Deep phenotyping meets big data: the Geoscience and hEalth Cohort COnsortium (GECCO) data to enable exposome studies in The Netherlands

Time-varying exposure to food retailers and cardiovascular disease hospitalization and mortality in the netherlands: a nationwide prospective cohort study

BACKGROUND

Very few studies to date investigated the prospective association of changes in exposure to the food environment with cardiovascular disease (CVD) risk. We aim to explore if time-varying exposure to the food environment was associated with hospitalization and mortality due to total and specific types of CVD in The Netherlands.

METHODS

In this prospective cohort study, 4,641,435 Dutch adults aged 35 + years who did not change residence in 2002-2018 were identified through registry data. Exposure to the food environment was defined as time-varying Food Environment Healthiness Index (FEHI) scores (range: - 5 to 5) and time-varying kernel density of specific food retailers (e.g., fast food outlets, supermarkets) around the home location between 2004 and 2018. The main outcome measures were hospitalization and mortality due to overall CVD, stroke, HF, and CHD occurring between 2004 and 2020, based on hospital and death registries.

RESULTS

In Cox regression models, each unit increase in the FEHI was associated with a lower hospitalization and mortality of CVD (hospitalization hazard ratio (HRh) = 0.90 (0.89 to 0.91), mortality hazard ratio (HRm) = 0.85 (0.82 to 0.89)), CHD (HRh = 0.88 (0.85 to 0.91), HRm = 0.80 (0.75 to 0.86)), stroke (HRh = 0.89 (0.84 to 0.93)), HRm = 0.89 (0.82 to 0.98)), and HF (HRh = 0.90 (0.84-0.96), HRm = 0.84 (0.76 to 0.92)). Increased density of local food shops, fast food outlets, supermarkets, and convenience stores and decreased density of food delivery outlets and restaurants were associated with a higher risk of CVD, CHD, stroke, and HF hospitalization and mortality.

CONCLUSIONS

In this observational longitudinal study, changes in exposure to a healthier food environment over 14 years were associated with a risk reduction in CVD hospitalization and mortality, in particular in urbanized areas and for younger adults and those with higher incomes.

Associations of neighborhood social cohesion and changes in BMI-The Maastricht Study

The role of the social environment can facilitate positive health outcomes through active community engagement, normalization of healthy behaviors, and stress buffering. We aim to examine the associations of neighborhood social cohesion with changes in BMI over time. A total of 7641 participants from The Maastricht Study between the ages of 40 and 75 years were analyzed. Weight and height were measured at baseline, and weight was self-reported annually up to 10 years of follow-up (median = 4.7 years). Perceived social cohesion was obtained by questionnaire. Home addresses for each participant were linked to geographic information system data from the Geoscience and Health Cohort Consortium to create neighborhood exposure variables including area level social cohesion, neighborhood walkability, and food environment within a 1000 m Euclidian buffer. Linear regression analyses were performed with BMI adjusted for socioeconomic variables. A mixed model analysis was carried out to examine changes in BMI. Living in the highest quartile area of individually perceived social cohesion was associated with lower BMI (Q4 B: -.53; 95% CI = -.79, -.28) compared to the lowest quartile. Similar findings were discovered using the area level measure (Q4 B: -.97; 95% CI = -1.29, -.65). There was no longitudinal association between social cohesion and BMI. Neighborhood social cohesion was associated with lower BMI classifying it as an obesogenic area characteristic that influences weight, independent of conventional built environment features.

Association of ambient air pollution with cognitive functioning and markers of structural brain damage: The Maastricht study

INTRODUCTION

Given the absence of curative interventions and the rising global incidence of dementia, research is increasingly focusing on lifestyle factors for prevention. However, identifying shared environmental risk for dementia, next to individual factors, is crucial for optimal risk reduction strategies. Therefore, in the present study we investigated the association between air pollution, cognitive functioning, and markers of structural brain damage.

METHODS

We used cross-sectional data from 4,002 participants of The Maastricht Study on volumetric markers of brain integrity (white and grey matter volume, cerebrospinal fluid volume, white matter hyperintensities volume, presence of cerebral small vessel disease) and cognitive functioning (memory, executive functioning and attention, processing speed, overall cognition). Individuals were matched by postal code of residence to nationwide data on air pollution exposure (particulate matter < 2.5 μm (PM2.5), particulate matter <10 μm (PM10), nitrogen dioxide (NO2), soot). Potentia linear and non-linear associations were investigated with linear, logistic, and restricted cubic splines regression. All analyses were adjusted for demographic characteristics and a compound score of modifiable dementia risk and protective factors.

RESULTS

Exposure to air pollutants was not related to cognitive functioning and most brain markers. We found curvilinear relationships between high PM2.5 exposures and grey matter and cerebrospinal fluid volume. Participants in the low and high range of exposure had lower grey matter volume. Higher cerebrospinal fluid volumes were only associated with high range of exposure, independent of demographic and individual modifiable dementia risk factors. After additional post hoc analyses, controlling for urbanicity, the associations for grey matter volume became non-significant. In men only, higher exposure to all air pollutants was associated with lower white matter volumes. No significant associations with white matter hyperintensities volume or cerebral small vessel disease were observed.

DISCUSSION

Our findings suggest that higher PM2.5 exposure is associated with more brain atrophy.

