Objective
We sought to investigate the relationship between a panel of angiogenic and inflammatory biomarkers measured in midpregnancy and small-for-gestational-age (SGA) outcomes in sub-Saharan Africa.
Study Design
Concentrations of 18 angiogenic and inflammatory biomarkers were determined in 432 pregnant women in Dar es Salaam, Tanzania, who participated in a trial examining the effect of multivitamins on pregnancy outcomes. Infants falling below the 10th percentile of birthweight for gestational age relative to the applied growth standards were considered SGA. Multivariate binomial regression models with the log link function were used to determine the relative risk of SGA associated with increasing quartiles of each biomarker. Restricted cubic splines were used to test for nonlinearity of these associations.
Results
A total of 60 participants (13.9%) gave birth to SGA infants. Compared to those in the first quartile, the risk of SGA was reduced among those in the fourth quartiles of vascular endothelial growth factor-A (adjusted risk ratio [RR], 0.38; 95% confidence interval [CI], 0.19–0.74), placental growth factor (adjusted RR, 0.28; 95% CI, 0.12–0.61), soluble fms-like tyrosine kinase-1 (adjusted RR, 0.48; 95% CI, 0.23–1.01), monocyte chemoattractant protein-1 (adjusted RR, 0.48; 95% CI, 0.25–0.92), and leptin (adjusted RR, 0.46; 95% CI, 0.22–0.96).
Conclusion
Our findings provide evidence of altered angiogenic and inflammatory mediators, at midpregnancy, in women who went on to deliver SGA infants.
Intrauterine growth restriction (IUGR) is a major public health problem that affects 4-8% of pregnancies in developed countries and an estimated 27% of pregnancies in low- and middle-income countries. The consequences of inadequate fetal growth can be lifelong. Growth-restricted infants have an increased risk of morbidity and mortality during the neonatal and postneonatal period; an increased risk of developmental delay, short stature, neurodevelopmental impairment, and cerebral palsy during childhood; as well as an increased risk of myriad cardiometabolic disorders during adulthood.
A small number of interventions have had modest success in reducing the occurrence of small for gestational age (SGA), a commonly used surrogate outcome for IUGR. Nonetheless, considerable demand still exists for effective preventive measures. Development of these measures requires an improved understanding of the pathogenesis of IUGR and the ability to identify pregnancies at risk in early and midgestation. The examination of biomarkers associated with reduced fetal growth can assist in both endeavors.
Previous studies have linked a number of biomarkers to IUGR but few have been conducted in resource-constrained settings where the burden of IUGR is greatest. Much of this research has focused on biomarkers of angiogenesis, the branching and nonbranching remodeling of the placental vasculature that is a crucial process for adequate perfusion of oxygen and nutrients to the fetus. Other evidence suggests that IUGR may involve a proinflammatory cytokine bias. For this reason, evaluating the association between inflammatory biomarkers and IUGR may be of particular relevance in developing countries, where common infections such as malaria can induce a proinflammatory microenvironment. In this study, we investigate the relationship between a range of angiogenic and inflammatory biomarkers during midpregnancy and IUGR as defined by SGA.
Materials and Methods
Study site and participants
We obtained data and specimens for these analyses from a randomized, double-blind, placebo-controlled trial of daily multivitamin supplementation during pregnancy. A detailed description of the trial has been published elsewhere. Trial participants were human immunodeficiency virus–negative, between 12-27 weeks of gestation, and planning to stay in Dar es Salaam for at least 1 year after delivery. At the time of enrollment, participants were randomly assigned to receive a daily oral dose of a multivitamin containing 20 mg of vitamin B 1 , 20 mg of vitamin B 2 , 25 mg of vitamin B 6 , 100 mg of niacin, 50 μg of vitamin B 12 , 500 mg of vitamin C, 30 mg of vitamin E, and 0.8 mg of folic acid or a placebo. All trial participants also received daily doses of 60 mg of elemental iron and 0.25 mg of folic acid. We limited the present analyses to primigravid women, since they are at higher risk for fetal growth restriction than multigravid women. In addition, we limited these analyses to singleton births. Multiple gestations are associated with both alterations in angiogenic markers and a higher risk of adverse pregnancy outcomes and could therefore confound the results of the study. From among the subset of primigravid trial participants with singleton pregnancies, known birth outcomes, and stored baseline plasma samples, we randomly selected 432 participants for these analyses.
Ethics statement
The institutional review boards at the Muhimbili University of Health and Allied Sciences in Dar es Salaam, Tanzania, and the Harvard School of Public Health in Boston, MA, granted ethical approval for the study.
