The research question centers on identifying modifiable environmental factors associated with well-being to inform public policy. The study acknowledges the existing literature linking demographic factors, particularly urbanization, to mental health outcomes. While urbanization offers benefits like economic growth and improved infrastructure, it's also associated with increased mental health disorders due to reduced green space, heightened social stress, and perceived neighborhood insecurity. The study highlights the role of genetic predisposition in influencing residential choices and environmental impact, citing research on urbanization and schizophrenia as an example. Record linkage, a key methodological advancement, allows for the integration of diverse datasets to uncover previously unseen patterns. This study utilizes record linkage to combine well-being data from the NTR with environmental data from GECCO, representing a significant step beyond the traditional "pick and choose" approach in environmental well-being research. The EnWAS approach, mirroring GWAS methodology, systematically tests various environmental variables against well-being, mitigating the risk of spurious findings associated with multiple testing. The study aims to provide a more comprehensive understanding of the multifaceted influences on well-being, ultimately informing prevention, intervention, and policy decisions at the neighborhood level.

The introduction extensively reviews existing literature on the relationship between environmental factors and well-being. Studies have shown a correlation between urbanization and mental health disorders, attributing this to factors such as limited access to green spaces, increased social stress, and decreased perceived neighborhood safety. The influence of genetic predisposition on residential choices and the consequent impact on mental health is also acknowledged. The authors point to the successful application of EnWAS in other studies examining various factors related to well-being, but note the absence of prior work focusing specifically on physical environmental factors at a neighborhood level. The authors highlight the importance of neighborhood-level analyses given the relevance of governmental policies and interventions at this scale.

The study utilized well-being data from the 6th (2002/2003) and 8th (2009/2010) waves of the Netherlands Twin Register (NTR) Adult sample, measuring well-being using the Satisfaction with Life Scale (SWL). Environmental data was sourced from the Geoscience and Health Cohort Consortium (GECCO) database, encompassing 1330 postal code-level variables across 34 domains. After pre-selection based on availability and representativeness, 139 variables were included in the analysis. Data linkage was performed using four-digit postal codes. The primary analysis involved generalized estimating equation (GEE) regression, controlling for sex, age, and age-squared, and accounting for familial relatedness. A Bonferroni-corrected significance threshold was applied. To address multicollinearity among environmental exposures, principal component regression (PCR) was employed. A genetically informative design was implemented, using polygenic scores (PGS) for the well-being spectrum to investigate gene-environment correlation. This involved testing the association between the well-being PGS and significant environmental correlates identified in the GEE analyses. The sample was also split into septiles based on the PGS to assess differences in environmental values across genetic susceptibility groups. Post-hoc analyses included visualizing correlations between significant variables using chord diagrams and repeating GEE analyses with corrections for socioeconomic status (SES) using individual educational attainment and neighborhood SES scores.

The GEE analyses revealed 21 environmental variables significantly associated with well-being, falling into the domains of housing stock, income, core neighborhood characteristics, livability, and SES scores. The polygenic risk score analysis showed no evidence of gene-environment correlation. Examination of correlations among significant variables revealed high inter-correlations. Principal component analysis (PCA) extracted independent principal components (PCs) that explained a substantial portion of the environmental data variance. PCs reflecting SES and livability were predictive of well-being. After correcting for individual and neighborhood SES, only neighborhood safety and the percentage of land devoted to greenhouse horticulture remained significantly associated with well-being. The effect sizes of the individual significant environmental predictors on well-being were small (0.2% to 0.5% of the variance explained), and the combined effect of all variables, as shown by PCA, explained only around 1% of the variance in well-being. The study did not find significant associations with previously suggested indicators, such as green space or air pollution, possibly due to the level of analysis (postal code), use of objective indicators, or the measure of well-being.

The study's findings support the importance of socioeconomic factors and neighborhood safety in predicting well-being at the postal code level. The small and context-dependent nature of environmental effects highlights the need for large-scale data-driven studies. The lack of gene-environment correlation suggests that genetic predisposition for well-being may not significantly influence residential choices or that the study lacked sufficient power to detect such an effect. The authors discuss the limitations of interpreting associations as causal relationships and the potential role of mediating factors, such as SES, in explaining some of the observed associations. The small effect sizes of individual environmental predictors and the overall limited variance in well-being explained by environmental factors are discussed in the context of the geographical scale of analysis and the use of objective environmental indicators.

This study successfully linked large datasets to investigate the complex relationship between environmental factors and well-being. The most important predictors of well-being at the postal code level were identified as socioeconomic status and neighborhood safety. The small and context-dependent nature of environmental effects emphasizes the need for large-scale, data-driven approaches. Future research could explore the creation of "poly-environmental" scores, similar to polygenic scores, to combine small environmental effects and develop personalized interventions. Further investigation into the direction of causality and the use of monozygotic twin pairs to disentangle genetic and environmental effects is also suggested.

The study's limitations include the use of postal code-level environmental data, which may not fully capture the heterogeneity of individual experiences within a postal code. The focus on objective environmental indicators, rather than subjective perceptions, may also limit the generalizability of findings. The relatively small effect sizes of the identified environmental predictors may limit the clinical or policy implications. Additionally, the absence of a strong gene-environment correlation may be due to a lack of statistical power or the use of existing well-being polygenic scores.