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Elisabete Cristina Bastos Pinto
FETAL GROWTH AND DIETARY INTAKE DURING PREGNANCY:
RESULTS OF PORTO BIRTH COHORT
Dissertação de candidatura ao grau de Doutor apresentada à
Faculdade de Medicina da Universidade do Porto
Porto, Março de 2010
II
Art.º 48º, § 3º
“A Faculdade não responde pelas doutrinas expendidas na dissertação.”
(Regulamento da Faculdade de Medicina da Universidade do Porto – Decreto-Lei nº 19337 de 29
de Janeiro de 1931)
III
Corpo Catedrático da Faculdade de Medicina do Porto
Professores Catedráticos Efectivos
Doutor Manuel Maria Paula Barbosa
Doutor Manuel Alberto Coimbra Sobrinho Simões
Doutor Jorge Manuel Mergulhão Castro Tavares
Doutora Maria Amélia Duarte Ferreira
Doutor José Agostinho Marques Lopes
Doutor Patrício Manuel Vieira Araújo Soares da Silva
Doutor Daniel Filipe Lima Moura
Doutor Alberto Manuel Barros da Silva
Doutor José Manuel Lopes Teixeira Amarante
Doutor José Henrique Dias Pinto de Barros
Doutora Maria de Fátima Machado Henriques Carneiro
Doutora Isabel Maria Amorim Pereira Ramos
Doutora Deolinda Maria Valente Alves Lima Teixeira
Doutora Maria Dulce Cordeiro Madeira
Doutor Altamiro Manuel Rodrigues Costa Pereira
Doutor Rui Manuel Almeida Mota Cardoso
Doutor António Carlos Freitas Ribeiro Saraiva
Doutor Álvaro Jerónimo Leal Machado de Aguiar
Doutor José Luís Medina Vieira
Doutor José Carlos Neves da Cunha Areias
Doutor Manuel Jesus Falcão Pestana Vasconcelos
Doutor João Francisco Montenegro Andrade Lima Bernardes
Doutora Maria Leonor Martins Soares David
Doutor Rui Manuel Lopes Nunes
Doutor Amadeu Pinto de Araújo Pimenta
Doutor António Albino Coelho Marques Abrantes Teixeira
Doutor José Eduardo Torres Eckenroth Guimarães
Doutor Francisco Fernando Rocha Gonçalves
Doutor José Manuel Pereira Dias de Castro Lopes
Doutor Manuel António Caldeira Pais Clemente
Doutor Abel Vitorino Trigo Cabral
IV
Professores Jubilados ou Aposentados
Doutor Abel José Sampaio Da Costa Tavares
Doutor Alexandre Alberto Guerra Sousa Pinto
Doutor Amândio Gomes Sampaio Tavares
Doutor António Augusto Lopes Vaz
Doutor António Carvalho Almeida Coimbra
Doutor António Fernandes da Fonseca
Doutor António Fernandes Oliveira Barbosa Ribeiro Braga
Doutor António Germano Pina Silva Leal
Doutor António José Pacheco Palha
Doutor António Luís Tomé da Rocha Ribeiro
Doutor António Manuel Sampaio de Araújo Teixeira
Doutor Artur Manuel Giesteira de Almeida
Doutor Belmiro dos Santos Patrício
Doutor Cândido Alves Hipólito Reis
Doutor Carlos Rodrigo Magalhães Ramalhão
Doutor Cassiano Pena de Abreu e Lima
Doutor Daniel Santos Pinto Serrão
Doutor Eduardo Jorge Cunha Rodrigues Pereira
Doutor Fernando de Carvalho Cerqueira Magro Ferreira
Doutor Fernando Tavarela Veloso
Doutor Francisco de Sousa Lé
Doutor Henrique José Ferreira Gonçalves Lecour de Menezes
Doutor João Silva Carvalho
Doutor Joaquim Germano Pinto Machado Correia da Silva
Doutor José Augusto Fleming Torrinha
Doutor José Carvalho de Oliveira
Doutor José Fernando Barros Castro Correia
Doutor José Manuel Costa Mesquita Guimarães
Doutor Levi Eugénio Ribeiro Guerra
Doutor Luís Alberto Martins Gomes de Almeida
Doutor Manuel Augusto Cardoso de Oliveira
Doutor Manuel Machado Rodrigues Gomes
Doutor Manuel Teixeira Amarante Júnior
Doutora Maria da Conceição Fernandes Marques Magalhães
Doutora Maria Isabel Amorim de Azevedo
Doutor Mário José Cerqueira Gomes Braga
Doutor Serafim Correia Pinto Guimarães
Doutor Valdemar Miguel Botelho dos Santos Cardoso
Doutor Walter Friedrich Alfred Osswald
V
Ao abrigo do Art.º 8º do Decreto-Lei n.º 388/70 fazem parte desta dissertação as seguintes
publicações:
Pinto E, Severo M, Correia S, Santos-Silva I, Lopes C, Barros H. Validity and reproducibility of a
semi-quantitative food frequency questionnaire for use among Portuguese pregnant women.
Maternal and Child Nutrition 2010; 6: 105-19.
Pinto E, Barros H, Santos-Silva I. Dietary intake and nutritional adequacy prior to conception and
during pregnancy: a follow-up study in the north of Portugal. Public Health Nutrition 2009
Jul;12(7):922-31.
Pinto E, Ramos E, Severo M, Casal S, Santos-Silva I, Lopes C, Barros H. Measurement of dietary
intake of fatty acids in pregnant women: comparison of self-reported intakes with adipose tissue
levels. Submitted
Pinto E, Severo M, De Stavola B, Cunha A, Rodrigues T, Santos-Silva I, Barros H. Prenatal exposure
to fatty acids and fetal growth trajectories. Submitted
Pinto E, Ramos E, Guimarães JT, Barros H. Maternal blood IGFs, cord blood IGFs and lipids, and size
at birth: implications for adult hypercholesterolemia. Submitted
VI
Esta investigação foi realizada no Serviço de Higiene e Epidemiologia da Faculdade de Medicina da
Universidade do Porto, sob a orientação do Senhor Professor Doutor Henrique Barros e co-
orientação da Senhora Professora Doutora Isabel dos Santos Silva.
Este projecto de investigação de base populacional – estudo de coorte, foi financiado pelo
Programa Operacional de Saúde – Saúde XXI, Quadro Comunitário de Apoio III e pela
Administração Regional de Saúde Norte.
No âmbito deste projecto de investigação, foi concedida a bolsa de Doutoramento pela Fundação
para a Ciência e a Tecnologia (SFRH / BD / 19803 / 2004).
VII
Aos meus Pais
À Susana
Ao Professor Doutor Henrique Barros
VIII
IX
Agradecimentos
Ao Professor Doutor Henrique Barros agradeço tudo o que me ensinou, o incentivo e os desafios
ao longo destes anos. Agradeço-lhe o facto de me ter feito “crescer” pessoal e profissionalmente e
de tantas vezes me lembrar que o céu é o limite e o trabalho o único caminho nessa direcção.
À Professora Isabel dos Santos Silva agradeço tudo o que me ensinou, todo o apoio, incentivo e
paciência.
À Carla e à Elisabete agradeço o apoio, a colaboração, os ensinamentos e acima de tudo a
amizade.
Ao Nuno, à Ana, à Raquel e à Sofia agradeço o companheirismo, a amizade, a boa disposição e as
muitas conversas informais que muito contribuíram para a consecução desta tese.
À Andreia agradeço a amizade de muitos anos, o apoio e a disponibilidade permanentes.
Ao Milton agradeço toda a ajuda que me deu na análise estatística deste trabalho e a quem
reconheço uma paciência sem limite.
A todos os colegas do Serviço de Higiene e Epidemiologia, particularmente aos que estiveram
ligados à Geração XXI, agradeço a amizade, a compreensão e o incentivo constantes ao longo
deste meu percurso.
Aos meus pais e à minha irmã agradeço toda a dedicação, apoio incondicional e confiança,
agradeço-lhes por serem sempre o meu porto de abrigo.
X
XI
Contents
Abstract 1
Resumo 7
Introduction 13
Aims of the study 25
Methods 29
Main results and discussion 47
Conclusions 65
References
69
Papers
I. Validity and reproducibility of a semi-quantitative food frequency
questionnaire for use among Portuguese pregnant women
II. Dietary intake and nutritional adequacy prior to conception and
during pregnancy: a follow-up study in the north of Portugal
III. Measurement of dietary intake of fatty acids in pregnant women:
comparison of self-reported intakes with adipose tissue levels
IV. Prenatal exposure to fatty acids and fetal growth trajectories
V. Maternal blood IGFs, cord blood IGFs and lipids, and size at birth:
implications for adult hypercholesterolemia
XII
PAPER I
Validity and reproducibility of a semi-quantitative food frequency
questionnaire for use among Portuguese pregnant women
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PAPER II
Dietary intake and nutritional adequacy prior to conception and during
pregnancy: a follow-up study in the north of Portugal
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PAPER III
Measurement of dietary intake of fatty acids in pregnant women: comparison
of self-reported intakes with adipose tissue levels
Measurement of dietary intake of fatty acids in pregnant women: comparison of self-
reported intakes with adipose tissue levels
Elisabete Pinto; Elisabete Ramos; Milton Severo; Susana Casal; Isabel dos Santos Silva; Carla
Lopes, Henrique Barros
ABSTRACT
Purpose: Dietary fatty acids affect several pregnancy outcomes, including foetal growth and
development. We compared self-reported intakes with concentrations of fatty acids in
adipose tissue in pregnant women.
Methods: The study was nested within Geração XXI, a birth cohort assembled in Portugal.
Intake was assessed by nine food diaries (FDs) completed throughout pregnancy and an FFQ
administered in the immediate postpartum period. A gluteal adipose tissue sample was
obtained from 23 women.
Results: FDs and FFQ estimated similar percentages of saturated (SFA), monounsaturated
(MUFA) and polyunsaturated fatty acids (PUFA), but the adipose tissue yielded a lower
percentage of SFA and higher percentages of MUFA and PUFA. Correlations between FDs
and adipose tissue ranged from r=0.50 for trans fatty acids to r=-0.19 for linolenic acid. The
proportion of women categorized in opposite tertiles by these two methods ranged from
4.3% to 30.4%. Correlations between FFQ and adipose tissue were even weaker and levels of
misclassification higher.
Conclusion: The correlations observed in this study between self-reported intakes and
tissue concentrations are weaker than those observed in a similar study conducted among
non-pregnant women, suggesting that adipose tissue levels of fatty acids may be a poor
biomarker of dietary intake in pregnancy.
INTRODUCTION
Essential fatty acids and their long-chain unsaturated derivatives are crucial for fetal growth
and development and their requirements increasing during pregnancy (1, 2), particularly in
the last ten weeks (3). In contrast, trans fatty acids (TFA) have adverse effects on fetal
growth and development (4-6). Some studies have suggested that fatty acids may also affect
pregnancy length (7) and risk of pre-eclampsia (8), but the evidence from systematic reviews
is less consistent (9, 10).
