Major dietary patterns in pregnancy and fetal growth

Abstract

To investigate possible associations between maternal diet during pregnancy and fetal growth.
Factor analysis was used to explore dietary patterns among pregnant women. The association between maternal dietary patterns and fetal growth (in terms of small for gestational age, SGA) was investigated by logistic regression. Prospective cohort study, including information on 44 612 women in Denmark.
Two major dietary patterns were defined: the first pattern was characterized by red and processed meat, high-fat dairy, and the second pattern was characterized by intake of vegetables, fruits, poultry and fish. Women were classified into three classes according to their diet: the first class had high intake of foods of the first dietary pattern, and was classified as 'the Western diet', the second class preferred foods of the second pattern and was classified as the 'Health Conscious'; and the third one had eaten foods of both patterns, and was classified as the 'Intermediate'. The odds ratio of having a small for gestational-age infant (with a birth weight below the 2.5th percentile for gestational age and gender) was 0.74 (95% CI 0.64-0.86) for women in the Health Conscious class compared with women in the Western Diet class. The analyses were adjusted for parity, maternal smoking, age, height, pre-pregnancy weight and father's height.
Our results indicated that a diet in pregnancy, based on red and processed meat and high-fat diary, was associated with increased risk for SGA. Further studies are warranted to identify specific macro-, or micronutrients that may be underlying these associations.

Full text

ORIGINAL ARTICLE
Major dietary patterns in pregnancy and fetal
growth
VK Knudsen
1
, IM Orozova-Bekkevold
1,2
, TB Mikkelsen
1
, S Wolff
1,3
and SF Olsen
1
1
Maternal Nutrition Group, Danish Epidemiology Science Centre, Statens Serum Institut, Copenhagen, Denmark;
2
Danmarks
TransportForskning, Denmark and
3
Institute of Preventive Medicine, Copenhagen University Hospital, Copenhagen, Denmark
Objectives: To investigate possible associations between maternal diet during pregnancy and fetal growth.
Method: Factor analysis was used to explore dietary patterns among pregnant women. The association between maternal
dietary patterns and fetal growth (in terms of small for gestational age, SGA) was investigated by logistic regression. Prospective
cohort study, including information on 44 612 women in Denmark.
Results: Two major dietary patterns were defined: the first pattern was characterized by red and processed meat, high-fat dairy,
and the second pattern was characterized by intake of vegetables, fruits, poultry and fish. Women were classified into three
classes according to their diet: the first class had high intake of foods of the first dietary pattern, and was classified as ‘the
Western diet’, the second class preferred foods of the second pattern and was classified as the ‘Health Conscious’; and the third
one had eaten foods of both patterns, and was classified as the ‘Intermediate’. The odds ratio of having a small for gestational-
age infant (with a birth weight below the 2.5th percentile for gestational age and gender) was 0.74 (95% CI 0.64–0.86) for
women in the Health Conscious class compared with women in the Western Diet class. The analyses were adjusted for parity,
maternal smoking, age, height, pre-pregnancy weight and father’s height.
Conclusions: Our results indicated that a diet in pregnancy, based on red and processed meat and high-fat diary, was associated
with increased risk for SGA. Further studies are warranted to identify specific macro-, or micronutrients that may be underlying
these associations.
European Journal of Clinical Nutrition (2008) 62, 463–470; doi:10.1038/sj.ejcn.1602745; published online 28 March 2007
Keywords: maternal dietary patterns; fetal growth; factor analysis; Danish national birth cohort
Introduction
Fetal growth is an important indicator of the individual’s
survival chances and health in later life (Barker, 1994). A
poor intrauterine growth rate has been associated with
increased risk of development of cardiovascular diseases
(Leon and Ben-Shlomo, 1997) and with type II diabetes
(Shiell et al., 2000), whereas high birth weight has been
associated with increased risk of breast cancer (Signorello
and Trichopoulos, 1998; Ahlgren et al., 2003) and childhood
obesity (Schwartz, 1990; Ong et al., 2000).
In elucidating the potential effect of maternal diet on fetal
growth, both diet compositions and single foods and
nutrients have been examined with varying results. No
association between macronutrient distribution and the risk
of fetal growth retardation was detected in one observational
study (Mathews et al., 1999). A high sugar consumption in
pregnancy was associated with increased risk of having a
small for gestational age (SGA) baby (Lenders et al., 1994),
and low consumption of seafood in pregnancy has been
associated with increased risk of low birth weight (Olsen and
Secher, 2002; Rogers et al., 2004). Intake of fruits and
vegetables in pregnancy has been associated with high birth
weight (Rao et al., 2001).
An approach which is becoming more and more wide-
spread in nutritional research is the use of factor analysis,
which has previously been used to investigate the influence
of diet in adult life on risk of adverse outcomes (Gittelsohn
et al., 1998; Slattery et al., 1998; Hu et al., 1999; Terry et al.,
2001; Fung et al., 2003; Newby et al., 2004; Togo et al., 2004).
Factor analysis has been employed in analysis of diet during
pregnancy in a Spanish study (Cuco et al., 2006), where diet
at different time points in pregnancy in relation to lifestyle
factors was examined, and in a Mexican-American survey
(Wolff and Wolff, 1995), where association between dietary
Received 13 June 2006; revised 13 February 2007; accepted 19 February
2007; published online 28 March 2007
Correspondence: VK Knudsen, Danish Epidemiology Science Centre, Statens
Serum Institut, Artillerivej 5, DK-2300 Copenhagen S, Denmark.
