Do high fasting glucose levels suggest nocturnal hypoglycaemia? The Somogyi effect-more fiction than fact?

Characteristics of Nocturnal Hypoglycaemic Events and Their Impact on Glycaemia

BACKGROUND

Nocturnal hypoglycaemia is a burden for people with diabetes, particularly when treated with multiple daily injections (MDI) therapy. However, the characteristics of nocturnal hypoglycaemic events in this patient group are only poorly described in the literature.

METHOD

Continuous glucose monitoring (CGM) data from 185 study participants with type 1 diabetes using MDI therapy were collected under everyday conditions for up to 13 weeks. Hypoglycaemic events were identified as episodes of consecutive CGM readings <70 mg/dl or <54 mg/dl for at least 15 minutes. Subsequently, the time <54 mg/dl (TB54), time below range (TBR), time in range (TIR), time above range (TAR), glucose coefficient of variation (CV), and incidence of hypoglycaemic events were calculated for diurnal and nocturnal periods. Furthermore, the effect of nocturnal hypoglycaemic events on glucose levels the following day was assessed.

RESULTS

The incidence of hypoglycaemic events <70 mg/dl was significantly lower during the night compared to the day, with 0.8 and 3.8 events per week, respectively, while the TBR, TB54, and incidence of events with CGM readings <54 mg/dl was not significantly different. Nocturnal hypoglycaemic events <70 mg/dl were significantly longer (60 vs 35 minutes) and enveloped by less rapidly changing glucose levels. On days following nights containing hypoglycaemic events, there was a decrease in TAR, mean CGM glucose level and morning glucose levels and an increase in TB54, TBR, and CV.

CONCLUSIONS

The results showed that nocturnal hypoglycaemic events are a common occurrence in persons with type 1 diabetes using MDI with significant differences between the characteristics of nocturnal and diurnal events.

Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring

Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.

Glucose Counterregulation: Clinical Consequences of Impaired Sympathetic Responses in Diabetic Dogs and Cats

Insulin induced hypoglycemia (IIH) is common in veterinary patients and limits the clinician's ability to obtain adequate glycemic control with insulin therapy. Not all diabetic dogs and cats with IIH exhibit clinical signs and hypoglycemia might be missed by routine blood glucose curve monitoring. In diabetic patients, counterregulatory responses to hypoglycemia are impaired (lack of decrease in insulin levels, lack of increase in glucagon, and attenuation of the parasympathetic and sympathoadrenal autonomic nervous systems) and have been documented in people and in dogs but not yet in cats. Antecedent hypoglycemic episodes increase the patient's risk for future severe hypoglycemia.

Confirmation of the Absence of Somogyi Effect in Patients with Type 2 Diabetes by Retrospective Continuous Glucose Monitoring Systems

BACKGROUND

The Somogyi effect is defined as fasting hyperglycemia secondary to nocturnal hypoglycemia. In past decades, this effect proved to be rare or absent. However, many endocrinologists still believe in this phenomenon in clinical practice. Does the Somogyi effect truly exist? We aimed to answer this question with a study based on a larger sample size.

METHODS

We collected retrospective CGMs data from 2,600 patients with type 2 diabetes with stable treatment of insulin. Nocturnal hypoglycemia was defined as a CGMs sensor glucose of less than 3.9 mmol/L for at least 15 min between 24:00 and 06:00. Morning fasting glucose was compared between people with nocturnal hypoglycemia and without nocturnal hypoglycemia.

RESULTS

Valid CGMs data were obtained on 4,705 of 5,200 nights. Morning fasting glucose was observed lower after nights with nocturnal hypoglycemia compared with nights without hypoglycemia (P < 0.001). 84 cases presented fasting glucose of more than 7 mmol/L after nocturnal glucose of less than 3.9 mmol/L. Only 27 cases presented fasting glucose of more than 7 mmol/L after nocturnal glucose of less than 3.0 mmol/L. Fasting glucose values below 3.9 mmol/l in the morning were associated with a 100% risk of nocturnal hypoglycemia, while fasting glucose values over 9.6 mmol/l in the morning were associated with no risk of nocturnal hypoglycemia. Correlation analysis showed that the nocturnal glucose nadir was significantly correlated with fasting glucose levels (r = 0.613, P < 0.001).

