Abstract
Background
We recently demonstrated that maternal dietary supplementation with
fish oil-derived n-3 long-chain polyunsaturated fatty acids (n-3
LCPUFAs) during pregnancy reduces the risk of asthma in the offspring
but the mechanisms involved are unknown.
Methods
Here we investigated potential metabolic mechanisms using untargeted
liquid chromatography-mass spectrometry-based metabolomics on 577
plasma samples collected at age 6 months in the offspring of mothers
participating in the n-3 LCPUFA randomized controlled trial. First,
associations between the n-3 LCPUFA supplementation groups and child
metabolite levels were investigated using univariate regression models
and data-driven partial least square discriminant analyses (PLS-DA).
Second, we analyzed the association between the n-3 LCPUFA metabolomic
profile and asthma development using Cox-regression. Third, we
conducted mediation analyses to investigate whether the protective
effect of n-3 LCPUFA on asthma was mediated via the metabolome.
Findings
The univariate analyses and the PLS-DA showed that maternal fish oil
supplementation affected the child's metabolome, especially with lower
levels of the n-6 LCPUFA pathway-related metabolites and saturated and
monounsaturated long-chain fatty acids-containing compounds, lower
levels of metabolites of the tryptophan pathway, and higher levels of
metabolites in the tyrosine and glutamic acid pathway. This fish
oil-related metabolic profile at age 6 months was significantly
associated with a reduced risk of asthma by age 5 and the metabolic
profile explained 24% of the observed asthma-protective effect in the
mediation analysis.
Interpretation
Several of the observed pathways may be involved in the
asthma-protective effect of maternal n-3 LCPUFA supplementation and act
as mediators between the intervention and disease development.
Funding
COPSAC is funded by private and public research funds all listed on
[39]www.copsac.com.
Keywords: Metabolomics, Fish oil, Childhood asthma
__________________________________________________________________
Research in context.
Evidence before this study
The global increase in childhood asthma prevalence has been observed in
parallel with an increasing dietary intake of n-6 polyunsaturated fatty
acids (PUFA) from e.g. vegetable oils and decreasing intake of n-3
LCPUFAs found in fish, suggesting a modifiable link between n-3 LCPUFA
deprived diet during pregnancy and offspring asthma risk. Recently, we
proved that hypothesis in a double-blind, randomized controlled trial
of supplementation with fish oil-derived n-3 LCPUFA or placebo during
third pregnancy trimester, showing 31% reduced risk of asthma in the
offspring by age 5. However, the underlying mechanism behind the
supplementation effect is unknown.
Added value of this study
In this study we used global, untargeted metabolomics profiles from
plasma samples collected at age 6 months in the children born to
mothers participating in the n-3 LCPUFA trial to investigate metabolic
perturbations related to the prenatal n-3 LCPUFA supplementation and
whether those perturbations explained some of the underlying biology of
the asthma-protective effect. We observed differences in several
metabolic pathways, which have previously been related to asthma such
as n-6 LCPUFA pathway-related metabolites and tryptophan metabolites as
well as metabolites in the tyrosine and glutamic acid pathway. Further,
we showed that these n-3 LCPUFA related metabolic perturbations were
associated with a reduced risk of asthma by age 5 and significantly
mediated one fourth of the asthma-protective effect observed in the
trial.
Implications of all the available evidence
These findings provide novel insight into the metabolic effects of
dietary n-3 LCPUFA supplementation and the pathogenesis of childhood
asthma. Further, this study is an example of translational research,
where mechanisms of a clinically import finding from a dietary,
randomized controlled trial are explored using metabolomics.
Alt-text: Unlabelled Box
1. Introduction
Asthma is the most common chronic disease in childhood and has doubled
in prevalence over the last decades in Westernized countries [[40]1].
This rapid increase in disease prevalence is believed to be caused by
changes in lifestyle and human diet, particularly, an increased intake
of n-6 long-chain polyunsaturated fatty acids (n-6 LCPUFAs) and a
decreased intake of n-3 LCPUFAs among pregnant mothers, has been
associated with an increased risk of asthma in the offspring [[41]2].
The main dietary source of n-3 LCPUFA comes from fish and other
seafood, especially from cold-water fatty fish such as salmon and
mackerel, and/or from dietary supplements such as dietary fish oil
capsules. Due to those observations, we conducted a double-blind
randomized controlled trial (DB-RCT) of supplementation with fish oil
during pregnancy in the Copenhagen Prospective Studies on Asthma in
Childhood 2010 (COPSAC[2010]) mother-child cohort. This resulted in a
31% reduced risk of asthma in the offspring during their first 5 years
of life [[42]3]. Thus, n-3 LCPUFA supplementation during pregnancy
holds promise for the primary prevention of childhood asthma, but the
underlying mechanisms of the asthma-protective effect of LCPUFA remain
to be elucidated.
Untargeted metabolomics provides a global assessment of the metabolome,
which is the complete set of small-molecule metabolites in a biological
sample. By generating a complete metabolomic profile of a specific
biofluid, such as plasma, from a cohort of individuals [[43]4],
metabolomics can be applied to disentangle molecular mechanisms of
complex diseases such as asthma or metabolic consequences of
environmental exposures, such as diet. Hitherto, metabolomic profiling
studies have shown promising results for identifying the underlying
mechanisms of asthma [[44]5]. Furthermore, metabolomics enables
studying the long-term downstream metabolic effects in the child of
micronutrient and macronutrient supplementation during pregnancy
[[45]6], such as n-3 LCPUFAs.
The objective of this study was to conduct plasma metabolomic profiling
using untargeted liquid chromatography-mass spectrometry (LC-MS)
technique on blood sampled at age 6 months in the COPSAC[2010] cohort
to explore potential biochemical mechanisms behind the
asthma-protective effect of n-3 LCPUFA supplementation during
pregnancy.
2. Methods
2.1. Study population
The Copenhagen Prospective Studies on Asthma in Childhood[2010]
(COPSAC[2010]) cohort is a population-based mother-child cohort in
which 700 pregnant women were recruited between 22 and 26 weeks of
gestation and their children subsequently followed prospectively during
their first five years of life at 12 scheduled clinical visits. All the
visits were conducted at the COPSAC research center, where trained
pediatricians examined the children and collected exposure information
at age 1 week, 1, 3, 6, 12, 18, 24, 30, and 36 months after birth, and
yearly thereafter. In addition, whenever the children experienced lung,
allergy or skin-related symptoms the families attended acute care
visits at the research unit, where the COPSAC pediatricians diagnosed
and treated asthma, allergy, and eczema in accordance with predefined
validated algorithms [[46]7]. Furthermore, the parents filled daily
diary cards prospectively from birth recording significant troublesome
lung symptoms, including cough, wheeze, and dyspnea, anti-asthmatic
treatment, skin symptoms and treatment, and infections.
2.2. n-3 LCPUFA trial
The pregnant women were enrolled in a double-blind, randomized
controlled trial (DB-RCT) [[47]3]. At enrollment during pregnancy week
22–26, the women were randomly assigned in a 1:1 ratio to receive 2.4 g
per day of n − 3 long-chain polyunsaturated fatty acids (LCPUFAs) in
triacylglycerol form from fish oil capsules containing 55%
eicosapentaenoic acid (20:5n–3, EPA) and 37% docosahexaenoic acid
(22:6n–3, DHA (Incromega TG33/22, Croda, Health Care) or placebo in the
form of lookalike olive oil capsules, containing 72% n–9 oleic acid and
12% n − 6 linoleic acid (Pharma-Tech A/S). The women continued taking
the daily supplement until 1 week after delivery, where they visited
the research unit with their child. A subgroup of the pregnant women
(n = 623) also participated in another DB-RCT with a nested 2 × 2
factorial design in which they were assigned to 2400 IU of vitamin D
per day or placebo from pregnancy week 22–26 till 1 week postpartum on
top of the recommended pregnancy supplement of 400 IU/d; i.e. the women
received 2800 vs. 400 IU/d of vitamin D, which did not significantly
affect the offspring's risk of developing asthma [[48]3].
