Glycemic Variability In Type Research Articles

Estimation of Meal-Time Insulin Based on Glycemic Load versus Carbohydrate Counting among Patients with Type 1 Diabetes Using Insulin Pump

Abstract Background Dietary carbohydrates have a major effect on postprandial glycemic response. Therefore, the current guidelines recommend the usage of insulin/carbohydrate ratio for calculation of prandial insulin dose based on the carbohydrate count in a given meal. However, some studies pointed to the importance of the quality of carbohydrates, as reflected by their glycemic index, in addition to their quantity on the regulation of postprandial glucose level. Objectives To identify a safe, feasible and effective method for calculation of prandial insulin dose based on the glycemic load of a meal. This could minimize postprandial glycemic variability in type 1 diabetic patients. Patients and Methods Our study included 15 type 1 diabetic participants; their ages ranged from 8 to 15 years. The participants were given a breakfast with the same amount of fat, protein and carbohydrates, which was prepared similarly in all studied patients. Sequence, quality and amounts of meals consumed during the first week of the study were replicated during the second week. Calculation of prandial insulin doses using one regimen for 1 week and a crossover to the other regimen for an additional week was done. A detailed medical history was collected, including the age of onset of diabetes, disease duration, and symptoms of diabetic complications. Results Blood glucose postprandial excursions was significantly lower during the glycemic load counting (GLC) than the carbohydrate counting (CC) period from baseline to 120 min (-10.20 ± 8.18 vs 8.47 ± 6.65 mg/dl, respectively, p = < 0.001), baseline to 180 min (-11.80 ± 8.16 vs 5.13 ± 7.01 mg/dl, respectively, p = < 0.001) and baseline to 240 min (-9.60 ± 6.07 vs -0.53 ± 7.31 mg/dl, respectively, p = 0.007). However, there was no postprandial glucose difference between the two periods from baseline to 60 min (-2.87 ± 8.09 vs 2.53 ± 7.76 mg/dl, respectively, p = 0.083). Conclusion The data from this study showed that glycemic load counting for prandial insulin doses is a feasible and effective method in short term glucose control.

The relationship between glycated hemoglobin and glycemic variability in type 1 diabetics

The relationship between glycated hemoglobin and glycemic variability in type 1 diabetics

Limitations of 14-Day Continuous Glucose Monitoring Sampling for Assessment of Hypoglycemia and Glycemic Variability in Type 1 Diabetes.

Continuous glucose monitoring (CGM) has become the standard of care in diabetes management with the recent advances in technology and accessibility in the last decade. An International Consensus was established to define CGM metrics and its goals in diabetes care. The 2019 International Consensus suggested 14 days of CGM sampling for the assessment of CGM metrics stating the limitations that may occur for hypoglycemia and glycemic variability metrics. Since then, several studies assessed the correlation between CGM metrics and duration of the sampling period. This review summarized the studies that investigated the relationship between 14-day CGM sampling to 90-day CGM data in >70% CGM users for all CGM metrics and highlighted possible solutions for more accurate CGM sampling durations in type 1 diabetes (T1D). Accumulating evidence showed that 14-day CGM sampling correlates well with 90-day CGM data for mean glucose, time in 70-180 mg/dL, and hyperglycemia metrics; however, it correlates weakly for hypoglycemia and glycemic variability metrics. In the studies included in this review, in adults with T1D, minimum sampling duration was 14 days for mean glucose, time in 70-180 mg/dL, and time in hyperglycemia (>180 and >250 mg/dL); however, minimum sampling duration varied between 21 to 30 days for time <70 mg/dL, 30 to 35 days for time <54 mg/dL, and 28 to 35 days for coefficient of variation. Longer than 14 days of CGM, sampling was required to properly assess hypoglycemia and glycemic variability in T1D.

