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Introduction: Growing evidence exists that greenspace exposure can reduce metabolic syndrome risk, a growing public health concern with well-documented inequities across population subgroups. We capitalize on the use of g-computation to simulate the influence of multiple possible interventions on residential greenspace on nine metabolic biomarkers and metabolic syndrome in adults (N = 555) from the 2014-2017 Community of Mine Study living in San Diego County, California., Methods: Normalized difference vegetation index (NDVI) exposure from 2017 was averaged across a 400-m buffer around the participants' residential addresses. Participants' fasting plasma glucose, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglyceride concentrations, systolic and diastolic blood pressure, hemoglobin A1c (%), waist circumference, and metabolic syndrome were assessed as outcomes of interest. Using parametric g-computation, we calculated risk differences for participants being exposed to each decile of the participant NDVI distribution compared to minimum NDVI. Differential health impacts from NDVI exposure by sex, ethnicity, income, and age were examined., Results: We found that a hypothetical increase in NDVI exposure led to a decrease in hemoglobin A1c (%), glucose, and high-density lipoprotein cholesterol concentrations, an increase in fasting total cholesterol, low-density lipoprotein cholesterol, and triglyceride concentrations, and minimal changes to systolic and diastolic blood pressure, waist circumference, and metabolic syndrome. The impact of NDVI changes was greater in women, Hispanic individuals, and those under 65 years old., Conclusions: G-computation helps to simulate the potential health benefits of differential NDVI exposure and identifies which subpopulations can benefit most from targeted interventions aimed at minimizing health disparities., Competing Interests: The authors declare that they have no conflicts of interest with regard to the content of this report., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.)

Background: Little is known about the impact of environmental exposure change on metabolic biomarkers associated with cancer risk. Furthermore, this limited epidemiological evidence on metabolic biomarkers focused on residential exposure, without considering the activity space which can be done by modelling dynamic exposures. In this longitudinal study, we aimed to investigate the impact of environmental exposures change on metabolic biomarkers using GPS-GIS based measurements., Methods: Among two weight loss interventions, the Reach for Health and the MENU studies, which included ∼460 women at risk of breast cancer or breast cancer survivors residing in Southern California, three metabolic biomarkers (insulin resistance, fasting glucose, and C-reactive protein) were assessed. Dynamic GPS-GIS based exposure to green spaces, recreation, walkability, NO 2 , and PM2.5 were calculated at baseline and 6 months follow-up using time-weighted spatial averaging. Generalized estimating equations models were used to examine the relationship between changes in environmental exposures and biomarker levels over time., Results: Overall, six-month environmental exposure change was not associated with metabolic biomarkers change. Stratified analyses by level of environmental exposures at baseline revealed that reduced NO2 and PM2.5 exposure was associated with reduced fasting glucose concentration among women living in a healthier environment at baseline (β -0.010, 95%CI -0.025, 0.005; β -0.019, 95%CI -0.034, -0.003, respectively). Women living in poor environmental conditions at baseline and exposed to greener environments had decreased C-reactive protein concentrations (β -1.001, 95%CI -1.888, -0.131)., Conclusions: The impact of environmental exposure changes on metabolic biomarkers over time may be modified by baseline exposure conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Study Objectives: Examining multiple dimensions of sleep health may better capture associations between sleep and health risks, including cardiometabolic disease (CMD). Hispanics have elevated risk for inadequate sleep and CMD biomarkers. Few studies have explored whether associations between sleep and CMD differ by Hispanic ethnicity., Methods: Leveraging data from the Community of Mine (CoM) study, a cross-sectional investigation of 602 ethnically diverse participants, we derived accelerometer-measured sleep duration and efficiency, and self-reported sleep quality. Accelerometer-measured sleep exposures were analyzed both as continuous and categorical variables. Multivariate and quantile regression models were used to assess associations between sleep and CMD biomarkers (insulin resistance, systolic blood pressure, and low-density-lipoprotein cholesterol), controlling for age, sex, ethnicity, education, smoking status, and body mass index. We examined the potential effect modification of Hispanic ethnicity., Results: We observed mixed results based on CMD biomarkers and sleep exposure. Increased sleep duration was significantly related to low-density lipoprotein cholesterol in adjusted models (estimate = 0.06; 95% CI: 0.02, 0.11). Poor sleep efficiency was associated with greater insulin resistance in the adjusted quantile (estimate = 0.20; 95% CI: 0.04, 0.36) model at the 90th percentile. Self-reported sleep quality was not associated with CMD outcomes. There was no evidence of effect modification by Hispanic ethnicity., Conclusions: In this cohort, sleep health measures were found to have mixed and at times opposing effects on CMD outcomes. These effects did not demonstrate an interaction with Hispanic ethnicity., (© The Author(s) 2023. Published by Oxford University Press on behalf of Sleep Research Society.)

