How can we monitor glycaemic variability in the clinical setting?

Association between short-term glucose fluctuations and cognition in patients with acute ischemic stroke complicated by type 2 diabetes mellitus

OBJECTIVES

Glucose fluctuations are more harmful than persistent hyperglycemia for chronic complications of diabetes. However, the relationship between cognition and glucose fluctuations in patients with acute ischemic stroke (AIS) complicated by type 2 diabetes mellitus (T2DM) remains unclear. We aimed to evaluate the association between short-term glucose fluctuations and cognition in patients with AIS complicated by T2DM.

DESIGN

A cohort study with a 2-year follow-up.

SETTING AND PARTICIPANTS

We included 554 patients with mild AIS (mean age: 62 years; 170 females and 384 males).

MEASUREMENTS

Glucose variability (GV) was evaluated using glycated hemoglobin (HbA1c), stress hyperglycemia (SHR), standard deviation of blood glucose (SDBG), mean postprandial blood glucose (MPBG), mean amplitude of glycemic excursion (MAGE), and time in range (TIR). We evaluated the relationship between GV, fasting blood glucose (FBG) and cognition during the acute phase using linear regression analysis. We evaluated the relationship between GV, FBG and the occurrence of post-stroke cognitive impairment (PSCI) using a logistic regression model. Mediation analyses were fitted to explore whether the relationships of HbA1c with cognition were mediated by cerebral small vessel disease (CSVD).

RESULTS

A clear pattern of age-related GV was observed. Higher SHR in middle-aged participants; higher HbA1c, and lower TIR in older participants; and higher MAGE, MPBG, and SDBG across a broad age range (50-80 years) were associated with cognitive impairment in the acute phase of AIS. Higher SHR and SDBG together with lower TIR in middle-aged participants, higher HbA1c in older participants, and higher FBG, MPBG, and MAGE across a broad age range (50-80 years) were associated with the occurrence of PSCI. The association between HbA1c and cognition was partially mediated (proportion: 7-16%) by CSVD.

CONCLUSIONS

Short-term glucose fluctuations are associated with cognition and a higher risk of PSCI in patients with AIS complicated by T2DM. CSVD might play an important role in the relationship between short-term glucose fluctuations and cognition.

Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations

BACKGROUND

The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual's longer term control.

METHODS

We introduce explainable machine learning to make predictions of hypoglycemia (<70 mg/dL) and hyperglycemia (>270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user.

RESULTS

Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model.

CONCLUSIONS

Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user's glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications.

The efficacy of intraoperatie continuous glucose monitoring in patients undergoing liver transplantation: a study protocol for a prospective randomized controlled superiority trial

BACKGROUND

The high incidence of intraoperative glucose dysregulations in liver transplantation (LT) is related to the lack of highly orchestrated control of intraoperative blood glucose. Glucose monitoring based on a single arterial blood gas test can only provide a simple glucose profile and is insufficient in monitoring intraoperative glycemic variability (GV), which is not conducive to controlling GV and may have a lag in the management of hyper/hypoglycemia. Continuous glucose monitor (CGM), which has been successfully applied in the management of chronic disease in diabetes, provides more detailed blood glucose records and reflect GV parameters such as coefficient of variation (CV%). However, its effectiveness and accuracy for guiding blood glucose management in major surgeries remains unclear.

METHODS

This is a single-center, randomized, controlled, superiority trial. One hundred and eighty patients scheduled for orthotopic LT will be recruited and randomized into two groups. All patients are monitored for intraoperative glucose using CGM combined with arterial blood gas (ABG). In the intervention group (group CG), ABG will be performed when CGM value is < 6.1 mmol/L or > 10.0 mmol/L, or the rate of change of CGM value > 1.67 mmol/(L·min). In the control group (group G), intraoperative ABG tests will be performed every 2 h, and the frequency of ABG tests will be adjusted based on the previous arterial glucose result. Patients in both groups will have their blood glucose adjusted according to arterial glucose values and a uniform protocol. Surgical and other anesthetic management is completed according to standard LT practices.

DISCUSSION

This study intends to investigate the effectiveness of CGM-based intraoperative glucose management and its impact on the prognosis of LT patients by comparing the GV, mean glucose values, and the incidence of hypo/hypoglycemic events guided by the above two glucose monitoring methods.

