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CLINICAL RESEARCH ARTICLE OPEN
Neurometabolic changes in neonates with congenital heart
defects and their relation to neurodevelopmental outcome
Céline Steger
1,2,3,4,5
, Maria Feldmann
3,4,5,6
, Julia Borns
2,3,7
, Cornelia Hagmann
3,5,8
, Beatrice Latal
3,5,6
, Ulrike Held
5,9
, András Jakab
1,3,4,5
,
Ruth O’Gorman Tuura
1,3,4,5,10
and Walter Knirsch
2,3,5,10
✉
© The Author(s) 2022
BACKGROUND: Altered neurometabolite ratios in neonates undergoing cardiac surgery for congenital heart defects (CHD) may
serve as a biomarker for altered brain development and neurodevelopment (ND).
METHODS: We analyzed single voxel 3T PRESS H
1
-MRS data, acquired unilaterally in the left basal ganglia and white matter of 88
CHD neonates before and/or after neonatal cardiac surgery and 30 healthy controls. Metabolite ratios to Creatine (Cr) included
glutamate (Glu/Cr), myo-Inositol (mI/Cr), glutamate and glutamine (Glx/Cr), and lactate (Lac/Cr). In addition, the developmental
marker N-acetylaspartate to choline (NAA/Cho) was evaluated. All children underwent ND outcome testing using the Bayley Scales
of Infant and Toddler Development Third Edition (BSID-III) at 1 year of age.
RESULTS: White matter NAA/Cho ratios were lower in CHD neonates compared to healthy controls (group beta estimate: −0.26,
std. error 0.07, 95% CI: −0.40 –0.13, pvalue <0.001, FDR corrected pvalue =0.010). We found no correlation between pre- or
postoperative white matter NAA/Cho with ND outcome while controlling for socioeconomic status and CHD diagnosis.
CONCLUSION: Reduced white matter NAA/Cho in CHD neonates undergoing cardiac surgery may reflect a delay in brain
maturation. Further long-term MRS studies are needed to improve our understanding of the clinical impact of altered metabolites
on brain development and outcome.
Pediatric Research; https://doi.org/10.1038/s41390-022-02253-y
IMPACT:
●NAA/Cho was reduced in the white matter, but not the gray matter of CHD neonates compared to healthy controls.
●No correlation to the 1-year neurodevelopmental outcome (Bayley-III) was found.
●While the rapid change of NAA/Cho with age might make it a sensitive marker for a delay in brain maturation, the relationship
to neurodevelopmental outcome requires further investigation.
INTRODUCTION
Congenital heart defects (CHD) are among the most common
birth defects, with one in a hundred newborns affected.
1,2
Mild to
moderate impairments in different areas of neurodevelopment
(ND), including motor and cognitive domains, are frequently
observed among children with complex CHD.
3,4
While ND
impairments arise due to a combination of patient-specific
internal (e.g., genetics, diagnosis) and external factors, they are
thought to be related to alterations in brain development.
Studies using magnetic resonance (MR) imaging have revealed
various patterns of altered brain development in patients with
CHD, such as total and regional brain volume,
5,6
and delayed brain
maturation.
7,8
In addition, an increased prevalence of white matter
injuries (WMI) has been described.
9,10
Importantly, these neuroi-
maging findings could be linked to the ND outcome in these
patients.
11–15
However, even in the absence of structural
abnormalities, brain metabolism can be altered due to a reduction
of cerebral oxygen delivery and consumption as well as altered
cerebral perfusion associated with the specific type of CHD.
16
This
may lead to more subtle changes on the metabolic level of
neurons and glial cells potentially affecting ND outcome.
Metabolic compounds can be studied by magnetic resonance
spectroscopy (MRS) and may serve as a biomarker reflecting
different aspects of cellular functions. For instance,
N-acetylaspartate (NAA), serves as a marker for neuronal function
and neuronal density, myo-Inositol (mI) for glia and intracellular
signal transduction (second messenger), choline-containing com-
pounds (Cho) reflect cell membrane content or membrane
turnover, glutamate (Glu) is a marker for neurotransmitter activity,
glutamate and glutamine (Glx) and creatine containing
Received: 12 April 2022 Revised: 7 July 2022 Accepted: 27 July 2022
1
Center for MR-Research, University Children’s Hospital, Zurich, Switzerland.
2
Pediatric Cardiology, Pediatric Heart Center, Department of Surgery, University Children’s Hospital,
Zürich, Switzerland.
3
Children’s Research Center, University Children’s Hospital, Zürich, Switzerland.
4
Neuroscience Center Zürich, University of Zürich, Zürich, Switzerland.
5
University of Zurich, Zurich, Switzerland.
6
Child Development Center, University Children’s Hospital, Zurich, Switzerland.
7
Pediatric Cardiology, Inselspital Bern, Bern, Switzerland.
8
Department of Neonatology and Pediatric Intensive Care, University Children’s Hospital, Zurich, Switzerland.
9
Department of Epidemiology, Biostatistics and Prevention Institute
UZH, Zürich, Switzerland.
