First Trimester Prediction of Preterm Delivery in the Absence of Other Pregnancy-Related Complications Using Cardiovascular-Disease Associated MicroRNA Biomarkers

MiRNA-26a-5p inhibits preterm labor initiation by targeting and regulating TRPC3 ion channel protein expression

The incidence of preterm birth (PTB) is increasing annually worldwide, leading to various health problems or even fetal deaths. Our previous work demonstrated the activation of transient receptor potential cation channel subfamily C 3 (TRPC3) in mice with PTB, and its activation could promote inward flow of calcium ions and uterine smooth muscle (USM) contraction via regulation of Cav3.2, Cav3.1, and Cav1.2. However, the upstream regulators of TRPC3 and its mechanisms remain unknown. In the present study, the binding of miR-26a-5p to the 3' untranslated region of TRPC3 was predicted by bioinformatics databases (TargetScanHuman and starBase v3.0) and confirmed by a dual-luciferase assay. MiR-26a-5p was downregulated, while TRPC3 was upregulated in the USM tissues of patients with PTB compared to people without PTB. The results showed that miR-26a-5p mimic transfection markedly reduced TRPC3 expression in LPS-stimulated USM cells. Additionally, miR-26a-5p regulated intracellular Ca2+ levels in USM cells by targeting TRPC3. Furthermore, miR-26a-5p inhibited the CPI17/PKC/PLCγ signaling pathway and reduced the expression of Cav3.2, Cav3.1, and Cav1.2. In conclusion, miR-26a-5p regulated the initiation of PTB by targeting TRPC3 and regulating intracellular Ca2+ levels. This study provides a promising diagnostic biomarker and therapeutic target for PTB.

First trimester prediction models for small-for- gestational age and fetal growth restricted fetuses without the presence of preeclampsia

We established efficient first trimester prediction models for small-for-gestational age (SGA) and fetal growth restriction (FGR) without the presence of preeclampsia (PE) regardless of the gestational age of the onset of the disease [early FGR occurring before 32 gestational week or late FGR occurring after 32 gestational week]. The retrospective study was performed on singleton Caucasian pregnancies (n = 6440) during the period 11/2012-3/2020. Finally, 4469 out of 6440 pregnancies had complete medical records since they delivered in the Institute for the Care of Mother and Child, Prague, Czech Republic. The study included all cases diagnosed with SGA (n = 37) or FGR (n = 82) without PE, and 80 selected normal pregnancies. Four microRNAs (miR-1-3p, miR-20a-5p, miR-146a-5p, and miR-181a-5p) identified 75.68 % SGA cases at 10.0 % false positive rate (FPR). Eight microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-126-3p, miR-130b-3p, miR-146a-5p, miR-181a-5p, and miR-499a-5p) identified 83.80 % SGA cases at 10.0 % FPR. The prediction model for SGA based on microRNAs was further improved via implementation of maternal clinical characteristics [maternal age and BMI, an infertility treatment by assisted reproductive technology (ART), first trimester screening for PE and/or FGR and for spontaneous preterm, both by FMF algorithm]. Then 81.08 % and 89.19 % pregnancies developing SGA were identified at 10.0 % FPR in case of utilization of 4 microRNA and 8 microRNA biomarkers. Simplified prediction model for SGA based on limited number of maternal clinical characteristics (maternal age and BMI, an infertility treatment by ART, and 4 microRNAs) does not improve the detection rate of SGA (70.27 % SGA cases at 10.0 % FPR) when compared with prediction model for SGA based just on the expression profile of 4 or 8 microRNAs biomarkers. Seven microRNAs only (miR-16-5p, miR-20a-5p, miR-145-5p, miR-146a-5p, miR-181a-5p, miR-342-3p, and miR-574-3p) identified 42.68 % FGR cases at 10.0 % FPR (AUC 0.725). However, the combination of 10 microRNAs only (miR-16-5p, miR-20a-5p, miR-100-5p, miR-143-3p, miR-145-5p, miR-146a-5p, miR-181a-5p, miR-195-5p, miR-342-3p, and miR-574-3p) reached a higher discrimination power (AUC 0.774). It identified 40.24 % FGR cases at 10.0 % FPR. The prediction model for any subtype of FGR based on microRNAs was further improved via implementation of maternal clinical characteristics [maternal age and BMI, an infertility treatment by ART, the parity (nulliparity), the occurrence of SGA or FGR in previous gestation, and the occurrence of any autoimmune disorder, and the presence of chronic hypertension]. Then 64.63 % and 65.85 % pregnancies destinated to develop FGR were identified at 10.0 % FPR in case of utilization of 7 microRNA biomarkers or 10 microRNA biomarkers. When other clinical variables next to those ones mentioned above such as first trimester screening for PE and/or FGR and for spontaneous preterm, both by FMF algorithm, were added to the prediction model for FGR, the detection power was even increased to 74.39 % cases and 78.05 % cases at 10.0 % FPR.

