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Kumar SH, Acharyya S, Chouksey A, Soni N, Nazeer N, Mishra PK. Air pollution-linked epigenetic modifications in placental DNA: Prognostic potential for identifying future foetal anomalies. Reprod Toxicol 2024; 129:108675. [PMID: 39074641 DOI: 10.1016/j.reprotox.2024.108675] [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] [Received: 05/18/2024] [Revised: 07/11/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024]
Abstract
Prenatal exposure to air pollution is a significant risk factor for the mother and the developing foetus. The accumulation of pollutants in the placenta can cause a self-cascade loop of pro-inflammatory cytokine responses and DNA double-strand breaks. Previous research has shown that airborne particulate matter can damage the epigenome and disturb mitochondrial machinery, ultimately impairing placental function. Mitochondria are essential for preserving cellular homeostasis, energy metabolism, redox equilibrium, and epigenetic reprogramming. As these organelles are subtle targets of environmental exposures, any disruption in the signaling pathways can result in epigenomic instability, which can impact gene expression and mitochondrial function. This, in turn, can lead to changes in DNA methylation, post-translational histone modifications, and aberrant expression of microRNAs in proliferating trophoblast cells. The placenta has two distinct layers, cytotrophoblasts, and syncytiotrophoblasts, each with its mitochondria, which play important roles in preeclampsia, gestational diabetes, and overall health. Foetal nucleic acids enter maternal circulation during placental development because of necrotic, apoptotic, and inflammatory mechanisms. These nucleic acids reflect normal or abnormal ongoing cellular changes during prenatal foetal development. Detecting cell-free DNA in the bloodstream can be a biomarker for predicting negative pregnancy-related outcomes and recognizing abnormalities in foetal growth. Hence, a thorough understanding of how air pollution induces epigenetic variations within the placenta could offer crucial insights into underlying mechanisms and prolonged repercussions on foetal development and susceptibility in later stages of life.
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Affiliation(s)
- Sruthy Hari Kumar
- Division of Environmental Biotechnology, Genetics & Molecular Biology (EBGMB), ICMR-National Institute for Research in Environmental Health (NIREH), Bhopal, India
| | - Sayanti Acharyya
- Division of Environmental Biotechnology, Genetics & Molecular Biology (EBGMB), ICMR-National Institute for Research in Environmental Health (NIREH), Bhopal, India
| | - Apoorva Chouksey
- Division of Environmental Biotechnology, Genetics & Molecular Biology (EBGMB), ICMR-National Institute for Research in Environmental Health (NIREH), Bhopal, India
| | - Nikita Soni
- Division of Environmental Biotechnology, Genetics & Molecular Biology (EBGMB), ICMR-National Institute for Research in Environmental Health (NIREH), Bhopal, India
| | - Nazim Nazeer
- Division of Environmental Biotechnology, Genetics & Molecular Biology (EBGMB), ICMR-National Institute for Research in Environmental Health (NIREH), Bhopal, India
| | - Pradyumna Kumar Mishra
- Division of Environmental Biotechnology, Genetics & Molecular Biology (EBGMB), ICMR-National Institute for Research in Environmental Health (NIREH), Bhopal, India.
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Muli S, Blumenthal A, Conzen CA, Benz ME, Alexy U, Schmid M, Keski-Rahkonen P, Floegel A, Nöthlings U. Association of ultra-processed foods intake with untargeted metabolomics profiles in adolescents and young adults in the DONALD cohort study. J Nutr 2024:S0022-3166(24)01040-X. [PMID: 39332770 DOI: 10.1016/j.tjnut.2024.09.023] [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: 08/02/2024] [Revised: 09/16/2024] [Accepted: 09/22/2024] [Indexed: 09/29/2024] Open
Abstract
BACKGROUND High consumption of ultra-processed foods (UPF) continues to draw significant public health interest due to the associated negative health outcomes. Metabolomics can contribute to the understanding of the biological mechanisms through which UPFs may influence health. OBJECTIVES To investigate urine and plasma metabolomic biomarkers of UPF intake in adolescents and young adults. METHODS We used data from the Dortmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study to investigate cross-sectional associations of UPF intake with concentrations of urine metabolites in adolescents using 3-d weighed dietary records (3d-WDR) and 24-h urine samples (n = 339), and associations of repeatedly assessed UPF intake with concentrations of circulating plasma metabolites in young adults with 3 to 6 3d-WDRs within 5 y preceding blood measurement (n = 195). Urine and plasma samples were analyzed using mass spectrometry-based metabolomics. Biosample-specific metabolite patterns were determined using robust sparse principal components analysis. Multivariable linear regression models were applied to assess the associations of UPF consumption (as a percentage of total food intake in g/d) with concentrations of individual metabolites and metabolite pattern scores. RESULTS The median proportion of UPF intake was 22.0% (interquartile range, IQR: 12.3, 32.9) in adolescents and 23.2% (IQR: 16.0, 31.6) in young adults. We identified 42 and 6 UPF intake-associated metabolites in urine and plasma samples, respectively. One urinary metabolite pattern, "xenobiotics and amino acids" (β = 0.042, 95% confidence interval, [CI]: 0.014, 0.070) and one plasma metabolite pattern, "lipids, xenobiotics, and amino acids" (β = 0.074, 95% CI: 0.031, 0.117) showed positive association with UPF intake. Both patterns shared 29 metabolites, mostly of xenobiotic metabolism. CONCLUSIONS We identified urine and plasma metabolites associated with UPF intake in adolescents and young adults, which may represent some of the biological mechanisms through which UPFs may influence metabolism and health.
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Affiliation(s)
- Samuel Muli
- Unit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
| | - Annika Blumenthal
- Unit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
| | - Christina-Alexandra Conzen
- Unit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
| | - Maike Elena Benz
- Unit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
| | - Ute Alexy
- Unit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
| | - Matthias Schmid
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, Bonn, Germany
| | | | - Anna Floegel
- Section of Dietetics, Faculty of Agriculture and Food Sciences, Hochschule Neubrandenburg, Neubrandenburg, Germany
| | - Ute Nöthlings
- Unit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany.
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Guo J, Zheng X, Du X, Li W, Lu L. BMA-based Mendelian randomization identifies blood metabolites as causal candidates in pregnancy-induced hypertension. Hypertens Res 2024; 47:2549-2560. [PMID: 38951678 DOI: 10.1038/s41440-024-01787-4] [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] [Received: 03/11/2024] [Revised: 05/25/2024] [Accepted: 06/15/2024] [Indexed: 07/03/2024]
Abstract
Pregnancy-induced hypertension (PIH), a prominent determinant of maternal mortality and morbidity worldwide, is hindered by the absence of efficacious biomarkers for early diagnosis, contributing to suboptimal outcomes. Here, we explored potential causal relationships between blood metabolites and the risk of PIH using Mendelian randomization (MR). We employed a two-sample univariable MR approach to empirically estimate the causal relationships between 249 circulating metabolites and PIH. Inverse variance weighted, MR-egger, weight median, simple mode, and weighted mode methods were used for causal estimates. The exposure-to-outcome directionality was confirmed with the MR Steiger test. The Bayesian model averaging MR (MR-BMA) method was applied to detect the predominant causal metabolic traits with alignment for pleiotropy effects. In the primary analysis, analyzing 249 metabolites, we identified 25 causally linked to PIH, including 11 lipid-related traits and 6 associated with fatty acid (un)saturation. Importantly, MR-BMA analyses corroborated the total concentration of branched-chain amino acids(total-BCAA) to be the highest rank causal metabolite, followed by leucine (Leu), phospholipids to total lipids ratio in medium LDL (M-LDL-PL-pct), and Val (all P < 0.05). The directionality of causality predicted by univariable MR and MR-BMA for these metabolites remained consistent. This study highlights the causal connection between metabolites and PIH risk. It highlighted BCAAs as the strongest causal candidates warranting further investigation. Since PIH typically occurs in the second and third trimesters, extending these findings could inform earlier strategies to reduce its risk. Directed acyclic graph of the MR framework investigating the causal relationship between metabolites and PIH. MR: Mendelian randomization; GIVs: genetic instrument variables; SNPs: single-nucleotide polymorphism; IVW: inverse variance weighted; WM: weighted median; PIH: pregnancy-induced hypertension; SM: significant metabolite; MR-BMA: Bayesian model averaging MR.
