循环免疫细胞和炎症因子水平与胎盘早剥的因果关联评估——一项孟德尔随机化研究
Assessment of the Causal Associations of Circulating Immune Cell and Inflammatory Factor Levels with Placental Abruption—A Mendelian Randomization Study
摘要: 目的:本研究采用孟德尔随机化(MR)和两步MR分析,探究免疫细胞和循环炎症因子与胎盘早剥的因果关系。方法:本研究从公开来源获取731个免疫细胞表型和91个循环炎症因子的遗传数据。胎盘早剥的结局数据来自芬兰基因组联盟(R9),其中包括8812例欧洲血统病例和425,678例欧洲血统对照。研究采用逆方差加权法、敏感性分析和两步孟德尔随机化方法,来评估免疫细胞与胎盘早剥之间的因果关系,以及炎症因子在其中的潜在中介作用。结果:16种免疫细胞表型和5种循环炎症因子与胎盘早剥显著相关。研究发现CD6和TNF介导CD39+ CD8br细胞上的CD8对胎盘早剥产生影响。结论:本研究揭示特异性免疫细胞和炎症因子与胎盘早剥之间具有潜在因果关系,确定了预防和管理这种严重产科并发症的潜在生物标志物和治疗靶点。
Abstract: Objective: To investigate the causal effects of immune cells and circulating inflammatory proteins on placental abruption using Mendelian randomization (MR) and two-step MR analyses. Methods: Genetic data for 731 immune cell traits and 91 circulating inflammatory proteins were obtained from publicly available sources. Outcome data for placental abruption were obtained from the FinnGen consortium (R9), including 8812 cases and 425,678 controls of European descent. Inverse variance-weighted MR, sensitivity analyses, and two-step MR were performed to assess causal relationships and potential mediating roles of inflammatory proteins between immune cells and placental abruption. Results: Sixteen immune cell traits and five circulating inflammatory proteins were significantly associated with placental abruption. CD6 and TNF were found to mediate the effect of CD8 on CD39+ CD8br cells on placental abruption. Conclusion: This study provides evidence for the causal role of specific immune cells and inflammatory proteins in the development of placental abruption, identifying potential biomarkers and therapeutic targets for prevention and management of this serious obstetric complication.
文章引用:杨慧, 李钦柯, 杨竹. 循环免疫细胞和炎症因子水平与胎盘早剥的因果关联评估——一项孟德尔随机化研究[J]. 临床医学进展, 2025, 15(7): 399-407. https://doi.org/10.12677/acm.2025.1572002

1. 引言

胎盘早剥是指胎盘在分娩前部分或完全从子宫壁剥离,是产科严重并发症之一,可导致母婴发病率和死亡率显著增加[1] [2]。全球范围内,胎盘早剥的发生率占所有妊娠的0.3%至1.0% [3] [4]。目前已确定的危险因素包括高龄产妇、吸烟、高血压、子痫前期、既往剖宫产和创伤[3]-[5]。流行病学研究显示,胎盘早剥的发生存在种族差异,非裔美国女性的发病率高于白人女性[6]。此外,低教育水平和缺乏产前护理等社会经济因素也与胎盘早剥风险增加相关[6] [7]。胎盘早剥并发症后果可能极为严重,潜在结局包括胎儿生长受限、早产和死胎[2] [5]。母体并发症则可能涉及出血、弥散性血管内凝血(DIC)和低血容量性休克[1] [2]。因此,早期发现和及时处理对改善母胎预后至关重要。流行病学研究强调,识别高危妊娠并实施适当干预措施,对降低胎盘早剥发生率和严重程度具有重要意义[1] [6]

胎盘早剥的发病机制复杂且受多因素影响,涉及多种可能导致胎盘从子宫壁过早分离的机制[8]。胎盘早剥的主要病理生理过程之一是子宫胎盘灌注不足,这是由于胎盘血流不足所致。这种灌注不足可由多种因素引起,包括母体高血压、子痫前期和血栓形成倾向[1] [2]。胎盘早剥发病机制的另一个关键机制是母胎界面的破坏,其特征是滋养细胞侵入蜕膜和螺旋动脉过程异常[5]。滋养细胞侵袭和螺旋动脉重塑的异常可导致胎盘功能不全,进而增加胎盘早剥的风险。氧化应激也与胎盘早剥的发病机制有关[1] [2]。活性氧(ROS)水平升高和抗氧化能力下降会导致胎盘损伤和功能障碍,从而增加胎盘早剥的风险。此外,氧化应激与促炎途径的激活有关,这可能会进一步加剧胎盘损伤[1] [5]

