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Bai X, Zhou Z, Zheng Z, Li Y, Liu K, Zheng Y, Yang H, Zhu H, Chen S, Pan H. Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy. BMC Med Inform Decis Mak 2024; 24:174. [PMID: 38902714 PMCID: PMC11188254 DOI: 10.1186/s12911-024-02556-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 05/28/2024] [Indexed: 06/22/2024] Open
Abstract
INTRODUCTION The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no prediction model yet. MATERIAL AND METHODS The data were collected from the National Free Preconception Health Examination Project in China. A sum of 455 neonates (42 SGA births and 423 non-LGA births) were included. A training set (n = 319) and a test set (n = 136) were created from the dataset at random. To develop prediction models for LGA neonates, conventional logistic regression (LR) method and six machine learning methods were used in this study. Recursive feature elimination approach was performed by choosing 10 features which made a big contribution to the prediction models. And the Shapley Additive Explanation model was applied to interpret the most important characteristics that affected forecast outputs. RESULTS The random forest (RF) model had the highest average area under the receiver-operating-characteristic curve (AUC) for predicting LGA in the test set (0.843, 95% confidence interval [CI]: 0.714-0.974). Except for the logistic regression model (AUC: 0.603, 95%CI: 0.440-0.767), other models' AUCs displayed well. Thereinto, the RF algorithm's final prediction model using 10 characteristics achieved an average AUC of 0.821 (95% CI: 0.693-0.949). CONCLUSION The prediction model based on machine learning might be a promising tool for the prenatal prediction of LGA births in women with radiation exposure before pregnancy.
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Affiliation(s)
- Xi Bai
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Department of Endocrinology, Ministry of Education, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
| | - Zhibo Zhou
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
| | - Zeyan Zheng
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
| | - Yansheng Li
- DHC Mediway Technology CO., Ltd, Beijing, China
| | - Kejia Liu
- DHC Mediway Technology CO., Ltd, Beijing, China
| | | | - Hongbo Yang
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
| | - Huijuan Zhu
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
| | - Shi Chen
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
| | - Hui Pan
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China.
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Bhat AA, Afzal M, Goyal A, Gupta G, Thapa R, Almalki WH, Kazmi I, Alzarea SI, Shahwan M, Paudel KR, Ali H, Sahu D, Prasher P, Singh SK, Dua K. The impact of formaldehyde exposure on lung inflammatory disorders: Insights into asthma, bronchitis, and pulmonary fibrosis. Chem Biol Interact 2024; 394:111002. [PMID: 38604395 DOI: 10.1016/j.cbi.2024.111002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/27/2024] [Accepted: 04/07/2024] [Indexed: 04/13/2024]
Abstract
Lung inflammatory disorders are a major global health burden, impacting millions of people and raising rates of morbidity and death across many demographic groups. An industrial chemical and common environmental contaminant, formaldehyde (FA) presents serious health concerns to the respiratory system, including the onset and aggravation of lung inflammatory disorders. Epidemiological studies have shown significant associations between FA exposure levels and the incidence and severity of several respiratory diseases. FA causes inflammation in the respiratory tract via immunological activation, oxidative stress, and airway remodelling, aggravating pre-existing pulmonary inflammation and compromising lung function. Additionally, FA functions as a respiratory sensitizer, causing allergic responses and hypersensitivity pneumonitis in sensitive people. Understanding the complicated processes behind formaldehyde-induced lung inflammation is critical for directing targeted strategies aimed at minimizing environmental exposures and alleviating the burden of formaldehyde-related lung illnesses on global respiratory health. This abstract explores the intricate relationship between FA exposure and lung inflammatory diseases, including asthma, bronchitis, allergic inflammation, lung injury and pulmonary fibrosis.
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Affiliation(s)
- Asif Ahmad Bhat
- School of Pharmacy, Suresh Gyan Vihar University, Jagatpura, 302017, Mahal Road, Jaipur, India
| | - Muhammad Afzal
- Department of Pharmaceutical Sciences, Pharmacy Program, Batterjee Medical College, P.O. Box 6231, Jeddah, 21442, Saudi Arabia
| | - Ahsas Goyal
- Institute of Pharmaceutical Research, GLA University, Mathura, U.P., India
| | - Gaurav Gupta
- School of Pharmacy, Graphic Era Hill University, Dehradun, 248007, India; Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates.
