Proper nutrition during pregnancy is considered important both for maternal health and foetal growth and development, and hence is addressed by dietary recommendations. An appropriate diet also helps the mother to recover from delivery and supports successful breast-feeding. The traditional way to assess the diet is to examine energy and nutrient intakes or the consumption of certain food items. Dietary pattern analysis is an approach that aims to describe the whole diet in combination. The use of dietary patterns might help to capture some of the complexity of diet that is often lost in nutrient-based analysesReference Jacques and Tucker1, and provide additional information in exploring the relationship between nutrition and health. All foods contribute to nutritional status and it is not the presence or absence of a single food but the appropriate selection of foods in suitable quantities and combinations that is important to healthReference North and Emmet2.
Maternal food and nutrient intake during pregnancy has been investigated, but as far as we know only two studies have used dietary pattern analysisReference Wolff and Wolff3, Reference Cucó, Fernández-Ballart, Sala, Viladrich, Iranzo and Vila4. The first was performed among Mexican American mothers and reported seven maternal eating patternsReference Wolff and Wolff3. The second was a longitudinal study that found two stable dietary patterns from preconception to postpartum in a small cohort (n = 80) of Spanish womenReference Cucó, Fernández-Ballart, Sala, Viladrich, Iranzo and Vila4.
Dietary patterns identified by exploratory factor analysis are reported to account for relatively large proportions of the variance in energy and macronutrient intakes in middle-aged women and menReference Schulze, Hoffmann, Kroke and Boeing5, and to relate to many sociodemographic and lifestyle factors both in women and menReference Cucó, Fernández-Ballart, Sala, Viladrich, Iranzo and Vila4–Reference Robinson, Crozier, Borland, Hammond, Barker and Inskip10. There is evidence from earlier studies that older, highly educated and non-smoking pregnant women eat more healthily than othersReference Haste, Brooke, Anderson, Bland, Shaw and Griffin11–Reference Arkkola, Uusitalo, Pietikäinen, Metsälä, Kronberg-Kippilä and Erkkola15.
The aim of the present study was to identify and describe dietary patterns in a cohort of pregnant Finnish women. We also examined whether the dietary pattern scores vary by energy and nutrient intakes or sociodemographic factors.
Subjects and methods
Subject sample
This analysis represents part of the Nutrition Study within the Finnish Type 1 Diabetes Prediction and Prevention Study (DIPP), which aims to evaluate the effects of both childhood diet and maternal nutrition during pregnancy and lactation on the development of β-cell autoimmunity and type 1 diabetes mellitus in the offspring. All families with newborn infants carrying increased HLA (human leucocyte antigen)-conferred susceptibility to type 1 diabetes (genotype HLA DQB1*02/*0302 or HLA DQB1*0302/x; x ≠ *02, *0301 or *0602) in the Oulu and Tampere University Hospital regions were invited to participate. The present analysis included mothers who gave birth between October 1997 and December 2002 (n = 5362). Complete nutrition information was received from 3730 mothers (70% of those invited), who formed the final study population. Sociodemographic data were collected using a structured questionnaire. Those women who did not provide complete nutritional information were less educated and had more children than those who provided nutritional information. Age and smoking during pregnancy did not differ between these two groups. The mean age of the mothers was 30 years, varying between 16 and 47 years. Selected sociodemographic factors are presented in Table 1.
Table 1 Characteristics of 3730 pregnant Finnish women
Data collection and processing
Diet during pregnancy was assessed by a food-frequency questionnaire (FFQ) comprising a list of 181 food items that was validated by Erkkola et al.Reference Erkkola, Karppinen, Javanainen, Räsänen, Knip and Virtanen16. The FFQ assessed the use of foods or food groups and the consumption frequency (number of times per day, week or month) as common serving sizes. The questionnaire was specifically designed to reflect Finnish food consumption habits. Mothers were asked to answer questions concerning their diet during the month preceding the maternity leave in Finland, i.e. the eighth month of pregnancy. A notice concerning the period of interest was repeated on each page of the questionnaire. Mothers received the questionnaire after delivery and it was returned and checked by a study nurse at the infant’s 3-month visit to the study centre. The food consumption data were entered into a dietary database using a software program of the National Public Health Institute, Helsinki, Finland. In-house software with the Fineli national food composition database17 was used to calculate daily nutrient intakes. The selected frequency category for each food item in the FFQ was converted to a daily intake. The detailed content of the FFQ and data processing have been described elsewhereReference Erkkola, Karppinen, Javanainen, Räsänen, Knip and Virtanen16.
