Abstract
Excessive maternal inflammatory response is involved in the pathogenesis of preeclampsia. Few epidemiologic studies have investigated the associations between genetic variations in the inflammatory mediator genes and preeclampsia risk, and these studies have reached inconsistent results. We examined 31 single-nucleotide polymorphisms in IL-1A, IL-1B, IL-1R1, IL-2RA, IL-5RA, IL-6, IL-6R, TNFSF11, TNFRSF11A, IL-28RA, IRAK4, and KIT genes and the risk of preeclampsia and its clinical subtypes in a nested case–control study including 203 preeclampsia cases and 233 controls. We found that IL-1R1, IL-5RA, IL-6R, and TNFSF11 were associated with the risk of preeclampsia. Although the significant associations observed for preeclampsia overall were mainly seen for late-onset preeclampsia and severe preeclampsia, IL-6R (rs2229238) and TNFSF11 (rs9525643) polymorphisms were associated with the risk of early-onset preeclampsia. TNFSF11 (rs2200287 and rs2148072) polymorphisms were associated with risk of mild preeclampsia. Our study provided the first evidence that genetic variations in inflammatory mediator genes IL-1R1, IL-6R, TNFSF11, and IL-5RA were associated with preeclampsia risk, and the risk varied by preeclampsia subtypes.
Keywords: preeclampsia, inflammatory mediator gene, genetic polymorphisms, IL-5RA, IL-1R1
Introduction
Preeclampsia, a major pregnancy complication characterized by gestational hypertension and proteinuria, affects about 5% to 8% of all pregnancies worldwide.1 It is among the leading causes of maternal and neonatal morbidity and mortality.2 Although significant effort has been made, the etiology of preeclampsia is poorly understood.
Preeclampsia has been linked to reduced perfusion of placenta, maternal vascular endothelia activation, and abnormal remodeling of uterine spiral arteries that induced oxidative stress.3 Emerging evidence suggests that excessive maternal inflammatory response with cytokine-mediated endothelia damage may play a role in the pathogenesis of preeclampsia.4 Cytokine signals play an important role in the different stages of health and diseases and especially in the genesis of preeclampsia. Potential hypoxia and ischemia of the placenta lead to an increased release of inflammatory cytokines such as interleukin (IL)-1α, IL-1β, or tumor necrosis factor (TNF)-α which might trigger endothelial dysfunction and result in preeclampsia.5,6 Besides type 1 T helper cell (Th1) cytokines, a number of inflammatory mediators including IL-5 and IL-6 are also associated with the regulation of inflammatory response involved in the pathogenesis of preeclampsia.7 Several studies investigated single-nucleotide polymorphisms (SNPs) in inflammatory mediator genes and risk of preeclampsia; however, the results have been inconsistent.4,8–15 Differences in ethnic groups and heterogeneity of preeclampsia included in different studies might have contributed to the conflicting results.16 Additionally, early studies only focused on IL-1R1, IL-1A, IL-1B, and IL-6 genes. In order to further clarify a potential role of SNPs in inflammatory mediator genes in preeclampsia risk, we investigated 31 SNPs in 12 inflammatory mediator genes, including IL-1A, IL-1B, IL-1R1, IL-2RA, IL-5RA, IL-6, IL-6R, TNFSF11, TNFRSF11A, IL-28RA, IRAK4, and KIT and the risk of preeclampsia in a Chinese Han population.
Materials and Methods
A birth cohort was carried out at the first affiliated hospital of Shanxi Medical University in Taiyuan, China, between March 2012 and November 2013. Eligible women included pregnant women who came to the hospital for delivery with gestational age of 20 weeks or more, who had no mental illness, and who were aged 18 years or older. A total of 4208 eligible women were contacted for participation. Among those, 523 refused to participate and 26 did not complete in-person interviews, which yielded 3659 (87%) women who completed in-person interviews and 3166 women who donated blood samples. All study procedures were approved by the Human Investigation Committee at the Shanxi Medical University. Eligible women were informed of study procedure upon their arrival at the hospital for delivery. After obtaining written consent, an in-person interview was conducted at the hospital by trained study interviewers using a standardized and structured questionnaire. The questionnaire collected information regarding demographic factors, reproductive and medical history, smoking, alcohol and tea consumption, occupational and residential histories, physical activity, and diet. Information on birth outcomes and pregnancy complications were abstracted from medical records.
Among those who had maternal blood samples available, who gave singleton live birth without major birth defects, and who had no chronic hypertension and cardiovascular diseases, a total of 203 had preeclampsia. Preeclampsia was defined as having a blood pressure of 140/90 mm Hg or more (measured twice 6 hours apart) and concurrent proteinuria (2 urine specimens containing at least 1+ protein by dipstick test) after 20 weeks of gestation.17 Preeclampsia was further classified as early-onset preeclampsia (EOPE), or late-onset preeclampsia (LOPE), mild preeclampsia, or severe preeclampsia (Table 1). We also randomly selected 233 controls from the same population and frequency matched the cases by age (±2 years), residency, and time of conception (±3 months).
Table 1.
