Evidence map›Paper›PMID 27399751›Full record

ArticleInternational journal of environmental research and public health2016

Types of Obesity and Its Association with the Clustering of Cardiovascular Disease Risk Factors in Jilin Province of China.

Peng Zhang, Rui Wang, Chunshi Gao, Yuanyuan Song, Xin Lv, Lingling Jiang, Yaqin Yu, Yuhan Wang, Bo Li

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.0field-weighted citation impact, top 8% 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

7 citing papers in PubMed, 17 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

9 authors at 1 institution in 1 country.

Peng ZhangDepartment of Epidemiology and Biostatistics, Jilin University School of Public Health, 1163 Xinmin Street, Changchun, Jilin 130021, China. peng14zhang@sina.com.
Rui WangDepartment of Epidemiology and Biostatistics, Jilin University School of Public Health, 1163 Xinmin Street, Changchun, Jilin 130021, China. ruiwang14@mails.jlu.edu.cn.
Chunshi GaoDepartment of Epidemiology and Biostatistics, Jilin University School of Public Health, 1163 Xinmin Street, Changchun, Jilin 130021, China. jlugcs@126.com.
Yuanyuan SongDepartment of Epidemiology and Biostatistics, Jilin University School of Public Health, 1163 Xinmin Street, Changchun, Jilin 130021, China. songyuanmei00@163.com.
Xin LvDepartment of Epidemiology and Biostatistics, Jilin University School of Public Health, 1163 Xinmin Street, Changchun, Jilin 130021, China. jdhkl_lx@163.com.
Lingling JiangDepartment of Epidemiology and Biostatistics, Jilin University School of Public Health, 1163 Xinmin Street, Changchun, Jilin 130021, China. jianglingling.2008@163.com.
Yaqin YuDepartment of Epidemiology and Biostatistics, Jilin University School of Public Health, 1163 Xinmin Street, Changchun, Jilin 130021, China. pengzhang14@mails.jlu.edu.cn.
Yuhan WangDepartment of Epidemiology and Biostatistics, Jilin University School of Public Health, 1163 Xinmin Street, Changchun, Jilin 130021, China. 8626699shiwo@163.com.
Bo LiDepartment of Epidemiology and Biostatistics, Jilin University School of Public Health, 1163 Xinmin Street, Changchun, Jilin 130021, China. li_bo@jlu.edu.cn.
Jilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular disease (CVD) has become a serious public health problem in recent years in China. Aggregation of CVD risk factors in one individual increases the risk of CVD and the risk increases substantially with each additional risk factor. This study aims to explore the relationship between the number of clustered CVD risk factors and different types of obesity. A multistage stratified random cluster sampling design was used in this population-based cross-sectional study in 2012. Information was collected by face to face interviews. One-way analysis of variance (ANOVA), chi-square test, Kruskal-Wallis test and multiple logistic regression were used in this study. The prevalence of general obesity, central obesity and compound obesity were 0.3%, 36.1% and 14.7%, respectively. The prevalence of hypertension, hyperlipidemia and diabetes in the compound obesity group were higher than those in other groups (compound obesity > central obesity > general obesity > non-obesity), while smoking rate in the non-obesity group was higher than those in other groups (non-obesity > general obesity > central obesity > compound obesity). People with obesity were more likely to have one or more CVD risk factor compared with non-obesity subjects (general obesity (OR: 2.27, 95% CI: 1.13-4.56), central obesity (OR: 2.64, 95% CI: 2.41-2.89), compound obesity (OR: 5.09, 95% CI: 4.38-5.90). The results were similar when the number of clustered CVD risk factors was ≥ 2 and ≥ 3. As a conclusion, more than half of the residents in Jilin Province have a problem of obesity, especially central obesity. Government and health department should take measures to improve people's awareness of central obesity in Jilin Province of China. The prevalence of hypertension, hyperlipidemia and diabetes are associated with obesity types. Compound obesity has a greater risk to cluster multiple CVD risk factors than central obesity and general obesity. Taking measures to control obesity will reduce the prevalence of CVD in Jilin Province.

Indexed as

AdolescentAdultCardiovascular DiseasesChinaCluster AnalysisCross-Sectional StudiesDiabetes MellitusFemaleHumansHyperlipidemiasMaleMiddle AgedObesityPrevalenceRisk FactorsSmokingcardiovascular diseasescentral obesityclusteringcompound obesityobesity type

Identifiers

PMID27399751
PMCPMC4962226
OpenAlexW2461876843

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.