Faster-acting insulin aspart reduces glycaemic variability in sensor-augmented pump treated type 1 diabetes patients

AimTo evaluate the effect of faster aspart over glycaemic variability in type 1 diabetes (T1D) patients treated with sensor-augmented pump (SAP) in a real-world scenario. MethodsObservational study with SAP-treated adult T1D patients treated with faster aspart for three months. The primary endpoint was the mean amplitude of glucose excursions (MAGE). ResultsFifty patients were treated with faster aspart. Eleven patients (23%) withdrew during the follow-up mainly due to worsening of diabetes control (9 patients). Mean age was 41.2 yrs. (range 21–59) and T1D duration 22.4±10.0 yrs. Mean SAP treatment duration was 3.6±3.1 yrs. We detected a reduction of -7.0 (95% CI −1.1, −12.9; p=0.021) in MAGE at the end of the study. Other glycemic variability indices were also improved: standard deviation of mean interstitial glucose (−3mg/dl; 95% CI, −1, −5; p=0.01), CONGA4 (−2.2; 95% CI −0.3, −4.2; p=0.029), CONGA6 (−2.6; 95% CI −0.6, −4.6; p=0.011), GRADE (−0.5; 95% CI −0.1, −0.9; p=0.022), HBGI (−0.7; 95% CI −0.2, −1.3; p=0.013), J-index (−2.9; 95% CI −0.7, −5.0; p=0.011) and MODD (−5.7; 95% CI −1.7, −9.7; p=0.006). A slight reduction in mean glucose management indicator was also detected (−0.14%; 95% CI, −0.02, −0.27; −1.4mmol/mol; 95% CI −0.1, −3.3; p=0.03). ConclusionsIn SAP-treated T1D patients, faster aspart insulin was associated with reduced glycaemic variability, but also a high percentage of dropouts due to worsened glycaemic control. NCT04233203.

Glycemic variability in type 1 diabetics treated with human insulin

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

THE ASSESSMENT OF COMPENSATION OF CARBOHYDRATE METABOLISM IN PATIENTS WITH TYPE 2 DIABETES MELLITUS WITH METABOLIC SYNDROME BEYOND THE LIMITS OF GLYCATED HEMOGLOBIN.

The aim: To investigate glycemic variability in type 2 diabetes patients with metabolic syndrome (MS) and to assess its effect on diabetes compensation. Materials and methods: We used traditional indicators of glycemia variability according to the recommendations of the American Diabetes Association Professional Practice Committee. We proved that patients with type 2 diabetes mellitus with MS reliably have worse CGM indicators: Time in range TIR: (3.9-10.0 mmol/l) - 53.30±5.90%; Time above range (TAR): (time above range) (>10.1 mmol/l) - 43.33±5.96%; Time above TAR range (>13.9 mmol/l) - 22.1±3.91%; Glucose Variability СV - 44.10±4.89% compared to patients with type 2 diabetes without MS, which proves the negative effect of insulin resistance on compensation of diabetes. Results: Determination of the level of EI in the blood, calculation of the Caro index, HOMA-IR are informative for verifying the presence of IR in patients with type 2 diabetes with MS. For optimal diabetes control, in addition to HbA1c, we must consider CGM data and % Time in range (TIR). Conclusions: TIR should be used as a target point and an indicator of glycemic control in routine clinical practice. TIR provides accurate data on a patient's glycemic status and helps better control diabetes.

Metabolic syndrome associated with higher glycemic variability in type 1 diabetes: A multicenter cross-sectional study in china.