10
These authors contributed equally: Ruth O’Gorman Tuura, Walter Knirsch. ✉email: walter.knirsch@kispi.uzh.ch
www.nature.com/pr
1234567890();,:
compounds (Cr) reflect metabolic activity, while lactate may serve
as an indicator for hypoxia.
17
During the fetal and neonatal period, neurometabolic concen-
trations undergo substantial physiological changes, reflecting
brain development and its underlying cellular processes. For
example, the intracerebral concentrations of Cr and NAA increase,
while concentrations of Cho and mI decrease.
18–20
Due to the
rapid increase in NAA and the corresponding decrease in Cho with
development,
21,22
the ratio of NAA/Cho has been suggested to
represent a marker for brain maturation, sensitive both to changes
in neuronal development (NAA) and cell membrane turnover and
myelination (Cho).
23
In neonates with severe CHD, studies with a
focus on neurometabolic changes found decreased NAA/Cho
24,25
and increased Lac/Cho ratios relative to values seen in healthy
neonates.
25
However, these studies did not report on the relationship
between the metabolic alterations and ND outcome, something
that has been investigated in preterm infants.
26
Preterm infants
are a patient population with a similar risk profile for impaired ND
and neurometabolites measured at term equivalent age might be
associated with outcome in this population.
27
Alterations in
neurometabolic concentrations and ratios determined by MRS
may therefore serve as biomarkers for delayed brain development
in children at risk for impaired ND, allowing for tailored timely
intervention to improve patient-individual outcome.
In our study, we hypothesized that neurometabolite ratios in
CHD neonates before and after neonatal cardiac surgery are
altered in comparison to those of healthy controls and may be
associated with impaired ND outcome at 1 year of age determined
by the Bayley Scales of Infant and Toddler Development, Third
Edition (BSID-III). Therefore, the aims of the study were twofold: (1)
to identify altered neurometabolites in the CHD group compared
to a healthy control group and (2) to investigate the relationship
between altered neurometabolite levels and the Bayley composite
scores (BCS) at 1 year of age.
METHODS
Subjects
Data were collected in a single-center prospective cohort study of
the Research Group Heart and Brain.
12,28
The cantonal ethical committee
approved the study (KEK StV-23/619/04), and parents or legal guardians
provided written informed consent. Between December 2009 and April
2020, 125 term newborns (>36 weeks of gestation) with CHD scheduled for
neonatal cardiac surgery were enrolled in the study. Neonates with
suspected or confirmed genetic disorder, born preterm (before the 36th
week of gestation), who did not undergo surgery during the neonatal
period (first 6 weeks of life) or with no available written consent were
excluded. Patients underwent cerebral MRI before and/or after cardiac
surgery. In addition to the patient population, 52 healthy term newborns
from the well-baby maternity unit of the University Hospital Zurich were
recruited between 2011 and 2016 as healthy controls. These healthy
controls were scanned once. A total of 88 CHD and 30 controls had MRS
data available and underwent a BSID-III assessment at 1 year of age. A
more detailed overview of the dataset is given in Supplementary Fig. 1. As
MRS was a secondary outcome of the overall study, the sample size was
not formally calculated prior to this analysis, but was based on the
availability of data.
Magnetic resonance spectroscopy
MR data were acquired on a 3.0T scanner (GE Healthcare, Milwaukee, WI)
using an eight-channel head coil. During the study, a scanner upgrade
from the HD.xt to the MR750 platform was performed. Neonates were
scanned during natural sleep. Earplugs and Minimuffs were used for noise
protection. Patients were in a stable hemodynamic state at the time of MRI,
as described previously.
6,12
Short-echo time (TE) H
1
-proton MRS was
acquired in two voxels placed in the basal ganglia/thalami
(16 × 16 × 16 mm
3
) and white matter (16 × 16 × 16 mm
3
) (see Fig. 1for
voxel positions). A single voxel Point RESolved Spectroscopy (PRESS)
sequence (repetition time =3000 ms, TE =35 ms, 96 averages) was used
for the acquisition. Spectra were quantified using LCModel, a fully
automated spectral fitting software package that estimates the concentra-
tion of the metabolites, uncertainty, and the concentration ratio to creatine
(Cr) of each metabolite. In LCModel uncertainties are calculated as the
percentages of the Cramer–Rao lower bound (CRLB) of the metabolite fit
(% SD). The following metabolite ratios to Creatine were analyzed:
glutamate (Glu/Cr), glycerophosphorylcholine with phosphorylcholine
(Cho/Cr), myo-Inositol (mI/Cr), glutamate and glutamine (Glx/Cr), lactate
(Lac/Cr). N-acetylaspartate/Cho (NAA/Cho) was additionally calculated from
the measured concentrations of NAA and Cho.
Collected spectra underwent visual quality control for fitting errors, and
spectra where the relative CRLB for NAA or Cr exceeded 10% were
excluded.