遗传因素也可能在胎盘早剥的发病机制中发挥作用。研究表明,与血栓形成倾向、炎症和血管功能有关的多个候选基因与胎盘早剥风险增加相关[1] [2]。但这些遗传因素如何具体促成胎盘早剥的发展仍需进一步阐明。免疫细胞在维持正常妊娠中起着至关重要的作用,其功能失调已被证实是胎盘早剥发病机制中的因素之一。母胎界面的免疫细胞(如蜕膜自然杀伤细胞(dNK)、巨噬细胞和T细胞)需维持巧妙平衡,来调节滋养层侵袭和螺旋动脉重塑这一过程[8]。这种免疫平衡若被打破(如这些免疫细胞的比例或功能特性发生改变),可能促使胎盘早剥的发生[9] [10]。炎症因子在胎盘早剥的发病机制中也起着关键作用,可导致胎盘功能障碍和胎盘与子宫壁过早分离[2]。多种促炎细胞因子、趋化因子和其他介质已被证实可通过多种机制参与胎盘早剥的发生[5] [8] [11]。在胎盘早剥女性的母体血清和胎盘组织中白细胞介素-6 (IL-6)和肿瘤坏死因子-α (TNF-α)等促炎细胞因子水平升高,提示其参与了该疾病病理过程[12] [13]

本研究旨在通过孟德尔随机化(MR)和两步MR分析,探究免疫细胞和循环炎症因子对胎盘早剥的因果效应。我们假设某些免疫细胞表型和炎症因子与胎盘早剥风险增加存在因果关联,且部分炎症因子可能介导免疫细胞对胎盘早剥的影响。

2. 资料与方法

2.1. 数据来源

本研究中使用的免疫表型数据来自3757名撒丁岛人的队列,涵盖了731种免疫表型和20,143,392个单核苷酸多态性(SNP)。该数据集包括118个细胞计数、389个与表面抗原相关的中位荧光强度(MFI)、32个细胞形态参数和192个相对计数,相对计数是指不同细胞之间的比率[14]。91种循环炎症因子来自对11个队列的荟萃分析,共有14,824名欧洲血统的参与者,原始文献详细描述了测量炎症因子的方法[15]。每种蛋白的全基因组关联研究(GWAS)汇总统计数据可在https://www.phpc.cam.ac.uk/ceu/proteins和EBI GWAS目录(登录号为GCST90274758至GCST90274848)下载。胎盘早剥数据来自芬兰基因组联盟(FinnGen consortium, R9) [16],包括8812例欧洲血统病例和425,678名欧洲血统对照。

2.2. 孟德尔随机化和敏感性分析

具体筛选步骤包括使用TwoSampleMR软件包提取相关SNP [17],我们在全基因组范围内选择具有显著差异的SNP (采用P < 5 × 108作为显著性阈值筛选大多数免疫细胞和炎症因子,采用P < 1 × 106作为筛选阈值以获取足够SNP进行下一步分析)。并测试其连锁不平衡(r2 = 0.001, kb = 10000)及去除回声序列。为了减少因弱工具变量引起的偏移,剔除F统计量(衡量SNP稳健性的指标,可提示SNP强度)低于10的SNP [18]。最终共纳入331个免疫细胞和63种炎症因子进行进一步研究。

在工具变量(IVs)无多效性的情况下,逆方差加权法(IVW)显示出最高的统计效能和有效性[19]。因此,本研究将IVW作为主要研究方法[20]。此外,采用MR-Egger、加权中位数、简单模式和加权模式的方法对最终结果进行验证[21] [22]。同时,为确保结果的稳健性,我们使用Cochran’s Q检验来评估IVW和MR Egger中SNP的异质性[23]。通过MR-Egger截距评估水平多效性[21],并进行留一法分析,以评估因果效应是否由单个潜在影响的SNP驱动[24]。使用MR-Presso检测多效性残差和异常值。采用MR-Steiger评估因果方向是否正确,如果暴露可能导致结局,则为“TRUE”,否则为“FALSE”[25]。所有统计分析均在R软件中进行。