TRIAL REGISTRATION

This study is registered at www.chictr.org.cn on January 4, 2022, under the registration number ChiCTR2200055236.

Minimed Medtronic 780G optimizes glucose control in patients with type 1 diabetes mellitus

BACKGROUND AND AIMS

The new advanced hybrid closed loop insulin infusion systems have the potential to significantly improve glycaemic control. The aim of this study was to evaluate the effectiveness of the Minimed 780G system in 59 patients with type 1 diabetes.

METHODS AND RESULTS

Glucose control obtained by using the system in automatic mode at 1-2 months of activation, at 2-4 months, at 4-6 months, and beyond 6 month was compared with those obtained with the system in manual mode. A significant improvement in time-in-range and in time-above-range throughout the follow-up was observed, as well as a significant reduction in time-below-range (<54 mg/dl) after 6 months, a significant reduction of the glucose variability and of HbA1c. After switching the mode, all target percentages lied on the average within the recommended ranges by literature consensus and no severe hypoglycemia nor ketoacidosis episodes were recorded.

CONCLUSION

The Minimed 780G allowed a rapid and progressive improvement of the overall glucose control.

The Multi-Domain Intervention Trial in Older Adults With Diabetes Mellitus for Prevention of Dementia in Japan: Study Protocol for a Multi-Center, Randomized, 18-Month Controlled Trial

Background: The Japan-Multi-domain Intervention Trial for Prevention of Dementia in Older Adults with Diabetes (J-MIND-Diabetes) is an 18-month, multi-centered, open-labeled, randomized controlled trial designed to identify whether multi-domain intervention targeting modifiable risk factors for dementia could prevent the progression of cognitive decline among older adults with type 2 diabetes mellitus (T2DM). This manuscript describes the study protocol for the J-MIND-Diabetes trial. Materials and Methods: Subjects of this trial will comprise a total of 300 T2DM outpatients aged 70-85 years with mild cognitive impairment. Subjects will be centrally randomized into intervention and control groups at a 1:1 allocation ratio using the stratified permuted-block randomization methods. The intervention group will participate in multi-domain intervention programs aimed at: (1) management of metabolic and vascular risk factors; (2) physical exercise and self-monitoring of physical activity; (3) nutritional guidance; and (4) social participation. The control group will receive usual T2DM care and general instructions on dementia prevention. The primary and secondary outcomes will be assessed at baseline, at 6- and 18-month follow-up. The primary outcome is change from baseline at 18 months in a global composite score combining several neuropsychological domains, including global cognitive function, memory, attention, executive function, processing speed and language. Secondary outcomes include: (1) cognitive changes in neuropsychological tests; (2) changes in geriatrics assessments; (3) metabolic control and diabetic complications; (4) changes in blood and urinary markers. Discussion: This trial will be the first trial to demonstrate the effectiveness of multi-domain intervention in preventing cognitive decline in older adults with T2DM at increased risk of dementia in Japan. Trial Registration: UMIN000035911; Registered on the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) 18 February 2019. (https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000040908).

Acarbose-metformin is more effective in glycemic variability control than repaglinide-metformin in T2DM patients inadequately controlled with metformin: a retrospective cohort study

Background

Acarbose and repaglinide are widely used either by themselves or in combination with other medications. However, their efficacy in diabetes control has not been compared when used in combination with metformin.

Methods

The present study aimed to compare their effects on glycemic variability (GV) control when taken with metformin for type 2 diabetes mellitus (T2DM) inadequately controlled with metformin alone. In this retrospective cohort study, T2DM patients who were treated with either acarbose-metformin or repaglinide-metformin combination were recruited. Either acarbose 100 mg or repaglinide 2 mg triple daily was taken for the subsequent 12 weeks in combination with metformin. Demographic data, biochemical data and 7-point glycemic self-monitoring conducted with capillary blood (SMBG) data were reviewed after one week and 12 weeks. The primary outcome including glucose control and changes in GV as well as other factors affecting GV and the incidence of hypoglycemia were also analyzed.

