The dawn phenomenon, an early morning glucose rise: implications for diabetic intraday blood glucose variation

Impact of early-morning administration of rapid-acting insulin on the increase in blood glucose levels related to the dawn phenomenon in individuals with type 1 diabetes

Aims/Introduction

An early-morning elevation of blood glucose levels known as the dawn phenomenon and consequent postbreakfast hyperglycemia occur in some individuals with type 1 diabetes (T1D). Whereas insulin pump therapy can mitigate this phenomenon, some individuals prefer or are limited to alternative treatments. We have now assessed the effectiveness of early-morning administration of rapid-acting insulin for amelioration of the dawn phenomenon in individuals with T1D.

Materials and Methods

Thirteen individuals with T1D who experienced the dawn phenomenon as determined by continuous glucose monitoring (CGM) and who received a small dose of rapid-acting insulin on waking were included in this retrospective study. We evaluated the change in sensor glucose levels during a 2-h period from before to after breakfast consumed at 0700 h. The change in blood glucose levels during additional time intervals, average daily sensor glucose values, CGM indices, and insulin dose were also evaluated.

Results

The early-morning administration of 0.5-1 unit of rapid-acting insulin was associated with a significant reduction in 2-h glucose variability between before (0700 h) and after breakfast from a median of 90.7-51.0 mg/dL. The glucose variability from 0300 to 0700 or 0900 h was also significantly decreased, from 67.7 to 29.0 mg/dL and from 172.5 to 78.3 mg/dL, respectively. Average sensor glucose levels throughout the day were significantly reduced (from 192.7 to 156.7 mg/dL), as was the daily total insulin dose.

Conclusion

Early-morning administration of rapid-acting insulin effectively managed the dawn phenomenon and subsequent postbreakfast hyperglycemia in individuals with T1D.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13340-024-00709-6.

Circadian desynchrony and glucose metabolism

The circadian timing system controls glucose metabolism in a time-of-day dependent manner. In mammals, the circadian timing system consists of the main central clock in the bilateral suprachiasmatic nucleus (SCN) of the anterior hypothalamus and subordinate clocks in peripheral tissues. The oscillations produced by these different clocks with a period of approximately 24-h are generated by the transcriptional-translational feedback loops of a set of core clock genes. Glucose homeostasis is one of the daily rhythms controlled by this circadian timing system. The central pacemaker in the SCN controls glucose homeostasis through its neural projections to hypothalamic hubs that are in control of feeding behavior and energy metabolism. Using hormones such as adrenal glucocorticoids and melatonin and the autonomic nervous system, the SCN modulates critical processes such as glucose production and insulin sensitivity. Peripheral clocks in tissues, such as the liver, muscle, and adipose tissue serve to enhance and sustain these SCN signals. In the optimal situation all these clocks are synchronized and aligned with behavior and the environmental light/dark cycle. A negative impact on glucose metabolism becomes apparent when the internal timing system becomes disturbed, also known as circadian desynchrony or circadian misalignment. Circadian desynchrony may occur at several levels, as the mistiming of light exposure or sleep will especially affect the central clock, whereas mistiming of food intake or physical activity will especially involve the peripheral clocks. In this review, we will summarize the literature investigating the impact of circadian desynchrony on glucose metabolism and how it may result in the development of insulin resistance. In addition, we will discuss potential strategies aimed at reinstating circadian synchrony to improve insulin sensitivity and contribute to the prevention of type 2 diabetes.

Severe dawn phenomenon predicts long-term risk of all-cause mortality in patients with type 2 diabetes

AIMS

The dawn phenomenon (DP) is an abnormal early morning blood glucose rise without nocturnal hypoglycaemia, which can be more easily and precisely assessed with continuous glucose monitoring (CGM). This prospective study aimed to explore the association between DP and the risk of all-cause mortality in patients with type 2 diabetes.

MATERIALS AND METHODS

A total of 5542 adult inpatients with type 2 diabetes in a single centre were analysed. The magnitude of DP (ΔG) was defined as the increment in the CGM-determined glucose value from nocturnal nadir (after 24:00) to prebreakfast. Participants were stratified into four groups by ΔG: ≤1.11, 1.12-3.33, 3.34-5.55, and >5.55 mmol/L. Cox proportional hazard regression models were used to evaluate the impact of DP on all-cause mortality risk.

RESULTS

During a median follow-up of 9.4 years, 1083 deaths were identified. The restricted cubic spline revealed a nonlinear (p for nonlinearity = 0.002) relationship between ΔG and the risk of all-cause mortality. A multivariate-adjusted Cox regression model including glycated haemoglobin A1c (HbA1c) showed that ΔG > 5.55 mmol/L was associated with 30% (95% CI, 1.01-1.66) higher risk of all-cause mortality, as compared with ΔG 1.12-3.33 mmol/L.

CONCLUSIONS

Higher ΔG is significantly related to an increased risk of all-cause mortality in type 2 diabetes, suggesting that severe DP should be given more attention as a part of glucose management to reduce the risk of long-term adverse outcomes.

Exercise-induced improvement of glycemic fluctuation and its relationship with fat and muscle distribution in type 2 diabetes

AIMS

Management of blood glucose fluctuation is essential for diabetes. Exercise is a key therapeutic strategy for diabetes patients, although little is known about determinants of glycemic response to exercise training. We aimed to investigate the effect of combined aerobic and resistance exercise training on blood glucose fluctuation in type 2 diabetes patients and explore the predictors of exercise-induced glycemic response.

MATERIALS AND METHODS

Fifty sedentary diabetes patients were randomly assigned to control or exercise group. Participants in the control group maintained sedentary lifestyle for 2 weeks, and those in the exercise group specifically performed combined exercise training for 1 week. All participants received dietary guidance based on a recommended diet chart. Glycemic fluctuation was measured by flash continuous glucose monitoring. Baseline fat and muscle distribution were accurately quantified through magnetic resonance imaging (MRI).

RESULTS

Combined exercise training decreased SD of sensor glucose (SDSG, exercise-pre vs exercise-post, mean 1.35 vs 1.10 mmol/L, p = .006) and coefficient of variation (CV, mean 20.25 vs 17.20%, p = .027). No significant change was observed in the control group. Stepwise multiple linear regression showed that baseline MRI-quantified fat and muscle distribution, including visceral fat area (β = -0.761, p = .001) and mid-thigh muscle area (β = 0.450, p = .027), were significantly independent predictors of SDSG change in the exercise group, as well as CV change.

CONCLUSIONS

Combined exercise training improved blood glucose fluctuation in diabetes patients. Baseline fat and muscle distribution were significant factors that influence glycemic response to exercise, providing new insights into personalized exercise intervention for diabetes.

A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring

In type 2 diabetes (T2D), the dawn phenomenon is an overnight glucose rise recognized to contribute to overall glycemia and is a potential target for therapeutic intervention. Existing CGM-based approaches do not account for sensor error, which can mask the true extent of the dawn phenomenon. To address this challenge, we developed a probabilistic framework that incorporates sensor error to assign a probability to the occurrence of dawn phenomenon. In contrast, the current approaches label glucose fluctuations as dawn phenomena as a binary yes/no. We compared the proposed probabilistic model with a standard binary model on CGM data from 173 participants (71% female, 87% Hispanic/Latino, 54 ± 12 years, with either a diagnosis of T2D for six months or with an elevated risk of T2D) stratified by HbA1c levels into normal but at risk for T2D, with pre-T2D, or with non-insulin-treated T2D. The probabilistic model revealed a higher dawn phenomenon frequency in T2D [49% (95% CI 37-63%)] compared to pre-T2D [36% (95% CI 31-48%), p = 0.01] and at-risk participants [34% (95% CI 27-39%), p < 0.0001]. While these trends were also found using the binary approach, the probabilistic model identified significantly greater dawn phenomenon frequency than the traditional binary model across all three HbA1c sub-groups (p < 0.0001), indicating its potential to detect the dawn phenomenon earlier across diabetes risk categories.

Attention to Innate Circadian Rhythm and the Impact of Its Disruption on Diabetes

Novel strategies are required to reduce the risk of developing diabetes and/or clinical outcomes and complications of diabetes. In this regard, the role of the circadian system may be a potential candidate for the prevention of diabetes. We reviewed evidence from animal, clinical, and epidemiological studies linking the circadian system to various aspects of the pathophysiology and clinical outcomes of diabetes. The circadian clock governs genetic, metabolic, hormonal, and behavioral signals in anticipation of cyclic 24-hour events through interactions between a "central clock" in the suprachiasmatic nucleus and "peripheral clocks" in the whole body. Currently, circadian rhythmicity in humans can be subjectively or objectively assessed by measuring melatonin and glucocorticoid levels, core body temperature, peripheral blood, oral mucosa, hair follicles, rest-activity cycles, sleep diaries, and circadian chronotypes. In this review, we summarized various circadian misalignments, such as altered light-dark, sleep-wake, rest-activity, fasting-feeding, shift work, evening chronotype, and social jetlag, as well as mutations in clock genes that could contribute to the development of diabetes and poor glycemic status in patients with diabetes. Targeting critical components of the circadian system could deliver potential candidates for the treatment and prevention of type 2 diabetes mellitus in the future.

Dual-basal-insulin regimen for the management of dawn phenomenon in children with type 1 diabetes: a retrospective cohort study

Background

Handling of the dawn phenomenon (DP) with multiple daily insulin injection (MDII) regimen is a real challenge.

