Severe hypoglycaemia unawareness is associated with an early decrease in vigilance during hypoglycaemia

The effect of chrono-nutritional manipulation of carbohydrate intake on sleep macrostructure: A randomized controlled trial

BACKGROUND & AIMS

Over the years, there is a rapid increase in the prevalence of inadequate sleep and its detrimental consequences. Yet, the impact of prolonged nutritional interventions on sleep optimization remains unexplored. To examine the effect of carbohydrate manipulation combined with exercise training on sleep macro-structure.

METHODS

Forty-two healthy, trained male volunteers were recruited for this study. The 4-week intervention consisted of three groups: i) Sleep Low-No Carbohydrates (SL-NCHO): participants consumed all their carbohydrate intake at regular intervals prior to evening training, ii) Sleep High-Low Glycemic Index (SH-LGI) and iii) Sleep High-High Glycemic Index (SH-HGI): Carbohydrate intake was spread throughout the day, both prior (60% of total CHO intake) and after evening training (40% of total CHO intake). The SH-LGI and SH-HGI groups differentiated by consuming either LGI or HGI foods in the evening, respectively. Alongside, participants performed a standardized exercise program combining resistance exercise and high-intensity interval training. Participants' sleep macro-structure was assessed with polysomnography, actigraphy, sleep diary, and sleep-wake questionnaires.

RESULTS

Objective assessments revealed a substantial time-effect on sleep initiation, duration, and continuity. After the intervention, sleep onset latency decreased (p < 0.001), sleep duration was prolonged (p = 0.006), sleep efficiency increased (p < 0.001), and wake after sleep onset decreased (p = 0.035). Sleep macroarchitecture did not significantly change, while the percentage of REM sleep stage to the total sleep time increased over time (p < 0.01). Consistent with the objective findings, subjects reported improved subjective sleep quality (p = 0.043) and reduced daytime sleepiness (p = 0.047).

CONCLUSION

The combination of a personalized dietary plan with exercise training enhances sleep initiation, sleep continuity, sleep duration, REM and N1 sleep stages, independently of carbohydrate type or timing. Lifestyle interventions should be investigated further to promote sleep quality and recovery.

REGISTRATION

The trial was registered at clinicaltrials.gov as NCT05464342.

Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments

The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.

The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients

The potential of using an electroencephalogram (EEG) to detect hypoglycemia in patients with type 1 diabetes (T1D) has been investigated in both time and frequency domains. Under hyperinsulinemic hypoglycemic clamp conditions, we have shown that the brain's response to hypoglycemic episodes could be described by the centroid frequency and spectral gyration radius evaluated from spectral moments of EEG signals. The aim of this paper is to investigate the effect of hypoglycemia on spectral moments in EEG epochs of different durations and to propose the optimal time window for hypoglycemia detection without using clamp protocols. The incidence of hypoglycemic episodes at night time in five T1D adolescents was analyzed from selected data of ten days of observations in this study. We found that hypoglycemia is associated with significant changes (P < 0.05) in spectral moments of EEG segments in different lengths. Specifically, the changes were more pronounced on the occipital lobe. We used effect size as a measure to determine the best EEG epoch duration for the detection of hypoglycemic episodes. Using Bayesian neural networks, this study showed that 30 second segments provide the best detection rate of hypoglycemia. In addition, Clarke's error grid analysis confirms the correlation between hypoglycemia and EEG spectral moments of this optimal time window, with 86% of clinically acceptable estimated blood glucose values. These results confirm the potential of using EEG spectral moments to detect the occurrence of hypoglycemia.

Effects of Gastric Bypass Surgery on the Brain: Simultaneous Assessment of Glucose Uptake, Blood Flow, Neural Activity, and Cognitive Function During Normo- and Hypoglycemia

