Lipid level and risk of Alzheimer′s disease: a systematic review and meta-analysis
Bibliographic record
Abstract
ObjectiveTo systematically evaluate the role of blood lipid level in predicting the risk of Alzheimer′s disease (AD). MethodsWe searched Chinese National Knowledge Infrastructure (CNKI), Wanfang Database, VIP Database for Chinese Technical Periodicals (VIP), Pubmed, Web of Science, Springer, and Cocharne Library for literatures on the correlation between AD incidence and blood lipid level published in Chinese or English till June 2021 and supplementary manual tracing for some of the references was also conducted. Newcastle-Ottawa-Scale literature quality evaluation scale was adopted to evaluate the quality of the included studies. Statistical analyses were performed with RevMan5.3 software. ResultsA total of 15 eligible studies were selected according to the inclusion and exclusion criteria, and the data on blood lipid level were collected from 1 435 healthy elderly people and 2 162 elderly people with AD. Meta-analysis showed that there was no significant difference in total cholesterol (TC) and triglyceride (TG) level between the AD sufferers and the healthy controls；low-density lipoprotein cholesterol (LDL-C) was significantly higher in the AD sufferers than that in the healthy controls (mean difference [MD] = 3.59，95% confidence interval [95% CI]: 0.98 – 6.21); while, high-density lipoprotein cholesterol (HDL-C) was significantly lower in the AD sufferers than that in the healthy controls (MD = – 3.47，95% CI: – 5.94 – – 0.99). ConclusionHigher LDL-C level but lower HDL-C level may indicate a higher risk of AD in older adults.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".