A Systematic Review of Placental Biomarkers Predicting Small-for-Gestational-Age Neonates

Abstract

Background:

Neonates born small for gestational age (SGA) face increased risk of neonatal mortality, childhood developmental problems, and adult disease. The placenta is a key factor in SGA development because of its multiple biological processes that underlie fetal growth. However, valid and reliable placental biomarkers of SGA have not been determined.

Objectives:

The objective of this article was to systematically identify and review studies examining associations between placental biomarkers and SGA and assess those biomarkers’ predictive value.

Methods:

Use of the matrix method and the PRISMA guidelines ensured systematic identification of relevant articles based on selection criteria. PubMed, CINAHL, and EMBASE were searched for English articles published in 2005–2016 that addressed relationships between placental biomarkers and SGA.

Results:

The search captured 466 articles; 13 met selection criteria. The review identified 14 potential placental biomarkers for SGA, with placental growth factor and soluble fms-like tyrosine kinase 1 being the most commonly studied. However, findings for these and other biomarkers have often been contradictory. Thus, no placental biomarkers have been confirmed as reliable for predicting SGA.

Conclusion:

The inconsistent findings suggest low placental biomarker reliability, perhaps due to the multifactorial nature of SGA. This review is novel in its focus on identifying potential placental biomarkers for SGA, producing a better understanding of how placental function underlies fetal growth. Nevertheless, use of placental biomarkers alone may not be adequate for predicting SGA. Therefore, combinations of biomarkers and other predictive tests should be evaluated for their ability to predict risk of SGA.

Keywords

small for gestational age intrauterine growth restriction placenta biomarkers neonate

Small for gestational age (SGA) is defined as a weight (either in utero estimated fetal weight or birth weight) less than the 10th percentile for a specific gestational age (Vayssiere et al., 2015) and is now the term recommended for describing small fetuses or neonates (Ego, 2013). This definition encompasses the entire spectrum of fetuses, including those who are constitutionally small and those with intrauterine growth restriction (IUGR) who have failed to achieve their growth potential (Wilcox, 2001; Wollmann, 2009). Being born SGA confers risk of increased perinatal, childhood, and adulthood complications (Saleem et al., 2011) such as stunted growth and undernutrition in childhood (Blake et al., 2016) and cardiovascular risk (Lenfant, 2008), metabolic problems (Barker et al., 1993), and poor neurodevelopmental outcomes in adulthood (Savchev et al., 2013). In low- and middle-income countries, an estimated 32.4 million babies were born SGA in 2010, constituting 27% of all live births (Lee et al., 2013), compared to 9–10% in the United States (Talge, Mudd, Sikorskii, & Basso, 2014). Thus, SGA is a greater public health problem in low- and middle-income countries than in developed countries; however, most SGA-prevention studies have been conducted in developed countries (Lee et al., 2013). Given the prevalence and adverse effects of SGA, it is critical that effective screening and treatment procedures be developed for SGA in pregnancy to avoid the adverse health outcomes faced by neonates born SGA.

Several studies have demonstrated that maternal factors are associated with SGA. These factors include age, marital status, education, income, and parity (Ota et al., 2014); body mass index (Frederick, Williams, Sales, Martin, & Killien, 2008); gestational weight gain (Boriboonhirunsarn, 2015); preeclampsia, chronic hypertension, and anemia (Jariyapitaksakul & Tannirandorn, 2013; Ota et al., 2014); alcohol and illicit substance use (Ota et al., 2014); and psychosocial factors such as stressful life events and lack of social support (Sanguanklin et al., 2014). Fetal factors are also associated with SGA, including fetal sex, malformation, and infections (Wollmann, 2009). In addition, placental factors are strongly associated with impaired fetal growth, particularly an abnormal placental vascular system that leads to poor maternal–fetal blood transfusion (Wollmann, 2009).

According to the developmental origins of health and disease (DOHaD) theoretical framework, the normal pattern of fetal development is disrupted by abnormal stimuli that occur at a critical point of in utero development and that influence changes in cell allocation differentiation, tissue and organ growth, and birth weight (Barker, 2007; Gluckman & Hanson, 2006; Rutherford, 2013). Placental adaptation occurs in response to the abnormal stimuli and produces changes in both placental structure and function, resulting in disrupted fetal development that leads to adverse health effects such as diabetes or hypertension in adulthood (Gillman et al., 2007; Myatt, 2006). Disruption of normal placental development also leads to functional alterations in the placenta (Myatt, 2006). Study findings have shown that abnormal placental function was accompanied by alterations in placental vasculogenesis and angiogenesis of the vascular system (Mayhew, Charnock-Jones, & Kaufmann, 2004), in the activity of amino acid transporters (Jansson, Ekstrand, Bjorn, Wennergren, & Powell, 2002), and in hormone and enzyme activities (McMullen et al., 2004). Because the placenta regulates fetal growth and development, playing an active role in programming the fetal experience in utero, placental dysfunction can lead to disease in adult life (Myatt, 2006).

