Classification and Regression Tree Model for the Differential Diagnosis of Preeclampsia Based on Clinicopathological Features and miR Signatures

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DOI: 10.21522/TIJPH.2013.SE.25.02.Art002

Authors : Vinaya Vijayan, Kannan R, Subhashini Y, Aparajita D’souza, Tarakeswari S, Aruna Yerra, B. Ram Reddy

Abstract:

Preeclampsia (PE) is a pregnancy complication characterized by the onset of high blood pressure after 20 weeks of gestation with proteinuria and abnormal liver enzymes. The early diagnosis and prophylactic use of aspirin can reduce the long-term complications of PE. In the current study, we utilized machine learning tools for the differential diagnosis of EOPE and LOPE based on demographic, clinical, and biochemical data. We employed SYBR green-based real-time PCR to study the differential expression of hsa-miR-4743-5p, miR-149-5p, miR-331-5p, and miR-483-5p in both forms of PE. A classification and regression tree (CART) model was developed to differentiate between EOPE and LOPE. This was achieved by determining thresholds of systolic blood pressure (SBP), Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), Body Mass Index (BMI), urine protein, and SGOT. The RT-PCR-based DEM profile identified an association of miR-4743-5p with both forms of PE; miR-149-5p with EOPE, and miR-331-5p and miR-483-5p with LOPE. MiRDip analysis revealed that genes targeted by these miRs influence TGF beta signaling in EOPE; cholesterol and lipid homeostasis and NOTCH2 signaling in LOPE. In conclusion, SBP, MAP, BMI, urine protein, DBP, and SGOT are key determinants of EOPE and LOPE. The DEM profile clearly distinguished EOPE and LOPE.


References:

[1].   Yang, Y., Le Ray, I., Zhu, J., Zhang, J., Hua, J., & Reilly, M., 2021, Preeclampsia prevalence, risk factors, and pregnancy outcomes in Sweden and China. JAMA Network Open, 4(5), e218401. https://doi.org/10.1001/jamanetworkopen.2021.8401.

[2].   Tyrmi, J. S., Kaartokallio, T., Lokki, A. I., Jääskeläinen, T., Kortelainen, E., et al., 2023, Genetic Risk Factors Associated with Preeclampsia and Hypertensive Disorders of Pregnancy. JAMA cardiology, 8(7), 674–683. https://doi.org/10.1001/jamacardio.2023.1312

[3].   Muldoon, K. A., McLean, C., El-Chaár, D., Corsi, D. J., Rybak, et al., 2023, Persisting risk factors for preeclampsia among high-risk pregnancies already using prophylactic aspirin: a multi-country retrospective investigation. The journal of maternal-fetal & neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians, 36(1), 2200879. https://doi.org/10.1080/14767058.2023.2200879

[4].   Hercus, A., Dekker, G., & Leemaqz, S., 2020, Primipaternity and birth interval; independent risk factors for preeclampsia. The Journal of maternal-fetal & neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians, 33(2), 303–306. https://doi.org/10.1080/14767058.2018.1489794

[5].   Stitterich, N., Shepherd, J., Koroma, M. M., & Theuring, S., 2021. Risk factors for preeclampsia and eclampsia at a main referral maternity hospital in Freetown, Sierra Leone: a case-control study. BMC pregnancy and childbirth, 21(1), 413. https://doi.org/10.1186/s12884-021-03874-7

[6].   Dai, F., Pan, S., Lan, Y., Tan, H., Li, J., & Hua, Y., 2022, Pregnancy outcomes and risk factors for preeclampsia in dichorionic twin pregnancies after in vitro fertilization: a five-year retrospective study. BMC pregnancy and childbirth, 22(1), 830. https://doi.org/10.1186/s12884-022-05184-y

[7].   Fox, N. S., Roman, A. S., Saltzman, D. H., Hourizadeh, T., Hastings, J., & Rebarber, A., 2014. Risk factors for preeclampsia in twin pregnancies. American journal of perinatology, 31(2), 163–166. https://doi.org/10.1055/s-0033-1343775

[8].   Jaatinen, N., Jääskeläinen, T., FINNPEC, Laivuori, H., & Ekholm, E., 2021, The non-traditional and familial risk factors for preeclampsia in the FINNPEC cohort. Pregnancy hypertension, 23, 48–55. https://doi.org/10.1016/j.preghy.2020.11.001