E-mail: vik@ssi.dk
European Journal of Clinical Nutrition (2008) 62, 463–470
&
2008 Nature Publishing Group All rights reserved 0954-3007/08 $
30.00
www.nature.com/ejcn

patterns in pregnancy and birth weight was investigated. As
the human diet consists of a large variety of nutrients, and
intakes of these will often correlate, it may be appropriate to
analyse dietary patterns as an alternative to examining a
potential effect of intakes of single nutrients on health
outcome. Information on intakes of major food groups is
analysed to identify patterns in dietary intakes represented
in the population, i.e., which foods and food groups are
related with each other. When relating the dietary patterns
with certain health outcomes of interest, the results of the
analyses may give a picture of which dietary patterns may
have health benefits. This may be an advantage in the work
of implementing dietary recommendations.
The aim of our study was to investigate possible associa-
tions between maternal dietary patterns and fetal growth.
Data on maternal diet collected in the Danish National Birth
Cohort (DNBC) (Olsen et al., 2001), given its large sample
size, provided a unique setting to examine possible relations
between diet during pregnancy and intrauterine growth.
Factor analysis was employed to derive major factors (dietary
patterns) from the information on food intake in pregnancy,
recorded by a food frequency questionnaire (FFQ) in week 25
of gestation.
Subjects and methods
Study population
From 1997 to 2002, the DNBC enrolled 101 042 pregnant
Danish women for a follow-up study of them and their
infants. Information on life style, health, socio-economic
factors, etc. was collected through a self-administered
recruitment form and four telephone interviews completed
by the mother. Data on maternal diet was collected through
a self-completed FFQ submitted in week 25 of gestation,
recording food intake in previous 4 weeks. Birth outcome
measures, such as birth weight and length, head and
abdominal circumferences, gestational age and sex of the
infant were extracted from the National Patient Registry in
Denmark. At the time of data assembling for the present
study, there were 95 786 live born infants, registered in the
DNBC; 4765 (approximately 5%) had missing or erroneously
registered birth weight (e.g. values less than 300 g or above
7000 g, or a birth weight less than 1000 g for full-term live
born child); in these cases, birth weight was considered
not available. Gestational age was determined from day 1 of
the last menstrual period (LMP) and the date of birth
(adjusted for the length of the menstrual cycle); when
data on LMP were unavailable, the gestational age reported
in the National Patient Registry (usually based upon
ultrasound measures) was used. All women, participating in
the DNBC, gave written informed consent in accordance
with the Helsinki II Declaration and the study was approved
by the Danish National Committee for Biomedical Research
Ethics, Copenhagen by protocol nos. KF-01-471/94 and
KF-01-012/97.
The FFQ was sent to 94 321 participants. For the present
study, dietary data were available for 44 612 women who had
delivered a live born, full-term, singleton child.
Dietary assessment
The FFQ was distributed in week 25 of gestation, and
included questions on intake of 360 food and beverage
items in the previous 4 weeks, and was validated with a 7-day
weighted food record, which showed statistically significant
correlations between intake of fruits and vegetables
(r ¼ 0.55), protein (r ¼ 0.39), folate (r ¼ 0.53) and retinol
(r ¼ 0.37) (Mikkelsen et al., 2006). Furthermore, energy
intakes estimated in the two methods were highly correlated
(P ¼ 0.001). The 360 food items recorded in the FFQ were
aggregated into 36 food groups. A detailed description of
each food group is reported in Appendix 1. The daily
intake (in grams) for each food group, eaten by a given
individual, was used in the factor analysis to identify dietary
patterns.
Extraction of dietary patterns
Factor analysis has been employed to detect underlying
common patterns among highly correlated variables (Hu
et al., 1999; Osler et al., 2002; Fung et al., 2003, 2004; Schulze
et al., 2003; Dixon et al., 2004; Newby et al., 2004; Sieri et al.,
2004). We applied factor analysis (PROC FACTOR in SAS,
principal components method), using the women’s daily
intake (in grams) of each of the 36 food groups as input.
We used the Kaiser’s measure of sampling (option MSA in
PROC FACTOR) to evaluate if a common factor model was
appropriate for our data. To decide the number of factors to
retain, we used the Scree plots and the eigenvalues of the
principal components (Mardia et al., 1980), and subjective
criteria (considerations on simplicity and interpretability).
We tested solutions with number of factors ranging from
two to 12 and looked at the eigenvalues of the principal
components, the Scree plots and the factor loadings in order
to evaluate the interpretability of the factors. Both the
examination of the principal components and the Scree
plots showed a flattering at the third eigenvalue, which
indicated that two factors were clearly distinguished from
the rest and explained the largest part of the variation.
Studies on dietary-pattern analysis have used between two
(Hu et al., 1999; Osler et al., 2001; Fung et al., 2003; Schulze
et al., 2003; Dixon et al., 2004) and 10 (Newby et al., 2004)
dietary patterns. A model two with factors was chosen for
further analyses, as it was considered to represent the data in
the best way. The factors were then rotated along the
direction of maximum variation by an orthogonal transfor-
mation (option VARIMAX in PROC FACTOR) in order to
obtain independent (i.e. non-correlated) factors. Factor
loadings were estimated for each of the 36 food groups
across the two factors. On the basis of the values of the
factor loadings, distinct dietary patterns were defined,
Maternal diet and fetal growth
VK Knudsen et al
464
European Journal of Clinical Nutrition

characterized by high loadings of specific food groups. For
each woman, a factor score in the respective factor (dietary
pattern) was estimated as a sum of the standardized daily
intake of each food group multiplied by the loading for the
food group; the final sums were standardized as well (PROC
FACTOR). Thus, food groups with high daily intake and high
factor loading contributed most to the individual’s score in
the respective dietary pattern.
