Keywords
acylcarnitines - fatty acids - amino acids - maternal obesity - pregnancy - longitudinal
Obesity has reached pandemic proportions, with the worldwide prevalence nearly doubling
since 1980.[1] Individuals with obesity have been shown to be at an increased risk of developing
chronic metabolic and cardiovascular diseases such as type-2 diabetes mellitus, coronary
heart disease, and stroke.[2]
[3] With the number of people affected growing at an exponential rate, much focus has
been placed on understanding the biological mechanisms related to obesity and its
associated comorbidities. Recent studies have identified differences in circulating
metabolic biomarkers between individuals with and without obesity that could be targeted
for clinical intervention.[2]
[4]
[5]
[6]
Obesity is of particular concern during pregnancy, when both the mother and developing
fetus are at risk for complications. An estimated one-third of all pregnancies are
complicated by obesity, and maternal obesity has been identified as one of the leading
causes of the unprecedented increase in obstetrical mortality seen over the last decade.[2]
[7] Women with obesity are at an increased risk of obstetric complications, including
gestational diabetes mellitus, preeclampsia, and thromboembolic events.[2] Diagnosis and intervention for gestational diabetes, a condition that often coexists
with obesity, has been successful in reducing pregnancy complications and maternal
mortality. However, intervention leading to a decreased risk for adverse pregnancy
outcomes due to overweight and obesity in women without gestational diabetes has not
yet been successfully introduced during pregnancy. Nutritional intervention can be
successfully implemented before and continuing during pregnancy. While recent reports
identify promising metabolic markers involved in obesity that may be targets of intervention,
these studies were not performed in pregnant women where the maternal metabolome is
a fusion of fetal and maternal metabolic outcomes.[2]
[7]
Many metabolic adaptations occur during pregnancy that are essential to ensure adequate
growth and development of the fetus while meeting the increased energy demands on
the mother.[8] While specific metabolites such as glucose and lipids undergo striking changes beginning
early in pregnancy, less is known about the changes that occur in the intermediate
metabolites involved in glucose and lipid metabolic pathways and how obesity status
may affect these changes.[8] Understanding maternal metabolism throughout pregnancy and the influence of maternal
obesity is a critical step in identifying potential pathways and mechanisms that may
contribute to pregnancy complications and poor infant outcomes in pregnancies complicated
by obesity.
Our primary objective was to assess the difference in amino and fatty acid biomarkers
throughout pregnancy in women with and without obesity. Our secondary aims were to
assess interactions between biomarkers and obesity status for associations with maternal
glucose, maternal weight gain, and infant birth weight. We hypothesize that amino
acid and fatty acid profiles will differ between trimesters among women with and without
obesity. In addition, we speculate that the association between biomarkers and maternal
and fetal metabolic measures will differ according to the obesity status.
Methods
Study Population
Women were enrolled in this study during their 15- to 20-weeks' visit to the Obstetrics
Clinic at the University of Iowa Hospitals and Clinics in Iowa City, IA. Enrollment
occurred between March 2012 and August 2013. Women aged 18 to 45 who were between
15 and 20 weeks' of pregnancy with a singleton gestation and prepregnancy body mass
index (BMI) of < 25 or ≥ 35 were eligible for this study. BMI categories were defined
according to the Centers for Disease Control and Prevention standards.[9] Exclusion criteria included type-1 diabetes, infectious diseases (human immunodeficiency
virus, hepatitis B/C, and syphilis), autoimmune diseases and known metabolic disease
or fetal anomaly. Additional exclusion criteria for subjects with BMI < 25 included
type-2 diabetes, hypertension, thyroid disease, or other chronic illness. Because
these conditions are closely associated with metabolic disease, women with obesity
who were diagnosed with type-2 diabetes, hypertension, or gestational diabetes were
included in the study sample. Study protocols were approved by the Institutional Review
Board at the University of Iowa (IRB no.: 201112755). Overall, 42 women were eligible
to participate in this study. Three women withdrew after the first visit and were
not included in the final analyses. The final sample population included 39 women,
24 women without obesity (BMI: 18–25) and 15 women with obesity (BMI: ≥ 35).
