Definition of Late Onset Alzheimer’s Disease and Anticipation Effect of Genome-Wide Significant Risk Variants: Pilot Study of the APOE e4 Allele
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: This study aims to investigate the role of apolipoprotein E (APOE) e4 influencing the age at onset (AAO) of Alzheimer's disease (AD). In AD, the AAO of dementia varies from 40 to 90 years. Usually, AD patients who develop symptoms before the age of 65 are considered as early-onset AD (EOAD). However, considering the heterogeneity of the AD onset, the definition of late-onset AD (LOAD) cannot rely on an arbitrary cut-off. Thus, we aim to validate the anticipation effect of the APOE e4 allele in LOAD. Methods/Overview: Firstly, the optimal number of AAO subgroups was determined using MCLUST for 3 AD samples from Italy, Brazil, and from the ADNI consortium. MCLUST selects the best-fitting model based on the Bayesian information criterion (BIC), and the ideal cut-off for separating early onset from late onset in each sample. Then, when the AAO was modeled for each sample, the finite mixture model (FMM) analysis was used to analyze the effect of the APOE e4 in determining the risk for anticipated onset in LOAD. For the Brazilian sample, the ancestry was incorporated as a covariate. The FMM results from the 3 samples were meta-analyzed using METAL. RESULTS: We performed the AAO analysis on the APOE e4 in 474 Italian patients enrolled at the IRCCS Santa Lucia Foundation in Italy, 135 AD from the Outpatients Reference Center for Geriatrics from the Federal University of Minas Gerais in Brazil, and 376 from the ADNI consortium. Using this distribution model, we found that the specific LOAD cut-off was ≥64 for the Italian sample, ≥67 for the ADNI sample, and ≥74 for the Brazilian sample. The APOE e4 showed a significant anticipatory effect specific for LOAD in all 3 samples. The METAL analysis for the anticipatory e4 effect was genome-wide significant when analyzing the LOAD effect size under the fixed model (beta = -8.1; p < 0.0001). However, when analyzing EOAD there was no genome-wide significant anticipation effect (beta = 1.9244; p = 0.0219). CONCLUSIONS: This study showed that the mixture analysis can refine the ideal cut-off for defining LOAD as a homogeneous genetic entity. We also validated the e4 allele anticipatory effect only in LOAD. In summary, the tool developed in this study is a sophisticated statistical pipeline to analyze the AAO in genome-wide association studies of AD, to find new molecular targets as a new line of translational research to foster drug discovery.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".