2.3. Ethics statement
The trial was approved by The National Committee on Health Research
Ethics (H-B-2008-093) and the Danish Data Protection Agency
(2015-41-3696). Both parents gave oral and written informed consent
before enrollment.
2.4. Asthma diagnosis
Asthma or persistent wheeze was the primary end point of the DB-RCT of
n-3 LCPUFA supplementation during pregnancy and was solely diagnosed by
the COPSAC pediatricians based on a quantitative validated symptom
algorithm [[49]8,[50]9], including all of the following four criteria:
(1) verified diary recordings of at least five episodes of troublesome
lung symptoms within six months, each lasting at least three
consecutive days; (2) symptoms typical of asthma, including
exercise-induced symptoms, prolonged nocturnal cough, and/or persistent
cough outside of common colds; (3) need for intermittent rescue use of
inhaled β2-agonist; and (4) response to a three-month course of inhaled
corticosteroids and relapse upon ending treatment [[51]8].
2.5. Covariates
2.5.1. Breastfeeding
Information about the duration of exclusive and total breastfeeding
duration was obtained during the scheduled visits to the COPSAC clinic
and recorded online in a dedicated database.
2.5.2. FADS genotyping
Maternal and child variation in the gene encoding fatty acid desaturase
(FADS2) was assessed by genotyping the single-nucleotide polymorphism
(SNP) rs1535. Genotyping was performed using the Illumina Infinium
HumanOmniExpressExome Bead chip, at the AROS Applied Biotechnology AS
center, Aarhus, Denmark. Genotypes were called with Illumina Genome
Studio software and rs1535 was genotyped on this array. We excluded
individuals with individual genotyping call rate < 0.95, gender
mismatch, genetic duplicates or outlying heterozygosity > 0.27 and
<0.037. Furthermore, maternal genotyping was solely done in mothers of
European descent as there are very few non-Caucasian mothers in the
cohort.
2.5.3. Blood sample collection and storage
A blood sample was collected from the child when visiting the research
clinic at age 6 months. The blood sample was collected in an EDTA tube
and left at room temperature for 30 min and thereafter spun down for
10 min at 4000 rpm. The supernatant was collected and stored at −80 °C
until further analysis.
2.6. Liquid Chromatography-Mass Spectrometry (LC-MS) metabolomics analysis
2.6.1. Sample preparation
The untargeted metabolomic analysis of the plasma samples was carried
out by Metabolon, Inc. (NC, USA). Briefly, the sample preparation was
done using the automated system MicroLab STAR® system from Hamilton
Company. Prior to extraction, the samples were fortified with recovery
standards for quality control (QC) purposes. Methanol was used to
extract the metabolites and precipitate the proteins during vigorous
shaking for 2 min (Glen Mills GenoGrinder 2000) followed by
centrifugation. The resulting extract was divided into four aliquots to
be subsequently analyzed by LC-MS/MS comprising of four platforms.
Samples were placed on a TurboVap® (Zymark) to remove the organic
solvent and stored overnight under nitrogen before preparation for
LC-MS/MS analysis.
2.6.2. LC-MS/MS analysis
All the four platforms utilized an ACQUITY Ultra-Performance Liquid
Chromatography (UPLC) (Waters, Milford, USA) and a Q Exactive™ Hybrid
Quadrupole-Orbitrap™ mass spectrometer interfaced with heated
electrospray ionization (HESI-II) source (ThermoFisher Scientific,
Waltham, Massachusetts, USA). The sample extracts were reconstituted in
solvents compatible to each of the four LC-MS methods: two separated
reverse phase UPLC-ESI(+)MS/MS methods optimized for hydrophilic and
hydrophobic compounds; one reverse phase UPLC-(−)MS/MS using basic
optimized conditions; and one HILIC/UPLC-(−)MS/MS. The MS analysis
alternated between full scan MS and data-dependent MS^n scans using
dynamic exclusion. The scan range for both ionization modes was
70–1000 m/z.
2.6.3. Data collection and quality control
The raw data was extracted, peak identified, and QC processed. Samples
were semi-quantified using area-under-the-curve. Peak identification
was done by automated comparison of the ion feature in the experimental
spectrum to the ions present in the library spectrum of authenticated
standards or recurrent unknown entities. The identification was based
on three matching criteria: narrow retention time/index (RI) range,
mass accuracy (±10 ppm) and MS/MS spectra. The identification level
reported in this paper follows the criteria described by Summer LW et
al. [[52]10]. Compounds labelled with “*” have identification level 2,
compounds labelled with “**” have level 3 (since no standards or
matching spectra are available). Compounds named with “X-#” are unknown
and therefore have level 4. If no label is applied, the identification
level is 1.
Throughout the analyses of the batches, different kinds of samples were
analyzed for instrument performance monitoring: a well-characterized
human plasma as a technical replicate, extracted water samples as
process blanks, and a mixture of QC standards (not interfering with the
measurement of endogenous compounds) spiked in every analyzed sample
prior to injection. The instrument variability was determined by
calculating the median relative standard deviation (RSD) for the QC
standards spiked in. It ranged around 7%. The total process variability
was determined by calculating the median RSD for all endogenous
metabolites (i.e., non-instrument standards) present in the technical
replicates. It resulted to be within 10%.
2.7. Statistical analysis
2.7.1. Data preprocessing
Each compound was corrected in run-day blocks by registering the
medians to equal one (1.00) and normalizing each variable, accordingly.
This data normalization procedure that was done for all the dataset
from the four platforms was performed to compensate for the inter-day
variation. The final set of variables from all the four platforms was
imported into Matlab (Version 9.3, the Mathworks, Inc., MA, USA) and
RStudio (Version 1.1, RStudio, Inc) for the statistical analysis.
In order to remove spurious information prior to data analysis, samples
having ≥30% of missing values as well as metabolites containing ≥70% of
missing values were discarded. Furthermore, since the aim of our
analysis was to investigate the effect of the n-3 LCPUFA
supplementation during pregnancy on the child metabolome at 6 months of
age, variables not present in at least 70% of the samples in either the
n-3 LCPUFA or placebo group were removed [[53]11]. Finally, missing
values were imputed with zeros.
2.7.2. Univariate linear regression analysis
Firstly, linear regression analysis was performed to compare the
relative concentrations of the imported set of metabolites (features)
in the n-3 LCPUFA supplementation vs. placebo group. Significant
features were reported at a nominal significance level in this
exploratory analysis (p-values ≤ .05 from “Wald test”).
Linear regression analysis was also used to correlate solely
breastfeeding duration with selected metabolite levels. The intercept
of such model also allowed to estimate the levels of the compounds
independent by the breastfeeding. Thereafter, stratifying the analysis
by the n-3 LCPUFA intervention allowed us to estimate the difference in
modelled metabolite levels at birth vs. at 6 months (time of sampling)
by the slope. In order to test if those differences were significant,
we used simultaneous tests for general linear hypotheses from the
“multcomp” R package.