Neurodevelopmental outcome
CHD patients and healthy controls underwent neurodevelopmental
outcome testing at 1 year of age using the BSID-III. BSID-III provides
composite scores for three domains, namely the motor composite score
(MCS), the language composite score (LCS), and the cognitive composite
score (CCS). BCS are age adjusted and were designed to have a mean score
of 100 and a standard deviation of ±15. The assessment was administered
by trained developmental pediatricians, who were not blinded to clinical
and major structural MRI findings.
Cardiac diagnosis and surgical procedure
Cardiac diagnoses for the CHD participants were divided into subgroups
including d-Transposition of the Great Arteries (dTGA), left ventricular
outflow tract obstructions (LVOTO), right ventricular outflow tract
obstructions (RVOTO), single ventricle physiology (SVP), and other.
Cardiac surgery included biventricular repair by arterial switch or Rastelli
operation for patients with dTGA, complex aortic arch surgery, systemic-
pulmonary shunt procedure, and neonatal Fallot repair. For univentricular
palliation, Norwood-type stage I palliation for patients with hypoplastic left
heart syndrome and other forms of univentricular physiology with a
hypoplastic aortic arch was performed. In the case of aortic arch surgery,
i.e., Norwood repair, cardiopulmonary bypass (CPB) surgery was performed
under moderate hypothermia (25 °C) with selective regional cerebral
perfusion. Modified ultrafiltration was used at the end of CPB surgery.
Postoperative intensive care and further follow-up were evaluated until the
end of the first year of life.
Variables
The dependent variable of the first research question was the metabolite
ratios: NAA/Cho, mI/Cr, Glu/Cr, Glx/Cr, and Lac/Cr. The variable scanner
software was introduced to account for whether participants were scanned
before or after the MR scanner upgrade. Gestational age at scan is a known
predictor for some metabolites and sex is a potential confounder
29,30
and
thus were included in the analyses. The dependent variable of the second
research question was ND outcome (CCS, LCS, and MCS) at 1 year. Due to
its known correlation with neurodevelopmental outcome,
31
the socio-
economic status (SES, ranging from 2 to 12) was estimated based on a sum
score of maternal education and paternal occupation (each 1–6) and
included as a covariate. To account for the diagnosis as a covariate for
Basal ganglia White matter
1
1
Fig. 1 MRS voxel locations within the basal ganglia and white
matter.
C. Steger et al.
2
Pediatric Research
outcome, a binary diagnosis variable dTGA and non-dTGA was introduced.
This binarization was done to keep variables in the model limited. Patients
with dTGA undergoing arterial switch were used as the most homogenous
CHD group and compared with other type of CHD, termed as non-dTGA
group, which included a more heterogenous group of CHD. In additional
exploratory analyses, the classification of moderate and severe types of
CHD was used
32
and the covariate WMI at scan (yes/no) was added.
Statistical analysis
Statistical analysis was carried out in R (R version 4.0.4).
33
Descriptive
statistics for both groups are shown as mean and standard deviation,
median and interquartile range, or number and percentages. Exploratory
group comparisons were performed using a t-test, Wilcoxon rank-sum test,
or χ
2
test, as appropriate. The metabolite ratios are presented for healthy
controls, CHD preoperative, and CHD postoperative as mean and standard
deviation and median and interquartile range. The percentage of missing
values for each metabolite is reported. Lac/Cr ratio was not normally
distributed and was close to zero. The data were therefore logarithmically
transformed after the addition of 0.001 to each value to avoid log
transformation of zero values.
To investigate differences in metabolite ratios between the CHD and the
control group, mixed effect models were employed (“lme4”package R).
Models consisted of the metabolite ratio as the dependent variable and
group (CHD or healthy control), centered gestational age at scan, sex, and
scanner software as independent variables. A random intercept for each
subject was introduced to account for the repeated measurements in
subjects of the CHD group, as the models' combined metabolite ratios
from pre- and post-surgery scans for patients. Linear models without
random effects were also fitted to qualitatively confirm the robustness of
the estimated models (data not shown). Normality of residuals was
checked by visual inspection of histograms, Residuals vs Fitted, and QQ
plots. Pvalues of the mixed-effects models were obtained using the
“lmertest”package in R. To account for multiple comparisons, pvalues
were adjusted according to the Benjamini–Hochberg procedure,
34
using
the “stats”package in R. Pvalues <0.05 were considered statistically
significant.
In addition, a post hoc analysis was carried out on the second cohort
only to explore the difference between groups in the absence of the
scanner upgrade confounder.
The correlation between white matter NAA/Cho and the neurodeve-
lopmental outcome was tested in the CHD group only, using linear
models. Separate models were generated for pre- and postoperative
metabolite data. The dependent variable of each linear model was one
of the three BSID-III composite scores, while independent variables
gestational age at MRI, metabolite ratio, SES, and diagnosis (dTGA vs
non-dTGA or severe vs moderate) were chosen. In addition, models
correcting for the presence of WMI at scan timepoint were explored.
Normality of residuals was checked by visual inspection of residuals
histograms, Residuals vs Fitted, and QQ plots. Two-sided pvalues <0.05
were considered significant.