The association of obesogenic environments with weight status, blood pressure, and blood lipids: A cross-sectional pooled analysis across five cohorts

In this observational cross-sectional study, we investigated the relationship between combined obesogenic neighbourhood characteristics and various cardiovascular disease risk factors in adults, including BMI, systolic blood pressure, and blood lipids, as well as the prevalence of overweight/obesity, hypertension, and dyslipidaemia. We conducted a large-scale pooled analysis, comprising data from five Dutch cohort studies (n = 183,871). Neighbourhood obesogenicity was defined according to the Obesogenic Built-environmental CharacterisTics (OBCT) index. The index was calculated for 1000m circular buffers around participants' home addresses. For each cohort, the association between the OBCT index and prevalence of overweight/obesity, hypertension and dyslipidaemia was analysed using robust Poisson regression models. Associations with continuous measures of BMI, systolic blood pressure, LDL-cholesterol, HDL-cholesterol, and triglycerides were analysed using linear regression. All models were adjusted for age, sex, education level and area-level socio-economic status. Cohort-specific estimates were pooled using random-effects meta-analyses. The pooled results show that a 10 point higher OBCT index score was significantly associated with a 0.17 higher BMI (95%CI: 0.10 to 0.24), a 0.01 higher LDL-cholesterol (95% CI: 0.01 to 0.02), a 0.01 lower HDL cholesterol (95% CI: -0.02 to -0.01), and non-significantly associated with a 0.36 mmHg higher systolic blood pressure (95%CI: -0.14 to 0.65). A 10 point higher OBCT index score was also associated with a higher prevalence of overweight/obesity (PR = 1.03; 95% CI: 1.02 to 1.05), obesity (PR = 1.04; 95% CI: 1.01 to 1.08) and hypertension (PR = 1.02; 95% CI: 1.00 to 1.04), but not with dyslipidaemia. This large-scale pooled analysis of five Dutch cohort studies shows that higher neighbourhood obesogenicity, as measured by the OBCT index, was associated with higher BMI, higher prevalence of overweight/obesity, obesity, and hypertension. These findings highlight the importance of considering the obesogenic environment as a potential determinant of cardiovascular health.

Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort

Effective personalized well-being interventions require the ability to predict who will thrive or not, and the understanding of underlying mechanisms. Here, using longitudinal data of a large population cohort (the Netherlands Twin Register, collected 1991-2022), we aim to build machine learning prediction models for adult well-being from the exposome and genome, and identify the most predictive factors (N between 702 and 5874). The specific exposome was captured by parent and self-reports of psychosocial factors from childhood to adulthood, the genome was described by polygenic scores, and the general exposome was captured by linkage of participants' postal codes to objective, registry-based exposures. Not the genome (R 2 = -0.007 [-0.026-0.010]), but the general exposome (R 2 = 0.047 [0.015-0.076]) and especially the specific exposome (R 2 = 0.702 [0.637-0.753]) were predictive of well-being in an independent test set. Adding the genome (P = 0.334) and general exposome (P = 0.695) independently or jointly (P = 0.029) beyond the specific exposome did not improve prediction. Risk/protective factors such as optimism, personality, social support and neighborhood housing characteristics were most predictive. Our findings highlight the importance of longitudinal monitoring and promises of different data modalities for well-being prediction.

Longitudinal associations of air pollution and green space with cardiometabolic risk factor clustering among children in the Netherlands

BACKGROUND

This study examines longitudinal associations of air pollution and green space with cardiometabolic risk among children in the Netherlands.

METHODS

Three Dutch prospective cohorts with a total of 13,822 participants aged 5 to 17 years were included: (1) the Amsterdam Born Children and their Development (ABCD) study from Amsterdam (n = 2,547), (2) the Generation R study from Rotterdam (n = 5,431), and (3) the Lifelines study from northern Netherlands (n = 5,844). Air pollution (PM2.5, PM10, NO2, and elemental carbon (EC)) and green space exposures (density in multiple Euclidean buffer sizes) from 2006 to 2017 at home address level were used. Cardiometabolic risk factor clustering was assessed by a MetScore, which was derived from a confirmatory factor analysis of six cardiometabolic risk factors to assess the overall risk. Linear regression models with change in Metscore as the dependent variable, adjusted for multiple confounders, were conducted for each cohort separately. Meta-analyses were used to pool cohort-specific estimates.

RESULTS

Exposure to higher levels of NO2 and EC was significantly associated with increases in MetScore in Lifelines (per SD higher exposure: βNO2 = 0.006, 95 % CI = 0.001 to 0.010; βEC = 0.008, 95 % CI = 0.002 to 0.014). In the other two cohort studies, these associations were in the same direction but these were not significant. Higher green space density in 500-meter buffer zones around participants' residential addresses was not significantly associated with decreases of MetScore in all three cohorts. Higher green space density in 2000-meter buffer zones was significantly associated with decreases of MetScore in ABCD and Lifelines (per SD higher green space density: βABCD = -0.008, 95 % CI = -0.013 to -0.003; βLifelines = -0.002, 95 % CI = -0.003 to -0.00003). The pooled estimates were βNO2 = 0.003 (95 % CI = -0.001 to 0.006) for NO2, βEC = 0.003 (95 % CI = -0.001, 0.007) for EC, and β500m buffer = -0.0014 (95 % CI = -0.0026 to -0.0001) for green space.

CONCLUSIONS

More green space exposure at residence was associated with decreased cardiometabolic risk in children. Exposure to more NO2 and EC was also associated with increased cardiometabolic risk.

