Pub Date : 2025-07-11DOI: 10.1038/s41370-025-00789-9
Seong-Uk Baek, Jin-Ha Yoon
Academic interest in the health impacts of air pollutant mixtures has increased in past years. Studies indicated that air pollutants exposure is linked to obesity and metabolic syndrome. This study aimed to explore the association of air pollutant mixture with metabolic obesity phenotypes. A nationwide sample of 68,675 adults was analyzed in our cross-sectional study. Participants were linked to modeled air pollution data from 2007 to 2019. The concentrations of PM2.5–10, PM2.5, NO2, CO, SO2, and O3 were estimated for 2-year moving averages. Metabolic obesity phenotypes were classified into metabolically healthy obesity (MHO; body mass index [BMI] ≥25 kg/m2; without metabolic abnormality) and metabolically unhealthy obesity (MUO; BMI ≥25 kg/m2; with metabolic abnormality). The quantile g-computation was used to determine the association of air pollutant mixture with MHO and MOU. In total, 46,061 individuals were classified as non-obese, 2724 individuals were classified as MHO, and 19,890 individuals were classified as MUO. In the quantile g-computation, one quartile increase in the concentration of air pollutant mixture was positively associated with MUO (OR [odds ratio]: 1.12, 95% CI [confidence interval]: 1.05–1.19) but not with MHO (OR: 1.00, 95% CI: 0.87–1.15). O3, CO, and PM2.5–10 accounted for 37.6%, 21.6%, and 21.3% of the positive association of air pollutant mixture with MUO, respectively. Mounting evidence shows that outdoor air pollution is linked to obesity. We explored the association between long-term exposure to air pollutant mixture and metabolic obesity phenotypes. Obesity phenotypes were classified as metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). A mixture analysis showed that quartile increase in the concentration of the air pollutant mixture is associated with 1.12-fold increase in the odds of MUO, but not with MHO. Our novel findings suggest that long-term exposure to air pollutants may affect both metabolic abnormalities and obesity, contributing to a shift towards a metabolically unfavorable obesity profile.
{"title":"Long-term exposure to ambient air pollutant mixture and metabolic obesity phenotypes: Results from a nationwide Korean study (2007–2019)","authors":"Seong-Uk Baek, Jin-Ha Yoon","doi":"10.1038/s41370-025-00789-9","DOIUrl":"10.1038/s41370-025-00789-9","url":null,"abstract":"Academic interest in the health impacts of air pollutant mixtures has increased in past years. Studies indicated that air pollutants exposure is linked to obesity and metabolic syndrome. This study aimed to explore the association of air pollutant mixture with metabolic obesity phenotypes. A nationwide sample of 68,675 adults was analyzed in our cross-sectional study. Participants were linked to modeled air pollution data from 2007 to 2019. The concentrations of PM2.5–10, PM2.5, NO2, CO, SO2, and O3 were estimated for 2-year moving averages. Metabolic obesity phenotypes were classified into metabolically healthy obesity (MHO; body mass index [BMI] ≥25 kg/m2; without metabolic abnormality) and metabolically unhealthy obesity (MUO; BMI ≥25 kg/m2; with metabolic abnormality). The quantile g-computation was used to determine the association of air pollutant mixture with MHO and MOU. In total, 46,061 individuals were classified as non-obese, 2724 individuals were classified as MHO, and 19,890 individuals were classified as MUO. In the quantile g-computation, one quartile increase in the concentration of air pollutant mixture was positively associated with MUO (OR [odds ratio]: 1.12, 95% CI [confidence interval]: 1.05–1.19) but not with MHO (OR: 1.00, 95% CI: 0.87–1.15). O3, CO, and PM2.5–10 accounted for 37.6%, 21.6%, and 21.3% of the positive association of air pollutant mixture with MUO, respectively. Mounting evidence shows that outdoor air pollution is linked to obesity. We explored the association between long-term exposure to air pollutant mixture and metabolic obesity phenotypes. Obesity phenotypes were classified as metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). A mixture analysis showed that quartile increase in the concentration of the air pollutant mixture is associated with 1.12-fold increase in the odds of MUO, but not with MHO. Our novel findings suggest that long-term exposure to air pollutants may affect both metabolic abnormalities and obesity, contributing to a shift towards a metabolically unfavorable obesity profile.","PeriodicalId":15684,"journal":{"name":"Journal of Exposure Science and Environmental Epidemiology","volume":"36 1","pages":"1-9"},"PeriodicalIF":4.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The accurate analysis of extractables and leachables (E&L) from medical devices is crucial for the reliable safety risk assessment of substances to which patients and users may be exposed. The extractable profile of medical devices is often complex and unpredictable, thus improper selection of reference standards can lead to irreproducible chemical analyses between laboratories. ISO 10993-18, the international consensus standard for chemical characterization of medical devices, does not specify a process for selection of appropriate chemical reference standards for non-targeted analysis of E&L, leading to a variety of approaches being used. This study seeks to set out requirements for building a comprehensive list of chemical reference standards for non-targeted analysis of E&L and propose suggestions for selecting appropriate standards to enhance the consistency of chemical analysis. Criteria for selecting reference standards for non-targeted analysis of E&L in medical devices were developed using relevant polymer additives as a model system. The Relative Response Factor (RRF) values of the selected reference standards were determined using GC-MS and LC-MS analysis across three different concentrations. A system was developed to rank the toxicological hazards of the selected reference standards. A list of 106 reference standards of polymer additives was compiled, encompassing a wide range of physicochemical properties and broad toxicological coverage. Statistical analyses of these chemicals revealed there was no significant correlation between their six physicochemical properties and the corresponding relative response factors measured by GC-MS and LC-MS techniques. Accurate chemical identification and quantification of extractable substances from medical devices is important for chemical characterization of medical devices. The accurate quantitation of extractable chemicals in medical devices through non-targeted analysis is dependent on the proper selection of reference standards. We have proposed a set of reference standards intended to enhance the confidence in quantitation of device extractables, covering a broad range of structural and physicochemical diversity. This set of reference standards may assist chemistry laboratories in developing robust screening methods for extractables in medical devices, supporting the accurate characterization of medical devices.
