Evidence map›Paper›PMID 39075353›Full record

ArticleBMC public health2024

Association between weight-adjusted waist index and risk of diabetes mellitus type 2 in United States adults and the predictive value of obesity indicators.

XinMeng Li, Dan Zhao, Hongkun Wang

Abstract read
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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 14 papers.

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14citing 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

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3 · Its place in the literature

Who cites it

14 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

3 authors.

XinMeng LiDepartment of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong Province, 256600, China.ORCID 0009-0001-5383-897X
Dan ZhaoDepartment of Anesthesiology, Binzhou Medical University Hospital, Binzhou, Shandong Province, 256600, China.
Hongkun WangDepartment of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong Province, 256600, China. wanghk_byfykfk@126.com.ORCID 0000-0003-0288-5925

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe weight-adjusted waist index (WWI) is a quantitative anthropometric index that can be applied to evaluate obesity. This study examined the relationship between adult United States (US) residents' risk of diabetes mellitus type 2 (T2DM) and WWI.

methodsThe NHANES (National Health and Nutrition Examination Survey) 2001-2018 provided the data for this investigation. This study used multifactorial logistic regression analysis, smoothed curve fitting, subgroup analysis, and interaction tests to assess the association between WWI and T2DM. Additionally, threshold effects were calculated using a two-stage linear regression model. The receiver operating characteristic(ROC) curves evaluated the diagnostic ability of the WWI and commonly used obesity indicators.

results20,477 participants were enrolled in the analysis, and patients with greater levels of WWI had a higher prevalence of T2DM. WWI and T2DM have a non-linear relationship, with a positive association found on the left side of the breakpoint (WWI = 12.35) (OR = 1.82, 95%CI:1.64-2.02), whereas, on the right side, no such relationship was found (OR = 0.9, 95%CI:0.61-1.34). For every unit rise in WWI, the probability of having T2DM increased by 67% after controlling for all other variables (OR:1.67,95%CI:1.53-1.83). Based on subgroup analyses, individuals under 40 had a higher correlation between WWI and T2DM (P < 0.001).ROC analyses showed that WWI had the best discrimination and accuracy in predicting T2DM compared to other obesity indicators (WC, BMI, and Weight).

conclusionHigher WWI values had a higher prevalence of T2DM in US individuals, especially in adults under 40. WWI has the strongest ability to predict T2DM. Therefore, the importance of WWI in the early identification of T2DM in US adults should be emphasized.

Indexed as

Diabetes Mellitus, Type 2Nutrition SurveysObesityAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedPredictive Value of TestsPrevalenceRisk AssessmentRisk FactorsUnited StatesWaist CircumferenceDiabetes mellitus type 2NHANESObesityWeight-adjusted-waist index

Identifiers

PMID39075353
PMCPMC11285432

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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.