Dietary patterns throughout childhood and associations with nutrient intakes.

Cribb, Victoria; Emmett, Pauline; Northstone, Kate

ObjectiveTo examine how the dietary patterns of children at various time points throughout childhood relate to estimated nutrient intakes.DesignFFQ at 3, 4, 7 and 9 years of age were completed by mothers. Dietary patterns were identified cross-sectionally using principal component analysis; ‘processed’, ‘health conscious’ and ‘traditional’ patterns were consistently obtained. Correlations between pattern scores and nutrient intakes and proportions of variance in nutrients explained by the patterns were calculated.SettingAvon Longitudinal Study of Parents and Children (ALSPAC), Bristol, UK.SubjectsChildren provided data between 3 and 9 years of age (n 8010 to 10 023).ResultsDietary patterns explained substantial proportions of the variance of the absolute intake for most nutrients (>25 % at 3 years of age, >40 % other ages). After energy adjustment, protein, fibre, K, Mg, Fe, Zn, folate, thiamin and vitamin B6 continued to be well explained. Strong correlations were observed between the ‘processed’ pattern and macronutrients including energy (r = 0·481–0·619), total fat (r = 0·529–0·662) and total sugar (r = 0·475–0·693). However correlations with most micronutrients were reversed after energy adjustment, suggesting that the ‘processed’ pattern is energy-dense but nutrient-poor. The ‘health conscious’ and ‘traditional’ patterns were strongly positively correlated with protein, fibre and most micronutrients, whether energy adjusted or not. Higher scores on these patterns were associated with a better nutrient profile.ConclusionsDietary patterns explain a reasonable amount of the variation in the nutrient content of diets. Higher scores on the ‘health conscious’ and ‘traditional’ dietary patterns were related to better nutrient profiles; conversely, with higher scores on the ‘processed’ pattern the nutrient profile was poorer.

CHILD nutrition; INGESTION; FOOD habits; ESTIMATION theory; CROSS-sectional method; PRINCIPAL components analysis; STATISTICAL correlation; MICRONUTRIENTS

Public Health Nutrition, 2013, Vol 16, Issue 10, p1801

1368-9800

Academic Journal

10.1017/S1368980012004132