Dietary intake can be assessed using several tools. Some attempt to estimate an
individual’s food intake (e.g. food frequency questionnaires (FFQ), food diaries (FDs)) and
others rely on assays to estimate the concentration of nutrients, or their metabolites, in
biological samples. Biomarkers are often considered to be the “gold standard” for dietary
evaluation because they are independent of an individual’s ability, or willingness, to
accurately report intake (11) and because they avoid interviewer bias (12). However,
biomarkers reflect not only an individual’s dietary intake but also his/her non-dietary
lifestyles, genetic background and metabolic profile. Biomarkers may also be affected by the
method of collection, sampling site and analytical technique used, and the inherent errors of
these procedures (13). Additionally, the value of a biomarker as a proxy for dietary intake
may be affected by specific physiological and biochemical adaptations, such as those
experienced during pregnancy (3, 14).
We have previously developed a specific FFQ for the Portuguese adult (non-
pregnant) population (available at: http://higiene.med.up.pt/freq.php). This FFQ was
validated against FDs and adipose tissue fatty acid concentrations, with the level of
agreement between the FFQ fatty acid estimates and adipose tissue concentrations being
within the usual ranges observed for these tools (15, 16). Recently, we assessed the validity
of this FFQ relative to FDs to measure usual intake of macro- and micro-nutrients other than
fatty acids among Portuguese pregnant women using a similar methodology (17). For a
subsample of the participants we also obtained a gluteal subcutaneous adipose tissue
sample to measure fatty acid concentrations. In this paper we compare dietary intake of
fatty acids during pregnancy, as estimated by the FFQ and multiple FDs, with levels of these
acids in the gluteal adipose tissue.
METHODS
Subjects and study design
Participants were pregnant women enrolled in the “Geração XXI”, a birth cohort
assembled in Porto, Portugal, during the years 2004-2005. Most participants (n=8,334) were
recruited during the immediate post-partum period but a “pregnancy” sub-sample (n=320)
was identified and followed regularly from the first trimester of pregnancy (gestational age
below 13 weeks). Of these, 101 participated in the FFQ validation study (17).
Women were interviewed in each trimester of pregnancy by trained interviewers
using structured questionnaires to obtain information on demographic and lifestyle
variables, past medical history and health status during pregnancy. They were also asked to
complete a 3-day FD (in non-consecutive days) in each trimester. Twenty-four women also
gave permission for the researchers to collect a sample of gluteal subcutaneous adipose
tissue in late pregnancy. The participants were re-interviewed in the immediate postpartum
period and the FFQ administered to estimate dietary intakes during the whole pregnancy.
Ethical approval was obtained from all relevant institutional ethics committees. Each
participant provided written informed consent, including specific consent for collection of a
sample of adipose tissue.
Food frequency questionnaire (FFQ) and food diaries (FDs)
Participants were given detailed instructions on how to complete the FDs. These
were checked for completeness and accuracy with the participants and coded by a trained
nutritionist. The semi-quantitative FFQ comprised 86 food items. Frequency of consumption
was recorded in nine pre-specified categories from “never or less than once per month” to
“six or more times per day”. Each food item was allocated a pre-specified portion size. Usual
intake of any given food was estimated by multiplying its frequency of intake by its portion
size (in grams) and, if appropriate, by a seasonal variation factor (16, 17). The Food
Processor software, version SQL 9.3.0 (ESHA Research, Salem, Oregon, 2004),
supplemented with nutritional composition data of Portuguese foods and recipes (17, 18),
was used to convert FFQ and FD food intakes into nutrient intakes. Further details on the
development of the FFQ, and on its validation for nutrients other than fatty acids, can be
found elsewhere (16, 17).
Gluteal subcutaneous adipose tissue sample
The biopsy was scheduled for 37-39 weeks of gestation and performed as
recommended by Beyen and Katan (19). The tissue samples were obtained from the upper
half of the buttock and stored at -80ºC until analysis (storage time: <6 months for all
samples). Each sample was divided in two portions, washed with 0.9% NaCl if blood
contamination was visible, and transferred to the reaction vials. The total fatty acids were
analyzed as methyl esters, prepared by direct transterification of the sample tissue with
sodium methoxide (0.5M, 30 min, 50ºC). After extraction with heptanes, the solution was
analyzed in a gas chromatograph Chrompack CP 9001 (Middelburg, the Netherlands),
equipped with a split/splitless injector and a flame ionization detector. Separation was
achieved in a capillary column CP-Sil 88 (50m x 0.22mm, 0.2µm, Varian) with helium as
carried gas. Quantification was based on the relative percentages of the fatty acid methyl
esters, corrected for the detector response obtained with standard mixtures (Supelco,
Spain). A total of 33 fatty acids methyl esters were quantified in all samples. The
methodology was validated by previous analysis of a certified reference material (CRM163,
BCR, European Commission).
Statistical methods
Mann-Whitney and chi-square tests were used to compare the characteristics of
women who provided an adipose tissue sample to the characteristics of other women in the
pregnancy sub-cohort. Cook’s distance was estimated for several fatty acids and a cut-off of
0.18 was used to define outliers (4/n-k-1, where n is the number of women and k is the
number of independent variables) (20). Spearman correlation coefficients were calculated to
measure the degree of association between the fatty acids intakes estimates by the two
dietary tools and the concentrations in adipose tissue, with a bootstrap method (21) used to
estimate their 95% confidence intervals.
The level of agreement between the FFQ, FD and adipose tissue estimates was
assessed by calculating the percentage of women who were classified into the same (perfect
agreement) and opposite tertiles (extreme disagreement) of the intake/concentration
distributions.
Statistical analyses were performed using SPSS (version 14.0) and R computer
packages (version 2.6.0).
RESULTS
Valid tissue concentrations of fatty acids were obtained for 23 women (one was excluded
because she was an outlier). These 23 participants were similar to the remaining women in
the pregnancy sub-cohort who provided complete dietary information (n=100) in terms of
their age [median (25th percentile; 75th percentile): 30.4 (27.0; 32.8) vs. 30.4 (27.9; 33.8)
years, respectively; p=0.59], educational level [9.0 (7.5; 12.0) vs. 9.0 (6.0; 12.0) completed
schooling years; p=0.35], pre-pregnancy body mass index (BMI) [23.0 (20.9; 26.7) vs. 23.8
(21.4; 26.0) kg/m2; p=0.45], and total energy and fat FFQ intakes [mean (SD): 2,444 (735) vs.
2,531 (642) kcal, p=0.58; 85.4 (29.6) vs. 89.6 (24.2) g fat; p=0.49].
The FFQ and the FDs yielded, on average, similar fatty acid percent distributions
(median: 33.1% vs. 33.6% for saturated (SFA), 40.4% vs. 43.2% for monounsaturated (MUFA)
and 16.9% vs. 15.0% for polyunsaturated (PUFA) fatty acids, respectively) (Table 1).
However, the adipose tissue profile generated a lower percentage of SFA (24.2%) and,
hence, higher percentages of MUFA (52.3%) and PUFA (20.5%) (Table 1).
Overall, adipose tissue levels were more strongly correlated with the FD than the FFQ
intake estimates (Table 1). The strongest associations between FDs and adipose tissue were
observed for TFA (r=0.50), SFA (r=0.45) and docosapentaenoic acid (DPA) (r=0.44).
Correlations between fatty acids measured by the FFQ and the adipose tissue
determinations were strongest for linoleic acid (r=0.26) and PUFA (r=0.23).
The proportion of women classified into the same tertile (perfect agreement) by both
the FDs and the adipose tissue determinations was highest for SFA (60.9%), TFA (56.5%) and
DPA (52.2%) and lowest for arachidonic acid (AA) (21.7%). Classification in opposite tertiles
(extreme disagreement) ranged from 4.3% for TFA and DPA to 30.4% for linolenic acid (Table
2). Perfect agreement between FFQ and adipose tissue levels was highest for TFA (47.8%)
and SFA (43.5%) and lowest for DPA (21.7%), with extreme disagreement ranging from 8.7%
for linoleic acid to 30.4% for DPA (Table 2).
DISCUSSION
To our knowledge this is the first study to compare two self-reported methods (FDs and FFQ)
of estimating fatty acids intake to adipose tissue concentrations, in pregnant women.
Although fatty acid levels in the adipose tissue were slightly better correlated with the FDs
than the FFQ estimates, the correlations were very weak for many fatty acids in both
comparisons. Fair correlations between FDs estimates and adipose tissue levels were
obtained only for TFA, SFA and DPA.
Fat intake is difficult to assess using self-reported dietary tools as participants may
misreport fat intake (e.g. due to its social undesirability) (11, 22) and fat used for cooking is
often disregarded. For FFQs, the advantages of using an interviewer to maximise
completeness and accuracy need to be balanced against the possibility of introducing
interviewer bias even when, as in the present study, the interviewers are trained and
procedures standardized.
It is often assumed that biomarkers provide a more accurate estimate of intake (13).
The fatty acids composition of white adipose tissue in pregnant women reflects maternal
lipids metabolism over of the last 6-12 months (2) and, consequently, a sample taken late in
pregnancy will reflect intake as well as the fast metabolic turnover characteristic of
pregnancy (3, 22). The only published study to have assessed the fatty acid composition of
adipose tissue in pregnant women (2), although it did not collect information on dietary
intake, reported higher percentages of linoleic acid, AA, eicosapentaenoic acid (EPA), DPA ,
and DHA, but a lower proportion of linolenic acid (approximately half), than those observed
in the present study.
The initial validation study of our FFQ, conducted among healthy males and non-
pregnant female adults (16) using the same methodology to that of the present study,
reported a similar overall adipose tissue profile in non-pregnant women to that observed
among pregnant women in the present study except for a lower percentage of oleic acid and
higher percentages of linolenic acid and TFA (Table 3). The associations between FD
estimates and adipose tissue levels among non-pregnant women were, however, stronger
than those observed among pregnant women in the present study (Table 3), particularly for
SFA (r=0.70 vs. r=0.45, respectively) and MUFA (r=0.67 vs. r=0.31, respectively) (Table 3).
Similarly, a fair agreement between FD estimates and adipose tissue levels was observed for
EPA and DHA, two important fatty acids for fetal growth, among non-pregnant women in the
previous validation study, but these associations were rather weak among pregnant women
in the present study (Table 3). As both studies used the same methodology, including the
same FFQ, FD methodology and adipose tissue analytical technique, the discrepancies found
may reflect specific effects of pregnancy. Greater within-subject variability in dietary intake
during pregnancy (e.g. due to nausea, vomits and appetite fluctuations) could account for
the poorer correlations among pregnant than non-pregnant women, but the main reason for
the poorer correlation between FD and adipose tissue fatty acid estimates in pregnant than
in non-pregnant women is probably the specific metabolic adjustments that occur during
pregnancy (3). Studies have reported lower DHA concentrations in maternal adipose tissue
relative to DHA concentration in fetal brain and fetal adipose tissue, suggesting differential
mechanisms in the placenta and/or fetus in the uptake and retention of n-3 and n-6 fatty
acids (3, 23, 24). We also observed a lower concentration of DHA, but not EPA, in adipose
tissue of pregnant than non-pregnant women (Table 3), but the sample sizes were too small
to be conclusive.
Our findings suggest that levels of fatty acids in adipose tissue collected in late
pregnancy may not be a good biomarker of dietary fat intake during the whole pregnancy as
they are likely to reflect the balance between maternal intake and fetal utilisation. Further
larger studies are needed to corroborate these findings.
