Evidence map›Paper›PMID 27545898›Full record

ArticleScientific reports2016

Body mass index and waist circumference combined predicts obesity-related hypertension better than either alone in a rural Chinese population.

Ming Zhang, Yang Zhao, Guoan Wang, Hongyan Zhang, Yongcheng Ren, Bingyuan Wang, Lu Zhang, Xiangyu Yang, Chengyi Han, Chao Pang and 3 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed, 60 citations in OpenAlex.

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  5. Predictors of High Obesity in Rural Nicaragua: A Cross-Sectional Study.International journal of environmental research and public health · 2025
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  9. Correlation between obesity-related indices and hypertension.American journal of translational research · 2024
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  17. Hypertensive patients' knowledge and practices on lifestyle modification in Extension 6, Middelburg.South African family practice : official journal of the South African Academy of Family Practice/Primary Care · 2022
    Article
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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

13 authors at 3 institutions in 1 country.

Ming ZhangDepartment of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
Yang ZhaoDepartment of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
Guoan WangDepartment of Pharmacology, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
Hongyan ZhangDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Yongcheng RenDepartment of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
Bingyuan WangDepartment of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
Lu ZhangDepartment of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
Xiangyu YangDepartment of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
Chengyi HanDepartment of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
Chao PangDepartment of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China.
Lei YinDepartment of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China.
Jingzhi ZhaoDepartment of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, Henan, People's Republic of China.
Dongsheng HuDepartment of Preventive Medicine, Shenzhen University Health Sciences Center, Shenzhen, Guangdong, People's Republic of China.
Zhengzhou University · CNShenzhen University Health Science Center · CNShenzhen Luohu People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Limited information is available on the association of obesity defined by both body mass index (BMI) and waist circumference (WC) with incident hypertension in rural China. A total of 9,174 participants ≥18 years old from rural areas in middle of China, free of hypertension, diabetes, myocardial infarction and stroke, were selected in this cohort study. Questionnaire interview and anthropometric and laboratory measurements were performed at baseline (2007-2008) and follow-up (2013-2014). During the 6 years of follow-up, hypertension developed in 733/3,620 men and 1,051/5,554 women. After controlling for age, education level, smoking, drinking, physical activity, and family history of hypertension, the relative risk of hypertension was lower for participants with high BMI but normal WC than those with both BMI and WC obesity for men 18-39 and 40-59 years old. Women 18-39 years old with normal BMI but high WC showed a 1.96-fold risk of hypertension, and being female with age 40-59 years and high BMI but normal WC was independently associated with hypertension incidence as compared with both normal BMI and WC. BMI is more associated with hypertension as compared with WC in both genders. High WC tends to add additional risk of hypertension in young women.

Indexed as

Body Mass IndexWaist CircumferenceAdolescentAdultChinaCohort StudiesFemaleFollow-Up StudiesHumansHypertensionIncidenceMaleMiddle AgedObesityRisk FactorsRural Population

Identifiers

PMID27545898
PMCPMC4992958
OpenAlexW2513758163

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LicenceCC BY
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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.