Impact of vaccination and variants of concern on long COVID clinical phenotypes

SARS-CoV-2 vaccination influence in the development of long-COVID clinical phenotypes

Although SARS-CoV-2 vaccination reduces hospitalization and mortality, its long-term impact on Long-COVID remains to be elucidated. The aim of the study was to evaluate the different development of Long-COVID clinical phenotypes according to the vaccination status of patients. Clinical and demographic characteristics were assessed for each patient, while Long-COVID symptoms were self-reported and later stratified into distinct clinical phenotypes. Vaccination was significantly associated with the avoidance of hospitalization, less invasive respiratory support, and less alterations of cardiopulmonary functions, as well as reduced lasting lung parenchymal damage. However, no association between vaccination status and the development of at least one Long-COVID symptom was found. Nevertheless, clinical phenotypes were differently associated with vaccination status, as neuropsychiatric were more frequent in unvaccinated patients and cardiorespiratory symptoms were reported mostly in vaccinated patients. Different progression of disease could be at play in the different development of specific Long-COVID clinical phenotypes, as shown by the different serological responses between unvaccinated and vaccinated patients. A higher anti-Spike (S) antibody titre was protective for vaccinated patients, while it was detrimental for unvaccinated patients. A better understanding of the mechanism underlying the development of Long-COVID symptoms might be reached by standardized methodologies and symptom classification.

The effect of pre-COVID and post-COVID vaccination on long COVID: A systematic review and meta-analysis

BACKGROUND

Long COVID affects millions of people and results in a substantial decrease in quality of life. Previous primary studies and reviews attempted to study the effect of vaccination against long COVID, but these studies varied in the cut-off time of long COVID. We adhered to the WHO's definition of long COVID and conducted a systematic review and meta-analysis on the effect of pre-COVID and post-COVID vaccination on long COVID.

METHODS

We obtained data from 13 databases up to 18 February 2024, including peer reviewed and preprint studies. Our inclusion criteria were: (1) long COVID definition as 3 months or beyond, (2) comparing long COVID symptoms between vaccinated and unvaccinated groups, (3) subjects received vaccinations either before or after infected with COVID, (4) the number of doses received by participants was specified. We extracted study characteristics and data and computed the summary odds ratio (OR) with the DerSimonian and Laird random effects model. We then performed subgroup analyses based on the main vaccine brand and long COVID assessment method. ROBINS-I framework was used for assessment of risk of bias and the GRADE approach was used for evaluating the certainty of evidence.

FINDINGS

We included data from 25 observational studies (n = 14,128,260) with no randomised controlled trials. One-dose pre-COVID vaccination did not have an effect on long COVID (number of studies = 10, summary OR = 1.01, 95% CI = 0.88-1.15, p-value = 0.896). Two-dose pre-COVID vaccination was associated with a 24% reduced odds of long COVID (number of studies = 15, summary OR = 0.76, 95% CI = 0.65-0.89, p-value = 0.001) and 4 symptoms (fatigue, headache, loss of smell, muscle pain) out of 10 symptoms analysed. The OR of three-dose pre-COVID vaccination against overall long COVID was statistically insignificant but was far away from 1 (number of studies = 3, summary OR = 0.31, 95% CI = 0.05-1.84, p-value = 0.198). One-dose post-COVID vaccination was associated with a 15% reduced odds of long COVID (number of studies = 5, summary OR = 0.85, 95% CI = 0.73-0.98, p-value = 0.024). The OR of two-dose post-COVID vaccination against long COVID was statistically insignificant but was far away from 1 (number of studies = 3, summary OR = 0.63, 95% CI = 0.38-1.03, p-value = 0.066).

INTERPRETATION

Our study suggests that 2-dose pre-COVID vaccination and 1-dose post-COVID vaccination are associated with a lower risk of long COVID. Since long COVID reduces quality of life substantially, vaccination could be a possible measure to maintain quality of life by partially protecting against long COVID.

Cluster analysis identifies long COVID subtypes in Belgian patients

Severe acute respiratory syndrome coronavirus infection presents complications known as long COVID, a multisystemic organ disease which allows multidimensional analysis. This study aims to uncover clusters of long COVID cases and establish their correlation with the clinical classification developed at the Clinical Research Unit of Brugmann University Hospital, Brussels. Such an endeavour is instrumental in customizing patient management strategies tailored to the unique needs of each distinct group. A two-stage multidimensional exploratory analysis was performed on a retrospective cohort of 205 long COVID patients, involving a factorial analysis of mixed data, and then hierarchical clustering post component analysis. The study's sample comprised 76% women, with an average age of 44.5 years. Three clinical forms were identified: long, persistent, and post-viral syndrome. Multidimensional analysis using demographic, clinical, and biological variables identified three clusters of patients. Biological data did not provide sufficient differentiation between clusters. This emphasizes the importance of identifying or classifying long COVID patients according to their predominant clinical syndrome. Long COVID phenotypes, as well as clinical forms, appear to be associated with distinct pathophysiological mechanisms or genetic predispositions. This underscores the need for further research.