| | - Riya Thapa
- School of Pharmacy, Suresh Gyan Vihar University, Jagatpura, 302017, Mahal Road, Jaipur, India
| | - Waleed Hassan Almalki
- Department of Pharmacology, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Imran Kazmi
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, 21589, Jeddah, Saudi Arabia
| | - Sami I Alzarea
- Department of Pharmacology, College of Pharmacy, Jouf University, 72341, Sakaka, Aljouf, Saudi Arabia
| | - Moyad Shahwan
- Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates; Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, 346, United Arab Emirates
| | - Keshav Raj Paudel
- Centre for Inflammation, Centenary Institute and University of Technology Sydney, Faculty of Science, School of Life Sciences, Sydney, NSW, 2050, Australia
| | - Haider Ali
- Centre for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, India; Department of Pharmacology, Kyrgyz State Medical College, Bishkek, Kyrgyzstan
| | - Dipak Sahu
- Department of Pharmacology, Amity University, Raipur, Chhattisgarh, India
| | - Parteek Prasher
- Department of Chemistry, University of Petroleum & Energy Studies, Energy Acres, Dehradun, 248007, India
| | - Sachin Kumar Singh
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab, 144411, India; Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW, 2007, Australia; School of Medical and Life Sciences, Sunway University, 47500 Sunway City, Malaysia
| | - Kamal Dua
- Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW, 2007, Australia; Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, NSW, 2007, Australia; Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, India.
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Takaguchi K, Nakaoka H, Tsumura K, Eguchi A, Shimatani K, Nakayama Y, Matsushita T, Ishizaka T, Kawashima A, Mori C, Suzuki N. The association between clustering based on composition of volatile organic compound in indoor air and building-related symptoms. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170197. [PMID: 38253092 DOI: 10.1016/j.scitotenv.2024.170197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/26/2023] [Accepted: 01/14/2024] [Indexed: 01/24/2024]
Abstract
Volatile organic compounds (VOCs) have been suspected to cause building-related symptoms (BRSs). Although some studies investigated the association between BRSs and VOCs in indoor air, those results were inconsistent. This study investigated the contamination status of VOCs in the indoor air of 154 houses in Japan. Additionally, these samples were grouped by hierarchical clustering analysis based on the VOC composition, and the relationship between a VOC cluster and the BRSs was investigated. The median concentration of the sum of VOCs (ΣVOCs) was 140 μg m-3 (range: 18-3500 μg m-3). The levels of acetaldehyde in four samples and p-dichlorobenzene in one sample exceeded the guideline value. As a result of the hierarchical clustering analysis, the samples in this study were divided into six characteristic clusters based on the VOC composition. The ΣVOCs in cluster 1 were significantly lower than those in other clusters. In cluster 2, acyclic and aromatic hydrocarbons were dominant. Cluster 3 had a relatively high proportion of limonene. In cluster 4, the concentrations and composition ratios of α-pinene were higher than those of other clusters. In cluster 5, p-dichlorobenzene accounted for 42 %-72 % of the total VOCs. Cluster 6 had a relatively high proportion of decamethyl cyclopentasiloxane. This clustering likely depended on the construction of houses and lifestyles. As a result of logistic regression analysis, cluster 5 was associated with the cough symptoms of the BRSs. The results of the present study suggest that investigating the association between VOCs and BRSs is necessary to consider not only total concentrations such as TVOC and ΣVOCs but also VOC composition.
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Affiliation(s)
- Kohki Takaguchi
- Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan.
| | - Hiroko Nakaoka
- Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
| | - Kayo Tsumura
- Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
| | - Akifumi Eguchi
- Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
| | - Keiichi Shimatani
- Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
| | - Yoshitake Nakayama
- Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
| | - Takafumi Matsushita
- Graduate School of Agriculture, Ehime University, 3-5-7, Tarumi, Matsuyama, Ehime 790-8566, Japan
| | - Takahiro Ishizaka
- Graduate School of Agriculture, Ehime University, 3-5-7, Tarumi, Matsuyama, Ehime 790-8566, Japan
| | - Ayato Kawashima
- Graduate School of Agriculture, Ehime University, 3-5-7, Tarumi, Matsuyama, Ehime 790-8566, Japan
| | - Chisato Mori
- Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan; Department of Bioenvironmental Medicine, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-0856, Japan
| | - Norimichi Suzuki
- Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
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Tang J, Huang J, He X, Zou S, Gong L, Yuan Q, Peng Z. The prediction of in-hospital mortality in elderly patients with sepsis-associated acute kidney injury utilizing machine learning models. Heliyon 2024; 10:e26570. [PMID: 38420451 PMCID: PMC10901004 DOI: 10.1016/j.heliyon.2024.e26570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
Background Sepsis-associated acute kidney injury (SA-AKI) is a severe complication associated with poorer prognosis and increased mortality, particularly in elderly patients. Currently, there is a lack of accurate mortality risk prediction models for these patients in clinic. Objectives This study aimed to develop and validate machine learning models for predicting in-hospital mortality risk in elderly patients with SA-AKI. Methods Machine learning models were developed and validated using the public, high-quality Medical Information Mart for Intensive Care (MIMIC)-IV critically ill database. The recursive feature elimination (RFE) algorithm was employed for key feature selection. Eleven predictive models were compared, with the best one selected for further validation. Shapley Additive Explanations (SHAP) values were used for visualization and interpretation, making the machine learning models clinically interpretable. Results There were 16,154 patients with SA-AKI in the MIMIC-IV database, and 8426 SA-AKI patients were included in this study (median age: 77.0 years; female: 45%). 7728 patients excluded based on these criteria. They were randomly divided into a training cohort (5,934, 70%) and a validation cohort (2,492, 30%). Nine key features were selected by the RFE algorithm. The CatBoost model achieved the best performance, with an AUC of 0.844 in the training cohort and 0.804 in the validation cohort. SHAP values revealed that AKI stage, PaO2, and lactate were the top three most important features contributing to the CatBoost model. Conclusion We developed a model capable of predicting the risk of in-hospital mortality in elderly patients with SA-AKI.
