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Record W7133013673

Advancing Precision Medicine in Clinical Depression: Insights from the (Epi)genomics of the Stress System

2025· dissertation· W7133013673 on OpenAlexfundno aff
Amanda Lisoway

Bibliographic record

VenueTSpace · 2025
Typedissertation
Language
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchCentre for Addiction and Mental Health Foundation
KeywordsAntidepressantdNaMMajor depressive disorderSerotonin transporterPrecision medicineDNA methylationGeneFluoxetineDrug development
DOInot available

Abstract

fetched live from OpenAlex

No reliable biomarkers currently exist to aid in identification of effective treatments for individuals experiencing depression. Dysregulation of the hypothalamic-pituitary-adrenal stress response system (HPA axis) is not only one of the foremost predictors in the development of depressive symptoms, but also plays an important role in the cascade and severity of these symptoms. Thus, it is conceivable that the HPA axis may influence psychotropic treatment response and investigating this influence has the potential to advance precision medicine. We aim to make a significant contribution to the development of the rapidly expanding pharmaco-epigenetic body of work that is emerging in the field. First, to refine our hypotheses, we surveyed the pharmaco-epigenetic literature in major depressive disorder (MDD) focusing on human studies that examined DNA methylation (DNAm). Second, given our findings combined with the role of serotonin in the stress system and in treatment of depression, we investigated the serotonin transporter gene (SLC6A4) 5-HTTLPR functional variant and included DNAm across the promoter region in relation to a quantitative measure of antidepressant treatment response (n=163). Linear regression with permutation testing revealed that increased DNAm at a specific site in the promoter of SLC6A4, but not 5-HTTLPR variation, was associated with impaired response to medication (β=522.13, SE=218.06, p=0.012). Third, we used a hypothesis driven approach to expand the (epi)genetic coverage and investigated markers and DNAm levels across seven HPA axis-related genes (CRHR1, CRHR2, FKBP5, HTR2A, NR3C1, SKA2, and SLC6A4) and their associations with the same phenotype of antidepressant response. The top results, while not surviving FDR correction, were for HTR2A genetic markers (n=978, β=4.97, p=1.30x10-3) and CRHR1 DNAm (n=163, β=3.82, p=0.062). Finally, a methylome-wide approach was used to identify novel DNAm targets that may be associated with clinical symptom improvement in depression. The most interesting result from this final approach was for cg14376548 in the CNP gene (n=163; β=16.33, p=2.65x10-4), a gene which has been previously associated with myelination and oligodendrocyte function, as well as with schizophrenia and depression. Our results suggest that (epi)genetics of the HPA axis and serotonin system may be involved in antidepressant treatment response in depression. (Epi)genetic markers show promise to become a useful clinical tool for early identification of antidepressant treatment responders. Further examination of the (epi)genetics of the stress system in depression treatment in larger, more diverse cohorts is underway.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.028
GPT teacher head0.356
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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