Multimorbidity, individuals suffering from two or more chronic diseases, has become a major health challenge worldwide, especially in populous and prosperous cities, where studies of this phenomenon in China are limited. We examined the prevalence, trends, patterns, and associated factors of multimorbidity from 2009 to 2018 among community-dwelling adults in Guangzhou, China. We conducted serial cross-sectional surveys for chronic diseases in Guangzhou, China, in 2009, 2013, and 2018. General and stratified prevalence were standardized using demographic data. Multivariable logistic regression and hierarchical cluster analysis were applied to identify associated factors and to assess the correlations and patterns of multimorbidity, respectively. This study included 23,284 adults aged 18 and over in 2009, 18,551 in 2013, and 15,727 in 2018. The standardized prevalence of multimorbidity increased substantially, with 12.69% (95% CI: 10.45-15.33) in 2009, 25.44% (95% CI: 23.47-27.52) in 2013, and 35.13% (95% CI:32.64-37.70) in 2018 (P for trend <0.001). The highest bi- and triple-conditions of multimorbidity were dyslipidemia (DP) and overweight or obesity (OO) (12.54%, 95% CI: 11.68-13.46), and DP, OO, and Hypertension (HT) (3.99%, 95% CI: 3.47-4.58) in 2018. From 2009 to 2018, (1) The majority of multimorbidity patterns showed a high prevalence; (2) The percentage of participants with only one chronic condition was found lower, while the percentage with multiple conditions was higher. The prevalence of chronic disease multimorbidity in Guangzhou China, has increased substantially among adults. Effective policies targeting multimorbidity are urgently needed, especially for the health management of primary medical institutions.

Background: This study aimed to examine the prevalence and associated factors of malnutrition in older community-dwellers and explore the interaction between associated factors. Methods: A total of 474,467 older community-dwellers aged 65 or above were selected in Guangzhou, China. We used a two-step methodology to detect the associated factors of malnutrition and constructed logistic regression models to explore the influencing factors and interactive effects on three patterns of malnutrition. Results: The prevalence of malnutrition was 22.28%. Older adults with both hypertension and diabetes (RERI = 0.13), both meat or fish diet and hypertension (RERI = 0.79), and both meat or fish diet and diabetes (RERI = 0.81) had positive additive interaction effects on the risk of obesity, whereas those on a vegetarian diet with hypertension (RERI = −0.25) or diabetes (RERI = −0.19) had negative additive interaction effects. Moreover, the interactions of physical activity with a meat or fish diet (RERI = −0.84) or dyslipidemia (RERI = −0.09) could lower the risk of obesity. Conclusions: Malnutrition was influenced by different health factors, and there were interactions between these influencing factors. Pertinent dietary instruction should be given according to different nutritional status indexes and the prevalence of metabolic diseases to avoid the occurrences of malnutrition among older adults.

Background: Optimizing nutritional care for people with multimorbidity requires effective and feasible tools for early risk identification. This study aimed to investigate the associations of conicity index (C-index) and body roundness index (BRI) with multimorbidity among adults in Guangzhou, and to explore their potential utility in identifying individuals who may benefit from prioritized nutritional assessment. Methods: Based on the Fourth Adult Chronic Disease and Risk Factor Surveillance Program in Guangzhou, a total of 15,532 adults were recruited in this cross-sectional study. Multiple logistic regression analysis, restricted cubic spline (RCS) analysis and receiver operating characteristic curve (ROC) analysis with DeLong’s test were performed. Results: The prevalence of multimorbidity was 38.95%. Higher C-index and BRI quartiles were significantly associated with increased multimorbidity risk. Compared with the lowest quartile, the adjusted OR for the highest C-index quartile was 1.98 (95% CI: 1.78–2.21), and for the highest BRI quartile it was 3.05 (95% CI: 2.74–3.41). A dose–response relationship was observed (p < 0.001). BRI showed statistically significantly higher discriminative ability relative to traditional anthropometric indices, though its overall discriminative performance was modest (AUC = 0.678). Conclusions: C-index and BRI are strongly and positively associated with multimorbidity. These simple, non-invasive indices may support community-based risk stratification and help identify individuals who warrant further clinical and nutritional assessment.