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Table 1. Percent distributions of dietary and tissue fatty acids and their correlations (n=23)
FDs – food diaries (average of three trimester-specific 3-day FDs); FFQ – food frequency questionnaire; AT – adipose tissue; P25; P75 – percentiles 25th and 75th; r (95% CI)
- Spearman correlation coefficient (95% confidence interval); SFA – saturated fatty acids; MUFA – monounsaturated fatty acids; PUFA – polyunsaturated fatty acids; C18:1 -
oleic acid; C18:2 - linoleic acid; C18:3 – linolenic acid; C20:4 – arachidonic acid (AA); C20:5 – eicosapentaenoic acid (EPA); C22:5 – docosapentaenoic acid (DPA); C22:6 –
docosahexaenoic acid (DHA); TFA – trans fatty acids
Fatty acid
Adipose tissue (%)
Median (P25; P75)
FDs (%)
Median (P25; P75)
FFQ
Median (P25; P75)
AT - FDs
r (95% CI)
AT - FFQ
r (95% CI)
SFA 24.2 (21.5; 26.7) 33.1 (31.4; 35.4) 33.6 (28.4; 36.3) 0.45 (-0.07; 0.75) -0.11 (-0.57; 0.37)
MUFA 52.3 (49.7; 54.6) 40.4 (38.9; 43.7) 43.2 (41.3; 45.7) 0.31 (-0.15; 0.65) -0.04 (-0.50; 0.38)
PUFA 20.5 (19.5; 21.6) 16.9 (15.1; 18.3) 15.0 (13.4; 16.5) -0.05 (-0.46; 0.41) 0.23 (-0.17; 0.60)
C18:1 43.2 (41.4; 44.9) 36.3 (34.9; 40.5) 36.9 (33.0; 39.5) 0.21 (-0.24; 0.60) 0.02 (-0.47; 0.49)
C18:2 17.7 (16.8; 19.1) 13.7 (11.4; 14.5) 10.2 (8.50; 11.7) -0.07 (-0.48; 0.40) 0.26 (-0.11; 0.60)
C18:3 0.61 (0.49; 0.66) 1.77 (1.44; 2.39) 1.80 (1.38; 2.12) -0.19 (-0.58; 0.28) 0.07 (-0.35; 0.46)
C20:4 0.47 (0.38; 0.57) 0.15 (0.13; 0.19) 0.16 (0.13; 0.21) 0.06 (-0.31; 0.42) 0.08 (-0.29; 0.44)
C20:5 0.04 (0.03; 0.06) 0.11 (0.08; 0.17) 0.14 (0.10; 0.18) -0.04 (-0.52; 0.45) -0.02 (-0.42; 0.41)
C22:5 0.11 (0.09; 0.15) 0.03 (0.02; 0.03) 0.26 (0.04; 0.40) 0.44 (0.03; 0.72) -0.05 (-0.49; 0.40)
C22:6 0.15 (0.11; 0.18) 0.28 (0.25; 0.36) 0.29 (0.22; 0.37) 0.09 (-0.35; 0.51) -0.01 (-0.40; 0.43)
TFA 1.33 (1.20; 1.44) 0.97 (0.70; 1.24) 1.56 (1.30; 1.95) 0.50 (0.03; 0.75) 0.12 (-0.41; 0.75)
Omega 3 FA 0.83 (0.74; 1.01) 1.50 (1.36; 1.77) 1.68 (1.61; 1.94) -0.15 (-0.56; 0.38) 0.09 (-0.43; 0.49)
Omega 6 FA 1.23 (1.05; 1.46) 14.6 (12.7; 15.4) 10.9 (9.26; 12.3) 0.23 (-0.22; 0.59) -0.09 (-0.55; 0.33)
Table 2. Level of agreement between self-reported fatty acid intakes and adipose tissue
concentrations (n=23)
Fatty acid
(%)
AT and FDs AT and FFQ
Agreement in the
same tertile
Extreme
disagreement
Agreement in
the same tertile
Extreme
disagreement
SFA 60.9 13.0 43.5 21.7
MUFA 47.8 17.4 39.1 17.4
PUFA 34.8 26.1 30.4 17.4
C18:1 39.1 17.4 30.4 17.4
C18:2 30.4 26.1 39.1 8.7
C18:3 26.1 30.4 26.1 21.7
C20:4 21.7 13.0 39.1 17.4
C20:5 39.1 26.1 39.1 26.1
C22:5 52.2 4.3 21.7 30.4
C22:6 26.1 17.4 30.4 21.7
TFA 56.5 4.3 47.8 17.4
Omega 3 FA 30.4 26.1 30.4 17.4
Omega 6 FA 30.4 17.4 26.1 21.7
AT – adipose tissue; FDs – food diaries (average of three trimester-specific 3-day FDs); FFQ – food frequency
questionnaire; SFA – saturated fatty acids; MUFA – monounsaturated fatty acids; PUFA – polyunsaturated fatty
acids; C18:1 - oleic acid; C18:2 - linoleic acid; C18:3 – linolenic acid; C20:4 – arachidonic acid (AA); C20:5 –
eicosapentaenoic acid (EPA); C22:5 – docosapentaenoic acid (DPA); C22:6 – docosahexaenoic acid (DHA); TFA –
trans fatty acids
Table 3. Relative concentrations of individual fatty acids in adipose tissue in pregnant and
non-pregnant* women, and their correlations with intake as estimated by multiple food
diaries
Fatty
acids
AT
Median (P25; P75)
FDs vs. AT
r (95%CI)
Pregnant
women
(n=23)
Non-pregnant
women
(n=35) *
Pregnant women
(n=23)
Non-pregnant
women *
(n=35)
SFA 24.2 (21.5; 26.7) 22.8 (21.0; 28.1) 0.45 (-0.07; 0.75) 0.70 (0.51; 0.84)
MUFA 52.3 (49.7; 54.6) 55.6 (52.7; 60.2) 0.31 (-0.15; 0.65) 0.67 (0.45; 0.80)
PUFA 20.5 (19.5; 21.6) 19.3 (16.6; 22.8) -0.05 (-0.46; 0.41) 0.21 (-0.12; 0.50)
C18:1 43.2 (41.4; 44.9) 50.0 (46.6; 53.8) 0.21 (-0.24; 0.60) 0.52 (0.20; 0.73)
C18:2 17.7 (16.8; 19.1) 18.4 (15.9; 21.9) -0.07 (-0.48; 0.40) 0.27 (-0.09; 0.61)
C18:3 0.61 (0.49; 0.66) 0.33 (0.29; 0.38) -0.19 (-0.58; 0.28) -0.12 (-0.47; 0.26)
C20:4 0.47 (0.38; 0.57) 0.42 (0.32; 0.50) 0.06 (-0.31; 0.42) 0.31 (-0.04; 0.62)
C20:5 0.04 (0.03; 0.06) 0.05 (0.04; 0.07) -0.04 (-0.52; 0.45) 0.50 (0.20; 0.69)
C22:6 0.15 (0.11; 0.18) 0.21 (0.17; 0.31) 0.09 (-0.35; 0.51) 0.37 (-0.02; 0.63)
TFA 1.33 (1.20; 1.44) 0.88 (0.73; 0.98) 0.50 (0.03; 0.75) 0.02 (-0.30; 0.37)
AT – adipose tissue; FDs – food diaries (average of three trimester-specific 3-day FDs for pregnant women;
average of four 7-days FDs for non-pregnant women); P25; P75 – percentiles 25 and 75; FDs – food diaries; r -
Spearman correlation coefficient; SFA – saturated fatty acids; MUFA – monounsaturated fatty acids; PUFA –
polyunsaturated fatty acids; C18:1 - oleic acid; C18:2 - linoleic acid; C18:3 – linolenic acid; C20:4 – arachidonic
acid (AA); C20:5 – eicosapentaenoic acid (EPA); C22:6 – docosahexaenoic acid (DHA); TFA – trans fatty acids
* Adapted from Lopes et al. (15, 16) to include only data from adult non-pregnant women who completed the
FDs and also provided a gluteal adipose tissue sample. These women also completed a FFQ reporting diet
during preceding year. The same FFQ, FD collection methods and adipose tissue collection and analytic
procedures were used in the two studies.
PAPER IV
Prenatal exposure to fatty acids and fetal growth trajectories
Prenatal exposure to fatty acids and fetal growth trajectories
Elisabete Pinto, Milton Severo, Bianca De Stavola, Ana Cunha, Teresa Rodrigues, Isabel dos
Santos Silva, Henrique Barros
ABSTRACT
Background: In well-nourished populations maternal diet has little impact on size at birth,
but but little is known about its impact on fetal growth trajectories.
Aims: To investigate the effect of maternal diet, particularly intakes of energy,
macronutrients and specific fatty acids, on fetal growth trajectories.
Study design: A sample of women enrolled in “Geração XXI”, a Portuguese birth cohort, was
followed throughout pregnancy and their diet ascertained using a food frequency
questionnaire. Linear mixed models were fitted to assess the association of diet with fetal
growth trajectories and term birth size.
Subjects: 222 mother-infant pairs.
Outcome measures: Fetal growth estimated from ultrasonographic fetal measures (weight,
head and abdominal circumferences, femur length) taken from mid-pregnancy onwards
(mean number of scans/woman: 2; range 2-6).
Results: Energy, protein and carbohydrate intakes were not associated with any measure of
fetal growth. The effect of gestational age on fetal growth was modified by maternal fat
intake, with fetus of women in the top intake fifths of total fat, saturated-, trans- and n-6
fatty acids having a similar weight as those of women in the other fifths at 18th gestational
week, but an increasing lower weight after that (6-8g less weekly). Similar effect
modifications were observed for abdominal circumference, but not for head circumference
or femur length.
Conclusions: Maternal energy, protein and carbohydrate intakes had no effect on fetal
growth. Highest intakes of total fat, and certain of its components, were associated with a
slowdown in the rate of fetal growth in the last half of pregnancy.
INTRODUCTION
Food deprivation is a major determinant of intrauterine growth restriction, in developing
countries (1), both as the result of macro (2) and micronutrients deficiencies (3). However, in
developed countries associations between maternal diet during pregnancy and size at birth
of their offspring are rarely observed and, when found, they tend to be weak (4, 5).
Fetal growth is a dynamic process and similar birth weights may be achieved through
different growth trajectories leading to variations in body composition and organ
development and maturation (6, 7). Animal experiments have suggested that fetal under-
nutrition in early pregnancy results in small but normally proportioned offspring, whereas
nutritional restrictions in late pregnancy may have several effects on body proportions but
little effect on birth weight (8). Data from children exposed in utero to the Dutch Hunger
Winter showed that birth weight and body proportions were only affected when exposure
occurred late in pregnancy (2). Maternal diet during pregnancy influences the balance
between the fetal demand for nutrients and the materno-placental capacity to supply
sufficient nutrients for the fetus to maintain its own innate growth trajectory (8). Failure to
support fetal nutrient requirements results in a range of fetal adaptations and
developmental changes and, although these adaptations may be beneficial for short-term
survival, they may lead to permanent alterations in the body’s structure and metabolism and
thereby to cardiovascular and metabolic disease in adult life (9).
Total fat and/or specific fatty acids are the only maternal nutrients for which there is
some, although inconsistent, evidence that they may affect fetal growth in well-nourished
populations. Some studies reported positive associations between maternal high intakes of
fish, or marine n-3 fatty acids, during pregnancy and newborn birth weight due to increases
in both pregnancy length and fetal growth rate (10, 11). However, other studies, including
randomized controlled trials, did not find any associations between maternal intake of total
fat, or of any of its components – i.e. proportions of saturated fatty acids (SFA),
monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), n-3 fatty acids or
n-6 fatty acids - and gestational length or newborn birth size (12-14). But, to our knowledge,
no study has so far investigated the role of these nutrients on fetal growth trajectories.