{"title":"Designing a set of reference standards for non-targeted analysis of polymer additives extracted from medical devices","authors":"Byeong Hwa Yun, Amali Herath, Ying Jin, Jamie Kim, Kerry Belton, Echoleah Rufer, Omar Rivera Betancourt","doi":"10.1038/s41370-025-00788-w","DOIUrl":"10.1038/s41370-025-00788-w","url":null,"abstract":"The accurate analysis of extractables and leachables (E&L) from medical devices is crucial for the reliable safety risk assessment of substances to which patients and users may be exposed. The extractable profile of medical devices is often complex and unpredictable, thus improper selection of reference standards can lead to irreproducible chemical analyses between laboratories. ISO 10993-18, the international consensus standard for chemical characterization of medical devices, does not specify a process for selection of appropriate chemical reference standards for non-targeted analysis of E&L, leading to a variety of approaches being used. This study seeks to set out requirements for building a comprehensive list of chemical reference standards for non-targeted analysis of E&L and propose suggestions for selecting appropriate standards to enhance the consistency of chemical analysis. Criteria for selecting reference standards for non-targeted analysis of E&L in medical devices were developed using relevant polymer additives as a model system. The Relative Response Factor (RRF) values of the selected reference standards were determined using GC-MS and LC-MS analysis across three different concentrations. A system was developed to rank the toxicological hazards of the selected reference standards. A list of 106 reference standards of polymer additives was compiled, encompassing a wide range of physicochemical properties and broad toxicological coverage. Statistical analyses of these chemicals revealed there was no significant correlation between their six physicochemical properties and the corresponding relative response factors measured by GC-MS and LC-MS techniques. Accurate chemical identification and quantification of extractable substances from medical devices is important for chemical characterization of medical devices. The accurate quantitation of extractable chemicals in medical devices through non-targeted analysis is dependent on the proper selection of reference standards. We have proposed a set of reference standards intended to enhance the confidence in quantitation of device extractables, covering a broad range of structural and physicochemical diversity. This set of reference standards may assist chemistry laboratories in developing robust screening methods for extractables in medical devices, supporting the accurate characterization of medical devices.","PeriodicalId":15684,"journal":{"name":"Journal of Exposure Science and Environmental Epidemiology","volume":"35 6","pages":"943-955"},"PeriodicalIF":4.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41370-025-00788-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-04DOI: 10.1038/s41370-025-00791-1
Elaine A. Cohen Hubal
{"title":"Six principles of exposure science: inspiring solutions that foster healthy environments","authors":"Elaine A. Cohen Hubal","doi":"10.1038/s41370-025-00791-1","DOIUrl":"10.1038/s41370-025-00791-1","url":null,"abstract":"","PeriodicalId":15684,"journal":{"name":"Journal of Exposure Science and Environmental Epidemiology","volume":"35 5","pages":"693-695"},"PeriodicalIF":4.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41370-025-00791-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-03DOI: 10.1038/s41370-025-00790-2
Thomas Boissiere-O’Neill, Nina Lazarevic, Peter D. Sly, Anne-Louise Ponsonby, Aimin Chen, Meghan B. Azad, Joseph M. Braun, Jeffrey R. Brook, David Burgner, Bruce P. Lanphear, Theo J. Moraes, Richard Saffery, Padmaja Subbarao, Stuart E. Turvey, Kimberly Yolton, CHILD investigator group, BIS investigator group, Dwan Vilcins
Exposure to plastic additives, such as phthalates and bisphenols, has been associated with a higher risk of allergic conditions, but the evidence is inconsistent for children younger than five. To examine the association between pre- and postnatal urinary phthalates and bisphenols, and allergic conditions, and potential effect modification by sex, in pre-school children, through a pooled analysis. We pooled data from the Barwon Infant Study (Australia), the Canadian Healthy Infant Longitudinal Development Study (Canada), the Health Outcomes and Measures of the Environment (United States) and the Environmental Influences on Child Health Outcomes–wide cohorts (United States). Urinary phthalates and bisphenols were measured during pregnancy and early childhood. We estimated daily intakes from urinary concentrations, except for mono-(3-carboxypropyl) phthalate (MCPP). Outcomes, including asthma, wheeze, eczema, and rhinitis, were assessed up to five years of age through questionnaires and clinical assessments. We used generalised estimating equations for single compounds and quantile G-computation for the chemical mixtures. 5306 children were included. A two-fold increase in prenatal dibutyl phthalates (DBP; risk ratio [RR] = 1.08; 95% confidence interval [CI]: 1.00–1.16) and benzyl butyl phthalate (BBzP; RR = 1.06; 95%CI: 1.00–1.12) increased the risk of asthma in children under five. Prenatal MCPP levels were associated with rhinitis (RR = 1.05; 95%CI: 1.01–1.09). Postnatal BBzP levels increased the risk of wheezing (RR = 1.05; 95%CI 1.01–1.09), as well as di(2-ethylhexyl) phthalate (DEHP; RR = 1.06; 95%CI: 1.01–1.11) and MCPP (RR = 1.09; 95%CI: 1.04–1.14). These were also inversely associated with eczema. A one-quartile increase in the postnatal chemical mixture increased the risk of wheezing (RR = 1.14; 95%CI: 1.02–1.26). There was limited evidence of effect modification by sex.
