Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment
Fig 6
Various metabolites (particularly long-chain fatty acids, sphingolipids and glucose), protein biomarkers (IL-6, IL-10, MIP-1α) are the best predictors of the genital inflammation.
Integrated vaginal microbiome, metabolome, and immunoproteome profiles (excluding the 7 cytokines used to score genital inflammation) were used as predictive features for training cross-validated Random Forest classifiers to predict whether a subject’s genital inflammation score was “no inflammation” (0), low (1–4), or high (≥ 5.0). Combined measurements predict inflammation score at an overall accuracy rate of 77.8%. A 1.7-fold improvement over baseline accuracy was observed. Receiver operating characteristics (ROC) analysis showing true and false positive rates for each group, indicating moderate average accuracy (micro-average AUC = 0.90) and weak to good predictive accuracy for each group (A). The confusion matrix illustrates the proportion of times each sample receives the correct classification when evaluating the classifier at a threshold of 0.5 (B). The graphs depict the 25 most strongly predictive features ranked by their mean Gini importance score across all 10 trained classifiers, a measure of their overall contribution to classifier accuracy (C).