Longitudinal analysis of dietary patterns in Chinese adults from 1991 to 2009

Abstract

Our aims were to identify the changes or stability in the structure of dietary patterns and the tracking, trends and factors related to the adherence of these patterns in China from 1991 to 2009. We used seven waves of the China Health and Nutrition Survey and included 9,253 adults with ≥3 waves complete. Diet was measured over a 3-day period with 24-hr recalls and a household food inventory. Using factor analysis in each wave we found that the structure of the two dietary patterns identified, remained stable over the studied period. The traditional southern pattern was characterized by high intake of rice, fresh leafy vegetables, low-fat red meat, pork, organ meats, poultry and fish/seafood and low intakes of wheat flour, corn/coarse grains; and the modern high-wheat pattern was characterized by high intake of wheat buns/breads, cakes/cookies/pastries, deep-fried wheat, nuts/seeds, starchy roots/tubers products, fruits, eggs/eggs products, soy milk, animal-based milk and instant noodles/frozen dumplings. Temporal tracking (maintenance of a relative position over time) was higher for the traditional southern, whereas adherence to the modern high-wheat had an upward trend over time. Higher income, education and urbanicity level were positively associated with both dietary patterns, but the association became smaller in the later years. These results suggest that even in the context of rapid economic changes in China; the way people chose to combine their foods remained relatively stable. However, the increasing popularity of the modern high-wheat pattern, a pattern associated with several energy-dense foods is cause of concern.

Using both principal component analysis and reduced rank regression to study dietary patterns and diabetes in Chinese adults

“…We previously found a similar dietary pattern in this population that we call “modern high-wheat.”. (21) The second factor from the PCA, which we call “traditional southern,” was positively related to intake of rice, high-fat pork, organ meats, poultry and game, and fish and seafood and inversely associated with intake of wheat flour, wheat buns and breads, and corn and coarse grains. The factor loadings from the RRR seemed to be close to the modern high-wheat dietary pattern (PCA 1) and the opposite of the traditional southern (PCA 2) at the same time.…”

“…(23, 24) To estimate the residuals, we ran several logistic regressions with each binary food group as the dependent variable and geographic region, urbanicity index, education, and income as the independent variables. Our variable selection for the estimation of residuals is based in previous work, (21) where we found that in this population these variables were the most influential on dietary patterns. We used these residuals (difference between the observed and the predicted probability) as intake variables on the RRR procedure.…”

Restaurants in the Neighborhood, Eating Away from Home and BMI in China

“…Data were recorded by trained interviewers through face-to-face interviews, and include the types, amounts, types of meals (breakfast, lunch, dinner, snack) and locations (home, school or the workplace, restaurant or food stand, relative’s or friend’s house, nursery school, festival or celebration) of consumption of all food items consumed[33]. …”

Dietary Patterns and Cognitive Decline Among Chinese Older Adults

“…16 After removing food groups with less than 5% consumers overall, to accommodate a high proportion of non-consumers for several food groups and to allow for consistent comparisons across groups, each of the 34 food and condiment groups was categorized as a binary variable (consumer/nonconsumer for food groups with less than 50% of consumers; or above/below median for those with higher intakes) and included in PCA. 26 We derived dietary patterns based on the polychoric correlation matrix and orthogonal varimax rotation in STATA. On the basis of eigenvalue>1.0, scree plot (eFigure 1) and interpretability, 2 factors were retained (see eTable 2 for food examples and factor loading), which together accounted for 23% of the variance in intakes (12% and 11% for pattern 1 and 2 respectively).…”