Temporal Trends in Air Pollution Exposure across Socioeconomic Groups in The Netherlands

Air pollution exposure has been linked to detrimental health outcomes. While cross-sectional studies have demonstrated socioeconomic disparities in air pollution exposure, longitudinal evidence on these disparities remains limited. The current study investigates trends in residential air pollution exposure across socioeconomic groups in the Netherlands from 2014 to 2019. Our dataset includes over 12.5 million individuals, aged 18 years and above, who resided in the Netherlands between 2014 and 2019, using Statistics Netherlands data. The address-level air pollution concentrations were estimated by dispersion models of the National Institute of Public Health and the Environment. We linked the exposure estimations of particulate matter < 10 or <2.5 μm (PM10, PM2.5) and nitrogen dioxide (NO2) to household-level socioeconomic data. In highly urbanized areas, individuals from both the lowest and highest socioeconomic groups were exposed to higher air pollution concentrations. Individuals from the lowest socioeconomic group were disproportionally located in highly urbanized and more polluted areas. The air pollution concentrations of PM10, PM2.5, and NO2 decreased between 2014 and 2019 for all the socioeconomic groups. The decrease in the annual average air pollution concentrations was the strongest for the lowest socioeconomic group, although differences in exposure between the socioeconomic groups remain. Further research is needed to define the health and equity implications.

The neighourhood obesogenic built environment characteristics (OBCT) index: Practice versus theory

BACKGROUND

Obesity is a key risk factor for major chronic diseases such as type 2 diabetes and cardiovascular diseases. To extensively characterise the obesogenic built environment, we recently developed a novel Obesogenic Built environment CharacterisTics (OBCT) index, consisting of 17 components that capture both food and physical activity (PA) environments.

OBJECTIVES

We aimed to assess the association between the OBCT index and body mass index (BMI) in a nationwide health monitor. Furthermore, we explored possible ways to improve the index using unsupervised and supervised methods.

METHODS

The OBCT index was constructed for 12,821 Dutch administrative neighbourhoods and linked to residential addresses of eligible adult participants in the 2016 Public Health Monitor. We split the data randomly into a training (two-thirds; n = 255,187) and a testing subset (one-third; n = 127,428). In the training set, we used non-parametric restricted cubic regression spline to assess index's association with BMI, adjusted for individual demographic characteristics. Effect modification by age, sex, socioeconomic status (SES) and urbanicity was examined. As improvement, we (1) adjusted the food environment for address density, (2) added housing price to the index and (3) adopted three weighting strategies, two methods were supervised by BMI (variable selection and random forest) in the training set. We compared these methods in the testing set by examining their model fit with BMI as outcome.

RESULTS

The OBCT index had a significant non-linear association with BMI in a fully-adjusted model (p<0.05), which was modified by age, sex, SES and urbanicity. However, variance in BMI explained by the index was low (<0.05%). Supervised methods increased this explained variance more than non-supervised methods, though overall improvements were limited as highest explained variance remained <0.5%.

DISCUSSION

The index, despite its potential to highlight disparity in obesogenic environments, had limited association with BMI. Complex improvements are not necessarily beneficial, and the components should be re-operationalised.

Exposome-Wide Association Study of Body Mass Index Using a Novel Meta-Analytical Approach for Random Forest Models

BACKGROUND

Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors.

OBJECTIVES

Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies.

METHODS

Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688-141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies.

RESULTS

Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in 5 -km buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to € 300,000 . The directions of associations were less consistent for walkability and share of single residents.

DISCUSSION

Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. https://doi.org/10.1289/EHP13393.

Social cohesion and associations with sedentary time, physical activity and dietary quality in The Maastricht Study

INTRO

We aim to investigate the relationship between social cohesion and sedentary behavior (SB), total physical activity (PA), moderate-to-vigorous PA (MVPA), and dietary quality. Additionally, we assess whether these associations are independent of neighborhood walkability and the food environment.

METHODS

A total of 7641 participants from The Maastricht Study in the Netherlands between the ages of 40 and 75 years were analyzed. Neighborhood social cohesion was obtained by participant questionnaire completed at baseline and measured by the Dutch Livability meter. Home addresses were linked to geographic information system (GIS) data from the Geoscience and Health Cohort Consortium (GECCO) to create neighborhood exposures of walkability and food environment. A thigh worn accelerometer collected data to measure sedentary time, total daily PA, and MVPA. Dietary quality was measured with a food frequency questionnaire. Multivariate linear regression analyses were adjusted for age, sex, socioeconomic position, neighborhood walkability, and food environment.

RESULTS

Those living in the highest quartile area of perceived social cohesion had statistically significant lower levels of SB (Q4 B: -13.04; 95% CI = -20.23, -5.85), higher total PA (Q4 B: 4.39; 95% CI = 1.69, 7.10), and higher MVPA (Q4 B: 2.57; 95% CI = 0.83, 4.31) and better diet quality (Q4 B: 1.12; 95% CI = 0.24, 2.01) compared to the lowest quartile independent of walkability and food environment. Similar results were found using the Livability meter.

CONCLUSION

We discovered neighborhood social cohesion as an important obesogenic determinant that should be considered in policymaking to encourage higher levels of PA and higher diet quality.

