APOE and APOC1 gene polymorphisms are associated with cognitive impairment progression in Chinese patients with late-onset Alzheimer′s disease
Bibliographic record
Abstract
Current evidence shows that apolipoprotein E (APOE), apolipoprotein CI (APOC1) and low density lipoprotein receptor-related protein (LRP) variations are related to late-onset Alzheimer's disease. However, it remains unclear if genetic polymorphisms in these genes are associated with cognitive decline in late-onset Alzheimer's disease patients. We performed a 30-month longitudinal cohort study to investigate the relationship between Alzheimer's disease and APOE, APOC1, and LRP. In this study, 78 Chinese Han patients with late-onset Alzheimer's disease were recruited form Guangxi Zhuang Autonomous Region in China. APOE, APOC1, and LRP genotyping was performed using polymerase chain reaction-restriction fragment length polymorphisms. The Mini-Mental State Examination and Clinical Dementia Rating Scale were used to assess patients' cognitive function. After a 30-month follow-up period, we found a significant reduction in Mini-Mental State Examination total score, a higher proportion of patients fulfilling cognitive impairment progression criteria, and a higher proportion of APOC1 H2 carriers in APOE ε4 carriers compared with non-carriers. In addition, the APOE ε4 allele frequency was significantly higher in the cognitive impairment progression group compared with the non-cognitive impairment progression group. In conclusion, APOE ε4 plays an important role in augmenting cognitive decline, and APOC1 H2 may act synergistically with APOE ε4 in increasing the risk of cognitive decline in Chinese patients with late-onset Alzheimer's disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".