Background
Prediction of preeclampsia risk is key to informing effective maternal care. Current
screening for preeclampsia at 11 to 13 weeks of gestation using maternal demographic
characteristics and medical history with measurements of mean arterial pressure, uterine
artery pulsatility index, and serum placental growth factor can identify approximately
75% of women who develop preterm preeclampsia with delivery at <37 weeks of gestation.
Further improvements to preeclampsia screening tests will likely require integrating
additional biomarkers. Recent research suggests the existence of distinct maternal
risk profiles. Therefore, biomarker evaluation should account for the possibility
that a biomarker only predicts preeclampsia in a specific maternal phenotype.
Objective
This study aimed to verify metabolite biomarkers as preterm preeclampsia predictors
early in pregnancy in all women and across body mass index groups.
Study Design
Observational case-control study drawn from a large prospective study on the early
prediction of pregnancy complications in women attending their routine first hospital
visit at King’s College Hospital, London, United Kingdom, in 2010 to 2015. Pregnant
women underwent a complete first-trimester assessment, including the collection of
blood samples for biobanking. In 11- to 13-week plasma samples of 2501 singleton pregnancies,
the levels of preselected metabolites implicated in the prediction of pregnancy complications
were analyzed using a targeted liquid chromatography-mass spectrometry method, yielding
high-quality quantification data on 50 metabolites. The ratios of amino acid levels
involved in arginine biosynthesis and nitric oxide synthase pathways were added to
the list of biomarkers. Placental growth factor and pregnancy-associated plasma protein
A were also available for all study subjects, serving as comparator risk predictors.
Data on 1635 control and 106 pregnancies complicated by preterm preeclampsia were
considered for this analysis, normalized using multiples of medians. Prediction analyses
were performed across the following patient strata: all subjects and the body mass
index classes of <25, 25 to <30, and ≥30 kg/m2. Adjusted median levels were compared between cases and controls and between each
body mass index class group. Odds ratios and 95% confidence intervals were calculated
at the mean ±1 standard deviation to gauge clinical prediction merits.
Results
The levels of 13 metabolites were associated with preterm preeclampsia in the entire
study population (P<.05) with particularly significant (P<.01) associations found for 6 of them, namely, 2-hydroxy-(2/3)-methylbutyric acid, 25-hydroxyvitamin D3, 2-hydroxybutyric acid, alanine,
dodecanoylcarnitine, and 1-(1Z-octadecenyl)-2-oleoyl-sn-glycero-3-phosphocholine.
Fold changes in 7 amino acid ratios, all involving glutamine or ornithine, were also
significantly different between cases and controls (P<.01). The predictive performance of some metabolites and ratios differed according
to body mass index classification; for example, ornithine (P<.001) and several ornithine-related ratios (P<.0001 to P<.01) were only strongly associated with preterm preeclampsia in the body mass index
of <25 kg/m2 group, whereas dodecanoylcarnitine and 3 glutamine ratios were particularly predictive
in the body mass index of ≥30 kg/m2 group (P<.01).
Conclusion
Single metabolites and ratios of amino acids related to arginine bioavailability and
nitric oxide synthase pathways were associated with preterm preeclampsia risk at 11
to 13 weeks of gestation. Differential prediction was observed according to body mass
index classes, supporting the existence of distinct maternal risk profiles. Future
studies in preeclampsia prediction should account for the possibility of different
maternal risk profiles to improve etiologic and prognostic understanding and, ultimately,
clinical utility of screening tests.
Key words
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Article info
Publication history
Published online: December 17, 2022
Accepted:
December 11,
2022
Received in revised form:
December 8,
2022
Received:
August 19,
2022
Publication stage
In Press Journal Pre-ProofFootnotes
A.P., L.W.B., P.O., and R.T. are employees of Metabolomic Diagnostics. G.T. is the owner of SQU4RE, an independent statistics and data mining provider. L.W.B., G.T., and R.T. are named inventors on several patent applications regarding the use of metabolites to predict preeclampsia risk. The associated rights are assigned to Metabolomic Diagnostics. A.S. and K.H.N. report no conflict of interest.
Metabolomic Diagnostics has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement number 822842 for parts of this research. Collection and storage of the maternal samples was funded by the Fetal Medicine Foundation (United Kingdom charity number 1037116). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Cite this article as: Tuytten R, Syngelaki A, Thomas G, et al. First-trimester preterm preeclampsia prediction with metabolite biomarkers: differential prediction according to maternal body mass index. Am J Obstet Gynecol 2022;XX:x.ex–x.ex.
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