Definition of Preeclampsia and Its Subtype.
| Preeclampsia | Preeclampsia Subtypes | ||||
|---|---|---|---|---|---|
| Mild | Severe | Early Onset | Late Onset | ||
| Systolic blood pressure | ≥140 mm Hg | 140 to <160 mm Hg | ≥160 mm Hg | ≥140 mm Hg | ≥140 mm Hg |
| Diastolic blood pressure | ≥90 mm Hg | 90 to <110 mm Hg | ≥110 mm Hg | ≥90 mm Hg | ≥90 mm Hg |
| Proteinuriaa | ≥1+ | 1+ | ≥2+ | ≥1+ | ≥1+ |
| Symptoms of severityb | Yes/no | No | Yes | Yes/no | Yes/no |
| Gestational age of diagnosis | >20 weeks | >20 weeks | >20 weeks | 21-<34 weeks | ≥34 weeks |
aDipstick test in 2 urine samples.
bIncluding headache, blurred vision, epigastric burning pain, decreased urine output, decreased or absent fetal kick, and so on.
DNA was extracted, isolated, and purified from whole blood samples according to a standard phenol–chloroform extraction method. Genotyping was conducted using an Illumina Golden Gate Platform. Duplicate samples (5%) were interspersed throughout the plates used for genotype analysis for quality control purposes. A total of 31 SNPs from 12 cytokine-mediated signaling pathway genes (IL-1A, IL-1B, IL-1R1, IL-2RA, IL-5RA, IL-6, IL-6R, TNFSF11, TNFRSF11A, IL-28RA, IRAK4, and KIT) were considered for this study. The completion rates for all SNPs were over 99% and the concordance rates for quality control samples were greater than 98% for all assays. Hardy-Weinberg equilibrium (HWE) was assessed in controls for each SNP using a χ2 test. Single-nucleotide polymorphisms with a P value >.05 from the χ2 test were considered to be in HWE. Of the 31 SNPs tested, 3 SNPs were not in HWE and were excluded from the final analyses (Supplementary Table S1).
Unconditional logistic regression was used to estimate the risk and calculate the odds ratio (OR) and 95% confidence intervals (CIs) for individual SNPs adjusting for age, education level, and body mass index. Additional adjustment for other variables including family income, history of preeclampsia, smoking, and alcohol consumption did not result in material changes of the results; therefore, they were not included in the final model. The common homozygous genotypes served as references. A linear trend test was conducted by assigning the ordinal values 1, 2, and 3 to the homozygous common, heterozygous, and homozygous rare alleles, respectively. The minimum P (minP) tests were conducted to examine an association at the gene level. The minP test was based on permutation resampling and was conducted to assess the true statistical significance of the smallest P trend within each gene region.18 Haplotype analyses were conducted for all genes in which more than 1 SNP was genotyped. Haplotype block structures were evaluated with Haploview using the 4-gamete rule.19 The individual haplotype frequencies were estimated through the expectation maximization algorithm under the assumption of the HWE. An unconditional logistic regression model was used to estimate the effect of individual haplotypes, with the most common haplotype as the reference. The false discovery rate method with a significant level of .2 was applied for multiple comparisons. All the statistical analyses were carried out using SAS vision 9.3 software (SAS Institute).
Results
Among 203 preeclampsia cases, 161 cases (79.3%) had severe preeclampsia and 93 (45.8%) had EOPE. As shown in Table 2, preeclampsia cases were likely to have lower educational level, to be overweight or obese, and to have had cesarean section compared to controls. No statistically significant differences were observed in parity, smoking during pregnancy, and infant gender between preeclampsia and control groups.
Table 2.
Distributions of Selected Characteristics of the Study Population.
| Preeclampsia (n = 203) | Controls (n = 233) | P | |||
|---|---|---|---|---|---|
| Number | % | Number | % | ||
| Age (years) | .252 | ||||
| <25 | 52 | 25.62 | 54 | 23.18 | |
| 25-29 | 88 | 43.35 | 89 | 38.2 | |
| ≥30 | 63 | 31.03 | 90 | 38.62 | |
| Education (years) | .004 | ||||
| <10 | 93 | 45.81 | 72 | 30.9 | |
| 11-15 | 105 | 51.73 | 150 | 64.38 | |
| >15 | 5 | 2.46 | 11 | 4.72 | |
| Body mass index (kg/m2) | .002 | ||||
| <24.9 | 153 | 75.37 | 204 | 87.55 | |
| 24.9-30 | 42 | 20.69 | 23 | 9.87 | |
| ≥30 | 8 | 3.94 | 4 | 1.72 | |
| Missing | 0 | 0 | 2 | 0.86 | |
| Parity | .531 | ||||
| Nulliparous | 91 | 44.82 | 98 | 42.06 | |
| Parous | 111 | 54.68 | 135 | 57.94 | |
| Missing | 1 | 0.50 | 0 | 0 | |
| Birthweight (g) | <.001 | ||||
| Low (<2500) | 114 | 56.16 | 31 | 13.30 | |
| Normal (2500-4000) | 79 | 38.92 | 181 | 77.68 | |
| High (≥4000) | 10 | 4.92 | 18 | 7.72 | |
| missing | 0 | 0 | 3 | 1.30 | |
| Gestational week | <.001 | ||||
| 36.07 ± 3.28 | 38.18 ± 2.29 | ||||
| Intrauterine growth | <.001 | ||||
| SGA | 94 | 46.30 | 29 | 12.45 | |
| AGA | 91 | 44.83 | 168 | 72.10 | |
| LGA | 18 | 8.87 | 36 | 15.45 | |
| Smoking (active) during pregnancy | |||||
| Yes | 0 | 0 | 0 | 0 | |
| No | 201 | 99.01 | 233 | 100 | |
| Missing | 2 | 0.99 | 0 | 0 | |
| Smoking (passive) during pregnancy | .214 | ||||
| Yes | 44 | 21.67 | 40 | 17.17 | |
| No | 159 | 78.33 | 193 | 82.83 | |
| Alcohol drinking during pregnancy | |||||
| Yes | 0 | 0 | 0 | 0 | |
| No | 201 | 99.01 | 233 | 100 | |
| Missing | 2 | 0.99 | 0 | 0 | |
| Caesarean section | <.001 | ||||
| Yes | 147 | 72.41 | 110 | 47.21 | |
| No | 56 | 27.59 | 119 | 51.07 | |
| Missing | 0 | 0 | 4 | 1.72 | |
| Gender | .923 | ||||
| Boy | 100 | 49.26 | 115 | 49.36 | |
| Girl | 101 | 49.75 | 114 | 48.93 | |
| Missing | 2 | 0.99 | 4 | 1.71 | |
Abbreviations: AGA, appropriate for gestational age; LGA, large for gestational age; SGA, small for gestational age.