First-Trimester Screening for Miscarriage or Stillbirth-Prediction Model Based on MicroRNA Biomarkers

We evaluated the potential of cardiovascular-disease-associated microRNAs to predict in the early stages of gestation (from 10 to 13 gestational weeks) the occurrence of a miscarriage or stillbirth. The gene expressions of 29 microRNAs were studied retrospectively in peripheral venous blood samples derived from singleton Caucasian pregnancies diagnosed with miscarriage (n = 77 cases; early onset, n = 43 cases; late onset, n = 34 cases) or stillbirth (n = 24 cases; early onset, n = 13 cases; late onset, n = 8 cases; term onset, n = 3 cases) and 80 selected gestational-age-matched controls (normal term pregnancies) using real-time RT-PCR. Altered expressions of nine microRNAs (upregulation of miR-1-3p, miR-16-5p, miR-17-5p, miR-26a-5p, miR-146a-5p, and miR-181a-5p and downregulation of miR-130b-3p, miR-342-3p, and miR-574-3p) were observed in pregnancies with the occurrence of a miscarriage or stillbirth. The screening based on the combination of these nine microRNA biomarkers revealed 99.01% cases at a 10.0% false positive rate (FPR). The predictive model for miscarriage only was based on the altered gene expressions of eight microRNA biomarkers (upregulation of miR-1-3p, miR-16-5p, miR-17-5p, miR-26a-5p, miR-146a-5p, and miR-181a-5p and downregulation of miR-130b-3p and miR-195-5p). It was able to identify 80.52% cases at a 10.0% FPR. Highly efficient early identification of later occurrences of stillbirth was achieved via the combination of eleven microRNA biomarkers (upregulation of miR-1-3p, miR-16-5p, miR-17-5p, miR-20a-5p, miR-146a-5p, and miR-181a-5p and downregulation of miR-130b-3p, miR-145-5p, miR-210-3p, miR-342-3p, and miR-574-3p) or, alternatively, by the combination of just two upregulated microRNA biomarkers (miR-1-3p and miR-181a-5p). The predictive power achieved 95.83% cases at a 10.0% FPR and, alternatively, 91.67% cases at a 10.0% FPR. The models based on the combination of selected cardiovascular-disease-associated microRNAs had very high predictive potential for miscarriages or stillbirths and may be implemented in routine first-trimester screening programs.

Maternal Plasma RNA in First Trimester Nullipara for the Prediction of Spontaneous Preterm Birth ≤ 32 Weeks: Validation Study

The first-trimester prediction of spontaneous preterm birth (sPTB) has been elusive, and current screening is heavily dependent on obstetric history. However, nullipara lack a relevant history and are at higher risk for spontaneous (s)PTB ≤ 32 weeks compared to multipara. No available objective first-trimester screening test has proven a fair predictor of sPTB ≤ 32 weeks. We questioned whether a panel of maternal plasma cell-free (PCF) RNAs (PSME2, NAMPT, APOA1, APOA4, and Hsa-Let-7g) previously validated at 16-20 weeks for the prediction of sPTB ≤ 32 weeks might be useful in first-trimester nullipara. Sixty (60) nulliparous women (40 with sPTB ≤ 32 weeks) who were free of comorbidities were randomly selected from the King's College Fetal Medicine Research Institute biobank. Total PCF RNA was extracted and the expression of panel RNAs was quantitated by qRT-PCR. The analysis employed, primarily, multiple regression with the main outcome being the prediction of subsequent sPTB ≤ 32 weeks. The test performance was judged by the area under the curve (AUC) using a single threshold cut point with observed detection rates (DRs) at three fixed false positive rates (FPR). The mean gestation was 12.9 ± 0.5 weeks (range 12.0-14.1 weeks). Two RNAs were differentially expressed in women destined for sPTB ≤ 32 weeks: APOA1 (p < 0.001) and PSME2 (p = 0.05). APOA1 testing at 11-14 weeks predicted sPTB ≤ 32 weeks with fair to good accuracy. The best predictive model generated an AUC of 0.79 (95% CI 0.66-0.91) with observed DRs of 41%, 61%, and 79% for FPRs of 10%, 20%, and 30%, including crown-rump length, maternal weight, race, tobacco use, and age.