Five-year follow-up of the iBerry Study: screening in early adolescence to identify those at risk of psychopathology in emerging adulthood

The iBerry Study, a Dutch population-based high-risk cohort (n = 1022) examines the transition from subclinical symptoms to psychiatric disorders in adolescents. Here, we present the first follow-up measurement, approximately 3 years after baseline assessment and 5 years after the screening based on self-reported emotional and behavioral problems (SDQ-Y). We give an update on the data collection, details on the (non)response, and the results on psychopathology outcomes. The first follow-up (2019-2022) had a response rate of 79% (n = 807). Our results at baseline (mean age 15.0 years) have shown the effectiveness of using the SDQ-Y to select a cohort oversampled for the risk of psychopathology. At first follow-up (mean age 18.1 years), the previously administered SDQ-Y remains predictive for selecting adolescents at risk. At follow-up, 47% of the high-risk adolescents showed significant mental health problems based on self- and parent reports and 46% of the high-risk adolescents met the criteria for multiple DSM-5 diagnoses. Compared to low-risk adolescents, high-risk adolescents had a sevenfold higher odds of significant emotional and behavioral problems at follow-up. Comprehensive assessment on psychopathology, substance abuse, psychotic symptoms, suicidality, nonsuicidal self-injury, addiction to social media and/or video gaming, and delinquency, as well as social development, and the utilization of healthcare and social services were conducted. This wave, as well as the ones to follow, track these adolescents into their young adulthood to identify risk factors, elucidate causal mechanisms, and discern pathways leading to both common and severe mental disorders. Results from the iBerry Study will provide leads for preventive interventions.

Walking to work: The role of walkability around the workplace in a Dutch adult commuting population

Current evidence on neighborhood walkability and active commuting focuses on residential rather than workplace environment. This cross-sectional study investigated whether higher workplace walkability (WW) was associated with commute walking, both independently and together with residential walkability, using data from 6769 respondents of the 2017 Dutch national travel survey. In a fully adjusted logistic regression model, 10% increase in WW was associated with 32% higher odds of commute walking (Odds ratio (OR): 1.31, 95% Confidence Interval (CI: 1.27-1.36). The estimates were stronger in rural dwellers than urban residents, (ORrural 1.49, 95%CI: 1.34-1.64 vs ORhighly.urban 1.19, 95%CI: 1.13-1.26). In participants with both high residential walkability and WW, we observed 215% higher odds (OR 3.15, 95% CI: 2.48-3.99) of commute walking compared to those with low walkability in both. Our study indicated the importance and complementary nature of walkable residence and workplace in contribution to physical activity of working individuals through active commuting.

Ethnic and socioeconomic inequalities in air pollution exposure: a cross-sectional analysis of nationwide individual-level data from the Netherlands

BACKGROUND

Air pollution contributes to a large disease burden and some populations are disproportionately exposed. We aimed to evaluate ethnic and socioeconomic differences in exposure to air pollution in the Netherlands.

METHODS

We did a nationwide, cross-sectional analysis of all residents of the Netherlands on Jan 1, 2019. Sociodemographic information was centralised by Statistics Netherlands and mainly originated from the National Population Register, the tax register, and education registers. Concentrations of NO2, PM2·5, PM10, and elemental carbon, modelled by the National Institute for Public Health and the Environment, were linked to the individual-level demographic data. We assessed differences in air pollution exposures across the 40 largest minority ethnic groups. Evaluation of how ethnicity intersected with socioeconomic position in relation to exposures was done for the ten largest ethnic groups, plus Chinese and Indian groups, in both urban and rural areas using multivariable linear regression analyses.

FINDINGS

The total study population consisted of 17 251 511 individuals. Minority ethnic groups were consistently exposed to higher levels of air pollution than the ethnic Dutch population. The magnitude of inequalities varied between the minority ethnic groups, with 3-44% higher exposures to NO2 and 1-9% higher exposures to PM2·5 compared with the ethnic Dutch group. Average exposures were highest for the lowest socioeconomic group. Ethnic inequalities in exposure remained after adjustment for socioeconomic position and were of similar magnitude in urban and rural areas.

INTERPRETATION

The variability in air pollution exposure across ethnic and socioeconomic subgroups in the Netherlands indicates environmental injustice at the intersection of social characteristics. The health consequences of the observed inequalities and the underlying processes driving them warrant further investigation.

FUNDING

The Gravitation programme of the Dutch Ministry of Education, Culture, and Science, the Netherlands Organization for Scientific Research, the Netherlands Organisation for Health Research and Development, and Amsterdam University Medical Center.

Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis

BACKGROUND

The exposome serves as a popular framework in which to study exposures from chemical and nonchemical stressors across the life course and the differing roles that these exposures can play in human health. As a result, data relevant to the exposome have been used as a resource in the quest to untangle complicated health trajectories and help connect the dots from exposures to adverse outcome pathways.

OBJECTIVES

The primary aim of this methods seminar is to clarify and review preprocessing techniques critical for accurate and effective external exposomic data analysis. Scalability is emphasized through an application of highly innovative combinatorial techniques coupled with more traditional statistical strategies. The Public Health Exposome is used as an archetypical model. The novelty and innovation of this seminar's focus stem from its methodical, comprehensive treatment of preprocessing and its demonstration of the positive effects preprocessing can have on downstream analytics.

DISCUSSION

State-of-the-art technologies are described for data harmonization and to mitigate noise, which can stymie downstream interpretation, and to select key exposomic features, without which analytics may lose focus. A main task is the reduction of multicollinearity, a particularly formidable problem that frequently arises from repeated measurements of similar events taken at various times and from multiple sources. Empirical results highlight the effectiveness of a carefully planned preprocessing workflow as demonstrated in the context of more highly concentrated variable lists, improved correlational distributions, and enhanced downstream analytics for latent relationship discovery. The nascent field of exposome science can be characterized by the need to analyze and interpret a complex confluence of highly inhomogeneous spatial and temporal data, which may present formidable challenges to even the most powerful analytical tools. A systematic approach to preprocessing can therefore provide an essential first step in the application of modern computer and data science methods. https://doi.org/10.1289/EHP12901.

