Bin Wang

Title: Associate Professor, Doctoral Supervisor

Duty: Vice Dean of Institute of Reproductive and Child Health of Peking University

E-mail: binwang@pku.edu.cn

Address: Xueyuanlu No. 38, Haidian district, Beijing, China

Personal profile

02/2024-to present  Associate Professor: Institute of Reproductive and Child Health Department of Epidemiology and Health Statistics, School of Public Health, Peking University, Beijing, P. R. China

01/2018-01/2024  Assistant Professor: Institute of Reproductive and Child Health Department of Epidemiology and Health Statistics, School of Public Health, Peking University, Beijing, P. R. China

08/2013-12/2017 Assistant Researcher: Institute of Reproductive and Child Health Department of Epidemiology and Health Statistics, School of Public Health, Peking University, Beijing, P. R. China

03/2014-05/2015 Visiting scholar: College of Veterinary Medicine, Michigan State University, East Lansing, MI, USA

09/2008 - 07/2013 PhD, College of Urban and Environmental Sciences, Peking University, Beijing, P.R. China

12/2010 - 12/2011 Visiting PhD candidate, Department of Energy, Environmental & Chemical Engineering, Washington University, St. Louis, MO, USA

09/2004 - 07/2008 BE, Department of Environmental Science and Technology, Dalian University of Technology, Dalian, P.R. China  

My main interest is to adopt multidisciplinary study methods such as environmental chemistry, epidemiology, clinical medicine, and artificial intelligence to explain the migration and transformation processes of environmental pollutants in human body, reveal the impact and biological pathways of regional environmental pollution exposure on population reproductive health and chronic diseases, and construct various models for human health risk assessment and prediction. I created the exposome platform ExposomeX (www.exposomex.cn), building a complex network relationship and machine learning prediction models to explore the link of "Exposome-Biological pathway-Disease". I have presided over three projects from National Natural Science Funding in China (NSFC) as the principal investigator, and participated in three national key research and development projects as backbone member. As the first or corresponding author, I have published a total of 55 papers in international authoritative journals (H-index = 42, he-cited more than 6500 times), e.g., Environ Health Persp, Environ Sci Tech, Environ Sci Tech Lett, The Innovation, and China CDC Weekly. I served as the Topic Editor of “Machine Learning and Public Health” in the TOP journal “Environmental Science & Technology”, as well as editorial board member of the authoritative journals (Environment & Health, Eco-Environmental Health, Hygiene and Environmental Health Advances, China CDC Weekly, and Journal of Environmental Hygiene). I teach the undergraduate course "Exposomics", the postgraduate course "Exposomics", and the international high-end global health public health master's program "Environment & Health". I served as the leader of the "Environment and Population Health" working group of the China Cohort Consortium platform. I won the Second Prize of Science and Technology of Beijing Preventive Medicine Association and the title of "Excellent Individual in the National Science and Technology System to Fight the COVID-19 Pandemic" in China.

Main research directions

Environment Health, Exposome, Bioinformatics and Artificial intelligence

Representative scientific research projects

(1) National Natural Science Foundation of China (NSFC), 42077390, Study on the Transfer Mechanism of Polycyclic Aromatic Hydrocarbons through the Blood-Follicular Barrier and the Influencing Factors among the Women of Childbearing Age, 2021/01-2024/12，570,000 RMB, Principal Investigator, Ongoing.

(2) National Key Research and Development Program: Assessment and Application Demonstration of the Air Pollution Health Effect in Typical Areas, 2023YFC3708305, Sub-project V: "Research on the Tracing the Whole Chain of the Causes in Air Pollution induced Diseases and Evaluating the Disease Burden", Principal Investigator of Sub-project II, 2023/12-2027/11, 360,000 RMB, Principal Investigator, Ongoing.

(3) National Key Research and Development Program: Intergovernmental International Cooperation on Science, Technology and Innovation (China and the United States), Exposure characteristics of typical emerging agrochemicals and their reproductive health risk assessment, 2022YFE0134900, Sub-project I: "Exposure characteristics of typical emerging agrochemicals and their correlation with reproductive health effects", Principal Investigator of Sub-project I, 2023/01-2025/12, 600,000 RMB, Principal Investigator, Ongoing.

(4) National Natural Science Foundation of China (NSFC), 41771527, Study on the External and Internal Exposure Levels of Polycyclic Aromatic Hydrocarbons among Childbearing-age Women in North China, 2018/01-2021/12, 630,000 RMB, Principal Investigator, Completed.

