Dataset Information

CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention.

ABSTRACT: Microbial variations in the human gut are harbored in temporal and spatial heterogeneity, and quantitative prediction of spatiotemporal dynamic changes in the gut microbiota is imperative for development of tailored microbiome-directed therapeutics treatments, e.g. precision nutrition. Given the high-degree complexity of microbial variations, subject to the dynamic interactions among host, microbial, and environmental factors, identifying how microbiota colonize in the gut represents an important challenge. Here we present COmputing the DYnamics of microbiota (CODY), a multiscale framework that integrates species-level modeling of microbial dynamics and ecosystem-level interactions into a mathematical model that characterizes spatial-specific in vivo microbial residence in the colon as impacted by host physiology. The framework quantifies spatiotemporal resolution of microbial variations on species-level abundance profiles across site-specific colon regions and in feces, independent of a priori knowledge. We demonstrated the effectiveness of CODY using cross-sectional data from two longitudinal metagenomics studies-the microbiota development during early infancy and during short-term diet intervention of obese adults. For each cohort, CODY correctly predicts the microbial variations in response to diet intervention, as validated by available metagenomics and metabolomics data. Model simulations provide insight into the biogeographical heterogeneity among lumen, mucus, and feces, which provides insight into how host physical forces and spatial structure are shaping microbial structure and functionality.

SUBMITTER: Geng J

PROVIDER: S-EPMC8020746 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo find variations in Chinese obesogenic environmental priorities from 2000 to 2011, predict spatiotemporal distribution of obesity prevalence aged 7-17 years in 31 provinces, and provide foundations for policy-makers to reduce obesity in children and adolescents.MethodsBased on data examination of provincial obesity prevalence aged 7-17 years from three rounds of China Health and Nutrition Surveys (in 9 [2000], 9 [2006], and 12 [2011] provinces) and corresponding years' environments in 31 provinces from China Statistical Yearbooks and other sources, 12 predictors were selected. We used 30 surveyed provinces in three rounds as training samples to fit three analytic models with partial least-square regressions and prioritized predictors by variable importance projection to find variations. And fitted a spatiotemporal prediction model with Bayesian analysis to infer in space-time.ResultsVariations of obesogenic environmental priorities were found at different times. A Bayesian spatiotemporal prediction model with deviance information criterion of 155.60 and statistically significant (P < 0.05) parameter estimates of intercept (-717.0400, 95% confidence intervals [CI]: -1186.0300, -248.0480), year (0.3584, CI: 0.1245, 0.5924), square of food industry level (0.0003, CI: 0.0002, 0.0004), and log (healthcare) (5.3742, CI: 2.5138, 8.2347) was optimized. Totally inferred average obesity prevalence among children and adolescents were 2.23%, 5.11%, 10.77%, 12.20%, 13.99%, and 17.58% in 31 provinces in China in 2000, 2006, 2011, 2015, 2020, and 2030, respectively. Obesity in north and east of China clusters on predicted maps.ConclusionsObesity prevalence in children and adolescents in China is rapidly increasing, growing at 0.3584% annually from 2000 to 2011. From longitudinal observation, prevalence was significantly influenced by food industry ("Amplifier") and healthcare service ("Balancer"). Targeted interventions in north and east of China are pressing. Further researches on the mechanisms underlying the influence of food industry, healthcare service, and so on in children and adolescents are needed.

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CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention.

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