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Affiliation(s)
- Jun Guo
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230001, Anhui, China
- Department of Radiology, The First Affiliate Hospital of Hunan Normal University (Hunan Provincial People's Hospital), Changsha, China
| | - Xiaofei Zheng
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xue Du
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230001, Anhui, China
| | - Weisheng Li
- Department of gynaecology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China.
| | - Likui Lu
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230001, Anhui, China.
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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 2024; 131:908-916. [PMID: 37984426 DOI: 10.1111/1471-0528.17723] [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: 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.
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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
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Selvaratnam RJ, Sovio U, Cook E, Gaccioli F, Charnock-Jones DS, Smith GCS. Objective measures of smoking and caffeine intake and the risk of adverse pregnancy outcomes. Int J Epidemiol 2023; 52:1756-1765. [PMID: 37759082 PMCID: PMC10749751 DOI: 10.1093/ije/dyad123] [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: 03/12/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND In pregnancy, women are encouraged to cease smoking and limit caffeine intake. We employed objective definitions of smoking and caffeine exposure to assess their association with adverse outcomes. METHODS We conducted a case cohort study within the Pregnancy Outcome Prediction study to analyse maternal serum metabolomics in samples from 12, 20, 28 and 36 weeks of gestational age. Objective smoking status was defined based on detectable cotinine levels at each time point and objective caffeine exposure was based on tertiles of paraxanthine levels at each time point. We used logistic and linear regression to examine the association between cotinine, paraxanthine and the risk of pre-eclampsia, spontaneous pre-term birth (sPTB), fetal growth restriction (FGR), gestational diabetes mellitus and birthweight. RESULTS There were 914 and 915 women in the smoking and caffeine analyses, respectively. Compared with no exposure to smoking, consistent exposure to smoking was associated with an increased risk of sPTB [adjusted odds ratio (aOR) = 2.58, 95% CI: 1.14 to 5.85)] and FGR (aOR = 4.07, 95% CI: 2.14 to 7.74) and lower birthweight (β = -387 g, 95% CI: -622 g to -153 g). On univariate analysis, consistently high levels of paraxanthine were associated with an increased risk of FGR but that association attenuated when adjusting for maternal characteristics and objective-but not self-reported-smoking status. CONCLUSIONS Based on objective data, consistent exposure to smoking throughout pregnancy was strongly associated with sPTB and FGR. High levels of paraxanthine were not independently associated with any of the studied outcomes and were confounded by smoking.
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Affiliation(s)
- Roshan J Selvaratnam
- The Ritchie Centre, Department of Obstetrics and Gynaecology, Monash University, VIC, Melbourne, Australia
| | - Ulla Sovio
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Emma Cook
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
| | - Francesca Gaccioli
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - D Stephen Charnock-Jones
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Gordon C S Smith
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
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Meek CL, Stewart ZA, Feig DS, Furse S, Neoh SL, Koulman A, Murphy HR. Metabolomic insights into maternal and neonatal complications in pregnancies affected by type 1 diabetes. Diabetologia 2023; 66:2101-2116. [PMID: 37615689 PMCID: PMC10542716 DOI: 10.1007/s00125-023-05989-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/19/2023] [Indexed: 08/25/2023]
Abstract
AIMS/HYPOTHESIS Type 1 diabetes in pregnancy is associated with suboptimal pregnancy outcomes, attributed to maternal hyperglycaemia and offspring hyperinsulinism (quantifiable by cord blood C-peptide). We assessed metabolomic patterns associated with risk factors (maternal hyperglycaemia, diet, BMI, weight gain) and perinatal complications (pre-eclampsia, large for gestational age [LGA], neonatal hypoglycaemia, hyperinsulinism) in the Continuous Glucose Monitoring in Women with Type 1 Diabetes in Pregnancy Trial (CONCEPTT). METHODS A total of 174 CONCEPTT participants gave ≥1 non-fasting serum sample for the biorepository at 12 gestational weeks (147 women), 24 weeks (167 women) and 34 weeks (160 women) with cord blood from 93 infants. Results from untargeted metabolite analysis (ultrahigh performance LC-MS) are presented as adjusted logistic/linear regression of maternal and cord blood metabolites, risk factors and perinatal complications using a modified Bonferroni limit of significance for dependent variables. RESULTS Maternal continuous glucose monitoring time-above-range (but not BMI or excessive gestational weight gain) was associated with increased triacylglycerols in maternal blood and increased carnitines in cord blood. LGA, adiposity, neonatal hypoglycaemia and offspring hyperinsulinism showed distinct metabolite profiles. LGA was associated with increased carnitines, steroid hormones and lipid metabolites, predominantly in the third trimester. However, neonatal hypoglycaemia and offspring hyperinsulinism were both associated with metabolite changes from the first trimester, featuring triacylglycerols or dietary phenols. Pre-eclampsia was associated with increased abundance of phosphatidylethanolamines, a membrane phospholipid, at 24 weeks. CONCLUSIONS/INTERPRETATION Altered lipid metabolism is a key pathophysiological feature of type 1 diabetes pregnancy. New strategies for optimising maternal diet and insulin dosing from the first trimester are needed to improve pregnancy outcomes in type 1 diabetes.
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Affiliation(s)
- Claire L Meek
- Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - Zoe A Stewart
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Denice S Feig
- Mount Sinai Hospital, Sinai Health System, New York, NY, USA
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Toronto, ON, Canada
| | - Samuel Furse
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Core Metabolomics and Lipidomics Laboratory, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Sandra L Neoh
- Department of Endocrinology, Austin Health, Melbourne, VIC, Australia
- Department of Endocrinology, Northern Health, Melbourne, VIC, Australia
| | - Albert Koulman
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Core Metabolomics and Lipidomics Laboratory, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Helen R Murphy
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Norwich Medical School, University of East Anglia, Norwich, UK
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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.
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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.
