Skip to main content
NCBI home page
ERJ Open logoLink to ERJ Open
. 2018 Aug 30;52(2):1800507. doi: 10.1183/13993003.00507-2018

Maternal dietary antioxidant intake in pregnancy and childhood respiratory and atopic outcomes: birth cohort study

Annabelle Bédard 1, Kate Northstone 2, John W Holloway 3, A John Henderson 2,4, Seif O Shaheen 1,4
PMCID: PMC6383601  PMID: 30093569

Abstract

Evidence for a possible protective effect of maternal dietary antioxidant intake during pregnancy on childhood asthma and other atopic outcomes is conflicting, and associations with childhood lung function have been little studied.

In the Avon Longitudinal Study of Parents and Children, we analysed associations between maternal intake of fruits, vegetables, vitamins C and E, carotene, zinc, and selenium in pregnancy and current doctor-diagnosed asthma, atopy and lung function in 8915 children at age 7–9 years. Potential modification of associations by maternal smoking and common maternal antioxidant gene polymorphisms was explored to strengthen causal inference.

After controlling for confounders, positive associations were observed between maternal intake of zinc and childhood forced expiratory volume in 1 s and forced vital capacity (difference in age-, height- and sex-adjusted sd units per quartile increase in maternal dietary zinc intake β 0.05 (95% CI 0.01–0.08); ptrend=0.01 and 0.05 (95% CI 0.02–0.09); ptrend=0.005, respectively). Weak evidence was found for an interaction between maternal zinc intake and maternal glutathione S-transferase GSTM1 genotype on childhood forced vital capacity (pinteraction=0.05); association among the GSTM1 null group β 0.11 (95% CI 0.05–0.17); ptrend=0.001.

Our results suggest that a higher maternal intake of zinc during pregnancy may be associated with better lung function in the offspring.

Short abstract

A higher maternal intake of zinc during pregnancy may improve lung function, and especially forced vital capacity, in the offspring http://ow.ly/oTt030l1rew

Introduction

A declining dietary intake of antioxidants has been proposed as a possible explanation for the large increase in the prevalence of asthma and atopy seen in the West in recent decades [1], and this has led to interest in the role of maternal antioxidant dietary intake in pregnancy in the aetiology of childhood asthma and atopic diseases. Although some studies have suggested a possible protective effect of maternal intake of vitamin E, zinc, fruits and vegetables during pregnancy [2–4], the evidence overall is conflicting. A recent meta-analysis concluded that, while there is some evidence for a protective effect of maternal intake of zinc and vitamin E on childhood wheeze, evidence regarding asthma and other atopic outcomes is inconclusive [5]. Evidence regarding childhood lung function is limited to one study [2].

A concern with all observational studies, and particularly in nutritional epidemiology, is that findings may be confounded [6]. One way to strengthen causal inference is to demonstrate biologically plausible interactions. Researchers have hypothesised that a diet low in antioxidants may increase susceptibility to oxidant injury and airway inflammation [7]. Maternal smoking during pregnancy has been associated with adverse respiratory outcomes in children [8, 9] and in the Avon Longitudinal Study of Parents and Children (ALSPAC), maternal smoking during pregnancy was associated with reduced mid-expiratory flows in childhood [10]. A recent randomised clinical trial suggested that vitamin C supplementation in pregnant, smoking females may reduce the deleterious effect of maternal smoking on infant pulmonary function [11]. However, to the best of our knowledge, no observational study has investigated potential interactions between maternal dietary antioxidant intake and maternal smoking during pregnancy on respiratory and atopic outcomes in later childhood. Similarly, interactions between maternal diet and common antioxidant gene polymorphisms have not been explored, although a few studies conducted in children have investigated possible interactions between common glutathione S-transferase (GST) gene polymorphisms and antioxidant intake on atopic and respiratory outcomes [12–14].

The aim of this study was to investigate the associations between maternal intake of dietary antioxidants in pregnancy and childhood respiratory and atopic outcomes (including lung function), and to explore whether these associations were modified by maternal smoking during pregnancy and common maternal antioxidant gene polymorphisms, which could potentially strengthen causal inference.

Methods

Participants

The ALSPAC is a population-based birth cohort that recruited 14 541 predominantly White pregnant females resident in Avon, UK with expected dates of delivery from April 1, 1991 to December 31, 1992. These pregnancies resulted in 13 613 singletons who were alive at 1 year of age. The cohort has been followed since birth with annual questionnaires and, since age 7 years, with objective measures in annual research clinics. The study protocol has been described previously [15, 16] and further information can be found at: http://www.alspac.bris.ac.uk, which contains details of all the data that are available (http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary/). Ethics approval was obtained from the ALSPAC Ethics and Law Committee (IRB 00003312) and the Local NHS Research Ethics Committees

Outcome assessment

Children were defined as having current doctor-diagnosed asthma at 7.5 years (primary outcome) if mothers responded positively to the question “Has a doctor ever actually said that your study child has asthma?” and positively to one or both of the questions “Has your child had any of the following in the past 12 months: wheezing with whistling; asthma?”. Parental reports of a doctor's diagnosis of asthma agree well with a general practitioner-recorded diagnosis in ALSPAC [17]. Atopy at 7 years was defined as a positive reaction (maximum diameter of any detectable weal) to Dermatophagoides pteronyssinus, cat or grass (after subtracting positive saline reactions from histamine and allergen weals, and excluding children unreactive to 1% histamine). Lung function was measured by spirometry (Vitalograph 2120; Vitalograph¸ Maids Moreton, UK) at age 8.5 years after withholding short-acting bronchodilators for at least 6 h and long-acting bronchodilators and theophyllines for at least 24 h. The best of three reproducible flow–volume curves was used to measure forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and maximal mid-expiratory flow (i.e. forced expiratory flow at 25–75% of FVC (FEF25–75)). Lung function measurements were transformed to age-, height- and sex-adjusted sd units [18]. The tests adhered to American Thoracic Society (ATS) criteria for standardisation and reproducibility of flow–volume measurement [19], with the exception of ATS recommendations for duration of expiration, since many young children cannot sustain exhalation for 6 s to establish FVC [20]. We therefore used no volume change over >1 s to define the plateau phase of the flow–volume curve as the end-of-test criterion in those unable to blow >6 s. Lung function at 15 years was also considered as a secondary outcome of interest in post hoc analyses (see Statistical analyses section).

Exposures of interest

Data on maternal diet in pregnancy were collected by a food frequency questionnaire (FFQ) sent out at 32 weeks gestation to mothers, covering all the main foods consumed in Britain [21]. The questionnaire included questions about the weekly frequency of consumption of 43 food groups and food items, with the possibility for respondents to tick one of the following options: never or rarely, once in 2 weeks, 1–3 times a week, 4–7 times a week, or more than once a day. One question on the weekly frequency of fresh fruit consumption and six questions on the weekly frequency of vegetables (peas, sweetcorn, broad beans; cabbage, brussels sprouts, kale and other green leafy vegetables; other green vegetables; carrots; other root vegetables; salad) were used to estimate weekly intake of fruits and vegetables, respectively, using standard portions [22]. The FFQ was used to estimate daily nutrient intakes for each female, by multiplying the daily frequency of consumption of a food by the nutrient content [23] of a standard portion [22] of that food and summing this for all the foods consumed. Daily intakes of vitamins C and E, zinc, selenium, and carotene were estimated in this way. To ensure consistency, all dietary exposure variables were categorised in quartiles. A maternal dietary antioxidant score was derived for each mother by adding the intake quartile for each of the five antioxidant nutrients, thus ranging from 5 to 20. Information on the child's intake of antioxidants at 3 years, and maternal and paternal antioxidant intake at 4 years post-partum, was collected using a similar FFQ.

Maternal smoking during pregnancy

Maternal smoking habits during the 3 months before pregnancy and at several time-points during pregnancy were recorded using self-reported questionnaires on an ordinal categorical scale (never, passive smoking only, 1–9 cigarettes per day, 10–19 cigarettes per day or ≥20 cigarettes per day). The highest category reported at any time during pre-pregnancy or pregnancy was used in the analysis.

