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Song Y, Lu R, Yu G, Rahman ML, Chen L, Zhu Y, Tsai MY, Fiehn O, Chen Z, Zhang C. Longitudinal lipidomic profiles during pregnancy and associations with neonatal anthropometry: findings from a multiracial cohort. EBioMedicine 2023; 98:104881. [PMID: 38006745 PMCID: PMC10709105 DOI: 10.1016/j.ebiom.2023.104881] [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: 06/04/2023] [Revised: 11/06/2023] [Accepted: 11/06/2023] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND Maternal lipidomic profiling offers promise for characterizing lipid metabolites during pregnancy, but longitudinal data are limited. This study aimed to examine associations of longitudinal lipidomic profiles during pregnancy with multiple neonatal anthropometry using data from a multiracial cohort. METHODS We measured untargeted plasma lipidome profiles among 321 pregnant women from the NICHD Fetal Growth Study-Singletons using plasma samples collected longitudinally during four study visits at gestational weeks (GW) 10-14, 15-26, 23-31, and 33-39, respectively. We evaluated individual lipidomic metabolites at each study visit in association with neonatal anthropometry. We also evaluated the associations longitudinally by constructing lipid networks using weighted correlation network analysis and common networks using consensus network analysis across four visits using linear mixed-effects models with the adjustment of false discover rate. FINDINGS Multiple triglycerides (TG) were positively associated with birth weight (BW), BW Z-score, length and head circumference, while some cholesteryl ester (CE), phosphatidylcholine (PC), sphingomyelines (SM), phosphatidylethanolamines (PE), and lysophosphatidylcholines (LPC 20:3) families were inversely associated with BW, length, abdominal and head circumference at different GWs. Longitudinal trajectories of TG, PC, and glucosylcermides (GlcCer) were associated with BW, and CE (18:2) with BW z-score, length, and sum of skinfolds (SS), while some PC and PE were significantly associated with abdominal and head circumference. Modules of TG at GW 10-14 and 15-26 mainly were associated with BW. At GW 33-39, two networks of LPC (20:3) and of PC, TG, and CE, showed inverse associations with abdominal circumference. Distinct trajectories within two consensus modules with changes in TG, CE, PC, and LPC showed significant differences in BW and length. INTERPRETATION The results demonstrated that longitudinal changes of TGs during early- and mid-pregnancy and changes of PC, LPC, and CE during late-pregnancy were significantly associated with neonatal anthropometry. FUNDING National Institute of Child Health and Human Development intramural funding.
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Affiliation(s)
- Yiqing Song
- Department of Epidemiology, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA
| | - Ruijin Lu
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Guoqi Yu
- Global Center for Asian Women's Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Bia-Echo Asia Centre for Reproductive Longevity and Equality (ACRLE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mohammad L Rahman
- National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Liwei Chen
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Yeiyi Zhu
- Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
| | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, Davis, CA, USA
| | - Zhen Chen
- Division of Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Health, Bethesda, MD, USA
| | - Cuilin Zhang
- Global Center for Asian Women's Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Bia-Echo Asia Centre for Reproductive Longevity and Equality (ACRLE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
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Thomas G, Syngelaki A, Hamed K, Perez-Montaño A, Panigassi A, Tuytten R, Nicolaides KH. Preterm preeclampsia screening using biomarkers: combining phenotypic classifiers into robust prediction models. Am J Obstet Gynecol MFM 2023; 5:101110. [PMID: 37752025 DOI: 10.1016/j.ajogmf.2023.101110] [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: 07/19/2023] [Accepted: 07/27/2023] [Indexed: 09/28/2023]
Abstract
