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. The optimal microbiome-only model, using binary-transformed ASV data, achieved a mean absolute error (MAE) of 6.39 ± 5.73 years on the test set. The model based solely on the seven age-correlated ARGs yielded an MAE of 7.53 ± 5.64 years. Importantly, a combined model utilizing only the 13 integrated features (six genera + seven ARGs) matched the accuracy of the best microbiome model (MAE = 6.39 years) while demonstrating significantly lower variability (± 4.35 years). These findings demonstrate that ARGs hold promise as novel biomarkers for forensic age estimation. More importantly, the dual-marker strategy of “microbiome-ARG” achieves effective age prediction using only a small number of features, offering a highly efficient and promising new approach for forensic age estimation.

Authors

• Daijing Yu (School of Forensic Medicine, Shanxi Medical University, China)
• Liwei Zhang (School of Forensic Medicine, Shanxi Medical University, China)
• Liuyaoxing Gao (School of Forensic Medicine, Shanxi Medical University, China)
• AGU DAMU (School of Forensic Medicine, Shanxi Medical University, China)
• Niu Gao (School of Forensic Medicine, Shanxi Medical University, China)
• Jingjing Xu (School of Forensic Medicine, Shanxi Medical University, China)
• Jiaxuan Hao (School of Forensic Medicine, Shanxi Medical University, China)
• Yuntian Xiao (School of Forensic Medicine, Shanxi Medical University, China)
• Jun Zhang (School of Forensic Medicine, Shanxi Medical University, China)
• Jiangwei Yan (School of Forensic Medicine, Shanxi Medical University, China)