Results

Of the 305 T2DM patients enrolled, data from 273 subjects, 136 in the acarbose-metformin group (M+A) and 137 in the repaglinide-metformin group (M+R) were analyzed. Both regimens improved glycemic control at 12 weeks post commencement of new medications. GV, expressed as the mean amplitude of plasma glycemic excursions (MAGE, 5.0 ± 2.6 vs. 2.8 ± 1.6 mmol/L, p < 0.001 in M+A; 5.1 ± 2.5 vs. 2.9 ± 1.3 mmol/L, p < 0.001 in M+R), standard deviation of blood glucose (SDBG, 3.6 ± 1.3 vs. 2.0 ± 0.9 mmol/L, p < 0.001 in M+A; 3.7 ± 1.3 vs. 2.4 ± 1.3 p < 0.001 in M+R), coefficient of variation of blood glucose (CVBG, (0.30 ± 0.09 vs. 0.21 ± 0.1, p < 0.001 in M+A; 0.31 ± 0.09 vs. 0.24 ± 0.12, p < 0.001 in M+R), postprandial amplitude of glycemic excursions (PPGE, 5.2 ± 2.6 vs. 2.8 ± 1.6 mmol/L, p < 0.001 in M+A; 5.3 ± 2.5 vs. 2.9 ± 1.3 mmol/L, p < 0.001 in M+R) or largest amplitude of glycemic excursions (LAGE, 9.8 ± 3.6 vs. 5.4 ± 2.4 mmol/L, p < 0.001 in M+A; 10.1 ± 3.4 vs. 6.3 ± 3.2 mmol/L, p < 0.001 in M+R) decreased significantly after the addition of acarbose or repaglinide (p < 0.05 respectively). Compared with repaglinide-metformin, acarbose-metformin was more effective in GV control at 12 weeks post commencement of new medications (p < 0.05). This study indicates that both acarbose-metformin and repaglinide-metformin combinations could effectively reduce GV and the acarbose-metformin combination seems to be more effective than the repaglinide-metformin combination. However, this conclusion should be confirmed by future large-scaled and more comprehensive studies due to the limitations of the present study.

The relationship between HbA1c and hypoglycaemia in patients with diabetes treated with insulin degludec versus insulin glargine 100 units/mL

AIM

Treat-to-target, randomized controlled trials have confirmed lower rates of hypoglycaemia at equivalent glycaemic control with insulin degludec (degludec) versus insulin glargine 100 units/mL (glargine U100) in patients with type 1 (T1D) or type 2 diabetes (T2D). Treat-to-target trials are designed to enable comparisons of safety and tolerability at a similar HbA1c level. In this post hoc analysis of the SWITCH 1 and 2 trials, we utilised a patient-level modelling approach to compare how glycaemic control might differ between basal insulins at a similar rate of hypoglycaemia.

MATERIALS AND METHODS

Data for HbA1c and symptomatic hypoglycaemia from the SWITCH 1 and SWITCH 2 trials were analyzed separately for patients with type 1 diabetes and type 2 diabetes, respectively. The association between the individual patient-level risk of hypoglycaemia and HbA1c was investigated using a Poisson regression model and used to estimate potential differences in glycaemic control with degludec versus glargine U100, at the same rate of hypoglycaemia.

RESULTS

Improvements in glycaemic control increased the incidence of hypoglycaemia with both basal insulins across diabetes types. Our analysis suggests that patients could achieve a mean HbA1c reduction of 0.70 [0.05; 2.20]_{95% CI} (for type 1 diabetes) or 0.96 [0.39; 1.99]_{95% CI} (for type 2 diabetes) percentage points (8 [1; 24]_{95% CI} or 10 [4; 22]_{95% CI} mmol/mol, respectively) further with degludec than with glargine U100 before incurring an equivalent risk of hypoglycaemia.

CONCLUSION

Our findings suggest that patients in clinical practice may be able to achieve lower glycaemia targets with degludec versus glargine U100, before incurring an equivalent risk of hypoglycaemia.

