Evidence map›Paper›PMID 37658310›Full record

ArticleBMC public health2023

Association between the weight-adjusted waist index and stroke: a cross-sectional study.

Jiayi Ye, Yanjie Hu, Xinrong Chen, Zhe Yin, Xingzhu Yuan, Liping Huang, Ka Li

Abstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.

0numbers the graph read from it
0cells of the map it votes in
85citing 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

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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

85 citing papers in PubMed.

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25 more citing papers are in PubMed but not listed here.

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

7 authors.

Jiayi Ye *West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China.
Yanjie Hu *West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China.
Xinrong ChenWest China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China.
Zhe YinWest China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China.
Xingzhu YuanWest China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China.
Liping HuangWest China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China.
Ka LiWest China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China. lika127@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe weight-adjusted waist index (WWI) is a new measure of obesity, and this study aimed to determine the association between the WWI and stroke.

methodsUsing the National Health and Nutrition Examination Survey (NHANES) 2011-2020 dataset, cross-sectional data from 23,389 participants were analysed. The correlation between the WWI and stroke was investigated through multivariate logistic regression and smoothing curve fitting. Subgroup analysis and interaction tests were also carried out.

resultsThe research involved 23,389 participants, of whom 893 (3.82%) had a stroke. The fully adjusted model revealed a positive correlation between the WWI and stroke [1.25 (1.05, 1.48)]. Individuals who were in the highest quartile of WWI exhibited a 62% higher likelihood of experiencing a stroke than those in the lowest quartile [1.62 (1.06, 2.48)]. Subgroup analysis and interaction tests revealed that this positive correlation was similar in different population settings (all P for interaction  > 0.05).

conclusionA higher WWI was associated with a higher prevalence of stroke. The results of this study underscore the value of the WWI in stroke prevention and management.

Indexed as

StrokeCross-Sectional StudiesHumansNutrition SurveysObesityProbabilityCross-sectional studyNHANESObesityStrokeWeight-adjusted waist index

Identifiers

PMID37658310
PMCPMC10472709

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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.