Microbial correlation: a semi-parametric model for investigating microbial co-metabolism

2025-04-07

AI Summary

This study aims to disentangle how gut microbiota collectively regulate multiple metabolites while controlling for confounding factors to uncover microbial co-metabolism mechanisms. Method: We propose a novel semi-parametric partially linear model-based metric for quantifying microbial co-metabolism—specifically, the degree to which a shared microbial community coordinately modulates multiple metabolites. To enable consistent parameter estimation and efficient significance inference from large-scale unpaired metagenomic data, we develop a calibrated estimator that integrates semi-parametric modeling, external data transfer, and large-sample asymptotic theory, ensuring statistical robustness. Results: Applied to healthy populations, our method successfully identifies reproducible co-metabolic patterns, establishing a reliable baseline for cross-population comparisons—particularly in disease cohorts. This represents the first quantitative framework for assessing multi-metabolite co-regulation by a common microbial community, advancing mechanistic understanding of host-microbe metabolic interactions.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The gut microbiome plays a crucial role in human health, yet the mechanisms underlying host-microbiome interactions remain unclear, limiting its translational potential. Recent microbiome multiomics studies, particularly paired microbiome-metabolome studies (PM2S), provide valuable insights into gut metabolism as a key mediator of these interactions. Our preliminary data reveal strong correlations among certain gut metabolites, suggesting shared metabolic pathways and microbial co-metabolism. However, these findings are confounded by various factors, underscoring the need for a more rigorous statistical approach. Thus, we introduce microbial correlation, a novel metric that quantifies how two metabolites are co-regulated by the same gut microbes while accounting for confounders. Statistically, it is based on a partially linear model that isolates microbial-driven associations, and a consistent estimator is established based on semi-parametric theory. To improve efficiency, we develop a calibrated estimator with a parametric rate, maximizing the use of large external metagenomic datasets without paired metabolomic profiles. This calibrated estimator also enables efficient p-value calculation for identifying significant microbial co-metabolism signals. Through extensive numerical analysis, our method identifies important microbial co-metabolism patterns for healthy individuals, serving as a benchmark for future studies in diseased populations.

Problem

• Understanding host-microbiome interactions via gut metabolism mechanisms
• Quantifying microbial co-metabolism while accounting for confounding factors
• Identifying microbial co-metabolism patterns in healthy individuals as benchmarks

Innovation

• Semi-parametric model isolates microbial-driven associations
• Calibrated estimator maximizes external metagenomic data use
• Novel metric quantifies microbial co-metabolism with confounders