First-Trimester Screening for HELLP Syndrome—Prediction Model Based on MicroRNA Biomarkers and Maternal Clinical Characteristics

We evaluated the potential of cardiovascular-disease-associated microRNAs for early prediction of HELLP (hemolysis, elevated liver enzymes, and low platelets) syndrome. Gene expression profiling of 29 microRNAs was performed on whole peripheral venous blood samples collected between 10 and 13 weeks of gestation using real-time RT-PCR. The retrospective study involved singleton pregnancies of Caucasian descent only diagnosed with HELLP syndrome (n = 14) and 80 normal-term pregnancies. Upregulation of six microRNAs (miR-1-3p, miR-17-5p, miR-143-3p, miR-146a-5p, miR-181a-5p, and miR-499a-5p) was observed in pregnancies destined to develop HELLP syndrome. The combination of all six microRNAs showed a relatively high accuracy for the early identification of pregnancies destined to develop HELLP syndrome (AUC 0.903, p < 0.001, 78.57% sensitivity, 93.75% specificity, cut-off > 0.1622). It revealed 78.57% of HELLP pregnancies at a 10.0% false-positive rate (FPR). The predictive model for HELLP syndrome based on whole peripheral venous blood microRNA biomarkers was further extended to maternal clinical characteristics, most of which were identified as risk factors for the development of HELLP syndrome (maternal age and BMI values at early stages of gestation, the presence of any kind of autoimmune disease, the necessity to undergo an infertility treatment by assisted reproductive technology, a history of HELLP syndrome and/or pre-eclampsia in a previous gestation, and the presence of trombophilic gene mutations). Then, 85.71% of cases were identified at a 10.0% FPR. When another clinical variable (the positivity of the first-trimester screening for pre-eclampsia and/or fetal growth restriction by the Fetal Medicine Foundation algorithm) was implemented in the HELLP prediction model, the predictive power was increased further to 92.86% at a 10.0% FPR. The model based on the combination of selected cardiovascular-disease-associated microRNAs and maternal clinical characteristics has a very high predictive potential for HELLP syndrome and may be implemented in routine first-trimester screening programs.

First Trimester Maternal Plasma Aberrant miRNA Expression Associated with Spontaneous Preterm Birth

Spontaneous Preterm Delivery (sPTD) is one of the leading causes of perinatal mortality and morbidity worldwide. The present case−control study aims to detect miRNAs differentially expressed in the first trimester maternal plasma with the view to identify predictive biomarkers for sPTD, between 320/7 and 366/7 weeks, that will allow for timely interventions for this serious pregnancy complication. Small RNA sequencing (small RNA-seq) of five samples from women with a subsequent sPTD and their matched controls revealed significant down-regulation of miR-23b-5p and miR-125a-3p in sPTD cases compared to controls, whereas miR-4732-5p was significantly overexpressed. Results were confirmed by qRT-PCR in an independent cohort of 29 sPTD cases and 29 controls. Statistical analysis demonstrated that miR-125a is a promising early predictor for sPTL (AUC: 0.895; 95% CI: 0.814-0.972; p < 0.001), independent of the confounding factors tested, providing a useful basis for the development of a novel non-invasive predictive test to assist clinicians in estimating patient-specific risk.

