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Identifying multimorbidity profiles associated with COVID-19 severity in chronic patients using network analysis in the PRECOVID Study.

ABSTRACT: A major risk factor of COVID-19 severity is the patient's health status at the time of the infection. Numerous studies focused on specific chronic diseases and identified conditions, mainly cardiovascular ones, associated with poor prognosis. However, chronic diseases tend to cluster into patterns, each with its particular repercussions on the clinical outcome of infected patients. Network analysis in our population revealed that not all cardiovascular patterns have the same risk of COVID-19 hospitalization or mortality and that this risk depends on the pattern of multimorbidity, besides age and sex. We evidenced that negative outcomes were strongly related to patterns in which diabetes and obesity stood out in older women and men, respectively. In younger adults, anxiety was another disease that increased the risk of severity, most notably when combined with menstrual disorders in women or atopic dermatitis in men. These results have relevant implications for organizational, preventive, and clinical actions to help meet the needs of COVID-19 patients.

SUBMITTER: Carmona-Pirez J

PROVIDER: S-EPMC8857317 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Identifying multimorbidity profiles associated with COVID-19 severity in chronic patients using network analysis in the PRECOVID Study.

Carmona-Pírez Jonás J Gimeno-Miguel Antonio A Bliek-Bueno Kevin K Poblador-Plou Beatriz B Díez-Manglano Jesús J Ioakeim-Skoufa Ignatios I González-Rubio Francisca F Poncel-Falcó Antonio A Prados-Torres Alexandra A Gimeno-Feliu Luis A LA

Scientific reports 20220218 1

A major risk factor of COVID-19 severity is the patient's health status at the time of the infection. Numerous studies focused on specific chronic diseases and identified conditions, mainly cardiovascular ones, associated with poor prognosis. However, chronic diseases tend to cluster into patterns, each with its particular repercussions on the clinical outcome of infected patients. Network analysis in our population revealed that not all cardiovascular patterns have the same risk of COVID-19 hos ...[more]

PMID: 35181720

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Project description:BackgroundMarkedly elevated levels of proinflammatory cytokines and defective type-I interferon responses were reported in patients with coronavirus disease 2019 (COVID-19).ObjectiveWe sought to determine whether particular cytokine profiles are associated with COVID-19 severity and mortality.MethodsCytokine concentrations and severe acute respiratory syndrome coronavirus 2 antigen were measured at hospital admission in serum of symptomatic patients with COVID-19 (N = 115), classified at hospitalization into 3 respiratory severity groups: no need for mechanical ventilatory support (No-MVS), intermediate severity requiring mechanical ventilatory support (MVS), and critical severity requiring extracorporeal membrane oxygenation (ECMO). Principal-component analysis was used to characterize cytokine profiles associated with severity and mortality. The results were thereafter confirmed in an independent validation cohort (N = 86).ResultsAt time of hospitalization, ECMO patients presented a dominant proinflammatory response with elevated levels of TNF-α, IL-6, IL-8, and IL-10. In contrast, an elevated type-I interferon response involving IFN-α and IFN-β was characteristic of No-MVS patients, whereas MVS patients exhibited both profiles. Mortality at 1 month was associated with higher levels of proinflammatory cytokines in ECMO patients, higher levels of type-I interferons in No-MVS patients, and their combination in MVS patients, resulting in a combined mortality prediction accuracy of 88.5% (risk ratio, 24.3; P < .0001). Severe acute respiratory syndrome coronavirus 2 antigen levels correlated with type-I interferon levels and were associated with mortality, but not with proinflammatory response or severity.ConclusionsDistinct cytokine profiles are observed in association with COVID-19 severity and are differentially predictive of mortality according to oxygen support modalities. These results warrant personalized treatment of COVID-19 patients based on cytokine profiling.

Project description:Individuals with the SARS-CoV-2 infection may experience a wide range of symptoms, from being asymptomatic to having a mild fever and cough to a severe respiratory impairment that results in death. MicroRNA (miRNA), which plays a role in the antiviral effects of SARS-CoV-2 infection, has the potential to be used as a novel marker to distinguish between patients who have various COVID-19 clinical severities. In the current study, the existing blood expression profiles reported in two previous studies were combined for deep analyses. The final profiles contained 1444 miRNAs in 375 patients from six categories, which were as follows: 30 patients with mild COVID-19 symptoms, 81 patients with moderate COVID-19 symptoms, 30 non-COVID-19 patients with mild symptoms, 137 patients with severe COVID-19 symptoms, 31 non-COVID-19 patients with severe symptoms, and 66 healthy controls. An efficient computational framework containing four feature selection methods (LASSO, LightGBM, MCFS, and mRMR) and four classification algorithms (DT, KNN, RF, and SVM) was designed to screen clinical miRNA markers, and a high-precision RF model with a 0.780 weighted F1 was constructed. Some miRNAs, including miR-24-3p, whose differential expression was discovered in patients with acute lung injury complications brought on by severe COVID-19, and miR-148a-3p, differentially expressed against SARS-CoV-2 structural proteins, were identified, thereby suggesting the effectiveness and accuracy of our framework. Meanwhile, we extracted classification rules based on the DT model for the quantitative representation of the role of miRNA expression in differentiating COVID-19 patients with different severities. The search for novel biomarkers that could predict the severity of the disease could aid in the clinical diagnosis of COVID-19 and in exploring the specific mechanisms of the complications caused by SARS-CoV-2 infection. Moreover, new therapeutic targets for the disease may be found.

Project description:PurposeCoronavirus disease 2019 (COVID-19) continues to threaten public health globally. Severe acute respiratory coronavirus type 2 (SARS-CoV-2) infection-dependent alterations in the host cell signaling network may unveil potential target proteins and pathways for therapeutic strategies. In this study, we aim to define early severity biomarkers and monitor altered pathways in the course of SARS-CoV-2 infection.Experimental designWe systematically analyzed plasma proteomes of COVID-19 patients from Turkey by using mass spectrometry. Different severity grades (moderate, severe, and critical) and periods of disease (early, inflammatory, and recovery) are monitored. Significant alterations in protein expressions are used to reconstruct the COVID-19 associated network that was further extended to connect viral and host proteins.ResultsAcross all COVID-19 patients, 111 differentially expressed proteins were found, of which 28 proteins were unique to our study mainly enriching in immunoglobulin production. By monitoring different severity grades and periods of disease, CLEC3B, MST1, and ITIH2 were identified as potential early predictors of COVID-19 severity. Most importantly, we extended the COVID-19 associated network with viral proteins and showed the connectedness of viral proteins with human proteins. The most connected viral protein ORF8, which has a role in immune evasion, targets many host proteins tightly connected to the deregulated human plasma proteins.Conclusions and clinical relevancePlasma proteomes from critical patients are intrinsically clustered in a distinct group than severe and moderate patients. Importantly, we did not recover any grouping based on the infection period, suggesting their distinct proteome even in the recovery phase. The new potential early severity markers can be further studied for their value in the clinics to monitor COVID-19 prognosis. Beyond the list of plasma proteins, our disease-associated network unravels altered pathways, and the possible therapeutic targets in SARS-CoV-2 infection by connecting human and viral proteins. Follow-up studies on the disease associated network that we propose here will be useful to determine molecular details of viral perturbation and to address how the infection affects human physiology.

Dataset Information

Identifying multimorbidity profiles associated with COVID-19 severity in chronic patients using network analysis in the PRECOVID Study.

Publications

Identifying multimorbidity profiles associated with COVID-19 severity in chronic patients using network analysis in the PRECOVID Study.

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