P3‐252: SERUM OXYLIPINS INDICATE VASCULAR COGNITIVE IMPAIRMENT IN ALZHEIMER'S DISEASE WITH COMORBID CEREBROVASCULAR DISEASE: A PRELIMINARY REPORT
Bibliographic record
Abstract
Subcortical ischemic vascular disease (SIVD) is commonly comorbid with Alzheimer's disease (AD) in late onset dementia cases. The soluble epoxide hydrolase (sEH) enzyme inactivates anti-inflammatory and vasoactive cytochrome p450 derived polyunsaturated fatty acid epoxides by converting them into cytotoxic dihydroxy oxylipin species. Previously, we found that the ratio of an sEH derived linoleic acid dihydroxy species to its parent epoxide in serum was associated with poorer executive function and white matter hyperintensity (WMH) volumes in patients with SIVD, but it remains to be seen if these oxylipins can identify SIVD and the associated cognitive deficits in patients with comorbid AD and SIVD. The present study investigated participants with AD or healthy controls with different degrees of SIVD as determined by visual rating of WMH on multimodal 3.0 T MRI, all without evidence of cortical stroke. The serum oxylipins were extracted thrugh solid phase extraction, then quantified with a targeted ultrahigh pressure liquid chromatography mass spectrometry lipidomics platform. A unit-weighted composite Z-score of speed, attention and executive function was derived from age, gender and education corrected norms from the Digit Symbol Substitution Test, Trial-Making Test Part B, Stroop Color-Word Interference Test, and FAS Verbal Fluency Test. Multivariate analyses of covariance, and linear regression models, were used to investigate the association among oxylipins, SIVD, and cognitive performance. In 30 participants with AD (n=14 with SIVD, and n=16 with minimal SIVD), and 54 participants without AD (n=29 with SIVD and n=25 with minimal SIVD), the concentration of 12,13-dihydroxyoctadecamonoenoic acid (12,13-DiHOME; an sEH derived linoleic acid oxylipin) to its epoxide substrate 12,13-epoxyoctadecenoic acid (12,13-EpOME) was higher among patients with extensive SIVD (F=6.386, p=0.014; Figure 1). The ratio was negatively associated with a composite score of executive function, processing speed, and attention in all participants with extensive WMH (β=−0.437, p=0.008, n=43), including subgroups without (β=−0.468, p=0.022, n=29; Figure 2: red) and with AD (β=−0.641, p=0.022, n=14; Figure 2: blue).
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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) | 0.002 | 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".