While Roux-en-Y gastric bypass (RYGB) surgery in obese individuals typically improves glycemic control and prevents diabetes, it also frequently causes asymptomatic hypoglycemia. Previous work showed attenuated counterregulatory responses following RYGB. The underlying mechanisms as well as the clinical consequences are unclear. In this study, 11 subjects without diabetes with severe obesity were investigated pre- and post-RYGB during hyperinsulinemic normo-hypoglycemic clamps. Assessments were made of hormones, cognitive function, cerebral blood flow by arterial spin labeling, brain glucose metabolism by ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography, and activation of brain networks by functional MRI. Post- versus presurgery, we found a general increase of cerebral blood flow but a decrease of total brain FDG uptake during normoglycemia. During hypoglycemia, there was a marked increase in total brain FDG uptake, and this was similar for post- and presurgery, whereas hypothalamic FDG uptake was reduced during hypoglycemia. During hypoglycemia, attenuated responses of counterregulatory hormones and improvements in cognitive function were seen postsurgery. In early hypoglycemia, there was increased activation post- versus presurgery of neural networks in brain regions implicated in glucose regulation, such as the thalamus and hypothalamus. The results suggest adaptive responses of the brain that contribute to lowering of glycemia following RYGB, and the underlying mechanisms should be further elucidated.

A Systematic Review, Meta-Analysis and Meta-Regression on the Effects of Carbohydrates on Sleep

This study aimed to assess the effects of quantity, quality and periodization of carbohydrates consumption on sleep. PubMed, SCOPUS and Cochrane Library were searched through October 2020. Data were pooled using random-effects meta-analysis. Eleven articles were included in the meta-analysis which consisted of 27 separate nutrition trials, resulting in 16 comparison data sets (sleep quantity n = 11; sleep quality n = 5). Compared to high carbohydrate (HCI), low carbohydrate intake (LCI) moderately increased duration and proportion of N3 sleep stage (ES = 0.37; 95% CI = 0.18, 0.56; p < 0.001 and ES = 0.51; 95% CI = 0.33, 0.69; p < 0.001, respectively). HCI prolonged rapid eye movement (REM) stage duration (ES = −0.38; 95% CI = 0.05, −8.05; p < 0.001) and proportion (ES = −0.46; 95% CI = −0.83, −0.01; p < 0.001), compared to LCI. The quality of carbohydrate intake did not affect sleep stages. Meta-regression showed that the effectiveness of carbohydrate quantity and quality in sleep onset latency was significantly explained by alterations of carbohydrate intake as a percentage of daily energy intake (R² = 25.87, p = 0.018) and alterations in the glycemic load (R² = 50.8, p = 0.048), respectively. Alterations in glycemic load partially explained the variance of the effectiveness of carbohydrate quality in sleep efficiency (R² = 89.2, p < 0.001) and wake after sleep onset (R² = 64.9, p = 0.018). Carbohydrate quantity was shown to affect sleep architecture, and especially N3 and REM sleep stages. Alterations in both quantity and quality of carbohydrate intake showed a significant effect on sleep initiation. Variations in carbohydrate quality significantly affected measures of sleep continuation. Further studies are needed to assess the effect of long-term carbohydrate interventions on sleep.

Disciplined sleep for healthy living: Role of noradrenaline

Sleep is essential for maintaining normal physiological processes. It has been broadly divided into rapid eye movement sleep (REMS) and non-REMS (NREMS); one spends the least amount of time in REMS. Sleep (both NREMS and REMS) disturbance is associated with most altered states, disorders and pathological conditions. It is affected by factors within the body as well as the environment, which ultimately modulate lifestyle. Noradrenaline (NA) is one of the key molecules whose level increases upon sleep-loss, REMS-loss in particular and it induces several REMS-loss associated effects and symptoms. The locus coeruleus (LC)-NAergic neurons are primarily responsible for providing NA throughout the brain. As those neurons project to and receive inputs from across the brain, they are modulated by lifestyle changes, which include changes within the body as well as in the environment. We have reviewed the literature showing how various inputs from outside and within the body integrate at the LC neuronal level to modulate sleep (NREMS and REMS) and vice versa. We propose that these changes modulate NA levels in the brain, which in turn is responsible for acute as well as chronic psycho-somatic disorders and pathological conditions.

Hypoglycemia-Induced Changes in the Electroencephalogram: An Overview

Hypoglycemia is defined by an abnormally low blood glucose level. The condition develops when rates of glucose entry into the systematic circulation are reduced relative to the glucose uptake by the tissues. A cardinal manifestation of hypoglycemia arises from inadequate supply of glucose to the brain, where glucose is the primary metabolic fuel. The brain is one of the first organs to be affected by hypoglycemia. Shortage of glucose in the brain, or neuroglycopenia, results in a gradual loss of cognitive functions causing slower reaction time, blurred speech, loss of consciousness, seizures, and ultimately death, as the hypoglycemia progresses. The electrical activity in the brain represents the metabolic state of the brain cells and can be measured by electroencephalography (EEG). An association between hypoglycemia and changes in the EEG has been demonstrated, although blood glucose levels alone do not seem to predict neuroglycopenia. This review provides an overview of the current literature regarding changes in the EEG during episodes of low blood glucose.

