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To develop a short food frequency questionnaire (FFQ) that can be used amongst young women in Southampton to assess compliance with a prudent dietary pattern characterised by high consumption of wholemeal bread, fruit and vegetables, and low consumption of sugar, white bread, and red and processed meat.
Diet was assessed using a 100-item interviewer-administered FFQ in 6,129 non-pregnant women aged 20-34 years. 94 of these women were re-interviewed two years later using the same FFQ. Subsequently diet was assessed in 378 women attending SureStart Children’s Centres in the Nutrition and Well-being Study using a 20-item FFQ. The 20 foods included were those that characterised the prudent dietary pattern.
The 20-item prudent diet score was highly correlated with the full 100-item score (r=0.94) in the Southampton Women’s Survey. Both scores were correlated with red blood cell folate (r=0.28 for the 100-item score and r=0.25 for the 20-item score). Amongst the women re-interviewed after two years, the change in prudent diet score was correlated with change in red cell folate for both the 20-item (rS=0.31) and 100-item scores (rS=0.32). In the Nutrition and Well-being Study a strong association between the 20-item prudent diet score and educational attainment (r=0.41) was observed, similar to that seen in the Southampton Women’s Survey (r=0.47).
The prudent diet pattern describes a robust axis of variation in diet. A 20-item FFQ based on the foods that characterise the prudent diet pattern has clear advantages in terms of time and resources, and is a helpful tool to characterise the diets of young women in Southampton.
Food frequency questionnaires (FFQs) are a popular tool in nutritional epidemiology and enable estimates of habitual diet in a large population. The aim of FFQs is usually to gain information about the whole diet, so that intakes of a broad range of nutrients can be estimated. However, in some situations there is interest in only one aspect of diet, such as vitamin D and calcium (Severo et al, 2009) or fat (Rohrmann et al, 2003) intake, and thus the use of a short FFQ, which determines consumption of only those foods most relevant to the nutrients under consideration, can be as efficient (Byers et al, 1985) but require fewer resources.
Dietary patterns analysis is becoming more popular in nutritional epidemiology. Kant reviews various methods of dietary patterns analysis and describes how patterns defined using cluster analysis and principal component analysis, or the related technique of factor analysis, have been shown to be predictive of mortality, morbidity and disease-related biomarkers (Kant, 2004). A common pattern described amongst populations from several countries and with varying demographics is a prudent/healthful dietary pattern, characterised by high intakes of fruit, vegetables, whole grain, fish and poultry (Kant, 2004). This pattern has been found to be positively associated with micronutrient intake (Kant, 2004) and negatively associated with saturated fat intake (Crozier et al, 2006). Since a prudent dietary pattern has previously been observed to be the most important dietary pattern amongst young women in Southampton (Robinson et al, 2004; Crozier et al, 2006; Crozier et al, 2008), the question arises whether a short FFQ that only includes these characteristic foods could be used to ascertain this same information. If dietary pattern scores using a reduced number of items are highly correlated with those using all items, a short FFQ could be a useful tool to provide information about variation in the prudent dietary pattern.
Two previous studies have investigated the use of reduced-item scores as derived by factor analysis. Schulze et al. used the six most influential variables from a factor analysis of 49 variables, and created a ‘simplified score’ that summed the standardised frequencies of consumption, without using the coefficients from the factor analysis (Schulze et al, 2003); this simplified reduced-item score had a correlation of >0.95 with the original factor. Mishra et al. utilised the same method on binary food intake data in an analysis of changes in adult dietary patterns in a large birth cohort study in the UK (Mishra et al, 2006). From a factor analysis of 126 variables they used between 19 and 32 variables to create simplified reduced-item scores for patterns in both men and women. However, they did not report the association between the reduced-item scores and the full factor scores.
It is important to understand the strengths and limitations of reduced-item scores in capturing dietary patterns if they are to be employed to develop short FFQs. In addition, no previous work has compared a reduced-item score with biomarkers, or determined whether reduced-item scores can capture change in diet over time. We have used data from a large sample of non-pregnant women to investigate these issues; a subset of the women had repeat data two years later. We have also developed a short FFQ based on the discriminating foods to assess dietary patterns in another study of women in the same city.
