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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Bayesian semiparametric inference in longitudinal metabolomics data.

Abhra Sarkar1, Ornella Cominetti2, Ivan Montoliu3,4

  • 1Department of Statistics and Data Sciences, University of Texas at Austin, Austin, 78712-1823, USA. abhra.sarkar@utexas.edu.

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Summary
This summary is machine-generated.

This study reveals how blood metabolites impact childhood obesity and glucose control, identifying key metabolic pathways. The findings offer new insights for understanding and managing these critical health issues in children.

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Area of Science:

  • Metabolomics
  • Pediatric Health
  • Statistical Modeling

Background:

  • Childhood obesity and glucose control are complex health issues.
  • Understanding the link between metabolites and longitudinal health outcomes is crucial.
  • Existing methods struggle with high-dimensional, dynamic data and missing values.

Purpose of the Study:

  • To develop a statistical model for analyzing the association between dynamic metabolites and childhood obesity/glucose control.
  • To identify key metabolites and metabolic pathways influencing glucose trajectories.
  • To provide a tool for generating new hypotheses in pediatric metabolic health.

Main Methods:

  • Proposed a Bayesian semiparametric joint model for outcome and covariate processes.
  • Utilized nonparametric mean processes, latent factor models, and continuous shrinkage priors.
  • Employed Markov chain Monte Carlo for efficient implementation and uncertainty quantification.

Main Results:

  • Developed a flexible model addressing high dimensionality and missing data.
  • Successfully integrated longitudinal glucose data with metabolite profiles from the EarlyBird cohort.
  • Identified specific metabolites and central energy metabolomic pathways associated with glucose trajectories from ages 5 to 16.

Conclusions:

  • The methodology effectively integrates molecular data for understanding pediatric metabolic health.
  • Circulating metabolites are significantly associated with glucose levels and trajectories in childhood.
  • The approach facilitates hypothesis generation for childhood obesity and glucose control research.