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CC BY-NC-ND 4.0 · AJP Rep 2020; 10(01): e68-e77
DOI: 10.1055/s-0040-1702928
Case Report
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Metabolomic Pathways Predicting Labor Dystocia by Maternal Body Mass Index

Nicole S. Carlson 
1   Emory University Nell Hodgson Woodruff School of Nursing, Atlanta, Georgia
,
Jennifer K. Frediani
1   Emory University Nell Hodgson Woodruff School of Nursing, Atlanta, Georgia
,
Elizabeth J. Corwin
2   Department of Physiology, Columbia University School of Nursing, New York, New York
,
Anne Dunlop
3   Departments of Family and Preventive Medicine, Epidemiology, and Nursing, Emory University, Atlanta, Georgia
,
Dean Jones
4   Division of Pulmonary, Allergy, and Critical Care, Department of Medicine, Emory University, Atlanta, Georgia
› Institutsangaben Funding Drs. Nicole Carlson and Jennifer Frediani were supported by grant number K01NR016984 from the National Institute of Nursing Research during manuscript production.
This manuscript was supported by funding from the National Institutes of Health National Institutes of Nursing Research (R01NR014800), the Office of the Director [3R01NR014800, UG3OD023318], the National Institute of Environmental Health Sciences (P50ES926071), and the US Environmental Protection Agency (83615301 Mod. 2).
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Publikationsverlauf

22. Juli 2019

20. Dezember 2019

Publikationsdatum:
04. März 2020 (online)

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Abstract

Objectives The purpose of this study was to evaluate the metabolic pathways activated in the serum of African-American women during late pregnancy that predicted term labor dystocia.

Study Design Matched case–control study (n = 97; 48 cases of term labor dystocia and 49 normal labor progression controls) with selection based on body mass index (BMI) at hospital admission and maternal age. Late pregnancy serum samples were analyzed using ultra-high-resolution metabolomics. Differentially expressed metabolic features and pathways between cases experiencing term labor dystocia and normal labor controls were evaluated in the total sample, among women who were obese at the time of labor (BMI ≥ 30 kg/m2), and among women who were not obese.

Results Labor dystocia was predicted by different metabolic pathways in late pregnancy serum among obese (androgen/estrogen biosynthesis) versus nonobese African-American women (fatty acid activation, steroid hormone biosynthesis, bile acid biosynthesis, glycosphingolipid metabolism). After adjusting for maternal BMI and age in the total sample, labor dystocia was predicted by tryptophan metabolic pathways in addition to C21 steroid hormone, glycosphingolipid, and androgen/estrogen metabolism.

Conclusion Metabolic pathways consistent with lipotoxicity, steroid hormone production, and tryptophan metabolism in late pregnancy serum were significantly associated with term labor dystocia in African-American women.

Keywords

labor dystocia - obesity - mechanisms - metabolomics - parturition

Declaration of Conflicting Interests per International Committee of Medical Journal Editors Guidelines

The authors declare that there is no conflict of interest.


Supplementary Material

 

