89 The Effect of Personality Traits on the Development of Predementia Cognitive States: Results from the Einstein Aging Study
Notice bibliographique
Résumé
Objective: Recent research has found associations between the Five Factor Model (FFM) personality traits (Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) and risk of developing subjective cognitive decline (SCD), mild cognitive impairment (MCI), and/or dementia. It has therefore been proposed that personality should be incorporated into conceptual models of dementia risk, as personality assessments have utility as readily available, low-cost measures to predict who is at greater risk for developing pathological cognitive decline. The objective of the present study was to explore the relationship between FFM personality traits and predementia cognitive syndromes including SCD, amnestic MCI (aMCI), and non-amnestic MCI (naMCI). The first aim was to compare baseline personality traits between participants who transitioned from healthy cognition or SCD to aMCI vs. naMCI. The second aim was to determine the relationship between FFM personality traits and risk of transition between predementia cognitive states. The third aim was to explore relationships between levels of FFM personality traits and performance on a comprehensive cognitive battery. Participants and Methods: The participants for this study were 562 (Aim 3; Mean Age = 78.90) older adults from the Einstein Aging Study, 378 of which had at least one follow-up assessment (Aims 1 & 2; Mean Age = 78.60). Baseline levels of FFM personality traits were measured in the EAS using the 50-item International Personality Item Pool (IPIP) version of the NEO-Personality Inventory. Baseline levels of anxiety and depressive symptoms, medical history, performance on a cognitive battery and age sex, and years of education were also collected. A multistate Markov approach was used to model the risk of transition across the four predementia states (cognitively healthy, SCD, aMCI, and naMCI) with each FFM personality trait as covariates. Results: Regarding Aim 1, Mann-Whitney U tests revealed no differences in levels of FFM personality traits between participants who developed aMCI compared to those who developed naMCI. Regarding Aim 2, the multistate Markov model revealed that higher levels of conscientiousness were protective against developing SCD while higher levels of neuroticism resulted in an increased risk of developing SCD. Further, the model revealed that higher levels of extraversion were protective against developing naMCI. Finally, regarding Aim 3, exploratory correlations revealed many positive associations between levels of openness to experience and performance on neuropsychological tests. Few associations were found for the other FFM personality traits. Conclusions: Results from this study suggest that premorbid personality traits may play a predictive role in the risk for or protection against specific predementia syndromes. Thus, FFM personality traits may be useful in improving predictions of who is at greatest risk for developing specific predementia syndromes. These personality measures could be used (in addition to other established risk factors for cognitive decline) to enrich clinical trials by targeting individuals who are at greatest risk for developing specific forms of cognitive decline. Such measures may also be useful in diagnostic prediction models for predementia syndromes. These results should be replicated in future studies with larger sample sizes and younger participants.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».