The placenta is the conduit for transferring maternal nutrients to the developing fetus, and research has implicated placental insufficiency in impaired fetal growth and reduced birth weight (Parra-Saavedra et al., 2013). This organ produces many substances that are associated with its function and related to birth outcomes (Ilekis et al., 2016; Myatt, 2006). Placental growth factor and vascular endothelial growth factor are among the substances produced specifically by the placenta (Charnock-Jones, Kaufmann, & Mayhew, 2004), while others are produced by both the placenta (which is primarily of embryonic origin) and the maternal tissue (Haig, 1996). For example, leptin, a polypeptide hormone that regulates body weight and homeostasis, is produced by both the mother and the placenta. Although maternal serum leptin levels during pregnancy are higher than those in nonpregnant women, this hormone is considered a good placental biomarker because leptin levels drop precipitously after the placenta is delivered, suggesting that the rise in leptin during pregnancy is largely due to the placenta (Henson & Castracane, 2000; Yura et al., 1998).

Placental biomarkers reflect placental functions that link placental physiology and fetal growth (Heazell, Whitworth, Duley, & Thornton, 2015). For instance, placental growth hormone (PGH) is predominantly expressed in the syncytiotrophoblast layer of the placenta. PGH can also be detected in maternal circulation from 12 to 20 weeks of gestation up to term, as it gradually replaces pituitary growth hormone. Researchers have observed lower maternal levels of PGH in pregnancies with fetal growth restriction (Evain-Brion & Malassine, 2003). Measurement of placental biomarkers in maternal blood is one common method of evaluating placental functions related to pregnancy outcomes (Zhong, Zhu, & Ding, 2015). However, these placental chemical products can be identified in various locations such as placental tissue, amniochorionic membranes, amniotic fluid, fetal blood, and cord blood as well as maternal blood and urine (Heazell et al., 2015).

Previous systematic reviews have shown that many biomarkers are associated with adverse pregnancy outcomes such as SGA, IUGR, stillbirth, preterm birth, and preeclampsia. These biomarkers include pregnancy-associated plasma protein A, free β-human chorionic gonadotrophin, placental growth factor, placental protein 13, α-fetoprotein, thyroid-stimulating hormone, urinary albumin, and vitamin D (Conde-Agudelo, Papageorghiou, Kennedy, & Villar, 2013; Zhong et al., 2015). However, these systematic reviews did not focus specifically on SGA as an outcome. Moreover, no valid and reliable placental biomarkers related to SGA have been determined. Thus, the purpose of the present article was to systematically identify and review studies examining associations between placental biomarkers and SGA and those biomarkers’ predictive value. By focusing only on potential placenta-specific biomarkers and SGA, we intended to provide a better understanding of how placental function regulates fetal growth as well as information that might help to improve pregnancy outcomes and reduce risks of SGA.

Method

In conducting this systematic review, we were guided by the matrix method of identifying, organizing, and critically evaluating previous research on a topic of interest (Garrard, 2017). The steps of this method, which we applied in the present review, are (1) establishing the purpose of the review, (2) organizing and conducting a search for studies that meet specific criteria, (3) abstracting relevant study components, (4) analyzing and summarizing information across studies, and (5) drawing conclusions based on review results. We also followed the PRISMA flow diagram to ensure systematic identification of relevant articles (Moher, Liberati, Tetzlaff, Altman, & the PRISMA Group, 2009).

Search Strategy

We conducted comprehensive searches in PubMed, CINAHL, and EMBASE, databases that index health science literature, for relevant articles using medical subject headings (MeSH) and the key words biomarkers and biological markers, placenta, small for gestational age, and intrauterine growth restriction. We also reviewed references and bibliographies of relevant articles to identify additional studies. The inclusion criteria for studies were articles (1) published in peer-reviewed journals from 2005 to 2016, (2) in English, (3) with full-text availability online, and (4) that addressed the relationship between placental biomarkers and SGA. Included studies could employ any type of design, experimental, or nonexperimental design. We excluded studies that (1) were case series, reports, editorials, comments, reviews, or unpublished manuscripts; (2) reported placental biomarkers in women with multiple pregnancies or who were diagnosed with preeclampsia or diabetes; or (3) used uterine artery Doppler, as this method is not employed to measure biomarkers.

Selection Process

The initial search of electronic databases produced 466 potentially relevant articles: 163 in PubMed, 126 in CINAHL, and 177 in EMBASE. After a review of all titles, we removed 311 duplicates and irrelevant titles and screened the remaining 155 studies by reading each abstract. Based on the screening, 23 studies warranted full-text retrieval. Also, two studies we identified in article reference lists and bibliographies warranted full-text retrieval. In the end, 13 studies met the inclusion criteria and were included in the systematic review. Figure 1 presents the PRISMA flow diagram depicting the study selection process (Moher et al., 2009).

Figure 1.

PRISMA flow diagram illustrating the selection process (Moher et al., 2009).

Quality Appraisal

Quality assessment is an integral part of any systematic review because it decreases the risk of bias in review results (Whiting, Rutjes, Reitsma, Bossuyt, & Kleijnen, 2003). In the present review, we used a modified version of the QUADAS tool for the quality assessment of diagnostic accuracy studies (Whiting et al., 2003) based on the approach of Conde-Agudelo, Papageorghiou, Kennedy, and Villar (2013). Evaluation criteria included 5 items important to study quality, each of which we scored as yes, no, or unclear (see Table 1).