  • References

  • 1 Hamilton BE, Martin JA, Osterman MJ. Births: preliminary data for 2015. Natl Vital Stat Rep 2016; 65 (03) 1-15
    PubMedGoogle Scholar
  • 2 The Joint Commission. Specifications Manual for Joint Commission National Quality Measures. Joint Commission 2016. Available at: https://manual.jointcommission.org/releases/TJC2016A/MIF0167.html
  • 3 Osterman MJ, Martin JA. Trends in low-risk cesarean delivery in the United States, 1990-2013. Natl Vital Stat Rep 2014; 63 (06) 1-16
    PubMedGoogle Scholar
  • 4 Martin JA, Hamilton BE, Osterman MJ, Driscoll AK, Drake P. Births: Final data for 2017. Hyattsville, MD: National Center for Health Statistics; 2018
    Google Scholar
  • 5 Clark EA, Silver RM. Long-term maternal morbidity associated with repeat cesarean delivery. Am J Obstet Gynecol 2011; 205 (6, Suppl): S2-S10
    CrossrefPubMedGoogle Scholar
  • 6 Silver RM, Landon MB, Rouse DJ. , et al; National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. Maternal morbidity associated with multiple repeat cesarean deliveries. Obstet Gynecol 2006; 107 (06) 1226-1232
    CrossrefPubMedGoogle Scholar
  • 7 Liu S, Liston RM, Joseph KS, Heaman M, Sauve R, Kramer MS. ; Maternal Health Study Group of the Canadian Perinatal Surveillance System. Maternal mortality and severe morbidity associated with low-risk planned cesarean delivery versus planned vaginal delivery at term. CMAJ 2007; 176 (04) 455-460
    CrossrefPubMedGoogle Scholar
  • 8 Petersen EE, Davis NL, Goodman D. , et al. Vital signs: pregnancy-related deaths, United States, 2011–2015, and strategies for prevention, 13 states, 2013–2017. MMWR Morb Mortal Wkly Rep 2019; 68 (18) 423-429
    PubMedGoogle Scholar
  • 9 Spong CY, Berghella V, Wenstrom KD, Mercer BM, Saade GR. Preventing the first cesarean delivery: summary of a joint Eunice Kennedy Shriver National Institute of Child Health and Human Development, Society for Maternal-Fetal Medicine, and American College of Obstetricians and Gynecologists Workshop. Obstet Gynecol 2012; 120 (05) 1181-1193
    CrossrefPubMedGoogle Scholar
  • 10 Barber EL, Lundsberg LS, Belanger K, Pettker CM, Funai EF, Illuzzi JL. Indications contributing to the increasing cesarean delivery rate. Obstet Gynecol 2011; 118 (01) 29-38
    CrossrefPubMedGoogle Scholar
  • 11 Zhang J, Landy HJ, Branch DW. , et al; Consortium on Safe Labor. Contemporary patterns of spontaneous labor with normal neonatal outcomes. Obstet Gynecol 2010; 116 (06) 1281-1287
    CrossrefPubMedGoogle Scholar
  • 12 Neal JL, Lowe NK, Patrick TE, Cabbage LA, Corwin EJ. What is the slowest-yet-normal cervical dilation rate among nulliparous women with spontaneous labor onset?. J Obstet Gynecol Neonatal Nurs 2010; 39 (04) 361-369
    CrossrefPubMedGoogle Scholar
  • 13 Kominiarek MA, Zhang J, Vanveldhuisen P, Troendle J, Beaver J, Hibbard JU. Contemporary labor patterns: the impact of maternal body mass index. Am J Obstet Gynecol 2011; 205 (03) 244.e1-244.e8
    CrossrefPubMedGoogle Scholar
  • 14 Cedergren MI. Non-elective caesarean delivery due to ineffective uterine contractility or due to obstructed labour in relation to maternal body mass index. Eur J Obstet Gynecol Reprod Biol 2009; 145 (02) 163-166
    CrossrefPubMedGoogle Scholar
  • 15 Chu SY, Kim SY, Schmid CH. , et al. Maternal obesity and risk of cesarean delivery: a meta-analysis. Obes Rev 2007; 8 (05) 385-394