[9].   Weitzner, O., Yagur, Y., Weissbach, T., Man El, G., & Biron-Shental, T., 2020, Preeclampsia: risk factors and neonatal outcomes associated with early- versus late-onset diseases. The journal of maternal-fetal & neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians, 33(5), 780–784. https://doi.org/10.1080/14767058.2018.1500551

[10].  Suksai, M., Geater, A., Phumsiripaiboon, P., & Suntharasaj, T., 2022, A new risk score model to predict preeclampsia using maternal factors and mean arterial pressure in early pregnancy. Journal of obstetrics and gynaecology: the journal of the Institute of Obstetrics and Gynaecology, 42(3), 437–442. https://doi.org/10.1080/01443615.2021.1916804

[11].  Choorakuttil, R. M., Rajalingam, B., Satarkar, S. R., Sharma, L. K., Gupta, A., et al., 2022, Effectiveness of the First Trimester Samrakshan Protocol for the Identification of Pregnant Women at High Risk for Preterm Pre-eclampsia. The Indian journal of radiology & imaging, 33(1), 98–100. https://doi.org/10.1055/s-0042-1759856

[12].  Chaemsaithong, P., Pooh, R. K., Zheng, M., Ma, R., Chaiyasit, N., Tokunaka, M., et al., 2019, Prospective evaluation of screening performance of first-trimester prediction models for preterm preeclampsia in an Asian population. American journal of obstetrics and gynecology, 221(6), 650.e1–650.e16. https://doi.org/10.1016/j.ajog.2019.09.041

[13].  Marić, I., Tsur, A., Aghaeepour, N., Montanari, A., Stevenson, D. K., Shaw, G. M., & Winn, V. D., 2020, Early prediction of preeclampsia via machine learning. American journal of obstetrics & gynecology MFM, 2(2), 100100. https://doi.org/10.1016/j.ajogmf.2020.100100.

[14].  Melinte-Popescu, A. S., Vasilache, I. A., Socolov, D., & Melinte-Popescu, M., 2023, Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia-A Prospective Study. Journal of clinical medicine, 12(2), 418. https://doi.org/10.3390/jcm12020418.

[15].  Zhao, X., Wang, Y., Li, L., Mei, J., Zhang, X., & Wu, Z.,2021, Predictive value of 4-Hydroxyglutamate and miR-149-5p on eclampsia. Experimental and molecular pathology, 119, 104618. https://doi.org/10.1016/j.yexmp.2021.104618

[16].  Wang, G., Li, Y., Liu, Z., Ma, X., Li, M., Lu, Q., et al., 2020, Circular RNA circ_0124644 exacerbates the ox-LDL-induced endothelial injury in human vascular endothelial cells through regulating PAPP-A by acting as a sponge of miR-149-5p. Molecular and cellular biochemistry, 471(1-2), 51–61. https://doi.org/10.1007/s11010-020-03764-0

[17].  Mayor-Lynn, K., Toloubeydokhti, T., Cruz, A. C., & Chegini, N., 2011, Expression profile of microRNAs and mRNAs in human placentas from pregnancies complicated by preeclampsia and preterm labor. Reproductive sciences (Thousand Oaks, Calif.), 18(1), 46–56. https://doi.org/10.1177/1933719110374115.

[18].  Calvier, L., Chouvarine, P., Legchenko, E., Hoffmann, N., Geldner, J., et al., 2017, PPARγ Links BMP2 and TGFβ1 Pathways in Vascular Smooth Muscle Cells, Regulating Cell Proliferation and Glucose Metabolism. Cell metabolism, 25(5), 1118–1134.e7. https://doi.org/10.1016/j.cmet.2017.03.011

[19].  Haider, S., Lackner, A. I., Dietrich, B., Kunihs, V., Haslinger, P., Meinhardt, G., et al., 2022, Transforming growth factor-β signaling governs the differentiation program of extravillous trophoblasts in the developing human placenta. Proceedings of the National Academy of Sciences of the United States of America, 119(28), e2120667119. https://doi.org/10.1073/pnas.2120667119

[20].  Antonić, T. D., Ardalić, D. Č., Vladimirov, S. S., Banjac, G. S., Cabunac, P. J., Zeljković, A. R., et al., 2021, Cholesterol homeostasis is dysregulated in women with preeclampsia. Polish archives of internal medicine, 131(12), 16144. https://doi.org/10.20452/pamw.16144

[21].  Zhao, W. X., Wu, Z. M., Liu, W., & Lin, J. H.,2017, Notch2 and Notch3 suppress the proliferation and mediate invasion of trophoblast cell lines. Biology open, 6(8), 1123–1129. https://doi.org/10.1242/bio.025767