Genotypes of interest

Maternal DNA was a mixture of samples extracted from blood collected during pregnancy and from lymphoblastoid cell lines. The majority of the children's DNA samples were extracted from cord blood or venous blood collected at age 7 years, with a small number extracted from venous blood collected at 43–61 months. The GSTT1 and GSTM1 gene deletion genotyping was performed using a real-time PCR method described previously [24]. Two single nucleotide polymorphisms (SNPs) were typed in mothers and children by LGC Genomics (formerly KBiosciences, Hoddesdon, UK), using a competitive allele-specific PCR system (KASPar): a SNP in GSTP1 (G313A, Ile105Val, rs1695) and a SNP in GPX4 (glutathione peroxidase 4; rs713041, at position 718). The GST gene polymorphisms are common and we have previously investigated their role (and interactions) in childhood asthma in ALSPAC [25, 26]. We have also reported interactions between pre-natal selenium status and childhood GPX4 genotype on childhood asthma (glutathione peroxidase 4 is a selenium-dependent enzyme) [27].

Potential confounders

We selected potential confounding factors that are known (from existing literature) to be associated with one or more of the outcomes of interest [28]. These included maternal age at delivery, sex of child, multiple pregnancy, season of birth, maternal history of atopic diseases (hay fever, asthma, eczema, allergies, or attacks of wheezing with whistling on the chest or attacks of breathlessness in the past 2 years), parity, highest educational qualification, housing tenure, financial difficulties, ethnicity, breastfeeding duration and maternal factors during pregnancy (smoking status, anxiety score (Crown–Crisp Experiential Index) [29], paracetamol use, antibiotic use, infections (urinary infection, influenza, rubella, thrush, genital herpes, other), supplement use and total energy intake (kJ·day−1)).

Statistical analyses

Logistic regression and linear regression were used to analyse associations between dietary exposure variables and binary and continuous outcomes, respectively. Dietary exposure variables were analysed in quartiles, first as a categorical variable using the lowest quartile as reference to allow for a nonlinear pattern of association and second as a continuous variable to test for linear trend (i.e. per increasing quartile effect). For all regression analyses, two stages of adjustment were used. In Model 1, we adjusted for total energy intake only. In Model 2, we adjusted additionally for all potential confounders listed previously.

Sensitivity analyses

When evidence for associations persisted after adjustment for potential confounders, we conducted a number of additional analyses: 1) additional adjustment for potential mediators (i.e. gestational age at delivery [30, 31], birthweight [32, 33], maternal pre-pregnancy body mass index (BMI) and weight gain during pregnancy [34–36], and child's BMI at age 7 years [37, 38]; see supplementary figure S1 for directed acyclic graph); 2) additional adjustment for maternal dietary intake of total polyunsaturated fatty acids [5]; 3) mutual adjustment for maternal dietary intake of antioxidants that were found to be associated with the same childhood outcome; 4) exclusion of mothers taking supplements in pregnancy (vitamins/zinc); and 5) exclusion of mothers with implausible energy intakes (<2500 or >25 000 kJ·day−1 [39]).

Further investigation of confounding

We also used two approaches to further investigate potential confounding of associations with pre-natal exposures: 1) we controlled additionally for child's intake of the same exposure at 3 years of age and 2) we used a parental comparison approach to investigate potential unmeasured confounding by genetic or shared environmental or lifestyle factors (further details in supplementary material) [40, 41].

To correct for potential loss to follow-up bias, we used inverse probability weighting and assigned to each female a weight that is the inverse of the probability of her selection for given values of covariates (further details in supplementary material) [42].

Exploration of interactions

To explore potential modification of dietary associations by maternal smoking we stratified by maternal smoking history (dichotomised) and tested for interaction. Maternal smoking during pregnancy has been found to be associated with reduced childhood FEF25–75 in ALSPAC [10]. To explore potential modification of this association by maternal dietary antioxidant exposures, we stratified by antioxidant intake (above versus below median) and tested for interaction. Distributions of allele frequencies for each polymorphism in mothers and children were formally tested for deviation from Hardy–Weinberg equilibrium using a likelihood ratio test. To investigate whether associations between dietary exposures and childhood outcomes were modified by maternal antioxidant genotype, we stratified by maternal GSTM1 and GSTT1 null genotypes, and by GSTP1 genotype. We also stratified the associations between maternal dietary selenium intake and outcomes by maternal GPX4 genotype (since glutathione peroxidase 4 is a selenium-dependent enzyme). All statistical analyses were carried out using Stata version 12.1 (StataCorp, College Station, TX, USA).

Results

Of the 13 972 singletons and twins alive at 1 year of age, information on maternal diet during pregnancy was available for 12 078, of whom there was information on at least one of the outcomes of interest for 8915 children (supplementary figure S2). Characteristics of the 8915 mother–child pairs who were included in the analyses and those of the 3163 mother–child pairs with information on maternal diet who were excluded because of incomplete outcome data are compared in table 1.

TABLE 1.

Characteristics of mothers and offspring who were included and excluded in the analyses#

Included Excluded p-value
Subjects n 8915 3163
Maternal intake in pregnancy
 Vitamin C mg·day−1 82±35 74±36 <0.001
 Vitamin E mg·day−1 8.7±4.1 8.0±4.1 <0.001
 Zinc mg·day−1 8.3±2.4 7.8±2.4 <0.001
 Selenium µg·day−1 72.2±27.9 66.1±27.2 <0.001
 Carotene µg·day−1 2170±1176 2018±1175 <0.001
 Fruits g·week−1 671±390 557±392 <0.001
 Vegetables g·week−1 949±474 888±505 <0.001
Mother's age years 28.9±4.6 26.5±5.1 <0.001
Parity
 0 45.5 42.8 <0.001
 1 36.1 34.1
 ≥2 18.5 23.0
Sex of child
 Male 51.1 52.2 0.28
 Female 48.9 47.8
Multiple pregnancy
 Singleton 97.6 97.1 0.14
 Twin 2.4 2.9
Season of birth
 Winter 16.2 15.8 0.65
 Spring 26.9 26.7
 Summer 30.1 31.3
 Autumn 26.7 26.2
Breastfeeding duration
 Never 21.2 35.4 <0.001
 <3 months 31.5 32.9
 3– <6 months 13.8 10.4
 ≥6 months 33.5 21.3
Mother's educational level
 Certificate of Secondary Education 15.4 32.7 <0.001
 Vocational 9.0 12.2
 Ordinary level 35.4 32.6
 Advanced level 25.1 15.6
 Degree 15.1 6.8
Maternal ethnicity
 White 98.1 95.5 <0.001
 Non-White 1.9 4.5
Housing tenure
 Owned/mortgaged 83.7 62.5 <0.001
 Council rented 9.4 24.0
 Noncouncil rented 6.9 13.5
Financial difficulties
 Yes 17.1 22.9 <0.001
Maternal history of atopic diseases
 Yes 68.3 68.9 0.62
Maternal anxiety score in pregnancy
 0–9 21.3 16.9 <0.001
 10–14 25.7 21.6
 15–19 25.9 24.6
 ≥20 27.2 36.9
Maximum maternal tobacco exposure
 None 26.5 17.5 <0.001
 Passive only 46.0 36.1
 1–9 cigarettes per day 8.0 9.5
 10–19 cigarettes per day 11.3 19.9
 ≥20 cigarettes per day 8.2 17.1
Maternal paracetamol use during pregnancy
 Yes 62.4 64.6 0.03
Maternal antibiotic use during pregnancy
 Yes 16.1 14.5 0.04
Maternal vitamin/zinc supplement use during pregnancy
 Yes 21.6 20.0 0.06
Maternal infections in pregnancy
 Yes 45.8 46.9 0.27
Total energy intake kJ·day−1 7260±1966 7162±2153 0.02
Maternal pre-pregnancy BMI
 <18.50 kg·m−2 4.3 6.4 <0.001
 18.50–24.99 kg·m−2 75.4 72.8
 25.00–29.99 kg·m−2 15.1 14.8
 ≥30.00 kg·m−2 5.2 6.0
Birthweight
 <2500 g 4.3 5.7 <0.001
 2500–2999 g 13.8 15.2
 3000–3499 g 35.4 36.6
 3500–3999 g 33.2 30.8
 ≥4000 g 13.3 11.7
Gestational age weeks 39.5±1.8 39.4±1.8 0.03
Child's BMI at 7 years
 <15.00 kg·m−2 28.1 29.6 0.51
 15.00–17.49 kg·m−2 52.5 45.5
 17.50–20.49 kg·m−2 15.2 19.3
 ≥20.50 kg·m−2 4.2 5.7
Maternal weight gain during pregnancy
 Quartile 1 25.3 28.4 <0.001
 Quartile 2 24.8 24.4
 Quartile 3 25.6 22.0
 Quartile 4 24.4 25.2
Open in a new tab

Data are presented as mean±sd or %, unless otherwise stated. BMI: body mass index. #: n=12 078; : Chi-squared tests were used for categorical variables, and t-tests and Wilcoxon tests were used for nonskewed- and skewed-distributed continuous variables, respectively.

