Evidence map›Paper›PMID 38799463›Full record

ArticleFrontiers in immunology2024

Lipidome is a valuable tool for the severity prediction of coronavirus disease 2019.

Shan-Shan Zhang, Zhiling Zhao, Wan-Xue Zhang, Rui Wu, Fei Li, Han Yang, Qiang Zhang, Ting-Ting Wei, Jingjing Xi, Yiguo Zhou and 5 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Observational
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

15 authors.

Shan-Shan Zhang *Department of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Zhiling Zhao *Department of Intensive Care Medicine, Peking University Third Hospital, Beijing, China.
Wan-Xue Zhang *Center for Infectious Disease and Policy Research and Global Health and Infectious Diseases Group, Peking University, Beijing, China.
Rui WuPulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Fei LiDepartment of General Surgery, Peking University Third Hospital, Beijing, China.
Han YangCenter for Infectious Disease and Policy Research and Global Health and Infectious Diseases Group, Peking University, Beijing, China.
Qiang ZhangDepartment of Intensive Care Medicine, Peking University Third Hospital, Beijing, China.
Ting-Ting WeiDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Jingjing XiDepartment of Intensive Care Medicine, Peking University Third Hospital, Beijing, China.
Yiguo ZhouCenter for Infectious Disease and Policy Research and Global Health and Infectious Diseases Group, Peking University, Beijing, China.
Tiehua WangDepartment of Intensive Care Medicine, Peking University Third Hospital, Beijing, China.
Juan DuDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Ninghua HuangDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Qinggang GeDepartment of Intensive Care Medicine, Peking University Third Hospital, Beijing, China.
Qing-Bin LuDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To describe the lipid metabolic profile of different patients with coronavirus disease 2019 (COVID-19) and contribute new evidence on the progression and severity prediction of COVID-19. Methods: This case-control study was conducted in Peking University Third Hospital, China. The laboratory-confirmed COVID-19 patients aged ≥18 years old and diagnosed as pneumonia from December 2022 to January 2023 were included. Serum lipids were detected. The discrimination ability was calculated with the area under the curve (AUC). A random forest (RF) model was conducted to determine the significance of different lipids. Results: Totally, 44 COVID-19 patients were enrolled with 16 mild and 28 severe patients. The top 5 super classes were triacylglycerols (TAG, 55.9%), phosphatidylethanolamines (PE, 10.9%), phosphatidylcholines (PC, 6.8%), diacylglycerols (DAG, 5.9%) and free fatty acids (FFA, 3.6%) among the 778 detected lipids from the serum of COVID-19 patients. Certain lipids, especially lysophosphatidylcholines (LPCs), turned to have significant correlations with certain immune/cytokine indexes. Reduced level of LPC 20:0 was observed in severe patients particularly in acute stage. The AUC of LPC 20:0 reached 0.940 in discriminating mild and severe patients and 0.807 in discriminating acute and recovery stages in the severe patients. The results of RF models also suggested the significance of LPCs in predicting the severity and progression of COVID-19. Conclusion: Lipids probably have the potential to differentiate and forecast the severity, progression, and clinical outcomes of COVID-19 patients, with implications for immune/inflammatory responses. LPC 20:0 might be a potential target in predicting the progression and outcome and the treatment of COVID-19.

Indexed as

COVID-19LipidomicsSARS-CoV-2Severity of Illness IndexAdultAgedBiomarkersCase-Control StudiesChinaFemaleHumansLipidsMaleMiddle AgedTriglyceridesBiomarkersLipidsTriglyceridesCOVID-19cytokineimmunelipidLPCSARS-CoV-2

Identifiers

PMID38799463
PMCPMC11116732

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.