In this study, we investigated the effect of maternal diet, particularly energy,
macronutrients and specific fatty acids, on fetal growth trajectories in a group of well-
nourished pregnant women. We also examined possible associations between maternal diet
and birth size of their offspring.
MATERIAL AND METHODS
Subjects
This study was based on data from pregnant women enrolled in Geração XXI, a
population-based prospective birth cohort assembled in Porto, Portugal (15). A sub-sample
of mothers in this cohort, recruited through two of the five participating maternity hospitals
– Júlio Dinis Maternity and S. João Hospital - between 1st December of 2004 and 31st
December of 2005, were consecutively invited to participate if they reported a gestational
age below 13 weeks. Those who agreed to participate were interviewed in each trimester of
pregnancy and in the immediate postpartum period. In accordance with Portuguese
antenatal care guidelines, each woman was scheduled to have three routine obstetric scans
in one of these hospitals - in early (gestational age <14 weeks), mid (gestational age between
18 and 25 weeks) and late pregnancy (gestational age ≥25 weeks).
A total of 430 pregnant women were enrolled (recruitment rate 96.2%): 300 at Júlio
Dinis Maternity and 130 at S. João Hospital. Of these, 21 were excluded immediately after
enrolment because the self-reported gestational age was not confirmed by the ultrasound
exam. Further women were excluded at a later stage because of miscarriage (n=24), medical
abortion (n=2), fetal death (n=4), severe intra-uterine growth restriction with major
malformations (n=1), twin pregnancies (n=9) and refusal to be further evaluated (n=59) or
inability to complete the food frequency questionnaire (FFQ) in the immediate post-partum
(n=35). An additional 52 women were excluded because only one ultrasound scan was
performed after the 17th gestational week due to very preterm deliveries, missed
appointments or scans having been performed somewhere else. In the analysis, a further
woman was excluded because the two available ultrasound evaluations were both
performed in mid-pregnancy (in weeks 18 and 22). This analysis is therefore based on data
from the 222 remaining participants.
Approval for the study was obtained from institutional ethics committees. Each
participant provided written informed consent.
Data collection
Trained interviewers administered structured questionnaires in each trimester of
pregnancy and in the post-partum to obtain information on demographic and lifestyle
variables, past medical history and health status during pregnancy. Education was recorded
as number of completed schooling years and categorized as ≤6, 7–9, 10–12 and >12 years.
Pre-pregnancy body mass index (BMI) was estimated from self-reported pre-pregnancy
weight or, if this was not known (for 13.2% of women), the weight measured at the first
ante-natal visit. BMI was analysed according to the follow categories: <25.0kg.m-2, 25.0–
29.9kg.m-2 and ≥30.0kg.m-2. Maternal smoking habits were categorised as having ever/never
smoked throughout pregnancy. Newborns’ anthropometric data were collected from
hospital records. Birth weight was measured in digital scales and reported to the nearest 2g
in S. João Hospital and to the nearest 5g in Júlio Dinis Maternity; length and head
circumference were reported to the nearest 0.1cm. Birth weight was measured shortly after
birth and length and head circumference were measured in the first 24h hours, by trained
paediatricians.
Maternal dietary intake during pregnancy
Dietary intake was ascertained by the application of a semi-quantitative FFQ. The FFQ
was administered four times. The first administration (FFQ1) occurred at the time of the first
ante-natal interview during the first trimester of pregnancy, and it aimed to estimate usual
dietary intake in the preceding year; the second (FFQ2) and third (FFQ3) administrations
took place in mid- and late pregnancy and sought to ascertain maternal diet during the first
and second pregnancy trimesters, respectively. The fourth administration (FFQ4) occurred a
few days after delivery and aimed to estimate usual dietary intake during the whole
pregnancy. In a previous study (16) we showed that the dietary intake in this group of
pregnant women varied little throughout pregnancy and that the FFQ applied in the
immediate post-partum is a valid tool to rank women in terms of their dietary intake. Thus,
we used the data provided by the FFQ4 in the present analysis.
Nutritional intake was estimated by multiplying the frequency of intake of each 86
FFQ food or food group items by its respective allocated portion size, in grams, and
subsequently converted into nutrients using Portuguese food composition tables (15). In this
study, we examined possible associations between maternal dietary intakes of energy and
macronutrients, with particular emphasis on total fat and its components, and fetal growth
trajectories.
Fetal ultrasonography
Fetal ultrasound examinations were carried out at the obstetric ultrasonographic
units in the two hospitals, using Aloka ultrasound equipments (SSD 2000, abdominal probe
of 3.5 Mhz). Ultrasound data were recorded in Astraia® - an obstetric and gynaecological
database, version 1.18, Germany. Gestational age was estimated on the basis of the first day
of the last menstrual period, as reported by the women, and subsequently confirmed by the
early-pregnancy ultrasound (taken before the 14th gestational week). If the two methods
yielded different estimates, the latter one was chosen.
The fetal growth measures examined included head and abdominal circumferences,
femur length and biparietal diameter (the latter was only used by Astraia® to automatically
estimate fetal weight, as proposed by Hadlock et al. (17)). Fetal ultrasound measurements
were taken by trained obstetricians, to the nearest millimetre, using standardized
procedures.
In this analysis, we only considered ultrasounds taken at mid- and late pregnancy (i.e.
in the 18th gestational week or later). The number of ultrasound scans available per woman
within this period ranged from 2 to 6 (with 122 (55.0%) having two scans, 80 (36.0%) having
three, and 20 (9.0%) having at least four). The mean gestational age at the first ultrasound
within this period was 21.2 weeks (SD: 2.0).
Statistical Analysis
The individual fetal growth trajectories over gestational age (measured in days but
expressed in weeks) were examined to assess data quality. These checks were performed
separately for each fetal growth dimension: weight, head and abdominal circumferences,
and femur length.
The nutrients investigated were total energy, proteins, carbohydrates, total fat,
saturated fatty acids (SFA), trans fatty acids (TFA), and n-3 and n-6 fatty acids. The 1st and 4th
quintiles of their distributions in the whole study population were used to categorise women
into three groups, with the bottom category corresponding to values less than the 1st
quintile, and the top category corresponding to values greater than the 4th quintile. The
bottom and top fifths were compared to the middle category (reference).
For each fetal growth dimension, mixed models were fitted, assuming a quadratic
relationship with gestational age, to estimate the effect of each categorized nutrient on
average fetal size at the 18th gestational week and the average weekly rate of growth from
then onwards. These models always included fetal gender and maternal smoking as a priori
predictors of average size and average rate of growth. Mixed models were fitted to estimate
the effect of categorized nutrients on newborn size as measured by weight, length and head
circumference at birth. Several potential confounders for the effect of the dietary variables
on fetal growth and birth size were also considered, including maternal age, education,
parity, height, and pre-pregnancy weight and BMI. Each of these variables was included in
the model and any changes in the effect of the nutrient assessed. Statistical analyses were
performed in SPSS (version 17.0) and R (version 2.6.0) computer packages.
RESULTS Participating women had a mean age of 28.8 years (standard deviation (SD) 5.9) at the time
of their enrolment into the study and a median number of completed schooling years of 9
(inter-quartile range (IR): 6, 12) (Table 1). About 90% of the women were married, and for
47% the current pregnancy was their first. Sixty percent of the participants had a pre-
pregnancy BMI<25.0kg.m-2, and 19% reported having ever smoked during pregnancy (Table
1).
Median daily energy intake during the whole pregnancy was 2423kcal (percentile 20;
percentile 80 (P20; P80): 1979; 3016), 18.3% as proteins, 51.4% as carbohydrates and 31.8%
as total fat (Table 1). SFA and TFA contributed to 10.9% and 0.54% of the total energy intake,
respectively.
The median gestational age at birth of their offspring was 39.1 weeks (P25, P75: 38.3,
40.0), similar for boys and girls. At birth girls were, on average, 240g lighter, 0.5cm shorter
and had a 0.5cm smaller head circumference than boys (Table 2).
At the 18th gestational week, there were no significant differences in fetal weight
according to the gender of the fetus or the smoking status of the mothers (Table 3). From
the 18th gestational week onwards fetal weight increased, on average, 85.7g per week. The
weekly increments in head circumference, abdominal circumference and femur length were,
on average, 14.3mm, 12.8mm and 2.94mm, respectively. The weekly increment in fetal
weight was 8.3g lower in females, but no clear gender differences were observed in relation
to the other three fetal measures (Table 3). Relative to fetus of non-smokers those of
smokers had a significantly lower rate of growth, with smaller weekly increments in weight
(less 8.7g), femur length (0.12mm) and abdominal circumference (0.51mm) from the 18th
gestational week onwards (Table 3).
At the 18th gestational week there were no associations between maternal intakes
and fetal size (Table 4). Maternal intakes of energy, protein and carbohydrates had no effect
on the rate of fetal growth from then onwards, but a high maternal fat consumption slowed
down the rate of fetal growth. Fetus of mothers in the top intake fifths of total fat, SFA, TFA
and n-6 fatty acids gained weekly less 5.5, 7.2, 8.3 and 7.7g, respectively, in fetal weight
from the 18th gestational week onwards (Table 4) and about 0.4mm less weekly in
abdominal circumference (i.e., 0.37mm for total fat, 0.47mm for SFA, 0.39mm for TFA and
0.38mm for n-6 fatty acids; Table 4). Fetus of pregnant women in the top fifth of TFA intake
also had a lower weekly rate of increase in femur length (β=-0.09mm, 95 percent confidence
interval (CI): -0.16, -0.03). In contrast, none of the maternal nutrients examined had an
effect on the rate of growth of head circumference (data not shown).
After controlling for gestational age, fetus gender, smoking and other potential
confounders there was no evidence of any association between maternal diet throughout
pregnancy and newborn anthropometry at birth, except that newborns whose mothers were
in the bottom fifth for SFA were, on average, 0.66cm (CI: -1.25, -0.07) shorter at birth than
those whose mothers were in the reference category (data not shown).
DISCUSSION
To the best of our knowledge, no previous study has examined the effect of maternal diet
during pregnancy on fetal growth trajectories. A recent study used a similar approach, but to
evaluate the effect of maternal smoking on fetal growth (7). We observed no associations
between maternal intake of energy or macronutrients and fetal size at the 18th gestational
week but, from then onwards, we found that average weekly increases in weight and
abdominal circumference were lower among fetus of mothers who were in the highest
intake fifths of total fat, SFA, TFA and n-6 fatty acids. In contrast, maternal fat intake had no
effect on the rate of growth of head circumference or femur length (only a weak interaction
between TFA intake and gestational age on the rate of increase in femur length was
observed). These findings are consistent with animal experiments, particularly those
conducted in sheep whose organogenesis during fetal development is comparable to that in
humans, that identified the third trimester of pregnancy as the time window of higher
plasticity of adipose tissue and muscle (18). The lack of associations between maternal diet
and rate of increase in head circumference and femur length in mid-late pregnancy suggest
that the rate of linear (skeletal) growth may be determined at an earlier stage of pregnancy
and is not affected by maternal diet later on.
