2026 ECNP Congress

PS02-2266

MTHFR burden-associated DNA methylation patterns in SSRI response in major depressive disorder

Nan Lyu — A. Fan, Q. Zhao, J. Yang, D. Liu, J. Zhou, X. Zhu, M. Liu, H. Wang, M. Wei, C. Zhang, R. Li, L. Zhang, G. Wang · Beijing Anding Hospital- Capital Medical University, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing, China

2026 ECNP Congress

Background

Response to selective serotonin reuptake inhibitors (SSRIs) in major depressive disorder (MDD) remains highly variable, and the molecular basis of this heterogeneity is still incompletely understood. Conventional clinical predictors explain only a limited proportion of treatment outcome, highlighting the need for biologically informed stratification markers. Given the central role of MTHFR in one-carbon metabolism, folate cycling, and methylation capacity, MTHFR genetic variation represents a plausible determinant of both antidepressant response and epigenetic regulation. We therefore investigated whether MTHFR genetic burden is associated with SSRI treatment outcome and, more importantly, whether it reshapes DNA methylation patterns linked to treatment response in patients with MDD.

Methods

A total of 791 patients with MDD receiving SSRI treatment were included in the clinical pharmacogenetic analysis, comprising 542 responders and 249 non-responders. Three functional MTHFR polymorphisms (rs1801133, rs1801131, and rs2274976) were genotyped, and cumulative risk-allele burden was analyzed in relation to treatment response. To further explore epigenetic mechanisms, genome-wide DNA methylation profiling was performed in a balanced methylation subcohort of 66 patients, including 33 responders and 33 non-responders. Patients were stratified according to MTHFR genotype burden into no-risk and risk-genotype groups, and differential methylation analyses were conducted to characterize methylomic signatures associated withMTHFR burden. We further assessed genotype-dependent response-associated methylation patterns using an interaction framework based on differences in methylation changes between responders and non-responders across genotype strata, followed by functional enrichment analysis of annotated genes.

Results

In the clinical cohort, higher MTHFR risk burden was associated with SSRI non-response, supporting a pharmacogenetic contribution to treatment heterogeneity. In the methylation subcohort, individuals with higher versus lowerMTHFRgenotype burden showed significant methylation differences at multiple CpG sites after false discovery rate (FDR) correction, with top signals showing Δβ values of 0.050–0.084 and FDR values as low as 0.003. These loci included CpGs annotated to LGALS12, NRCAM, CEACAM6, TRH, SLC28A3, and CIRBP, indicating that MTHFR genetic variation exerts a measurable and widespread effect on the methylome. Functional enrichment analysis of these MTHFR-associated DMPs implicated neurodevelopmental, cellular signaling, and transcriptional regulatory pathways. Most importantly, interaction analysis revealed genotype-dependent differences in methylation patterns associated with treatment response. Eight biologically prioritized candidate CpG sites—cg05911749, cg17888482, cg13830226, cg20266028, cg21527417, cg06001045, cg03689082, and cg15277108—showed distinct opposite or attenuated response-related methylation trends across MTHFR genotype-burden strata in exploratory interaction plots. Although the top interaction CpGs did not remain significant after multiple-testing correction, the observed interaction effects, with absolute inter-Δβ values reaching up to 0.195 in candidate loci, together with functional enrichment of interaction-associated genes, support the potential biological relevance of an MTHFR-dependent epigenetic modulation of antidepressant response.

Conclusions

MTHFR risk genotype burden is associated with both SSRI treatment outcome and broad DNA methylation changes in MDD. Beyond this main methylomic effect, MTHFRvariation may shape genotype-dependent epigenetic signatures of antidepressant response, highlighting an interaction-based pharmacogenomic–epigenomic framework for precision treatment stratification in depression.

References

No references