Influence of maternal and child FADS2 genotype were also investigated
using linear regression models. Genotype was associated with selected
metabolites level in the n-6 pathway stratified by treatment and
accounting for each other genotype and exclusively breastfeeding
duration. The association is reported per copy of the minor allele G in
terms of beta-estimate, standard error (SE) and significance (p-value
from “Wald test”).
2.7.3. Multivariate partial least squares discriminant analysis (PLS-DA)
After the univariate analysis, PLS-DA was employed to classify the
differences between metabolites in the n-3 LC-PUFA supplement group
compared to placebo. Initially, the dataset was imported into the
PLS_Toolbox (Version 8.61, Eigenvector Research, Inc., MA, USA) and
auto-scaled prior to analysis. The PLS-DA model validation was done by
iterating 100×, using a 10-fold cross-validation model in the training
set in which the number of PLS-DA components was chosen based on the
lowest misclassification error. During each iteration, the rank of each
feature was recorded based on its selectivity ratio (SR) and the model
was validated based on misclassification error on a random test set
selected up-front and using 20% of the original dataset. The product of
the ranks was used to sort the variables ascending (low rank, high
importance) and successively to select the most important ones based on
the lowest misclassification error on the random test set (20% of the
total samples) when models were built including 10 variables at the
time and starting with 11 initial features [[54]12]. Furthermore, a
permutation test using 100× iterations was applied to assess the
classification performance of the final PLS-DA model with the selected
variables, where the p-values are from a randomization t-test.
2.7.4. Rotated model
The PLSDA model estimates the linear combination of the original matrix
(X (n,p)) that most optimally relates to the intervention (f (n,1)) by
finding a so called score matrix (T = XW (n,k)). This score matrix by
definition captures the structure in the metabolomics data table (X)
that has maximum covariance with the intervention. However, as the
estimation do not utilize information of the clinical outcome (asthma
at 6y of age: o (n,1)), the metabolomics intervention fingerprint
related to asthma will be distributed across all k components. In order
to make a more parsimonious model, that in the first component (T[1])
reflects the invention fingerprint related to outcome while leaving the
part that is only related to intervention, but not outcome in the
remaining components (T[2],…, T[k]) the score matrix is rotated towards
the outcome using orthogonal procrustes rotation [[55]13].
2.7.5. Enrichment analysis
One-sided Fisher's exact test was performed to investigate for any
pathway enrichment within the selected metabolites. For each pathway, a
contingency table (2 × 2) was made, see Table S1 in the Supplementary
Material for further details. Enrichment is defined for significantly
(p-value < .05) higher odds for inclusion of a metabolite for the i'the
pathway compared to all other pathways.
2.7.6. Cox-regression and mediation analysis
Cox regression analysis using the “survival” R package was employed to
assess the correlation between the components of the rotated PLS-DA
model (which were z-scored), that describes the difference between n-3
LCPUFA and placebo, and the risk of developing asthma during the first
5 years of life. Reported p-values are from Wald test. A Kaplan-Meier
curve was used to representing the association between the PLS-DA
component scores divided into tertiles and risk of developing asthma.
To assess the mediation effect between the components of the rotated
PLS-DA model and the development of asthma during the first 5 years of
life, the R package “mediation” was used with 95% CIs based on
quasi-Bayesian approximation using 10,000 Monte-Carlo draws. The
mediation analysis was built in the following way:
firstly, a linear model was built associating the rsLV1 and the n-3
LCPUFA supplementation (Model 1), followed by a binomial model
associating the asthma diagnosed till age 5 years (n = 116) and the n-3
LCPUFA supplementation plus the rsLV1 (Model 2). Afterwards, a third
model was built combining these two models together and imposing rsLV1
as mediating factor between the asthma development and the n-3 LCPUFA
supplementation (Model 3).
[MATH: Model1InterventionvsrLV1:lmrsLv1~intervent
ion :MATH]
(1)
[MATH: Model2asthma:glmasthma~intervention+rLV1
:MATH]
(2)
[MATH: Model3Model1+Model2:med
iationModel1Model2mediator<
mo
linebreak="goodbreak">="rLV1"<
/mrow> :MATH]
(3)
2.8. Data availability
De-identified metabolomics data is deposited in the publicly available
data repository, “Metabolomics Workbench” under Study ID: ST001212, and
it is also available with this article (Supplementary material 1).
3. Results
3.1. Baseline characteristics of the study
The COPSAC[2010] mother-child cohort of 736 pregnant women and their
695 children has previously been described in detail [[56]7]. Blood
samples were collected for metabolomic profiling from 602 children at
6 months up to 2 years of age. Of those, only samples from children
aged 4–8 months were included in the analysis as their metabolomic
profiles were similar in a principal component analysis (PCA) compared
to samples collected from children at older ages (see Supplementary
Material). These metabolomic profiles contained 1138 unique metabolites
that were identified using mass to charge ratio, retention time, and a
library of metabolites (see Supplementary Material). Following the
subsequent quality control steps removing samples with ≥30% missing
values as well as features with ≥70% missing values resulted in a final
dataset including samples from 577 children (51% boys) with 831
metabolites. The composition of the remaining metabolites after these
quality control procedures is shown in Figure SF3 (in the Supplementary
Material), where the metabolites are grouped based on sub pathway
(Panel a) and the most abundant pathways with >10 compounds, which are
lipids and amino acids (Panel b).
A total of 51 (8.9%) children were exclusively breastfed for less than
a week from birth, 553 (96.2%) terminated the exclusive breastfeeding
before the time of metabolomic sampling, whereas 22 (3.8%) were still
exclusively breastfed at the time of metabolomic sampling, and
breastfeeding status was unknown for 2 children. An overview of the
baseline characteristics is presented in Table S2 (see Supplementary
Material).
3.2. n-3 LCPUFA supplementation during pregnancy and the child metabolome
In order to investigate differences in the plasma metabolomic profiles
between children whose mothers received n-3 LCPUFA supplementation or
placebo, we used univariate linear regression models for each
metabolite followed by a partial least squares discriminant analysis
(PLS-DA) model.
3.3. Univariate linear regression analysis
Among the 831 features investigated, 42 showed a nominally significant
difference at a p = .05 level between the n-3 LCPUFA supplementation
and the placebo group. The volcano plot in Figure SF5 [in the
Supplementary Material] summarizes the results showing the effect
estimates vs. p-values. The results are further detailed in the heatmap
in Figure SF6 [in the Supplementary Material] that shows the
significant metabolites and their associated sub-pathways. As shown in
Figure SF6, the lipids and the amino acid pathways are the ones most
affected by the n-3 LCPUFA supplementation with higher
hydroxy-3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid
(hydroxy-CMPF) and lower n-6 docosapentaenoic acid (DPA, 22:5n-6) being
the two most perturbed compounds. Among the significant metabolites, 7
have unknown identities, see Table S3 [in the Supplementary Material]
and will not be mentioned or discussed further. Due to natural
collinearity between metabolites and the scope of the univariate models
being merely exploratory supportive analyses, we did not apply FDR
correction. However, the univariate analyses results should be
interpreted with caution.
3.4. Multivariate PLS-DA and pathway enrichment analysis
Based on all the metabolome features for classifying children according
to pregnancy supplementation, the best PLS-DA model comprised 5
components and 121 metabolites. This model had a misclassification
error of 0.29, an AUC of 0.77 and a permutation test p-value = .005 for
discriminating between n-3 LCPUFA supplementation and placebo. The
score plot of the first two components (LV1 and LV2, explaining 10.99%
and 9.39% of the variation, respectively) is shown in [57]Fig. 1 Panel
A, illustrating that children in the n-3 LCPUFA supplementation group
have higher scores in both components compared to children in the
placebo group. The corresponding loading plot is shown in [58]Fig. 1
Panel a, where the significant compounds from the univariate linear
regression analysis are labelled.
