Evidence map›Paper›PMID 42566560›Full record

ArticleMedicine2026

Relationship of weight-adjusted waist index and body mass index with infertility: A NHANES 2015 to 2018 cross-sectional study.

Siqi Zhang, Baoyi Liao, Yulu Zhou, Yixuan He, Junjie Cao, Xiaoming Huang

Abstract read
In one paragraph

Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Siqi ZhangSchool of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, China.
Baoyi LiaoSchool of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, China.
Yulu ZhouSchool of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, China.
Yixuan HeSchool of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, China.
Junjie CaoSchool of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, China.
Xiaoming HuangDepartment of Clinical Nutrition for Consultation, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, China.ORCID 0000-0002-8448-8140

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Obesity is a significant risk factor for female infertility, representing a growing global health concern. Body mass index (BMI) has traditionally been used as the standard measure of obesity; however, the weight-adjusted waist index (WWI) has recently appeared as a novel metric that may provide a more accurate assessment of obesity. This study aims to investigate the association of both BMI and WWI with female infertility. The "2015-2018 National Health and Nutrition Examination Survey" (NHANES) was utilized to derive the data. The study included 2393 women between the ages of 20 and 50 years. Among them, 251 were diagnosed with infertility. Multivariate logistic regression, subgroup analysis, interaction testing, smooth curve fitting, and receiver operating characteristic curve analysis were conducted to examine the associations of WWI and BMI with infertility. Among women, the infertility's prevalence was positively associated with both WWI and BMI levels. Higher WWI was significantly related with raised infertility prevalence (OR = 3.63, 95% CI: 1.96-6.73), as was higher BMI (OR = 2.27, 95% CI: 1.29-4.01). In the WWI subgroup analysis, the association was consistently observed in most subgroups, except for age, race, and education level. In the BMI subgroup analysis, the association was consistent across all subgroups. In the population-based cross-sectional data of this study, WWI and BMI were positively associated with the odds of infertility among US women. This finding suggests that obesity-related measures may have potential value in clinical practice and may help to identify potentially high-risk groups of women with infertility. In addition, the existing results suggest that weight management-related interventions may have potential effects on fertility outcomes, but whether they can reduce the infertility rate, explain the biological mechanism, and guide clinical decision-making needs to be verified by prospective, mechanistic, and interventional trials in the future.

Indexed as

Body Mass IndexBody WeightInfertility, FemaleObesityWaist CircumferenceAdultCross-Sectional StudiesFemaleHumansMiddle AgedNutrition SurveysPrevalenceRisk FactorsYoung Adultbody mass indexinfertilityNHANESobesityweight-adjusted-waist index

Identifiers

PMID42566560
PMCPMC13456793

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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.