Multiomics Profiling Reveals Signatures of Dysmetabolism in Urban Populations in Central India

Background: Non-communicable diseases (NCDs) have become a major cause of morbidity and mortality in India. Perturbation of host-microbiome interactions may be a key mechanism by which lifestyle-related risk factors such as tobacco use, alcohol consumption, and physical inactivity may influence metabolic health. There is an urgent need to identify relevant dysmetabolic traits for predicting risk of metabolic disorders, such as diabetes, among susceptible Asian Indians where NCDs are a growing epidemic. Methods: Here, we report the first in-depth phenotypic study in which we prospectively enrolled 218 adults from urban and rural areas of Central India and used multiomic profiling to identify relationships between microbial taxa and circulating biomarkers of cardiometabolic risk. Assays included fecal microbiota analysis by 16S ribosomal RNA gene amplicon sequencing, quantification of serum short chain fatty acids by gas chromatography-mass spectrometry, and multiplex assaying of serum diabetic proteins, cytokines, chemokines, and multi-isotype antibodies. Sera was also analysed for N-glycans and immunoglobulin G Fc N-glycopeptides. Results: Multiple hallmarks of dysmetabolism were identified in urbanites and young overweight adults, the majority of whom did not have a known diagnosis of diabetes. Association analyses revealed several host-microbe and metabolic associations. Conclusions: Host-microbe and metabolic interactions are differentially shaped by body weight and geographic status in Central Indians. Further exploration of these links may help create a molecular-level map for estimating risk of developing metabolic disorders and designing early interventions.

Automated summary

Non-communicable diseases have become a major cause of morbidity and mortality in India, particularly among susceptible Asian Indians. This study identified multiple dysmetabolic traits in urbanites and young overweight adults, highlighting the complex interactions between host-microbiome and metabolic factors in predicting risk of metabolic disorders like diabetes. Further exploration of these links may help in designing early interventions to manage and prevent NCDs.