A meta-analysis of lipid peroxidation markers in major depression
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) may be associated with oxidative damage to lipids, which can potentially affect mood-regulating pathways. This meta-analysis summarizes current knowledge regarding lipid peroxidation markers in clinical samples of MDD and the effects of antidepressant pharmacotherapy on those markers. METHODS: MEDLINE, EMBASE, CINAHL, PsycINFO, and Cochrane Collaboration were searched for original, peer-reviewed articles measuring markers of lipid peroxidation in patients with MDD and nondepressed healthy controls up to April 2015. Standardized mean differences (SMDs) were generated from random effects models summarizing mean (± standard deviations) concentrations of selected markers. RESULTS: Lipid peroxidation was greater in MDD than in controls (studies =17, N=857 MDD/782 control, SMD =0.83 [0.56-1.09], z=6.11, P<0.01, I (2)=84.0%) and was correlated with greater depressive symptom severity (B=0.05, df=8, P<0.01). Antidepressant treatment was associated with a reduction in lipid peroxidation in MDD patients (studies=5, N=222, SMD=0.71 [0.40-0.97], P<0.01; I (2)=42.5%). LIMITATIONS: Lipid peroxidation markers were sampled from peripheral blood, included studies comparing MDD to controls were all cross-sectional, and only five antidepressant treatment studies were eligible for inclusion. CONCLUSION: Increased lipid peroxidation was associated with MDD and may be normalized by antidepressants. Continued investigation of lipid peroxidation in MDD is warranted.
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 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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.051 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".