Urban green space, human heat perception and sleep quality: A repeated cross-sectional study.

Urban heat poses significant challenges to public health, as exposure to high temperatures is associated to heat stress, resulting in heat strain, sleep deprivation, and cardiovascular morbidity and mortality. As the frequency of heat waves is increasing due to global warming, urban green spaces are often proposed as a nature-based solution to mitigate urban heat stress. This study investigated the impact of urban green space on perceived heat stress and sleep quality, using questionnaires and detailed land cover data. We surveyed 584 respondents during four heat and four control events in the summers of 2021 and 2022, assessing perceived heat stress, sleep quality, and mental health. Using structural equation models, this study analysed the influence of both tree cover and grass and shrub cover on perceived heat stress and sleep quality, while controlling for risk and vulnerability factors. The outcomes revealed that during heat events, enhanced tree cover was associated with reduced heat stress (B = -0.484, 95% CI [-0.693, -0.275], p = 0.001), while increased grass and shrub cover was associated with both reduced heat stress (B = -0.361 [-0.529, -0.193], p = 0.000) and improved sleep quality (B = -0. 241 [-0.399, -0.083], p = 0.003). Conversely, during control events, stress indicators were more strongly associated with individual vulnerability factors rather than surrounding green space. These results emphasize the importance of combining trees with lower vegetation in urban planning to mitigate heat-related stress and enhance sleep quality, thereby improving overall well-being during heat events.

Parental arsenic exposure and tissue-specific DNA methylation in Bangladeshi infants with spina bifida.

An emerging hypothesis linking arsenic toxicity involves altered epigenetic mechanisms, such as DNA methylation. In this study, we examined the relationship between parents' arsenic exposure and DNA methylation in tissues obtained from 28 infants with spina bifida from Bangladesh. We analyzed arsenic in parents' toenails using inductively coupled plasma mass spectrometry (ICP-MS). DNA methylation was measured in infants' dural tissue, buccal swabs, and whole blood using the Illumina Infinium MethylationEPIC BeadChip. We performed epigenome-wide association analyses (EWAS) and tested differentially methylated regions (DMRs). In EWAS, DNA methylation at cg24039697 in dural tissue was positively associated (ß = 0.59, p = 7.6 × 10-9) with father's toenail arsenic concentrations, adjusting for covariates. We did not identify any CpG sites related to father's arsenic exposure in the other tissues, or any CpG sites related to mother's arsenic exposure. Gene ontology analysis identified many biological pathways of interest, including the Wnt signaling pathways. We identified several DMRs across the tissues related to arsenic exposure that included probes mapping to genes that have previously been identified in studies of neural tube defects. This study emphasizes the potential impact of arsenic exposure in fathers, often understudied in epidemiological studies, on DNA methylation in a unique neurological tissue specific to spina bifida.

The Multi-Country Multi-City Collaborative Research Network: An international research consortium investigating environment, climate, and health.

Research on the health risks of environmental factors and climate change requires epidemiological evidence on associated health risks at a global scale. Multi-center studies offer an excellent framework for this purpose, but they present various methodological and logistical problems. This contribution illustrates the experience of the Multi-Country Multi-City Collaborative Research Network, an international collaboration working on a global research program on the associations between environmental stressors, climate, and health in a multi-center setting. The article illustrates the collaborative scheme based on mutual contribution and data and method sharing, describes the collection of a huge multi-location database, summarizes published research findings and future plans, and discusses advantages and limitations. The Multi-Country Multi-City represents an example of a collaborative research framework that has greatly contributed to advance knowledge on the health impacts of climate change and other environmental factors and can be replicated to address other research questions across various research fields.

Greenness Exposure and Mortality Risk in a Cardio-Oncologic Population.