BACKGROUND Preeclampsia screening is a critical component of antenatal care worldwide. Currently, the most developed screening test for preeclampsia at 11 to 13 weeks' gestation integrates maternal demographic characteristics and medical history with 3 biomarkers-serum placental growth factor, mean arterial pressure, and uterine artery pulsatility index-to identify approximately 75% of women who develop preterm preeclampsia with delivery before 37 weeks of gestation. It is generally accepted that further improvements to preeclampsia screening require the use of additional biomarkers. We recently reported that the levels of specific metabolites and metabolite ratios are associated with preterm preeclampsia. Notably, for several of these markers, preterm preeclampsia prediction varied according to maternal body mass index class. These findings motivated us to study whether patient classification allowed for combining metabolites with the current biomarkers more effectively to improve prediction of preterm preeclampsia. OBJECTIVE This study aimed to investigate whether metabolite biomarkers can improve biomarker-based preterm preeclampsia prediction in 3 screening resource scenarios according to the availability of: (1) placental growth factor, (2) placental growth factor+mean arterial pressure, and (3) placental growth factor+mean arterial pressure+uterine artery pulsatility index. STUDY DESIGN This was an observational case-control study, drawn from a large prospective screening study at 11 to 13 weeks' gestation on the prediction of pregnancy complications, conducted at King's College Hospital, London, United Kingdom. Maternal blood samples were also collected for subsequent research studies. We used liquid chromatography-mass spectrometry to quantify levels of 50 metabolites previously associated with pregnancy complications in plasma samples from singleton pregnancies. Biomarker data, normalized using multiples of medians, on 1635 control and 106 preterm preeclampsia pregnancies were available for model development. Modeling was performed using a methodology that generated a prediction model for preterm preeclampsia in 4 consecutive steps: (1) z-normalization of predictors, (2) combinatorial modeling of so-called (weak) classifiers in the unstratified patient set and in discrete patient strata based on body mass index and/or race, (3) selection of classifiers, and (4) aggregation of the selected classifiers (ie, bagging) into the final prediction model. The prediction performance of models was evaluated using the area under the receiver operating characteristic curve, and detection rate at 10% false-positive rate. RESULTS First, the predictor development methodology itself was evaluated. The patient set was split into a training set (2/3) and a test set (1/3) for predictor model development and internal validation. A prediction model was developed for each of the 3 different predictor panels, that is, placental growth factor+metabolites, placental growth factor+mean arterial pressure+metabolites, and placental growth factor+mean arterial pressure+uterine artery pulsatility index+metabolites. For all 3 models, the area under the receiver operating characteristic curve in the test set did not differ significantly from that of the training set. Next, a prediction model was developed using the complete data set for the 3 predictor panels. Among the 50 metabolites available for modeling, 26 were selected across the 3 prediction models; 21 contributed to at least 2 out of the 3 prediction models developed. Each time, area under the receiver operating characteristic curve and detection rate were significantly higher with the new prediction model than with the reference model. Markedly, the estimated detection rate with the placental growth factor+mean arterial pressure+metabolites prediction model in all patients was 0.58 (95% confidence interval, 0.49-0.70), a 15% increase (P<.001) over the detection rate of 0.43 (95% confidence interval, 0.33-0.55) estimated for the reference placental growth factor+mean arterial pressure. The same prediction model significantly improved detection in Black (14%) and White (19%) patients, and in the normal-weight group (18.5≤body mass index<25) and the obese group (body mass index≥30), with respectively 19% and 20% more cases detected, but not in the overweight group, when compared with the reference model. Similar improvement patterns in detection rates were found in the other 2 scenarios, but with smaller improvement amplitudes. CONCLUSION Metabolite biomarkers can be combined with the established biomarkers of placental growth factor, mean arterial pressure, and uterine artery pulsatility index to improve the biomarker component of early-pregnancy preterm preeclampsia prediction tests. Classification of the pregnant women according to the maternal characteristics of body mass index and/or race proved instrumental in achieving improved prediction. This suggests that maternal phenotyping can have a role in improving the prediction of obstetrical syndromes such as preeclampsia.