After controlling for energy intake only, maternal intakes of fruits and vitamin C during pregnancy were negatively associated with childhood asthma. However, these associations attenuated towards the null after further adjustment for potential confounders (table 2). No other association was found between other dietary antioxidant exposures and childhood asthma or atopy (table 2). After controlling for energy intake and all other potential confounders, there was weak evidence for a positive association between maternal intake of vegetables and childhood FEV1 and FEF25–75 (table 3). Positive associations were observed between maternal intake of zinc and childhood FEV1 and FVC, with evidence of a dose–response relationship. There was weaker evidence for positive associations between maternal carotene intake and childhood FEV1 and FVC, and between maternal selenium intake and childhood FVC (table 3). Positive associations were observed between the maternal antioxidant score and childhood FEV1 and FVC, with evidence of a dose–response relationship (table 3). If zinc intake was omitted from the antioxidant score, the latter was no longer significantly associated with childhood lung function (data not shown).

TABLE 2.

Associations between maternal dietary antioxidant intake and childhood asthma and atopy

Asthma# Atopy
Model 1+ Model 2§ Model 1+ Model 2§
Total fruits
 Quartile 1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference)
 Quartile 2 0.80 (0.61–1.05) 0.87 (0.66–1.15) 0.89 (0.68–1.16) 0.84 (0.63–1.10)
 Quartile 3 0.73 (0.57–0.94) 0.86 (0.66–1.12) 1.06 (0.82–1.36) 0.92 (0.70–1.20)
 Quartile 4 0.68 (0.52–0.88) 0.82 (0.62–1.10) 1.06 (0.82–1.37) 0.85 (0.65–1.13)
 Per quartile 0.90 (0.83–0.97) 0.95 (0.88–1.04) 1.06 (0.99–1.13) 0.98 (0.91–1.06)
 ptrend 0.004 0.26 0.12 0.58
Total vegetables
 Quartile 1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference)
 Quartile 2 0.94 (0.77–1.15) 0.96 (0.79–1.18) 0.98 (0.82–1.18) 0.94 (0.78–1.14)
 Quartile 3 0.90 (0.75–1.09) 0.93 (0.77–1.14) 0.96 (0.81–1.15) 0.90 (0.75–1.08)
 Quartile 4 0.84 (0.69–1.02) 0.88 (0.71–1.08) 1.07 (0.89–1.28) 0.96 (0.79–1.15)
 Per quartile 0.95 (0.89–1.01) 0.96 (0.90–1.02) 1.02 (0.96–1.08) 0.98 (0.93–1.04)
 ptrend 0.08 0.22 0.52 0.57
Vitamin C
 Quartile 1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference)
 Quartile 2 0.83 (0.68–1.02) 0.90 (0.74–1.11) 1.03 (0.85–1.24) 0.94 (0.78–1.14)
 Quartile 3 0.86 (0.70–1.04) 0.98 (0.79–1.20) 1.06 (0.88–1.28) 0.93 (0.76–1.13)
 Quartile 4 0.77 (0.63–0.95) 0.89 (0.71–1.11) 1.20 (0.99–1.44) 0.98 (0.80–1.20)
 Per quartile 0.93 (0.87–0.99) 0.97 (0.91–1.05) 1.06 (1.00–1.13) 1.00 (0.93–1.06)
 ptrend 0.03 0.46 0.05 0.93
Vitamin E
 Quartile 1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference)
 Quartile 2 0.83 (0.67–1.02) 0.90 (0.73–1.12) 1.03 (0.86–1.25) 0.97 (0.80–1.17)
 Quartile 3 1.06 (0.87–1.31) 1.18 (0.95–1.46) 1.10 (0.91–1.32) 1.00 (0.82–1.22)
 Quartile 4 0.88 (0.70–1.10) 0.99 (0.78–1.26) 1.12 (0.91–1.37) 0.99 (0.80–1.22)
 Per quartile 0.99 (0.92–1.06) 1.03 (0.95–1.11) 1.04 (0.98–1.11) 1.00 (0.94–1.07)
 ptrend 0.80 0.46 0.23 0.98
Zinc
 Quartile 1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference)
 Quartile 2 1.05 (0.85–1.30) 1.15 (0.92–1.43) 1.00 (0.82–1.21) 0.91 (0.75–1.11)
 Quartile 3 1.01 (0.80–1.27) 1.15 (0.90–1.47) 1.13 (0.92–1.40) 0.98 (0.78–1.22)
 Quartile 4 0.97 (0.73–1.30) 1.15 (0.85–1.57) 1.07 (0.82–1.38) 0.85 (0.64–1.12)
 Per quartile 0.99 (0.90–1.08) 1.04 (0.94–1.15) 1.04 (0.95–1.13) 0.96 (0.88–1.05)
 ptrend 0.78 0.42 0.39 0.41
Selenium
 Quartile 1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference)
 Quartile 2 0.95 (0.77–1.17) 1.04 (0.84–1.29) 1.09 (0.90–1.31) 1.00 (0.83–1.22)
 Quartile 3 0.95 (0.77–1.18) 1.04 (0.83–1.31) 1.14 (0.94–1.39) 0.99 (0.80–1.21)
 Quartile 4 0.90 (0.71–1.15) 1.03 (0.79–1.35) 1.07 (0.86–1.33) 0.87 (0.68–1.10)
 Per quartile 0.97 (0.90–1.05) 1.01 (0.93–1.10) 1.02 (0.95–1.10) 0.95 (0.89–1.03)
 ptrend 0.44 0.83 0.53 0.23
Carotene
 Quartile 1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference)
 Quartile 2 0.95 (0.78–1.15) 0.94 (0.77–1.15) 0.91 (0.76–1.09) 0.91 (0.75–1.09)
 Quartile 3 0.91 (0.74–1.10) 0.94 (0.77–1.16) 1.03 (0.87–1.23) 1.01 (0.84–1.21)
 Quartile 4 0.89 (0.73–1.09) 0.93 (0.75–1.14) 1.00 (0.84–1.20) 0.93 (0.77–1.12)
 Per quartile 0.96 (0.90–1.02) 0.98 (0.91–1.05) 1.01 (0.96–1.07) 0.99 (0.93–1.05)
 ptrend 0.23 0.51 0.67 0.68
Antioxidant score
 Quartile 1 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference)
 Quartile 2 0.98 (0.79–1.21) 1.09 (0.88–1.36) 1.04 (0.85–1.27) 0.94 (0.76–1.15)
 Quartile 3 0.91 (0.72–1.15) 1.06 (0.83–1.37) 1.20 (0.96–1.48) 1.00 (0.80–1.26)
 Quartile 4 0.81 (0.62–1.05) 0.98 (0.74–1.31) 1.19 (0.94–1.51) 0.92 (0.71–1.20)
 Per quartile 0.93 (0.86–1.01) 0.98 (0.90–1.08) 1.07 (0.99–1.15) 0.98 (0.91–1.07)
 ptrend 0.07 0.72 0.08 0.70
Open in a new tab

Data are presented as odds ratio (95% CI), unless otherwise stated. #: n=7677; : n=6117; +: model controlling for energy intake only; §: model controlling for energy intake, smoking, infections, supplements, antibiotics and paracetamol use during pregnancy, and maternal educational level, housing tenure, financial difficulties, ethnicity, age, parity, history of atopic diseases, anxiety, sex of child, season of birth, multiple pregnancy and breastfeeding duration.

TABLE 3.

