Cognitive Functioning Trajectories and Their Association With Mental Health in Older Adults With Hypertension: Secondary Analysis of the Chinese Longitudinal Healthy Longevity Survey Data
Notice bibliographique
Résumé
Background: There is a lack of research on the trajectories of cognitive functioning in older people with hypertension, as well as possible contributing factors and correlations between cognitive functioning and mental health. Objective: This study aimed to conduct a secondary longitudinal analysis to examine cognitive functioning trajectories and their associated factors in older people with hypertension. Methods: The data used in our study were retrieved from the Chinese Longitudinal Healthy Longevity Survey. The Chinese version of the Mini-Mental State Examination was used to assess cognitive functioning. The trajectories of cognitive functioning of older individuals with hypertension were determined by using group-based trajectory modeling. The binary logistic regression analyses were performed to examine how participant factors affected the trajectories of cognitive functioning in older individuals with hypertension. The relationships between cognitive functioning and mental health were investigated using multivariable linear regression models. Data were analyzed using SPSS (version 20.0; SPSS Inc) and Stata (version 16.0; StataCorp LLC). Results: A total of 642 older people with hypertension were included. Cognitive functioning was categorized into 2 trajectories according to group-based trajectory modeling: "rapid decline" (48/642, 7.9%) and "slow decline" (594/642, 92.1%). Binary logistic regression results showed that older adults with hypertension aged equal to or greater than 80 years had an elevated risk of rapid decline in cognitive functioning (odds ratio 5.484, 95% CI 2.365-12.719), and higher score in mental health was the protector of rapid decline in cognitive functioning during the following 13 years (odds ratio 0.918, 95% CI 0.852-0.988). In the unadjusted model, mental health was positively associated with cognitive functioning (β=.246, 95% CI 0.125-0.234, P<.001), and this association was maintained after partial or complete adjustment for covariates (β=.159, 95% CI 0.059-0.174, P<.001; β=.138, 95% CI 0.043-0.158, P=.001). Subgroup analyses by age showed that this positive correlation was only seen in the 60-69 years age group (β=.183, 95% CI 0.036-0.193, P=.004), while subgroup analyses by sex revealed that the association between these 2 indicators was no longer presented among males in the fully adjusted model (β=.082, 95% CI -0.024 to 0.119, P=.19), and BMI fully stratification demonstrated this association persisted in the healthy weight group (β=.125, 95% CI 0.039-0.210, P=.004). Conclusions: Our research showed that the decline in cognitive functioning is associated with lower mental health and occurs more rapidly in older adults with hypertension who are older than 80 years.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».