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Olga L, Sovio U, Wong H, Smith G, Aiken C. Association between antenatal diagnosis of late fetal growth restriction and educational outcomes in mid-childhood: A UK prospective cohort study with long-term data linkage study. PLoS Med 2023; 20:e1004225. [PMID: 37093852 PMCID: PMC10166482 DOI: 10.1371/journal.pmed.1004225] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 05/08/2023] [Accepted: 03/28/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND Fetal growth restriction (FGR) is associated with a suboptimal intrauterine environment, which may adversely impact fetal neurodevelopment. However, analysing neurodevelopmental outcomes by observed birthweight fails to differentiate between true FGR and constitutionally small infants and cannot account for iatrogenic intervention. This study aimed to determine the relationship between antenatal FGR and mid-childhood (age 5 to 7 years) educational outcomes. METHODS AND FINDINGS The Pregnancy Outcome Prediction Study (2008-2012) was a prospective birth cohort conducted in a single maternity hospital in Cambridge, United Kingdom. Clinicians were blinded to the antenatal diagnosis of FGR. FGR was defined as estimated fetal weight (EFW) <10th percentile at approximately 36 weeks of gestation, plus one or more indicators of placental dysfunction, including ultrasonic markers and maternal serum levels of placental biomarkers. A total of 2,754 children delivered at term were divided into 4 groups: FGR, appropriate-for-gestational age (AGA) with markers of placental dysfunction, healthy small-for-gestational age (SGA), and healthy AGA (referent). Educational outcomes (assessed at 5 to 7 years using UK national standards) were assessed with respect to FGR status using regression models adjusted for relevant covariates, including maternal, pregnancy, and socioeconomic factors. Compared to healthy AGA (N = 1,429), children with FGR (N = 250) were at higher risk of "below national standard" educational performance at 6 years (18% versus 11%; aOR 1.68; 95% CI 1.12 to 2.48, p = 0.01). By age 7, children with FGR were more likely to perform below standard in reading (21% versus 15%; aOR 1.46; 95% CI 0.99 to 2.13, p = 0.05), writing (28% versus 23%; aOR 1.46; 95% CI 1.02 to 2.07, p = 0.04), and mathematics (24% versus 16%; aOR 1.49; 95% CI 1.02 to 2.15, p = 0.03). This was consistent whether FGR was defined by ultrasound or biochemical markers. The educational attainment of healthy SGA children (N = 126) was comparable to healthy AGA, although this comparison may be underpowered. Our study design relied on linkage of routinely collected educational data according to nationally standardised metrics; this design allowed a high percentage of eligible participants to be included in the analysis (75%) but excludes those children educated outside of government-funded schools in the UK. Our focus on pragmatic and validated measures of educational attainment does not exclude more subtle effects of the intrauterine environment on specific aspects of neurodevelopment. CONCLUSIONS Compared to children with normal fetal growth and no markers of placental dysfunction, FGR is associated with poorer educational attainment in mid-childhood.
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Affiliation(s)
- Laurentya Olga
- Department of Obstetrics and Gynaecology and NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Ulla Sovio
- Department of Obstetrics and Gynaecology and NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Hilary Wong
- Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom
| | - Gordon Smith
- Department of Obstetrics and Gynaecology and NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Catherine Aiken
- Department of Obstetrics and Gynaecology and NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, United Kingdom
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Han L, Holland OJ, Da Silva Costa F, Perkins AV. Potential biomarkers for late-onset and term preeclampsia: A scoping review. Front Physiol 2023; 14:1143543. [PMID: 36969613 PMCID: PMC10036383 DOI: 10.3389/fphys.2023.1143543] [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: 01/13/2023] [Accepted: 02/21/2023] [Indexed: 03/12/2023] Open
Abstract
Preeclampsia is a progressive, multisystem pregnancy disorder. According to the time of onset or delivery, preeclampsia has been subclassified into early-onset (<34 weeks) and late-onset (≥34 weeks), or preterm (<37 weeks) and term (≥37 weeks). Preterm preeclampsia can be effectively predicted at 11-13 weeks well before onset, and its incidence can be reduced by preventively using low-dose aspirin. However, late-onset and term preeclampsia are more prevalent than early forms and still lack effective predictive and preventive measures. This scoping review aims to systematically identify the evidence of predictive biomarkers reported in late-onset and term preeclampsia. This study was conducted based on the guidance of the Joanna Briggs Institute (JBI) methodology for scoping reviews. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR) was used to guide the study. The following databases were searched for related studies: PubMed, Web of Science, Scopus, and ProQuest. Search terms contain "preeclampsia," "late-onset," "term," "biomarker," or "marker," and other synonyms combined as appropriate using the Boolean operators "AND" and "OR." The search was restricted to articles published in English from 2012 to August 2022. Publications were selected if study participants were pregnant women and biomarkers were detected in maternal blood or urine samples before late-onset or term preeclampsia diagnosis. The search retrieved 4,257 records, of which 125 studies were included in the final assessment. The results demonstrate that no single molecular biomarker presents sufficient clinical sensitivity and specificity for screening late-onset and term preeclampsia. Multivariable models combining maternal risk factors with biochemical and/or biophysical markers generate higher detection rates, but they need more effective biomarkers and validation data for clinical utility. This review proposes that further research into novel biomarkers for late-onset and term preeclampsia is warranted and important to find strategies to predict this complication. Other critical factors to help identify candidate markers should be considered, such as a consensus on defining preeclampsia subtypes, optimal testing time, and sample types.
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Affiliation(s)
- Luhao Han
- School of Pharmacy and Medical Sciences, Griffith University, Gold Coast, QLD, Australia
| | - Olivia J. Holland
- School of Pharmacy and Medical Sciences, Griffith University, Gold Coast, QLD, Australia
| | - Fabricio Da Silva Costa
- Maternal Fetal Medicine Unit, Gold Coast University Hospital, Gold Coast, QLD, Australia
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD, Australia
| | - Anthony V. Perkins
- School of Pharmacy and Medical Sciences, Griffith University, Gold Coast, QLD, Australia
- School of Health, University of the Sunshine Coast, Sunshine Coast, QLD, Australia
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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.
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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
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11
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Sovio U, Clayton GL, Cook E, Gaccioli F, Charnock-Jones DS, Lawlor DA, Smith GCS. Metabolomic Identification of a Novel, Externally Validated Predictive Test for Gestational Diabetes Mellitus. J Clin Endocrinol Metab 2022; 107:e3479-e3486. [PMID: 35436338 PMCID: PMC9282248 DOI: 10.1210/clinem/dgac240] [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: 01/11/2022] [Indexed: 11/19/2022]
Abstract
CONTEXT Undiagnosed gestational diabetes mellitus (GDM) is a major preventable cause of stillbirth. In the United Kingdom, women are selected for diagnostic testing for GDM based on risk factors, including body mass index (BMI) > 30 kg/m2. OBJECTIVE To improve the prediction of GDM using metabolomics. METHODS We performed metabolomics on maternal serum from the Pregnancy Outcome Prediction (POP) study at 12 and 20 weeks of gestational age (wkGA; 185 GDM cases and 314 noncases). Predictive metabolites were internally validated using the 28 wkGA POP study serum sample and externally validated using 24- to 28-wkGA fasting plasma from the Born in Bradford (BiB) cohort (349 GDM cases and 2347 noncases). The predictive ability of a model including the metabolites was compared with BMI > 30 kg/m2 in the POP study. RESULTS Forty-seven predictive metabolites were identified using the 12- and 20-wkGA samples. At 28 wkGA, 4 of these [mannose, 4-hydroxyglutamate, 1,5-anhydroglucitol, and lactosyl-N-palmitoyl-sphingosine (d18:1/16:0)] independently increased the bootstrapped area under the receiver operating characteristic curve (AUC) by >0.01. All 4 were externally validated in the BiB samples (P = 2.6 × 10-12, 2.2 × 10-13, 6.9 × 10-28, and 2.6 × 10-17, respectively). In the POP study, BMI > 30 kg/m2 had a sensitivity of 28.7% (95% CI 22.3-36.0%) and a specificity of 85.4% whereas at the same level of specificity, a predictive model using age, BMI, and the 4 metabolites had a sensitivity of 60.2% (95% CI 52.6-67.4%) and an AUC of 0.82 (95% CI 0.78-0.86). CONCLUSIONS We identified 4 strongly and independently predictive metabolites for GDM that could have clinical utility in screening for GDM.