AimsThe comorbidity of metabolic syndrome (MetS) and type 1 diabetes mellitus (T1DM) is an obstacle to glucose control in patients with T1DM. We compared glycemic profiles using continuous glucose monitoring (CGM) systems in patients with T1DM with or without MetS.MethodsThis was a multicenter cross-sectional study of patients with T1DM (N = 207) with or without MetS. CGM data were collected from study enrollment until discharge during a 1-week study session. We analyzed baseline HbA1c, average glucose, estimated HbA1c, time in range (TIR), time above range (TAR), time below range (TBR), coefficient of variation (CV), postprandial glucose excursions (PPGE) and other glycemic variability (GV) metrics. Logistic regression was developed to investigate the association between MetS and CGM metrics.ResultsThe results showed higher average baseline HbA1c levels, and a higher percentage of patients with baseline HbA1c levels ≥7.5%, in the T1DM with MetS group. Furthermore, MetS was associated with GV, which indicated a higher CV in patients with T1DM with MetS. However, our results showed that TAR, TIR, TBR and other GV metrics were comparable between the two groups. The T1DM with MetS group also had a higher proportion of patients with high CV (≥ 36%) than the group without MetS. In multivariable logistic regression analysis, the presence of MetS was a risk factor for high CV (≥ 36%) in our study participants.ConclusionsT1DM patients with MetS in our study had better β-cell function. However, MetS was associated with worse glycemic control characterized by higher GV and HbA1c levels. Efforts should be expanded to improve treatment of MetS in patients with T1DM to achieve better glycemic control.

Faster-acting insulin aspart reduces glycaemic variability in sensor-augmented pump treated type 1 diabetes patients

AimTo evaluate the effect of faster aspart over glycaemic variability in type 1 diabetes (T1D) patients treated with sensor-augmented pump (SAP) in a real-world scenario. MethodsObservational study with SAP-treated adult T1D patients treated with faster aspart for three months. The primary endpoint was the mean amplitude of glucose excursions (MAGE). ResultsFifty patients were treated with faster aspart. Eleven patients (23%) withdrew during the follow-up mainly due to worsening of diabetes control (9 patients). Mean age was 41.2 yrs. (range 21–59) and T1D duration 22.4±10.0 yrs. Mean SAP treatment duration was 3.6±3.1 yrs. We detected a reduction of -7.0 (95% CI −1.1, −12.9; p=0.021) in MAGE at the end of the study. Other glycemic variability indices were also improved: standard deviation of mean interstitial glucose (−3mg/dl; 95% CI, −1, −5; p=0.01), CONGA4 (−2.2; 95% CI −0.3, −4.2; p=0.029), CONGA6 (−2.6; 95% CI −0.6, −4.6; p=0.011), GRADE (−0.5; 95% CI −0.1, −0.9; p=0.022), HBGI (−0.7; 95% CI −0.2, −1.3; p=0.013), J-index (−2.9; 95% CI −0.7, −5.0; p=0.011) and MODD (−5.7; 95% CI −1.7, −9.7; p=0.006). A slight reduction in mean glucose management indicator was also detected (−0.14%; 95% CI, −0.02, −0.27; −1.4mmol/mol; 95% CI −0.1, −3.3; p=0.03). ConclusionsIn SAP-treated T1D patients, faster aspart insulin was associated with reduced glycaemic variability, but also a high percentage of dropouts due to worsened glycaemic control. NCT04233203.

Comparison of glycemic variability in type 2 diabetes mellitus patients on oral anti diabetic drugs (OAD) with and without insulin using ambulatory glucose profile (AGP)

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Effect of Hydroxychloroquine on Glycemic Variability in Type 2 Diabetes Patients Uncontrolled on Glimepiride and Metformin Therapy.

