Establishment of a Highly Accurate and Sensitive Age Prediction Model for Multiple Body Fluids: Blood, Saliva, and...

DNA methylation-based age prediction has become a reliable method for individual identification. While current models have achieved high accuracy when targeting a single type of biological fluid, crime scenes often contain multiple body fluids. Applying single fluid models to other fluid types may result in significant prediction errors, potentially misleading investigations. Therefore, age prediction models applicable to multiple biological fluids are of critical importance. In this study, we screened three age-related methylation sites (cg05940966, cg10501210, and cg10528482) from blood, saliva, and semen samples by analysis of public databases. These sites are associated with age. We then quantified methylation levels in peripheral blood samples from 101 healthy individuals via pyrosequencing. Based on these data, machine learning algorithms were applied to construct multiple age prediction models. These models were evaluated for their prediction accuracy, applicability to other body fluids, sensitivity, inhibitor tolerance, and utility with aged forensic samples. A multiple linear regression model constructed using the pyrosequencing results displayed the highest prediction accuracy, with mean absolute deviation (MAD) values of 2.717, 3.506, and 4.154 years in blood, saliva, and semen, respectively. Pyrosequencing also demonstrated high sensitivity, with MAD remaining within 4 years, even in trace samples (0.5 ng of unconverted genomic DNA). However, when the concentration of oxidized heme exceeds 1 ng/µL and the concentration of humic acid exceeds 2 ng/µL, pyrosequencing cannot accurately measure the methylation values at the CpG sites. In summary, this study provides an accurate and reliable tool for age prediction based on multiple bodily fluids. From a practical standpoint, this technology enables rapid age estimation without requiring prior identification of fluid type, thereby conserving valuable biological samples at crime scenes. This breakthrough will significantly enhance the efficiency of criminal investigations, strengthen the reliability of biological evidence interpretation in complex scenarios, and provide scientific support for judicial processes.