DIABETES TECHNOLOGY & THERAPEUTICS Volume 17, Number 8, 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, PhD 2
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 AN24 device (Monica Healthcare Ltd., Nottingham, United Kingdom), the recordings of which were matched 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. decisions, contributing to better perinatal outcomes.7 Recent technological advances in the field of glucose monitoring and noninvasive fetal heart rate (FHR) monitoring made it possible to observe the fetal–maternal dependencies in a continuous manner. Using two devices—a continuous glucose monitoring (CGM) system and a FHR monitor—we investigated association between maternal glycemic fluctuations and FHR variability.
Introduction
A
dequate metabolic control of diabetes in pregnant women is a crucial method of preventing neonatal and obstetric complications. The rate of all perinatal complications correlates with the mother’s glycated hemoglobin (HbA1c) concentration.1–6 Unfortunately, HbA1c is a retrospective parameter, and thus it is not particularly useful during pregnancy when self-glucose monitoring is essential. However, achieving and maintaining normoglycemia during pregnancy in women with diabetes are major challenges. Adding the continuous monitoring to standard treatment could improve metabolic control and facilitate therapeutic
Subjects and Methods
The study group consisted of 14 white pregnant women 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. 3 Feto-Maternal and Gynecology Department, Research Institute, Polish Mother’s Memorial Hospital, Lodz, Poland. 4 Department of Structural Biology, Faculty of Biomedical Sciences and Postgraduate Education, Medical University of Lodz, Lodz, Poland.
1
2
CYPRYK ET AL.
treated during the 2012–2013 period were assessed for eligibility and asked to participate in the study. The inclusion criteria were set as following: singleton pregnancy > 30 weeks of gestation and established type 1 diabetes. We excluded patients with any comorbidities necessitating pharmacological treatment, patients after in vitro fertilization, pregnancies at risk of premature termination, abnormal findings in prenatal ultrasonography, or triple test. The study was approved by the Bioethics Committee of the Medical University of Lodz, Lodz, Poland. Eligible patients invited to the study, after their informed consent was obtained, were fitted with a CGM recorder (iPro2; Medtronic, Minneapolis, MN) for a standard, at least 48-h-long, recording. Throughout the study the patients had the option of staying in the hospital or remaining in their own home. The sensor was placed in the subcutaneous tissue using a dedicated insertion device. Calibration of the CGM device was done using the ACCU-CHEK glucometer (Roche, Basel, Switzerland), and measurements were performed at least four times per day. The obstetrics team was blinded to CGM recordings during the study, and its results did not influence their clinical management during the study. The endocrinological team was blinded to fetal electrocardiography data but could adjust insulin treatment depending on current glucose levels when necessary (blood glucose concentration less than 70 mg/dL [3.9 mmol/L] and more than 126 mg/dL [7 mmol/L]). Fetal electrocardiogram (ECG) data were recorded using the Monica AN24 device (Monica Healthcare Ltd., Nottingham, United Kingdom). All patients were fitted with this device between 1 p.m. and 5 p.m. after a run-in period of at least 3 h of CGM. After a calibration period of the ECG, the recording was started and continued for at least 20 h. After removal of both devices, the ECG recording was analyzed for a 5-min time frame using the manufacturer’s software (Monica DK; Monica Healthcare Ltd.), matched
