Prediction of gestational diabetes based on cardiovascular miRNA expression profiles

Abstract

A method for predicting gestational diabetes, including both mild and more severe forms, comprises screening a pregnant woman and measuring the expression profile of two or more miRNAs in peripheral venous whole blood collected between 10 and 13 weeks of pregnancy, the two or more miRNAs being selected from the group consisting of 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.

Description

[Technical Field]

[0001] The present invention relates to the field of molecular biology methods, primarily the analysis of non-coding nucleic acids and expression markers, in particular microRNAs (miRNAs), using reverse transcription quantitative polymerase chain reaction (RT-qPCR), and their use in screening for predicting the development of metabolic disorders during pregnancy. [Background technology]

[0002] Gestational diabetes mellitus (GDM), also known as gestational diabetes, is a maternal metabolic disorder characterized by impaired glucose tolerance that manifests during pregnancy and spontaneously resolves during the postpartum period. Without appropriate prenatal diabetes care, in the form of dietary modification or, in more severe cases, targeted therapy, GDM increases the maternal risk of pregnancy complications and neonatal morbidity, as well as the risk of developing other metabolic disorders, such as obesity and type 2 diabetes, with long-term consequences for both mother and child. Clinically, GDM is diagnosed through an oral glucose tolerance test performed during the second or third trimester of pregnancy. However, this test can only detect GDM that has already developed. Therefore, modern medicine strives to predict this metabolic disorder early and, ideally, prevent its onset or, at least, mitigate its course and impact through early intervention.

[0003] Methods for predicting the onset of GDM in mothers during the first trimester of pregnancy are known in the prior art, e.g., Non-Patent Documents 1-10. However, these methods are primarily based on data about the mother and her clinical parameters, such as age, weight, height, race, subcutaneous fat mass, blood pressure, family history of diabetes, smoking, physical activity, use of fertility drugs, or the occurrence of GDM in previous pregnancies. Some of the above-mentioned methods supplement these data with blood counts and an incomplete set of biochemical markers, such as coagulation factors, indicators of glycolipid metabolism, serum glutamine, urinary ethanolamine and 1,3-diphosphoglycerate, soluble CD163 receptor, tumor necrosis factor α (TNF-α), placental protein 13 (PP13), or pregnancy-associated plasma protein A (PAPP-A). These methods rely on the above-mentioned maternal data to predict the risk of developing GDM, but these data are often incomplete, dependent on the mother's current physical and mental state, and often inaccurate or false. This degree of subjectivity significantly affects the accuracy of predictions in actual clinical conditions and constitutes a fundamental flaw in the state of the art. At the same time, the methods known from the prior art make it virtually impossible to make predictions without knowledge of this information, making it impossible to reliably analyze anonymous samples or to evaluate samples on a large scale in laboratories. Another drawback of this state of the art is the use of non-specific biochemical markers, whose values may vary greatly within a population, further raising questions about the reliability of predictions.

[0004] Examples of changes in selected circulating microRNA (miRNA) levels associated with the onset of GDM have been described in the scientific literature. Non-Patent Document 11 describes changes in miRNA levels associated with nervous system function in women with GDM during the second and third trimesters compared to first trimester levels. However, these findings cannot be used to predict the onset of GDM. Non-Patent Documents 12-17 describe dozens of miRNAs whose expression profiles change during the first trimester in women who subsequently develop GDM, and mention the possibility of using these findings to predict the onset of GDM. However, these documents do not examine the statistical significance of individual miRNAs, which is necessary to determine which miRNAs are suitable for such prediction in clinical settings. Furthermore, the cited literature describes markers that circulate freely in serum and plasma or as part of plasma exosomes. Processing plasma exosomes is a laborious method that is not suitable for widespread clinical use.

