In this study we interviewed a large sample of young women both before and during pregnancy. A particular strength of the SWS is that the data were collected prospectively, thus providing a valuable opportunity to assess dietary change when women become pregnant. Data are available from a large cohort of women with a good response rate: 75% of the women contacted agreed to take part in the study. The complete cohort of 12,583 non-pregnant women has been shown to be broadly representative of women of this age group in the UK in terms of smoking and educational profile, although the proportion of white women is higher than the national figure of 88% (17
). Diet was assessed using an FFQ administered by trained research nurses (18
). Although there is concern that FFQs may be subject to bias (25
), they have been shown to identify similar patterns of diet to weighed food records (5
). Since data were interviewer-collected, there were few missing food items, a particular advantage for PCA where complete dietary data is required. Characterizations of individual tracking in dietary scores have often used only correlation methods (1
); these measure linear association but not agreement. Here we have used Bland-Altman plots (21
), which are able to highlight any consistent shifts in pattern scores between time points.
We have used dietary data collected before, in early and late pregnancy to derive prudent and high-energy dietary patterns at these time points using principal component analysis. The continuous nature of PCA has been seen to be more advantageous than a two-cluster solution resulting from a cluster analysis of SWS dietary data (24
). The first component was termed the ‘prudent’ diet score, in line with published data (5
); women with high scores had diets in line with recommendations from the UK Department of Health (29
) and other agencies. The second component was termed the ‘high-energy’ diet score; similar patterns in the literature have been labeled ‘high-fat’(31
) and ‘high energy-density’(32
). In common with other studies (1
) we found that the prudent and high-energy patterns were replicated with only slight variation across the three time points.
The prudent and high-energy diet score together explain over 14% of the variation in the 48 food and food groups at each time point. Direct comparisons of the proportion of variation explained by a set of components cannot be made across the literature since it is highly dependent on the number of variables entered into a PCA and the number of components retained. However, when the SWS results were compared to dietary analyses with a similar number of variables entered and components retained, the proportion of variation explained by the SWS was highly comparable (5
We have used dietary patterns in the SWS to address the question of whether natural or applied scores are preferable to assess tracking of individual diet over time. Three problems are apparent with natural scores: firstly, since they are generated with a mean of zero, if, for example, on average diets became less prudent in early pregnancy, this effect would not be apparent. Secondly, it is common for dietary patterns to be calculated on differing numbers of subjects in longitudinal studies, for example where attrition has occurred over time. In this case any apparent change in natural dietary scores could simply be due to the characteristics of subjects with data at both time points, as demonstrated by the changes in natural scores (), and thus be an artifact of the sub-sample on which differences can be calculated, rather than illustrating true change. Thirdly, although dietary patterns tended to be replicated across time points within the SWS, there is inevitably some variation; therefore changes in natural scores reflect both changes in diet and subtle variations in the patterns, whereas by calculating applied scores we know that any changes in scores are due to changes solely in the participants’ diets themselves because the scale of measurement (the dietary pattern) is kept constant.
For these reasons, applied scores are preferred to natural scores. Another study (13
) inferred that natural scores are more appropriate, but the FFQ in this study differed somewhat at the second time point, causing difficulties with implementing applied scores, whereas in the SWS the FFQs were identical. If FFQs did change substantially over time within a study then it may not be possible to calculate applied scores, and natural scores would have to be used. A pertinent example might be when different FFQs are used through infancy and childhood because it is impossible for one tool to be appropriate at all ages.
A further advantage of natural scores cited by Northstone and Emmett (13
) is that their use enables researchers to identify new patterns at follow-up. We therefore suggest that an important step in exploratory work is to calculate natural as well as applied scores to ensure that dietary patterns used for applied scores are relevant to the follow-up time point.
There was moderate tracking in dietary scores from before pregnancy into pregnancy. Most women’s prudent diet scores in pregnancy were within −1.44 and 1.39 standard deviations of their score before pregnancy. There was very slightly lower tracking of the high-energy diet score, which in pregnancy was mainly between −1.60 and 1.69 standard deviations of their score before pregnancy. We found that women’s applied prudent diet scores did not increase in pregnancy compared to before pregnancy. In early pregnancy women’s prudent diet scores were on average 0.01 standard deviations lower than before pregnancy, and in late pregnancy they were 0.03 standard deviations lower. These changes reflect the differences seen in food consumption in pregnancy: there was decreased consumption of rice and pasta, vegetables and vegetable dishes, all of which were positively associated with the prudent diet score, alongside increases in consumption of foods that were negatively associated with the prudent diet scores including white bread, cakes and biscuits, red and processed meat, crisps, confectionery, full-fat spread and soft drinks. These influences were offset to a large extent by increases in consumption of breakfast cereals, fruit and fruit juices, dried fruit, and cooking fat and salad oils, that were positively associated with the prudent diet score, and decreases in intake of tea and coffee, which was negatively associated with the prudent diet score.
Women’s applied high-energy diet scores did not change between early and before pregnancy, but were on average 0.07 standard deviations higher in late pregnancy than before pregnancy. This change reflects increases in consumption of foods in late pregnancy that were positively associated with the high-energy diet score, such as cakes and biscuits, processed meat, crisps, fruit, sweet spreads, puddings, cream, full-fat milk, cheese, full-fat spread and soft drinks.
Of the 48 foods and food groups studied, the intake of 21 increased in pregnancy, and 10 decreased. Few studies have been able to collect dietary data prospectively before and during pregnancy. However, Brown and Kahn (33
) describe data from 550 US women whose dietary intake reported before pregnancy was compared with that at four time points during pregnancy. Whilst food consumption data was not provided, there was a noticeable increase in energy intake in pregnancy, a pattern that is consistent with the broad picture of increases in consumption in pregnancy in the SWS. Rifas-Sherman et al.
) describe changes in the diets of American women from the 1st
trimester of pregnancy. Although their time points do not match directly with those in the SWS, their observations of increases in dairy foods, and red and processed meat through pregnancy are consistent with the changes seen in the SWS.
Adequate nutrition during pregnancy is important for the health of both the mother and her child (35
). Since there were very small reductions in prudent diet score into pregnancy in the SWS, it is concerning that women were not able to improve their diet in pregnancy. This small change is likely to be an effect of pregnancy itself rather than repeating the questionnaire; we have previously reported (14
) that dietary patterns are reasonably stable in a subset of 94 SWS women who did not become pregnant but who were re-interviewed two years after their initial interview, and if anything women’s applied prudent diet scores increased (by 0.13 standard deviations).
Women are able to respond to dietary public health messages in pregnancy (23
) as demonstrated by the reductions in liver and kidney, and caffeinated drink intake in pregnancy. However, the overall quality of the diet, as measured by the prudent diet score, has not improved in the SWS. Appropriate nutrition during pregnancy is an important public health issue, and therefore interventions to improve dietary quality may need to take into account reasons for changes in diet such as nausea and changes in appetite.