2.3. 两步孟德尔随机化分析

我们采用两步孟德尔随机化方法研究了循环炎症因子在免疫细胞和胎盘早剥之间的潜在介导作用。首先,我们假设免疫细胞对胎盘早剥的影响未知,并分别计算了免疫细胞在胎盘早剥中的效应值(β1)、炎性因子在胎盘早剥中的效应值(β2)和免疫细胞在胎盘早剥过程中的总效应值(β0)。此时,中介效应为β1 × β2,中介比例为β1 × β2/β0,以此量化循环炎症因子在免疫细胞与胎盘早剥关系中的中介作用。

2.4. 软件

所有分析均在R Studio环境中使用R版本4.2.3完成。特定孟德尔随机化分析采用R包“TwoSampleMR”。统计结果中,P < 0.05被认为存在潜在的因果关系。Bonferroni检验(1.5e−4, 0.05/331)用作多重检验假设的阳性阈值。但考虑到检验次数增加可能导致II型错误的增加,P < 0.05的结果仍被纳入进一步的中介分析。

3. 结果

3.1. 暴露因素的工具变量

与胎盘早剥显著相关的16种免疫细胞表型,用作工具变量(IVs)的SNP数量为5至53个(中位数8个);对于5种循环炎症因子,SNP数量为6至12个(中位数7个)。工具变量的F统计量均高于10,表明MR分析具有足够的强度。通过关联散点图(显示与16种免疫细胞亚群(P < 8.17 × 105)显著相关的SNP及其对胎盘早剥风险影响)来直观评估通过MR分析确定的因果关系的一致性和强度。通过留一法进行敏感性分析,该分析通过迭代剔除单个SNP并重新计算因果估计来评估MR结果的稳健性。

3.2. 免疫细胞对胎盘早剥的因果效应

使用逆方差加权(IVW)方法评估免疫细胞对胎盘早剥的因果效应时,发现16个免疫细胞表型与胎盘早剥有关(图1)。其中,13种免疫细胞表型显示出正相关,NK细胞的效应最强(OR 1.785, 95% CI 1.294~2.463, P < 0.0001)。三种免疫细胞表型,包括幼稚CD4+ T细胞、幼稚CD4+细胞上的CD4和CD62L + 髓系树突状细胞上的CD80,与胎盘早剥呈负相关。在反向MR分析中,发现胎盘早剥与幼稚CD4+上的CD4呈正相关(OR 1.085, 95% CI 1.002~1.175, P = 0.045)。

Figure 1. Two-sample Mendelian randomization analysis of the impact of circulating immune cell levels on the incidence of placental abruption. IVW, Inverse Variance Weighted; OR, Odds Ratio; 95% CI, 95% Confidence Interval

1. 循环免疫细胞水平对胎盘早剥发生率影响的两样本孟德尔随机化分析。IVW,逆方差加权;OR,比值比;95%置信区间

3.3. 循环炎症因子对胎盘早剥的因果效应

采用逆方差加权法(IVW)分析发现,五种循环炎症因子与胎盘早剥有关(图2)。CD244、CD6和ST1A1显示出正相关,其中ST1A1的效应最强(OR 2.024, 95% CI 1.286~3.188, P = 0.002)。TNFB和TNF与胎盘早剥呈负相关。

Figure 2. Two-sample Mendelian randomization analysis of the impact of circulating inflammatory factor levels on the incidence of placental abruption. IVW, Inverse Variance Weighted; OR, Odds Ratio; 95% CI, 95% Confidence Interval

2. 循环炎症因子水平对胎盘早剥发生率影响的两样本孟德尔随机化分析。IVW,逆方差加权;OR,比值比;95%置信区间

3.4. 循环炎症蛋白的介导分析

我们采用两步孟德尔随机化进一步验证循环炎症细胞因子是否介导免疫细胞对胎盘早剥的产生影响。首先,我们发现两种循环炎症因子CD6和TNF与胎盘早剥相关(β2 = −0.611和0.24,图3(A))。CD6与胎盘早剥呈正相关(OR 1.271, 95% CI 1.02~1.585, P = 0.033),而TNF呈负相关(OR 0.543, 95% CI 0.368~0.8, P = 0.002)。随后,我们评估了与胎盘早剥有因果关系的16种免疫细胞表型对CD6和TNF的影响,发现CD39+ CD8br细胞上的CD8与CD6和TNF-均显著相关(β1 = −0.099和−0.095,图3(B))。CD39+ CD8br细胞上的CD8降低了CD6 (OR 0.905, 95% CI 0.853~0.961, P = 0.001)和TNF (OR 0.91, 95% CI 0.857~0.966, P = 0.002)的水平。我们计算了CD39+ CD8br细胞上CD8对CD6和TNF介导的胎盘早剥的间接效应和中介比例(图3(C))。CD6的间接效应为−0.0238 (95% CI −0.0392至−0.00844,P = 0.002),中介比例为−8.02% (95% CI −13.2%至−2.84%)。TNF的间接效应为0.0578 (95% CI 0.0209~0.0948, P = 0.002),中介比例为19.4% (95% CI 7.02%~31.9%)。因此,研究结果表明,CD6和TNF是CD39+ CD8br细胞上CD8介导的胎盘早剥的重要介质。图3(D)显示了TNF或CD6通过CD39+ CD8br细胞上的CD8介导的胎盘早剥的计算过程。

