A dual-marker approach for forensic age prediction: integrating the salivary microbiome and antibiotic resistome

Age prediction is a critical challenge in forensic science. Current mainstream approaches, such as DNA methylation analysis, often involve complex sample processing (e.g., bisulfite conversion), which can be costly and time-consuming and may compromise DNA integrity, thereby limiting multi-analyte recovery from trace evidence. Recent studies have highlighted the potential of the human microbiome as a source of forensic biomarkers, including for age estimation. Notably, the profile of antibiotic resistance genes (ARGs) in the microbiome has been reported to vary with host age, yet their utility as forensic age biomarkers remains unexplored. To address this, we characterized the salivary microbiome of volunteers via 16S rRNA gene (V3-V4) amplicon sequencing and quantified the abundance of 32 preselected ARGs using high-throughput quantitative PCR (HT-qPCR). Our analysis identified six bacterial genera and seven ARGs whose relative abundances were significantly correlated with chronological age (P < 0.05). We then constructed and compared random forest regression models for age prediction based on (i) microbiome features (Amplicon Sequence Variants, ASVs), (ii) ARG abundances alone, and (iii) an integrated set combining both marker types. Our results showed that the microbiome-only and ARG-only models both yielded higher mean absolute error (MAE) than the integrated model. In contrast, the combined model, built on just 13 features (six bacterial genera and seven ARGs), achieved a test MAE of 6.22 ± 3.75 years (MAE ± SD). This study reveals that ARGs hold promise as novel biomarkers for forensic age estimation. More importantly, the dual‑marker "microbiome‑ARG" strategy achieves effective age prediction using only a small number of features, offering a highly efficient and promising new approach for forensic age estimation.