We examined the role of maternal diet on overall fetal weight as well as on body
proportions, as birth weight is likely to be a rather crude surrogate of fetal growth and
development. We found that the associations of maternal fat intake with the rate of fetal
growth observed in this study were mainly driven by similar associations with abdominal
circumference. Rates of increase in abdominal circumference are likely to reflect mainly
rates in liver growth because this organ grows rapidly in late gestation to become the most
important abdominal organ at birth (19). Interestingly, Barker et al. found that subjects with
a reduced abdominal circumference at birth had hypercholesterolemia in adulthood (19),
suggesting that the mechanisms controlling cholesterol metabolism in later life may be
programmed in utero.
Our observation of a lower rate of fetal growth in fetus whose mothers consumed
the highest quantities of TFA is also largely supported by the literature (20-22). Placenta
allows the transport of TFA to the fetus (21) and both experimental data, from in vitro and
animal studies, and human data, from studies of preterm infants and children, indicate that
trans fatty acids may impair growth (21). Albeit some studies reported an association
between maternal intake of n-3 fatty acids and birth weight no association was found with
rate of fetal growth in mid- and late pregnancy in this study. One previous study showed
that birth weight was positively associated with maternal serum levels of n-3 fatty acids in
early pregnancy (as a proxy for dietary intake), but negatively associated with serum levels
of n-6 fatty acids and TFA (20). However, no such associations were reported by another
study which ascertained dietary intake in the last trimester of pregnancy (12).
On the basis of the observed associations between maternal fat intake and the rate
of fetal growth, one would have predicted similar association with size at birth. However,
and similarly to others (14, 23, 24), we found no associations between maternal diet and any
of the birth size dimensions examined. The large majority of women had their last
ultrasonographic evaluation around the 30-34th gestational weeks; thus, it is possible that
further changes in the rate of fetal growth may have occurred between the time of the last
ultrasound scan and birth. Besides, as fetal weight was estimated indirectly from ultrasound
measurements and birth weight was measured directly, the two measures may not be
entirely comparable.
This study has strengths but also some weaknesses. It was nested within a
prospective population-based cohort study of pregnant women who were followed up
throughout pregnancy and for whom detailed data on dietary data and ultrasonographic
fetal measurements were collected. There were no differences in maternal or newborn
characteristics between the participants and those women who would have been eligible
but did not complete the required ultrasound evaluations or the FFQ, except that the
proportion of ever-smokers during pregnancy was lower among participants (18% vs. 32%;
P=0.009). A few women developed gestational diabetes (n=18) or gestational hypertensive
disorders (n=6), but their exclusion from the analysis did not affect the results. Reassuringly,
the major determinants of the fetal growth rate in the second half of pregnancy were, as
expected, gestational age, fetal gender and maternal smoking during pregnancy, despite the
fact that none of these variables was associated with fetal size at the 18th gestational week.
The observed effect of maternal smoking on fetal growth is consistent with data from a large
Dutch birth cohort (7), which reported severe growth fetal retardation for mothers who
smoked later in pregnancy relative to those who smoked only during mid pregnancy.
Because our sample size was much smaller we were unable to examine trimester-specific
smoking effects. Our analyses of the association between maternal diet and both fetal
growth and birth size adjusted for fetus gender and maternal smoking, as well as other
potential confounding variables, the possibility of residual confounding cannot be ruled out.
Our examination of growth trajectories was restricted to gestational age greater than
17th weeks because earlier ultrasounds evaluations did not provide estimates for all the fetal
size measures examined in this study. Data on usual diet during pregnancy was collected
retrospectively by administering a previously validated FFQ in the immediate post-partum
period; this is unlikely to have introduced bias as both the interviewers and the women were
unaware of the specific study hypothesis. The present analysis was restricted to intake of
energy and macronutrients because micro-nutrient intakes were within recommended levels
for practically all study women and a large proportion took supplements (15). Our a priori
decision to focus on the role of specific fatty acids and their association with fetal growth
trajectories was informed by previously published work (20, 25). As our study population
comprised well nourished women we hypothesized that any dietary effects on fetal growth
were likely to be modest (4, 5). However, as the maturation of many organs – e.g. lungs,
kidneys, organs of the digestive tract – takes place during the last trimester (9), even small
variations within the normal range of growth and development in late fetal life may have
life-long health consequences.
In summary, our study showed that in well nourished women levels of intake of total
energy, protein and carbohydrate throughout pregnancy had no effects on fetal growth.
However, highest intakes of total fat, SFA, TFA and n-6 fatty acids, were associated with a
slowdown in the rate of fetal growth in the last half of pregnancy, reflecting mainly a
slowdown in the rate of increase in abdominal circumference. Although the observed effects
on fetal growth rate were small they may, nevertheless, be of relevance in the context of the
“fetal origins of adult diseases” hypothesis.
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Table 1. Characteristics of the Study Sample of Pregnant Women, Geração XXI, Porto, Portugal
n (%)
Maternal age at enrolment into the study
(years)
≤ 20
21 – 34
≥ 35
27 (12.2)
159 (71.6)
36 (16.2)
Education (completed schooling years)
≤ 6
7 – 9
10 – 12
≥ 13
67 (30.2)
68 (30.6)
59 (26.6)
28 (12.6)
Marital status at enrolment
Married/ Fact union
199 (89.6)
Gravidity
First pregnancy
105 (47.3)
Maternal BMI* before pregnancy (kg.m-2)
< 25.0
25.0 – 29.9
> 29.9
122 (59.8)
56 (27.5)
26 (12.7)
Smoking during pregnancy
Ever
41 (18.5)
Median (P20; P80)†
Energy (kcal) 2423 (1979; 3016)
Protein (g) 111.6 (91.0; 134.7)
Carbohydrates (g) 303.3 (245.8; 393.8)
Total fat (g) 82.5 (66.2; 112.7)
Saturated fatty acids (g) 28.2 (21.9; 39.2)
Trans fatty acids (g) 1.42 (1.05; 1.95)
n-3 fatty acids (g) 1.47 (1.08; 1.84)
n-6 fatty acids (g) 9.00 (6.80; 12.64) BMI – body mass index; * data available for 204; † Percentiles 20 and 80 are shown because they were used to define the three nutrient intake categories presented in Table 4.
Table 2. Characteristics of the Study Sample of Newborns, Geração XXI, Porto, Portugal
Total sample
Median (P25; P75)¥
Boys §
Median (P25; P75)¥
Girls §
Median (P25; P75)¥
n (%) 222 (100.0) 104 (46.8) 118 (53.2)
Gestational age at birth (wks) 39.1 (38.3; 40.0)
(n=220)
39.1 (38.2; 40.0)
(n=102)
39 (38.6; 40.0)
(n=118)
Birth weight (g) 3216 (2868; 3483)
(n=222)
3320 (2961; 3590)
(n=104)
3080 (2842; 3349)
(n=118)
Birth length (cm) 49.0 (47.5; 50.0)
(n=221)
49.5 (48.0; 51.0)
(n=104)
49.0 (47.0; 50.0)
(n=117)
Birth head circumference (cm) 34.0 (33.5; 35.0)
(n=216)
34.5 (33.5; 35.5)
(n=102)
34.0 (33.0; 34.5)
(n=114)
§ The numbers of boys and girls may be smaller than the total number because of missing values.
¥ Unless otherwise specified
Median (P25; P75) – median (percentile 25th; percentile 75th); wks - weeks
Table 3. Fetal Growth Trajectories in Relation to Fetal Gender and Maternal Smoking Status, Geração XXI, Porto, Portugal
Fetal weight (g)
Head circumference (mm) Femur length (mm) Abdominal circumference
(mm)
Coef 95% CI Coef 95% CI Coef 95% CI Coef 95% CI Fixed effects
Intercept Reference 117.8 (28.7; 206.8) 148.2 (143.8; 152.6) 26.3 (25.2; 27.4) 126.3 (120.6; 131.9) (at 18th gestational wks) Female gender 6.5 (-46.0; 59.0) -3.1 (-5.7; -0.5) -0.32 (-0.98; 0.34) -3.1 (-6.4; 0.3)
Maternal smoking 31.5 (-36.3; 99.4) 0.38 (-3.0; 3.7) 0.22 (-0.63; 1.08) 2.7 (-1.7; 7.0)
Gestational age (wks) Reference 85.7 (74.9; 96.5) 14.3 (13.8; 14.8) 2.94 (2.81; 3.07) 12.8 (12.1; 13.4) Female gender -8.3 (-12.3; -3.0) -0.11 (-0.31; 0.08) 0.04 (-0.01; 0.09) -0.12 (-0.37; 0.12) Maternal smoking -8.7 (-14.3; -3.0) -0.21 (-0.47; 0.06) -0.12 (-0.18; -0.05) -0.51 (-0.85; -0.17)
Gestational age
2 (wks) Reference 3.7 (3.2; 4.1) -0.24 (-0.26; -0.22) -0.04 (-0.04; -0.03) -0.11 (-0.14; -0.08)
Random effects
Between babies SD 100.8 5.68 1.37 7.41 Within babies SD 140.5 6.55 1.70 8.38
Coef – beta coefficient; 95% CI – 95% confidence interval; wks – weeks; Gestational age 2
– quadratic term for gestational age; SD – standard deviation
Table 4. Maternal Dietary Intake During Pregnancy and Fetal Growth Trajectories, Geração XXI, Porto, Portugal
Average size at the 18th
gestational week
(95% CI) §
in ref. category ¥
Change in average size at the 18th
gestational week §
from ref. category ¥
Average weekly fetal
growth rate (95% CI) §Ŧ
in ref. category¥
Change in average weekly fetal growth rate
(95% CI) §
from ref. category
Ŧ
Bottom fifth ¥ Top fifth
¥ Bottom fifth
¥ Top fifth
¥
Fetal weight (g)
Energy (kcal) 116.0 (25.4; 206.6) -6.7 (-74.5; 61.2) 15.4 (-51.9; 82.6) 85.9 (75.0; 96.8) 1.45 (-3.9; 6.8) -1.7 (-7.2; 3.7)
Proteins (g) 115.2 (23.5; 207.0) 11.3 (-55.7; 78.3) 7.7 (-59.8; 75.2) 86.1 (75.2; 97.1) -2.4 (-7.6; 2.8) -0.8 (-6.3; 4.6)
Carbohydrates (g) 119.6 (26.7; 212.4) -11.7 (-79.1; 55.7) 15.9 (-51.3; 83.0) 84.6 (73.7; 95.6) 4.6 (-0.7; 9.8) -0.9 (-6.1; 4.4)
Total fat (g) 109.5 (18.2; 200.8) 6.9 (-60.7; 74.4) 36.6 (-30.5; 103.7) 87.0 (76.1; 97.9) -1.4 (-6.7; 3.9) -5.5 (-10.9; -0.1)
SFA (g) 106.2 (14.7; 197.6) 6.6 (-61.3; 74.6) 35.6 (-31.2; 102.5) 87.8 (76.9; 98.7) -1.4 (-6.8; 3.9) -7.2 (-12.6; -1.8)
TFA (g) 115.1 (22.6; 207.5) -22.9(-89.8; 43.9) 22.6 (-44.3; 89.5) 88.1 (77.2; 99.0) -0.8 (-6.1; 4.5) -8.3 (-13.6; -3.0)
n-3 FA (g) 120.1 (28.6; 211.6) -9.8 (-76.8; 57.3) -4.5 (-72.7; 63.6) 85.4 (74.4; 96.3) 2.2 (-3.1; 7.5) -1.3 (-6.8; 4.1)
n-6 FA (g) 111.8 (20.4; 203.2) 0.6 (-66.6; 67.9) 32.5 (-33.5; 98.6) 86.7 (75.9; 97.6) 0.2 (-5.1; 5.5) -7.7 (-12.9; -2.5)
Abdominal circumference (mm)
Energy (kcal) 126.0 (120.2; 131.7) 0.59 (-3.73; 4.92) 1.66 (-2.62; 5.95) 12.8 (12.2; 13.5) -0.01 (-0.33; 0.31) -0.11 (-0.43; 0.22)
Proteins (g) 125.7 (119.9; 131.5) 2.22 (-2.05; 6.48) 1.35 (-2.94; 5.64) 12.8 (12.2; 13.5) -0.23 (-0.54; 0.08) -0.08 (-0.41; 0.25)
Carbohydrates (g) 126.2 (120.3; 132.1) -0.59 (-4.88; 3.71) 2.03 (-2.24; 6.31) 12.7 (12.1; 13.4) 0.28 (-0.03; 0.60) -0.08 (-0.39; 0.24)
Total fat (g) 125.3 (119.5; 131.0) 2.15 (-2.15; 6.45) 3.66 (-0.61; 7.93) 12.9 (12.2; 13.5) -0.26 (-0.58; 0.05) -0.37 (-0.70; -0.05)
SFA (g) 125.0 (119.2; 130.8) 1.56 (-2.77; 5.88) 3.76 (-0.51; 8.02) 12.9 (12.3; 13.6) -0.20 (-0.52; 0.12) -0.47 (-0.80; -0.15)
TFA (g) 126.2 (120.3; 132.1) -0.50 (-4.78; 3.77) 0.74 (-3.55; 5.02) 12.9 (12.3; 13.6) -0.17 (-0.49; 0.15) -0.39 (-0.71; -0.07)
n-3 FA (g) 126.3 (120.5; 132.1) 0.29 (-3.99; 4.56) -1.34 (-5.68; 3.00) 12.8 (12.1; 13.4) 0.06 (-0.26; 0.38) 0.03 (-0.29; 0.36)
n-6 FA (g) 125.7 (119.8; 131.5) 1.05 (-3.25; 5.35) 2.04 (-2.19; 6.28) 12.9 (12.2; 13.5) -0.06 (-0.38; 0.26) -0.38 (-0.69; -0.06)
§ Mixed models were fitted adjusting for fetus gender, maternal smoking status, gestational age (as linear and quadratic terms), interaction between gestational age and maternal smoking status, and interaction between gestational age and fetus gender. Further adjustment for maternal age, educational level, parity, height, and pre-pregnancy weight or BMI did not affect these findings.
