Oxolipidomics profile in major depressive disorder: Comparing remitters and non-remitters to repetitive transcranial magnetic stimulation treatment
Bibliographic record
Abstract
BACKGROUND: Repetitive Transcranial Magnetic Stimulation [rTMS] is increasingly being used to treat Major Depressive Disorder [MDD]. Given that not all patients respond to rTMS, it would be clinically useful to have reliable biomarkers that predict treatment response. Oxidized phosphatidylcholine [OxPC] and some oxylipins are important plasma biomarkers of oxidative stress and inflammation. Not only is depression associated with oxidative stress, but rTMS has been shown to have anti-oxidative effects. OBJECTIVES: To investigate whether plasma oxolipidomics profiles could predict treatment response in patients with treatment resistant MDD. METHODS: Fourty-eight patients undergoing rTMS treatment for MDD were recruited along with nine healthy control subjects. Plasma OxPCs and oxylipins were extracted and analyzed through high performance liquid chromatography coupled with mass spectrometry. Patients with a Hamilton Depression Rating Scale score [Ham-D] ≤7 post-treatment were defined as having entered remission. RESULTS: Fifty-seven OxPC and 32 oxylipin species were identified in our subjects. MDD patients who entered remission following rTMS had significantly higher pre-rTMS levels of total and fragmented OxPCs compared to non-remitters and controls [one-way ANOVA, p<0.05]. However, no significant changes in OxPC levels were found as a result of rTMS, regardless of treatment response [p>0.05]. No differences in plasma oxylipins were found between remitters and non-remitters at baseline. CONCLUSION: Certain categories of OxPCs may be useful predictive biomarkers for response to rTMS treatment in MDD. Given that elevated oxidized lipids may indicate higher levels of oxidative stress and inflammation in the brain, patients with this phenotype of depression may be more receptive to rTMS treatment.
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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 | 0.001 | 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".