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Association between volatile organic compounds exposure and cardiometabolic function

A population-based study

Bibliographic Data

ID22086233
AuthorsQiuyu Wang (0000-0001-8571-5715, Wannan Medical College), Yongping Cao (0000-0003-0550-5991), Fei Ma (0000-0002-5472-4763), Hengyang Zhang (0000-0002-1711-2020), Yuelin Hu (0000-0002-3864-5942, Wannan Medical College, corresponding author), Wenwen Xiao (0000-0001-6817-7732, corresponding author)
Year2025
Volume13
Pages1570752-1570752
Publication date2025-04-23
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1570752
PMID40337734
OpenAlexW4409710194
LanguageEN
References cited56

Background: The Cardiometabolic Index (CMI) is a new metric used to assess an individual's cardiovascular and metabolic status. Volatile Organic Compounds (VOCs) are common environmental pollutants associated with cardiovascular diseases and lipid metabolism disorders. This study aims to investigate the relationship between VOC exposure and CMI. Methods: Data from NHANES (2011-2020) were used to assess the impact of VOC exposure on cardiometabolic function. Multivariable linear regression was used to analyze the association between VOCs and the CMI. Restricted cubic spline models were applied to model the nonlinear relationship between VOCs and CMI. BKMR (bayesian kernel machine regression), WQS (weighted quantile sum), and Q-gcomp (quantile g-computation) models were employed to explore the association between VOC mixture exposure and CMI. Subgroup analyses were conducted to investigate the relationship between VOCs and CMI across different subgroups. Results: = 0.28, 95% CI = 0.01, 0.55, P for trend = 0.010). AAMC and SBMA showed a nonlinear relationship with CMI. Results from mixture exposure models indicated that CEMA contributed most significantly to the impact on CMI. BKMR, WQS, and Q-gcomp models showed a positive trend between overall VOC exposure and CMI. Subgroup analysis revealed significant interactions of BMI and diabetes status in the relationship between VOC exposure and CMI, especially among individuals with BMI ≥ 30 and those with diabetes. Conclusion: This study found significant positive associations between specific VOC exposures and CMI. Additionally, BMI and diabetes status play important roles in moderating the relationship between VOC exposure and CMI. These findings highlight the potential impact of environmental VOCs on cardiovascular and metabolic health which provides new evidence for public health interventions

Environmental health · Population · Air Quality and Health Impacts · Carcinogens and Genotoxicity Assessment · Indoor Air Quality and Microbial Exposure · Medicine · Psychology

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