D. Stephen Charnock-Jones
Professor of Reproductive Biology
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D. Stephen Charnock-Jones is a Professor of Reproductive Biology at the University of Cambridge, UK. His research primarily focuses on the human placenta and its implications for reproductive outcomes, including adverse pregnancy outcomes and predictive modeling using metabolomic data. Recent articles highlight his contributions to understanding gestational diabetes mellitus, preterm birth prediction, and placental biomarkers in early pregnancy.
Articles (13)
Year:
2025
Year:
2024
Year:
2023
Year:
2023
Year:
2023
Objectives To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites. Design Case–cohort design within a prospective cohort study. Setting Cambridge, UK. Population or sample A total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB. Methods An untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB. Main outcome measures sPTB and sETB. Results We identified 47 metabolites, mostly lipids, as important predictors of sPTB by two or more methods and 22 were identified by three or more methods. The best 4‐predictor model had an optimism‐corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12‐week samples (0.606, 95% CI 0.544–0.667) and 20‐week samples (0.657, 95% CI 0.597–0.717) and it predicted sETB in 36‐week samples (0.727, 95% CI 0.606–0.849). A lysolipid, 1‐palmitoleoyl‐GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548–0.670), 20 weeks (0.630, 95% CI 0.569–0.690) and 28 weeks (0.660, 95% CI 0.599–0.722), and of sETB at 36 weeks (0.739, 95% CI 0.618–0.860). Conclusions We identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1‐palmitoleoyl‐GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required.
Year:
2023
Year:
2023
Year:
2022
Year:
2022
Year:
2022
Year:
2021
Year:
2020
Year:
2020
Collaborators (11)
Andrew Murray
Reader in Metabolic Physiology
University of Cambridge
Neil Sebire
University College London
Gemma Clayton
University of Bristol
Neil Goulding
University of Bristol
Paul Robson
Professor
Jackson Laboratory - Farmington
Irving L. M. H. Aye
University of Cambridge
Nancy McBride
University of Bristol
Stephen Tong
Professor
University of Melbourne
Ulla Sovio
-
Gordon Smith
Professor & Head of Department
University of Cambridge
Saverio Tardito
University of Glasgow
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