Wildfires constitute a growing source of extremely high levels of particulate matter that is less than 2.5 microns in diameter (PM2.5). Recently, toxicologic and epidemiologic studies have shown that PM2.5 generated from wildfires may have a greater health burden than PM2.5 generated from other pollutant sources. This study examined the impact of PM2.5 on hospitalizations for respiratory diseases in California between 2006 and 2019 using a health impact assessment approach that considers differential concentration-response functions (CRF) for PM2.5 from wildfire and non-wildfire sources of emissions. We quantified the burden of respiratory hospitalizations related to PM2.5 exposure at the zip code level through two different approaches: (a) naïve (considering the same CRF for all PM2.5 emissions) and (b) nuanced (considering different CRFs for PM2.5 from wildfires and from other sources). We conducted a Geographically Weighted Regression to analyze spatially varying relationships between the delta (i.e., the difference between the naïve and nuanced approaches) and the Centers for Disease Control and Prevention's Social Vulnerability Index (SVI). A higher attributable number of respiratory hospitalizations was found when accounting for the larger health burden of wildfire PM2.5. We found that, between 2006 and 2019, the number of hospitalizations attributable to PM2.5 may have been underestimated by approximately 13% as a result of not accounting for the higher CRF of wildfire-related PM2.5 throughout California. This underestimation was higher in northern California and areas with higher SVI rankings. The relationship between delta and SVI varied spatially across California. These findings can be useful for updating future air pollution guideline recommendations., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2023 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

Evidence linking traffic noise to insulin resistance and diabetes is limited and unanswered questions remain regarding the potential effect modification by neighborhood socioeconomic status (nSES). We aimed to assess socioeconomic inequalities in noise exposure, whether road and aircraft noise exposures were associated with insulin resistance or diabetes, and whether nSES modified these relationships. Among the Community of Mine Study in San Diego County, road and aircraft noise exposure at enrollment was calculated based on the static (participant's administrative boundary, and circular buffer around participant homes), and dynamic (mobility data by global positioning system, GPS) spatio-temporal aggregation methods. Associations of noise with insulin resistance (HOMA-IR) or type 2 diabetes (T2DM) were quantified using generalized estimating equation models adjusted for sex, age, ethnicity, individual income, and air pollution (nitrogen dioxide) exposure. Additive interaction between noise and nSES was assessed. Among 573 participants (mean age 58.7 y), participants living in low nSES were exposed to higher levels of aircraft and road noise using noise level at the census tract, circular buffer, or Kernel Density Estimation (KDE) of GPS data. Participants exposed to road noise greater or equal to the median (53 dB(A)) at the census tract and living in low nSES had an increased level of insulin resistance (β = 0.15, 95%CI: -0.04, 0.34) and higher odds of T2DM (Odds Ratio = 2.34, 95%CI: 1.12, 4.90). A positive additive interaction was found as participants living in low nSES had higher odds of T2DM. The impact of noise exposure on insulin resistance and T2DM differs substantially by nSES. Public health benefits of reducing exposure to road or aircraft noise would be larger in individuals living in low nSES., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: We investigated whether hypertension may be a mediator in the pathway linking environmental noise exposure to incident MI and stroke., Methods: Separately for MI and stroke, we built two population-based cohorts from linked health administrative data. Participants were residents of Montreal (Canada) between 2000 and 2014, aged 45 years and older who were free of hypertension and MI or stroke at time of entry. MI, stroke and hypertension were ascertained from validated case definitions. Residential long-term environmental noise exposure, expressed as the annual mean level acoustic equivalent 24 h (L Aeq24h ), was estimated from a land use regression model. We performed mediation analysis based on the potential outcomes framework. We used a Cox proportional hazards model for the exposure-outcome model and a logistic regression for the exposure-mediator model. In sensitivity analysis we applied a marginal structural approach to estimate the natural direct and indirect effects., Results: Each cohort included approximately 900 000 individuals, with 26 647 incident cases of MI and 16 656 incident cases of stroke. 