DIABETES TECHNOLOGY & THERAPEUTICS Volume 17, Number 9, 2015 ª Mary Ann Liebert, Inc. DOI: 10.1089/dia.2014.0255 ORIGINAL ARTICLE Continuous Glucose Monitoring in Type 1 Diabetes Pregnancy Shows that Fetal Heart Rate Correlates with Maternal Glycemia Katarzyna Cypryk, MD, PhD,1 Lukasz Bartyzel, MD,1 Monika Zurawska-Klis, MD, PhD,1 Wojciech Mlynarski, MD, PhD,2 Agnieszka Szadkowska, MD, PhD,2 Jan Wilczynski, MD, PhD,3 Dorota Nowakowska, MD, PhD,3 Lucyna A. Wozniak, PhD,4 and Wojciech Fendler, MD, PhD2 Abstract Background: Much evidence has shown that pregnancies in women with preexisting diabetes are affected by an increased risk of maternal and fetal adverse outcomes, probably linked to poor glycemic control. Despite great progress in medical care, the rate of stillbirths remains much higher in diabetes patients than in the general population. Recent technological advances in the field of glucose monitoring and noninvasive fetal heart rate monitoring made it possible to observe the fetal–maternal dependencies in a continuous manner. Subjects and Methods: Fourteen type 1 diabetes patients were involved into the study and fitted with a blinded continuous glucose monitoring (CGM) recorder. Fetal electrocardiogram data were recorded using the Monica AN24Ô device (Monica Healthcare Ltd., Nottingham, United Kingdom), the recordings of which were mat- ched with CGM data. Statistical analysis was performed using a generalized mixed-effect logistic regression to account for individual factors. Results: The mean number of paired data points per patient was 254 – 106, representing an observation period of 21.2 – 8.8 h. Mean glycemia equaled 5.64 – 0.68 mmol/L, and mean fetal heart rate was 135 – 6 beats/min. Higher glycemia correlated with fetal heart rate (R = 0.32; P < 0.0001) and was associated with higher odds of the fetus developing small accelerations (odds ratio = 1.05; 95% confidence interval, 1.00–1.10; P = 0.04). Conclusions: Elevated maternal glycemia of mothers with diabetes is associated with accelerations of fetal heart rate. Introduction decisions, contributing to better perinatal outcomes.7 Recent technological advances in the field of glucose monitoring and dequate metabolic control of diabetes in pregnant noninvasive fetal heart rate (FHR) monitoring made it pos- women is a crucial method of preventing neonatal and A sible to observe the fetal–maternal dependencies in a con- obstetric complications. The rate of all perinatal complica- tinuous manner. Using two devices—a continuous glucose tions correlates with the mother’s glycated hemoglobin monitoring (CGM) system and a FHR monitor—we inves- (HbA1c) concentration.1–6 Unfortunately, HbA1c is a retro- tigated association between maternal glycemic fluctuations spective parameter, and thus it is not particularly useful and FHR variability. during pregnancy when self-glucose monitoring is essential. However, achieving and maintaining normoglycemia during Subjects and Methods pregnancy in women with diabetes are major challenges. Adding the continuous monitoring to standard treatment The study group consisted of 14 white pregnant women could improve metabolic control and facilitate therapeutic with diabetes, treated with insulin. All available patients Departments of 1Diabetology and Metabolic Diseases and 2Pediatrics, Oncology, Hematology and Diabetology, Medical University of Lodz, Poland. 3Feto-Maternal and Gynecology Department, Research Institute, Polish Mother’s Memorial Hospital, Lodz, Poland. 4Department of Structural Biology, Faculty of Biomedical Sciences and Postgraduate Education, Medical University of Lodz, Lodz, Poland. 619 620 CYPRYK ET AL. treated during the 2012–2013 period were assessed for with the CGM recording up to the nearest minute. Small FHR eligibility and asked to participate in the study. The in- accelerations and decelerations were defined, respectively, as clusion criteria were set as following: singleton pregnancy increases in the FHR from the baseline greater than 10 beats/ > 30 weeks of gestation and established type 1 diabetes. We min (bpm) and lasting for at least 15 s and as decreases of excluded patients with any comorbidities necessitating ‡ 10 bpm for ‡ 10 s. A large acceleration event was defined as pharmacological treatment, patients after in vitro fertil- an increase greater than 15 bpm and lasting for more than ization, pregnancies at risk of premature termination, ab- 15 s. A large deceleration event was defined as a fall in FHR normal findings in prenatal ultrasonography, or triple test. from the baseline, where the area below the baseline was The study was approved by the Bioethics Committee of the greater than 20 beats. Imputation of missing data was not Medical University of Lodz, Lodz, Poland. Eligible patients