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Updated: Jul 16, 2026

Optimized Analysis of DNA Methylation and Gene Expression from Small, Anatomically-defined Areas of the Brain
Published on: July 12, 2012
A Critical Review of Longitudinal DNA Methylomic Changes Associated with Treatment Response in Major Depressive
Rosana Carvalho Silva1, Danae Zareifi2,3, Danai Giannakou3,4
1Department of Molecular and Translational Medicine, University of Brescia, 25121 Brescia, Italy.
Abstract:
Major depressive disorder (MDD) is a prevalent psychiatric disorder in which epigenetic mechanisms, particularly DNA methylation (DNAm), may contribute to disease vulnerability and treatment response. Epigenome-wide association studies (EWAS) have increasingly investigated longitudinal methylomic changes associated with therapeutic interventions in depression; however, methodological heterogeneity limits comparability across studies. This critical review examined the methodologies and findings of longitudinal EWAS evaluating DNAm changes related to treatment response in MDD and treatment-resistant depression (TRD). A literature search identified seven studies published up to 20 June 2026. Six studies investigated non-pharmacological interventions, including electroconvulsive therapy, trauma-focused psychotherapy, and cognitive interventions, and one study explored pharmacotherapy. Considerable heterogeneity was observed regarding sample size, biospecimen type, methylation platforms, preprocessing pipelines, covariate adjustment, statistical modeling, and longitudinal sampling schedules. Most studies used Illumina EPIC array-based workflows and mixed-model analytical approaches, while one study employed sequencing-based methylation profiling. Overall, treatment-related methylation changes were modest and often limited to specific CpG sites or differentially methylated regions associated with immune, inflammatory, stress-related, and neurobiological pathways. Current evidence supports the feasibility of longitudinal EWAS approaches in depression research but highlights the need for larger cohorts, methodological standardization, and integration with multi-omics and clinical data to improve reproducibility and biomarker discovery.
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