Evidence map›Paper›PMID 27213419›Full record

ArticleInternational journal of environmental research and public health2016

Cross-Sectional Associations between Body Mass Index and Hyperlipidemia among Adults in Northeastern China.

Wenwang Rao, Yingying Su, Guang Yang, Yue Ma, Rui Liu, Shangchao Zhang, Shibin Wang, Yingli Fu, Changgui Kou, Yaqin Yu and 1 more

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 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
6.1field-weighted citation impact, top 3% 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

31 citing papers in PubMed, 68 citations in OpenAlex.

  1. Article
  2. [Identifying genetic etiology of ischemic stroke based on pleiotropy of obesity related genes: A sibling study].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025
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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 1 institution in 2 countries.

Wenwang RaoDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China. raoww14@mails.jlu.edu.cn.
Yingying SuDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China. suyy14@mails.jlu.edu.cn.
Guang YangDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China. 5143086676@163.com.
Yue MaDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China. mayue205@163.com.
Rui LiuDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China. ruiliu15@mails.jlu.edu.cn.
Shangchao ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China. zhangsc15@mails.jlu.edu.cn.
Shibin WangDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China. spiriorwang@126.com.
Yingli FuDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China. fuyingli318@126.com.
Changgui KouDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China. koucg@jlu.edu.cn.
Yaqin YuDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China. yuyaqin5540@163.com.
Qiong YuDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China. yuqiong@jlu.edu.cn.
Jilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is evidence that body mass index (BMI) is closely related to hyperlipidemia. This study aimed to estimate the cross-sectional relationship between Body Mass Index (BMI) and hyperlipidemia.

methodsWe recruited 21,435 subjects (aged 18-79 years and residing in Jilin province, China) using the multistage stratified cluster random sampling method. Subjects were interviewed with a standardized questionnaire and physically examined. We analyzed the cross-sectional relationship between BMI and hyperlipidemia.

resultsThe prevalence of hyperlipidemia was 51.09% (52.04% in male and 50.21% in female). The prevalence of overweight and obesity was 31.89% and 6.23%, respectively. Our study showed that underweight (OR = 0.499, 95% CI: 0.426-0.585), overweight (OR = 2.587, 95% CI: 2.428-2.756), and obesity (OR = 3.614, 95% CI: 3.183-4.104) were significantly associated with hyperlipidemia (p < 0.001) in the age- and sex-adjusted logistic regression. After further adjusting for age, gender, region, district, ethnicity, education, marital status, main occupation, monthly family income per capita, smoking, drinking, exercise, central obesity, waist and hip, underweight (OR = 0.729, 95% CI: 0.616-0.864), overweight (OR = 1.651, 95% CI: 1.520-1.793), and obesity (OR = 1.714, 95% CI: 1.457-2.017) were independently associated with hyperlipidemia (p < 0.001). The restricted cubic spline model illustrated a nonlinear dose-response relationship between levels of BMI and the prevalence of hyperlipidemia (Pnonlinearity < 0.001).

conclusionOur study demonstrated that the continuous variance of BMI was significantly associated with the prevalence of hyperlipidemia.

Indexed as

Body Mass IndexAdultChinaCross-Sectional StudiesExerciseFemaleHumansHyperlipidemiasLogistic ModelsMaleMiddle AgedOdds RatioOverweightPrevalenceRisk FactorsSmokingBMIhyperlipidemiarelationship

Identifiers

PMID27213419
PMCPMC4881141
OpenAlexW2407203763

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.