Evidence map›Paper›PMID 37461090›Full record

ArticleClinical epigenetics2023

Association of immune cell composition with the risk factors and incidence of acute coronary syndrome.

Xian Shi, Minghan Qu, Yi Jiang, Ziwei Zhu, Chengguqiu Dai, Minghui Jiang, Lin Ding, Yu Yan, Chaolong Wang, Xiaomin Zhang and 2 more

Open access · goldAbstract read
In one paragraph

Article in Clinical epigenetics, 2023. 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
0.7field-weighted citation impact, top 23% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Increased CD14PeerJ · 2024
    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

12 authors at 1 institution in 1 country.

Xian ShiDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Minghan QuDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yi JiangDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Ziwei ZhuDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Chengguqiu DaiDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Minghui JiangDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Lin DingDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yu YanDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Chaolong WangDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiaomin ZhangDepartment of Occupational and Environmental Health, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Shanshan ChengDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. sscheng@hust.edu.cn.
Xingjie HaoDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. xingjie@hust.edu.cn.
Huazhong University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough immune cells are involved in acute coronary syndrome (ACS), few studies have explored the association of incident ACS with the relative immune cell proportions. We aimed to investigate the association of immune cell proportions with the incidence and risk factors of ACS in the Dongfeng-Tongji cohort.

methodsWe conducted the analyses with 38,295 subjects from the first follow-up of the Dongfeng-Tongji cohort, including DNA methylation profiles for 1570 individuals. The proportions of immune cell types were observed from routine blood tests or estimated from DNA methylation profiles. For both observed and estimated immune cell proportions, we tested their associations with risk factors of ACS by multivariable linear regression models. In addition, the association of each immune cell proportion with incident ACS was assessed by the Cox regression model and conditional logistic regression model, respectively, adjusting for the risk factors of ACS.

findingsThe proportions of lymphocytes, monocytes, and neutrophils showed strong associations with sex, followed by diabetes. Moreover, sex and current smoking were the two factors with strongest association with the proportions of lymphocyte subtypes. The hazard ratio (HR) and 95% confidence interval (CI) of incident ACS per standard deviation (SD) increase in proportions of lymphocytes and neutrophils were 0.91 (0.85-0.96) and 1.10 (1.03-1.16), respectively. Furthermore, the OR (95% CI) of incident ACS per SD increase in proportions of NK cells, CD4

interpretationThe proportions of immune cells were associated with several risk factors of ACS, including sex, diabetes, and current smoking. In addition, proportion of neutrophils had a risk effect, while proportion of lymphocytes had a protective effect on the incidence of ACS. The protective effect of lymphocytes was probably driven by NK cells.

Indexed as

Acute Coronary SyndromeDiabetes MellitusDNA MethylationHumansIncidenceKiller Cells, NaturalRisk FactorsAcute coronary syndromeDNA methylationImmune cell compositionRisk factor

Identifiers

PMID37461090
PMCPMC10353119
OpenAlexW4384560574

What OpenQuestion 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 OpenQuestion graph.