Objective

We aimed to demonstrate the effectiveness of a dual-basal-insulin (a long-acting glargine and an intermediate-acting neutral protamine Hagedorn (NPH)) regimen for the management of DP in children with type 1 diabetes mellitus (T1DM). The primary efficacy outcome was to overcome morning hyperglycemia without causing hypoglycemia during the non-DP period of the night.

Design

Retrospective cohort study.

Method

Charts of 28 children with T1DM (12 female; 42.8%, mean age 13.7 ± 2.1 years) treated with MDII were retrospectively reviewed. The median duration of diabetes was 4.5 years (range 2-13.5 years). DP was diagnosed using a threshold difference of 20 mg/dL (0.1 mmol/L) between fasting capillary blood glucose at 3 a.m. and prebreakfast. NPH was administered at midnight in addition to daily bedtime (08.00-09.00 p.m.) glargine (dual-basal-insulin regimen). Midnight, 03:00 a.m., prebreakfast and postprandial capillary blood glucose readings, insulin-carbohydrate ratios, and basal-bolus insulin doses were recorded the day before the dual-basal-insulin regimen was started and the day after the titration of the insulin doses was complete. Body mass index standard deviation scores (BMI SDS) at the onset-3rd-12th month of treatment were noted.

Results

Before using dual basal insulin, prebreakfast capillary blood glucose levels were greater than those at midnight and at 03:00 a.m. (F = 64.985, p < 0.01). After titration of the dual-basal-insulin doses, there were significant improvements such that there were no statistically significant differences in the capillary blood glucose measurements at the three crucial time points (midnight, 03.00 a.m., and prebreakfast; F = 1.827, p = 0.172). No instances of hypoglycemia were reported, and the total daily insulin per kilogram of body weight did not change. The BMI SDS remained steady over the course of the 1-year follow-up.

Conclusion

In this retrospective cohort study, the dual-basal-insulin regimen, using a long-acting glargine and an intermediate-acting NPH, was effective in overcoming early morning hyperglycemia due to insulin resistance in the DP. However, the effectiveness of the dual-basal-insulin regimen needs to be verified by prospective controlled studies using continuous glucose monitoring metrics or frequent blood glucose monitoring.

Nrf2 modulates the benefits of evening exercise in type 2 diabetes

Exercise has well-characterized therapeutic benefits in the management of type 2 diabetes mellitus (T2DM). Most of the beneficial effects of exercise arise from the impact of nuclear factor erythroid 2 related factor-2 (Nrf2) activation of glucose metabolism. Nrf2 is an essential controller of cellular anti-oxidative capacity and circadian rhythms. The circadian rhythm of Nrf2 is influenced by circadian genes on its expression, where the timing of exercise effects the activation of Nrf2 and the rhythmicity of Nrf2 and signaling, such that the timing of exercise has differential physiological effects. Exercise in the evening has beneficial effects on diabetes management, such as lowering of blood glucose and weight. The mechanisms responsible for these effects have not yet been associated with the influence of exercise on the circadian rhythm of Nrf2 activity. A better understanding of exercise-induced Nrf2 activation on Nrf2 rhythm and signaling can improve our appreciation of the distinct effects of morning and evening exercise. This review hypothesizes that activation of Nrf2 by exercise in the morning, when Nrf2 level is already at high levels, leads to hyperactivation and decrease in Nrf2 signaling, while activation of Nrf2 in the evening, when Nrf2 levels are at nadir levels, improves Nrf2 signaling and lowers blood glucose levels and increases fatty acid oxidation. Exploring the effects of Nrf2 activators on rhythmic signaling could also provide valuable insights into the optimal timing of their application, while also holding promise for timed treatment of type 2 diabetes.

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling

Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR), and heart rate variability (HRV) in 25 healthy participants over 14 days. We develop a Bayesian inference framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modeling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm for glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40%-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals. A more complex model incorporating activity, HR, and HRV explains up to 15% of additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.

The Effect of High-Intensity Interval Exercise on Short-Term Glycaemic Control, Serum Level of Key Mediator in Hypoxia and Pro-Inflammatory Cytokines in Patients with Type 1 Diabetes-An Exploratory Case Study

Type 1 diabetes (T1D) is associated with hyperglycaemia-induced hypoxia and inflammation. This study assessed the effects of a single bout of high-intensity interval exercise (HIIE) on glycaemia (BG) and serum level of pro-inflammatory cytokines, and an essential mediator of adaptive response to hypoxia in T1D patients. The macronutrient intake was also evaluated. Nine patients suffering from T1D for about 12 years and nine healthy individuals (CG) were enrolled and completed one session of HIIE at the intensity of 120% lactate threshold with a duration of 4 × 5 min intermittent with 5 min rests after each bout of exercise. Capillary and venous blood were withdrawn at rest, immediately after and at 24 h post-HIIE for analysis of BG, hypoxia-inducible factor alpha (HIF-1α), tumour necrosis factor alpha (TNF-α) and vascular-endothelial growth factor (VEGF). Pre-exercise BG was significantly higher in the T1D patients compared to the CG (p = 0.043). HIIE led to a significant decline in T1D patients' BG (p = 0.027) and a tendency for a lower BG at 24 h post-HIIE vs. pre-HIIE. HIF-1α was significantly elevated in the T1D patients compared to CG and there was a trend for HIF-1α to decline, and for VEGF and TNF-α to increase in response to HIIE in the T1D group. Both groups consumed more and less than the recommended amounts of protein and fat, respectively. In the T1D group, a tendency for a higher digestible carbohydrate intake and more frequent hyperglycaemic episodes on the day after HIIE were observed. HIIE was effective in reducing T1D patients' glycaemia and improving short-term glycaemic control. HIIE has the potential to improve adaptive response to hypoxia by elevating the serum level of VEGF. Patients' diet and level of physical activity should be screened on a regular basis, and they should be educated on the glycaemic effects of digestible carbohydrates.

Exploring the Impact of Dawn Phenomenon on Glucose-Guided Eating Thresholds in Individuals With Type 2 Diabetes Using Continuous Glucose Monitoring: Observational Study

BACKGROUND

Glucose-guided eating (GGE) improves metabolic markers of chronic disease risk, including insulin resistance, in adults without diabetes. GGE is a timed eating paradigm that relies on experiencing feelings of hunger and having a preprandial glucose level below a personalized threshold computed from 2 consecutive morning fasting glucose levels. The dawn phenomenon (DP), which results in elevated morning preprandial glucose levels, could cause typically derived GGE thresholds to be unacceptable or ineffective among people with type 2 diabetes (T2DM).

OBJECTIVE

The aim of this study is to quantify the incidence and day-to-day variability in the magnitude of DP and examine its effect on morning preprandial glucose levels as a preliminary test of the feasibility of GGE in adults with T2DM.

METHODS

Study participants wore a single-blinded Dexcom G6 Pro continuous glucose monitoring (CGM) system for up to 10 days. First and last eating times and any overnight eating were reported using daily surveys over the study duration. DP was expressed as a dichotomous variable at the day level (DP day vs non-DP day) and as a continuous variable reflecting the percent of days DP was experienced on a valid day. A valid day was defined as having no reported overnight eating (between midnight and 6 AM). ∂ Glucose was computed as the difference in nocturnal glucose nadir (between midnight and 6 AM) to morning preprandial glucose levels. ∂ Glucose ≥20 mg/dL constituted a DP day. Using multilevel modeling, we examined the between- and within-person effects of DP on morning preprandial glucose and the effect of evening eating times on DP.

RESULTS

In total, 21 adults (59% female; 13/21, 62%) with non-insulin-treated T2DM wore a CGM for an average of 10.5 (SD 1.1) days. Twenty out of 21 participants (95%) experienced DP for at least 1 day, with an average of 51% of days (SD 27.2; range 0%-100%). The mean ∂ glucose was 23.7 (SD 13.2) mg/dL. People who experience DP more frequently had a morning preprandial glucose level that was 54.1 (95% CI 17.0-83.9; P<.001) mg/dL higher than those who experienced DP less frequently. For within-person effect, morning preprandial glucose levels were 12.1 (95% CI 6.3-17.8; P=.008) mg/dL higher on a DP day than on a non-DP day. The association between ∂ glucose and preprandial glucose levels was 0.50 (95% CI 0.37-0.60; P<.001). There was no effect of the last eating time on DP.

CONCLUSIONS

DP was experienced by most study participants regardless of last eating times. The magnitude of the within-person effect of DP on morning preprandial glucose levels was meaningful in the context of GGE. Alternative approaches for determining acceptable and effective GGE thresholds for people with T2DM should be explored and evaluated.

Relationship Between Time in Range and Dusk Phenomenon in Outpatients with Type 2 Diabetes Mellitus

Purpose

The dusk phenomenon refers to a spontaneous and transient pre-dinner hyperglycemia that affects glucose fluctuation and glycemic control, and the increasing use of continuous glucose monitoring (CGM) has facilitated its diagnosis. We investigated the frequency of the dusk phenomenon and its relationship with the time in range (TIR) in patients with type 2 diabetes mellitus (T2DM).

Patients and Methods

This study involved 102 patients with T2DM who underwent CGM for 14 days. CGM-derived metrics and clinical characteristics were evaluated. A consecutive dusk blood glucose difference (pre-dinner glucose minus 2-hour post-lunch glucose) of ≥ 0 or once-only dusk blood glucose difference of < 0 was diagnosed as the clinical dusk phenomenon (CLDP).

