Metabolic and vascular contributions to dementia: Soluble epoxide hydrolase‐derived linoleic acid oxylipins and glycemic status are related to cerebral small vessel disease markers, atrophy, and cognitive performance
Bibliographic record
Abstract
INTRODUCTION: Type 2 diabetes mellitus (T2DM) is a risk factor for dementia and cerebral small vessel disease, but there remains a need to identify targetable molecular pathways involved in the underlying pathophysiology. METHODS: In participants with Alzheimer's disease, related dementias, or cerebrovascular diseases, we assessed associations between ratios of unesterified linoleic acid (LA)-derived soluble epoxide hydrolase (sEH) metabolites (diols) and substrates (epoxides), with imaging-derived white matter hyperintensities (WMHs), brain parenchymal fraction (BPF), and cognitive performance. Potential moderation effects by glycemic control (hemoglobin A1c [HbA1c]) were examined. RESULTS: With elevated HbA1c, greater LA-derived diol/epoxide ratios were associated with greater WMH volume (β [95% CI] = 0.565 [0.100, 1.030], p = 0.017), lower global BPF (β [95% CI] = -0.476 [-0.903, -0.048], p = 0.029), and poorer memory performance (β [95% CI] = -0.603 [-1.070, -0.136], p = 0.012), such that detrimental associations were observed only in T2DM. DISCUSSION: Cytochrome P450-sEH metabolites may indicate a novel metabolic-vascular contribution to dementia in individuals with T2DM. CLINICAL TRIALS REGISTRATION INFORMATION: ClinicalTrials.gov Identifier NCT04104373. HIGHLIGHTS: LA-derived sEH metabolite (diol) to substrate (epoxide) ratio was lower in individuals with diabetes. The diol/epoxide ratio with high HbA1c contributed to SVD and brain atrophy. The CYP450-sEH pathway may link metabolic and vascular contributions to dementia. sEH may be a potential therapeutic target in individuals with diabetes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".