CONCLUSIONS

Our data provided no support for the existence of the Somogyi effect. If fasting glucose exceeds 9.6 mmol/L, we do not have to worry about asymptomatic nocturnal hypoglycemia in patients with type 2 diabetes.

Characterization of Post-Hypoglycemic Hyperglycemia in Children and Adolescents With Type 1 Diabetes: The EPHICA Study

BACKGROUND

In patients with diabetes, the dynamics in which hypoglycemia recovers impacts cardiovascular disease risk. Our study investigated the extents of "post-hypoglycemic hyperglycemia (PHH)" (i.e. hypoglycemia that recover to hyperglycemia in any circumstance) and factors likely to influence PHH characteristics in a pediatric cohort of patients with type 1 diabetes (T1D).

METHODS

We collected retrospective continuous glucose monitoring (CGM) data from 142 pediatric patients with T1D to characterize episodes of PHH during a two-month follow-up period. Factors influencing PHH were determined using univariate and multivariate analyses.

RESULTS

In our EPHICA cohort, PHH rate was 0.6 ± 0.3 episode/day and correlated (r=0.33; p<0.0001) with hyperglycemia rate (2.6 ± 0.5 episodes/day). The global proportion of hyperglycemia corresponding to PHH was 0.22 ± 0.1, yet 14.8% of patients had more than 1/3 of hyperglycemia related to PHH. Episodes of PHH lasted 239.6 ± 124.8 minutes with a hyperglycemic peak of 258.8 ± 47.1 mg/dL. Only 12.2% of PHH occurred at night. While a younger age (<12 years) and lower body mass index (BMI) (SDS: -2 to 1.6) were associated with higher daily PHH rates, teenagers (≥12 years) and obese patients experienced longer PHH and higher hyperglycemic peaks. Parameters of glycemic variability (i.e. HbA_1C, IDAA_1C and GTAA_1C) moderately correlated with PHH duration and related hyperglycemic peak. Multivariate analysis confirmed these results, as factors likely to influence PHH rate were phenotype (age and BMI) and glycemic variability parameters (time in range, mean glycemia, HbA_1C and GTAA_1C).

CONCLUSION

Our EPHICA study highlights the importance of PHH as a prominent component of hyperglycemia in some children and adolescents with T1D. Factors associated with PHH features are age, BMI and parameters of glycemic control. Young and lean children are more prone to experience hypoglycemia that recover with hyperglycemia, but adolescents and obese children tend to experience hyperglycemia of longer duration.

Is it possible to predict the onset of nocturnal asymptomatic hypoglycemia in patients with type 1 diabetes receiving insulin degludec? Potential role of previous day and next morning glucose values

AIMS/INTRODUCTION

To determine whether the occurrence of nocturnal asymptomatic, serious, clinically important hypoglycemia (NSH) could be predicted based on glucose values on the previous day and the following morning of the day of onset.

MATERIALS AND METHODS

This study examined patients with type 1 diabetes who underwent continuous glucose monitoring assessments and received insulin degludec. NSH was defined as glucose level <54 mg/dL detected between 24.00 and 06.00 hours. The participants were evaluated to determine the following: (i) glucose level at bedtime (24.00 hours) on the previous day (BG); (ii) fasting glucose level (FG); and (iii) the range of post-breakfast glucose elevation. The patients were divided into those with NSH and those without, and compared using t-tests. Optimal cut-off values for relevant parameters for predicting NSH were determined using receiver operating characteristic analysis.

