Evidence map›Paper›PMID 37081416›Full record

ArticleBMC cardiovascular disorders2023

Predicting hypertension by obesity- and lipid-related indices in mid-aged and elderly Chinese: a nationwide cohort study from the China Health and Retirement Longitudinal Study.

Yuqing Li, Jiaofeng Gui, Xiaoyun Zhang, Ying Wang, Yujin Mei, Xue Yang, Haiyang Liu, Lei-Lei Guo, Jinlong Li, Yunxiao Lei and 11 more

Open access · goldAbstract read
In one paragraph

Article in BMC cardiovascular disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
9.9field-weighted citation impact, top 1% 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

27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.

  1. Pooled it
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  6. Body Roundness Index Mediates the Association Between Triglyceride-Glucose Index and Hypertension: A Cross-Sectional Study.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2026
    Article
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  10. Assessment of six surrogate insulin resistance indexes for predicting hypertension risk in rural Chinese adults.Hypertension research : official journal of the Japanese Society of Hypertension · 2025
    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

21 authors at 3 institutions in 1 country.

Yuqing LiDepartment of Graduate School, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Jiaofeng GuiDepartment of Graduate School, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Xiaoyun ZhangDepartment of Graduate School, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Ying WangDepartment of Graduate School, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Yujin MeiDepartment of Graduate School, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Xue YangDepartment of Graduate School, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Haiyang LiuStudent Health Center, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Lei-Lei GuoDepartment of Surgical Nursing, School of Nursing, Jinzhou Medical University, No.40, Section 3, Songpo Road, Linghe District, Jinzhou City, Liaoning Province, People's Republic of China.
Jinlong LiDepartment of Occupational and Environmental Health, Key Laboratory of Occupational Health and Safety for Coal Industry in Hebei Province, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, People's Republic of China.
Yunxiao LeiObstetrics and Gynecology Nursing, School of Nursing, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Xiaoping LiDepartment of Emergency and Critical Care Nursing, School of Nursing, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Lu SunDepartment of Emergency and Critical Care Nursing, School of Nursing, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Liu YangDepartment of Internal Medicine Nursing, School of Nursing, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Ting YuanObstetrics and Gynecology Nursing, School of Nursing, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Congzhi WangDepartment of Internal Medicine Nursing, School of Nursing, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Dongmei ZhangDepartment of Pediatric Nursing, School of Nursing, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Huanhuan WeiObstetrics and Gynecology Nursing, School of Nursing, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Jing LiDepartment of Surgical Nursing, School of Nursing, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Mingming LiuDepartment of Surgical Nursing, School of Nursing, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Ying HuaRehabilitation Nursing, School of Nursing, Wanna Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China.
Lin ZhangDepartment of Internal Medicine Nursing, School of Nursing, Wannan Medical College, 22 Wenchang West Road, Higher Education Park, Wuhu City, An Hui Province, People's Republic of China. yaoran2008@163.com.
Wannan Medical College · CNJinzhou Medical University · CNNorth China University of Science and Technology · CN

Funding

China Health and Retirement Longitudinal StudyR01AG037031 · NIA · PEKING UNIVERSITY · PI STRAUSS, JOHN A, WANG, YAFENG · 2010 to 2024
$15.1M
Studies of health, retirement and living arrangements of the aging population inR03TW008358 · FIC · PEKING UNIVERSITY · PI STRAUSS, JOHN A, ZHAO, YAOHUI · 2009 to 2011
$152k
FIC NIH HHS R03 TW008358NIA NIH HHS R01 AG037031
6 · The paper itself

Abstract

backgroundCurrently, the study outcomes of anthropometric markers to predict the risk of hypertension are still inconsistent due to the effect of racial disparities. This study aims to investigate the most effective predictors for screening and prediction of hypertension (HTN) in the Chinese middle-aged and more elderly adult population and to predict hypertension using obesity and lipid-related markers in Chinese middle-aged and older people.

methodsThe data for the cohort study came from the China Health and Retirement Longitudinal Study (CHARLS), including 4423 middle-aged and elderly people aged 45 years or above. We examined 13 obesity- and lipid-related indices, including waist circumference (WC), body mass index (BMI), waist-height ratio (WHtR), visceral adiposity index (VAI), a body shape index (ABSI), body roundness index (BRI), lipid accumulation product index (LAP), conicity index (CI), Chinese visceral adiposity index (CVAI), triglyceride-glucose index (TyG-index) and their combined indices (TyG-BMI, TyG-WC, TyG-WHtR). To compare the capacity of each measure to forecast the probability of developing HTN, the receiver operating characteristic curve (ROC) was used to determine the usefulness of anthropometric indices for screening for HTN in the elderly and determining their cut-off value, sensitivity, specificity, and area under the curve (AUC). Association analysis of 13 obesity-related anthropometric indicators with HTN was performed using binary logistic regression analysis.

resultsDuring the four years, the incident rates of HTN in middle-aged and elderly men and women in China were 22.08% and 17.82%, respectively. All the above 13 indicators show a modest predictive power (AUC > 0.5), which is significant for predicting HTN in adults (middle-aged and elderly people) in China (P < 0.05). In addition, when WHtR = 0.501 (with an AUC of 0.593, and sensitivity and specificity of 63.60% and 52.60% respectively) or TYg-WHtR = 4.335 (with an AUC of 0.601, and sensitivity and specificity of 58.20% and 59.30% respectively), the effect of predicting the incidence risk of men is the best. And when WHtR = 0.548 (with an AUC of 0.609, and sensitivity and specificity of 59.50% and 56.50% respectively) or TYg-WHtR = 4.781(with an AUC of 0.617, and sensitivity and specificity of 58.10% and 60.80% respectively), the effect of predicting the incidence risk of women is the best.

conclusionsThe 13 obesity- and lipid-related indices in this study have modest significance for predicting HTN in Chinese middle-aged and elderly patients. WHtR and Tyg-WHtR are the most cost-effective indicators with moderate predictive value of the development of HTN.

Indexed as

HypertensionObesityAgedBody Mass IndexChinaCohort StudiesEast Asian PeopleFemaleHumansLipidsLongitudinal StudiesMaleMiddle AgedPredictive Value of TestsRisk FactorsWaist CircumferenceLipidsCohort studyHypertensionMiddle-aged and elderlyReceiver operating characteristic curve

Identifiers

PMID37081416
PMCPMC10120116
OpenAlexW4366586879

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LicenceCC BY
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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.