Gestational Diabetes in Korea: Incidence and Risk Factors of Diabetes in Women with Previous Gestational Diabetes

Korean women with a history of gestational diabetes mellitus (GDM) have a 3.5 times greater risk of developing postpartum diabetes than the general population. The incidence of type 2 diabetes mellitus in early postpartum is reported as 10-15% in Korean women. A prospective follow-up study on Korean women with GDM showed that approximately 40% of women with previous GDM were expected to develop diabetes within 5 years postpartum. Independent risk factors for the development of diabetes in Korean women with previous GDM are pre-pregnancy body weight, gestational age at diagnosis, antepartum hyperglycemia on oral glucose tolerance test, low insulin response to oral glucose load, and family history of diabetes. Women with postpartum diabetes have greater body mass indexes, body weight, and waist circumferences than women with normal glucose tolerance. Multiple logistic regression analysis has revealed that waist circumference is the strongest obesity index along with systolic blood pressure and that triglyceride levels are a major independent risk factor for developing diabetes. These results in Korean women with previous GDM underline the importance of postpartum testing in Korean women diagnosed with GDM, and demonstrate that impaired B-cell function, obesity, and especially visceral obesity, are associated with the development of diabetes

Glucose Monitoring During Pregnancy

Self-monitoring of blood glucose in women with mild gestational diabetes has recently been proven to be useful in reducing the rates of fetal overgrowth and gestational weight gain. However, uncertainty remains with respect to the optimal frequency and timing of self-monitoring. A continuous glucose monitoring system may have utility in pregnant women with insulin-treated diabetes, especially for those women with blood sugars that are difficult to control or who experience nocturnal hypoglycemia; however, continuous glucose monitoring systems need additional study as part of larger, randomized trials

Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment

[EN] As one of the main aims of obstetrics is to be able to detect imminent delivery in patients with threatened preterm labor, the techniques currently used in clinical practice have serious limitations in this respect. The electrohysterogram (EHG) has now emerged as an alternative technique, providing relevant information about labor onset when recorded in controlled checkups without administration of tocolytic drugs. The studies published to date mainly focus on EHG-burst analysis and, to a lesser extent, on whole EHG window analysis. The study described here assessed the ability of EHG signals to discriminate imminent labor (The ability of EHG recordings to predict imminent labor (<7days) was analyzed in preterm threatened patients undergoing tocolytic therapies by means of EHG-burst and whole EHG window analysis. The non-linear features were found to have better performance than the temporal and spectral parameters in separating women who delivered in less than 7days from those who did not.Mas-Cabo, J.; Prats-Boluda, G.; Perales Marín, AJ.; Garcia-Casado, J.; Alberola Rubio, J.; Ye Lin, Y. (2019). Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Medical & Biological Engineering & Computing. 57:401-411. https://doi.org/10.1007/s11517-018-1888-yS40141157Aboy M, Cuesta-Frau D, Austin D, Micó-Tormos P (2007) Characterization of sample entropy in the context of biomedical signal analysis. Conf Proc IEEE Eng Med Biol Soc:5942–5945. https://doi.org/10.1109/IEMBS.2007.4353701Aboy M, Hornero R, Abásolo D, Álvarez D (2006) Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis. IEEE Trans Biomed Eng 53:2282–2288. https://doi.org/10.1109/TBME.2006.883696Chkeir A, Fleury MJ, Karlsson B, Hassan M, Marque C (2013) Patterns of electrical activity synchronization in the pregnant rat uterus. Biomed 3:140–144. https://doi.org/10.1016/j.biomed.2013.04.007Crandon AJ (1979) Maternal anxiety and neonatal wellbeing. J Psychosom Res 23:113–115. https://doi.org/10.1016/0022-3999(79)90015-1Devedeux D, Marque C, Mansour S, Germain G, Duchêne J (1993) Uterine electromyography: a critical review. Am J Obstet Gynecol 169:1636–1653. https://doi.org/10.1016/0002-9378(93)90456-SFele-Žorž G, Kavšek G, Novak-Antolič Ž, Jager F (2008) A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups. Med Biol Eng Comput 46:911–922. https://doi.org/10.1007/s11517-008-0350-yFergus P, Cheung P, Hussain A, al-Jumeily D, Dobbins C, Iram S (2013) Prediction of preterm deliveries from EHG signals using machine learning. PLoS One 8:e77154. https://doi.org/10.1371/journal.pone.0077154Garfield RE, Maner WL (2006) Biophysical methods of prediction and prevention of preterm labor: uterine electromyography and cervical light-induced fluorescence—new obstetrical diagnostic techniques. In: Preterm Birth pp 131–144Garfield RE, Maner WL (2007) Physiology and electrical activity of uterine contractions. Semin Cell Dev Biol 18:289–295. https://doi.org/10.1016/j.semcdb.2007.05.004Garfield RE, Maner WL, MacKay LB et al (2005) Comparing uterine electromyography activity of antepartum patients versus term labor patients. Am J Obstet Gynecol 193:23–29. https://doi.org/10.1016/j.ajog.2005.01.050Goldenberg RL, Culhane JF, Iams JD, Romero R (2008) Epidemiology and causes of preterm birth. Lancet 371:75–84. https://doi.org/10.1016/S0140-6736(08)60074-4American College of Obstetricians and Gynecologists and Committee on Practice Bulletins— Obstetrics (2012) Practice bulletin no. 127. Obstet Gynecol 119(6):1308–1317.Hadar E, Biron-Shental T, Gavish O, Raban O, Yogev Y (2015) A comparison between electrical uterine monitor, tocodynamometer and intra uterine pressure catheter for uterine activity in labor. J Matern Neonatal Med 28:1367–1374. https://doi.org/10.3109/14767058.2014.954539Hans P, Dewandre P, Brichant JF, Bonhomme V (2005) Comparative effects of ketamine on Bispectral Index and spectral entropy of the electroencephalogram under sevoflurane anaesthesia. Br J Anaesth 94:336–340. https://doi.org/10.1093/bja/aei047Hassan M, Terrien J, Marque C, Karlsson B (2011) Comparison between approximate entropy, correntropy and time reversibility: application to uterine electromyogram signals. Med Eng Phys 33:980–986. https://doi.org/10.1016/j.medengphy.2011.03.010Hassan M, Terrien J, Muszynski C et al (2013) Better pregnancy monitoring using nonlinear correlation analysis of external uterine electromyography. IEEE Trans Biomed Eng 60:1160–1166. https://doi.org/10.1109/TBME.2012.2229279Horoba K, Jezewski J, Matonia A, Wrobel J, Czabanski R, Jezewski M (2016) Early predicting a risk of preterm labour by analysis of antepartum electrohysterograhic signals. Biocybern Biomed Eng 36:574–583. https://doi.org/10.1016/j.bbe.2016.06.004Lawn JE, Wilczynska-Ketende K, Cousens SN (2006) Estimating the causes of 4 million neonatal deaths in the year 2000. Int J Epidemiol 35:706–718. https://doi.org/10.1093/ije/dyl043Lemancewicz A, Borowska M, Kuć P, Jasińska E, Laudański P, Laudański T, Oczeretko E (2016) Early diagnosis of threatened premature labor by electrohysterographic recordings—the use of digital signal processing. Biocybern Biomed Eng 36:302–307. https://doi.org/10.1016/j.bbe.2015.11.005M L WLM, LR C (2012) Noninvasive uterine electromyography for prediction of preterm delivery. Am J Obstet Gynecol 204:1–20. https://doi.org/10.1016/j.ajog.2010.09.024.NoninvasiveManer WL, Garfield RE (2007) Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann Biomed Eng 35:465–473. https://doi.org/10.1007/s10439-006-9248-8Maner WL, Garfield RE, Maul H, Olson G, Saade G (2003) Predicting term and preterm delivery with transabdominal uterine electromyography. Obstet Gynecol 101:1254–1260. https://doi.org/10.1016/S0029-7844(03)00341-7Marque C, Gondry J (1999) Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Trans Biomed Eng 46:1222–1229Maul H, Maner WL, Olson G, Saade GR, Garfield RE (2004) Non-invasive transabdominal uterine electromyography correlates with the strength of intrauterine pressure and is predictive of labor and delivery. J Matern Fetal Neonatal Med 15:297–301Mischi M, Chen C, Ignatenko T, de Lau H, Ding B, Oei SGG, Rabotti C (2018) Dedicated entropy measures for early assessment of pregnancy progression from single-channel electrohysterography. IEEE Trans Biomed Eng 65:875–884. https://doi.org/10.1109/TBME.2017.2723933Most O, Langer O, Kerner R, Ben David G, Calderon I (2008) Can myometrial electrical activity identify patients in preterm labor? Am J Obstet Gynecol 199:378. https://doi.org/10.1016/j.ajog.2008.08.003Petrou S (2005) The economic consequences of preterm birth during the first 10 years of life. BJOG 112:10–15. https://doi.org/10.1111/j.1471-0528.2005.00577.xRabotti C, Sammali F, Kuijsters N, et al (2015) Analysis of uterine activity in nonpregnant women by electrohysterography: a feasibility study. In: Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS pp 5916–5919Schlembach D, Maner WL, Garfield RE, Maul H (2009) Monitoring the progress of pregnancy and labor using electromyography. Eur J Obstet Gynecol Reprod Biol 144:2–8. https://doi.org/10.1016/j.ejogrb.2009.02.016Sikora J, Matonia A, Czabański R et al (2011) Recognition of premature threatening labour symptoms from bioelectrical uterine activity signals. Arch Perinat Med 17:97–103Vinken MPGC, Rabotti C, Mischi M, van Laar JOEH, Oei SG (2010) Nifedipine-induced changes in the electrohysterogram of preterm contractions: feasibility in clinical practice. Obstet Gynecol Int 2010:325635. https://doi.org/10.1155/2010/325635Vrhovec J, Lebar AM (2012) An uterine electromyographic activity as a measure of labor progression. Appl EMG Clin Sport Med 243–268. doi: https://doi.org/10.5772/25526Vrhovec J, Macek-Lebar A, Rudel D (2007) Evaluating uterine electrohysterogram with entropy. 11th Mediterr Conf Med Biomed Eng Comput 144–147. https://doi.org/10.1007/978-3-540-73044-6_36Ye-Lin Y, Bueno-Barrachina JM, Prats-boluda G, Rodriguez de Sanabria R, Garcia-Casado J (2017) Wireless sensor node for non-invasive high precision electrocardiographic signal acquisition based on a multi-ring electrode. Measurement 97:195–202. https://doi.org/10.1016/J.MEASUREMENT.2016.11.009Ye-Lin Y, Garcia-Casado J, Prats-Boluda G, Alberola-Rubio J, Perales A (2014) Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions. Comput Math Methods Med 2014:1–11. https://doi.org/10.1155/2014/470786Zhang XS, Roy RJ, Jensen EW (2001) EEG complexity as a measure of depth of anesthesia for patients. IEEE Trans Biomed Eng 48:1424–1433. https://doi.org/10.1109/10.96660