[0005] Non-Patent Document 18 describes postpartum profiles of selected miRNAs in women who developed GDM during pregnancy. However, because the observed changes in miRNA expression profiles were caused by metabolic disorders that only developed in the third trimester, the knowledge gained from the cited document cannot be used to develop methods for predicting the onset of GDM during the first trimester, i.e., between weeks 10 and 13 of pregnancy. In other words, increases or decreases in specific miRNA levels after a pregnant woman develops a metabolic disorder do not correspond in any way to their pre-onset values and are completely independent. Because the findings in the cited document are based on changes resulting from the onset of this metabolic disorder, it is not possible to use these changes to predict the disorder before its onset.

[0006] Non-Patent Documents 19-21 describe methods for predicting pregnancy complications in the form of preeclampsia, fetal growth restriction, pregnancy-induced hypertension, small-for-gestational-age infants, and preterm birth (spontaneous preterm birth or premature rupture of membranes), as well as methods for detecting undiagnosed chronic hypertension, using selected cardiovascular miRNAs as biomarkers. However, because the aforementioned complications are associated with placental dysfunction and abnormal activity in the maternal cardiovascular system, it is not possible to clearly use the knowledge already known from the cited documents, i.e., the use of cardiovascular miRNAs, to develop methods for predicting the onset of GDM, a metabolic disorder, rather than complications related to placental function or the maternal cardiovascular system. [Prior art documents] [Non-patent literature]

[0007] [Non-Patent Document 1] Syngelaki et al.,Fetal.Diagn.Ther.2015,38,p14-21 [Non-patent document 2] Sweeting et al.,Diabetes Res.Clin.Pract.2017,127,p44-50 [Non-patent document 3] Sweeting et al.,Fetal Diagn.Ther.2019,45,p76-84 [Non-patent document 4] Wang et al.,Diabetes Res.Clin.Pract.2018,142,p130-138 [Non-Patent Document 5] Sakurai et al.,J.Diabetes Investig.2019,10,p513-520 [Non-patent document 6] Tenenbaum-Gavish et al., Placenta 2020, 101, p80-89 [Non-Patent Document 7] Zheng et al.,Front.Public Health 2022,10,p850191 [Non-patent document 8] Zhang et al.,Exp.Ther.Med.2020,20,p293-300

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Non-licensed Document 11

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[0008] The object of the present invention is to overcome the drawbacks of the prior art by developing a method that can be easily implemented on a large scale in molecular genetic laboratories with standard equipment and that can predict the onset of gestational diabetes in the third trimester of pregnancy, which cannot be predicted in current clinical practice, in a robust manner and with high reliability, even in the first trimester, even using anonymous samples, i.e. without knowing data about the mother and her clinical parameters. [Means for solving the problem]

[0009] The present invention is based on RT-qPCR analysis of the levels of 11 specific miRNAs (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) in peripheral venous whole blood samples collected from pregnant women during standard first-trimester screening, i.e., between 10 and 13 weeks of pregnancy. This allows for highly reliable prediction of GDM onset. Selection of an appropriate subset of this miRNA group further enables identification of mild GDM, requiring only dietary modification, versus more severe GDM, requiring dietary modification and other therapeutic interventions, such as drug therapy.

[0010] Although the relationship between changes in the levels of the above miRNAs and the onset of GDM or the prediction of pregnancy complications associated with abnormal placental and maternal cardiovascular function has already been demonstrated in the scientific literature and is therefore known from the prior art, their use for predicting GDM is not clear or easily inferred from these publications. This is due to the large number of miRNAs described, their specific association with the cardiovascular system rather than metabolism, the wide variability of possible changes in expression levels, and the lack of clarity regarding the specific subset of miRNAs that should be selected as these markers.

[0011] The method was developed based on the analysis of samples selected from 4,187 women in the first trimester of pregnancy, statistical processing of these results, and comparison with the subsequent pregnancy progress of the women under observation. Of these women, 3,028 had a complete health profile throughout their pregnancies, including all complications and delivery progress. Of these 3,028 women, 121 developed GDM without other pregnancy complications. Of these 121, 101 had mild GDM requiring dietary modification alone, and 20 had more severe GDM requiring dietary modification plus medical intervention.