Exposure
Maternal peripheral blood samples were collected in EDTA vacutainer tubes at enrollment, plasma separated, and stored at −80°C prior to testing. For the present study, we selected 18 biomarkers associated with inflammatory and angiogenic pathways previously studied in pregnancy, infection, or endothelial activation pathways. These included angiopoietin (Ang)-1, Ang-2, Ang-like 3, vascular endothelial growth factor (VEGF)-A, soluble fms-like tyrosine kinase (sFlt)-1, soluble tumor necrosis factor receptor 2, placental growth factor (PGF), macrophage inflammatory protein-1β (MIP1β)/chemokine (CC motif) ligand 4 (CCL4), monocyte chemoattractant protein (MCP)-1/chemokine (CC motif) ligand 2 (CCL2), leptin, interleukin-1 β, interleukin-18 binding protein, soluble intercellular adhesion molecule (sICAM)1, complement factor D, soluble endoglin, C-reactive protein, chitinase-3-like protein-1, and complement component C5a. All analyses utilized commercially available enzyme-linked immunosorbent assays (Duosets; R&D Systems, Minneapolis, MN). To increase sensitivity, samples were incubated for 2 hours at room temperature (18-28°C) for the analyses of C-reactive protein, complement component C5a, complement factor D, and VEGF-A, and overnight at 4°C for the analyses of all other biomarkers. Enzyme-linked immunosorbent assay analysis was blinded to infant size for gestational age at birth.
Outcomes
At enrollment, participants reported the date of their last menstrual period. These dates were used to calculate gestational ages. Research midwives measured birthweights of newborns to the nearest 10 g following delivery. We defined SGA births as those falling below the 10th percentile of birthweight for gestational age according to growth standards from a US population as in the parent study.
Statistical analysis
We tested the distributions of each biomarker for normality using the Shapiro-Wilks test. Because the distributions of each biomarker deviated from normality, we used the Wilcoxon rank sum test to nonparametrically examine whether the levels of each biomarker differed between SGA and appropriate-for-gestational-age (AGA) infants. We estimated the relative association between each biomarker and SGA by categorizing biomarker values into quartiles and using log binomial regression to determine the risk ratios and 95% confidence intervals (CIs) for SGA for participants in each of the upper 3 quartiles compared to those in the lowest quartile. In most cases, the log binomial models failed to converge and were replaced with log Poisson models, which provide consistent but not fully efficient estimates of the risk ratio and its CI. Multivariate models also contained terms for covariates that predicted SGA at an alpha level of <0.2 in univariate log binomial models. These variables included literacy (yes/no), marital status (yes/no), gestational age at study entry (<20, 20-25, >25 weeks), and district of recruitment (Ilala/Temeke/Kinondoni). Although randomization should have yielded a balanced distribution of all biomarkers between study arms, we repeated the analysis adjusting for trial regimen assignment, since multivitamin supplementation reduced the risk of SGA in the parent trial.
We nonparametrically examined the possibility of nonlinear relationships between continuous biomarker levels and SGA status using restricted cubic splines with 4 knots placed at the points corresponding to the 20th, 40th, 60th, and 80th percentiles. For biomarkers that did not depart from linearity in relation to SGA status, we then tested for the presence of linear trends by assigning each quartile the median value and modeling this variable as a continuous variable.
We considered assigned treatment arm in the parent trial as a potential modifier of the relationship between each biomarker and SGA births. To do so, we dichotomized each biomarker at the median to create “high” and “low” categories, computed cross-product terms by multiplying the dichotomous biomarker variables by the indicator variable for treatment arm, and assessed the significance of this cross-product term using a likelihood ratio test that compared the −2 log likelihoods of the models containing the cross-product term to the models without cross-product terms. All statistical analyses were performed using software (SAS 9.2; SAS Institute, Cary, NC).