BACKGROUND AND AIMS: Knowledge is lacking on the relationship between greenness and mortality in cancer survivors who experience coronary artery disease (CAD), a cardio-oncologic population. We aimed to investigate the association between residential greenness exposure and all-cause mortality in a cardio-oncologic population. METHODS: Cancer survivors undergoing percutaneous coronary intervention at the Rabin Medical Center in Israel between 2004 and 2014 were included in the study. Clinical data were collected from medical records during index hospitalization and from the Israeli National Cancer Registry. Residential greenness was estimated by the normalized difference vegetation index (NDVI), a satellite-based index derived from Landsat imagery at a 30-meter spatial resolution, with larger values indicating higher levels of vegetative density (ranging between -1 to 1). Mortality follow-up data were obtained through the end of 2021. Cox models were used to assess the hazard ratios (HRs) for all-cause mortality per 1SD increase in NDVI. RESULTS: Among 1,331 patients analyzed [mean (SD) age, 75.6 (10.2) years, 373 (28%) females], the mean (SD) NDVI within a 300-meter radius was 0.12 (0.03). During a median follow-up period of 12.0 (IQR 9.2-14.7) years, 883 (66%) participants died. After adjustment for potential confounding factors, including residential socioeconomic status, air pollution, and smoking, NDVI was inversely associated with mortality hazard [HR (95% CI) = 0.93 (0.86, 0.99); p=.042]. The association was stronger among individuals with more recently (<10 years) diagnosed cancer [HR (95% CI) = 0.89 (0.81, 0.98); p=.019]. CONCLUSION: In a cohort of cardio-oncologic patients, greenness was independently associated with lower mortality.

Construction of a minute ventilation model to address inter-individual inhaled dose variability within identical exposure scenarios using wearable devices.

Inhaled dose is crucial for accurately assessing exposure to air pollution, determined by pollutant concentration and minute ventilation (VE). However, the VE predictive models and its application to assess the health effects of air pollution are still lacking. In this study, we developed VE predictive models using machine learning techniques, utilizing data obtained from eighty participants who underwent a laboratory cardiopulmonary exercise test (CPET). VE predictive models were developed using generalized additive model (GAM), random forest model (RF) and extreme gradient boosting (XGBoost) and analyzed for explanation of input variables. The Random Forest model, cross-validated, exhibited outstanding performance with an R2 of 0.986 and a MAE of 1.816 L/min. The median difference between the measured VE and the predicted VE was 0.18 L/min, and the median difference between the black carbon (BC) inhaled dose based on predicted VE and measured VE was 0.02 ng. Employing explainable machine learning, the results showed that metabolic equivalent (METs), heart rate, and body weight are the three top important variables, emphasizing the significance of incorporating METs variables when constructing VE models. Through multiple linear regression models and an adjusted stratified analysis model, the significant adverse association between BC concentration and inhaled dose on diastolic blood pressure (DBP) was only observed in female. The disparity in the effect of BC inhaled dose compared to BC concentration on DBP reached up to 115 %. This study is the first to explore the ability of different machine learning algorithms to construct VE prediction models and directly apply the models to assess health effects of an example pollutant. This study contributes to the accurate assessment of air pollution exposure leveraging wearable devices, an approach useful for environmental epidemiology studies.

Metabolic profiles associated with exposure to ambient particulate air pollution: findings from the Betula cohort.

Introduction: Air pollution is a significant contributor to morbidity and mortality globally and has been linked to an increased risk of dementia. Previous studies within the Betula cohort in Northern Sweden have demonstrated associations between air pollution and dementia, as well as distinctive metabolomic profiles in dementia patients compared to controls. This study aimed to investigate whether air pollution is associated with quantitative changes in metabolite levels within this cohort, and whether future dementia status would modify this association. Methods: Both short-term and long-term exposure to air pollution were evaluated using high spatial resolution models and measured data. Air pollution from vehicle exhaust and woodsmoke were analyzed separately. Metabolomic profiling was conducted on 321 participants, including 58 serum samples from dementia patients and a control group matched for age, sex, and education level, using nuclear magnetic resonance spectroscopy. Results: No statistically significant associations were found between any metabolites and any measures of short-term or long-term exposure to air pollution. However, there were trends potentially suggesting associations between both long-term and short-term exposure to air pollution with lactate and glucose metabolites. Notably, these associations were observed despite the lack of correlation between long-term and short-term air pollution exposure in this cohort. There were also tendencies for associations between air pollution from woodsmoke to be more pronounced in participants that would later develop dementia, suggesting a potential effect depending on urban/rural factors. Discussion: While no significant associations were found, the trends observed in the data suggest potential links between air pollution exposure and changes in lactate and glucose metabolites. These findings provide some new insights into the link between air pollution and metabolic markers in a low-exposure setting. However, addressing existing limitations is crucial to improve the robustness and applicability of future research in this area. The pronounced associations in participants who later developed dementia may indicate an influence of urban/rural factors, warranting further investigation.

