Combining a novel ensemble model and multiplex methylation SNaPshot assays for saliva age prediction and cross-platform data analysis

DNA methylation is a pivotal biomarker for age prediction. However, most studies focus on blood-derived data, with limited research on saliva, and the inability to directly analyze methylation data across diverse platforms constrains predictive accuracy. We identified 10 age-related CpG sites in saliva (cg00481951, cg07547549, cg10501210, cg13654588, cg14361627, cg15480367, cg17110586, cg17885226, cg19671120, cg21296230) through six Illumina HumanMethylation450 BeadChip datasets and developed two multiplex SNaPshot assays. Leveraging methylation SNaPshot data from 239 saliva samples (13–69 years), we constructed an ensemble model with 17 neural network classifiers, each categorizing ages with a 17-year bin width and shifting bins by one year in subsequent classifiers. Validated by an independent testing set consisting of 44 samples (13–66 years), the model achieved a mean absolute error (MAE) of 4.39 years, outperforming some advanced linear and nonlinear models. Notably, the model also showed improved prediction performance when applied to other datasets, demonstrating its robustness and generalizability. Additionally, by incorporating dummy variables into our model, we effectively mitigated platform-specific biases, facilitating integrated multi-platform methylation data analysis for age prediction. In this study, we identified ten age-associated CpG sites in saliva and developed an ensemble model with 17 neural network classifiers for precise age prediction. Moreover, by introducing dummy variables, our model effectively mitigates platform-dependent variations. In summary, we offered a novel framework for age prediction for saliva and cross-platform data analysis.