    CrossrefPubMedGoogle Scholar
  • 16 Vahratian A, Siega-Riz AM, Savitz DA, Zhang J. Maternal pre-pregnancy overweight and obesity and the risk of cesarean delivery in nulliparous women. Ann Epidemiol 2005; 15 (07) 467-474
    CrossrefPubMedGoogle Scholar
  • 17 Hautakangas T, Palomäki O, Eidstø K, Huhtala H, Uotila J. Impact of obesity and other risk factors on labor dystocia in term primiparous women: a case control study. BMC Pregnancy Childbirth 2018; 18 (01) 304
    CrossrefPubMedGoogle Scholar
  • 18 Tse WT, Chaemsaithong P, Chan WWY. , et al. Labor progress determined by ultrasound is different in women requiring cesarean delivery from those who experience a vaginal delivery following induction of labor. Am J Obstet Gynecol 2019; 221 (04) 335.e1-335.e18
    CrossrefPubMedGoogle Scholar
  • 19 Parkington HC, Stevenson J, Tonta MA. , et al. Diminished hERG K+ channel activity facilitates strong human labour contractions but is dysregulated in obese women. Nat Commun 2014; 5: 4108
    CrossrefPubMedGoogle Scholar
  • 20 Shmygol A, Noble K, Wray S. Depletion of membrane cholesterol eliminates the Ca2+-activated component of outward potassium current and decreases membrane capacitance in rat uterine myocytes. J Physiol 2007; 581 (Pt 2): 445-456
    CrossrefPubMedGoogle Scholar
  • 21 Jie Zhang, Kendrick A, Quenby S, Wray S. Contractility and calcium signaling of human myometrium are profoundly affected by cholesterol manipulation: implications for labor?. Reprod Sci 2007; 14 (05) 456-466
    CrossrefPubMedGoogle Scholar
  • 22 Gam CMBF, Larsen LH, Mortensen OH. , et al. Unchanged mitochondrial phenotype, but accumulation of lipids in the myometrium in obese pregnant women. J Physiol 2017; 595 (23) 7109-7122
    CrossrefPubMedGoogle Scholar
  • 23 Frayn KN. Metabolic Regulation: A Human Perspective. 3rd edition. Chichester, U.K.; Malden, MA: Wiley-Blackwell Pub.; 2010
    Google Scholar
  • 24 Carlson NS, Hernandez TL, Hurt KJ. Parturition dysfunction in obesity: time to target the pathobiology. Reprod Biol Endocrinol 2015; 13 (01) 135
    CrossrefPubMedGoogle Scholar
  • 25 Jones DP. Redefining oxidative stress. Antioxid Redox Signal 2006; 8 (9-10): 1865-1879
    CrossrefPubMedGoogle Scholar
  • 26 Abu Bakar MH, Sarmidi MR, Cheng KK. , et al. Metabolomics - the complementary field in systems biology: a review on obesity and type 2 diabetes. Mol Biosyst 2015; 11 (07) 1742-1774
    CrossrefPubMedGoogle Scholar
  • 27 Jones DP, Park Y, Ziegler TR. Nutritional metabolomics: progress in addressing complexity in diet and health. Annu Rev Nutr 2012; 32: 183-202
    CrossrefPubMedGoogle Scholar
  • 28 Patti GJ, Yanes O, Siuzdak G. Innovation: Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 2012; 13 (04) 263-269
    PubMedGoogle Scholar
  • 29 Xie B, Waters MJ, Schirra HJ. Investigating potential mechanisms of obesity by metabolomics. J Biomed Biotechnol 2012; 2012: 805683
    PubMedGoogle Scholar
  • 30 Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014; 311 (08) 806-814
    CrossrefPubMedGoogle Scholar
  • 31 Martin JA, Hamilton BE, Osterman MJK, Curtin SC, Matthews TJ. Births: Final Data for 2013. Hyattsville, MD: National Center for Health Statistics; 2015
    Google Scholar
  • 32 Aron DC, Gordon HS, DiGiuseppe DL, Harper DL, Rosenthal GE. Variations in risk-adjusted cesarean delivery rates according to race and health insurance. Med Care 2000; 38 (01) 35-44