The Omicron Variant Is Associated with a Reduced Risk of the Post COVID-19 Condition and Its Main Phenotypes Compared to the Wild-Type Virus: Results from the EuCARE-POSTCOVID-19 Study

Post COVID-19 condition (PCC) is defined as ongoing symptoms at ≥1 month after acute COVID-19. We investigated the risk of PCC in an international cohort according to viral variants. We included 7699 hospitalized patients in six centers (January 2020-June 2023); a subset of participants with ≥1 visit over the year after clinical recovery were analyzed. Variants were observed or estimated using Global Data Science Initiative (GISAID) data. Because patients returning for a post COVID-19 visit may have a higher PCC risk, and because the variant could be associated with the probability of returning, we used weighted logistic regressions. We estimated the proportion of the effect of wild-type (WT) virus vs. Omicron on PCC, which was mediated by Intensive Care Unit (ICU) admission, through a mediation analysis. In total, 1317 patients returned for a post COVID visit at a median of 2.6 (IQR 1.84-3.97) months after clinical recovery. WT was present in 69.6% of participants, followed by the Alpha (14.4%), Delta (8.9%), Gamma (3.9%) and Omicron strains (3.3%). Among patients with PCC, the most common manifestations were fatigue (51.7%), brain fog (32.7%) and respiratory symptoms (37.2%). Omicron vs. WT was associated with a reduced risk of PCC and PCC clusters; conversely, we observed a higher risk with the Delta and Alpha variants vs. WT. In total, 42% of the WT effect vs. Omicron on PCC risk appeared to be mediated by ICU admission. A reduced PCC risk was observed after Omicron infection, suggesting a possible reduction in the PCC burden over time. A non-negligible proportion of the variant effect on PCC risk seems mediated by increased disease severity during the acute disease.

Long COVID is not the same for everyone: a hierarchical cluster analysis of Long COVID symptoms 9 and 12 months after SARS-CoV-2 test

BACKGROUND

Identifying symptom clusters in Long COVID is necessary for developing effective therapies for this diverse condition and improving the quality of life of those affected by this heterogeneous condition. In this study, we aimed to identify and compare symptom clusters at 9 and 12 months after a SARS-CoV-2 positive test and describe each cluster regarding factors at infection.

METHODS

This is a cross-sectional study with individuals randomly selected from the Portuguese National System of Epidemiological Surveillance (SINAVE) database. Individuals who had a positive RT-PCR SARS-CoV-2 test in August 2022 were contacted to participate in a telephonic interview approximately 9 and 12 months after the test. A hierarchical clustering analysis was performed, using Euclidean distance and Ward's linkage. Clustering was performed in the 35 symptoms reported 9 and 12 months after the SARS-CoV-2 positive test and characterised considering age, sex, pre-existing health conditions and symptoms at time of SARS-CoV-2 infection.

RESULTS

552 individuals were included at 9 months and 458 at 12 months. The median age was 52 years (IQR: 40-64 years) and 59% were female. Hypertension and high cholesterol were the most frequently reported pre-existing health conditions. Memory loss, fatigue or weakness and joint pain were the most frequent symptoms reported 9 and 12 months after the positive test. Four clusters were identified at both times: no or minor symptoms; multi-symptoms; joint pain; and neurocognitive-related symptoms. Clusters remained similar in both times, but, within the neurocognitive cluster, memory loss and concentration issues increased in frequency at 12 months. Multi-symptoms cluster had older people, more females and more pre-existing health conditions at 9 months. However, at 12 months, older people and those with more pre-existing health conditions were in joint pain cluster.

CONCLUSIONS

Our results suggest that Long COVID is not the same for everyone. In our study, clusters remained similar at 9 and 12 months, except for a slight variation in the frequency of symptoms that composed each cluster. Understanding Long COVID clusters might help identify treatments for this condition. However, further validation of the observed clusters and analysis of its risk factors is needed.

Early inflammatory profiles predict maximal disease severity in COVID-19: An unsupervised cluster analysis

Background

The inflammatory changes that underlie the heterogeneous presentations of COVID-19 remain incompletely understood. In this study we aimed to identify inflammatory profiles that precede the development of severe COVID-19, that could serve as targets for optimised delivery of immunomodulatory therapies and provide insights for the development of new therapies.

Methods

We included individuals sampled <10 days from COVID-19 symptom onset, recruited from both inpatient and outpatient settings. We measured 61 biomarkers in plasma, including markers of innate immune and T cell activation, coagulation, tissue repair and lung injury. We used principal component analysis and hierarchical clustering to derive biomarker clusters, and ordinal logistic regression to explore associations between cluster membership and maximal disease severity, adjusting for known risk factors for severe COVID-19.

