Predictability of polygenic risk score for progression to dementia and its interaction with APOE ε4 in mild cognitive impairment
Bibliographic record
Abstract
Abstract Background The combinatorial effect of multiple genetic factors calculated as a polygenic risk score (PRS) has been studied to predict disease progression to Alzheimer’s disease (AD) from mild cognitive impairment (MCI). Previous studies have investigated the performance of PRS in the prediction of disease progression to AD by including and excluding single nucleotide polymorphisms within the region surrounding the APOE gene. These studies may have missed the APOE genotype-specific predictability of PRS for disease progression to AD. Methods We analyzed 732 MCI from the Alzheimer’s Disease Neuroimaging Initiative cohort, including those who progressed to AD within 5 years post-baseline ( n = 270) and remained stable as MCI ( n = 462). The predictability of PRS including and excluding the APOE region (PRS + APOE and PRS − APOE ) on the conversion to AD and its interaction with the APOE ε4 carrier status were assessed using Cox regression analyses. Results PRS + APOE (hazard ratio [HR] 1.468, 95% CI 1.335–1.615) and PRS − APOE (HR 1.293, 95% CI 1.157–1.445) were both associated with a significantly increased risk of MCI progression to dementia. The interaction between PRS + APOE and APOE ε4 carrier status was significant with a P -value of 0.0378. The association of PRSs with the progression risk was stronger in APOE ε4 non-carriers (PRS + APOE : HR 1.710, 95% CI 1.244–2.351; PRS − APOE : HR 1.429, 95% CI 1.182–1.728) than in APOE ε4 carriers (PRS + APOE : HR 1.167, 95% CI 1.005–1.355; PRS − APOE : HR 1.172, 95% CI 1.020–1.346). Conclusions PRS could predict the conversion of MCI to dementia with a stronger association in APOE ε4 non-carriers than APOE ε4 carriers. This indicates PRS as a potential genetic predictor particularly for MCI with no APOE ε4 alleles.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".