Current status of cardiovascular and metabolic comorbidity in elderly patients with chronic diseases
10.3969/j.issn.1006-2483.2026.05.033
- VernacularTitle:老年慢性病患者心血管代谢性共病现状调查
- Author:
Xiaomin YANG
1
;
Xinrong ZHAI
2
;
Jing LYU
1
Author Information
1. Geriatric Cardiac Care Unit, Huadong Hospital Affiliated to Fudan University, Shanghai 200000, China
2. Cardiovascular Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai 200000, China
- Publication Type:Journal Article
- Keywords:
Elderly;
Chronic disease;
Cardiometabolic disease;
Comorbidity
- From:
Journal of Public Health and Preventive Medicine
2026;37(5):155-159
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the status of cardiometabolic comorbidity among elderly patients with chronic diseases, analyze its influencing factors, and construct a model based on cross-sectional data to identify a set of key characteristics associated with cardiometabolic comorbidity. Methods A cross-sectional investigation was conducted among 443 elderly patients with chronic diseases to determine the incidence of cardiometabolic comorbidity. Patients were divided into the comorbidity group and the non-comorbidity group based on the presence or absence of cardiometabolic comorbidity. The prevalence of cardiometabolic comorbidity was compared between patients of different genders and ages. The influencing factors for cardiometabolic comorbidity in elderly patients with chronic diseases were analyzed. A model for identifying cardiometabolic comorbidity in elderly patients with chronic diseases was constructed and its application value was evaluated. Results Among the 443 elderly patients with chronic diseases, the prevalence rate of cardiometabolic comorbidity was 68.62% (304/443), and the prevalence rate increased significantly with age (P<0.05). Logistic regression analysis identified age, smoking history, insufficient sleep, poor dietary habits, and family history of chronic diseases as independent risk factors. The model constructed based on these factors demonstrated good discriminative efficacy (AUC=0.808), and its visualized nomogram had high clinical discriminative value. Conclusion Elderly patients with chronic diseases have a high risk of cardiometabolic multimorbidity. Factors such as age, smoking history, and insufficient sleep are independent influencing factors. The nomogram model constructed based on these factors shows good discriminative efficacy.