(A)

(B)

(C)

(D)

(A) CD8对CD38+ CD8br细胞对炎症因子CD6和TNF(β1)影响的两样本孟德尔随机化结果。(B) 炎症因子CD6和TNF对胎盘早剥(β2)发生的影响的两样本孟德尔随机化分析。(C) CD8对CD38+ CD8br细胞通过CD6或TNF对胎盘早剥的调节作用值(β1*β2)和调节作用百分比(β1*β2/β0) × 100%。(D) 中介孟德尔随机化分析示意图。IVW,逆方差加权;OR,比值比;95%置信区间。IVW,逆方差加权;OR,比值比;95%置信区间。

Figure 3. Mediation Mendelian randomization analysis of the effect of circulating immune cell levels on placental abruption

3. 循环免疫细胞水平对胎盘早剥影响的孟德尔随机化分析

IVW检验和MR-Egger回归的Cochran’s Q统计均未显示显著异质性。MR Egger截距的P值在0.501至0.901之间,表明水平多效性极低。使用其他MR方法的敏感性分析结果与IVW结果一致。

4. 讨论

近年来,免疫细胞、炎性因子和胎盘早剥之间的关系一直是研究的热点。本研究采用孟德尔随机化(MR)和两步MR分析探究了免疫细胞和循环炎症因子对胎盘早剥的因果效应。结果发现,16种免疫细胞特征和5种循环炎症因子与胎盘早剥显著相关,其中CD6和TNF介导了CD39+ CD8br细胞对胎盘早剥的影响。

既往研究表明,TNF-α水平升高与妊娠并发症风险增加相关,尤其在复发性自然流产、子痫前期、胎膜早破和宫内胎儿生长受限等情况下[26] [27]。然而,我们的研究显示,TNF可能在胎盘早剥中具有保护作用,这与既往观点相悖。这种差异可能与样本选择、地域和种族差异以及研究设计有关。此外,抗TNF-α治疗在特定情况下的潜在益处表明,TNF-α的作用可能具有剂量依赖性——适当水平可能有益,而过量则可能导致不良妊娠结局[28] [29]。值得注意的是,胎盘的特异性反应可能解释了TNF-α在胎盘早剥中的保护作用。例如,TNF-α在胎盘中的调控可能有助于维持其结构和功能,防止过早剥离。遗传背景和环境因素也可能在个体对TNF-α反应的差异中起着关键作用。因此,我们的研究结果为TNF-α在妊娠并发症中的作用提供了新的视角,特别是在探索其在胎盘早剥中的潜在保护作用方面。未来的研究应进一步探索TNF-α的剂量反应效应及其在妊娠不同阶段和条件下的具体机制,以优化抗TNF治疗策略[27]

研究已证实多种免疫失调与胎盘早剥风险增加相关[30] [31]。调节性T细胞(Treg细胞)在妊娠期间维持免疫耐受、防止母体免疫系统排斥胎儿方面起着关键作用。Treg细胞的失调与各种妊娠并发症有关,包括子痫前期和复发性流产[32] [33]。虽然Treg细胞在胎盘早剥中的具体作用尚未得到广泛研究,但Treg细胞功能障碍可能通过促进母胎界面的促炎环境而导致胎盘早剥风险增加[34]。B细胞负责产生抗体,并与各种妊娠并发症的发病机制有关[35] [36]。但目前仍缺乏关于它们与胎盘早剥发生机制的研究。自然杀伤(NK)细胞是先天免疫系统的关键组成部分,在妊娠的建立和维持中起着关键作用。NK细胞在蜕膜(胎盘的母体部分)中含量丰富,参与调节滋养细胞侵袭和螺旋动脉重塑[37]。虽然NK细胞在胎盘早剥中的具体作用尚未被充分研究,但NK细胞功能障碍可能通过扰乱胎盘发育和功能的正常过程,导致胎盘早剥风险增加。最近的研究强调了母胎界面免疫细胞的动态作用,表明这些细胞对于维持妊娠和调节母体对胎儿同种异体的免疫耐受至关重要。例如,蜕膜NK细胞、巨噬细胞和T细胞因其促进这种耐受的作用而受到关注,这对成功妊娠至关重要[38] [39]

