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
Email	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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Sample Preparation:

Sampleprep ID:	SP002393
Sampleprep Summary:	Plasma was obtained after the collection of samples into heparinized tubes followed by centrifugation for 5 minutes (3000g). The samples were stored at –80 ºC for long-term storage until further metabolomics analysis. An aliquot of plasma sample into a microcentrifuge tube and add cold methanol into the sample at 3:1 v/v (i.e., 30 μL sample, add 90 μL cold methanol) vortex and allow to sit in –20ºC for two hrs. Next, centrifuge the samples at 20,817 x g for 15 min at 4ºC. Then, transfer the supernatant to a new microcentrifuge tube. Usually, transfer three times the original sample volume (i.e., for 30 μL sample, add 90 μL cold methanol, then transfer 90 μL supernatant). Dry down the sample using Speed vac at 30 – 40°C. Store the dried sample in a –80ºC freezer for further use or dissolve it in solvent for LC-MS/MS analysis

Sampleprep ID:

SP002393

Sampleprep Summary:

Plasma was obtained after the collection of samples into heparinized tubes followed by centrifugation for 5 minutes (3000g). The samples were stored at –80 ºC for long-term storage until further metabolomics analysis. An aliquot of plasma sample into a microcentrifuge tube and add cold methanol into the sample at 3:1 v/v (i.e., 30 μL sample, add 90 μL cold methanol) vortex and allow to sit in –20ºC for two hrs. Next, centrifuge the samples at 20,817 x g for 15 min at 4ºC. Then, transfer the supernatant to a new microcentrifuge tube. Usually, transfer three times the original sample volume (i.e., for 30 μL sample, add 90 μL cold methanol, then transfer 90 μL supernatant). Dry down the sample using Speed vac at 30 – 40°C. Store the dried sample in a –80ºC freezer for further use or dissolve it in solvent for LC-MS/MS analysis