RESULTS

The study included a total of 31 patients with type 1 diabetes (mean glycated hemoglobin value 7.8 ± 0.7%). NSH occurred in eight patients (26%). BG and FG were significantly lower in those with NSH than in those without (P = 0.044, P < 0.001). The range of post-breakfast glucose elevation was significantly greater in those with NSH than in those without. The cut-off glucose values for predicting NSH were as follows: BG = 90 mg/dL (sensitivity 0.83/specificity 0.75/area under the curve 0.79, P = 0.017) and FG = 69 mg/dL (0.83/0.75/0.86, P = 0.003).

CONCLUSIONS

The results showed that in patients with type 1 diabetes receiving insulin degludec, BG <90 mg/dL and FG <69 mg/dL had an approximately 80% probability of predicting the occurrence of NSH.

Glucose concentrations after insulin-induced hypoglycemia and glycemic variability in healthy and diabetic cats

Background

Little information is available about posthypoglycemic hyperglycemia (PHH) in diabetic cats, and a causal link between hypoglycemia and subsequent hyperglycemia is not clear. Fluctuations in blood glucose concentrations might only represent high glycemic variability.

Hypothesis

Insulin induces PHH in healthy cats, and PHH is associated with poorly regulated diabetes and increased glycemic variability in diabetic cats.

Animals

Six healthy cats, 133 diabetic cats.

Methods

Insulin (protamine‐zinc and degludec; 0.1‐0.3 IU/kg) administered to healthy cats. Blood glucose curves were generated with portable glucose meter to determine the percentage of curves with PHH. Data from insulin‐treated diabetic cats with blood glucose curves showing hypoglycemia included data of cats with and without PHH. Post‐hypoglycemic hyperglycemia was defined as blood glucose concentrations <4 mmol/L followed by blood glucose concentrations >15 mmol/L within 12 hours. Glycemic variability was calculated as the standard deviation of the blood glucose concentrations.

Results

In healthy cats, all insulin doses caused hypoglycemia but PHH was not observed; glycemic variability did not differ between insulin preparations. Among diabetic cats with hypoglycemia, 33 (25%) had PHH. Compared with cats without PHH, their daily insulin dose was higher (1.09 ± 0.55 versus 0.65 ± 0.56 IU/kg; P < .001), serum fructosamine concentration was higher (565 ± 113 versus 430 ± 112 µmol/L; P < .001), remission was less frequent (10% versus 56%; P < .001), and glycemic variability was larger (8.1 ± 2.4 mmol/L versus 2.9 ± 2.2 mmol/L; P < .001).

Conclusions and Clinical Importance

Insulin‐induced hypoglycemia did not cause PHH in healthy cats but it occurred in 25% of diabetic cats with hypoglycemia, particularly when diabetes was poorly controlled. Glycemic variability was increased in cats with PHH.

The risks of nocturnal hypoglycaemia in insulin-treated diabetes

Over half of all episodes of severe hypoglycaemia (requiring external help) occur during sleep, but nocturnal hypoglycaemia is often asymptomatic and unrecognised. The precise incidence of nocturnal hypoglycaemia is difficult to determine with no agreed definition, but continuous glucose monitoring has shown that it occurs frequently in people taking insulin. Attenuation of the counter-regulatory responses to hypoglycaemia during sleep may explain why some episodes are undetected and more prolonged, and modifies cardiovascular responses. The morbidity and mortality associated with nocturnal hypoglycaemia is probably much greater than realised, causing seizures, coma and cardiovascular events and affecting quality of life, mood and work performance the following day. It may induce impaired awareness of hypoglycaemia. Cardiac arrhythmias that occur during nocturnal hypoglycaemia include bradycardia and ectopics that may provoke dangerous arrhythmias. Treatment strategies are discussed that may help to minimise the frequency of nocturnal hypoglycaemia.

Prediction of nocturnal hypoglycemia unawareness by fasting glucose levels or post-breakfast glucose fluctuations in patients with type 1 diabetes receiving insulin degludec: A pilot study

OBJECTIVE

To evaluate whether nocturnal asymptomatic hypoglycemia (NAH) can be predicted by fasting glucose levels or post-breakfast glucose fluctuations in patients with type 1 diabetes (T1D) receiving insulin degludec.