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Affiliation(s)
- Jie Tang
- Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
| | - Jian Huang
- Department of Diagnostic Ultrasound and Echocardiography, Sir Run Run Shaw Hospital, Zhejiang University College of Medicine, Hangzhou, Zhejiang, China
| | - Xin He
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Sijue Zou
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Li Gong
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qiongjing Yuan
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National International Joint Research Center for Medical Metabolomices, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Medical Research Center for Geriatric Diseases, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhangzhe Peng
- Organ Fibrosis Key Lab of Hunan Province, Central South University, Changsha, Hunan, China
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National International Joint Research Center for Medical Metabolomices, Xiangya Hospital, Central South University, Changsha, Hunan, China
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Yan M, Zhu H, Luo H, Zhang T, Sun H, Kannan K. Daily Exposure to Environmental Volatile Organic Compounds Triggers Oxidative Damage: Evidence from a Large-Scale Survey in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:20501-20509. [PMID: 38033144 DOI: 10.1021/acs.est.3c06055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Volatile organic compounds (VOCs) are ubiquitous environmental pollutants and have been implicated in adverse health outcomes. In this study, concentrations of 11 VOC metabolites (mVOCs) and three oxidative stress biomarkers (8-oxo-7,8-dihydro-2'-deoxyguanosine, 8-oxo-7,8-dihydro-guanosine, and dityrosine) were determined in 205 urine samples collected from 12 cities across mainland China. Urinary ∑11mVOC concentrations ranged from 498 to 1660 ng/mL, with a geometric mean (GM) value of 1070 ng/mL. The factorial analysis revealed that cooking, solvents, and vehicle emissions were the three primary sources of VOC exposure. A significant regional variation was clearly found in ∑11mVOC concentrations across four regions in China, with high urine VOC concentrations found in North and South China (GM: 1450 and 1340 ng/mL). The multiple linear regression model revealed that most mVOCs were significantly positively correlated with three oxidative stress markers (β range: 0.06-0.22). Mixture effect regression showed that isoprene, crotonaldehyde, acrolein, and benzene were the strongest contributors to oxidative stress. Approximately 80% of the participants have HQ values greater than 1.0 for 1,3-butadiene and benzene, suggesting that their exposure doses were close to potential adverse health effects. Our findings provide comprehensive information on human exposure and potential health risks of VOCs in China.
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Affiliation(s)
- Mengqi Yan
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Hongkai Zhu
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Haining Luo
- Center for Reproductive Medicine, Tianjin Central Hospital of Gynecology Obstetrics, Tianjin 300100, China
| | - Tao Zhang
- School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
| | - Hongwen Sun
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Kurunthachalam Kannan
- Wadsworth Center, New York State Department of Health, Albany, New York 12237, United States
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Tokuda N, Ishikawa R, Yoda Y, Araki S, Shimadera H, Shima M. Association of air pollution exposure during pregnancy and early childhood with children's cognitive performance and behavior at age six. ENVIRONMENTAL RESEARCH 2023; 236:116733. [PMID: 37507042 DOI: 10.1016/j.envres.2023.116733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/21/2023] [Accepted: 07/23/2023] [Indexed: 07/30/2023]
Abstract
BACKGROUND The impact of air pollution on neurodevelopment in children has attracted much attention in recent times. We aim to clarify the association between prenatal and postnatal air pollutant exposure and children's cognitive performance and behavior at age six. METHODS This study was conducted based on a birth cohort study in Japan. Children's intelligence quotient (IQ) was assessed using the Wechsler Intelligence Scale for Children and a score <85 was deemed as low intelligence. A score ≥60 on the Child Behavior Checklist indicated behavioral problems. Exposure to outdoor fine particulate matter (PM2.5) during pregnancy and early childhood was estimated using a spatiotemporal model, while indoor concentrations of air pollutants inside subjects' homes were measured for a week when the child was of ages 1.5 and 3. The associations of exposure to air pollution during pregnancy and after childbirth with cognitive performance and behavior were analyzed using logistic regression models. RESULTS The estimated exposure to outdoor PM2.5 during pregnancy and early childhood was not associated with decreased cognitive performance. However, exposure during the first trimester, 0-1 and 3-5 years of age was associated with children's externalizing problems (odds ratios (ORs) were 2.77 [95% confidence interval (CI): 1.05-7.29], 1.66 [95%CI: 1.05-2.62], and 1.80 [95%CI: 1.19-2.74] per interquartile range (IQR) increase, respectively). Exposure to indoor PM2.5 and coarse particles after childbirth was associated with lower full scale IQ (ORs were 1.46 [95%CI: 1.03-2.08] and 1.85 [95%CI: 1.12-3.07] per IQR increase, respectively). However, some inverse associations were also observed. CONCLUSIONS These results suggest associations between prenatal and postnatal exposure to outdoor air pollution and behavioral problems, and between indoor air pollution after childbirth and cognitive performance at age six. However, the effects of exposure to outdoor PM2.5 during pregnancy on cognitive performance were not observed.
