Association of white matter hyperintensities with lipoprotein (a) levels: insights from a cohort study
Bibliographic record
Abstract
Background Little is known about the relationship between lipoprotein (a) [Lp(a)] and cerebral white matter hyperintensities (WMH). The aim of the study was to examine if elevated Lp(a) levels are associated with higher burden of WMH. Methods We retrospectively investigated associations between Lp(a) and the burden of WMH among patients with confirmed diagnosis of acute ischemic stroke or transient ischemic attacks. WMH burden was assessed using 3-Tesla brain MRI and graded according to the Fazekas score. Multivariable models were generated to determine the contribution of Lp(a) to the presence and extent of WMH. Results One hundred and fifty-three patients were included (mean age, 45.9 years; 35.9% women). When the study population was stratified by Lp(a) level into three categories, low (<75 nmol/L), moderate (75 to <125 nmol/L), and high (≥125 nmol/L), the distribution of the three groups was 60.8, 15.0 and 24.2%, respectively. High Lp(a) Level was associated with higher burden of both periventricular WMH and deep WMH compared to the lower level (odds ratio [OR], 4.4; 95% confidence interval [CI], 1.60–12.07; p = 0.004; and OR, 5.6; CI, 1.69–14.7; p = 0.001, respectively). Conclusion We show in this cohort of patients that a higher burden of WMH was observed in patients with higher level of Lp(a). Further studies are needed to confirm this observation and assess whether lowering Lp(a) level may be a potential therapeutic target for mitigating the development of WMH.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".