Fig. 1.
[59]Fig. 1
[60]Open in a new tab
PLS-DA analysis. a) Loadings plot of the first two components of the
PLS-DA analysis. The compounds are colored based on their sub-pathway.
The labelled compounds are the ones in common with the linear
regression analysis. b) Scores plot of the first two components of the
PLS-DA analysis. The samples are colored based on the intervention (n-3
LCPUFA or placebo).
Most of the observed association of the pregnancy n-3 LCPUFA
supplementation on the child metabolome is related to lower levels of
lipid-related compounds containing n-6 unsaturated, monounsaturated or
saturated long-chain fatty acids (FAs). Especially, arachidonic acid
(AA, 20:4n6) and its esterified forms (plasmalogens,
lysophosphatidylcholines, phosphatidylcholines, phosphatidylinositols,
diacylglycerols), dihomo-linolenic acid (20:3n-6) and n-6
docosapentaenoic acid (n6-DPA, 22:5n-6) were lower in the n-3 LCPUFA
supplementation group. This is illustrated in [61]Table 1, which
provides an overview of the metabolites contributing the most in the
first two PLS-DA components grouped by subpathway, including the fold
change. For the remaining set of metabolites (N = 69), see Table S5 in
the Supplementary Material.
Table 1.
Main metabolites influencing the PLS-DA model, reported in terms of
effect size when stratifying by the intervention. A negative effect
size represents the effect towards the placebo group.
Compound, Sub-pathway Treatment Effect size Metabolism
1-(1-Enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4)*, Plasmalogen
Placebo n3/n6 Fatty Acid Metabolism
n3 LCPUFA −0.2
1-(1-Enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)*, Plasmalogen
Placebo
n3 LCPUFA −0.1
1-Arachidonoyl-GPC (20:4n6)*, Lysophospholipid Placebo
n3 LCPUFA −0.2
1-Arachidonoyl-GPE (20:4n6)*, Lysophospholipid Placebo
n3 LCPUFA −0.2
1-Palmitoyl-2-arachidonoyl-GPC (16:0/20:4n6), Phosphatidylcholine (PC)
Placebo
n3 LCPUFA −0.2
1-Stearoyl-2-arachidonoyl-GPC (18:0/20:4), Phosphatidylcholine (PC)
Placebo
n3 LCPUFA −0.2
1-Stearoyl-2-arachidonoyl-GPI (18:0/20:4), Phosphatidylinositol (PI)
Placebo
n3 LCPUFA −0.2
Dihomo-linolenate (20:3n3 or n6), Polyunsaturated Fatty Acid (n3 and
n6) Placebo
n3 LCPUFA −0.1
Docosapentaenoate (n6 DPA; 22:5n6), Polyunsaturated Fatty Acid (n3 and
n6) Placebo
n3 LCPUFA −0.3
Palmitoyl-arachidonoyl-glycerol (16:0/20:4) [2]*, Diacylglycerol
Placebo
n3 LCPUFA −0.1
Stearidonate (18:4n3), Polyunsaturated Fatty Acid (n3 and n6) Placebo
n3 LCPUFA −0.2
Stearoyl-arachidonoyl-glycerol (18:0/20:4) [1]*, Diacylglycerol Placebo
n3 LCPUFA −0.2
Stearoyl-arachidonoyl-glycerol (18:0/20:4) [2]*, Diacylglycerol Placebo
n3 LCPUFA −0.2
3-Hydroxybutyrate (BHBA), Ketone Bodies Placebo Lipid oxidation
n3 LCPUFA −0.1
Carnitine, Carnitine Metabolism Placebo
n3 LCPUFA 0.1
Decanoylcarnitine (C10), Fatty Acid Metabolism(Acyl Carnitine) Placebo
n3 LCPUFA −0.2
Myristoleoylcarnitine (C14:1)*, Fatty Acid Metabolism(Acyl Carnitine)
Placebo
n3 LCPUFA −0.2
Octanoylcarnitine (C8), Fatty Acid Metabolism(Acyl Carnitine) Placebo
n3 LCPUFA −0.2
Propionylcarnitine (C3), Fatty Acid Metabolism (also BCAA Metabolism)
Placebo
n3 LCPUFA 0.2
3-(4-Hydroxyphenyl)lactate, Tyrosine Metabolism Placebo Tyrosine
Metabolism
n3 LCPUFA 0.1
3-Methoxytyramine sulfate, Tyrosine Metabolism Placebo
n3 LCPUFA 0.1
4-Hydroxyphenylpyruvate, Tyrosine Metabolism Placebo
n3 LCPUFA 0.2
Gentisate, Tyrosine Metabolism Placebo
n3 LCPUFA 0.2
Tyrosine, Tyrosine Metabolism Placebo
n3 LCPUFA 0.1
Vanillylmandelate (VMA), Tyrosine Metabolism Placebo
n3 LCPUFA 0.1
4-Hydroxyglutamate, Glutamate Metabolism Placebo Glutamate Metabolism
n3 LCPUFA 0.3
Glutamate, Glutamate Metabolism Placebo
n3 LCPUFA 0.2
3-Indoxyl sulfate, Tryptophan Metabolism Placebo Tryptophan Metabolism
n3 LCPUFA 0.1
Xanthurenate, Tryptophan Metabolism Placebo
n3 LCPUFA −0.2
3-Carboxy-4-methyl-5-pentyl-2-furanpropionate (3-Cmpfp)**, Fatty Acid,
Dicarboxylate Placebo Furan Fatty Acid Metabolism
n3 LCPUFA −0.2
Hydroxy-CMPF*, Fatty Acid, Dicarboxylate Placebo
n3 LCPUFA 0.8
N-Behenoyl-sphingadienine (d18:2/22:0)*, Sphingolipid Metabolism
Placebo Other
n3 LCPUFA −0.1
Behenoyl sphingomyelin (d18:1/22:0)*, Sphingolipid Metabolism Placebo
n3 LCPUFA −0.1
Sphingomyelin (d17:1/16:0, d18:1/15:0, d16:1/17:0)*, Sphingolipid
Metabolism Placebo
n3 LCPUFA −0.1
Sphingomyelin (d17:2/16:0, d18:2/15:0)*, Sphingolipid Metabolism
Placebo
n3 LCPUFA −0.1
Sphingomyelin (d18:1/19:0, d19:1/18:0)*, Sphingolipid Metabolism
Placebo
n3 LCPUFA −0.1
Sphingomyelin (d18:1/24:1, d18:2/24:0)*, Sphingolipid Metabolism
Placebo
n3 LCPUFA −0.1
Sphingomyelin (d18:2/21:0, d16:2/23:0)*, Sphingolipid Metabolism
Placebo
n3 LCPUFA −0.1
Eicosanodioate (C20-DC), Fatty Acid, Dicarboxylate Placebo
n3 LCPUFA 0.1
Sphingomyelin (d18:2/23:0, d18:1/23:1, d17:1/24:1)*, Sphingolipid
Metabolism Placebo
n3 LCPUFA −0.1
Sphingomyelin (d18:2/23:1)*, Sphingolipid Metabolism Placebo
n3 LCPUFA −0.1
Stearoyl sphingomyelin (d18:1/18:0), Sphingolipid Metabolism Placebo
n3 LCPUFA −0.1
1-(1-Enyl-palmitoyl)-2-linoleoyl-GPE (P-16:0/18:2)*, Plasmalogen
Placebo
n3 LCPUFA 0.1
Ceramide (d18:1/17:0, d17:1/18:0)*, Ceramides Placebo
n3 LCPUFA −0.2
Eicosenoate (20:1), Long Chain Fatty Acid Placebo
n3 LCPUFA −0.2
Erucate (22:1n9), Long Chain Fatty Acid Placebo
n3 LCPUFA −0.2
Glycerophosphoglycerol, Glycerolipid Metabolism Placebo
n3 LCPUFA 0.1
Glycosyl ceramide (d18:1/23:1, d17:1/24:1)*, Ceramides Placebo
n3 LCPUFA −0.3
Glycosyl ceramide (d18:2/24:1, d18:1/24:2)*, Ceramides Placebo
n3 LCPUFA −0.2
Glycosyl-N-behenoyl-sphingadienine (d18:2/22:0)*, Ceramides Placebo
n3 LCPUFA −0.2
Myristoleate (14:1n5), Long Chain Fatty Acid Placebo
n3 LCPUFA −0.1
Nonadecanoate (19:0), Long Chain Fatty Acid Placebo
n3 LCPUFA −0.2
[62]Open in a new tab
The n-3 LCPUFA supplementation also resulted in a decrease in
xanthurenic acid in the tryptophan pathway and an increase in the
glutamic acid and tyrosine pathway-related compounds. We also observed
association with the carnitine metabolism pathway with increase of
carnitines and decrease of acylcarnitine-related compounds. Finally, a
decrease of ceramides and sphingolipids containing 18:0 and 22:0 FAs
and an increase in hydroxy-CMPF, a metabolite associated with fish or
fish oil intake, was prevalent in the children, whose mothers received
n-3 LCPUFA ([63]Table 1).