Analysis of the effect of liraglutide on glycemic variability in patients with type 2 diabetes

The efficacy of liraglutide in the treatment of glycemic variability in type 2 diabetic patients remains to be fully elucidated. Some studies evaluated the efficacy and safety of liraglutide in glycemic variability, and this meta-analysis was performed to evaluate the accuracy of the results of existing studies on the efficacy of liraglutide. We conducted a comprehensive search for all relevant studies published in PubMed, EMBASE, Cochrane Library, and China Academic Journal Full-Text Database from the beginning of 2011 to October 31, 2019. The mean ± SD and 95% confidence interval were used for evaluation, and subgroup and sensitivity analysis were carried out. Publication bias was estimated by funnel plots and Egger's tests. A total of 16 studies were included in the meta-analysis involving 492 participants. MAGE (mean amplitude of glycemic excursion), LAGE (largest amplitude of glycemic excursions), SD (standard deviation of blood glucose), and MODD (mean of daily differences) were collected to reflect the variability of blood glucose. The glycemic variability indexes of patients before and after treatment with liraglutide were compared. Patients with treatment had lower glycemic variability compared with patients receiving treatment of liraglutide. Compared with the patients before the treatment, the patients after the treatment had a smaller glycemic variability (MAGE: I² = 92%, p < 0.01, Z = 11.91, p < 0.01, MD = -2.78, 95%CI: -3.24 - -2.32; LAGE: I² = 76%, p = 0.08, Z = 9.94, p < 0.01, MD = -2.20, 95%CI: -2.59 - -1.81; MODD: I² = 74%, p = 0.002, Z = 14.03, p < 0.01, MD = -0.90, 95%CI: -1.02 - -0.77; SD: I² = 93%, p < 0.01, Z = 3.62, p < 0.01, SMD = -1.77, 95%CI: -2.73 - -0.81). Sensitivity analysis showed that our results were reliable and no evidence of significant publication bias was detected. The results of this study suggest that patients with type 2 diabetes treated with liraglutide are associated with lower glycemic variability.

Circulating Wnt1-inducible signaling pathway protein-1 (WISP-1/CCN4) is a novel biomarker of adiposity in subjects with type 2 diabetes

BACKGROUND

Wnt1-inducible signaling pathway protein 1, or cellular communication network factor 4 (CCN4), a member of CCN family of secreted, extracellular matrix associated signaling proteins, recently was validated as a novel adipose tissue derived cytokine.

OBJECTIVE

To assess the relationships between circulating CCN4, adipose tissue distribution and function, and chronic low-grade inflammation in subjects with type 2 diabetes.

METHODS

We observed 156 patients with type 2 diabetes and 24 healthy controls. Serum levels of CCN4, hsCRP and alpha1-acid glycoprotein (alpha1-AGP) were measured by ELISA. Serum concentrations of leptin, resistin, visfatin, adipsin, adiponectin, IL-6, IL-8, IL-18 and TNF-alpha were determined by multiplex analysis. Fat mass and distribution was assessed by DEXA. Mean diameter of adipocytes was estimated in samples of subcutaneous adipose tissue.

RESULTS

Patients with diabetes had higher levels of circulating CCN4, leptin, resistin, adipsin, visfatin, hsCRP, alpha1-AGP, and IL-6 (all p < 0.02). The CCN4 concentration correlated positively with percentage of fat mass in central abdominal area, as well as with leptin, resistin and visfatin levels; negative correlation was found between CCN4 and mean adipocyte diameter. In multiple regression analysis fat mass in central abdominal area was independent predictor for CCN4 concentration.

CONCLUSION

In subjects with type 2 diabetes serum levels of CCN4 are associated with central abdominal fat mass and adipose tissue dysfunction.

Effectiveness of Insulin Degludec in Thai Patients with Diabetes Mellitus: Real-World Evidence From a Specialized Diabetes Center

Background Insulin degludec, an ultra-long-acting insulin analogue, has been available in Thailand since October 2016. Although clinical trial results revealed less hypoglycemia, data from real-world settings is limited especially in Asian patients. This study aimed to evaluate prospectively the real-world effectiveness, safety, quality of life (QOL) and patient satisfaction with insulin degludec among Thai patients with diabetes mellitus (DM).

Methods From October 2016 to September 2017, all patients who had started insulin degludec for at least 3 months were observed and evaluated at baseline, 3, 6, and 12 months. QOL was assessed using WHOQOL-BREF-THAI and level of satisfaction was measured by 7-point Likert scale. Glycemic fluctuation from paired iPro2 continuous glucose monitoring (CGM) obtained 4–6 weeks apart were also evaluated from a subset of patients with T1DM who switched from insulin glargine to insulin degludec.