In a post hoc analysis, we explored whether patients with WMI had
lower white matter NAA/Cho than patients without.
The study was reported according to STROBE guidelines.
RESULTS
Study population
Demographic characteristics and other variables included in the
study of CHD patients and healthy controls are given in Table 1.
In the CHD group, 62 preoperative spectra and 67 postoperative
spectra were available (out of the 88 CHD subjects, 41 CHD cases
had two MRS spectra, 21 had preoperative spectra only, and 26
had postoperative spectra only) (Fig. 2). The most frequent
diagnosis was dTGA (56.8%) and 61.9% of CHD patients
underwent CPB surgery between the two MR timepoints. Eleven
CHD patients had white matter lesions in the preoperative scan
and ten patients had white matter lesions in the postoperative
scan. For further details see Supplementary Table 1. CHD
patients had an SES of 9 IQR [7, 10] (median [IQR]) that was
lower than that of the control group who had an SES of 12 IQR
[11, 12] (p=0.001). A total of 63 CHD (71.6%) and 20 (66.7%)
control patients were scanned after the scanner upgrade (χ
2
test:
pvalue: 0.781).
Metabolite ratio alterations
An increase of Naa/Cho with gestational age can be observed in
both the white matter and the basal ganglia, while mI/Cr
decreases with gestational age. Figure 3illustrates the relationship
of each metabolite ratio with the gestational age at the time of
measurement. Table 2shows the measured cerebral metabolites
for healthy controls and CHD neonates before and after cardiac
surgery. More data were missing for the white matter measure-
ment, possibly due to the order of the spectra in the scanning
protocol, in which the basal ganglia spectrum is acquired before
the white matter spectrum. For the groupwise analysis in the full
cohort, the beta coefficients of the group for each metabolite and
location are reported in Fig. 4. The main effect of group was
significant for NAA/Cho in the white matter. Belonging to the CHD
group was associated with a 12.3% reduction in NAA/Cho ratio in
the white matter (beta estimate: −0.26, std. error 0.07, 95% CI:
−0.40 to 0.13, pvalue <0.001, FDR corrected pvalue =0.010).
NAA/Cho ratio in the basal ganglia was not significant after
correction for multiple testing (beta estimate: −0.14, std.error:
0.07, 95% CI: −0.27 to −0.01, pvalue =0.035, FDR corrected p
value =0.175). A positive relationship with gestational age at scan
was modeled in both locations for NAA/Cho and for Glu/Cr, while
a negative relationship was described in both locations for mI/Cr
and in the basal ganglia for log (Lac/Cr). The analysis on only the
second cohort (after scanner software upgrade) had less power,
but reproduced the main finding of a decrease in NAA/Cho within
the white matter in the CHD group. A detailed result table of each
model is provided in Supplementary Tables 2 and 3. The necessity
of an interaction term of gestational age at scan and group (CHD
vs control) was explored, but the interaction was not significant
and therefore not included in the models (data not shown).
In an additional post hoc exploratory analysis we found no
evidence that patients with WMI had different white matter NAA/
Cho ratios than patients without WMI (data not shown).
Neurodevelopmental outcome
Overall, the healthy controls and the CHD patients had a good
outcome. BCS were found to be reduced in the CHD group,
particularly for the cognitive and motor domains (exploratory linear
model pvalues: CCS p=0.04, MCS p=0.01, LCS p=0.57) (Table 3).
Correlation between metabolite ratio and
neurodevelopmental outcome
There was no evidence for a correlation between metabolite ratio
(white matter NAA/Cho) and BCS (Supplementary Tables 4–7) in
the explored models. The categorization of moderate vs severe
did lead to higher R
2
in the models compared to non-TGA vs TGA
categorization. The highest R
2
of 0.34 (adjusted R
2
0.27) was found
for the linear model with dependent variable MCS and
independent variables preoperative NAA/Cho, diagnosis (moder-
ate vs severe), age at MRI, and SES. Severe CHD was associated
with a reduced, while higher SES was associated with an increased
motor score both pre- and postoperatively. As expected SES was
associated with outcome in most of our explored models. WMI in
postoperative scans was associated with language outcome
scores, but not other scores. As the correlation of interest (NAA/
Cho and outcome) was not significant in any of the models, no
correction for multiple testing was performed.
DISCUSSION
In this study, we analyzed H
1
-MRS spectra of CHD neonates and
healthy controls to evaluate metabolic alterations in CHD patients
undergoing early neonatal cardiac surgery. We found significantly
reduced NAA/Cho ratios for the CHD group compared to healthy
controls in the white matter, but not the basal ganglia. The remaining
cerebral metabolite ratios such as Lac/Cr, Glx/Cr, Glu/Cr, and mI/Cr
were comparable between the CHD patients and healthy controls.
C. Steger et al.
3
Pediatric Research
Furthermore, we could confirm the physiological age-related rapid
changes within the first month of life including a significant increase
in NAA/Cho. We found no evidence for a correlation between white
matter NAA/Cho and ND outcome at 1 year of age.