Results

We found that the percentage of CLDP was 11.76% (10.34% in men, 13.64% in women). Compared with the non-CLDP group, the CLDP group tended to be younger and have a lower percentage of TIR (%TIR^3.9-10) and higher percentage of time above range (%TAR^>10 and %TAR^>13.9) (P ≤ 0.05). Adjusted for confounding factors, the binary logistic regression analysis showed a negative association of CLDP with %TIR (odds ratio < 1, P < 0.05). We repeated the correlation analysis based on 70%TIR and found significant differences in hemoglobin A1c, fasting blood glucose, mean blood glucose, standard deviation of the sensor glucose values, glucose coefficient of variation, largest amplitude of glycemic excursions, mean amplitude of glycemic excursions, glucose management indicator, and percentage of CLDP between the two subgroups of TIR ≤ 70% and TIR > 70% (P < 0.05). The negative association between TIR and CLDP still remained after adjustment by the binary logistic regression analysis.

Conclusion

The CLDP was frequently present in patients with T2DM. The TIR was significantly correlated with the CLDP and could serve as an independent negative predictor.

Circadian clock, diurnal glucose metabolic rhythm, and dawn phenomenon

The circadian clock provides cue-independent anticipatory signals for diurnal rhythms of baseline glucose levels and glucose tolerance. The central circadian clock is located in the hypothalamic suprachiasmatic nucleus (SCN), which comprises primarily GABAergic neurons. The SCN clock regulates physiological diurnal rhythms of endogenous glucose production (EGP) and hepatic insulin sensitivity through neurohumoral mechanisms. Disruption of the molecular circadian clock is associated with the extended dawn phenomenon (DP) in type 2 diabetes (T2D), referring to hyperglycemia in the early morning without nocturnal hypoglycemia. The DP affects nearly half of patients with diabetes, with poorly defined etiology and a lack of targeted therapy. Here we review neural and secreted factors in physiological diurnal rhythms of glucose metabolism and their pathological implications for the DP.

Capturing the real-world benefit of residual β-cell function during clinically important time-periods in established Type 1 diabetes

AIMS

Many individuals with type 1 diabetes retain residual β-cell function, with increased endogenous insulin secretion associated with reduced hyperglycaemia, hypoglycaemia and glycaemic variability. However, it is unknown when these improvements occur during the day. Dysglycaemia is common in overnight and postprandial periods and associated with diabetes complications. Therefore, this study aimed to determine the influence of residual β-cell function upon nocturnal and postprandial glycaemic control in established type 1 diabetes.

METHODS

Under free-living conditions, 66 participants wore a blinded continuous glucose monitor (CGM), kept a food diary, and completed a stimulated urine C-peptide creatinine (UCPCR) test. Nocturnal, and postprandial CGM outcomes (participant means and discrete event analysis) were compared between UCPCR groups: undetectable (Cpep_und ), low (Cpep_low : 0.001-0.19 nmol/mmol) and high (Cpep_high : ≥0.2 nmol/mmol).

RESULTS

Greater β-cell function was associated with incremental improvements in glycaemia. Cpep_high spent significantly greater time in normoglycaemia than Cpep_und overnight (76 ± 20% vs. 58 ± 20%, p = 0.005) and 0-300 mins postprandially (68 ± 22% vs. 51 ± 22%, p = 0.045), while also having reducing nocturnal variability (SD 1.12 ± 0.41 vs. 1.52 ± 0.43 mmol/L, p = 0.010). Analysis of individual events, controlling for diabetes duration, BMI, basal insulin, use of a continuous or flash glucose monitor and (for postprandial) meal type, carbohydrate and bolus insulin intake, replicated the group findings, additionally demonstrating Cpep_und had increased hyperglycaemia versus Cpep_low overnight and increased postprandial hypoglycaemic events compared with Cpep_high . For all participants, breakfast had a significantly higher incremental area under the curve than lunch and dinner.

CONCLUSIONS

Residual β-cell function is associated with improved nocturnal and postprandial glycaemic control. These data may be of clinical importance for identifying specific periods and individuals where further glycaemic management strategies would be beneficial.

The northeast glucose drift: Stratification of post-breakfast dysglycemia among predominantly Hispanic/Latino adults at-risk or with type 2 diabetes

BACKGROUND

There is minimal experience in continuous glucose monitoring (CGM) among underserved racial/ethnic minority populations with or at risk of type 2 diabetes (T2D), and therefore a lack of CGM-driven insight for these individuals. We analyzed breakfast-related CGM profiles of free-living, predominantly Hispanic/Latino individuals at-risk of T2D, with pre-T2D, or with non-insulin treated T2D.

METHODS

Starting February 2019, 119 participants in Santa Barbara, CA, USA, (93 female, 87% Hispanic/Latino [predominantly Mexican-American], age 54·4 [±12·1] years), stratified by HbA_1c levels into (i) at-risk of T2D, (ii) with pre-T2D, and (iii) with non-insulin treated T2D, wore blinded CGMs for two weeks. We compared valid CGM profiles from 106 of these participants representing glucose response to breakfast using four parameters.

FINDINGS

A "northeast drift" was observed in breakfast glucose responses comparing at-risk to pre-T2D to T2D participants. T2D participants had a significantly higher pre-breakfast glucose level, glucose rise, glucose incremental area under the curve (all p < 0·0001), and time to glucose peak (p < 0·05) compared to pre-T2D and at-risk participants. After adjusting for demographic and clinical covariates, pre-breakfast glucose and time to peak (p < 0·0001) were significantly associated with HbA_1c. The model predicted HbA_1c within (0·55 ± 0·67)% of true laboratory HbA_1c values.

INTERPRETATION

For predominantly Hispanic/Latino adults, the average two-week breakfast glucose response shows a progression of dysglycemia from at-risk of T2D to pre-T2D to T2D. CGM-based breakfast metrics have the potential to predict HbA_1c levels and monitor diabetes progression.

FUNDING

US Department of Agriculture (Grant #2018-33800-28404), a seed grant from the industry board fees of the NSF Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) (Award #1648451), and the Elsevier foundation.

Exercise, type 1 diabetes mellitus and blood glucose: The implications of exercise timing

The scientific literature shows that exercise has many benefits for individuals with type 1 diabetes. Yet, several barriers to exercise in this population exist, such as post-exercise hypoglycaemia or hyperglycaemia. Several studies suggest that the timing of exercise may be an important factor in preventing exercise-induced hypoglycaemia or hyperglycaemia. However, there is a paucity of evidence solely focused on summarising findings regarding exercise timing and the impact it has on glucose metabolism in type 1 diabetes. This report suggests that resistance or high-intensity interval exercise/training (often known as HIIT) may be best commenced at the time of day when an individual is most likely to experience a hypoglycaemic event (i.e., afternoon/evening) due to the superior blood glucose stability resistance and HIIT exercise provides. Continuous aerobic-based exercise is advised to be performed in the morning due to circadian elevations in blood glucose at this time, thereby providing added protection against a hypoglycaemic episode. Ultimately, the evidence concerning exercise timing and glycaemic control remains at an embryonic stage. Carefully designed investigations of this nexus are required, which could be harnessed to determine the most effective, and possibly safest, time to exercise for those with type 1 diabetes.

Identification of Movements and Postures Using Wearable Sensors for Implementation in a Bi-Hormonal Artificial Pancreas System

BACKGROUND

Closed loop bi-hormonal artificial pancreas systems, such as the artificial pancreas (AP™) developed by Inreda Diabetic B.V., control blood glucose levels of type 1 diabetes mellitus patients via closed loop regulation. As the AP™ currently does not classify postures and movements to estimate metabolic energy consumption to correct hormone administration levels, considerable improvements to the system can be made. Therefore, this research aimed to investigate the possibility to use the current system to identify several postures and movements.

METHODS

seven healthy participants took part in an experiment where sequences of postures and movements were performed to train and assess a computationally sparing algorithm.

RESULTS

Using accelerometers, one on the hip and two on the abdomen, user-specific models achieved classification accuracies of 86.5% using only the hip sensor and 87.3% when including the abdomen sensors. With additional accelerometers on the sternum and upper leg for identification, 90.0% of the classified postures and movements were correct.

CONCLUSIONS

The current hardware configuration of the AP™ poses no limitation to the identification of postures and movements. If future research shows that identification can still be done accurately in a daily life setting, this algorithm may be an improvement for the AP™ to sense physical activity.

Long-term Efficacy and Safety of Ultra Rapid Lispro in Japanese Patients With Type 1 Diabetes: Subpopulation Analysis of the 52-Week PRONTO-T1D Study

INTRODUCTION

The PRONTO-T1D study evaluated the efficacy and safety of ultra rapid lispro (URLi) versus lispro in adults with type 1 diabetes mellitus. After 26 weeks of treatment, mealtime and postmeal URLi provided effective and comparable glycemic control in a prespecified subpopulation analysis of Japanese patients from PRONTO-T1D. We present the results of a 52-week study which evaluated the long-term efficacy and safety of URLi in Japanese patients.

METHODS

After an 8-week lead-in period to optimize basal insulin treatment, Japanese patients were randomized to one of three treatment groups: the 52-week double-blind mealtime URLi (n = 62) or mealtime lispro (n = 59) group, respectively, or the 52-week open-label postmeal URLi (n = 46) group.