Among the 121 metabolites in the final PLS-DA model, 29 compounds are
of unknown identity and are reported in Table S5 in the Supplementary
Material. Performing an enrichment analysis based on sub-pathways on
the remaining 92 metabolites, we observed significant enrichment in the
tyrosine pathway, as shown in [64]Fig. 2.
Fig. 2.
[65]Fig. 2
[66]Open in a new tab
Results from the enrichment analysis using Fisher's exact test. The
odds ratio from each sub-pathway and the corresponding CI at 95% are
colored based on the −log10(p-value), whereas the size of the colored
circles is based on the number of compounds (from the PLS-DA) in the
corresponding sub-pathway. (*): p.value < .05.
3.5. Maternal and child FADS2 genotype and LCPUFA levels at age 6 month
It has been shown that levels of n-3 LCPUFAs are related to the
activity of the fatty acid desaturase (FADS) enzymes (Δ5-desaturase and
Δ6-desaturase), which are involved in the formation of the LCPUFAs from
essential FAs [[67]14]. Those enzymes are encoded by the FADS1 and
FADS2 genes located on chromosome 11 [[68]15,[69]16].
In this DB-RCT of n-3 LCPUFA supplementation during pregnancy, we
showed that the mother's levels of eicosapentaenoic acid (EPA) and
docosahexaenoic acid (DHA) at enrollment at pregnancy week 24 were
associated with the FADS2 genotype (rs1535) with lower levels in
mothers carrying the minor allele (G) [[70]3]. Therefore, we tested
whether maternal and/or child FADS2 genotype influenced the levels of
the FAs and esterified forms in the n-6 pathway, that we found were
significantly associated with the n-3 LCPUFA supplementation in our
analysis. The analyses were stratified by n-3 LCPUFA supplementation or
placebo using both maternal and child FADS2 genotype in models adjusted
for each other. The results are presented in [71]Table 2 showing that
maternal FADS2 genotype was significantly and negatively associated
with the levels of all the n-6 pathway-related metabolites in the
placebo strata with lower levels per number of the G minor allele,
whereas no associations were observed for maternal FADS2 genotype in
the n-3 LCPUFA group. No associations were observed between child FADS2
genotype and the n-6 pathway-related metabolites in either the n-3
LCPUFA or the placebo strata.
Table 2.
Associations between FADS2 polymorphism (rs1535) and child n-6
pathway-related metabolite levels stratified by the intervention (n-3
LCPUFA supplement or placebo). Reported p-values are from “Wald test”.
Metabolite, Sub-pathway Intervention Mother's genotype
__________________________________________________________________
Child's genotype
__________________________________________________________________
beta-Estimate p.value CI beta-Estimate p.value CI
1-(1-Enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4)*, Plasmalogen
Placebo −0.29 0.01 −0.51 -0.08 0.07 0.52 −0.15 0.29
1-(1-Enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4)*, Plasmalogen
LCPUFA −0.10 0.32 −0.3 0.1 0.01 0.93 −0.2 0.22
1-(1-Enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)*, Plasmalogen
Placebo −0.34 <0.001 −0.51 -0.18 0.09 0.29 −0.08 0.27
1-(1-Enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)*, Plasmalogen
LCPUFA −0.06 0.53 −0.25 0.13 −0.01 0.90 −0.21 0.19
1-Arachidonoyl-GPC (20:4n6)*, Lysophospholipid Placebo −0.24 0.02 −0.45
-0.04 −0.05 0.66 −0.26 0.17
1-Arachidonoyl-GPC (20:4n6)*, Lysophospholipid LCPUFA −0.13 0.16 −0.32
0.05 −0.12 0.24 −0.32 0.08
1-Arachidonoyl-GPE (20:4n6)*, Lysophospholipid Placebo −0.19 0.07 −0.4
0.02 0.17 0.12 −0.05 0.39
1-Arachidonoyl-GPE (20:4n6)*, Lysophospholipid LCPUFA −0.17 0.09 −0.36
0.03 −0.01 0.95 −0.21 0.2
1-Palmitoyl-2-arachidonoyl-GPC (16:0/20:4n6), Phosphatidylcholine (PC)
Placebo −0.43 <0.001 −0.62 -0.24 0.08 0.45 −0.12 0.28
1-Palmitoyl-2-arachidonoyl-GPC (16:0/20:4n6), Phosphatidylcholine (PC)
LCPUFA −0.05 0.61 −0.25 0.15 −0.22 0.04 −0.43 -0.01
1-Stearoyl-2-arachidonoyl-GPC (18:0/20:4), Phosphatidylcholine (PC)
Placebo −0.34 <0.001 −0.53 -0.15 0.00 0.97 −0.19 0.2
1-Stearoyl-2-arachidonoyl-GPC (18:0/20:4), Phosphatidylcholine (PC)
LCPUFA −0.05 0.61 −0.25 0.15 −0.17 0.12 −0.39 0.04
1-Stearoyl-2-arachidonoyl-GPI (18:0/20:4), Phosphatidylinositol (PI)
Placebo −0.23 0.03 −0.45 -0.02 0.09 0.40 −0.13 0.32
1-stearoyl-2-arachidonoyl-GPI (18:0/20:4), Phosphatidylinositol (PI)
LCPUFA −0.13 0.22 −0.33 0.08 −0.05 0.67 −0.26 0.17
Arachidonate (20:4n6), Polyunsaturated Fatty Acid (n3 and n6) Placebo
−0.17 0.11 −0.39 0.04 0.07 0.54 −0.15 0.29
Arachidonate (20:4n6), Polyunsaturated Fatty Acid (n3 and n6) LCPUFA
−0.09 0.35 −0.28 0.1 0.02 0.84 −0.18 0.22
Dihomo-linolenate (20:3n3 or n6), Polyunsaturated Fatty Acid (n3 and
n6) Placebo −0.17 0.10 −0.37 0.03 0.17 0.11 −0.04 0.38
Dihomo-linolenate (20:3n3 or n6), Polyunsaturated Fatty Acid (n3 and
n6) LCPUFA −0.07 0.49 −0.25 0.12 0.15 0.14 −0.05 0.35
Docosapentaenoate (n6 DPA; 22:5n6), Polyunsaturated Fatty Acid (n3 and
n6) Placebo −0.24 0.04 −0.46 -0.01 0.22 0.07 −0.02 0.45
Docosapentaenoate (n6 DPA; 22:5n6), Polyunsaturated Fatty Acid (n3 and
n6) LCPUFA −0.01 0.87 −0.19 0.16 −0.01 0.96 −0.2 0.18
Palmitoyl-arachidonoyl-glycerol (16:0/20:4) [2]*, Diacylglycerol
Placebo −0.30 0.01 −0.52 -0.08 0.19 0.10 −0.04 0.42
Palmitoyl-arachidonoyl-glycerol (16:0/20:4) [2]*, Diacylglycerol LCPUFA
−0.13 0.11 −0.3 0.03 0.03 0.77 −0.15 0.2
Stearidonate (18:4n3), Polyunsaturated Fatty Acid (n3 and n6) Placebo
−0.09 0.48 −0.33 0.16 0.03 0.84 −0.23 0.28
Stearidonate (18:4n3), Polyunsaturated Fatty Acid (n3 and n6) LCPUFA
−0.02 0.81 −0.18 0.14 −0.05 0.54 −0.22 0.12
Stearoyl-arachidonoyl-glycerol (18:0/20:4) [1]*, Diacylglycerol Placebo
−0.30 0.01 −0.51 -0.09 −0.10 0.36 −0.32 0.12
Stearoyl-arachidonoyl-glycerol (18:0/20:4) [1]*, Diacylglycerol LCPUFA
−0.21 0.02 −0.39 -0.04 −0.16 0.09 −0.34 0.02
Stearoyl-arachidonoyl-glycerol (18:0/20:4) [2]*, Diacylglycerol Placebo