Table 1.

Quality Evaluation Criteria for the Studies.

Evaluation Criterion	Interpretation
Adequate study design
Yes	Pregnant women were consecutively or randomly selected, and a prospective cohort design was used
No	Convenience sampling or a case–control/retrospective design was used
Adequate selection criteria
Yes	The study included “normotensive” SGA (not involving preeclampsia, chronic hypertension, congenital or chromosomal anomalies, infection, diabetes, thrombophilia, systematic lupus erythematosus, or substance abuse)
No	SGA fetuses were associated with one or more known conditions
Appropriate reference standard
Yes	Birth weight was below the 10th percentile for neonates of the same gestational age.
No	Birth weight was at or above the 10th percentile, or no reference standard was reported for SGA.
Adequate description of test
Yes	The study included a detailed description of the test procedures and the time at which the sample was collected
No	Information on the study’s test procedure and sample collection time was absent or incomplete
Blinding
Yes	The study included masking of laboratory technicians to pregnancy outcomes and clinicians to test results
No	The study did not include such masking, or the information was absent
Unclear	The study did not include sufficient information to make a judgment about blinding

Note. SGA = small for gestational age.

Data Extraction and Analysis

After examining all eligible studies, we developed a review matrix using Garrard’s matrix method as a guide (Garrard, 2017). Some studies reported multiple outcomes; however, we only include findings related to SGA in the discussion below.

Results

Study Quality

We have summarized the quality assessments of the 13 reviewed studies in Figure 2. Most studies (85%) used a prospective design with consecutive recruitment. All 13 studies employed appropriate selection criteria that included normotensive SGA neonates with a birth weight lower than the 10th percentile. Moreover, most studies (85%) provided detailed descriptions of test procedures and sample collection times. However, only 15% of the studies masked laboratory technicians and clinicians to test results.

Figure 2.

Summary of the quality assessment of the 13 studies reviewed.

Study Characteristics

A total of 13 studies, which comprised a combined sample of 56,041 women and evaluated a total of 14 placental biomarkers, met the inclusion criteria. We list placental biomarkers evaluated in these studies for their ability to predict SGA in Table 2 and summarize the characteristics of the included studies in Table 3.

Table 2.

Placental Biomarkers Predicting SGA in Reviewed Studies.

Biomarker	Studies
Angiogenesis-related biomarkers
Placental growth factor (PlGF)	Andraweera et al. (2012); Asvold et al. (2011); Bakalis et al. (2015); Darling et al. (2014); Fadigas et al. (2015); Karagiannis et al. (2011); Rizos et al. (2013); Romero et al. (2008); Valino et al. (2016)
Soluble fms-like tyrosine kinase 1 (sFlt1)	Andraweera et al. (2012); Asvold et al. (2011); Bakalis et al. (2015); Darling et al. (2014); Fadigas et al. (2015); Rizos et al. (2013); Romero et al. (2008); Valino et al. (2016)
Soluble endoglin (s-Eng)	Asvold et al. (2011); Darling et al. (2014); Romero et al. (2008)
Vascular endothelial growth factor A (VEGF-A)	Andraweera et al. (2012); Darling et al. (2014)
Kinase insert domain receptor (KDR)	Andraweera et al. (2012)
Transforming growth factor beta-1 (TGF-β1)	Singh et al. (2013)
Fibroblast growth factor-basic (FGF-b)	Ozkan et al. (2008)
Placental hormone/protein-related biomarkers
Insulin-like growth factor (IGF-I)	Ozkan et al. (2008); Sifakis et al. (2012a)
Insulin-like growth factor binding protein-1 (IGFBP-1)	Sifakis et al. (2012a)
Insulin-like growth factor binding protein-3 (IGFBP-3)	Sifakis et al. (2012a)
A disintegrin and metalloprotease 12 (ADAM12)	Karagiannis et al. (2011)
Placenta protein 13 (PP13)	Karagiannis et al. (2011)
Placental growth hormone (PGH)	Sifakis et al. (2012b)
Neural cell adhesion molecule (N-CAM)	Ozkan et al. (2008)

Note. SGA = small for gestational age.

Table 3.

Summary of Reviewed Studies on Placental Biomarkers Predicting SGA.