Age-related and groupwise differences in MRS metabolite
ratios
Age-related changes were evident in both CHD patients and
healthy controls. Our data showed a rapid increase in NAA/Cho
with age, while mI/Cr ratios declined with gestational age,
consistent with findings from previous studies.
18,20
The main alteration observed in the CHD was a 12.3% reduction
in NAA/Cho ratios in the white matter group compared to the
healthy controls. This observation supports previous reports of a
10% overall reduction of NAA/Cho ratios in neonates with CHD.
25
A cross-sectional study in fetuses discussed the possibility of
progressively lower NAA/Cho during the third trimester in CHD
patients compared to healthy controls.
35
We explored the
Control CHD presurgery CHD postsurgery
mI
Cho
Cre
Gln
Glu
NAA
Lac+Lip
mI
ChoCre
Gln
Glu
NAA
Lac+Lip
mI
Cho Cre
Gln
Glu
NAA
Lac+Lip
mI
Cho
Cre
Gln
Glu
NAA
Lac+Lip
mI
Cho
Cre
Gln
Glu
NAA
Lac+Lip
mI
Cho
Cre
Gln
Glu
NAA
Lac+Lip
Chemical shift (ppm) Chemical shift (ppm) Chemical shift (ppm)
Chemical shift (ppm)
Chemical shift (ppm)
4.5
0
3.8E+06
0
395910
0
2.2E+06
0
425604
0
2.6E+06
0
274230
0
3.0E+06
0
546229
0
2.1E+06
0
325365
0
2.2E+06
0
559221
4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.50
4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.50 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.50 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.50
4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.50
Chemical shift (ppm)
4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.50
LCModel (Version 6.3-1M) Copyright: S.W. Provencher. Ref.: Magn. Reson. Med. 30:672-679 (1993). 10-June-2021 00:11
LCModel (Version 6.3-1M) Copyright: S.W. Provencher. Ref.: Magn. Reson. Med. 30:672-679 (1993). 10-June-2021 00:10
LCModel (Version 6.3-1M) Copyright: S.W. Provencher. Ref.: Magn. Reson. Med. 30:672-679 (1993).
Data of: Division of Magnetic Resonance, Kinderspital, University of Zurich Data of: Division of Magnetic Resonance, Kinderspital, University of Zurich
Data of: Division of Magnetic Resonance, Kinderspital, University of Zurich 13-July-2021 18:45
LCModel (Version 6.3-1M) Copyright: S.W. Provencher. Ref.: Magn. Reson. Med. 30:672-679 (1993).
Data of: Division of Magnetic Resonance, Kinderspital, University of ZurichData of: Division of Magnetic Resonance, Kinderspital, University of Zurich Data of: Division of Magnetic Resonance, Kinderspital, University of Zurich
13-July-2021 18:49
LCModel (Version 6.3-1M) Copyright: S.W. Provencher. Ref.: Magn. Reson. Med. 30:672-679 (1993). 13-July-2021 18:5
5
LCModel (Version 6.3-1M) Copyright: S.W. Provencher. Ref.: Magn. Reson. Med. 30:672-679 (1993). 13-July-2021 18:55
Fig. 2 Representative spectra of control and patient pre- and postoperatively. Basal ganglia spectra are depicted in the top row, while
white matter spectra are shown in the bottom row. The chemical shift (ppm) is shown on the X axis, and the spectral data are shown in black
with the LCModel fit overlaid in red. The residuals between the data and the fit are shown above each spectrum. NAA N-acetylaspartate, Cho
choline, Cre creatine, mI myo-Inositol, Glx glutamate and glutamine, Glu glutamate, Lac lactate, Lip lipid.
Table 1. Baseline characteristics.
CHD Healthy controls pvalue
a
Number (n)8830
Male sex 63 (71.6%) 15 (50.0%) 0.046* (c)
Gestational age (weeks) 39.32 (1.24) 39.63 (1.24) 0.245 (a)
Birth weight (kg) 3.33 (0.45) 3.37 (0.41) 0.460 (a)
Diagnosis 88 (100%)
dTGA (n, %) 50 (56.8) ––
LVOTO ( n, %) 9 (10.2) ––
RVOTO (n, %) 9 (10.2) ––
SVP (n, %) 11 (12.5) ––
Other
b
(n, %) 9 (10.2) ––
Cardiopulmonary bypass (n, %) 73 (61.9) ––
Socioeconomic status 8 [7–10] 12 [11–12] <0.001* (b)
Characteristic variables stratified by group. Categorical and binominal variables presented as number and percentage (n(%)). Mean and standard deviation (m
(SD)) or median and interquartile range (m [IQR]) for continuous variables. The variable scanner software was a binominal categorical variable introduced to
account for a scanner upgrade during the study.