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Affiliation(s)
- Ulla Sovio
- Department of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Gemma L Clayton
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Emma Cook
- Department of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Francesca Gaccioli
- Department of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - D Stephen Charnock-Jones
- Department of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Deborah A Lawlor
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Gordon C S Smith
- Department of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
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12
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Farhan HA, Yaseen IF. Biomarker profile and risk stratification in cardiovascular disease during pregnancy: Action to move forward. INTERNATIONAL JOURNAL OF CARDIOLOGY CONGENITAL HEART DISEASE 2022. [DOI: 10.1016/j.ijcchd.2022.100393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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13
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Mayrink J, Leite DF, Nobrega GM, Costa ML, Cecatti JG. Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review. BMJ Open 2022; 12:e054697. [PMID: 35470187 PMCID: PMC9039389 DOI: 10.1136/bmjopen-2021-054697] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 04/05/2022] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVE To determine the accuracy of metabolomics in predicting hypertensive disorders in pregnancy. DESIGN Systematic review of observational studies. DATA SOURCES AND STUDY ELIGIBILITY CRITERIA An electronic literature search was performed in June 2019 and February 2022. Two researchers independently selected studies published between 1998 and 2022 on metabolomic techniques applied to predict the condition; subsequently, they extracted data and performed quality assessment. Discrepancies were dealt with a third reviewer. The primary outcome was pre-eclampsia. Cohort or case-control studies were eligible when maternal samples were taken before diagnosis of the hypertensive disorder. STUDY APPRAISAL AND SYNTHESIS METHODS Data on study design, maternal characteristics, how hypertension was diagnosed, metabolomics details and metabolites, and accuracy were independently extracted by two authors. RESULTS Among 4613 initially identified studies on metabolomics, 68 were read in full text and 32 articles were included. Studies were excluded due to duplicated data, study design or lack of identification of metabolites. Metabolomics was applied mainly in the second trimester; the most common technique was liquid-chromatography coupled to mass spectrometry. Among the 122 different metabolites found, there were 23 amino acids and 21 fatty acids. Most of the metabolites were involved with ammonia recycling; amino acid metabolism; arachidonic acid metabolism; lipid transport, metabolism and peroxidation; fatty acid metabolism; cell signalling; galactose metabolism; nucleotide sugars metabolism; lactose degradation; and glycerolipid metabolism. Only citrate was a common metabolite for prediction of early-onset and late-onset pre-eclampsia. Vitamin D was the only metabolite in common for pre-eclampsia and gestational hypertension prediction. Meta-analysis was not performed due to lack of appropriate standardised data. CONCLUSIONS AND IMPLICATIONS Metabolite signatures may contribute to further insights into the pathogenesis of pre-eclampsia and support screening tests. Nevertheless, it is mandatory to validate such methods in larger studies with a heterogeneous population to ascertain the potential for their use in clinical practice. PROSPERO REGISTRATION NUMBER CRD42018097409.
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Affiliation(s)
- Jussara Mayrink
- Department of Obstetrics and Gynecology, State University of Campinas Faculty of Medical Sciences, Campinas, Brazil
| | - Debora F Leite
- Department of Obstetrics and Gynecology, State University of Campinas Faculty of Medical Sciences, Campinas, Brazil
| | - Guilherme M Nobrega
- Department of Obstetrics and Gynecology, State University of Campinas Faculty of Medical Sciences, Campinas, Brazil
| | - Maria Laura Costa
- Department of Obstetrics and Gynecology, State University of Campinas Faculty of Medical Sciences, Campinas, Brazil
| | - Jose Guilherme Cecatti
- Department of Obstetrics and Gynecology, State University of Campinas Faculty of Medical Sciences, Campinas, Brazil
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14
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Sovio U, Goulding N, McBride N, Cook E, Gaccioli F, Charnock-Jones DS, Lawlor DA, Smith GCS. A Maternal Serum Metabolite Ratio Predicts Large for Gestational Age Infants at Term: A Prospective Cohort Study. J Clin Endocrinol Metab 2022; 107:e1588-e1597. [PMID: 34897472 PMCID: PMC8947792 DOI: 10.1210/clinem/dgab842] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Indexed: 12/02/2022]
Abstract
CONTEXT Excessive birth weight is associated with maternal and neonatal complications. However, ultrasonically estimated large for gestational age (LGA; >90th percentile) predicts these complications poorly. OBJECTIVE To determine whether a maternal serum metabolite ratio developed for fetal growth restriction (FGR) is predictive of birth weight across the whole range, including LGA at birth. METHODS Metabolites were measured using ultrahigh performance liquid chromatography-tandem mass spectroscopy. The 4-metabolite ratio was previously derived from an analysis of FGR cases and a random subcohort from the Pregnancy Outcome Prediction study. Here, we evaluated its relationship at 36 weeks of gestational age (wkGA) with birth weight in the subcohort (n = 281). External validation in the Born in Bradford (BiB) study (n = 2366) used the metabolite ratio at 24 to 28 wkGA. RESULTS The inverse of the metabolite ratio at 36 wkGA predicted LGA at term [the area under the receiver operating characteristic curve (AUROCC) = 0.82, 95% CI 0.73 to 0.91, P = 6.7 × 10-5]. The ratio was also inversely associated with birth weight z score (linear regression, beta = -0.29 SD, P = 2.1 × 10-8). Analysis in the BiB cohort confirmed that the ratio at 24 to 28 wkGA predicted LGA (AUROCC = 0.60, 95% CI 0.54 to 0.67, P = 8.6 × 10-5) and was inversely associated with birth weight z score (beta = -0.12 SD, P = 1.3 × 10-9). CONCLUSIONS A metabolite ratio which is strongly predictive of FGR is equally predictive of LGA birth weight and is inversely associated with birth weight across the whole range.
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Affiliation(s)
- Ulla Sovio
- Department of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Neil Goulding
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Nancy McBride
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Emma Cook
- Department of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Francesca Gaccioli
- Department of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - D Stephen Charnock-Jones
- Department of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Deborah A Lawlor
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, Bristol, UK
| | - Gordon C S Smith
- Department of Obstetrics and Gynaecology, University of Cambridge; NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
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15
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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.
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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
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16
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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: 87] [Impact Index Per Article: 43.5] [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.
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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.
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17
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Farhan HA, Yaseen IF. Biomarker profile and risk stratification in cardiovascular disease during pregnancy: Action to move forward. INTERNATIONAL JOURNAL OF CARDIOLOGY CONGENITAL HEART DISEASE 2021. [DOI: 10.1016/j.ijcchd.2021.100206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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18
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Taylor K, McBride N, J Goulding N, Burrows K, Mason D, Pembrey L, Yang T, Azad R, Wright J, A Lawlor D. Metabolomics datasets in the Born in Bradford cohort. Wellcome Open Res 2021. [DOI: 10.12688/wellcomeopenres.16341.2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks’ gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts’ extensive phenotype data from questionnaires, medical, educational and social record linkage, and other ‘omics data.
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19
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Taylor K, McBride N, J Goulding N, Burrows K, Mason D, Pembrey L, Yang T, Azad R, Wright J, A Lawlor D. Metabolomics datasets in the Born in Bradford cohort. Wellcome Open Res 2021; 5:264. [PMID: 38778888 PMCID: PMC11109709 DOI: 10.12688/wellcomeopenres.16341.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 05/25/2024] Open
Abstract
Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks' gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts' extensive phenotype data from questionnaires, medical, educational and social record linkage, and other 'omics data.