Association of perceived and objective neighborhood walkability with accelerometer-measured physical activity and sedentary time in the Maastricht Study

BACKGROUND

We investigated the association of neighborhood walkability with accelerometer-measured physical activity (PA) and sedentary behavior (SB) and examined whether objective and subjective measures of walkability resulted in similar findings.

METHODS

PA and SB from the first 7689 Maastricht Study participants ages 40-75 from 2010 to 2017 were measured using accelerometers for 7 days. Mean daily step count, light-intensity PA, moderate- to vigorous- intensity PA (MVPA), and SB were calculated. Objective walkability was measured by the 7-component Dutch Walkability Index within 500 m Euclidean buffers around residential addresses of participants. Subjective walkability was obtained from the Abbreviated Neighborhood Environment Walkability Scale. Linear regression models analyzed the associations of walkability with PA and SB, controlling for potential confounders.

RESULTS

Objective walkability was negatively associated with light intensity PA in the most walkable quartile (b = -14.58, 95% CI = -20.94, -8.23). Compared to participants living in the least walkable neighborhoods, those in the most walkable quartile had statistically significantly higher SB levels (b = 11.64, 95% CI = 4.95, 18.32). For subjective walkability, mean daily step count was significantly higher in the most walkable quartile (b = 509.60, 95% CI = 243.38, 775.81). Higher subjective walkability was positively associated with MVPA (b = 4.40, 95% CI = 2.56, 6.23).

CONCLUSION

Living in a neighborhood with higher objective walkability was associated with lower levels of PA and higher SB levels while higher subjective walkability was associated with higher levels of PA. These results show discordant findings and thus, the effect of walkability on participant PA and SB within our sample is to be determined.

Associations between the urban environment and psychotic experiences in adolescents

OBJECTIVE

In 2050 two-thirds of the world's population is predicted to live in cities, which asks for a better understanding of how the urban environment affects mental health. Urbanicity has repeatedly been found to be a risk factor, in particular for psychosis. Here, we explored what factors of the urban exposome underlie the association between urban characteristics and psychotic experiences (PE) in adolescents.

METHODS

Participants were 815 adolescents (mean age 14.84 years, SD 0.78) from an at-risk cohort (greater Rotterdam area, the Netherlands) oversampled on their self-reported emotional and behavioral problems. We used linear regression analysis to examine the association with detailed geodata on urbanicity (surrounding address density), green space density (high and low vegetation), and mixed noise levels (road, rail, air, industry, and wind power) with PE in adolescents. Analyses were adjusted for multiple socio-economic and parental confounders. Furthermore, we explored sex-interaction effects.

RESULTS

Higher surrounding address density and low greenspace density were each independently associated with more PE (B = 0.18, 95 % CI 0.02; 0.34 and B = 0.17, 95 % CI 0.01; 0.32, respectively). High mixed noise levels were only associated with more PE in boys (B = 0.23, 95 % CI 0.01; 0.46). A sex-interaction effect was found for high urbanicity (B = -0.46, 95 % CI -0.77; -0.14) and low greenspace density (B = -0.49, 95 % CI -0.73; -0.11), illustrating that these associations with PE were specific for boys.

CONCLUSION

Multiple characteristics of living in an urban area are associated with more PE in adolescent boys. Our observations provide leads for prevention of mental health problems via urban designing.

A positive neighborhood walkability is associated with a higher magnitude of leisure walking in adults upon COVID-19 restrictions: a longitudinal cohort study

BACKGROUND

Previous cross-sectional and longitudinal observational studies revealed positive relationships between contextual built environment components and walking behavior. Due to severe restrictions during COVID-19 pandemic lockdowns, physical activity was primarily performed within the immediate living area. Using this unique opportunity, we evaluated whether built environment components were associated with the magnitude of change in walking activity in adults during COVID-19 restrictions.

METHODS

Data on self-reported demographic characteristics and walking behaviour were extracted from the prospective longitudinal Lifelines Cohort Study in the Netherlands of participants ≥ 18 years. For our analyses, we made use of the data acquired between 2014-2017 (n = 100,285). A fifth of the participants completed the questionnaires during COVID-19 restrictive policies in July 2021 (n = 20,806). Seven spatial components were calculated for a 500m and 1650m Euclidean buffer per postal code area in GIS: population density, retail and service destination density, land use mix, street connectivity, green space density, sidewalk density, and public transport stops. Additionally, the walkability index (WI) of these seven components was calculated. Using multivariable linear regression analyses, we analyzed the association between the WI (and separate components) and the change in leisure walking minutes/week. Included demographic variables were age, gender, BMI, education, net income, occupation status, household composition and the season in which the questionnaire was filled in.

RESULTS

The average leisure walking time strongly increased by 127 min/week upon COVID-19 restrictions. All seven spatial components of the WI were significantly associated with an increase in leisure walking time; a 10% higher score in the individual spatial component was associated with 5 to 8 more minutes of leisure walking/week. Green space density at the 500m Euclidean buffer and side-walk density at the 1650m Euclidean buffer were associated with the highest increase in leisure walking time/week. Subgroup analysis revealed that the built environment showed its strongest impact on leisure walking time in participants not engaging in leisure walking before the COVID-19 pandemic, compared to participants who already engaged in leisure walking before the COVID-19 pandemic.