attempted. invited to the study, after their informed consent was ob- The paired CGM/ECG data points were used in the analysis. tained, were fitted with a CGM recorder (iProÒ2; Medtronic, Frequency of hypoglycemia ( < 70 mg/dL [3.9 mmol/L] and Minneapolis, MN) for a standard, at least 48-h-long, re- < 54 mg/dL [ < 3 mmol/L]) and hyperglycemia ( > 126 mg/dL cording. Throughout the study the patients had the option of [7 mmol/L]) were presented as percentage of the total CGM staying in the hospital or remaining in their own home. time. Mean glucose values and three parameters of glycemic The sensor was placed in the subcutaneous tissue using a variability (SD, M100, and J index) were calculated using an dedicated insertion device. Calibration of the CGM device online CGM variability calculator designed by the authors was done using the ACCU-CHEKÒ glucometer (Roche, (GlyCulator) and verified using Microsoft (Redmond, WA) Basel, Switzerland), and measurements were performed at ExcelÔ. These indices of glycemic variability were selected as least four times per day. The obstetrics team was blinded to their methodology allows for noncontinuous data, which was CGM recordings during the study, and its results did not the case in our study.8–11 influence their clinical management during the study. Statistical analysis was performed using a generalized The endocrinological team was blinded to fetal electro- mixed-effect logistic regression model to estimate the cardiography data but could adjust insulin treatment de- glucose-dependent risk of FHR disturbances while account- pending on current glucose levels when necessary (blood ing for individual effects. The lme4 package for R was used glucose concentration less than 70 mg/dL [3.9 mmol/L] and for this analysis. more than 126 mg/dL [7 mmol/L]). Fetal electrocardiogram (ECG) data were recorded using the Monica AN24Ô device Results (Monica Healthcare Ltd., Nottingham, United Kingdom). All patients were fitted with this device between 1 p.m. and 5 Mean age of patients was 30.4 – 4.2 years (range, 25–37 p.m. after a run-in period of at least 3 h of CGM. After a years). Mean duration of diabetes was 14.6 – 7.6 years. Two calibration period of the ECG, the recording was started and patients had a body mass index of > 30 kg/m2, with the mean continued for at least 20 h. value in the whole group being 28.1 – 3.8 kg/m2.Gestational After removal of both devices, the ECG recording was week at evaluation was 33.5 – 1.0 (range, 32–36) (Table 1). No analyzed for a 5-min time frame using the manufacturer’s adverse events during the CGM/ECG recordings were noted in software (Monica DK; Monica Healthcare Ltd.), matched any of the studied patients. The mean number of paired data Table 1. Study Group Characteristics Duration BMI Third- Gestational Neonatal Maternal of (kg/m2) trimester age Place Gestational birth Patient age Type of diabetes Insulin before HbA1c (weeks) at of age (weeks) weight ID (years) diabetes (years) therapy pregnancy (%) evaluation observation at delivery (g) 1 36 T1DM 26 MDI 28.6 6.20 33 Hospital 38 3,660 2 29 T1DM 8 CSII 26.5 5.86 33 Hospital 36 3,200 3 26 T1DM 17 CSII 27.9 6.45 33 Hospital 38 3,460 4 31 T1DM 23 CSII 34.1 5.30 33 Home 37 3,800 5 30 T1DM 18 CSII 29.4 6.03 33 Home 38 4,160 6 36 T1DM 8 CSII 24.3 5.55 33 Home 33 3,140 7 27 T1DM 2 MDI 25.1 5.60 33 Hospital 39 3,390 8 29 T1DM 25 CSII 28.8 5.68 33 Home 37 4,080 9 28 T1DM 22 CSII 37.6 6.12 34 Hospital 35 2,950 10 31 T1DM 7 CSII 24.7 6.10 35 Hospital 39 3,900 11 35 T1DM 15 MDI 25.5 6.30 34 Hospital 40 3,770 12 25 T1DM 12 CSII 28.6 6.00 36 Hospital 38 3,680 13 25 T1DM 15 CSII 23.9 6.97 32 Home 33 2,690 14 37 T1DM 7 CSII 27.8 5.16 34 Hospital 39 4,160 Mean – SD 30.4 – 4.2 — 14.6 – 7.6 — 28.1 – 3.8 5.95 – 0.48 33.5 – 1.0 — 37.1 – 2.17 3,574 – 456 Individual data of all 14 participants are presented in the respective rows. Means with SDs are presented in the last row. BMI, body mass index; CSII, continuous subcutaneous insulin infusion; HbA1c, glycated hemoglobin; MDI, multiple daily injections; T1DM, type 1 diabetes mellitus. Table 2. Individual Data Recording Characteristics Patient ID Mean – SD or 1 2 3 4 5 6 7 8 9 10 11 12 13 14 median (25–75%) Glucose concentration (mmol/L) Mean 5.34 4.71 6.52 5.52 6.90 5.06 5.00 5.84 5.85 5.06 5.59 5.03 6.67 5.92 5.64 – 0.68 SD 2.00 1.50 2.07 1.76 2.14 1.10 1.29 0.90 2.06 1.37 2.15 1.49 2.11 1.68 1.69 – 0.42 M100 9.55 6.47 4.66 4.12 5.22 2.03 2.90 0.96 5.01 2.54 5.66 3.37 5.27 3.06 4.34 – 2.15 J index 17.45 12.46 23.87 17.2 26.5 12.29 12.86 17.87 20.28 13.39 19.41 13.77 24.96 18.7 17.93 – 4.74 Glucose concentration (mmol/L) Minimum 2.83 2.44 3 2.33 3.22 2.22 3.83 4.56 2.44 2.78 2.83 2.67 4.67 3.17 3.07 – 0.78 Maximum 9.89 10 10.78 8.11 9.78 8.22 7.61 8.11 9.89 8.11 10.61 10.22 10.78