Interplay between Socioeconomic Markers and Polygenic Predisposition on Timing of Dementia Diagnosis
Bibliographic record
Abstract
OBJECTIVES: Identifying the interplay between socioeconomic markers (education and financial resources) and polygenetic predisposition influencing the time of dementia and the diagnosis of clinical Alzheimer's disease (AD) dementia is of central relevance for preventive strategies. DESIGN: Prospective cohort design. SETTING: The English Longitudinal Study of Aging is a household survey data set of a representative sample. PARTICIPANTS: A total of 7,039 individuals aged 50 years and older participated in the study. Of these, 320 (4.6%) were diagnosed with dementia over the 10-year follow-up. MEASUREMENTS: Polygenic score (PGS) for Alzheimer's disease (AD-PGS) was calculated using summary statistics from the International Genomics of Alzheimer's Project. An accelerated failure time survival model was used to investigate interactions between AD-PGS and socioeconomic markers on the timing of dementia and clinical AD dementia diagnosis. RESULTS: A one standard deviation increase in AD-PGS was associated with an accelerated time to dementia diagnosis by 4.8 months. The presence of the apolipoprotein E gene (APOE-ε4) was associated with an earlier dementia onset by approximately 24.9 months, whereas intermediate and low levels of wealth were associated with an accelerated time to dementia diagnosis by 12.0 months and 18.7 months, respectively. A multiplicative interaction between AD-PGS and years of completed schooling in decelerating the time to clinical AD dementia by 3.0 months suggests educational attainment may serve as a protective mechanism against AD diagnosis among older people with a higher polygenic risk. Interaction between AD-PGS and lower wealth accelerated the time to clinical AD dementia diagnosis by 21.1 to 24.1 months. CONCLUSION: Socioeconomic markers influence the time to dementia and clinical AD dementia diagnosis, particularly in those with a higher polygenic predisposition. J Am Geriatr Soc 68:1529-1536, 2020.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".