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Affiliation(s)
- Kurt Taylor
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
| | - Nancy McBride
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Bristol NIHR Biomedical Research Centre, University of Bristol, Bristol, BS1 2NT, UK
| | - Neil J Goulding
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
| | - Kimberley Burrows
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Lucy Pembrey
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Tiffany Yang
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Rafaq Azad
- Department of Biochemistry, Bradford Royal Infirmary, Bradford, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
- Wolfson Centre for Applied Health Research, Bradford Hospitals National Health Service Trust, Bradford, BD9 6RJ, UK
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Bristol NIHR Biomedical Research Centre, University of Bristol, Bristol, BS1 2NT, UK
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Minakata S, Manabe S, Inai Y, Ikezaki M, Nishitsuji K, Ito Y, Ihara Y. Protein C-Mannosylation and C-Mannosyl Tryptophan in Chemical Biology and Medicine. Molecules 2021; 26:molecules26175258. [PMID: 34500691 PMCID: PMC8433626 DOI: 10.3390/molecules26175258] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 12/25/2022] Open
Abstract
C-Mannosylation is a post-translational modification of proteins in the endoplasmic reticulum. Monomeric α-mannose is attached to specific Trp residues at the first Trp in the Trp-x-x-Trp/Cys (W-x-x-W/C) motif of substrate proteins, by the action of C-mannosyltransferases, DPY19-related gene products. The acceptor substrate proteins are included in the thrombospondin type I repeat (TSR) superfamily, cytokine receptor type I family, and others. Previous studies demonstrated that C-mannosylation plays critical roles in the folding, sorting, and/or secretion of substrate proteins. A C-mannosylation-defective gene mutation was identified in humans as the disease-associated variant affecting a C-mannosylation motif of W-x-x-W of ADAMTSL1, which suggests the involvement of defects in protein C-mannosylation in human diseases such as developmental glaucoma, myopia, and/or retinal defects. On the other hand, monomeric C-mannosyl Trp (C-Man-Trp), a deduced degradation product of C-mannosylated proteins, occurs in cells and extracellular fluids. Several studies showed that the level of C-Man-Trp is upregulated in blood of patients with renal dysfunction, suggesting that the metabolism of C-Man-Trp may be involved in human kidney diseases. Together, protein C-mannosylation is considered to play important roles in the biosynthesis and functions of substrate proteins, and the altered regulation of protein C-manosylation may be involved in the pathophysiology of human diseases. In this review, we consider the biochemical and biomedical knowledge of protein C-mannosylation and C-Man-Trp, and introduce recent studies concerning their significance in biology and medicine.
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Affiliation(s)
- Shiho Minakata
- Department of Biochemistry, Wakayama Medical University, 811-1 Kimiidera, Wakayama, Wakayama 641-0012, Japan; (S.M.); (Y.I.); (M.I.); (K.N.)
| | - Shino Manabe
- Pharmaceutical Department, The Institute of Medicinal Chemistry, Hoshi University, 2-4-41 Ebara, Shinagawa, Tokyo 142-8501, Japan;
- Research Center for Pharmaceutical Development, Graduate School of Pharmaceutical Science & Faculty of Pharmaceutical Sciences, Tohoku University, 6-3 Aoba, Sendai, Miyagi 980-8578, Japan
| | - Yoko Inai
- Department of Biochemistry, Wakayama Medical University, 811-1 Kimiidera, Wakayama, Wakayama 641-0012, Japan; (S.M.); (Y.I.); (M.I.); (K.N.)
| | - Midori Ikezaki
- Department of Biochemistry, Wakayama Medical University, 811-1 Kimiidera, Wakayama, Wakayama 641-0012, Japan; (S.M.); (Y.I.); (M.I.); (K.N.)
| | - Kazuchika Nishitsuji
- Department of Biochemistry, Wakayama Medical University, 811-1 Kimiidera, Wakayama, Wakayama 641-0012, Japan; (S.M.); (Y.I.); (M.I.); (K.N.)
| | - Yukishige Ito
- Department of Chemistry, Graduate School of Science, Osaka University, 1-1 Machikaneyama, Toyonaka, Osaka 560-0043, Japan;
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Yoshito Ihara
- Department of Biochemistry, Wakayama Medical University, 811-1 Kimiidera, Wakayama, Wakayama 641-0012, Japan; (S.M.); (Y.I.); (M.I.); (K.N.)
- Correspondence: ; Tel.: +81-73-441-0628
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21
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McBride N, Yousefi P, Sovio U, Taylor K, Vafai Y, Yang T, Hou B, Suderman M, Relton C, Smith GCS, Lawlor DA. Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation. Metabolites 2021; 11:530. [PMID: 34436471 PMCID: PMC8399752 DOI: 10.3390/metabo11080530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/23/2021] [Accepted: 07/30/2021] [Indexed: 12/01/2022] Open
Abstract
Many women who experience gestational diabetes (GDM), gestational hypertension (GHT), pre-eclampsia (PE), have a spontaneous preterm birth (sPTB) or have an offspring born small/large for gestational age (SGA/LGA) do not meet the criteria for high-risk pregnancies based upon certain maternal risk factors. Tools that better predict these outcomes are needed to tailor antenatal care to risk. Recent studies have suggested that metabolomics may improve the prediction of these pregnancy-related disorders. These have largely been based on targeted platforms or focused on a single pregnancy outcome. The aim of this study was to assess the predictive ability of an untargeted platform of over 700 metabolites to predict the above pregnancy-related disorders in two cohorts. We used data collected from women in the Born in Bradford study (BiB; two sub-samples, n = 2000 and n = 1000) and the Pregnancy Outcome Prediction study (POPs; n = 827) to train, test and validate prediction models for GDM, PE, GHT, SGA, LGA and sPTB. We compared the predictive performance of three models: (1) risk factors (maternal age, pregnancy smoking, BMI, ethnicity and parity) (2) mass spectrometry (MS)-derived metabolites (n = 718 quantified metabolites, collected at 26-28 weeks' gestation) and (3) combined risk factors and metabolites. We used BiB for the training and testing of the models and POPs for independent validation. In both cohorts, discrimination for GDM, PE, LGA and SGA improved with the addition of metabolites to the risk factor model. The models' area under the curve (AUC) were similar for both cohorts, with good discrimination for GDM (AUC (95% CI) BiB 0.76 (0.71, 0.81) and POPs 0.76 (0.72, 0.81)) and LGA (BiB 0.86 (0.80, 0.91) and POPs 0.76 (0.60, 0.92)). Discrimination was improved for the combined models (compared to the risk factors models) for PE and SGA, with modest discrimination in both studies (PE-BiB 0.68 (0.58, 0.78) and POPs 0.66 (0.60, 0.71); SGA-BiB 0.68 (0.63, 0.74) and POPs 0.64 (0.59, 0.69)). Prediction for sPTB was poor in BiB and POPs for all models. In BiB, calibration for the combined models was good for GDM, LGA and SGA. Retained predictors include 4-hydroxyglutamate for GDM, LGA and PE and glycerol for GDM and PE. MS-derived metabolomics combined with maternal risk factors improves the prediction of GDM, PE, LGA and SGA, with good discrimination for GDM and LGA. Validation across two very different cohorts supports further investigation on whether the metabolites reflect novel causal paths to GDM and LGA.
