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

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.

Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis

BACKGROUND & AIMS

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes.

METHODS

Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome.

RESULTS

Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease.

CONCLUSION

GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without 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.

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.

Decreasing complexity of glucose time series derived from continuous glucose monitoring is correlated with deteriorating glucose regulation

Most information used to evaluate diabetic statuses is collected at a special time-point, such as taking fasting plasma glucose test and providing a limited view of individual's health and disease risk. As a new parameter for continuously evaluating personal clinical statuses, the newly developed technique "continuous glucose monitoring" (CGM) can characterize glucose dynamics. By calculating the complexity of glucose time series index (CGI) with refined composite multi-scale entropy analysis of the CGM data, the study showed for the first time that the complexity of glucose time series in subjects decreased gradually from normal glucose tolerance to impaired glucose regulation and then to type 2 diabetes (P for trend < 0.01). Furthermore, CGI was significantly associated with various parameters such as insulin sensitivity/secretion (all P < 0.01), and multiple linear stepwise regression showed that the disposition index, which reflects β-cell function after adjusting for insulin sensitivity, was the only independent factor correlated with CGI (P < 0.01). Our findings indicate that the CGI derived from the CGM data may serve as a novel marker to evaluate glucose homeostasis.

Clinical application guidelines for blood glucose monitoring in China (2022 edition)

Glucose monitoring is an important component of diabetes management. The Chinese Diabetes Society (CDS) has been producing evidence-based guidelines on the optimal use of glucose monitoring since 2011. In recent years, new technologies in glucose monitoring and more clinical evidence, especially those derived from Chinese populations, have emerged. In this context, the CDS organised experts to revise the Clinical application guidelines for blood glucose monitoring in China in 2021. In this guideline, we focus on four methods of glucose monitoring that are commonly used in clinical practice, including capillary glucose monitoring, glycated haemoglobin A1c, glycated albumin, and continuous glucose monitoring. We describe the definitions and technical characteristics of these methods, the factor that may interfere with the measurement, the advantages and caveats in clinical practice, the interpretation of glucose metrics, and the relevant supporting evidence. The recommendations for the use of these methods are also provided. The various methods of glucose monitoring have their strengths and limitations and cannot be replaced by one another. We hope that these guidelines could aid in the optimal application of common methods of glucose monitoring in clinical practice for better diabetes care.

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.

The Reproducibility and Usefulness of Estimated Average Glucose for Hyperglycemia Management during Health Checkups: A Retrospective Cross-Sectional Study

HbA1c reflects average glucose levels over 3 months, but it does not measure glycemic variability. This study aimed to determine the reproducibility and usefulness of HbA1c-derived estimated average glucose (eAG) and to analyze the factors associated with eAG during health checkups. This cross-sectional retrospective study consecutively selected subjects who had undergone health checkups at 16 health-promotion centers in 13 Korean cities in 2020. The subjects comprised 182,848 healthy subjects with normoglycemia, 109,555 with impaired fasting glucose (IFG), and 35,632 with diabetes. eAG was calculated using Nathan’s regression equation. In all subjects, fasting plasma glucose (FPG) was found to be fairly strongly correlated with eAG (r = 0.811). When the subjects were divided into FPG subgroups, the strength of the correlation decreased among those with normoglycemia and IFG (p < 0.001). Higher eAG levels were associated with older age, females, higher FPG, and lower HDL-C and triglycerides (p < 0.05). The proportion of subjects with a higher value of FPG than eAG was 46.3% in poorly controlled diabetic patients, compared with only 1.5% in normoglycemic subjects. This suggests eAG could help patients to understand their glycemic variability intuitively and healthcare providers to identify patients who might worsen in hyperglycemia control through measuring the difference between eAG and FPG.

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.

Time-in-range as a target in type 2 diabetes: An urgent need

Time-in-range emerged as a valuable blood glucose metric, 'beyond HbA1c' for a deeper insight into glycemic control in people with diabetes. It denotes the proportion of time that a person's glucose level remains within the desired target range (usually 70-180 mg/dL or 3.9-10.0 mmol/L). Though clinical targets in the current recommendations for type 1 and type 2 diabetes are close enough, their clinical profiles and prevalences are quite different. Type 2 diabetes is the commonest form of diabetes. Many clinical trials have challenged the usefulness of HbA1c as a glycemic target for Type 2 diabetes mellitus. On account of the higher prevalence and complications of type 2 diabetes, more outcomes-based studies are needed to associate time-in-range with its ongoing risk. These studies strongly support the dependability of time-in-range to identify patients with elevated risk in type 2 diabetes. We discuss the utility of time-in-range, a new metric of continuous glucose monitoring as an outcome measure to correlate with type 2 diabetes risks and complications and to analyze the effectiveness of type 2 diabetes management. This approach may support the use of time-in-range as a metric for long-term health outcomes in the type 2 diabetes population.