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Dietary patterns and gallstone risks in Chinese adults: a cross-sectional analysis of China Multi-Ethnic Cohort Study.

Chan NieTingting YangZiyun WangDeji SuolangSongmei WangKangzhuo BaimaLi WeiHua LingLeilei LiuQibing ZengZixiu QinHaojiang ZuoFeng Hong
Published in: Journal of epidemiology (2022)
Background Little is known about the association between a plant-based diet and the risk of Gallstone disease (GD), especially in developing counties. We tested the hypothesis that the shifting dietary pattern would be related to the risk of GD, and the Mediterranean diet (MED) adjusted for China was still beneficial for the lower risk of GD.Methods Data were extracted from the baseline survey of the China Multi-Ethnic Cohort study. An alternative Mediterranean diet (aMED) score was accessed based on the food frequency questionnaire and three posteriori dietary patterns (the modern dietary pattern, the coarse grain dietary pattern, and the rice dietary pattern) were identified by factor analysis. Multivariable logistic regression models were developed to evaluate the association between dietary patterns and GD risks.Results A total of 89 544 participants were included. The prevalence of GD was 7.5%. Comparing the highest with lowest quintiles, aMED was associated with an increased risk of GD (OR:1.13; 95% CI:1.04-1.24; p trend = 0.003), whereas the rice dietary pattern was inversely related to GD risk (OR: 0.79; 95% CI:0.71-0.87; p trend < 0.001). After the stratified analysis, the rice dietary pattern has a stronger inverse association in the subgroup of female, the older, the urban, the fat, and diabetes-factors associated with higher rates of GD in previous studies.Conclusion Higher adherence to the rice dietary pattern was associated with a lower risk of GD. For the high-risk populations, making some shift to a traditional agricultural diet might help with the primary prevention of GD.
Keyphrases
  • physical activity
  • human health
  • cross sectional
  • cardiovascular disease
  • machine learning
  • molecular dynamics
  • clinical trial
  • big data