(5) National Key Research and Development Program, 2020YFC0846300, Study on the Epidemiological Characteristics and Evaluation of Prevention and Control Strategies of the COVID-19 Spreading, 2020/04-2021/04, 4,000,000 RMB, Backbone Member, Completed.

(6) National Natural Science Foundation of China (NSFC), 41401583, Urban-Rural Differences in Exposure to Fine Particulate Matter and Levels of Oxidative Stress Damage among Women of Childbearing Age, 2015/01-2017/12, 250,000 RMB, Principal Investigator, Completed.

(7) Undergraduate Teaching Reform Project of Peking University, JG2023137, Teaching Practice of Environmental Health Major Based on Exposome Big Data Platform, 2023/03-2014/02, 40,000 RMB, Principal Investigator, Completed.

10 representative papers

(1) Zhang G, Lin W, Gao N, Lan C, Ren M, Yan L, Pan B, Xu J, Han B, Hu L, Chen Y, Wu Y, Zhuang L*, Qun Lu*, Bin Wang*, Fang M. Using Machine Learning to Construct the Blood−Follicle Distribution Models of Various Trace Elements and Explore the Transport-Related Pathways with Multiomics Data. Environmental Science & Technology. 2024, https://doi.org/10.1021/acs.est.3c10904

(2) Zhao F, Li L, Lin P, Chen Y, Xing S, Du H, Wang Z, Yang J, Huan T, Long C, Zhang L, Wang B*, Fang M*. HExpPredict: In Vivo Exposure Prediction of Human Blood Exposome using A Random Forest Model and Its Application in Chemical Risk Prioritization, Environ Health Perspect, 2023, 131:37009.

(3) Wang B, Pang Y, Li K, Jiang J, Zhu Y, Li Z, Pan B, Zhang L, Zhang Y, Ye R, Li Z*. First evidence on the adverse effect of maternal germanium exposure on fetal neural tube defects, Environ Sci Technol Lett. 2023, 10: 192–197.

(4) Feng Y, Su S, Lin W, Ren M, Gao N, Pan B, Zhang L, Jin L, Zhang Y, Li Z, Ye R, Ren A, Wang B*. Using Machine Learning to Expedite the Screening of Environmental Factors Associated with the Risk of Spontaneous Preterm Birth: From Exposure Mixtures to Key Molecular Events, Environ Sci Technol Lett, 2023, 10, 11, 1036–1044.

(5) Fang M., Hu L., Chen D., Guo Y, Liu J, Lan C, Gong J*, Wang B*. Exposome in human health: Utopia or wonderland? The Innovation, 2021, 2: 100172.

(6) Liu X, Chen D*, Wang B*, Xu F, Pang Y, Zhang L, Zhang Y, Jin L, Li Z, Ren A. Does low maternal exposure to per- and polyfluoroalkyl substances elevate the risk of spontaneous preterm birth? A nested case-control study in China. Environ Sci Technol. 2020, 54: 8259-8268.

(7) Ren M#, Pei R#, Jiangtulu B#, Chen J#, Xue T, Shen S, Yuan X, Li K, Lan C, Chen Z, Chen X, Wang Y, Jia X, Li Z, Rashid A, Prapamontol T, Zhao X, Dong Z, Zhang Y, Zhang L, Ye R, Li Z, Guan W*, Wang B*. Contribution of temperature increase to restrain the transmission of COVID-19 in China. The Innovation, 2020, 2: 100071.

(8) Jia X#, Chen J#, Li L#, Na Jia*, Jiangtulu B, Xue T, Zhang L, Li Z, Ye R, Wang B*, Modeling the prevalence of asymptomatic COVID-19 infections in the Chinese Mainland. The Innovation, 2020, 1: 100026.

(9) Wang B#, Jin L#, Ren A*, Yuan Y, Liu J, Li Z, Zhang L, Yi D, Wang LL, Zhang Y, Wang X, Tao S, Finnell RH. Levels of polycyclic aromatic hydrocarbons in maternal serum and risk of neural tube defects in offspring. Environ Sci Technol, 2015, 49: 588-596.

(10) Wang B, Li K, Jin W, Lu Y, Zhang Y, Shen G, Wang R, Shen H, Li W, Huang Y, Zhang Y, Wang X, Li X, Liu W, Cao H, Tao S*. Properties and inflammatory effects of various size fractions of ambient particulate matter from Beijing on A549 and J774A.1 cells. Environ Sci Technol, 2013, 47: 10583-10590.