with the CGM recording up to the nearest minute. Small FHR accelerations and decelerations were defined, respectively, as increases in the FHR from the baseline greater than 10 beats/ min (bpm) and lasting for at least 15 s and as decreases of ‡ 10 bpm for ‡ 10 s. A large acceleration event was defined as an increase greater than 15 bpm and lasting for more than 15 s. A large deceleration event was defined as a fall in FHR from the baseline, where the area below the baseline was greater than 20 beats. Imputation of missing data was not attempted. The paired CGM/ECG data points were used in the analysis. Frequency of hypoglycemia ( < 70 mg/dL [3.9 mmol/L] and < 54 mg/dL [ < 3 mmol/L]) and hyperglycemia ( > 126 mg/dL [7 mmol/L]) were presented as percentage of the total CGM time. Mean glucose values and three parameters of glycemic variability (SD, M100, and J index) were calculated using an online CGM variability calculator designed by the authors (GlyCulator) and verified using Microsoft (Redmond, WA) Excel. These indices of glycemic variability were selected as their methodology allows for noncontinuous data, which was the case in our study.8–11 Statistical analysis was performed using a generalized mixed-effect logistic regression model to estimate the glucose-dependent risk of FHR disturbances while accounting for individual effects. The lme4 package for R was used for this analysis. Results
Mean age of patients was 30.4 – 4.2 years (range, 25–37 years). Mean duration of diabetes was 14.6 – 7.6 years. Two patients had a body mass index of > 30 kg/m2, with the mean value in the whole group being 28.1 – 3.8 kg/m2. Gestational week at evaluation was 33.5 – 1.0 (range, 32–36) (Table 1). No adverse events during the CGM/ECG recordings were noted in any of the studied patients. The mean number of paired data
Table 1. Study Group Characteristics
Patient ID
Neonatal BMI Third- Gestational Duration birth age Place Gestational (kg/m2) trimester of Maternal of age (weeks) weight HbA1c (weeks) at age Type of diabetes Insulin before (g) evaluation observation at delivery (%) (years) diabetes (years) therapy pregnancy
1 36 2 29 3 26 4 31 5 30 6 36 7 27 8 29 9 28 10 31 11 35 12 25 13 25 14 37 Mean – SD 30.4 – 4.2
T1DM 26 T1DM 8 T1DM 17 T1DM 23 T1DM 18 T1DM 8 T1DM 2 T1DM 25 T1DM 22 T1DM 7 T1DM 15 T1DM 12 T1DM 15 T1DM 7 — 14.6 – 7.6
MDI CSII CSII CSII CSII CSII MDI CSII CSII CSII MDI CSII CSII CSII —
28.6 6.20 33 26.5 5.86 33 27.9 6.45 33 34.1 5.30 33 29.4 6.03 33 24.3 5.55 33 25.1 5.60 33 28.8 5.68 33 37.6 6.12 34 24.7 6.10 35 25.5 6.30 34 28.6 6.00 36 23.9 6.97 32 27.8 5.16 34 28.1 – 3.8 5.95 – 0.48 33.5 – 1.0
Hospital Hospital Hospital Home Home Home Hospital Home Hospital Hospital Hospital Hospital Home Hospital —
38 3,660 36 3,200 38 3,460 37 3,800 38 4,160 33 3,140 39 3,390 37 4,080 35 2,950 39 3,900 40 3,770 38 3,680 33 2,690 39 4,160 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.
3
4.71 1.50 6.47 12.46 2.44 10 10.0 46.0 11.2 138 7.19 116 165 0.41 0.24 0.25a
5.34 2.00 9.55 17.45 2.83 9.89 23.4 8.1 1.4 128 6.03 117 142 0.43 0.18 0.29a
2
0.33 0.31 0.21b
101 156
131 7.49
3 10.78 15.9 11.7 2.2
6.52 2.07 4.66 23.87
3
0.37 0.31 0.09
105 147
123 9.06
2.33 8.11 3.3 15.7 6.2
5.52 1.76 4.12 17.2
4
0.38 0.18 0.36a
115 153
135 6.05
3.22 9.78 53.6 5.3 0.0
6.90 2.14 5.22 26.5
5
0.39 0.09 - 0.02
112 164
133 8.24
2.22 8.22 4.4 13.7 3.4
5.06 1.10 2.03 12.29
6
7
0.49 0.18 0.11
122 150
138 4.57
3.83 7.61 9.9 1.7 0.0
5.00 1.29 2.90 12.86
P < 0.0001, bP < 0.05. bpm, beats/min; FHR, fetal heart rate; NA, not applicable; R, Pearson’s correlation coefficient.
a
Glucose concentration (mmol/L) Mean SD M100 J index Glucose concentration (mmol/L) Minimum Maximum % > 7 mmol/L % < 3.9 mmol/L % < 3.0 mmol/L FHR (bpm) Mean SD FHR (bpm) Minimum Maximum Percentage small Acceleration Deceleration R for FHR/glycemia correlation
1
0.5 0.13 0.21a
113 163
137 8.49
4.56 8.11 28.4 0.0 0.0
5.84 0.90 0.96 17.87
8
Patient ID
0.4 0.18 0.38a
125 179
144 8.26
2.44 9.89 23.4 7.7 2.4
5.85 2.06 5.01 20.28
9
0.43 0.18 0.11b
115 166
136 7.84
2.78 8.11 4.4 12.4 1.7
5.06 1.37 2.54 13.39
10
Table 2. Individual Data Recording Characteristics
0.23 0.25 0.35a
117 157
132 6.23
2.83 10.61 21.3 28.7 2.4
5.59 2.15 5.66 19.41
11
0.46 0.29 0.40a
112 159
133 9.52
2.67 10.22 14.7 14.9 2.2
5.03 1.49 3.37 13.77
12
0.44 0.19 0.10
133 167
145 5.35
4.67 10.78 54.1 0.0 0.0
6.67 2.11 5.27 24.96
13
0.47 0.32 0.34a
114 157
134 8.11
3.17 9.44 17.4 5.5 0.0
5.92 1.68 3.06 18.7
14
0.41 – 0.07 0.22 – 0.07 NA
116 – 8 159 – 9
135 – 6 7.32 – 1.46
3.07 – 0.78 9.40 – 1.28 16.65 (9.93–23.40) 9.90 (5.35–14.60) 1.95 (0.00–2.40)
5.64 – 0.68 1.69 – 0.42 4.34 – 2.15 17.93 – 4.74
Mean – SD or median (25–75%)
4
CYPRYK ET AL.