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Affiliation(s)
- Narumi Tokuda
- Department of Public Health, School of Medicine, Hyogo Medical University, Nishinomiya, 663-8501, Japan; Hyogo Regional Center for the Japan Environment and Children's Study, Hyogo Medical University, Nishinomiya, 663-8501, Japan
| | - Rina Ishikawa
- Hyogo Regional Center for the Japan Environment and Children's Study, Hyogo Medical University, Nishinomiya, 663-8501, Japan
| | - Yoshiko Yoda
- Department of Public Health, School of Medicine, Hyogo Medical University, Nishinomiya, 663-8501, Japan
| | - Shin Araki
- Graduate School of Engineering, Osaka University, Suita, 565-0871, Japan
| | - Hikari Shimadera
- Graduate School of Engineering, Osaka University, Suita, 565-0871, Japan
| | - Masayuki Shima
- Department of Public Health, School of Medicine, Hyogo Medical University, Nishinomiya, 663-8501, Japan; Hyogo Regional Center for the Japan Environment and Children's Study, Hyogo Medical University, Nishinomiya, 663-8501, Japan.
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Jung CR, Chen WT, Young LH, Hsiao TC. A hybrid model for estimating the number concentration of ultrafine particles based on machine learning algorithms in central Taiwan. ENVIRONMENT INTERNATIONAL 2023; 175:107937. [PMID: 37088007 DOI: 10.1016/j.envint.2023.107937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 05/03/2023]
Abstract
Modeling is a cost-effective measure to estimate ultrafine particle (UFP) levels. Previous UFP estimates generally relied on land-use regression with insufficient temporal resolution. We carried out in-situ measurements for UFP in central Taiwan and developed a model incorporating satellite-based measurements, meteorological variables, and land-use data to estimate daily UFP levels at a 1-km resolution. Two sampling campaigns were conducted for measuring hourly UFP concentrations at six sites between 2008-2010 and 2017-2021, respectively, using scanning mobility particle sizers. Three machine learning algorithms, namely random forest, eXtreme gradient boosting (XGBoost), and deep neural network, were used to develop UFP estimation models. The performances were evaluated with a 10-fold cross-validation, temporal, and spatial validation. A total of 1,022 effective sampling days were conducted. The XGBoost model had the best performance with a training coefficient of determination (R2) of 0.99 [normalized root mean square error (nRMSE): 6.52%] and a cross-validation R2 of 0.78 (nRMSE: 31.0%). The ten most important variables were surface pressure, distance to the nearest road, temperature, calendar year, day of the year, NO2, meridional wind, the total length of roads, PM2.5, and zonal wind. The UFP levels were elevated along the main roads across different seasons, suggesting that traffic emission is an important contributor to UFP. This hybrid model outperformed prior land use regression models and thus can provide more accurate estimates of UFP for epidemiological studies.