{"title":"Phthalates and bisphenols early-life exposure, and childhood allergic conditions: a pooled analysis of cohort studies","authors":"Thomas Boissiere-O’Neill, Nina Lazarevic, Peter D. Sly, Anne-Louise Ponsonby, Aimin Chen, Meghan B. Azad, Joseph M. Braun, Jeffrey R. Brook, David Burgner, Bruce P. Lanphear, Theo J. Moraes, Richard Saffery, Padmaja Subbarao, Stuart E. Turvey, Kimberly Yolton, CHILD investigator group, BIS investigator group, Dwan Vilcins","doi":"10.1038/s41370-025-00790-2","DOIUrl":"10.1038/s41370-025-00790-2","url":null,"abstract":"Exposure to plastic additives, such as phthalates and bisphenols, has been associated with a higher risk of allergic conditions, but the evidence is inconsistent for children younger than five. To examine the association between pre- and postnatal urinary phthalates and bisphenols, and allergic conditions, and potential effect modification by sex, in pre-school children, through a pooled analysis. We pooled data from the Barwon Infant Study (Australia), the Canadian Healthy Infant Longitudinal Development Study (Canada), the Health Outcomes and Measures of the Environment (United States) and the Environmental Influences on Child Health Outcomes–wide cohorts (United States). Urinary phthalates and bisphenols were measured during pregnancy and early childhood. We estimated daily intakes from urinary concentrations, except for mono-(3-carboxypropyl) phthalate (MCPP). Outcomes, including asthma, wheeze, eczema, and rhinitis, were assessed up to five years of age through questionnaires and clinical assessments. We used generalised estimating equations for single compounds and quantile G-computation for the chemical mixtures. 5306 children were included. A two-fold increase in prenatal dibutyl phthalates (DBP; risk ratio [RR] = 1.08; 95% confidence interval [CI]: 1.00–1.16) and benzyl butyl phthalate (BBzP; RR = 1.06; 95%CI: 1.00–1.12) increased the risk of asthma in children under five. Prenatal MCPP levels were associated with rhinitis (RR = 1.05; 95%CI: 1.01–1.09). Postnatal BBzP levels increased the risk of wheezing (RR = 1.05; 95%CI 1.01–1.09), as well as di(2-ethylhexyl) phthalate (DEHP; RR = 1.06; 95%CI: 1.01–1.11) and MCPP (RR = 1.09; 95%CI: 1.04–1.14). These were also inversely associated with eczema. A one-quartile increase in the postnatal chemical mixture increased the risk of wheezing (RR = 1.14; 95%CI: 1.02–1.26). There was limited evidence of effect modification by sex.","PeriodicalId":15684,"journal":{"name":"Journal of Exposure Science and Environmental Epidemiology","volume":"35 6","pages":"965-980"},"PeriodicalIF":4.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41370-025-00790-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-30DOI: 10.1038/s41370-025-00787-x
Emily Lasher, Jessica Trowbridge, Alison Gemmill, Rachel Morello-Frosch, Erin DeMicco, Kurunthachalam Kannan, Jessie P. Buckley, Tracey J. Woodruff
Research suggests exposure to chemical and non-chemical stressors may increase the risk of pregnancy complications, including gestational diabetes mellitus (GDM). Exposure to melamine and aromatic amines (AAs) is ubiquitous among pregnant people. However, studies investigating the maternal and fetal health effects of prenatal exposure are limited. This cross-sectional study aimed to (1) evaluate relationships between exposure to aromatic amines, melamine and its derivatives, and gestational diabetes in a pregnancy cohort in San Francisco, California, USA, (2) explore if non-chemical stressors modify these relationships, and (3) assess fetal sex differences using stratification. We measured 36 AAs, melamine, and three of its derivatives in second-trimester urine samples (n = 607). Financial strain and psychosocial stress were assessed using self-reported questionnaires. GDM status was abstracted from medical records. We used unadjusted and adjusted logistic regression models to calculate the odds of GDM associated with an interquartile range increase in urinary concentrations of melamine and AAs or higher levels of non-chemical stress, overall and stratified by infant sex. Interaction terms between each chemical and non-chemical stressor were used to test for effect modification. Eight analytes were detected in >65% of participants, with 100% detection of melamine and cyanuric acid. Among male infants, summed urinary concentrations of melamine and its analogs and o-Anisidine were associated with increased odds of GDM (OR: 1.08 [1.00, 1.17], OR: 1.18 [1.03, 1.36], respectively). Higher levels of perceived stress and discrimination were also associated with increased odds of GDM (OR: 1.41 [0.73, 2.70], OR: 2.33 [1.16, 4.67], respectively). We found limited evidence of interaction between chemical and non-chemical stressors. This study revealed positive associations between melamine and its analogs, some aromatic amines, and gestational diabetes, especially among pregnant women carrying male fetuses. We also found that levels of perceived stress and discrimination were associated with gestational diabetes.
