Serum Arachidonic Acid Oxylipins Indicate Small Vessel Injury And the Related Executive Function Decline In Stroke Patients
Bibliographic record
Abstract
Abstract Background Cerebral small vessel disease (SVD) frequently co‐occurs with other neurodegenerative pathologies and collectively lead to cognitive deficits. Previously, we found that the ratios of soluble epoxide hydrolase (sEH)‐ derived diols to the parent epoxides (generated by CYP2J/2Cs) linoleic acids were associated with greater white matter hyperintensities (WMH) and poorer executive function in patients with transient ischemic attack. However, the associations with the diol and epoxide species derived from arachidonic acid (AA) have not been explored. Methods The study cohort is comprised of 159 stroke patients (mean age = 69.2, 32.1% female) from the Ontario Neurodegenerative Degenerative Research Initiative. EpETrEs (AA epoxides) and DiHETrEs (AA diols) were quantified by a targeted ultrahigh pressure liquid chromatography mass spectrometry (UPLC‐MS/MS). Executive function was assessed using a composite score that included Digit Symbol Substitution Test (DSST), Trial‐Making Test Part B, Stroop Color‐Word Interference Test, and FAS Verbal Fluency Test. WMHs were quantified from T2‐Fluid‐Attenuated‐Inversion‐Recovery MRI through Lesion Explorer (https://imaging.brainlab.ca/lesion‐explorer.html).In linear models controlling for age, sex, education or age, sex, glucose, cholesterol, and waist circumferences were used for cognitive and imaging out comes, respectively. Results 11(12)‐EpETrE was found to be associated with less WHM (ρ = ‐0.175, p = 0.040) and better executive function (ρ = 0.166, p = 0.039). In a sub‐cohort of patients with small vessel stroke (n = 50), the relationship between WMH and 11(12)‐EpETrE became stronger (ρ = ‐0.390, p = 0.014). 14(15)‐EpETrE was also associated with less WMH (ρ = ‐0.416, p = 0.009). Similar relationships were not seen with the diols or diol to epoxide ratios. Conclusions Oxylipins derived from sEH activity may be potential biomarkers of vascular cognitive impairment and small vessel disease in stroke patients.
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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 | 0.001 |
| 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) | 0.003 | 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".