Chemical exposome and children health: Identification of dose-response relationships from meta-analyses and epidemiological studies.

BACKGROUND: Health impact assessment studies quantifying the impact of the chemical exposome on children's health generally consider a small fraction of the exposome. Synthetizing available dose-response relationships is an essential step to fill this gap. We reviewed the literature for dose-response relationships relating the chemical exposome with children health. METHOD: We focused on 78 substance-outcome pairs for which the level of evidence had previously been classified as 'likely' or 'very likely'. We searched for dose-response relationships for these pairs from meta-analyses and, if none was available, from single epidemiological studies, from which we conducted meta-analyses whenever possible. RESULTS: We identified dose-response relationships for 50 of the 78 prioritized substance-outcome pairs (64%). Dose-response relationships stemmed from meta-analyses for 21 pairs, from de novo meta-analyses for 1 pair and single studies for 28 pairs. Dose-response relationships were available for tobacco (fetal and infant death, congenital heart defects, birth outcomes, orofacial clefts, respiratory health), lead (asthma, cognition, delayed puberty onset and iron deficiency anaemia), polychlorobiphenyls (PCBs) (cognition, respiratory infections and birth outcomes), bisphenol A (cognition), hexachlorobenzene (HCB) (respiratory health), Polybrominated diphenyl ethers (neurodevelopment), DDT (hypospadias, cryptorchidism, miscarriage), pesticides (neurodevelopment), methylmercury (cognition), PFAS (immune system, birth weight, behavior, miscarriage), arsenic (cognition, birth weight, death, respiratory health), cadmium (cognition, birth weight), manganese (behavior), sodium (blood pressure) and thallium (birth weight). For 28 of the 78 substance-outcome pairs (36%), no dose-response relationship was available from epidemiological studies in children. CONCLUSIONS: We identified dose-response relationships for 50 substance-outcome pairs, corresponding to 20 chemicals and 17 health outcomes. These can be used to perform more comprehensive quantitative health impact assessment of the exposome on child health. We also identified 28 substance-outcome pairs corresponding to 'likely' or 'very likely' effects for which research generating dose-response functions in children would be relevant.

Heat, humidity and health impacts: how causal diagrams can help tell the complex story.

The global health burden associated with exposure to heat is a grave concern and is projected to further increase under climate change. While physiological studies have demonstrated the role of humidity alongside temperature in exacerbating heat stress for humans, epidemiological findings remain conflicted. Understanding the intricate relationships between heat, humidity, and health outcomes is crucial to inform adaptation and drive increased global climate change mitigation efforts. This article introduces 'directed acyclic graphs' (DAGs) as causal models to elucidate the analytical complexity in observational epidemiological studies that focus on humid-heat-related health impacts. DAGs are employed to delineate implicit assumptions often overlooked in such studies, depicting humidity as a confounder, mediator, or an effect modifier. We also discuss complexities arising from using composite indices, such as wet-bulb temperature. DAGs representing the health impacts associated with wet-bulb temperature help to understand the limitations in separating the individual effect of humidity from the perceived effect of wet-bulb temperature on health. General examples for regression models corresponding to each of the causal assumptions are also discussed. Our goal is not to prioritize one causal model but to discuss the causal models suitable for representing humid-heat health impacts and highlight the implications of selecting one model over another. We anticipate that the article will pave the way for future quantitative studies on the topic and motivate researchers to explicitly characterize the assumptions underlying their models with DAGs, facilitating accurate interpretations of the findings. This methodology is applicable to similarly complex compound events.

Compilation of open access time-series datasets for studying temperature-mortality association.