FIG. 1. Correlation between the time-point-matched maternal glycemia and fetal heart rate for pooled data points of all available patients/recordings. points per patient was 254 – 106, representing an observation period of 21.2 – 8.8 h. The mean glucose level equaled 5.64 – 0.68 mmol/L, and the mean FHR was 135 – 6 bpm. Glycemic variability data and FHR characteristics are summarized in Table 2. In nine patients FHR showed significant, positive correlation with the time-point-matched maternal glycemia, which consequently held true for an aggregate correlation analysis (R = 0.32; P < 0.0001) (Fig. 1). In five patients no such correlations were noted, and in one case a significant negative correlation was noted. In mixedeffect logistic regression analysis, higher glucose levels were associated with higher odds of the fetus developing small accelerations (odds ratio = 1.05; 95% confidence interval, 1.00–1.10; P = 0.04). None of the analyzed individuallevel factors, like patient’s age (P = 0.38), body mass index (P = 0.66), duration of diabetes (P = 0.32), gestational week (P = 0.93), or nighttime measurements (P = 0.07), significantly affected the association between maternal glucose and FHR accelerations. The risk of small decelerations did not, however, depend significantly on current maternal glucose levels (odds ratio = 0.97; 95% confidence interval, 0.92–1.03; P = 0.32), with nighttime measurements significantly decreasing the odds of deceleration events (odds ratio = 0.49; 95% confidence interval, 0.41–0.58; P < 0.0001). Similarly, with acceleration events, none of the evaluated individual factors—patient’s age (P = 0.51), body mass index (P = 0.56), duration of diabetes (P = 0.83), or gestational week (P = 0.35)—showed any associations with the odds of deceleration events.
In our study we did not observe any large, long-lasting deceleration, the most dangerous heart disturbance, probably because diabetes control achieved in the studied patients was excellent. The mean HbA1c was 5.95 – 0.48%, and glycemia during the observation ranged from 2.22 to 10.78 mmol/L. Discussion
Adequate metabolic control of diabetes in pregnant women is a crucial method of preventing neonatal and obstetric complications. Hyperglycemia in pregnant women can cause fetal malformations, growth disturbances, development delay of the central nervous system, chronic hypoxemia, and, finally, spontaneous abortion, stillbirth, prematurity, and many other fetal complications, including hypoglycemia, macrosomy, etc. Those are well documented in the in vitro and in human studies.1–5 A Swedish study proved that stillbirth rate in women with type 1 diabetes was more than three times higher than in the background population.6 Cardiotocography (fetal ECG) is an established method to monitor fetal well-being and is essential to avoid intrauterine death by early detection of fetal compromise. Bjo¨rklund et al.12 revealed that maternal hypoglycemia induced during hyperinsulinemic/hypoglycemic clamp with induction and maintenance of an arterial blood glucose level of about 2.2 mmol/L was associated with an increase in frequency and amplitude of FHR accelerations and a slight decrease in the pulsatility index of the umbilical artery and with a rise in the maternal catecholamine levels.