{"title":"Prenatal melamine, aromatic amine, and psychosocial stress exposures and their association with gestational diabetes mellitus in a San Francisco pregnancy cohort","authors":"Emily Lasher, Jessica Trowbridge, Alison Gemmill, Rachel Morello-Frosch, Erin DeMicco, Kurunthachalam Kannan, Jessie P. Buckley, Tracey J. Woodruff","doi":"10.1038/s41370-025-00787-x","DOIUrl":"10.1038/s41370-025-00787-x","url":null,"abstract":"Research suggests exposure to chemical and non-chemical stressors may increase the risk of pregnancy complications, including gestational diabetes mellitus (GDM). Exposure to melamine and aromatic amines (AAs) is ubiquitous among pregnant people. However, studies investigating the maternal and fetal health effects of prenatal exposure are limited. This cross-sectional study aimed to (1) evaluate relationships between exposure to aromatic amines, melamine and its derivatives, and gestational diabetes in a pregnancy cohort in San Francisco, California, USA, (2) explore if non-chemical stressors modify these relationships, and (3) assess fetal sex differences using stratification. We measured 36 AAs, melamine, and three of its derivatives in second-trimester urine samples (n = 607). Financial strain and psychosocial stress were assessed using self-reported questionnaires. GDM status was abstracted from medical records. We used unadjusted and adjusted logistic regression models to calculate the odds of GDM associated with an interquartile range increase in urinary concentrations of melamine and AAs or higher levels of non-chemical stress, overall and stratified by infant sex. Interaction terms between each chemical and non-chemical stressor were used to test for effect modification. Eight analytes were detected in >65% of participants, with 100% detection of melamine and cyanuric acid. Among male infants, summed urinary concentrations of melamine and its analogs and o-Anisidine were associated with increased odds of GDM (OR: 1.08 [1.00, 1.17], OR: 1.18 [1.03, 1.36], respectively). Higher levels of perceived stress and discrimination were also associated with increased odds of GDM (OR: 1.41 [0.73, 2.70], OR: 2.33 [1.16, 4.67], respectively). We found limited evidence of interaction between chemical and non-chemical stressors. This study revealed positive associations between melamine and its analogs, some aromatic amines, and gestational diabetes, especially among pregnant women carrying male fetuses. We also found that levels of perceived stress and discrimination were associated with gestational diabetes.","PeriodicalId":15684,"journal":{"name":"Journal of Exposure Science and Environmental Epidemiology","volume":"35 6","pages":"907-920"},"PeriodicalIF":4.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41370-025-00787-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-18DOI: 10.1038/s41370-025-00786-y
Alison Gauthier, William Behymer, Jennifer Bare, Mandie Kramer, Wade T. Barranco, Joseph P. Longtin, Susan Borghoff, Andrew Jaques
Vinyl acetate monomer (VAM) (CAS 108-05-4) is employed in the creation of an array of polymers and copolymers used in the manufacture of consumer products. There is no direct use of VAM in consumer products. However, residual amounts of unreacted VAM in (co)polymer products have been identified as a possible general population exposure concern. The objective of this evaluation was to provide a contemporary review of exposure to VAM via residual VAM (co)polymer in a range of consumer products in the United States. The study authors conducted a market-basket sampling of residual VAM levels in 71 consumer products purchased in the United States that met the selection criteria. Subsequently, exposure assessments were conducted using ConsExpo (version 1.1.1) and the United States Environmental Protection Agency’s Consumer Exposure Model (CEM; version 2.1) on a subset of those identified products. Of the products analyzed, 40 had VAM concentrations below the lowest detection limits (0.1–2 ppmw), 19 were non-detectable but the product materials demonstrated atypical or nonlinear calibration behavior, four had detectable VAM in the 2–10 ppmw range, and eight had detectable VAM from 10 to 648 ppmw. Eleven use scenarios were developed based on seven categories of consumer products from the evaluation. Resulting exposure estimates were all less than both acute and chronic non-cancer human health thresholds.