Note：*Corresponding author, #Co-first author

Main research progress

Environmental exposure is a significant risk factor affecting human health. However, assessing health risks under conditions of actual multi-pollutant exposure presents considerable challenges, which are closely related to the complexity of exposure scenarios, limitations of assessment methods, and diversity of health outcomes. Emissions of environmental pollutants exhibit typical spatiotemporal heterogeneity, coupled with factors such as component types, exposure pathways, and intake doses, leading to significant bias in exposure assessment. Additionally, the process of pollutants from the external environment to the human body is extremely complex, making it one of the bottlenecks in determining the quantitative relationship between internal and external exposure for effective environmental health risk assessment. Furthermore, among the health outcomes resulting from pollutant exposure, adverse reproductive health outcomes during pregnancy are particularly sensitive. Therefore, determining exposure dose thresholds for such sensitive health endpoints is crucial for efficiently conducting health risk assessments. To address the aforementioned constraints in environmental health risk assessment, the research team selected typical regions with high pollutant emission density and adopted a multidisciplinary research approach. They conducted research based on the regional investigation, focusing on the "Environmental exposure - Internal-external relationship - Reproductive health" paradigm. Typical work includes:

1. We proposed a new approach to select key exposure windows for conducting environmental health research, establishing multiple simultaneous analysis methods for trace pollutants, and demonstrating the reliability of specific exposure biomarkers.  

Exposome is at the forefront of revealing the etiology of human diseases, with its core idea being to consider all environmental exposures throughout the human lifecycle, revealing important factors influencing human health through exposome correlation analysis. Addressing the theoretical and practical challenges it faces, the applicant proposed the idea of conducting exposome research around sensitive population divisions to identify key environmental exposure factors affecting human diseases more easily and to adopt effective disease prevention measures for susceptible populations. However, accurately assessing individual environmental exposure levels poses significant challenges. For example, due to the multitude of environmental exposure factors surrounding the human body, and the fact that most harmful substances are often present at trace levels, the available amount of biological specimens often cannot meet the requirements of instrumental analysis. In addition, the biological sample matrix is complex, leading to serious matrix effects on quantitative analysis of multiple components. The applicant has overcome relevant technical challenges and developed a series of simultaneous analysis methods, such as polycyclic aromatic hydrocarbons (PAHs), organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), inorganic elements, etc., in blood, as well as polycyclic aromatic hydrocarbons, nicotine/cotinine, halogenated endocrine disruptors, inorganic elements, etc., in hair, providing strong support for the development of relevant exposure assessment studies.

2. We elucidated key factors influencing the quantitative relationship between internal and external exposure to typical environmental pollutants in human populations, and constructed statistical models to predict the levels of common pollutants in the human body for a given region. This provides a new method for optimizing the level of regional environmental risk factor control by combining information on internal and external exposure.
The concentration of organic compounds in human blood is an important indicator of internal exposure. Based on the basic assumption that pollutant concentrations in human blood are influenced by factors such as exposure dose, exposure pathway, metabolic characteristics, and physicochemical properties, the research team quantified these factors to develop statistical models for predicting pollutant concentrations in blood. The team constructed a database for assessing organic pollutant exposure in regional populations through extensive literature review. Additionally, high-quality survey data, including Human Exposome and Metabolome Database (HExpMetDB), US Environmental Protection Agency (US-EPA) ExpoCast and ToxCast, National Health and Nutrition Examination Survey (NHANES) from the Centers for Disease Control and Prevention, and the International Agency for Research on Cancer (IARC) Exposome-Explorer, were integrated. In total, data for 10 categories of chemicals were included, such as polycyclic aromatic hydrocarbons (PAHs), perfluoroalkyl substances (PFASs), flame retardants, pesticides, phthalates, and bisphenols.

The study found that exposure intake, exposure pathway, volume of distribution in the body, and metabolic half-life were the four most important parameters affecting the predictive performance of the models. Therefore, machine learning models were established to predict the concentrations of these 10 categories of organic compounds (a total of 8074 compounds), obtaining important model parameters (Environ Health Perspect 2023, 131, 37009). Currently, the International Agency for Research on Cancer (IARC) has established the world's largest exposome database, providing information on the internal and external exposure relationships for 274 substances. Compared to previous research efforts, this study represents a significant breakthrough in terms of the number of pollutants, key influencing factors, and model accuracy. Professor Courtney Carignan from Michigan State University commented in the Environmental Health Perspectives journal that "predicting the concentrations of thousands of pollutants in blood seems almost impossible, but the research team innovatively provides clever computational methods" (Environ Health Perspect. 2023, 131:31304).

