OBJECTIVE: To examine how the dietary patterns of children at various time points throughout childhood relate to estimated nutrient intakes. DESIGN: FFQ 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. SETTING: Avon Longitudinal Study of Parents and Children (ALSPAC), Bristol, UK. SUBJECTS: Children provided data between 3 and 9 years of age (n 8010 to 10,023). RESULTS: Dietary 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. CONCLUSIONS: Dietary 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.
OBJECTIVE: To examine how the dietary patterns of children at various time points throughout childhood relate to estimated nutrient intakes. DESIGN: FFQ 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. SETTING: Avon Longitudinal Study of Parents and Children (ALSPAC), Bristol, UK. SUBJECTS: Children provided data between 3 and 9 years of age (n 8010 to 10,023). RESULTS: Dietary 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. CONCLUSIONS: Dietary 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.
Authors: Maria Jose Romo-Palafox; Nalini Ranjit; Sara J Sweitzer; Cindy Roberts-Gray; Courtney E Byrd-Williams; Margaret E Briley; Deanna M Hoelscher Journal: J Acad Nutr Diet Date: 2018-03-01 Impact factor: 4.910
Authors: Sarah C Bath; Emilie Combet; Patrick Scully; Michael B Zimmermann; Katharine H C Hampshire-Jones; Margaret P Rayman Journal: Eur J Nutr Date: 2015-08-15 Impact factor: 5.614