    CrossrefPubMedGoogle Scholar
  • 33 Cribbs SK, Park Y, Guidot DM. , et al. Metabolomics of bronchoalveolar lavage differentiate healthy HIV-1-infected subjects from controls. AIDS Res Hum Retroviruses 2014; 30 (06) 579-585
    CrossrefPubMedGoogle Scholar
  • 34 Fitzpatrick AM, Park Y, Brown LA, Jones DP. Children with severe asthma have unique oxidative stress-associated metabolomic profiles. J Allergy Clin Immunol 2014; 133 (01) 258-61.e1 , 8
    CrossrefPubMedGoogle Scholar
  • 35 Roede JR, Uppal K, Park Y. , et al. Serum metabolomics of slow vs. rapid motor progression Parkinson's disease: a pilot study. PLoS One 2013; 8 (10) e77629
    CrossrefPubMedGoogle Scholar
  • 36 Frediani JK, Jones DP, Tukvadze N. , et al. Plasma metabolomics in human pulmonary tuberculosis disease: a pilot study. PLoS One 2014; 9 (10) e108854
    CrossrefPubMedGoogle Scholar
  • 37 Neujahr DC, Uppal K, Force SD. , et al. Bile acid aspiration associated with lung chemical profile linked to other biomarkers of injury after lung transplantation. Am J Transplant 2014; 14 (04) 841-848
    CrossrefPubMedGoogle Scholar
  • 38 Corwin EJ, Hogue CJ, Pearce B. , et al. Protocol for the Emory University African American Vaginal, Oral, and Gut Microbiome in Pregnancy Cohort Study. BMC Pregnancy Childbirth 2017; 17 (01) 161
    CrossrefPubMedGoogle Scholar
  • 39 Pearce N. Analysis of matched case-control studies. BMJ 2016; 352: i969
    CrossrefPubMedGoogle Scholar
  • 40 Hellmuth C, Lindsay KL, Uhl O. , et al. Association of maternal prepregnancy BMI with metabolomic profile across gestation. Int J Obes 2017; 41 (01) 159-169
    CrossrefPubMedGoogle Scholar
  • 41 Fattuoni C, Mandò C, Palmas F. , et al. Preliminary metabolomics analysis of placenta in maternal obesity. Placenta 2018; 61: 89-95
    CrossrefPubMedGoogle Scholar
  • 42 Rogers AJG, Harper LM, Mari G. A conceptual framework for the impact of obesity on risk of cesarean delivery. Am J Obstet Gynecol 2018; 219 (04) 356-363
    CrossrefPubMedGoogle Scholar
  • 43 Snowden JM, Tilden EL, Odden MC. Formulating and answering high-impact causal questions in physiologic childbirth science: concepts and assumptions. J Midwifery Womens Health 2018; 63 (06) 721-730
    CrossrefPubMedGoogle Scholar
  • 44 Li S, Park Y, Duraisingham S. , et al. Predicting network activity from high throughput metabolomics. PLOS Comput Biol 2013; 9 (07) e1003123
    CrossrefPubMedGoogle Scholar
  • 45 Soltow QA, Zeanah EH, Lira VA, Criswell DS. Cessation of cyclic stretch induces atrophy of C2C12 myotubes. Biochem Biophys Res Commun 2013; 434 (02) 316-321
    CrossrefPubMedGoogle Scholar
  • 46 Hoffman JM, Soltow QA, Li S, Sidik A, Jones DP, Promislow DE. Effects of age, sex, and genotype on high-sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster. Aging Cell 2014; 13 (04) 596-604
    CrossrefPubMedGoogle Scholar
  • 47 Yu T, Park Y, Li S, Jones DP. Hybrid feature detection and information accumulation using high-resolution LC-MS metabolomics data. J Proteome Res 2013; 12 (03) 1419-1427
    CrossrefPubMedGoogle Scholar
  • 48 Uppal K, Soltow QA, Strobel FH. , et al. xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data. BMC Bioinformatics 2013; 14: 15
    CrossrefPubMedGoogle Scholar
  • 49 Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007; 8 (01) 118-127
    CrossrefPubMedGoogle Scholar