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Affiliation(s)
- Chau-Ren Jung
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan; Japan Environment and Children's Study Programme Office, Health and Environmental Risk Division, National Institute for Environmental Studies, Tsukuba, Japan.
| | - Wei-Ting Chen
- Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
| | - Li-Hao Young
- Department of Occupational Safety and Health, China Medical University, Taichung, Taiwan
| | - Ta-Chih Hsiao
- Graduate Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan
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Baeza_Romero MT, Dudzinska MR, Amouei Torkmahalleh M, Barros N, Coggins AM, Ruzgar DG, Kildsgaard I, Naseri M, Rong L, Saffell J, Scutaru AM, Staszowska A. A review of critical residential buildings parameters and activities when investigating indoor air quality and pollutants. INDOOR AIR 2022; 32:e13144. [PMID: 36437669 PMCID: PMC9828800 DOI: 10.1111/ina.13144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/27/2022] [Accepted: 10/08/2022] [Indexed: 06/16/2023]
Abstract
Indoor air in residential dwellings can contain a variety of chemicals, sometimes present at concentrations or in combinations which can have a negative impact on human health. Indoor Air Quality (IAQ) surveys are often required to characterize human exposure or to investigate IAQ concerns and complaints. Such surveys should include sufficient contextual information to elucidate sources, pathways, and the magnitude of exposures. The aim of this review was to investigate and describe the parameters that affect IAQ in residential dwellings: building location, layout, and ventilation, finishing materials, occupant activities, and occupant demography. About 180 peer-reviewed articles, published from 01/2013 to 09/2021 (plus some important earlier publications), were reviewed. The importance of the building parameters largely depends on the study objectives and whether the focus is on a specific pollutant or to assess health risk. When considering classical pollutants such as particulate matter (PM) or volatile organic compounds (VOCs), the building parameters can have a significant impact on IAQ, and detailed information of these parameters needs to be reported in each study. Research gaps and suggestions for the future studies together with recommendation of where measurements should be done are also provided.
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Affiliation(s)
- María Teresa Baeza_Romero
- Universidad de Castilla‐La Mancha. Dpto. Química‐Física, Escuela de Ingeniería Industrial y AeroespacialToledoSpain
| | | | - Mehdi Amouei Torkmahalleh
- Division of Environmental and Occupational Health Sciences, School of Public HealthUniversity of Illinois ChicagoChicagoIllinoisUSA
- Department of Chemical and Materials Engineering, School of Engineering and Digital SciencesNazarbayev UniversityAstanaKazakhstan
| | - Nelson Barros
- UFP Energy, Environment and Health Research Unit (FP‐ENAS)University Fernando PessoaPortoPortugal
| | - Ann Marie Coggins
- School of Natural Sciences & Ryan InstituteNational University of IrelandGalwayIreland
| | - Duygu Gazioglu Ruzgar
- School of Mechanical EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Metallurgical and Materials Engineering DepartmentBursa Technical UniversityBursaTurkey
| | | | - Motahareh Naseri
- Department of Chemical and Materials Engineering, School of Engineering and Digital SciencesNazarbayev UniversityAstanaKazakhstan
| | - Li Rong
- Department of Civil and Architectural EngineeringAarhus UniversityAarhus CDenmark
| | | | | | - Amelia Staszowska
- Faculty of Environmental EngineeringLublin University of TechnologyLublinPoland
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Wang X, Han X, Fan L, Li L, Wang C, Gong S, Qi J, Ge T, Liu H, Li X, Cao Y, Liu M, Wang Q, Su L, Yao X, Wang X. The relationship of residential formaldehyde pollution in 11 Chinese cities to schoolchildren pneumonia prevalence in actual living condition. ENVIRONMENTAL RESEARCH 2022; 214:114162. [PMID: 36027964 DOI: 10.1016/j.envres.2022.114162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/14/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
Residential formaldehyde pollution is one of the leading residential harmful pollutants with a large production and consumption globally and remains much uncertainty in Chinese families with huge health burden for children worldwide. A multi-center observation study from 11 cities was conducted to investigate residential formaldehyde pollution levels measured by phenol reagent spectrophotometry. Data on household characteristics and schoolchildren's health were collected by questionnaire. The median concentration of residential formaldehyde was 0.025 (0.002-0.281) mg/m3 among 11 cities with the total exceeding standard rate of 7.40% according to the reference value of 0.10 mg/m3 (1-h average). Residential formaldehyde pollution in warm season, bedrooms and northern cities was more serious than that in cold season, living rooms and southern cities, respectively. The potential influencing factors of residential formaldehyde included household characteristics (distance from a traffic road, building history, residence duration, window glass layers, decoration and furniture) and use of air conditioner. The positive regulation effect of temperature on residential formaldehyde was explored with the approximately turning-point temperature of 28.9 °C for peak concentration. Long-term exposure to residential formaldehyde of low concentrations (0.010-0.090 mg/m3) would increase the prevalence of childhood pneumonia and a more stringent criteria value for residential formaldehyde should be discussed cautiously.
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Affiliation(s)
- Xinqi Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Xu Han
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Lin Fan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Li Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Chong Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Shuhan Gong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Jing Qi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Tanxi Ge
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Hang Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Xu Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Yun Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Mengmeng Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Qin Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Liqin Su
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Xiaoyuan Yao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
| | - Xianliang Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli Road, Chaoyang District, Beijing, 100021, PR China.