{"title":"Measurement of vinyl acetate monomer in consumer products and modeled estimates of consumer exposure","authors":"Alison Gauthier, William Behymer, Jennifer Bare, Mandie Kramer, Wade T. Barranco, Joseph P. Longtin, Susan Borghoff, Andrew Jaques","doi":"10.1038/s41370-025-00786-y","DOIUrl":"10.1038/s41370-025-00786-y","url":null,"abstract":"Vinyl acetate monomer (VAM) (CAS 108-05-4) is employed in the creation of an array of polymers and copolymers used in the manufacture of consumer products. There is no direct use of VAM in consumer products. However, residual amounts of unreacted VAM in (co)polymer products have been identified as a possible general population exposure concern. The objective of this evaluation was to provide a contemporary review of exposure to VAM via residual VAM (co)polymer in a range of consumer products in the United States. The study authors conducted a market-basket sampling of residual VAM levels in 71 consumer products purchased in the United States that met the selection criteria. Subsequently, exposure assessments were conducted using ConsExpo (version 1.1.1) and the United States Environmental Protection Agency’s Consumer Exposure Model (CEM; version 2.1) on a subset of those identified products. Of the products analyzed, 40 had VAM concentrations below the lowest detection limits (0.1–2 ppmw), 19 were non-detectable but the product materials demonstrated atypical or nonlinear calibration behavior, four had detectable VAM in the 2–10 ppmw range, and eight had detectable VAM from 10 to 648 ppmw. Eleven use scenarios were developed based on seven categories of consumer products from the evaluation. Resulting exposure estimates were all less than both acute and chronic non-cancer human health thresholds.","PeriodicalId":15684,"journal":{"name":"Journal of Exposure Science and Environmental Epidemiology","volume":"35 6","pages":"933-942"},"PeriodicalIF":4.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41370-025-00786-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traffic-related air pollution (TRAP) poses significant risks to human health, particularly in urban areas with high traffic volumes. Intake fraction (iF) quantifies the relationship between emissions and exposure, defined as the ratio of the total inhalation increment of all exposed individuals in a target population to the emissions from specific pollution sources over a certain period. The overarching objective of this study is to unravel the underlying value and significance of the iF method in evaluating TRAP exposure risks, while also exploring its future development trajectories and potential avenues for application. We conducted a comprehensive review of iF to assess TRAP exposure. We employed a search strategy to identify and analyze literature on iF methods related to TRAP exposure across academic databases covering the period from 2002 to 2024. After deduplication, title and abstract screening, and full-text review, we ultimately included 25 studies on iF related to TRAP. We classified the measurement methods of iF into four types: simple estimation method, dispersion simulation method, numerical simulation method, and exposure monitoring method. We found orders of magnitude of differences in iF among studies. Population density, pollutant concentration, and breathing rate explain a significant portion of the variations. iF values of nitrogen oxides (NOx), carbon monoxide (CO), and fine particulate matter (PM2.5) are higher than those of diesel particulate matter (DPM), ultrafine particles (UFP), and benzene. Compared to power plants, TRAP has higher iF values, emphasizing the control priority of TRAP. Future research should expand to under-researched regions, strengthen investigations on UFP and secondary pollutants, and refine iF calculation methods using high-resolution and mobility data.
