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A maternal serum metabolite ratio predicts fetal growth restriction at term


Fetal growth restriction (FGR) is the major single cause of stillbirth1 and is also associated with neonatal morbidity and mortality2,3, impaired health and educational achievement in childhood4,5 and with a range of diseases in later life6. Effective screening and intervention for FGR is an unmet clinical need. Here, we performed ultrahigh performance liquid chromatography–tandem mass spectroscopy (UPLC–MS/MS) metabolomics on maternal serum at 12, 20 and 28 weeks of gestational age (wkGA) using 175 cases of term FGR and 299 controls from the Pregnancy Outcome Prediction (POP) study, conducted in Cambridge, UK, to identify predictive metabolites. Internal validation using 36 wkGA samples demonstrated that a ratio of the products of the relative concentrations of two positively associated metabolites (1-(1-enyl-stearoyl)-2-oleoyl-GPC (P-18:0/18:1) and 1,5-anhydroglucitol) to the product of the relative concentrations of two negatively associated metabolites (5α-androstan-3α,17α-diol disulfate and N1,N12-diacetylspermine) predicted FGR at term. The ratio had approximately double the discrimination as compared to a previously developed angiogenic biomarker7, the soluble fms-like tyrosine kinase 1:placental growth factor (sFLT1:PlGF) ratio (AUC 0.78 versus 0.64, P = 0.0001). We validated the predictive performance of the metabolite ratio in two sub-samples of a demographically dissimilar cohort, Born in Bradford (BiB), conducted in Bradford, UK (P = 0.0002). Screening and intervention using this metabolite ratio in conjunction with ultrasonic imaging at around 36 wkGA could plausibly prevent adverse events through enhanced fetal monitoring and targeted induction of labor.

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Fig. 1: Levels of predictive metabolites at four gestational time points.
Fig. 2: Receiver operating characteristic curve analyses for the prediction of fetal growth restriction.

Data availability

Source data for Figs. 1,2 and Extended Data Figs. 2,5,6,7 are available online. As the individual patient data contain confidential information, it can be supplied only in an anonymized format to suitably qualified researchers who can make appropriate institutional commitments relating to data security and confidentiality. Data requests should be addressed to U.S. or G.C.S.S.


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The work was supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (Women’s Health theme), the Medical Research Council (MRC) (G1100221 to G.C.S.S. and D.S.C.-J. and MR/N024397/1 to D.A.L.), the Wellcome Trust (WT101597MA), National Institutes of Health (R01 DK10324), the European Research Council (669545), and the NIHR Biomedical Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol (Reproductive and Perinatal Health theme), which funds N.M.’s PhD studentship. N.G., N.M. and D.A.L. work in a unit that receives support from the MRC (MC_UU_00011/6) and University of Bristol. The funders did not have any role in the design, analysis or preparation of the manuscript for publication. We are grateful to the participants in the POP and BiB studies and staff who recruited and assessed these participants. We thank L. Bibby, S. Ranawaka, K. Holmes, J. Gill and R. Millar for technical assistance.

Author information

Authors and Affiliations



G.C.S.S. had the original idea. G.C.S.S., D.S.C.-J. and D.A.L. designed the experiments. U.S. and G.C.S.S. conceived the analysis. N.G., N.M. and U.S. conducted the analysis. E.C., F.G. and D.S.C.-J. conducted the laboratory work. U.S. and G.C.S.S. drafted the initial version of the manuscript. All authors have seen and approved the final version of the manuscript.

Corresponding author

Correspondence to Ulla Sovio.

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Competing interests

Direct: Cambridge Enterprise (UK) have filed a patent relating to the associations described in this paper with U.S., D.S.C.-J. and G.C.S.S. as the named inventors. Indirect: G.C.S.S. reports research support in kind from GE Healthcare and Roche, and financial support of research from GlaxoSmithKline (GSK) and Sera Prognostics. G.C.S.S. has been paid to attend advisory boards by GSK and Roche. G.C.S.S. has acted as a paid consultant to GSK and is a member of a Data Safety and Monitoring Committee for a GSK vaccine trial. D.A.L. has received support in kind from Roche Diagnostics and Medtronic Ltd.

Additional information

Peer review information Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Flow diagram of the selection of cases and controls using a case-cohort design in the POP study cohort.

One of the six women who did not have any blood samples available for analysis had FGR at term. POP, Pregnancy Outcome Prediction; wkGA, weeks of gestational age; FGR, fetal growth restriction; BW, birth weight, ACGVD1, abdominal circumference growth velocity in the lowest decile.

Extended Data Fig. 2 Distribution of P values from the composite Chi-squared test (two-sided) for the measurements at 20/28 wkGA.