Fetal growth measurements and confounders
The birth weight for each infant was transformed into a
z-score using the expected mean birth weight for the
respective sex and gestational age, estimated from the
intrauterine growth curve as reported in (Marsal et al.,
1996). An SGA infant was defined as a child whose z-score
was below the 2.5th percentile for the respective sex and
gestational age.
Data on pre-pregnancy weight, height of the mother and
the father, maternal smoking, parity, maternal age at
conception, etc. were reported in the recruitment form and
in the first telephone interview. The variables were all
considered as potential confounders, and were classified as
follows: maternal age at conception (o20, 20–29, 30–39,
40 þ years); smoking in pregnancy (never vs ever); parity
(primiparous vs multiparous); mother’s height (o160, 160–
169, 170–179, 180 þ cm); pre-pregnancy weight (o50,
50–59, 60–69, 70–79, 80 þ kg); and father’s height (o160,
160–169, 170–179, 180–189, 190 þ cm). The total energy
intake was not included in the analyses, as it was derived
from the daily intake of food groups upon which our dietary
exposure variables were created. Thus these quantities were
not independent variables from the food groups, and were
therefore not included as confounders.
Statistical analysis
The dietary exposure variables included in the analyses
derived from the factor scores, referred to as factor 1 and
factor 2. The women were divided into quintiles of the two
factors. The quintile ranking in one factor does not
necessarily mirror the distribution in the other factor (e.g.
a person can belong to the highest quintile group both in
factor 1 and in factor 2, although the expectation would be
that a person with high intake in factor 1 would display low
intake in factor 2). To clarify further the possible differences
between the two dietary patterns, the following classification
was implemented to achieve a better pronounced exposure
contrast between the two factors: if the woman was in the
upper two (i.e. 4th or 5th) quintiles for factor 1, whereas she
was in the lower two (i.e. 1st or 2nd) quintiles for factor 2,
she was assigned to Class 1. If the woman was in upper end
(i.e. 4th or 5th quintile) of factor score 2, whereas she was in
the lower end (i.e. 1st or 2nd quintile) in factor score 1, she
was assigned to Class 2. If neither of these conditions was
fulfilled, the woman was assigned to Class 3. As a result, the
women were grouped into three mutually exclusive classes,
reflecting their food preferences.
We used analysis of variance (ANOVA) and the Cochran–
Armitage and Tukey–Kramer tests to analyse the differences
between the various groups of exposure (classes) with respect
to birth weight and SGA, adjusting for the potential
confounders. The odds for having an SGA baby were
calculated by logistic regression and were adjusted for
potential confounders (parity, maternal age and smoking
habits, mother’s height and weight and father’s height). The
statistical analyses were conducted with the SAS software
(SAS version 8.2, SAS Inst. Inc., Cary, NC, USA).
Results
The average age of the mother was 29 years; the mean pre-
pregnancy weight was 67 kg, and the mean height 169 cm.
About 24% of the women smoked during pregnancy and
47% of them were primiparous. The average birth weight was
3680 g for boys (51.3% of the births) and 3547 g for girls.
Incidence of SGA with respect to the confounder variables is
shown in Table 1. The statistical analyses (data not shown)
showed that smoking in pregnancy, pre-pregnancy weight
below 60 kg, low stature of the mother (o160 cm), young age
of the mother (o20 years) and parity ¼ 0 (i.e. primiparous)
were significantly associated with low birth weight of the
child.
The factor loadings for each food group in the
respective factor are shown in Table 2. Factor loadings were
interpreted similarly to correlation coefficients: high values
indicate high correlation between the given food group
and the respective factor. Animal fat (i.e. butter and
lard), processed and red meat, margarine, refined grains
and potatoes showed high loadings (above 0.3 indicated
by bold) in factor 1 and low loadings in factor 2.
Vegetables, tomatoes, green leafy vegetables, fruits, fish and
poultry were heavily loaded in factor 2 and weakly loaded in
factor 1.
To separate the exposure groups more clearly, the mutually
exclusive classes, described in the Subjects and methods were
included in the analyses. When using the classification based
on the top two/lower two quintiles for the factor scores,
there were 7619 (17.1%) women in Class 1, 7479 (16.8%) in
Class 2 and 29 514 (66.2%) in Class 3.
The average daily consumption (in grams) for each food
group and for total energy, fat, protein and carbohydrates
in the three classes is reported in Table 3. Women in class 1
were characterized by the highest intake of high-fat dairy,
refined grains, processed and red meat, animal fat (butter
and lard), potatoes, sweets, beer, coffee and high-energy
drinks. This group had also the highest energy intake; 35% of
the energy was coming from fat. Class 1 will hereafter be
referred to as the Western Diet class. The mothers in Class 2
had a high intake of fruits, vegetables, fish, poultry, breakfast
cereals, vegetable juice and water. These women avoided
Maternal diet and fetal growth
VK Knudsen et al
465
European Journal of Clinical Nutrition

foods with high fat content. This group had the lowest
energy intake, and 25% from this energy was coming from
fat. Class 2 is referred to as the Health Conscious class.