Associations between maternal dietary antioxidant intake and childhood lung function

FEV1 FVC FEF25–75
Model 1# Model 2 Model 1# Model 2 Model 1# Model 2
Subjects n 6062 6157 6157
Total fruits
 Quartile 1 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference)
 Quartile 2 −0.02 (−0.13–0.09) −0.03 (−0.14–0.08) −0.01 (−0.11–0.08) −0.04 (−0.15–0.07) −0.01 (−0.12–0.10) −0.03 (−0.14–0.08)
 Quartile 3 0.04 (−0.06–0.15) 0.02 (−0.09–0.12) −0.01 (−0.10–0.07) −0.03 (−0.14–0.08) 0.07 (−0.04–0.17) 0.04 (−0.07–0.14)
 Quartile 4 0.06 (−0.05–0.17) 0.03 (−0.09–0.14) 0.04 (−0.05–0.13) 0.02 (−0.10–0.13) 0.05 (−0.06–0.15) 0.01 (−0.10–0.13)
 Per quartile 0.03 (0.00–0.06) 0.02 (−0.01–0.05) 0.02 (0.00–0.05) 0.02 (−0.01–0.05) 0.02 (−0.01–0.05) 0.01 (−0.02–0.04)
 ptrend 0.04 0.25 0.09 0.26 0.12 0.41
Total vegetables
 Quartile 1 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference)
 Quartile 2 0.06 (−0.01–0.14) 0.05 (−0.03–0.13) 0.02 (−0.05–0.10) 0.02 (−0.06–0.09) 0.08 (0.00–0.15) 0.06 (−0.02–0.14)
 Quartile 3 0.02 (−0.05–0.09) 0.01 (−0.07–0.08) −0.01 (−0.08–0.06) −0.02 (−0.09–0.05) 0.06 (−0.01–0.13) 0.05 (−0.03–0.12)
 Quartile 4 0.10 (0.03–0.17) 0.08 (0.01–0.16) 0.07 (0.00–0.15) 0.06 (−0.01–0.14) 0.10 (0.03–0.18) 0.09 (0.01–0.16)
 Per quartile 0.03 (0.00–0.05) 0.02 (0.00–0.05) 0.02 (0.00–0.04) 0.01 (−0.01–0.04) 0.03 (0.01–0.05) 0.02 (0.00–0.05)
 ptrend 0.03 0.09 0.12 0.22 0.01 0.05
Vitamin C
 Quartile 1 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference)
 Quartile 2 0.05 (−0.02–0.13) 0.04 (−0.04–0.12) 0.06 (−0.01–0.14) 0.05 (−0.02–0.13) 0.02 (−0.05–0.10) 0.01 (−0.07–0.09)
 Quartile 3 0.05 (−0.03–0.12) 0.02 (−0.05–0.10) 0.05 (−0.02–0.12) 0.03 (−0.04–0.11) 0.02 (−0.05–0.10) 0.00 (−0.07–0.08)
 Quartile 4 0.06 (−0.02–0.14) 0.04 (−0.05–0.12) 0.05 (−0.02–0.13) 0.04 (−0.05–0.12) 0.05 (−0.02–0.13) 0.03 (−0.05–0.12)
 Per quartile 0.02 (−0.01–0.04) 0.01 (−0.02–0.03) 0.01 (−0.01–0.04) 0.01 (−0.02–0.03) 0.02 (−0.01–0.04) 0.01 (−0.02–0.04)
 ptrend 0.17 0.53 0.30 0.62 0.19 0.45
Vitamin E
 Quartile 1 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference)
 Quartile 2 −0.03 (−0.10–0.05) −0.03 (−0.11–0.04) 0.00 (−0.07–0.08) 0.01 (−0.07–0.08) −0.02 (−0.09–0.06) −0.04 (−0.11–0.04)
 Quartile 3 −0.02 (−0.10–0.06) −0.03 (−0.11–0.05) 0.02 (−0.06–0.10) 0.02 (−0.06–0.10) −0.02 (−0.10–0.05) −0.05 (−0.13–0.03)
 Quartile 4 0.01 (−0.07–0.10) −0.01 (−0.09–0.08) 0.04 (−0.04–0.13) 0.04 (−0.04–0.13) 0.00 (−0.08–0.09) −0.03 (−0.12–0.05)
 Per quartile 0.01 (−0.02–0.03) 0.00 (−0.03–0.03) 0.01 (−0.01–0.04) 0.01 (−0.01–0.04) 0.00 (−0.02–0.03) −0.01 (−0.04–0.02)
 ptrend 0.63 0.98 0.26 0.28 0.90 0.48
Zinc
 Quartile 1 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference)
 Quartile 2 0.05 (−0.03–0.13) 0.04 (−0.04–0.12) 0.04 (−0.04–0.12) 0.03 (−0.05–0.11) 0.05 (−0.03–0.13) 0.03 (−0.05–0.11)
 Quartile 3 0.11 (0.02–0.20) 0.08 (−0.01–0.17) 0.13 (0.04–0.21) 0.11 (0.02–0.20) 0.06 (−0.03–0.15) 0.02 (−0.07–0.11)
 Quartile 4 0.18 (0.07–0.29) 0.14 (0.03–0.25) 0.16 (0.05–0.26) 0.14 (0.03–0.25) 0.13 (0.02–0.23) 0.07 (−0.04–0.18)
 Per quartile 0.06 (0.03–0.09) 0.05 (0.01–0.08) 0.06 (0.02–0.09) 0.05 (0.02–0.09) 0.04 (0.00–0.07) 0.02 (−0.02–0.06)
 ptrend 0.001 0.01 0.001 0.005 0.03 0.28
Selenium
 Quartile 1 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference)
 Quartile 2 0.05 (−0.03–0.13) 0.03 (−0.05–0.11) 0.05 (−0.03–0.13) 0.05 (−0.03–0.13) 0.04 (−0.04–0.12) 0.00 (−0.07–0.08)
 Quartile 3 0.06 (−0.02–0.14) 0.04 (−0.04–0.13) 0.05 (−0.03–0.13) 0.05 (−0.04–0.18) 0.06 (−0.02–0.14) 0.02 (−0.07–0.10)
 Quartile 4 0.09 (0.00–0.18) 0.06 (−0.03–0.16) 0.11 (0.02–0.20) 0.10 (0.01–0.20) 0.05 (−0.04–0.14) −0.01 (−0.10–0.09)
 Per quartile 0.03 (0.00–0.06) 0.02 (−0.01–0.05) 0.03 (0.00–0.06) 0.03 (0.00–0.06) 0.02 (−0.01–0.05) 0.00 (−0.03–0.03)
 ptrend 0.05 0.22 0.03 0.06 0.25 0.95
Carotene
 Quartile 1 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference)
 Quartile 2 0.01 (−0.07–0.08) −0.01 (−0.09–0.06) 0.02 (−0.05–0.09) 0.01 (−0.06–0.09) −0.02 (−0.09–0.05) −0.05 (−0.12–0.02)
 Quartile 3 0.05 (−0.02–0.12) 0.03 (−0.05–0.10) 0.02 (−0.05–0.10) 0.01 (−0.06–0.09) 0.03 (−0.04–0.11) 0.00 (−0.07–0.07)
 Quartile 4 0.10 (0.03–0.18) 0.08 (0.01–0.16) 0.08 (0.01–0.16) 0.08 (0.00–0.15) 0.07 (0.00–0.15) 0.04 (−0.03–0.12)
 Per quartile 0.04 (0.01–0.06) 0.03 (0.00–0.05) 0.03 (0.00–0.05) 0.02 (0.00–0.05) 0.03 (0.00–0.05) 0.02 (−0.01–0.04)
 ptrend 0.004 0.02 0.03 0.05 0.03 0.17
Antioxidant score
 Quartile 1 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference) 0.00 (reference)
 Quartile 2 −0.01 (−0.09–0.07) −0.03 (−0.11–0.06) 0.01 (−0.07–0.09) 0.02 (−0.06–0.10) −0.01 (−0.09–0.07) −0.04 (−0.13–0.04)
 Quartile 3 0.03 (−0.06–0.12) 0.01 (−0.09–0.10) 0.03 (−0.05–0.12) 0.04 (−0.06–0.13) 0.04 (−0.05–0.12) −0.01 (−0.10–0.09)
 Quartile 4 0.12 (0.02–0.22) 0.08 (−0.02–0.19) 0.13 (0.03–0.22) 0.12 (0.02–0.23) 0.07 (−0.03–0.17) 0.01 (−0.10–0.12)
 Per quartile 0.04 (0.01–0.08) 0.03 (0.00–0.07) 0.04 (0.01–0.07) 0.04 (0.01–0.07) 0.03 (0.00–0.06) 0.01 (−0.02–0.04)
 ptrend 0.004 0.04 0.005 0.01 0.06 0.52
Open in a new tab

Data are presented as β (difference in age-, height- and sex-adjusted sd units) (95% CI), unless otherwise stated. FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; FEF25–75: forced expiratory flow at 25–75% of FVC. #: model controlling for energy intake only; : model controlling for energy intake, smoking, infections, supplements, antibiotics and paracetamol use during pregnancy, and maternal educational level, housing tenure, financial difficulties, ethnicity, age, parity, history of atopic diseases, anxiety, sex of child, season of birth, multiple pregnancy and breastfeeding duration.

