RESUMO
BACKGROUND: The prevalence rate of multiple chronic diseases among the elderly is relatively high, posing a risk to their health and also imposing a financial burden on them. Optimal dietary patterns have positive effects on multiple chronic diseases. This study aimed to identify dietary patterns associated with multiple chronic diseases in older adults. METHODS: Dietary intake was assessed through two non-consecutive 24-hour dietary recalls. The presence of multiple chronic diseases was assessed based on the existence of dyslipidemia, hypertension, chronic kidney disease, sleep disorders, diabetes, moderate or severe depressive symptoms, and cognitive impairment, with two or more of these conditions being considered. Latent class analysis was used to identify types of multiple chronic diseases, and two-step cluster analysis was used to determine individual dietary patterns. Logistic regression analysis with robust standard errors was conducted to determine the associations between dietary patterns and types of multiple chronic diseases. RESULTS: Three dietary patterns and three types of multiple chronic diseases were identified. Individuals following a diet rich in legumes, meat, vegetables and fruits (HLMVF dietary pattern) were 59% less likely to have the cardiometabolic cognitive impairment comorbidity (CCC) than those following a diet rich in milk and eggs but with low grain intake (HME-LG) (OR = 0.41, 95% CI: 0.27-0.64, P < 0.001) and 66% less likely to have the especially sleep disorders comorbidity (ESC) than those following a diet rich in grains but lacking milk and eggs (HG-LME) (OR = 0.34, 95% CI: 0.14-0.87, P < 0.05). DISCUSSION: The HLMVF dietary pattern may serve as a healthy dietary pattern to reduce the incidence of multiple chronic diseases and should be promoted among the older adult population.
RESUMO
Background: The prevalence of cardiovascular metabolic comorbidities (CMM) among adults is relatively high, imposing a heavy burden on individuals, families, and society. Dietary patterns play a significant role in the occurrence and development of CMM. This study aimed to identify the combined types of CMM in adult populations and investigate the impact of dietary patterns on CMM. Methods: Participants in this study were from the sixth wave of the China Health and Nutrition Survey (CHNS). Dietary intake was assessed using a three-day 24-hour dietary recall method among 4,963 participants. Latent profile analysis was used to determine dietary pattern types. Two-step cluster analysis was performed to identify the combined types of CMM based on the participants' conditions of hyperuricemia, dyslipidemia, diabetes, renal dysfunction, hypertension, and stroke. Logistic regression analysis with robust standard errors was used to determine the impact of dietary patterns on CMM. Results: Participants were clustered into three dietary patterns (Pattern 1 to 3) and five CMM types (Class I to V). Class I combined six diseases, with a low proportion of diabetes. Class II also combined six diseases but with a high proportion of diabetes. Class III combined four diseases, with a high proportion of hypertension. Class IV combined three diseases, with the highest proportions of hyperuricemia, diabetes, and renal dysfunction. Class V combined two diseases, with high proportions of dyslipidemia and renal dysfunction. Patients with Class III CMM had a significantly higher average age than the other four classes (P ≤ 0.05). Compared to those with isolated dyslipidemia, individuals with a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern had a higher risk of developing dyslipidemia combined with renal dysfunction (Class V CMM) with an odds ratio of 2.001 (95% CI 1.011-3.960, P≤ 0.05). Conclusion: For individuals with isolated dyslipidemia, avoiding a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern may help reduce the risk of developing dyslipidemia combined with renal dysfunction (Class V CMM).