No study to date has assessed the effect of hydroxychloroquine on various parameters of glycaemic variability. To assess the effect of hydroxychloroquine on glycaemic variability in type 2 diabetes patients uncontrolled on glimepiride and metformin. A total of 30 T2DM patients aged 18-65 years uncontrolled on glimepiride and metformin therapy with HbA1c 7.5-10% (58-86 mmol/mol) were given adjunctive hydroxychloroquine 400 mg during the 12-week study period. The glycaemic variability parameters such as standard deviation of 24 hours of blood glucose, mean of daily differences (MODD) and mean amplitude of glycaemic excursion (MAGE) were assessed by continuous glucose monitoring system (CGMS) data at baseline and at 12 weeks after the addition of hydroxychloroquine 400 mg. Efficacy was assessed by change in fasting, postprandial plasma glucose and HbA1c from baseline to 12 weeks of addition of 400 mg hydroxychloroquine. There was a significant reduction in all parameters of glycaemic variability including MAGE, MODD, standard deviation of 24-hour blood glucose and average blood glucose as well as a significant reduction in fasting, postprandial blood glucose and glycated haemoglobin post 12 weeks of adjunctive treatment with hydroxychloroquine. At the end of 12 weeks of adjunctive treatment with hydroxychloroquine, there was a significant improvement in the percentage of time spent in the target glucose range of 3.9-8.3 mmol/L (70-150 mg/dL). The addition of hydroxychloroquine in uncontrolled diabetes significantly reduces all glycaemic parameters including all parameters of glycaemic variability and hence can be an effective add-on to patients uncontrolled on glimepiride and metformin therapy.

The Role of Glucagon in Glycemic Variability in Type 1 Diabetes: A Narrative Review.

Type 1 diabetes mellitus (T1DM) is a progressive disease as a result of the severe destruction of islet β-cell function, which leads to high glucose variability in patients. However, α-cell function is also compromised in patients with T1DM, characterized by aberrant fasting and postprandial glucagon secretion. According to recent studies, this aberrant glucagon secretion plays an increasing role in hyperglycemia, insulin-induced hypoglycemia and exercise-associated hypoglycemia in patients with T1DM. With application of continuous glucose monitoring system, dozens of metrics enable the assessment of glycemic variability, which is an integral component of glycemic control for patients with T1DM. There is growing evidences to illustrate the contribution of glucagon secretion to the glycemic variability in patients with T1DM, which may promote the development of new treatment strategies aiming to mitigate glycemic variability associated with aberrant glucagon secretion.

Cardiovascular Adverse Outcomes as Challenges on Glycemic Variability Among Patients with Type 2 Diabetes on Intensive Insulin Therapy: The Role of Medical Nutrition Therapy with Automatic Snacking

The glycemic variability (GV) is the fluctuation of the blood glucose (BG) in relation to the daily mean BG or mean glycosylated hemoglobin (HbA1c). BG oscillations can be influenced by the type of food and timing of intake, activity, psychological condition (fear of hypoglycemia), and systemic hormonal cross-talking between insulin and glucagon. Pharmacologically, these BG oscillations can be affected by the regulators of insulin secretion like sulfonylurea and insulin. The increase in GV with very high and very low BG excursions has been associated with the development of macrovascular and microvascular complications among type 2 diabetes mellitus (DM) patients. Increased mortality has been demonstrated in the past among elderly patients on intensive insulin therapy due to severe hypoglycemia. Prompt intake of glucose has obviously compromised glycemic control and worsens the GV. Automatic snacking (AS) as a part of medical nutrition therapy (MNT) is the provision of snacks 2 h after meals which are taken even in the absence of hunger. This review will showcase our published papers among patients with type 2 DM where AS was instituted to obtain long-term glycemic control and prevent the occurrence of fatal postprandial hypoglycemia. Although, further research is needed, AS is a promising dietary management to address GV in type 2 DM patients on intensive insulin therapy.

Central pontine myelinolysis secondary to glycemic variability in type 1 diabetes: a case report and a systematic review of the literature

Central pontine myelinolysis (CPM) is a rare manifestation of osmotic demyelination syndrome (ODS) which involves the pons and causes significant morbidity and mortality. CPM usually occurs in the setting of rapid correction of severe chronic hyponatremia. A rare case of CPM due to hyperglycemia in a 27-year-old man with type 1 diabetes is reported. During the patient’s hospitalization, his plasma glucose level showed a wide variability ranging from 38 mg/dL to 530 mg/dL, while plasma sodium level was constantly normal. At computed tomography (CT) scans, areas of hypodensity with a hyperdense ring were identified in the anterior part of the pons. At magnetic resonance imaging (MRI) scan, pontine abnormalities compatible with CPM were observed. According to laboratory tests, we concluded that CPM resulted from rapid and wide shifts in osmolar gradient owing to variability in plasma glucose levels. While universally recognized in several clinical settings, CPM is rarely observed in diabetic patients. Our report supports the notion that hyperosmolarity per se plays a key role in the pathogenesis of CPM, which may occur independently of sodium abnormalities.