RESULTS

At week 52, there were no statistically significant differences in change from baseline in hemoglobin A1c (HbA1c) between Japanese patients on URLi and those on lispro; the least-squares mean (LSM) treatment difference was 0.04% (95% confidence interval [CI] - 0.18, 0.25) between mealtime URLi and lispro, and 0.04% (95% CI - 0.19, 0.28) between postmeal URLi and mealtime lispro. No significant between-group differences were observed in the number of patients achieving the HbA1c target of < 7.0% (20.0, 30.5 and 16.3% of those on mealtime URLi, mealtime lispro and postmeal URLi, respectively). Daily average blood glucose levels in the 10-point self-monitored blood glucose profiles at week 52 were similar between treatments. However, compared with lispro, lower blood glucose levels were observed for the mealtime URLi group at the morning 1- and 2-h postmeal time points with LSM differences of - 32.7 mg/dL (- 1.82 mmol/L) (p = 0.005) and - 23.2 mg/dL (- 1.29 mmol/L) (p = 0.029), respectively. There were no significant treatment differences in the incidences of treatment-emergent adverse events, documented hypoglycemia and severe hypoglycemia; however, the rate of documented hypoglycemia was lower in the mealtime URLi arm compared with the lispro arm.

CONCLUSIONS

Overall glycemic control and improved postprandial glucose via self-monitoring was maintained in Japanese patients following 52 weeks of treatment with URLi versus lispro, including postmeal URLi administration.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT03214367.

The dawn phenomenon in type 2 diabetes: its association with glucose excursions and changes after oral glucose-lowering drugs

Background:

We investigated the association between glucose excursions and the dawn phenomenon, and the effects of oral-glucose lowering drugs on the dawn phenomenon in patients with type 2 diabetes (T2D).

Methods:

We conducted a post hoc analysis using data from a previous randomized trial. Patients with T2D on metformin monotherapy were randomized to receive add-on acarbose or glibenclamide for 16 weeks. Ambulatory continuous glucose monitoring (CGM) was conducted before randomization and at the end of the study. Using the CGM data, we assessed glucose excursions as indicated by mean amplitude of glycemic excursions (MAGE). The magnitude of the dawn phenomenon was calculated as the difference between the nocturnal nadir (0:00 to 6:00 a.m.) and prebreakfast glucose level.

Results:

A total of 50 patients with T2D [mean age 53.5 ± 8.2 years, mean glycated hemoglobin (HbA1c) 8.4 ± 1.2%] were analyzed. There was an independent association between MAGE and the dawn phenomenon [β coefficient 0.199, 95% confidence interval (CI) 0.074–0.325, p = 0.003]. HbA1c improved significantly after treatment with acarbose or glibenclamide. However, only treatment with acarbose significantly improved glucose excursions. The dawn phenomenon decreased significantly only in patients treated with acarbose (from 35.9 ± 15.7–28.3 ± 16.5 mg/dl, p = 0.037), but not in those treated with glibenclamide (from 35.9 ± 20.6–34.6 ± 17.0 mg/dl, p = 0.776).

Conclusion:

Glucose excursions were independently associated with the dawn phenomenon in patients with T2D on metformin monotherapy. Both glucose excursions and the dawn phenomenon improved after treatment with acarbose, but not after treatment with glibenclamide.

The impact of dusk phenomenon on total glucose exposure in Chinese people with type 2 diabetes

This study was aimed at assessing the impact of the dusk phenomenon on the total glucose exposure in Chinese people with type 2 diabetes.A total of 380 type 2 diabetes who received a retrospective continuous glucose monitoring system (CGMs) for 72 hours were enrolled in our study, 32 of them failed in CGMs. The patients were first divided into 2 groups: dusk phenomenon (n = 95) and non dusk phenomenon group (n = 253). The magnitude of the dusk phenomenon (δDusk) was quantified by pre-dinner glucose minus post-lunch 2 hours glucose. A persistent δDusk ≥ 0 or a once only δDusk < 0 can be diagnosed with the dusk phenomenon. The participants were secondarily matched for the post-lunch 2 hours glucose to assess the impact of the dusk phenomenon on the overall glucose exposure. The impact of the dusk phenomenon was assessed on high-performance liquid chromatography assay (HbA1c) and 24-hour mean glucose.There were 95 of 348 (27.3%) participants with the dusk phenomenon in the overall population, and the median of δDusk level was -0.8 (-1.8, 0.2) mmol/L. The median of glucose differences between the 2 paired groups were 0.4 (-0.4, 1.0)% for HbA1c, 0.9 (0.2, 1.4) mmol/L for 24 hours mean glucose. The correlation analysis showed no relationship between the magnitude of dawn phenomenon and the dusk phenomenon (r = 0.052, P = .472).The incidence of dusk phenomenon is about 27.3% in people with type 2 diabetes. The impacts of dusk phenomenon on HbA1c and 24-hour mean glucose were about 0.4% and 0.9 mmol/L and the dusk phenomenon was not related with the dawn phenomenon.

Medical Food Assessment Using a Smartphone App With Continuous Glucose Monitoring Sensors: Proof-of-Concept Study

Background

Novel wearable biosensors, ubiquitous smartphone ownership, and telemedicine are converging to enable new paradigms of clinical research. A new generation of continuous glucose monitoring (CGM) devices provides access to clinical-grade measurement of interstitial glucose levels. Adoption of these sensors has become widespread for the management of type 1 diabetes and is accelerating in type 2 diabetes. In parallel, individuals are adopting health-related smartphone-based apps to monitor and manage care.

Objective

We conducted a proof-of-concept study to investigate the potential of collecting robust, annotated, real-time clinical study measures of glucose levels without clinic visits.

Methods

Self-administered meal-tolerance tests were conducted to assess the impact of a proprietary synbiotic medical food on glucose control in a 6-week, double-blind, placebo-controlled, 2×2 cross-over pilot study (n=6). The primary endpoint was incremental glucose measured using Abbott Freestyle Libre CGM devices associated with a smartphone app that provided a visual diet log.

Results

All subjects completed the study and mastered CGM device usage. Over 40 days, 3000 data points on average per subject were collected across three sensors. No adverse events were recorded, and subjects reported general satisfaction with sensor management, the study product, and the smartphone app, with an average self-reported satisfaction score of 8.25/10. Despite a lack of sufficient power to achieve statistical significance, we demonstrated that we can detect meaningful changes in the postprandial glucose response in real-world settings, pointing to the merits of larger studies in the future.

Conclusions

We have shown that CGM devices can provide a comprehensive picture of glucose control without clinic visits. CGM device usage in conjunction with our custom smartphone app can lower the participation burden for subjects while reducing study costs, and allows for robust integration of multiple valuable data types with glucose levels remotely.

Trial Registration

ClinicalTrials.gov NCT04424888; http://clinicaltrials.gov/ct2/show/NCT04424888.

A Role for Exercise to Counter Skeletal Muscle Clock Disruption

Disruption of the skeletal muscle circadian clock leads to a preferential shift toward lipid oxidation while reducing carbohydrate oxidation. These effects are apparent at the whole-body level, including glucose intolerance, increased energy expenditure, and fasting hyperglycemia. We hypothesize that exercise counters these metabolic disturbances by modifying the skeletal muscle clock and reverting substrate metabolism back toward an optimal substrate balance.

Effect of growth hormone on insulin signaling

Growth hormone (GH) is a pleiotropic hormone that coordinates an array of physiological processes, including effects on bone, muscle, and fat, ultimately resulting in growth. Metabolically, GH promotes anabolic action in most tissues except adipose, where its catabolic action causes the breakdown of stored triglycerides into free fatty acids (FFA). GH antagonizes insulin action via various molecular pathways. Chronic GH secretion suppresses the anti-lipolytic action of insulin and increases FFA flux into the systemic circulation; thus, promoting lipotoxicity, which causes pathophysiological problems, including insulin resistance. In this review, we will provide an update on GH-stimulated adipose lipolysis and its consequences on insulin signaling in liver, skeletal muscle, and adipose tissue. Furthermore, we will discuss the mechanisms that contribute to the diabetogenic action of GH.

Fasting May Alter Blood Glucose Responses to High-Intensity Interval Exercise in Adults With Type 1 Diabetes: A Randomized, Acute Crossover Study

OBJECTIVES

In individuals with type 1 diabetes (T1D), changes in blood glucose (BG) during high-intensity interval exercise (HIIE) are smaller than those observed during aerobic exercise. Study outcomes, however, have been variable, with some demonstrating significant BG decreases and others showing BG increases. This study compared BG outcomes between fasting (AME) and postprandial (PME) HIIE in T1D to test the hypothesis that AME would produce a BG increase, yet PME would cause BG to decline.

METHODS

Twelve (6 men and 6 women) physically active individuals with T1D performed two 45-minute exercise sessions (AME at 7:00 AM, PME at 5:00 PM) in random order, separated by at least 48 hours. Sessions consisted of a 10-minute warmup (50%VO_2peak), followed by 10-second sprints every 2 minutes for 24 minutes, and then an 11-minute cooldown. Capillary glucose was measured pre- and postexercise, and then 60 minutes postexercise. Interstitial glucose was recorded for 24 hours postexercise using continuous glucose monitoring.

RESULTS

AME caused capillary glucose to increase (from 7.6±1.4 to 9.2±2.9 mmol/L during exercise, and 9.9±2.8 mmol/L in recovery), whereas PME produced a decline in capillary glucose (from 9.9±3.1 to 9.5±3.4 mmol/L during exercise and 8.9±2.7 mmol/L during recovery; time × treatment interaction, p=0.014). PME was associated with a higher frequency of hyperglycemic events in the 6 hours and overnight (midnight to 6:00 AM) after exercise.

CONCLUSIONS

Fasting HIIE results in a different BG trajectory than postprandial exercise in T1D, and may be beneficial for hypoglycemia avoidance during exercise.

The dawn phenomenon across the glycemic continuum: Implications for defining dysglycemia

AIMS

To investigate the frequency of dawn phenomenon (DP) and its relationship with time in range (TIR) and glycemic variability (GV) using continuous glucose monitoring (CGM).

