First Trimester Detection of Placental Disease: Challenges and Opportunities

Carolyn M. Salafia; Diana M. Thomas; Drucilla J. Roberts; Jennifer K. Straughen; Patrick M. Catalano; Gabriela Perez-Avilan

doi:10.1055/s-0036-1586508

American Journal of Perinatology, Inhaltsverzeichnis

Am J Perinatol 2016; 33(13): 1306-1312
DOI: 10.1055/s-0036-1586508

Original Article

Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

First Trimester Detection of Placental Disease: Challenges and Opportunities

Authors

Carolyn M. Salafia

¹Placental Modulation Laboratory, Institute for Basic Research, Staten Island, New York

²Division of Predictive Modeling, Placental Analytics, New Rochelle, New York
Diana M. Thomas

³Center for Quantitative Obesity Research, Montclair State University, Montclair, New Jersey
Drucilla J. Roberts

⁴Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
Jennifer K. Straughen

⁵Department of Public Health Sciences, Henry Ford Hospital, Detroit, Michigan
Patrick M. Catalano

⁶Clinical Research Unit, Case Western Reserve University, Cleveland, Ohio

⁷Center for Reproductive Health, Department of Obstetrics and Gynecology, MetroHealth Medical Center, Case Western Reserve University, Cleveland, Ohio
Gabriela Perez-Avilan

²Division of Predictive Modeling, Placental Analytics, New Rochelle, New York

Abstract

It is generally agreed that placental pathology accounts for the majority of perinatal morbidity and mortality. If a placental prodrome could be diagnosed in vivo, risk for maternal or fetal complications could be estimated and acted upon before clinical symptoms are apparent. This is especially relevant in early diagnoses of gestational diabetes mellitus, which can be controlled through carefully monitored diet and activity changes. To meet this important need, there have been increased efforts to identify early gestation biomarkers of placental dysfunction using innovative imaging technologies. Here we outline innovative quantitative markers of placental shape and their relationship to placental function, clinical implications of these quantifiers, and the most recent mathematical models that utilize placental images to delineate at risk from normal pregnancies. We propose that novel contexts of readily available placental measures and routine collection of in vivo placental images in all pregnancies may be all that are needed to advance the identification of early risk determination of complicated pregnancies from placental images.