The Southampton Women’s Survey (SWS) has assessed the diet, body composition, physical activity and social circumstances of a large group of non-pregnant women aged 20 to 34 years living in the city of Southampton, UK. Full details of the study have been published previously (Inskip et al, 2006). Women were recruited between April 1998 and December 2002 through general practices across the city. In total 12,583 women agreed to take part in the survey, 75% of all women contacted. Trained research nurses obtained dietary information using a 100-item validated food frequency questionnaire (FFQ). Prompt cards were used to ensure standardised responses to the FFQ (Robinson et al, 1996). Data are presented here from the first 6,129 women who were recruited from practices in the western half of the city between April 1998 and June 2000. A subgroup of 94 SWS women who had not become pregnant were followed up two years after their initial interview for a second assessment of diet, lifestyle and body composition (between November 2000 and February 2001) (Borland et al, 2008). Women were asked to give a blood sample during the second half of their menstrual cycle and again if they were part of the repeat study. Immediately after collection the samples had a full blood count performed and red cell folate was assessed by microparticle enzyme immunoassay using an Abbott IMX machine (Steijns et al, 1996). Coefficients of variation of the assays were less than 10%. Measurements are available for 3,983 (65%) of the initial sample and 51 (54%) of the repeat sample. The SWS was approved by the Southampton and South West Hampshire Local Research Ethics Committee and written consent was obtained from participants.
The Nutrition and Well-being Study (NWS) has assessed diet and a range of psychological measures amongst a group of 378 women attending SureStart Children’s Centres in 19 locations around the city of Southampton. SureStart Centres provide universal and social care services for families with children under the age of five. They offer a range of services including parenting support, employment advice, women’s groups, cooking and food preparation classes. Women in these Centres were interviewed between June 2007 and November 2007 (Barker et al, 2009). The NWS was approved by the University of Southampton School of Medicine Research Ethics Committee and written consent was obtained from participants.
Food frequency responses for the SWS initial and repeat data, and in the NWS, were standardised using the mean and standard deviation of the relevant food item in the SWS initial data. Principal components analysis (PCA) is a statistical technique that produces new variables that are uncorrelated linear combinations of the dietary variables that maximise the explained variance (Joliffe et al, 1992). PCA was performed on the reported weekly frequencies of consumption of 100 foods in the SWS. Individual dietary pattern scores were calculated by multiplying the coefficients for the 100 foods by each individual’s standardised reported frequencies of consumption, to provide a score for every participant. On the repeat data ‘applied’ scores (Northstone et al, 2008) were generated using the coefficients from the initial data; both the initial and repeat scores were standardised using the mean and standard deviation of the initial scores. The reduced-item scores were calculated by multiplying the coefficients for the reduced number of foods by each individual’s standardised reported frequencies of consumption. Both initial and repeat reduced-item scores were standardised using the mean and standard deviation of the initial reduced-item score.
Red blood cell folate measures were transformed to normality using a logarithm transformation. T-tests were used to compare two groups of normally distributed variables and Mann-Whitney rank-sum tests to compare two groups of non-normally distributed variable. Pearson’s correlation coefficients were used to measure association between two normally distributed continuous variables and Spearman’s correlation coefficient where at least one variable was not normally distributed. Normally distributed variables were age, educational attainment, prudent diet scores and log red blood cell folate levels; non-normally distributed variables were number of children in the household, change in prudent diet scores, change in red blood cell folate levels and frequency of food intakes. Agreement was assessed using Bland-Altman limits of agreement (Bland et al, 1986). Statistical analysis was performed using Stata 10.0 (StataCorp, 2007).
Of the first 6,129 women recruited to the SWS, 6,125 provided complete dietary information. These 6,125 women form the SWS analysis sample in this paper. Similarly of the 378 NWS women, 377 provided complete dietary information and form the NWS analysis sample in this paper. Study characteristics of the 6,125 SWS participants and 377 NWS participants are given in Table 1. The participants in the two studies were of a similar age with a mean around 28 years. The SWS women had somewhat higher educational attainment and tended to live with fewer children (both P<0.001). 33% of the SWS women smoked at the time of interview and they had a median BMI of 23.9; this information was not collected in the NWS.