Women in Class 3 had high intake of low-fat dairy and fruit
juice. Their consumption of the remaining food groups was
in between Class 1 and Class 2, and is referred to as the
Intermediate class, thus these women had eaten fruits and
vegetables, as well as red meat and dairy products, and 30%
of the energy intake came from fat. There was no significant
difference in wine drinking among the three classes and, in
Table 1 Confounders and incidence of SGA in the study population
Variable (level) n SGA, n (%)
Parity
Primipara (0) 19 978 704 (3.4)
Multipara (1) 23 122 408 (1.7)
Smoking in pregnancy
Ever (1) 10 072 477 (4.5)
Never (0) 33 028 634 (1.9)
Mother’s age (years)
o 20 (1) 334 21 (5.8)
20–29 (2) 23 385 625 (2.6)
30–39 (3) 18 993 444 (2.3)
440 (4) 388 22 (5.3)
Mother’s pre-pregnant weight (kg)
o 50 (1) 1034 86 (8.0)
50–59 (2) 11 483 424 (3.6)
60–69 (3) 16 196 344 (2.1)
70–79 (4) 8426 134 (1.6)
4 80 (5) 5961 116 (1.9)
Mother’s height (cm)
o160 (1) 2171 125 (5.6)
160–169 (2) 20 995 642 (3.0)
170–179 (3) 17 938 328 (1.8)
4180 (4) 1996 15 (0.7)
Father’s height (cm)
o160 (1) 23 1 (4.4)
160–169 (2) 968 54 (5.5)
170–179 (3) 13 212 408 (3.0)
180–189 (4) 22 402 508 (2.2)
4190 (5) 6495 100 (1.5)
Abbreviation: SGA, small for gestational age.
Table 2 Factor loadings of the food groups across the two factors
(dietary patterns)
Food group Factor 1 Factor 2
Animal fat 0.70 0.11
Margarine 0.61 0.01
Processed meat 0.54 0.12
Red meat 0.52 0.03
Refined grains 0.49 0.06
Eggs 0.47 0.28
Potatoes 0.45 0.12
Snacks 0.42 0.17
Sweets 0.34 0.05
High-fat dairy 0.34 0.03
Vegetables 0.05 0.76
Tomatoes 0.03 0.71
Green leafy vegetables 0.07 0.66
Fruit 0.10 0.50
Fish 0.25 0.45
Water 0.02 0.40
Vegetable fats 0.07 0.36
Poultry 0.02 0.33
Values above 0.3 is indicated by bold.
Table 3 Mean daily intake of total energy, macronutrients and food
groups among the women in the three classes (Western Diet,
Intermediate and Health Conscious)
Western diet
(n ¼ 7 619)
Intermediate
(n ¼ 29 514)
Health conscious
(n ¼ 7479)
Energy and macro
nutrients
Mean (s.d.) Mean (s.d.) Mean (s.d.)
Energy (KJ/day) 11 498 (1965)
1
10 161 (2570)
3
9522 (1745)
2
Fat (% of energy) 34.8 (5.6)
1
29.6 (5.4)
3
25.2 (4.5)
2
Protein (% of
energy)
13.8 (2.2)
1
15.3 (2.3)
3
16.0 (2.2)
2
Carbohydrate (%
of energy)
50.9 (5.7)
1
54.6 (5.6)
3
58.3 (5.1)
2
Food group (g)
Low-fat dairy 526 (413) 551 (399)
1
536 (369)
High-fat dairy 134 (203)
1
97 (38)
3
70 (63)
2
Ice-cream 5 (7)
1
5(7)
3
4(4)
2
Breakfast cereals 18 (22)
1
29 (27)
3
41 (31)
2
Whole grains 173 (97) 176 (100)
1
176 (91)
Refined grains 114 (62)
1
78 (48)
3
59 (31)
2
Fruit 95 (77)
1
153 (104)
3
228 (109)
2
Organ meat 0.3 (1.6) 0.3 (1.8) 0.2 (0.9)
1
Processed meat 48 (23)
1
41 (26)
3
29 (17)
2
Red meat 62 (27)
1
48 (25)
3
34 (18)
2
Fish 19 (14)
1
26 (23)
3
29 (18)
2
Shellfish 1.1 (1.5) 1.3 (2.2)
1
1.1 (1.5)
Poultry 18 (13)
1
24 (18)
3
30 (22)
2
Eggs 19 (10)
1
18 (12)
3
15 (8)
2
Animal fat 35 (22)
1
19 (16)
3
10 (7)
2
Vegetable fat 1.4 (1.3)
1
2 (29)
3
2.6 (2.8)
2
Margarine 19 (13)
1
13 (10)
3
8(5)
2
Sweets 41 (29)
1
34 (24)
3
28 (18)
2
Wine 10 (15)
1
12 (189) 12 (16)
Beer 15 (44)
1
12 (38)
3
8(19)
2
Tea 118 (191)
1
157 (211)
3
187 (225)
2
Coffee 213 (295)
1
140 (214)
3
98 (157)
2
High-energy drink 202 (316)
1
86 (1419
3
42 (64)
2
Low-energy drink 44 (166)
1
37 (125)
3
28 (88)
2
Water 880 (500)
1
1108 (550)
3
1356 (505)
2
Alcohol 0.4 (1.6) 0.4 (4.3) 0.2 (0.9)
1
Snacks 18 (14)
1
12 (10)
3
7(6)
2
Vegetable juice 0.6 (6.7)
1
2 (22)
3
5(37)
2
Fruit juice 149 (221)
1
175 (241) 176 (215)
Marmalade 59 (69)
1
49 (61)
3
36 (48)
2
Nuts 1.1 (1.8)
1
1.5 (3.2)
3
1.8 (3.8)
2
Other vegetables 57 (31)
1
92 (68)
3
138 (82)
2
Potatoes 143 (89)
1
122 (85)
3
95 (56)
2
Green leafy
vegetables
2.8 (3.2)
1
6(8)
3
13 (14)
2
Tomatoes 12 (8)
1
21 (820)
3
35 (24)
2
Soy 0.1 (4.0)
1
0.8 (15.8)
3
3.2 (34)
2
Abbreviation: SGA, small for gestational age.
