{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:05:37Z","timestamp":1790244337117,"version":"4.1.0"},"reference-count":50,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>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\u2019s method, implemented a 5 \u00d7 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 \u2264 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 \u00d7 10\u221244), 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\u20131.00 with unstable confidence intervals). GSE60655 showed same-tissue endurance-training directional concordance (15\/15 testable CpGs sign-concordant, 4\/15 raw p &lt; 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.<\/jats:p>","DOI":"10.3390\/ijms27198523","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:07:54Z","timestamp":1790240874000},"page":"8523","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Batch-Aware, Subject-Grouped Pipeline to Prioritize HIIT-Associated DNA Methylation Candidates with Cross-Cohort Evaluation in Skeletal Muscle"],"prefix":"10.3390","volume":"27","author":[{"given":"Bruce Mingyeung","family":"Song","sequence":"first","affiliation":[{"name":"Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"}]},{"given":"Kenneth Hon Kim","family":"Ban","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4323-3635","authenticated-orcid":false,"given":"Caroline Guat Lay","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore"},{"name":"Cancer and Stem Cell Biology Programme, Duke-NUS Medical School, Singapore 169857, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"given":"Xavier Wezen","family":"Chee","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nrg3230","article-title":"Functions of DNA methylation: Islands, start sites, gene bodies and beyond","volume":"13","author":"Jones","year":"2012","journal-title":"Nat. Rev. Genet."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1101\/gad.947102","article-title":"DNA methylation patterns and epigenetic memory","volume":"16","author":"Bird","year":"2002","journal-title":"Genes Dev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.4161\/15592294.2014.982445","article-title":"An integrative analysis reveals coordinated reprogramming of the epigenome and the transcriptome in human skeletal muscle after training","volume":"9","author":"Lindholm","year":"2014","journal-title":"Epigenetics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1898","DOI":"10.1038\/s41598-018-20287-3","article-title":"Human Skeletal Muscle Possesses an Epigenetic Memory of Hypertrophy","volume":"8","author":"Seaborne","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.2337\/dc18-0296","article-title":"Exercise Response Variations in Skeletal Muscle PCr Recovery Rate and Insulin Sensitivity Relate to Muscle Epigenomic Profiles in Individuals With Type 2 Diabetes","volume":"41","author":"Stephens","year":"2018","journal-title":"Diabetes Care"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"180213","DOI":"10.1038\/sdata.2018.213","article-title":"Methylome of human skeletal muscle after acute & chronic resistance exercise training, detraining & retraining","volume":"5","author":"Seaborne","year":"2018","journal-title":"Sci. Data"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1113\/jphysiol.2011.224725","article-title":"Physiological adaptations to low-volume, high-intensity interval training in health and disease","volume":"590","author":"Gibala","year":"2012","journal-title":"J. Physiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1136\/bjsports-2013-092576","article-title":"High-intensity interval training in patients with lifestyle-induced cardiometabolic disease: A systematic review and meta-analysis","volume":"48","author":"Weston","year":"2014","journal-title":"Br. J. Sports Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.cmet.2017.02.009","article-title":"Enhanced Protein Translation Underlies Improved Metabolic and Physical Adaptations to Different Exercise Training Modes in Young and Old Humans","volume":"25","author":"Robinson","year":"2017","journal-title":"Cell Metab."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e23184","DOI":"10.1096\/fj.202300840RR","article-title":"Methylome and proteome integration in human skeletal muscle uncover group and individual responses to high-intensity interval training","volume":"37","author":"Jacques","year":"2023","journal-title":"FASEB J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1186\/s13059-016-1066-1","article-title":"Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling","volume":"17","author":"Pidsley","year":"2016","journal-title":"Genome Biol."},{"key":"ref_12","first-page":"e22","article-title":"Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes","volume":"45","author":"Zhou","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"C258","DOI":"10.1152\/ajpcell.00423.2024","article-title":"Human skeletal muscle possesses an epigenetic memory of high-intensity interval training","volume":"328","author":"Pilotto","year":"2025","journal-title":"Am. J. Physiol.-Cell Physiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2305082","DOI":"10.1080\/15592294.2024.2305082","article-title":"Resistance and aerobic training increases genome-wide DNA methylation in women with polycystic ovary syndrome","volume":"19","author":"Furtado","year":"2024","journal-title":"Epigenetics"},{"key":"ref_15","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vabalas, A., Gowen, E., Poliakoff, E., and Casson, A.J. (2019). Machine learning algorithm validation with a limited sample size. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224365"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","article-title":"Adjusting batch effects in microarray expression data using empirical Bayes methods","volume":"8","author":"Johnson","year":"2007","journal-title":"Biostatistics"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Widschwendter, M., and Teschendorff, A.E. (2012). A comparison of feature selection and classification methods in DNA methylation studies using the Illumina Infinium platform. BMC Bioinform., 13.","DOI":"10.1186\/1471-2105-13-59"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1038\/nrg.2017.86","article-title":"Statistical and integrative system-level analysis of DNA methylation data","volume":"19","author":"Teschendorff","year":"2018","journal-title":"Nat. Rev. Genet."