Exploring Risk Factors Related to Low Calf Circumference in Older Adults With Multimorbidity: Cross-Sectional Latent Class Analysis
Notice bibliographique
Résumé
Background: As the global population continues to age, the prevalence of sarcopenia is gradually increasing, and the loss of skeletal muscle mass is one of the manifestations of sarcopenia. Low calf circumference (CC) is often used as a predictor of poor skeletal muscle mass or sarcopenia. Older adults usually have a combination of multiple chronic diseases. There is a lack of evidence to explore the risk factors for low CC with multimorbidity in Chinese, community-dwelling, older adults. Objective: This study aimed to explore the risk factors and potential categories in older adult patients with low CC and multimorbidity from an individual-centered perspective. Methods: We selected 15,874 participants from the Chinese Longitudinal Healthy Longevity Survey in 2018 and screened for low CC in older adult patients. The individual-centered latent class analysis was used to classify potential multimorbidity groups. Multiple logistic regression was used to explore the risk factors associated with low CC and multimorbidity by applying the elastic net to screen for reliable risk variables. Results: A total of 7956 older individuals were eligible for the study, of whom 3960 (49.8%) were aged >90 years and 2166 (27.2%) had multimorbidity with low CC. The prevalence of multimorbidity increases between the ages of 65 and 89 years. However, the majority of older adults remain in reasonably good health beyond the age of 90 years. Five multimorbidity groups were identified by latent class analysis: multisystem morbidity diseases (78/2166, 3.6%), arthritis-rheumatism or rheumatoid diseases (400/2166, 18.47%), diabetes-hypertension diseases (330/2166, 15.23%), respiratory-heart diseases (347/2166, 16.02%), and cardiovascular diseases (1011/2166, 46.68%). Through 12 variables screened by the elastic net, multiple logistic regression showed different impacts on multimorbidity groups, including demographic background, behavioral characteristics, and physical and mental health factors. In particular, older patients who self-report poor health and live in urban areas need more attention. Conclusions: The results revealed that low CC is a common phenomenon among community-dwelling older adults, and a substantial proportion also present with multimorbidity. In the older adult population with low CC, the proportion of multimorbidity does not simply increase with age. Multimorbidity in low CC has been identified in 5 potential groups. Different groups have distinctive risk factors. Public health authorities should pay attention to low CC in older adult patients with multimorbidity and carry out targeted interventions, thereby enhancing health outcomes.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».