1,2,3
Means not sharing a common superscript letter are significantly different
at Po0.05 level (Tukeys test).
Maternal diet and fetal growth
VK Knudsen et al
466
European Journal of Clinical Nutrition

general, the intake of alcohol in the study population is low,
which was expected for pregnant women.
Some general characteristics for the women and birth
outcomes in the three classes described above are given in
Table 4. The lowest average birth weight and the highest
incidence of SGA babies were observed among women in the
Western Diet class; the highest proportion of smokers was
found in this group. Women in the Intermediate and Health
Conscious classes had significantly lower percentages of SGA
babies compared with the mothers in Western Diet class.
Women in Health Conscious class had the lowest pre-
pregnancy weight of the mother, and the highest occurrence
of non-smokers. Mothers in the Intermediate class had
infants with the highest average birth weight; the occurrence
of SGA babies was slightly higher compared with the women
of the Health Conscious class, although not statistically
significant.
The odds for giving birth to an SGA infant across the three
groups are shown in Table 5 using the women in the Western
Diet class as reference group. The analyses were adjusted for
potential confounders. Compared with the reference group
odds ratio (OR) for SGA among infants born in the
Intermediate class was 0.68 (95% CI 0.55–0.84) and in the
Health Conscious class was 0.74 (95% CI 0.64–0.86). No
significant difference in the risk for SGA was detected
between the offsprings of women in the Intermediate and
the Health Conscious classes.
Discussion
We identified two major dietary patterns among pregnant
Danish women using factor analysis. The study population
was further classified into three groups: a Western Diet class,
who had the highest intake of red and processed meat,
potatoes and high-fat dairy, and the lowest intake of fruits
and vegetables; a Health Conscious class who had the
highest intake of fruits, vegetables, fish and poultry, and
the lowest intake of meat and fat of animal origin; and an
Intermediate class, whose intakes of all food groups were in
between of the former two classes. The women in Western
Diet class had significantly higher OR of having SGA infants
compared with the women in the Intermediate and the
Health Conscious classes, whereas the risk of having an SGA
infant among mothers in the Intermediate class was not
significantly different from the risk among those following
the Health Conscious diet.
The mothers of the first group had the highest energy
intake, and this group had also the highest number of
smokers, which might indicate that those mothers were
prone to unhealthier life style as compared to the other two
groups. The women in the Health Conscious class had the
lowest number of smokers, and the lowest energy intake,
both pointing towards the fact that these women in general
had a healthier life style compared with those in the Western
Diet class. The women in the Intermediate class had intakes
of foods from both dietary patterns in a balanced way, and
may as such have had the most varied diet. The number of
smokers in this group was between the other two as well,
which indicated that the women are in between the two
others regarding attitudes towards their own health. How-
ever, information on physical activity was not included in
the analyses, as well as the data on maternal weight gain
during pregnancy, both parameters are likely to be associated
with energy intake. These factors could explain some of the
difference in the total energy intake between the three
classes.
A dietary survey using factor analysis among 549 pregnant
women showed that a dietary pattern characterised by high
Table 4 Population characteristics and fetal growth variables among women in the three classes
Western diet (n ¼ 7619) Intermediate (n ¼ 29 514) Health conscious (n ¼ 7479)
Covariates
Mother’s age (years), mean (s.d.) 28.5 (4.3)
1
29.2 (4.2)
2
29.8 (3.9)
3
Pre-pregnancy weight (kg), mean (s.d.) 67.3 (13.7)
1
67.1 (12.5)
2
65.2 (10.8)
3
Mother’s height (cm), mean (s.d.) 168.3 (6.2)
1
168.8 (6.0)
2
169.3 (5.9)
3
Father’s height (cm), mean (s.d.) 181.2 (7.2)
1
182.0 (7.0)
2
182.7 (6.9)
3
Smoker (%) 38.1%
1
23.0%
2
13.9%
3
Primiparous (%) 37.5%
1
46.0%
2
58.5%
3
Fetal growth variables
Birth weight (g), mean (s.d.) 3583 (515)
1
3623 (490)
2
3616 (486)
2
Small for gestational age (SGA), % 3.53%
1
2.31%
2
2.15%
2
1,2,3
Means across the groups, not sharing a common superscript letter, are significantly different at Po0.05 level (Tukey–Kramer test).
Table 5 Odds ratio (OR) and 95% confidence intervals (95% CI) for
SGA among mothers across the three classes, Western Diet, Intermediate
and Health Conscious
Class OR (95 % CI) P-value
Western diet 1.00 (reference)
Intermediate 0.68 (0.55–0.84), P ¼ 0.0004
Health conscious 0.74 (0.64–0.86), P ¼ 0.0001
Abbreviations: OR, odds ratio; CI, confidence interval.
