Understanding Interactions Between Life Satisfaction and Genetic Predisposition on Risk of Alzheimer's Disease up to 14 Years Later: Findings From the UK Biobank
Bibliographic record
Abstract
OBJECTIVES: Previous research investigating associations between life satisfaction and risk of Alzheimer's disease (AD) has been mixed. This association may differ depending on genetic risk for AD. The aim of this study was to test interactions between life satisfaction and genetic predisposition on the future incidence of AD diagnosis. METHODS: Data were used from 66,668 participants aged 60+ from the UK Biobank. Participants attended an assessment centre at baseline, and data were linked to hospital admissions data and death records up to 14 years later. Cox proportional hazards models were used to test interactions between life satisfaction and a polygenic risk score (PRS) for AD on incident AD diagnosis. Models were also run stratified by genetic risk for AD. RESULTS: Models adjusted for age, sex, ethnicity, deprivation, education, and depression showed main effects of both life satisfaction (OR = 0.78, 95% CI = 0.68-0.90, p = 0.001) and the AD PRS (OR = 2.26, 95% CI = 2.12-2.40, p < 0.001) on incident AD. There was a significant interaction between the two (OR = 1.21, 95% CI = 1.09-1.35, p < 0.001). Stratified models showed that life satisfaction was associated with lower incident AD in the low, but not in the high genetic risk group (low: OR = 0.56, 95% CI = 0.42-0.75, p < 0.001; high: OR = 0.88, 95% CI = 0.75-1.04, p = 0.13). CONCLUSIONS: Results show that genetic risk for AD modified the relationship between life satisfaction and the risk of AD. This suggests that genetic risk may weaken associations between life satisfaction and AD risk. The findings clarify the mixed results of previous research on this topic and may contribute to more tailored approaches to the prevention of AD in the future.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".