A Batch-Aware, Subject-Grouped Pipeline to Prioritize HIIT-Associated DNA Methylation Candidates with Cross-Cohort Evaluation in Skeletal Muscle.

Skeletal muscle DNA methylation is closely associated with molecular adaptation to exercise training, but longitudinal studies in this area are constrained by the difficulty of repeated muscle biopsy sampling and extended follow-up. The resulting datasets are typically small, with repeated measurements within individuals and complex sub-cohort or batch structure. To address these limitations, we developed a methylation candidate-discovery pipeline that integrates batch-aware statistical screening, subject-grouped nested cross-validation, and a binary/multi-class/temporal three-axis consensus screen. We applied this pipeline to the public skeletal-muscle HIIT cohort GSE171140 (195 samples from 52 unique participants; EPIC v1.0; vastus lateralis), in which the 8- and 12-week time points derived from a single sub-cohort. The pipeline estimated per-locus evidence within each sub-cohort, combined these estimates using Fisher's method, implemented a 5 × 3 nested cross-validation grouped by individual_id, and required candidate CpGs to pass all three task axes. In internal evaluation, LR-L2 achieved AUC = 0.754 (subject-level bootstrap 95% CI [0.689, 0.832]; permutation