Statistical methods
The 181 food items were aggregated into 52 separate food groups (Table 2). The grouping scheme was based on culinary use and nutrient profiles. Principal components analysis with varimax rotation was used to identify patterns among the food groups. The factor model is driven by the idea that correlated variables belong together, and they should be recognised as distinct from groups of variables with which they are not correlated. A plot of eigenvalues (i.e. the Scree test) indicated a break between the seventh and eighth factor which could be used as a separate criterion to the solution of seven factors that were retained for further analyses. After varimax rotation of the factors, food groups with absolute factor loading ≤−0.2 or ≥0.2 were considered as significantly contributing to a pattern. Factor scores were calculated for each person in each pattern in terms of how closely they fit the pattern. Factor scores were computed by weighting each factor loading by the factor’s eigenvalue, multiplying these weights with the subject’s corresponding food group intake, and summing these products. Factor scores were used to rank individuals.
Table 2 Food groupings used in the dietary pattern analysis
Pearson’s correlation coefficients were calculated between dietary patterns and energy and nutrient intakes. Multiple linear regression analysis was used to test how age, educational level, smoking during pregnancy, living area and the number of earlier deliveries explained the variance in pattern scores. All statistical analyses were performed using SPSS for Windows v. 14.0 (SPSS Inc.).
Results
Dietary patterns
Seven factors were identified to describe the dietary patterns of the pregnant Finnish women (Table 3). Collectively these factors explained 29.5% of the variability within the sample. Food items with loadings of ≥0.2 on a factor were considered to have a strong association with that factor. Negative loading (≤−0.2) represents an inverse association between the food item and the factor. The seven factors were named according to the food item loadings as ‘Healthy’, ‘Fast foods’, ‘Traditional bread’, ‘Traditional meat’, ‘Low-fat foods’, ‘Coffee’ and ‘Alcohol and butter’ (Table 3).
Table 3 Factor loadings ≤−0.2 or ≥0.2 of different food items in the seven dietary factors identified using principal components analysis with varimax rotation
Dietary intake and sociodemographic factors
Pattern scores were differently associated with energy and nutrient intakes (Table 4). Energy intake correlated positively with ‘Healthy’, ‘Fast food’, ‘Traditional bread’ and ‘Traditional meat’ patterns and inversely with the ‘Alcohol and butter’ pattern. Intake of dietary fibre correlated positively with ‘Healthy’, ‘Traditional bread’ and ‘Low-fat foods’ patterns and inversely with the ‘Alcohol and butter’ pattern. The seven dietary patterns seemed to account for rather large proportions (over 50%) of the variance in energy and nutrient intakes except for the intake of vitamin D, vitamin C, carotenoids and calcium (Table 4).
Table 4 Pearson correlation coefficients between dietary pattern score and energy and energy-adjusted nutrient intakes, and proportion of explained variance in energy and nutrient intakes in pregnant Finnish women (n = 3730)
** P < 0.01, ***P < 0.001; the strongest associations (≥0.40 and ≤−0.40) are shown in bold.
† The sum of squared correlations between absolute nutrient intake and pattern scores.