The P values of 829 metabolites with a known structural identity were calculated from the test for interaction between term FGR and gestational age. The analysis included metabolite measurements from 175 FGR cases and 299 controls. wkGA, weeks of gestational age; FGR, fetal growth restriction.

Source data

Extended Data Fig. 3 Flow diagram of the selection of cases and controls in the first BiB study sample.

The sample selection was performed to provide a subsample of 1,000 women suitable for multi-omics assessment. Hence, only women with GWAS and DNA samples suitable for DNA methylation analyses at the time of selection were included. BiB, Born in Bradford; GWAS, genome-wide association study; FGR, fetal growth restriction.

Extended Data Fig. 4 Flow diagram of the selection of cases and controls in the second BiB study sample.

The selection of 2,000 women was performed using a case-cohort design. BiB, Born in Bradford; GWAS, genome-wide association study; HDP, hypertensive disorders of pregnancy; FGR, fetal growth restriction.

Extended Data Fig. 5 Sensitivity and false positive rate across 100 cutoff points of EFW and metabolite ratio at 36wkGA in relation to delivery of an infant with FGR at term.

There were 160 FGR cases and 273 controls in the analysis. Both EFW and the metabolite ratio were expressed as percentiles. In a, the first cutoff point was EFW <1st and metabolite ratio >99th. The remaining cutoff points were defined by progressively increasing the EFW threshold by 1 and decreasing the metabolite threshold by 1 (that is EFW <ith and metabolite ratio >100-ith percentile, i=1, …, 99). The cutoff point corresponding to the combination of EFW<20th and metabolite ratio >80th percentile is marked on the graph. The screening statistics (95%CI) for this combination were positive LR 10.8 (6.4 to 18.4), negative LR 0.47 (0.39–0.56), sensitivity 55.6% (47.8%–63.2%), specificity 94.9% (91.5%–96.9%), PPV 33.1% (21.8%–46.7%), NPV 97.9% (97.3%–98.4%), and DOR 23.2 (12.6–41.6). In b, the cutoff points were EFW <ith and/or metabolite ratio >100-ith percentile, i=1, …, 99. The cutoff point corresponding to EFW<20th and/or metabolite ratio >80th percentile is marked on the graph. The screening statistics (95%CI) for this were positive LR 2.5 (2.2 to 2.9), negative LR 0.07 (0.03–0.15), sensitivity 95.6% (91.0%–97.9%), specificity 61.9% (56.0%–67.5%), PPV 10.3% (8.2%–12.8%), NPV 99.7% (99.3%–99.9%), and DOR 35.5 (16.3–77.3). EFW, estimated fetal weight; wkGA, weeks of gestational age; FGR, fetal growth restriction; LR, likelihood ratio; PPV, positive predictive value; NPV, negative predictive value; DOR, diagnostic odds ratio.

Source data

Extended Data Fig. 6 Odds ratios (95% confidence intervals) of metabolite measurements at ~24–28 wkGA in relation to subsequent FGR (defined as birth weight <3rd percentile corrected only for GA and fetal sex) in the POP study and the BiB study samples 1 and 2.

Odds ratios are given for one standard deviation increase in the log-transformed metabolite ratio. The POP study included 136 FGR cases and 294 controls, the BiB 1 study included 20 FGR cases and 950 controls, and the BiB 2 study included 41 cases and 1513 controls. wkGA, weeks of gestational age; FGR, fetal growth restriction; SGA, small for gestational age; 1-(1-enyl-stear)-2-o-GPC, 1-(1-enyl-stearoyl)-2-oleoyl-GPC (P-18:0/18:1); 1,5-AG, 1,5-anhydroglucitol; 5α-androstan, 5α-androstan-3α,17α-diol disulfate; 4-androsten-3β2, 4-androsten-3β,17β-diol monosulfate (2); POP, Pregnancy Outcome Prediction; BiB, Born in Bradford.

Source data

Extended Data Fig. 7 Receiver operating characteristic (ROC) curve analysis of the metabolite ratio at 28wkGA comparing preterm FGR (n=32) and controls (n=305) in the POP study.

Area under the ROC curve (AUC) = 0.60 (95% CI: 0.48 to 0.72). For the null hypothesis of AUC=0.5, z test P=0.10 (two-sided). Preterm FGR was defined as delivery at <37wkGA with customized birth weight <10th percentile. FGR, fetal growth restriction; wkGA, weeks of gestational age; POP, Pregnancy Outcome Prediction.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1–10.

Reporting Summary

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Sovio, U., Goulding, N., McBride, N. et al. A maternal serum metabolite ratio predicts fetal growth restriction at term. Nat Med 26, 348–353 (2020).

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