The Relationship between Dietary Patterns and High Blood Glucose among Adults Based on Structural Equation Modelling

Abstract

The aim of this study was to examine the association between dietary patterns and high blood glucose in Jiangsu province of China by using structural equation modelling (SEqM). Methods: Participants in this cross-sectional study were recruited through the 2015 Chinese Adult Chronic Disease and Nutrition Surveillance Program in Jiangsu province using a multistage stratified cluster random sampling method. Dietary patterns were defined by exploratory factor analysis (EFA). Confirmatory factor analysis (CFA) was used to test the fitness of EFA. SEqM was used to investigate the association between dietary patterns and high blood glucose. Results: After exclusion, 3137 participants with complete information were analysed for this study. The prevalence of high blood glucose was 9.3% and 8.1% in males and females, respectively. Two dietary patterns: the modern dietary pattern (i.e., high in red meats and its products, vegetables, seafood, condiments, fungi and algae, main grains and poultry; low in other grains, tubers and preserves), and the fruit–milk dietary pattern (i.e., high in milk and its products, fruits, eggs, nuts and seeds and pastry snacks, but low in vegetable oils) were established. Modern dietary pattern was found to be positively associated with high blood glucose in adults in Jiangsu province (multivariate logistic regression: OR = 1.561, 95% CI: 1.025~2.379; SEqM: β = 0.127, p < 0.05). Conclusion: The modern dietary pattern—high intake of red meats—was significantly associated with high blood glucose among adults in Jiangsu province of China, while the fruit–milk dietary pattern was not significantly associated with high blood glucose.

Aging Population, Balanced Diet and China’s Grain Demand

“…It is important to make more accurate GD forecasting to provide a basis for policymaking in grain production, consumption and trade to ensure grain security, a topic that has become a major international issue. During the past few decades, a wide range of models and data sources have been applied to GD prediction (e.g., [18][19][20]). The research methods can be roughly classified into three categories [16].…”

“…The second category uses a simple mechanistic approach to predict demand based on a simple (Engel's Law) relationship between income and kilocalorie consumption [18,[25][26][27][28][29]. The third kind, phenomenological models, assume that current trends of increasing kilocalorie consumption will continue [20,[30][31][32] There were various projections of future GD in China (Table S1) and around the world, and they varied widely; the projected global GD growth from 2005 to 2050 ranges from 35 percent to 110 percent [5]. Even the near-term projections for China's GD in 2020, varied from 480 to 741 million tons (Table S1).…”

“…During the past few decades, a wide range of models and data sources have been applied to GD prediction (e.g., [18][19][20]). The research methods can be roughly classified into three categories [16].…”

“…The second category uses a simple mechanistic approach to predict demand based on a simple (Engel's Law) relationship between income and kilocalorie consumption [18,[25][26][27][28][29]. The third kind, phenomenological models, assume that current trends of increasing kilocalorie consumption will continue [20,[30][31][32]. Different modeling methods had resulted in a variety of forecasts for global or regional GD.…”

“…[10,33]. Other sources of variation are the projected population, age structure, and especially the dietary structure [20]. Uncertainties in the growth of meat and dairy consumption in South and East Asia, particularly China and India, were the primary sources of uncertainty in global GD forecasts [34].…”