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Affiliation(s)
- Grégoire Thomas
- SQU4RE, Lokeren, Belgium (Dr Thomas); Metabolomic Diagnostics, Cork, Ireland (Drs Thomas, Panigassi, and Tuytten)
| | - Argyro Syngelaki
- The Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, United Kingdom (Drs Syngelaki, Hamed, Perez-Montaño, and Nicolaides)
| | - Karam Hamed
- The Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, United Kingdom (Drs Syngelaki, Hamed, Perez-Montaño, and Nicolaides)
| | - Anais Perez-Montaño
- The Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, United Kingdom (Drs Syngelaki, Hamed, Perez-Montaño, and Nicolaides)
| | - Ana Panigassi
- Metabolomic Diagnostics, Cork, Ireland (Drs Thomas, Panigassi, and Tuytten)
| | - Robin Tuytten
- Metabolomic Diagnostics, Cork, Ireland (Drs Thomas, Panigassi, and Tuytten).
| | - Kypros H Nicolaides
- The Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, United Kingdom (Drs Syngelaki, Hamed, Perez-Montaño, and Nicolaides)
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Villar J, Ochieng R, Gunier RB, Papageorghiou AT, Rauch S, McGready R, Gauglitz JM, Barros FC, Vatish M, Fernandes M, Zammit V, Carrara VI, Munim S, Craik R, Barsosio HC, Carvalho M, Berkley JA, Ismail LIC, Norris SA, Tshivuila-Matala COO, Nosten F, Ohuma EO, Stein A, Lambert A, Winsey A, Uauy R, Eskenazi B, Bhutta ZA, Kennedy SH. Association between fetal abdominal growth trajectories, maternal metabolite signatures early in pregnancy, and childhood growth and adiposity: prospective observational multinational INTERBIO-21st fetal study. Lancet Diabetes Endocrinol 2022; 10:710-719. [PMID: 36030799 PMCID: PMC9622423 DOI: 10.1016/s2213-8587(22)00215-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/07/2022] [Accepted: 07/12/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Obesity predominantly affects populations in high-income countries and those countries facing epidemiological transition. The risk of childhood obesity is increased among infants who had overweight or obesity at birth, but in low-resource settings one in five infants are born small for gestational age. We aimed to study the relationships between: (1) maternal metabolite signatures; (2) fetal abdominal growth; and (3) postnatal growth, adiposity, and neurodevelopment. METHODS In the prospective, multinational, observational INTERBIO-21st fetal study, conducted in maternity units in Pelotas (Brazil), Nairobi (Kenya), Karachi (Pakistan), Soweto (South Africa), Mae Sot (Thailand), and Oxford (UK), we enrolled women (≥18 years, with a BMI of less than 35 kg/m2, natural conception, and a singleton pregnancy) who initiated antenatal care before 14 weeks' gestation. Ultrasound scans were performed every 5±1 weeks until delivery to measure fetal growth and feto-placental blood flow, and we used finite mixture models to derive growth trajectories of abdominal circumference. The infants' health, growth, and development were monitored from birth to age 2 years. Early pregnancy maternal blood and umbilical cord venous blood samples were collected for untargeted metabolomic analysis. FINDINGS From Feb 8, 2012, to Nov 30, 2019, we enrolled 3598 pregnant women and followed up their infants to 2 years of age. We identified four ultrasound-derived trajectories of fetal abdominal circumference growth that accelerated or decelerated within a crucial 20-25 week gestational age window: faltering growth, early accelerating growth, late accelerating growth, and median growth tracking. These distinct phenotypes had matching feto-placental blood flow patterns throughout pregnancy, and different growth, adiposity, vision, and neurodevelopment outcomes in early childhood. There were 709 maternal metabolites with positive effect for the faltering growth phenotype and 54 for the early accelerating growth phenotype; 31 maternal metabolites had a negative effect for the faltering growth phenotype and 76 for the early accelerating growth phenotype. Metabolites associated with the faltering growth phenotype had statistically significant odds ratios close to 1·5 (ie, suggesting upregulation of metabolic pathways of impaired fetal growth). The metabolites had a reciprocal relationship with the early accelerating growth phenotype, with statistically significant odds ratios close to 0.6 (ie, suggesting downregulation of fetal growth acceleration). The maternal metabolite signatures included 5-hydroxy-eicosatetraenoic acid, and 11 phosphatidylcholines linked to oxylipin or saturated fatty acid sidechains. The fungicide, chlorothalonil, was highly abundant in the early accelerating growth phenotype group. INTERPRETATION Early pregnancy lipid biology associated with fetal abdominal growth trajectories is an indicator of patterns of growth, adiposity, vision, and neurodevelopment up to the age of 2 years. Our findings could contribute to the earlier identification of infants at risk of obesity. FUNDING Bill & Melinda Gates Foundation.