[0012] The method according to the present invention is carried out as follows. First, collected blood is processed to produce a leukocyte lysate. The contained RNA is then extracted, followed by isolation of short-chain RNAs. The resulting purified solution is analyzed using a two-step RT-qPCR reaction in the presence of standard and sequence-specific chemicals, namely, a miRNA-specific stem-loop RT primer, miRNA-specific forward and reverse PCR primers, and a miRNA-specific MGB probe, in an instrument that maintains ideal temperature conditions for the individual steps that are repeated cyclically. At each step, the fluorescent signal emitted by the probe is measured, and this cycle is typically repeated a total of 40-45 times. After the program is completed, C is measured in individual channels. tThe value is read. The result corresponds to the original number of nucleic acid molecules observed. RNA extraction from leukocyte lysates and RT-qPCR analysis are currently the standard in molecular diagnostics. This procedure is fast, easy to perform, and can be easily automated. Therefore, this diagnostic method is suitable for implementation in most genetic laboratories and for widespread use in clinical settings.

[0013] A normal distribution of the expression of selected miRNAs was determined based on samples from women with uncomplicated physiological pregnancies. These levels were then compared with miRNA levels in samples from women who developed GDM during pregnancy, and the results were statistically analyzed using the Kruskal-Wallis test and the Mann-Whitney test. Based on these results, a specific set of 11 miRNAs (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 selected. All of these miRNAs are upregulated before the onset of GDM, and their levels can be used to predict GDM with sufficiently high sensitivity and a sufficiently low false-positive rate. A generally acceptable false-positive rate is 10%. Of this set of 11 miRNAs, measurement of eight specific miRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-100-5p, miR-125b-5p, miR-195-5p, miR-499a-5p, miR-574-3p) makes it possible to identify whether the disease will be mild, requiring only dietary improvement, while measurement of three specific miRNAs (miR-20a-5p, miR-20b-5p, miR-195-5p) makes it possible to identify whether the disease will be more severe, requiring therapeutic intervention in addition to dietary improvement.

[0014] As can be seen from the results presented below, this method can effectively predict GDM based solely on the analysis of miRNA profiles in peripheral venous blood, i.e., without additional information from the woman's clinical examination or medical history. Therefore, this method is highly objective and not susceptible to distortion by false or erroneous data, while allowing for the testing of large samples, including evaluations, while maintaining patient anonymity. However, this method can also be combined with maternal clinical characteristics, further increasing the probability of successful prediction.

[0015] Gestational diabetes mellitus (GDM) In women who developed GDM during pregnancy, the best predictive value was found to be elevated expression of 11 miRNAs in peripheral venous whole blood collected during the first trimester: 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. This method, without additional information from the woman's clinical examination or medical history, allowed prediction in 47.9% of cases with a false positive rate (FPR) of 10%. Combining these 11 miRNAs with three basic maternal clinical characteristics (age at first trimester, body mass index (BMI) at first trimester, and treatment with assisted reproductive technology) predicted 69.2% of cases with an FPR of 10%. Combining these 11 miRNAs with seven maternal clinical characteristics (age at first trimester, BMI at first trimester, treatment with assisted reproductive technology, maternal history of miscarriage, presence of thrombophilia gene mutations, positive first-trimester prenatal screening results for early preeclampsia and spontaneous preterm birth before 34 weeks of gestation and fetal growth restriction before 37 weeks of gestation using a default predictive routing algorithm in a computer application for obstetrics and gynecology, and incidence of diabetes among first-degree relatives) predicted 72.5% of cases with an FPR of 10%.