CONCLUSIONS

These results provide strong evidence that the built environment, corrected for individual-level characteristics, directly links to changes observed in leisure walking time during COVID-19 restrictions. Since this relation was strongest in those who did not engage in leisure walking before the COVID-19 pandemic, our results encourage new perspectives in health promotion and urban planning.

The association between the food environment and adherence to healthy diet quality: the Maastricht Study

OBJECTIVE

The purpose of this study is to determine if healthier neighbourhood food environments are associated with healthier diet quality.

DESIGN

This was a cross-sectional study using linear regression models to analyse data from the Maastricht Study. Diet quality was assessed using data collected with a FFQ to calculate the Dutch Healthy Diet (DHD). A buffer zone encompassing a 1000 m radius was created around each participant home address. The Food Environment Healthiness Index (FEHI) was calculated using a Kernel density analysis within the buffers of available food outlets. The association between the FEHI and the DHD score was analysed and adjusted for socio-economic variables.

SETTING

The region of Maastricht including the surrounding food retailers in the Netherlands.

PARTICIPANTS

7367 subjects aged 40-75 years in the south of the Netherlands.

RESULTS

No relationship was identified between either the FEHI (B = 0·62; 95 % CI = -2·54, 3·78) or individual food outlets, such as fast food (B = -0·07; 95 % CI = -0·20, 0·07) and diet quality. Similar null findings using the FEHI were identified at the 500 m (B = 0·95; 95 % CI = -0·85, 2·75) and 1500 m (B = 1·57; 95 % CI = -3·30, 6·44) buffer. There was also no association between the food environment and individual items of the DHD including fruits, vegetables and sugar-sweetened beverages.

CONCLUSION

The food environment in the Maastricht area appeared marginally unhealthy, but the differences in the food environment were not related to the quality of food that participants reported as intake.

The quality of OpenStreetMap food-related point-of-interest data for use in epidemiological research

We assessed the quality of food-related OpenStreetMap (OSM) data in urban areas of five European countries. We calculated agreement statistics between point-of-interests (POIs) from OSM and from Google Street View (GSV) in five European regions. We furthermore assessed correlations between exposure measures (distance and counts) from OSM data and administrative data from local data sources on food environment data in three European countries. Agreement between POI data in OSM compared to GSV was poor, but correlations were moderate to high between exposures from OSM and local data sources. OSM data downloaded in 2020 seems to be an acceptable source of data for generating count-based food exposure measures for research in selected European regions.

Longitudinal effects of environmental noise and air pollution exposure on autism spectrum disorder and attention-deficit/hyperactivity disorder during adolescence and early adulthood: The TRAILS study

BACKGROUND

Exposure to ambient noise and air pollution may affect the manifestation and severity of Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD). However, evidence is limited, and most studies solely assessed environmental exposures during pregnancy and early childhood.

OBJECTIVE

To examine the longitudinal effects of ambient noise and air pollutants on ASD and ADHD symptom severity during adolescence and early adulthood.

METHODS

Using a longitudinal design, we included 2750 children between 10 and 12 years old from the TRacking Adolescents' Individual Lives Survey (TRAILS) in the Netherlands, who were assessed in 6 waves from 2001 to 2017. ASD was measured by the Children's Social Behavior Questionnaire and the Adult Social Behavior Questionnaire. ADHD was measured by Child Behavior Checklist and the Adult Behavior Checklist. Ambient noise and air pollution exposures, including Ozone (O₃), soot, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), particulate matter 2.5 (PM_2.5), and PM₁₀ were modeled at the residential level according to standardized protocols. The longitudinal associations between exposures and symptom outcomes were examined using linear mixed models.

RESULTS

We found evidence that higher levels of exposure to PM were associated with more severe ASD and ADHD symptoms. This association decreased over time. We did not observe any other consistent associations of noise or other air pollutants with ASD and ADHD severity.

CONCLUSION

The current study provides evidence for the negative impact of PM on ASD and ADHD symptoms. We did not find evidence of the negative health impact of other air pollutants and noise exposures on ASD or ADHD symptoms. Our study adds more evidence on the presence of associations between PM air pollution and neurodevelopmental diseases among adolescents and young adults.

Social and physical neighbourhood characteristics and 10-year incidence of depression and anxiety in older adults: Results from the Longitudinal Aging Study Amsterdam

OBJECTIVE

A growing literature suggests that neighbourhood characteristics are associated with mental health outcomes, but the evidence in older adults is inconsistent. We investigated the association of neighbourhood characteristics, pertaining to demographic, socio-economic, social and physical environment domains, with the subsequent 10-year incidence of depression and anxiety, in Dutch older adults.

METHODS

In the Longitudinal Aging Study Amsterdam depressive and anxiety symptoms were assessed four times between 2005/2006 and 2015/2016, using the Center for Epidemiological Studies Depression Scale (n = 1365) and the Anxiety subscale of the Hospital Anxiety and Depression Scale (n = 1420). Neighbourhood-level data on urban density, percent population over 65 years of age, percent immigrants, average house price, average income, percent low-income earners, social security beneficiaries, social cohesion, safety, proximity to retail facilities, housing quality, percent green space, percent water coverage, air pollution (particulate matter (PM2.5)), and traffic noise, were obtained for study baseline years 2005/2006. Cox proportional hazard regression models, clustered within neighbourhood, were used to estimate the association between each neighbourhood-level characteristic and the incidence of depression and anxiety.

