Abstract 4139938: Lipidomic signature of acute ischemic and hemorrhagic stroke
Bibliographic record
Abstract
Introduction: Stroke is the second leading cause of death worldwide and the leading cause of disability. Without a plasma biomarker care is often delayed. The brain is rich in lipids, which readily cross the blood-brain barrier, and therefore represent a target for biomarkers of stroke. The study aimed to determine the lipidomic changes in plasma in stroke patients through an untargeted and targeted analysis. Methods: A cohort of 482 patients from the INTERSTROKE study was included in the analysis. This included 241 stroke patients (120 ISCH, 121 HEM) and 241 age and sex-matched controls. A LC/MS/MS platform was used to perform a detailed lipidomic and oxylipidomic analysis of plasma. Biomarker analysis was performed using the Random Forest Classification algorithm (RFC). Results: Lipidomic analysis identified 141 lipid species and 32 oxylipins significantly altered in ISCH stroke compared to control and 167 lipid species and 34 oxylipins in HEM stroke. Between ISCH and HEM stroke there were in 87 significant lipids. There was a 141 % increase in phosphatidylserine (PS) 40:6 (p< 0.0001) in patients with HEM stroke compared to ISCH, whereas prostaglandin E2 was found to be 94% higher (p<0.0001) in ISCH stroke. RFC model identified lysophosphatidylcholine (LPC) 14:0 and 15-oxoETE as the most important features contributing to classification between control and stroke with a mean AUC score of 0.81 and 0.69 respectively. PS 40:6 and 14-HDoHE are the most important features contributing to separating ISCH and HEM stroke, with mean AUC scores of 0.84 and 0.67, respectively. PC 34:1 and PC-O 36:3 were found to be the biggest predictors of stroke outcomes at 30 days, measured by The Modified Rankin Score (mRS) score. In addition, four lipids: Ceramide 24:1, C14 Tetradecanoyl Carnitine, trihexosylceramide 16:0, and PS 40:6 were found to be the independent predictors of mortality in patients with stroke. Conclusion: We have demonstrated significant alterations in the human plasma lipidome during acute stroke, revealing distinct differences between HEM and ISCH subtypes. These lipid profiles not only differentiate between stroke subtypes but also predict clinical outcomes. Given the current lack of plasma biomarkers for stroke, our study underscores the potential of lipid molecules as valuable biomarkers for stroke diagnosis and prognosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".