[0016] Mild GDM requiring only dietary modification For women who developed mild GDM during pregnancy, elevated expression of the following eight miRNAs in peripheral venous whole blood collected during the first trimester was found to be the most predictive: 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. Using this method, it was possible to predict 34.7% of cases with a 10% FPR without additional information from the woman's clinical examination or medical history. Combining these eight miRNAs with three basic maternal clinical characteristics (age at the start of pregnancy, BMI at the start of pregnancy, and infertility treatment with assisted reproductive technology) allowed us to predict 50.5% of cases with a 10% FPR. Combining these eight miRNAs with seven maternal clinical characteristics (age at the start of pregnancy, BMI at the start of pregnancy, infertility treatment with assisted reproductive technology, maternal history of miscarriage, presence or absence of thrombophilia gene mutations, positive results of first-trimester prenatal screening for early preeclampsia and spontaneous preterm birth before 34 weeks of gestation and fetal growth restriction before 37 weeks of gestation using a default predictive routing algorithm approach in a computer application for obstetrics and gynecology databases, and incidence of diabetes among first-degree relatives) allowed us to predict 56.4% of cases with an FPR of 10%.

[0017] More severe GDM that requires therapeutic intervention in addition to dietary modification In women who developed more severe GDM during pregnancy, we found that elevated expression of three miRNAs in peripheral venous whole blood collected during the first trimester was the most predictive: miR-20a-5p, miR-20b-5p, and miR-195-5p. In these cases, we did not observe significant elevated expression of miR-1-3p, miR-100-5p, miR-125b-5p, miR-499a-5p, or miR-574-3p, as these miRNAs are only significantly elevated in mild GDM. Using this method, we were able to predict 30.0% of cases with a 10% FPR without additional information from the woman's clinical examination or medical history. Combining these three miRNAs with three basic maternal clinical characteristics (age at the start of pregnancy, BMI at the start of pregnancy, and infertility treatment with assisted reproductive technology) enabled us to predict 79.0% of cases with a 10% FPR. Combining these three miRNAs with seven maternal clinical characteristics (age at the start of pregnancy, BMI at the start of pregnancy, infertility treatment with assisted reproductive technology, maternal history of miscarriage, presence or absence of thrombophilia gene mutations, positive results of first-trimester prenatal screening for early preeclampsia and spontaneous preterm birth before 34 weeks of gestation and fetal growth restriction before 37 weeks of gestation using a default predictive routing algorithm approach in a computer application for obstetrics and gynecology databases, and incidence of diabetes among first-degree relatives) predicted 89.5% of cases with an FPR of 10%. [Brief explanation of the drawings]