METHODS

781 subjects of a multicenter CGM study in China were included: those with normal glucose tolerance (NGT n = 360); impaired glucose regulation (IGR n = 173); newly diagnosed type 2 diabetes mellitus (T2D n = 248). Analysis of the magnitude of DP (ΔG) was conducted with the primary definition of 1.11 mmol/L and a secondary definition of 0.56 mmol/L.

RESULTS

The frequency of DP was 8.9%, 30.1% and 52.4% in NGT, IGR and T2D group, respectively, using the primary definition. In all three groups, TIR was lower (all P < 0.05), coefficient of variation (CV) was higher in DP subgroup (all P < 0.05). In DP subgroup of T2D, TIR was 7.0% (1.68 h) lower and CV was 3.0% higher, and HbA_1c was 0.6% (7 mmol/mol) higher using the primary definition (all P < 0.05).

CONCLUSIONS

DP was present in a high percent of subjects with NGT and IGR. In newly diagnosed T2D group, the presence of DP was associated with poorer overall glycemic control.

A low-carbohydrate protein-rich bedtime snack to control fasting and nocturnal glucose in type 2 diabetes: A randomized trial

In type 2 diabetes, liver insulin resistance and excess hepatic glucose production results in elevated fasting glucose. A bedtime snack has been recommended to improve fasting glucose, yet there is little evidence supporting this recommendation. Moreover, the optimal composition of a bedtime snack is unknown.

PURPOSE

To determine whether a low-carbohydrate protein-rich bedtime snack (Egg) could reduce fasting plasma glucose levels in people with type 2 diabetes when compared to a high-carbohydrate protein-rich bedtime snack (Yogurt) or a No Bedtime Snack condition. Secondary outcomes included glucose control assessed by continuous glucose monitoring (CGM) and fasting insulin sensitivity markers.

METHODS

Using a randomized crossover design, participants with type 2 diabetes (N = 15) completed three separate isocaloric conditions: i) Egg, ii) Yogurt, and iii) No Bedtime Snack, each lasting three days. CGM was collected throughout and duplicate fasting blood samples were obtained on the morning of day 4 in each condition.

RESULTS

Fasting plasma glucose (P = 0.04, d = 0.68), insulin (P = 0.04, d = 0.45), and nocturnal glucose (P = 0.02, d = 0.94) were significantly lower, and quantitative insulin sensitivity check index (QUICKI; P = 0.003) was improved, in the Egg compared to the Yogurt bedtime snack. There were no significant differences between either bedtime snack and No Bedtime Snack.

CONCLUSION

In the short-term, a low-carbohydrate bedtime snack (Egg) lowered fasting glucose and improved markers of insulin sensitivity when compared to a high-carbohydrate protein-matched bedtime snack (Yogurt). However, consuming a low- or high-carbohydrate bedtime snack did not appear to lower fasting glucose compared to consuming an isocaloric diet with no bedtime snack. CLINICAL TRIAL REGISTRY: clinicaltrials.gov (NCT03207269).

Effects of Exercise on Blood Glucose and Glycemic Variability in Type 2 Diabetic Patients with Dawn Phenomenon

Background

The dawn phenomenon (DP) is the primary cause of difficulty in blood glucose management in type 2 diabetic (T2D) patients, and the use of oral hypoglycemic agents has shown weak efficacy in controlling DP. Thus, this study is aimed at investigating the effect of moderate-intensity aerobic exercise before breakfast on the blood glucose level and glycemic variability in T2D patients with DP.

Methods

A total of 20 T2D patients with DP confirmed via continuous glucose monitoring (CGM) participated in the current study. After collecting baseline measurements by CGM as a control, CGM was reinstalled and 30 minutes of moderate-intensity aerobic exercise was performed prior to breakfast. Dawn blood glucose increase, blood glucose levels, and glycemic variability were measured before and after exercise.

Results

Dawn blood glucose increase (ΔGlu, 1.25 ± 0.84vs.2.15 ± 1.07, P = 0.005), highest blood glucose value before breakfast (Gmax, 8.01 ± 1.16vs. 8.78 ± 1.09, P = 0.005), highest blood glucose value before breakfast (Gmax, 8.01 ± 1.16vs. 8.78 ± 1.09, P = 0.005), highest blood glucose value before breakfast (Gmax, 8.01 ± 1.16vs. 8.78 ± 1.09, P = 0.005), highest blood glucose value before breakfast (Gmax, 8.01 ± 1.16vs. 8.78 ± 1.09, P = 0.005), highest blood glucose value before breakfast (Gmax, 8.01 ± 1.16vs.2.15 ± 1.07, P = 0.005), highest blood glucose value before breakfast (Gmax, 8.01 ± 1.16vs.2.15 ± 1.07, P = 0.005), highest blood glucose value before breakfast (Gmax, 8.01 ± 1.16vs.2.15 ± 1.07, P = 0.005), highest blood glucose value before breakfast (Gmax, 8.01 ± 1.16

Conclusions

Acute moderate-intensity aerobic exercise before breakfast reduced the morning rise of blood glucose in T2D patients, partially counteracting DP. Furthermore, exercise significantly reduced blood glucose fluctuations and improved blood glucose control throughout the day. We recommend that T2D patients with DP take moderate-intensity aerobic exercise before breakfast to improve DP and glycemic control.

Morning (Fasting) vs Afternoon Resistance Exercise in Individuals With Type 1 Diabetes: A Randomized Crossover Study

OBJECTIVE

To determine the effect of morning exercise in the fasting condition vs afternoon exercise on blood glucose responses to resistance exercise (RE).

RESEARCH DESIGN AND METHODS

For this randomized crossover design, 12 participants with type 1 diabetes mellitus [nine females; aged 31 ± 8.9 years; diabetes duration, 19.1 ± 8.3 years; HbA1c, 7.4% ± 0.8% (57.4 ± 8.5 mmol/mol)] performed ∼40 minutes of RE (three sets of eight repetitions, seven exercises, at the individual's predetermined eight repetition maximum) at either 7 am (fasting) or 5 pm. Sessions were performed at least 48 hours apart. Venous blood samples were collected immediately preexercise, immediately postexercise, and 60 minutes postexercise. Interstitial glucose was monitored overnight postexercise by continuous glucose monitoring (CGM).

RESULTS

Data are presented as mean ± SD. Blood glucose rose during fasting morning exercise (9.5 ± 3.0 to 10.4 ± 3.0 mmol/L), whereas it declined with afternoon exercise (8.2 ± 2.5 to 7.4 ± 2.6 mmol/L; P = 0.031 for time-by-treatment interaction). Sixty minutes postexercise, blood glucose concentration was significantly higher after fasting morning exercise than after afternoon exercise (10.9 ± 3.2 vs 7.9 ± 2.9 mmol/L; P = 0.019). CGM data indicated more glucose variability (2.7 ± 1.1 vs 2.0 ± 0.7 mmol/L; P = 0.019) and more frequent hyperglycemia (12 events vs five events; P = 0.025) after morning RE than after afternoon RE.

CONCLUSIONS

Compared with afternoon RE, morning (fasting) RE was associated with distinctly different blood glucose responses and postexercise profiles.

Feature Selection for Blood Glucose Level Prediction in Type 1 Diabetes Mellitus by Using the Sequential Input Selection Algorithm (SISAL)

Feature selection is a primary exercise to tackle any forecasting task. Machine learning algorithms used to predict any variable can improve their performance by lessening their computational effort with a proper dataset. Anticipating future glycemia in type 1 diabetes mellitus (DM1) patients provides a baseline in its management, and in this task, we need to carefully select data, especially now, when novel wearable devices offer more and more information. In this paper, a complete characterization of 25 diabetic people has been carried out, registering innovative variables like sleep, schedule, or heart rate in addition to other well-known ones like insulin, meal, and exercise. With this ground-breaking data compilation, we present a study of these features using the Sequential Input Selection Algorithm (SISAL), which is specially prepared for time series data. The results rank features according to their importance, regarding their relevance in blood glucose level prediction as well as indicating the most influential past values to be taken into account and distinguishing features with person-dependent behavior from others with a common performance in any patient. These ideas can be used as strategies to select data for predicting glycemia depending on the availability of computational power, required speed, or required accuracy. In conclusion, this paper tries to analyze if there exists symmetry among the different features that can affect blood glucose levels, that is, if their behavior is symmetric in terms of influence in glycemia.

Analysis of Prevalence, Magnitude and Timing of the Dawn Phenomenon in Adults and Adolescents With Type 1 Diabetes: Descriptive Analysis of 2 Insulin Pump Trials

OBJECTIVES

To better understand the dawn phenomenon in type 1 diabetes, we sought to determine its prevalence, timing and magnitude in studies specifically designed to assess basal insulin requirements in patients using insulin pumps.

METHODS

Thirty-three participants from 2 sensor-augmented insulin pump studies were analyzed. Twenty participants were obtained from a methodologically ideal semiautomated basal analysis trial in which basal rates were determined from repeated fasting tests (the derivation set) and 13 from an artificial pancreas trial in which duration of fasting was variable (the "confirmation" set). Prevalence was determined for the total cohort and for individual trials using the standard definition of an increase in insulin exceeding 20% and lasting ≥90 minutes. Among cases, time of onset and percent change in the magnitude of basal delivery were determined.