The relationship between glycosylated hemoglobin, time-in-range and glycemic variability in type 1 diabetes patients under flash glucose monitoring

ObjectivesFlash glucose monitoring in patients with type 1 diabetes provides new glucometric data that allow for the assessment of glycemic control beyond HbA1c. The objective of the study was to evaluate the relationship between HbA1c, time-in-range (TIR) and glycemic variability in a cohort of paediatric and adult patients with type 1 diabetes and treatment with flash glucose monitoring. Material and methodsThis was a cross-sectional study in 195 patients with type 1 diabetes (42.6% females, 70 paediatric, 26.2% continuous subcutaneous insulin infusion, 28.7% coefficient of variation [CV]≤36%) in intensive treatment and flash glucose monitoring. Clinical, analytical and glucometric data were evaluated. ResultsThe relationship between the TIR and HbA1c showed a strong negative linear correlation (R=−0.746; R2=0.557; P<.001), modified in those patients with CV≤36% (R=−0.852; R2=0.836) compared to CV>36% (R=−0.703; R2=0.551). A similar correlation was found when evaluating the TIR and the Glucose Management Indicator (R=−0.846; R2=0.715; P<.001); in patients with CV≤36% (R=−0.980; R2=0.960) versus CV>36% (R=−0.837; R2=0.701); P<.001. Both correlations remained stable in the paediatric population (R=−0.724; R2=0.525; P<.001) and adults (R=−0.706; R2=0.498; P<.001) and by type of treatment: multiple doses of insulin (R=−0.747; R2=0.558; P<.001) and continuous subcutaneous insulin infusion (R=−0.711; R2=0.506; P<.001). In a multiple regression analysis evaluating HbA1c as dependent variable, the only parameters that maintained statistical significance were the TIR (β=−0,031; P<.001), CV (β=0.843; P<.05) and TIR-CV interaction (β=−0.017; P<.01). ConclusionsThe glycemic variability defined by the CV modifies the relationship between the TIR and HbA1c/Glucose Management Indicator and should be taken into account when individualising TIR targets, regardless of age or the type of treatment used.

Association of HbA1c With All-cause Mortality Across Varying Degrees of Glycemic Variability in Type 2 Diabetes.

ContextThe interaction of glycated hemoglobin A1c (HbA1c) and glycemic variability in relation to diabetes-related outcomes remains unknown.ObjectiveTo evaluate the relationship between HbA1c and all-cause mortality across varying degrees of glycemic variability in patients with type 2 diabetes.Design, Setting, and PatientsThis was a prospective study conducted in a single referral center. Data of 6090 hospitalized patients with type 2 diabetes was analyzed. Glucose coefficient of variation [coefficient of variation (CV)] was obtained as the measure of glycemic variability by using continuous glucose monitoring for 3 days. Cox proportional hazards regression models were used to estimate hazard ratios and 95% CIs for all-cause mortality.ResultsDuring a median follow-up of 6.8 years, 815 patients died. In patients with the lowest and middle tertiles of glucose CV, HbA1c ≥ 8.0% was associated with 136% (95% CI 1.46-3.81) and 92% (95% CI 1.22-3.03) higher risks of all-cause mortality, respectively, as compared with HbA1c 6.0%-6.9%, after adjusting for confounders. However, a null association of HbA1c with mortality was found in patients with the highest tertile of glucose CV.ConclusionsHbA1c may not be a robust marker of all-cause mortality in patients with high degree of glycemic variability. New metrics of glycemic control may be needed in these individuals to achieve better diabetes management.