METHODS

Patients with T1D receiving insulin degludec underwent at-home CGM assessments. Indices for glycemic variability before and after breakfast included fasting glucose levels and the range of post-breakfast glucose elevation. For comparison, the patients were classified into those with NAH and those without. The optimal cut-off values for the relevant parameters were determined to predict NAH using ROC analysis.

RESULTS

The study included a total of 31 patients (mean HbA1c values, 7.8 ± 0.7%), and 16 patients (52%) had NAH. Those with NAH had significantly lower fasting glucose levels than did those without (82 ± 48 mg/dL vs. 144 ± 69 mg/dL; P = 0.009). The change from pre- to post-breakfast glucose levels was significantly greater among those with NAH (postprandial 1-h, P = 0.028; postprandial 2-h, P = 0.028). The cut-off values for prediction of NAH were as follows: fasting glucose level <84 mg/dL (sensitivity 0.80/specificity 0.75/AUC 0.80; P = 0.004), 1-h postprandial elevation >69 mg/dL (0.75/0.67/0.73; P = 0.033), and 2-h postprandial elevation >99 mg/dL (0.69/0.67/0.71; P = 0.044).

CONCLUSIONS

The results suggest that fasting glucose level of < 84 mg/dL had approximately 80% probability of predicting the occurrence of NAH in T1D receiving insulin degludec. It was also shown that the occurrence of hypoglycemia led to greater post-breakfast glucose fluctuations and steeper post-breakfast glucose gradients.

Rebound hyperglycaemia in diabetic cats

OBJECTIVES

Rebound hyperglycaemia (also termed Somogyi effect) is defined as hyperglycaemia caused by the release of counter-regulatory hormones in response to insulin-induced hypoglycaemia, and is widely believed to be common in diabetic cats. However, studies in human diabetic patients over the past quarter century have rejected the common occurrence of this phenomenon. Therefore, we evaluated the occurrence and prevalence of rebound hyperglycaemia in diabetic cats.

METHODS

In a retrospective study, 10,767 blood glucose curves of 55 cats treated with glargine using an intensive blood glucose regulation protocol with a median of five blood glucose measurements per day were evaluated for evidence of rebound hyperglycaemic events, defined in two different ways (with and without an insulin resistance component).

RESULTS

While biochemical hypoglycaemia occurred frequently, blood glucose curves consistent with rebound hyperglycaemia with insulin resistance was confined to four single events in four different cats. In 14/55 cats (25%), a median of 1.5% (range 0.32-7.7%) of blood glucose curves were consistent with rebound hyperglycaemia without an insulin resistance component; this represented 0.42% of blood glucose curves in both affected and unaffected cats.

CONCLUSIONS AND RELEVANCE

We conclude that despite the frequent occurrence of biochemical hypoglycaemia, rebound hyperglycaemia is rare in cats treated with glargine on a protocol aimed at tight glycaemic control. For glargine-treated cats, insulin dose should not be reduced when there is hyperglycaemia in the absence of biochemical or clinical evidence of hypoglycaemia.

Predictors of recurrent hypoglycemia following a severe hypoglycemic event among hospitalized patients

OBJECTIVES

Severe hypoglycemia is associated with poor hospital outcomes, but variables contributing to the adequacy of treatment have not been described. The objective of this study was to determine predictors of recurrent hypoglycemia among hospitalized patients with a severe hypoglycemic event.

METHODS

Patients with severe hypoglycemia (glucose <40 mg/dl) with a concomitant insulin order were identified using the study institution's Information Warehouse. The primary outcome was the prevalence of recurrent hypoglycemia (defined as <70 mg/dl within 24 hours) and to identify independent predictors of recurrent hypoglycemia. Secondary outcomes included time to blood glucose recheck, time to blood glucose ≥ 70 mg/dl, and rebound hyperglycemia (defined as glucose >300 mg/dl within 24 hours). Multivariable linear and logistic regression models were performed.

