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Record W4386116203 · doi:10.2196/49652

Association Between Urinary Bisphenols and Body Composition Among American Adults: Cross-Sectional National Health and Nutrition Examination Survey Study

2023· article· en· W4386116203 on OpenAlexvenueno aff
Jiakun Li, Zilong Zhang, Chichen Zhang, Qiyu Zhu, Jing Zhao, Hui Zong, Qi Deng, Jiaming Zheng, Erman Wu, Rongrong Wu, Tong Tang, Yihang Zhang, Qiaosen Dong, Yifan Li, Jiao Wang, Lu Yang, Shi Qiu, Bairong Shen, Qiang Wei

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsnot available
FundersSichuan University
KeywordsNational Health and Nutrition Examination SurveyQuartileMedicineCross-sectional studyLean body massBody mass indexConfoundingEnvironmental healthPopulationInternal medicineConfidence intervalPathologyBody weight

Abstract

fetched live from OpenAlex

BACKGROUND: Bisphenol A (BPA), bisphenol S (BPS), and bisphenol F (BPF) are widely used in various consumer products. They are environmental contaminants with estrogenic properties that have been linked to various health outcomes. Understanding their impact on body composition is crucial for identifying potential health risks and developing preventive strategies. However, most current studies have only focused on their relationship with BMI. OBJECTIVE: This study aimed to investigate the association between urinary levels of BPA, BPS, and BPF and body composition, including BMI, lean mass, and fat mass, in a large population-based sample. METHODS: We conducted a cross-sectional analysis using data from the National Health and Nutrition Examination Survey 2003-2016. Body composition data were assessed using dual-energy X-ray absorptiometry, which provided precise measurements of lean mass, fat mass, and other indicators. We used multivariate linear regression models to estimate the associations, adjusting for potential confounders such as age, gender, race, socioeconomic factors, and lifestyle variables. RESULTS: The results revealed significant associations between bisphenol exposure and body composition. After adjusting for covariates, BPS showed a positive association with BMI, with quartiles 3 and 4 having 0.91 (95% CI 0.34-1.48) and 1.15 (95% CI 0.55-1.74) higher BMI, respectively, compared with quartile 1 (P<.001). BPA was negatively associated with total lean mass (TLM) and appendicular lean mass, with quartiles 2, 3, and 4 having -7.85 (95% CI -11.44 to -4.25), -12.33 (95% CI -16.12 to -8.54), and -11.08 (95% CI -15.16 to -7.01) lower TLM, respectively, compared with quartile 1 (P<.001). BPS was negatively associated with TLM, with quartiles 3 (β=-10.53, 95% CI -16.98 to -4.08) and 4 (β=-11.14, 95% CI -17.83 to -4.45) having significantly lower TLM (P=.005). Both BPA and BPS showed a positive dose-response relationship with trunk fat (BPA: P=.002; BPS: P<.001) and total fat (BPA: P<.001; BPS: P=.01). No significant association was found between BPF and any body composition parameter. CONCLUSIONS: This large-sample study highlights the associations between urinary levels of BPA and BPS and alterations in body composition, including changes in lean mass, fat mass, and regional fat distribution. These findings underscore the importance of understanding the potential health risks associated with bisphenol exposure and emphasize the need for targeted interventions to mitigate adverse effects on body composition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.375
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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