Author (Year); Aim	Sample, Setting, Design	Timing, Methods	Outcomes	Placental Biomarkers	Findings
Andraweera, Dekker, Laurence, and Roberts (2012); Aim: To evaluate the expression of VEGF-A, PlGF, sFlt1, and KDR mRNA in pregnancies complicated by preeclampsia, gestational hypertension, SGA, or preterm birth	Sample: N = 86 (preeclampsia = 18, hypertension = 15, SGA = 13, preterm = 10, control = 30). Setting: Multicenters in Adelaide, Australia, and Auckland, New Zealand, 2005–2008. Design: Prospective (nested case–control)	Timing: Placenta samples were collected within 30 min of delivery. Method: Quantitative RT-PCR	Preeclampsia, hypertension, normotensive, SGA, preterm	VEGF-A, PlGF, sFlt1, KDR	Analysis via Kruskal Wallis test (one-way ANOVA). Placental expression of VEGF-A mRNA was reduced by 42% (p = .03) in normotensive SGA. No significant differences were observed in the expression of PlGF (p = .08), KDR (p = .5), and of sFlt1 (p = .5)
Asvold et al. (2011) Aim: To evaluate the associations between PlGF, sFlt1, and s-Eng and fetal growth restriction	Sample: N = 595 (SGA = 217, control = 378). Setting: Antenatal clinics in 11 counties in Norway, 1992–1994. Design: Prospective (nested case–control)	Timing: Maternal serum collected at pregnancy Week 10 and Week 23. Method: ELISA	SGA	PlGF, sFlt1, and s-Eng	Analyses via logistic regression (adjusted). PlGF: Women with low PIGF at both time points were at higher risk of SGA delivery (OR = 3.8, 95% CI = [1.6, 8.8]). sFlt1: Low levels of sFlt1 at the first time point were associated with higher risk of SGA delivery. However, at the second time point, high sFlt1 levels were associated with higher risk of SGA delivery. s-Eng: High levels of s-Eng at the second time point were associated with higher risk of SGA delivery (OR = 3.5, 95% CI = [1.3, 9.1]).
Bakalis, Peeva, Gonzalez, Poon, & Nicolaides (2015); Aim: To investigate the potential value of combined screening by maternal factors, estimated fetal weight, uterine artery PI, MAP, PlGF, and sFlt1 at 30–34 weeks’ gestation for the prediction of SGA birth in the absence of preeclampsia	Sample: N = 9,472 (SGA = 469, control = 9,003). Setting: King’s College Hospital and Medway Maritime Hospital, United Kingdom, 2011–2013. Design: Prospective (nested case–control)	Timing: Maternal blood samples collected at 30–34 weeks’ gestation. Method: No report	SGA	PlGF and sFlt1	Analyses via logistic regression (adjusted). PlGF: Significantly lower levels in the SGA group. sFlt1: Significantly higher levels in the SGA group. When PlGF and sFlt1 were added to a model that combined maternal factors, estimated fetal weight, uterine artery PI, MAP, and sFlt1 did not remain a significant predictor of SGA
Darling et al. (2014) ; Aim: To investigate the relationship between a panel of angiogenic and inflammatory biomarkers measured in mid-pregnancy and SGA outcomes	Sample: N = 432 (SGA = 60, control = 372). Setting: Dar es Salaam, Tanzania. Design: Prospective (nested case–control)	Timing: Maternal blood samples collected at 12–27 weeks’ gestation. Method: ELISA	SGA	VEGF-A, PlGF, sFlt1, and s-Eng	Analyses via log binomial regression. VEGF-A: Significantly lower levels in the SGA group (OR = 9.43, 95% CI = [7.81, 85.38]). PlGF: Significantly lower levels in the SGA group (OR = 1.18, 95% CI = [0.65, 1.98]). sFlt1: Significantly lower levels in the SGA group (OR = 0.94, 95% CI = [0.23, 2.38]). s-Eng: No significant differences
Fadigas, Peeva, Mendez, Poon, and Nicolaides (2015) ; Aim: To investigate the potential value of maternal serum PlGF and sFlt1 at 35–37 weeks’ gestation in the prediction of delivery of SGA infants in the absence of preeclampsia	Sample: N = 3,859 (SGA = 158, control = 3,701). Setting: King’s College Hospital and Medway Maritime Hospital, United Kingdom, 2014. Design: Prospective (nested case–control)	Timing: Maternal blood samples collected at 35–37 weeks’ gestation. Method: Electrochemiluminescence immunoassay	SGA	PlGF and sFlt1	Analyses via logistic regression (adjusted). PlGF: Significantly lower levels in the SGA group. sFlt1: Significantly higher levels in the SGA group. When PlGF and sFlt1 were added to a model that combined maternal factors and estimated fetal weight, sFlt1 did not remain a significant predictor of SGA
Karagiannis, Akolekar, Sarquis, Wright, and Nicolaides (2011) ; Aim: To develop a model for prediction of SGA neonates in the absence of preeclampsia based on maternal factors, biophysical, and biochemical markers at 11–13 weeks’ gestation	Sample: N = 32,850 (SGA = 1,536, control = 31,314). Setting: King’s College Hospital, United Kingdom, 2006–2009. Design: Prospective (nested case–control)	Timing: Maternal blood samples collected at 11–13 weeks’ gestation. Method: ELISA, DELFIA^®, and fluorescent immunoassay	SGA	PlGF, PP13, and ADAM12	Analyses via Mann–Whitney U test, chi-square, and Fisher’s exact test. PlGF (OR = 0.9, 95% CI = [0.63, 1.24]), PP13 (OR = 0.82, 95% CI = [0.62, 1.07]), and ADAM12 (OR = 0.86, 95% CI = [0.69, 1.08]) were significantly decreased in the SGA group compared to the control group