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Affiliation(s)
- Jose Villar
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK.
| | | | - Robert B Gunier
- Center for Environmental Research and Community Health, School of Public Health, University of California, Berkeley, CA, USA
| | - Aris T Papageorghiou
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Stephen Rauch
- Center for Environmental Research and Community Health, School of Public Health, University of California, Berkeley, CA, USA
| | - Rose McGready
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
| | | | - Fernando C Barros
- Programa de Pós-Graduação em Saúde e Comportamento, Universidade Católica de Pelotas, Pelotas, Brazil
| | - Manu Vatish
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Michelle Fernandes
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Medical Research Council Lifecourse Epidemiology Centre & Human Development and Health Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Victor Zammit
- Biomedical Sciences, Translational & Experimental Medicine, Warwick Medical School, University of Warwick, Coventry, UK
| | - Verena I Carrara
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
| | - Shama Munim
- Department of Obstetrics and Gynaecology, Division of Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Rachel Craik
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK
| | - Hellen C Barsosio
- Kenya Medical Research Institute-Coast Centre for Geographical Medicine and Research, University of Oxford, Kilifi, Kenya
| | - Maria Carvalho
- Department of Obstetrics & Gynaecology, Faculty of Health Sciences, Aga Khan University Hospital, Nairobi, Kenya
| | - James A Berkley
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Kenya Medical Research Institute-Coast Centre for Geographical Medicine and Research, University of Oxford, Kilifi, Kenya
| | - Leila I Cheikh Ismail
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Clinical Nutrition and Dietetics Department, University of Sharjah, Sharjah, United Arab Emirates
| | - Shane A Norris
- South African Medical Research Institute Developmental Pathways For Health Research Unit, Department of Paediatrics & Child Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Chrystelle O O Tshivuila-Matala
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; South African Medical Research Institute Developmental Pathways For Health Research Unit, Department of Paediatrics & Child Health, University of the Witwatersrand, Johannesburg, South Africa; Health, Nutrition & Population Global Practice, World Bank Group, Washington, DC, USA
| | - Francois Nosten
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
| | - Eric O Ohuma
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Maternal, Adolescent, Reproductive & Child Health Centre, London School of Hygiene & Tropical Medicine, London, UK
| | - Alan Stein
- Department of Psychiatry, University of Oxford, Oxford, UK; Medical Research Council and Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; African Health Research Institute, KwaZulu-Natal, South Africa
| | - Ann Lambert
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Adele Winsey
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK
| | - Ricardo Uauy
- Department of Nutrition and Public Health Interventions Research, London School of Hygiene and Tropical Medicine, London, UK
| | - Brenda Eskenazi
- Center for Environmental Research and Community Health, School of Public Health, University of California, Berkeley, CA, USA
| | - Zulfiqar A Bhutta
- Centre of Excellence in Women and Child Health, Aga Khan University, Nairobi, Kenya; Center for Global Child Health, Hospital for Sick Children, Toronto, ON, Canada
| | - Stephen H Kennedy
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
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The Exploration of Fetal Growth Restriction Based on Metabolomics: A Systematic Review. Metabolites 2022; 12:metabo12090860. [PMID: 36144264 PMCID: PMC9501562 DOI: 10.3390/metabo12090860] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/03/2022] [Accepted: 09/04/2022] [Indexed: 11/30/2022] Open
Abstract