3. We quantitatively assessed the association between pollution exposure levels in pregnant women in high-pollution areas and adverse reproductive health outcomes, particularly providing key evidence of the impact of maternal exposure to germanium and polycyclic aromatic hydrocarbons (PAHs) on fetal neural tube development.

The relationship between environmental pollution and human health risks is complex, and the accuracy of related research results is influenced by various factors, such as the diversity of environmental exposures, the reliability of exposure biomarkers, and the complexity of pathogenic mechanisms. To address these challenges, the research team focused on four aspects, including (1) spatial differences: conducting comparative analyses involving regionally representative populations, particularly considering the regional representativeness of pollutant emissions; (2) sensitive outcomes: selecting important reproductive indicators for pregnant women as health endpoints; (3) critical windows: emphasizing the selection of biological specimens from critical exposure time windows; and (4) biological evidence: comprehensively utilizing information from animal experimental models, reliable exposure biomarkers, and high-quality biological information databases to elucidate influencing mechanisms. Based on these approaches, the research team conducted multiple applied case studies in regions characterized by high pollutant emissions, quantitatively assessing the relationship between maternal exposure to PAHs, PFASs, metal elements, and adverse reproductive health outcomes. Four representative works are as follows:

(1) In regions with high PAH emissions in Hebei and Shanxi, a case-control study was conducted, revealing a clear dose-response relationship between maternal serum PAH concentrations and the risk of fetal neural tube defects (NTDs), with high-molecular-weight PAHs showing a more pronounced trend of increased risk compared to low-molecular-weight PAHs (Environ Sci Technol, 2015, 49: 588-596).

(2) In heavily polluted areas of Hebei and Shanxi, advantages of hair sample analysis were utilized to investigate the exposure characteristics of pregnant women during the critical time window of neural tube closure. It was found that the content of germanium in maternal hair during early pregnancy was significantly associated with the risk of fetal NTDs, with consistent conclusions observed in two local populations. Additionally, using other independent birth cohorts for validation, it was suggested that maternal exposure to germanium may increase the risk of fetal NTDs by increasing DNA oxidative damage levels or reducing immune function. Furthermore, animal model validation showed that the content of germanium in rat hair effectively indicated its intake level, demonstrating the reliability of hair germanium as an indicator of environmental intake dose. Overall, this study provided important evidence of the relationship between germanium and NTDs (Environ Sci Technol Lett. 2023, 10: 192–197).

(3) In Shanxi, Beijing, and Shandong, a cohort study was conducted among women of childbearing age, revealing that at current exposure levels, PFASs did not increase the risks of early miscarriage and spontaneous preterm birth, two important reproductive health outcomes. However, at the molecular level, it was found that even at lower PFASs exposure levels, they may inhibit the expression of crucial immune factors (interleukin-8 and monocyte chemoattractant protein-1) (Environ Sci Technol. 2020, 54:8259-8268).

(4) Integrating information on PFASs, inorganic elements, immune effects, oxidative damage markers, and lipid characteristics (a total of 1080 exposure data points), multiple omics machine learning models were constructed to predict the risk of spontaneous preterm birth, screening relevant important biological pathways and molecular events, particularly revealing the significant contributions of glycerophospholipids and 21-carbon fatty acids (C21:0) in predicting the risk of spontaneous preterm birth (Environ Sci Technol Lett, 2023, 10, 11, 1036–1044).

Future work

Based on the regional emission characteristics of new pollutants in China, representative regions will be selected to recruit women of childbearing age and conduct assessments of regional features of exposure to new pollutants and their impacts on reproductive health. Additionally, utilizing big data from exposome studies and artificial intelligence technology, a "exposome-biological pathway-disease" network model will be constructed to address the following scientific questions:

(1) What are the typical regional characteristics of exposure to new pollutants among women of childbearing age, and what are the influencing factors?

(2) How can we predict the regional internal exposure levels of new pollutants among women of childbearing age?

(3) How can we evaluate the combined effects of these typical new pollutants on the reproductive health of women of childbearing age?