Results A total of 55 patients (T2DM 76.4%, females 54.5%, mean age 57.1±16.1 years, duration of diabetes 16.7±8.8 years, BMI 27.3±5.5 kg/m ² , baseline A1C 9.3±2.3%, median duration of treatment 8 months) were included in the study. In T1DM patients (n=13), the overall mean A1C reduction at 12 months was 0.5% with minimal weight gain of 0.9 kgs at 12 months. In T2DM patients (n=42), the overall mean A1C reduction at 12 months was 0.8% with minimal weight loss of 0.4 kgs at 12 months. The proportion of T1DM patients who could achieve optimal glycemic control increased slightly from 14.3 to 18.2% but the proportion of T2DM patients who could achieve optimal glycemic control increased from 30.8 to 53.8%. Patient satisfaction showed a sustained improvement throughout the duration of study. In four T1DM patients who had paired CGM data, insulin degludec provided greater reductions in glycemic variability endpoints with increased time-in-range when compared with previous insulin glargine.

Discussion Our data suggested that the effectiveness of insulin degludec was consistent with the results seen in clinical trials with lower risk of patients-reported hypoglycemia, and a significant improvement in glycemic control. Patients also reported higher treatment satisfaction. More long-term and cost-effectiveness data are needed to establish the role of this ultra-long-acting insulin in real-world settings.

Diabetes and Ramadan: Utility of flash-glucose monitoring derived markers of glycaemic control and comparison with glycosylated haemoglobin

AIMS

Flash glucose monitoring (FGM)-derived markers of glucose control and variability and laboratory measured HbA1c among patients with diabetes on insulin in context of Ramadan fasting (RF) were examined and compared.

METHODS

FGM data on insulin-treated patients (n = 20, age 42.3 ± 11.4 years; 18 male, 2 female; 13 with type 1 and 7 with type 2 diabetes) who fasted during Ramadan were used to calculate Q-score as an indicator of glycaemia before, during and after RF. Post-hoc analysis in a group of patients (n = 12) who had HbA1c available and appropriate for these periods was performed. Other relevant data were extracted from patient records.

RESULTS

Mean glucose (9.6 ± 1.32 v 10.78 ± 1.64 mmol/l; P < 0.0001) and Q-score increased significantly with Ramadan fasting and reduced after Ramadan. Post-hoc subgroup analysis showed a significant rise in eA1c (7.2 ± 0.4%; 55.0 ± 4.4 mmol/mol v 7.7 ± 0.5%; 61.0 ± 5.5 mmol/mol) but not in laboratory HbA1c with Ramadan fasting; eA1c reduced after Ramadan (P = 0.018).

CONCLUSIONS

Ramadan fasting was associated with a deterioration in overall glucose control and time in hyperglycaemia in insulin-treated patients. FGM-derived markers are useful and a preferable alternative to HbA1c in Ramadan studies.

Insulin glargine/lixisenatide fixed-ratio combination improves glycaemic variability and control without increasing hypoglycaemia

Maintaining optimal glycaemic control reduces the risk of micro- and macrovascular complications in patients with type 2 diabetes. Typically, glycaemic control is based on glycated haemoglobin (HbA1c) as a measure of mean glucose concentration; however, this marker does not accurately reflect glycaemic variability (GV), which is characterized by the amplitude, frequency and duration of hypo- and hyperglycaemic fluctuations. In the present study, we analysed data from the LixiLan-O trial, which compared iGlarLixi, a titratable fixed-ratio combination of the glucagon-like peptide-1 receptor agonist lixisenatide (Lixi) and long-acting basal insulin glargine 100 units/mL (iGlar), with its individual components, and the LixiLan-L trial, which compared iGlarLixi with iGlar. The GV features that were measured were mean and SD of self-measured plasma glucose (SMPG), high blood glucose index (HBGI) and low blood glucose index, area under the SMPG curve for each patient (AUCn), mean absolute glucose (MAG) and mean amplitude of glycaemic excursions (MAGE). By week 30, iGlarLixi improved all GV markers from baseline, with no increased hypoglycaemia risk. Significant improvements were observed in SMPG, SD of SMPG, HBGI, AUCn, MAG and MAGE compared with iGlar, and in SMPG, HBGI and AUCn, compared with Lixi.

