Metabolomic Identification of a Novel, Externally Validated Predictive Test for Gestational Diabetes Mellitus

Sovio, Ulla; Clayton, Gemma; Gaccioli, Francesca; Charnock-Jones, D. Stephen; Lawlor, Debbie A.; Smith, Gordon C. S.

doi:10.1210/clinem/dgac240

Cited by 9 publications

(9 citation statements)

References 29 publications

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“…Given that SEP and NMR metabolic traits can inform on adverse pregnancy and perinatal outcomes [4][5][6][7][8][9] , our findings suggest SEP might influence adverse pregnancy and perinatal outcomes via effects on metabolic traits.…”

Section: Discussionmentioning

confidence: 80%

“…We found that lower SEP associated with higher levels of the inflammatory marker glycoprotein acetyls corroborates findings from a study of 605 women showing that higher educational level was associated with lower inflammatory biomarkers in pregnancy 27 . Given that SEP and NMR metabolic traits can inform on adverse pregnancy and perinatal outcomes 4–9 , our findings suggest SEP might influence adverse pregnancy and perinatal outcomes via effects on metabolic traits.…”

Section: Discussionmentioning

confidence: 84%

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Social inequalities in pregnancy metabolic profile: findings from the multi-ethnic Born in Bradford cohort study

Elhakeem,

Clayton,

Soares

et al. 2024

Preprint

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Background Lower socioeconomic position (SEP) is associated with adverse pregnancy and perinatal outcomes and with less favourable metabolic profile in nonpregnant adults. However, socioeconomic differences in pregnancy metabolic profile are unknown. We investigated association between a composite measure of SEP and pregnancy metabolic profile in White European (WE) and South Asian (SA) women. Methods We included 3,905 WE and 4,404 SA pregnant women from a population-based UK cohort. Latent class analysis was applied to nineteen individual, household, and area-based SEP indicators (collected by questionnaires or linkage to residential address) to derive a composite SEP latent variable. Targeted nuclear magnetic resonance spectroscopy was used to determine 148 metabolic traits from mid-pregnancy serum samples. Associations between SEP and metabolic traits were examined using linear regressions adjusted for gestational age and weighted by latent class probabilities. An interactive application was developed for exploring all association results (https://aelhak.shinyapps.io/SEP_NMR_BiB/). Results Five SEP sub-groups were identified and labelled Highest SEP (48% WE and 52% SA), High-Medium SEP (77% and 23%), Medium SEP (56% and 44%) Low-Medium SEP (21% and 79%), and Lowest SEP (52% and 48%). Lower SEP was associated with more adverse levels of 113 metabolic traits, including lower high-density lipoprotein (HDL) and higher triglycerides and very low-density lipoprotein (VLDL) traits. For example, mean standardized difference (95%CI) in concentration of small VLDL particles (vs. Highest SEP) was 0.12 standard deviation (SD) units (0.05 to 0.20) for Medium SEP and 0.25SD (0.18 to 0.32) for Lowest SEP. There was statistical evidence of ethnic differences in associations of SEP with 31 traits, primarily characterised by stronger associations in WE women e.g., mean difference in HDL cholesterol in WE and SA women respectively (vs. Highest-SEP) was -0.30SD (-0.41 to -0.20) and -0.16SD (-0.27 to -0.05) for Medium SEP, and -0.62SD (-0.72 to -0.52) and -0.29SD (-0.40 to -0.20) for Lowest SEP. Conclusions We found widespread socioeconomic differences in metabolic traits in pregnant WE and SA women residing in the UK, and clearer socioeconomic gradient for some traits in WE women. Supporting all pregnant women in the most disadvantaged socioeconomic groups may provide the greatest benefit for perinatal health.

