Use of a hybrid method to derive dietary patterns in 7 years olds with explanatory ability of body mass index at age 10

Eur J Clin Nutr. 2021 Nov;75(11):1598-1606. doi: 10.1038/s41430-021-00883-9. Epub 2021 Mar 5.


Background/objectives: The usual definition of dietary patterns only accounts for the explanation of dietary choices and not a specific health outcome. This could partially explain the lack of consistent associations between diet and related diseases. This study aims to identify dietary patterns in 7 years olds explaining body mass index (BMI) at age 10 and to assess their association with early-life factors (sociodemographic, birth, and infancy characteristics).

Methods: Children from the birth cohort Generation XXI at ages 7 and 10 were included (n = 4698). Diet was assessed by a validated food-frequency questionnaire. Measured BMI z-scores (zBMI) were calculated. Principal component analysis (PCA) and partial least squares (PLS) were run to derive dietary patterns.

Results: The component scores of PCA was able to explain 13.0% of food groups and only 0.2% of zBMI, while the PLS scores explained the variance of both food groups (10.1%) and zBMI at age 10 (4.2%). By using PLS, two dietary patterns were derived, but only one, higher in processed meats and energy-dense foods and lower in vegetable soup consumption, was significantly associated with an increased zBMI in 10 years olds (adjusted β̂ 0.032; 95% CI:0.017; 0.047). It was more likely followed by children from younger and less educated mothers and who were born heavier.

Conclusions: A dietary pattern higher in processed and energy-dense foods and with lower vegetable soup intake in 7 years olds significantly explained zBMI of 10 years olds, and was predicted by early-life characteristics. The other dietary patterns were not significantly associated with zBMI at age 10.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Body Mass Index
  • Child
  • Diet*
  • Feeding Behavior
  • Female
  • Humans
  • Mothers
  • Principal Component Analysis
  • Vegetables*