1
|
Elhakeem A, Clayton GL, Soares AG, Taylor K, Maitre L, Santorelli G, Wright J, Lawlor DA, Vrijheid M. Social inequalities in pregnancy metabolic profile: findings from the multi-ethnic Born in Bradford cohort study. BMC Pregnancy Childbirth 2024; 24:333. [PMID: 38689215 PMCID: PMC11061950 DOI: 10.1186/s12884-024-06538-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 04/22/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Lower socioeconomic position (SEP) associates with adverse pregnancy and perinatal outcomes and with less favourable metabolic profile in nonpregnant adults. 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. 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. Further research is needed to understand whether the socioeconomic differences we observe here reflect pre-conception differences or differences in the metabolic pregnancy response. If replicated, it would be important to explore if these differences contribute to socioeconomic differences in pregnancy outcomes.
Collapse
Affiliation(s)
- Ahmed Elhakeem
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Gemma L Clayton
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ana Goncalves Soares
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kurt Taylor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Léa Maitre
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Gillian Santorelli
- Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service Foundation Trust, Bradford, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service Foundation Trust, Bradford, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| |
Collapse
|
2
|
Ewington L, Black N, Leeson C, Al Wattar BH, Quenby S. Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review. BJOG 2024. [PMID: 38465451 DOI: 10.1111/1471-0528.17802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/08/2024] [Accepted: 02/22/2024] [Indexed: 03/12/2024]
Abstract
BACKGROUND The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies. OBJECTIVES To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice. SEARCH STRATEGY MEDLINE, EMBASE and Cochrane Library were searched until June 2022. SELECTION CRITERIA We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance. DATA COLLECTION AND ANALYSIS Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool). MAIN RESULTS A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias. CONCLUSIONS There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.
Collapse
Affiliation(s)
- Lauren Ewington
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
| | - Naomi Black
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
| | - Charlotte Leeson
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
| | - Bassel H Al Wattar
- Beginnings Assisted Conception Unit, Epsom and St Helier University Hospitals, London, UK
- Comprehensive Clinical Trials Unit, Institute for Clinical Trials and Methodology, University College London, London, UK
| | - Siobhan Quenby
- Division of Biomedical Sciences, University of Warwick, Coventry, UK
- University Hospitals Coventry and Warwickshire, Coventry, UK
| |
Collapse
|
3
|
Al Ghadban Y, Du Y, Charnock-Jones DS, Garmire LX, Smith GCS, Sovio U. Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study. BJOG 2023. [PMID: 37984426 DOI: 10.1111/1471-0528.17723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 09/11/2023] [Accepted: 11/02/2023] [Indexed: 11/22/2023]
Abstract
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.
Collapse
Affiliation(s)
- Yasmina Al Ghadban
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Yuheng Du
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - D Stephen Charnock-Jones
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research (CTR), Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Gordon C S Smith
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research (CTR), Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Ulla Sovio
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research (CTR), Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| |
Collapse
|
4
|
Miller JJ, Higgins V, Ren A, Logan S, Yip PM, Fu L. Advances in preeclampsia testing. Adv Clin Chem 2023; 117:103-161. [PMID: 37973318 DOI: 10.1016/bs.acc.2023.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Preeclampsia is a multisystem hypertensive disorder and one of the leading causes of maternal and fetal morbidity and mortality. The clinical hallmarks such as hypertension and proteinuria, and additional laboratory tests currently available including liver enzyme testing, are neither specific nor sufficiently sensitive. Therefore, biomarkers for timely and accurate identification of patients at risk of developing preeclampsia are extremely valuable to improve patient outcomes and safety. In this chapter, we will first discuss the clinical characteristics of preeclampsia and current evidence of the role of angiogenic factors, such as placental growth factor (PlGF) and soluble FMS like tyrosine kinase 1 (sFlt-1) in the pathogenesis of preeclampsia. Second, we will review the clinical practice guidelines for preeclampsia diagnostic criteria and their recommendations on laboratory testing. Third, we will review the currently available PlGF and sFlt-1 assays in terms of their methodologies, analytical performance, and clinical diagnostic values. Finally, we will discuss the future research needs from both an analytical and clinical perspective.