综上,本研究有助于深入理解特定免疫细胞特征如何影响胎盘早剥,增加了越来越多的证据,强调了免疫系统在妊娠相关疾病中的复杂作用。这些发现凸显了通过调节免疫细胞功能来预防或减轻胎盘早剥和其他妊娠相关的并发症的靶向治疗的治疗潜力。

NOTES

*通讯作者。

参考文献

[1] Ananth, C.V., Keyes, K.M., Hamilton, A., Gissler, M., Wu, C., Liu, S., et al. (2015) An International Contrast of Rates of Placental Abruption: An Age-Period-Cohort Analysis. PLOS ONE, 10, e0125246.
https://doi.org/10.1371/journal.pone.0125246
[2] Tikkanen, M. (2010) Placental Abruption: Epidemiology, Risk Factors and Consequences. Acta Obstetricia et Gynecologica Scandinavica, 90, 140-149.
https://doi.org/10.1111/j.1600-0412.2010.01030.x
[3] Ananth, C.V., Oyelese, Y., Yeo, L., Pradhan, A. and Vintzileos, A.M. (2005) Placental Abruption in the United States, 1979 through 2001: Temporal Trends and Potential Determinants. American Journal of Obstetrics and Gynecology, 192, 191-198.
https://doi.org/10.1016/j.ajog.2004.05.087
[4] Pariente, G., Wiznitzer, A., Sergienko, R., Mazor, M., Holcberg, G. and Sheiner, E. (2010) Placental Abruption: Critical Analysis of Risk Factors and Perinatal Outcomes. The Journal of Maternal-Fetal & Neonatal Medicine, 24, 698-702.
https://doi.org/10.3109/14767058.2010.511346
[5] Oyelese, Y. and Ananth, C.V. (2006) Placental Abruption. Obstetrics & Gynecology, 108, 1005-1016.
https://doi.org/10.1097/01.aog.0000239439.04364.9a
[6] Shen, T.T., DeFranco, E.A., Stamilio, D.M., Chang, J.J. and Muglia, L.J. (2008) A Population-Based Study of Race-Specific Risk for Placental Abruption. BMC Pregnancy and Childbirth, 8, Article No. 43.
https://doi.org/10.1186/1471-2393-8-43
[7] Ananth, C. (1999) Incidence of Placental Abruption in Relation to Cigarette Smoking and Hypertensive Disorders during Pregnancy: A Meta-Analysis of Observational Studies. Obstetrics & Gynecology, 93, 622-628.
https://doi.org/10.1016/s0029-7844(98)00408-6
[8] Lockwood, C.J., Krikun, G., Caze, R., Rahman, M., Buchwalder, L.F. and Schatz, F. (2008) Decidual Cell-Expressed Tissue Factor in Human Pregnancy and Its Involvement in Hemostasis and Preeclampsia‐Related Angiogenesis. Annals of the New York Academy of Sciences, 1127, 67-72.
https://doi.org/10.1196/annals.1434.013
[9] Saito, S., Nakashima, A., Shima, T. and Ito, M. (2010) REVIEW ARTICLE: Th1/Th2/Th17 and Regulatory T-Cell Paradigm in Pregnancy. American Journal of Reproductive Immunology, 63, 601-610.
https://doi.org/10.1111/j.1600-0897.2010.00852.x
[10] Cerdeira, A.S., Rajakumar, A., Royle, C.M., Lo, A., Husain, Z., Thadhani, R.I., et al. (2013) Conversion of Peripheral Blood NK Cells to a Decidual NK-Like Phenotype by a Cocktail of Defined Factors. The Journal of Immunology, 190, 3939-3948.
https://doi.org/10.4049/jimmunol.1202582
[11] Rusterholz, C., Hahn, S. and Holzgreve, W. (2007) Role of Placentally Produced Inflammatory and Regulatory Cytokines in Pregnancy and the Etiology of Preeclampsia. Seminars in Immunopathology, 29, 151-162.
https://doi.org/10.1007/s00281-007-0071-6
[12] Germain, S.J., Sacks, G.P., Soorana, S.R., Sargent, I.L. and Redman, C.W. (2007) Systemic Inflammatory Priming in Normal Pregnancy and Preeclampsia: The Role of Circulating Syncytiotrophoblast Microparticles. The Journal of Immunology, 178, 5949-5956.
https://doi.org/10.4049/jimmunol.178.9.5949
