P3‐149: Does multilingualism reduce the risk or delay the onset of dementia?: Findings from the Nun Study
Bibliographic record
Abstract
The ability to speak multiple languages has been associated with increased executive function and may thus help to resist age-related cognitive decline. The objective of this study was to assess whether multilingualism was associated with reduced risk or delayed onset of dementia. Analyses were based on data from the Nun Study, a longitudinal study of aging in religious sisters aged 75+ years living in the United States. Dementia likelihood was assessed in 325 participants using discrete-time survival analyses; subsample analyses (n=106) examined the influence of linguistic ability (idea density measured from autobiographical essays). Multilingualism was self-reported; dementia was diagnosed according to DSM-IV criteria. Age at onset of dementia was not associated with multilingualism: monolingual participants were no more likely to develop dementia in earlier time periods than multilinguals. When adjusted for significant covariates (baseline age and apolipoprotein E-ε4 allele [APOE-ε4] status), participants speaking 4+ languages were significantly less likely to develop dementia than monolingual participants (conditional odds ratio [OR] = 0.14; 95% CI = 0.01–0.66); those speaking two or three languages did not differ in dementia risk from monolinguals. An interaction of multilingualism with baseline age and ApoE-ε4 status was observed: the dementia risk in participants 85+ years with an APOE-ε4 allele who spoke 4+ languages was similar to that of participants <80 years without an APOE-ε4 allele who spoke one language. In models including idea density and adjusted for significant covariates (baseline age, APOE-ε4 status and education), a suggestive although nonsignificant decrease in risk was noted for participants speaking 4+ languages (conditional OR = 0.53; 95% CI = 0.06–4.91). Multilingualism did not delay onset of dementia. While a dose-response trend of decreasing dementia risk with increasing number of languages spoken was not observed, speaking 4+ languages significantly decreased the risk of dementia compared to speaking one language; this association weakened in the idea density subsample. The presence of an APOE-ε4 allele and low idea density had a strong and consistent significant association with dementia risk and its relationship to multilingualism. The role of these variables in the relationship between multilingualism and dementia warrants further exploration.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".