Principal component analysis was performed in the SWS using the 100 foods in the food frequency questionnaire. This revealed a first principal component that was characterised by high intakes of vegetables (peppers, tomatoes, vegetable dishes, courgettes, green salad, onions, spinach), wholemeal bread, vegetarian food and pasta, and low intakes of full-fat milk, beef, crisps and savoury snacks, Yorkshire pudding and savoury pancakes, white bread, sugar, gravy, sausages, meat pies and roast potatoes. The coefficients of the 20 most influential foods (those with coefficients of greatest magnitude) are given in Table 2, alongside the means and standard deviations used to standardise the foods in the analysis.
The first component was termed the ‘100-item prudent diet score’, in line with published data (Slattery et al, 1998; Hu et al, 1999; Osler et al, 2001); women with high scores had diets in accordance with recommendations from the Department of Health (Department of Health, 1994; Department of Health, 1998) and other agencies. The 100-item prudent diet score had a strong and graded association with educational qualifications (r=0.47) (Figure 1a), as seen previously (Robinson et al, 2004). 56% of the women who left school with no qualifications were in the lowest quarter of the prudent diet score, compared with only three percent of women with university degrees. The correlation between the 100-item prudent diet score and women’s red blood cell folate measurements was 0.28 (P<0.0001).
For women in the SWS repeat study it was possible to calculate change in 100-item prudent diet score and change in red blood cell folate measures. Amongst these 51 women, Spearman’s correlation between change in 100-item prudent diet score and change in red blood cell folate measurements was 0.32 (P=0.02), indicating that changes in prudent diet score over time were associated with changes in measurements of this biomarker.
To investigate how many foods are optimal to include in a short FFQ, principal component scores based on various reduced numbers of foods were derived. Scores were derived for between 2 and 30 items and correlations calculated between these scores and the SWS 100-item prudent diet score. Figure 2 shows how the correlation increases as the number of items increases, as would be expected. However, the aim of a short FFQ is to have a substantially reduced number of foods. The 20 most influential foods (Table 2) have a correlation with the 100-item score of 0.94 and appear to be a pragmatic choice when comparing number of items with association (Figure 2). Since it is possible to have a high correlation without high agreement, Bland and Altman limits of agreement were calculated; there was no average difference in the scores, with 95% limits of agreement −0.7 to 0.7 SDs.
Like the 100-item prudent diet score, the 20-item prudent diet score was correlated with red blood cell folate levels at the initial time point (r=0.25, P<0.0001). Similarly, the change in 20-item prudent diet score was associated with change in red blood cell folate levels (rS=0.31, P=0.03).
The 20-item FFQ was used in the Nutrition and Well-being Study. The mean (SD) prudent diet score based on the 20 foods was 0.00 (0.99) in the SWS and −0.01 (1.14) in the NWS. Thus the variability in scores in the Nutrition and Well-being Study was as great as that within the SWS and there was no difference in mean scores between the two studies (P=0.85). The association with educational qualifications in the NWS was very similar to that in the SWS (r=0.41) (Figure 1b).
Using PCA we have found that prudent diet scores calculated using the 100-item SWS FFQ are correlated with red blood cell folate status. A 20-item prudent diet score, based on the 20 most influential foods for this pattern, was highly correlated with the full 100-item score and was similarly correlated with red blood cell folate. Furthermore, amongst a subset of women (Borland et al, 2008), the change in prudent diet score over two years was correlated with change in red cell folate for both the 20-item and 100-item scores. A 20-item FFQ can capture very similar information about the prudent diet score, but has clear advantages of being simpler to administer and requiring fewer resources.
The SWS had a good response rate (75%) and participants have been shown to be broadly representative of young women in England and Wales, although those from ethnic minorities are somewhat under-represented (Inskip et al, 2006). The FFQ was interviewer-administered meaning that there was little missing information, a particular advantage for principal component analyses where complete dietary data is required. There is concern that FFQs may be subject to bias (Byers, 2004). However, in the context of dietary patterns analysis, Hu et al. showed that an FFQ revealed similar patterns of diet as weighed diet records and that individuals’ scores on both were strongly positively correlated (Hu et al, 1999). In a Southampton cohort we have also shown that prudent diet scores calculated from FFQ and diary data are highly correlated (Crozier et al, 2008).
A reduced-item score is able to account for a large proportion of the variability in diet because the food consumption frequencies are correlated. In the SWS, for example, mushroom consumption, which is not included in the 20-item score, is correlated with pepper consumption (rS=0.31), which is included. Byers et al. suggest that between 15 and 20 items may be all that is required for assessment of a single nutrient for epidemiological purposes (Byers et al, 1985).
