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 p ≤ 0.002), supported by synthetic-signal recovery and method comparison. The consensus screen reduced 95,477 high-variance CpGs to 21 L6 candidates and one L5 sentinel (cg06639166 near SIM1). In participant-aware mixed-model reanalysis, 20 of the 21 candidates remained associated with training status and 19 of 21 were genome-wide significant after false-discovery-rate correction, with little evidence of sub-cohort heterogeneity in sensitivity analyses. Within the extended MHC (6p21.33), the TNXB five-CpG cluster formed an internally coherent regional signal supported by a 25-CpG DMR (Stouffer p = 3.15 × 10^-44), although genetic, ancestry-related, and copy-number contributions cannot be ruled out on the basis of methylation-only data. We treated external evaluations as bounded checks. Specifically, GSE268211 provided a small partner-laboratory, same-platform, same-tissue consistency check (n = 10; five paired subjects; transfer AUROC 0.96-1.00 with unstable confidence intervals). GSE60655 showed same-tissue endurance-training directional concordance (15/15 testable CpGs sign-concordant, 4/15 raw p < 0.05, 0/15 BH-FDR, 34 analytical samples from 17 paired participants). Finally, GSE213363 provided a cross-tissue/cross-disease stress test (AUC = 0.673), in which all 21 candidates were testable, but locus-level concordance was limited in paired analyses. In the paired DNAm-age analysis (15 participants), only Horvath2013 remained significant after multiple-testing correction (mean change +1.40 years, 95% CI +0.56 to +2.23; BH-FDR = 0.015); the other clocks did not. This study provides a reusable analytical framework and a conservative shortlist of exploratory methylation candidates for evaluation in larger, genotype-aware, same-tissue exercise cohorts.