Evidence map›Paper›PMID 37655481›Full record

ArticleJournal of the American Heart Association2023

Causal Effects of YKL-40 on Ischemic Stroke and Its Subtypes: A 2-Sample Mendelian Randomization Study.

Qingyun Xu, Lulu Sun, Yinan Wang, Ruirui Wang, Yiming Jia, Daoxia Guo, Mengyao Shi, Pinni Yang, Yonghong Zhang, Zhengbao Zhu

Open access · goldAbstract read
In one paragraph

Article in Journal of the American Heart Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 22% 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

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

  1. Pooled it
  2. Review
  3. Article
  4. Article
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

10 authors at 1 institution in 1 country.

Qingyun XuDepartment of Epidemiology School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University Suzhou China.ORCID 0000-0001-7733-3813
Lulu SunDepartment of Epidemiology School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University Suzhou China.ORCID 0000-0002-9927-0654
Yinan WangDepartment of Epidemiology School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University Suzhou China.ORCID 0000-0003-2967-6058
Ruirui WangDepartment of Epidemiology School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University Suzhou China.ORCID 0000-0002-2725-5228
Yiming JiaDepartment of Epidemiology School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University Suzhou China.ORCID 0000-0002-3624-7879
Daoxia GuoSchool of Nursing Medical College of Soochow University Suzhou China.ORCID 0000-0002-4537-0705
Mengyao ShiDepartment of Epidemiology School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University Suzhou China.ORCID 0000-0002-5883-0416
Pinni YangDepartment of Epidemiology School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University Suzhou China.ORCID 0000-0001-7327-3294
Yonghong ZhangDepartment of Epidemiology School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University Suzhou China.ORCID 0000-0001-6188-3490
Zhengbao ZhuDepartment of Epidemiology School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University Suzhou China.ORCID 0000-0001-5500-0014
Soochow University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Chitinase-3 like protein 1 (CHI3L1, YKL-40) was reported to be implicated in the development of ischemic stroke, but whether the association between them was causal remained unclear. We conducted a 2-sample Mendelian randomization study to explore the associations of genetically determined plasma YKL-40 with ischemic stroke and its subtypes (large artery stroke, small vessel stroke, and cardioembolic stroke). Methods and Results Based on genome-wide association study data of 3394 European-descent individuals, we selected 13 single-nucleotide polymorphisms associated with plasma YKL-40 as genetic instruments. Summary data about ischemic stroke and its subtypes were obtained from the Multiancestry Genome-wide Association Study of Stroke Consortium, involving 34 217 ischemic stroke cases and 406 111 controls of European ancestry. We used the inverse-variance weighted method followed by a series of sensitivity analyses to assess the causal associations of plasma YKL-40 with ischemic stroke and its subtypes. The primary analysis showed that genetically determined high YKL-40 levels were associated with increased risks of large artery stroke (odds ratio [OR], 1.08 [95% CI, 1.04-1.12];

Indexed as

Embolic StrokeIschemic StrokeStrokeChitinase-3-Like Protein 1Genome-Wide Association StudyHumansMendelian Randomization AnalysisChitinase-3-Like Protein 1large artery strokeMendelian randomizationsmall vessel strokeYKL‐40

Identifiers

PMID37655481
PMCPMC10547358
OpenAlexW4386346612

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.