Evidence map›Paper›PMID 34260764›Full record

ArticleJournal of clinical laboratory analysis2021

Low serum level of apolipoprotein A1 may predict the severity of COVID-19: A retrospective study.

Zhe Zhu, Yayun Yang, Lingyan Fan, Shuyuan Ye, Kehong Lou, Xin Hua, Zuoan Huang, Qiaoyun Shi, Guosheng Gao

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 16% of its field
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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

  1. Pooled it
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  4. The prognostic value of serum apolipoprotein A1 levels in elderly patients withFrontiers in cellular and infection microbiology · 2025
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  8. Alterations in platelet proteome signature and impaired platelet integrin αJournal of thrombosis and haemostasis : JTH · 2023
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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

9 authors at 1 institution in 1 country.

Zhe ZhuDepartment of Blood Transfusion, HwaMei Hospital, University of Chinese Academy of Sciences, Ningbo, China.ORCID https://orcid.org/0000-0003-3450-7259
Yayun YangNingbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China.
Lingyan FanNingbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China.
Shuyuan YeNingbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China.
Kehong LouNingbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China.
Xin HuaNingbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China.
Zuoan HuangNingbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China.
Qiaoyun ShiNingbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China.
Guosheng GaoNingbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China.
University of Chinese Academy of Sciences · CN

Funding

COVID-19 Platform Program of Hwa Mei Hospital, University of Chinese Academy of Sciences 2020HMZD21COVID-19 Platform Program of Hwa Mei Hospital, University of Chinese Academy of Sciences 2020HMZD27Zhejiang Provincial Natural Science Foundation of China LGF21H010005
6 · The paper itself

Abstract

backgroundDyslipidemia has been observed in patients with coronavirus disease 2019 (COVID-19). This study aimed to investigate blood lipid profiles in patients with COVID-19 and to explore their predictive values for COVID-19 severity.

methodsA total of 142 consecutive patients with COVID-19 were included in this single-center retrospective study. Blood lipid profile characteristics were investigated in patients with COVID-19 in comparison with 77 age- and gender-matched healthy subjects, their predictive values for COVID-19 severity were analyzed by using multivariable logistic regression analysis, and their prediction efficiencies were evaluated by using receiver operator characteristic (ROC) curves.

resultsThere were 125 and 17 cases in the non-severe and severe groups, respectively. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein A1 (ApoA1) gradually decreased across the groups in the following order: healthy controls, non-severe group, and severe group. ApoA1 was identified as an independent risk factor for COVID-19 severity (adjusted odds ratio [OR]: 0.865, 95% confidence interval [CI]: 0.800-0.935, p < 0.001), along with interleukin-6 (IL-6) (adjusted OR: 1.097, 95% CI: 1.034-1.165, p = 0.002). ApoA1 exhibited the highest area under the ROC curve (AUC) among all single markers (AUC: 0.896, 95% CI: 0.834-0.941); moreover, the risk model established using ApoA1 and IL-6 enhanced prediction efficiency (AUC: 0.977, 95% CI: 0.932-0.995).

conclusionBlood lipid profiles in patients with COVID-19 are quite abnormal compared with those in healthy subjects, especially in severe cases. Serum ApoA1 may represent a good indicator for predicting the severity of COVID-19.

Indexed as

AdultAgedApolipoprotein A-IArea Under CurveBiomarkersCase-Control StudiesCholesterol, HDLCholesterol, LDLComorbidityCOVID-19FemaleHumansLipidsMaleMiddle AgedRetrospective StudiesAPOA1 protein, humanApolipoprotein A-IBiomarkersCholesterol, HDLCholesterol, LDLLipidsApoA1blood lipidCOVID-19disease severitySARS-CoV-2

Identifiers

PMID34260764
PMCPMC8373354
OpenAlexW3180684432

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.