Research on Influencing Factors and Risk Prediction of Cognitive Function in Community-dwelling Middle-aged and Elderly People
Notice bibliographique
Résumé
Background The incidence of cognitive impairment is rising year by year among middle-aged and elderly individuals, yet its pathogenesis remains unclear and effective treatments are lacking. Integrating multidimensional factors to construct a predictive model can enhance the early identification and intervention of high-risk populations for cognitive impairment. Objective To explore and construct a risk prediction model for cognitive impairment in community-dwelling middle-aged and elderly adults based on a biomarkers-genetic-environment multidimensional perspective. Methods A total of 2 243 middle-aged and elderly people in the community who underwent health examinations at Songjiang District Sijing Community Health Center of Shanghai from April to September 2021 were included as the research subjects. Their sociodemographic data, lifestyle, personal disease history and physical examination indicators were collected. The homocysteine (Hcy) concentration was measured by fully automatic biochemical analyzer to determine whether it was hyperhomocysteinemia (HHcy), and single nucleotide polymorphism (SNP) gene sites rs429358 and rs7412 were detected by ligase detection reaction technology to determine the Apolipoprotein E (APOE) genotype. Cognitive function was assessed using Two-tiered Cognitive Self-Assessment (TCSA), and the subjects were divided into normal cognitive group and cognitive impairment risk group according to the assessment results. The general data and physical examination indicators of the two groups were compared. The multivariate logistic stepwise regression method was used to screen independent predictors, and a nomogram prediction model for the risk of cognitive impairment in middle-aged and elderly people was constructed. The Bootstrap self-sampling method was used for internal validation to determine the accuracy of the prediction model. Results The incidence rate of cognitive impairment risk in the community-dwelling middle-aged and elderly people was 16.72%. Multivariate Logistic regression analysis revealed that advanced age (OR=1.064, 95%CI=1.040-1.088, P<0.001), smoking (OR=1.746, 95%CI=1.277-2.386, P<0.001), hypertension (OR=2.584, 95%CI=1.761-3.793, P<0.001), stroke (OR=1.451, 95%CI=1.048-2.008, P=0.025), HHcy (OR=2.421, 95%CI=1.827-3.207, P<0.001) and E4 carrier (OR=2.034, 95%CI=1.473-2.808, P<0.001) were risk factors for cognitive impairment in middle-aged and elderly people in the community, while long years of education (OR=0.922, 95%CI=0.893-0.952, P<0.001) and appropriate sleep duration (OR=0.614, 95%CI=0.470-0.802, P<0.001) were protective factors for cognitive impairment. The nomogram prediction model was constructed based on the influencing factors in the multivariate Logistic regression analysis. The consistency index of the model was 0.743 (95%CI=0.712-0.771) . Conclusion Years of education, smoking, adequate sleep, history of hypertension and stroke, hyperhomocysteinemia (HHcy), and E4 carrier are influencing factors for cognitive impairment in middle-aged and elderly people. A risk prediction model based on multi-dimensional prediction of "biomarkers-genetics-environment" can provide guidance for screening the risk of cognitive impairment in community-dwelling middle-aged and elderly people.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».