A Systematic Review and Meta-Analysis of Lipid Signatures in Post-traumatic Stress Disorder
Bibliographic record
Abstract
Background Research assessing lipid levels in individuals diagnosed with post-traumatic stress disorder (PTSD) has yielded mixed results. This study aimed to employ meta-analytic techniques to characterize the relationship between the levels of lipid profiles and PTSD. Methods We performed meta-analyses of studies comparing profiles and levels of lipids between PTSD patients and healthy individuals by searching Embase, Ovid Medline, Scopus, PsycINFO, and Cochrane databases for the studies until March 2021. Meta-analyses were performed using random-effects models with the restricted maximum-likelihood estimator to synthesize the effect size assessed by standardized mean difference (SMD) across studies. Findings A total of 8,657 abstracts were identified, and 17 studies were included. Levels of total cholesterol (TC) (SMD = 0.57 95% CI, 0.27–0.87, p = 0.003), low-density lipoprotein (LDL) (SMD = 0.48, 95% CI, 0.19–0.76, p = 0.004), and triglyceride (TG) (SMD = 0.46, 95% CI, 0.22–0.70, p = 0.001) were found to be higher, while levels of high-density lipoprotein (HDL) (SMD = –0.47, –0.88 to –0.07, p = 0.026) were found to be lower in PTSD patients compared to healthy controls. Subgroup analysis showed that TG levels were higher in PTSD patients who were on or off of psychotropic medications, both < 40 and ≥ 40 years of age, and having body mass index of < 30 and ≥ 30 compared to healthy controls. Interpretation This work suggested dysregulation of lipids in PTSD that may serve as biomarker to predict the risk. The study will be useful for physicians considering lipid profiles in PTSD patients to reduce cardiovascular morbidity and mortality.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".