Results
Participant characteristics are presented in Table 1 . At the time of enrollment into the parent trial, participants had a median age of 20.6 years (interquartile range, 18.6–22.6) and a median gestational age of 22 weeks (interquartile range, 19.4–24.6). The majority of participants (67.4%) were of normal weight for height, were able to read (90.5%), had completed <8 years of schooling (73.8%), and were married (79.2%). Approximately one fifth of participants reported that their household spent ≤500 Tanzanian shillings (US $0.31) per person per day on food. Of the 432 participants, 60 (13.9%) delivered SGA infants. Table 2 displays the median values of each biomarker among SGA and AGA deliveries. Compared to women who gave birth to AGA infants, women who gave birth to SGA infants had notably lower median levels of VEGF-A, sFlt-1, PGF, MCP-1/CCL2, and leptin, and notably higher levels of sICAM1.
| Characteristics | n (%) or median (IQR) |
|---|---|
| Age, y | 20.6 (18.6–22.6) |
| Gestational age at enrollment, wk | 22 (19.4–24.6) |
| Body mass index, kg/m 2 | |
| <18.5 | 5 (1.3) |
| 18.5-24.9 | 252 (67.4) |
| 25-29.9 | 98 (26.2) |
| ≥30.0 | 19 (5.1) |
| Mid upper arm circumference, cm | |
| ≤22.5 | 47 (11.0) |
| 22.6-25.0 | 172 (40.4) |
| 25.1-29.0 | 165 (38.7) |
| >29.0 | 42 (9.9) |
| Education, y | |
| 0-4 | 30 (6.9) |
| 5-7 | 289 (66.9) |
| 8-11 | 97 (22.5) |
| ≥12 | 16 (3.7) |
| Literate | 389 (90.5) |
| Marital status | |
| Married | 339 (79.2) |
| Divorced/single/widowed | 89 (20.8) |
| Per day spending for food ≤TSh 500 | 87 (20.1) |
| District of recruitment | |
| Ilala | 287 (66.4) |
| Temeke | 45 (10.4) |
| Kinondoni | 100 (23.2) |
| Received multivitamin regimen | 226 (52.3) |
| Biomarker | n a | Appropriate for gestational age | n a | Small for gestational age | P value b |
|---|---|---|---|---|---|
| Median [IQR] | Median [IQR] | ||||
| Ang-1, ng/mL | 362 | 19.63 [11.68–29.11] | 60 | 18.13 [11.90–27.26] | .47 |
| Ang-2, ng/mL | 362 | 4.64 [2.04–8.21] | 60 | 4.05 [2.05–9.10] | .93 |
| Angptl3, ng/mL | 363 | 124.74 [86.50–167.15] | 60 | 108.05 [74.19–152.41] | .16 |
| VEGF-A, pg/mL | 362 | 51.08 [7.81–363.54] | 60 | 9.43 [7.81–85.38] | .0006 |
| sFlt-1, ng/mL | 363 | 1.31 [0.55–4.11] | 60 | 0.94 [0.23–2.38] | .03 |
| sTNFR2, ng/mL | 363 | 5.68 [3.87–8.20] | 60 | 5.62 [4.30–7.71] | .86 |
| PGF, ng/mL | 343 | 1.61 [0.91–2.84] | 59 | 1.18 [0.65–1.98] | .01 |
| MIP1β/CCL4, pg/mL | 350 | 163.76 [81.14–346.06] | 59 | 135.31 [63.64–295.72] | .30 |
| MCP-1/CCL2, pg/mL | 353 | 48.14 [11.56–247.91] | 60 | 20.91 [7.81–187.09] | .07 |
| Leptin, ng/mL | 362 | 7.74 [4.76–12.88] | 60 | 6.36 [4.04–10.42] | .10 |
| IL1β, pg/mL | 362 | 19.96 [3.91–76.40] | 60 | 19.99 [3.91–65.39] | .40 |
| IL-18 BP, ng/mL | 363 | 13.57 [9.29–18.78] | 60 | 13.33 [8.76–18.97] | .83 |
| sICAM1, ng/mL | 363 | 157.68 [109.40–237.01] | 60 | 184.53 [120.93–241.80] | .03 |
| Complement factor D, ng/mL | 356 | 478.00 [322.72–674.42] | 57 | 505.07 [366.77–994.25] | .34 |
| sEng, ng/mL | 362 | 22.41 [16.42–29.67] | 60 | 22.90 [15.27–28.24] | .71 |
| CRP, μg/mL | 350 | 1.86 [0.78–4.17] | 57 | 2.05 [0.05–4.61] | .81 |
| CHI3L1, ng/mL | 363 | 38.69 [22.52–66.53] | 60 | 41.15 [25.87–69.48] | .45 |
| C5a, ng/mL | 352 | 32.49 [16.80–64.74] | 58 | 28.92 [16.89–52.38] | .48 |
a Sample sizes were not equal for all biomarkers due to indeterminate assay results
Since the biomarkers examined have unique profiles with respect to gestational age, we examined biomarker levels according to the gestational age at which the plasma sample was collected. Median levels of VEGF-A and sFlt-1 were consistently lower in the SGA group compared to the AGA group at all gestational ages ( Figure 1 , A and C). Median levels of PGF, MCP-1/CCL2, and leptin were higher in the SGA group compared to the AGA group among women between 12-16 weeks of gestation, but were lower among women at other gestational ages ( Figure 1 , B, D, and E). Higher median levels of sICAM1 were observed among the SGA group at all weeks of gestation ( Figure 1 , E and F).