Novel First-Trimester Prediction Model for Any Type of Preterm Birth Occurring before 37 Gestational Weeks in the Absence of Other Pregnancy-Related Complications Based on Cardiovascular Disease-Associated MicroRNAs and Basic Maternal Clinical Characteristics

The goal of the study was to establish an efficient first-trimester predictive model for any type of preterm birth before 37 gestational weeks (spontaneous preterm birth (PTB) or preterm prelabor rupture of membranes (PPROM)) in the absence of other pregnancy-related complications, such as gestational hypertension, preeclampsia, fetal growth restriction, or small for gestational age. The retrospective study was performed in the period from 11/2012 to 3/2020. Peripheral blood samples were collected from 6440 Caucasian individuals involving 41 PTB and 65 PPROM singleton pregnancies. A control group with 80 singleton term pregnancies was selected on the basis of equal sample-storage time. A combination of only six microRNAs (miR-16-5p, miR-21-5p, miR-24-3p, miR-133a-3p, miR-155-5p, and miR-210-3p; AUC 0.812, p < 0.001, 70.75% sensitivity, 78.75% specificity, cut-off > 0.652) could predict preterm delivery before 37 gestational weeks in early stages of gestation in 52.83% of pregnancies with a 10.0% FPR. This predictive model for preterm birth based on aberrant microRNA expression profile was further improved via implementation of maternal clinical characteristics (maternal age and BMI at early stages of gestation, infertility treatment with assisted reproductive technology, occurrence of preterm delivery before 37 gestational weeks in previous pregnancy(ies), and presence of any kind of autoimmune disease (rheumatoid arthritis, systemic lupus erythematosus, antiphospholipid syndrome, type 1 diabetes mellitus, or other autoimmune disease)). With this model, 69.81% of pregnancies destined to deliver before 37 gestational weeks were identified with a 10.0% FPR at early stages of gestation. When other clinical variables as well as those mentioned above—such as positive first-trimester screening for early preeclampsia with onset before 34 gestational weeks and/or fetal growth restriction with onset before 37 gestational weeks using the Fetal Medicine Foundation algorithm, as well as positive first-trimester screening for spontaneous preterm birth with onset before 34 gestational weeks using the Fetal Medicine Foundation algorithm—were added to the predictive model for preterm birth, the predictive power was even slightly increased to 71.70% with a 10.0% FPR. Nevertheless, we prefer to keep the first-trimester screening for any type of preterm birth occurring before 37 gestational weeks in the absence of other pregnancy-related complications as simple as possible.

Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications

We assessed the diagnostic potential of cardiovascular disease-associated microRNAs for the early prediction of gestational diabetes mellitus (GDM) in singleton pregnancies of Caucasian descent in the absence of other pregnancy-related complications. Whole peripheral venous blood samples were collected within 10 to 13 weeks of gestation. This retrospective study involved all pregnancies diagnosed with only GDM (n = 121) and 80 normal term pregnancies selected with regard to equality of sample storage time. Gene expression of 29 microRNAs was assessed using real-time RT-PCR. Upregulation of 11 microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-100-5p, miR-125b-5p, miR-126-3p, miR-181a-5p, miR-195-5p, miR-499a-5p, and miR-574-3p) was observed in pregnancies destinated to develop GDM. Combined screening of all 11 dysregulated microRNAs showed the highest accuracy for the early identification of pregnancies destinated to develop GDM. This screening identified 47.93% of GDM pregnancies at a 10.0% false positive rate (FPR). The predictive model for GDM based on aberrant microRNA expression profile was further improved via the implementation of clinical characteristics (maternal age and BMI at early stages of gestation and an infertility treatment by assisted reproductive technology). Following this, 69.17% of GDM pregnancies were identified at a 10.0% FPR. The effective prediction model specifically for severe GDM requiring administration of therapy involved using a combination of these three clinical characteristics and three microRNA biomarkers (miR-20a-5p, miR-20b-5p, and miR-195-5p). This model identified 78.95% of cases at a 10.0% FPR. The effective prediction model for GDM managed by diet only required the involvement of these three clinical characteristics and eight microRNA biomarkers (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-100-5p, miR-125b-5p, miR-195-5p, miR-499a-5p, and miR-574-3p). With this, the model identified 50.50% of GDM pregnancies managed by diet only at a 10.0% FPR. When other clinical variables such as history of miscarriage, the presence of trombophilic gene mutations, positive first-trimester screening for preeclampsia and/or fetal growth restriction by the Fetal Medicine Foundation algorithm, and family history of diabetes mellitus in first-degree relatives were included in the GDM prediction model, the predictive power was further increased at a 10.0% FPR (72.50% GDM in total, 89.47% GDM requiring therapy, and 56.44% GDM managed by diet only). Cardiovascular disease-associated microRNAs represent promising early biomarkers to be implemented into routine first-trimester screening programs with a very good predictive potential for GDM.