In this article, we present a comprehensive compilation of open access daily time-series datasets tailored to assess the temperature-mortality association. The data consists of daily mortality counts and average ambient temperature at various levels of geographic aggregation, including data from four cities, ten regions, and two counties, which have been utilised in previously published studies. These datasets are applicable for time-series regression analysis to estimate location-specific temperature-mortality associations. Additionally, the availability of data from multiple geographical locations enabled the exploration of geographical differences by pooling associations using meta-analysis. This compilation aims to serve as a valuable resource for researchers, educators, and students, facilitating their application of time-series regression modelling for research endeavours and training activities.

Oil and gas development exposure and atrial fibrillation exacerbation: a retrospective study of atrial fibrillation exacerbation using Colorado's all payer claims dataset.

Introduction: Emerging risk factors for atrial fibrillation (AF) incidence and episodes (exacerbation), the most common and clinically significant cardiac arrhythmia, include air and noise pollution, both of which are emitted during oil and natural gas (O&G) well site development. Methods: We evaluated AF exacerbation risk and proximity to O&G well site development by employing a novel data source and interrupted time-series design. We retrospectively followed 1,197 AF patients living within 1-mile of an O&G well site (at-risk of exposure) and 9,764 patients living >2 miles from any O&G well site (unexposed) for AF claims in Colorado's All Payer Claims Dataset before, during, and after O&G well site development. We calculated AF exacerbation risk with multi-failure survival analysis. Results: The analysis of the total study population does not provide strong evidence of an association between AF exacerbation and proximity to O&G wells sites during (HR = 1.07, 95% CI: 0.94, 1.22) or after (HR = 1.01, 95% CI: 0.88, 1.16) development. However, AF exacerbation risk differed by patient age and sex. In patients >80 years living within 0.39 miles (2,059 feet) of O&G well site development, AF exacerbation risk increased by 83% (HR = 1.83, 95% CI: 1.25, 2.66) and emergency room visits for an AF event doubled (HR = 2.55, 95% CI: 1.50, 4.36) during development, with risk increasing with proximity. In female patients living within 0.39 miles of O&G well site development, AF exacerbation risk increased by 56% percent (95% CI: 1.13, 2.15) during development. AF exacerbation risk did not persist past the well development period. We did not observe increased AF exacerbation risk in younger or male patients. Discussion: The prospect that proximity to O&G well site development, a significant noise and air pollution source, may increase AF exacerbation risk in older and female AF patients requires attention. These findings support appropriate patient education to help mitigate risk and development of mitigation strategies and regulations to protect the health of populations in O&G development regions.

Promotion of environmental public health and environmental justice in communities affected by large and long lasting industrial contamination: methods applied and lessons learned from the case study of Porto Torres (Italy).

Introduction: Communities affected by large scale and long lasting industrial contamination are often keen to understand whether their health has been impaired by such contamination. This requires answers that integrate environmental public health and environmental justice perspectives. At these sites, exposure scenarios from environmental contamination over time by multiple chemicals, often involving different environmental matrices, are complex and challenging to reconstruct. Methods: An approach for describing the health of such communities in association with environmental contamination is presented, with the methods applied across the three domains of environmental contamination, population exposure and toxicology, environmental and social epidemiology, and environmental public health communication. The approach is described with examples from its application to the case study of Porto Torres, a town with a substantial industrially conditioned evolution. Results: Activities in the field of environmental contamination, population exposure and toxicology focus on the collection and systematization of available contamination data, the identification of priority pollutants based on their toxicological profiles, the qualitative assessment of the likelihood of exposure for the population to priority pollutants and their known health effects. Environmental and social epidemiology methods are applied to describe the health profiles and socioeconomic conditions of the local population, taking into account multiple health outcomes from local information systems and considering specific diseases based on exposure and toxicological assessments. The environmental public health communication methods are directed to produce a communication plan and for its implementation through interaction with local institutional and social actors. The interpretation of health profiles benefits from a transdisciplinary analysis of the results. Discussion: The proposed approach combines the needs of environmental public health and environmental justice allowing the integration of multidisciplinary knowledge to define recommendations for reducing and/or preventing hazardous environmental exposures and adverse health effects, stimulating the interactions between stakeholders, and making the study results more accessible to citizens.

Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis.

Background and objective: Heavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy metal exposures and the incidence of DKD. Methods: We analyzed data from the NHANES (2005-2020), using machine learning, and cross-sectional survey. Our study also involved a bidirectional two-sample Mendelian randomization (MR) analysis. Results: Machine learning reveals correlation coefficients of -0.5059 and - 0.6510 for urinary Ba and urinary Tl with DKD, respectively. Multifactorial logistic regression implicates urinary Ba, urinary Pb, blood Cd, and blood Pb as potential associates of DKD. When adjusted for all covariates, the odds ratios and 95% confidence intervals are 0.87 (0.78, 0.98) (p = 0.023), 0.70 (0.53, 0.92) (p = 0.012), 0.53 (0.34, 0.82) (p = 0.005), and 0.76 (0.64, 0.90) (p = 0.002) in order. Furthermore, multiplicative interactions between urinary Ba and urinary Sb, urinary Cd and urinary Co, urinary Cd and urinary Pb, and blood Cd and blood Hg might be present. Among the diabetic population, the OR of urinary Tl with DKD is a mere 0.10, with a 95%CI of (0.01, 0.74), urinary Co 0.73 (0.54, 0.98) in Model 3, and urinary Pb 0.72 (0.55, 0.95) in Model 2. Restricted Cubic Splines (RCS) indicate a linear linkage between blood Cd in the general population and urinary Co, urinary Pb, and urinary Tl with DKD among diabetics. An observable trend effect is present between urinary Pb and urinary Tl with DKD. MR analysis reveals odds ratios and 95% confidence intervals of 1.16 (1.03, 1.32) (p = 0.018) and 1.17 (1.00, 1.36) (p = 0.044) for blood Cd and blood Mn, respectively. Conclusion: In the general population, urinary Ba demonstrates a nonlinear inverse association with DKD, whereas in the diabetic population, urinary Tl displays a linear inverse relationship with DKD.

Exposome-wide association study of cognition among older adults in the National Health and Nutrition Examination Survey.

Cognitive impairment among older adults is a growing public health challenge and environmental chemicals may be modifiable risk factors. A wide array of chemicals has not yet been tested for association with cognition in an environment-wide association framework. In the US National Health and Nutrition Examination Survey (NHANES) 1999-2000 and 2011-2014 cross-sectional cycles, cognition was assessed using the Digit Symbol Substitution Test (DSST, scores 0-117) among participants aged 60 years and older. Concentrations of environmental chemicals measured in blood or urine were log2 transformed and standardized. Chemicals with at least 50% of measures above the lower limit of detection were included (nchemicals=147, nclasses=14). We tested for associations between chemical concentrations and cognition using parallel survey-weighted multivariable linear regression models adjusted for age, sex, race/ethnicity, education, smoking status, fish consumption, cycle year, urinary creatinine, and cotinine. Participants with at least one chemical measurement (n=4,982) were mean age 69.8 years, 55.0% female, 78.2% non-Hispanic White, and 77.0% at least high school educated. The mean DSST score was 50.4 (standard deviation (SD)=17.4). In adjusted analyses, 5 of 147 exposures were associated with DSST at p-value<0.01. Notably, a SD increase in log2-scaled cotinine concentration was associated with 2.71 points lower DSST score (95% CI -3.69, -1.73). A SD increase in log2-scaled urinary tungsten concentration was associated with 1.34 points lower DSST score (95% CI -2.11, -0.56). Exposure to environmental chemicals, particularly heavy metals and tobacco smoke, may be modifiable factors for cognition among older adults.

Air pollution and incident sarcoidosis in central Pennsylvania.