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10
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Bai X, Zhou Z, Su M, Li Y, Yang L, Liu K, Yang H, Zhu H, Chen S, Pan H. Predictive models for small-for-gestational-age births in women exposed to pesticides before pregnancy based on multiple machine learning algorithms. Front Public Health 2022; 10:940182. [PMID: 36003638 PMCID: PMC9394741 DOI: 10.3389/fpubh.2022.940182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/19/2022] [Indexed: 11/25/2022] Open
Abstract
Background The association between prenatal pesticide exposures and a higher incidence of small-for-gestational-age (SGA) births has been reported. No prediction model has been developed for SGA neonates in pregnant women exposed to pesticides prior to pregnancy. Methods A retrospective cohort study was conducted using information from the National Free Preconception Health Examination Project between 2010 and 2012. A development set (n = 606) and a validation set (n = 151) of the dataset were split at random. Traditional logistic regression (LR) method and six machine learning classifiers were used to develop prediction models for SGA neonates. The Shapley Additive Explanation (SHAP) model was applied to determine the most influential variables that contributed to the outcome of the prediction. Results 757 neonates in total were analyzed. SGA occurred in 12.9% (n = 98) of cases overall. With an area under the receiver-operating-characteristic curve (AUC) of 0.855 [95% confidence interval (CI): 0.752–0.959], the model based on category boosting (CatBoost) algorithm obtained the best performance in the validation set. With the exception of the LR model (AUC: 0.691, 95% CI: 0.554–0.828), all models had good AUCs. Using recursive feature elimination (RFE) approach to perform the feature selection, we included 15 variables in the final model based on CatBoost classifier, achieving the AUC of 0.811 (95% CI: 0.675–0.947). Conclusions Machine learning algorithms can develop satisfactory tools for SGA prediction in mothers exposed to pesticides prior to pregnancy, which might become a tool to predict SGA neonates in the high-risk population.
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Affiliation(s)
- Xi Bai
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Zhibo Zhou
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | | | - Yansheng Li
- DHC Mediway Technology Co., Ltd, Beijing, China
| | | | - Kejia Liu
- DHC Mediway Technology Co., Ltd, Beijing, China
| | - Hongbo Yang
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Huijuan Zhu
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Shi Chen
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
- *Correspondence: Hui Pan
| | - Hui Pan
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
- Shi Chen
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Taniguchi Y, Yamazaki S, Nakayama SF, Sekiyama M, Michikawa T, Isobe T, Iwai-Shimada M, Kobayashi Y, Takagi M, Kamijima M. Baseline Complete Blood Count and Chemistry Panel Profile from the Japan Environment and Children's Study (JECS). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063277. [PMID: 35328967 PMCID: PMC8951634 DOI: 10.3390/ijerph19063277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/04/2022] [Accepted: 03/05/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND The Japan Environment and Children's Study (JECS) is a nationwide birth cohort study of environmental factors affecting children's health and development. We summarize the complete blood count and chemistry panel statistical data from pregnant women enrolled in JECS. METHODS Statistical data of up to 58,056 mother's sample in their first (gestational age ≤ 13 weeks) and second trimester (22-27 weeks) were calculated. RESULTS Mean (SD) values in the first trimester were: white blood cell count, 7866 (1906)/μL; red blood cell count, 417 (33) 104/μL; hemoglobin, 12.4 (1.0) g/dL; hematocrit, 37.1 (2.6)%; mean corpuscular volume, 89.2 (4.7) fL; mean corpuscular hemoglobin, 29.8 (1.9) pg; mean corpuscular hemoglobin concentration, 33.3 (0.9)%; platelet count, 24.8 (5.2) 104/μL; HbA1c, 5.26 (0.26)%; total cholesterol, 181 (28) mg/dL; low density lipoprotein cholesterol, 95 (23) mg/dL; high density lipoprotein cholesterol, 73 (13) mg/dL; free cholesterol, 41 (7) mg/dL; triglycerides, 109 (47) mg/dL; total protein, 6.9 (0.4) g/dL; and albumin, 4.1 (0.2) g/dL. Mean values (SD) in the second trimester were: total cholesterol, 246 (38) mg/dL; free cholesterol, 61 (9) mg/dL; triglycerides, 183 (70) mg/dL; total protein, 6.5 (0.4) g/dL; and albumin, 3.6 (0.2) g/dL. CONCLUSIONS These data will be useful for future JECS studies.
