Prediction of Biological Age and Blood Biomarkers from DNA Methylation Profiles Measured by the Methylation Screening Array: Development and Validation of Models on Japanese Data

Abstract Background Epigenetic clocks based on DNA methylation (DNAm) are widely used indicators of biological aging; however, most established models have been developed using EPIC arrays and non-Japanese populations. The Methylation Screening Array (MSA), a cost-efficient platform with reduced CpG content, has not been evaluated for its capacity to support biological age estimation and biomarker prediction in Japanese cohorts. Methods DNAm profiles and clinical laboratory measurements were obtained from 166 Japanese participants for model development; an independent cohort of 48 individuals processed at a separate institute was used for validation. A linear regression model was trained using the Elastic Net method to predict phenotypic age from MSA-derived methylation data, and a two-stage modeling (residual learning) framework integrating EPIC-based clock predictions with MSA-specific residual predictions was evaluated. Additional models were constructed to examine the predictability of 59 clinical biomarkers and their log-transformed variants, including sex-stratified analyses. Results The MSA-based model accurately predicted phenotypic age in the validation dataset; prediction performance improved when the EPIC-based estimates were incorporated through the residual learning framework. Several clinical biomarkers, particularly those related to leukocyte composition and sex hormone regulation, were also predicted from the MSA data, although some markers were strongly affected by sex. Some of the nine constituent phenotypic age biomarkers were not individually predicted. Conclusions MSA methylation profiles contain sufficient biological information for reliable prediction of epigenetic aging markers in Japanese individuals. These findings demonstrate the feasibility of applying cost-efficient MSA-based DNAm profiling for biological age prediction and provide a methodological foundation for expanding epigenetic biomarker applications in Japan.