Glucose variability indices predict the episodes of nocturnal hypoglycemia in elderly type 2 diabetic patients treated with insulin

AIM

O determine the applicability of glucose variability (GV) indices derived from continuous glucose monitoring (CGM) data for prediction of nocturnal hypoglycemia (NH) in elderly patients with type 2 diabetes treated with insulin.

METHODS

We observed 83 insulin-treated in-patients, 65-80 years of age. Blinded CGM data for 176 nights were analyzed. Daytime (06:00-22:59) mean glucose, Standard Deviation (SD), 2-h Continuous Overlapping Net Glycemic Action (CONGA2) and Mean Absolute Glucose (MAG), pre-midnight (23:00-23:59) mean glucose, SD and MAG, 24-h Mean Amplitude of Glucose Excursions (MAGE), were estimated. Pre-midnight glucose trends were estimated as the absolute difference between glucose values at 23:00 and 23:59 (deltaG). Episode of interstitial glucose ≤70mg/dL observed from 0:00 to 5:59 was considered as NH.

RESULTS

NH was present in 68 out of 176 24-h recordings (39%). Lower daytime mean glucose and CONGA2, and higher MAG values were found in patients with NH as compared to those without (p=0.0002, p=0.0001 and p=0.02, respectively). Pre-midnight mean glucose was lower, while pre-midnight deltaG was higher in patients with NH (p<0.0001 and p=0.02). Antecedent daytime hypoglycemia increased the risk of NH (p<0.0001). In logistic regression analysis, the combination of daytime MAG and pre-midnight mean glucose was the most reliable predictor of subsequent NH (accuracy 75.6%, p=0.0004).

CONCLUSION

The analysis of CGM-derived GV parameters could improve prediction of NH in elderly patients with type 2 diabetes treated with insulin.

HbA1c, systolic blood pressure variability and diabetic retinopathy in Asian type 2 diabetics

BACKGROUND

The aim of the present study was to examine the association between variability in HbA1c or systolic blood pressure (SBP) and diabetes-specific moderate retinopathy in Asians with type 2 diabetes (T2D).

METHODS

A retrospective study was conducted of 172 cases of moderate diabetic retinopathy (DR) cases and 226 controls without DR, matched for age, sex, and ethnicity. Serial HbA1c and SBP (range 3-6 readings) over the 2 years prior to photographic screening of DR were collected. Intrapersonal mean and SD values for HbA1c (iM-HbA1c and iSD-HbA1c) and SBP (iM-SBP and iSD-SBP) were derived. Moderate DR was assessed from digital retinal photographs and defined as levels >43 using the Early Treatment Diabetic Retinopathy Study scale.

RESULTS

Cases of moderate DR had higher iM-HbA1c (8.2 % vs 7.3 %; P = 0.001), iSD-HbA1c (1.22 vs 0.64; P = 0.001), iM-SBP (136.8 vs 129.6 mmHg; P = 0.001) and iSD-SBP (13.3 vs 11.1; P = 0.002) than controls. In the multivariate regression model adjusted for age, gender, ethnicity, duration of diabetes, SBP, and HbA1c, iM-HbA1c and iM-SBP were significantly associated with moderate DR (odds ratio [OR] 1.80, 95 % confidence interval [CI] 1.37-2.36; and OR 1.03, 95 % CI 1.01-1.05, respectively). Neither iSD-HbA1c nor iSD-SBP were associated with moderate DR. When stratified by HbA1c <7 %, only iSD-SBP remained significantly associated with moderate DR (OR 1.11, 95 % CI 1.01-1.21).

CONCLUSION

In a cohort of Asian patients with T2D, both higher mean HbA1c levels and SBP, but not their variability, were associated with moderate DR. Among those with good glycemic control, wider variability of SBP is associated with moderate DR.