The significant associations observed between maternal zinc intake and the maternal antioxidant score during pregnancy and childhood FEV1 and FVC remained unattenuated in all the sensitivity analyses (see Statistical analyses section), whereas associations with the other dietary exposures weakened (data not shown). The significant associations observed between maternal zinc intake and the maternal antioxidant score and childhood FEV1 and FVC also remained unattenuated after adjusting for child dietary zinc intake and antioxidant score, respectively, at age 3 years. In subsets of the cohort with complete data for paternal (respectively, maternal) zinc intake after pregnancy, no association was found between paternal (respectively, maternal) zinc intake or antioxidant score after pregnancy and childhood lung function (data not shown). The inverse probability weighting analysis did not alter the main results (data not shown). Post hoc analyses of the associations between maternal zinc intake and childhood FEV1 and FVC at 15 years (n=3669) showed similar findings to those observed at 8 years (difference in age-, height- and sex-adjusted sd units per quartile increase in maternal dietary zinc intake β 0.06 (95% CI 0.01–0.11); ptrend=0.01 and 0.06 (95% CI 0.01–0.10); ptrend=0.02, respectively). However, no association was found between the maternal antioxidant score and childhood FEV1 and FVC at 15 years (data not shown).

When we stratified maternal dietary associations by maternal smoking, there was no evidence of effect modification by smoking on any childhood outcome (table 4). Conversely, when we stratified the association between maternal smoking during pregnancy and childhood FEF25–75 (β per smoking category increase in the whole cohort 0.05 (95% CI −0.07– −0.02); ptrend=0.0001) by maternal intake (above and below median) of dietary antioxidants, associations between maternal smoking and childhood FEF25–75 were stronger for mothers with below median intakes of fruits, vitamin C, vitamin E and the maternal antioxidant score, although there was no statistical evidence of interaction (supplementary table S1).

TABLE 4.

Associations between maternal dietary antioxidant intake and childhood outcomes stratified by maternal smoking during pregnancy

Asthma Atopy FEV1 FVC FEF25−75
OR# (95% CI) ptrend OR# (95% CI) ptrend β# (95% CI) ptrend β# (95% CI) ptrend β# (95% CI) ptrend
Subjects n 7677 6117 6062 6157 6157
Total fruits intake          
 Non/passive smokers 0.99 (0.89–1.09) 0.81 1.02 (0.93–1.12) 0.61 0.02 (−0.02–0.05) 0.41 0.02 (−0.02–0.05) 0.35 0.01 (−0.02–0.05) 0.43
 Active smokers 0.90 (0.78–1.04) 0.16 0.87 (0.76–1.01) 0.07 0.02 (−0.04–0.08) 0.49 0.02 (−0.04–0.08) 0.45 0.00 (−0.06–0.06) 1.00
 pinteraction 0.59 0.14 0.60 0.90 0.93
Total vegetables intake
 Non/passive smokers 1.00 (0.92–1.08) 0.91 0.99 (0.93–1.06) 0.85 0.02 (−0.01–0.05) 0.15 0.02 (−0.01–0.05) 0.16 0.02 (−0.01–0.04) 0.23
 Active smokers 0.88 (0.78–1.00) 0.05 0.94 (0.83–1.07) 0.34 0.02 (−0.03–0.07) 0.36 0.00 (−0.05–0.05) 0.96 0.04 (0.00–0.09) 0.07
 pinteraction 0.18 0.70 0.96 0.35 0.25
Vitamin C intake
 Non/passive smokers 1.00 (0.92–1.09) 0.99 1.01 (0.94–1.09) 0.75 0.00 (−0.03–0.03) 0.81 0.01 (−0.02–0.04) 0.53 0.00 (−0.03–0.03) 0.86
 Active smokers 0.92 (0.81–1.05) 0.23 0.93 (0.82–1.06) 0.30 0.02 (−0.03–0.07) 0.49 0.00 (−0.05–0.05) 0.95 0.02 (−0.03–0.07) 0.41
 pinteraction 0.54 0.57 0.55 0.65 0.30
Vitamin E intake
 Non/passive smokers 1.06 (0.97–1.16) 0.19 0.97 (0.90–1.05) 0.44 0.00 (−0.03–0.03) 0.96 0.02 (−0.01–0.05) 0.25 −0.01 (−0.05–0.02) 0.38
 Active smokers 0.96 (0.84–1.11) 0.60 1.12 (0.98–1.28) 0.11 0.00 (−0.06–0.05) 0.91 0.00 (−0.05–0.05) 0.95 0.00 (−0.05–0.06) 0.94
 pinteraction 0.39 0.09 0.83 0.51 0.53
Zinc intake
 Non/passive smokers 1.06 (0.94–1.19) 0.36 0.96 (0.86–1.06) 0.39 0.04 (0.00–0.08) 0.05 0.05 (0.01–0.09) 0.02 0.01 (−0.03–0.05) 0.58
 Active smokers 1.01 (0.85–1.21) 0.90 1.01 (0.85–1.21) 0.90 0.05 (−0.02–0.12) 0.15 0.05 (−0.02–0.12) 0.14 0.04 (−0.03–0.11) 0.30
 pinteraction 0.92 0.61 0.74 0.72 0.76
Selenium intake
 Non/passive smokers 0.99 (0.90–1.10) 0.87 0.93 (0.86–1.02) 0.12 0.03 (−0.01–0.06) 0.13 0.03 (0.00–0.07) 0.07 0.01 (−0.03–0.04) 0.76
 Active smokers 1.05 (0.90–1.22) 0.56 1.03 (0.89–1.19) 0.72 0.00 (−0.06–0.06) 0.93 0.03 (−0.03–0.08) 0.40 −0.01 (−0.07–0.04) 0.64
 pinteraction 0.50 0.31 0.32 0.52 0.67
Carotene intake
 Non/passive smokers 0.98 (0.91–1.07) 0.70 0.98 (0.91–1.05) 0.53 0.02 (0.00–0.05) 0.10 0.02 (−0.01–0.05) 0.12 0.01 (−0.02–0.04) 0.52
 Active smokers 0.97 (0.85–1.10) 0.59 1.02 (0.90–1.15) 0.78 0.04 (−0.01–0.09) 0.11 0.03 (−0.02–0.08) 0.25 0.04 (−0.01–0.08) 0.15
 pinteraction 0.83 0.47 0.75 0.85 0.32
Antioxidant score
 Non/passive smokers 1.01 (0.91–1.13) 0.85 0.96 (0.87–1.05) 0.35 0.03 (−0.01–0.07) 0.18 0.04 (0.00–0.08) 0.03 0.00 (−0.04–0.04) 0.93
 Active smokers 0.94 (0.80–1.11) 0.44 1.08 (0.92–1.27) 0.37 0.05 (−0.01–0.11) 0.13 0.04 (−0.03–0.10) 0.23 0.04 (−0.02–0.10) 0.22
 pinteraction 0.70 0.25 0.86 0.64 0.35
Open in a new tab

β: difference in age-, height- and sex-adjusted sd units; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; FEF25–75: forced expiratory flow at 25–75% of FVC. #: per category/quartile of dietary intake, controlling for energy intake, infections, supplements, antibiotics and paracetamol use during pregnancy, and maternal educational level, housing tenure, financial difficulties, ethnicity, age, parity, history of atopic diseases, anxiety, sex of child, season of birth, multiple pregnancy and breastfeeding duration; : treating smoking as a binary variable and dietary exposures as continuous variables.

