Objective: This study aimed to examine the impact of different dietary patterns on stroke outcomes among type 2 diabetes mellitus (T2DM) patients in China.
Design: Participants were enrolled by a stratified random cluster sampling method in the study. After collecting dietary data using a quantified food frequency questionnaire, latent class analysis was used to identify dietary patterns, and propensity score matching was used to reduce confounding effects between different dietary patterns. Binary logistic regression and conditional logistic regression were used to analyze the relationship between dietary patterns and stroke in patients with T2DM.
Setting: A cross-sectional survey available from December 2013 to January 2014.
Participants: A total of 13731 Chinese residents aged 18 years or over.
Results: Two dietary patterns were identified: 61.2% of T2DM patients were categorized in the High-fat dietary pattern while 38.8% of patients were characterized by the Balanced dietary pattern. Compared to the High-fat dietary pattern, the Balanced dietary pattern was associated with reduced stroke risk (OR=0.63, 95%CI: 0.52-0.76, P<0.001) after adjusting for confounding factors. The protective effect of the balanced model did not differ significantly (interaction P>0.05).
Conclusion: This study provides sufficient evidence to support the dietary intervention strategies to prevent stroke effectively. Maintaining a Balanced dietary pattern, especially with moderate consumption of foods rich in quality protein and fresh vegetables in T2DM patients, might decrease the risk of stroke in China.
Keywords: Dietary patterns; Latent class analysis; Propensity score matching; Stroke; Type 2 diabetes.