Ecological associations between ambient air pollution, gut microbiome composition, and metabolic markers in urban populations of central India

ObjectiveTo investigate the ecological level associations and spatial co-variation among ambient air pollutants, Type 2 diabetes (T2D) related clinical markers, and gut microbiome composition in urban populations of Bhopal, India.MethodsA cross-sectional ecological study was conducted involving 95 participants from three urban locations in Bhopal. Ambient air quality data, including PM2.5, PM10, NOx, and SO2, were obtained from local monitoring stations. Exposure assessment was based on location level ambient monitoring data corresponding to participants’ residential localities rather than individual level personal exposure measurements. Clinical assessments were performed to measure T2D related metabolic parameters, and fecal samples were analyzed using full-length 16S rRNA sequencing to profile gut microbiome composition. Associations between pollutant levels, metabolic markers, and microbial taxa were evaluated using exploratory correlation and compositional analyses.ResultsVariability was observed in both air pollutant concentrations and metabolic parameters. NOx levels were positively correlated with glycemic indices, while particulate pollutants (PM2.5 and PM10) showed inconsistent and non-directional associations with metabolic markers. Gut microbiome analyses revealed broadly similar microbial community structures with subtle differences in diversity and composition across locations. Although variations were observed in selected microbial taxa, most genus-level differences did not remain statistically significant following multiple testing correction.ConclusionAir pollution and gut microbiome composition appear to have a descriptive role in metabolic health. The findings describe ecological level co-variation between ambient air pollution indicators, metabolic parameters, and gut microbiome composition across the study populations. Because the study design was cross-sectional and exposure assessment relied on location level monitoring data, the results should be interpreted as descriptive and hypothesis-generating rather than causal. Further longitudinal studies incorporating individual level exposure assessment and functional microbiome analyses are required to better evaluate these relationships.