When the study population was restricted to mother–child pairs with complete data on maternal genotype, the main findings described above were similar (results not shown). Maternal and child genotype frequencies did not deviate significantly from Hardy–Weinberg equilibrium. When we stratified associations between maternal intake of fruits and vegetables during pregnancy and childhood outcomes by maternal GST gene polymorphisms, there was weak evidence for an interaction between vegetables intake and GSTM1 genotype on FVC (pinteraction=0.07), with a positive association only if mothers were GSTM1 null (table 5). When we investigated interactions between maternal intake of other antioxidants and maternal GST gene polymorphisms on childhood outcomes, weak evidence was found for an interaction between zinc and GSTM1 on childhood FVC (β 0.11 (95% CI 0.05–0.17); ptrend=0.001 and 0.02 (95% CI −0.05–0.08); ptrend=0.57, respectively, for the null and non-null maternal GSTM1 genotype groups; pinteraction=0.05). No interaction was found between maternal intake of other antioxidant nutrients or the antioxidant score and GST gene polymorphisms on any childhood outcome (data not shown). No interaction was found between maternal intake of selenium during pregnancy and maternal GPX4 genotype on childhood outcomes (supplementary table S2). As a post hoc analysis, we studied the associations between maternal zinc intake and childhood FVC, stratified by combinations of maternal and child GSTM1 genotypes. We observed positive associations if mothers were GSTM1 null, regardless of the child's GSTM1 genotype (table 6). Post hoc analysis did not show evidence of an interaction between maternal zinc intake and maternal GSTM1 on childhood FVC at 15 years (pinteraction=0.16).

TABLE 5.

Associations between maternal fruits and vegetables intake and childhood outcomes stratified by maternal glutathione S-transferase gene polymorphisms

Asthma Atopy FEV1 FVC FEF25−75
OR# (95% CI) ptrend OR# (95% CI) ptrend β# (95% CI) ptrend β# (95% CI) ptrend β# (95% CI) ptrend
Subjects 4953 3911 4011 4080 4080
Total fruits intake          
 GSTT1          
  Non-null (n=4376) 0.91 (0.81–1.02) 0.10 1.01 (0.90–1.12) 0.92 0.01 (−0.03–0.06) 0.52 0.02 (−0.02–0.06) 0.39 0.00 (−0.04–0.04) 0.95
  Null (n=870) 1.00 (0.77–1.30) 0.99 0.92 (0.71–1.18) 0.49 0.04 (−0.06–0.14) 0.42 0.04 (−0.06–0.13) 0.48 0.04 (−0.06–0.14) 0.46
  pinteraction 0.71 0.47 0.44 0.50 0.48
 GSTM1
  Non-null (n=2476) 0.84 (0.72–0.98) 0.03 1.01 (0.87–1.17) 0.93 0.01 (−0.05–0.06) 0.77 0.03 (−0.03–0.08) 0.30 −0.03 (−0.09–0.03) 0.29
  Null (n=2799) 0.94 (0.82–1.09) 0.44 0.98 (0.85–1.12) 0.73 0.04 (−0.02–0.09) 0.20 0.02 (−0.03–0.08) 0.46 0.04 (−0.01–0.10) 0.13
  pinteraction 0.12 0.54 0.53 0.87 0.26
 GSTP1, rs1695
  A:A (n=2289) 0.96 (0.81–1.13) 0.61 0.99 (0.85–1.16) 0.94 0.01 (−0.05–0.07) 0.66 0.01 (−0.05–0.07) 0.75 0.02 (−0.04–0.08) 0.47
  G:A (n=2529) 0.91 (0.78–1.06) 0.23 0.98 (0.85–1.13) 0.76 0.02 (−0.04–0.08) 0.55 0.02 (−0.04–0.08) 0.46 0.00 (−0.06–0.06) 0.97
  G:G (n=670) 0.83 (0.61–1.13) 0.25 0.80 (0.59–1.10) 0.17 0.00 (−0.11–0.12) 0.94 0.01 (−0.10–0.12) 0.83 0.01 (−0.11–0.13) 0.85
  pinteraction 0.60 0.94 0.59 0.87 0.31
Total vegetables intake
 GSTT1
  Non-null (n=4376) 0.91 (0.83–1.00) 0.05 1.01 (0.93–1.10) 0.85 0.04 (0.00–0.07) 0.03 0.05 (0.01–0.07) 0.006 0.02 (−0.01–0.05) 0.21
  Null (n=870) 1.00 (0.80–1.25) 0.98 0.99 (0.80–1.23) 0.95 0.01 (−0.07–0.09) 0.84 −0.03 (−0.11–0.05) 0.46 0.04 (−0.04–0.12) 0.30
  pinteraction 0.25 0.98 0.81 0.22 0.68
 GSTM1
  Non-null (n=2476) 0.90 (0.79–1.02) 0.09 0.96 (0.86–1.08) 0.54 0.01 (−0.03–0.06) 0.54 0.01 (−0.03–0.05) 0.59 0.03 (−0.01–0.07) 0.19
  Null (n=2799) 0.94 (0.84–1.06) 0.32 1.05 (0.95–1.17) 0.33 0.06 (0.01–0.10) 0.01 0.06 (0.02–0.10) 0.004 0.02 (−0.02–0.06) 0.41
  pinteraction 0.52 0.15 0.24 0.07 0.41
 GSTP1, rs1695
  A:A (n=2289) 0.98 (0.86–1.12) 0.76 1.02 (0.90–1.15) 0.77 0.06 (0.01–0.11) 0.01 0.06 (0.02–0.11) 0.008 0.04 (−0.01–0.08) 0.12
  G:A (n=2529) 0.98 (0.87–1.10) 0.72 1.06 (0.94–1.18) 0.33 0.02 (−0.03–0.06) 0.41 0.02 (−0.02–0.07) 0.27 0.00 (−0.04–0.05) 0.88
  G:G (n=670) 0.89 (0.69–1.15) 0.37 0.81 (0.64–1.02) 0.08 0.01 (−0.07–0.10) 0.78 −0.01 (−0.10–0.07) 0.76 0.06 (−0.03–0.15) 0.21
  pinteraction 0.42 0.19 0.35 0.18 0.82
Open in a new tab

β: difference in age-, height- and sex-adjusted sd units; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; FEF25–75: forced expiratory flow at 25–75% of FVC. #: per category/quartile of fruits/vegetables intake, controlling for energy intake, smoking, infections, supplements, antibiotics and paracetamol use during pregnancy, and maternal educational level, housing tenure, financial difficulties, ethnicity, age, parity, history of atopic diseases, anxiety, sex of child, season of birth, multiple pregnancy and breastfeeding duration.

TABLE 6.

Associations between maternal zinc intake and childhood forced vital capacity (FVC) stratified by combinations of maternal and child glutathione S-transferase GSTM1 genotypes

GSTM1 n FVC#
Mother Child β (95% CI) ptrend
Non-null Non-null 956 0.04 (−0.12–0.05) 0.41
Non-null Null 452 0.04 (−0.11–0.18) 0.60
Null Non-null 439 0.13 (−0.01–0.27) 0.07
Null Null 1167 0.15 (0.07–0.23) 0.0002
Open in a new tab

β: difference in age-, height- and sex-adjusted sd units. #: n=3014; : per quartile of zinc intake, controlling for energy intake, smoking, infections, supplements, antibiotics and paracetamol use during pregnancy, and maternal educational level, housing tenure, financial difficulties, ethnicity, age, parity, history of atopic diseases, anxiety, sex of child, season of birth, multiple pregnancy and breastfeeding duration.

Discussion

In this large, population-based, birth cohort study, we found that a higher maternal zinc intake during pregnancy was associated, in a dose–response fashion, with higher FEV1 and FVC in the offspring, after controlling for potential confounders. To the best of our knowledge this is a novel finding. Only one other birth cohort study has investigated the relation between maternal diet in pregnancy and childhood lung function, and did not report any association between maternal zinc intake and lung function in the offspring at age 5 years [2], but the sample size was much smaller than ours. We also found weak evidence for an interaction between maternal zinc intake during pregnancy and maternal GSTM1 genotype on childhood FVC. Interactions between maternal intake of antioxidants and antioxidant genotype on childhood lung function and other respiratory and atopic outcomes have not previously been investigated. The graded nature of the associations between maternal zinc intake and lung function is in keeping with a causal effect on lung growth and development, and persistence of the association from childhood to adolescence strengthens causal inference further. While we also found positive associations between the maternal antioxidant score during pregnancy (derived from five antioxidant nutrients) and childhood lung function, these were largely explained by maternal zinc intake.

A surprising observation was the lack of interaction between maternal intake of antioxidants and maternal smoking on childhood outcomes. We hypothesised that a higher intake of antioxidants might be particularly beneficial if the fetus was exposed to tobacco smoke, a source of oxidative stress, but no such effect modification was seen. To the best of our knowledge, this has not been investigated before. However, when we examined the detrimental effect of maternal smoking on FEF25–75, we found that effect estimates were generally larger if mothers had below average intakes of antioxidants, especially vitamin C and vitamin E, than if their intakes were above average. While these differences were not statistically significant on formal testing for interaction, they are in keeping with a trial in pregnant smokers which showed that the detrimental effect of smoking on infant lung function was reduced by vitamin C supplementation in pregnancy [11]. One possible explanation for why we did not see a statistically significant interaction between antioxidant intake and smoking is that the estimated maximum vitamin C intake from food alone in ALSPAC pregnant females was 256 mg·day−1, which is much lower than the 500 mg daily intake of vitamin C taken by mothers in the trial [11], although ∼20% of ALSPAC females were also taking vitamin supplements.

