Increases in Neuroticism May Be an Early Indicator of Dementia: A Coordinated Analysis
Bibliographic record
Abstract
OBJECTIVES: Although personality change is typically considered a symptom of dementia, some studies suggest that personality change may be an early indication of dementia. One prospective study found increases in neuroticism preceding dementia diagnosis (Yoneda, T., Rush, J., Berg, A. I., Johansson, B., & Piccinin, A. M. (2017). Trajectories of personality traits preceding dementia diagnosis. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 72, 922-931. doi:10.1093/geronb/gbw006). This study extends this research by examining trajectories of personality traits in additional longitudinal studies of aging. METHODS: Three independent series of latent growth curve models were fitted to data from the Longitudinal Aging Study Amsterdam and Einstein Aging Study to estimate trajectories of personality traits in individuals with incident dementia diagnosis (total N = 210), in individuals with incident Mild Cognitive Impairment (N = 135), and in individuals who did not receive a diagnosis during follow-up periods (total N = 1740). RESULTS: Controlling for sex, age, education, depressive symptoms, and the interaction between age and education, growth curve analyses consistently revealed significant linear increases in neuroticism preceding dementia diagnosis in both datasets and in individuals with mild cognitive impairment. Analyses examining individuals without a diagnosis revealed nonsignificant change in neuroticism overtime. DISCUSSION: Replication of our previous work in 2 additional datasets provides compelling evidence that increases in neuroticism may be early indication of dementia, which can facilitate development of screening assessments.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".