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Affiliation(s)
- Nancy McBride
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol BS8 2BN, UK
- Department of Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Paul Yousefi
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
- Department of Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Ulla Sovio
- NIHR Cambridge Biomedical Research Centre, Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge CB2 0QQ, UK; (U.S.); (G.C.S.S.)
| | - Kurt Taylor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
| | - Yassaman Vafai
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford BD9 6DA, UK; (Y.V.); (T.Y.); (B.H.)
| | - Tiffany Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford BD9 6DA, UK; (Y.V.); (T.Y.); (B.H.)
| | - Bo Hou
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford BD9 6DA, UK; (Y.V.); (T.Y.); (B.H.)
| | - Matthew Suderman
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
- Department of Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Caroline Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
- Department of Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Gordon C. S. Smith
- NIHR Cambridge Biomedical Research Centre, Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge CB2 0QQ, UK; (U.S.); (G.C.S.S.)
| | - Deborah A. Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; (P.Y.); (K.T.); (M.S.); (C.R.); (D.A.L.)
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol BS8 2BN, UK
- Department of Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
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23
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Zhao X, Wang Y, Li L, Mei J, Zhang X, Wu Z. Predictive value of 4-Hydroxyglutamate and miR-149-5p on eclampsia. Exp Mol Pathol 2021; 119:104618. [PMID: 33582167 DOI: 10.1016/j.yexmp.2021.104618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/03/2021] [Accepted: 02/09/2021] [Indexed: 11/24/2022]
Abstract
This research aimed at exploring the predictive value of 4-Hydroxyglutamate and miR-149-5p on eclampsia. Preeclampsia patients admitted to our hospital (n = 204), with 112 mild patients and 92 severe patients. Thereinto, pregnant women who underwent physical examination were regarded as a normal group (NG) (n = 100). Serum 4-Hydroxyglutamate levels and miR-149-5p in each group were detected. The serum 4-Hydroxyglutamate level in pregnant women in the NG was markedly lower than that in preeclampsia, while the miR-149-5p level was higher (p = 0.001). The serum 4-Hydroxyglutamate level in severe preeclampsia was higher than that in mild preeclampsia, while the miR-149-5p level was lower (p = 0.001). Partial thromboplastin time (APTT) and prothrombin time (PT) of preeclampsia patients were lower than those of the NG, while Fibrinogen (Fib) was higher (p = 0.001). With the aggravation of the condition of patients, PT, APTT decreased and Fib index increased. In preeclampsia patients, serum 4-Hydroxyglutamate was negatively correlated with PT and APTT, positively correlated with Fib content (p < 0.001); serum miR-149-5p was dramatically positively correlated with PT and APTT, negatively correlated with Fib content (p < 0.001). 4-Hydroxyglutamate and miR-149-5p were relevant to the occurrence time of preeclampsia; 4-Hydroxyglutamate, miR-149-5p and their combination could be used for preeclampsia diagnosis. According to the situation of newborn, they were divided into good and poor groups. The 4-Hydroxyglutamate level in the good group was lower than that in the poor group, while the miR-149-5p level was higher. The adverse prognosis of preeclampsia patients was predicted by 4-Hydroxyglutamate and miR-149-5p. 4-Hydroxyglutamate is highly expressed in preeclampsia, while miR-149-5p is low. Single and combined detection of 4-Hydroxyglutamate, miR-149-5p can be used for preeclampsia diagnosis and prediction.
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Affiliation(s)
- Xiaolan Zhao
- Department of Obstetrics and Gynecology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 610072, P.R. China
| | - Yujue Wang
- Department of Obstetrics and Gynecology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 610072, P.R. China
| | - Lingling Li
- Department of Obstetrics and Gynecology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 610072, P.R. China
| | - Jie Mei
- Department of Obstetrics and Gynecology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 610072, P.R. China
| | - Xun Zhang
- Department of Obstetrics and Gynecology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 610072, P.R. China
| | - Zhao Wu
- Department of Obstetrics and Gynecology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 610072, P.R. China.
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24
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Harville EW, Li YY, Pan K, McRitchie S, Pathmasiri W, Sumner S. Untargeted analysis of first trimester serum to reveal biomarkers of pregnancy complications: a case-control discovery phase study. Sci Rep 2021; 11:3468. [PMID: 33568690 PMCID: PMC7876105 DOI: 10.1038/s41598-021-82804-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 01/15/2021] [Indexed: 11/23/2022] Open
Abstract
Understanding of causal biology and predictive biomarkers are lacking for hypertensive disorders of pregnancy (HDP) and preterm birth (PTB). First-trimester serum specimens from 51 cases of HDP, including 18 cases of pre-eclampsia (PE) and 33 cases of gestational hypertension (GH); 53 cases of PTB; and 109 controls were obtained from the Global Alliance to Prevent Prematurity and Stillbirth repository. Metabotyping was conducted using liquid chromatography high resolution mass spectroscopy and nuclear magnetic resonance spectroscopy. Multivariable logistic regression was used to identify signals that differed between groups after controlling for confounders. Signals important to predicting HDP and PTB were matched to an in-house physical standards library and public databases. Pathway analysis was conducted using GeneGo MetaCore. Over 400 signals for endogenous and exogenous metabolites that differentiated cases and controls were identified or annotated, and models that included these signals produced substantial improvements in predictive power beyond models that only included known risk factors. Perturbations of the aminoacyl-tRNA biosynthesis, L-threonine, and renal secretion of organic electrolytes pathways were associated with both HDP and PTB, while pathways related to cholesterol transport and metabolism were associated with HDP. This untargeted metabolomics analysis identified signals and common pathways associated with pregnancy complications.
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Affiliation(s)
- E W Harville
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, Epidemiology #8318, 1440 Canal St. Ste. 2001, New Orleans, LA, 70112, USA.
| | - Y-Y Li
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill School of Public Health, CB#74612, Chapel Hill, NC, 27599-7461, USA
| | - K Pan
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, Epidemiology #8318, 1440 Canal St. Ste. 2001, New Orleans, LA, 70112, USA
| | - S McRitchie
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill School of Public Health, CB#74612, Chapel Hill, NC, 27599-7461, USA
| | - W Pathmasiri
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill School of Public Health, CB#74612, Chapel Hill, NC, 27599-7461, USA
| | - S Sumner
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill School of Public Health, CB#74612, Chapel Hill, NC, 27599-7461, USA.
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Jääskeläinen T, Kärkkäinen O, Jokkala J, Klåvus A, Heinonen S, Auriola S, Lehtonen M, Hanhineva K, Laivuori H. A non-targeted LC-MS metabolic profiling of pregnancy: longitudinal evidence from healthy and pre-eclamptic pregnancies. Metabolomics 2021; 17:20. [PMID: 33515103 PMCID: PMC7846510 DOI: 10.1007/s11306-020-01752-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 11/25/2020] [Indexed: 12/25/2022]
Abstract
INTRODUCTION Maternal metabolism changes substantially during pregnancy. However, few studies have used metabolomics technologies to characterize changes across gestation. OBJECTIVES AND METHODS We applied liquid chromatography-mass spectrometry (LC-MS) based non-targeted metabolomics to determine whether the metabolic profile of serum differs throughout the pregnancy between pre-eclamptic and healthy women in the FINNPEC (Finnish Genetics of Preeclampsia Consortium) Study. Serum samples were available from early and late pregnancy. RESULTS Progression of pregnancy had large-scale effects to the serum metabolite profile. Altogether 50 identified metabolites increased and 49 metabolites decreased when samples of early pregnancy were compared to samples of late pregnancy. The metabolic signatures of pregnancy were largely shared in pre-eclamptic and healthy women, only urea, monoacylglyceride 18:1 and glycerophosphocholine were identified to be increased in the pre-eclamptic women when compared to healthy controls. CONCLUSIONS Our study highlights the need of large-scale longitudinal metabolomic studies in non-complicated pregnancies before more detailed understanding of metabolism in adverse outcomes could be provided. Our findings are one of the first steps for a broader metabolic understanding of the physiological changes caused by pregnancy per se.