  • 50 Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004 ; doi: 10.2202/1544-6115.1027
    PubMedGoogle Scholar
  • 51 Hochberg Y, Benjamini Y. More powerful procedures for multiple significance testing. Stat Med 1990; 9 (07) 811-818
    CrossrefPubMedGoogle Scholar
  • 52 Uppal K, Walker DI, Liu K, Li S, Go YM, Jones DP. Computational metabolomics: a framework for the Million Metabolome. Chem Res Toxicol 2016; 29 (12) 1956-1975
    CrossrefPubMedGoogle Scholar
  • 53 Serkova NJ, Jackman M, Brown JL. , et al. Metabolic profiling of livers and blood from obese Zucker rats. J Hepatol 2006; 44 (05) 956-962
    CrossrefPubMedGoogle Scholar
  • 54 Desert R, Canlet C, Costet N, Cordier S, Bonvallot N. Impact of maternal obesity on the metabolic profiles of pregnant women and their offspring at birth. Metabolomics 2015; 11: 1896-1907
    CrossrefPubMedGoogle Scholar
  • 55 Zhou Y, Qiu L, Xiao Q. , et al. Obesity and diabetes related plasma amino acid alterations. Clin Biochem 2013; 46 (15) 1447-1452
    CrossrefPubMedGoogle Scholar
  • 56 Norman SM, Tuuli MG, Odibo AO, Caughey AB, Roehl KA, Cahill AG. The effects of obesity on the first stage of labor. Obstet Gynecol 2012; 120 (01) 130-135
    CrossrefPubMedGoogle Scholar
  • 57 Kominiarek MA, Vanveldhuisen P, Hibbard J. , et al; Consortium on Safe Labor. The maternal body mass index: a strong association with delivery route. Am J Obstet Gynecol 2010; 203 (03) 264.e1-264.e7
    CrossrefPubMedGoogle Scholar
  • 58 Pevzner L, Powers BL, Rayburn WF, Rumney P, Wing DA. Effects of maternal obesity on duration and outcomes of prostaglandin cervical ripening and labor induction. Obstet Gynecol 2009; 114 (06) 1315-1321
    CrossrefPubMedGoogle Scholar
  • 59 Berkane N, Liere P, Oudinet JP. , et al. From pregnancy to preeclampsia: a key role for estrogens. Endocr Rev 2017; 38 (02) 123-144
    CrossrefPubMedGoogle Scholar
  • 60 Palmer BF, Clegg DJ. The sexual dimorphism of obesity. Mol Cell Endocrinol 2015; 402: 113-119
    CrossrefPubMedGoogle Scholar
  • 61 Wan J, Hu Z, Zeng K. , et al. The reduction in circulating levels of estrogen and progesterone in women with preeclampsia. Pregnancy Hypertens 2018; 11: 18-25
    CrossrefPubMedGoogle Scholar
  • 62 Zhang P, Yang H, Feng Y. , et al. Polymorphisms in sex hormone metabolism genes and risk of preeclampsia in Taiyuan, China. Gynecol Obstet Invest 2018; 83 (02) 179-186
    CrossrefPubMedGoogle Scholar
  • 63 Gostynski M, Gutzwiller F, Kuulasmaa K. , et al; WHO MONICA Project. Analysis of the relationship between total cholesterol, age, body mass index among males and females in the WHO MONICA Project. Int J Obes Relat Metab Disord 2004; 28 (08) 1082-1090
    CrossrefPubMedGoogle Scholar
  • 64 Bodnar LM, Catov JM, Klebanoff MA, Ness RB, Roberts JM. Prepregnancy body mass index and the occurrence of severe hypertensive disorders of pregnancy. Epidemiology 2007; 18 (02) 234-239
    CrossrefPubMedGoogle Scholar
  • 65 Mesiano S. Endocrinology of human pregnancy and fetal-placental neuroendocrine development. In: Strauss J, Barbieri R, Gargiulo A. , eds. Yen & Jaffe's Reproductive Endocrinology. 8th edition. Elsevier; 2019: 1001
    Google Scholar
  • 66 Zhang S, Chen G, Li N. , et al. CYP2J2 overexpression ameliorates hyperlipidemia via increased fatty acid oxidation mediated by the AMPK pathway. Obesity 2015; 23 (07) 1401-1413