36% of incident MI and 40% of incident stokes had previously developed hypertension. The estimated total effect per interquartile range increase (from 55.0 to 60.5 dB A) in the annual mean L Aeq24h was 1.073 (95% confidence interval (CI): 1.070-1.077) for both MI for stroke. We found no evidence of exposure-mediator interaction for both outcomes. The relationships between environmental noise and MI and stroke was not mediated by hypertension., Conclusions: This population-based cohort study suggests that the main route by which environmental noise exposure may cause MI or stroke is not through hypertension., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Accumulating evidence links cardiometabolic health with social and environmental neighborhood exposures, which may contribute to health inequities. We examined whether environmental characteristics were individually or jointly associated with insulin resistance, hypertension, obesity, type 2 diabetes, and metabolic syndrome in San Diego County, CA. As part of the Community of Mine Study, cardiometabolic outcomes of insulin resistance, hypertension, BMI, diabetes, and metabolic syndrome were collected in 570 participants. Seven census tract level characteristics of participants' residential environment were assessed and grouped as follows: economic, education, health care access, neighborhood conditions, social environment, transportation, and clean environment. Generalized estimating equation models were performed, to take into account the clustered nature of the data and to estimate β or relative risk (RR) and 95 % confidence intervals (CIs) between each of the seven environmental characteristics and cardiometabolic outcomes. Quantile g-computation was used to examine the association between the joint effect of a simultaneous increase in all environmental characteristics and cardiometabolic outcomes. Among 570 participants (mean age 58.8 ± 11 years), environmental economic, educational and health characteristics were individually associated with insulin resistance, diabetes, obesity, and metabolic syndrome. In the mixture analyses, a joint quartile increase in all environmental characteristics (i.e., improvement) was associated with decreasing insulin resistance (β, 95 %CI: -0.09, -0.18-0.01)), risk of diabetes (RR, 95 %CI: 0.59, 0.36-0.98) and obesity (RR, 95 %CI: 0.81, 0.64-1.02). Environmental characteristics synergistically contribute to cardiometabolic health and independent analysis of these determinants may not fully capture the potential health impact of social and environmental determinants of health., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Limited previous work has identified a relationship between exposure to ambient air pollution and aggressive somatic lung tumor mutations. More work is needed to confirm this relationship, especially using spatially resolved air pollution. We aimed to quantify the association between different air pollution metrics and aggressive tumor biology. Among patients treated at City of Hope Comprehensive Cancer Center in Duarte, CA (2013-2018), three non-small cell lung cancer somatic tumor mutations, TP53 , KRAS , and KRAS G12C / V , were documented. PM 2.5 exposure was assessed using state-of-the art ensemble models five and ten years before lung cancer diagnosis. We also explored the role of NO 2 using inverse-distance-weighting approaches. We fitted logistic regression models to estimate odds ratio (OR) and their 95% confidence intervals (CIs). Among 435 participants (median age: 67, female: 51%), an IQR increase in NO 2 exposure (3.5 μg/m 3 ) five years before cancer diagnosis was associated with an increased risk in TP53 mutation (OR, 95% CI: 1.30, 0.99-1.71). We found an association between highly-exposed participants to PM 2.5 (>12 μg/m 3 ) five and ten years before cancer diagnosis and TP53 mutation (OR, 95% CI: 1.61, 0.95-2.73; 1.57, 0.93-2.64, respectively). Future studies are needed to confirm this association and better understand how air pollution impacts somatic profiles and the molecular mechanisms through which they operate.