Ŧ From the 18
th gestational week onwards;
¥ The cutoff points used to define these categories for each nutrient are shown in Table 1
95% CI – 95% confidence interval; Ref. – reference; SFA – saturated fatty acids; TFA – trans-fatty acids; FA – fatty acids
PAPER V
Maternal blood IGFs, cord blood IGFs and lipids, and size at birth: implications
for adult hypercholesterolemia
Maternal blood IGFs, cord blood IGFs and lipids, and size at birth: implications for adult
hypercholesterolemia
Elisabete Pinto, Elisabete Ramos, João Tiago Guimarães, Isabel dos Santos Silva, Henrique
Barros
ABSTRACT
Background: There is on-going interest in unraveling the pathways linking the intra-uterine
environment with adult conditions such as cardiovascular diseases.
Aims: To quantify the associations between cord and maternal insulin-like growth factors
(IGFs) and the birth size, and the association of these factors with cord lipids.
Study design: A sample of 196 mother-infant pairs enrolled in “Geração XXI”, a Portuguese
birth cohort. Maternal and umbilical blood samples were drawn at the time of delivery.
Serum IGF-1, IGF-2 and IGFBP-3 levels were determined in all maternal and cord samples,
and serum total cholesterol, HDL cholesterol (HDLc) and triglycerides (TG) measured in cord
samples. Linear regression models were used to examine association between IGFs, cord
lipids and several birth size measures.
Results: Both maternal and cord IGF-1 levels were positively associated with birth size, but in
mutually-adjusted analyses only the cord IGF-1 effect persisted (for each standard deviation
increment in log IGF-1 cord levels birth weight increased by 371g (95%CI: 287; 456). Similar
positive associations of cord IGF-1 were observed with length, head circumference and
ponderal index at birth. Neither maternal nor cord IGF-2 or IGFBP-3 were independently
associated with birth size. Cord IGF-1 levels were positively associated with cord HDLc and
negatively associated with cord TG. Birth size was also negatively associated with cord TG,
but the effect disappeared on adjustment for cord IGF-1.
Conclusions: Cord IGF-1 plays a more important role in fetal growth than maternal IGF-1.
Cord IGF-1 affects cord lipids raising, thus, the hypothesis that cord lipids might be a possible
link between the intrauterine environment and adult diseases.
INTRODUCTION
There is an increasing interest in the life-course influence of the intrauterine environment on
cardiovascular and metabolic diseases. Adults who had experienced reduced rates of fetal
growth have increased risks of high blood pressure (1), impaired glucose tolerance (2),
abnormal blood coagulation (3), higher serum levels of total and low density lipoproteins
cholesterol (LDLc) (4), and higher mortality from coronary heart disease mortality (5, 6).
Birth weight (BW) is commonly used as a surrogate measure of intrauterine exposures,
because it reflects the role of both genetic and environmental factors, including parental
socio-economical status, maternal nutrition and endocrine regulation of fetal growth (7).
The importance of environmental factors on BW was highlighted by Brooks et al. (8) in their
study of pregnancies involving ovum donation. They found that BW was affected by the
weight of the recipient mother, but not by the weight of the donor mother. BW is the most
widely investigated measure of fetal growth, partly because it is routinely collected and
partly because it is less likely to be affected by measurement error than other newborn
anthropometric measures, such as length, arm circumference or abdominal circumference.
Retarded intrauterine growth affects body proportions at birth as well as birth size and,
hence, availability of data on anthropometric measures other than BW, as markers of
adiposity and body proportionality, may be of relevance as they have been linked to health
outcomes later in life in some studies (9).
Since the 1980s there is growing evidence that Insulin-like Growth Factors (IGFs) are
associated with fetal growth (10, 11). IGFs are produced by fetal, placental and maternal
tissues but it is unclear whether IGFs influence fetal growth regardless of their origin (7).
IGFs have mitogenic properties, inducing somatic cell growth and proliferation, and the
ability to influence the transport of glucose and amino acids across the placenta. Alterations
in the IGF axis are associated with fetal growth restriction in both animal models and human
studies (7). Cord blood IGF-1 and IGFBP-3 have been consistently associated with larger birth
anthropometric parameters (12-18), while IGF-2 has been shown to have either a positive
(14) or a null (12, 18, 19) association with BW. Cord IGF-1 levels have been shown to be
associated with cord blood concentration of high density lipoproteins cholesterol (HDLc) and
of triglycerides (TG) (20) and these, in turn, are known to be associated with lipid profiles in
adult life (21). Thus, one can hypothesise that IGFs may play an important role in the way the
prenatal environment may permanently influence lipid metabolism and, possibly, other
health outcomes later in life.
This study aims to investigate associations between cord and maternal IGFs, cord
lipids and birth size, and their implications in terms of potential pathways linking in utero
exposures to adult diseases and, in particular, adult hypercholesterolemia.
SUBJECTS AND METHODS
Subjects
Participants in this study were selected from Geração XXI, a birth cohort assembled in Porto,
Portugal. All mothers resident in the metropolitan area of Porto who delivered a live-born
baby between 1st May 2005 and 31st August 2006 in five public level III maternity units were
invited to participate in Geração XXI. These hospitals are responsible for 91.6% of the
deliveries in the whole catchment population, with the remaining occurring in private
hospitals/clinics. A total of 8654 babies were enrolled into the study.
Maternal and cord blood samples were available for a sub-sample of 1581 Caucasian
mothers and babies. We excluded twin pregnancies (n=21), mothers with a diagnosis of
diabetes or hypertensive disorders before or during pregnancy (n=100), preterm deliveries
(gestational duration < 37 weeks) (n=78) and newborns for whom anthropometry data at
birth were not available (n=9). The eligible study population included 1373 mother-newborn
pairs, comprising 41 low birth BW (<2500g) and 55 macrossomic newborns (BW> 3999g).
The final sample size for this analysis included these 96 newborns (and their mothers) as well
as a random sample of 100 newborns with BW between 2500g to 3999g (inclusive).
Data collection
All mothers were invited and interviewed in the 24 to 72 hours after delivery by
trained interviewers, using a structured questionnaire to elicit information on demographic
and lifestyle variables, past medical history and health status during pregnancy. Education
was recorded as the number of completed years of schooling and categorized as <6, 7–9,
10–12 and >12. Maternal weight gain during pregnancy was estimated from self-reported
pre-pregnancy weight and the weight at the end of pregnancy. Pre-pregnancy body mass
index (BMI) was estimated from self-reported pre-pregnancy weight and the height
measurement taken during the interview (if the latter was not possible, the measured height
recorded on the national identity card was used (n=17)) and it was analyzed according to
WHO categories (underweight: <18.5kg/m2; normal weight: 18.5–24.9kg/m2; overweight:
25.0–29.9kg/m2; obesity: ≥30.0kg/m2). Tobacco smoking was recorded as the daily number
of cigarettes smoked in each trimester of pregnancy and women subsequently dichotomized
as being either ever- or never-smokers.
Information on gestational length (estimated from date of last menstrual period),
newborn gender, and neonate birth size (i.e. weight (in g), length (in cm), head
circumference (in cm) and ponderal index (PI) (weight (kg)/length (m)3)) were collected from
the clinical records. BW was measured shortly after birth, using digital scales. BL was
measured with infantometers and head circumference (HC) with tapes, with both sets of
measurements taken within the first 24 hours after birth.
Biochemical analysis
Mothers provided a venous blood sample during labour or in the first hours after
delivery (maximum 72h). In addition, a blood sample from the umbilical cord was also
collected during labour. Blood were collected in Vacutainer serum tubes (Becton-
Dickinson, USA). All specimens were separated and stored in aliquots at -80º until assayed.
The median storage time of the samples was 1004 days (range: 905 to 1245). Samples from
mother-newborn pairs were examined in the same batch, to reduce bias, and laboratory
staff was kept blind to the characteristics of the samples.
Serum total cholesterol and triglycerides were measured using conventional
colorimetric methods, while HDL Cholesterol was measured with a direct method. The
within- and between-batch variability for the serum concentrations of these variables were
all less than 5%. All this measurements were made using an Olympus AU5400 automated
clinical chemistry analyzer. (Beckman-Coulter, Izasa, Porto, Portugal).
IGF-I and IGFBP3 were both measured with a solid-phase, enzyme-labeled
chemiluminescent immunometric assay using a Siemens IMMULITE 2000 automated
analyzer (Siemens, Lisboa, Portugal). Analytical sensitivity is 20.0ng/dl for IGF-I and 0.1μg/ml
for IGFBP3. Within- and between-batch coefficients of variation were below 10% for both
IGF-1 and IGFBP-3. Samples for the determination of IGF-2 were measured in duplicate by an
immunoradiometric assay (IRMA), from Diagnostics Systems Laboratories, Inc., that includes
an extraction step (DSL-9100 Active IGF-II IRMA kit, Arium, Lisboa, Portugal). The detection
limit for this method was 12.0ng/dl, with the within- and between-batch coefficients of
variation being less than 11%. Samples for the determination of IGF-2 were analyzed using
kits from the same production lot.
