Total Cholesterol and <i>APOE</i> -Related Risk for Alzheimer’s Disease in the Alzheimer’s Disease Neuroimaging Initiative
Bibliographic record
Abstract
BACKGROUND: APOEɛ4 allele confers greatest genetic risk for Alzheimer's disease (AD), yet mechanisms underlying this risk remain elusive. APOE is involved in lipid metabolism, and literature suggest relationships between high total cholesterol, APOE, and AD. Further investigation is needed to elucidate the potential role of total cholesterol in AD risk. OBJECTIVE: To investigate the relationship between total cholesterol and APOE-related AD risk in the Alzheimer's Disease Neuroimaging Initiative. METHODS: Participants (N = 1,534) were classified as controls (cognitively normal; N = 404), early mild cognitive impairment (MCI; N = 294), late MCI (N = 539), or AD (N = 297). Total cholesterol levels were compared across APOE genotype and diagnosis. Mendelian randomization was performed to examine causality between total cholesterol and AD risk using APOE as a genetic instrument. RESULTS: Total cholesterol was higher in APOE4+ compared to APOE3 and APOE2+ (ps < 0.04) carriers. Those with AD and late MCI (ps < 0.001) had higher total cholesterol than the control group. Comparing APOE4+ to APOE3 carriers, the predicted odds ratios per mg/dL greater total cholesterol were 1.11 for MCI (95% confidence interval, 1.04-7.32), 1.05 for early MCI (1.01-3.22), 1.13 for late MCI (1.05-11.70), 1.21 for AD (1.09-54.05), and 1.13 for composite dementia (MCI or AD; 1.06-11.59) (ps < 0.05, F-statistics > 10). CONCLUSION: Higher total cholesterol may be a significant contributor to AD risk, particularly in APOE4 carriers who, based on existing literature, tend to have impaired cholesterol metabolism. Our findings highlight a possible mechanism by which APOE confers AD risk and indicate potential for AD risk modification through maintenance of healthy total cholesterol levels.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".