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Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 84%

Social inequalities in pregnancy metabolic profile: findings from the multi-ethnic Born in Bradford cohort study

Elhakeem,

Clayton,

Soares

et al. 2024

Preprint

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“…This limitation in diversity may limit the external validity of the findings. However, previous POP study findings have been successfully validated in the Born in Bradford (BiB) study, whose population is significantly more diverse 11,35,36 . For the present study, external validation using the BiB study samples was not performed because of the unavailability of some of the metabolites included in the models developed in the POP study (https://osf.io/axqsu, accessed 11 September 2023).…”

Section: Discussionmentioning

confidence: 99%

“…7 Metabolomics has shown potential to contribute to the improved prediction of various pathologies of pregnancy, such as pre-eclampsia, fetal growth restriction and gestational diabetes mellitus. [8][9][10][11][12] In addition, several studies have been published on the metabolomic prediction of sPTB or time to spontaneous labor. [12][13][14][15][16][17][18][19][20][21][22][23][24] However, metabolomics analyses involve complex data sets, with a vast number of predictors and relatively small sample sizes, known as the 'large p, small n problem'.…”

Section: Introductionmentioning

confidence: 99%

“…Pregnancy initiates substantial and widespread metabolic changes 7 . Metabolomics has shown potential to contribute to the improved prediction of various pathologies of pregnancy, such as pre‐eclampsia, fetal growth restriction and gestational diabetes mellitus 8–12 . In addition, several studies have been published on the metabolomic prediction of sPTB or time to spontaneous labor 12–24 .…”

Section: Introductionmentioning

confidence: 99%

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Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case–cohort study

Al Ghadban,

Du,

Charnock‐Jones

et al. 2023

BJOG

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ObjectivesTo 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.DesignCase–cohort design within a prospective cohort study.SettingCambridge, UK.Population or sampleA total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB.MethodsAn 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 measuressPTB and sETB.ResultsWe 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).ConclusionsWe 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.

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Systematic Review of risk score prediction models using maternal characteristics with and without biomarkers for the prediction of GDM

Parkhi,

Sampathkumar,

Weldeselassie

et al. 2023

Preprint

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Background: GDM is associated with adverse maternal and fetal complications. By the time GDM is diagnosed, continuous exposure to the hyperglycaemic intrauterine environment can adversely affect the fetus. Hence, early pregnancy prediction of GDM is important. Aim: To systematically evaluate whether composite risk score prediction models can accurately predict GDM in early pregnancy. Method: Systematic review of observational studies involving pregnant women of <20 weeks of gestation was carried out. The search involved various databases, grey literature, and reference lists till August 2022. The primary outcome was the predictive performance of the models in terms of the AUC, for <14 weeks and 14-20 weeks of gestation. Results: Sixty-seven articles for <14 weeks and 22 for 14-20 weeks of gestation were included (initial search - 4542). The sample size ranged from 42 to 1,160,933. The studies were from Canada, USA, UK, Europe, Israel, Iran, China, Taiwan, South Korea, South Africa, Australia, Singapore, and Thailand. For <14 weeks, the AUC ranges were 0.59-0.88 and 0.53-0.95, respectively for models that used only maternal characteristics and for those that included biomarkers. For 14-20 weeks these AUCs were 0.68-0.71 and 0.65-0.92. Age, ethnicity, BMI, family history of diabetes, and prior GDM were the 5 most commonly used risk factors. The addition of systolic BP improved performance in some models. Triglycerides, PAPP-A, and lipocalin-2, combined with maternal characteristics, have the highest predictive performance. AUC varied according to the population studied. Pooled analyses were not done due to high heterogeneity. Conclusion: Accurate GDM risk prediction may be possible if common risk factors are combined with biomarkers. However, more research is needed in populations of high GDM risk. Artificial Intelligence-based risk prediction models that incorporate fetal biometry data may improve accuracy.

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Metabolomic Identification of a Novel, Externally Validated Predictive Test for Gestational Diabetes Mellitus

Cited by 9 publications

References 29 publications

Social inequalities in pregnancy metabolic profile: findings from the multi-ethnic Born in Bradford cohort study

Social inequalities in pregnancy metabolic profile: findings from the multi-ethnic Born in Bradford cohort study

Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case–cohort study

Systematic Review of risk score prediction models using maternal characteristics with and without biomarkers for the prediction of GDM

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