FETAL HEART RATE AND MATERNAL GLYCEMIA
However, Reece et al.13 did not observe an influence of maternal hypoglycemia in pregnant women with diabetes on the mean number of fetal limb, body, and breathing movements or a heart rate, although maternal epinephrine and growth hormone levels were significantly increased. In a study by Serra-Serra et al.14 the impact of maternal blood glucose concentration on cardiotocographic results in pre- and postmeal stage was measured. The authors concluded that FHR parameters are unaffected by prandial glycemic changes over a wide range (4.2–14.8 mmol/L) of maternal glucose levels in any of the groups of women with gestational diabetes, pregestational diabetes, and healthy pregnant volunteers without diabetes.14 On the other hand, Buscicchio et al.15 showed that gestational diabetes did impact FHR. The alteration was slight but evident, and it correlated with neonatal reactivity.15 Unfortunately, in this study the blood glucose level was measured only once, at delivery. Wiener et al.16 revealed significantly reduced FHR variation as well as frequency of accelerations in fetuses of woment with well-controlled diabetes compared with fetuses of mothers without diabetes. In this study, published in 1996, HbA1c was only estimated every 3 months during pregnancy, self-monitoring of glucose was performed, and there were no data about blood glucose concentration during the test. Costa et al.17 also found a positive correlation between basal FHR and mean glycemia. A significant negative correlation was observed in this study between short-term variation and mean glycemia. All these studies did not combine directly maternal blood glucose concentration with fetal ECG data. Our study seems to confirm direct relationship between maternal glycemia and fetal well-being as it demonstrates that elevated maternal glycemia of mothers with diabetes is associated with accelerations of FHR, even in very well-controlled diabetes. We are aware that the study does have its limitations due to a single time point of CGM/ECG examination, a lack of a healthy control group, and limited sample size. It would be, however, extremely difficult to justify such an experiment on healthy pregnant women without any a priori reason to conduct a cumbersome CGM examination. Moreover, the dynamic range of glycemic changes in healthy women is incomparably narrower than that observed in women with diabetes. This ultimately convinced us that if any meaningful associations between GCM and ECG variability are to be discovered, it should be done first in a group in which fluctuations of both heart rate and glycemia may have direct consequences. Finally, having established that FHR does correlate with maternal glycemia, we are able to focus on the impact of further inquiries on fetal outcomes of either parameter fluctuations. This is the first study conducted in real life that shows this correlation in a continuous manner. We believe that investigation using a combined fetal ECG/maternal CGM is feasible and provides insight into impact of maternal diabetes on fetal well-being. Conclusions
Elevated maternal glycemia of mothers with diabetes is associated with accelerations of FHR. Further studies are needed to explore the association between glucose variability
5
of the mother and fetal well-being in various levels of glycemic control. Acknowledgments
This study was supported financially by funds of the Medical University of Lodz (project number 502-03/0-16001/502-04-014). Author Disclosure Statement
No competing financial interests exist. References
1. Vargas R, Repke JT, Ural SH: Type 1 diabetes mellitus and pregnancy. Rev Obstet Gynecol 2010;3:92–100. 2. Inkster ME, Fahey TP, Donnan PT, Leese GP, Mires GJ, Murphy DJ: Poor glycated haemoglobin control and adverse pregnancy outcomes in type 1 and type 2 diabetes mellitus: systematic review of observational studies. BMC Pregnancy Childbirth 2006;30:6–30. 3. Jensen DM, Korsholm L, Ovesen P, Beck-Nielsen H, Moelsted-Pedersen L, Westergaard JG, Moeller M, Damm P: Peri-conceptional A1C and risk of serious adverse pregnancy outcome in 933 women with type 1 diabetes. Diabetes Care 2009;32:1046–1048. 4. Gizzo S, Patrelli TS, Rossanese M, Noventa M, Berretta R, Di Gangi S, Bertin M, Gangemi M, Nardelli GB: An update on diabetic women obstetrical outcomes linked to preconception and pregnancy glycemic profile: a systematic literature review. Sci World J 2013;6;254901. 5. Jensen DM, Damm P, Moelsted-Pedersen L, Ovesen P, Westergaard JG, Moeller M, Beck-Nielsen H: Outcomes in type 1 diabetic pregnancies: a nationwide, population-based study. Diabetes Care 2004;27:2819–2823. 6. Persson M, Norman M, Hanson U: Obstetric and perinatal outcomes in type 1 diabetic pregnancies: a large, population-based study. Diabetes Care 2009;32:2005– 2009. 7. Murphy HR, Rayman G, Lewis K, Kelly S, Johal B, Duffield K, Fowler D, Campbell PJ, Temple RC: Effectiveness of continuous glucose monitoring in pregnant women with diabetes: randomised clinical trial. BMJ 2008;25;337: a1680. 8. Dobbe JGG, Lunshof S, Boer K, Wolf H, Grimbergen CA: The technique and algorithms for computerized analysis of long-term fetal heart rate recordings. Prenatal Neonatal Med 2001;6:280–289. 9. Schlichtkrull J, Munck O, Jersild M: The M-value, an index of blood-sugar control in diabetics. Acta Med Scand 1965; 177:95–102. 10. Wojcicki JM: ‘‘J’’-index. A new proposition of the assessment of current glucose control in diabetic patients. Horm Metab Res 1995;27:41–42. 11. Czerwoniuk D, Fendler W, Walenciak L, Mlynarski W: GlyCulator: a glycemic variability calculation tool for continuous glucose monitoring data. J Diabetes Sci Technol 2011;5:447–451. 12. Bjo¨rklund AO, Adamson UK, Almstro¨m NH, Enocksson EA, Gennser GM, Lins PE, Westgren LM: Effects of hypoglycaemia on fetal heart activity and umbilical artery Doppler velocity waveforms in pregnant women with insulindependent diabetes mellitus. Br J Obstet Gynaecol 1996; 103:413–420.