Statistical analysis
The Kolmogorov–Smirnoff test was used to assess the assumption of normality.
Logarithmic transformations were made on each of the IGFs and IGFBP-3 values to achieve
normality. Gender differences in mean levels of cord IGFs and lipids were examined using
the Mann-Whitney U test.
Correlations between hormone/lipid levels and newborn size measures were
quantified using Pearson correlation coefficients. Linear regression models were fitted to
examine the independent role of each IGF on birth size measures (BW, BL, HC and ponderal
index (PI, defined as length (m) / weight (kg)3) . The effect of each IGF on birth size was
examined for increments of one standard deviation (SD) of the log transformed IGF values to
facilitate interpretation. The potential confounding effect of several variables was assessed,
including storage time, laboratory batch, maternal age, education, gravidity, tobacco
smoking during pregnancy, pre-pregnancy BMI, weight gain during pregnancy, gestational
length and newborn gender. Models were fitted to provide three different estimates: (i)
firstly, estimates of the effect of maternal and newborn IGFs and IGFBP-3 on birth size
measures adjusted for other maternal and newborn characteristics (only the variables
shown to be confounders were included in the final model as indicated in the tables); (ii)
estimates of each cord (or maternal) IGF effects on birth size further adjusted for the other
two cord (or maternal) IGF levels; and, finally, (iii) estimates of the cord and maternal IGF-1
effects on birth size adjusted for all the variables in (i) and for each other. To assess whether
these associations were being driven by the extreme values at the birth size distribution we
conducted further analyses restricted to newborns (and their mothers) with BW between
2500g and 3999g (inclusive).
Linear regression models were also fitted to quantify the independent effect of cord
IGF-1 and birth size measures on cord lipid levels.
Statistical analyses were performed using SPSS, version 17.0.
RESULTS
Mothers had a mean age of 28.5 (standard deviation (SD): 5.4) years, 54.1% were
primiparae and 25% reported having ever smoker during pregnancy. 35% of the women
were overweight or obese prior to becoming pregnant. The mean weight gain during
pregnancy was 13.7 (SD: 5.8) kg. Ninety-eight newborns were male. The mean gestational
duration was 39.4 (SD: 1.2) weeks. The median BW was 2395g among low BW newborns
(interquartile range (IQR): 2290; 2450), 3217g among babies with adequate BW (IQR: 310;
3486) and 4130g for the macrossomic newborns (IQR: 4080; 4260) (Table 1).
Medians and IQR of the levels of cord and maternal blood IGF-1, IGF-2, IGFBP-3, total
cholesterol, HDLc are also shown in Table 1. As expected, for all parameters, maternal
concentrations were much higher than the corresponding cord blood ones.
There were no statistically significant gender (female vs. male) differences in cord
blood concentrations of IGFs and IGFBP-3 (IGF-1 (ng/ml): 64.2 vs. 61.3, p=0.215; IGF-2
(ng/ml): 443.3 vs. 446.2, p= 0.707; IGFBP3 (mcg/ml): 1.4 vs. 1.4, p=0.101), or in cord TG
levels (0.36 vs. 0.33g/L, p=0.590), but the total cholesterol and HDLc concentrations were
significantly higher in females (total cholesterol (g/L): 0.67 vs. 0.60, p=0.026; HDLc (g/L): 0.27
vs. 0.25, p=0.026).
Correlations between cord and maternal levels of IGFs and IGFBP-3, cord lipids and
birth size measures are shown in Table 2. Each one of the IGFs measured in cord blood was
positively correlated with its counterpart measured in maternal blood; all cord IGFs were
positively correlated with BW, BL, HC and PI, whereas only maternal IGF-1 was correlated
significantly with birth size. The correlation coefficients between maternal and cord blood
specimens were: 0.32 (p<0.001) for IGF-1, 0.16 (p=0.009) for IGF-2 and 0.24 (p=0.010) for
IGFBP-3. IGFs and IGFBP3 levels in cord blood were positively correlated with cord levels of
HDLc, but negatively correlated with cord levels of TG. There were, however, no correlations
between cord lipid levels and birth size measures except for a weak inverse correlation
between cord TG and HC (Table 2).
After adjustment for maternal and newborn characteristics, cord IGF-1, IGF-2 and
IGFBP-3 were all positively associated with BW, BL and HC (Table 3, model 1), but only IGF-1
levels were associated with PI. The strongest effects were observed for IGF-1 – for each SD
increment in log IGF-1 cord concentration BW increased by 319.6g (95% CI: 237.3; 401.9), BL
by 1.24cm (95% CI: 0.96; 1.53), HC by 0.83cm (95% CI: 0.59; 1.07) and PI by 3.2kg/m3 (0.7;
5.6) (Table 3). In contrast, maternal IGFs were not associated with birth size measures,
except for an association between maternal IGF-1 and BL. Cord IGF levels are highly
correlated (Table 2) and mutually-adjusted analyses showed that the effect of IGF-1 on birth
size persisted after adjustment for the other two cord IGFs, whereas the effects of IGF-2 and
IGFBP-3 disappeared (Table 3, model 2). The effect of maternal IGF-1 on birth size was
slightly strengthened after adjustment for the other two maternal IGFs, with IGF-1 being
positively and independently associated with both weight and length at birth. However, the
magnitude of the IGF-1 association was much stronger for cord levels (for each SD increment
in log IGF-1 cord and maternal levels BW increased by 342.2g (95% CI: 214.2; 476.3) and
147.8g (95% CI: 3.2; 292.3), respectively, and BL increased by 1.01cm (95% CI: 0.54; 1.48)
and 0.80cm (95% CI: 0.27; 1.34), respectively) (Table 3; model 2). Thus, when both maternal
and cord IGF-1 levels were included simultaneously in the same model (Table 3, model 3),
only the effect of cord IGF-1 on birth size persisted (for each SD increment of log IGF-1 cord
levels BW increased by 371.3g (95% CI: 286.9; 455.8), BL by 1.29cm (95% CI: 0.96; 1.62), HC
by 0.85cm (95% CI: 0.58; 1.12) and PI by 0.9kg/m3 (95% CI: 0.4; 1.4)) (Table 3). Restricting the
analyses to newborns (and their mothers) with BW between 2500g and 3999g (inclusive) did
not affect these findings.
After adjustment for relevant maternal and newborn characteristics, cord IGF-1 levels
were significantly associated with cord levels of HDLc and TG (Table 4). These associations
persisted after adjustment for birth size measures (HDLc increased by 0.01g/L and TG
decreased by 0.06g/L for each SD increment in the log IGF-1 cord levels) (Table 4). In
contrast, the effect of BW, BL, HC and PI on cord levels of HDLc and TG disappeared after
further adjustment for cord IGF-1 levels (Table 4).
DISCUSSION
Cord blood levels of IGF-1, IGF-2 and IGFBP-3 were all associated with newborn BW,
BL, HC and PI, but in mutually adjusted analyses only IGF-1 was found to be an independent
correlate of birth size. Maternal IGF-1 was also found to be associated with BW and BL, but
its effects disappeared on adjustment for cord IGF-1. Cord IGF-1 levels were also positively
associated with cord HDLc, and negatively associated with TG levels, with these associations
persisting after adjustment for birth size measures. In contrast, birth size measures were not
associated with cord HDLc, but were negatively associated with cord TG; however, these
birth size effects on cord TG disappeared on adjustment for cord IGF-1 levels.
Serum concentrations of IGF-1 and IGF-2 are higher in pregnant women than non-
pregnant women (22) with concentrations increasing even further by the third trimester
(23), suggesting a possible role of maternal IGFs on fetal growth. In our study, only maternal
IGF-1 was found to be associated with BW, and its effect disappeared on adjustment for cord
IGF-1. Other previous studies have also shown no association between maternal IGFs and
BW (24, 25).
Our data favour a stronger independent role of cord, rather than maternal, IGFs on
fetal growth. The positive association between cord IGF-1 and BW is extensively
corroborated by the literature (12-18, 26, 27). The same is true for IGFBP-3 (12-14, 16). The
findings for IGF-2 are not so consistent. Two previous studies (10, 14, 16) reported a positive
correlation between IGF-2 and BW, but no correlations were found in others (12, 15, 18, 19,
27). The effect of IGF axis on the other newborns’ anthropometric measures has been less
studied but, similarly to our study, positive associations of cord IGF-1 with BL (16, 28, 29) and
PI (29, 30) have been described. In the present study, cord IGF-2 and IGFBP-3 were positively
associated with birth size but these associations disappeared on adjustment for cord IGF-1.
Maternal IGF-2 and IGFBP-3 were not associated with birth size, before or after adjustment
for maternal IGF-1.
We did not find differences in the IGF axis according to the gender of the fetus.
Previous studies reported higher IGF-2 concentrations in cord blood from male newborns
(11), or higher cord IGF-1 and IGFBP-3 in females (29). However, similarly to our study, a
recent one (20) found no gender differences in cord IGF levels.
One of the strengths of the present study is that it is based on a random sample of
mothers-newborn pairs, with the sampling procedure designed to oversample low BW and
macrossomic newborns so that the IGF associations could be examined across the full birth
size distribution. Most previous studies had had a case-control design, whereby newborns
from uncomplicated pregnancies were compared with newborns from mothers who
developed gestational diabetes or hypertensive disorders. These disorders, in addition with
twin pregnancies, are important causes of extreme birth size. We excluded such conditions
from our study, to allow examination of the role of the IGFs axis on birth size in the absence
of any obvious pathology.
Badiee et al. (31) has revised data on cord lipid levels in several populations. Our
Portuguese sample of newborns had concentrations of total cholesterol, HDLc and TG similar
to those observed in Chile, Brazil and USA; in Poland, higher levels of TG and lower levels of
HDLc were reported, while in Japan the opposite was observed. Iranian data showed higher
concentrations of all measured parameters. A previous study in the Portuguese population
(32) showed a higher total cholesterol and HDLc concentrations in cord blood and similar TG
levels. We compared the two studies regarding the timing of the blood collection, average
BW, gestational age, maternal blood levels and methods of assay and we cannot find
differences, except in the sample size (other study figured out only 39 newborns of non pre-
eclamptic mothers). It is possible that the differences could be attributable to differences in
sample size. Furthermore, procedures in the collection and storage and of biological samples
can compromise the validity of the findings.
In the present study, cord IGF-1 was positively associated with cord HDLc and
negatively associated with cord TG. Similar results were reported in a previous study (20),
with the authors postulating a direct effect of IGF-1 on HDLc metabolism, as well as
alterations in the IGF-1 mediated fetal liver production of TG (20). Although little is known
about the control and function of circulating fetal lipoproteins in utero (33), in adults IGF-1
and IGFBP-3 levels are associated with several features of the metabolic syndrome (34),
namely with circulating levels of HDLc and TG (35).
There is evidence to suggest that the intrauterine environment may permanently
influence lipid metabolism (21, 36, 37). Some studies showed that BW correlates negatively
with serum TG levels (38, 39) and positively with serum HDLc (38, 40) in adult life. Barker (4)
advanced as a possible explanation for that the impairment of the liver growth in low BW
children which leads to permanent changes in LDLc metabolism. One study reported that
LDLc concentration was negatively associated with BW and HC in full-term neonates (40).
