Genome-wide DNA methylation profiling of blood samples from patients with major depressive disorder: correlation with symptom heterogeneity
Bibliographic record
Abstract
Background: Alterations in DNA, such as DNA methylation, may be key molecular events involved in the development of major depressive disorder (MDD). We sought to clarify correlations between DNA methylation profiles and symptom heterogeneity among patients with MDD. Methods: We conducted a genome-wide DNA methylation analysis of blood samples from patients with MDD and controls, using the Infinium MethylationEPIC BeadChip. Results: We analyzed 283 blood samples, including 141 from an initial cohort (69 patients with MDD, 72 controls) and 142 from a second validation cohort (67 patients with MDD, 75 controls). After adjustment for age, sex, and blood cell heterogeneity, DNA methylation status at 2699 CpG sites tended to differ between patients with MDD and controls in both the initial and second cohorts. Hierarchical clustering of patients based on DNA methylation status at these 2699 CpG sites revealed a significant correlation with scores for GRID-Hamilton Depression Rating Scale (GRID-HAMD) items (depressed mood, guilt, early insomnia, middle insomnia, work and activities, psychic anxiety, loss of appetite, general somatic symptoms, and total score), suggesting the feasibility of severity diagnostics based on blood DNA methylation testing. Pathway over-representation analysis revealed that genes whose DNA methylation status was correlated with epigenetic clustering were accumulated in molecular pathways involved in various cellular functions, especially nerve development. For PLEKHD1, STK10, and FOXK1, DNA methylation levels were inversely correlated with expression levels in the Clinical Proteomic Tumor Analysis Consortium database. DNA hypomethylation of PLEKHD1, STK10, and FOXK1 was correlated with higher GRID-HAMD scores in both cohorts. Limitations: Although we performed marker exploration using 2 cohorts including 283 participants, the heterogeneity of the molecular mechanisms operating in MDD might necessitate a larger cohort for establishment of criteria with sufficient diagnostic impact. Conclusion: These findings indicate that the DNA methylation status of specific genes may correlate with the severity of MDD symptoms, and that genome-wide DNA methylation analysis of blood samples would be useful for clarifying the DNA methylation profiles related to symptom heterogeneity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".