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13. Reece EA, Hagay Z, Roberts AB, DeGennaro N, Homko CJ, Connolly-Diamond M, Sherwin R, Tamborlane WV, Diamond MP: Fetal Doppler and behavioral responses during hypoglycemia induced with the insulin clamp technique in pregnant diabetic women. Am J Obstet Gynecol 1995;172:151–155. 14. Serra-Serra V, Camara R, Sarrio´n P, Jaren˜o M, Cervera J, Bellver J, Perales A: Effects of prandial glycemic changes on objective fetal heart rate parameters. Acta Obstet Gynecol Scand 2000;79:953–957. 15. Buscicchio G, Gentilucci L, Giannubilo SR, Tranquilli AL: Computerized analysis of fetal heart rate in pregnancies complicated by gestational diabetes mellitus. Gynecol Endocrinol 2010;26:270–274. 16. Weiner Z, Thaler I, Farmakides G, Barnhard Y, Maulik D, Divon MY: Fetal heart rate patterns in pregnancies com-
CYPRYK ET AL.
plicated by maternal diabetes. Eur J Obstet Gynecol Reprod Biol 1996;27;70:111–115. 17. Costa VN, Nomura RM, Reynolds KS, Miyadahira S, Zugaib M: Effects of maternal glycemia on fetal heart rate in pregnancies complicated by pregestational diabetes mellitus.Eur J Obstet Gynecol Reprod Biol 2009;143:14–17.
Address correspondence to: Katarzyna Cypryk, MD, PhD Department of Diabetology and Metabolic Diseases Medical University of Lodz ul. Pomorska 251 92-216 Lodz, Poland E-mail: [email protected]
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, PhD 2
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 AN24 device (Monica Healthcare Ltd., Nottingham, United Kingdom), the recordings of which were matched 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. decisions, contributing to better perinatal outcomes.7 Recent technological advances in the field of glucose monitoring and noninvasive fetal heart rate (FHR) monitoring made it possible to observe the fetal–maternal dependencies in a continuous manner. Using two devices—a continuous glucose monitoring (CGM) system and a FHR monitor—we investigated association between maternal glycemic fluctuations and FHR variability.
Introduction
A
dequate metabolic control of diabetes in pregnant women is a crucial method of preventing neonatal and obstetric complications. The rate of all perinatal complications correlates with the mother’s glycated hemoglobin (HbA1c) concentration.1–6 Unfortunately, HbA1c is a retrospective parameter, and thus it is not particularly useful during pregnancy when self-glucose monitoring is essential. However, achieving and maintaining normoglycemia during pregnancy in women with diabetes are major challenges. Adding the continuous monitoring to standard treatment could improve metabolic control and facilitate therapeutic
Subjects and Methods
The study group consisted of 14 white pregnant women 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. 3 Feto-Maternal and Gynecology Department, Research Institute, Polish Mother’s Memorial Hospital, Lodz, Poland. 4 Department of Structural Biology, Faculty of Biomedical Sciences and Postgraduate Education, Medical University of Lodz, Lodz, Poland.
1
2
CYPRYK ET AL.
treated during the 2012–2013 period were assessed for eligibility and asked to participate in the study. The inclusion criteria were set as following: singleton pregnancy > 30 weeks of gestation and established type 1 diabetes. We excluded patients with any comorbidities necessitating pharmacological treatment, patients after in vitro fertilization, pregnancies at risk of premature termination, abnormal findings in prenatal ultrasonography, or triple test. The study was approved by the Bioethics Committee of the Medical University of Lodz, Lodz, Poland. Eligible patients invited to the study, after their informed consent was obtained, were fitted with a CGM recorder (iPro2; Medtronic, Minneapolis, MN) for a standard, at least 48-h-long, recording. Throughout the study the patients had the option of staying in the hospital or remaining in their own home. The sensor was placed in the subcutaneous tissue using a dedicated insertion device. Calibration of the CGM device was done using the ACCU-CHEK glucometer (Roche, Basel, Switzerland), and measurements were performed at least four times per day. The obstetrics team was blinded to CGM recordings during the study, and its results did not influence their clinical management during the study. The