Most of the original studies linking birth size with disease outcomes later in life were
limited, due to their retrospective nature, by the unavailability of detailed newborn
anthropometric data. The present study benefited from the availability of such detailed
newborn anthropometric data, which were collected using standardised procedures, as well
as from the availability of cord and maternal samples.
Both cord lipid profile and birth size have been shown to be associated with
hypercholesterolemia later in life. We found that cord IGF-1, but not birth size, was
independently associated with cord lipid levels and therefore hypothesized that cord lipids,
rather than birth size, could be the relevant marker of in utero programming of lipid
metabolism. IGF-1 levels affect both birth size and therefore it could simply be a surrogate of
IGF-1 and/or of the mechanism linking IGF-1 and cord lipid profile to adult
hypercholesterolemia (Figure 1). The fact that the association between birth size and cord
lipids was not independent of cord IGF-1, strengthens the hypothesis that birth size may be a
surrogate for cord IGF-1. Our results are based on cross-sectional data and further
prospective studies are needed to unravel the mechanisms underlying the associations
between birth size, cord IGFs and cord lipids, and hypercholesterolemia later in life.
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Table 1. Mothers and newborns characteristics, including maternal and cord levels of IGFs
and lipids
Maternal characteristics
Age (years) Mean (SD) 28.5 (5.4)
Education (years) Median (IR) 10 (7; 12)
Ever smoking during pregnancy n (%) 47 (24.6)
Gravidity (=1) n (%) 106 (54.1)
Pre-pregnancy BMI (kg/m2) Median (IR) 23.7 (21.4; 26.3)
Pre-pregnancy weight (kg) Mean (SD) 63.4 (13.9)
Weight gain during pregnancy (kg) Mean (SD) 13.7 (5.8)
IGF-1 (ng/ml) Median (IR) 244.0 (150.0; 339.0)
IGF-2 (ng/ml) Median (IR) 998.0 (770.4; 1249.3)
IGFBP-3 (mcg/ml) Median (IR) 4.6 (3.6; 5.5)
Newborn characteristics
Gender (male) n (%) 98 (50.0)
Gestational age (weeks) Mean (SD) 39.4 (1.2)
Birth weight (g)
Low birth weight (<2500g)
Adequate birth weight (2500<BW<4000g)
Macrossomic (>3999g)
Median (IR)
2395 (2290; 2450)
3217 (3010; 3486)
4130 (4080; 4260)
IGF-1 (ng/ml) Median (IR) 63.4 (40.7; 79.3)
IGF-2 (ng/ml) Median (IR) 444.8 (355.7; 514.6)
IGFBP-3 (mcg/ml) Median (IR) 1.4 (1.2; 1.6)
Total cholesterol (g/L) Median (IR) 0.62 (0.50; 0.73)
HDL cholesterol (g/L) Median (IR) 0.25 (0.21; 0.30)
Triglycerides (g/L) Median (IR) 0.35 (0.27; 0.46)
BMI – body mass index, SD – standard deviation; IR – interquartile range, BW – birth weight
* Corresponding median (IQR) in each birth weight category were 45.5 (44.8; 47.0), 49.5 (48.0; 50.5) and 52.0
(51.0; 52.6) cm for birth length; 24.8 (23.5; 26.7), (26.5 (25.4; 28.4) and 29.7 (28.5; 31.3) kg/m3 for PI; and 32.5
(31.5; 33.4), 34.0 (34.0; 35.5) and 36.0 (35.4; 37.0) cm for head circumference.
Table 2. Correlation coefficients between maternal and cord IGFs and lipids levels, and birth size measures
Newborn Mother
IGF-1 IGF-2 IGFBP-3 Total c HDLc TG IGF-1 IGF-2 IGFBP-3 BW BL HC PI
Ne
wb
orn
IGF-1 1 0.32** 0.81** 0.10 0.31** -0.45** 0.32** 0.08 0.20* 0.53** 0.44** 0.42** 0.39**
IGF-2 1 0.45** 0.05 0.22** -0.23** 0.28** 0.16* 0.19* 0.26** 0.26** 0.24** 0.14
IGFBP-3 1 0.12 0.21** -0.22** 0.38**
0.08 0.24* 0.48** 0.43** 0.39** 0.30**
Total c 1 0.68** 0.06 0.13 0.16* 0.21** -0.08 -0.11 -0.02 0.00
HDLc 1 -0.29** 0.13 0.07 0.11 0.01 -0.02 0.06 0.06
TG 1 -0.04 -0.05 0.01 0.01 -0.06 -0.17* -0.13
Mo
the
r
IGF-1 1 0.33** 0.73** 0.33** 0.31** 0.22** 0.20**
IGF-2 1 0.57** <0.01 0.06 -0.03 -0.10
IGFBP-3 1 0.10 0.11 0.08 0.03
Total c – total cholesterol; HDLc – HDL cholesterol; TG – triglycerides; BW – birth weight; BL – birth length; HC – head circumference; PI – ponderal index
** P<0.01; *P<0.05
Levels of IGFs and IGFBP-3 were log-transformed to achieve normality
Table 3. Associations between cord and maternal blood levels of IGF-1, IGF-2 and IGFBP-3 on birth size
Birth weight
(cm)
Birth length
(cm)
Head circumference
(cm)
Ponderal index
(m/kg3)
Model I
β (95% CI)
Newborn IGF-1 (SD) 319.6 (237.3; 401.9) 1.24 (0.96; 1.53) 0.83 (0.59; 1.07) 0.32 (0.07; 0.56)
Newborn IGF-2 (SD) 110.1 (26.7; 193.5) 0.58 (0.26; 0.91) 0.30 (0.04; 0.56) 0.04 (-0.24; 0.31)
Newborn IGFBP-3 (SD) 241.0 (151.7; 330.1) 1.12 (0.82; 1.43) 0.70 (0.44; 0.95) 0.25 (-0.23; 0.73)
Maternal IGF-1 (SD) 75.4 (-13.8; 164.6) 0.49 (0.15; 0.84) 0.17 (-0.09; 0.43) 0.00 (-0.04; 0.04)
Maternal IGF-2 (SD) 19.6 (-69.1; 108.4) 0.25 (-0.09; 0.58) 0.03 (-0.23; 0.29) -0.02 (-0.06; 0.03)
Maternal IGFBP-3 (SD) 23.8 (-60.9; 108.6) 0.22 (-0.11; 0.55) 0.10 (-0.15; 0.35) -0.01 (-0.05; 0.04)
Model II
β (95% CI)
Newborn IGF-1 (SD) 342.2 (214.2; 476.3) 1.01 (0.54; 1.48) 0.76 (0.36; 1.16) 0.11 (0.03; 0.19)
Newborn IGF-2 (SD) 27.6 (-56.4; 111.6) 0.19 (-0.12; 0.50) 0.08 (-0.18; 0.34) -0.00 (-0.05; 0.05)
Newborn IGFBP-3 (SD) -39.0 (-177.5; 99.4) 0.23 (-0.27; 0.74) 0.05 (-0.37; 0.48) -0.56 (-1.36; 0.24)
Maternal IGF-1 (SD) 147.8 (3.2; 292.3) 0.80 (0.27; 1.34) 0.21 (-0.21; 0.63) 0.01 (-0.06; 0.08)
Maternal IGF-2 (SD) 0.7 (-118.4; 119.7) 0.21 (-0.24; 0.66) -0.09 (-0.44; 0.26) -0.03 (-0.09; 0.03)
Maternal IGFBP-3 (SD) -87.2 (-242.8; 68.3) -0.49 (-1.07; 0.009) 0.00 (-0.45; 0.45) 0.01 (-0.07; 0.08)
Model III
β (95% CI)
Newborn IGF-1 (SD) 371.3 (286.9; 455.8) 1.29 (0.96; 1.62) 0.85 (0.58; 1.12) 0.09 (0.04; 0.14)
Maternal IGF-1 (SD) -14.3 (-88.0; 59.3) 0.09 (-0.21; 0.40) -0.07 (-0.31; 0.18) -0.02 (-0.06; 0.03)
Levels of IGF-1, IGF-2 and IGFBP-3 were log-transformed
β (95% CI) – Change in BW, BL, HC and PI per one SD increment in log IGFs/IGFBP-3 cord levels (95% confidence interval); SD – standard deviation
Model 1: BW analyses: adjusted for maternal pre-pregnancy BMI, maternal weight gain, gestation length, and newborn gender; BL, HC, PI analyses: adjusted for
maternal pre-pregnancy BMI, gestation length, and newborn gender
Model 2: Cord (and maternal) effects adjusted for all variables considered in model 1 and for the other two cord (or maternal) IGFs in the same group
Model 3: Cord and maternal IGF-1 effects adjusted for all variables considered in model 1 and mutually for each other
Table 4. Independent effects of cord IGF-1 levels and birth size on cord lipid levels
Total cholesterol
(g/L)
HDLc
(g/L)
TG
(g/L)
IGF-1 (SD)ϕ
β (95% CI)a -0.01 (-0.02; 0.02) 0.01 (0.00; 0.01) -0.06 (-0.08; -0.04)
β (95% CI)b -0.01 (-0.03; 0.02) 0.01 (0.00; 0.02) -0.06 (-0.09; -0.03)
β (95% CI)c -0.00 (-0.02; 0.02) 0.01 (0.00; 0.02) -0.06 (-0.09; -0.03)
β (95% CI)d -0.00 (-0.02; 0.02) 0.01 (0.00; 0.02) -0.05 (-0.08; -0.03)
β (95% CI)e -0.01 (-0.02; 0.01) 0.01 (0.00; 0.02) -0.06 (-0.08; -0.03)
Birth weight (SD)
β (95% CI)a 0.00 (-0.03; 0.02) 0.00 (-0.01; 0.01) -0.05 (-0.07; -0.02)
β (95% CI)f 0.01 (-0.02; 0.03) -0.01 (-0.02; 0.00) 0.01 (-0.04; 0.03)
Birth length (SD)
β (95% CI)a -0.01 (-0.03; 0.02) 0.01 (-0.01; 0.01) -0.04 (-0.07; -0.01)
β (95% CI)f 0.01 (-0.02; 0.02) -0.00 (-0.01; 0.01) 0.00 (-0.03; 0.03)
Head circumference (SD)
β (95% CI)a 0.01 (-0.01; 0.03) 0.00 (-0.01; 0.01) -0.05 (-0.07; -0.02)
β (95% CI)f 0.01 (-0.01; 0.03) -0.00 (-0.01; 0.01) -0.02 (-0.05; 0.01)
Ponderal index (SD)
β (95% CI)a 0.00 (-0.02; 0.02) 0.00 (-0.01; 0.01) -0.03 (-0.05; -0.00)
β (95% CI)f 0.01 (-0.01; 0.02) -0.00 (-0.01; 0.00) -0.01 (-0.03; 0.02)
ϕ Variable log-transformed; SD – standard deviation
β (95% CI) – Change in cord levels of total cholesterol, HDLc and TG associated with one SD increment in log
IGF-1 cord levels and with one SD increment in BW, BL, HC and PI aAdjusted for gestational length and newborn gender, and for the other two cord lipids
bAs (a) but further adjusted BW
cAs (a) but further adjusted for BL
dAs (a) but further adjusted for g HC
eAs (a) but further adjusted for PI
fAdjusted for gestational length, newborn gender, cord IGF-1 and the other two cord lipids
Figure 1. Hypothetical mechanisms underlying the fetal origins of adult hypercholesterolemia