D-TGA d-Transposition of the Great Arteries, LVOTO left ventricular outflow tract obstruction, RVOTO right ventricular outflow tract obstruction, SVP single
ventricle physiology.
a
Exploratory pvalues show uncorrected pvalues, asterisks (*) indicate pvalue <0.05, the used test is specified in brackets as follows:
(a) t-test, (b) Wilcoxon rank-sum test, and (c) χ
2
test.
b
Other included: ventricular septal defect, L-TGA, truncus arteriosus communis, and total anomalous pulmonary venous connection.
C. Steger et al.
4
Pediatric Research
Table 2. Metabolite ratios.
Healthy controls CHD pre-surgery CHD post-surgery Missing data
n30 62 67
Gestational age at scan 42.5 (1.9) 40.2 (1.4) 43.0 (2.0) 0
Basal ganglia
NAA/Cho 2.2 (0.31) 1.76 (0.21) 2.13 (0.44) 2
Cho/Cr 0.36 (0.04) 0.39 (0.04) 0.37 (0.07) 2
NAA/Cr 0.78 (0.1) 0.69 (0.07) 0.79 (0.12) 2
mI/Cr 0.75 (0.19) 0.82 (0.15) 0.77 (0.24) 2
Glu/Cr 1.02 (0.19) 0.97 (0.19) 1.08 (0.25) 2
Glx/Cr 1.53 (0.37) 1.56 (0.38) 1.71 (0.49) 2
Lac/Cr 0.09 [0.00, 0.15] 0.11 [0.04, 0.17] 0.11 [0.04, 0.15] 2
White matter
NAA/Cho 2.14 (0.35) 1.57 (0.26) 2.00 (0.45) 17
Cho/Cr 0.49 (0.07) 0.54 (0.07) 0.48 (0.08) 17
NAA/Cr 1.04 (0.15) 0.83 (0.11) 0.96 (0.16) 17
mI/Cr 1.4 (0.27) 1.55 (0.29) 1.35 (0.31) 17
Glu/Cr 1.35 (0.29) 1.31 (0.32) 1.40 (0.32) 17
Glx/Cr 2.15 (0.55) 2.18 (0.52) 2.12 (0.53) 17
Lac/Cr 0.17 [0.08, 0.29] 0.24 [0.14, 0.33] 0.19 [0.07, 0.32] 17
Stratified by group and timepoint of measurement. Reported as mean and standard deviation (m (SD)). Lac/Cr is reported as median and interquartile range
(m [IQR]) as it was nonnormally distributed. Missing data show the percentage of data missing in the dataset that was imputed.
NAA N-acetylaspartate, Cho choline, Cr creatine, mI myo-Inositol, Glx glutamate and glutamine, Glu glutamate, Lac lactate.
38
1.0 10.0 0.5
1.0
1.5
2.0
0.2
0.4
0.6
2
3
4
1.5
2.0
40 42 44 46 48
38
0.75 1.0 0.0 0.5
1.0
1.5
2.0
0.2
0.4
0.6
1.5
2.0
2.5
3.0
1.00
1.25
1.50
40 42 44 46 48 38 40 42 44 46 48 38 40 42 44 46 48 38 40 42 44 46 48
38
wm Glu/Cr
bg Glu/Cr bg Glx/Cr bg Lac/Cr bg ml/Cr
wm Lac/Cr wm ml/Crwm Glx/Cr
Metabolite ratio
1
2
3
1.0
1.5
2.0
2.5
3.0
40 42 44
bg Naa/Cho wm Naa/Cho
46 48 38 40 42 44 46 48
CHD
Controls
38 40 42 44 46 48
Gestational age at MR
38 40 42 44 46 48 38 40 42 44 46 48
Fig. 3 Metabolite ratios versus gestational age. X axis: gestational age in weeks, Y axis: metabolite ratio. First row =white matter (wm), third
row =basal ganglia (bg), middle row =NAA/Cho in wm and bg. datapoints: blue =CHD cohort, black =healthy controls.
C. Steger et al.
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interaction between the group and gestational age at scan, which
was non-significant and therefore not included in our model. By
using a linear model for our analysis, we assume and limit
ourselves to a linear relationship between gestational age and
metabolite ratios. Given the rapid changes with age, the inter- and
intra-subject variability (reproducibility of MRS measurement),
36,37
large longitudinal comparative studies are required to further
investigate whether metabolites change at a similar rate over time
in healthy controls and CHD patients.
Since our study design included voxels in the gray matter (basal
ganglia) as well as the cerebral white matter, we also investigated
the regional specificity of altered metabolite ratios in CHD. While
NAA/Cho levels were reduced in the white matter, alterations in
the basal ganglia were not significant, both with and without
imputation for missing data. Metabolite ratios might therefore be
altered in a regionally specific manner. NAA and Cho are thought
to reflect neural integrity and brain development during the
neonatal period, with NAA increasing
38
and Cho decreasing with
age.
20
Cho plays a role in membrane turnover,
17,39
while NAA is
present in neurons and oligodendrocytes and could serve as a
metabolite trafficking system supporting oligodendrocyte
metabolism during brain development and in response to brain
injury.
40
While patients with CHD are at risk for brain injuries,
10
a
recent multi-center study showed that WMI seem to occur in a
characteristic distribution pattern, due to the regional differences
in brain maturation.
