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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Project:

Project ID:	PR001474
Project DOI:	doi: 10.21228/M86998
Project Title:	Serum metabolomics profiling identifies new predictive biomarkers for disease severity in COVID-19 patients
Project 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:	+971 50 159 4048

Subject:

Subject ID:	SU002387
Subject Type:	Human
Subject Species:	Homo sapiens
Taxonomy ID:	9606

Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id	local_sample_id	Severity of Disease
SA226414	Plasma45-02_51_1_2375	Asymptomatic
SA226415	Plasma46-01_52_1_2376	Asymptomatic
SA226416	Plasma45-01_51_1_2374	Asymptomatic
SA226417	Plasma44-01_50_1_2372	Asymptomatic
SA226418	Plasma43-01_49_1_2370	Asymptomatic
SA226419	Plasma46-02_52_1_2377	Asymptomatic
SA226420	Plasma44-02_50_1_2373	Asymptomatic
SA226421	Plasma47-01_53_1_2378	Asymptomatic
SA226422	Plasma49-01_55_1_2382	Asymptomatic
SA226423	Plasma49-02_55_1_2383	Asymptomatic
SA226424	Plasma48-02_54_1_2381	Asymptomatic
SA226425	Plasma48-01_54_1_2380	Asymptomatic
SA226426	Plasma47-02_53_1_2379	Asymptomatic
SA226427	Plasma42-02_48_1_2369	Asymptomatic
SA226428	Plasma41-02_47_1_2357	Asymptomatic
SA226429	Plasma37-02_43_1_2349	Asymptomatic
SA226430	Plasma38-01_44_1_2350	Asymptomatic
SA226431	Plasma37-01_43_1_2348	Asymptomatic
SA226432	Plasma36-02_42_1_2347	Asymptomatic
SA226433	Plasma35-02_41_1_2345	Asymptomatic
SA226434	Plasma36-01_42_1_2346	Asymptomatic
SA226435	Plasma38-02_44_1_2351	Asymptomatic
SA226436	Plasma39-01_45_1_2352	Asymptomatic
SA226437	Plasma41-01_47_1_2356	Asymptomatic
SA226438	Plasma50-01_56_1_2384	Asymptomatic
SA226439	Plasma40-02_46_1_2355	Asymptomatic
SA226440	Plasma40-01_46_1_2354	Asymptomatic
SA226441	Plasma39-02_45_1_2353	Asymptomatic
SA226442	Plasma42-01_48_1_2368	Asymptomatic
SA226443	Plasma51-01_57_1_2386	Asymptomatic
SA226444	Plasma60-02_66_1_2409	Asymptomatic
SA226445	Plasma61-01_67_1_2410	Asymptomatic
SA226446	Plasma60-01_66_1_2408	Asymptomatic
SA226447	Plasma59-02_65_1_2407	Asymptomatic
SA226448	Plasma58-02_64_1_2405	Asymptomatic
SA226449	Plasma59-01_65_1_2406	Asymptomatic
SA226450	Plasma61-02_67_1_2411	Asymptomatic
SA226451	Plasma62-01_68_1_2412	Asymptomatic
SA226452	Plasma64-01_70_1_2416	Asymptomatic
SA226453	Plasma64-02_70_1_2417	Asymptomatic
SA226454	Plasma63-02_69_1_2415	Asymptomatic
SA226455	Plasma63-01_69_1_2414	Asymptomatic
SA226456	Plasma62-02_68_1_2413	Asymptomatic
SA226457	Plasma58-01_64_1_2404	Asymptomatic
SA226458	Plasma57-02_63_1_2403	Asymptomatic
SA226459	Plasma53-01_59_1_2390	Asymptomatic
SA226460	Plasma53-02_59_1_2391	Asymptomatic
SA226461	Plasma52-02_58_1_2389	Asymptomatic
SA226462	Plasma52-01_58_1_2388	Asymptomatic
SA226463	Plasma35-01_41_1_2344	Asymptomatic
SA226464	Plasma51-02_57_1_2387	Asymptomatic
SA226465	Plasma54-01_60_1_2392	Asymptomatic
SA226466	Plasma54-02_60_1_2393	Asymptomatic
SA226467	Plasma56-02_62_1_2401	Asymptomatic
SA226468	Plasma57-01_63_1_2402	Asymptomatic
SA226469	Plasma56-01_62_1_2400	Asymptomatic
SA226470	Plasma55-02_61_1_2395	Asymptomatic
SA226471	Plasma55-01_61_1_2394	Asymptomatic
SA226472	Plasma50-02_56_1_2385	Asymptomatic
SA226473	Plasma43-02_49_1_2371	Asymptomatic
SA226474	Plasma13-02_19_1_2297	Mild
SA226475	Plasma13-01_19_1_2296	Mild
SA226476	Plasma12-02_18_1_2295	Mild
SA226477	Plasma14-01_20_1_2298	Mild
SA226478	Plasma14-02_20_1_2299	Mild
SA226479	Plasma16-01_22_1_2302	Mild
SA226480	Plasma15-02_21_1_2301	Mild
SA226481	Plasma15-01_21_1_2300	Mild
SA226482	Plasma12-01_18_1_2294	Mild
SA226483	Plasma11-02_17_1_2293	Mild
SA226484	Plasma08-02_14_1_2287	Mild
SA226485	Plasma02-02_8_1_2275	Mild
SA226486	Plasma02-01_8_1_2274	Mild
SA226487	Plasma09-01_15_1_2288	Mild
SA226488	Plasma09-02_15_1_2289	Mild
SA226489	Plasma11-01_17_1_2292	Mild
SA226490	Plasma10-02_16_1_2291	Mild
SA226491	Plasma10-01_16_1_2290	Mild
SA226492	Plasma16-02_22_1_2303	Mild
SA226493	Plasma08-01_14_1_2286	Mild
SA226494	Plasma71-02_77_1_2431	Severe
SA226495	Plasma71-01_77_1_2430	Severe
SA226496	Plasma70-02_76_1_2429	Severe
SA226497	Plasma70-01_76_1_2428	Severe
SA226498	Plasma72-01_78_1_2432	Severe
SA226499	Plasma72-02_78_1_2433	Severe
SA226500	Plasma74-02_80_1_2437	Severe
SA226501	Plasma74-01_80_1_2436	Severe
SA226502	Plasma73-02_79_1_2435	Severe
SA226503	Plasma73-01_79_1_2434	Severe
SA226504	Plasma69-02_75_1_2427	Severe
SA226505	Plasma69-01_75_1_2426	Severe
SA226506	Plasma66-01_72_1_2420	Severe
SA226507	Plasma65-02_71_1_2419	Severe
SA226508	Plasma65-01_71_1_2418	Severe
SA226509	Plasma32-01_38_1_2338	Severe
SA226510	Plasma66-02_72_1_2421	Severe
SA226511	Plasma67-01_73_1_2422	Severe
SA226512	Plasma68-02_74_1_2425	Severe
SA226513	Plasma68-01_74_1_2424	Severe

