Aging in the city: Linking neighborhood features with whole-brain structural and micro-structural changes during aging

The association between the physical living environment and brain structural changes in older adults remains considerably unexplored despite its potential to benefit a rapidly-growing proportion of the world's population. We investigated whether neighborhood-level environmental features are associated with changes in whole-brain structure and microstructure during healthy aging. By means of structural equation modeling, we estimated change in gross brain features over about two years in a longitudinal sample of participants from the Berlin Aging Study II (at T1: N=334, mean age = 70 years, 128 women; https://www.base2.mpg.de/en). We used the machine learning algorithm BORUTA on neighborhood-level (Lifeworld-Oriented Spaces) environmental information (N = 201) to identify correlates of brain absolute measures and brain change. We observed typical trajectories of brain aging at the construct and measurement levels. Our findings indicate that of the 25 neighborhood features examined, air pollutants, population density and the land-use composition of individuals' neighborhood are associated with changes in subcortical gray matter volume, cortical integrity, white matter volume, and ventricle size. Weaker associations were found for mean diffusivity and cortical thickness. These findings underscore the relevance of the physical environment for neural outcomes during aging.

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Publication Details

Journal
Environmental Research
Published
2026-05-01
DOI
https://doi.org/10.1016/j.envres.2026.124671
Primary Topic
Functional Brain Connectivity Studies
Type
article
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article

Aging in the city: Linking neighborhood features with whole-brain structural and micro-structural changes during aging

S Kuhn, Johanna Drewelies, Sandra Düzel, Juan F. Quinones et al.
Environmental Research
Functional Brain Connectivity Studies
article

Aging in the city: Linking neighborhood features with whole-brain structural and micro-structural changes during aging

S Kuhn, Johanna Drewelies, Sandra Düzel, Juan F. Quinones, Ulman Lindenberger, Ilja Demuth, Denis Gerstorf
article en

Abstract

The association between the physical living environment and brain structural changes in older adults remains considerably unexplored despite its potential to benefit a rapidly-growing proportion of the world's population. We investigated whether neighborhood-level environmental features are associated with changes in whole-brain structure and microstructure during healthy aging. By means of structural equation modeling, we estimated change in gross brain features over about two years in a longitudinal sample of participants from the Berlin Aging Study II (at T1: N=334, mean age = 70 years, 128 women; https://www.base2.mpg.de/en). We used the machine learning algorithm BORUTA on neighborhood-level (Lifeworld-Oriented Spaces) environmental information (N = 201) to identify correlates of brain absolute measures and brain change. We observed typical trajectories of brain aging at the construct and measurement levels. Our findings indicate that of the 25 neighborhood features examined, air pollutants, population density and the land-use composition of individuals' neighborhood are associated with changes in subcortical gray matter volume, cortical integrity, white matter volume, and ventricle size. Weaker associations were found for mean diffusivity and cortical thickness. These findings underscore the relevance of the physical environment for neural outcomes during aging.

Environmental Research
Universität Hamburg (DE), Humboldt-Universität zu Berlin (DE), University Medical Center Hamburg-Eppendorf (DE), Max Planck Institute for Human Development (DE), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
Openalex Percentile: Top 9%
Functional Brain Connectivity Studies
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Aging in the city: Linking neighborhood features with whole-brain structural and micro-structural changes during aging — S Kuhn, Johanna Drewelies, et al. · Environmental Research (2026) | TGRS Research Map | TGRS