Pulmonary Recovery 12 Months after Non-Severe and Severe COVID-19: The Prospective Swiss COVID-19 Lung Study

Lenoir, Alexandra; Christe, Andreas; Ebner, Lukas; Bridevaux, Pierre‐Olivier; Brutsche, Martin; Clarenbach, Christian F.; Erkoşar, Berra; Garzoni, Christian; Geiser, Thomas; Guler, Sabina; Heg, Dik; Lador, Frédéric; Mancinetti, Marco; Ott, Sebastian Robert; Piquilloud, Lise; Prella, Maura; Que, Yok-Ai; Garnier, Christophe von; Funke-Chambour, Manuela

doi:10.1159/000528611

Cited by 11 publications

(17 citation statements)

References 37 publications

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“…Severe acute COVID-19 is associated with more breathlessness and a lower DLCO, %, value in post-COVID-19 conditions. 73,92 Perhaps, the two different causes of restrictive lung disease in post-COVID-19 conditions explain why we found different proteins associated with FVC, %, than with DLCO, %; the dysregulation of different proteins may cause respiratory muscle weakness versus the interstitial lung disease in post-COVID-19 conditions.…”

Section: Forced Vital Capacity and Post-covid-19mentioning

confidence: 83%

Proteomic Evolution from Acute to Post-COVID-19 Conditions

Mohammed,

Tran,

Carlsten

et al. 2023

J. Proteome Res.

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Many COVID-19 survivors have post-COVID-19 conditions, and females are at a higher risk. We sought to determine (1) how protein levels change from acute to post-COVID-19 conditions, (2) whether females have a plasma protein signature different from that of males, and (3) which biological pathways are associated with COVID-19 when compared to restrictive lung disease. We measured protein levels in 74 patients on the day of admission and at 3 and 6 months after diagnosis. We determined protein concentrations by multiple reaction monitoring (MRM) using a panel of 269 heavy-labeled peptides. The predicted forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (DLCO) were measured by routine pulmonary function testing. Proteins associated with six key lipid-related pathways increased from admission to 3 and 6 months; conversely, proteins related to innate immune responses and vasoconstriction-related proteins decreased. Multiple biological functions were regulated differentially between females and males. Concentrations of eight proteins were associated with FVC, %, and they together had c-statistics of 0.751 (CI:0.732− 0.779); similarly, concentrations of five proteins had c-statistics of 0.707 (CI:0.676−0.737) for DLCO, %. Lipid biology may drive evolution from acute to post-COVID-19 conditions, while activation of innate immunity and vascular regulation pathways decreased over that period.

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Section: Forced Vital Capacity and Post-covid-19mentioning

confidence: 83%

Proteomic Evolution from Acute to Post-COVID-19 Conditions

Mohammed,

Tran,

Carlsten

et al. 2023

J. Proteome Res.

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“…A total of 2517 studies were identified, of which 43 met the inclusion (N = 8858 patients) ( Figure 1 ) [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ]. The majority of studies were from China (14 studies [ 51 , 58 , 59 , 60 , 61 , 64 , 65 , …”

Section: Resultsmentioning

confidence: 99%

“…The median age of the patients was 60.3 years (57–63), with 61.5% being males and 37.8% being current/former smokers. Thirty-nine studies [ 47 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 65 , 66 , 67 , 68 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ] reported outcomes stratified by disease severity; 17 for non-severe disease, 31 for severe disease, and 7 for mixed disease severity ( Table 1 ). The most common comorbidities were hypertension (35.8%), cardiovascular disease, (28%), and obesity (24.9%) ( Supplemental Table S1 ).…”

Section: Resultsmentioning

confidence: 99%

Short- and Long-Term Chest-CT Findings after Recovery from COVID-19: A Systematic Review and Meta-Analysis