As shown in Table 3, cases with EOPE were likely to be younger, to give birth to low birthweight infants, to have smaller gestational week, and to have had cesarean section compared to LOPE. Cases with severe preeclampsia were more likely to have lower educational level, to give birth to low birthweight infants, and to have smaller gestational week compared to mild preeclampsia.
Table 3.
Distributions of Selected Characteristics of the Preeclampsia Subtypes.
| EOPE (n = 93), N % | LOPE (n = 110), N % | P | Mild (n = 42), N % | Severe (n = 161), N % | P | |
|---|---|---|---|---|---|---|
| Age (years) | .026 | .080 | ||||
| <25 | 16 (17.2) | 36 (32.7) | 6 (14.4) | 46 (28.6) | ||
| 25-29 | 42 (45.2) | 46 (41.8) | 18 (42.8) | 70 (43.4) | ||
| ≥30 | 35 (37.6) | 28 (25.5) | 18 (42.8) | 45 (28.0) | ||
| Education (years) | .217 | .045 | ||||
| <10 | 39 (41.9) | 54 (49.1) | 15 (35.7) | 78 (48.4) | ||
| 11-15 | 50 (53.7) | 55 (50.0) | 24 (57.1) | 81 (50.3) | ||
| >15 | 4 (4.4) | 1 (0.9) | 3 (7.2) | 2 (1.3) | ||
| Body mass index (kg/m2) | .493 | .444 | ||||
| <24.9 | 68 (73.1) | 87 (79.1) | 30 (71.4) | 125 (77.6) | ||
| 24.9-30 | 20 (21.5) | 20 (18.2) | 11 (26.2) | 29 (18.0) | ||
| ≥30 | 5 (5.4) | 3 (2.7) | 1 (2.4) | 7 (4.4) | ||
| Parity | .184 | .182 | ||||
| Nulliparous | 37 (39.8) | 54 (49.1) | 15 (35.7) | 76 (47.2) | ||
| Parous | 56 (60.2) | 55 (50.0) | 27 (64.3) | 84 (52.2) | ||
| Missing | 0 | 1 (0.9) | 0 | 1 (0.6) | ||
| Birthweight (g) | <.001 | .003 | ||||
| Low (<2500) | 72 (77.4) | 42 (38.2) | 14 (33.4) | 100 (62.1) | ||
| Normal (2500-4000) | 19 (20.4) | 60 (54.5) | 24 (57.1) | 55 (34.2) | ||
| High (≥4000) | 2 (2.2) | 8 (7.3) | 4 (9.5) | 6 (3.7) | ||
| Gestational week | <.001 | <.001 | ||||
| 34.10 ± 3.51 | 37.75 ± 1.83 | 37.45 ± 2.07 | 35.71 ± 3.45 | |||
| Intrauterine growth | .287 | .441 | ||||
| SGA | 48 (51.6) | 46 (41.8) | 16 (38.1) | 78 (48.4) | ||
| AGA | 39 (41.9) | 52 (47.3) | 21 (50.0) | 70 (43.5) | ||
| LGA | 6 (6.5) | 12 (10.9) | 5 (11.9) | 13 (8.1) | ||
| Smoking (passive) during pregnancy | .280 | .643 | ||||
| Yes | 17 (18.1) | 27 (24.5) | 8 (19.0) | 36 (22.4) | ||
| No | 76 (81.9) | 83 (75.5) | 34 (81.0) | 125 (77.6) | ||
| Caesarean section | .036 | .186 | ||||
| Yes | 74 (79.5) | 73 (66.4) | 27 (64.3) | 120 (74.5) | ||
| No | 19 (20.5) | 37 (33.6) | 15 (35.7) | 41 (25.5) | ||
| Gender | .534 | .914 | ||||
| Boy | 48 (51.6) | 52 (47.3) | 21 (50.0) | 79 (49.1) | ||
| Girl | 44 (47.3) | 57 (51.8) | 21 (50.0) | 80 (49.7) | ||
| Missing | 1 (1.1) | 1 (0.9) | 0 | 2 (1.2) | ||
Abbreviations: AGA, appropriate for gestational age; EOPE, early onset of preeclampsia; LGA, large for gestational age; LOPE, late onset of preeclampsia; SGA, small for gestational age.