RESULTS

The incidence of depression and anxiety was 19.9 and 13.2 per 1000 person-years, respectively. Neighbourhood characteristics were not associated with the incidence of depression. However, various neighbourhood characteristics were associated with an increased incidence of anxiety, including: higher urban density level, higher percent immigrants, greater proximity to retail facilities, lower housing quality score, lower safety score, higher PM2.5 levels and less green space.

CONCLUSION

Our results indicate that several neighbourhood characteristics are associated with anxiety but not with depression incidence in older age. Several of these characteristics have the potential to be modifiable and thus could serve as a target for interventions at the neighbourhood-level in improving anxiety, provided that future studies replicate our findings and provide further evidence for a causal effect.

Cohort profile: Netherlands Longitudinal Study on Hearing (NL-SH)

PURPOSE

The Netherlands Longitudinal Study on Hearing (NL-SH) was set up to examine associations of hearing ability with psychosocial, work and health outcomes in working age adults.

PARTICIPANTS

Inclusion started in 2006 and is ongoing. Currently the sample comprises 2800 adults with normal and impaired hearing, aged 18-70 years at inclusion. Five-year follow-up started in 2011, 10-year follow-up in 2016 and 15-year follow-up in 2021. All measurements are web-based. Participants perform a speech-in-noise recognition test to measure hearing ability and fill out questionnaires about their hearing status, hearing aid use, self-reported hearing disability and coping, work status and work-related outcomes (work performance, need for recovery), physical and psychosocial health (depression, anxiety, distress, somatisation, loneliness), healthcare usage, lifestyle (smoking, alcohol), and technology use.

FINDINGS TO DATE

The NL-SH has shown the vast implications of reduced hearing ability for the quality of life and health of working-age adults. A selection of results published in 27 papers is presented. Age-related deterioration of hearing ability accelerates after the age of 50 years. Having a history of smoking is associated with a faster decline in hearing ability, but this relationship is not found for other cardiovascular risk factors. Poorer hearing ability is associated with increased distress, somatisation, depression and loneliness. Adults with impaired hearing ability are more likely to be unemployed or unfit for work, and need more time to recuperate from work effort.

FUTURE PLANS

Participant data will be linked to a national database to enable research on the association between hearing ability and mortality. Linking to environmental exposure data will facilitate insight in relations between environmental factors, hearing ability and psychosocial outcomes. The unique breadth of the NL-SH data will also allow for further research on other functional problems, for instance, hearing ability and fall risk.

TRIAL REGISTRATION NUMBER

NL12015.029.06.

Neighbourhood walkability in relation to cognitive functioning in patients with disorders along the heart-brain axis

This study examined associations of neighbourhood walkability with cognitive functioning (i.e., global cognition, memory, language, attention-psychomotor speed, and executive functioning) in participants without or with either heart failure, carotid occlusive disease, or vascular cognitive impairment. Neighbourhood walkability at baseline was positively associated with global cognition and attention-psychomotor speed. These associations were stronger in patients with vascular cognitive impairment. Individuals who live in residential areas with higher walkability levels were less likely to have impairments in language and executive functioning at two-year follow-up. These findings highlight the importance of the built environment for cognitive functioning in healthy and vulnerable groups.

Individual and environmental determinants of serum ferritin levels: A structural equation model

BACKGROUND AND OBJECTIVES

Serum ferritin levels are increasingly being used to assess iron stores. Considerable variation in ferritin levels within and between individuals has been observed, but our current understanding of factors that explain this variation is far from complete. We aim to combine multiple potential determinants in an integrative model, and investigate their relative importance and potential interactions.

METHODS

We use ferritin measurements collected by Sanquin Blood Bank on both prospective (N = 59 596) and active blood donors (N = 78 318) to fit a structural equation model with three latent constructs (individual characteristics, donation history, and environmental factors). Parameters were estimated separately by sex and donor status.

RESULTS

The model explained 25% of ferritin variance in prospective donors, and 40% in active donors. Individual characteristics and donation history were the most important determinants of ferritin levels in active donors. The association between environmental factors and ferritin was smaller but still substantial; higher exposure to air pollution was associated with higher ferritin levels, and this association was considerably stronger for active blood donors than for prospective donors.

DISCUSSION

In active donors, individual characteristics explain 20% (17%) of ferritin variation, donation history explains 14% (25%) and environmental factors explain 5% (4%) for women (men). Our model presents known ferritin determinants in a broader perspective, allowing for comparison with other determinants as well as between new and active donors, or between men and women.

Development of an objectively measured walkability index for the Netherlands

Background

Walkability indices have been developed and linked to behavioural and health outcomes elsewhere in the world, but not comprehensively for Europe. We aimed to 1) develop a theory-based and evidence-informed Dutch walkability index, 2) examine its cross-sectional associations with total and purpose-specific walking behaviours of adults across socioeconomic (SES) and urbanisation strata, 3) explore which walkability components drive these associations.

Methods

Components of the index included: population density, retail and service density, land use mix, street connectivity, green space, sidewalk density and public transport density. Each of the seven components was calculated for three Euclidean buffers: 150 m, 500 m and 1000 m around every 6-digit postal code location and for every administrative neighbourhood in GIS. Componential z-scores were averaged, and final indices normalized between 0 and 100. Data on self-reported demographic characteristics and walking behaviours of 16,055 adult respondents (aged 18–65) were extracted from the Dutch National Travel Survey 2017. Using Tobit regression modelling adjusted for individual- and household-level confounders, we assessed the associations between walkability and minutes walking in total, for non-discretionary and discretionary purposes. By assessing the attenuation in associations between partial indices and walking outcomes, we identified which of the seven components drive these associations. We also tested for effect modification by urbanization degree, SES, age and sex.

