ABC Transporters, APOE, CYP46A1, and LRP1 Gene Polymorphisms as Markers of Dementia Development in Patients with Hyperlipidemia
Bibliographic record
Abstract
In an aging society, solving problems associated with the diagnosis and treatment of dementia-related diseases represents a serious challenge. The aim of the study was to evaluate the possibility of applying molecular biology methods to test polymorphisms recognized in the global literature as potentially useful in assessing the risk of developing dementia in a group of patients with hyperlipidemia. A sample of 203 patients: 109 diagnosed with both dementia and hyperlipidemia, 94 with hyperlipidemia, and 101 individuals as an allele frequency control group—were genotyped. Additional data about cognitive decline and neuropsychological assessment were collected. Among all the studied polymorphisms, the frequency of the ABCA1 rs2230806 polymorphism differed between the analyzed groups. The GG genotype (p = 0.0002, RR = 3.22, CI = 1.63 ÷ 6.37) and the G allele (p = 0.0007, RR = 1.53, CI = 1.19 ÷ 1.97) were more frequent in patients diagnosed with dementia, specifically in those with Alzheimer’s disease. Furthermore, the GG genotype was more common in individuals with a shorter disease duration and lower scores on the Montreal Cognitive Assessment (MoCA) scale, and consequently, with greater cognitive function deficits during early stages of the diagnostic process. ABCA1 rs2230806 genotyping is a potential marker for the early identification of dementia risk in patients with hyperlipidemia, which supports the validity of exploring options for incorporating diagnostics based on molecular biology methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".