Fig. 5: The characteristics (A) and distributions of identified subpopulations (B, C), and the associations of subpopulations with various aging metrics (D).

SOM, self-organizing map. We used SOM analyses to recognize group structure. Populations were differentiated into air subpopulation (red, specific name was used only to refer to the main environmental exposure feature, not all features), green space subpopulation (green), rural–urban fringe subpopulation (yellow), noise subpopulation (black), blue space subpopulation (blue), and others (gray). A Characteristics of subpopulations. A larger sector size represents the larger amount of a specific environmental factor. B Distributions of subpopulations and C Distributions separately. We used multiple linear regression models to evaluate the associations of subpopulations with various aging metrics (D). Linear regression models were performed to examine the associations of subpopulations with various aging metrics. All models were adjusted for age, sex, ethnicity, neighborhood socioeconomic status (nSES), smoking status, BMI (category variable), alcohol intake frequency, regular exercise, healthy diet, history of cardiovascular disease (CVD), and cancer at baseline. Benjamini–Hochberg procedure was used to control the family-wise error rate in the main analyses (n = 285). Two-sided P value of <0.05 was considered statistically significant (values are represented as a coefficient ± standard error of the mean. *P < 0.05, **P < 0.01, ***P < 0.005; different subpopulations vs. green space subpopulation). The blank map of the UK can be freely downloaded from GADM version 4.1 (https://www.gadm.org/). Source data are provided as a Source Data file.