At the gene level, IL-5RA was significantly associated with the risk of preeclampsia (minP = .002), EOPE (minP = .003), and severe preeclampsia (minP = .0003). TNFSF11 was associated with EOPE (minP = .02) and mild preeclampsia (min P = .002). After adjustment for multiple comparisons, all these associations remained statistically significant (Table 4).
Table 4.
Associations Between Evaluated Gene Regions and Risk of PE and Subtype.
| Gene | SNP Number | SNP Database ID | MinP test P | ||||
|---|---|---|---|---|---|---|---|
| PE | EOPE | LOPE | Mild | Severe | |||
| IL6R | 2 | rs7549250 rs2229238 | .040 | .035 | .076 | .300 | .061 |
| TNFSF11 | 3 | rs2200287 rs2148072 rs9525643 | .049 | .020 | .016 | .002 | .060 |
| TNFASF11A | 2 | rs9646629 rs9951012 | .792 | .695 | .930 | .232 | .614 |
| IL1A | 3 | rs2071374 rs3783550 rs17561 | .807 | .293 | .737 | .206 | .946 |
| IL1B | 2 | rs1143633 rs1143643 | .870 | .831 | .477 | .770 | .694 |
| IL28RA | 2 | rs6696471 rs16860738 | .863 | .443 | .493 | .491 | .927 |
| IRAK4 | 5 | rs4251513 rs1461567 rs4251569 rs6582484 rs17121283 | .963 | .894 | .868 | .513 | .856 |
| IL5RA | 5 | rs6794523 rs334809 rs340813 rs163551 rs163550 | .002 | .003 | .058 | .176 | .0003 |
Abbreviations: EOPE, early onset of preeclampsia; LOPE, late onset of preeclampsia; minP, minimum P; PE, preeclampsia; SNP, single nucleotide polymorphism.
After adjustment for multiple comparisons, statistically significant associations between inflammatory mediator gene SNPs and risk of preeclampsia or its subtypes are presented in Table 5 and nonstatistically significant associations are presented in Supplementary Table 2. A reduced risk of preeclampsia was observed among women who carried IL-5RA rs163550 CC genotype compared to GG genotype (ORCC = 0.31; 95% CI, 0.17-0.57; P trend = .0003), IL-5RA rs163551 GG genotype compared to AA genotype (ORGG = 0.39; 95% CI, 0.22-0.69; P trend = .002), IL-6R rs7549250 AG genotype compared to AA genotype (ORAG = 0.58; 95% CI, 0.37-0.91; P trend = .021), IL-6R rs7549250 GG genotype compared to AA genotype (ORGG = 0.55; 95% CI, 0.32-0.96; P trend = .021), TNFSF11 rs9525643 AG genotype compared to AA genotype (ORAG = 0.60; 95% CI, 0.38-0.96; P trend = .020), and TNFSF11 rs9525643 GG genotype compared to AA genotype (ORGG = 0.52; 95% CI, 0.30-0.91; P trend = .020). An increased risk of preeclampsia was observed among women who carried IL-1R1 rs3771202 CG genotype compared to GG genotype (ORCG = 1.85; 95% CI, 1.24-2.76; P trend = .013), TNFSF11 rs2200287 AA&AG genotype compared to GG genotype (ORAA&AG = 1.61; 95% CI, 1.07-2.41; P trend = .022), and TNFSF11 rs2148072 TC&TT genotype compared to CC genotype (ORTC&TT = 1.64; 95% CI, 1.09-2.47; P trend = .013).
Table 5.
Associations between Inflammatory Mediator Genes Polymorphisms and Risk of PE Overall and Its Common Subtypes Including EOPE, LOPE, Mild PE, and Severe PE.