9
The vulnerability of the oligodendrocyte
progenitor cells that are present in the neonatal CHD brain due to
delayed maturation is a proposed pathological mechanism
underlying WMI.
41,42
In addition, decreased NAA/Cho ratios have
previously been associated with preoperative brain injuries in this
population.
43
This indicates that the location of MRS acquisition
might be of importance when trying to establish a relation to
outcome, and MRS data from the white matter might be more
sensitive to pathological changes associated with CHD.
In our cohort, we only had a few patients with WMI, and we
could not find evidence that this was associated with lower white
matter NAA/Cho compared to patients without WMI. However,
this post hoc analysis was exploratory and has to be interpreted
with care, given the low frequency of WMI in our cohort. We found
no evidence for groupwise alterations in any of the other
metabolite ratios, although previous studies reported increased
levels of lactate
25
in the brains of CHD children, and lactate levels
Table 3. Bayley composite scores.
Score Healthy controls CHD pvalue Missing
CCS 116 (11.92) 104.77 (14.12) 0.04* (a) 0
LCS 98.60 (10.85) 94.07 (12.48) 0.57 (a) 2
MCS 104.70 (9.77) 92.23 (15.17) 0.01* (a) 0
Determined at 1 year of age in patients with CHD and healthy controls. Missing data show the percentage of data missing in the dataset. Composite scores
from Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III).
CCS Bayley cognition composite score, LCS Bayley language composite score, MCS Bayley motor composite score.
Exploratory pvalues show uncorrected pvalues, asterisks (*) indicate pvalue <0.05, the used test is specified in brackets as follows:
(a) pvalues of group variable in linear model including socioeconomic status as covariate.
–0.4
bg Naa/Cho
wm Naa/Cho
bg Glu/Cr
wm Glu/Cr
bg Glx/Cr
bg ml/Cr
wm ml/Cr
bg log(Lac/Cr)
wm log(Lac/Cr)
wm Glx/Cr
–0.3 –0.2 –0.1 0.0 0.1 0.2
Model
0.3
Estimated beta and Cl for group (CHD or control)
0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3
P-value
group
0.103
0.122
0.779
0.456
0.739
0.120
0.398
0.571
0.001
0.035
0.244
0.244
0.779
0.651
0.779
0.244
0.651
0.714
0.010
0.175
Corr. P-
value
Fig. 4 Metabolite differences between groups: estimated betas and confidence intervals. For each model, the estimated beta coefficient of
the group, its 95% confidence interval (CI), and pvalues are listed. Complete models are described in Supplementary Table 3. The figure to the
right illustrates the results as follows: A beta coefficient for the group that is positive describes a larger metabolite ratio for the CHD group. CI
crossing 0.0 implies that there is no significant effect. Lactate was logarithmically transformed, for results see Supplementary Table 2. Asterisk
(*) indicates the results that remained significant after false discovery rate correction. NAA N-acetylaspartate, Cho choline, Cr creatine, mI myo-
Inositol, Glx glutamate+glutamine, Glu glutamate, Lac lactate.
C. Steger et al.
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are also increased in neonates suffering from hypoxia or ischemic
injuries.
44
BSID-III outcome after 1 year
The overall good outcome in CHD patients is likely to be the result
of a variety of factors, as ND in itself is multifactorial. In our cohort,
we report a high SES, a factor known to impact ND, and low WMI,
which potentially can impact ND negatively. As in this MR study,
only hemodynamically stable neonates could be scanned, and
critically ill patients might be missing, which is not fully
representative of the clinical population. This may have con-
tributed to an overall low rate of WMI and overall good outcome.
Altered cerebral metabolites at neonatal age and BSID-III
outcome after 1 year
Few studies have investigated the relationship between cerebral
metabolites and BSID in CHD children. In a previous study Park
et al.
45
reported a potential link between altered metabolite ratios
measured at 1 year and BSID-II scores of a dTGA patient cohort,
suggesting that neurodevelopmental delay may reflect conse-
quences of altered metabolism. Importantly, they correlated MRS
findings with ND both obtained at an age of 1 year, while we
aimed to examine the predictive value of altered perioperative
MRS on the subsequent outcome. With this approach, we found
no evidence for a correlation between NAA/Cho ratios and ND
outcome at 1 year of age. Serial MRS data may provide important
insight into whether children with CHD demonstrate an ongoing
impaired brain metabolism during the first year of life or a
“normalization”of brain metabolism until the end of the first year
of life. Furthermore, the sensitivity of the used Bayley scales (II or III
generation) and the long-term effect of altered metabolism on ND
until school age has to be determined.
More data have been reported on the relationship between
cerebral metabolite ratios and ND outcome for preterm-born
children. The relationship between ND and altered NAA/Cho
specifically has been studied more extensively, still it is unclear
whether an association exists. In a recent systematic review, the
authors showed that NAA/Cho ratios measured at term equivalent
age can serve as a potential surrogate marker for the short-term
outcome.