Principal component analysis has previously been performed on 49 foods and food groups formed by grouping the 100 foods in the SWS data (Robinson et al, 2004; Crozier et al, 2006). The 100-item and 49-item prudent diet scores are strongly correlated (r=0.97) (Crozier et al, 2006). Since we aimed to produce a simple short FFQ, we based our investigations on the 100-item prudent diet score.
We have demonstrated how using the most influential foods from a 100-item prudent diet score in the SWS we were able to generate a 20-item score with necessarily reduced information, but able to capture a large amount of the variability in the 100-item prudent diet score, with which it was highly correlated (r=0.94) and had strong agreement (Bland-Altman 95% limits of agreement = 0.0 (−0.7, 0.7) SDs). The correlation of our 20-item score with the full score compares well with that found by Schulze et al. (r>0.95) (Schulze et al, 2003), although these results are not directly comparable because different numbers of foods were chosen for the reduced-item score. In contrast to Mishra et al. we used the standardised food frequencies, rather than binary intake data (Mishra et al, 2006). When using standardised food frequencies it is important to standardise the foods at a second time point or in an alternative study using the mean and standard deviation from the initial foods (as shown in Table 2), because otherwise it is not possible to assess change or differences (if foods are standardised to an internal mean of zero there is no change or difference by definition).
The SWS 20-item prudent diet score had a correlation with red cell folate measurement very similar to that for the 100-item prudent diet score (r=0.25 compared to r=0.28), indicating that the reduced-item prudent diet score has moderate associations with a biomarker that reflects variation in intake of fruit and vegetables, wholemeal bread and breakfast cereals. Previous analyses (Borland et al, 2008) have demonstrated that the initial and repeat prudent diet scores are strongly associated (rS=0.81), and showed a slight increase in score over a two-year period (mean increase = 0.13 SDs). Change in red cell folate intake between these two time points is reflected by change in the 20-item prudent diet score (rS=0.31), a very similar association as when using the 100-item prudent diet score (rS=0.32).
A particular strength of this work has been the opportunity to create a short FFQ and to use this amongst a cohort of young women attending SureStart Children’s Centres in Southampton: the Nutrition and Well-being Study. Since limited time was available in each NWS interview we were keen to develop a short FFQ with which to assess diet. Women in the NWS had the same average age as those in the SWS (28 years), and both studies included women with a wide range of educational achievements. The scores in the NWS showed the same variation as those in the SWS with the same average score. Furthermore, the 20-item prudent diet score within the NWS had a similar correlation with educational qualifications as the 100-item prudent diet score in the SWS (r=0.41 compared to r=0.47), indicating a consistent association with an important predictor of dietary quality (Robinson et al, 2004).
The prudent diet pattern has been found to describe a robust axis of variation in a range of studies (Kant, 2004) and in a variety of analyses within the SWS (Crozier et al, 2006). Amongst young women in Southampton a 20-item prudent diet score is strongly associated with the initial 100-item prudent diet score, and with red blood cell folate measures. There is evidence from the Nutrition and Well-being Study that a 20-item FFQ based on this score might be a helpful tool for use amongst young women in Southampton where time available for ascertainment of diet is limited. However, it is unlikely that it would be appropriate for researchers in other settings to use a 20-item FFQ based on the foods in Table 2. Differences in food consumption patterns amongst men, participants of different ages and ethnicity, or those from different geographical areas mean that the 20 foods in Table 2 may not correlate as well with a full prudent diet score. In order to develop a short FFQ to assess a prudent diet score the researcher should ideally apply the techniques described in this paper to an existing dataset in order to generate a population-specific short FFQ.
The 20-item FFQ we have developed has clear advantages for use in large surveys in terms of the time and resources required for completion and analysis. Our data from young women in Southampton show that this short FFQ can be used to provide useful information about variation in their compliance with the prudent dietary pattern.
We are grateful to the women of Southampton who took part in these studies and the research nurses and other staff who collected and processed the data. The SWS was funded by the Medical Research Council, the University of Southampton and the Dunhill Medical Trust. This NWB was funded by an award from Danone Institute International.
Conflict of interest: The authors declare no conflict of interest.