Geographical Distribution of Dietary Patterns and Their Association with T2DM in Chinese Adults Aged 45 y and Above: A Nationwide Cross-Sectional Study

Abstract

Background: This study aimed to investigate the geographical distribution of dietary patterns and their association with T2DM among Chinese adults aged 45 years and above.

Methods: Data was from the China Adults Chronic Diseases and Nutrition Surveillance (2015). Dietary intake for each participant was determined through a combination of 3-day 24-h dietary recall interviews and food frequency questionnaires. Principal component analysis was used to extract dietary patterns and spatial analysis was employed to investigate the geographic distribution of them. T2DM was diagnosed using criteria of ADA 2018, and binary logistic regression was employed to examine the relationship between dietary patterns and T2DM.

Results: A total of 36,648 participants were included in the study; 10.9% of them were diagnosed as T2DM. Three dietary patterns were identified with the name of plant-based pattern, animal-based pattern, and oriental traditional pattern, which were represented located in northern, northwest, and southern regions, respectively. After adjusting for potential confounders, participants in the highest quartile of the plant-based pattern were associated with lower T2DM odds (OR = 0.82, 95% CI: 0.74, 0.90) when comparing with the lowest quartile. However, participants inclined to higher quartiles of animal-based pattern had a higher risk of T2DM (OR = 1.15, 95% CI: 1.04, 1.27) compared with those in the lower quartiles. No significant association was found between the oriental traditional pattern and T2DM (OR = 1.03, 95% CI: 0.93, 1.14).

Conclusion: Dietary patterns of Chinese population revealed geographical disparities, with plant-based dietary pattern showing protective effects and animal-based pattern carrying high risks for T2DM. Regional dietary variations and food environment are paramount in T2DM prevention and management.

Keywords: T2DM; cross-sectional study; dietary patterns; spatial statistical analysis.

Figure 1

Characteristics of dietary patterns. Line colors indicate the significance (p-value) of the correlations between dietary patterns and food groups/nutrients, while line thickness represents the strength of these correlations (R-1, R-3). The triangular heat maps on both sides depict the internal correlations among the food groups and nutrients (R-2, R-4) (a). After categorizing the sscores into quartiles according to dietary patterns, (b) depicts the food groups and nutrient intake levels within the top quartile (Q4) of each dietary pattern. The width of the ribbon corresponds to the quantity of intake. In order to present the information as clearly as possible, selectively showcases the food groups and nutrients that exhibit significant differences in intake among the three dietary patterns. For comprehensive intake details, refer to Table S3.

Figure 2

The spatial distribution of the three dietary patterns. (a-1–a-3) represent the actual distribution of three patterns, (b-1–b-3) represent the local spatial autocorrelation results of three patterns, and (c-1–c-3) represent the distribution of hot and cold spots of three dietary patterns. High-High Cluster: Regions with high dietary pattern scores clustering spatially with regions with high dietary pattern scores. Low-Low Cluster: Regions with low dietary pattern scores clustering spatially with regions with low dietary pattern scores. High-Low Outlier: Regions with high dietary pattern scores clustering spatially with regions with low dietary pattern scores. Low-High Outlier: Regions with low dietary pattern scores clustering spatially with regions with high dietary pattern scores. Hotspot area: Regions with statistically significant high dietary pattern scores that were surrounded by neighboring areas with similar high dietary pattern scores. Coldspot area: Regions with statistically significant low dietary pattern scores that were surrounded by neighboring areas with similar low dietary pattern scores.

Figure 3

Logistic regression analysis on the association between the dietary patterns and T2DM. Adjusted for age, gender, income, educational level, marital status, region, ethnicity, chronic family history, BMI, physical activity, smoke, drinking, and total energy intake.