| SNPs | Controls | PE overall | EOPE | LOPE | Mild | Severe | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cases | ORa | 95% CI | P | Cases | ORa | 95% CI | P | Cases | ORa | 95% CI | P | Cases | ORa | 95% CI | P | Cases | ORa | 95% CI | P | ||
| IL1R1 | rs3771202 | ||||||||||||||||||||
| GG | 148 | 102 | 1 | 49 | 1 | 53 | 1 | 24 | 1 | 78 | 1 | ||||||||||
| CG | 77 | 95 | 1.85 | (1.24-2.76 | .003 | 39 | 1.58 | 0.95-2.62 | .078 | 56 | 2.08 | 1.28-3.38 | .003 | 17 | 1.40 | 0.70-2.77) | .340 | 78 | 1.95 | 1.27-3.00) | .002 |
| CC | 7 | 6 | 1.10 | 0.35-3.44) | .865 | 5 | 1.95 | 0.59-6.50) | .276 | 1 | 0.33 | 0.04-2.79) | .306 | 1 | 0.87 | 0.10-7.44) | .900 | 5 | 1.13 | 0.34-3.77) | .844 |
| Trend | .013 | .056 | .046 | .486 | .012 | ||||||||||||||||
| CC and CG | 84 | 101 | 1.78 | 1.20-2.63 | .004 | 44 | 1.61 | 0.99-2.64 | .056 | 57 | 1.91 | 1.18-3.08 | .008 | 18 | 1.35 | 0.69-2.64 | .382 | 83 | 1.87 | 1.23-2.85 | .004 |
| IL6R | rs7549250 | ||||||||||||||||||||
| AA | 59 | 76 | 1 | 34 | 1 | 42 | 1 | 13 | 1 | 63 | 1 | ||||||||||
| AG | 118 | 87 | 0.58 | 0.37-0.91 | .018 | 40 | 0.61 | 0.35-1.06 | .082 | 47 | 0.53 | 0.31-0.91 | .022 | 24 | 0.93 | 0.44-1.96 | .845 | 63 | 0.51 | 0.31-0.82 | 0006 |
| GG | 53 | 38 | 0.55 | 0.32-0.96 | .034 | 18 | 0.60 | 0.30-1.20 | .150 | 20 | 0.51 | 0.26-0.99 | .046 | 5 | 0.43 | 0.14-1.29 | .134 | 33 | 0.59 | 0.33-1.05 | .071 |
| Trend | .021 | .115 | .028 | .162 | .039 | ||||||||||||||||
| AG and GG | 171 | 125 | 0.57 | 0.38-0.87 | .009 | 58 | 0.61 | 0.36-1.02 | .061 | 67 | 0.52 | 0.32-0.87 | .012 | 29 | 0.77 | 0.38-1.59 | .488 | 96 | 0.53 | 0.34-0.83 | .006 |
| IL6R | rs2229238 | ||||||||||||||||||||
| CC | 191 | 179 | 1 | 86 | 1 | 93 | 1 | 36 | 1 | 143 | 1 | ||||||||||
| TC | 39 | 21 | 0.59 | 0.33-1.04 | .068 | 5 | 0.28 | 0.11-0.75 | .011 | 16 | 0.88 | 0.46-1.68 | .689 | 6 | 0.8 | 0.31-2.03 | .640 | 15 | 0.55 | 0.29-1.04 | .068 |
| TT | 3 | 1 | 0.42 | 0.04-4.23 | .462 | 1 | 0.71 | 0.07-7.05 | .771 | 0 | .985 | 0 | .990 | 1 | 0.57 | 0.05-5.86 | .634 | ||||
| Trend | .054 | .021 | .437 | .440 | .071 | ||||||||||||||||
| TC and TT | 42 | 22 | 0.58 | 0.33-1.01 | .054 | 6 | 0.31 | 0.13-0.77 | .011 | 16 | 0.82 | 0.43-1.57 | .555 | 6 | 0.74 | 0.29-1.88 | .527 | 16 | 0.55 | 0.29-1.03 | .061 |
| TNFSF11 | rs2200287 | ||||||||||||||||||||
| GG | 163 | 123 | 1 | 61 | 1 | 62 | 1 | 23 | 1 | 100 | 1 | ||||||||||
| AG | 62 | 68 | 1.53 | 1.00-2.35 | .048 | 27 | 1.23 | 0.71-2.13 | .451 | 41 | 1.82 | 1.09-3.02 | .022 | 12 | 1.40 | 0.65-2.99 | .389 | 56 | 1.59 | 1.01-2.51 | .044 |
| AA | 8 | 12 | 2.18 | 0.85-5.58 | .105 | 5 | 1.75 | 0.55-5.60 | .347 | 7 | 2.70 | 0.91-8.04 | .074 | 7 | 6.22 | 2.06-18.80 | .001 | 5 | 1.19 | 0.37-3.87 | .768 |
| Trend | .017 | .267 | .007 | .005 | .089 | ||||||||||||||||
| AA and AG | 70 | 80 | 1.61 | 1.07-2.41 | .022 | 32 | 1.29 | 0.77-2.17 | .331 | 48 | 1.91 | 1.17-3.11 | 0.009 | 19 | 1.96 | 1.00-3.85 | .050 | 61 | 1.55 | 1.00-2.40 | .051 |
| TNFSF11 | rs2148072 | ||||||||||||||||||||
| CC | 162 | 118 | 1 | 59 | 1 | 59 | 1 | 21 | 1 | 97 | 1 | ||||||||||
| TC | 62 | 67 | 1.56 | 1.02-2.39 | .041 | 26 | 1.23 | 0.71-2.14 | .467 | 41 | 1.87 | 1.12-3.12 | 0.016 | 13 | 1.67 | 0.78-3.56 | .183 | 54 | 1.56 | 0.99-2.47 | .055 |
| TT | 8 | 12 | 2.25 | 0.88-5.77 | .091 | 5 | 1.81 | 0.56-5.81 | .319 | 7 | 2.77 | 0.93-8.22 | 0.067 | 7 | 6.83 | 2.24-20.85 | .0007 | 5 | 1.21 | 0.37-3.90 | .752 |