Ozkan, Vural, Dalcik, Tas, and Dalcik (2008) ; Aim: To investigate placental expression of IGF-I, FGF-b, and N-CAM in the context of their potential role in the pathogenesis of pregnancies with SGA fetuses	Sample: N = 22 (SGA = 12, control = 10). Setting: Kacaeli University Hospital, Turkey. Design: Case–control	Timing: Placental samples and amniochorionic membranes collected in delivery room. Method: Immunohistochemical analysis and immunostaining pattern	SGA	IGF-I, FGF-b, and N-CAM	Analyses via Mann–Whitney U test. IGF-I, FGF-b, and N-CAM immunostainings showed significantly increased immunoreactivities in placenta chorionic villi of SGA cases
Rizos et al. (2013) ; Aim: To determine maternal serum concentrations of PlGF and sFlt1 longitudinally in normal pregnancies, in pregnancies that develop preeclampsia, and in pregnancies that deliver an SGA infant	Sample: N = 116 (preeclampsia = 12, SGA = 14, and control = 90). Setting: Aretaieio Hospital, Athens, Greece, 2008–2010. Design: Prospective (nested case–control)	Timing: Three maternal sera samples collected at 11–14 weeks’, 20–26 weeks’, and 28–35 weeks’ gestation. Method: ELISA	Preeclampsia, normotensive, and SGA	PlGF and sFlt1	Analyses via logistic regression (adjusted). No significant differences in PlGF and sFlt1 levels between the SGA and control groups
Romero et al. (2008) ; Aim: To determine if changes in maternal plasma concentrations of angiogenic and antiangiogenic factors differ prior to development of disease among patients with normal pregnancies and those who develop preeclampsia or deliver an SGA neonate	Sample: N = 144 (preeclampsia = 42, SGA = 56, control = 46). Setting: Sotero del Rio Hospital, Santiago, Chile. Design: Prospective (nested case–control)	Timing: Maternal blood samples collected 6 times from the first or early second trimester until delivery. Method: ELISA	Preeclampsia, normotensive, and SGA	PlGF, sFlt1, and s-Eng	Analyses via Mann–Whitney U test, chi-square, and Fisher’s exact test. PlGF: Significantly lower levels in the SGA group than the control group. sFlt1: No significant differences between the SGA and control groups. s-Eng: Significantly higher levels in the SGA group compared to the control group
Sifakis et al. (2012a); Aim: To investigate the possible value of maternal serum concentration of IGF-I, IGFBP-1, and IGFBP-3 at 11–13 weeks’ gestation in the prediction of SGA neonates	Sample: N = 180 (SGA = 60, control = 120). Setting: King’s College Hospital, United Kingdom. Design: Prospective (nested case–control)	Timing: Maternal blood samples collected at 11–13 weeks’ gestation. Method: ELISA	SGA	IGF-I, IGFBP-1, and IGFBP-3	Analyses via Mann–Whitney U test, chi-square, and Fisher’s exact test. Median IGF-I (p = .002) and IGFBP-1 (p = .002) but not IGFBP-3 (p = .402) were significantly lower in the SGA group than in the control group. However, after logistic regression analysis and adjustment for maternal characteristics, these biomarkers were not useful in predicting SGA
Sifakis et al. (2012b); Aim: To investigate whether the maternal serum concentration of PGH at 11–13 weeks’ gestation is altered in pregnancies that deliver SGA neonates	Sample: N = 180 (SGA = 60, control = 120). Setting: King’s College Hospital, United Kingdom. Design: Prospective (nested case–control)	Timing: Maternal blood samples collected at 11–13 weeks’ gestation. Method: ELISA	SGA	PGH	Analyses via Mann–Whitney U test, chi-square, and Fisher’s exact test. PGH levels did not differ significantly between the SGA and control groups
Singh, Orazulike, Ashmore, and Konje (2013); Aim: To examine changes in levels of TGF-β1 in maternal serum during gestation and compare changes in the third trimester in women with SGA and appropriate-for-gestational-age fetuses	Sample: N = 17 (SGA = 10, control = 7). Setting: A hospital in the United Kingdom. Design: Cross-sectional	Timing: Maternal serum collected at 34 and 38 weeks’ gestation. Method: The quantitative sandwich enzyme immunoassay technique (manufacturer: R&D Systems)	SGA	TGF-β1	Comparison of means and standard deviations. The mean TGF-β1 levels in SGA pregnancies at 34 weeks’ gestation were significantly lower than those in normal pregnancies, while at 38 weeks’ gestation, the levels were similar in the two groups
Valino, Giunta, Gallo, Akolekar, and Nicolaides (2016) ; Aim: To investigate the potential value of biophysical and biochemical markers at 30–34 weeks’ gestation in the prediction of adverse perinatal outcomes	Sample: N = 8,268 (preeclampsia = 223, SGA = 882, stillbirth = 23, fetal distress = 512, low cord pH = 84, low APGAR score = 70, control = 6,474). Setting: King’s College Hospital, University College London Hospital, and Medway Maritime Hospital, United Kingdom, 2011–2014 Design: Case–control	Timing: Maternal blood samples collected at 30–34 weeks’ gestation. Method: Not reported	Preeclampsia, normotensive, SGA, stillbirth, fetal distress, umbilical cord arterial blood pH ≤ 7.0 or venous blood pH ≤ 7.1, APGAR score < 7, admission to the NNU	(1) PlGF, (2) sFlt1	Analyses via logistic regression (adjusted). PlGF: PlGF level was significantly lower in the preterm SGA group (OR = 0.33, 95% CI = [0.16, −0.61]) and the term SGA group (OR = 0.64, 95% CI = [0.38, 1.06]) compared to the control group. sFlt1: sFlt1 level was significantly higher in the preterm SGA group (OR = 1.43, 95% CI = [1.06, 3.13]) and the term SGA group (OR = 1.10, 95% CI = [0.78, 1.52]) compared to the control group