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Affiliation(s)
- Yu Taniguchi
- Japan Environment and Children’s Study Programme Office, National Institute for Environmental Studies, Tsukuba 305-8506, Japan; (Y.T.); (S.F.N.); (M.S.); (T.I.); (M.I.-S.); (Y.K.); (M.T.)
| | - Shin Yamazaki
- Japan Environment and Children’s Study Programme Office, National Institute for Environmental Studies, Tsukuba 305-8506, Japan; (Y.T.); (S.F.N.); (M.S.); (T.I.); (M.I.-S.); (Y.K.); (M.T.)
- Correspondence: ; Tel.: +81-(29)-850-2226
| | - Shoji F. Nakayama
- Japan Environment and Children’s Study Programme Office, National Institute for Environmental Studies, Tsukuba 305-8506, Japan; (Y.T.); (S.F.N.); (M.S.); (T.I.); (M.I.-S.); (Y.K.); (M.T.)
| | - Makiko Sekiyama
- Japan Environment and Children’s Study Programme Office, National Institute for Environmental Studies, Tsukuba 305-8506, Japan; (Y.T.); (S.F.N.); (M.S.); (T.I.); (M.I.-S.); (Y.K.); (M.T.)
| | - Takehiro Michikawa
- Department of Environmental and Occupational Health, Toho University School of Medicine, Ota 143-8540, Japan;
| | - Tomohiko Isobe
- Japan Environment and Children’s Study Programme Office, National Institute for Environmental Studies, Tsukuba 305-8506, Japan; (Y.T.); (S.F.N.); (M.S.); (T.I.); (M.I.-S.); (Y.K.); (M.T.)
| | - Miyuki Iwai-Shimada
- Japan Environment and Children’s Study Programme Office, National Institute for Environmental Studies, Tsukuba 305-8506, Japan; (Y.T.); (S.F.N.); (M.S.); (T.I.); (M.I.-S.); (Y.K.); (M.T.)
| | - Yayoi Kobayashi
- Japan Environment and Children’s Study Programme Office, National Institute for Environmental Studies, Tsukuba 305-8506, Japan; (Y.T.); (S.F.N.); (M.S.); (T.I.); (M.I.-S.); (Y.K.); (M.T.)
| | - Mai Takagi
- Japan Environment and Children’s Study Programme Office, National Institute for Environmental Studies, Tsukuba 305-8506, Japan; (Y.T.); (S.F.N.); (M.S.); (T.I.); (M.I.-S.); (Y.K.); (M.T.)
| | - Michihiro Kamijima
- Department of Occupational and Environmental Health, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan;
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Cova CM, Rincón E, Espinosa E, Serrano L, Zuliani A. Paving the Way for a Green Transition in the Design of Sensors and Biosensors for the Detection of Volatile Organic Compounds (VOCs). BIOSENSORS 2022; 12:51. [PMID: 35200311 PMCID: PMC8869180 DOI: 10.3390/bios12020051] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/13/2022] [Accepted: 01/18/2022] [Indexed: 05/06/2023]
Abstract
The efficient and selective detection of volatile organic compounds (VOCs) provides key information for various purposes ranging from the toxicological analysis of indoor/outdoor environments to the diagnosis of diseases or to the investigation of biological processes. In the last decade, different sensors and biosensors providing reliable, rapid, and economic responses in the detection of VOCs have been successfully conceived and applied in numerous practical cases; however, the global necessity of a sustainable development, has driven the design of devices for the detection of VOCs to greener methods. In this review, the most recent and innovative VOC sensors and biosensors with sustainable features are presented. The sensors are grouped into three of the main industrial sectors of daily life, including environmental analysis, highly important for toxicity issues, food packaging tools, especially aimed at avoiding the spoilage of meat and fish, and the diagnosis of diseases, crucial for the early detection of relevant pathological conditions such as cancer and diabetes. The research outcomes presented in the review underly the necessity of preparing sensors with higher efficiency, lower detection limits, improved selectivity, and enhanced sustainable characteristics to fully address the sustainable manufacturing of VOC sensors and biosensors.
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Affiliation(s)
- Camilla Maria Cova
- Department of Chemistry, University of Florence and CSGI, Via della Lastruccia 3, 50019 Sesto Fiorentino, FI, Italy;
| | - Esther Rincón
- BioPren Group, Inorganic Chemistry and Chemical Engineering Department, Faculty of Sciences, University of Cordoba, 14014 Cordoba, Spain; (E.R.); (E.E.); (L.S.)
| | - Eduardo Espinosa
- BioPren Group, Inorganic Chemistry and Chemical Engineering Department, Faculty of Sciences, University of Cordoba, 14014 Cordoba, Spain; (E.R.); (E.E.); (L.S.)
| | - Luis Serrano
- BioPren Group, Inorganic Chemistry and Chemical Engineering Department, Faculty of Sciences, University of Cordoba, 14014 Cordoba, Spain; (E.R.); (E.E.); (L.S.)