26
Findings of a relationship between white matter NAA/
Cho ratios have been shown for motor outcome in preterm infants
at 1 year of age
46–48
as well as for cognitive and language
outcome
27,46
continuing at 18–24 months of corrected age for all
developmental domains, but the relationship to longer-term
outcome needs to be studied.
26
Interestingly, metabolite ratios in preterm-born teenagers and
adults remain altered and appear to be associated with cognitive
function.
49,50
Studying the relationship between metabolite ratios
and ND is challenging as both parameters change throughout
childhood. However, even in the absence of an association with the
1-year neurodevelopmental outcome,NAA/Choasanexpressionof
altered brain maturation could still be associated with more complex
cognitive and language functions that only evolve later during
childhood and may not be captured as early as 1 year of age. Further
studies would be needed to evaluate the link between neurometa-
bolite changes and outcome at a later developmental stage.
The underlying mechanisms of impaired ND outcome at 1 year
of age are multifactorial. In this analysis, we focused solely on the
impact of altered metabolic ratios, but further studies are needed
to improve our understanding of the multiple pathological
findings in CHD patients. For an early identification of children
at risk for ND impairment, which is needed for early therapeutic
support, a risk score derived from a variety of pathological MR
findings as well as various clinical variables and psychosocial
factors may help to stratify patients according to their risk of a
negative outcome. However, further research is needed to
elucidate the interplay between these various factors and their
combined impact on ND.
Limitations
The following limitations of our study merit mentioning. During
our data collection, a scanner software upgrade was performed,
but the effects of the scanner upgrade were investigated both
by introducing a variable in our model to account for the
upgrade and by performing a separate analysis on the second
(larger) cohort only. We had several missing values in our
metabolites because of incomplete scanning protocols due to
the nature of the data collection, during natural sleep. It also has
to be taken into account, that our cohort is not fully
representative of the clinical population as only hemodynami-
cally stable neonates could be scanned, while more critically ill
patients might be missing in our cohort. Further limitations
regarding the studied patients include their heterogeneity due
to the different types of CHD and the different degrees of
surgical invasiveness. While we could compare the patients to a
healthy control group, the controls only underwent one scan at
a time between the two times of the pre- and postoperative
scans. This limits the time-corrected analysis of the metabolite
rate change as a linear increase with age was assumed and a
longitudinal comparison was not possible. For the outcome
correlation analysis, controlling for the diagnosis was challen-
ging, but we explored two binary categorization options, non-
TGA vs TGA and moderate vs severe CHD. Differentiated
comparison between CHD subtypes might be necessary, ideally
in larger or more homogenous cohorts. In addition, the overall
good outcome of most infants in the cohort might limit the
ability to detect a relationship between altered metabolite ratios
and ND outcome. Finally, the investigators were not blinded to
the MR findings.
CONCLUSION
We found that NAA/Cho is reduced in our CHD patients compared
to the healthy control group, while other neurometabolites follow
the physiological course of metabolic brain development within
the first month of life compared to healthy controls. No evidence
for a correlation between white matter NAA/Cho and individual
BCSs at 1 year of age was found. Further investigation is needed to
clarify if a link exists between altered neonatal cerebral metabolite
levels and outcome at a later timepoint.
DATA AVAILABILITY
The datasets generated during and/or analyzed during the current study are available
from the corresponding author on reasonable request.
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ACKNOWLEDGEMENTS
The authors would like to express their sincere gratitude to the families participating
in this study. Furthermore, the authors would like to thank the whole Research Group
Heart and Brain and the clinical staff of the University Children’s Hospital involved in
making the data collection possible.
AUTHOR CONTRIBUTIONS
C.S.: conceptualization of the analysis, formal analysis, methodology, writing—
original draft, visualization. M.F.: data curation, writing—review and editing. J.B.: data
curation, reviewing. C.H.: reviewing, examination of study patients, supervision. B.L.:
study conceptualization, examination of study patients, reviewing manuscript. U.H.:
methodology and supervision of statistical analysis, interpretation of results,
reviewing of the final draft. A.J.: conceptualization, methodology, reviewing,
supervision. R.O.T.: conceptualization, methodology, software, resources, supervision,
writing—review and editing. W.K.: conceptualization, methodology, investigation,
resources, writing, reviewing, supervision, project administration, funding acquisition.
FUNDING
This work was supported by the Swiss National Science Foundation (SNSF
320030_184932), Vontobel Foundation, OPO Foundation, Prof. Dr. Max Cloetta
Foundation, Anna Müller Grocholski Foundation, Foundation for Research in Science
and the Humanities at the University of Zurich, EMDO Foundation, FZK Grant, Swiss
National Science Foundation SPARK Grant (CRSK534 3_190638). Open access funding
provided by University of Zurich.
COMPETING INTERESTS
The authors declare no competing interests.
CONSENT TO PARTICIPATE
Consent for the study was required and obtained.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41390-022-02253-y.
Correspondence and requests for materials should be addressed to Walter Knirsch.
Reprints and permission information is available at http://www.nature.com/
reprints
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims
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