{"title":"Comprehensive review of intake fraction methods for assessing traffic-related air pollution exposure: insights, variations, and future directions","authors":"Shuyan Meng, Ling Qi, Pengpeng Wu, Suzhen Cao, Kai Zhang, Zongshuang Wang, Xiaoli Duan","doi":"10.1038/s41370-025-00775-1","DOIUrl":"10.1038/s41370-025-00775-1","url":null,"abstract":"Traffic-related air pollution (TRAP) poses significant risks to human health, particularly in urban areas with high traffic volumes. Intake fraction (iF) quantifies the relationship between emissions and exposure, defined as the ratio of the total inhalation increment of all exposed individuals in a target population to the emissions from specific pollution sources over a certain period. The overarching objective of this study is to unravel the underlying value and significance of the iF method in evaluating TRAP exposure risks, while also exploring its future development trajectories and potential avenues for application. We conducted a comprehensive review of iF to assess TRAP exposure. We employed a search strategy to identify and analyze literature on iF methods related to TRAP exposure across academic databases covering the period from 2002 to 2024. After deduplication, title and abstract screening, and full-text review, we ultimately included 25 studies on iF related to TRAP. We classified the measurement methods of iF into four types: simple estimation method, dispersion simulation method, numerical simulation method, and exposure monitoring method. We found orders of magnitude of differences in iF among studies. Population density, pollutant concentration, and breathing rate explain a significant portion of the variations. iF values of nitrogen oxides (NOx), carbon monoxide (CO), and fine particulate matter (PM2.5) are higher than those of diesel particulate matter (DPM), ultrafine particles (UFP), and benzene. Compared to power plants, TRAP has higher iF values, emphasizing the control priority of TRAP. Future research should expand to under-researched regions, strengthen investigations on UFP and secondary pollutants, and refine iF calculation methods using high-resolution and mobility data.","PeriodicalId":15684,"journal":{"name":"Journal of Exposure Science and Environmental Epidemiology","volume":"36 1","pages":"112-124"},"PeriodicalIF":4.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144319185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-09DOI: 10.1038/s41370-025-00785-z
A. Eklund, T. Taj, L. Dunder, P. M. Lind, L. Lind, S. Salihovic
Perfluoroalkyl substances (PFAS) constitute a diverse group of chemical compounds used in various consumer products. While the associations between PFAS and certain adverse human health effects are well-documented, their impact on kidney function remains less known. The main aim of this study is to investigate the relationship between PFAS levels and kidney function (estimated glomerular filtration rate [eGFR]) utilizing a longitudinal design. The population-based Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study included 997 individuals at baseline (all aged 70 years, 50% females). Follow-up investigations were performed at 75 and 80 years of age. Seven major PFAS were determined in plasma using ultra-high performance liquid chromatography-mass spectrometry. Longitudinal and cross-sectional associations between PFAS and eGFR were analyzed using linear regression and mixed effects models following adjustment for sex, HDL and LDL-cholesterol, triglycerides, glucose, BMI, statin use and smoking. Longitudinal models demonstrated statistically significant positive associations between perfluoroundecanoic acid (PFUnDA), perfluorononanoic acid (PFNA), and perfluorodecanoic acid (PFDA)and eGFR (all P < 0.001). The associations between linear perfluorooctane sulfonic acid (PFOS) and perfluorohexanesulfonic acid (PFHxS) followed a similar trend. In contrast, an inverse relationship between perfluoroheptanoic acid (PFHpA) and perfluorooctanesulfonamide (PFOSA) with eGFR was observed. The findings were largely corroborated by cross-sectional analyses. This longitudinal study found that changes in certain PFAS concentrations were positively associated with the change in kidney function, though the direction of association varied across PFAS. These findings were further supported by cross-sectional analysis. The complexity of associations remains incompletely understood as some PFAS showed positive associations while others were inverse. Further longitudinal studies with repeated measures are needed to better elucidate the relationship between PFAS exposure and kidney function.