endocrinological team was blinded to fetal electrocardiography data but could adjust insulin treatment depending on current glucose levels when necessary (blood glucose concentration less than 70 mg/dL [3.9 mmol/L] and more than 126 mg/dL [7 mmol/L]). Fetal electrocardiogram (ECG) data were recorded using the Monica AN24 device (Monica Healthcare Ltd., Nottingham, United Kingdom). All patients were fitted with this device between 1 p.m. and 5 p.m. after a run-in period of at least 3 h of CGM. After a calibration period of the ECG, the recording was started and continued for at least 20 h. After removal of both devices, the ECG recording was analyzed for a 5-min time frame using the manufacturer’s software (Monica DK; Monica Healthcare Ltd.), matched
with the CGM recording up to the nearest minute. Small FHR accelerations and decelerations were defined, respectively, as increases in the FHR from the baseline greater than 10 beats/ min (bpm) and lasting for at least 15 s and as decreases of ‡ 10 bpm for ‡ 10 s. A large acceleration event was defined as an increase greater than 15 bpm and lasting for more than 15 s. A large deceleration event was defined as a fall in FHR from the baseline, where the area below the baseline was greater than 20 beats. Imputation of missing data was not attempted. The paired CGM/ECG data points were used in the analysis. Frequency of hypoglycemia ( < 70 mg/dL [3.9 mmol/L] and < 54 mg/dL [ < 3 mmol/L]) and hyperglycemia ( > 126 mg/dL [7 mmol/L]) were presented as percentage of the total CGM time. Mean glucose values and three parameters of glycemic variability (SD, M100, and J index) were calculated using an online CGM variability calculator designed by the authors (GlyCulator) and verified using Microsoft (Redmond, WA) Excel. These indices of glycemic variability were selected as their methodology allows for noncontinuous data, which was the case in our study.8–11 Statistical analysis was performed using a generalized mixed-effect logistic regression model to estimate the glucose-dependent risk of FHR disturbances while accounting for individual effects. The lme4 package for R was used for this analysis. Results
Mean age of patients was 30.4 – 4.2 years (range, 25–37 years). Mean duration of diabetes was 14.6 – 7.6 years. Two patients had a body mass index of > 30 kg/m2, with the mean value in the whole group being 28.1 – 3.8 kg/m2. Gestational week at evaluation was 33.5 – 1.0 (range, 32–36) (Table 1). No adverse events during the CGM/ECG recordings were noted in any of the studied patients. The mean number of paired data
Table 1. Study Group Characteristics
Patient ID
Neonatal BMI Third- Gestational Duration birth age Place Gestational (kg/m2) trimester of Maternal of age (weeks) weight HbA1c (weeks) at age Type of diabetes Insulin before (g) evaluation observation at delivery (%) (years) diabetes (years) therapy pregnancy
1 36 2 29 3 26 4 31 5 30 6 36 7 27 8 29 9 28 10 31 11 35 12 25 13 25 14 37 Mean – SD 30.4 – 4.2
T1DM 26 T1DM 8 T1DM 17 T1DM 23 T1DM 18 T1DM 8 T1DM 2 T1DM 25 T1DM 22 T1DM 7 T1DM 15 T1DM 12 T1DM 15 T1DM 7 — 14.6 – 7.6
MDI CSII CSII CSII CSII CSII MDI CSII CSII CSII MDI CSII CSII CSII —
28.6 6.20 33 26.5 5.86 33 27.9 6.45 33 34.1 5.30 33 29.4 6.03 33 24.3 5.55 33 25.1 5.60 33 28.8 5.68 33 37.6 6.12 34 24.7 6.10 35 25.5 6.30 34 28.6 6.00 36 23.9 6.97 32 27.8 5.16 34 28.1 – 3.8 5.95 – 0.48 33.5 – 1.0
Hospital Hospital Hospital Home Home Home Hospital Home Hospital Hospital Hospital Hospital Home Hospital —
38 3,660 36 3,200 38 3,460 37 3,800 38 4,160 33 3,140 39 3,390 37 4,080 35 2,950 39 3,900 40 3,770 38 3,680 33 2,690 39 4,160 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.
3
4.71 1.50 6.47 12.46 2.44 10 10.0 46.0 11.2 138 7.19 116 165 0.41 0.24 0.25a
5.34 2.00 9.55 17.45 2.83 9.89 23.4 8.1 1.4 128 6.03 117 142 0.43 0.18 0.29a
2
0.33 0.31 0.21b
101 156
131 7.49
3 10.78 15.9 11.7 2.2
6.52 2.07 4.66 23.87
3
0.37 0.31 0.09
105 147
123 9.06
2.33 8.11 3.3 15.7 6.2
5.52 1.76 4.12 17.2
4
0.38 0.18 0.36a
115 153
135 6.05
3.22 9.78 53.6 5.3 0.0
6.90 2.14 5.22 26.5
5
0.39 0.09 - 0.02
112 164
133 8.24
2.22 8.22 4.4 13.7 3.4
5.06 1.10 2.03 12.29
6
7
0.49 0.18 0.11
122 150
138 4.57
3.83 7.61 9.9 1.7 0.0
5.00 1.29 2.90 12.86
P < 0.0001, bP < 0.05. bpm, beats/min; FHR, fetal heart rate; NA, not applicable; R, Pearson’s correlation coefficient.