Results

In 312 individuals, median (IQR) 7 (4-9) days from symptom onset, we found four clusters. Cluster 1 was characterised by low overall inflammation, cluster 2 was characterised by higher levels of growth factors and markers of endothelial activation (EGF, VEGF, PDGF, TGFα, PAI-1 and p-selectin). Cluster 3 and 4 both had higher overall inflammation. Cluster 4 had the highest levels of most markers including markers of innate immune activation (IL6, procalcitonin, CRP, TNFα), and coagulation (D-dimer, TPO), in contrast cluster 3 had the highest levels of alveolar epithelial injury markers (RAGE, ST2), but relative downregulation of growth factors and endothelial activation markers, suggesting a dysfunctional inflammatory pattern. In unadjusted and adjusted analysis, compared to cluster 1, cluster 3 had the highest odds of progressing to more severe disease (unadjusted OR (95%CI) 9.02 (4.53-17.96), adjusted OR (95%CI) 6.02 (2.70-13.39)).

Conclusion

Early inflammatory profiles predicted subsequent maximal disease severity independent of risk factors for severe COVID-19. A cluster with downregulation of growth factors and endothelial activation markers, and early evidence of alveolar epithelial injury, had the highest risk of severe COVID-19.

The gut microbiome associates with phenotypic manifestations of post-acute COVID-19 syndrome

The mechanisms underlying the many phenotypic manifestations of post-acute COVID-19 syndrome (PACS) are poorly understood. Herein, we characterized the gut microbiome in heterogeneous cohorts of subjects with PACS and developed a multi-label machine learning model for using the microbiome to predict specific symptoms. Our processed data covered 585 bacterial species and 500 microbial pathways, explaining 12.7% of the inter-individual variability in PACS. Three gut-microbiome-based enterotypes were identified in subjects with PACS and associated with different phenotypic manifestations. The trained model showed an accuracy of 0.89 in predicting individual symptoms of PACS in the test set and maintained a sensitivity of 86% and a specificity of 82% in predicting upcoming symptoms in an independent longitudinal cohort of subjects before they developed PACS. This study demonstrates that the gut microbiome is associated with phenotypic manifestations of PACS, which has potential clinical utility for the prediction and diagnosis of PACS.

Identification of inflammatory clusters in long-COVID through analysis of plasma biomarker levels

Mechanisms underlying long COVID remain poorly understood. Patterns of immunological responses in individuals with long COVID may provide insight into clinical phenotypes. Here we aimed to identify these immunological patterns and study the inflammatory processes ongoing in individuals with long COVID. We applied an unsupervised hierarchical clustering approach to analyze plasma levels of 42 biomarkers measured in individuals with long COVID. Logistic regression models were used to explore associations between biomarker clusters, clinical variables, and symptom phenotypes. In 101 individuals, we identified three inflammatory clusters: a limited immune activation cluster, an innate immune activation cluster, and a systemic immune activation cluster. Membership in these inflammatory clusters did not correlate with individual symptoms or symptom phenotypes, but was associated with clinical variables including age, BMI, and vaccination status. Differences in serologic responses between clusters were also observed. Our results indicate that clinical variables of individuals with long COVID are associated with their inflammatory profiles and can provide insight into the ongoing immune responses.

The knowns and unknowns of long COVID-19: from mechanisms to therapeutical approaches

The coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 has been defined as the greatest global health and socioeconomic crisis of modern times. While most people recover after being infected with the virus, a significant proportion of them continue to experience health issues weeks, months and even years after acute infection with SARS-CoV-2. This persistence of clinical symptoms in infected individuals for at least three months after the onset of the disease or the emergence of new symptoms lasting more than two months, without any other explanation and alternative diagnosis have been named long COVID, long-haul COVID, post-COVID-19 conditions, chronic COVID, or post-acute sequelae of SARS-CoV-2 (PASC). Long COVID has been characterized as a constellation of symptoms and disorders that vary widely in their manifestations. Further, the mechanisms underlying long COVID are not fully understood, which hamper efficient treatment options. This review describes predictors and the most common symptoms related to long COVID's effects on the central and peripheral nervous system and other organs and tissues. Furthermore, the transcriptional markers, molecular signaling pathways and risk factors for long COVID, such as sex, age, pre-existing condition, hospitalization during acute phase of COVID-19, vaccination, and lifestyle are presented. Finally, recommendations for patient rehabilitation and disease management, as well as alternative therapeutical approaches to long COVID sequelae are discussed. Understanding the complexity of this disease, its symptoms across multiple organ systems and overlapping pathologies and its possible mechanisms are paramount in developing diagnostic tools and treatments.