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

  • Carolina Batis (a1), Michelle A Mendez (a1), Penny Gordon-Larsen (a1), Daniela Sotres-Alvarez (a2), Linda Adair (a1) and Barry Popkin (a1)...
Abstract
Objective

We examined the association between dietary patterns and diabetes using the strengths of two methods: principal component analysis (PCA) to identify the eating patterns of the population and reduced rank regression (RRR) to derive a pattern that explains the variation in glycated Hb (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR) and fasting glucose.

Design

We measured diet over a 3 d period with 24 h recalls and a household food inventory in 2006 and used it to derive PCA and RRR dietary patterns. The outcomes were measured in 2009.

Setting

Adults (n 4316) from the China Health and Nutrition Survey.

Results

The adjusted odds ratio for diabetes prevalence (HbA1c≥6·5 %), comparing the highest dietary pattern score quartile with the lowest, was 1·26 (95 % CI 0·76, 2·08) for a modern high-wheat pattern (PCA; wheat products, fruits, eggs, milk, instant noodles and frozen dumplings), 0·76 (95 % CI 0·49, 1·17) for a traditional southern pattern (PCA; rice, meat, poultry and fish) and 2·37 (95 % CI 1·56, 3·60) for the pattern derived with RRR. By comparing the dietary pattern structures of RRR and PCA, we found that the RRR pattern was also behaviourally meaningful. It combined the deleterious effects of the modern high-wheat pattern (high intakes of wheat buns and breads, deep-fried wheat and soya milk) with the deleterious effects of consuming the opposite of the traditional southern pattern (low intakes of rice, poultry and game, fish and seafood).

Conclusions

Our findings suggest that using both PCA and RRR provided useful insights when studying the association of dietary patterns with diabetes.

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Corresponding author
*Corresponding author: Email popkin@unc.edu
References
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Tables S1-S6 and Figure S1

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