[0018] [Figure 1] Figure 1 depicts the receiver operating characteristic (ROC) curve obtained from statistical analysis of the elevated expression of 11 selected miRNAs (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) for the prediction of GDM. [Figure 2]Statistical analysis of the elevated expression of 11 selected miRNAs (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) for predicting GDM. ROC curves are plotted in combination with three basic maternal clinical characteristics (age at the beginning of pregnancy, BMI at the beginning of pregnancy, and infertility treatment with assisted reproductive technology). [Figure 3] The figure shows the receiver operating characteristic curve (ROC) obtained by combining 11 selected miRNAs (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) for predicting GDM with seven maternal clinical characteristics: age at first pregnancy, BMI at first pregnancy, infertility treatment with assisted reproductive technology, maternal history of miscarriage, presence or absence of a thrombophilic gene mutation, positive results of first-trimester prenatal screening for early preeclampsia and spontaneous preterm birth before 34 weeks of gestation and fetal growth restriction before 37 weeks of gestation using the default predictive routing algorithm of a computer application for obstetrics and gynecology, and incidence of diabetes among first-degree relatives. [Figure 4] Figure 1 depicts the ROC curve obtained from the statistical analysis of the elevated expression of eight selected miRNAs (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) for the prediction of mild GDM requiring only dietary modification. [Figure 5]Figure 1 depicts the ROC curve obtained by combining statistical analysis of elevated expression of eight selected miRNAs (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 three basic maternal clinical characteristics (age at first pregnancy, BMI at first pregnancy, and infertility treatment with assisted reproductive technology) for the prediction of mild GDM requiring only dietary modification. [Figure 6] The figure shows the receiver operating characteristic curve (ROC) obtained by combining statistical analysis of elevated expression of eight selected miRNAs (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 seven maternal clinical characteristics (age at the beginning of pregnancy, BMI at the beginning of pregnancy, infertility treatment by assisted reproductive technology, maternal history of miscarriage, presence or absence of thrombophilia gene mutations, positive results of first-trimester prenatal screening for early preeclampsia and spontaneous preterm birth before 34 weeks of gestation and fetal growth restriction before 37 weeks of gestation using the default predictive routing algorithm method of a computer application for obstetrics and gynecology, and incidence of diabetes among first-degree relatives) for the prediction of mild GDM requiring only dietary modification. [Figure 7] Figure 1 depicts the ROC curve obtained from the statistical analysis of elevated expression of three selected miRNAs (miR-20a-5p, miR-20b-5p, and miR-195-5p) for predicting more severe GDM, which requires therapeutic intervention in addition to dietary modification. [Figure 8] Figure 1 depicts the ROC curve obtained by combining the statistical analysis of elevated expression of three selected miRNAs (miR-20a-5p, miR-20b-5p, and miR-195-5p) with three basic maternal clinical characteristics (age at the start of pregnancy, BMI at the start of pregnancy, and infertility treatment with assisted reproductive technology) for the prediction of more severe GDM, which requires therapeutic intervention in addition to dietary improvement. [Figure 9]The figure shows the receiver operating characteristic curve (ROC) obtained by combining statistical analysis of increased expression of three selected miRNAs (miR-20a-5p, miR-20b-5p, and miR-195-5p) with seven maternal clinical characteristics (age at the start of pregnancy, BMI at the start of pregnancy, infertility treatment by assisted reproductive technology, maternal history of miscarriage, presence or absence of thrombophilia gene mutations, positive results of first-trimester prenatal screening for early preeclampsia and spontaneous preterm birth before 34 weeks of gestation and fetal growth restriction before 37 weeks of gestation using the default predictive routing algorithm method of a computer application for obstetrics and gynecology databases, and incidence of diabetes among first-degree relatives) for predicting more severe GDM, which requires therapeutic intervention in addition to dietary improvement. DETAILED DESCRIPTION OF THE INVENTION [Example]

[0019] Example 1 describes a general example of an RT-qPCR assay to measure miRNA levels in test and standard samples of peripheral venous whole blood, normalized to the levels of specific endogenous controls (RNU58A and RNU38B) measured simultaneously.

[0020] RNA is isolated from thawed leukocyte lysates using a mixture of acidic phenol and chloroform. Long-chain RNA is further removed from the resulting RNA, and short-chain RNA is enriched using a glass fiber filter column and different concentrations of ethanol in each isolation step. The isolated RNA, including short-chain RNA, is used directly as a template for a two-step RT-qPCR reaction. Reverse transcription is performed at 16°C for 30 minutes, 42°C for 30 minutes, and then 85°C for 5 minutes. Polymerase chain reaction is then performed at 50°C for 2 minutes, 95°C for 10 minutes, and then cycling at 95°C for 15 seconds and 60°C for 1 minute. After each cycle, fluorescence is measured in the FAM and ROX channels (passive reference for fluorescence normalization). This cycle is repeated 40–45 times in total. After the program is completed, the C values for each channel are measured. tWithin a sample, the C values of the FAM channel for the evaluated miRNA or the short RNA used as an endogenous control are read. t These values correspond to the individual gene expression in the biological sample. The normalized expression values are the C of the miRNA in the measured sample. t C values of endogenous controls t The expression of all tested miRNAs and endogenous controls is measured simultaneously in a standard sample used in all analyses performed. [Example]

[0021] Example 2 describes a general example of statistical analysis of data describing the levels of selected miRNAs.