| Trend | .013 | .260 | 0.006 | .002 | .102 | ||||||||||||||||
| TC and TT | 70 | 79 | 1.64 | 1.09-2.47 | .018 | 31 | 1.30 | 0.77-2.19 | .334 | 48 | 1.97 | 1.21-3.21 | 0.007 | 20 | 2.28 | 1.15-4.49 | .018 | 59 | 1.52 | 0.98-2.37 | .061 |
| TNFSF11 | rs9525643 | ||||||||||||||||||||
| AA | 49 | 61 | 1 | 29 | 1 | 32 | 1 | 15 | 1 | 46 | 1 | ||||||||||
| AG | 126 | 100 | 0.60 | 0.38-0.96 | .035 | 50 | 0.67 | 0.38-1.19 | .177 | 50 | 0.54 | 0.30-0.95 | 0.034 | 14 | 0.37 | 0.16-0.82 | .015 | 86 | 0.67 | 0.41-1.11 | .120 |
| GG | 58 | 40 | 0.52 | 0.30-0.91 | .022 | 13 | 0.38 | 0.18-0.80 | .012 | 27 | 0.63 | 0.32-1.21 | 0.167 | 12 | 0.68 | 0.29-1.58 | .367 | 28 | 0.46 | 0.25-0.86 | .015 |
| Trend | .020 | .011 | 0.169 | .357 | .014 | ||||||||||||||||
| AG and GG | 184 | 140 | 0.58 | 0.37-0.90 | .015 | 63 | 0.58 | 0.33-1.00 | .050 | 77 | 0.57 | 0.33-0.97 | 0.039 | 26 | 0.47 | 0.23-0.95 | .036 | 114 | 0.60 | 0.37-0.98 | .040 |
| IL5RA | rs163551 | ||||||||||||||||||||
| AA | 64 | 75 | 1 | 38 | 1 | 37 | 1 | 11 | 1 | 64 | 1 | ||||||||||
| AG | 108 | 97 | 0.75 | 0.49-1.17 | .209 | 41 | 0.64 | 0.37-1.09 | .102 | 56 | 0.89 | 0.52-1.52 | 0.677 | 22 | 1.19 | 0.54-2.62 | .666 | 75 | 0.68 | 0.43-1.08 | .104 |
| GG | 60 | 28 | 0.39 | 0.22-0.69 | .001 | 12 | 0.33 | 0.16-0.70 | .004 | 16 | 0.45 | 0.23-0.91 | 0.026 | 8 | 0.76 | 0.28-2.02 | .581 | 20 | 0.32 | 0.17-0.60 | .0004 |
| Trend | .002 | .003 | 0.035 | .620 | .0005 | ||||||||||||||||
| AG and GG | 168 | 125 | 0.62 | 0.41-0.94 | .026 | 53 | 0.53 | 0.32-0.88 | .014 | 72 | 0.73 | 0.44-1.21 | 0.226 | 30 | 1.03 | 0.49-2.19 | .930 | 95 | 0.55 | 0.35-0.85 | .008 |
| IL5RA | rs163550 | ||||||||||||||||||||
| GG | 65 | 77 | 1 | 39 | 1 | 38 | 1 | 10 | 1 | 67 | 1 | ||||||||||
| GC | 109 | 101 | 0.77 | 0.50-1.19 | .243 | 43 | 0.66 | 0.39-1.14 | .135 | 58 | 0.90 | 0.53-1.52 | 0.690 | 25 | 1.52 | 0.68-3.37 | .305 | 76 | 0.66 | 0.42-1.05 | .080 |
| CC | 57 | 22 | 0.31 | 0.17-0.57 | .0002 | 9 | 0.26 | 0.11-0.58 | .001 | 13 | 0.37 | 0.18-0.78 | 0.008 | 6 | 0.67 | 0.23-1.96 | .462 | 16 | 0.26 | 0.13-0.50 | .00007 |
| Trend | .0003 | .001 | 0.014 | .58 | .00008 | ||||||||||||||||
| CC and GC | 166 | 123 | 0.61 | 0.41-0.92 | .019 | 52 | 0.52 | 0.31-0.87 | .012 | 71 | 0.71 | 0.43-1.17 | 0.182 | 31 | 1.22 | 0.56-2.63 | .614 | 92 | 0.52 | 0.34-0.81 | .003 |
Abbreviations: CI, confidence interval; EOPE, early onset of preeclampsia; LOPE, late onset of preeclampsia; PE, preeclampsia.
aAdjusted for maternal age by years, education, and body mass index.
Although significant associations observed for preeclampsia overall were mainly seen for LOPE (Table 4), IL-5RA rs163550 and rs163551 showed similar association with LOPE and EOPE. A significant reduced risk was also observed for IL-6R rs2229238 TC genotypes compared to CC genotypes and TNFSF11 rs9525643 GG genotype compared to AA genotype for EOPE but not for LOPE.
After being stratified according to the severity of preeclampsia, the majority of significant associations remained for severe preeclampsia but not for mild preeclampsia (Table 5). Two SNPs of TNFSF11 (rs2200287 and rs2148072), however, showed significantly increased risk of mild preeclampsia if women carried TNFSF11 rs2200287 AA genotype compared to GG genotype (ORAA = 6.22; 95% CI, 2.06-18.80; P trend = .005) or TNFSF11 rs2148072 TT genotype compared to CC genotype (ORTT = 6.83; 95% CI, 2.24-20.85; P trend = .002).
