Correlation of Subjective Cognitive Decline with Multimorbidity among Elderly People
Notice bibliographique
Résumé
Background Subjective cognitive decline (SCD) is a target for early prevention of Alzheimer's disease (AD). AD is closely related to multimorbidity, but the correlation of SCD with multimorbidity has not been well defined. Objective To investigate the correlation between SCD and multimorbidity in the elderly, providing a theoretical basis for early prevention and intervention of AD. Methods From January 2021 to June 2022, 612 elderly people aged≥60 years were sampled by convenience sampling method in urban communities and elderly care institutions in Guangzhou. The objective cognitive function was assessed using the Chinese version of Montreal Cognitive Assessment-Basic (MoCA-BC), Chinese version of Clinical Dementia Rating Scale (CDR-C), and Chinese version of Hachinski Ischemic Scale (HIS-C). SCD was diagnosed using the conceptual framework proposed by the working group of the Subjective Cognitive Decline Initiative and Chinese version of Subjective Cognitive Decline-Questionnaire 9 (SCD-Q9-C). Then according to the assessment results, participants were divided into SCD group (having normal overall objective cognitive function, SCD and SCD-Q9-C score≥5) and normal cognitive (NC) group (having normal overall objective cognitive function, and SCD-Q9-C score<5). A general information questionnaire to collect socio-demographics〔gender, age, place of residence (community or elderly care institution), years of education, marital status, type of occupation before retirement, monthly income〕and health-related information〔body mass index, waist circumference, habits of smoking, alcohol consumption and drinking tea, exercise frequency, habit and average duration of siesta, sleep quality assessed using the Chinese version of Pittsburgh Sleep Quality Index (PSQI-C), depressive symptoms assessed using the Chinese version of Patient Health Questionnaire (PHQ-9-C), anxiety symptoms assessed using the Chinese version of Generalized Anxiety Disorder Scale-7 (GAD-7-C), and activities of daily living (ADLs) assessed using the ADL Scale for Chinese Adults〕. Besides, another questionnaire to collect the history of chronic illness. The level of multimorbidity was classified into three categories〔no multimorbidity (0-1), low multimorbidity (2-4) and high multimorbidity (≥5) 〕by the number of chronic conditions. A binary Logistic regression analysis was used to explore the effect of multimorbidity on the SCD. Results The mean SCD-Q9-C score was (4.20±1.95) in 612 elderly people in this survey. Two hundred and fifty cases (40.8%) and 362 cases (59.2%) were assigned to the SCD group, and NC group, respectively. Univariate analysis showed statistically significant differences in gender, age, years of education, type of occupation before retirement, monthly income, tea drinking habits, sleep quality, depressive symptoms, anxiety symptoms and ADL scores between SCD and NC groups (P<0.05). Five hundred and seventy-four cases (93.8%) had chronic diseases, and 475 (77.6%) of them had multimorbidity, including 352 (57.5%) with low multimorbidity level and 123 (20.1%) with high multimorbidity level. The differences in multimorbidity prevalence, multimorbidity level, diabetes, arthritis and osteoporosis between SCD and NC groups were statistically significant (P<0.05). Binary Logistic regression analysis showed that older age, poor sleep quality, presence of anxiety symptoms, poor ADLs, and high level of multimorbidity were statistically significant risk factors for SCD (P<0.05), with the risk of SCD being 1.826〔95%CI (1.037, 3.216) 〕times higher for high multimorbidity level than for no multimorbidity (P<0.05). Longer years of education was a protective factor for SCD (P<0.05) . Conclusion High multimorbidity level is associated with increased risk of SCD. Community and elderly care providers can use multimorbidity as an assessment indicator of cognitive decline, and collaboratively implement management of multimorbidity and related factors to actively identify and intervene in SCD in order to delay the development of AD in older adults and promote healthy ageing.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 ».