Babar,

Jamil,

Mehta

et al. 2024

Diagnostics

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While ground-glass opacity, consolidation, and fibrosis in the lungs are some of the hallmarks of acute SAR-CoV-2 infection, it remains unclear whether these pulmonary radiological findings would resolve after acute symptoms have subsided. We conducted a systematic review and meta-analysis to evaluate chest computed tomography (CT) abnormalities stratified by COVID-19 disease severity and multiple timepoints post-infection. PubMed/MEDLINE was searched for relevant articles until 23 May 2023. Studies with COVID-19-recovered patients and follow-up chest CT at least 12 months post-infection were included. CT findings were evaluated at short-term (1–6 months) and long-term (12–24 months) follow-ups and by disease severity (severe and non-severe). A generalized linear mixed-effects model with random effects was used to estimate event rates for CT findings. A total of 2517 studies were identified, of which 43 met the inclusion (N = 8858 patients). Fibrotic-like changes had the highest event rate at short-term (0.44 [0.3–0.59]) and long-term (0.38 [0.23–0.56]) follow-ups. A meta-regression showed that over time the event rates decreased for any abnormality (β = −0.137, p = 0.002), ground-glass opacities (β = −0.169, p < 0.001), increased for honeycombing (β = 0.075, p = 0.03), and did not change for fibrotic-like changes, bronchiectasis, reticulation, and interlobular septal thickening (p > 0.05 for all). The severe subgroup had significantly higher rates of any abnormalities (p < 0.001), bronchiectasis (p = 0.02), fibrotic-like changes (p = 0.03), and reticulation (p < 0.001) at long-term follow-ups when compared to the non-severe subgroup. In conclusion, significant CT abnormalities remained up to 2 years post-COVID-19, especially in patients with severe disease. Long-lasting pulmonary abnormalities post-SARS-CoV-2 infection signal a future public health concern, necessitating extended monitoring, rehabilitation, survivor support, vaccination, and ongoing research for targeted therapies.

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“…Respiratory failure is one of the main causes of death in severe COVID-19 patients ( Ohuabunwa et al., 2022 ). Meanwhile, studies found that patients with severe COVID-19 had severe respiratory symptoms during hospitalization, including lower oxygen saturation, and diminished lung function compared to those after non-severe COVID-19 ( Bieńkowski et al., 2022 ; Lenoir et al., 2022 ). Compared to patients with non-severe coronavirus pneumonia, lower oxygen saturation and lung function in the first year in patients with severe COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Identifying key genes related to inflammasome in severe COVID-19 patients based on a joint model with random forest and artificial neural network

Fan

Guo

et al. 2023

Front. Cell. Infect. Microbiol.

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BackgroundThe coronavirus disease 2019 (COVID-19) has been spreading astonishingly and caused catastrophic losses worldwide. The high mortality of severe COVID-19 patients is an serious problem that needs to be solved urgently. However, the biomarkers and fundamental pathological mechanisms of severe COVID-19 are poorly understood. The aims of this study was to explore key genes related to inflammasome in severe COVID-19 and their potential molecular mechanisms using random forest and artificial neural network modeling.MethodsDifferentially expressed genes (DEGs) in severe COVID-19 were screened from GSE151764 and GSE183533 via comprehensive transcriptome Meta-analysis. Protein-protein interaction (PPI) networks and functional analyses were conducted to identify molecular mechanisms related to DEGs or DEGs associated with inflammasome (IADEGs), respectively. Five the most important IADEGs in severe COVID-19 were explored using random forest. Then, we put these five IADEGs into an artificial neural network to construct a novel diagnostic model for severe COVID-19 and verified its diagnostic efficacy in GSE205099.ResultsUsing combining P value < 0.05, we obtained 192 DEGs, 40 of which are IADEGs. The GO enrichment analysis results indicated that 192 DEGs were mainly involved in T cell activation, MHC protein complex and immune receptor activity. The KEGG enrichment analysis results indicated that 192 GEGs were mainly involved in Th17 cell differentiation, IL-17 signaling pathway, mTOR signaling pathway and NOD-like receptor signaling pathway. In addition, the top GO terms of 40 IADEGs were involved in T cell activation, immune response-activating signal transduction, external side of plasma membrane and phosphatase binding. The KEGG enrichment analysis results indicated that IADEGs were mainly involved in FoxO signaling pathway, Toll-like receptor, JAK-STAT signaling pathway and Apoptosis. Then, five important IADEGs (AXL, MKI67, CDKN3, BCL2 and PTGS2) for severe COVID-19 were screened by random forest analysis. By building an artificial neural network model, we found that the AUC values of 5 important IADEGs were 0.972 and 0.844 in the train group (GSE151764 and GSE183533) and test group (GSE205099), respectively.ConclusionThe five genes related to inflammasome, including AXL, MKI67, CDKN3, BCL2 and PTGS2, are important for severe COVID-19 patients, and these molecules are related to the activation of NLRP3 inflammasome. Furthermore, AXL, MKI67, CDKN3, BCL2 and PTGS2 as a marker combination could be used as potential markers to identify severe COVID-19 patients.

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Pulmonary Recovery 12 Months after Non-Severe and Severe COVID-19: The Prospective Swiss COVID-19 Lung Study

Cited by 11 publications

References 37 publications

Proteomic Evolution from Acute to Post-COVID-19 Conditions

Proteomic Evolution from Acute to Post-COVID-19 Conditions

Short- and Long-Term Chest-CT Findings after Recovery from COVID-19: A Systematic Review and Meta-Analysis

Identifying key genes related to inflammasome in severe COVID-19 patients based on a joint model with random forest and artificial neural network

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