While some studies have suggested a possible protective effect of maternal intake of vitamin E, zinc, fruits and vegetables during pregnancy on childhood asthma and atopy [2–4], we found no evidence to support this, nor were the other antioxidant nutrients associated with these outcomes, which is concordant with two recent systematic reviews and meta-analyses [5, 43]. Given the size of our study, we therefore believe that the new totality of evidence (including ALSPAC) indicates that there is unlikely to be a causal relationship between dietary antioxidant intake in pregnancy and risk of childhood asthma and atopy.

Mechanisms

A plausible explanation for the associations we observed between maternal zinc intake during pregnancy and childhood lung function, and especially FVC, could be that pre-natal zinc status influences growth and development of fetal lungs. In support of this hypothesis, zinc deficiency has been associated with impaired fetal lung growth in rats [44]. According to the FFQ completed in pregnancy, the main sources of dietary zinc were red meat and poultry in ALSPAC pregnant females. Although zinc is generally considered to be an antioxidant, it can serve such a function only indirectly and the term “pro-antioxidant” is more appropriate [45]. While the interaction between maternal zinc intake and maternal GSTM1 genotype on childhood FVC is in keeping with a pro-antioxidant effect of zinc on pre-natal lung growth (stronger association if the mother was GSTM1 null and therefore had compromised enzymatic antioxidant defences), the lack of effect modification by maternal smoking and the lack of effect modification by maternal GSTM1 genotype on FVC in adolescence do not support such an interpretation. However, zinc influences growth through multiple, complex pathways [46] and effects on fetal lung growth may not involve its pro-antioxidant properties.

Strengths and limitations

Strengths of the ALSPAC birth cohort include its population-based prospective design, rich information on numerous potential confounders and detailed phenotypic outcome measurements. ALSPAC's size gave us greater statistical power than previous, smaller birth cohorts that have investigated this research question. Another major strength of the ALSPAC birth cohort is that maternal DNA was collected, enabling maternal genotyping and exploration of interactions with pre-natal exposures, which is not possible in most other birth cohort studies.

Although the FFQ that we used had not been formally calibrated against other instruments such as diet diaries, it was based on the one used by Yarnell et al. [47], which has been validated against weighed dietary records, and modified in the light of a more recent weighed dietary survey [21]. While there will have been some misclassification of dietary exposures, this is likely to be nondifferential with respect to the outcomes of interest and would be expected to bias effect estimates towards the null; in other words, the magnitude of associations may have been underestimated and small or modest effects may have been missed. The possibility of uncontrolled or residual confounding cannot be ruled out. However, we think that confounding of the main findings by lifestyle or other aspects of maternal diet in pregnancy is unlikely, as we controlled for numerous potential confounders in the analyses, including post-natal zinc intake. The null findings for maternal and paternal zinc intakes after pregnancy make confounding by unmeasured familial behaviours linked to zinc intake and offspring lung function a less likely explanation for the main findings.

As with any longitudinal study, we cannot rule out the possibility that exclusion of mother–child pairs without complete information might have biased our findings. However, it could be argued that, for our results to be totally spurious for maternal zinc intake and childhood lung function in those included in our analysis (and for the associations to be truly null in the population as a whole), associations in the excluded mother–child pairs would have to be at least of equal magnitude in the opposite direction, which seems unlikely. Furthermore, loss to follow-up bias has been shown to only slightly modify associations in longitudinal studies, including in ALSPAC [48], and the results of our inverse probability weighting analysis confirmed that loss to follow-up is unlikely to have biased our results. In view of the multiple analyses carried out, we cannot exclude the possibility that the main findings occurred by chance; hence, they should be interpreted with caution and require replication in another birth cohort study. Given the a priori nature of the general hypothesis being tested and the fact that some outcomes of interest were highly correlated, it did not seem appropriate to correct for multiple testing.

Conclusions

We conclude that a higher maternal intake of zinc during pregnancy may improve lung function, and especially FVC, in the offspring, but further studies are needed to confirm these results. A Mendelian randomisation approach could be used to strengthen causal inference. If the association with pre-natal zinc status is causal, this may have greater implications in developing countries where zinc deficiency is a bigger problem today than it is in the West [49]. In contrast, we found no evidence that maternal dietary antioxidant intake in pregnancy is associated with risk of childhood asthma or atopy, suggesting that intervening in pregnancy to increase antioxidant intake would be unlikely to succeed as a strategy to prevent these conditions.

Supplementary material

Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.

Supplementary material ERJ-00507-2018_Supplement (331.1KB, pdf)

Acknowledgements

We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. This paper is the work of the authors, and A.J. Henderson and S.O. Shaheen will serve as guarantors for its contents. The views expressed are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Dept of Health.

Footnotes

This article has supplementary material available from erj.ersjournals.com

Author contributions: A. Bédard and S.O. Shaheen conceived the study and drafted the manuscript. All authors were involved in the analysis strategy, K. Northstone gave advice on the dietary data, and A. Bédard performed the statistical analyses. A.J. Henderson was responsible for all clinical respiratory and allergy data collection. J.W. Holloway was responsible for generation of the genotyping data. All authors participated in the interpretation of the findings, reviewed the manuscript and revised it critically before submission. All authors have seen and approved the final version of the manuscript.

Conflict of interest: A.J. Henderson reports grants from the Medical Research Council and Wellcome, during the conduct of the study.

Support statement: The UK Medical Research Council, the Wellcome Trust (grant 102215/2/13/2) and the University of Bristol currently provide core support for ALSPAC. A. Bédard is funded by a European Respiratory Society Long-Term Research Fellowship (LTRF 2015-5838). Funding information for this article has been deposited with the Crossref Funder Registry.