Collapse
Affiliation(s)
| | - Victoria Higgins
- DynaLIFE Medical Labs, Edmonton, AB, Canada; Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada
| | - Annie Ren
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Samantha Logan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Paul M Yip
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Precision Diagnostics and Therapeutics Program (Laboratory Medicine), Sunnybrook Health Sciences Center, Toronto, ON, Canada; Sunnybrook Research Institute, Toronto, ON, Canada
| | - Lei Fu
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Precision Diagnostics and Therapeutics Program (Laboratory Medicine), Sunnybrook Health Sciences Center, Toronto, ON, Canada; Sunnybrook Research Institute, Toronto, ON, Canada.
| |
Collapse
|
5
|
Elhakeem A, Ronkainen J, Mansell T, Lange K, Mikkola TM, Mishra BH, Wahab RJ, Cadman T, Yang T, Burgner D, Eriksson JG, Järvelin MR, Gaillard R, Jaddoe VWV, Lehtimäki T, Raitakari OT, Saffery R, Wake M, Wright J, Sebert S, Lawlor DA. Effect of common pregnancy and perinatal complications on offspring metabolic traits across the life course: a multi-cohort study. BMC Med 2023; 21:23. [PMID: 36653824 PMCID: PMC9850719 DOI: 10.1186/s12916-022-02711-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 12/14/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Common pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age. METHODS We used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1-1.6 years), childhood (4.2-7.5 years); adolescence (12.0-16.0 years), and adulthood (22.0-67.8 years). RESULTS Offspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of -0.89 standard deviation (SD) units for albumin with PTB (95% CI: -1.10 to -0.69, P=1.3×10-17) and -0.41 SD for total lipids in medium HDL with SGA (95% CI: -0.56 to -0.25, P=2.6×10-7), with some evidence of persistence to older ages. LGA was inversely associated with 19 metabolic traits including lower levels of cholesterol, lipoproteins, fatty acids, and amino acids, with associations emerging in adolescence, (e.g. -0.11 SD total fatty acids, 95% CI: -0.18 to -0.05, P=0.0009), and attenuating with older age across adulthood. CONCLUSIONS These reassuring findings suggest little evidence of wide-spread and long-term impact of common pregnancy and perinatal complications on offspring metabolic traits, with most associations only observed for newborns rather than older ages, and for perinatal rather than pregnancy complications.
Collapse
Affiliation(s)
- Ahmed Elhakeem
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Justiina Ronkainen
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Toby Mansell
- Murdoch Children's Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
| | - Katherine Lange
- Murdoch Children's Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
| | - Tuija M Mikkola
- Folkhälsan Research Center, Helsinki, Finland
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Binisha H Mishra
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Rama J Wahab
- Department of Paediatrics, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Centre, Rotterdam, Netherlands
| | - Tim Cadman
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Tiffany Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service Foundation Trust, Bradford, UK
| | - David Burgner
- Murdoch Children's Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Department of Paediatrics, Monash University, Clayton, VIC, Australia
| | - Johan G Eriksson
- Folkhälsan Research Center, Helsinki, Finland
- Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Obstetrics & Gynecology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Singapore Institute for Clinical Sciences (SICS), Agency for Science and Technology (A*STAR), Singapore, Singapore
| | - Marjo-Riitta Järvelin
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Romy Gaillard
- Department of Paediatrics, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Centre, Rotterdam, Netherlands
| | - Vincent W V Jaddoe
- Department of Paediatrics, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, Netherlands
- The Generation R Study Group, Erasmus MC, University Medical Centre, Rotterdam, Netherlands