Keywords

diabetes - obesity - placenta - fetus - ultrasound - Doppler - growth model

Volltext

Referenzen

References
1 Desoye G, van Poppel M. The feto-placental dialogue and diabesity. Best Pract Res Clin Obstet Gynaecol 2015; 29 (1) 15-23
2 Simmons D. Diabetes and obesity in pregnancy. Best Pract Res Clin Obstet Gynaecol 2011; 25 (1) 25-36
3 Rani V, Deep G, Singh RK, Palle K, Yadav UC. Oxidative stress and metabolic disorders: pathogenesis and therapeutic strategies. Life Sci 2016; 148: 183-193
4 Verdile G, Keane KN, Cruzat VF , et al. Inflammation and oxidative stress: the molecular connectivity between insulin resistance, obesity, and Alzheimer's disease. Mediators Inflamm 2015; 2015: 105828
5 Dimasuay KG, Boeuf P, Powell TL, Jansson T. Placental responses to changes in the maternal environment determine fetal growth. Front Phys 2016; 7: 12
6 Leon-Garcia SM, Roeder HA, Nelson KK , et al. Maternal obesity and sex-specific differences in placental pathology. Placenta 2016; 38: 33-40
7 Arizawa M, Nakayama M, Kidoguchi K. Correlation of placental villous immaturity and dysmaturity with clinical control of maternal diabetes [in Japanese]. Nippon Sanka Fujinka Gakkai Zasshi 1991; 43 (6) 595-602
8 Seidmann L, Suhan T, Unger R, Gerein V, Kirkpatrick CJ. Transient CD15-positive endothelial phenotype in the human placenta correlates with physiological and pathological fetoplacental immaturity. Eur J Obstet Gynecol Reprod Biol 2014; 180: 172-179
9 Huynh J, Dawson D, Roberts D, Bentley-Lewis R. A systematic review of placental pathology in maternal diabetes mellitus. Placenta 2015; 36 (2) 101-114
10 Saini P, Pankaj JP, Jain A, Agarwal GC. Effect of gestational diabetes mellitus on gross morphology of placenta: a comparative study. Int J Anat Res 2015; 3 (1) 889-894
11 Basnet K, Bentley-Lewis R, Wexler DJ, Kilic F, Roberts DJ. The prevalence of intervillous thrombi is increased in placentas from pregnancies complicated by diabetes. Pediatr Dev Pathol 2015;
12 Li Y, Hadden C, Singh P , et al. GDM-associated insulin deficiency hinders the dissociation of SERT from ERp44 and down-regulates placental 5-HT uptake. Proc Natl Acad Sci U S A 2014; 111 (52) E5697-E5705
13 Starikov R, Inman K, Chen K , et al. Comparison of placental findings in type 1 and type 2 diabetic pregnancies. Placenta 2014; 35 (12) 1001-1006
14 Smyth S, Heron A. Diabetes and obesity: the twin epidemics. Nat Med 2006; 12 (1) 75-80
15 Aye IL, Lager S, Ramirez VI , et al. Increasing maternal body mass index is associated with systemic inflammation in the mother and the activation of distinct placental inflammatory pathways. Biol Reprod 2014; 90 (6) 129
16 Straughen JK, Misra DP, Kumar P, Misra VK. The influence of overweight and obesity on maternal soluble FMS-like tyrosine kinase 1 and its relationship with leptin during pregnancy. Reprod Sci 2013; 20 (3) 269-275
17 Misra VK, Straughen JK, Trudeau S. Maternal serum leptin during pregnancy and infant birth weight: the influence of maternal overweight and obesity. Obesity (Silver Spring) 2013; 21 (5) 1064-1069
18 Catalano P, deMouzon SH. Maternal obesity and metabolic risk to the offspring: why lifestyle interventions may have not achieved the desired outcomes. Int J Obes 2015; 39 (4) 642-649
19 Kleiber M. Body size and metabolic rate. Physiol Rev 1947; 27 (4) 511-541
20 Salafia CM, Kiryankova N, Inany H , et al. Metabolic scaling and twin placentas. Placenta 2016; 37: 16-18
21 Salafia CM, Yampolsky M. Metabolic scaling law for fetus and placenta. Placenta 2009; 30 (5) 468-471
22 Salafia CM, Misra DP, Yampolsky M, Charles AK, Miller RK. Allometric metabolic scaling and fetal and placental weight. Placenta 2009; 30 (4) 355-360
23 Thomas DM, Clapp JF, Shernce S. A foetal energy balance equation based on maternal exercise and diet. J R Soc Interface 2008; 5 (21) 449-455
24 Thomas DM, Navarro-Barrientos JE, Rivera DE , et al. Dynamic energy-balance model predicting gestational weight gain. Am J Clin Nutr 2012; 95 (1) 115-122
25 Heymsfield SB, Thomas D, Bosy-Westphal A, Shen W, Peterson CM, Müller MJ. Evolving concepts on adjusting human resting energy expenditure measurements for body size. Obes Rev 2012; 13 (11) 1001-1014
26 Melanson EL, Coelho LB, Tran ZV, Haugen HA, Kearney JT, Hill JO. Validation of the BodyGem hand-held calorimeter. Int J Obes Relat Metab Disord 2004; 28 (11) 1479-1484
27 Salafia CM, Zhang J, Charles AK , et al. Placental characteristics and birthweight. Paediatr Perinat Epidemiol 2008; 22 (3) 229-239
28 Misra DP, Salafia CM, Charles AK, Miller RK. Birth weights smaller or larger than the placenta predict BMI and blood pressure at age 7 years. J Dev Orig Health Dis 2010; 1 (2) 123-130
29 Misra DP, Salafia CM, Charles AK, Miller RK. Placental measurements associated with intelligence quotient at age 7 years. J Dev Orig Health Dis 2012; 3 (3) 190-197
30 Misra DP, Salafia CM, Miller RK, Charles AK. Non-linear and gender-specific relationships among placental growth measures and the fetoplacental weight ratio. Placenta 2009; 30 (12) 1052-1057
31 Hiroi R, Carbone DL, Zuloaga DG, Bimonte-Nelson HA, Handa RJ. Sex-dependent programming effects of prenatal glucocorticoid treatment on the developing serotonin system and stress-related behaviors in adulthood. Neuroscience 2016; 320: 43-56
32 Yampolsky M, Salafia CM, Shlakhter O, Haas D, Eucker B, Thorp J. Modeling the variability of shapes of a human placenta. Placenta 2008; 29 (9) 790-797
33 Shingleton A. Allometry: the study of biological scaling. Nature Education Knowledge 2010; 3 (10) 2
34 Salafia CM, Yampolsky M, Shlakhter A, Mandel DH, Schwartz N. Variety in placental shape: when does it originate?. Placenta 2012; 33 (3) 164-170
35 Hageman JA, Hendriks MM, Westerhuis JA, van der Werf MJ, Berger R, Smilde AK. Simplivariate models: ideas and first examples. PLoS ONE 2008; 3 (9) e3259
36 Shah RG, Salafia CM, Girardi T, Conrad L, Keaty K, Bartleotc A. Shape matching algorithm to validate the tracing protocol of placental chorionic surface vessel networks. Placenta 2015; 36 (8) 944-946
37 Chan DY, Hughes BD, Leong AS, Reed WJ. Stochastically evolving networks. Phys Rev E Stat Nonlin Soft Matter Phys 2003; 68 (6, Pt 2): 066124
38 Calkins K, Devaskar SU. Fetal origins of adult disease. Curr Probl Pediatr Adolesc Health Care 2011; 41 (6) 158-176
39 Dyer JS, Rosenfeld CR. Metabolic imprinting by prenatal, perinatal, and postnatal overnutrition: a review. Semin Reprod Med 2011; 29 (3) 266-276
40 Ingelfinger JR, Nuyt AM. Impact of fetal programming, birth weight, and infant feeding on later hypertension. J Clin Hypertens (Greenwich) 2012; 14 (6) 365-371
41 Santos MS, Joles JA. Early determinants of cardiovascular disease. Best Pract Res Clin Endocrinol Metab 2012; 26 (5) 581-597