Evidence map›Paper›PMID 37814169›Full record

ArticleBiological trace element research2024

Liver Iron Overload Drives COVID-19 Mortality: a Two-Sample Mendelian Randomization Study.

Huimin Tian, Xiangjie Kong, Fulei Han, Fangjie Xing, Shuai Zhu, Tao Xu, Weijing Wang, Ning Song, Yili Wu

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In one paragraph

Article in Biological trace element research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 26% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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 2 institutions in 1 country.

Huimin TianZhonglou District Center for Disease Control and Prevention, Changzhou, Jiangsu, China. tianhuimin78@163.com.ORCID http://orcid.org/0000-0002-7849-8414
Xiangjie KongDepartment of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China.
Fulei HanDepartment of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China.
Fangjie XingDepartment of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China.
Shuai ZhuDepartment of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China.
Tao XuDepartment of Pulmonary and Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Weijing WangDepartment of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China.
Ning SongDepartment of Physiology, Shandong Provincial Key Laboratory of Pathogenesis and Prevention of Neurological Disorder, School of Basic Medicine, Qingdao University, Qingdao, Shandong, China.
Yili WuDepartment of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China.
Qingdao University · CNJiangsu Provincial Center for Disease Control and Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Iron overload has been associated with an increased risk of COVID-19 severity and mortality in observational studies, but it remains unclear whether these associations represent causal effects. We performed a two-sample Mendelian randomization (MR) to determine associations between genetic liability to iron overload and the risk of COVID-19 severity and mortality. From genome-wide association studies of European ancestry, single-nucleotide polymorphisms associated with liver iron (n = 32,858) and ferritin (n = 23,986) were selected as exposure instruments, and summary statistics of the hospitalization (n = 16,551) and mortality (n = 15,815) of COVID-19 were utilized as the outcome. We used the inverse-variance weighted (IVW) method as the primary analysis to estimate causal effects, and other alternative approaches as well as comprehensive sensitivity analysis were conducted for estimating the robustness of identified associations. Genetically predicted high liver iron levels were associated with an increased risk of COVID-19 mortality based on the results of IVW analysis (OR = 1.38, 95% CI: 1.05-1.82, P = 0.02). Likewise, sensitivity analyses showed consistent and robust results in general (all P > 0.05). A higher risk of COVID-19 hospitalization trend was also observed in patients with high liver iron levels without statistical significance. This study suggests that COVID-19 mortality might be partially driven by the iron accumulation in the liver, supporting the classification of iron overload as one of the independent death risk factors. Therefore, avoiding iron overload and maintaining normal iron levels may be a powerful measure to reduce COVID-19 mortality.

Indexed as

COVID-19Genome-Wide Association StudyIron OverloadLiverMendelian Randomization AnalysisPolymorphism, Single NucleotideFerritinsHumansIronSARS-CoV-2FerritinsIronCOVID-19Iron overloadMendelian randomization analysisMortalitySeverity

Identifiers

PMID37814169
OpenAlexW4387446097

What Socratic holds

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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.