Fetal growth restriction (FGR) is a common complication of pregnancy and a significant cause of neonatal morbidity and mortality. The adverse effects of FGR can last throughout the entire lifespan and increase the risks of various diseases in adulthood. However, the etiology and pathogenesis of FGR remain unclear. This study comprehensively reviewed metabolomics studies related with FGR in pregnancy to identify potential metabolic biomarkers and pathways. Relevant articles were searched through two online databases (PubMed and Web of Science) from January 2000 to July 2022. The reported metabolites were systematically compared. Pathway analysis was conducted through the online MetaboAnalyst 5.0 software. For humans, a total of 10 neonatal and 14 maternal studies were included in this review. Several amino acids, such as alanine, valine, and isoleucine, were high frequency metabolites in both neonatal and maternal studies. Meanwhile, several pathways were suggested to be involved in the development of FGR, such as arginine biosynthesis, arginine, and proline metabolism, glyoxylate and dicarboxylate metabolism, and alanine, aspartate, and glutamate metabolism. In addition, we also included 8 animal model studies, in which three frequently reported metabolites (glutamine, phenylalanine, and proline) were also present in human studies. In general, this study summarized several metabolites and metabolic pathways which may help us to better understand the underlying metabolic mechanisms of FGR.
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Guo P, Furnary T, Vasiliou V, Yan Q, Nyhan K, Jones DP, Johnson CH, Liew Z. Non-targeted metabolomics and associations with per- and polyfluoroalkyl substances (PFAS) exposure in humans: A scoping review. ENVIRONMENT INTERNATIONAL 2022; 162:107159. [PMID: 35231839 PMCID: PMC8969205 DOI: 10.1016/j.envint.2022.107159] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 01/29/2022] [Accepted: 02/21/2022] [Indexed: 05/13/2023]
Abstract
OBJECTIVE To summarize the application of non-targeted metabolomics in epidemiological studies that assessed metabolite and metabolic pathway alterations associated with per- and polyfluoroalkyl substances (PFAS) exposure. RECENT FINDINGS Eleven human studies published before April 1st, 2021 were identified through database searches (PubMed, Dimensions, Web of Science Core Collection, Embase, Scopus), and citation chaining (Citationchaser). The sample sizes of these studies ranged from 40 to 965, involving children and adolescents (n = 3), non-pregnant adults (n = 5), or pregnant women (n = 3). High-resolution liquid chromatography-mass spectrometry was the primary analytical platform to measure both PFAS and metabolome. PFAS were measured in either plasma (n = 6) or serum (n = 5), while metabolomic profiles were assessed using plasma (n = 6), serum (n = 4), or urine (n = 1). Four types of PFAS (perfluorooctane sulfonate(n = 11), perfluorooctanoic acid (n = 10), perfluorohexane sulfonate (n = 9), perfluorononanoic acid (n = 5)) and PFAS mixtures (n = 7) were the most studied. We found that alterations to tryptophan metabolism and the urea cycle were most reported PFAS-associated metabolomic signatures. Numerous lipid metabolites were also suggested to be associated with PFAS exposure, especially key metabolites in glycerophospholipid metabolism which is critical for biological membrane functions, and fatty acids and carnitines which are relevant to the energy supply pathway of fatty acid oxidation. Other important metabolome changes reported included the tricarboxylic acid (TCA) cycle regarding energy generation, and purine and pyrimidine metabolism in cellular energy systems. CONCLUSIONS There is growing interest in using non-targeted metabolomics to study the human physiological changes associated with PFAS exposure. Multiple PFAS were reported to be associated with alterations in amino acid and lipid metabolism, but these results are driven by one predominant type of pathway analysis thus require further confirmation. Standardizing research methods and reporting are recommended to facilitate result comparison. Future studies should consider potential differences in study methodology, use of prospective design, and influence from confounding bias and measurement errors.