{"title":"Longitudinal and cross-sectional analysis of perfluoroalkyl substances and kidney function","authors":"A. Eklund, T. Taj, L. Dunder, P. M. Lind, L. Lind, S. Salihovic","doi":"10.1038/s41370-025-00785-z","DOIUrl":"10.1038/s41370-025-00785-z","url":null,"abstract":"Perfluoroalkyl substances (PFAS) constitute a diverse group of chemical compounds used in various consumer products. While the associations between PFAS and certain adverse human health effects are well-documented, their impact on kidney function remains less known. The main aim of this study is to investigate the relationship between PFAS levels and kidney function (estimated glomerular filtration rate [eGFR]) utilizing a longitudinal design. The population-based Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study included 997 individuals at baseline (all aged 70 years, 50% females). Follow-up investigations were performed at 75 and 80 years of age. Seven major PFAS were determined in plasma using ultra-high performance liquid chromatography-mass spectrometry. Longitudinal and cross-sectional associations between PFAS and eGFR were analyzed using linear regression and mixed effects models following adjustment for sex, HDL and LDL-cholesterol, triglycerides, glucose, BMI, statin use and smoking. Longitudinal models demonstrated statistically significant positive associations between perfluoroundecanoic acid (PFUnDA), perfluorononanoic acid (PFNA), and perfluorodecanoic acid (PFDA)and eGFR (all P < 0.001). The associations between linear perfluorooctane sulfonic acid (PFOS) and perfluorohexanesulfonic acid (PFHxS) followed a similar trend. In contrast, an inverse relationship between perfluoroheptanoic acid (PFHpA) and perfluorooctanesulfonamide (PFOSA) with eGFR was observed. The findings were largely corroborated by cross-sectional analyses. This longitudinal study found that changes in certain PFAS concentrations were positively associated with the change in kidney function, though the direction of association varied across PFAS. These findings were further supported by cross-sectional analysis. The complexity of associations remains incompletely understood as some PFAS showed positive associations while others were inverse. Further longitudinal studies with repeated measures are needed to better elucidate the relationship between PFAS exposure and kidney function.","PeriodicalId":15684,"journal":{"name":"Journal of Exposure Science and Environmental Epidemiology","volume":"35 6","pages":"1041-1049"},"PeriodicalIF":4.7,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41370-025-00785-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-06DOI: 10.1038/s41370-025-00783-1
Ji-Young Son, Brandon M. Lewis, Michelle L. Bell
Animal feeding operations (AFOs), including concentrated animal feeding operations (CAFOs), pose significant environmental degradation and health risks. These facilities are often disproportionately located in disadvantaged communities, however, findings are inconsistent. We investigated disparities in AFO/CAFO exposure across seven US states, focusing on variables related to environmental justice (EJ) and at-risk populations. We linked AFO/CAFO data from seven states (Iowa, North Carolina, Pennsylvania, South Carolina, Texas, Virginia, and Wisconsin) to ZIP code-level census variables. We assessed exposure by calculating area-weighted number of AFO/CAFO within 15 km buffers and categorized ZIP codes into no, low, medium, and high exposure groups. Our analysis compared the spatial distributions of AFO/CAFO exposure and variables related to EJ and at-risk populations by exposure intensity. We found differences in the distributions of AFO/CAFO exposure and variables related to EJ and at-risk populations among states. In some states (e.g., North Carolina, Pennsylvania), AFOs/CAFOs were densely clustered in specific areas, while in others (e.g., Iowa, Wisconsin), they were more evenly distributed. We found disproportionate exposure to AFO/CAFO in disadvantaged communities such as communities with high percentages of racial/ethnic minority persons and low socioeconomic status in some states, whereas other states showed different patterns. Trends varied by state, with some showing increasing Non-Hispanic Black and Hispanic populations with higher exposure (e.g., North Carolina), while others showed opposite trends (e.g., Pennsylvania). Education, poverty, and income levels also varied, with some states (e.g., North Carolina, South Carolina) showing higher poverty rates, lower education level, and lower incomes in higher exposure groups and other states showing reverse trends (e.g., Wisconsin).
{"title":"Disparities in exposure to concentrated animal feeding operations (CAFOs) and other animal feeding operations across multiple states in USA","authors":"Ji-Young Son, Brandon M. Lewis, Michelle L. Bell","doi":"10.1038/s41370-025-00783-1","DOIUrl":"10.1038/s41370-025-00783-1","url":null,"abstract":"Animal feeding operations (AFOs), including concentrated animal feeding operations (CAFOs), pose significant environmental degradation and health risks. These facilities are often disproportionately located in disadvantaged communities, however, findings are inconsistent. We investigated disparities in AFO/CAFO exposure across seven US states, focusing on variables related to environmental justice (EJ) and at-risk populations. We linked AFO/CAFO data from seven states (Iowa, North Carolina, Pennsylvania, South Carolina, Texas, Virginia, and Wisconsin) to ZIP code-level census variables. We assessed exposure by calculating area-weighted number of AFO/CAFO within 15 km buffers and categorized ZIP codes into no, low, medium, and high exposure groups. Our analysis compared the spatial distributions of AFO/CAFO exposure and variables related to EJ and at-risk populations by exposure intensity. We found differences in the distributions of AFO/CAFO