Biomarker-anchored screening of dietary microplastics via a gut microbiota–informed machine learning model

Microplastics (MPs) are increasingly recognized as emerging contaminants in the human diet, yet the absence of unified biomarker-anchored screening thresholds hampers quantitative risk assessment. This study establishes a gut microbiota-anchored machine learning framework that links Firmicutes/Bacteroidetes (F/B) ratio thresholds with real-world dietary MP exposure data, enabling a biomarker-anchored screening of dysbiosis-related signals associated with disease risk. Across the compiled disease cohorts, obesity provided the largest and most data-sufficient contrast in this dataset and was therefore selected as the anchor condition for threshold derivation. Receiver operating characteristic (ROC) analysis determined a model-derived screening cutoff for the F/B ratio (cutoff = 1.08), which was further used to define a three-tier risk classification system. A dataset comprising 558 dietary MP records from 129 studies across 25 countries was incorporated to predict food- and region-specific risk levels. Results reveal that seafood represents the dominant global dietary MP risk source, with mollusca contributing the highest predicted F/B-related risk signal. Spatial stratification indicates that the United States and Ecuador were classified into the highest model-derived tier based on national mean predicted F/B values, followed by high-risk regions such as Vietnam, Turkey, and Ireland, while countries in the Middle East and North Africa remain at moderate risk. These geographic contrasts suggest the combined influence of dietary structure and regional exposure intensity. Overall, we present a biomarker-anchored, machine-learning-enabled screening framework that links dietary MP exposure to predicted changes in a dysbiosis-associated indicator (F/B). This approach can support monitoring prioritization, identification of data gaps, and hypothesis generation for future validation studies.