We further examined the combination of significant SNPs in relation to the risk of preeclampsia and its subtypes (data not shown). We found that combination of IL5RA rs163551 GG genotype and IL5RA rs163551 CC genotype was associated with a reduced risk of EOPE compared to other genotypes (OR = 0.32; 95% CI, 0.15-0.68; P = .003; 9 exposed cases and 57 controls). Combination of L1R1 rs3771201 GG genotype, IL6R rs7549250 AG genotype, TNFSF11 rs2200287 AG genotype, and TNFSF11 rs2148072 AG genotype was associated with an increased risk of LOPE (OR = 2.46; 95% CI, 1.06-5.70; P = .036; 14 exposed cases and 12 controls). IL5RA rs163551 GG genotype and IL5RA rs163551 CC genotype together was associated with a reduced risk of LOPE (OR = 0.40; 95% CI, 0.21-0.78; P = .003; 13 exposed cases and 57 controls). Additionally, women who carried both TNFSF11 rs2200287 AA genotype and TNFSF11 rs2148072 TT genotype experienced an increased risk of mild preeclampsia compared to other genotypes (OR = 5.61; 95% CI, 1.91-16.46; P = .002; 7 exposed cases and 8 controls). Women who carried both IL5RA rs163551 GG genotype and IL5RA rs163551 CC genotype had a reduced risk of mild preeclampsia (OR = 0.33; 95% CI, 0.18-0.61; P = .0003; 16 exposed cases and 57 controls).
We observed strong linkage disequilibrium of 5 blocks in 28 SNPs (data not shown). Haplotype analyses were consistent with the results of the individual SNP analyses and did not provide additional insight into these associations (data not shown).
Discussion
The present study found that genetic polymorphisms in IL-1R1, IL-6R, TNFSF11, and IL-5RA were associated with the risk of preeclampsia in a Chinese Han population, and the risk varied by preeclampsia subtypes. Specifically, genetic polymorphisms in IL-6R (rs2229238), TNFSF11 (rs9525643), and IL-5RA (rs163551, rs163550) were associated with EOPE risk, IL-6R (rs7549250), TNFSF11 (rs2200287, rs2148072), and IL-5RA (rs163551, rs163550) were associated with LOPE risk, TNFSF11 (rs2200287, rs2148072) were associated with mild preeclampsia, whereas IL-1R1 (rs3771202), TNFSF11 (rs9525643), and IL-5RA (rs163551, rs163550) were associated with severe preeclampsia risk in this population.
No published studies have investigated genetic polymorphisms in TNFSF11, TNFSF11A, IL-2RA, IL-5RA, IL-28RA, IRAK4, and KIT genes in relation to preeclampsia. An early study by Goddard et al from the United States including 394 preeclampsia and 602 normotensive pregnant women examined the association between IL-1A (rs3783550, rs2071374, rs17561) and preeclampsia and found no association between them.13 Another study from the United States by Haggerty et al involving 150 preeclampsia and 661 normotensive pregnant women reported that genetic polymorphisms in IL-1A rs17561 were associated with preeclampsia risk both in black women and white women.14 A study conducted by Andraweera et al including 175 preeclampsia and 171 normal pregnant women from Sri Lanka investigated polymorphisms in IL-1A (rs1800587, rs17561) and found IL-1A rs1800587, but not rs17561, was associated with preeclampsia risk.9 Ghasemi et al conducted a study in Iran involving 305 preeclampsia women and 325 controls and reported that carriage of the variant C allele in IL-1A rs1800587 was associated with preeclampsia.8 The only study conducted in China, which included 402 preeclampsia cases and 554 controls, investigated polymorphism IL-1A rs17561 and found that carriage of the variant C allele in IL-1A rs17561 was associated with preeclampsia.10 However, our study did not find that polymorphisms in IL-1A (rs3783550, rs2071374, rs17561) were associated with preeclampsia risk.
Five studies also investigated genetic polymorphisms in IL-1B genes in relation to preeclampsia risk9,11–13,15; none found any association, which was consistent with our study.
Only one study conducted by Franchim et al, which included 109 preeclampsia cases and 174 controls, investigated polymorphisms in IL-1R1 rs2234650 and preeclampsia and found no association.4 Although our study did not examine the SNP rs2234650, we investigated IL-1R1 rs3771202 and found it was associated with the risk of preeclampsia, and the association was mainly seen for severe preeclampsia. The IL-1R1 encodes a receptor located on type 2 T helper cell (Th2) cells and binds with IL-1R AcP and IL-1A or IL-1B to form a trimeric complex. In this complex, the Toll- and IL-1R-like domains get close to each other, which subsequently results in the recruitment of myeloid differentiation primary response gene 88 (MYD88), toll-interacting protein, and IL-1 receptor-associated kinase 4 (IRAK4). This then affects inflammatory response through the protein-related pathways involved in innate immunity.20,21 Additionally, rs3771202 was located in the noncoding intron, where it may regulate exon transcription. To confirm our results and identify potential regulatory SNPs, we used ESEfinder22 to look for potential ESEs and modifications of the splicing factors binding site pattern resulting from the mutation. Although rs3771202 was associated with the risk of severe preeclampsia in our population, we did not find rs3771202 could change binding sites on RNA splicing. Its function still needs further study.
Substantial evidence supports a role of cytokines in the etiology of preeclampsia during both the early placental stage and the later systemic stage. The Th1-type cytokines including IL-2, TNF-α, and IFN-γ can induce several cell-mediated inflammatory responses. On the other hand, Th2-type cells secrete the Th2 cytokines IL-4, IL-5, IL-6, and IL-10, which are associated with help for humoral immunity.23 Th2-type immunity is associated with normal pregnancy, whereas an excessive Th1 reactivity is associated with adverse pregnancy complications including spontaneous miscarriage and preeclampsia.24,25 It is biologically plausible that polymorphisms in inflammatory mediator genes involved in cytokine-mediated signaling pathways might influence preeclampsia risk.