exposure and variables related to EJ and at-risk populations among states. In some states (e.g., North Carolina, Pennsylvania), AFOs/CAFOs were densely clustered in specific areas, while in others (e.g., Iowa, Wisconsin), they were more evenly distributed. We found disproportionate exposure to AFO/CAFO in disadvantaged communities such as communities with high percentages of racial/ethnic minority persons and low socioeconomic status in some states, whereas other states showed different patterns. Trends varied by state, with some showing increasing Non-Hispanic Black and Hispanic populations with higher exposure (e.g., North Carolina), while others showed opposite trends (e.g., Pennsylvania). Education, poverty, and income levels also varied, with some states (e.g., North Carolina, South Carolina) showing higher poverty rates, lower education level, and lower incomes in higher exposure groups and other states showing reverse trends (e.g., Wisconsin).","PeriodicalId":15684,"journal":{"name":"Journal of Exposure Science and Environmental Epidemiology","volume":"36 1","pages":"167-174"},"PeriodicalIF":4.7,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-04DOI: 10.1038/s41370-025-00776-0
Sourav Biswas, Aparajita Chattopadhyay, Kathrin Schilling, Ayushi Das
One-fourth of Indians are hypertensive, and the majority relies on groundwater for drinking. But the role of groundwater physicochemical properties and contamination in hypertension remains understudied. The study investigates the association between physicochemical groundwater characteristics andcontaminants and hypertension risk in India. This study used data from the fifth round of the National Family Health Survey (NFHS-5 collected 2019–2021), including health, socio-demographics, and food and dietary information (n = 712,666 individuals). The physicochemical characteristics of groundwater data were derived from the Central Groundwater Board (CGWB, 2019–2021). This groundwater data from raster maps was linked to NFHS-5 records using cluster shapefiles and merging them with individual records via cluster IDs. Bivariate and multivariable regressions were used to identify factors associated with hypertension at the individual level. Moran’s I statistics, Local Indicator of Spatial Association (LISA) cluster maps, and the Spatial Error Model (SEM) were used at district levels to investigate the spatial association. Machine learning models, including Artificial Neural Networks (ANN), Random Forest and Extreme Gradient Boosting (XGBoost), were used to predict hypertension risk zones. Physicochemical drinking water composition is a key factor in hypertension risk. Elevated groundwater pH (>8.5, Adjusted Odds Ratio (AOR): 2.12), electrical conductivity (>300 μS/cm, AOR: 1.06), sulphate (>200 mg/L, AOR: 1.16), arsenic (>0.01 mg/L, AOR: 1.09), nitrate (>45 mg/L, AOR: 1.07), and magnesium (>30 mg/L, AOR: 1.03) are associated to higher odds of hypertension. The Random Forest model demonstrated the highest predictive performance, with a coefficient of determination (R²) of 0.9970, mean absolute error (MAE) of 0.0012, and mean squared error (MSE) of 0.0077. It effectively identified high-risk zones in the northwestern (Delhi, Punjab, Haryana, and Rajasthan) and eastern (West Bengal and Bihar) regions of India.
{"title":"Investigating the association between groundwater contaminants and hypertension risk in India: a machine learning-based analysis","authors":"Sourav Biswas, Aparajita Chattopadhyay, Kathrin Schilling, Ayushi Das","doi":"10.1038/s41370-025-00776-0","DOIUrl":"10.1038/s41370-025-00776-0","url":null,"abstract":"One-fourth of Indians are hypertensive, and the majority relies on groundwater for drinking. But the role of groundwater physicochemical properties and contamination in hypertension remains understudied. The study investigates the association between physicochemical groundwater characteristics andcontaminants and hypertension risk in India. This study used data from the fifth round of the National Family Health Survey (NFHS-5 collected 2019–2021), including health, socio-demographics, and food and dietary information (n = 712,666 individuals). The physicochemical characteristics of groundwater data were derived from the Central Groundwater Board (CGWB, 2019–2021). This groundwater data from raster maps was linked to NFHS-5 records using cluster shapefiles and merging them with individual records via cluster IDs. Bivariate and multivariable regressions were used to identify factors associated with hypertension at the individual level. Moran’s I statistics, Local Indicator of Spatial Association (LISA) cluster maps, and the Spatial Error Model (SEM) were used at district levels to investigate the spatial association. Machine learning models, including Artificial Neural Networks (ANN), Random Forest and Extreme Gradient Boosting (XGBoost), were used to predict hypertension risk zones. Physicochemical drinking water composition is a key factor in hypertension risk. Elevated groundwater pH (>8.5, Adjusted Odds Ratio (AOR): 2.12), electrical conductivity (>300 μS/cm, AOR: 1.06), sulphate (>200 mg/L, AOR: 1.16), arsenic (>0.01 mg/L, AOR: 1.09), nitrate (>45 mg/L, AOR: 1.07), and magnesium (>30 mg/L, AOR: 1.03) are associated to higher odds of hypertension. The Random Forest model demonstrated the highest predictive performance, with a coefficient of determination (R²) of 0.9970, mean absolute error (MAE) of 0.0012, and mean squared error (MSE) of 0.0077. It effectively identified high-risk zones in the northwestern (Delhi, Punjab, Haryana, and Rajasthan) and eastern (West Bengal and Bihar) regions of India.","PeriodicalId":15684,"journal":{"name":"Journal of Exposure Science and Environmental Epidemiology","volume":"36 1","pages":"125-142"},"PeriodicalIF":4.7,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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