RESULTS

A total of 129 patients with severe hypoglycemia were identified. The median time to repeat glucose measurement was 29 (IQR 15-61) minutes, while the time to resolution of hypoglycemia was 49 (IQR 26-103) minutes. Recurrent hypoglycemia occurred in 49% of patients, while 19% of patients experienced rebound hyperglycemia. Independent predictors of recurrent hypoglycemia included lower repeat glucose (p = 0.025), low glomerular filtration rate (p = 0.033), and lack of insulin adjustment (p = 0.012). Independent predictors of maximum glucose post-event were type 1 diabetes (p = 0.0003), history of any diabetes (p = 0.013), and total bolus dose of insulin (p < 0.0001). Overnight timing of events was the only predictor of shorter time to hypoglycemia resolution (p < 0.0001).

CONCLUSIONS

Recurrent hypoglycemia following severe hypoglycemia is common in the hospital, suggesting the need for enhanced monitoring in such patients. Further research is needed to identify methods to reduce the incidence of recurrent hypoglycemia.

Can Fasting Glucose Levels or Post-Breakfast Glucose Fluctuations Predict the Occurrence of Nocturnal Asymptomatic Hypoglycemia in Type 1 Diabetic Patients Receiving Basal-Bolus Insulin Therapy with Long-Acting Insulin?

Objective

To investigate whether the occurrence of nocturnal asymptomatic hypoglycemia may be predicted based on fasting glucose levels and post-breakfast glucose fluctuations.

Patients and Methods

The study subjects comprised type 1 diabetic patients who underwent CGM assessments and received basal-bolus insulin therapy with long-acting insulin. The subjects were evaluated for I) fasting glucose levels and II) the range of post-breakfast glucose elevation (from fasting glucose levels to postprandial 1- and 2-hour glucose levels). The patients were divided into those with asymptomatic hypoglycemia during nighttime and those without for comparison. Optimal cut-off values were also determined for relevant parameters that could predict nighttime hypoglycemia by using ROC analysis.

Results

64 patients (mean HbA1c 8.7 ± 1.8%) were available for analysis. Nocturnal asymptomatic hypoglycemia occurred in 23 patients (35.9%). Fasting glucose levels (I) were significantly lower in those with hypoglycemia than those without (118 ± 35 mg/dL vs. 179 ± 65 mg/dL; P < 0.001). The range of post-breakfast glucose elevation (II) was significantly greater in those with hypoglycemia than in those without (postprandial 1-h, P = 0.003; postprandial 2-h, P = 0.005). The cut-off values determined for relevant factors were as follows: (I) fasting glucose level < 135 mg/dL (sensitivity 0.73/specificity 0.83/AUC 0.79, P < 0.001); and (II) 1-h postprandial elevation > 54 mg/dL (0.65/0.61/0.71, P = 0.006), 2-h postprandial elevation > 78 mg/dL (0.65/0.73/0.71, P = 0.005).

Conclusions

Nocturnal asymptomatic hypoglycemia was associated with increases in post-breakfast glucose levels in type 1 diabetes. Study findings also suggest that fasting glucose levels and the range of post-breakfast glucose elevation could help predict the occurrence of nocturnal asymptomatic hypoglycemia.

Technology to Reduce Hypoglycemia

Hypoglycemia is a major barrier toward achieving glycemic targets and is associated with significant morbidity (both psychological and physical) and mortality. This article reviews technological strategies, from simple to more advanced technologies, which may help prevent or mitigate exposure to hypoglycemia. More efficient insulin delivery systems, bolus advisor calculators, data downloads providing information on glucose trends, continuous glucose monitoring with alarms warning of hypoglycemia, predictive algorithms, and finally closed loop insulin delivery systems are reviewed. The building blocks to correct use and interpretation of this range of available technology require patient education and appropriate patient selection.