Evidence map›Paper›PMID 29642198›Full record

SynthesisMedicine2018

Association between interleukin 37 (rs3811047) polymorphism and multiple autoimmune diseases in a Chinese population: A PRISMA-compliant meta-analysis.

Xiao-Yan Lin, Xue-Jun Guo, Yu-Zhuo He, Shu-Fang Hou, Hong-Bin Zhu, Ying Cheng, Zhen Nan

Abstract readMeta-Analysis
In one paragraph

Synthesis in Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

7 authors.

Xiao-Yan LinDepartment of Hemopathology, Puyang City Oilfield General Hospital, Puyang City, Henan, P.R. China.
Xue-Jun Guo
Yu-Zhuo He
Shu-Fang Hou
Hong-Bin Zhu
Ying Cheng
Zhen Nan

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveEmerging evidence suggests that interleukin 37 (IL-37) plays an important role in the pathogenesis of several autoimmune diseases (ADs), but the correlations are still unclear. We conducted a meta-analysis to explore whether IL-37 gene (rs3811047) polymorphism was associated with susceptibility to multiple ADs in a Chinese population.

methodsRelevant studies were searched in the PubMed, Embase, Chinese National Knowledge Infrastructure, and Chinese Wangfang databases up to August 31, 2017. Odds ratio (OR) and its 95% confidence interval (95% CI) was used to estimate the strength of the association in different genetic models. The results of fixed or random models were adopted according to the heterogeneity. Publication bias and sensitive analysis were also performed to evaluate the reliability of results.

resultsA total of 3161 patients and 4078 controls from 6 studies were included in this meta-analysis. Pooling all data together, a significant association between IL-37 gene (rs3811047 A/G) polymorphism and susceptibility to ADs in the Chinese population was found in all 4 genetic models (allelic model A vs G: OR = 0.73 95% CI = 0.67∼0.79; recessive model AA + AG vs GG: OR = 0.72, 95% CI = 0.65∼0.79; dominant model AA vs AG + GG: OR = 0.59, 95% CI = 0.45∼0.77; homozygous model AA vs GG: OR = 0.55, 95% CI = 0.42∼0.72). No heterogeneity and publication bias was detected in all models. Sensitive analysis indicated that all of the positive results are reliable.

conclusionThe IL- 37 (rs3811047) polymorphism contributes to the development of ADs in a Chinese population.

Indexed as

AllelesAsian PeopleAutoimmune DiseasesGenetic Predisposition to DiseaseGenotypeHumansInterleukin-1Polymorphism, GeneticIL37 protein, humanInterleukin-1

Identifiers

PMID29642198
PMCPMC5908614

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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 OpenQuestion graph.