[13] Luppi, P., Tse, H., Lain, K.Y., Markovic, N., Piganelli, J.D. and DeLoia, J.A. (2006) Preeclampsia Activates Circulating Immune Cells with Engagement of the NF-κB Pathway. American Journal of Reproductive Immunology, 56, 135-144.
https://doi.org/10.1111/j.1600-0897.2006.00386.x
[14] Orrù, V., Steri, M., Sidore, C., Marongiu, M., Serra, V., Olla, S., et al. (2020) Complex Genetic Signatures in Immune Cells Underlie Autoimmunity and Inform Therapy. Nature Genetics, 52, 1036-1045.
https://doi.org/10.1038/s41588-020-0684-4
[15] Zhao, J.H., Stacey, D., Eriksson, N., Macdonald-Dunlop, E., Hedman, Å.K., Kalnapenkis, A., et al. (2023) Genetics of Circulating Inflammatory Proteins Identifies Drivers of Immune-Mediated Disease Risk and Therapeutic Targets. Nature Immunology, 24, 1540-1551.
https://doi.org/10.1038/s41590-023-01588-w
[16] Kurki, M.I., Karjalainen, J., Palta, P., Sipilä, T.P., Kristiansson, K., Donner, K.M., et al. (2023) FinnGen Provides Genetic Insights from a Well-Phenotyped Isolated Population. Nature, 613, 508-518.
[17] Hemani, G., Zheng, J., Elsworth, B., Wade, K.H., Haberland, V., Baird, D., et al. (2018) The MR-Base Platform Supports Systematic Causal Inference across the Human Phenome. eLife, 7, e34408.
https://doi.org/10.7554/elife.34408
[18] Burgess, S., Timpson, N.J., Ebrahim, S. and Davey Smith, G. (2015) Mendelian Randomization: Where Are We Now and Where Are We Going? International Journal of Epidemiology, 44, 379-388.
https://doi.org/10.1093/ije/dyv108
[19] Burgess, S., Bowden, J., Fall, T., Ingelsson, E. and Thompson, S.G. (2017) Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology, 28, 30-42.
https://doi.org/10.1097/ede.0000000000000559
[20] Davies, N.M., Holmes, M.V. and Davey Smith, G. (2018) Reading Mendelian Randomisation Studies: A Guide, Glossary, and Checklist for Clinicians. BMJ, 362, k601.
https://doi.org/10.1136/bmj.k601
[21] Bowden, J., Davey Smith, G. and Burgess, S. (2015) Mendelian Randomization with Invalid Instruments: Effect Estimation and Bias Detection through Egger Regression. International Journal of Epidemiology, 44, 512-525.
https://doi.org/10.1093/ije/dyv080
[22] Hartwig, F.P., Davey Smith, G. and Bowden, J. (2017) Robust Inference in Summary Data Mendelian Randomization via the Zero Modal Pleiotropy Assumption. International Journal of Epidemiology, 46, 1985-1998.
https://doi.org/10.1093/ije/dyx102
[23] Bowden, J., Del Greco M., F., Minelli, C., Davey Smith, G., Sheehan, N.A. and Thompson, J.R. (2016) Assessing the Suitability of Summary Data for Two-Sample Mendelian Randomization Analyses Using MR-Egger Regression: The Role of the I2 Statistic. International Journal of Epidemiology, 45, 1961-1974.
https://doi.org/10.1093/ije/dyw220
[24] Burgess, S. and Thompson, S.G. (2017) Interpreting Findings from Mendelian Randomization Using the MR-Egger Method. European Journal of Epidemiology, 32, 377-389.
https://doi.org/10.1007/s10654-017-0255-x
[25] Hemani, G., Tilling, K. and Davey Smith, G. (2017) Orienting the Causal Relationship between Imprecisely Measured Traits Using GWAS Summary Data. PLOS Genetics, 13, e1007081.
https://doi.org/10.1371/journal.pgen.1007081