RESULTS

Seventeen participants (52%) experienced the dawn phenomenon (11 of 20 [55%] in the derivation set and 6 of 13 [46%] in the confirmation set). Time of onset was 3 AM (interquartile range [IQR], 3 to 4:15 AM) in the derivation set and 3 AM (IQR, 3 to 4 AM) in the confirmation set. The magnitude of the dawn phenomenon was a 58.1% (IQR, 28.8% to 110.6%) increase in insulin requirements in the derivation set and 65.5% (IQR, 45.6% to 87.4%) in the confirmation set.

CONCLUSIONS

The dawn phenomenon occurs in approximately half of patients with type 1 diabetes; when present, it has predictable timing of onset (generally 3 AM) and a substantial, but highly variable, magnitude. These findings imply that optimization of glycemic control requires clinical emphasis on fasted overnight basal insulin assessment.

The fluctuation in sympathetic nerve activity around wake-up time was positively associated with not only morning but also daily glycemic variability in subjects with type 2 diabetes

AIMS

It is known that autonomic nerve activity (ANA) affects glucose metabolism by regulating the secretion of insulin and glucagon. Sympathetic nerve stimulation results in increased blood glucose levels. ANA also showed a circadian variation, and sympathetic nerve activity was minimal at night and began to rise at arousal. Therefore, a drastic alteration in ANA around wake-up would be associated with glycemic variability (GV) known risk factor for cardiovascular disease. We investigated the relation between ANA around wake-up and either morning or daily GV.

METHODS

We simultaneously performed Holter ECG and continuous glucose monitoring system in 41 patients with type 2 diabetes (T2D). ANA was assessed by heart rate variability (HRV) analysis. Delta (Δ) wake-up was defined as the difference between the maximum and minimum value during 1 h before and after wake-up time, before breakfast.

RESULTS

Δ of low frequency/high frequency (LF/HF) around wake-up time (Δ LF/HF _wake-up) was positively associated with Δ glucose _wake-up, standard deviation (SD) glucose _wake-up, the mean amplitude of glucose excursions (MAGE_24h), and SD glucose_24h after adjustment for age, sex, BMI, the duration of diabetes, and the prevalence of diabetic polyneuropathy (β = 0.47, p = 0.011, β = 0.48, p = 0.009, β = 0.54, p = 0.002 and β = 0.41, p = 0.0025, respectively). No association was found between Δ LF/HF_wake-up and either mean blood glucose for 24 h, or HbA1c as parameters of chronic hyperglycemia.

CONCLUSIONS

In T2D, the fluctuation in fasting sympathetic nerve activity around wake-up was positively associated with not only morning but also daily GV.

Carbohydrate Restriction in Type 1 Diabetes: A Realistic Therapy for Improved Glycaemic Control and Athletic Performance?

Around 80% of individuals with Type 1 diabetes (T1D) in the United States do not achieve glycaemic targets and the prevalence of comorbidities suggests that novel therapeutic strategies, including lifestyle modification, are needed. Current nutrition guidelines suggest a flexible approach to carbohydrate intake matched with intensive insulin therapy. These guidelines are designed to facilitate greater freedom around nutritional choices but they may lead to higher caloric intakes and potentially unhealthy eating patterns that are contributing to the high prevalence of obesity and metabolic syndrome in people with T1D. Low carbohydrate diets (LCD; <130 g/day) may represent a means to improve glycaemic control and metabolic health in people with T1D. Regular recreational exercise or achieving a high level of athletic performance is important for many living with T1D. Research conducted on people without T1D suggests that training with reduced carbohydrate availability (often termed "train low") enhances metabolic adaptation compared to training with normal or high carbohydrate availability. However, these "train low" practices have not been tested in athletes with T1D. This review aims to investigate the known pros and cons of LCDs as a potentially effective, achievable, and safe therapy to improve glycaemic control and metabolic health in people with T1D. Secondly, we discuss the potential for low, restricted, or periodised carbohydrate diets in athletes with T1D.

Contribution of pancreatic α-cell function to insulin sensitivity and glycemic variability in patients with type 1 diabetes

AIMS/INTRODUCTION

To evaluate the contribution of pancreatic α-cell function to the dawn phenomenon, insulin sensitivity, hepatic glucose uptake and glycemic variability in patients with type 1 diabetes.

MATERIALS AND METHODS

In 40 patients with type 1 diabetes, arginine stimulation tests were carried out, and the area under the curve (AUC) of glucagon was measured using radioimmunoassays (AUCglc RIA ) and enzyme-linked immunosorbent assays (AUCglc ELISA ). The ratio of the insulin dose delivered by an artificial pancreas to maintain euglycemia between 04.00 and 08.00 hours or between 00.00 and 04.00 hours was measured as the dawn index. The glucose infusion rate and hepatic glucose uptake were measured using hyperinsulinemic euglycemic clamp and clamp oral glucose loading tests. Glycemic variability in 96 h was measured by continuous glucose monitoring.

RESULTS

The median dawn index (1.7, interquartile range 1.0-2.8) was not correlated with AUCglc RIA (R2  = 0.03, P = 0.39) or AUCglc ELISA (R2  = 0.04, P = 0.32). The median glucose infusion rate (7.3 mg/kg/min, interquartile range 6.4-9.2 mg/kg/min) was significantly correlated with AUCglc RIA (R2  = 0.20, P = 0.02) and AUCglc ELISA (R2  = 0.21, P = 0.02). The median hepatic glucose uptake (65.3%, interquartile range 40.0-87.3%) was not correlated with AUCglc RIA (R2  = 0.07, P = 0.26) or AUCglc ELISA (R2  = 0.26, P = 0.79). The standard deviation of glucose levels measured by continuous glucose monitoring was significantly correlated with AUCglc RIA (R2  = 0.11, P = 0.049), but not with AUCglc ELISA (R2  = 0.01, P = 0.75).

CONCLUSIONS

Pancreatic α-cell function contributed to insulin sensitivity in patients with type 1 diabetes.

A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning

Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations. The proposed adaptive basal-bolus algorithm (ABBA) supports inputs from either SMBG or CGM devices to provide personalised suggestions for the daily basal rate and prandial insulin doses on the basis of the patients' glucose level on the previous day. The ABBA is based on reinforcement learning, a type of artificial intelligence, and was validated in silico with an FDA-accepted population of 100 adults under different realistic scenarios lasting three simulated months. The scenarios involve three main meals and one bedtime snack per day, along with different variabilities and uncertainties for insulin sensitivity, mealtime, carbohydrate amount, and glucose measurement time. The results indicate that the proposed approach achieves comparable performance with CGM or SMBG as input signals, without influencing the total daily insulin dose. The results are a promising indication that AI algorithmic approaches can provide personalised adaptive insulin optimization and achieve glucose control-independent of the type of glucose monitoring technology.

Growth hormone controls lipolysis by regulation of FSP27 expression

Growth hormone (GH) has long been known to stimulate lipolysis and insulin resistance; however, the molecular mechanisms underlying these effects are unknown. In the present study, we demonstrate that GH acutely induces lipolysis in cultured adipocytes. This effect is secondary to the reduced expression of a negative regulator of lipolysis, fat-specific protein 27 (FSP27; aka Cidec) at both the mRNA and protein levels. These effects are mimicked in vivo as transgenic overexpression of GH leads to a reduction of FSP27 expression. Mechanistically, we show GH modulation of FSP27 expression is mediated through activation of both MEK/ERK- and STAT5-dependent intracellular signaling. These two molecular pathways interact to differentially manipulate peroxisome proliferator-activated receptor gamma activity (PPARγ) on the FSP27 promoter. Furthermore, overexpression of FSP27 is sufficient to fully suppress GH-induced lipolysis and insulin resistance in cultured adipocytes. Taken together, these data decipher a molecular mechanism by which GH acutely regulates lipolysis and insulin resistance in adipocytes.

Clinical diagnosis for dusk phenomenon of diabetes

The diabetes dusk phenomenon (spontaneous and transient pre-dinner hyperglycemia) anecdotally exists but has not been investigated.A total of 80 diabetic patients that received continuous subcutaneous insulin infusions were retrospectively studied. They were grouped into a routine group (R) (consecutive δDG [dusk blood glucose difference] <0 mmol/L) and a classic dusk phenomenon group (CDP, consecutive δDG≥0 mmol/L). δDG represents differences in blood glucose measurements between pre-dinner and post-lunch (δDG: dusk blood glucose difference). Other patients were placed in a suspicious group (S). The suspicious group was further divided into 3 groups based on the frequency at which the δDG occurred: suspicious 1 group (S1), δDG≥0 mmol/L occurred once only; suspicious 3 group (S3), δDG < 0 mmol/L occurred once only, and the remaining patients were grouped in the suspicious 2 group (S2).We identified the CDP and S3 groups as the "clinical dusk phenomenon" group (CLDP). We confirmed that the S1 and R groups to be in the "clinical routine" group. The S2 group was significantly different from the CDP group. In addition, the S2 group had significant differences in δDG measurements and post-lunch blood glucose values compared with the R group, but no differences in other parameters were seen. Multiple comparisons with the other suspicious groups also showed no statistical difference in many parameters. Thus, we placed these patients into the "suspicious clinical dusk phenomenon" group (SDP). The δDG cut-off for the CLDP group was 1.0167 mmol/L. The pre-dinner-pre-lunch blood glucose cut-off for this group was 2.72 mmol/L. The δDG cut-off for the SDP group was -0.95 mmol/L. The pre-dinner-pre-lunch blood glucose cut-off for this group was 0.87 mmol/L. The cut-off points for the post-dinner-post-lunch blood glucose measurements in the CLDP and SDP groups were both 1.2667 mmol/L.A consecutive δDG≥0 or a once only δDG < 0 could be diagnosed as falling into the CLDP group. The CLDP could be excluded when a consecutive δDG < 0 or a once only δDG≥0 was found. Patients falling into other categories were placed into the SDP group.

