TY - JOUR
T1 - Dietary preferences and diabetic risk in China
T2 - A large-scale nationwide Internet data-based study
AU - Zhao, Zhiyun
AU - Li, Mian
AU - Li, Chao
AU - Wang, Tiange
AU - Xu, Yu
AU - Zhan, Zhizheng
AU - Dong, Weishan
AU - Shen, Zhiyong
AU - Xu, Min
AU - Lu, Jieli
AU - Chen, Yuhong
AU - Lai, Shenghan
AU - Fan, Wei
AU - Bi, Yufang
AU - Wang, Weiqing
AU - Ning, Guang
N1 - Funding Information:
This work is funded by National Natural Science Foundation of China (No.81500660, 81500610, 81622011, 81621061). It is also supported by National Key R&D Program of Ministry of Science and Technology of the People's Republic of China (No.2016YFC1305600, 2017YFC1310700, 2016YFC0901200, 2016YFC1304904, 2018YFC1311800), the Program for Professor of Special Appointment (Younger Eastern Scholar) at Shanghai Institutions of Higher Learning (No.QD2016007) and Shanghai Municipal Commission of Health and Family Planning (No.20174Y0014).
Funding Information:
This work is funded by National Natural Science Foundation of China (No.81500660, 81500610, 81622011, 81621061). It is also supported by National Key R&D Program of Ministry of Science and Technology of the People's Republic of China (No.2016YFC1305600, 2017YFC1310700, 2016YFC0901200, 2016YFC1304904, 2018YFC1311800), the Program for Professor of Special Appointment (Younger Eastern Scholar) at Shanghai Institutions of Higher Learning (No.QD2016007) and Shanghai Municipal Commission of Health and Family Planning (No.20174Y0014).
Publisher Copyright:
© 2019 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd
PY - 2020/4/1
Y1 - 2020/4/1
N2 - 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.
AB - 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.
KW - diabetes mellitus
KW - dietary preferences
KW - geographical distribution
KW - large-scale internet data
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U2 - 10.1111/1753-0407.12967
DO - 10.1111/1753-0407.12967
M3 - Article
C2 - 31290214
AN - SCOPUS:85074403622
SN - 1753-0393
VL - 12
SP - 270
EP - 278
JO - Journal of diabetes
JF - Journal of diabetes
IS - 4
ER -