Sarcoidosis is a chronic granulomatous disease predominantly affecting the lungs and inducing significant morbidity and elevated mortality rate. The etiology of the disease is unknown but may involve exposure to an antigenic agent and subsequent inflammatory response resulting in granuloma formation. Various environmental and occupational risk factors have been suggested by previous observations, such as moldy environments, insecticides, and bird breeding. Our study investigated the association of air pollution with diagnosis of sarcoidosis using a case-control design. Penn State Health electronic medical records from 2005 to 2018 were examined for adult patients with (cases) and without (controls) an International Classification of Disease (ICD)-9 or -10 code for sarcoidosis. Patient addresses were geocoded and 24-hr residential-level air pollution concentrations were estimated using spatio-temporal models of particulate matter <2.5 µm (PM2.5), ozone, and PM2.5 elemental carbon (EC) and moving averages calculated. In total, 877 cases and 34,510 controls were identified. Logistic regression analysis did not identify significant associations between sarcoidosis incidence and air pollution exposure estimates. However, the odds ratio (OR) for EC for exposures occurring 7-10 years prior did approach statistical significance, and ORs exhibited an increasing trend for longer averaging periods. Data suggested a latency period of more than 6 years for PM2.5 and EC for reasons that are unclear. Overall, results for PM2.5 and EC suggest that long-term exposure to traffic-related air pollution may contribute to the development of sarcoidosis and emphasize the need for additional research and, if the present findings are substantiated, for public health interventions addressing air quality as well as increasing disease surveillance in areas with a large burden of PM2.5 and EC.

Health effects of air pollution on respiratory symptoms: A longitudinal study using digital health sensors.

Previous studies of air pollution and respiratory disease often relied on aggregated or lagged acute respiratory disease outcome measures, such as emergency department (ED) visits or hospitalizations, which may lack temporal and spatial resolution. This study investigated the association between daily air pollution exposure and respiratory symptoms among participants with asthma and chronic obstructive pulmonary disease (COPD), using a unique dataset passively collected by digital sensors monitoring inhaled medication use. The aggregated dataset comprised 456,779 short-acting beta-agonist (SABA) puffs across 3,386 people with asthma or COPD, between 2012 and 2019, across the state of California. Each rescue use was assigned space-time air pollution values of nitrogen dioxide (NO2), fine particulate matter with diameter ≤ 2.5 µm (PM2.5) and ozone (O3), derived from highly spatially resolved air pollution surfaces generated for the state of California. Statistical analyses were conducted using linear mixed models and random forest machine learning. Results indicate that daily air pollution exposure is positively associated with an increase in daily SABA use, for individual pollutants and simultaneous exposure to multiple pollutants. The advanced linear mixed model found that a 10-ppb increase in NO2, a 10 µg m-3 increase in PM2.5, and a 30-ppb increase in O3 were respectively associated with incidence rate ratios of SABA use of 1.025 (95 % CI: 1.013-1.038), 1.054 (95 % CI: 1.041-1.068), and 1.161 (95 % CI: 1.127-1.233), equivalent to a respective 2.5 %, 5.4 % and 16 % increase in SABA puffs over the mean. The random forest machine learning approach showed similar results. This study highlights the potential of digital health sensors to provide valuable insights into the daily health impacts of environmental exposures, offering a novel approach to epidemiological research that goes beyond residential address. Further investigation is warranted to explore potential causal relationships and to inform public health strategies for respiratory disease management.

Air quality and kidney health: Assessing the effects of PM10, PM2.5, CO, and NO2 on renal function in primary glomerulonephritis.