Hypoglycemia-Associated EEG Changes in Prepubertal Children With Type 1 Diabetes

BACKGROUND

The purpose of this study was to explore the possible difference in the electroencephalogram (EEG) pattern between euglycemia and hypoglycemia in children with type 1 diabetes (T1D) during daytime and during sleep. The aim is to develop a hypoglycemia alarm based on continuous EEG measurement and real-time signal processing.

METHOD

Eight T1D patients aged 6-12 years were included. A hyperinsulinemic hypoglycemic clamp was performed to induce hypoglycemia both during daytime and during sleep. Continuous EEG monitoring was performed. For each patient, quantitative EEG (qEEG) measures were calculated. A within-patient analysis was conducted comparing hypoglycemia versus euglycemia changes in the qEEG. The nonparametric Wilcoxon signed rank test was performed. A real-time analyzing algorithm developed for adults was applied.

RESULTS

The qEEG showed significant differences in specific bands comparing hypoglycemia to euglycemia both during daytime and during sleep. In daytime the EEG-based algorithm identified hypoglycemia in all children on average at a blood glucose (BG) level of 2.5 ± 0.5 mmol/l and 18.4 (ranging from 0 to 55) minutes prior to blood glucose nadir. During sleep the nighttime algorithm did not perform.

CONCLUSIONS

We found significant differences in the qEEG in euglycemia and hypoglycemia both during daytime and during sleep. The algorithm developed for adults detected hypoglycemia in all children during daytime. The algorithm had too many false alarms during the night because it was more sensitive to deep sleep EEG patterns than hypoglycemia-related EEG changes. An algorithm for nighttime EEG is needed for accurate detection of nocturnal hypoglycemic episodes in children. This study indicates that a hypoglycemia alarm may be developed using real-time continuous EEG monitoring.

Combining genetic algorithm and Levenberg-Marquardt algorithm in training neural network for hypoglycemia detection using EEG signals

Hypoglycemia is the most common but highly feared complication induced by the intensive insulin therapy in patients with type 1 diabetes mellitus (T1DM). Nocturnal hypoglycemia is dangerous because sleep obscures early symptoms and potentially leads to severe episodes which can cause seizure, coma, or even death. It is shown that the hypoglycemia onset induces early changes in electroencephalography (EEG) signals which can be detected non-invasively. In our research, EEG signals from five T1DM patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected efficiently using EEG signals from only two channels. This paper demonstrates that by implementing a training process of combining genetic algorithm and Levenberg-Marquardt algorithm, the classification results are improved markedly up to 75% sensitivity and 60% specificity on a separate testing set.

Hyperglycemia is associated with simultaneous alterations in electrical brain activity in youths with type 1 diabetes mellitus

OBJECTIVE

To assess the association between hyperglycemia and electrical brain activity in type 1 diabetes mellitus (T1DM).

METHODS

Nine youths with T1DM were monitored simultaneously and continuously by EEG and continuous glucose monitor system, for 40 h. EEG powers of 0.5-80 Hz frequency bands in all the different brain regions were analyzed according to interstitial glucose concentration (IGC) ranges of 4-11 mmol/l, 11-15.5 mmol/l and >15.5 mmol/l. Analysis of variance was used to examine the differences in EEG power of each frequency band between the subgroups of IGC. Analysis was performed separately during wakefulness and sleep, controlling for age, gender and HbA1c.

RESULTS

Mean IGC was 11.49 ± 5.26 mmol/l in 1253 combined measurements. IGC>15.5 mmol/l compared to 4-11 mmol/l was associated during wakefulness with increased EEG power of low frequencies and with decreased EEG power of high frequencies. During sleep, it was associated with increased EEG power of low frequencies in all brain areas and of high frequencies in frontal and central areas.

CONCLUSIONS

Asymptomatic transient hyperglycemia in youth with T1DM is associated with simultaneous alterations in electrical brain activity during wakefulness and sleep.

SIGNIFICANCE

The clinical implications of immediate electrical brain alterations under hyperglycemia need to be studied and may lead to adaptations of management.

