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Sun P, Guo X, Ding E, Li C, Ren H, Xu Y, Qian J, Deng F, Shi W, Dong H, Lin EZ, Guo P, Fang J, Zhang Q, Zhao W, Tong S, Lu X, Pollitt KJG, Shi X, Tang S. 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. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:77005. [PMID: 39028628 PMCID: PMC11259245 DOI: 10.1289/ehp13865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 05/25/2024] [Accepted: 06/24/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND Evidence 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. OBJECTIVES Our 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. METHODS From 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. RESULTS The 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 ( FD R BH ) < 0.05 ]. The BKMR model demonstrated a positive correlation between contaminants (anthracene, ATQ, copaene, di-epi-α -cedrene, and DMIP) with VFA. Mediation analysis revealed that phosphatidylcholine [PC, PC(16:1e/18:1), PC(16:2e/18:0)] and sphingolipid [SM, SM(d18:2/24:1)] mediated a significant portion, ranging from 12.27% to 26.03% (p-value < 0.05 ), of the observed increase in VFA. 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.
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Affiliation(s)
- Peijie Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Toxicology, School of Public Health, China Medical University, Shenyang, China
| | - Xiaojie Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Enmin Ding
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chenfeng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Occupational Health and Environment Health, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Huimin Ren
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Toxicology, School of Public Health, China Medical University, Shenyang, China
| | - Yibo Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, China
| | - Jiankun Qian
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Toxicology, School of Public Health, China Medical University, Shenyang, China
| | - Fuchang Deng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Wanying Shi
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Haoran Dong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Elizabeth Z. Lin
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Pengfei Guo
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Jianlong Fang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qian Zhang
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
| | - Wenhua Zhao
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
| | - Shilu Tong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Xiaobo Lu
- Department of Toxicology, School of Public Health, China Medical University, Shenyang, China
| | - Krystal J. Godri Pollitt
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Song Tang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
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Bartolini ME, Wilson K, Raja M, Raymer GH, Thompson RT, Webber CE, Moran GR. Dual X-ray absorptiometry model for characterizing water in the human forearm using multiple frequency bioimpedance analysis. Can J Physiol Pharmacol 2006; 84:181-93. [PMID: 16900944 DOI: 10.1139/y05-125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The purpose of this study was to develop a method for measuring intracellular (ICW) and extracellular water (ECW) in the human forearm using multiple frequency bioimpedance analysis (MFBIA). The approach was (i) to measure whole-body and forearm fat-free mass using dual X-ray absorptiometry (DXA); (ii) to use these measurements to estimate the fat-free mass (FFM) resistivity in both the forearm and in the whole body; and (iii) to use the ratio of these FFM resistivities to estimate the resistivity in the ICW and ECW compartments of the forearm. To first demonstrate the accuracy of the DXA software in differentiating lean body mass from fat and bone within a volume of tissue, ex-vivo bovine muscle tissue samples (n = 3) were used to approximate the physical properties of the human forearm. It was found that although the human whole-body software overestimates FFM, it was slightly underestimated by the small animal software. Using this technique, DXA measures of FFM were obtained from human volunteers (n = 11; age = 20 ± 5 years; height = 170 ± 12 cm; mass = 64 ± 16 kg). These measures were used in conjunction with MFBIA measures of impedance of the whole body and of the forearm to determine the resistivities of the ICW and ECW compartments of the forearm, namely 375.8 ± 25.2 Ωcm and 55.6 ± 3.7 Ωcm, respectively. These were used in MFBIA equations to calculate the ICW, ECW, and total arm water (TAW) volumes of the human forearm. The calculated TAW and the ECW (± SD) volume fraction (667.29 ± 200.15 mL and 0.169 ± 0.039 mL, respectively) were in agreement with literature values. MFBIA results were compared with those obtained using nuclear magnetic resonance relaxometry (NMRR). MFBIA was performed on 15 subjects before and after an intense maximal handgrip exercise to estimate changes in water volume in muscle. Following exercise, the total and intracellular water of the forearm increased on average by 8% ± 3% and 10% ± 4% (mean ± SD), respectively. In 5 healthy volunteers, MFBIA and NMRR were performed before and after a similar exercise of the forearm muscle. The changes with exercise of intracellular and total arm water volumes as measured by MFBIA were estimated. The percent increases in total water were found to be 9.4% ± 4.2% and 9.4% ± 2.6% and in intracellular water were found to be 10.6% ± 4.6% and 12.0% ± 2.8% (mean ± SD) for NMRR and MFBIA, respectively. The results show that the exercise-induced changes in ICW and TAW determined with the MFBIA model are consistent with those observed with NMRR and radiotracer literature.
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Affiliation(s)
- Miria E Bartolini
- Medical Physics and Applied Radiation Sciences Unit, McMaster University, 1280 Main St. W, Hamilton, ON L8S 4K1, Canada
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