Table 3 shows the relative risks for an SGA delivery according to quartiles of biomarker values. Women with VEGF-A levels in the third and fourth quartiles had a reduced risk of giving birth to an SGA infant compared to women in the first quartile. Spline analysis confirmed a significant nonlinear relationship ( P = .001) between VEGF-A and SGA status that appeared L-shaped ( Figure 2 ). Women with plasma PGF in the highest quartile had a reduced risk of delivering an SGA infant (72%; 95% CI, 59–88%) compared to those in the lowest quartile. Similar to the results for VEGF-A, spline analysis showed that PGF had a significant nonlinear relationship to SGA ( P = .008) that showed an L-shape ( Figure 3 ).
| Variable | Q1 | Q2 | Q3 | Q4 | P trend b |
|---|---|---|---|---|---|
| Ang-1, ng/mL | 1.00 (Ref) | 1.19 (0.63–2.24) | 0.90 (0.46–1.77) | 0.69 (0.33–1.44) | .20 |
| Ang-2, ng/mL | 1.00 (Ref) | 1.14 (0.60–2.17) | 0.72 (0.34–1.53) | 1.14 (0.58–2.24) | .84 |
| Angptl3, ng/mL | 1.00 (Ref) | 0.67 (0.37–1.23) | 0.59 (0.30–1.17) | 0.55 (0.28–1.07) | .09 |
| VEGF-A, pg/mL | 1.00 (Ref) | 0.56 (0.30–1.05) | 0.24 (0.11–0.53) | 0.38 (0.19–0.74) | |
| sFlt-1, ng/mL | 1.00 (Ref) | 0.93 (0.52–1.68) | 0.65 (0.34–1.23) | 0.48 (0.23–1.01) | .05 |
| sTNFR2, ng/mL | 1.00 (Ref) | 1.32 (0.67–2.60) | 1.32 (0.66–2.63) | 1.09 (0.52–2.25) | .98 |
| PGF, ng/mL | 1.00 (Ref) | 0.72 (0.39–1.34) | 0.64 (0.34–1.20) | 0.28 (0.12–0.61) | |
| MIP1β/CCL4, pg/mL | 1.00 (Ref) | 0.83 (0.43–1.60) | 0.71 (0.36–1.40) | 0.78 (0.41–1.50) | .65 |
| MCP-1/CCL2, pg/mL | 1.00 (Ref) | 0.44 (0.23–0.87) | 0.53 (0.29–0.98) | 0.48 (0.25–0.92) | .30 |
| Leptin, ng/mL | 1.00 (Ref) | 0.63 (0.33–1.21) | 0.80 (0.44–1.46) | 0.46 (0.22–0.96) | .06 |
| IL1β, pg/mL | 1.00 (Ref) | 0.42 (0.19–0.93) | 0.87 (0.48–1.56) | 0.75 (0.41–1.40) | .94 |
| IL-18 BP, ng/mL | 1.00 (Ref) | 0.87 (0.45–1.69) | 0.89 (0.45–1.76) | 1.08 (0.57–2.09) | .69 |
| sICAM1, ng/mL | 1.00 (Ref) | 1.33 (0.66–2.68) | 1.34 (0.67–2.70) | 1.52 (0.75–3.11) | |
| Complement factor D, ng/mL | 1.00 (Ref) | 1.03 (0.51–2.09) | 1.03 (0.51–2.10) | 1.36 (0.70–2.66) | .35 |
| sEng, ng/mL | 1.00 (Ref) | 0.68 (0.32–1.44) | 1.27 (0.70–2.30) | 0.74 (0.37–1.50) | .66 |
| CRP, μg/mL | 1.00 (Ref) | 1.01 (0.51–2.03) | 1.18 (0.59–2.37) | 1.25 (0.62–2.52) | .68 |
| CHI3L1, ng/mL | 1.00 (Ref) | 1.46 (0.71–2.98) | 1.39 (0.67–3.87) | 1.79 (0.89–3.62) | .52 |
| C5a, ng/mL | 1.00 (Ref) | 1.06 (0.55–2.06) | 1.17 (0.61–2.24) | 0.72 (0.34–1.54) | .32 |
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