Evidence map›Paper›PMID 29416119›Full record

ArticleJournal of human hypertension2018

Comparison of body mass index, waist circumference, conicity index, and waist-to-height ratio for predicting incidence of hypertension: the rural Chinese cohort study.

Xu Chen, Yu Liu, Xizhuo Sun, Zhaoxia Yin, Honghui Li, Kunpeng Deng, Cheng Cheng, Leilei Liu, Xinping Luo, Ruiyuan Zhang and 17 more

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Journal of human hypertension, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
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

22 citing papers in PubMed.

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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

27 authors.

Xu ChenDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Yu LiuThe Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
Xizhuo SunThe Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
Zhaoxia YinThe Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
Honghui LiThe Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
Kunpeng DengYantian Entry-exit Inspection and Quarantine Bureau, Shenzhen, Guangdong, People's Republic of China.
Cheng ChengDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Leilei LiuDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Xinping LuoDepartment of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
Ruiyuan ZhangDepartment of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
Feiyan LiuDepartment of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
Qionggui ZhouDepartment of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
Chongjian WangDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Linlin LiDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Lu ZhangDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Bingyuan WangDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Yang ZhaoDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Junmei ZhouDepartment of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
Chengyi HanDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 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.
Xiangyu YangDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 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.
Tianping FengDepartment 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.
Ming ZhangDepartment of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, Guangdong, People's Republic of China.
Dongsheng HuDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China. hud@szu.edu.cn.ORCID 0000-0002-9998-8041

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study compared the ability of body mass index (BMI), waist circumference (WC), conicity index, and waist-to-height ratio (WHtR) to predict incident hypertension and to identify the cutoffs of obesity indices for predicting hypertension in rural Chinese adults. This prospective cohort study recruited 9905 participants aged 18-70 years during a median follow-up of 6 years in rural China. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to assess the association, predictive ability, and optimal cutoffs (in terms of hypertension risk factors) of the four obesity indices: BMI, WC, conicity index, and WHtR. The 6-year cumulative incidence of hypertension was 19.89% for men and 18.68% for women, with a significant upward trend of increased incident hypertension with increasing BMI, WC, conicity index, and WHtR (P for trend < 0.001) for both men and women. BMI and WHtR had the largest area under the ROC curve for identifying hypertension for both genders. The optimal cutoff values for BMI, WC, conicity index, and WHtR for predicting hypertension were 22.65 kg/m

Indexed as

Body Mass IndexWaist CircumferenceWaist-Height RatioAdultChinaFemaleHumansHypertensionIncidenceMaleMiddle AgedObesityProspective StudiesROC CurveRural Population

Identifiers

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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.