Survivors to COVID-19 have described long-term symptoms after acute disease. These signs constitute a heterogeneous group named long COVID or persistent COVID. The aim of this study is to describe persisting symptoms 6 months after COVID-19 diagnosis in a prospective cohort in the Northwest Spain. This is a prospective cohort study performed in the COHVID-GS. This cohort includes patients in clinical follow-up in a health area of 569,534 inhabitants after SARS-CoV-2/COVID-19 diagnosis. Clinical and epidemiological characteristics were collected during the follow up. A total of 248 patients completed 6 months follow-up, 176 (69.4%) required hospitalization and 29 (10.2%) of them needed critical care. At 6 months, 119 (48.0%) patients described one or more persisting symptoms. The most prevalent were: extra-thoracic symptoms (39.1%), chest symptoms (27%), dyspnoea (20.6%), and fatigue (16.1%). These symptoms were more common in hospitalized patients (52.3% vs. 38.2%) and in women (59.0% vs. 40.5%). The multivariate analysis identified COPD, women gender and tobacco consumption as risk factors for long COVID. Persisting symptoms are common after COVID-19 especially in hospitalized patients compared to outpatients (52.3% vs. 38.2%). Based on these findings, special attention and clinical follow-up after acute SARS-CoV-2 infection should be provided for hospitalized patients with previous lung diseases, tobacco consumption, and women.

COVID-19 manifests with a wide diversity of clinical phenotypes characterized by dysfunctional and exaggerated host immune responses. Many results have been described on the status of the immune system of patients infected with SARS-CoV-2, but there are still aspects that have not been fully characterized or understood. In this study, we have analyzed a cohort of patients with mild, moderate and severe disease. We performed flow cytometric studies and correlated the data with the clinical characteristics and clinical laboratory values of the patients. Both conventional and unsupervised data analyses concluded that patients with severe disease are characterized, among others, by a higher state of activation in all T cell subsets (CD4, CD8, double negative and T follicular helper cells), higher expression of perforin and granzyme B in cytotoxic cells, expansion of adaptive NK cells and the accumulation of activated and immature dysfunctional monocytes which are identified by a low expression of HLA-DR and an intriguing shift in the expression pattern of CD300 receptors. More importantly, correlation analysis showed a strong association between the alterations in the immune cells and the clinical signs of severity. These results indicate that patients with severe COVID-19 have a broad perturbation of their immune system, and they will help to understand the immunopathogenesis of COVID-19.

Background The epigenetic landscape underlying cardiovascular disease (CVD) is not completely understood and the clinical value of the identified biomarkers is still limited. We aimed to identify differentially methylated loci associated with acute myocardial infarction (AMI) and assess their validity as predictive and causal biomarkers. Results We designed a case–control, two-stage, epigenome-wide association study on AMI (ndiscovery = 391, nvalidation = 204). DNA methylation was assessed using the Infinium MethylationEPIC BeadChip. We performed a fixed-effects meta-analysis of the two samples. 34 CpGs were associated with AMI. Only 12 of them were available in two independent cohort studies (n ~ 1800 and n ~ 2500) with incident coronary and cardiovascular disease (CHD and CVD, respectively). The Infinium HumanMethylation450 BeadChip was used in those two studies. Four of the 12 CpGs were validated in association with incident CHD: AHRR-mapping cg05575921, PTCD2-mapping cg25769469, intergenic cg21566642 and MPO-mapping cg04988978. We then assessed whether methylation risk scores based on those CpGs improved the predictive capacity of the Framingham risk function, but they did not. Finally, we aimed to study the causality of those associations using a Mendelian randomization approach but only one of the CpGs had a genetic influence and therefore the results were not conclusive. Conclusions We have identified 34 CpGs related to AMI. These loci highlight the relevance of smoking, lipid metabolism, and inflammation in the biological mechanisms related to AMI. Four were additionally associated with incident CHD and CVD but did not provide additional predictive information.