An estimated 10% of coronavirus disease (COVID‐19) survivors suffer from persisting symptoms referred to as long COVID (LC), a condition for which approved treatment options are still lacking. This systematic review (PROSPERO: CRD42024499281) aimed to explore the pathophysiological mechanisms underlying LC and potential treatable traits across symptom‐based phenotypes. We included studies with primary data, written in English, focusing on omics analyses of human samples from LC patients with persistent symptoms of at least 3 months. Our search in PubMed and Embase, conducted on January 8, 2024, identified 642 studies, of which 29 met the inclusion criteria after full‐text assessment. The risk of bias was evaluated using the Joanna Briggs Institute appraisal tool. The synthesis of omics data, including genomics, transcriptomics, proteomics, metabolomics, and metagenomics, revealed common findings associated with fatigue, cardiovascular, pulmonary, neurological, and gastrointestinal phenotypes. Key findings included mitochondrial dysfunction, dysregulated microRNAs associated with pulmonary dysfunction, tissue impairment, blood–brain barrier disruption, coagulopathy, vascular dysfunction, microbiome disturbances, microbial‐derived metabolite production and persistent inflammation. Limitations include cross‐study heterogeneity and variability in sampling methods. Our review emphasizes the complexity of LC and the need for further longitudinal omics‐integrated studies to advance the development of biomarkers and targeted treatments.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has led to the death of almost 7 million people, however, with a cumulative incidence of 0.76 billion, most people survive COVID-19. Several studies indicate that the acute phase of COVID-19 may be followed by persistent symptoms including fatigue, dyspnea, headache, musculoskeletal symptoms, and pulmonary functional-and radiological abnormalities. However, the impact of COVID-19 on long-term health outcomes remains to be elucidated. Aims: The Precision Medicine for more Oxygen (P4O2) consortium COVID-19 extension aims to identify long COVID patients that are at risk for developing chronic lung disease and furthermore, to identify treatable traits and innovative personalized therapeutic strategies for prevention and treatment. This study aims to describe the study design and first results of the P4O2 COVID-19 cohort. Methods: The P4O2 COVID-19 study is a prospective multicenter cohort study that includes nested personalized counseling intervention trial. Patients, aged 40–65 years, were recruited from outpatient post-COVID clinics from five hospitals in The Netherlands. During study visits at 3–6 and 12–18 months post-COVID-19, data from medical records, pulmonary function tests, chest computed tomography scans and biological samples were collected and questionnaires were administered. Furthermore, exposome data was collected at the patient’s home and state-of-the-art imaging techniques as well as multi-omics analyses will be performed on collected data. Results: 95 long COVID patients were enrolled between May 2021 and September 2022. The current study showed persistence of clinical symptoms and signs of pulmonary function test/radiological abnormalities in post-COVID patients at 3–6 months post-COVID. The most commonly reported symptoms included respiratory symptoms (78.9%), neurological symptoms (68.4%) and fatigue (67.4%). Female sex and infection with the Delta, compared with the Beta, SARS-CoV-2 variant were significantly associated with more persisting symptom categories. Conclusions: The P4O2 COVID-19 study contributes to our understanding of the long-term health impacts of COVID-19. Furthermore, P4O2 COVID-19 can lead to the identification of different phenotypes of long COVID patients, for example those that are at risk for developing chronic lung disease. Understanding the mechanisms behind the different phenotypes and identifying these patients at an early stage can help to develop and optimize prevention and treatment strategies.

An estimated 10% of COVID-19 survivors have been reported to suffer from complaints after at least three months. The intestinal microbiome has been shown to impact long COVID through the gut–lung axis and impact the severity. We aimed to investigate the relationship between the gut microbiome and clinical characteristics, exploring microbiome heterogeneity through clustering. Seventy-nine patients with long COVID evaluated at 3 to 6 months after infection were sampled for fecal metagenome analysis. Patients were divided into two distinct hierarchical clusters, based solely on the microbiome composition. Compared to cluster 1 (n = 67), patients in cluster 2 (n = 12) showed a significantly reduced lung function (FEV₁, FVC, and DLCO) and during acute COVID-19 showed a longer duration of hospital admissions (48 compared to 7 days) and higher rates of ICU admissions (92% compared to 22%). Additionally, the microbiome composition showed a reduced alpha diversity and lower proportion of butyrate-producing bacteria in cluster 2 together with higher abundances of Ruminococcus gnavus, Escherichia coli, Veillonella spp. and Streptococcus spp. and reduced abundances of Faecalibacterium prausnitzii and Eubacteria spp. Further research could explore the effect of pre- and pro-biotic supplementation and its impact on lung function and societal participation in long COVID.