References

  • 1.Seaton A, Godden DJ, Brown K. Increase in asthma: a more toxic environment or a more susceptible population? Thorax 1994; 49: 171–174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Devereux G, Turner SW, Craig LC, et al. Low maternal vitamin E intake during pregnancy is associated with asthma in 5-year-old children. Am J Respir Crit Care Med 2006; 174: 499–507. [DOI] [PubMed] [Google Scholar]
  • 3.Nurmatov U, Devereux G, Sheikh A. Nutrients and foods for the primary prevention of asthma and allergy: systematic review and meta-analysis. J Allergy Clin Immunol 2011; 127: 724–733. [DOI] [PubMed] [Google Scholar]
  • 4.Garcia-Larsen V, Del Giacco SR, Moreira A, et al. Asthma and dietary intake: an overview of systematic reviews. Allergy 2016; 71: 433–442. [DOI] [PubMed] [Google Scholar]
  • 5.Beckhaus AA, Garcia-Marcos L, Forno E, et al. Maternal nutrition during pregnancy and risk of asthma, wheeze, and atopic diseases during childhood: a systematic review and meta-analysis. Allergy 2015; 70: 1588–1604. [DOI] [PubMed] [Google Scholar]
  • 6.Willett W. Nutritional epidemiology: issues and challenges. Int J Epidemiol 1987; 16: 312–317. [DOI] [PubMed] [Google Scholar]
  • 7.Romieu I, Castro-Giner F, Kunzli N, et al. Air pollution, oxidative stress and dietary supplementation: a review. Eur Respir J 2008; 31: 179–197. [DOI] [PubMed] [Google Scholar]
  • 8.Burke H, Leonardi-Bee J, Hashim A, et al. Prenatal and passive smoke exposure and incidence of asthma and wheeze: systematic review and meta-analysis. Pediatrics 2012; 129: 735–744. [DOI] [PubMed] [Google Scholar]
  • 9.Guerra S, Stern DA, Zhou M, et al. Combined effects of parental and active smoking on early lung function deficits: a prospective study from birth to age 26 years. Thorax 2013; 68: 1021–1028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Henderson AJ, Newson RB, Rose-Zerilli M, et al. Maternal Nrf2 and gluthathione-S-transferase polymorphisms do not modify associations of prenatal tobacco smoke exposure with asthma and lung function in school-aged children. Thorax 2010; 65: 897–902. [DOI] [PubMed] [Google Scholar]
  • 11.McEvoy CT, Schilling D, Clay N, et al. Vitamin C supplementation for pregnant smoking women and pulmonary function in their newborn infants. JAMA 2014; 311: 2074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Chung J, Kwon SO, Ahn H, et al. Association between dietary patterns and atopic dermatitis in relation to GSTM1 and GSTT1 polymorphisms in young children. Nutrients 2015; 7: 9440–9452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Gref A, Rautiainen S, Gruzieva O, et al. Dietary total antioxidant capacity in early school age and subsequent allergic disease. Clin Exp Allergy 2017; 47: 751–759. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Romieu I, Sienra-Monge JJ, Ramírez-Aguilar M, et al. Genetic polymorphism of GSTM1 and antioxidant supplementation influence lung function in relation to ozone exposure in asthmatic children in Mexico City. Thorax 2004; 59: 8–10. [PMC free article] [PubMed] [Google Scholar]
  • 15.Boyd A, Golding J, Macleod J, et al. Cohort profile: the “Children of the 90s” – the index offspring of the Avon Longitudinal Study of Parents and Children. Int J Epidemiol 2013; 42: 111–127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Fraser A, Macdonald-Wallis C, Tilling K, et al. Cohort profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int J Epidemiol 2013; 42: 97–110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Cornish RP, Henderson J, Boyd AW, et al. Validating childhood asthma in an epidemiological study using linked electronic patient records. BMJ Open 2014; 4: e005345. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Chinn S, Rona RJ. Height and age adjustment for cross sectional studies of lung function in children aged 6–11 years. Thorax 1992; 47: 707–714. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.American Thoracic Society. Standardization of spirometry, 1994 update. Am J Respir Crit Care Med 1995; 152: 1107–1136. [DOI] [PubMed] [Google Scholar]
  • 20.Arets HGM, Brackel HJL, Van Der Ent CK. Forced expiratory manoeuvres in children: do they meet ATS and ERS criteria for spirometry? Eur Respir J 2001; 18: 655–660. [DOI] [PubMed] [Google Scholar]
  • 21.Rogers I, Emmett P. Diet during pregnancy in a population of pregnant women in South West England. ALSPAC Study Team. Avon Longitudinal Study of Pregnancy and Childhood. Eur J Clin Nutr 1998; 52: 246–250. [DOI] [PubMed] [Google Scholar]
  • 22.Ministry of Agriculture, Fisheries and Food. Food Portion Sizes. London, HMSO, 1991. [Google Scholar]
  • 23.Ministry of Agriculture, Fisheries and Food. McCance and Widdowson's The Composition of Foods. 5th Edn London, Royal Society of Chemistry, 1991. [Google Scholar]
  • 24.Rose-Zerilli MJ, Barton SJ, Henderson AJ, et al. Copy-number variation genotyping of GSTT1 and GSTM1 gene deletions by real-time PCR. Clin Chem 2009; 55: 1680–1685. [DOI] [PubMed] [Google Scholar]
  • 25.Minelli C, Granell R, Newson R, et al. Glutathione-S-transferase genes and asthma phenotypes: a Human Genome Epidemiology (HuGE) systematic review and meta-analysis including unpublished data. Int J Epidemiol 2010; 39: 539–562. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Shaheen SO, Newson RB, Ring SM, et al. Prenatal and infant acetaminophen exposure, antioxidant gene polymorphisms, and childhood asthma. J Allergy Clin Immunol 2010; 126: 1141–1148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Shaheen SO, Rutterford CM, Lewis SJ, et al. Maternal selenium status in pregnancy, offspring glutathione peroxidase 4 genotype, and childhood asthma. J Allergy Clin Immunol 2015; 135: 1083–1085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Nurmatov U, Nwaru BI, Devereux G, et al. Confounding and effect modification in studies of diet and childhood asthma and allergies. Allergy 2012; 67: 1041–1059. [DOI] [PubMed] [Google Scholar]
  • 29.Birtchnell J, Evans C, Kennard J. The total score of the Crown-Crisp Experiential Index: a useful and valid measure of psychoneurotic pathology. Br J Med Psychol 1988; 61: 255–266. [DOI] [PubMed] [Google Scholar]
  • 30.Gernand AD, Schulze KJ, Stewart CP, et al. Micronutrient deficiencies in pregnancy worldwide: health effects and prevention. Nat Rev Endocrinol 2016; 12: 274–289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Jaakkola JJK, Ahmed P, Ieromnimon A, et al. Preterm delivery and asthma: a systematic review and meta-analysis. J Allergy Clin Immunol 2006; 118: 823–830. [DOI] [PubMed] [Google Scholar]
  • 32.Grieger JA, Clifton VL. A review of the impact of dietary intakes in human pregnancy on infant birthweight. Nutrients 2015; 7: 153–178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Brooks AM, Byrd RS, Weitzman M, et al. Impact of low birth weight on early childhood asthma in the United States. Arch Pediatr Adolesc Med 2001; 155: 401–406. [DOI] [PubMed] [Google Scholar]
  • 34.Tielemans MJ, Erler NS, Leermakers ETM, et al. A priori and a posteriori dietary patterns during pregnancy and gestational weight gain: the generation R study. Nutrients 2015; 7: 9383–9399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Forno E, Young OM, Kumar R, et al. Maternal obesity in pregnancy, gestational weight gain, and risk of childhood asthma. Pediatrics 2014; 134: e535–e546. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Bédard A, Dumas O, Kauffmann F, et al. Potential confounders in the asthma–diet association: how causal approach could help? Allergy 2012; 67: 1461–1462. [DOI] [PubMed] [Google Scholar]
  • 37.Reynolds CM, Gray C, Li M, et al. Early life nutrition and energy balance disorders in offspring in later life. Nutrients 2015; 7: 8090–8111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Litonjua AA, Gold DR. Asthma and obesity: common early-life influences in the inception of disease. J Allergy Clin Immunol 2008; 121: 1075–1084. [DOI] [PubMed] [Google Scholar]
  • 39.Maslova E, Rytter D, Bech BH, et al. Maternal protein intake during pregnancy and offspring overweight 20 y later. Am J Clin Nutr 2014; 100: 1139–1148. [DOI] [PubMed] [Google Scholar]
  • 40.Smith GD. Assessing intrauterine influences on offspring health outcomes: can epidemiological studies yield robust findings? Basic Clin Pharmacol Toxicol 2008; 102: 245–256. [DOI] [PubMed] [Google Scholar]
  • 41.Shaheen SO, Newson RB, Smith GD, et al. Prenatal paracetamol exposure and asthma: further evidence against confounding. Int J Epidemiol 2010; 39: 790–794. [DOI] [PubMed] [Google Scholar]
  • 42.Hernán MA, Hernandez-Diaz S, Robins JM. A structural approach to selection bias. Epidemiology 2004; 15: 615–625. [DOI] [PubMed] [Google Scholar]
  • 43.Garcia-Larsen V, Ierodiakonou D, Jarrold K, et al. Diet during pregnancy and infancy and risk of allergic or autoimmune disease: a systematic review and meta-analysis. PLoS Med 2018; 15: e1002507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Vojnik C, Hurley LS. Abnormal prenatal lung development resulting from maternal zinc deficiency in rats. J Nutr 1977; 107: 862–872. [DOI] [PubMed] [Google Scholar]
  • 45.Maret W. Zinc biochemistry: from a single zinc enzyme to a key element of life. Adv Nutr 2013; 4: 82–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.MacDonald RS. Zinc and health: current status and future directions the role of zinc in growth and cell proliferation. J Nutr 2000; 130: 1500S–1508S. [DOI] [PubMed] [Google Scholar]
  • 47.Yarnell JW, Fehily AM, Milbank JE, et al. A short dietary questionnaire for use in an epidemiological survey: comparison with weighed dietary records. Hum Nutr Appl Nutr 1983; 37: 103–112. [PubMed] [Google Scholar]
  • 48.Howe LD, Tilling K, Galobardes B, et al. Loss to follow-up in cohort studies. Epidemiology 2013; 24: 1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Wessells KR, Brown KH. Estimating the global prevalence of zinc deficiency: results based on zinc availability in national food supplies and the prevalence of stunting. PLoS One 2012; 7: e50568. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.

Supplementary material ERJ-00507-2018_Supplement (331.1KB, pdf)

Articles from The European Respiratory Journal are provided here courtesy of European Respiratory Society

RESOURCES