Note. ADAM12 = A disintegrin and metalloprotease-12; ANOVA = analysis of variance; DELPHIA = dissociation-enhanced lanthanide fluorescent immunoassay; ELISA = enzyme-linked immunosorbent assay; FGF-b = fibroblast growth factor-basic; IGF-I = insulin-like growth factor; IGFBP-I = insulin-like growth factor binding protein-1; IGFBP-3 = insulin-like growth factor binding protein-3; KDR = kinase insert domain receptor; MAP = mean arterial pressure; N-CAM = neural cell adhesion molecule; NNU = neonatal unit; PIGF = placental growth factor; PGH = placental growth hormone; PI = pulsatility index; PP13 = placenta protein 13; RT-PCR = reverse transcription polymerase chain reaction; s-Eng = soluble endoglin; sFlt1 = soluble fms-like tyrosine kinase-1; SGA = small for gestational age; TGF-β1 = transforming growth factor beta-1; VEGF-A = vascular endothelial growth factor-A.

Placental Biomarkers

In the reviewed studies, researchers measured biomarkers in maternal serum (11 studies), placental tissues (2 studies), and amniochorionic membranes (1 study; see Table 3). Placental biomarkers that these studies found to be associated with SGA can be divided into angiogenesis-related biomarkers and placental hormone/protein-related biomarkers.

Angiogenesis-related biomarkers

Functional alterations in the placenta are often caused by abnormal development of the placental vascular system, which is the product of vasculogenesis and angiogenesis (Salafia, Charles, & Maas, 2006; Triunfo et al., 2014). Vasculogenesis is responsible for creation of the primary vascular plexus in the placenta, whereas angiogenesis is responsible for creation of blood vessels and their development into a mature placental vascular system (Charnock-Jones et al., 2004). Vasculogenesis occurs in placental villi between Days 21 and 31 of pregnancy, whereas angiogenesis occurs in placental villi from Day 32 until term. Vasculogenesis is a complex process, and investigators have identified few of the factors involved. It is, therefore, less difficult to detect factors involved in angiogenesis.

Investigators have identified the vascular endothelial growth factor (VEGF) family as a regulator of both vasculogenesis and angiogenesis (Arroyo & Winn, 2008; Vrachnis et al., 2013). The VEGF family includes seven proteins: VEGF-A (commonly referred to as VEGF), VEGF-B, VEGF-C, VEGF-D, VEGF-E, VEGF-F, and placental growth factor (PlGF; Vrachnis et al., 2013). The various VEGF proteins bind to two tyrosine kinase receptors, VEGF-1 (flt1) and VEGF-2 (kinase insert domain receptor [KDR]; Neufeld, Cohen, Gengrinovitch, & Poltorak, 1999). In addition to the VEGF family, investigators have identified other placental products as angiogenesis-related factors, including angiopoietins (Ang-1 and Ang-2), transforming growth factor beta-1 (TGF-β1), fibroblast growth factor-basic (FGF-b), and soluble endoglin (s-Eng; Charnock-Jones et al., 2004; Levine et al., 2006; Mayhew et al., 2004).

Of the 13 studies in this review, 9 reported data on PlGF, 8 on soluble fms-like tyrosine kinase-1 (sFlt1), 3 on s-Eng, 2 on VEGF-A, and 1 each on KDR, TGF-β1, and FGF-b (see Table 2). Of the nine studies reporting on PIGF, seven reported that levels were significantly lower in the SGA group than in the appropriate-for-gestational-age (AGA) group, whereas two reported no significant differences. Likewise, three studies reported that sFlt1 levels were significantly higher in the SGA group than in the AGA group, while three others reported no significant differences. In contrast, Darling et al. (2014) reported that sFlt1 levels were significantly lower in the SGA group. Furthermore, Asvold et al. (2011) found that sFlt1 levels were significantly lower in the SGA group than in the AGA group at the first blood sampling (Week 10) but significantly higher in the second sampling (Week 23). For s-Eng, two studies showed higher levels in the SGA group than in the AGA group, and one study showed no significant difference in levels between the groups. In addition, two studies reported that low levels of VEGF-A were associated with higher risk of SGA delivery. Andraweera, Dekker, Laurence, and Roberts (2012) observed no significant differences in the expression of KDR between the SGA and the AGA groups. Moreover, Singh, Orazulike, Ashmore, and Konje (2013) found that the mean TGF-β1 levels in the SGA group were significantly lower than in the AGA group. Finally, Ozkan, Vural, Dalcik, Tas, and Dalcik (2008) found that FGF-b levels were increased in SGA cases (see Table 3).