Dietary pattern scores were differently associated with age, educational level, smoking during pregnancy, living area and the number of earlier deliveries (Table 5). Positive associations were observed for age and the ‘Healthy’ and ‘Alcohol and butter’ patterns, while the ‘Fast foods’ and ‘Traditional meat’ patterns showed inverse associations. Positive associations were seen between educational level and the ‘Healthy’, ‘Low-fat foods’ and ‘Alcohol and butter’ patterns. Smoking during pregnancy was associated with ‘Fast foods’, ‘Traditional meat’ and with the ‘Coffee’ pattern, in particular. The number of earlier deliveries was positively associated with ‘Traditional bread’, ‘Traditional meat’ and ‘Coffee’ patterns, while inverse associations were observed for ‘Fast foods’ and ‘Low-fat foods’ patterns.
Table 5 Selected sociodemographic factors explaining the variance in dietary pattern scores among pregnant women; regression parameters (95% confidence interval) of multiple linear regression analysisFootnote †
* Significant (P < 0.05).
† Pattern score as a dependent variable and the sociodemographic variables as independent variables.
Discussion
We have identified and described seven dietary patterns among pregnant Finnish women. The patterns were differently related to energy and nutrient intakes and the sociodemographic factors of the women.
The extensive DIPP birth cohort with a high participation rate provided an excellent opportunity for examining the dietary patterns of pregnant Finnish women. The possible effects of the knowledge that the child carried increased HLA-conferred susceptibility to type 1 diabetes mellitus on maternal dietary habits should be considered. We collected information retrospectively concerning maternal diet during the eighth month of pregnancy, although diet during the early stages of pregnancy is perceived to be more important for foetal growth and development. However, earlier findings regarding maternal diet during pregnancy suggest that dietary patterns do not change significantly from preconception to 6 months postpartumReference Cucó, Fernández-Ballart, Sala, Viladrich, Iranzo and Vila4. The FFQ used in this study was developed for the Nutrition Study within the DIPP. To effectively study the putative effects of maternal diet during pregnancy on the development of type 1 diabetes in the offspring, we needed a dietary instrument that could be administered after delivery when the genetic disease susceptibility of the offspring had already been determined. In the validation study by Erkkola et al.Reference Erkkola, Karppinen, Javanainen, Räsänen, Knip and Virtanen16, the correlation coefficients between the second questionnaire, completed 1 month after delivery, and the food records were similar to those obtained between the first questionnaire, completed during the period of interest (eighth month of pregnancy), and the food records.
The influence of current diet is an important possible source of bias for the assessment of remote diet, and the diet during past pregnancy is recalled with perhaps slightly lower accuracy than adult diet generallyReference Bunin, Gyllstrom, Brown, Kahn and Kushi18. However, in the study of Bunin et al.Reference Bunin, Gyllstrom, Brown, Kahn and Kushi18 the time gap between assessment and the period of interest was 3–7 years whereas it was only a few months in our study. Some recent investigations have reported reasonable validity for questionnaires concerning adolescent diet recalled by adults many years laterReference Maruti, Feskanich, Colditz, Frazier, Sampson and Michels19, Reference Maruti, Feskanich, Rockett, Colditz, Sampson and Willett20.
Dietary pattern analysis is used increasingly in nutritional research but it still has weaknesses. It is well known that factor analysis requires decisions to be made at several steps, starting with aggregation of dietary variables, the number of factors to be retained and concluding with naming the factorsReference Martinez, Marshall and Sechrest21. These decisions may affect the final results. Patterns are retained in an explorative way rather than established a priori, and are therefore unlikely to be reproducible in populations with different dietary habitsReference Schulze, Hoffmann, Kroke and Boeing5. On the other hand, Balder et al.Reference Balder, Virtanen, Brants, Krogh, Dixon and Tan22 suggest that some important eating patterns may be shared by various populations.
