Dataset Information

Distinct blood inflammatory biomarker clusters stratify host phenotypes during the middle phase of COVID-19.

ABSTRACT: The associations between clinical phenotypes of coronavirus disease 2019 (COVID-19) and the host inflammatory response during the transition from peak illness to convalescence are not yet well understood. Blood plasma samples were collected from 129 adult SARS-CoV-2 positive inpatient and outpatient participants between April 2020 and January 2021, in a multi-center prospective cohort study at 8 military hospitals across the United States. Plasma inflammatory protein biomarkers were measured in samples from 15 to 28 days post symptom onset. Topological Data Analysis (TDA) was used to identify patterns of inflammation, and associations with peak severity (outpatient, hospitalized, ICU admission or death), Charlson Comorbidity Index (CCI), and body mass index (BMI) were evaluated using logistic regression. The study population (n = 129, 33.3% female, median 41.3 years of age) included 77 outpatient, 31 inpatient, 16 ICU-level, and 5 fatal cases. Three distinct inflammatory biomarker clusters were identified and were associated with significant differences in peak disease severity (p < 0.001), age (p < 0.001), BMI (p < 0.001), and CCI (p = 0.001). Host-biomarker profiles stratified a heterogeneous population of COVID-19 patients during the transition from peak illness to convalescence, and these distinct inflammatory patterns were associated with comorbid disease and severe illness due to COVID-19.

SUBMITTER: Blair PW

PROVIDER: S-EPMC9795438 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Distinct blood inflammatory biomarker clusters stratify host phenotypes during the middle phase of COVID-19.

Blair Paul W PW Brandsma Joost J Chenoweth Josh J Richard Stephanie A SA Epsi Nusrat J NJ Mehta Rittal R Striegel Deborah D Clemens Emily G EG Alharthi Sultanah S Lindholm David A DA Maves Ryan C RC Larson Derek T DT Mende Katrin K Colombo Rhonda E RE Ganesan Anuradha A Lalani Tahaniyat T Colombo Christopher J CJ Malloy Allison A AA Snow Andrew L AL Schully Kevin L KL Lanteri Charlotte C Simons Mark P MP Dumler John S JS Tribble David D Burgess Timothy T Pollett Simon S Agan Brian K BK Clark Danielle V DV

Scientific reports 20221228 1

The associations between clinical phenotypes of coronavirus disease 2019 (COVID-19) and the host inflammatory response during the transition from peak illness to convalescence are not yet well understood. Blood plasma samples were collected from 129 adult SARS-CoV-2 positive inpatient and outpatient participants between April 2020 and January 2021, in a multi-center prospective cohort study at 8 military hospitals across the United States. Plasma inflammatory protein biomarkers were measured in ...[more]

PMID: 36577783

Similar Datasets

Project description:BackgroundTranscriptomic analysis has been used to elucidate the complex pathogenesis of heterogeneous disease and may also contribute to identify potential therapeutic targets by delineating the hub genes. This study aimed to investigate whether blood transcriptomic clustering can distinguish clinical and immune phenotypes of asthmatics, and microbiome in asthmatics.MethodsTranscriptomic expression of peripheral blood mononuclear cells (PBMCs) from 47 asthmatics and 21 non-asthmatics was measured using RNA sequencing. A hierarchical clustering algorithm was used to classify asthmatics. Differentially expressed genes, clinical phenotypes, immune phenotypes, and microbiome of each transcriptomic cluster were assessed.ResultsIn asthmatics, three distinct transcriptomic clusters with numerously different transcriptomic expressions were identified. The proportion of severe asthmatics was highest in cluster 3 as 73.3%, followed by cluster 2 (45.5%) and cluster 1 (28.6%). While cluster 1 represented clinically non-severe T2 asthma, cluster 3 tended to include severe non-T2 asthma. Cluster 2 had features of both T2 and non-T2 asthmatics characterized by the highest serum IgE level and neutrophil-dominant sputum cell population. Compared to non-asthmatics, cluster 1 showed higher CCL23 and IL1RL1 expression while the expression of TREML4 was suppressed in cluster 3. CTSD and ALDH2 showed a significant positive linear relationship across three clusters in the order of cluster 1 to 3. No significant differences in the diversities of lung and gut microbiomes were observed among transcriptomic clusters of asthmatics and non-asthmatics. However, our study has limitations in that small sample size data were analyzed with unmeasured confounding factors and causal relationships or function pathways were not verified.ConclusionsGenetic clustering based on the blood transcriptome may provide novel immunological insight, which can be biomarkers of asthma immune phenotypes. Trial registration Retrospectively registered.