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Affiliation(s)
- Tiina Jääskeläinen
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland.
| | - Olli Kärkkäinen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Jenna Jokkala
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Anton Klåvus
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Seppo Heinonen
- Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Seppo Auriola
- School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Marko Lehtonen
- School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Kati Hanhineva
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- Department of Biochemistry, Food Chemistry and Food Development Unit, University of Turku, Turku, Finland
| | - Hannele Laivuori
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, Faculty of Medicine and Health Technology, Tampere University Hospital and University of Tampere, Tampere, Finland
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26
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McBride N, Yousefi P, White SL, Poston L, Farrar D, Sattar N, Nelson SM, Wright J, Mason D, Suderman M, Relton C, Lawlor DA. Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? Findings from a UK birth cohort with independent validation. BMC Med 2020; 18:366. [PMID: 33222689 PMCID: PMC7681995 DOI: 10.1186/s12916-020-01819-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 10/19/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Prediction of pregnancy-related disorders is usually done based on established and easily measured risk factors. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders. METHODS We used data collected from women in the Born in Bradford (BiB; n = 8212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n = 859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of (1) risk factors (maternal age, pregnancy smoking, body mass index (BMI), ethnicity and parity) to (2) nuclear magnetic resonance-derived metabolites (N = 156 quantified metabolites, collected at 24-28 weeks gestation) and (3) combined risk factors and metabolites. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a multi-ethnic study of obese pregnant women. RESULTS Maternal age, pregnancy smoking, BMI, ethnicity and parity were retained in the combined risk factor and metabolite models for all outcomes apart from PTB, which did not include maternal age. In addition, 147, 33, 96, 51 and 14 of the 156 metabolite traits were retained in the combined risk factor and metabolite model for GDM, HDP, SGA, LGA and PTB, respectively. These include cholesterol and triglycerides in very low-density lipoproteins (VLDL) in the models predicting GDM, HDP, SGA and LGA, and monounsaturated fatty acids (MUFA), ratios of MUFA to omega 3 fatty acids and total fatty acids, and a ratio of apolipoprotein B to apolipoprotein A-1 (APOA:APOB1) were retained predictors for GDM and LGA. In BiB, discrimination for GDM, HDP, LGA and SGA was improved in the combined risk factors and metabolites models. Risk factor area under the curve (AUC 95% confidence interval (CI)): GDM (0.69 (0.64, 0.73)), HDP (0.74 (0.70, 0.78)) and LGA (0.71 (0.66, 0.75)), and SGA (0.59 (0.56, 0.63)). Combined risk factor and metabolite models AUC 95% (CI): GDM (0.78 (0.74, 0.81)), HDP (0.76 (0.73, 0.79)) and LGA (0.75 (0.70, 0.79)), and SGA (0.66 (0.63, 0.70)). For GDM, HDP and LGA, but not SGA, calibration was good for a combined risk factor and metabolite model. Prediction of PTB was poor for all models. Independent validation in UPBEAT at 24-28 weeks and 15-18 weeks gestation confirmed similar patterns of results, but AUCs were attenuated. CONCLUSIONS Our results suggest a combined risk factor and metabolite model improves prediction of GDM, HDP and LGA, and SGA, when compared to risk factors alone. They also highlight the difficulty of predicting PTB, with all models performing poorly.
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Affiliation(s)
- Nancy McBride
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK. .,NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK. .,Population Health Sciences, University of Bristol, Bristol, UK.
| | - Paul Yousefi
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Population Health Sciences, University of Bristol, Bristol, UK
| | - Sara L White
- Department of Women and Children's Health, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Lucilla Poston
- Department of Women and Children's Health, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Diane Farrar
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Naveed Sattar
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK.,Cardiovascular and Medical Sciences, British Heart Foundation Glasgow, Cardiovascular Research Centre, University of Glasgow, Glasgow, UK.,School of Medicine, University of Glasgow, Glasgow, UK
| | - Scott M Nelson
- NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK.,Cardiovascular and Medical Sciences, British Heart Foundation Glasgow, Cardiovascular Research Centre, University of Glasgow, Glasgow, UK.,School of Medicine, University of Glasgow, Glasgow, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Matthew Suderman
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Population Health Sciences, University of Bristol, Bristol, UK
| | - Caroline Relton
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,Population Health Sciences, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK.,Population Health Sciences, University of Bristol, Bristol, UK
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27
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Abstract
In the last years, 'omics' technologies, and especially metabolomics, emerged as expanding scientific disciplines and promising technologies in the characterization of several pathophysiological processes.In detail, metabolomics, able to detect in a dynamic way the whole set of molecules of low molecular weight in cells, tissues, organs, and biological fluids, can provide a detailed phenotypic portray, representing a metabolic "snapshot."Thanks to its numerous strength points, metabolomics could become a fundamental tool in human health, allowing the exact evaluation of individual metabolic responses to pathophysiological stimuli including drugs, environmental changes, lifestyle, a great number of diseases and other epigenetics factors.Moreover, if current metabolomics data will be confirmed on larger samples, such technology could become useful in the early diagnosis of diseases, maybe even before the clinical onset, allowing a clinical monitoring of disease progression and helping in performing the best therapeutic approach, potentially predicting the therapy response and avoiding overtreatments. Moreover, the application of metabolomics in nutrition could provide significant information on the best nutrition regimen, optimal infantile growth and even in the characterization and improvement of commercial products' composition.These are only some of the fields in which metabolomics was applied, in the perspective of a precision-based, personalized care of human health.In this review, we discuss the available literature on such topic and provide some evidence regarding clinical application of metabolomics in heart diseases, auditory disturbance, nephrouropathies, adult and pediatric cancer, obstetrics, perinatal conditions like asphyxia, neonatal nutrition, neonatal sepsis and even some neuropsychiatric disorders, including autism.Our research group has been interested in metabolomics since several years, performing a wide spectrum of experimental and clinical studies, including the first metabolomics analysis of human breast milk. In the future, it is reasonable to predict that the current knowledge could be applied in daily clinical practice, and that sensible metabolomics biomarkers could be easily detected through cheap and accurate sticks, evaluating biofluids at the patient's bed, improving diagnosis, management and prognosis of sick patients and allowing a personalized medicine. A dream? May be I am a dreamer, but I am not the only one.
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Affiliation(s)
- Flaminia Bardanzellu
- Neonatal Intensive Care Unit, Department of Surgical Sciences, AOU University of Cagliari, SS 554 km 4,500, 09042, Monserrato, CA, Italy.
| | - Vassilios Fanos
- Neonatal Intensive Care Unit, Department of Surgical Sciences, AOU University of Cagliari, SS 554 km 4,500, 09042, Monserrato, CA, Italy
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28
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A maternal serum metabolite ratio predicts fetal growth restriction at term. Nat Med 2020; 26:348-353. [PMID: 32161413 DOI: 10.1038/s41591-020-0804-9] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 01/30/2020] [Indexed: 11/08/2022]
Abstract
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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Abstract
Preeclampsia is a medical condition affecting 5-10% of pregnancies. It has serious effects on the health of the pregnant mother and developing fetus. While possible causes of preeclampsia are speculated, there is no consensus on its etiology. The advancement of big data and high-throughput technologies enables to study preeclampsia at the new and systematic level. In this review, we first highlight the recent progress made in the field of preeclampsia research using various omics technology platforms, including epigenetics, genome-wide association studies (GWAS), transcriptomics, proteomics and metabolomics. Next, we integrate the results in individual omic level studies, and show that despite the lack of coherent biomarkers in all omics studies, inhibin is a potential preeclamptic biomarker supported by GWAS, transcriptomics and DNA methylation evidence. Using network analysis on the biomarkers of all the literature reviewed here, we identify four striking sub-networks with clear biological functions supported by previous molecular-biology and clinical observations. In summary, omics integration approach offers the promise to understand molecular mechanisms in preeclampsia.
