With the accelerated progress of population aging and the shift in the disease spectrum, the prevention and management of chronic diseases and multimorbidity are facing great challenges. Multimorbidity measurement is a process that assesses and quantifies the complexity and severity of multiple disease states in a population, which is of great significance for understanding the epidemiology and disease burden of multimorbidity, as well as for optimizing medical services and policy. This article reviews existing multimorbidity measurement indicators, tools, and applications from two dimensions, static and dynamic attributes, aiming to improve the accuracy and comprehensiveness of multimorbidity epidemiology, diagnosis, and treatment.

With the accelerated progress of population aging and the shift in the disease spectrum, the prevention and management of chronic diseases and multimorbidity are facing great challenges. Multimorbidity measurement is a process that assesses and quantifies the complexity and severity of multiple disease states in a population, which is of great significance for understanding the epidemiology and disease burden of multimorbidity, as well as for optimizing medical services and policy. This article reviews existing multimorbidity measurement indicators, tools, and applications from two dimensions, static and dynamic attributes, aiming to improve the accuracy and comprehensiveness of multimorbidity epidemiology, diagnosis, and treatment.

Objective:To estimate the longitudinal association between serum lipid biomarkers and the development of cardiometabolic multimorbidity (CMM) in middle-aged and old adults (≥45) in China, while examining effect differences among degree of dyslipidemia aggregation and various dyslipidemia combination patterns.Methods:Based on data from the China Health and Retirement Longitudinal Study (2011-2018), logistic regression analysis was used to evaluate the associations of TC, LDL-C, HDL-C, TG (4 forms of dyslipidemias), degree and pattern of dyslipidemia combination with CMM. We also used restricted cubic splines to show the dose-response associations between 4 lipid biomarkers and CMM development.Results:Of the 6 522 participants included, 590 (9.05%) developed CMM. After adjusting for covariates, all 4 forms of dyslipidemias were positively associated with CMM development (high TC: OR=1.33, 95% CI: 1.03-1.71; high LDL-C: OR=1.35, 95% CI: 1.05-1.75; low HDL-C: OR=1.45, 95% CI: 1.19-1.77; high TG: OR=1.50, 95% CI: 1.20-1.88). The U-shaped dose-response relationship between LDL-C and CMM development was observed ( P for non-linear =0.022). The odds of CMM increased with the increase of dyslipidemias forms, which was highest in those with ≥3 forms of dyslipidemias ( OR=2.02, 95% CI: 1.33-3.06). In various dyslipidemia form combinations, the possibility of CMM development was highest in those with high TC, high LDL-C and low HDL-C ( OR=3.54, 95% CI: 1.40-8.67). High TC and high LDL-C were significantly associated with CMM development in people without cardiometabolic diseases. Low HDL-C was positively associated with diabetes and CMM development in participants without cardiometabolic diseases, cardiovascular disease (CVD) followed by diabetes, and diabetes followed by CVD. High TG was positively associated with diabetes and CMM in participants without cardiometabolic diseases, and diabetes followed by CVD. Conclusions:A total of 4 forms of dyslipidemia were all independently associated with CMM development in middle-aged and old adults in China. The dose-response relationship between LDL-C level and CMM development was U-shaped. The aggregation of 4 forms of dyslipidemia were associated with the development of CMM. Low HDL-C and high TG were significantly associated with multiple patterns of cardiometabolic diseases development.

Objective:To analyze the dynamic development of physical, psychological, and cognitive degenerative multimorbidity among middle-aged and older Chinese adults (≥45 years old) while estimating the longitudinal association between socioeconomic status (SES) and the progression of multimorbidity.Methods:Based on data from the China Health and Retirement Longitudinal Study (2011-2020), the Sankey diagram was used to show the dynamic development of physical, psychological, and cognitive degenerative multimorbidity from 2011 to 2020. SES was constructed based on the level of education and total household wealth. Logistic regression was used to estimate OR and 95% CI to evaluate the association between SES and the progression of multimorbidity. Results:Of the 5 393 participants included, 4 484 (83.14%) of them developed new diseases, and the prevalence of physical, psychological, and cognitive degenerative multimorbidity increased from 38.04% to 74.23%. Compared to those with no reported disorders at baseline, participants with psychological disorder (for newly developed physical-cognitive multimorbidity: OR=4.59,95% CI: 2.89-7.29), cognitive disorder (for newly developed physical-psychological multimorbidity: OR=2.24,95% CI: 1.40-3.60), or their multimorbidity at baseline were more likely to progress to physical, psychological, and cognitive degenerative multimorbidity. After adjusting covariates, individuals with low SES were more likely to develop physical diseases ( OR=1.45, 95% CI: 1.11-1.89), cognitive disorder ( OR=1.84, 95% CI: 1.16-2.91), physical-psychological multimorbidity ( OR=1.87, 95% CI: 1.37-2.56), physical-cognitive multimorbidity ( OR=3.58, 95% CI: 2.54-5.06), psychological-cognitive multimorbidity ( OR=5.66, 95% CI: 3.04-10.55), and physical-psychological-cognitive multimorbidity ( OR=3.21, 95% CI: 2.06-5.01) in comparison to those with high SES. There is a dose-response relationship between SES and the multimorbidity progression (all trend P<0.001). Conclusions:The prevalence of physical, psychological, and cognitive degenerative multimorbidity increased significantly among middle-aged and older Chinese adults. Lower SES was associated with multiple patterns of physical, psychological, and cognitive disorders progression.