Evidence map›Paper›PMID 39028628›Full record

ArticleEnvironmental health perspectives2024

Association between Personal Abiotic Airborne Exposures and Body Composition Changes among Healthy Adults (60-69 Years Old): A Combined Exposome-Wide and Lipidome Mediation Approach from the China BAPE Study.

Peijie Sun, Xiaojie Guo, Enmin Ding, Chenfeng Li, Huimin Ren, Yibo Xu, Jiankun Qian, Fuchang Deng, Wanying Shi, Haoran Dong and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environmental health perspectives, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Global research trends on the human exposome: a bibliometric analysis (2005-2024).Environmental science and pollution research international · 2025
    Review
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

20 authors.

Peijie SunChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiaojie GuoChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Enmin DingChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Chenfeng LiChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Huimin RenChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Yibo XuChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Jiankun QianChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Fuchang DengChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Wanying ShiDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
Haoran DongChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Elizabeth Z LinDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA.
Pengfei GuoDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA.
Jianlong FangChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Qian ZhangChinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China.
Wenhua ZhaoChinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China.
Shilu TongChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiaobo LuDepartment of Toxicology, School of Public Health, China Medical University, Shenyang, China.
Krystal J Godri PollittDepartment of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA.
Xiaoming ShiChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0002-7071-571X
Song TangChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0003-3219-1422

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEvidence suggested that abiotic airborne exposures may be associated with changes in body composition. However, more evidence is needed to identify key pollutants linked to adverse health effects and their underlying biomolecular mechanisms, particularly in sensitive older adults.

objectivesOur research aimed to systematically assess the relationship between abiotic airborne exposures and changes in body composition among healthy older adults, as well as the potential mediating mechanisms through the serum lipidome.

methodsFrom September 2018 to January 2019, we conducted a monthly survey among 76 healthy adults (60-69 years old) in the China Biomarkers of Air Pollutant Exposure (BAPE) study, measuring their personal exposures to 632 abiotic airborne pollutions using MicroPEM and the Fresh Air wristband, 18 body composition indicators from the InBody 770 device, and lipidomics from venous blood samples. We used an exposome-wide association study (ExWAS) and deletion/substitution/addition (DSA) model to unravel complex associations between exposure to contaminant mixtures and body composition, a Bayesian kernel machine regression (BKMR) model to assess the overall effect of key exposures on body composition, and mediation analysis to identify lipid intermediators.

resultsThe ExWAS and DSA model identified that 2,4,5-T methyl ester (2,4,5-TME), 9,10-Anthracenedione (ATQ), 4b,8-dimethyl-2-isopropylphenanthrene, and 4b,5,6,7,8,8a,9,10-octahydro-(DMIP) were associated with increased body fat mass (BFM), fat mass indicators (FMI), percent body fat (PBF), and visceral fat area (VFA) in healthy older adults [Bonferroni-Hochberg false discovery rate DISCUSSION: Based on the evidence from multiple model results, ATQ and DMIP were statistically significantly associated with the increased VFA levels of healthy older adults, potentially regulated through lipid intermediators. These findings may have important implications for identifying potentially harmful environmental chemicals and developing targeted strategies for the control and prevention of chronic diseases in the future, particularly as the global population is rapidly aging. https://doi.org/10.1289/EHP13865.

Indexed as

Air PollutantsBody CompositionEnvironmental ExposureExposomeLipidomicsAgedAir PollutionBiomarkersChinaFemaleHumansMaleMiddle AgedAir PollutantsBiomarkers

Identifiers

PMID39028628
PMCPMC11259245

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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.