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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)

Treatment:

Treatment ID:	TR002399
Treatment Summary:	No treatment, study examines the predictive ability of metabolomic profiling models to predict covid disease severity

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

Combined analysis:

Analysis ID	AN003757
Analysis type	MS
Chromatography type	Reversed phase
Chromatography system	Bruker Elute
Column	Hamilton Intensity Solo 2 C18
MS Type	ESI
MS instrument type	QTOF
MS instrument name	Bruker timsTOF
Ion Mode	POSITIVE
Units	AU

Chromatography:

Chromatography ID:	CH002780
Instrument Name:	Bruker Elute
Column Name:	Hamilton Intensity Solo 2 C18
Chromatography Type:	Reversed phase

MS:

MS ID:	MS003500
Analysis ID:	AN003757
Instrument Name:	Bruker timsTOF
Instrument Type:	QTOF
MS Type:	ESI
MS Comments:	The ESI source with dry nitrogen gas was 10 L/min, and the drying temperature was equal to 220℃ with nebulizer gas pressure set to 2.2 bar. The capillary voltage of the ESI was 4500 V and the Plate Offset 500 V. MS acquisition scan was set at 20-1300 m/z and the collision energy at 7 eV. Sodium formate was injected as an external calibrant between 0.1 and 0.3 minutes. A total volume of 10 µL sample was injected into the TIMS-TOF MS.
Ion Mode:	POSITIVE