    CrossrefPubMedGoogle Scholar
  • 67 Badoud F, Perreault M, Zulyniak MA, Mutch DM. Molecular insights into the role of white adipose tissue in metabolically unhealthy normal weight and metabolically healthy obese individuals. FASEB J 2015; 29 (03) 748-758
    CrossrefPubMedGoogle Scholar
  • 68 Xu YH, Barnes S, Sun Y, Grabowski GA. Multi-system disorders of glycosphingolipid and ganglioside metabolism. J Lipid Res 2010; 51 (07) 1643-1675
    CrossrefPubMedGoogle Scholar
  • 69 Muir R, Ballan J, Clifford B. , et al. Modelling maternal obesity: the effects of a chronic high-fat, high-cholesterol diet on uterine expression of contractile-associated proteins and ex vivo contractile activity during labour in the rat. Clin Sci (Lond) 2016; 130 (03) 183-192
    CrossrefPubMedGoogle Scholar
  • 70 Boyle KE, Patinkin ZW, Shapiro ALB. , et al. Maternal obesity alters fatty acid oxidation, AMPK activity, and associated DNA methylation in mesenchymal stem cells from human infants. Mol Metab 2017; 6 (11) 1503-1516
    CrossrefPubMedGoogle Scholar
  • 71 Badawy AA. Tryptophan metabolism, disposition and utilization in pregnancy. Biosci Rep 2015; 35 (05) e00261
    CrossrefPubMedGoogle Scholar
  • 72 Ryding EL, Wijma B, Wijma K, Rydhström H. Fear of childbirth during pregnancy may increase the risk of emergency cesarean section. Acta Obstet Gynecol Scand 1998; 77 (05) 542-547
    CrossrefPubMedGoogle Scholar
  • 73 Ogunyemi D, Jovanovski A, Liu J. , et al. The contribution of untreated and treated anxiety and depression to prenatal, intrapartum, and neonatal outcomes. AJP Rep 2018; 8 (03) e146-e157
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 74 Huang J, Li HJ, Wang J. , et al. Prenatal emotion management improves obstetric outcomes: a randomized control study. Int J Clin Exp Med 2015; 8 (06) 9667-9675
    PubMedGoogle Scholar
  • 75 Zhao Y, Munro-Kramer ML, Shi S, Wang J, Luo J. A randomized controlled trial: effects of a prenatal depression intervention on perinatal outcomes among Chinese high-risk pregnant women with medically defined complications. Arch Women Ment Health 2017; 20 (02) 333-344
    CrossrefPubMedGoogle Scholar
  • 76 Cordeaux Y, Pasupathy D, Bacon J, Charnock-Jones DS, Smith GC. Characterization of serotonin receptors in pregnant human myometrium. J Pharmacol Exp Ther 2009; 328 (03) 682-691
    CrossrefPubMedGoogle Scholar
  • 77 Horgan RP, Clancy OH, Myers JE, Baker PN. An overview of proteomic and metabolomic technologies and their application to pregnancy research. BJOG 2009; 116 (02) 173-181
    CrossrefPubMedGoogle Scholar
  • 78 Bahado-Singh RO, Akolekar R, Mandal R. , et al. First-trimester metabolomic detection of late-onset preeclampsia. Am J Obstet Gynecol 2013; 208 (01) 58.e1-58.e7
    CrossrefPubMedGoogle Scholar
  • 79 Heazell AE, Brown M, Worton SA, Dunn WB. Review: The effects of oxygen on normal and pre-eclamptic placental tissue--insights from metabolomics. Placenta 2011; 32 (Suppl 2): S119-S124
    CrossrefPubMedGoogle Scholar
  • 80 Bahado-Singh RO, Akolekar R, Mandal R. , et al. Metabolomics and first-trimester prediction of early-onset preeclampsia. J Matern Fetal Neonatal Med 2012; 25 (10) 1840-1847
    CrossrefPubMedGoogle Scholar
  • 81 Heazell AE, Brown M, Dunn WB. , et al. Analysis of the metabolic footprint and tissue metabolome of placental villous explants cultured at different oxygen tensions reveals novel redox biomarkers. Placenta 2008; 29 (08) 691-698