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1038\/nature12787","article-title":"An atlas of active enhancers across human cell types and tissues","volume":"507","author":"Andersson","year":"2014","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/1756-8935-8-6","article-title":"De novo identification of differentially methylated regions in the human genome","volume":"8","author":"Peters","year":"2015","journal-title":"Epigenet. Chromatin"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3042","DOI":"10.1172\/JCI68016","article-title":"Rare variants in single-minded 1 (SIM1) are associated with severe obesity","volume":"123","author":"Ramachandrappa","year":"2013","journal-title":"J. Clin. Investig."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"573","DOI":"10.18632\/aging.101414","article-title":"An epigenetic biomarker of aging for lifespan and healthspan","volume":"10","author":"Levine","year":"2018","journal-title":"Aging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e73420","DOI":"10.7554\/eLife.73420","article-title":"DunedinPACE, a DNA methylation biomarker of the pace of aging","volume":"11","author":"Belsky","year":"2022","journal-title":"eLife"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"R115","DOI":"10.1186\/gb-2013-14-10-r115","article-title":"DNA methylation age of human tissues and cell types","volume":"14","author":"Horvath","year":"2013","journal-title":"Genome Biol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.molcel.2012.10.016","article-title":"Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates","volume":"49","author":"Hannum","year":"2013","journal-title":"Mol. Cell"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.18632\/aging.101508","article-title":"Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies","volume":"10","author":"Horvath","year":"2018","journal-title":"Aging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1038\/s41580-022-00528-0","article-title":"Emerging roles and functional mechanisms of PIWI-interacting RNAs","volume":"24","author":"Wang","year":"2023","journal-title":"Nat. Rev. Mol. Cell Biol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"419","DOI":"10.18632\/aging.101168","article-title":"Epigenetic clock analysis of diet, exercise, education, and lifestyle factors","volume":"9","author":"Quach","year":"2017","journal-title":"Aging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e13828","DOI":"10.1111\/acel.13828","article-title":"Physical activity is associated with slower epigenetic ageing\u2014Findings from the Rhineland study","volume":"22","author":"Fox","year":"2023","journal-title":"Aging Cell"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1056\/NEJMoa002939","article-title":"A recessive form of the Ehlers-Danlos syndrome caused by tenascin-X deficiency","volume":"345","author":"Schalkwijk","year":"2001","journal-title":"N. Engl. J. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1002\/ajmg.c.30071","article-title":"Tenascin-X, collagen, elastin, and the Ehlers-Danlos syndrome","volume":"139C","author":"Bristow","year":"2005","journal-title":"Am. J. Med. Genet. Part C"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1152\/physrev.00031.2003","article-title":"Role of extracellular matrix in adaptation of tendon and skeletal muscle to mechanical loading","volume":"84","author":"Kjaer","year":"2004","journal-title":"Physiol. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s13395-020-00236-3","article-title":"A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations","volume":"10","author":"Spector","year":"2020","journal-title":"Skelet. Muscle"},{"key":"ref_35","unstructured":"Fisher, R.A. (1925). Statistical Methods for Research Workers, Oliver and Boyd."},{"key":"ref_36","unstructured":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates. [2nd ed.]."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"963","DOI":"10.2307\/2529876","article-title":"Random-effects models for longitudinal data","volume":"38","author":"Laird","year":"1982","journal-title":"Biometrics"},{"key":"ref_38","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"vbad048","DOI":"10.1093\/bioadv\/vbad048","article-title":"nestedcv: An R package for fast implementation of nested cross-validation with embedded feature selection designed for transcriptomics and high-dimensional data","volume":"3","author":"Lewis","year":"2023","journal-title":"Bioinform. Adv."},{"key":"ref_40","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2026, April 21). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems 30 (NeurIPS\/NIPS 2017). Available online: https:\/\/papers.nips.cc\/paper\/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1093\/bioinformatics\/btu049","article-title":"Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays","volume":"30","author":"Aryee","year":"2014","journal-title":"Bioinformatics"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"41","DOI":"10.12688\/wellcomeopenres.17598.2","article-title":"The EWAS Catalog: A database of epigenome-wide association studies","volume":"7","author":"Battram","year":"2022","journal-title":"Wellcome Open Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1093\/bioinformatics\/btx183","article-title":"annotatr: Genomic regions in context","volume":"33","author":"Cavalcante","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., and Wang, J. (2015). Integrative analysis of 111 reference human epigenomes. Nature, 518, 317\u2013330.","DOI":"10.1038\/nature14248"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"GTEx Consortium (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science, 369, 1318\u20131330.","DOI":"10.1126\/science.aaz1776"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1111\/j.1420-9101.2005.00917.x","article-title":"Combining probability from independent tests: The weighted Z-method is superior to Fisher\u2019s approach","volume":"18","author":"Whitlock","year":"2005","journal-title":"J. Evol. Biol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1038\/s41576-018-0004-3","article-title":"DNA methylation-based biomarkers and the epigenetic clock theory of ageing","volume":"19","author":"Horvath","year":"2018","journal-title":"Nat. Rev. Genet."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.18632\/aging.101020","article-title":"DNA methylation-based measures of biological age: Meta-analysis predicting time to death","volume":"8","author":"Chen","year":"2016","journal-title":"Aging"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B Methodol."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/19\/8523\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:13:43Z","timestamp":1790241223000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/19\/8523"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":50,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["ijms27198523"],"URL":"https:\/\/doi.org\/10.3390\/ijms27198523","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}