Hypoglycemia-associated changes in the electroencephalogram in patients with type 1 diabetes and normal hypoglycemia awareness or unawareness

Hypoglycemia is associated with increased activity in the low-frequency bands in the electroencephalogram (EEG). We investigated whether hypoglycemia awareness and unawareness are associated with different hypoglycemia-associated EEG changes in patients with type 1 diabetes. Twenty-four patients participated in the study: 10 with normal hypoglycemia awareness and 14 with hypoglycemia unawareness. The patients were studied at normoglycemia (5-6 mmol/L) and hypoglycemia (2.0-2.5 mmol/L), and during recovery (5-6 mmol/L) by hyperinsulinemic glucose clamp. During each 1-h period, EEG, cognitive function, and hypoglycemia symptom scores were recorded, and the counterregulatory hormonal response was measured. Quantitative EEG analysis showed that the absolute amplitude of the θ band and α-θ band up to doubled during hypoglycemia with no difference between the two groups. In the recovery period, the θ amplitude remained increased. Cognitive function declined equally during hypoglycemia in both groups and during recovery reaction time was still prolonged in a subset of tests. The aware group reported higher hypoglycemia symptom scores and had higher epinephrine and cortisol responses compared with the unaware group. In patients with type 1 diabetes, EEG changes and cognitive performance during hypoglycemia are not affected by awareness status during a single insulin-induced episode with hypoglycemia.

Analyzing EEG signals under insulin-induced hypoglycemia in type 1 diabetes patients

Hypoglycemia is dangerous and considered as a limiting factor of the glycemic control therapy for patients with type 1 diabetes mellitus (T1DM). Nocturnal hypoglycemia is especially feared because early warning symptoms are unclear during sleep so an episode of hypoglycemia may lead to a fatal effect on patients. The main objective of this paper is to explore the correlation between hypoglycemia and electroencephalography (EEG) signals. To do this, the EEG of five T1DM adolescents from an overnight insulin-induced study is analyzed by spectral analysis to extract four different parameters. We aim to explore the response of these parameters during the clamp study which includes three main phases of normal, hypoglycemia and recovery. We also look at data at the blood glucose level (BGL) of 3.3-3.9 mmol/l to find a threshold to distinguish between non-hypoglycemia and hypoglycemia states. The results show that extracted EEG parameters are highly correlated with patients' conditions during the study. It is also shown that at the BGL of 3.3 mmol/l, responses to hypoglycemia in EEG signals start to significantly occur.

An adaptive strategy of classification for detecting hypoglycemia using only two EEG channels

Hypoglycemia is the most common but highly feared side effect of the insulin therapy for patients with Type 1 Diabetes Mellitus (T1DM). Severe episodes of hypoglycemia can lead to unconsciousness, coma, and even death. The variety of hypoglycemic symptoms arises from the activation of the autonomous central nervous system and from reduced cerebral glucose consumption. In this study, electroencephalography (EEG) signals from five T1DM patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected non-invasively using EEG signals from only two channels. This paper demonstrates that a significant advantage can be achieved by implementing adaptive training. By adapting the classifier to a previously unseen person, the classification results can be improved from 60% sensitivity and 54% specificity to 75% sensitivity and 67% specificity.

Identification of hypoglycemic states for patients with T1DM using various parameters derived from EEG signals

For patients with Type 1 Diabetes Mellitus (T1DM), hypoglycemia is a very common but dangerous complication which can lead to unconsciousness, coma and even death. The variety of hypoglycemia symptoms is originated from the inadequate supply of glucose to the brain. In this study, we explore the connection between hypoglycemic episodes and the electrical activity of neurons within the brain or electroencephalogram (EEG) signals. By analyzing EEG signals from a clinical study of five children with T1DM, associated with hypoglycemia at night, we find that some EEG parameters change significantly under hypoglycemia condition. Based on these parameters, a method of detecting hypoglycemic episodes using EEG signals with a feed-forward multi-layer neural network is proposed. In our application, the classification results are 72% sensitivity and 55% specificity when the EEG signals are acquired from 2 electrodes C3 and O2. Furthermore, signals from different channels are also analyzed to observe the contributions of each channel to the performance of hypoglycemia classification.