Results

In fully adjusted models, a 10% increase in walkability was associated with a maximum increase of 8.5 min of total walking per day (95%CI: 7.1–9.9). This association was consistent across buffer sizes and purposes of walking. Public transport density was driving the index’s association with walking outcomes. Stratified results showed that associations with minutes of non-discretionary walking were stronger in rural compared to very urban areas, in neighbourhoods with low SES compared to high SES, and in middle-aged (36–49 years) compared to young (18–35 years old) and older adults (50–65 years old).

Conclusions

The walkability index was cross-sectionally associated with Dutch adult’s walking behaviours, indicating its validity for further use in research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-022-01270-8.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

Developing the building blocks to elucidate the impact of the urban exposome on cardiometabolic-pulmonary disease: The EU EXPANSE project

By 2030, more than 80% of Europe's population will live in an urban environment. The urban exposome, consisting of factors such as where we live and work, where and what we eat, our social network, and what chemical and physical hazards we are exposed to, provides important targets to improve population health. The EXPANSE (EXposome Powered tools for healthy living in urbAN SEttings) project will study the impact of the urban exposome on the major contributors to Europe's burden of disease: Cardio-Metabolic and Pulmonary Disease. EXPANSE will address one of the most pertinent questions for urban planners, policy makers, and European citizens: "How to maximize one's health in a modern urban environment?" EXPANSE will take the next step in exposome research by (1) bringing together exposome and health data of more than 55 million adult Europeans and OMICS information for more than 2 million Europeans; (2) perform personalized exposome assessment for 5,000 individuals in five urban regions; (3) applying ultra-high-resolution mass-spectrometry to screen for chemicals in 10,000 blood samples; (4) evaluating the evolution of the exposome and health through the life course; and (5) evaluating the impact of changes in the urban exposome on the burden of cardiometabolic and pulmonary disease. EXPANSE will translate its insights and innovations into research and dissemination tools that will be openly accessible via the EXPANSE toolbox. By applying innovative ethics-by-design throughout the project, the social and ethical acceptability of these tools will be safeguarded. EXPANSE is part of the European Human Exposome Network.

Associations of changes in neighbourhood walkability with changes in walking activity in older adults: a fixed effects analysis

Background

Supporting older adults to engage in physically active lifestyles requires supporting environments. Walkable environments may increase walking activity in older adults, but evidence for this subgroup is scarce, and longitudinal studies are lacking. This study therefore examined whether changes in neighbourhood walkability were associated with changes in walking activity in older adults, and whether this association differed by individual-level characteristics and by contextual conditions beyond the built environment.

Methods

Data from 668 participants (57.8–93.4 years at baseline) across three waves (2005/06, 2008/09 and 2011/12) of the Longitudinal Aging Study Amsterdam (LASA) were used. These individuals did not relocate during follow-up. Self-reported outdoor walking activity in minutes per week was assessed using the LASA Physical Activity Questionnaire. Composite exposure measures of neighbourhood walkability (range: 0 (low)-100 (high)) within 500-m Euclidean buffer zones around each participant’s residential address were constructed by combining objectively measured high-resolution Geographic Information System data on population density, retail and service destination density, land use mix, street connectivity, green space density, and sidewalk density. Fixed effects linear regression analyses were applied, adjusted for relevant time-varying confounders.

Results

Changes in neighbourhood walkability were not statistically significantly associated with changes in walking activity in older adults (β_500m = − 0.99, 95% CI = -6.17–4.20). The association of changes in neighbourhood walkability with changes in walking activity did not differ by any of the individual-level characteristics (i.e., age, sex, educational level, cognitive impairment, mobility disability, and season) and area-level characteristics (i.e., road traffic noise, air pollution, and socioeconomic status).

Conclusions

This study did not show evidence for an association between changes in neighbourhood walkability and changes in walking activity in older adults. If neighbourhood walkability and walking activity are causally linked, then changes in neighbourhood walkability between 2005/06 and 2011/12 might have been not substantial enough to produce meaningful changes in walking activity in older adults.

Neighborhood walkability, physical activity and changes in glycemic markers in people with type 2 diabetes: The Hoorn Diabetes Care System cohort

Studies investigating neighborhood walkability and physical activity in people with type 2 diabetes (T2D) mainly used self-report measures, and only few studies assessed the association with glycemic control. This study assessed the associations between objectively measured (i.e. GIS based) and subjectively measured (i.e. questionnaire-based) neighborhood walkability and changes in glycemic markers in people with T2D, and whether this association was mediated by device-measured physical activity (PA), in the Diabetes Care System Cohort (n = 1230). Neither objective or subjectively measured walkability was associated with glycemic control. In mediation analyses we observed no overall mediation by PA.

Centralizing environmental datasets to support (inter)national chronic disease research: Values, challenges, and recommendations

Whereas environmental data are increasingly available, it is often not clear how or if datasets are available for health research. Exposure metrics are typically developed for specific research initiatives using disparate exposure assessment methods and no mechanisms are put in place for centralizing, archiving, or distributing environmental datasets. In parallel, potentially vast amounts of environmental data are emerging due to new technologies such as high resolution imagery and machine learning.