Periodic Reporting for period 1 - URBANE (Early-life URBAN environmental exposures and childhood multimorbidity: An Exposome-wide approach)
Okres sprawozdawczy: 2023-09-01 do 2026-01-31
More than half of the world’s population lives in cities, and this proportion is expected to increase to nearly 70% by 2050. While urban living offers many benefits, it also exposes people to environmental factors such as air pollution, noise, heat, traffic and limited green spaces. These exposures are known to affect health, but their impact on children—especially during the earliest stages of life—is still not well understood. Pregnancy and early childhood are particularly sensitive periods when environmental exposures can influence health throughout life. At the same time, many children experience more than one health condition simultaneously, a situation known as multimorbidity. Common childhood conditions such as asthma, obesity and neurodevelopmental disorders often occur together, yet the environmental factors contributing to this pattern remain largely unexplored. The URBANE project will investigate how multiple aspects of the urban environment during pregnancy and childhood influence the risk of multimorbidity in children. The project will use an exposome approach, which considers the combined effects of many environmental exposures across the life course, providing a more comprehensive understanding of environmental impacts on health. By identifying harmful urban environmental factors and the most sensitive periods of exposure, URBANE will help inform prevention strategies and support healthier urban planning and policies to improve children’s health.
In Work Package 1, urban environmental exposure data from birth to 12 years were compiled and pre-processed, covering air pollution, natural spaces, built environment characteristics, traffic, meteorology, and social environment indicators. Data cleaning procedures included management of missing data, outlier detection, and standardisation of exposure variables. Multimorbidity indicators were developed based on seven health outcomes across cardiometabolic, respiratory/allergic, and neurodevelopmental domains. A multimorbidity risk score was constructed and clustering approaches were explored to identify patterns of disease co-occurrence. Machine learning methods were implemented to evaluate the predictive contribution of early-life urban environmental exposures to multimorbidity.
In Work Package 2, DNA methylation data underwent quality control and normalization procedures to remove low-quality samples and probes and to correct for technical variation and batch effects. The dataset was prepared for downstream analyses. Reproducible analytical pipelines were developed for epigenome-wide association studies and epigenetic clock analyses. Epigenetic research questions were also further refined and integrated into new project applications to support the continuation of research in this area.
In Work Package 3, analytical work focused on the assessment of associations between specific childhood urban environmental exposures and multimorbidity indicators and cardiometabolic health outcomes. Longitudinal trajectories of exposure to green and blue spaces were modelled, and annual and period-specific exposures to air pollutants were estimated. Regression-based approaches were applied to evaluate associations with multimorbidity risk score, multimorbidity clusters, and cardiometabolic health indicators.
In Work Package 2, DNA methylation data underwent quality control and normalization procedures to remove low-quality samples and probes and to correct for technical variation and batch effects. The dataset was prepared for downstream analyses. Reproducible analytical pipelines were developed for epigenome-wide association studies and epigenetic clock analyses. Epigenetic research questions were also further refined and integrated into new project applications to support the continuation of research in this area.
In Work Package 3, analytical work focused on the assessment of associations between specific childhood urban environmental exposures and multimorbidity indicators and cardiometabolic health outcomes. Longitudinal trajectories of exposure to green and blue spaces were modelled, and annual and period-specific exposures to air pollutants were estimated. Regression-based approaches were applied to evaluate associations with multimorbidity risk score, multimorbidity clusters, and cardiometabolic health indicators.
Analyses in 2,371 children revealed that multimorbidity at age 13 was common, with multiple co-occurring conditions across cardiometabolic, respiratory/allergic, and neurodevelopmental domains. Machine learning models using urban environmental exposures from birth to 12 years showed moderate predictive performance, and no single environmental exposure emerged as a strong predictor of multimorbidity. Complementary analyses assessing longitudinal trajectories of green and blue space exposure, as well as annual and period-specific exposures to PM2.₅ and NO2, found no robust associations with multimorbidity indicators or cardiometabolic outcomes after adjusting for confounders and multiple testing. The datasets, analytical frameworks, and methodological approaches produced by this project provide a platform for further research within ongoing and newly funded studies. These resources will support continued international collaboration, the investigation of environmental and molecular determinants of multimorbidity, and the refinement of analytical methods to study complex life-course health trajectories.