[0022] Because the Shapiro-Wilk test confirmed that the data were not normally distributed, nonparametric tests were used to evaluate the experimental data. MiRNA gene expression was compared between individual groups using the Mann-Whitney test. When more than two groups were compared, the Kruskal-Wallis test was used, followed by post-hoc analysis. The statistical significance level was determined by the corrected p-value after applying the Benjamini-Hochberg correction. Receiver operating characteristic (ROC) curves were also constructed for each miRNA. The area under the curve, the sensitivity and specificity of each miRNA, and the optimal cutoff value (the so-called criterion) were evaluated. Furthermore, the optimal cutoff value and sensitivity of a specific miRNA biomarker were determined at a specificity of 90.0%, which corresponds to information about the proportion of women with elevated or decreased expression of a specific miRNA with a false positive rate (FPR) of 10.0%. Furthermore, a statistical analysis combining logistic regression and ROC analysis was performed to select the optimal combination of miRNA biomarkers for a specific situation. This application yields parameters such as area under the curve, sensitivity, specificity, optimal cut-off value, and sensitivity of a particular combination of miRNA biomarkers at a specificity of 90.0%. [Example]

[0023] Example 3 describes the collection and selection of a suitable set of biological samples for developing a method to predict pregnancy complications using cardiovascular miRNAs as biomarkers.

[0024] 200 μL of peripheral venous whole blood was collected from 4,187 women 10-13 weeks pregnant. Red blood cells were removed, and white blood cell lysates were prepared, then flash-frozen and stored at -80°C. After delivery, samples were selected exclusively from women whose complete health status during pregnancy, including all complications and delivery history, was known. Eighty patients with a normal pregnancy history and negative first-trimester prenatal screening results using a predefined method utilizing a predictive routing algorithm in a computerized obstetric and gynecological database application were selected as the control group. For the GDM prediction test, 121 patients diagnosed with GDM during pregnancy were selected. Blood samples from these selected patients are analyzed according to the procedure described in Example 1 to measure the levels of 11 selected miRNAs (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). To predict GDM with sufficient reliability as described in Examples 4 to 12, more than half of the preferred miRNAs shown in each Example are used each time. Using all of the preferred miRNAs shown in each Example provides the highest reliability. [Example]

[0025] Example 4 describes the successful prediction of GDM using selected miRNA markers in selected patient samples.

[0026] 121 of the observed patients developed GDM during pregnancy. Blood samples from these selected patients were analyzed according to the procedure described in Example 1 to measure the levels of 11 selected miRNAs (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). Increased expression of these selected miRNA biomarkers, whose levels exceeded the minimum value determined by statistical analysis at an FPR of 10%, was observed in 58 of the 121 patients, corresponding to successful prediction in 47.9% of cases. Statistical analysis of the obtained data showed the specificity, sensitivity, 95% CI, and reference value as follows:

[0027] [Table 1] [Example]

[0028] Example 5 describes the successful prediction of GDM using selected miRNA markers and baseline maternal clinical characteristics in selected patient samples.

[0029] 121 of the observed patients developed GDM during pregnancy. The blood samples of these selected patients were analyzed according to the procedure described in Example 1 to measure the levels of 11 selected miRNAs (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). The elevated expression of these selected miRNA biomarkers and the values of three basic maternal clinical characteristics (age at early pregnancy, BMI at early pregnancy, and infertility treatment by assisted reproductive technology) exceeded the minimum value determined by statistical analysis at an FPR of 10%, and were observed in 84 of the 121 patients, corresponding to successful prediction in 69.4% of cases. Statistical analysis of the data obtained shows the specificity, sensitivity, 95% CI, and reference values as follows:

[0030] [Table 2] [Example]

[0031] Example 6 describes the successful prediction of GDM using selected miRNA markers and maternal clinical characteristics in selected patient samples.

