q2-predict-dysbiosis

q2-predict-dysbiosis (Q2PD): Metabolism-Centric Metagenomic Health Index for Disease Prediction

q2-predict-dysbiosis (Q2PD) computes a metagenomic health index that distinguishes healthy and dysbiotic states of the human gut microbiota by quantifying metabolic functional potential rather than relying on taxonomic classification.

Key Features:

• Metabolism-Centric Modeling: Quantifies metabolic functional potential of microbiomes to capture ecological interactions among microbial species.
• Functional Potential Analysis: Replaces Linnean phylogenetic classification and species richness metrics with metabolic function–based assessment of phenotypic and metabolic diversity.
• Cross-Dataset Validation: Demonstrates superior performance relative to Gut Microbiome Health Index (GMHI) and high-dimensional principal component analysis (hiPCA) across inflammatory bowel disease (IBD) and 27 additional clinical datasets.
• Longitudinal Robustness: Validated in a longitudinal COVID-19 cohort; outperforms GMHI and hiPCA and remains stable across sequencing depth variation.
• Complementary Benchmarking: Applies multiple benchmarking strategies to evaluate discrimination between healthy and disease states.

Scientific Applications:

• Dysbiosis Detection: Identifies microbiome-associated disease states, including inflammatory bowel disease and COVID-19, using metagenomic profiles.
• Clinical Stratification: Supports disease classification across diverse clinical cohorts using functional microbiome signatures.
• Microbiome Research: Enables development and evaluation of function-based microbiome health indices.

Methodology:

Q2PD analyzes metagenomic datasets to quantify microbial metabolic functional potential, integrates comparative benchmarking against GMHI and hiPCA, and evaluates performance across cross-sectional and longitudinal cohorts to derive a health index robust to sequencing depth variability.