Deep-learning analysis of greenspace and metabolic syndrome: A street-view and remote-sensing approach

Abstract

Evidence linking greenspace exposure to metabolic syndrome (MetS) remains sparse and inconsistent. This exploratory study evaluate the relationship between green visibility index (GVI) and normalized difference vegetation index (NDVI) with MetS prevalence, and quantifies the potential reduction in MetS burden from increased greenspace exposure. Participants were selected from the baseline survey of the Wuhan Chronic Disease Cohort. Street-view imagry was procured within buffer zones ranging from 50 to 500-m surrounding participants' residences. GVI was extracted from street-view images using a convolutional neural network model trained on CityScapes, while the NDVI was ascertained from satellite remote sensing data. We employed generalized linear mixed-effects models to assess the associations between greenspace with the risk of MetS. Additionally, restricted cubic spline function was applied to generate exposure-response curve. Leveraging a counterfactual causal inference framework, we quantified the potential diminution in MetS cases consequent to an elevation in NDVI levels within Wuhan. Within the 150-m buffer zone, each 0.1-unit increase in GVI and NDVI corresponded to 13% and 31% decline in the odds of MetS in the fully adjusted regression models, respectively. A negative non-linear relationship between GVI and MetS was observed when the GVI level exceeded 0.209, while a negative linear association for NDVI when its level exceeded 0.299. Assuming causality, 74,183 cases of MetS can be avoided by achieving greenness threshold of NDVI, amounting for 8.16% of total MetS prevalence in 2019. Our findings offer a compelling justification for the integration of greening policies in initiatives aimed at promoting metabolic health.