Sample Collection and Preparation
At enrollment (15–20 weeks' of gestation) subjects completed a brief food questionnaire
to indicate when and what they had eaten in the last 4 hours, an extra tube of blood
was drawn along with their routine obstetrics laboratories and fresh blood was spotted
on Whatman 903 filter paper card (GE Healthcare Ltd, Cardiff, UK), dried at room temperature
then stored at −80°C. Prepregnancy BMI was calculated using weight and height measurements
recorded by nurses in patient medical charts (all measurements were recorded before
15 weeks' of gestation). Information on age at delivery, race/ethnicity, gestational
age, infant birth weight, maternal weight gain, and infant gender were abstracted
from the patient medical charts by research team members. Maternal weight gain was
calculated using weight measurements recorded by nurses in patient medical charts
before 15 weeks' of gestation and at the time of delivery.
Glucose and Metabolite Measurements
Glucose was measured at each visit with the Accu-Check system (Roche Diagnostics,
Indianapolis, IN). Subjects identified as being eligible for study participation at
the first study visit returned fasted for a follow-up visit 1 to 8 days later. Women
who had not eaten within 4 hours of sample collection were considered fasted. A third
blood sample was collected at the time of the subjects' oral glucose tolerance test
which was performed between the 26th and 30th weeks' of pregnancy. Samples were prepared
and handled at the State of Iowa Hygienic Laboratory according to the Clinical Laboratory
Standards Institute (CLSI) guidelines[10] in a similar fashion as those reported previously for routine newborn screening.[11]
A total of 43 metabolites, including 11 amino acids, 31 acylcarnitines, and free carnitine,
were measured using tandem mass spectrometry. Of these metabolites, 11 had low variability
(standard deviation < 0.01) among subjects and were excluded from analysis (Table 1, Supplementary Material available in the online version only). Tandem mass spectrometry was performed with
the Waters Quattro Micro triple quadrupole tandem mass spectrometer (Waters Corporation,
Milford, MA), equipped with an electrospray ionization source operated in the positive
ion mode.
Statistical Analysis
Statistical analyses were conducted using SAS 9.3 software (SAS Institute, Cary, NC).
Demographic and clinical characteristics of our cohort were compared between women
with and without obesity with Fisher exact tests for dichotomous or categorical exposures
and Wilcoxon–Mann–Whitney tests for continuous exposures. All metabolite values were
natural log transformed to account for the nonnormality in their distribution. Metabolites
that had noncontinuous distributions were categorized into quantiles that grouped
observations as evenly as possible.
Measurements were compared between trimesters and between the fasted and nonfasted
state for all women using a linear mixed-effects regression with maximum-likelihood
estimation for continuous metabolites and an unstructured random effects covariance
matrix. Mixed-effects cumulative logistic regression was used for categorical metabolites
with an independent random effects correlation matrix. Measurements were compared
between women with and without obesity, for each trimester using general linear regression
for continuous metabolites and logistic regression for categorical metabolites.
The p values were adjusted for using the Bonferroni correction. Spearman rank correlation
test was used to assess the correlation between each metabolite and maternal glucose
levels, maternal weight gain, and infant birth weight in women with and without obesity
separately. Because little variation in gestational age at birth was present in the
study population (the vast majority of infants were born term), birth weight was not
adjusted for gestational age. Maternal glucose, maternal weight gain, and infant birth
weight were separately regressed on significant metabolites, obesity status, and the
interaction between the metabolite and obesity. All analyses were performed stratified
by trimester using the nonfasted samples.
Results
A total of 10 amino acids, 21 acylcarnitines, and free carnitine were examined in
39 women, 24 without obesity (BMI: 18–25) and 15 with obesity (BMI: ≥ 35). The majority
of women in our study were non-Hispanic, white women with a term delivery of an infant
of average birth weight ([Table 1]). None of the women without obesity in this study were diagnosed with type-2 diabetes
mellitus, hypertension, or gestational diabetes mellitus. Six women with obesity had
existing hypertension and two had existing type-2 diabetes mellitus, none developed
gestational diabetes mellitus. Nonfasted second- and third-trimester glucose measurements
were marginally higher in women with obesity compared with women without obesity,
while fasting glucose measurements were significantly higher in women with obesity.
Weight gain during pregnancy was significantly higher for women without obesity compared
with women with obesity.