a
Glucose concentration (mmol/L) Mean SD M100 J index Glucose concentration (mmol/L) Minimum Maximum % > 7 mmol/L % < 3.9 mmol/L % < 3.0 mmol/L FHR (bpm) Mean SD FHR (bpm) Minimum Maximum Percentage small Acceleration Deceleration R for FHR/glycemia correlation
1
0.5 0.13 0.21a
113 163
137 8.49
4.56 8.11 28.4 0.0 0.0
5.84 0.90 0.96 17.87
8
Patient ID
0.4 0.18 0.38a
125 179
144 8.26
2.44 9.89 23.4 7.7 2.4
5.85 2.06 5.01 20.28
9
0.43 0.18 0.11b
115 166
136 7.84
2.78 8.11 4.4 12.4 1.7
5.06 1.37 2.54 13.39
10
Table 2. Individual Data Recording Characteristics
0.23 0.25 0.35a
117 157
132 6.23
2.83 10.61 21.3 28.7 2.4
5.59 2.15 5.66 19.41
11
0.46 0.29 0.40a
112 159
133 9.52
2.67 10.22 14.7 14.9 2.2
5.03 1.49 3.37 13.77
12
0.44 0.19 0.10
133 167
145 5.35
4.67 10.78 54.1 0.0 0.0
6.67 2.11 5.27 24.96
13
0.47 0.32 0.34a
114 157
134 8.11
3.17 9.44 17.4 5.5 0.0
5.92 1.68 3.06 18.7
14
0.41 – 0.07 0.22 – 0.07 NA
116 – 8 159 – 9
135 – 6 7.32 – 1.46
3.07 – 0.78 9.40 – 1.28 16.65 (9.93–23.40) 9.90 (5.35–14.60) 1.95 (0.00–2.40)
5.64 – 0.68 1.69 – 0.42 4.34 – 2.15 17.93 – 4.74
Mean – SD or median (25–75%)
4
CYPRYK ET AL.
FIG. 1. Correlation between the time-point-matched maternal glycemia and fetal heart rate for pooled data points of all available patients/recordings. points per patient was 254 – 106, representing an observation period of 21.2 – 8.8 h. The mean glucose level equaled 5.64 – 0.68 mmol/L, and the mean FHR was 135 – 6 bpm. Glycemic variability data and FHR characteristics are summarized in Table 2. In nine patients FHR showed significant, positive correlation with the time-point-matched maternal glycemia, which consequently held true for an aggregate correlation analysis (R = 0.32; P < 0.0001) (Fig. 1). In five patients no such correlations were noted, and in one case a significant negative correlation was noted. In mixedeffect logistic regression analysis, higher glucose levels were associated with higher odds of the fetus developing small accelerations (odds ratio = 1.05; 95% confidence interval, 1.00–1.10; P = 0.04). None of the analyzed individuallevel factors, like patient’s age (P = 0.38), body mass index (P = 0.66), duration of diabetes (P = 0.32), gestational week (P = 0.93), or nighttime measurements (P = 0.07), significantly affected the association between maternal glucose and FHR accelerations. The risk of small decelerations did not, however, depend significantly on current maternal glucose levels (odds ratio = 0.97; 95% confidence interval, 0.92–1.03; P = 0.32), with nighttime measurements significantly decreasing the odds of deceleration events (odds ratio = 0.49; 95% confidence interval, 0.41–0.58; P < 0.0001). Similarly, with acceleration events, none of the evaluated individual factors—patient’s age (P = 0.51), body mass index (P = 0.56), duration of diabetes (P = 0.83), or gestational week (P = 0.35)—showed any associations with the odds of deceleration events.
In our study we did not observe any large, long-lasting deceleration, the most dangerous heart disturbance, probably because diabetes control achieved in the studied patients was excellent. The mean HbA1c was 5.95 – 0.48%, and glycemia during the observation ranged from 2.22 to 10.78 mmol/L. Discussion
Adequate metabolic control of diabetes in pregnant women is a crucial method of preventing neonatal and obstetric complications. Hyperglycemia in pregnant women can cause fetal malformations, growth disturbances, development delay of the central nervous system, chronic hypoxemia, and, finally, spontaneous abortion, stillbirth, prematurity, and many other fetal complications, including hypoglycemia, macrosomy, etc. Those are well documented in the in vitro and in human studies.1–5 A Swedish study proved that stillbirth rate in women with type 1 diabetes was more than three times higher than in the background population.6 Cardiotocography (fetal ECG) is an established method to monitor fetal well-being and is essential to avoid intrauterine death by early detection of fetal compromise. Bjo¨rklund et al.12 revealed that maternal hypoglycemia induced during hyperinsulinemic/hypoglycemic clamp with induction and maintenance of an arterial blood glucose level of about 2.2 mmol/L was associated with an increase in frequency and amplitude of FHR accelerations and a slight decrease in the pulsatility index of the umbilical artery and with a rise in the maternal catecholamine levels.
