Evidence map›Paper›PMID 34847893›Full record

ArticleBMC cardiovascular disorders2021

Inverse correlation between serum irisin and cardiovascular risk factors among Chinese overweight/obese population.

Ruoyi Liu, Qiao Zhang, Nianchun Peng, Shujing Xu, Miao Zhang, Ying Hu, Zhengyi Chen, Kun Tang, Xi He, Yi Li and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC cardiovascular disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.3field-weighted citation impact, top 20% 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

5 citing papers in PubMed, 13 citations in OpenAlex.

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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

11 authors at 2 institutions in 1 country.

Ruoyi LiuDepartment of Endocrinology and Metabolism, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, China.
Qiao ZhangDepartment of Endocrinology and Metabolism, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, China.
Nianchun PengDepartment of Endocrinology and Metabolism, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, China.
Shujing XuDepartment of Endocrinology and Metabolism, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, China.
Miao ZhangDepartment of Endocrinology and Metabolism, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, China.
Ying HuDepartment of Endocrinology and Metabolism, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, China.
Zhengyi ChenDepartment of Endocrinology and Metabolism, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, China.
Kun TangDepartment of Endocrinology and Metabolism, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, China.
Xi HeDepartment of Endocrinology and Metabolism, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, China.
Yi LiDepartment of Endocrinology and Metabolism, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, China.
Lixin ShiDepartment of Endocrinology and Metabolism, Affiliated Hospital of Guizhou Medical University, No. 28 Guiyi Road, Yunyan District, Guiyang, 550001, China. liuruoyi731@126.com.
Guiyang Medical University · CNAffiliated Hospital of Guizhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIrisin is a novel myokine associated with obesity, which is a traditional cardiovascular risk factor (CVRF). The present study aimed to investigate the association between serum irisin and a single CVRF as well as the clustering of CVRFs among Chinese overweight/obese population.

methodsA total of 98 overweight and 93 obese subjects without clinical treatments were enrolled in this study. Subjects were then divided into two groups, based on the serum irisin level: a low irisin group (1.10-13.44 ng/ml) and a high irisin group (13.49-29.9 ng/ml). The clustering of CVRFs, smoking, diabetes mellitus, dyslipidemia and hypertension, was classified as 0, 1, 2 and ≥ 3 CVRFs. The demographic and baseline clinical characteristics of all participants were collected and serum irisin was measured.

resultsThe high serum irisin group had significantly higher high-density lipoprotein cholesterol but lower fasting plasma glucose than the low serum irisin group. Additionally, the high serum irisin group had a significantly lower prevalence of smoking, diabetes mellitus and dyslipidemia than the low serum irisin group. Increased serum irisin was significantly associated with a reduced risk of smoking and dyslipidemia in both the unadjusted and adjusted models. Furthermore, high serum irisin significantly reduced the risk of the prevalence of 1, 2 and ≥ 3 CVRFs.

conclusionsamong the Chinese overweight/obese populations, high serum irisin is negatively associated with smoking, dyslipidemia and the clustering of CVRFs. Thus, high serum irisin is potentially associated with a low risk of cardiovascular diseases in the Chinese overweight/obese population.

Indexed as

AgedBiomarkersCardiovascular DiseasesChinaCross-Sectional StudiesDiabetes MellitusDyslipidemiasFemaleFibronectinsHeart Disease Risk FactorsHumansHypertensionMaleMiddle AgedObesityRisk AssessmentBiomarkersFibronectinsFNDC5 protein, humanCardiovascular diseaseDiabetes mellitusDyslipidemiaHypertensionIrisin

Identifiers

PMID34847893
PMCPMC8638330
OpenAlexW3217312004

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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.