The analyses were adjusted for confounders.
Maternal age at conception, smoking in pregnancy, parity, mother’s height,
pre-pregnant weight, and father’s height.
Maternal diet and fetal growth
VK Knudsen et al
467
European Journal of Clinical Nutrition

intake of fruits and vegetables, or by protein-rich foods as
fish and poultry were associated positively with birth weight,
and that a dietary pattern characterised by a high intake of
fat, bread, cereals and sugar was associated with decreased
birth weight (Wolff and Wolff, 1995). An Indian study
showed that high intake of green leafy vegetables in
pregnancy increased the birth weight (Rao et al., 2001).
Our findings that maternal diet, rich in vegetables, fruits,
fish and poultry was associated with higher birth weight
compared with a diet high in animal fat and processed meat,
agree with these findings.
With the dietary characteristics of the three classes defined
in this study, it is obvious to conclude, that the women in
the Health Conscious class may have had a higher intake of
vitamins and minerals playing an essential role in fetal
growth. Women in the Western Type class had the highest
intakes of red and processed meat, butter and lard and high-
fat dairy products, and might thus have been exposed to a
higher intake of saturated fats and trans fatty acids. Zhang
et al. (2006) found an increased risk of gestational diabetes
in women following a Western diet in contrast to women
following a Prudent diet, and the difference was suggested to
be because of the higher intake of red and processed meat,
leading to a higher intake of saturated fatty acid and
cholesterol. Trans-fatty acids were found to be negatively
correlated with birth weight (Koletzko, 1992; Elias and Innis
2001; Larque et al., 2001).
Earlier studies found no associations between maternal
intake of macronutrients and infant birth size among well
nourished women (Mathews et al., 1999; Lagiou et al., 2004),
whereas positive association between energy from protein
and fetal growth has been observed in one study (Moore
et al., 2004), and a high intake of carbohydrate and low
protein intake was associated with increased risk of low birth
weight (Godfrey et al., 1996). Our study population was not
undernourished, and all three classes had protein energy %
within the range of what is recommended by the Nordic
Nutrition Recommendations (Nordiska Ministerra
˚
det, 1996),
but we found a higher risk of SGA infants in the Western Diet
class, who had the lowest protein energy %.
The strength of our study is the large sample size, as we
included 44 612 mother–child pairs in the analyses. The
dietary intake was assessed in week 25 of gestation, and
intake in the previous 4 weeks was recorded. Cuco et al.
(2006) found that food intake at one point in pregnancy
reflects well the dietary patterns throughout pregnancy, and
thus we find it reasonable to assume that the dietary intake
does not change substantially. Furthermore, the FFQ has
been validated with respect to intake of fruits and vegetables
showing that the data recorded in the FFQ are applicable to
analyses on food group level (Mikkelsen et al., 2006), which
provide the basis for the construction of the dietary patterns
defined in this study.
The dietary information was collected through food
frequency questionnaire and all subsequent calculations
were based on assumptions of average portions, sizes and
standard recipes for complex dishes (e.g. casseroles and
lasagne), which may have introduced bias in the estimations.
However, the FFQ was validated against a 7-day weighed
food record, which showed that the FFQ was useful in
separating the women into quintiles of dietary intakes
(Mikkelsen et al., 2006). Another potential limitation was
that measurements of confounders (such as exposure to
smoking and drinking) had some inherent imprecision, and
residual confounding cannot be excluded. Lastly, our data
might not represent all Danish pregnant women in their
behaviour, as participants in surveys like this are more likely
to have a healthier lifestyle than non-participants
(Hennekens and Buring, 1987). This would likely reduce
the variation in nutrient intake, and thereby the variation in
exposure, but it is assumed not to affect the direction of the
findings in the study, that is, the association between dietary
habits and fetal growth.
In conclusion, we found a significant association between
diet in pregnancy and fetal growth among pregnant Danish
women, indicating that intrauterine growth conditions
might be improved through adopting certain dietary
patterns during pregnancy. From the data from the present
study, it is not possible to say which nutrients, or combina-
tion of nutrients, could influence fetal growth. Further
studies are warranted to identify specific macro-, or micro-
nutrients that may be underlying the associations, found in
the present study.
Acknowledgements
We thank Mr Kenn S Nielsen for the technical and data
management support and the managerial team of the Danish
National Birth Cohort, which consisted of: Jørn Olsen
(Chair), Mads Melbye, Anne Marie Nybo Andersen, Sjurdur
F Olsen, Thorkild IA Sørensen and Peter Aabye. Financial
support for the Danish National Birth Cohort was obtained
from the March of Dimes Birth Defects Foundation, the
Danish National Research Foundation, the European Union
(QLK1-2000-00083), the Pharmacy Foundation, the Egmont
Foundation, the Augustinus Foundation and the Health
Foundation.
Sponsorship: The March of Dimes Birth Defects Foundation,
the Danish National Research Foundation, the European
Union (QLK1-2000-00083), the Pharmacy Foundation, the
Egmont Foundation, the Augustinus Foundation and the
Health Foundation.
References
Ahlgren M, Sorensen T, Wohlfahrt J, Haflidadottir A, Holst C, Melbye
M (2003). Birth weight and risk of breast cancer in a cohort of
106,504 women. Int J Cancer 107, 997–1000.
Barker DJ (1994). Mothers, babies, and disease in later life. BMJ
Publishing Group: London.
