Discovery of blood transcriptomic markers for depression in animal models and pilot validation in subjects with early-onset major depression
Bibliographic record
Abstract
Early-onset major depressive disorder (MDD) is a serious and prevalent psychiatric illness in adolescents and young adults. Current treatments are not optimally effective. Biological markers of early-onset MDD could increase diagnostic specificity, but no such biomarker exists. Our innovative approach to biomarker discovery for early-onset MDD combined results from genome-wide transcriptomic profiles in the blood of two animal models of depression, representing the genetic and the environmental, stress-related, etiology of MDD. We carried out unbiased analyses of this combined set of 26 candidate blood transcriptomic markers in a sample of 15-19-year-old subjects with MDD (N=14) and subjects with no disorder (ND, N=14). A panel of 11 blood markers differentiated participants with early-onset MDD from the ND group. Additionally, a separate but partially overlapping panel of 18 transcripts distinguished subjects with MDD with or without comorbid anxiety. Four transcripts, discovered from the chronic stress animal model, correlated with maltreatment scores in youths. These pilot data suggest that our approach can lead to clinically valid diagnostic panels of blood transcripts for early-onset MDD, which could reduce diagnostic heterogeneity in this population and has the potential to advance individualized treatment strategies.
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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 | 0.001 | 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) | 0.000 | 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".