798-P: Luseogliflozin Suppresses Fine Glycemic Fluctuation Late at Night: New Aspect of SGLT2 Inhibitor

Sodium-glucose cotransporter 2 (SGLT2) inhibitors block reabsorption of glucose by inhibiting SGLT2 in kidney, promotes the renal excretion of glucose. They have been proven to have a good glucose-lowering effect. To evaluating the effect of SGLT2 inhibitor on fine glycemic variability in type 2 diabetes mellitus (T2D) was used a continuous glucose monitoring system (CGMS) Freestyle Libre. The coefficient of variation (CV) of glucose levels every 15 minutes in the midnight zone (11pm-3am), which is less associated with diet and stress, was measured just before and just after the first administration of the SGLT2 inhibitor. Luseogliflozin (2.5mg per day) was administered to 6 patients with T2D. In Addition, fasting plasma glucose, postprandial plasma glucose, C-peptide, other hormones and metabolites were assessed. Luseogliflozin strongly suppressed CV of glucose (11.6±4.8 to 4.1±2.5, p=0.0012) (Figure). SGLT2 inhibitor suppressed fine glycemic fluctuations. There was no correlation with other factors. This inhibitory effect did not appear with sulfonylurea or DPP-4 inhibitors. (data not shown) Although the result is a new aspect of SGLT2 inhibitor, the clinical significance is not clear. Since Risso A et al reported that glycemic fluctuation cause harm to vascular endothelial cells (AJP 2001), SGLT2 inhibitors may reduce damage of blood vessel via regulation of fine glycemic fluctuation.View largeDownload slideView largeDownload slide DisclosureK. Yamauchi: None.

Classification of Gastroparesis from Glycemic Variability in Type 1 Diabetes: A Proof-of-Concept Study.

Delayed gastric emptying is a substantial challenge for people with diabetes, affecting quality of life and blood glucose regulation. The complication is underdiagnosed, and current diagnostic tests are expensive or time consuming or have modest accuracy. The assessment of glycemic variations has potential use in gastroparesis screening. The aim of this study was to investigate the differences in glycemic variability between type 1 diabetes patients with gastroparesis and without a diagnosis of gastroparesis and the potential for using a classification model to differentiate between groups. Continuous glucose monitoring (CGM) from 425 patients with diabetes was included in the analytic cohort, including 16 patients with a diagnosis of gastroparesis and 409 without a known gastroparesis diagnosis. Sixteen features (9 daytime features and 7 nighttime features) describing glucose dynamics were extracted to assess differences between patients with and without a diagnosis of gastroparesis. A logistic regression model was trained using forward selection and cross-validation. In total, 3 features were included in the model utilizing forward selection of features and cross-validation: mean absolute glucose (MAG), span, and standard deviation during the night. The Receiver operating characteristic (ROC) AUC for the classification model was 0.76. Gastroparesis seems to have an impact on glucose variability, especially during the night. Moreover, CGM could possibly be used as a part of the screening process for delayed gastric emptying, but more studies are needed to determine a realistic accuracy.

Log-linear relationship between endogenous insulin secretion and glycemic variability in patients with type 2 diabetes on continuous glucose monitoring

The contribution of endogenous insulin secretion to glycemic variability (GV) may differ between patients with impaired insulin secretion and those with preserved secretion. Our objective was to determine the linearity of the relationship between fasting C-peptide (CPR) as a marker of endogenous insulin secretion and GV in type 2 diabetes (T2DM), regardless of the type of antidiabetic treatment. We conducted a prospective observational study using continuous glucose monitoring obtained from 284 Japanese outpatients with T2DM with various HbA1c values and antidiabetic treatment. We constructed a prediction curve of base-line CPR versus coefficient of variation (CV) and identified the clinical factors associated with CV using multiple regression analysis. Fasting CPR showed a significant negative log-linear relationship with CV (P < 0.0001), and the latter being strikingly high in the low-CPR group. The multiple regression analysis showed that low CPR was an independent predictor of high CV (P < 0.0001). The significant correlations were sustained in both patients with/without insulin treatment. The contribution of endogenous insulin secretion to GV depends on the extent of insulin secretion impairment. Fasting CPR may represent a useful indicator of GV instability in T2DM.

