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Record W7117293373 · doi:10.1002/alz70856_104608

Soluble epoxide hydrolase‐derived linoleic acid diols, cerebral small vessel disease markers and white matter microstructural integrity in type 2 diabetes mellitus

2025· article· en· W7117293373 on OpenAlexaff
Si Won Ryoo, William Z. Lin, Myuri Ruthirakuhan, Natasha Z. Anita, Malcolm A. Binns, Manuel Montero‐Odasso, Stephen R. Arnott, Carmela Tartaglia, Anthony E. Lang, Sean Symons, Robert A. Hegele, Bradley J. MacIntosh, Krista L. Lanctôt, Ameer Y. Taha, Sandra E. Black, Walter Swardfager

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEicosanoids and Hypertension Pharmacology
Canadian institutionsToronto Dementia Research AllianceHeart and Stroke FoundationToronto Western HospitalIndoc ResearchSunnybrook Health Science CentreLawson Health Research InstituteUniversity of TorontoWestern UniversityBaycrest HospitalPublic Health OntarioUniversity Health NetworkHealth Sciences CentreSunnybrook Hospital
Fundersnot available
KeywordsType 2 Diabetes MellitusWhite matterEpoxide hydrolase 2DiseaseDiabetes mellitusNeurovascular bundleType 2 diabetes

Abstract

fetched live from OpenAlex

BACKGROUND: While polyunsaturated fatty acids can be metabolized into beneficial, anti-inflammatory epoxides, they are subsequently converted to inactive, cytotoxic diols by soluble epoxide hydrolase (sEH). Increased linoleic acid (LA)-derived diols have been associated with poorer cognitive performance in type 2 diabetes mellitus (T2DM). Furthermore, increased LA-derived diol to epoxide ratio has been associated with greater white matter hyperintensities (WMH) in patients with stroke. However, the relationship between diols and neuroimaging measures has yet to be explored in T2DM. Here, we examined LA-derived diols in relation to markers of cerebral small vessel disease and white matter microstructural integrity in T2DM. METHOD: 6.5%, or antidiabetic medication use. Two unesterified LA-derived diols (9,10-dihydroxyoctadecamonoenoic acid [DiHOME] and 12,13-DiHOME) were quantified via ultra-high-performance liquid chromatography-mass spectrometry/mass spectrometry from serum samples collected after fasting. Perivascular spaces (PVS), WMH, and lacunar infarct volumes were quantified through structural MRI. White matter microstructural integrity was measured as fractional anisotropy (FA) and mean diffusivity (MD) using diffusion tensor imaging. Multiple linear regressions controlling for demographic factors, intracranial volume, hypertension, APOE-ε4 status, HDL, HbA1c, neurodegenerative diagnoses, and antidiabetic medication use, were used. Results were Bonferroni adjusted for the two diol species tested, with p <0.025 considered as statistically significant. RESULT: Among 90 T2DM participants (20.0% female, age=69.4±7.2 years, MoCA=25±3.5), 9,10-DiHOME was associated with higher WMH (β=0.385, p <0.001) and lacunar infarct (β=0.283, p = 0.010) volumes, and higher MD (β=0.267, p = 0.018). The 12,13-DiHOME was associated with higher WMH volume (β=0.339, p = 0.001). In exploratory regional analyses, 9,10-DiHOME was associated with greater WMH (β=0.400, p <0.001) and lacunar infarct (β=0.281, p = 0.013) volumes, higher MD (β=0.364, p = 0.001), and lower FA (β=-0.245, p = 0.022) in the parietal lobe. The 12,13-DiHOME was associated with more WMH volume (β=0.352, p = 0.001) and higher MD (β=0.263, p = 0.018) in the parietal lobe. CONCLUSION: In diabetes, LA-derived sEH diols were associated with greater cerebral small vessel disease pathology and white matter microstructural damage. sEH may be a potential therapeutic target to mitigate neurovascular and white matter damage in diabetes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.244
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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