Table 1
Demographic and Clinical Characteristics of the Study Population
|
Characteristics
|
Women without obesity (N = 24)
|
Women with obesity (N = 15)
|
p Value
|
|
Age at delivery, y
|
31.1 ± 1.7
|
30.3 ± 4.9
|
0.85
|
|
Prepregnancy BMI
|
21.8 ± 1.9
|
42.9 ± 5.0
|
< 0.01[a]
|
|
Race/ethnicity[b]
|
|
|
0.44
|
|
White
|
22 (91.7)
|
13 (86.7)
|
|
|
Asian
|
1 (4.2)
|
0 (0.0)
|
|
|
Hispanic
|
1 (4.2)
|
2 (13.3)
|
|
|
Gestational age second trimester, d
|
128.0 ± 11.9
|
123.1 ± 12.5
|
0.19
|
|
Gestational age second trimester (fasting), d[c]
|
131.4 ± 11.9
|
126.9 ± 12.7
|
0.35
|
|
Gestational age third trimester, d
|
193.0 ± 5.5
|
192.9 ± 5.7
|
0.60
|
|
Gestational age at delivery, d
|
279.3 ± 6.2
|
270.7 ± 18.5
|
0.19
|
|
Glucose second trimester
|
83.6 ± 7.2
|
98.3 ± 24.4
|
0.02[a]
|
|
Glucose second trimester (fasting)[a]
|
82.2 ± 10.5
|
98.9 ± 23.3
|
< 0.01[a]
|
|
Glucose third trimester
|
104.5 ± 17.7
|
124.7 ± 34.6
|
0.04[a]
|
|
Infant birth weight, g
|
3,404.2 ± 335.1
|
3,358.1 ± 651.9
|
0.77
|
|
Maternal weight gain, lb
|
29.5 ± 11.5
|
20.7 ± 12.9
|
0.03[a]
|
|
Infant gender[b]
|
|
|
0.51
|
|
Male
|
9 (37.5)
|
8 (53.3)
|
|
|
Female
|
15 (62.5)
|
7 (46.7)
|
|
Abbreviations: ANOVA, analysis of variance; BMI, body mass index; SD, standard deviation.
Note: Mean ± SD and N (%) were calculated using untransformed variables. ANOVA was done using log transformed
variables.
a Statistically significant at α < 0.05.
b Data are expressed as N (%). All other data are expressed as mean ± SD.
c 28 (n = 19 without obesity and n = 9 with obesity) women returned for a fasting visit.
Of the 39 participating women, 28 (72%) had eaten within 4 hours of the first visit
and returned fasted for at least 4 hours 1 to 8 days later for the second visit. A
few metabolites (C8, C10, C10:1,C12:1, and C14:1) differed in concentration by whether
the sample was collected in the fasted or nonfasted state, accounting for the number
of tests performed and adjusting for gestational age at the time of blood draw (p > 0.002) (Table 2, Supplementary Material available in the online version only). These metabolites were removed from further
analyses.
There were substantial changes in amino acids and acylcarnitine metabolites between
the second and third trimesters (nonfasted state) of pregnancy that were significant
after correcting for multiple testing (p < 0.002) ([Table 2]). Specifically, levels of leucine, methionine, phenylalanine, tyrosine, valine,
free carnitine, C2, C4, C5, C5-OH, C6, C12, C18, C18:1, and C18:2 were all markedly
lower in the third trimester compared with the second trimester of pregnancy. Conversely,
glutamate levels were found to be significantly higher in the third trimester compared
with the second.
Table 2
Changes in metabolite measures from second to third trimester of pregnancy
|
Second trimester[c]
N = 39
|
Third trimester[c]
N = 39
|
Second vs. third trimester
p Values
|
|
ALA
|
159.39 ± 32.38
|
157.25 ± 29.69
|
0.80
|
|
ARG
|
6.56 ± 2.48
|
6.43 ± 3.08
|
0.50
|
|
CIT
|
12.29 ± 2.92
|
11.15 ± 2.47
|
0.01
|
|
GLU
|
75.91 ± 16.56
|
90.55 ± 21.02
|
1.2 × 10−4
[a]
|
|
LEU
|
87.05 ± 23.38
|
66.78 ± 20.34
|
6.8 × 10−6
[a]
|
|
MET
|
13.04 ± 3.59
|
11.19 ± 2.99
|
3.4 × 10−3
|
|
ORN
|
17.00 ± 3.69
|
17.95 ± 4.46
|
0.32
|
|
PHE
|
42.02 ± 9.09
|
36.67 ± 6.47
|
1.5 × 10−3
[a]
|
|
TYR
|
35.36 ± 10.92
|
29.35 ± 7.72
|
7.9 × 10−4
[a]
|
|
VAL
|
119.80 ± 27.21
|
93.18 ± 21.51
|
1.8 × 10−6
[a]
|
|
C0
|
13.61 ± 3.06
|
11.46 ± 2.18
|
2.8 × 10−6
[a]
|
|
C2
|
8.34 ± 1.79
|
7.58 ± 2.14
|
6.2 × 10−4
[a]
|
|
C3
|
1.14 ± 0.31
|
1.06 ± 0.37
|
0.03
|
|
C4
|
0.18 ± 0.06
|
0.13 ± 0.04
|
5.2 × 10−7
[a]
|
|
C4-DC
|
0.25 ± 0.10
|
0.23 ± 0.08
|
0.30
|
|
C4-OH[b]
|
|
|
0.02
|
|
Q1 (= 0.03 μmol/L)
|
6 (15.38)
|
9 (23.08)
|
|
|
Q2 (= 0.04 μmol/L)
|
12 (30.77)
|
16 (41.03)
|
|
|
Q3 (= 0.05 μmol/L)
|
12 (30.77)
|
8 (20.51)
|
|
|
Q4 (≥ 0.06 μmol/L)
|
9 (23.08)
|
6 (15.38)
|
|
|
C5
|
0.07 ± 0.02
|
0.06 ± 0.02
|
1.2 × 10−3
[a]
|
|
C5-OH
|
0.14 ± 0.06
|
0.12 ± 0.06
|
8.2 × 10−7
[a]
|
|
C6[b]
|
|
|
2.8 × 10−4
[a]
|
|
Q1 (= 0.01 μmol/L)
|
2 (5.13)
|
8 (20.51)
|
|
|
Q2 (= 0.02 μmol/L)
|
8 (20.51)
|
15 (38.46)
|
|
|
Q3 (= 0.03 μmol/L)
|
14 (35.90)
|
11 (28.21)
|
|
|
Q4 (≥ 0.04 μmol/L)
|
15 (38.46)
|
5 (12.82)
|
|
|
C8:1
|
0.05 ± 0.02
|
0.04 ± 0.03
|
0.01
|
|
C12[b]
|
|
|
8.4 × 10−6
[a]
|
|
Q1 (≤ 0.02 μmol/L)
|
6 (15.38)
|
22 (56.41)
|
|
|
Q2 (= 0.03 μmol/L)
|
17 (43.59)
|
13 (33.33)
|
|
|
Q3 (= 0.04 μmol/L)
|
9 (23.08)
|
4 (10.26)
|
|
|
Q4 (≥0.05 μmol/L)
|
7 (17.95)
|
0 (0)
|
|
|
C14[b]
|
|
|
0.36
|
|
Q1 (≤ 0.05 μmol/L)
|
7 (17.95)
|
13 (33.33)
|
|
|
Q2 (= 0.06 μmol/L)
|
14 (35.90)
|
9 (23.08)
|
|
|
Q3 (= 0.07 μmol/L)
|
8 (20.51)
|
9 (23.08)
|
|
|
Q4 (≥ 0.08 μmol/L)
|
10 (25.64)
|
8 (20.51)
|
|
|
C16
|
0.63 ± 0.16
|
0.58 ± 0.15
|
0.03
|
|
C16:1[b]
|
|
|
0.14
|
|
Q1 (≤ 0.02 μmol/L)
|
7 (17.95)
|
7 (17.95)
|
|
|
Q2 (= 0.03 μmol/L)
|
13 (33.33)
|
19 (48.72)
|
|
|
Q3 (= 0.04 μmol/L)
|
9 (23.08
|
8 (20.51)
|
|
|
Q4 (≥ 0.05 μmol/L)
|
10 (25.64)
|
5 (12.82)
|
|
|
C18
|
0.36 ± 0.11
|
0.30 ± 0.07
|
1.6 × 10−6
[a]
|
|
C18:1
|
0.61 ± 0.15
|
0.50 ± 0.14
|
6.2 × 10−7
[a]
|
|
C18:2
|
0.22 ± 0.05
|
0.18 ± 0.05
|
4.8 × 10−6
[a]
|
Abbreviation: ALA, alanine; ARG, arginine; CIT, citrulline; GLU, glutamic acid; LEU,
leucine; MET, methionine; ORN, ornithine; PHE, phenylalanine; SD, standard deviation;
TYR, tyrosine; VAL, valine.
Note: Mean ± SD were calculated using nontransformed variables. Linear (continuous
metabolites) and cumulative logistic (categorical metabolites) mixed-effects regression
was performed using log-transformed variables.
a Statistically significant at α < 0.002.
b Data are expressed as N (%). All other data are expressed as mean ± SD.
c Metabolites were measured in μmol/L.
Examining differences by maternal obesity, C8:1 was found to be significantly higher
in women with obesity compared with women without obesity for second trimester nonfasted
women, after correcting for multiple testing (p < 0.002) ([Fig. 1] and Table 3, Supplementary Material available in the online version only). A similar trend was observed in women in their
third trimester, with women, with obesity showing significantly higher measurements
for C2, C4:OH, and C18:1 than women without obesity, after correcting for multiple
testing.
Fig. 1 Differences in metabolite measurements by maternal obesity status. The X-axis is
a list of all metabolites. The Y-axis is the −log10 of the p value from the regression analyses. The horizontal dashed lines represent the p-value cutoffs. *p value = 0.05; **p value = 2.00 × 10−3.
Several metabolites were marginally (0.002 < p < 0.05) correlated with birth weight, maternal glucose, and maternal weight gain
stratified by obesity status and trimester (Tables 4–6, Supplementary Material available in the online version only); however, none met the correction for multiple
testing thresholds (p < 0.002). Notably, a significant interaction between second-trimester methionine
levels and obesity was associated with infant birth weight (Table 7, Supplementary Material available in the online version only and [Fig. 2]). Levels of methionine were associated with lower birth weight in both women with
and without obesity, with the negative relationship being more pronounced in women
with obesity (p = 0.04).
Fig. 2 Significant interactions between obesity status and metabolites for birth weight,
glucose, and maternal weight gain outcomes. Women with obesity are represented by
the circles and solid lines. Women without obesity are represented by the plus signs
and dashed lines.
Interactions between citrulline, C14, and C16:1 with obesity were significantly associated
with maternal glucose levels (Table 7, Supplementary Material available in the online version only and [Fig. 2]). In the second trimester higher C16:1 levels were associated with significantly
lower glucose levels for women without obesity (p = 4.6 × 10−6), while higher citrulline levels were associated with lower glucose levels in women
with obesity but not women without obesity (p = 0.01). In the third trimester, higher C14 levels were associated with higher glucose
levels for women without obesity and lower glucose levels for women with obesity (p = 0.02).
In assessing associations with maternal weight gain, interactions between leucine
and C16 with obesity were found to have a significant relationship (Table 7, Supplementary Material available in the online version only and [Fig. 2]). In the third trimester, higher leucine levels were associated with increased maternal
weight gain for women without obesity and decreased weight gain for women with obesity
(p = 0.04). This effect was also observed for C16 (p = 0.01).
Discussion
Metabolic adaptations during pregnancy are necessary to ensure adequate nutrient supply
for both the developing fetus and mother. This metabolic balance is well understood
for lipid metabolism and glucose, through which clinical conditions, such as hyperlipidemia
and gestational diabetes, can complicate pregnancy.[8] Intermediates of glucose and lipid metabolism, including branched-chain amino acid
metabolites and carnitine levels, have been reported to decline during pregnancy but
little has been documented on specific amino acid and acylcarnitine species throughout
pregnancy, particularly with respect to maternal obesity.[8]
[12] We identified substantial changes in several amino acids and acylcarnitine metabolites
from the second to third trimester in pregnancy, both supporting and expanding on
the findings from previous studies.
Importantly, we noted the little difference between analyte measurements in the fasted
or nonfasted state. While it is recognized that serum levels of amino acids and acylcarnitine
measurements are sensitive to fasting,[13] our data demonstrate that whole blood measurement collected as dried blood spots
during routine obstetric care are relatively stable regardless of a fasting or nonfasting
state. This suggests that screening amino acid and acylcarnitine levels using procedures
similar to routine newborn screening may offer a cost-effective and efficient method
for monitoring fatty acid and amino acid levels during pregnancy. Although future
studies measuring metabolic levels after a longer duration of fasting (8–10 hours)
may be ideal, the dietary needs of pregnant women limit the ability of researchers
to impose such strict criteria.
We identified several acylcarnitine species that differed between women with and without
obesity, particularly in the third trimester of pregnancy. Notably, women with obesity
in our study had higher levels of C2, C4-OH, and C18:1 than women without obesity.
This expounds on previous findings illustrating that the normal reduction in peripheral
insulin sensitivity observed in late gestation is reduced in obese women, contributing
to increased circulatory levels of metabolic fuels, including glucose, lipids, and
amino acids.[14]
[15]
Although two prior studies have assessed the association between BMI and fatty acid
concentration in pregnant women, the cross-sectional design of these studies restricted
them from drawing conclusions about the directionality of the relationship.[16]
[17] In addition, to being able to assess changes in metabolite levels across pregnancy
trimesters, we were able to more broadly assess differences between specific acylcarnitine
species (such as short, medium, and long-chain acylcarnitines) in pregnant women with
and without obesity, which has, to our knowledge, not previously been performed.
An increase in long-chain polyunsaturated fatty acids has been seen in women with
pregnancy-induced hypertension compared with normotensive women, with the difference
occurring later in pregnancy.[18] We found increased C18:1 level in women with obesity in both the second and third
trimester with a more marked difference in the third trimester. Higher baseline blood
pressure and hypertension are comorbidities of obesity; in fact, 40% of the women
with obesity in our study had existing hypertension. Because fetal circulation is
compromised in hypertensive women, overcompensation of fatty acids can occur to ensure
an adequate supply of nutrients is provided to the fetus.[19] While, due to sample size, we could not tease apart the effects of hypertension
from those of obesity, this mechanism could be a plausible explanation for the higher
levels of long-chain acylcarnitines in the women with obesity in our study.
Several metabolites were marginally correlated with birth weight, maternal glucose,
and maternal weight gain stratified by obesity status and trimester. For example,
a significant interaction between second-trimester methionine levels and obesity was
associated with infant birth weight. These results are consistent with recent studies
that found that increased maternal serum homocysteine levels, a metabolite of methionine,
were associated with low birth weight.[20]
[21]
[22]
[23]
[24] We found that higher levels of maternal methionine were associated with lower birth
weight in women with obesity but not in women without obesity.
The relationship between several metabolites and maternal metabolic measures (i.e.,
glucose levels and weight gain) were also found to differ by obesity status. Both
increased prepregnancy BMI and increased gestational weight gain, regardless of prepregnancy
BMI have been associated with dyslipidemia and insulin resistance during pregnancy
through the accumulation of visceral fat.[15]
[16] Normal circulatory increases in metabolic fuels, such as glucose, amino acids, and
free fatty acids, are often exaggerated as a consequence of obesity-impaired insulin
resistance.[15] In line with previous research, we saw increased glucose levels among women with
obesity in both the second trimester, fasting and nonfasting states, and third trimesters
and decreased maternal weight gain among women with obesity compared with women without
obesity. The relationship between several metabolites and maternal weight gain and
glucose levels differed significantly depending on maternal obesity status. This indicates
that the effect of maternal glucose levels and weight gain on metabolic disruption
during pregnancy may not be acting independent of prepregnancy BMI.
While our findings add considerable knowledge to the present state of research on
this topic, we acknowledge that there are limitations to our study findings. With
a small sample size, we may have been underpowered to detect several important associations.
For example, branched chain amino acids have been suggested as markers that differentiate
subjects with and without obesity,[7] but in our pregnant population, we did not detect these differences. In addition,
due to a relatively homogenous population of non-Hispanic white women from a single
hospital clinic, the generalizability of our findings may be hindered. The exclusion
criteria were altered for cases, allowing women with obesity with type-2 diabetes,
hypertension, thyroid disease, or other chronic diseases to be included in the sample
population, which could induce a selection bias. However, these conditions are common
comorbidities of obesity and are difficult to disentangle particularly during pregnancy
when the maternal metabolome is in constant flux.
In light of our preliminary findings, further investigation into the impact of obesity
on maternal metabolism is warranted. Large longitudinal metabolic profiling studies
are needed to evaluate the influence of maternal metabolism such as amino acids and
acylcarnitines on pregnancy (weight gain, pregnancy complications) and neonatal birth
outcomes (birth weight, gestational age). In addition, further investigation of the
microbiome during pregnancy and the impact on fatty acid metabolism in relationship
to obesity will be important for identifying other potential targets for intervention
including pro- or antibiotics.