Placental hormone/protein-related biomarkers

The placenta is an organ that secretes many different hormones and metabolites into maternal blood (Evain-Brion & Malassine, 2003). Placental hormone activities are important in maintaining pregnancy and inducing metabolic adaptations between mother and fetus, whereas placental proteins regulate adaptive maternal–fetal immune response (Blackburn, 2007; Than et al., 2014). The placenta synthesizes various peptide growth factors, which are usually proteins or steroid hormones such as insulin-like growth factor (IGF-I) and PGH (Evain-Brion & Malassine, 2003). These factors regulate cell growth and differentiation, healing, hormone release at the local level, and uterine contractility (Blackburn, 2007).

Of the studies in the present review reporting on the relationship between SGA birth and placental hormones or proteins, two reported data on IGF-I and one each reported on insulin-like growth factor binding protein-1 and -3 (IGFBP-1 and -3), A disintegrin and metalloprotease-12 (ADAM12), placenta protein 13 (PP13), PGH, and neural cell adhesion molecule (N-CAM; see Table 2). Ozkan et al. (2008) reported significantly lower IGF-I levels in an SGA group versus an AGA group. In addition, Sifakis, Akolekar, Kappou, Mantas, and Nicolaides (2012a) found significantly lower IGF-I and IGFBP-1 levels in an SGA group than in an AGA group but no difference in levels of IGFBP-3 between the groups. Karagiannis, Akolekar, Sarquis, Wright, and Nicolaides (2011) found that ADAM12 and PP13 levels were significantly lower in the SGA group. Furthermore, Sifakis, Akolekar, Kappou, Mantas, and Nicolaides (2012b) found that PGH was not significantly associated with SGA delivery. Finally, Ozkan et al. (2008) found that N-CAM levels were increased in SGA cases (see Table 3).

Discussion

Main Findings

In the present systematic review, we found that research has not yet determined a single placental biomarker that reliably predicts SGA, a result similar to the findings of other recent reviews of SGA, IUGR, preeclampsia, stillbirth, and preterm birth (Conde-Agudelo et al., 2013; Heazell et al., 2015; Hui et al., 2012). However, the present review is unique in targeting SGA (apart from preeclampsia, stillbirth, and preterm birth) and placental biomarkers, thus enhancing understanding of how placental function regulates fetal growth.

The generally accepted definition for SGA is a fetus or neonate whose weight is less than the 10th percentile for a specific gestational age (Vayssiere et al., 2015). This definition is not linked to pathology because it includes both constitutionally small and IUGR fetuses/neonates (Wilcox, 2001; Wollmann, 2009). Therefore, when using percentile as a reference standard for categorizing SGA births, it is important that researchers differentiate between constitutionally normal SGA and pathological SGA groups. However, IUGR usually corresponds with SGA that is associated with evidence of abnormal fetal growth confirmed by repeated ultrasound and/or a specific cause can be identified, such as abnormal placental function, toxic effects, or fetal infections (Vayssiere et al., 2015; Wollmann, 2009). Thus, placental biomarkers appear to be more clearly associated with pathological SGA (IUGR) than constitutionally normal SGA.

Moreover, the inconsistent results we evaluated in the present review suggest low reliability of placental biomarkers, which could be attributable to the multifactorial nature of SGA. SGA has many causes, including maternal, fetal, and placental factors (Wollmann, 2009). The limited clinical ability to identify reliable placental biomarkers is thus understandable. Moreover, some of the placental biomarkers covered in this review appeared only in a single study, including KDR, IGFBP-1, IGFBP-3, ADAM12, PP13, PGH, TGF-β1, FGF-b, and N-CAM (see Table 2). Therefore, the studies of the ability of placental biomarkers to predict SGA infants are still too small in scope and number to draw conclusions, and further research on these biomarkers is warranted.

The placental biomarkers most commonly reported in the 13 studies were PlGF and sFlt1. This prevalence is likely due to their major roles in the establishment and maturation of the vascular system of the human placenta (Vrachnis et al., 2013), which is the product of two processes, vasculogenesis and angiogenesis. Researchers have hypothesized that low concentrations of PlGF and high concentrations of sFlt1 are associated with impaired angiogenesis and placental development, leading to pregnancy complications such as preeclampsia and SGA (Arroyo & Winn, 2008). Most of the studies in the present review support this hypothesis (see Table 3). However, some studies reported contradictory findings. For example, Andraweera et al. (2012) and Rizos et al. (2013) found no significant differences in PlGF levels between SGA and AGA groups. Moreover, Andraweera et al. (2012), Rizos et al. (2013), and Romero et al. (2008) found no significant differences in the sFlt1 levels between SGA and AGA groups. These contradictory findings may be the result of differences in sampling times or frequencies, laboratory methods, or statistical analyses; Table 3 shows the variety of approaches used in the studies. The predictive accuracy of PlGF and sFlt1 thus remains unclear, and further research is required to confirm their value in predicting SGA.