−0.31 <0.001 −0.52 -0.09 −0.08 0.50 −0.3 0.15
Stearoyl-arachidonoyl-glycerol (18:0/20:4) [2]*, Diacylglycerol LCPUFA
−0.17 0.06 −0.35 0.01 −0.04 0.70 −0.23 0.16
[72]Open in a new tab
beta-Estimates are per copy of the G minor allele in rs1535. All the
metabolites were autoscaled prior to the analysis.
3.6. Breastfeeding and child metabolite levels at age 6 months
Many metabolites can be transferred from mothers to children via the
breastmilk and breastfeeding status may therefore influence the child
metabolome. To disentangle this, we investigated the association
between duration of exclusively breastfeeding and levels of each of the
main metabolites driving the PLS-DA model. We used the exclusively
breastfeeding duration rather than the total breastfeeding duration,
since we consider this a better estimate of breastfeeding exposure.
However, in order to exclude possible differences due to any
breastfeeding at the time of sampling, we repeated the analysis using
the difference in days between the sampling date and the end of any
breastfeeding. No substantial differences were observed. The results
from these correlation models are depicted in Figure SF7 (in the
Supplementary Material), showing that the levels of all the lipid
related compounds (long chain fatty acids, sphingolipids,
lysophospholipids, ceramides, carnitines, etc) are positively
associated with exclusive breastfeeding duration in both the n-3 LCPUFA
and placebo strata; i.e. increasing levels with increasing
breastfeeding duration independent of supplementation status. A few of
the metabolites showed inverse associations with breastfeeding such as
xanthurenic acid, 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)
and eicosanodioic acid (C20-DC).
To further explore the role of breastfeeding, we used the intercept
from the metabolite vs. breastfeeding length models to estimate
metabolite levels for a breastfeeding length of 0 days, which might
mainly reflect the in-utero metabolic changes. We thereafter
investigated differences in metabolite levels for an estimated
breastfeeding length of 0 days in the n-3 LCPUFA vs. placebo group and
compared that to the same difference at time of sampling; i.e.
6 months. These results are depicted in [73]Fig. 3, and the estimated
0 days of breastfeeding results only showed a significant difference
for sphingomyelin (C37n1) and (C33n1), palmitoyl-arachidonoyl-glycerol,
hydroxy-CMPF, N-behenoyl-sphingadienine, and 3-(4-hydroxyphenyl)
lactate between n-3 LCPUFA and placebo. All these differences, except
for hydroxy-CMPF, were no longer present at 6 months of age, suggesting
that what we observe is mainly mediated by the breastfeeding. However,
these results should be interpreted with caution, due to the
uncertainty of the estimation.
Fig. 3.
[74]Fig. 3
[75]Open in a new tab
Difference between metabolite levels in the n-3 LCPUFA and placebo
group at estimated age 0 (estimated from the intercept) and at age
6 months. Bars show the difference in the beta-estimates and they are
color coded based on the p-values. The metabolites were autoscaled
prior analysis. Reported p-values are from “Wald test” (*):
p-value < .05.
3.7. High-dose vitamin D intervention and child metabolomics at age 6 months
As 623 of the pregnant women in the n-3 LCPUFA trial also participated
in high-dose vs. standard dose vitamin D supplementation DB-RCT with a
nested 2 × 2 factorial design, we also investigated whether the vitamin
D supplementation affected the child's metabolite levels at age
6 months. No effect of the vitamin D supplementation was observed on
the child's metabolites levels using univariate linear modelling
(Figure SF8 in the Supplementary Material).
3.8. The n-3 LCPUFA metabolome and risk of asthma at age 5
We have previously demonstrated a 30.7% relative reduction in the risk
of developing asthma in the first 5 years of life in children, whose
mothers were supplemented with n-3 LCPUFA during pregnancy compared to
placebo [[76]3]. As we hypothesized that part of this risk reduction
could be mediated via the child metabolome, we tested possible
associations between the components of the PLS-DA model and asthma
development by age 5.
We initially found a significant association between the second latent
score (sLV2) of the PLS-DA model and risk of asthma using a Cox
regression analysis: hazard ratio (HR) per std. sLV2 increase: 0.80,
95% CI 0.65–1.26, p = .02 [Wald test]. In order to better disentangle
the n-3 LCPUFA supplementation effect from the asthma association, we
rotated the PLS-DA model to capture the asthma mediated direction in
the first component, leaving the supplementation effect in component
two to five. In [77]Fig. 4, a Kaplan-Meier curve shows the risk of
developing asthma in the first 5 years of life in children with LV1
score from the rotated PLS-DA model (rsLV1) divided into tertiles. The
curve shows that children with a low rsLV1 n-3 LCPUFA-like metabolome
score, i.e. a metabolome which is opposite to the n-3 LCPUFA
metabolome, have an increased risk of developing asthma irrespective of
supplementation group: HR 0.76, (0.64–0.90), p = .002 [Wald test], with
a change of one standard deviation, i.e. lower risk from increasing n-3
LCPUFA-like metabolome. Stratifying the analysis by the n-3 LCPUFA
supplementation, we observed a significant association in the placebo
strata but not in the n-3 LCPUFA strata, which is probably due to a
saturation effect of the supplementation on the metabolome in the n-3
LCPUFA group: per std. rsLV1, HR[placebo] 0.65, (0.50–0.85), p = .002
[Wald test] and HR[LCPUFA] 0.89, (0.69–1.15), p = .38 [Wald test],
respectively, see [78]Fig. 5.
Fig. 4.
[79]Fig. 4
[80]Open in a new tab
Kaplan-Meier curve of the first component of the rotated PLS-DA model
(rLV1) divided into tertiles and the risk of developing asthma by age
5.