Project description:BACKGROUND:Although individual cardiac biomarkers are associated with heart failure risk and all-cause mortality in HIV-infected individuals, their combined use for prediction has not been well studied. METHODS AND RESULTS:Unsupervised k-means cluster analysis was performed blinded to the study outcomes in 332 HIV-infected participants on 8 biomarkers: ST2, NT-proBNP (N-terminal pro-B-type natriuretic peptide), hsCRP (high-sensitivity C-reactive protein), GDF-15 (growth differentiation factor 15), cystatin C, IL-6 (interleukin-6), D-dimer, and troponin. We evaluated cross-sectional associations of each cluster with diastolic dysfunction, pulmonary hypertension (defined as echocardiographic pulmonary artery systolic pressure ≥35 mm Hg), and longitudinal associations with all-cause mortality. The biomarker-derived clusters partitioned subjects into 3 groups. Cluster 3 (n=103) had higher levels of CRP, IL-6, and D-dimer (inflammatory phenotype). Cluster 2 (n=86) displayed elevated levels of ST2, NT-proBNP, and GDF-15 (cardiac phenotype). Cluster 1 (n=143) had lower levels of both phenotype-associated biomarkers. After multivariable adjustment for traditional and HIV-related risk factors, cluster 3 was associated with a 51% increased risk of diastolic dysfunction (95% confidence interval, 1.12-2.02), and cluster 2 was associated with a 67% increased risk of pulmonary hypertension (95% confidence interval, 1.04-2.68), relative to cluster 1. Over a median 6.9-year follow-up, 48 deaths occurred. Cluster 3 was independently associated with a 3.3-fold higher risk of mortality relative to cluster 1 (95% confidence interval, 1.3-8.1), and cluster 2 had a 3.1-fold increased risk (95% confidence interval, 1.1-8.4), even after controlling for diastolic dysfunction, pulmonary hypertension, left ventricular mass, and ejection fraction. CONCLUSIONS:Serum biomarkers can be used to classify HIV-infected individuals into separate clusters for differentiating cardiopulmonary structural and functional abnormalities and can predict mortality independent of these structural and functional measures.