Strengths and Limitations

This systematic review involved a comprehensive examination of the literature on SGA currently available, rigorous assessment of the quality of the studies, and reporting on a wide range of placental biomarkers. Nevertheless, the review has some limitations, which are similar to those of other systematic reviews on maternal serum biomarkers and biomarkers of IUGR (Conde-Agudelo et al., 2013; Hui et al., 2012). First, the heterogeneity of the placental biomarkers and procedures used to examine them made the comparison of studies challenging. Second, not all studies reported the procedures and techniques researchers used to examine biomarkers, and most studies did not report whether they masked laboratory technicians to pregnancy outcomes and clinicians to test results. Therefore, the predictive accuracy reported for a given placental biomarkers may be biased. Third, we included only studies published in English, and there may well be studies published in other languages that could shed further light on placental biomarkers. Fourth, there are many causes of SGA, including maternal, fetal, and placental factors (Zabransky, 2013); thus, studies that examine only placental biomarkers, excluding maternal and fetal factors, may be too limited in identifying reliable predictors of SGA infants. Finally, the adverse pregnancy outcomes of some studies included in this review were not independent of one another. For example, authors reported coexisting adverse outcomes such as preeclampsia and SGA. It is possible that these were competitive outcomes that decreased the apparent predictive ability and accuracy of placental biomarkers for SGA alone.

Implications for Future Research

Several placental biomarkers for predicting SGA (see Table 2) have been too little investigated to draw meaningful conclusions about their value. We found that PlGF and sFlt1 were the most commonly examined placental biomarkers, but their predictive accuracy and reliability for SGA remain unclear. Additional research is warranted to further explore and confirm the relationships between these placental biomarkers and SGA.

Further research should also be performed to determine the best combinations of tests for predicting SGA (e.g., placental biomarkers plus uterine artery Doppler and associated maternal, fetal, and placental factors). Some investigators have, in fact, recently reported on the accuracy of combined tests for predicting SGA (Bakalis, Peeva, Gonzalez, Poon, & Nicolaides, 2015; Karagiannis et al., 2011; Valino, Giunta, Gallo, Akolekar, & Nicolaides, 2016). For example, Bakalis, Peeva, Gonzalez, Poon, and Nicolaides (2015) developed a screening instrument combining maternal factors, estimated fetal weight, uterine pulsatility index, mean arterial pressure, PlGF, and sFlt1 at 30–34 weeks of gestation for prediction of SGA. They reported that combined screening at a 10% false-positive rate predicted 89%, 94%, and 96% of SGA deliveries at 32–36 weeks of gestation with birth weights of <10th, <5th, and <3rd percentiles, respectively; moreover, the combined screening predicted 57%, 65%, and 72% of SGA deliveries at ≥37 weeks at the respective percentiles. Combined screening for maternal, biophysical, and biochemical factors could thus potentially identify a high proportion of pregnancies that will deliver SGA neonates. Further screening intervention studies are needed to determine whether these combined screenings produce results that can be used to improve pregnancy outcomes.

At the same time, the current revolution in genetic knowledge has markedly challenged and altered our understanding of health and disease. Therefore, researchers should also give more attention to studying associations between genetic markers and SGA in order to better understand the molecular mechanisms underlying fetal growth and development. Research findings in this area will be critical for developing screening and treatment procedures to prevent adverse pregnancy outcomes such as SGA (Ilekis et al., 2016).

Finally, there is an opportunity to link placental biomarkers and associated birth outcomes to long-term developmental processes and adult health (Rutherford, 2013). Future research should evaluate the predictive value of placental biomarkers related to neonatal outcomes such as stillbirth, neonatal death, preterm birth, neonatal intensive care unit admission, fetal abnormality, and serious neonatal health outcomes (e.g., necrotizing enterocolitis, chronic lung disease, intraventricular hemorrhage, sepsis, and seizure) as well as long-term health outcomes in childhood and adulthood (Heazell et al., 2015). In particular, researchers should give greater attention to studying these relationships in developing countries where SGA is a major public health problem. Improved understanding of the potential relationship between placental biomarkers and adverse neonatal health outcomes may help to decrease risks of neonatal mortality and morbidity as well as risks of adverse health outcomes throughout the life span.

Conclusion

It is vital that researchers continue to seek to increase our understanding of the relationship of placental biological processes to SGA and fetal growth. Such research will deepen our understanding of the normal physiology of fetal development, give clinicians the opportunity to screen pregnant women with valid placental biomarkers, and expand our arsenal of safe and effective treatments for SGA fetuses during pregnancy.

Footnotes

Authors’ Note

Ruchob, Rutherford, and Bell contributed to conception and design; contributed to acquisition, analysis, and interpretation; drafted manuscript; critically revised manuscript; gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy.

This article was completed while Rungnapa Ruchob was a doctoral student at the University of Illinois at Chicago.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Rungnapa Ruchob

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