Fig. 5.
[81]Fig. 5
[82]Open in a new tab
Kaplan-Meier curve of the first component of the rotated PLS-DA model
(rLV1) divided into tertiles and the risk of developing asthma by age
5, stratified by intervention.
As we observed influence on the child metabolome from both FADS2
genotype and breastfeeding length, we adjusted the model for these
parameters. From such sensitivity analysis we did not observe
dependency from either exclusive breastfeeding duration or FADS2
genotype on the relationship between rLV1 and asthma risk: adjusted HR
0.75 (0.63–0.91), p = .003 [Wald test]. Furthermore, we also tested
whether adjustments either for current breastfeeding at the time of
sampling and total breastfeeding duration would influence the
association with asthma development, but no influence was found.
Finally, we investigated whether the asthma-protective effect from the
n-3 LCPUFA supplementation during pregnancy was mediated via the
metabolome by using rLV1 as a proxy of the n-3 LCPUFA-associated
metabolome by age 6 months. The result from this mediation analysis
showed that 24% of the asthma-protective effect is mediated through the
metabolome (see Table S4).
4. Discussion
4.1. n-3 LCPUFA supplementation during pregnancy and the child metabolome
We observed a decrease in metabolite levels linked to the n-6 pathway
in children, whose mothers received fish oil during pregnancy. It has
previously been observed that a dietary increased intake of 20:5n-3
eicosapentaenoic acid (n3-EPA) and 22:6n-3 docosahexaenoic acid
(n3-DHA) leads to a decrease of arachidonic acid (20:4n-6, AA)
biosynthesis due to inhibition of Δ6 desaturation of linoleic acid
(18:2n-6) [[83]17,[84]18]. In line with this, we observed decreased
levels of AA in the n-3 LCPUFA supplementation group along with a
reduction in levels of the AA precursor dihomo-linolenic acid
(20:3n-6). We also registered lower levels of AA esterified
glycerophospholipids, possibly due to acylation affinity for AA
compared to the subsequent products in the pathway, which also undergo
peroxisomal beta-oxidation, partially leading back to AA [[85]19].
Finally, we observed decreased levels in the n-3 LCPUFA group of the
end product in the n-6 pathway, docosapentaenoic acid (22:5n-6), which
is probably due to a lack of AA as substrate as well as for the
plasmalogens and diacylglycerols containing AA in the sn-2 position.
Overall, it might be speculated that the n-3 LCPUFA supplementation led
to some of the AA being replaced by EPA or DHA in the structural
lipids; however, in our untargeted metabolomics analysis we did not
observe an increase in the n-3 LCPUFAs or in their phospholipids.
With regard to lipids, we observed that the n-3 LCPUFA supplementation
induced changes in the child metabolome with a decrease in levels of
ceramides and sphingolipids containing 18:0 and 22:0 FAs. It can be
speculated that there is an influence in the fatty acid metabolism from
the stearic acid (18:0) to docosanoic acid (22:0) formation and a
successive decrease in phospholipid formation.
The n-3 LCPUFA supplementation led to decreased levels of xanthurenic
acid, which is a catabolic product from tryptophan in the kynurenine
pathway. Xanthurenic acid is produced by the transamination of
3-hydroxykynurenine, a compound that can generate free radicals and
apoptosis [[86]20]. The indoleamine 2,3-dioxygenase 1 and 2 (IDO1/IDO2)
enzymes are the main rate limiting enzymes activating tryptophan
catabolism through the kynurenine pathway. IDO expression is induced by
lipopolysaccharides and cytokines, especially by interferon gamma
(IFN-y), that acts on T-cells and promotes differentiation and
apoptosis via the T-cell receptor [[87]21]. Our findings suggest that
n-3 LCPUFA supplementation has an impact on the regulation of IDO
expression, that may be caused by anti-inflammatory activity and
reduced IFN-y levels, leading to a reduction in tryptophan catabolism
in the kynurenine pathway.
Medium- and short-chain acyl-carnitine were also affected with lower
levels in the n-3 LCPUFA group. Acyl-carnitines are reversibly formed
by the reaction of the acyl-CoA esters with carnitine, a process
catalyzed by carnitine acyl-transferase. This mechanism takes place to
transport the FAs with >14 carbons into the mitochondrial membrane for
their subsequent beta-oxidation [[88]22]. The concentration of
acyl-carnitines in plasma also reflects the nutritional state and the
contribution from other tissues, in particular, medium and long-chain
acyl-carnitine are directly derived from the fatty acid metabolism. It
can be speculated that n-3 LCPUFA supplementation may influence the
beta-oxidation in the mitochondria, probably increasing the
beta-oxidation, and therefore limiting the accumulation of intermediate
lipid products. We also observed an increase in carnitine biosynthesis,
with both carnitine and its precursor trimethyllysine being enhanced in
the n-3 LCPUFA group.
In the n-3 LCPUFA group, we also observed an increase in tyrosine
catabolism and its derived catecholamines breakdown products.
Specifically, we observed an increase in vanillylmandelic acid, the
main inactive catabolic product of norepinephrine and epinephrine.
These catecholamines are produced from dopamine and act as hormones and
neurotransmitters and are synthesized in the brain and in the adrenal
gland. The other upregulated compounds in this pathway,
3-(4-hydroxyphenyl)lactate and 3-methoxytyramine sulfate, are
metabolites originating from dopamine catabolism. It has been shown
that rats chronically deficient in n-3 FAs have effects on the
monoaminergic system during development with different effect in
different regions of the cortex [[89]23]. Furthermore, rats receiving a
n-3 LCPUFA-enriched diet that was poor in linoleic acid (18:2n-6)
[[90]24] showed significant differences in the FA composition in the
cerebral membranes with higher levels of docosahexaenoic acid
(22:6n-3), that is the most abundant n-3 FA in the brain, lower levels
of AA and higher levels of dopamine. It has also been shown in mice
that fish oil supplementation upregulates the expression of the
mitochondrial uncoupling protein 1 in the brown and beige adipose
tissue, which affects fat accumulation. The brown adipose tissue
activity is primarily regulated by the sympathetic nervous system,
which releases norepinephrine that successively binds the
beta-adrenergic receptors and promotes the expression. This aligns with
observations of increased release of norepinephrine in the urine of
mice receiving fish oil supplementation [[91]25] and may partly explain
the relationship between the n-3 LCPUFA supplementation and
upregulation of the tyrosine pathway.
The n-3 LCPUFA supplementation was also associated with an increase in
glutamate, 4-hydroxy-glutamate, which is a byproduct of
4-hydroxy-2-oxoglutarate, and alpha-ketoglutarate which is a
deamination product and precursor of glutamate. Glutamate is a
non-essential amino acid that can be synthetized from
alpha-ketoglutarate, glutamine and pyroglutamic acid. Glutamate is
involved in several pathways both as substrate and product and is also
an excitatory neurotransmitter [[92]26]. So far, no direct associations
have been found between n-3 LCPUFA intake and the glutamate metabolism.
Hydroxy-CMPF and 3-carboxy-4-methyl-5-pentyl-2-furanpropanoic acid are
metabolites, which in humans come from beta-oxidation and
omega-oxidation of furan FAs. Furan FAs are formed in plants, bacteria,
and algae and they accumulate in the tissues of fish and crustaceans
[[93]27]. In humans, the uptake of the furan FAs is through food such
as vegetables and vegetable oils, fish and fish oil. However, there is
a hypothesis of de novo synthesis of CMPF by the gut microbiota
[[94]27]. We observed a much higher level of hydroxy-CMPF in the
children, whose mothers received fish oil-derived n-3 LCPUFA compared
to placebo, which is presumably directly related to the fish oil
supplementation and therefore serves as a biological validation of the
metabolomics dataset.