    CrossrefPubMedGoogle Scholar
  • 82 Horgan RP, Broadhurst DI, Dunn WB. , et al. Changes in the metabolic footprint of placental explant-conditioned medium cultured in different oxygen tensions from placentas of small for gestational age and normal pregnancies. Placenta 2010; 31 (10) 893-901
    CrossrefPubMedGoogle Scholar
  • 83 Powell KL, Carrozzi A, Stephens AS. , et al. Utility of metabolic profiling of serum in the diagnosis of pregnancy complications. Placenta 2018; 66: 65-73
    CrossrefPubMedGoogle Scholar
  • 84 Kelly RS, Giorgio RT, Chawes BL. , et al. Applications of metabolomics in the study and management of preeclampsia; a review of the literature. Metabolomics 2017; 13 (07) 86
    CrossrefPubMedGoogle Scholar
  • 85 Kadakia R, Scholtens DM, Rouleau GW. , et al. Cord blood metabolites associated with newborn adiposity and hyperinsulinemia. J Pediatr 2018; 203: 144-149.e1
    CrossrefPubMedGoogle Scholar
  • 86 Caboni P, Meloni A, Lussu M. , et al. Urinary metabolomics of pregnant women at term: a combined GC/MS and NMR approach. J Matern Fetal Neonatal Med 2014; 27 (02) (Suppl. 02) 4-12
    CrossrefPubMedGoogle Scholar
  • 87 Meloni A, Palmas F, Barberini L. , et al. PROM and labour effects on urinary metabolome: a pilot study. Dis Markers 2018; 2018: 1042479
    PubMedGoogle Scholar
  • 88 Virgiliou C, Gika HG, Witting M. , et al. Amniotic fluid and maternal serum metabolic signatures in the second trimester associated with preterm delivery. J Proteome Res 2017; 16 (02) 898-910
    CrossrefPubMedGoogle Scholar
  • 89 Bafor EE, Rowan EG, Edrada-Ebel R. Toward understanding myometrial regulation: metabolomic investigation reveals new pathways of oxytocin and ritodrine activity on the myometrium. Reprod Sci 2017; 24 (05) 691-705
    CrossrefPubMedGoogle Scholar
  • 90 Zaki MN, Hibbard JU, Kominiarek MA. Contemporary labor patterns and maternal age. Obstet Gynecol 2013; 122 (05) 1018-1024
    CrossrefPubMedGoogle Scholar
  • 91 Harper LM, Caughey AB, Odibo AO, Roehl KA, Zhao Q, Cahill AG. Normal progress of induced labor. Obstet Gynecol 2012; 119 (06) 1113-1118
    CrossrefPubMedGoogle Scholar
  • 92 Min CJ, Ehrenthal DB, Strobino DM. Investigating racial differences in risk factors for primary cesarean delivery. Am J Obstet Gynecol 2015; 212 (06) 814.e1-814.e14
    CrossrefPubMedGoogle Scholar
  • 93 Creanga AA, Syverson C, Seed K, Callaghan WM. Pregnancy-related mortality in the United States, 2011-2013. Obstet Gynecol 2017; 130 (02) 366-373
    CrossrefPubMedGoogle Scholar
  • 94 Metcalfe A, Wick J, Ronksley P. Racial disparities in comorbidity and severe maternal morbidity/mortality in the United States: an analysis of temporal trends. Acta Obstet Gynecol Scand 2017
    PubMedGoogle Scholar
  • 95 Patel MJ, Batch BC, Svetkey LP. , et al. Race and sex differences in small-molecule metabolites and metabolic hormones in overweight and obese adults. OMICS 2013; 17 (12) 627-635
    CrossrefPubMedGoogle Scholar
  • 96 Hill M, Reed KL, Cohen WR. Oxytocin utilization for labor induction in obese and lean women. J Perinat Med 2014
    PubMedGoogle Scholar
  • 97 Walsh J, Foley M, O'Herlihy C. Dystocia correlates with body mass index in both spontaneous and induced nulliparous labors. J Matern Fetal Neonatal Med 2011; 24 (06) 817-821
    CrossrefPubMedGoogle Scholar