Detection of nocturnal hypoglycemic episodes using EEG signals

Hypoglycemia (low blood glucose) or the fear of hypoglycemia constitutes a significant barrier to the achievement of good glycemic control in the insulin treated diabetic patients. By measuring physiological responses derived from EEG and analyzing these, we establish that hypoglycemia can be detected non-invasively. From a clinical study of six children with type 1 diabetes (T1D), associated with hypoglycemic episodes at night, their centroid (centre of gravity) alpha frequency reduced significantly (P〈0.001) and their centroid theta frequency increased significantly (P〈0.02). The overall data were organized into a training set (3 patients) and a test set (another 3 patients) randomly selected. Using the optimal Bayesian neural network which was derived from the training set with the highest log evidence, the estimated blood glucose profiles produced a significant correlation (P〈0.005) against measured values in the test set.

Automated detection of hypoglycemia-induced EEG changes recorded by subcutaneous electrodes in subjects with type 1 diabetes--the brain as a biosensor

AIMS

Hypoglycemia unawareness is a common condition associated with increased risk of severe hypoglycemia. We test the hypothesis that specific changes in the electroencephalogram (EEG) during hypoglycemia can be recorded by subcutaneous electrodes and processed by a general mathematical algorithm, and that hypoglycemia associated EEG changes appear before the development of severe hypoglycemia.

METHODS

Fifteen patients with type 1 diabetes were exposed to insulin-induced hypoglycemia and EEG was recorded. The cognitive function was evaluated by repeated cognitive testing. Insulin infusion was terminated when plasma glucose reached 1.8mmol/l or when the subjects showed obvious signs of cognitive dysfunction. EEG was analyzed by an automated mathematical algorithm with a predefined threshold of hypoglycemia.

RESULTS

Hypoglycemia associated EEG changes were detected by the mathematical algorithm in all subjects. Plasma glucose at the time of EEG changes above the threshold value ranged from 2.0 to 3.4mmol/l and occurred 29+/-28min (range 3-113min) before termination of insulin infusion.

CONCLUSIONS

Hypoglycemia associated EEG changes could be detected by an automated mathematical algorithm in all subjects exposed to insulin-induced hypoglycemia. In 12 of 15 patients, EEG changes occurred before severe hypoglycemia as evaluated by the cognitive testing.

Mechanisms of the blunting of the sympatho-adrenal response: a theory

Development of therapeutic measures to reduce the risk of potentially fatal episodes of hypoglycaemia and thus to achieve the full benefits of intensive insulin therapy in diabetic patients requires a complete understanding of the multi-factorial mechanisms for repeated hypoglycaemia-induced blunting of the sympatho-adrenal response (BSAR). After critical analysis of the hypotheses, this review paper suggests a heuristic theory. This theory suggests two mechanisms for the BSAR, each involving a critical role for the central brain noradrenergic system. Furthermore, this theory also suggests that the lateral hypothalamus (LH) plays an important role in this phenomenon. Within the framework of this theory, explanations for 1) sexual dimorphism in the adrenomedullary response (AR), 2) dissociation in the blunting of the AR and the sympathetic response (SR) and 3) antecedent exercise-induced blunting of the AR are provided. In addition, habituation of orexin-A neurons is suggested to cause defective awakening. Moreover, potential therapeutics measures have been also suggested that will reduce or prevent severe episodes of hypoglycaemia.

Hypoglycemia: a complication of diabetes therapy in children

The experience of hypoglycemia is probably the most feared and hated consequence of life with type 1 diabetes among pediatric patients and their parents. Although transient detrimental effects are clearly disturbing and may have severe results, there is surprisingly little evidence of long-term CNS damage, even after multiple hypoglycemic episodes, except in rare instances. Despite the latter evidence, we advocate that every treatment regimen be designed to prevent hypoglycemia without inducing unacceptable hyperglycemia and increasing the risk of micro- and macrovascular complications.

The effects of hypoglycemia and ethanol on rat performance in the radial-arm maze

A deficit in blood glucose (BG) (hypoglycemia, HG) causes a characteristic array of physiological sequelae, symptoms and cognitive impairment in human subjects. However, the performance of hypoglycemic animal subjects in the standard paradigms of behavioral pharmacology has received little research attention. The primary purpose of these experiments was to determine the effect of insulin-produced HG on spatial working memory in rats trained in the radial-arm maze (RAM), using a noninterrupted (win-shift) procedure. A second aim was to further investigate possible interaction between HG and ethanol administration since potentiation between the drugs' depressant effects has been reported for rat spontaneous motor activity (SMA). Insulin administration (resulting in BG levels approximately 65% of control levels) was combined factorially with ethanol treatment in two experiments. HG significantly increased time required to complete RAM trials in both experiments, but did not impair accuracy of arm choice. In the second experiment, ethanol was administered only once to minimize development of tolerance, and under these conditions, ethanol at 1500 mg/kg impaired arm-choice accuracy and marginally potentiated HG-produced slowing of running time. The current results appear somewhat similar to previously reported effects of HG on reaction time in human subjects.