| | - Alessio Zuliani
- Department of Chemistry, University of Florence and CSGI, Via della Lastruccia 3, 50019 Sesto Fiorentino, FI, Italy;
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Madaniyazi L, Jung CR, Fook Sheng Ng C, Seposo X, Hashizume M, Nakayama SF. Early life exposure to indoor air pollutants and the risk of neurodevelopmental delays: The Japan Environment and Children's Study. ENVIRONMENT INTERNATIONAL 2022; 158:107004. [PMID: 34991264 DOI: 10.1016/j.envint.2021.107004] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 11/07/2021] [Accepted: 11/22/2021] [Indexed: 06/14/2023]
Abstract
Air pollution has been associated with childhood neurodevelopment. However, the role of indoor air pollution, especially volatile organic compounds (VOCs), on childhood neurodevelopment has been poorly explored to date. We investigated the association between indoor air pollutants and childhood neurodevelopment in 5,017 randomly selected children from the Japan Environment and Children's Study. When the participants reached 1.5 and 3 years of age, they were followed up with home visits and neurodevelopmental tests using the Ages and Stages Questionnaire (ASQ). At both ages, we collected indoor air samples for 1 week and measured 13 indoor air pollutants: particulate matter with an aerodynamic diameter of ≤2.5 μm, ozone, nitrogen dioxide, sulfur dioxide, and nine VOCs. The associations between air pollutants and ASQ scores were estimated using linear mixed effects models and weighted quantile sum regressions (WQS) at each age separately. Stratified analysis by sex was conducted. Exposure to m,p-xylene at the age of 3 was associated with lower communication, fine motor, and overall ASQ scores (coefficients: -0.18 [99% confidence intervals (CI): -0.35, -0.02], -0.23 [99 %CI: -0.43, -0.03], and - 0.72 [99 %CI: -1.41, -0.04] per 1 µg/m3 increase, respectively). Exposure to o-xylene at the age of 3 was associated with lower communication, gross motor, fine motor, and overall ASQ scores (coefficients: -0.48 [99 %CI: -0.90, -0.07], -0.45 [99 %CI: -0.78, -0.13], -0.65 [99 %CI: -1.14, -0.16], and -2.15 [99 %CI: -3.83, -0.47] per 1 µg/m3 increase, respectively). The WQS index was associated with lower gross motor ASQ scores at the age of 3 (coefficient: -0.27 [95 %CI: -0.51, -0.03] for one-unit WQS index increases), which was attributed to benzene (33.96%), toluene (26.02%), o-xylene (13.62%), and ethylbenzene (9.83%). Stratified analysis showed similar results. Although further investigations are required, our results suggest an association of neurodevelopmental delays with indoor low-level exposure to m,p-xylene and o-xylene in early life.
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Affiliation(s)
- Lina Madaniyazi
- Department of Pediatric Infectious Diseases, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan; School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan; Japan Environment and Children's Study Programme Office, National Institute for Environmental Studies, Tsukuba, Japan
| | - Chau-Ren Jung
- Japan Environment and Children's Study Programme Office, National Institute for Environmental Studies, Tsukuba, Japan; Department of Public Health, China Medical University, Taiwan
| | - Chris Fook Sheng Ng
- Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Xerxes Seposo
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Masahiro Hashizume
- Department of Pediatric Infectious Diseases, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan; Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shoji F Nakayama
- Japan Environment and Children's Study Programme Office, National Institute for Environmental Studies, Tsukuba, Japan.
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Urinary Metabolites of Organophosphate Pesticides among Pregnant Women Participating in the Japan Environment and Children's Study (JECS). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18115929. [PMID: 34073036 PMCID: PMC8199379 DOI: 10.3390/ijerph18115929] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 12/17/2022]
Abstract
Organophosphate pesticides (OPPs) exhibit neurodevelopmental toxicity. To evaluate the effect of prenatal exposure to OPPs in the Japan Environment and Children's Study, a nationally representative birth cohort study, 4575 maternal urine samples were analysed for six OPP metabolites, i.e., dialkylphosphates (DAPs). This study aimed to investigate predictors of urinary DAPs using machine learning approaches and to assess the cumulative risk based on relative potency factors among Japanese pregnant women. The median creatinine-normalised urinary concentrations (interquartile ranges) of dimethylphosphate, dimethylthiophosphate and diethylphosphate, which had a detection rate of 50% or higher, were 3.53 (1.91-6.78), 4.09 (1.66-10.8) and 3.28 (1.88-5.98) µg/g-creatinine, respectively. Possible predictors of urinary DAP concentrations were the month of urine sampling, consumption of apple and maternal body mass index. When fenitrothion was used as an index chemical for cumulative risk assessment, 0.36% of participants exceeded the lower 95% confidence limit of the benchmark dose10.
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