Maternal diet and fetal growth
VK Knudsen et al
468
European Journal of Clinical Nutrition

Cuco G, Fernandez-Ballart J, Sala J, Viladrich C, Iranzo R, Vila J et al.
(2006). Dietary patterns and associated lifestyles in preconception,
pregnancy and postpartum. Eur J Clin Nutr 60, 364–371.
Dixon LB, Balder HF, Virtanen MJ, Rashidkhani B, Mannisto S, Krogh
V et al. (2004). Dietary patterns associated with colon and rectal
cancer: results from the Dietary Patterns and Cancer (DIETSCAN)
Project. Am J Clin Nutr 80, 1003–1011.
Elias SL, Innis SM (2001). Infant plasma trans, n-6 and n-3 fatty acids
and conjugated linoleic acids are related to maternal plasma fatty
acids, length of gestation, and birth weight and length. Am J Clin
Nutr 73, 807–814.
Fung T, Hu FB, Fuchs C, Giovannucci E, Hunter DJ, Stampfer MJ et al.
(2003). Major dietary patterns and the risk of colorectal cancer in
women. Arch Intern Med 163, 309–314.
Fung TT, Stampfer MJ, Manson JE, Rexrode KM, Willett WC, Hu FB
(2004). Prospective study of major dietary patterns and stroke risk
in women. Stroke 35, 2014–2019.
Gittelsohn J, Wolever TM, Harris SB, Harris-Giraldo R, Hanley AJ,
Zinman B (1998). Specific patterns of food consumption and
preparation are associated with diabetes and obesity in a native
Canadian community. J Nutr 128, 541–547.
Godfrey K, Robinson S, Barker DJ, Osmond C, Cox V (1996).
Maternal nutrition in early and late pregnancy in relation to
placental and fetal growth. BMJ 312, 410–414.
Hennekens CH, Buring JE (1987). Epidemiology in Medicine. Little,
Brown and Company: Boston.
Hu FB, Rimm E, Smith-Warner SA, Feskanich D, Stampfer MJ,
Ascherio A et al. (1999). Reproducibility and validity of dietary
patterns assessed with a food-frequency questionnaire [see com-
ments]. Am J Clin Nutr 69, 243–249.
Koletzko B (1992). Trans fatty acids may impair biosynthesis of
long-chain polyunsaturates and growth in man. Acta Paediatr 81,
302–306.
Lagiou P, Tamimi RM, Mucci LA, Adami HO, Hsieh CC, Trichopoulos
D (2004). Diet during pregnancy in relation to maternal weight
gain and birth size. Eur J Clin Nutr 58, 231–237.
Larque E, Zamora S, Gil A (2001). Dietary trans fatty acids in early
life: a review. Early Hum Dev 65 (Suppl), S31–S41.
Lenders CM, Hediger ML, Scholl TO, Khoo CS, Slap GB, Stallings VA
(1994). Effect of high-sugar intake by low-income pregnant
adolescents on infant birth weight. J Adolesc Health 15,
596–602.
Leon D, Ben-Shlomo Y (1997). Pre-adult influences on cardiovascular
disease and cancer. In: ‘A Life-course Approach to Chronic Disease and
Cancer’ (D. Kuh and Y. Ben-Shlomo Eds.). Oxford University Press:
Oxford, UK. pp. 45–77.
Mardia KV, Kent JM, Bibby JM (1980). Multivariate analysis. London:
Academic Press.
Marsal K, Persson PH, Larsen T, Lilja H, Selbing A, Sultan B (1996).
Intrauterine growth curves based on ultrasonically estimated
foetal weights. Acta Paediatr 85, 843–848.
Mathews F, Yudkin P, Neil A (1999). Influence of maternal nutrition
on outcome of pregnancy: prospective cohort study. BMJ 319,
339–343.
Mikkelsen TB, Osler M, Olsen SF (2006). Validity of protein, retinol,
folic acid and n-3 fatty acid intakes estimated from the food-
frequency questionnaire used in the Danish National Birth
Cohort. Public Health Nutr 9, 771–778.
Mikkelsen TB, Osler M, Orozova-Bekkevold I, Knudsen VK, Olsen SF
(2006). Association between fruit and vegetable consumption and
birth weight: a prospective study among 43,585 Danish women.
Scand J Publ Health 21, 76–86.
Moore VM, Davies MJ, Willson KJ, Worsley A, Robinson JS (2004).
Dietary composition of pregnant women is related to size of the
baby at birth. J Nutr 134, 1820–1826.
Newby PK, Muller D, Hallfrisch J, Andres R, Tucker KL (2004). Food
patterns measured by factor analysis and anthropometric changes
in adults. Am J Clin Nutr 80, 504–513.
Nordiska Ministerra
˚
det 1996. Nordiska Na
¨
ringsrekommendationer
1996. Nordiska Ministerra
˚
det: Ko
¨
benhamn.
Olsen J, Melbye M, Olsen SF, Sorensen TI, Aaby P, Andersen AM et al.
(2001). The Danish National Birth Cohort – its background,
structure and aim. Scand J Public Health 29, 300–307.
Olsen SF, Secher NJ (2002). Low consumption of seafood in early
pregnancy as a risk factor for preterm delivery: prospective cohort
study. BMJ 324, 447.
Ong KK, Ahmed ML, Emmett PM, Preece MA, Dunger DB (2000).
Association between postnatal catch-up growth and obesity in
childhood: prospective cohort study. BMJ 320, 967–971.