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Richard Saffery
- Murdoch Children's Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
| | - Melissa Wake
- Murdoch Children's Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service Foundation Trust, Bradford, UK
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| |
Collapse
|
6
|
Villar J, Ochieng R, Gunier RB, Papageorghiou AT, Rauch S, McGready R, Gauglitz JM, Barros FC, Vatish M, Fernandes M, Zammit V, Carrara VI, Munim S, Craik R, Barsosio HC, Carvalho M, Berkley JA, Ismail LIC, Norris SA, Tshivuila-Matala COO, Nosten F, Ohuma EO, Stein A, Lambert A, Winsey A, Uauy R, Eskenazi B, Bhutta ZA, Kennedy SH. Association between fetal abdominal growth trajectories, maternal metabolite signatures early in pregnancy, and childhood growth and adiposity: prospective observational multinational INTERBIO-21st fetal study. Lancet Diabetes Endocrinol 2022; 10:710-719. [PMID: 36030799 PMCID: PMC9622423 DOI: 10.1016/s2213-8587(22)00215-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/07/2022] [Accepted: 07/12/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Obesity predominantly affects populations in high-income countries and those countries facing epidemiological transition. The risk of childhood obesity is increased among infants who had overweight or obesity at birth, but in low-resource settings one in five infants are born small for gestational age. We aimed to study the relationships between: (1) maternal metabolite signatures; (2) fetal abdominal growth; and (3) postnatal growth, adiposity, and neurodevelopment. METHODS In the prospective, multinational, observational INTERBIO-21st fetal study, conducted in maternity units in Pelotas (Brazil), Nairobi (Kenya), Karachi (Pakistan), Soweto (South Africa), Mae Sot (Thailand), and Oxford (UK), we enrolled women (≥18 years, with a BMI of less than 35 kg/m2, natural conception, and a singleton pregnancy) who initiated antenatal care before 14 weeks' gestation. Ultrasound scans were performed every 5±1 weeks until delivery to measure fetal growth and feto-placental blood flow, and we used finite mixture models to derive growth trajectories of abdominal circumference. The infants' health, growth, and development were monitored from birth to age 2 years. Early pregnancy maternal blood and umbilical cord venous blood samples were collected for untargeted metabolomic analysis. FINDINGS From Feb 8, 2012, to Nov 30, 2019, we enrolled 3598 pregnant women and followed up their infants to 2 years of age. We identified four ultrasound-derived trajectories of fetal abdominal circumference growth that accelerated or decelerated within a crucial 20-25 week gestational age window: faltering growth, early accelerating growth, late accelerating growth, and median growth tracking. These distinct phenotypes had matching feto-placental blood flow patterns throughout pregnancy, and different growth, adiposity, vision, and neurodevelopment outcomes in early childhood. There were 709 maternal metabolites with positive effect for the faltering growth phenotype and 54 for the early accelerating growth phenotype; 31 maternal metabolites had a negative effect for the faltering growth phenotype and 76 for the early accelerating growth phenotype. Metabolites associated with the faltering growth phenotype had statistically significant odds ratios close to 1·5 (ie, suggesting upregulation of metabolic pathways of impaired fetal growth). The metabolites had a reciprocal relationship with the early accelerating growth phenotype, with statistically significant odds ratios close to 0.6 (ie, suggesting downregulation of fetal growth acceleration). The maternal metabolite signatures included 5-hydroxy-eicosatetraenoic acid, and 11 phosphatidylcholines linked to oxylipin or saturated fatty acid sidechains. The fungicide, chlorothalonil, was highly abundant in the early accelerating growth phenotype group. INTERPRETATION Early pregnancy lipid biology associated with fetal abdominal growth trajectories is an indicator of patterns of growth, adiposity, vision, and neurodevelopment up to the age of 2 years. Our findings could contribute to the earlier identification of infants at risk of obesity. FUNDING Bill & Melinda Gates Foundation.