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Affiliation(s)
- Pengfei Guo
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, USA; Yale Center for Perinatal, Pediatric, and Environmental Epidemiology, Yale School of Public Health, New Haven, USA
| | - Tristan Furnary
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, USA
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, USA
| | - Qi Yan
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, USA
| | - Kate Nyhan
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, USA; Harvey Cushing / John Hay Whitney Medical Library, Yale University, New Haven, USA
| | - Dean P Jones
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, USA; Department of Biochemistry, Emory University School of Medicine, Atlanta, USA
| | - Caroline H Johnson
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, USA
| | - Zeyan Liew
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, USA; Yale Center for Perinatal, Pediatric, and Environmental Epidemiology, Yale School of Public Health, New Haven, USA.
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6
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Byeon SK, Khanam R, Rahman S, Hasan T, Rizvi SJR, Madugundu AK, Ramarajan MG, Jung JH, Chowdhury NH, Ahmed S, Raqib R, Kim KP, Piazza AL, Rinaldo P, Pandey A, Baqui AH, Amanhi Bio-Banking Study Group. Maternal serum lipidomics identifies lysophosphatidic acid as a predictor of small for gestational age neonates. Mol Omics 2021; 17:956-966. [PMID: 34519752 DOI: 10.1039/d1mo00131k] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
To discover lipidomic alterations during pregnancy in mothers who subsequently delivered small for gestational age (SGA) neonates and identify predictive lipid markers that can help recognize and manage these mothers, we carried out untargeted lipidomics on maternal serum samples collected between 24-28 weeks of gestation. We used a nested case-control study design and serum from mothers who delivered SGA and appropriate for gestational age babies. We applied untargeted lipidomics using mass spectrometry to characterize lipids and discover changes associated with SGA births during pregnancy. Multivariate pattern recognition software Collaborative Laboratory Integrated Reports (CLIR) was used for the post-analytical recognition of range differences in lipid ratios that could differentiate between SGA and control mothers and their integration for complete separation between the two groups. Here, we report changes in lipids from serum collected during pregnancy in mothers who delivered SGA neonates. In contrast to normal pregnancies where lysophosphatidic acid increased over the course of the pregnancy owing to increased activity of lysophospholipase D, we observed a decrease (32%; P = 0.05) of 20:4-lysophosphatidic acid in SGA mothers, which could potentially compromise fetal growth and development. Integration of lipid ratios in an interpretive tool (CLIR) could completely separate SGA mothers from controls demonstrating the power of untargeted lipidomic analyses for identifying novel predictive biomarkers. Additional studies are required for further assessment of the lipid biomarkers identified in this report.
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Affiliation(s)
- Seul Kee Byeon
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA. .,McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Rasheda Khanam
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
| | | | - Tarik Hasan
- Projahnmo Research Foundation, Dhaka, Bangladesh
| | | | - Anil K Madugundu
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA. .,Institute of Bioinformatics, International Technology Park, Bangalore 560006, India.,Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences, Hosur Road, Bangalore, 560029, Karnataka, India
| | - Madan Gopal Ramarajan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA. .,Institute of Bioinformatics, International Technology Park, Bangalore 560006, India.,Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Jae Hun Jung
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA. .,McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,Department of Chemistry, Kyung Hee University, Yongin 17104, South Korea
| | | | | | - Rubhana Raqib
- Division of Infectious Diseases, International Centre for Diarrhoeal Disease Research, Bangladesh
| | - Kwang Pyo Kim
- Department of Chemistry, Kyung Hee University, Yongin 17104, South Korea
| | - Amy L Piazza
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA.
| | - Piero Rinaldo
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA.
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA. .,McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Abdullah H Baqui
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
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