Introduction: Exposure to air pollution disproportionately affects racial/ethnic minorities that could contribute to health inequalities including metabolic disorders. However, most existing studies used a static assessment of air pollution exposure (mostly using the residential address) and do not account for activity space when modelling exposure to air pollution. The aim of this study is to understand how exposure to air pollution impacts metabolic disorders biomarkers, how this effect differs according to ethnicity, and for the first time compare these findings with two methods of exposure assessment: dynamic and static measures., Methods: Among the Community of Mine study, a cross-sectional study conducted in San Diego County, insulin resistance, diabetes, hypertension, obesity, dyslipidemia, and metabolic syndrome (MetS) were assessed. Exposure to air pollution (PM 2.5 , NO 2 , traffic) was calculated using static measures around the home, and dynamic measures of mobility derived from Global Positioning Systems (GPS) traces using kernel density estimators to account for exposure variability across space and time. Associations of air pollution with metabolic disorders were quantified using generalized estimating equation models to account for the clustered nature of the data., Results: Among 552 participants (mean age 58.7 years, 42% Hispanic/Latino), Hispanics/Latinos had a higher exposure to PM 2.5 compared to non-Hispanics using static measures. In contrast, Hispanics/Latinos had less exposure to PM 2.5 using dynamic measures. For all participants, higher dynamic exposure to PM 2.5 and NO 2 was associated with increased insulin resistance and cholesterol levels, and increased risk of obesity, dyslipidemia and MetS (RR 1.17, 95% CI: 1.07-1.28; RR 1.21, 95% CI: 1.12-1.30, respectively). The association between dynamic PM 2.5 exposure and MetS differed by Hispanic/Latino ethnicity., Conclusion: These results highlight the importance of considering people's daily mobility in assessing the impact of air pollution on health., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Air pollution exposure is one of the modifiable risk factors of cognitive decline. We aimed to test the association between exposure to several outdoor air pollutants and domain-specific cognitive performance., Methods: In this cross-sectional study, we used data from the enrolment phase of the French CONSTANCES cohort. From the 220 000 people (aged 18-69 years) randomly recruited in the French CONSTANCES cohort, participants aged 45 years old or older (104 733 people) underwent a comprehensive cognitive assessment (verbal episodic memory, language skills, and executive functions). After exclusion of those who were not suitable for our analysis, 61 462 participants with available data were included in the analyses. We used annual mean concentrations at residential addresses, derived from land-use regression models, to assign exposure to particulate matter with aerodynamic diameters less than 2·5 μm (PM 2·5 ), nitrogen dioxide (NO 2 ), and black carbon. We used multiple linear regression models with different covariate adjustments to test the associations between each pollutant and cognitive outcomes. We did several sensitivity analyses, including multilevel modelling, meta-analysis by centre of recruitment, and exclusion of specific population groups., Findings: We found significantly poorer cognitive function, especially on semantic fluency and domains of executive functions, with an increase in exposure to black carbon and NO 2 . Exposure to PM 2·5 was mainly significant for the semantic fluency test. We found that decrease in cognitive performance with an increase of one interquartile range of exposure ranged from 1% to nearly 5%. The largest effect size (percentage decrease) for both PM 2.5 and NO 2 was found for the semantic fluency test (PM 2.5 4·6%, 95% CI 2·1-6·9 and NO 2 3·8%, 1·9-5·7), whereas for black carbon, the largest effect size was found for the digit symbol substitution test of the domains of executive functions (4·5%, 2·7-6·3). Monotonic and linear exposure-response associations were found between air pollution exposure and cognitive performance, starting from a low level of exposures., Interpretation: Significantly poorer cognitive performance was associated with exposure to outdoor air pollution even at low levels of exposure. This highlights the importance of further efforts to reduce exposure to air pollution., Funding: The Caisse Nationale d'Assurance Maladie, and partly funded by Merck Sharp & Dohme and L'Oréal, the French National Research Agency, and Fondation de France., Translation: For the French translation of the abstract see Supplementary Materials section., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Solvents are used in many workplaces and may be airway irritants but few studies have examined their association with asthma. We studied this question in CONSTANCES (cohort of 'CONSulTANts des Centres d'Examens de Santé'), a large French cohort. Current asthma and asthma symptom scores were defined by participant-reported respiratory symptoms, asthma medication or attacks, and the sum of 5 symptoms, in the past 12 months, respectively. Lifetime exposures to 5 organic solvents, paints and inks were assessed by questionnaire and a population-based Job-Exposure Matrix (JEM). Cross-sectional associations between exposures and outcomes were evaluated by gender using logistic and negative binomial regressions adjusted for age, smoking habits and body mass index. Analyses included 115,757 adults (54% women, mean age 47 years, 9% current asthma). Self-reported exposure to ≥1 solvent was significantly associated with current asthma in men and women, whereas using the JEM, a significant association was observed only in women. Significant associations between exposures to ≥1 solvent and asthma symptom score were observed for both self-report (mean score ratio, 95%CI, women: 1.36, 1.31-1.42; men: 1.34, 1.30-1.40) and JEM (women: 1.10, 1.07-1.15; men: 1.14, 1.09-1.18). Exposure to specific solvents was significantly associated with higher asthma symptom score. Occupational exposure to solvents should be systematically sought when caring for asthma.