In the randomized double-blind placebo-controlled CounterCOVID study, oral imatinib treatment conferred a positive clinical outcome and a signal for reduced mortality in COVID-19 patients. High concentrations of alpha-1 acid glycoprotein (AAG) were observed in these patients and were associated with increased total imatinib concentrations. This post-hoc study aimed to compare the difference in exposure following oral imatinib administration in COVID-19 patients to cancer patients and assess assocations between pharmacokinetic (PK) parameters and pharmacodynamic (PD) outcomes of imatinib in COVID-19 patients. We hypothesize that a relatively higher drug exposure of imatinib in severe COVID-19 patients leads to improved pharmacodynamic outcome parameters. 648 total concentration plasma samples obtained from 168 COVID-19 patients were compared to 475 samples of 105 cancer patients, using an AAG-binding model. Total trough concentration at steady state (Ct_trough) and total average area under the concentration-time curve (AUCt_ave) were associated with ratio between partial oxygen pressure and fraction of inspired oxygen (P/F), WHO ordinal scale (WHO-score) and liberation of oxygen supplementation (O₂lib). Linear regression, linear mixed effects models and time-to-event analysis were adjusted for possible confounders. AUCt_ave and Ct_trough were respectively 2.21-fold (95%CI 2.07-2.37) and 1.53-fold (95%CI 1.44-1.63) lower for cancer compared to COVID-19 patients. Ct_trough, not AUCt_ave, associated significantly with P/F (β=-19,64; p-value=0.014) and O₂lib (HR 0.78; p-value= 0.032), after adjusting for sex, age, neutrophil-lymphocyte ratio, dexamethasone concomitant treatment, AAG and baseline P/F-and WHO-score. Ct_trough, but not AUCt_ave associated significantly with WHO-score. These results suggest an inverse relationship between PK-parameters, Ct_trough and AUCt_ave, and PD outcomes. COVID-19 patients exhibit higher total imatinib exposure compared to cancer patients, attributed to differences in plasma protein concentrations. Higher imatinib exposure in COVID-19 patients did not associate with improved clinical outcomes. Ct_trough and AUCt_ave inversely associated with some PD-outcomes, which may be biased by disease course, variability in metabolic rate and protein binding. Therefore, additional PKPD analyses into unbound imatinib and its main metabolite may better explain exposure-response.

Background: While some long-term effects of COVID-19 are respiratory in nature, a non-respiratory effect gaining attention has been a decline in hemoglobin, potentially mediated by inflammatory processes. In this study, we examined the correlations between hemoglobin levels and inflammatory biomarkers and evaluated the association between hemoglobin and fatigue in a cohort of Long-COVID patients. Methods: This prospective cohort study in the Netherlands evaluated 95 (mostly hospitalized) patients, aged 40–65 years, 3–6 months post SARS-CoV-2 infection, examining their venous hemoglobin concentration, anemia (hemoglobin < 7.5 mmol/L in women and <8.5 mmol/L in men), inflammatory blood biomarkers, average FSS (Fatigue Severity Score), demographics, and clinical features. Follow-up hemoglobin was compared against hemoglobin during acute infection. Spearman correlation was used for assessing the relationship between hemoglobin concentrations and inflammatory biomarkers, and the association between hemoglobin and fatigue was examined using logistic regression. Results: In total, 11 (16.4%) participants were suffering from anemia 3–6 months after SARS-CoV-2 infection. The mean hemoglobin value increased by 0.3 mmol/L 3–6 months after infection compared to the hemoglobin during the acute phase (p-value = 0.003). Whilst logistic regression showed that a 1 mmol/L greater increase in hemoglobin is related to a decrease in experiencing fatigue in Long-COVID patients (adjusted OR 0.38 [95%CI 0.13–1.09]), we observed no correlations between hemoglobin and any of the inflammatory biomarkers examined. Conclusion: Our results indicate that hemoglobin impairment might play a role in developing Long-COVID fatigue. Further investigation is necessary to identify the precise mechanism causing hemoglobin alteration in these patients.

Background: Although the coronavirus disease 2019 (COVID-19) pandemic is no longer a public health emergency of international concern, 30% of COVID-19 patients still have long-term complaints. A better understanding of the progression of symptoms after COVID-19 is needed to reduce the burden of the post COVID-19 condition. Objective: This study aims to investigate the progression of symptoms, identify patterns of symptom progression, and assess their associations with patient characteristics. Methods: Within the P4O2 COVID-19 study, patients aged 40–65 years were recruited from five Dutch hospitals. At 3–6 and 12–18 months post COVID-19, medical data were collected, and pulmonary function tests were performed. In between, symptoms were assessed monthly with a questionnaire. Latent class mixed modelling was used to identify symptom progression patterns over time, with multinomial logistic regression to examine associations with patient characteristics. Results: Eighty-eight patients (aged 54.4 years, 48.9% males) were included. Three trajectories were identified for fatigue and dyspnoea: decreasing, high persistent, and low persistent. The odds of “decreasing fatigue” was higher for never smokers and participants in the lifestyle intervention and lower for those having a comorbidity. The odds of “decreasing dyspnoea” was higher for moderate COVID-19 patients and lifestyle intervention participants and lower for males, mild COVID-19 patients, and those with a higher age. Conclusions: Three distinct trajectories were identified for fatigue and dyspnoea, delineating patterns of symptom persistence following COVID-19. Sex, age, smoking status, participation in lifestyle interventions and COVID-19 severity were associated with the likelihood of belonging to different trajectories. These findings highlight the heterogeneity of the long-term symptoms experienced by post COVID-19 patients and emphasise the importance of personalised treatment strategies.