Dietary patterns have not been reported previously in pregnant Finnish women. In three Finnish cohort studies among women and menReference Balder, Virtanen, Brants, Krogh, Dixon and Tan22–Reference Montonen, Knekt, Härkänen, Järvinen, Heliövaara and Aromaa24 two important dietary patterns emerged: healthy and traditional. The dietary data of these studies were collected in the 1960sReference Montonen, Knekt, Härkänen, Järvinen, Heliövaara and Aromaa24 and the 1980sReference Balder, Virtanen, Brants, Krogh, Dixon and Tan22, Reference Mikkilä, Räsänen, Raitakari, Pietinen and Viikari23. Compared with our results of seven dietary patterns, it can be presumed that today there is more variation in peoples’ eating habits than before. This is partly due to the wider selection of food products available and the presence of different eating styles, even during pregnancy.
According to our results it seems that age and education are positively correlated with ‘Healthy’ and ‘Alcohol and butter’ dietary patterns, whereas ‘Fast foods’ shows an inverse association. Earlier findings also revealed that healthy eating patterns are related to older ageReference Williams, Prevost, Whichelow, Cox, Day and Wareham6, Reference Costacou, Bamia, Ferrari, Riboli, Trichopoulos and Trichopoulou8, Reference Montonen, Knekt, Härkänen, Järvinen, Heliövaara and Aromaa25, Reference Slattery, Boucher, Caan, Potter and Ma26 and higher educational levelReference Williams, Prevost, Whichelow, Cox, Day and Wareham6, Reference Slattery, Boucher, Caan, Potter and Ma26 in women and men. However, in the study by Sánchez-Villegas et al.Reference Sánchez-Villegas, Delgado-Rodríguez, Martínez-González and de Irala-Estévez9, higher educational level among women was associated with greater adherence to a ‘Western’ dietary pattern (similarities with our ‘Fast foods’ pattern). Smoking was strongly associated with the ‘Coffee’ and ‘Fast foods’ dietary patterns. The consumption of coffee was highly correlated with smoking also in pregnant Mexican American womenReference Wolff and Wolff3, although in that study coffee was omitted from the dietary pattern analysis. Furthermore, social, demographic and lifestyle factors related to the mother have been implicated to have an influence on the early eating patterns of the offspringReference North and Emmet2. It is suggested that food behaviour and concrete food choices are already established in childhood or adolescence and may track significantly into adulthoodReference Mikkilä, Räsänen, Raitakari, Pietinen and Viikari23. This emphasises the importance of identifying risk groups for targeted dietary guidance among mothers.
The seven dietary patterns identified provide a meaningful interpretation of the dietary data among pregnant Finnish women. Dietary patterns offer a framework for further research concerning diet and health outcomes among both mothers and their offspring. Next we will focus on the associations between dietary patterns and maternal weight gain during pregnancy.
Acknowledgements
Sources of funding: The DIPP Nutrition Study was supported by the Academy of Finland (grants 63672, 79685, 79686, 80846, 201988 and 210632), the Finnish Diabetes Association, the Finnish Diabetes Research Foundation, the Finnish Paediatric Research Foundation, the Juho Vainio Foundation, the Medical Research Fund of Tampere University Hospital, the Yrjo Jahnsson Foundation, and the Alma and KA Snellman Foundation. The DIPP Core Study was supported by Special Public Grants for Medical Research at the participating university hospitals, the Academy of Finland, the Juvenile Diabetes Research Foundation International (grants 197032 and 4-1998-274), the Novo Nordisk Foundation and EU Biomed 2 (BMH4-CT98-3314).
Conflict of interest declaration: None.
Authorship responsibilities: S.M.V. designed the Nutrition Study in DIPP and is responsible for the study. M.K. participated in the protocol development. S.M.V., T.A., U.U., R.V. and M.-L.O. were responsible for the present study concept and design. Statistical analysis was designed by M.V., T.A., S.M., M.G.K. and S.M.V., and performed by T.A. C.K.-K. was responsible for coordination of the field study and the data acquisition. U.U., C.K.-K., T.A. and S.M.V. were responsible for data processing and its supervision. The first draft of the manuscript was written by T.A. All authors contributed to the interpretation of the results and revising the manuscript.
Acknowledgements: We are grateful to all the mothers, study nurses and research scientists who took part in this study.