[0032] 121 patients observed developed GDM during pregnancy. Blood samples from these selected patients were analyzed according to the procedure described in Example 1 to measure the levels of 11 selected miRNAs (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). Increased expression of these selected miRNA biomarkers and seven maternal clinical characteristics (age at the beginning of pregnancy, BMI at the beginning of pregnancy, infertility treatment with assisted reproductive technology, maternal history of miscarriage, presence or absence of thrombophilic gene mutations, positive results of first-trimester prenatal screening for early preeclampsia and spontaneous preterm birth before 34 weeks of gestation and fetal growth restriction before 37 weeks of gestation using a predefined predictive routing algorithm method in a computer application for obstetrics and gynecology, and incidence of diabetes among first-degree relatives) whose levels exceeded the minimum determined by statistical analysis with an FPR of 10% were observed in 88 of 121 patients, corresponding to successful prediction in 72.7% of cases. Statistical analysis of the obtained data showed the specificity, sensitivity, 95% CI, and reference values as follows:

[0033] [Table 3] [Example]

[0034] Example 7 describes the successful prediction of mild GDM, requiring only dietary modification, using selected miRNA markers in selected patient samples.

[0035] 101 of the observed patients developed mild GDM during pregnancy, requiring only dietary modification. Blood samples from these selected patients were analyzed according to the procedure described in Example 1 to measure the levels of eight selected miRNAs (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). Increased expression of these selected miRNA biomarkers, whose levels exceeded the minimum value determined by statistical analysis at an FPR of 10%, was observed in 35 of the 101 patients, corresponding to successful prediction in 34.7% of cases. Statistical analysis of the obtained data showed the specificity, sensitivity, 95% CI, and reference value as follows:

[0036] [Table 4] [Example]

[0037] Example 8 describes the successful prediction of mild GDM, requiring only dietary modification, using selected miRNA markers and baseline maternal clinical characteristics in selected patient samples.

[0038] 101 of the observed patients developed mild GDM during pregnancy, requiring only dietary modification. Blood samples from these selected patients were analyzed according to the procedure described in Example 1 to measure the levels of eight selected miRNAs (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). Increased expression of these selected miRNA biomarkers and the values of three basic maternal clinical characteristics (age at early pregnancy, BMI at early pregnancy, and infertility treatment by assisted reproductive technology) exceeded the minimum values determined by statistical analysis with an FPR of 10%. This was observed in 51 of the 101 patients, corresponding to successful prediction in 50.5% of cases. Statistical analysis of the obtained data showed the following specificity, sensitivity, 95% CI, and reference values:

[0039] [Table 5] [Example]

[0040] Example 9 describes the successful prediction of mild GDM requiring only dietary modification using selected miRNA markers and maternal clinical characteristics in selected patient samples.

[0041] 101 patients observed developed mild GDM during pregnancy, requiring only dietary modification. Blood samples from these selected patients were analyzed according to the procedure described in Example 1 to measure the levels of eight selected miRNAs (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). Increased expression of these selected miRNA biomarkers and seven maternal clinical characteristics (age at the beginning of pregnancy, BMI at the beginning of pregnancy, infertility treatment with assisted reproductive technology, maternal history of miscarriage, presence or absence of thrombophilic gene mutations, positive results of first-trimester prenatal screening for early preeclampsia and spontaneous preterm birth before 34 weeks of gestation and fetal growth restriction before 37 weeks of gestation using a predefined predictive routing algorithm method in a computer application for obstetrics and gynecology, and incidence of diabetes among first-degree relatives) whose levels exceeded the minimum determined by statistical analysis with an FPR of 10% were observed in 57 of 101 patients, corresponding to successful prediction in 56.4% of cases. Statistical analysis of the obtained data showed the specificity, sensitivity, 95% CI, and reference values as follows:

[0042] [Table 6] [Example]

[0043] Example 10 describes the successful prediction of more severe GDM, which requires therapeutic intervention in addition to dietary modification, using selected miRNA markers in selected patient samples.