Effects of Providing High-Fat versus High-Carbohydrate Meals on Daily and Postprandial Physical Activity and Glucose Patterns: a Randomised Controlled Trial

We determined the effects of altering meal timing and diet composition on temporal glucose homeostasis and physical activity measures. Eight sedentary, overweight/obese men (mean ± SD, age: 36 ± 4 years; BMI: 29.8 ± 1.8 kg/m²) completed two × 12-day (12-d) measurement periods, including a 7-d habitual period, and then 5 d of each diet (high-fat diet [HFD]: 67:15:18% fat:carbohydrate:protein versus high-carbohydrate diet [HCD]: 67:15:18% carbohydrate:fat:protein) of three meals/d at ±30 min of 0800 h, 1230 h, and 1800 h, in a randomised order with an 8-d washout. Energy intake (EI), the timing of meal consumption, blood glucose regulation (continuous glucose monitor system (CGMS)), and activity patterns (accelerometer and inclinometer) were assessed across each 12-d period. Meal provision did not alter the patterns of reduced physical activity, and increased sedentary behaviour following dinner, compared with following breakfast and lunch. The HCD increased peak (+1.6 mmol/L, p < 0.001), mean (+0.5 mmol/L, p = 0.001), and total area under the curve (+670 mmol/L/min, p = 0.001), as well as 3-h postprandial meal glucose concentrations (all p < 0.001) compared with the HFD. In overweight/obese males, the provision of meals did not alter physical activity patterns, but did affect glycaemic control. Greater emphasis on meal timing and composition is required in diet and/or behaviour intervention studies to ensure relevance to real-world behaviours.

Computerized Generation of Circadian Sensor Modal Days with Continuous Glucose Monitoring for Comparison of Various Insulin Regimens Based on Insulin Glargine in Type 1 Diabetes

Aim

Our aims were (1) to design and standardize a statistical approach for data reduction in continuous glucose monitoring, allowing comparison of circadian glycemic patterns in therapeutic subcohorts of patients with type 1 diabetes, and (2) to investigate the applicability of this approach for CGMS® assessment in clinical study of basal insulin replacement quality with various timings of basal injections (pre-breakfast, dinner, bedtime) of a new insulin analog.

Methods

Prospective randomized three-arm parallel study with switch over after 6 months for another 3 months of free choice injection time point (options pre-breakfast, pre-dinner and bedtime) of the new insulin analog in 16 type 1 diabetic subjects on functional insulin treatment (FIT: basal, prandial and correctional dosages). CGMS® was used at the end of each follow up period of a clinical study. Representative daily profiles were off-line computed as “circadian sensor modal days” for each insulin regimen consisting of consecutive means of hourly glucose values.

Results

Although the overall quality of glycemic control (HbAIC) for different regimens did not reach statistical differences, CGMS® displayed slightly divergent maximal swings in the course of glycemia (p=0.04–0.08) and allowed – with delineated data reduction procedure – a reliable between treatment comparison.

Conclusion

Off-line computation of “hourly circadian sensor modal days” for data reduction can be effectively used with CGMS® for description of circadian glycemic patterns in type 1 diabetes. (Int J Artif Organs 2003; 26: 728–34)

Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus

BACKGROUND

Personalized blood glucose (BG) prediction for diabetes patients is an important goal that is pursued by many researchers worldwide. Despite many proposals, only a few projects are dedicated to the development of complete recommender system infrastructures that incorporate BG prediction algorithms for diabetes patients. The development and implementation of such a system aided by mobile technology is of particular interest to patients with gestational diabetes mellitus (GDM), especially considering the significant importance of quickly achieving adequate BG control for these patients in a short period (ie, during pregnancy) and a typically higher acceptance rate for mobile health (mHealth) solutions for short- to midterm usage.

OBJECTIVE

This study was conducted with the objective of developing infrastructure comprising data processing algorithms, BG prediction models, and an appropriate mobile app for patients' electronic record management to guide BG prediction-based personalized recommendations for patients with GDM.

METHODS

A mobile app for electronic diary management was developed along with data exchange and continuous BG signal processing software. Both components were coupled to obtain the necessary data for use in the personalized BG prediction system. Necessary data on meals, BG measurements, and other events were collected via the implemented mobile app and continuous glucose monitoring (CGM) system processing software. These data were used to tune and evaluate the BG prediction model, which included an algorithm for dynamic coefficients tuning. In the clinical study, 62 participants (GDM: n=49; control: n=13) took part in a 1-week monitoring trial during which they used the mobile app to track their meals and self-measurements of BG and CGM system for continuous BG monitoring. The data on 909 food intakes and corresponding postprandial BG curves as well as the set of patients' characteristics (eg, glycated hemoglobin, body mass index [BMI], age, and lifestyle parameters) were selected as inputs for the BG prediction models.

RESULTS

The prediction results by the models for BG levels 1 hour after food intake were root mean square error=0.87 mmol/L, mean absolute error=0.69 mmol/L, and mean absolute percentage error=12.8%, which correspond to an adequate prediction accuracy for BG control decisions.

CONCLUSIONS

The mobile app for the collection and processing of relevant data, appropriate software for CGM system signals processing, and BG prediction models were developed for a recommender system. The developed system may help improve BG control in patients with GDM; this will be the subject of evaluation in a subsequent study.

Effect of a dosing-time on quetiapine-induced acute hyperglycemia in mice

Although rare, second-generation antipsychotic drugs cause severe hyperglycemia within several days after the initiation of therapy. Because glucose tolerance exhibits circadian rhythmicity, we evaluated an effect of a dosing-time on quetiapine-induced acute hyperglycemia in mice. A single intraperitoneal dose of quetiapine dosing-time-independently induced insulin resistance in fasted C57BL/6J mice. However, acute hyperglycemic effect was detected only after dosing of the drug at the beginning of an active phase. Under the conditions in which hepatic glucose production was stimulated by pyruvate administration, hyperglycemic effect of quetiapine was dosing-time-independently observed. In addition, the dosing-time-dependent hyperglycemic effect of quetiapine disappeared in the liver-specific circadian clock-disrupted mice in which circadian rhythmicity in hepatic glucose production is deranged. Furthermore, the dosing-time had little impact on the pharmacokinetics of quetiapine in normal mice. These results suggest that quetiapine acutely causes hyperglycemia only when hepatic glucose production elevates. Therefore, quetiapine therapy with once daily dosing at a rest phase might be safer than that at an active phase. Further studies are needed to confirm the hypothesis.

Investigating the Relationship between Morning Glycemic Variability and Patient Characteristics Using Continuous Glucose Monitoring Data in Patients with Type 2 Diabetes

Objective To investigate the relationship between patient characteristics and morning glycemic variability. Methods We retrospectively evaluated 106 patients with type 2 diabetes who underwent continuous glucose monitoring during admission. The highest postprandial glucose level (within 3 hours after breakfast; 'highest level'), the time from the start of breakfast to the highest postprandial glucose level ('highest time'), the difference between the pre-breakfast and highest postprandial breakfast glucose level ('increase'), the area under the curve (AUC; ≥180 mg/dL) for the glycemic variability within 3 hours after breakfast ('morning AUC'), and the post-breakfast glucose gradient ('gradient') were calculated. We analyzed the associations between these factors and nocturnal hypoglycemia and the patients' characteristics by using a regression analysis. Results After stepwise multivariate adjustment, significant independent associations were found between 'highest level' and high age, low BMI, and high HbA1c; 'highest time' and high HbA1c, low C-peptide immunoreactivity (CPR), and low fasting plasma glucose (FPG); the 'increase' and high age, low BMI, high HbA1c, low FPG and hypoglycemia; 'morning AUC' and high age, high HbA1c and hypoglycemia; and 'gradient' and long duration of diabetes and low BMI. Conclusion Higher age and lower BMI are associated with higher 'highest' and 'increase' levels. Higher HbA1c levels were linked to a longer 'highest time', and longer durations of the diabetes, while lower BMI values were related to a higher 'gradient'.

Self-Monitoring of Blood Glucose to Assess Dawn Phenomenon in Chinese People with Type 2 Diabetes Mellitus

Aims. We investigated whether self-monitoring of blood glucose could be used to assess dawn phenomenon in Chinese people with type 2 diabetes mellitus (T2DM). Methods. A total of 306 people with T2DM underwent continuous glucose monitoring and self-monitoring of blood glucose for 72 h. A linear model was used to fit the optimal linear formula of the magnitude of dawn phenomenon (ΔDawn) and self-monitoring of blood glucose values. Results. The prevalence of dawn phenomenon was similar within different oral antidiabetic drug groups (42.5%, 31.5%, and 40.9%, P = 0.216). Multiple variable linear regression showed that prebreakfast, prelunch, and predinner glucose measurements were independently and significantly correlated with ΔDawn. The linear formula between ΔDawn and blood glucose was as follows: ΔDawn (mg/dL) = 0.557 × prebreakfast - 0.065 × prelunch - 0.164 × predinner - 20.894 (mg/dL) (adjusted R² = 0.302, P = 0.000). Conclusions. Dawn phenomenon could be partly assessed by blood glucose self-monitoring in Chinese people with T2DM using the abovementioned formula. The incidence of dawn phenomenon was similar among patients in different oral antidiabetic drug groups.