Osler M, Heitmann BL, Gerdes LU, Jorgensen LM, Schroll M (2001).
Dietary patterns and mortality in Danish men and women: a
prospective observational study. Br J Nutr 85, 219–225.
Osler M, Helms Andreasen A, Heitmann B, Hoidrup S, Gerdes U,
Morch Jorgensen L et al. (2002). Food intake patterns and
risk of coronary heart disease: a prospective cohort study
examining the use of traditional scoring techniques. Eur J Clin
Nutr 56, 568–574.
Rao S, Yajnik CS, Kanade A, Fall CH, Margetts BM, Jackson AA et al.
(2001). Intake of micronutrient-rich foods in rural Indian mothers
is associated with the size of their babies at birth: Pune Maternal
Nutrition Study. J Nutr 131, 1217–1224.
Rogers I, Emmett P, Ness A, Golding J (2004). Maternal fish intake
in late pregnancy and the frequency of low birth weight
and intrauterine growth retardation in a cohort of British infants.
J Epidemiol Community Health 58, 486–492.
Schulze MB, Hoffmann K, Kroke A, Boeing H (2003). Risk of
hypertension among women in the EPIC-Potsdam Study: compar-
ison of relative risk estimates for exploratory and hypothesis-
oriented dietary patterns. Am J Epidemiol 158, 365–373.
Schwartz R (1990). Hyperinsulinemia and macrosomia. N Engl J Med
323, 340–342.
Shiell AW, Campbell DM, Hall MH, Barker DJ (2000). Diet in late
pregnancy and glucose-insulin metabolism of the offspring 40
years later. BJOG 107, 890–895.
Sieri S, Krogh V, Pala V, Muti P, Micheli A, Evangelista A et al. (2004).
Dietary patterns and risk of breast cancer in the ORDET cohort.
Cancer Epidemiol Biomarkers Prev 13, 567–572.
Signorello LB, Trichopoulos D (1998). Perinatal determinants
of adult cardiovascular disease and cancer. Scand J Soc Med 26,
161–165.
Slattery ML, Boucher KM, Caan BJ, Potter JD, Ma KN (1998). Eating
patterns and risk of colon cancer. Am J Epidemiol 148, 4–16.
Terry P, Suzuki R, Hu FB, Wolk A (2001). A prospective study of major
dietary patterns and the risk of breast cancer. Cancer Epidemiol
Biomarkers Prev 10, 1281–1285.
Togo P, Osler M, Sorensen TI, Heitmann BL (2004). A longitudinal
study of food intake patterns and obesity in adult Danish men and
women. Int J Obes Relat Metab Disord 28, 583–593.
Wolff CB, Wolff HK (1995). Maternal eating patterns and
birth weight of Mexican American infants. Nutr Health 10,
121–134.
Zhang C, Schulze MB, Solomon CG, Hu FB (2006). A prospective
study of dietary patterns, meat intake and the risk of gestational
diabetes mellitus. Diabetologia 49, 2604–2613.
Maternal diet and fetal growth
VK Knudsen et al
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Appendix 1
Food grouping used in the dietary pattern analyses
Food group Food items
Low-fat dairy Skimmed milk, partly skimmed milk, butter milk, low-fat yoghurt, curd cheese 5 þ low-fat soured fresh cream dressing and
whey
High-fat dairy Whole milk, yoghurt, hard cheese and cream soured fresh cream dressing
Ice-cream Ice-cream based on whole milk and cream
Breakfast cereals Oats, cornflakes and mu
¨
sli
Whole grains Rye bread, white bread with whole kernels, rye flour, barley grouts, wheat kernels, wheat bran, brown rice and whole meal
spaghetti
Refined grains White bread, rice, spaghetti, bread rolls, crisp bread, crackers and biscuits
Fruit Apples, pears, oranges, grape fruit, bananas, peaches, strawberry, kiwi, plums, melon and grapes
Organ meat Liver, heart and kidney
Processed meat Bacon, ham and sausages
Red meat Beef, lamb and pork meat
Fish Plaice, cod, herring, mackerel, salmon, trout, garfish, halibut, cod roe, shellfish, sushi, seal and whale
Shellfish Shrimps
Poultry Chicken, hen, turkey, duck, goose, pheasant
Eggs Eggs
Animal fats Butter and lard
Vegetable fats Vegetable oil (mayonnaise, tartar sauce)
Margarine Margarine
Sweets and deserts Cakes, pastry, cookies, chocolates and candy
Wine Red, white and rose
´
wine
Beer Beer
Tea Tea
Coffee Coffee
High-energy drinks Soft drinks
Low-energy drinks Soft drinks, unsweetened
Water Tap water and carbonated water
Alcoholic beverage Spirits and dessert wine
Snacks Potato crisps, peanuts, popcorn
Vegetable juice Carrot or tomato juice
Fruit juice Orange, apple, pineapple juice
Fruit syrup and
marmalade
Fruit syrup and marmalade
Nuts Nuts
Green leafy vegetables Spinach, lettuce, kale, Chinese cabbage, celery, iceberg salad
Potatoes Potatoes and potato products
Tomatoes Fresh and canned tomatoes, ketchup
Other vegetables Leek, cucumber, eggplant, zucchini, capsicum, carrot, peas, corn, green beans, kidney beans, onions, garlic and mushroom
Soy products Soy beans, soybean curd
Spices Salt, pepper, yeast, vinegar
Maternal diet and fetal growth
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European Journal of Clinical Nutrition