BACKGROUND: While extensive studies have elucidated the relationships between exposure to air pollution and chronic diseases, such as cardiovascular disorders and diabetes, the intricate effects on specific kidney diseases, notably primary glomerulonephritis (GN)-an immune-mediated kidney ailment-are less well understood. Considering the escalating incidence of GN and conspicuous lack of investigative focus on its association with air quality, investigation is dedicated to examining the long-term effects of air pollutants on renal function in individuals diagnosed with primary GN. METHODS: This retrospective cohort analysis was conducted on 1394 primary GN patients who were diagnosed at Seoul National University Bundang Hospital and Seoul National University Hospital. Utilizing time-varying Cox regression and linear mixed models (LMM), we examined the effect of yearly average air pollution levels on renal function deterioration (RFD) and change in estimated glomerular filtration rate (eGFR). In this context, RFD is defined as sustained eGFR of less than 60 mL/min per 1.73 m2. RESULTS: During a mean observation period of 5.1 years, 350 participants developed RFD. Significantly, elevated interquartile range (IQR) levels of air pollutants-including PM10 (particles ≤10 micrometers, HR 1.389, 95 % CI 1.2-1.606), PM2.5 (particles ≤2.5 micrometers, HR 1.353, 95 % CI 1.162-1.575), CO (carbon monoxide, HR 1.264, 95 % CI 1.102-1.451), and NO2 (nitrogen dioxide, HR 1.179, 95 % CI 1.021-1.361)-were significantly associated with an increased risk of RFD, after factoring in demographic and health variables. Moreover, exposure to PM10 and PM2.5 was associated with decreased eGFR. CONCLUSIONS: This study demonstrates a substantial link between air pollution exposure and renal function impairment in primary GN, accentuating the significance of environmental determinants in the pathology of immune-mediated kidney diseases.

Prenatal and childhood exposure to bisphenols and bone mineral density in 7-year-old children from the Odense Child Cohort.

BACKGROUND: Bisphenol A (BPA) is a well-known endocrine disrupter used in several consumer products. Restricted use of BPA has led to increased use of bisphenol F (BPF) and bisphenol S (BPS). While previous studies found no associations between prenatal BPA and BPF exposure and bone mineral density (BMD), two recent cohort studies found that prenatal BPS exposure was negatively associated with bone mineral density in the offspring. AIM: To determine possible associations between maternal and child urinary bisphenol concentrations, BMD and bone mineral content (BMC) in 7-year-old healthy children. METHODS: Pregnant women were recruited in 2010-2012 to participate in the Odense Child Cohort (OCC), Denmark. Maternal urine samples were collected in gestational week 28 and urinary BPA concentration was measured by isotope diluted LC-MS/MS. The children delivered a urine sample at age 7 years in which BPA, BPF and BPS were measured by an extended LS-MS/MS method based on the original method. At age 7 years DXA scans were performed and BMC and Z-score for BMD calculated. Associations between osmolality adjusted urinary maternal BPA and child BPA, BPF and BPS concentrations and BMC and BMD Z-score were examined by multiple linear regression analysis adjusted for potential confounders. Additionally, a combined effect of the bisphenols were evaluated by including the sum of child urinary BPA, BPF and BPS concentrations in the statistical analyses. RESULTS: A total of 546 mothers and 453 children aged 7 years participated. BPA was detected in 84% and 96% of the maternal and child urine samples, respectively. We found no significant association between maternal urinary BPA concentration during pregnancy and BMC and BMD Z-score in 7-year-old children. In addition, no association between current bisphenol exposure in tertiles and bone density was found, interestingly, current BPA and summed bisphenol exposure in the highest 10% was associated with lower BMD Z-score at age 7-years, statistically significant for boys. CONCLUSION: In these low exposed children we found no association between prenatal or current bisphenol exposure in tertiles and BMD in healthy children, however, the highest 10% exposed children had lower BMD, significant for boys, suggesting a negative impact with high bisphenol exposure. The short half-lives of bisphenols and the cross-sectional nature of the child exposure prompt more longitudinal studies to further clarify this topic.

Biomonitoring Environmental Exposure in Syrian Refugees in Lebanon.

Over one million Syrian refugees have been residing in substandard living conditions in Lebanon for the past decade. Non-invasive biomonitoring of fractional exhaled nitric oxide (FeNO) as a pulmonary inflammation biomarker was conducted following and preceding indoor environmental assessments (which revealed elevated mould counts in informal tented settlements and non-residential shelters) to further evaluate effects of environmental exposure to indoor contaminants. Results of biomonitoring (n = 57) provided some insight regarding existing respiratory conditions and the possible implementation of minimally invasive methods to establish susceptibility profiles in Syrian refugees amid limited access to healthcare. The clinical interpretation of FeNO results suggested possible persistent exposure to allergens in addition to significant type 2 inflammation in some subjects. These findings warrant the need to expand this study, investigate other biomarkers, and attempt to correlate findings with environmental conditions to evaluate if a dose-response relationship exists.