Background: Emerging epidemiological evidence suggests a relationship between exposure to air pollution and dementia. However, most of the existing studies relied on health administrative databases for the diagnosis of dementia. In a large French population-based cohort (the 3C Study), we assessed the effects of particulate matter ≤2.5 µm (PM2.5), nitrogen dioxide (NO 2 ) and black carbon (BC) on the risk of dementia diagnosed with reliable tools., Methods: Participants aged ≥65 years were recruited between 1999 and 2001 and followed for 12 years. At baseline and every 2 years, dementia was suspected on the basis of the neuropsychological and neurological examination and confirmed by an independent committee of clinicians. Exposure to NO 2 , BC and PM2.5 at the participants' residential address was estimated using land use regression models. For each pollutant and year of follow-up, the 10-year moving average of past exposure was estimated. Multilevel spatial random-effects Cox proportional hazards models were used in which exposure was included as a time-varying variable. Analyses were adjusted for individual (age, sex, education, APOE4 genotype, health behaviours) and contextual (neighbourhood deprivation index) confounders., Results: At baseline, the median age of the 7066 participants was 73.4 years, and 62% were women. The median follow-up duration was 10.0 years during which 791 participants developed dementia (n = 541 Alzheimer's disease (AD) and n = 155 vascular/mixed dementia (VaD)). The 10-year moving average of PM2.5 concentrations ranged from 14.6 to 31.3 µg/m 3 . PM2.5 concentration was positively associated with dementia risk: HR = 1.20, 95% CI (1.08-1.32) for all-cause dementia, 1.20 (1.09-1.32) for AD, and 1.33 (1.05-1.68) for VaD per 5 µg/m 3 PM2.5 increase. No association was detected between NO 2 or BC exposure and dementia risk., Conclusion: In this large cohort of older adults, long-term PM2.5 exposure was associated with increased dementia incidence. Reducing PM2.5 emissions might lessen the burden of dementia in aging populations., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objectives: Despite the recent awareness of the environment impact on brain ageing, the influence of the neighbourhood socioeconomic status on cognitive impairment remains unclear. Here, we investigated the effects of individual and neighbourhood deprivation on cognitive impairment in middle-aged and young-old people., Design: Cross-sectional study., Settings: 21 Health Screening Centres in the entire French metropolitan territory., Participants: A total of 44 648 participants (age range: 45 to 69 years) from the French CONSTANCES cohort were included in the analyses., Main Outcomes: Associations between the overall cognitive score (based on a standardised battery of cognitive tests administered by neuropsychologists) and individual deprivation (Evaluation of Deprivation and Inequalities in Health Screening Centres; EPICES score) and geographical deprivation (French Deprivation Index; FDep index)., Results: Based on the EPICES score (validated cut-off ≥30.17), 12% of participants were considered to be deprived. After mutual adjustment, individual and geographical deprivation were associated with higher cognitive impairment in a multilevel logistic regression analysis that was also adjusted for sociodemographic, lifestyle and health factors. Specifically, individual deprivation was associated with an odds increase of 55% (OR=1.55, 95% CI: 1.45 to 1.66). The risk of global cognitive impairment progressively increased with the neighbourhood deprivation level, evaluated by the FDep index (reference Q1; Q2: OR=1.09, 95% CI: 0.98 to 1.20; Q3: OR=1.15, 95% CI: 1.04 to 1.27; Q4: OR=1.15, 95% CI: 1.04 to 1.28; Q5: OR=1.25, 95% CI: 1.13 to 1.39)., Conclusion: Our results suggest that the neighbourhood socioeconomic deprivation level is associated with cognitive impairment, independently of the individual deprivation level. A better understanding of this association could help to define new prevention strategies to target high-risk residents and high-risk geographical areas in order to reduce social health inequalities., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Socioeconomic level of residential environment was found to influence cognitive performance. However, individuals from the same place of residence may be affected differently. We aim to investigate for the first time the influence of individual activity space on the association between neighborhood socioeconomic status (NSES) and the risk of dementia., Methods: In the frame of the Three-City cohort, a French population-based study, we followed longitudinally (12 years) 7009 participants aged over 65. The activity space (i.e., the spatial area through which a person moves daily) was defined using two questions from Lawton's Instrumental Activities of Daily Living scale ("Goes shopping independently","Travels alone"), and one question about mobility restriction. The survival analysis was performed using a Cox marginal model that takes into account intra-neighborhood correlations and includes a large number of potential confounders., Results: Among people with a limited activity space (n = 772, 11%), risk of dementia is increased in subjects living in a deprived area (characterized by high GINI index or low median income) compared to those living in more favored., Conclusion: This study shows that the individual activity space modifies the association between NSES and the risk of dementia providing a more complete picture of residential inequalities. If confirmed in different populations, these findings suggest that people with limited activity space and living in a deprived neighborhood are particularly at risk and should be targeted for prevention.