Roles for lipoprotein lipase in Alzheimer's disease: An association study
Bibliographic record
Abstract
Lipoprotein lipase (LPL) assists lipid transport by transferring lipids between lipoprotein particles and cells. LPL binds apolipoprotein E (apoE) lipoprotein particles and a major apoE receptor, low density lipoprotein receptor related protein (LRP). Because apoE and LRP polymorphisms alter Alzheimer's disease (AD) risk, and LPL itself is found in AD amyloid plaques, we examined whether LPL variants also affect AD risk. In case-control studies in the United States and Canada, the frequencies of two LPL alleles known to affect LPL enzymatic activity were measured in Caucasian AD or elderly normal (N) subjects. Pathologically confirmed subjects in both studies exhibited similar trends toward fewer 447Ter and more 291Ser alleles in AD. Combining results from both countries gave allele frequencies for 447Ter of 13.7% (26/190) in N and 9.4% (80/852) in AD (P = 0.10), and for 291Ser of 0.0% (0/184) in N and 1. 3% (8/636) in AD (P = 0.21). The trend appeared even greater for homozygous 447Ter subjects: 4.2% (4/95) of N vs. 1.4% (6/426) of AD (P = 0.09). These trends are consistent with a putative protective effect of 447Ter and causative effect of 291Ser on AD. Furthermore, brains of AD patients with 447Ter showed trends toward fewer plaques, tangles, and glia, and more neurons and cortical thickness than AD patients without 447Ter. Hippocampal plaques were significantly reduced. LPL might affect hippocampal function and thus dementia via its role as supplier of membrane components or antioxidants to neurons. Alternatively, LPL may play a part in plaque formation through its interaction with apoE and LRP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".