Ruey Leng Loo1,2, Qinwei Lu3, Ellison M Carter4, Si Liu3,5, Sierra Clark6, Yulan Wang7, Jill Baumgartner6, Huiru Tang8,9, Queenie Chan10,11. 1. Centre for Computational and Systems Medicine, Institute of Health Futures, Murdoch University, Perth, WA, Australia. 2. Australian National Phenome Centre, Murdoch University, Perth, WA, Australia. 3. State Key Laboratory of Genetic Engineering, Zhongshan Hospital and School of Life Sciences, Human Phenome Institute, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Fudan University, Shanghai, China. 4. Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, USA. 5. CAS Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Centre for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences (CAS), Wuhan, China. 6. Institute for Health and Social Policy and Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada. 7. Singapore Phenome Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, Experimental Medicine Building, Singapore, Singapore. 8. State Key Laboratory of Genetic Engineering, Zhongshan Hospital and School of Life Sciences, Human Phenome Institute, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Fudan University, Shanghai, China. huiru_tang@fudan.edu.cn. 9. CAS Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Centre for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences (CAS), Wuhan, China. huiru_tang@fudan.edu.cn. 10. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. q.chan@imperial.ac.uk. 11. MRC Centre for Environment and Health, Imperial College London, London, UK. q.chan@imperial.ac.uk.
Abstract
BACKGROUND: Exposure-response studies and policy evaluations of household air pollution (HAP) are limited by current methods of exposure assessment which are expensive and burdensome to participants. METHODS: We collected 152 dried blood spot (DBS) specimens during the heating and non-heating seasons from 53 women who regularly used biomass-burning stoves for cooking and heating. Participants were enrolled in a longitudinal study in China. Untargeted metabolic phenotyping of DBS were generated using ultra-high performance liquid chromatography coupled with mass spectrometry to exemplify measurement precision and assessment for feasibility to detect exposure to HAP, evaluated by season (high pollution vs. low pollution) and measured personal exposure to fine particulate matter <2.5 μm diameters (PM2.5) and black carbon (BC) in the 48-h prior to collecting the DBS specimen. RESULTS: Metabolites e.g., amino acids, acyl-carnitines, lyso-phosphorylcholines, sphinganine, and choline were detected in the DBS specimens. Our approach is capable of detecting the differences in personal exposure to HAP whilst showing high analytical reproducibility, coefficient of variance (CV) <15%, meeting the U.S. Food and Drug Administration criteria. CONCLUSIONS: Our results provide a proof of principle that high-resolution metabolic phenotypic data can be generated using a simple DBS extraction method thus suitable for exposure studies in remote, low-resource settings where the collection of serum and plasma is logistically challenging or infeasible. The analytical run time (19 min/specimen) is similar to most global phenotyping methods and therefore suitable for large-scale application.
BACKGROUND: Exposure-response studies and policy evaluations of household air pollution (HAP) are limited by current methods of exposure assessment which are expensive and burdensome to participants. METHODS: We collected 152 dried blood spot (DBS) specimens during the heating and non-heating seasons from 53 women who regularly used biomass-burning stoves for cooking and heating. Participants were enrolled in a longitudinal study in China. Untargeted metabolic phenotyping of DBS were generated using ultra-high performance liquid chromatography coupled with mass spectrometry to exemplify measurement precision and assessment for feasibility to detect exposure to HAP, evaluated by season (high pollution vs. low pollution) and measured personal exposure to fine particulate matter <2.5 μm diameters (PM2.5) and black carbon (BC) in the 48-h prior to collecting the DBS specimen. RESULTS: Metabolites e.g., amino acids, acyl-carnitines, lyso-phosphorylcholines, sphinganine, and choline were detected in the DBS specimens. Our approach is capable of detecting the differences in personal exposure to HAP whilst showing high analytical reproducibility, coefficient of variance (CV) <15%, meeting the U.S. Food and Drug Administration criteria. CONCLUSIONS: Our results provide a proof of principle that high-resolution metabolic phenotypic data can be generated using a simple DBS extraction method thus suitable for exposure studies in remote, low-resource settings where the collection of serum and plasma is logistically challenging or infeasible. The analytical run time (19 min/specimen) is similar to most global phenotyping methods and therefore suitable for large-scale application.
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