Evidence map›Paper›PMID 38799469›Full record

ArticleFrontiers in immunology2024

Metabolic predictors of COVID-19 mortality and severity: a survival analysis.

Abdallah Musa Abdallah, Asmma Doudin, Theeb Osama Sulaiman, Omar Jamil, Rida Arif, Fatima Al Sada, Hadi M Yassine, Mohamed A Elrayess, Abdel-Naser Elzouki, Mohamed M Emara and 2 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Abdallah Musa AbdallahCollege of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar.
Asmma DoudinBiomedical Research Center (BRC), Qatar University, Doha, Qatar.
Theeb Osama SulaimanDepartment of Medicine, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar.
Omar JamilDepartment of Radiology, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar.
Rida ArifEmergency Medicine Department, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar.
Fatima Al SadaNeurosurgery Department, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar.
Hadi M YassineBiomedical Research Center (BRC), Qatar University, Doha, Qatar.
Mohamed A ElrayessCollege of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar.
Abdel-Naser ElzoukiCollege of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar.
Mohamed M EmaraCollege of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar.
Nagendra Babu ThillaiappanCollege of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar.
Farhan S CyprianCollege of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The global healthcare burden of COVID-19 pandemic has been unprecedented with a high mortality. Metabolomics, a powerful technique, has been increasingly utilized to study the host response to infections and to understand the progression of multi-system disorders such as COVID-19. Analysis of the host metabolites in response to SARS-CoV-2 infection can provide a snapshot of the endogenous metabolic landscape of the host and its role in shaping the interaction with SARS-CoV-2. Disease severity and consequently the clinical outcomes may be associated with a metabolic imbalance related to amino acids, lipids, and energy-generating pathways. Hence, the host metabolome can help predict potential clinical risks and outcomes. Methods: In this prospective study, using a targeted metabolomics approach, we studied the metabolic signature in 154 COVID-19 patients (males=138, age range 48-69 yrs) and related it to disease severity and mortality. Blood plasma concentrations of metabolites were quantified through LC-MS using MxP Quant 500 kit, which has a coverage of 630 metabolites from 26 biochemical classes including distinct classes of lipids and small organic molecules. We then employed Kaplan-Meier survival analysis to investigate the correlation between various metabolic markers, disease severity and patient outcomes. Results: A comparison of survival outcomes between individuals with high levels of various metabolites (amino acids, tryptophan, kynurenine, serotonin, creatine, SDMA, ADMA, 1-MH and carnitine palmitoyltransferase 1 and 2 enzymes) and those with low levels revealed statistically significant differences in survival outcomes. We further used four key metabolic markers (tryptophan, kynurenine, asymmetric dimethylarginine, and 1-Methylhistidine) to develop a COVID-19 mortality risk model through the application of multiple machine-learning methods. Conclusions: Metabolomics analysis revealed distinct metabolic signatures among different severity groups, reflecting discernible alterations in amino acid levels and perturbations in tryptophan metabolism. Notably, critical patients exhibited higher levels of short chain acylcarnitines, concomitant with higher concentrations of SDMA, ADMA, and 1-MH in severe cases and non-survivors. Conversely, levels of 3-methylhistidine were lower in this context.

Indexed as

COVID-19MetabolomicsSARS-CoV-2Severity of Illness IndexAgedBiomarkersFemaleHumansMaleMetabolomeMiddle AgedProspective StudiesSurvival AnalysisTryptophanBiomarkersTryptophanbiomarkersCOVID-19criticalmetabolitesmortalitysevere

Identifiers

PMID38799469
PMCPMC11127595

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.