPX and cytokines that progressively decreased thereafter the acute infection. IL-1β, MIP-1α and TNF-α at month 1 were the only cytokines that showed a significant difference between nPC and PC. These findings suggest that a persistent inflammatory state one month after the onset of SARS-CoV-2 infection related to specific cytokines (IL-1β, MIP-1α, and TNF-α) might guide to predicting post COVID-19 symptomatology.