An early study conducted in China found that alterations in IL-6 and its corresponding receptors IL-6R play a role in the etiology of severe preeclampsia.26 There were 6 studies that investigated genetic variation in IL6 rs1800795 and risk of preeclampsia and none found an association.27–32 Although our study did not examine the SNP rs1800795, we investigated IL-6 rs7805828 and also found no association. Although no existing study has investigated polymorphisms in IL-6R and preeclampsia, our study examined IL-6R (rs7549250 and rs2229238) and found that IL-6R rs7549250 was associated with LOPE and that IL-6R rs2229238 was associated with EOPE. Using ESEfinder, we found that rs7549250 could change SF2/ASF (IgM-BRCA1) binding sites, whereas rs2229238 could modify SC35 binding sites on splicing. These altered splicing binding site patterns may induce splicing switching or exons skiping33 and may subsequently result in affected synthesis of IL-6R.
The ligand-binding component IL-6R together with signal-transducing component gp130 form a cell surface cytokine receptor complex, which plays a key role in the IL-6 signaling.34 The complex can also activate multiple intracellular signaling cascades including janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway and mitogen-activated protein kinase pathway, affecting maternal inflammation that is associated with preeclampsia.
Tumor necrosis factor receptor superfamily, member 11a (TNFRSF11A), is also known as a receptor activator of nuclear factor-κB (RANK). TNFRSF11A and its ligand (TNFSF11 or RANKL) are 2 functionally linked genes and are frequently considered together when considering their role in determination of various traits, such as dendritic cell/T-cell communication, T-cell activation, or early B-lymphocyte development. Their primary function is to activate the transcription factor nuclear factor κb (NF-κb),35 which is implicated with dendritic cells, T-cell activation, and subsequently proinflammatory cytokines secretion, such as IL-1, IL-6, and IL-12.36 Furthermore, activation of NF-κb is also a key process involved in the toll-like receptor associated with innate immunity, including Myd88-dependent or independent pathways. These biological functions are implicated in the pathogenesis of preeclampsia.37 Our study found that TNFSF11 (rs2200287 and rs2148072) was associated with LOPE and mild preeclampsia, whereas TNFSF11 rs9525643 was associated with EOPE and severe preeclampsia. Using ESEfinder, we found that rs2200287 could change SF2/ASF (IgM-BRCA1)-binding site, whereas rs9525643 could change SRp40-binding sites on splicing. These altered splicing binding site patterns may induce splicing switching or exon skipping,33 subsequently resulting in affected synthesis of TNFSF11. Although rs2148072 was associated with the risk of preeclampsia in our population, its function was unclear.
The IL-5RA encodes an important receptor of IL-5, which binds to IL-5 and is activated in at least 3 signaling pathways including JAK2/STAT5, Btk, and Ras/ERK pathways.38 Among them, the activation of the Ras/ERK pathway can promote cell survival and proliferation against trophoblastic apoptosis and aponecrosis, which have been implicated as key pathologic events in preeclampsia.39 Our study found that polymorphisms in IL5-RA (rs163551, rs163550) were associated with EOPE, LOPE, and severe preeclampsia. We found that rs163551 could change SF2/ASF and SF2/ASF (IgM-BRCA1) binding sites, whereas rs163551 could change SF2/ASF and SC35 binding sites on splicing, suggesting that these SNPs have the potential to affect the synthesis of IL-5RA.
The present study was a candidate gene study. We recognized the possibility that one or more additional SNPs, in cis, could be causal SNPs. Additional studies are needed to survey the common genetic variation in these 4 genes, explore more functional SNPs associated with the risk of preeclampsia, and determine the pattern of linkage disequilibrium between each other. Relatedly, functional studies should investigate the functional consequences of these SNPs. Future experimental studies are needed to characterize these SNPs biological function.
Strengths and limitations should be considered when interpreting the study results. Diagnosis of preeclampsia in our study was based on medical records not self-reporting, which minimized potential disease misclassification. We have collected detailed information on various potential confounding factors and have controlled them in the model. Our study sample size was moderate and might have limited power for subtype analyses. Future larger studies are warranted to replicate the observed associations. The current study was hospital based, which might have limited generalizability. However, the rate of preeclampsia (6.4%) in our study population was within the range (2%-8%) reported worldwide.1
In conclusion, our study provided the first evidence that genetic polymorphisms in IL-1R1, IL-6R, TNFSF11, and IL-5RA were associated with the pathogenesis of preeclampsia and the association varied by preeclampsia subtypes. These findings warrant further investigation in larger population-based epidemiologic studies among different populations.
Supplementary Material
Footnotes
Authors’ Note: SW and YZ contributed equally to this work.
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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by theNational Institutes of Health grants (No. K02HD70324), National Natural Science Foundation of China grants (No. 81473061), Natural Science Foundation of Shanxi province grants (No. 2013021033-2), “100 Talent Plan” Award of Shanxi Province, Construction Project of Characteristic Key Disciplines for Universities of Shanxi Province and “10 Talent Plan” Award of Shanxi Medical University.
Supplemental Material: The online supplemental tables are available at http://journals.sagepub.com/doi/suppl/10.1177/1933719116660844.
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