Poor Sleep Quality Is Associated with Dawn Phenomenon and Impaired Circadian Clock Gene Expression in Subjects with Type 2 Diabetes Mellitus

Aims. We investigated whether poor sleep quality is associated with both dawn phenomenon and impaired circadian clock gene expression in subjects with diabetes. Methods. 81 subjects with diabetes on continuous glucose monitoring were divided into two groups according to the Pittsburgh Sleep Quality Index. The magnitude of dawn phenomenon was quantified by its increment from nocturnal nadir to prebreakfast. Peripheral leucocytes were sampled from 81 subjects with diabetes and 28 normal controls at 09:00. Transcript levels of circadian clock genes (BMAL1, PER1, PER2, and PER3) were determined by real-time quantitative polymerase chain reaction. Results. The levels of HbA1c and fasting glucose and the magnitude of dawn phenomenon were significantly higher in the diabetes group with poor sleep quality than that with good sleep quality. Peripheral leucocytes from subjects with poor sleep quality expressed significantly lower transcript levels of BMAL1 and PER1 compared with those with good sleep quality. Poor sleep quality was significantly correlated with magnitude of dawn phenomenon. Multiple linear regression showed that sleep quality and PER1 were significantly independently correlated with dawn phenomenon. Conclusions. Dawn phenomenon is associated with sleep quality. Furthermore, mRNA expression of circadian clock genes is dampened in peripheral leucocytes of subjects with poor sleep quality.

9th Annual Symposium on Self-Monitoring of Blood Glucose, April 28-30, 2016, Madrid, Spain

International experts in the field of diabetes and diabetes technology met in Madrid, Spain, for the 9th Annual Symposium on Self-Monitoring of Blood Glucose. The goal of these meetings is to establish a global network of experts, thus facilitating new collaborations and research projects to improve the lives of people with diabetes. The 2016 meeting comprised a comprehensive scientific program, parallel interactive workshops, and two keynote lectures.

Spontaneous and transient predinner hyperglycemia in some patients with diabetes: Dusk phenomenon

Blood glucose fluctuations have higher risk than absolute blood glucose level in diabetic chronic complications. At present, "dawn phenomenon" is well known by clinicians, but "dusk phenomenon" has not been recognized. This study explored the objective existence of "dusk phenomenon" (spontaneous and transient predinner hyperglycemia) and its clinical significance.The data of 54 patients with diabetes, who received routine insulin pump therapy between December 2010 and October 2012 in our hospital, were retrospectively analyzed. These patients included 4 patients with type 1 diabetes mellitus (DM) (T1DM) and 50 patients with type 2 DM (T2DM). According to the difference between predinner and postlunch blood glucose levels, the 50 patients with T2DM were divided into dusk phenomenon group (4 patients, all the differences ≥0 mmol/L during insulin pump therapy), nondusk phenomenon group (12 patients, all the differences <0 mmol/L during insulin pump therapy), and suspicious group (34 patients, the differences were uncertain during insulin pump therapy). In the 4 patients with T1DM of this study, the differences all were more than 0 mmol/L during insulin pump therapy. The changes in blood glucose levels were observed, and the correlations of blood glucose level with other factors were analyzed in T1DM and T2DM patients, respectively.In T1DM patients, blood glucose level was significantly higher in predinner than in prebreakfast and prelunch (all P < 0.01), and in postdinner 2 hour than in postlunch 2 hour (P = 0.021). The predinner blood level had no significant correlations with the blood glucose level at other time points and insulin dosages (all P > 0.05). In T2DM patients, the predinner blood glucose level was significantly higher in dusk phenomenon group than in suspicious group and nondusk phenomenon group (all P < 0.05). In dusk phenomenon group, the blood glucose level remained rising from predinner to prebed, and the predinner blood glucose level was only significantly correlated with postdinner 2-hour blood glucose level (P < 0.05).The "dusk phenomenon" (spontaneous and transient predinner hyperglycemia) is an objective existence in some patients with diabetes. The predinner hyperglycemia can affect blood glucose control between postdinner and prebed. Awareness of the "dusk phenomenon" has important clinical significance.

Methods of Measuring Insulin Sensitivity

Insulin resistance is a component of several health disorders, most notably impaired glucose tolerance and type 2 diabetes mellitus. Insulin-resistant individuals have an impaired biological response to the usual action of insulin; that is, they have reduced insulin sensitivity. Various methods are used to assess insulin sensitivity both in individuals and in study populations. Validity, reproducibility, cost, and degree of subject burden are important factors for both clinicians and researchers to consider when weighing the merits of a particular method. This article describes several in vivo methods used to assess insulin sensitivity and presents the advantages and disadvantages of each.

Daily Fasting Blood Glucose Rhythm in Male Mice: A Role of the Circadian Clock in the Liver

Fasting blood glucose (FBG) and hepatic glucose production are regulated according to a circadian rhythm. An early morning increase in FBG levels, which is pronounced among diabetic patients, is known as the dawn phenomenon. Although the intracellular circadian clock generates various molecular rhythms, whether the hepatic clock is involved in FBG rhythm remains unclear. To address this issue, we investigated the effects of phase shift and disruption of the hepatic clock on the FBG rhythm. In both C57BL/6J and diabetic ob/ob mice, FBG exhibited significant daily rhythms with a peak at the beginning of the dark phase. Light-phase restricted feeding altered the phase of FBG rhythm mildly in C57BL/6J mice and greatly in ob/ob mice, in concert with the phase shifts of mRNA expression rhythms of the clock and glucose production-related genes in the liver. Moreover, the rhythmicity of FBG and Glut2 expression was not detected in liver-specific Bmal1-deficient mice. Furthermore, treatment with octreotide suppressed the plasma growth hormone concentration but did not affect the hepatic mRNA expression of the clock genes or the rise in FBG during the latter half of the resting phase in C57BL/6J mice. These results suggest that the hepatic circadian clock plays a critical role in regulating the daily FBG rhythm, including the dawn phenomenon.

Major Increases between Pre- and Post-breakfast Glucose Levels May Predict Nocturnal Hypoglycemia in Type 2 Diabetes

Objective The aim of this study was to determine whether nocturnal hypoglycemia may be predicted according to morning glucose levels. Methods We retrospectively evaluated 106 patients with type 2 diabetes who underwent continuous glucose monitoring during admission. The pre-breakfast glucose level (Pre-breakfast level), highest postprandial glucose level within 3 hours after breakfast (Highest level), time from the start of breakfast to the highest postprandial glucose level (Highest time), difference between the pre-breakfast and highest postprandial breakfast glucose levels (Increase), area under the glucose curve (≥180 mg/dL) within 3 hours after breakfast (Morning AUC), post-breakfast glucose gradient (Gradient), and the increase-to-pre-breakfast ratio (Increase/Pre-breakfast) were calculated. The subjects were divided into hypoglycemic and non-hypoglycemic patients and compared for the above parameters using the t-test. A receiver operating characteristic analysis was used to determine the optimal cut-off values to predict nocturnal hypoglycemia (Hypoglycemia). Results Twenty-eight patients (26.4%) had hypoglycemia. The Pre-breakfast levels were significantly lower in patients with hypoglycemia than those without (p=0.03). The Increases were significantly higher in patients with hypoglycemia than those without (p=0.047). The Increase/Pre-breakfast ratio were significantly larger in patients with hypoglycemia than those without (p=0.0002). Their cut-off values were as follows (level, sensitivity, specificity, and area under the curve): 123 mg/dL, 0.89, 0.55, and 0.78 (p<0.0001); 90.5 mg/dL, 0.75, 0.64, and 0.76 (p<0.0001); and 90.2%, 0.75, 0.76, and 0.78 (p<0.0001), respectively. Conclusion Major increases between the pre- and post-breakfast glucose levels may predict nocturnal hypoglycemia in patients with type 2 diabetes.

Nocturnal Glucose Metabolism in Type 1 Diabetes: A Study Comparing Single Versus Dual Tracer Approaches

BACKGROUND

Understanding the effect size, variability, and underlying physiology of the dawn phenomenon is important for next-generation closed-loop control algorithms for type 1 diabetes (T1D).

SUBJECTS AND METHODS

We used an iterative protocol design to study 16 subjects with T1D on individualized insulin pump therapy for two successive nights. Endogenous glucose production (EGP) rates at 3 a.m. and 7 a.m. were measured with [6,6-(2)H(2)]glucose as a single tracer, infused from midnight to 7 a.m. in all subjects. To explore possibility of tracer recycling due to prolonged [6,6-(2)H(2)]glucose infusion, which was highly probable after preplanned interim data analyses, we infused a second tracer, [6-(3)H]glucose, from 4 a.m. to 7 a.m. in the last seven subjects to measure EGP at 7 a.m.

RESULTS

Cortisol concentrations increased during both nights, but changes in glucagon and insulin concentration were inconsistent. Although the plasma glucose concentrations rose from midnight to 7 a.m. during both nights, EGP measured with [6,6-(2)H(2)]glucose between 3 a.m. and 7 a.m. did not differ during Night 1 but fell in Night 2. However, EGP measured with [6-(3)H]glucose at 7 a.m. was higher than that measured with [6,6-(2)H(2)]glucose during both nights, thereby suggesting tracer recycling probably underestimating EGP calculated at 7 a.m. with [6,6-(2)H(2)]glucose. Likewise, EGP was higher at 7 a.m. with [6-(3)H]glucose than at 3 a.m. with [6,6-(2)H(2)]glucose during both nights.

CONCLUSIONS

The data demonstrate a consistent overnight rise in glucose concentrations through increased EGP, mediated likely by rising cortisol concentrations. The observations with the dual tracer approach imply significant tracer recycling leading to underestimation of EGP measured by longer-duration tracer infusion.