Nutrients (May 2022)

Dietary and Food Consumption Patterns and Their Associated Factors in the Tibetan Plateau Population: Results from 73 Counties with Agriculture and Animal Husbandry in Tibet, China

  • Chang Kong,
  • Linsheng Yang,
  • Hongqiang Gong,
  • Li Wang,
  • Hairong Li,
  • Yonghua Li,
  • Binggan Wei,
  • Cangjue Nima,
  • Yangzong Deji,
  • Shengcheng Zhao,
  • Min Guo,
  • Lijuan Gu,
  • Jiangping Yu,
  • Zongji Gesang,
  • Rujun Li

DOI
https://doi.org/10.3390/nu14091955
Journal volume & issue
Vol. 14, no. 9
p. 1955

Abstract

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Dietary imbalances are an important cause of morbidity and mortality, both in China and globally. Abnormal element content in the natural environment and the unbalanced dietary structure of populations coexist in the Tibetan Plateau. This study analyzed the dietary and food consumption patterns of 617 Tibetan residents and their associated factors. Cluster analysis revealed three modes of dietary pattern; the food consumption scores (FCSs) of subjects in modes with relatively high consumption frequency of staple food and relatively singular dietary structure were the lowest. Although the FCSs of most subjects were acceptable (FCS > 35), subjects with relatively low FCSs were more dependent on locally cultivated highland barley that is probably low in selenium. Hierarchical linear models revealed both individual–family and regional factors were significantly related (p values < 0.05) with the food consumption of subjects as follows: age, travel time from township to county, and cultivation area of highland barley were negatively related; numbers of individuals aged 40–60 years and pork, beef, and mutton production were positively related. Individuals with secondary or higher education had higher FCSs. A single indicator may be incomprehensive in dietary and food consumption studies. For people with a relatively unbalanced diet, an analysis of the main foods they consume is critical. Dietary and food consumption patterns might have relatively large inter-regional and intra-regional variations; therefore, factors that influence it might be multi-level and multi-scale.

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