Collapse
Affiliation(s)
- Jose Villar
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK.
| | | | - Robert B Gunier
- Center for Environmental Research and Community Health, School of Public Health, University of California, Berkeley, CA, USA
| | - Aris T Papageorghiou
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Stephen Rauch
- Center for Environmental Research and Community Health, School of Public Health, University of California, Berkeley, CA, USA
| | - Rose McGready
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
| | | | - Fernando C Barros
- Programa de Pós-Graduação em Saúde e Comportamento, Universidade Católica de Pelotas, Pelotas, Brazil
| | - Manu Vatish
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Michelle Fernandes
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Medical Research Council Lifecourse Epidemiology Centre & Human Development and Health Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Victor Zammit
- Biomedical Sciences, Translational & Experimental Medicine, Warwick Medical School, University of Warwick, Coventry, UK
| | - Verena I Carrara
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
| | - Shama Munim
- Department of Obstetrics and Gynaecology, Division of Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Rachel Craik
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK
| | - Hellen C Barsosio
- Kenya Medical Research Institute-Coast Centre for Geographical Medicine and Research, University of Oxford, Kilifi, Kenya
| | - Maria Carvalho
- Department of Obstetrics & Gynaecology, Faculty of Health Sciences, Aga Khan University Hospital, Nairobi, Kenya
| | - James A Berkley
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Kenya Medical Research Institute-Coast Centre for Geographical Medicine and Research, University of Oxford, Kilifi, Kenya
| | - Leila I Cheikh Ismail
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Clinical Nutrition and Dietetics Department, University of Sharjah, Sharjah, United Arab Emirates
| | - Shane A Norris
- South African Medical Research Institute Developmental Pathways For Health Research Unit, Department of Paediatrics & Child Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Chrystelle O O Tshivuila-Matala
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; South African Medical Research Institute Developmental Pathways For Health Research Unit, Department of Paediatrics & Child Health, University of the Witwatersrand, Johannesburg, South Africa; Health, Nutrition & Population Global Practice, World Bank Group, Washington, DC, USA
| | - Francois Nosten
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
| | - Eric O Ohuma
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Maternal, Adolescent, Reproductive & Child Health Centre, London School of Hygiene & Tropical Medicine, London, UK
| | - Alan Stein
- Department of Psychiatry, University of Oxford, Oxford, UK; Medical Research Council and Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; African Health Research Institute, KwaZulu-Natal, South Africa
| | - Ann Lambert
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Adele Winsey
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK
| | - Ricardo Uauy
- Department of Nutrition and Public Health Interventions Research, London School of Hygiene and Tropical Medicine, London, UK
| | - Brenda Eskenazi
- Center for Environmental Research and Community Health, School of Public Health, University of California, Berkeley, CA, USA
| | - Zulfiqar A Bhutta
- Centre of Excellence in Women and Child Health, Aga Khan University, Nairobi, Kenya; Center for Global Child Health, Hospital for Sick Children, Toronto, ON, Canada
| | - Stephen H Kennedy
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| |
Collapse
|
7
|
Yao M, Xiao Y, Yang Z, Ge W, Liang F, Teng H, Gu Y, Yin J. Identification of Biomarkers for Preeclampsia Based on Metabolomics. Clin Epidemiol 2022; 14:337-360. [PMID: 35342309 PMCID: PMC8943653 DOI: 10.2147/clep.s353019] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/25/2022] [Indexed: 01/15/2023] Open
Abstract
Background Preeclampsia (PE) is a significant cause of maternal and neonatal morbidity and mortality worldwide. However, the pathogenesis of PE is unclear and reliable early diagnostic methods are still lacking. The purpose of this review is to summarize potential metabolic biomarkers and pathways of PE, which might facilitate risk prediction and clinical diagnosis, and obtain a better understanding of specific metabolic mechanisms of PE. Methods This review included human metabolomics studies related to PE in the PubMed, Google Scholar, and Web of Science databases from January 2000 to November 2021. The reported metabolic biomarkers were systematically examined and compared. Pathway analysis was conducted through the online software MetaboAnalyst 5.0. Results Forty-one human studies were included in this systematic review. Several metabolites, such as creatinine, glycine, L-isoleucine, and glucose and biomarkers with consistent trends (decanoylcarnitine, 3-hydroxyisovaleric acid, and octenoylcarnitine), were frequently reported. In addition, eight amino acid metabolism-related, three carbohydrate metabolism-related, one translation-related and one lipid metabolism-related pathways were identified. These biomarkers and pathways, closely related to renal dysfunction, insulin resistance, lipid metabolism disorder, activated inflammation, and impaired nitric oxide production, were very likely to contribute to the progression of PE. Conclusion This study summarized several metabolites and metabolic pathways, which may be associated with PE. These high-frequency differential metabolites are promising to be biomarkers of PE for early diagnosis, and the prominent metabolic pathway may provide new insights for the understanding of the pathogenesis of PE.