Normalization of pregnancy outcome in pregestational diabetes through functional insulin treatment and modular out-patient education adapted for pregnancy

AIM

To investigate whether modular out-patient group education for flexible, Functional Insulin Treatment (FIT) adapted for pregnancy can eliminate diabetes-associated neonatal complications in pregestational diabetes.

RESEARCH DESIGN AND METHODS

Outcome analysis of the modular out-patient group education and FIT based on separate insulin dosages for fasting, eating or correcting hyperglycaemia in 76 consecutive pregnancies (in 20 cases first after conception) of 59 patients with pregestational diabetes (Type 1 diabetes, n = 54).

CONTROLS

(a) diabetic pregnancies: historical controls; (b) non-diabetic pregnancies: retrospective case-controlled study; (c) population-based data of all Austrian newborns registered within the respective time period.

RESULTS

HbA1c of 113 +/- 18% of mean value (= 100%) of non-diabetic, non-pregnant population (103 +/- 14% during the last pregnancy trimester), and self-monitored blood glucose of 5.6 +/- 0.7 mmol/l (5.3 +/- 0.7 mmol/l during the last trimester) was achieved throughout all FIT pregnancies. Severe hypoglycaemia occurred in 14 pregnancies. The gestational age at delivery was 39.2 +/- 1.5 weeks (four cases (5.4%) < 37 weeks) with a birth weight of 3305 +/- 496 g. Four newborns (5.3%) were above the 90th, and nine (11.8%) below the 10th percentile for weight of reference population-based data. Hypoglycaemia was recorded in six newborns (8%). Malformations were found in two infants whose mothers booked for diabetes FIT education only after conception. The caesarean delivery rate was 25%. In comparison with historical diabetic pregnancy controls we demonstrated a reduction in major complications, and compared with non-diabetic women, a lowering of diabetes-related neonatal complication rates to general population levels.

CONCLUSIONS

Structured, comprehensive, modular out-patient group education promoting self-choice of insulin dose for flexible, normal eating prior to conception normalizes pregnancy outcome in diabetes.

Decrease of vigilance assessed by EEG-mapping in type I diabetic patients with history of recurrent severe hypoglycaemia

In Type I diabetic patients with history of recurrent severe hypoglycaemia, a more rapid decrease in vigilance (slowing of brain function) during hypoglycaemia in comparison to patients without history of such events was found. Our aims were: (1) to study EEG parameters of vigilance in non-hypoglycaemic state in representative groups of Type I diabetic patients with and without previous recurrent severe hypoglycaemia; and (2) to compare them with non-diabetic controls. A vigilance-controlled EEG mapping (10-20 system, significance probability maps) was performed in a non-hypoglycaemic state (blood glucose 4.0-10.0 mmol/l) in a group of 13 Type I diabetic patients with a history of recurrent severe hypoglycaemia and compared to that of 14 Type I diabetic patients without history of severe hypoglycaemia, matched for HbA1c, age and gender, and to age- and gender-matched non-diabetic controls. When compared to non-diabetic controls, hypoglycaemia patients demonstrated a reduction in absolute power in beta band (13-35 Hz) and slowing of centroid frequencies of beta and total frequency bands (1.3-35 Hz) (up to P < 0.01), whereas patients without history of severe hypoglycaemia showed only a borderline reduction of absolute power in delta (1.3-3.5 Hz) band. Deceleration in hypoglycaemia patients versus those without recurrent hypoglycaemia was most remarkable (P < .01) in centroid frequency of total frequency band. Patients with history of recurrent severe hypoglycaemia demonstrated in non-hypoglycaemic state significantly reduced vigilance when compared to the group without hypoglycaemia history and to the controls, as well. Lower vigilance may be at least in part responsible for impaired hypoglycaemia perception in these patients, but, as it resembles EEG patterns seen in pathologic ageing, it might also represent a consequence of recurrent episodes of severe hypoglycaemia.