Dietary preferences and diabetic risk in China: A large‐scale nationwide Internet data‐based study

Abstract

Background Unhealthy diet is one of the important risk factors of diabetes, which is one of the major public health problems in China. The Internet tools provide large‐scale passively collected data that show people's dietary preferences and their relationship with diabetes risk. Methods 212 341 708 individuals' dietary preference labels were created based on Internet data from online search and shopping software. Metabolic data obtained from the 2010 China Noncommunicable Disease Surveillance, which had 98 658 participants, was used to estimate the relation between dietary preferences geographical distribution and diabetes risk. Results Chinese dietary preferences had different geographical distribution, which is related to the local climate and consumption level. Fried food preference proportion distribution was significantly positively correlated with diabetes prevalence, hypertension prevalence and body mass index (BMI). Similarly, grilled food preference proportion distribution had significantly positive correlation with the prevalence of diabetes and hypertension. In contrast, spicy food preference proportion distribution was negatively correlated with diabetes prevalence. Sweet food preference proportion distribution was positively related to diabetes prevalence. Using dietary preferences data to predict regional prevalence of diabetes, hypertension and BMI, the average values of error (95% CI) between the three paired predicted and observed values were 9.8% (6.9%‐12.7%), 7.5% (5.0%‐10.0%) and 1.6% (1.2%‐2.0%), respectively. Conclusions Fried food, grilled food, and sweet food preferences were positively related to diabetes risk whereas spicy food preference was negatively correlated with diabetes risk. Dietary preferences based on passively collected Internet data could be used to predict regional prevalence of diabetes, hypertension, and BMI and showed good value for public health monitoring.

Characteristics of spicy food consumption and its relation to lifestyle behaviours: results from 0.5 million adults

“…Partly consistent with previous studies (Dovey et al 2016;Park et al 2017;He et al 2019), we found that spicy food consumption metrics, including frequency and pungency degree, were clustered with diverse factors, such as weekly tea/alcohol drinking, current tobacco smoking and different dietary habits. Several studies have recently reported the health effects of spicy food on various diseases (McCarty et al 2015;Chen et al 2017b;Zhao et al 2020). However, such associations could be modified by these lifestyle factors according to this study.…”

“…However, such associations could be modified by these lifestyle factors according to this study. For example, a study only adjusted for age and sex found that spicy food preference was negatively correlated with diabetes prevalence (Zhao et al 2020), and another prospective study found the same result, but the result was found statistically insignificant after further adjustment for smoking, alcohol consumption, physical activity and BMI (Lv et al 2015). In addition, details of these lifestyle behaviours differed across spicy food intake frequency and pungency degree, which might affect health outcomes in different ways.…”

Gut Microbiota Mediates the Preventive Effects of Dietary Capsaicin Against Depression-Like Behavior Induced by Lipopolysaccharide in Mice

“…Kang et al found that different doses of CAP in the diet altered the gut microbiota of healthy subjects for two weeks. The results showed that CAP boosted the Firmicutes/Bacteroidetes ratio and the number of Faecalibacterium (Zhao et al, 2020). This suggests that CAP can regulate the composition of the microbiome, and several studies have shown that it can improve related diseases via regulation of microbes.…”

Utilization of text mining as a big data analysis tool for food science and nutrition

“…One's search queries related to food on the Internet could also be reflective of their dietary habits. Zhao et al (2019) conducted a correlation analysis between Chinese dietary preferences from Baidu search data and diabetes risk from government statistics and found a geographical distribution pattern. Besides, online food recipes have been recognized as a good source of Internet data to study recipe production, consumption, and innovation (Rong, Liu, Huo, & Sun, 2019).…”