Project description:BackgroundAsthma is an inflammatory heterogeneous disease. Asthma inflammatory phenotypes based on blood eosinophil and neutrophil counts have never been identified and characterized in population-based studies.MethodsAdults with current asthma and available blood eosinophil and neutrophil counts from the French population-based CONSTANCES cohort were included. Current asthma was defined by reports of asthma attacks, symptoms or treatments in the last 12 months. Inflammatory phenotypes were based on low (L) and high (H) blood (B) eosinophil (E) (LBE/HBE: </⩾0·25 × 109/L, respectively) and neutrophil (N) (LBN/HBN: </⩾5 × 109/L, respectively) cut-offs. Associations between inflammatory phenotypes and the clinical expressions of asthma were studied using logistic models adjusted for age, sex, smoking status, body mass index, education level, French deprivation index and treatment. Other cut-offs were applied. Stratified analyses according to age or sex were performed.FindingsAmong 15,019 adults with asthma (56% women, 59%≥40 years), the LBE/LBN (reference), LBE/HBN, HBE/LBN and HBE/HBN phenotypes accounted for 57%, 6%, 33% and 4% respectively. The LBE/HBN phenotype was associated with being awaken by an attack of coughing, chronic bronchitis, and dyspnoea (adjusted(a)OR ranging from 1·21 to 1·42). The HBE/LBN and HBE/HBN phenotypes were associated with asthma attacks (aOR=1·31[1·20-1·42], 1·25[1·02-1·53]) and asthma symptom score (p for trend<0·0001, p for trend=0·001, respectively). The HBE/LBN phenotype was also associated with being awaken with chest tightness (aOR=1·30[1·20-1·40]). Results were unchanged whatever the cut-offs used. No statistically significant heterogeneity was observed according to age or sex.InterpretationDifferences in the clinical expressions of asthma were found between the phenotypes, reproducible whatever the cut-offs used, and similar to those observed in case-control and clinical studies. Such phenotypes are of interest to improve asthma management and study its environmental risk factors.FundingThe CONSTANCES cohort receives grants from ANR (ANR-11-INBS-0002), the Caisse nationale d'assurance maladie-CNAM and the Ministry of research. CONSTANCES also receives funding from MSD, AstraZeneca, Lundbeck and L'Oréal, managed by INSERM-Transfert. T.Tsiavia is supported by a PhD grant from the Fondation pour le Recherche Médicale (ECO202006011654).

Project description:BackgroundMultisystem inflammatory syndrome in children (MIS-C) is a severe disease with an unpredictable course and a substantial risk of cardiogenic shock. Our objectives were to (a) compare MIS-C phenotypes across the COVID-19 pandemic, (b) identify features associated with intensive care need and treatment with biologic agents.MethodsYouth aged 0-18 years, fulfilling the World Health Organization case definition of MIS-C, and admitted to the Alberta Children's Hospital during the first four waves of the COVID-19 pandemic (May 2020-December 2021) were included in this cohort study. Demographic, clinical, biochemical, imaging, and treatment data were captured.ResultsFifty-seven MIS-C patients (median age 6 years, range 0-17) were included. Thirty patients (53%) required intensive care. Patients in the third or fourth wave (indicated as phase 2 of the pandemic) presented with higher peak ferritin (µg/l, median (IQR) = 1134 (409-1806) vs. 370 (249-629), P = 0.001), NT-proBNP (ng/l, median (IQR) = 12,217 (3013-27,161) vs. 3213 (1216-8483), P = 0.02) and D-dimer (mg/l, median (IQR) = 4.81 (2.24-5.37) vs. 2.01 (1.27-3.34), P = 0.004) levels, and higher prevalence of liver enzyme abnormalities (n(%) = 17 (68) vs. 11 (34), P = 0.02), hypoalbuminemia (n(%) = 24 (100) vs. 25 (81), P = 0.03) and thrombocytopenia (n(%) 18 (72) vs. 11 (34), P = 0.007) compared to patients in the first two waves (phase 1). These patients had a higher need of non-invasive/mechanical ventilation (n(%) 4 (16) vs. 0 (0), P = 0.03). Unsupervised clustering analyses classified 47% of the patients in the correct wave and 74% in the correct phase of the pandemic. NT-proBNP was the only significant contributor to the need for intensive care in all applied multivariate regression models. Treatment with biologic agents was significantly associated with peak CRP (mg/l (median, IQR = 240.9 (132.9-319.4) vs. 155.8 (101.0-200.7), P = 0.02) and ferritin levels (µg/l, median (IQR) = 1380 (509-1753) vs. 473 (280-296)).ConclusionsMIS-C patients in a later stage of the pandemic displayed a more severe phenotype, reflecting the impact of distinct SARS-CoV-2 variants. NT-proBNP emerged as the most crucial feature associated with intensive care need, underscoring the importance of monitoring.

Dataset Information

Distinct blood inflammatory biomarker clusters stratify host phenotypes during the middle phase of COVID-19.

Publications

Distinct blood inflammatory biomarker clusters stratify host phenotypes during the middle phase of COVID-19.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets