METHODS
2.1 Study
design
The VIRTUAL Placenta cohort was embedded in the ongoing prospective
Rotterdam Periconception Cohort 21, 22. Between
January 2017 and March 2018, women who were at least 18 years old,
carried a singleton pregnancy <10 weeks gestational age (GA),
and gave written informed consent were recruited from an academic
hospital. Both naturally conceived pregnancies and pregnancies achieved
via in vitro fertilization (IVF) with or without intracytoplasmic sperm
injection (ICSI) were eligible for inclusion. Pregnancies achieved via
oocyte donation and miscarriages were excluded from analyses. At
enrolment, participants filled out a questionnaire on general
characteristics, medical and obstetrical history and lifestyle
behaviours, and a Food Frequency Questionnaire (FFQ).
For all participants, two or more study visits were scheduled in the
first trimester at 7, 9 and 11 weeks GA during which 3D PD transvaginal
ultrasound scans of the whole gestational sac including the placenta and
utero-placental vasculature were obtained using the GE Voluson E8 (GE,
Zipf, Austria). Standardized ultrasound settings were previously
described (quality: max; pulse repetition frequency (PRF): 0.6; wall
motion filter (WMF): low1; compound resolution imaging (CRI): off; power
Doppler (PD) gain: -8.0) 19. Ultrasound examinations
were performed according to international guidelines on safe use of
Doppler ultrasound in the first trimester of pregnancy (ALARA-principle)23.
At the first study visit, height and weight were measured according to
protocol to calculate the body-mass index (BMI). Pregnancy outcomes were
collected through a questionnaire filled out by the participant within 1
month after giving birth and complemented with medical delivery records.
2.2 Pregnancy
dating
For naturally conceived pregnancies in regular cycles (25-35 days), GA
was calculated from the first day of last menstrual period (LMP). In
case of unknown LMP or irregular cycle, GA was calculated from
Crown-Rump-Length (CRL). If the two methods varied >6 days,
the CRL-based GA was assumed the true GA. For fresh IVF/ICSI
pregnancies, GA was calculated from oocyte pick-up day +14 days. In case
of cryopreserved embryo transfer, GA was calculated from transfer date
+19 days.
2.3 Periconceptional maternal dietary
intake
We used a standardized semi-quantitative food frequency questionnaire
(FFQ) validated for women in the reproductive age 24.
The FFQ consists of 191 food and beverage items and collects detailed
information about dietary intake, the frequency of consumption, portion
size and method of preparation over the previous four weeks. Energy and
nutritional intake of each food item was determined with the Dutch food
composition table by Wageningen University.
First, we extracted total daily energy intake (kcal/day) from the FFQ.
Using the Goldberg cut-off, designed for an average population as
described by Black 25, participants reporting an
unrealistically low value of energy-intake were excluded from analysis.
Next, we calculated the percentage energy intake (PEI) of each food
item. Then, using the NOVA classification, each food item in the FFQ was
categorized as ‘unprocessed or minimally processed food’, ‘processed
culinary ingredient’, ‘processed food’ or ‘ultra-processed food’9. The classification of all items was performed by
three researchers independently. In case of discrepancies, items were
discussed with a nutritional epidemiologist until consensus was reached.
Hereafter, we calculated the percentage of energy intake from
ultra-processed food consumption (PEI-UPF, %) for each participant.
To assess the intake of macronutrients, we used the FFQ to calculate the
total daily intake of carbohydrates, proteins and fats (g/day). In
addition, we calculated the total daily intake of macronutrient
compounds, for which we distinguished between mono-/disaccharides and
polysaccharides, animal proteins and plant-based proteins, and saturated
fatty acids and unsaturated fatty acids (g/day).
To identify distinct dietary patterns, we first reduced all 191 food
items into 25 food groups based on similarities in origin and nutrient
content, which we adapted from the European Prospective Investigation
into Cancer and Nutrition (EPIC) project 26, see Table
S1. All food groups were entered in a principal component analysis (PCA)
to identify dietary patterns (principal components) based on the degree
of reciprocal correlation between specific food groups. We extracted
dietary patterns with eigenvalues >1.0 and used a scree
plot to only select dietary patterns that explain a large proportion of
the variance in the food groups and exclude the residual components27. We provided a nutritional summary per dietary
pattern. The PCA automatically calculated a factor loading for each food
group, showing the extent to which that specific food group is
correlated with each dietary pattern. Finally, participants received a
factor score representing their adherence to each dietary pattern.
2.4 Imaging markers of first-trimester
utero-placental vascular
development
Image quality was scored on a four-point scale ranging between zero
(optimal) and three (unusable) based on the presence of artefacts, the
ability to distinguish between myometrium and trophoblastic tissue, and
completeness of the placenta. Images with a quality score of three were
excluded from the analyses.
The placental volume (PV) was measured using VOCAL software according to
the previously published validation study 28. In
short, the placental outline and gestational sac contours were
repeatedly traced in rotational steps of 15 degrees to calculate total
pregnancy volume and gestational sac volume respectively. The
gestational sac volume was subtracted from the total pregnancy volume to
calculate PV (cm3) 28.
The utero-placental vascular volume (uPVV) was measured using a virtual
reality (VR) desktop system with the V-Scope volume rendering
application. First, the threshold for 8-bit Doppler magnitude data was
set at a value of 100 and PD artefacts were removed with a virtual
eraser. Then, VR segmentation was used to erase the Doppler signal in
the embryo, the umbilical cord and the uterine tissue surrounding the
placenta (Figure S1A-B). The V-Scope application automatically
calculated the volume of all remaining PD voxels to measure the uPVV
(cm3), a volumetric vascular characteristic, as
published previously 19 (Figure S1C).
The utero-placental vascular skeleton (uPVS) was generated by applying a
skeletonization algorithm to the uPVV segmentations20. The skeletonization algorithm repeatedly peels off
the outermost layer of voxels from the uPVV, reducing the diameter of
the PD signal at each point in the vascular network until one central
voxel remains, thereby creating a network-like structure representing
the vascular morphology (Figure S1D) (18). Following
the construction of the network, the skeletonization algorithm
classifies each 26-connected voxel based on the number of neighbouring
voxels as endpoint (n) (1 neighbour), bifurcation point (n) (3
neighbours), crossing point (n) (4 neighbours) or as normal vessel point
(n) (2 neighbours). Voxels with >4 neighbours are
considered an anomaly and excluded from analyses. Further, the algorithm
measures total network length and average vascular thickness (mm)
(Figure S1E). The 6 uPVS characteristics represent absolute morphologic
development of the first-trimester utero-placental vasculature. Also, we
calculated ratios of the uPVS end-, bifurcation- and crossing points to
the uPVV (n/cm3) to identify 3 imaging markers to
represent the density of vascular branching in the utero-placental
vascular volume. Women who had no PV, uPVV or uPVS measurement available
were excluded from analysis.
2.5 Statistical
analysis
Baseline characteristics were presented as mean with standard deviation.
If needed, non-volumetric parameters were transformed using a square
root transformation to approximate a normal distribution. For volumetric
parameters and ratios a cubic root and natural log transformation were
used, respectively.
We used linear mixed models to estimate the association between maternal
intake of PEI-UPF, total energy, macronutrients and their compounds and
dietary patterns, and imaging markers of utero-placental vascular
development, assessed with PV, uPVV and uPVS morphologic and density
characteristics. We constructed three different models to explore the
potential effects of confounding: model 1 (adjusted for gestational age
only); model 2 (model 1 additionally adjusted for maternal age, BMI,
parity, conception mode, foetal sex and periconceptional alcohol
consumption, smoking and folic acid supplement use); and model 3 (model
2 additionally adjusted for total energy intake). Possible confounders
were selected based on literature and discussion amongst authors using a
directed acyclic graph.
All analyses were performed using SPSS (version 25.0; SPSS Inc.,
Chicago, IL, USA) and R (version 4.2.2, R Core Team, Vienna, Austria,
2022). P-values <0.05 were considered statistically
significant.