Sarah Shaw1,2, Sarah Crozier3, Sofia Strömmer3,4, Hazel Inskip3,4, Mary Barker3,4, Christina Vogel3,4. 1. MRC Lifecourse Epidemiology Unit, Southampton General Hospital, University of Southampton, Tremona Road, Southampton, SO16 6YD, UK. ss@mrc.soton.ac.uk. 2. NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK. ss@mrc.soton.ac.uk. 3. MRC Lifecourse Epidemiology Unit, Southampton General Hospital, University of Southampton, Tremona Road, Southampton, SO16 6YD, UK. 4. NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.
Abstract
BACKGROUND: UK adolescents consume fewer fruits and vegetables and more free sugars than any other age group. Established techniques to understand diet quality can be difficult to use with adolescents because of high participant burden. This study aimed to identify key foods that indicate variation in diet quality in UK adolescents for inclusion in a short food frequency questionnaire (FFQ) and to investigate the associations between adolescent diet quality, nutritional biomarkers and socio-demographic factors. METHODS: Dietary, demographic and biomarker data from waves 1-8 of the National Diet and Nutrition Survey rolling programme were used (n=2587; aged 11-18 years; 50% boys; n=≤997 biomarker data). Principal component analysis (PCA) was applied to 139 food groups to identify the key patterns within the data. Two diet quality scores, a 139-group and 20-group, were calculated using the PCA coefficients for each food group and multiplying by their standardised reported frequency of consumption and then summing across foods. The foods with the 10 strongest positive and 10 strongest negative coefficients from the PCA results were used for the 20-group score. Scores were standardised to have a zero mean and standard deviation of one. RESULTS: The first PCA component explained 3.0% of variance in the dietary data and described a dietary pattern broadly aligned with UK dietary recommendations. A correlation of 0.87 was observed between the 139-group and 20-group scores. Bland-Altman mean difference was 0.00 and 95% limits of agreement were - 0.98 to 0.98 SDs. Correlations, in the expected direction, were seen between each nutritional biomarker and both scores; results attenuated slightly for the 20-group score compared to the 139-group score. Better diet quality was observed among girls, non-white populations and in those from higher socio-economic backgrounds for both scores. CONCLUSIONS: The diet quality score based on 20 food groups showed reasonable agreement with the 139-group score. Both scores were correlated with nutritional biomarkers. A short 20-item FFQ can provide a meaningful and easy-to-implement tool to assess diet quality in large scale observational and intervention studies with adolescents.
BACKGROUND: UK adolescents consume fewer fruits and vegetables and more free sugars than any other age group. Established techniques to understand diet quality can be difficult to use with adolescents because of high participant burden. This study aimed to identify key foods that indicate variation in diet quality in UK adolescents for inclusion in a short food frequency questionnaire (FFQ) and to investigate the associations between adolescent diet quality, nutritional biomarkers and socio-demographic factors. METHODS: Dietary, demographic and biomarker data from waves 1-8 of the National Diet and Nutrition Survey rolling programme were used (n=2587; aged 11-18 years; 50% boys; n=≤997 biomarker data). Principal component analysis (PCA) was applied to 139 food groups to identify the key patterns within the data. Two diet quality scores, a 139-group and 20-group, were calculated using the PCA coefficients for each food group and multiplying by their standardised reported frequency of consumption and then summing across foods. The foods with the 10 strongest positive and 10 strongest negative coefficients from the PCA results were used for the 20-group score. Scores were standardised to have a zero mean and standard deviation of one. RESULTS: The first PCA component explained 3.0% of variance in the dietary data and described a dietary pattern broadly aligned with UK dietary recommendations. A correlation of 0.87 was observed between the 139-group and 20-group scores. Bland-Altman mean difference was 0.00 and 95% limits of agreement were - 0.98 to 0.98 SDs. Correlations, in the expected direction, were seen between each nutritional biomarker and both scores; results attenuated slightly for the 20-group score compared to the 139-group score. Better diet quality was observed among girls, non-white populations and in those from higher socio-economic backgrounds for both scores. CONCLUSIONS: The diet quality score based on 20 food groups showed reasonable agreement with the 139-group score. Both scores were correlated with nutritional biomarkers. A short 20-item FFQ can provide a meaningful and easy-to-implement tool to assess diet quality in large scale observational and intervention studies with adolescents.
Entities:
Keywords:
Adolescents; Diet quality; Dietary assessment; National Diet and nutrition survey; Short food frequency questionnaire
Authors: Anthea Magarey; Jane Watson; Rebecca K Golley; Tracy Burrows; Rachel Sutherland; Sarah A McNaughton; Elizabeth Denney-Wilson; Karen Campbell; Clare Collins Journal: Int J Pediatr Obes Date: 2010-09-28
Authors: George C Patton; Susan M Sawyer; John S Santelli; David A Ross; Rima Afifi; Nicholas B Allen; Monika Arora; Peter Azzopardi; Wendy Baldwin; Christopher Bonell; Ritsuko Kakuma; Elissa Kennedy; Jaqueline Mahon; Terry McGovern; Ali H Mokdad; Vikram Patel; Suzanne Petroni; Nicola Reavley; Kikelomo Taiwo; Jane Waldfogel; Dakshitha Wickremarathne; Carmen Barroso; Zulfiqar Bhutta; Adesegun O Fatusi; Amitabh Mattoo; Judith Diers; Jing Fang; Jane Ferguson; Frederick Ssewamala; Russell M Viner Journal: Lancet Date: 2016-05-09 Impact factor: 79.321