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Lawlor DA, Lewcock M, Rena-Jones L, Rollings C, Yip V, Smith D, Pearson RM, Johnson L, Millard LAC, Patel N, Skinner A, Tilling K. The second generation of The Avon Longitudinal Study of Parents and Children (ALSPAC-G2): a cohort profile. Wellcome Open Res 2019; 4:36. [PMID: 31984238 PMCID: PMC6971848 DOI: 10.12688/wellcomeopenres.15087.2] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/08/2019] [Indexed: 01/19/2023] Open
Abstract
Background: The Avon Longitudinal Study of Parents and Children-Generation 2 (ALSPAC-G2) was set up to provide a unique multi-generational cohort. It builds on the existing ALSPAC resource, which recruited 14,541 pregnancies to women resident in the South West of England who were expected to deliver between 01/04/1991 and 31/12/1992. Those women and their partners (Generation 0; ALSPAC-G0) and their offspring (ALSPAC-G1) have been followed for the last 26 years. This profile describes recruitment and data collection on the next generation (ALSPAC-G2)-the grandchildren of ALSPAC-G0 and children of ALSPAC-G1. Recruitment: Recruitment began on the 6 th of June 2012 and we present details of recruitment and participants up to 30 th June 2018 (~6 years). We knew at the start of recruitment that some ALSPAC-G1 participants had already become parents and ALSPAC-G2 is an open cohort; we recruit at any age. We hope to continue recruiting until all ALSPAC-G1 participants have completed their families. Up to 30 th June 2018 we recruited 810 ALSPAC-G2 participants from 548 families. Of these 810, 389 (48%) were recruited during their mother's pregnancy, 287 (35%) before age 3 years, 104 (13%) between 3-6 years and 30 (4%) after 6 years. Over 70% of those invited to early pregnancy, late pregnancy, second week of life, 6-, 12- and 24-month assessments (whether for their recruitment, or a follow-up, visit) have attended, with attendance being over 60% for subsequent visits up to 7 years (to few are eligible for the 9- and 11-year assessments to analyse). Data collection: We collect a wide-range of social, lifestyle, clinical, anthropometric and biological data on all family members repeatedly. Biological samples include blood (including cord-blood), urine, meconium and faeces, and placental tissue. In subgroups detailed data collection, such as continuous glucose monitoring and videos of parent-child interactions, are being collected.
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Affiliation(s)
- Deborah A. Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Melanie Lewcock
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- ALSPAC, University of Bristol, Bristol, UK
| | - Louise Rena-Jones
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- ALSPAC, University of Bristol, Bristol, UK
| | - Claire Rollings
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- ALSPAC, University of Bristol, Bristol, UK
| | - Vikki Yip
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- ALSPAC, University of Bristol, Bristol, UK
| | - Daniel Smith
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- ALSPAC, University of Bristol, Bristol, UK
| | - Rebecca M. Pearson
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Centre for Academic Mental Health, University of Bristol, Bristol, UK
| | - Laura Johnson
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Centre for Exercise, Nutrition and Health Science, School for Policy Studies, University of Bristol, Bristol, UK
| | - Louise A. C. Millard
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Intelligent Systems Laboratory, University of Bristol, Bristol, UK
| | - Nashita Patel
- Department of Women and Children’s Health, School of Life Course Sciences, Kings College London, London, UK
| | - Andy Skinner
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - ALSPAC Executive
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- ALSPAC, University of Bristol, Bristol, UK
- Centre for Academic Mental Health, University of Bristol, Bristol, UK
- Centre for Exercise, Nutrition and Health Science, School for Policy Studies, University of Bristol, Bristol, UK
- Intelligent Systems Laboratory, University of Bristol, Bristol, UK
- Department of Women and Children’s Health, School of Life Course Sciences, Kings College London, London, UK
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31
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Lawlor DA, Lewcock M, Rena-Jones L, Rollings C, Yip V, Smith D, Pearson RM, Johnson L, Millard LAC, Patel N, Skinner A, Tilling K. The second generation of The Avon Longitudinal Study of Parents and Children (ALSPAC-G2): a cohort profile. Wellcome Open Res 2019. [PMID: 31984238 DOI: 10.12688/wellcomeopenres.15087.1] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Background: The Avon Longitudinal Study of Parents and Children-Generation 2 (ALSPAC-G2) was set up to provide a unique multi-generational cohort. It builds on the existing ALSPAC resource, which recruited 14,541 pregnancies to women resident in the South West of England who were expected to deliver between 01/04/1991 and 31/12/1992. Those women and their partners (Generation 0; ALSPAC-G0) and their offspring (ALSPAC-G1) have been followed for the last 26 years. This profile describes recruitment and data collection on the next generation (ALSPAC-G2)-the grandchildren of ALSPAC-G0 and children of ALSPAC-G1. Recruitment: Recruitment began on the 6 th of June 2012 and we present details of recruitment and participants up to 30 th June 2018 (~6 years). We knew at the start of recruitment that some ALSPAC-G1 participants had already become parents and ALSPAC-G2 is an open cohort; we recruit at any age. We hope to continue recruiting until all ALSPAC-G1 participants have completed their families. Up to 30 th June 2018 we recruited 810 ALSPAC-G2 participants from 548 families. Of these 810, 389 (48%) were recruited during their mother's pregnancy, 287 (35%) before age 3 years, 104 (13%) between 3-6 years and 30 (4%) after 6 years. Over 70% of those invited to early pregnancy, late pregnancy, second week of life, 6-, 12- and 24-month assessments (whether for their recruitment, or a follow-up, visit) have attended, with attendance being over 60% for subsequent visits up to 7 years (to few are eligible for the 9- and 11-year assessments to analyse). Data collection: We collect a wide-range of social, lifestyle, clinical, anthropometric and biological data on all family members repeatedly. Biological samples include blood (including cord-blood), urine, meconium and faeces, and placental tissue. In subgroups detailed data collection, such as continuous glucose monitoring and videos of parent-child interactions, are being collected.
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Affiliation(s)
- Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Melanie Lewcock
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,ALSPAC, University of Bristol, Bristol, UK
| | - Louise Rena-Jones
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,ALSPAC, University of Bristol, Bristol, UK
| | - Claire Rollings
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,ALSPAC, University of Bristol, Bristol, UK
| | - Vikki Yip
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,ALSPAC, University of Bristol, Bristol, UK
| | - Daniel Smith
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,ALSPAC, University of Bristol, Bristol, UK
| | - Rebecca M Pearson
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, Bristol, UK.,Centre for Academic Mental Health, University of Bristol, Bristol, UK
| | - Laura Johnson
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK.,NIHR Bristol Biomedical Research Centre, Bristol, UK.,Centre for Exercise, Nutrition and Health Science, School for Policy Studies, University of Bristol, Bristol, UK
| | - Louise A C Millard
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,Intelligent Systems Laboratory, University of Bristol, Bristol, UK
| | - Nashita Patel
- Department of Women and Children's Health, School of Life Course Sciences, Kings College London, London, UK
| | - Andy Skinner
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, BS8 2BN, UK.,Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, Bristol, UK
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