41 The Role of Physical Activity, Social Support and Genetic Risk in Age-Related Cognitive Decline Over Time: A UK Biobank Study
Notice bibliographique
Résumé
Objective: This study aimed to determine how modifiable risk factors, such as physical exercise and social support, and non-modifiable risk factors, such as genetic risk may affect cognitive function over time in older adults. As well, the study explored how changes in modifiable risk factors (i.e., increase in exercise) may affect cognitive function over time. This research question was shaped with the help of a patient partner team. Participants and Methods: The study used UK Biobank data, and patient partners were involved in shaping research questions/goals. The UK Biobank study had participants complete comprehensive baseline assessments (2006-2010), with subgroups also completing repeat assessments (2012-2013), imaging assessments (2014-ongoing) and/or repeat imaging assessments (2019-ongoing; i.e., 2-4 data points per participant). Age, sex, education, ethnicity, and apolipoprotein E (APOE) e4 status (at least one e4 allele present) data were collected at baseline. Employment, physical activity, social support, and recent depressive symptom data were collected across timepoints. A Fluid intelligence score was obtained at each timepoint via a series of thirteen 1-pt. reasoning tasks (range: 0-13). Participants who did not complete cognitive testing at baseline and at least one other time point, and those with neurological conditions or events (e.g., stroke, epilepsy, dementia) were excluded (final N=17,409). Multi-Level Modeling (with Maximum Likelihood) was utilized, with fluid intelligence as the primary outcome measure. We ran Model 1: fully unconditioned, Model 2: with time predictor in years (baseline= 0), and Model 3: with baseline physical activity, social support and APOE e-4 predictors and covariates (mean-centered as appropriate), time-varying physical activity and social support predictors, and interaction terms. Nonsignificant interaction terms were trimmed from Model 3 to facilitate interpretation. Results: Model 1 was significant (p<.001) with an intraclass correlation (ICC) of 0.64, suggesting that 64% of the total variance in fluid intelligence in this sample is due to interindividual differences. Model 2 revealed that the average fluid intelligence score at baseline mean age (55.85) was 6.79 and significantly decreased with each year increase since baseline. Results from Model 3 (trimmed) revealed that being male, white, and having at least a university degree were associated with higher score at baseline, while being older and having more recent depressive symptoms were associated with lower scores. Higher social support quality was associated with higher scores while higher social support quantity was associated with lower scores at baseline; however, higher social support quantity at baseline was associated with less decline in scores over time. Surprisingly, having at least one e4 allele was associated with higher scores. Engaging in more moderate physical activity was associated with lower scores at baseline, however, individuals who increased the length of their moderate physical activity sessions over time showed higher timepoint-specific fluid intelligence scores. Additional significant interactions will be elaborated. Conclusions: Results suggest that increases in the length of moderate physical activity exercise sessions were associated with better cognitive function over time. Having better social support quality was also associated with better cognitive function, while higher social support quantity was associated with less cognitive decline over time. These findings suggest that positive lifestyle changes in older adulthood may slow cognitive decline.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».