Summary of Study ST002301
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001474. The data can be accessed directly via it's Project DOI: 10.21228/M86998 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST002301 |
| Study Title | Serum metabolomics profiling identifies new predictive biomarkers for disease severity in COVID-19 patients |
| Study Summary | Over the last three years, numerous groups have reported on different predictive models of disease severity in COVID-19 patients. However, almost all such models, which relied on serum biomarkers, clinical data or a combination of both, were subsequently deemed as cumbersome, inadequate and/or subject to bias. Moreover, although serum metabolomics profiling has shown significant differences among patients with different degrees of disease severity, the use of serum metabolomics profiling to identify prognostic biomarkers has, so far, been neglected. Herein, we sought to develop highly predictive models of disease severity by integrating routine laboratory findings and serum metabolomics profiling which identified several metabolites including K_4_aminophenol, acetaminophen and cytosine as potential biomarkers of disease severity in COVID-19 patients. Two models were subsequently developed and internally validated on the basis of ROC-AUC values. The predictive accuracy of the first model was 0.998 (95% CI: 0.992 to 1.000) with an optimal cut-off risk score of 4 biomarkers from among 8 linearly-related biomarkers (D-dimer, ferritin, neutrophil counts, Hp, sTfR, K_4_aminophenol, acetaminophen and cytosine). The predictive accuracy of the second model was 0.996 (95% CI: 0.989 to 1.000) with an optimal cut-off risk score of 3 biomarkers from among 6 biomarkers (D-dimer, ferritin, neutrophil counts, Hp, sTfR and cytosine). The two models are of high predictive power, need a small number of variables that can be acquired at minimal cost and effort, and can be applied independent of non-empirical clinical data. In conclusion, the metabolomics profiling data and the modeling work stemming from it, as presented here, could further explain the cause of COVID-19 disease prognosis and patient management. |
| Institute | Sharjah Institute for Medical Research |
| Last Name | Soares |
| First Name | Nelson |
| Address | M32, SIMR, College of Pharmacy, Health Sciences, University of Sharjah, Sharjah, UAE, Sharjah, 000, United Arab Emirates |
| nsoares@sharjah.ac.ae | |
| Phone | +971501594048 |
| Submit Date | 2022-09-21 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | d |
| Analysis Type Detail | LC-MS |
| Release Date | 2023-03-01 |
| Release Version | 1 |
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Collection:
| Collection ID: | CO002380 |
| Collection Summary: | In this retrospective cohort study, blood samples were collected from donors who tested positive for COVID-19 and presented with no, mild or severe symptoms between March 20 until July 17, 2020. Patients were diagnosed with COVID-19 using a nasal swab PCR test and later divided into three groups (asymptomatic, mild, and severe) based on their clinical presentation. Each donor gave a 10 ml blood sample, one half of which was collected in a plain tube and the other half in an EDTA vacutainer. A total of 85 samples were collected (30 COVID-19-positive asymptomatic, 10 COVID-19-positive with mild symptoms, and 45 COVID-19-positive with severe symptoms) for the purpose of this study. COVID-19-positive asymptomatic individuals were identified as a result of the national screening campaigns. Symptomatic COVID-19 patients were classified into mild or severe based on guidelines published by Abu Dhabi Department of Health (circular number 33, 19th April 2020). Patients with mild disease presented with upper respiratory tract infection and symptoms like fever, dry cough, sore throat, runny nose, muscle and joint pains without shortness of breath. Patients with severe disease presented with severe pneumonia and symptoms like fever, cough, dyspnea and fast breathing (>30 per minute), in addition to oxygen saturation <90%. Immediately upon sample collection, the hospital laboratory staff separated and tested the serum for CRP, D-dimer, ferritin, IL-6 and LDH; a complete blood count was also performed on each sample. Whole blood samples were also aliquoted and frozen at −80 0C for subsequent processing and analysis. The study was jointly approved by the Ministry of Health, Abu Dhabi and Dubai Health Authority (DOH/CVDC/2020/1949) on the understanding that samples will be number-coded to hide patient identity, that no personal information will be shared with a third party and that no sample analysis can be performed by entities other than the Research Institute of Medical and Health Sciences (RIMHS), the University of Sharjah (UOS) without prior written approval. |
| Sample Type: | Blood (plasma) |