FETAL HEART RATE AND MATERNAL GLYCEMIA
However, Reece et al.13 did not observe an influence of maternal hypoglycemia in pregnant women with diabetes on the mean number of fetal limb, body, and breathing movements or a heart rate, although maternal epinephrine and growth hormone levels were significantly increased. In a study by Serra-Serra et al.14 the impact of maternal blood glucose concentration on cardiotocographic results in pre- and postmeal stage was measured. The authors concluded that FHR parameters are unaffected by prandial glycemic changes over a wide range (4.2–14.8 mmol/L) of maternal glucose levels in any of the groups of women with gestational diabetes, pregestational diabetes, and healthy pregnant volunteers without diabetes.14 On the other hand, Buscicchio et al.15 showed that gestational diabetes did impact FHR. The alteration was slight but evident, and it correlated with neonatal reactivity.15 Unfortunately, in this study the blood glucose level was measured only once, at delivery. Wiener et al.16 revealed significantly reduced FHR variation as well as frequency of accelerations in fetuses of woment with well-controlled diabetes compared with fetuses of mothers without diabetes. In this study, published in 1996, HbA1c was only estimated every 3 months during pregnancy, self-monitoring of glucose was performed, and there were no data about blood glucose concentration during the test. Costa et al.17 also found a positive correlation between basal FHR and mean glycemia. A significant negative correlation was observed in this study between short-term variation and mean glycemia. All these studies did not combine directly maternal blood glucose concentration with fetal ECG data. Our study seems to confirm direct relationship between maternal glycemia and fetal well-being as it demonstrates that elevated maternal glycemia of mothers with diabetes is associated with accelerations of FHR, even in very well-controlled diabetes. We are aware that the study does have its limitations due to a single time point of CGM/ECG examination, a lack of a healthy control group, and limited sample size. It would be, however, extremely difficult to justify such an experiment on healthy pregnant women without any a priori reason to conduct a cumbersome CGM examination. Moreover, the dynamic range of glycemic changes in healthy women is incomparably narrower than that observed in women with diabetes. This ultimately convinced us that if any meaningful associations between GCM and ECG variability are to be discovered, it should be done first in a group in which fluctuations of both heart rate and glycemia may have direct consequences. Finally, having established that FHR does correlate with maternal glycemia, we are able to focus on the impact of further inquiries on fetal outcomes of either parameter fluctuations. This is the first study conducted in real life that shows this correlation in a continuous manner. We believe that investigation using a combined fetal ECG/maternal CGM is feasible and provides insight into impact of maternal diabetes on fetal well-being. Conclusions
Elevated maternal glycemia of mothers with diabetes is associated with accelerations of FHR. Further studies are needed to explore the association between glucose variability
5
of the mother and fetal well-being in various levels of glycemic control. Acknowledgments
This study was supported financially by funds of the Medical University of Lodz (project number 502-03/0-16001/502-04-014). Author Disclosure Statement
No competing financial interests exist. References
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13. Reece EA, Hagay Z, Roberts AB, DeGennaro N, Homko CJ, Connolly-Diamond M, Sherwin R, Tamborlane WV, Diamond MP: Fetal Doppler and behavioral responses during hypoglycemia induced with the insulin clamp technique in pregnant diabetic women. Am J Obstet Gynecol 1995;172:151–155. 14. Serra-Serra V, Camara R, Sarrio´n P, Jaren˜o M, Cervera J, Bellver J, Perales A: Effects of prandial glycemic changes on objective fetal heart rate parameters. Acta Obstet Gynecol Scand 2000;79:953–957. 15. Buscicchio G, Gentilucci L, Giannubilo SR, Tranquilli AL: Computerized analysis of fetal heart rate in pregnancies complicated by gestational diabetes mellitus. Gynecol Endocrinol 2010;26:270–274. 16. Weiner Z, Thaler I, Farmakides G, Barnhard Y, Maulik D, Divon MY: Fetal heart rate patterns in pregnancies com-
CYPRYK ET AL.
plicated by maternal diabetes. Eur J Obstet Gynecol Reprod Biol 1996;27;70:111–115. 17. Costa VN, Nomura RM, Reynolds KS, Miyadahira S, Zugaib M: Effects of maternal glycemia on fetal heart rate in pregnancies complicated by pregestational diabetes mellitus.Eur J Obstet Gynecol Reprod Biol 2009;143:14–17.
Address correspondence to: Katarzyna Cypryk, MD, PhD Department of Diabetology and Metabolic Diseases Medical University of Lodz ul. Pomorska 251 92-216 Lodz, Poland E-mail: [email protected]