Collapse
Affiliation(s)
- Mengxin Yao
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Yue Xiao
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Zhuoqiao Yang
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Wenxin Ge
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Fei Liang
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Haoyue Teng
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
| | - Yingjie Gu
- Department of Obstetrics and Gynecology, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
| | - Jieyun Yin
- Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, People’s Republic of China
- Correspondence: Jieyun Yin, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou, Jiangsu, People’s Republic of China, Tel/Fax +86 0512 6588036, Email
| |
Collapse
|
8
|
MacDonald TM, Walker SP, Hannan NJ, Tong S, Kaitu'u-Lino TJ. Clinical tools and biomarkers to predict preeclampsia. EBioMedicine 2022; 75:103780. [PMID: 34954654 PMCID: PMC8718967 DOI: 10.1016/j.ebiom.2021.103780] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/01/2021] [Accepted: 12/10/2021] [Indexed: 11/04/2022] Open
Abstract
Preeclampsia is pregnancy-specific, and significantly contributes to maternal, and perinatal morbidity and mortality worldwide. An effective predictive test for preeclampsia would facilitate early diagnosis, targeted surveillance and timely delivery; however limited options currently exist. A first-trimester screening algorithm has been developed and validated to predict preterm preeclampsia, with poor utility for term disease, where the greatest burden lies. Biomarkers such as sFlt-1 and placental growth factor are also now being used clinically in cases of suspected preterm preeclampsia; their high negative predictive value enables confident exclusion of disease in women with normal results, but sensitivity is modest. There has been a concerted effort to identify potential novel biomarkers that might improve prediction. These largely originate from organs involved in preeclampsia's pathogenesis, including placental, cardiovascular and urinary biomarkers. This review outlines the clinical imperative for an effective test and those already in use and summarises current preeclampsia biomarker research.
Collapse
Affiliation(s)
- Teresa M MacDonald
- Department of Obstetrics and Gynaecology, Mercy Hospital for Women, University of Melbourne. Heidelberg, Victoria, Australia; Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia
| | - Susan P Walker
- Department of Obstetrics and Gynaecology, Mercy Hospital for Women, University of Melbourne. Heidelberg, Victoria, Australia; Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia
| | - Natalie J Hannan
- Department of Obstetrics and Gynaecology, Mercy Hospital for Women, University of Melbourne. Heidelberg, Victoria, Australia; Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia; Translational Obstetrics Group, Mercy Hospital for Women, Heidelberg, Victoria, Australia
| | - Stephen Tong
- Department of Obstetrics and Gynaecology, Mercy Hospital for Women, University of Melbourne. Heidelberg, Victoria, Australia; Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia; Translational Obstetrics Group, Mercy Hospital for Women, Heidelberg, Victoria, Australia
| | - Tu'uhevaha J Kaitu'u-Lino
- Department of Obstetrics and Gynaecology, Mercy Hospital for Women, University of Melbourne. Heidelberg, Victoria, Australia; Mercy Perinatal, Mercy Hospital for Women, Heidelberg, Victoria, Australia; Translational Obstetrics Group, Mercy Hospital for Women, Heidelberg, Victoria, Australia.
| |
Collapse
|