[26] Azizieh, F.Y. and Raghupathy, R.G. (2014) Tumor Necrosis Factor-Α and Pregnancy Complications: A Prospective Study. Medical Principles and Practice, 24, 165-170.
https://doi.org/10.1159/000369363
[27] Romanowska-Próchnicka, K., Felis-Giemza, A., Olesińska, M., Wojdasiewicz, P., Paradowska-Gorycka, A. and Szukiewicz, D. (2021) The Role of TNF-α and Anti-TNF-α Agents during Preconception, Pregnancy, and Breastfeeding. International Journal of Molecular Sciences, 22, Article 2922.
https://doi.org/10.3390/ijms22062922
[28] Carpentier, P.A., Dingman, A.L. and Palmer, T.D. (2011) Placental TNF-α Signaling in Illness-Induced Complications of Pregnancy. The American Journal of Pathology, 178, 2802-2810.
https://doi.org/10.1016/j.ajpath.2011.02.042
[29] Wu, H., You, Q., Jiang, Y. and Mu, F. (2021) Tumor Necrosis Factor Inhibitors as Therapeutic Agents for Recurrent Spontaneous Abortion (Review). Molecular Medicine Reports, 24, Article No. 847.
https://doi.org/10.3892/mmr.2021.12487
[30] Romero, R., Grivel, J., Tarca, A.L., Chaemsaithong, P., Xu, Z., Fitzgerald, W., et al. (2015) Evidence of Perturbations of the Cytokine Network in Preterm Labor. American Journal of Obstetrics and Gynecology, 213, 836.E1-836.E18.
https://doi.org/10.1016/j.ajog.2015.07.037
[31] Saji, F., Samejima, Y., Kamiura, S., Sawai, K., Shimoya, K. and Kimura, T. (2000) Cytokine Production in Chorioamnionitis. Journal of Reproductive Immunology, 47, 185-196.
https://doi.org/10.1016/s0165-0378(00)00064-4
[32] Sasaki, Y., Darmochwal-Kolarz, D., Suzuki, D., Sakai, M., Ito, M., Shima, T., et al. (2007) Proportion of Peripheral Blood and Decidual CD4+ CD25BRIGHT Regulatory T Cells in Pre-Eclampsia. Clinical and Experimental Immunology, 149, 139-145.
https://doi.org/10.1111/j.1365-2249.2007.03397.x
[33] Inada, K., Shima, T., Ito, M., Ushijima, A. and Saito, S. (2015) Helios-Positive Functional Regulatory T Cells Are Decreased in Decidua of Miscarriage Cases with Normal Fetal Chromosomal Content. Journal of Reproductive Immunology, 107, 10-19.
https://doi.org/10.1016/j.jri.2014.09.053
[34] Bowen, J.M., Chamley, L., Keelan, J.A. and Mitchell, M.D. (2002) Cytokines of the Placenta and Extra-Placental Membranes: Roles and Regulation during Human Pregnancy and Parturition. Placenta, 23, 257-273.
https://doi.org/10.1053/plac.2001.0782
[35] Muzzio, D., Zygmunt, M. and Jensen, F. (2014) The Role of Pregnancy-Associated Hormones in the Development and Function of Regulatory B Cells. Frontiers in Endocrinology, 5, Article 39.
https://doi.org/10.3389/fendo.2014.00039
[36] Fettke, F., Schumacher, A., Costa, S. and Zenclussen, A.C. (2014) B Cells: The Old New Players in Reproductive Immunology. Frontiers in Immunology, 5, Article 285.
https://doi.org/10.3389/fimmu.2014.00285
[37] Faas, M.M. and de Vos, P. (2017) Uterine NK Cells and Macrophages in Pregnancy. Placenta, 56, 44-52.
https://doi.org/10.1016/j.placenta.2017.03.001
[38] Piccinni, M. (2003) Role of Immune Cells in Pregnancy. Autoimmunity, 36, 1-4.
https://doi.org/10.1080/0891693031000067287
[39] Yang, F., Zheng, Q. and Jin, L. (2019) Dynamic Function and Composition Changes of Immune Cells during Normal and Pathological Pregnancy at the Maternal-Fetal Interface. Frontiers in Immunology, 10, Article 2317.
https://doi.org/10.3389/fimmu.2019.02317