The Effects of Dietary Nutrition Intake on Glycemic Variability in Type 1 Diabetes Mellitus Adults.

IntroductionType 1 diabetes mellitus (T1DM) is characterized by an absolute deficiency of insulin and dependence on insulin therapy. Therefore, glycemic control and management are important for T1DM patients, particularly glycemic variability, which is associated with the development of diabetic complications. However, insufficient attention has been paid to the glycemic variability in T1DM patients so far. Our objective was to identify the effects of food intake on glycemic variability in T1DM patients.MethodsThis was a single-center study that took place in the outpatient clinics of Peking Union Medical College Hospital. A total of 68 Chinese T1DM patients between June 2018 and June 2019 were enrolled. After the baseline demographic and clinical characteristics were evaluated, each participant underwent 14-day flash glucose monitoring (FGM). They recorded caloric intake of breakfast, lunch, and dinner at least 3 days/week using a “Menthol Health” app. After 2 weeks, we obtained the FGM data and did further data analysis. Baseline characteristics and glycemic variability index generated by FGM were compared among groups. A general linear model was used to compare data among groups after adjusting for potential confounding factors. The quantitative relationship between two continuous variables was explored by constructing a linear regression equation.ResultsThe results showed that the C-peptide level was independently correlated with the mean of daily differences (MODD) after adjusting for the possible confounders (β = − 0.239, p = 0.046). The dietary nutrition intake had no effect on glycemic variability. However, the nutritional composition of carbohydrate, fat, and protein was an independent risk factor for time spent in hypoglycemia (TBR) post adjustment (β = − 0.213, p = 0.054). However, there was no impact of daily total energy intake on glycemic variability index.ConclusionIn our study, dietary nutrition intake had no effect on glycemic variability, but residual β-cell function was identified as an influencing factor for glycemic variability in T1DM adults. However, nutritional macronutrient composition played some roles in the occurrence of hypoglycemia. This might provide new evidence for the clinical glycemic control and management of T1DM in the Chinese population.

Inhibition of SGLT1 protects against glycemic variability-induced cardiac damage and pyroptosis of cardiomyocytes in diabetic mice

AimsGlycemic variability has been shown to be more harmful in the development of diabetic complication than sustained chronic hyperglycemia. In this present study, we tried to reveal the effects of glycemic variability on cardiac damage in diabetic mice and investigate whether sodium-glucose cotransporter 1 (SGLT1), an important cardiac glucose transporter, functions as an important mediator in the process. Materials and methodsType 2 diabetes mellitus (DM) mice were induced by a high-fat diet and intraperitoneal injection of streptozotocin (STZ), and then glycemic variability in type 2 diabetes mellitus (GVDM) was induced by alternately injecting insulin and glucose to DM mice. In order to determine the roles of SGLT1 in GVDM mice, SGLT1 inhibition was performed using shRNA against SGLT1. The blood glucose level, the cardiac function and myocardial injury were assessed. And the expressions of SGLT1 and the activations of NLRP3/caspase-1 pathway and NF-κB in left ventricular tissues were measured. Key findingsThe results showed that SGLT1 was highly expressed in heart of GVDM mice compared to control and DM groups, and knockdown of SGLT1 reduced glycemic variability in GVDM mice. Moreover, glycemic variability impaired cardiac function, aggravated cardiac injury and induced NLRP3/caspase-1-mediated inflammatory response and pyroptosis. And knockdown of SGLT1 significantly attenuated the cardiac damages that induced by glycemic variability. SignificanceThe results indicated that glycemic variability could cause cardiac damage and induce inflammatory response and pyroptosis of cardiomyocytes in diabetic mice, which could be partially blocked by SGLT1 silence.