Relationship Between Lipoprotein (a) [Lp(a)] and Cognition in Different Ischemic Stroke Subtypes
Bibliographic record
Abstract
Background and Purpose: Although elevated serum lipoprotein (a) [Lp(a)] is considered to be a risk factor of ischemic stroke, the relationship between Lp(a) and cognitive impairment after stroke remains unclear. This study investigated the association between serum Lp(a) and cognitive function after acute ischemic stroke (AIS) or transient ischemic attack (TIA). Methods: The study included 1,017 patients diagnosed with AIS or TIA from the cognition subgroup of the Third China National Stroke Registry (CNSR3). Montreal Cognitive Assessment (MoCA) at 2 weeks or discharge, 3 months, and 1 year was evaluated. The primary outcome was cognitive impairment at 1 year, defined as MoCA ≤ 22. The secondary outcome was cognition improvement at 1 year compared with 2 weeks. The association between Lp(a) levels and cognitive function was analyzed. Results: Among the 1,017 patients included, 326 (32.1%) had cognitive impairment at 1 year. Patients with MoCA ≤ 22 at 1 year were older, received less education, and had higher baseline NIHSS, higher proportion of ischemic stroke history, large artery atherosclerosis (LAA) subtype, and multiple infarctions (P < 0.05 for all). Patients with highest Lp(a) quartile had slightly higher percentage of cognitive impairment at 1 year but without statistical difference. In subgroup analysis of LAA subtype, the patients with highest Lp(a) quartile had higher percentage of cognitive impairment at 1 year (adjusted OR:2.63; 95% CI: 1.05–6.61, P < 0.05). What is more, the patients with highest Lp(a) quartile in LAA subtype had lower percentage of cognition improvement at 1 year. However, similar results were not found in small artery occlusion (SAO) subtype. Conclusion: Higher Lp(a) level was associated with cognitive impairment and less improvement of cognition in patients after AIS or TIA with large-artery atherosclerosis subtype.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".