Evidence map›Paper›PMID 39497061›Full record

ArticleBMC public health2024

Associations between various anthropometric indices and hypertension and hyperlipidaemia: a cross-sectional study in China.

Chuyao Feng, Cihang Lu, Kang Chen, Bo Song, Zhongyan Shan, Weiping Teng

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
–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

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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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

6 authors.

Chuyao Feng *Department of Endocrinology and Metabolism, Institute of Endocrinology, National Health Commission Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, The First Hospital of China Medical University, Liaoning Province, Shenyang, 110001, People's Republic of China.
Cihang Lu *Department of Endocrinology and Metabolism, Institute of Endocrinology, National Health Commission Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, The First Hospital of China Medical University, Liaoning Province, Shenyang, 110001, People's Republic of China.
Kang Chen *Department of Ophthalmology, The First Hospital of China Medical University, Liaoning Province, Shenyang, 110001, People's Republic of China.
Bo SongDepartment of Endocrinology and Metabolism, Institute of Endocrinology, National Health Commission Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, The First Hospital of China Medical University, Liaoning Province, Shenyang, 110001, People's Republic of China.
Zhongyan ShanDepartment of Endocrinology and Metabolism, Institute of Endocrinology, National Health Commission Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, The First Hospital of China Medical University, Liaoning Province, Shenyang, 110001, People's Republic of China.
Weiping TengDepartment of Endocrinology and Metabolism, Institute of Endocrinology, National Health Commission Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, The First Hospital of China Medical University, Liaoning Province, Shenyang, 110001, People's Republic of China. twp@vip.163.com.

Funding

The Research Fund for Public Welfare, National Health and Family Planning Commission of China Grant No. 201402005
6 · The paper itself

Abstract

backgroundThis study aims to explore the association and determine the distinguished potential of anthropometric adiposity indices in screening for hypertension and hyperlipidaemia in the Chinese population.

methodsA recent nationwide cross-sectional study, called the Thyroid Disorders, Iodine State, and Diabetes Epidemiological Survey (TIDE 2015-2017), provided the newest data on the relationships between anthropometric adiposity indices and hypertension and hyperlipidaemia and included 65,231 subjects. The area under the curve (AUC) was used to assess the feasibility of using these indices to distinguish hypertension and hyperlipidaemia. After age stratification, a restricted cubic spline (RCS) fitted for generalized linear regression was used to visualize the relationships of the body mass index (BMI), waist circumference (WC), the waist-to-height ratio (WHtR), the body roundness index (BRI), and the "a body shape index" (ABSI) with hypertension and hyperlipidaemia.

resultsThe results showed that there were significant differences in the BMI, WC, the WHtR, the BRI, and the ABSI among the different age groups (P < 0.0001). After adjusting for sex, age, education, income, smoking status, urban or rural residence, and ethnicity in model, The WHtR and BRI had greater discriminatory power in identifying hypertension (AUC = 0.665, 95% confidence interval (CI) 0.660-0.671 for both), hypercholesterolaemia (AUC = 0.629, 95% CI 0.624-0.634 for both), and high low-density lipoprotein cholesterol (LDL-C; AUC = 0.659, 95% CI 0.653-0.664 for both) status in the overall population. When distinguishing hypertriglyceridaemia among the general population, the BMI (AUC = 0.711, 95% CI 0.706-0.716) and WC (AUC = 0.715, 95% CI 0.710-0.720) had greater discriminatory ability than the other anthropometric indices did. The BMI (AUC = 0.631, 95% CI 0.625-0.637) had the highest power for low high-density lipoprotein cholesterol (HDL-C) status in the general population.

conclusionsSeveral anthropometric indices show significant correlation with hypertension and hyperlipidaemia. The WHtR and BRI were better in distinguishing hypertension, hypercholesterolaemia and high LDL-C status, while the BMI was better in hypertriglyceridaemia and low HDL-C status. The use of combined indices, such as the BMI, WC, the WHtR and the BRI, can be included in an individual's medical history and can be used as tools for cardiovascular health screening, which may yield superior results for public health.

Indexed as

AnthropometryHyperlipidemiasHypertensionAdultAgedBody Mass IndexChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedWaist-Height RatioYoung AdultAnthropometric indicesBody mass indexBody roundness indexHyperlipidaemiaHypertensionWaist-to-height ratio

Identifiers

PMID39497061
PMCPMC11536874

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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.