Age-related macular degeneration (AMD) is the leading cause of vision loss and impairment among individuals aged 50 and above worldwide, with an estimated 288 million people projected to be affected by 2040.
To investigate the association between various chronic diseases and AMD, analyze the interaction effects of different chronic disease combinations on AMD risk, and assess how the interaction between chronic diseases impacts AMD risk.
Based on the Cheeloo LEAD database, individuals aged 50 years and above were included. The AMD group was identified using ICD-10 coding (H35.3), and a 1∶4 matching by age and gender was conducted to select control group without AMD. Significant differences in baseline characteristics and chronic disease prevalence were found between the AMD and non-AMD groups. Multivariate Logistic regression models were used to assess the association between AMD and chronic conditions such as hypertension, diabetes, and heart disease. Collinearity was evaluated using the variance inflation factor (VIF) to ensure the robustness of the model. Interaction terms were introduced to evaluate the synergistic effects of different chronic disease combinations on AMD risk.
A total of 16 780 participants were included in this study, with 3 356 in the AMD group and 13 424 in the control group. After adjusting for confounding factors, multivariate Logistic regression analysis showed that hypertension (OR=2.81, 95%CI=2.59-3.04), heart disease (OR=2.02, 95%CI=1.86-2.19), stroke (OR=1.82, 95%CI=1.66-1.99), diabetes (OR=2.72, 95%CI=2.47-2.99), dyslipidemia (OR=2.01, 95%CI=1.78-2.28), chronic gastric or digestive system diseases (OR=1.90, 95%CI=1.72-2.10), chronic liver diseases (OR=2.29, 95%CI=2.04-2.57), emotional and mental disorders (OR=2.86, 95%CI=2.49-3.29), and memory-related diseases (OR=1.86, 95%CI=1.52-2.28) were all significant risk factors for AMD (P<0.05). Interaction effect analysis revealed that the predicted probability of AMD was 0.40 when hypertension and diabetes coexisted; 0.40 for the coexistence of diabetes and dyslipidemia; and 0.45 for the coexistence of chronic liver diseases and diabetes. The predicted probability of AMD was 0.30 for the combination of hypertension and heart disease, 0.30 for the combination of stroke and heart disease, 0.30 for the combination of chronic gastric diseases and chronic liver diseases, 0.45 for the combination of emotional and mental disorders and memory-related diseases, and 0.45 for the combination of hypertension and emotional and mental disorders.
Hypertension, diabetes, chronic liver diseases, and other conditions were significantly associated with the occurrence of AMD. Notably, the combinations of chronic liver diseases and diabetes, emotional and mental disorders with memory-related diseases, and hypertension with diabetes had an even more pronounced impact on AMD.
With the acceleration of population aging, there has been a continuous increase in the number of elderly individuals suffering from multiple chronic conditions and impaired activities of daily living (ADL), imposing a substantial healthcare burden on society. While multiple chronic conditions are highly associated with impairment in ADL, the specific mechanisms and combinatorial effects have not been fully elucidated.
This study aims to analyze the current status of multiple chronic conditions among the elderly in China and explore the association between different comorbidity combinations and ADL, thereby providing scientific evidence for chronic diseases management and functional maintenance in older adults.
Utilizing data from the 2018 China Health and Retirement Longitudinal Study (CHARLS), this study focused on individuals aged 60 years and older to compare the prevalence of impaired ADL across demographic subgroups. The Apriori algorithm was employed to perform association rules analysis to identify primary comorbidity combinations. Binary Logistic regression models were used to assess the impact of these comorbidity combinations on ADL impairment.
The study included 10 999 elderly participants, and the prevalence of multiple chronic conditions was 64.91% (7 140/10 999). 3 819 individuals (34.72%) exhibited ADL impairment, 1 149 (10.45%) demonstrated basic activities of daily living (BADL) impairment, and 3 662 (33.29%) showed instrumental activities of daily living (IADL) impairment. Statistically significant differences (P<0.05) in ADL, BADL and IADL impairment rates were observed across gender, age, education level, marital status, type of residence, and the presence of multiple chronic conditions (P<0.05). The Apriori algorithm identified 8 association rules, with the highest support rule being "dyslipidemia and hypertension" (support=8.237%), the highest confidence rule being "dyslipidemia, diabetes or high blood sugar and hypertension" (confidence=78.707%), and the highest lift rule being "asthma and chronic pulmonary diseases" (lift=4.188). Hypertension exhibited the highest frequency across all comorbidity combinations. Adjusted binary Logistic regression revealed that, multiple comorbidity combinations "stroke and hypertension" "asthma and chronic pulmonary diseases", and "kidney disease, stomach or other digestive diseases, and arthritis or rheumatism" significantly impacted ADL, BADL, and IADL impairment (P<0.05). Notably, the "stroke and hypertension" combination posed the highest risk for BADL impairment, the risk of being one level more severely impaired in BADL for individuals with this comorbidity combination was 4.480 times higher than that of the population without this comorbidity combination (OR=4.480, 95%CI=3.754-5.347).
Hypertension serves as a central hub in elderly comorbidities networks, demonstrating strong associations with multiple chronic conditions. Multiple comorbidity combinations significantly increase the risk of ADL impairment, with the "stroke and hypertension" combination being the most pronounced. Healthcare systems should prioritize elderly populations with comorbidities, develop effective long term care policies tailored to different comorbidities, reduce the risk of disability, delay functional decline, and enhance quality of life in elderly population.
Along with the continuous change of lifestyle, the incidence and hospitalization rate of coronary heart disease are increasing and becoming younger in average age, patients are also generally suffering from other chronic diseases, namely facing the problems of comorbidities, but currently the relevant research in China is still relatively lacking.
To understand the prevalence and patterns of comorbidities in premature coronary heart disease, and to explore the correlation between comorbidities, in order to provide reference for early detection and management of comorbidities in premature coronary heart disease in China.
Electronic medical record data of 5 124 premature coronary heart disease patients (males≤55 years old, females≤65 years old) admitted to the Department of Cardiovascular Medicine, Norman Bethune Second Hospital of Jilin University from 2010 to 2022 were collected and analyzed to determine the current status of comorbidities in premature coronary heart disease. SPSS 26.0 statistical software and Python 3.11.0 were used, along with the Apriori algorithm, to mine strong association rules for premature coronary heart disease and explore the comorbidity patterns.
The top three comorbidities with the highest prevalence in patients with premature coronary heart disease were hypertension (3 707 cases, 72.35%), dyslipidemia (2 134 cases, 41.65%), and diabetes (1 811 cases, 35.34%). Thirty five notable comorbidity patterns observed among the 13 chronic diseases. The binary comorbidity pattern of premature coronary heart disease takes "hypertension" as the core comorbidity, which can be accompanied by anemia, kidney disease, stroke, etc. The ternary comorbidity pattern takes "anemia + kidney disease" as the core comorbidity, which may be accompanied by diabetes and lung disease, etc. The quaternary comorbidity pattern takes "diabetes + hypertension" as the core comorbidity, which may be accompanied by stomach disease, liver disease, etc. According to the results of the association rule and cluster analysis, hypertension, diabetes, dyslipidemia, anemia, and kidney disease all had significant comorbidity rates, with a complex comorbidity relationship.
The predominant comorbidity disease in the population of premature coronary heart disease is hypertension, and the comorbidity pattern of "anemia + kidney disease" of early-onset coronary heart disease can be examined in future studies.