4.2. FADS2 genotype and n-3 LCPUFA supplementation
The amount of n-3/n-6 LCPUFA is dependent on food or supplementation
but also on the activity of the Δ5- and Δ6-desaturase enzymes, which
converts the essential FAs into LCPUFAs. The mother's FADS2 genotype
was strongly associated with levels of metabolites in the n-6 pathway
whereas the child's genotype was not. It might be that the child's
metabolite levels in this pathway are primarily a reflection of the
maternal levels via breastfeeding [[95]28]. Furthermore, the quasi
significant association of the main FAs is in line with previous
studies showing that the minor allele is associated with lower enzyme
activity and therefore lower levels of fatty acid products
[[96]28,[97]29], in our case AA, n-6 DPA and 20:3n6. The reason why we
observe a stronger significance for the AA derived products could be
due to a better sensitivity for the compounds further down the cascade
process, but still directly derived from AA. The effect of the genotype
is predominant in the placebo strata compared to the fish oil strata,
probably due to the effect of the supplementation, which affects the
activity of the desaturase enzyme and probably masking and compensating
for the genotype effect.
4.3. Breastfeeding effect on the child metabolome
We observed a strong influence of breastfeeding duration on metabolites
in both the n-3 LCPUFA and placebo strata and used this information to
address whether the n-3 LCPUFA supplementation induced a pre- or
post-natal programming effect on the child metabolome. Our results
suggest that the n-3 LCPUFA supplementation effects on the child's
metabolome are mainly originating from the composition of the mother's
breast milk. However, most of the differences in metabolite levels
between n-3 LCPUFA and placebo for estimated 6 months and 0 days of
breastfeeding have the same directionality and the fit of the
regression model may suffer from fewer cases of children not being
exclusively breastfed for a long period of time, thus effecting the
estimation at 0 days. We assume that breastfeeding duration plays an
important role in enhancing the level of n-3 LCPUFAs in the children,
but at the same time, we speculate that some of the compounds seem to
be transferred through the placenta or are altered due to prenatal
changes, even though we need metabolomics profiles of the children's
plasma at birth to verify this assumption. Further, assessments of
maternal metabolomics profiles would enable us to disentangle whether
the n-3 LCPUFA supplementation during pregnancy lead to sustained
changes in mother's metabolite levels, which could be transferred to
the child via the breastmilk. If it is correct that most of the
metabolite differences in relation to n-3 LCPUFA supplementation are a
result of breastfeeding, it suggests that breastfeeding may accentuate
the effects of prenatal n-3 LCPUFA supplementation.
4.4. n-3 LCPUFA-associated metabolome and asthma-protective effect
We aimed to relate the effect of the n-3 LCPUFA supplementation on the
child metabolome to the observed asthma-protective effect in our DB-RCT
to disentangle the underlying biochemical mechanisms. Our analysis
suggested that the n-3 LCPUFA supplementation associated metabolome
mediated 24% of the asthma-protective effect.
Part of the mediation effect on asthma may be explained by the
down-regulation of the n-6 LCPUFAs and their esterified compounds.
Particularly, AA is one of the most abundant LCPUFAs in the cells
involved in the inflammatory process leading to asthma symptoms. AA is
a substrate for cyclooxygenase, lipoxygenase and cytochrome P450
enzymes to produce eicosanoids, which are potent mediators of airway
inflammation. Thus, the prostaglandins 2-series (PGE2) are involved in
the induction of the proinflammatory interleukin 6 (IL-6) [[98]30] and
increased levels of the leukotrienes LTC4 and LTD4 resulting in
bronchoconstriction, increased mucus secretion, and airway
hyperreactivity, that leads to airflow obstruction in asthma patients
[[99]31]. In line with this, a case-control study among adults with
asthma have previously shown increased circulating AA levels [[100]32].
We also observed an association of the n-3 LCPUFA supplementation on
the glutamate pathway. Glutamate is involved in several pathways,
including the gamma-glutamyl cycle in the liver, where glutathione is
produced from glutamate cysteine and glycine [[101]33]. Glutathione is
an antioxidant compound and lower levels of glutamic acid, glycine and
tyrosine have previously been demonstrated in asthmatic children
[[102]34], thus connecting childhood asthma with a decreased
antioxidant defense. In our study, we observed that children, whose
mother received n-3 LCPUFA had higher levels of tyrosine, glutamic acid
as well as dimethylglycine and sarcosine, which are precursors of
glycine, thereby linking the n-3 LCPUFA supplementation to an improved
antioxidant system, which may have played an asthma-protective role.In
this study we demonstrate that n-3 LCPUFA supplementation with fish oil
during pregnancy significantly associates with the offspring metabolome
by age 6 months. The observed effects were primarily a decrease in
metabolites in the n-6 LCPUFA and tryptophan pathways, as well as an
increase in the tyrosine and glutamate pathways. We further show that
the fish oil metabolomic profile was associated with a reduced risk of
asthma by age 5 and that these biochemical alterations mediated one
fourth of the asthma-protective effect of the supplementation. These
findings provide new important insight into the effects of dietary n-3
LCPUFA supplementation and the pathogenesis of childhood asthma.
Authors contributions
H.B. had full access to all of the data in the study and takes
responsibility for the integrity of the data and accuracy of the data
analysis.
H.B. and B.L.C. contributed equally to the manuscript.
Study concept design and acquisition: H.B., B.L.C., K.B., J.S., M.A.R.
Statistical Analysis: D.R., M.A.R.
Interpretation of data: All authors.
Drafting of Manuscript: D.R.
Critical revision of the manuscript for important intellectual content:
All authors.
Source of funding
All funding received by COPSAC is listed on [103]www.copsac.com. The
Lundbeck Foundation (Grant no R16-A1694); The Ministry of Health,
Denmark (Grant no 903516); Danish Council for Strategic Research (Grant
no 0603-00280B); The Capital Region Research Foundation have provided
core support to the COPSAC research center. In addition, COPSAC and
VDAART have received funding from NIH (Grant no. R01HL141826).
Governance
We are aware of and comply with recognized codes of good research
practice, including the Danish Code of Conduct for Research Integrity.
We comply with national and international rules on the safety and
rights of patients and healthy subjects, including Good Clinical
Practice (GCP) as defined in the EU's Directive on Good Clinical
Practice, the International Conference on Harmonisation's (ICH) good
clinical practice guidelines and the Helsinki Declaration. Privacy is
important to us which is why we follow national and international
legislation on General Data Protection Regulation (GDPR), the Danish
Act on Processing of Personal Data and the practice of the Danish Data
Inspectorate.
Declaration of Competing Interest
All authors declare no potential, perceived, or real conflict of
interest regarding the content of this manuscript. The funding agencies
did not have any role in design and conduct of the study; collection,
management, and interpretation of the data; or preparation, review, or
approval of the manuscript. No pharmaceutical company was involved in
the study.
Footnotes
^Appendix A
Supplementary data to this article can be found online at
[104]https://doi.org/10.1016/j.ebiom.2019.07.057.
Contributor Information
Bo L. Chawes, Email: chawes@copsac.com.
Hans Bisgaard, Email: bisgaard@copsac.com.
Appendix A. Supplementary data
Supplementary material 1
[105]mmc1.csv^ (5MB, csv)
Supplementary material 2
[106]mmc2.docx^ (2.3MB, docx)
Supplementary material 3
[107]mmc3.pdf^ (66.7KB, pdf)
References