[0044] Twenty of the observed patients developed more severe GDM during pregnancy, requiring therapeutic intervention in addition to dietary modification. Blood samples from these selected patients were analyzed according to the procedure described in Example 1 to measure the levels of three selected miRNAs (miR-20a-5p, miR-20b-5p, and miR-195-5p). Increased expression of these selected miRNA biomarkers, whose levels exceeded the minimum value determined by statistical analysis at an FPR of 10%, was observed in 6 of the 20 patients, corresponding to successful prediction in 30.0% of cases. Statistical analysis of the obtained data showed the specificity, sensitivity, 95% CI, and reference value as follows:

[0045] [Table 7] [Example]

[0046] Example 11 describes the successful prediction of more severe GDM, requiring therapeutic intervention in addition to dietary modification, using selected miRNA markers and baseline maternal clinical characteristics in selected patient samples.

[0047] Twenty of the observed patients developed more severe GDM during pregnancy, requiring therapeutic intervention in addition to dietary modification. Blood samples from these selected patients were analyzed according to the procedure described in Example 1 to measure the levels of three selected miRNAs (miR-20a-5p, miR-20b-5p, and miR-195-5p). Increased expression of these selected miRNA biomarkers and the values of three basic maternal clinical characteristics (age at early pregnancy, BMI at early pregnancy, and infertility treatment by assisted reproductive technology) exceeded the minimum value determined by statistical analysis at an FPR of 10% and was observed in 16 of the 20 patients, corresponding to successful prediction in 80.0% of cases. Statistical analysis of the obtained data showed the following specificity, sensitivity, 95% CI, and reference value:

[0048] [Table 8] [Example]

[0049] Example 12 describes the successful prediction of more severe GDM, requiring therapeutic intervention in addition to dietary modification, using selected miRNA markers and maternal clinical characteristics in selected patient samples.

[0050] Twenty of the observed patients developed more severe GDM during pregnancy, requiring therapeutic intervention in addition to dietary modification. The blood samples of these selected patients were analyzed according to the procedure described in Example 1 to measure the levels of three selected miRNAs (miR-20a-5p, miR-20b-5p, miR-195-5p). The elevated expression levels of these selected miRNA biomarkers and seven maternal clinical characteristics (age at the beginning of pregnancy, BMI at the beginning of pregnancy, infertility treatment by assisted reproductive technology, maternal history of miscarriage, presence or absence of thrombophilic gene mutations, positive results of first-trimester prenatal screening for early preeclampsia and spontaneous preterm birth before 34 weeks of pregnancy and fetal growth restriction before 37 weeks of pregnancy using a predefined predictive routing algorithm method in a computer application for obstetrics and gynecology, and the incidence of diabetes in first-degree relatives) exceeded the minimum value determined by statistical analysis at an FPR of 10%, and were observed in 18 of the 20 patients, which corresponds to successful prediction in 90.0% of cases. Statistical analysis of the data obtained shows the specificity, sensitivity, 95% CI, and reference values as follows:

[0051] [Table 9] [Industrial Applicability]

[0052] The method for predicting gestational diabetes based on the expression profile of cardiovascular miRNAs can be industrially applied in clinical practice in obstetrics and gynecology for laboratory analysis of collected biological samples.

Claims

1. 1. A method for predicting gestational diabetes, comprising: Pregnant women are screened, and expression profiles of two or more miRNAs are measured in peripheral venous whole blood collected between 10 and 13 weeks of pregnancy, and gestational diabetes is predicted based on elevated expression of the two or more miRNAs, wherein the two or more miRNAs are selected from the group consisting of 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. A method characterized by:

2. The miRNAs to be screened for the prediction of mild gestational diabetes are 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. The method of claim 1.

3. The miRNAs to be screened for predicting more severe gestational diabetes are miR-20a-5p, miR-20b-5p, and miR-195-5p. The method of claim 1.

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