Not Only Glycaemic But Also Other Metabolic Factors Affect T Regulatory Cell Counts and Proinflammatory Cytokine Levels in Women with Type 1 Diabetes

Type 1 diabetic (T1D) patients suffer from insulinopenia and hyperglycaemia. Studies have shown that if a patient's hyperglycaemic environment is not compensated, it leads to complex immune dysfunctions. Similarly, T1D mothers with poor glycaemic control exert a negative impact on the immune responses of their newborns. However, questions concerning the impact of other metabolic disturbances on the immune system of T1D mothers (and their newborns) have been raised. To address these questions, we examined 28 T1D women in reproductive age for the relationship between various metabolic, clinical, and immune parameters. Our study revealed several unexpected correlations which are indicative of a much more complex relationship between glucose and lipid factors (namely, glycosylated haemoglobin Hb1Ac, the presence of one but not multiple chronic diabetic complications, and atherogenic indexes) and proinflammatory cytokines (IL-1alpha and TNF-alpha). Regulatory T cell counts correlated with HbA1c, diabetic neuropathy, lipid spectra parameters, and IL-6 levels. Total T-helper cell count was interconnected with BMI and glycaemia variability correlated with lipid spectra parameters, insulin dose, and vitamin D levels. These and other correlations revealed in this study provide broader insight into the association of various metabolic abnormalities with immune parameters that may impact T1D mothers or their developing child.

Continuous glucose monitoring in patients with type 2 diabetes treated with glucagon-like peptide-1 receptor agonist dulaglutide in combination with prandial insulin lispro: an AWARD-4 substudy

AIM

To conduct a substudy, using 24-hour continuous glucose monitoring (CGM), of the AWARD-4 trial, which was designed to compare insulin + glucagon-like peptide-1 receptor agonist treatment with an insulin-only regimen.

METHODS

The AWARD-4 trial randomized 884 conventional insulin regimen-treated patients to dulaglutide 1.5 mg, dulaglutide 0.75 mg and glargine, all in combination with prandial insulin lispro. The CGM substudy included 144 patients inserted with a Medtronic CGMS iPro CGM device to enable 3-day glucose monitoring. CGM sessions were completed at weeks 0, 13, 26 and 52. CGM measures included mean 24-hour glucose, percentage time in target glucose ranges, hyper- and hypoglycaemia and glucose variability. The primary objective was treatment comparison for percentage time spent with CGM glucose values in the 3.9-7.8 mmol/L range after 26 weeks.

RESULTS

At week 26, mean CGM values decreased in all treatment groups (change from baseline -2.8 ± 0.3, -2.4 ± 0.3 and -2.5 ± 0.3 mmol/L for dulaglutide 1.5 mg, dulaglutide 0.75 mg and glargine, respectively); between-group differences were not statistically significant. Treatment groups were similar for percentage time in the 3.9-7.8 mmol/L range. Percentage time in the 3.9-10.0 mmol/L range was greater for dulaglutide 1.5 mg than for glargine (p < 0.05). Dulaglutide and glargine were associated with decreased glucose variability for all CGM variability indices. The overall within-patient standard deviation (s.d.) was significantly reduced with dulaglutide 1.5 mg versus glargine (p < 0.05). At week 52, there were no significant differences among the groups with regard to measures of normoglycaemia or near-normoglycaemia and for the overall within-patient s.d. Treatment with glargine was associated with greater increases in percentage time spent with glucose values ≤3.9 mmol/L, with statistically significant differences between the groups at 52 weeks (p < 0.05).

CONCLUSIONS

In combination with prandial lispro, treatment with dulaglutide and glargine resulted in similar proportions of glucose values in the normoglycaemic range, but dulaglutide provided an improved balance between the proportion of values within the near-normoglycaemia range and values within the hypoglycaemic range.

Taking a Closer Look--Continuous Glucose Monitoring in Non-Critically Ill Hospitalized Patients with Type 2 Diabetes Mellitus Under Basal-Bolus Insulin Therapy

BACKGROUND

Inpatient glucose management is based on four daily capillary blood glucose (BG) measurements. The aim was to test the capability of continuous glucose monitoring (CGM) for assessing the clinical impact and safety of basal-bolus insulin therapy in non-critically ill hospitalized patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

Eighty-four patients with T2DM (age, 68±10 years; glycosylated hemoglobin, 72±28 mmol/mol; body mass index, 31±7 kg/m(2)) were treated with basal-bolus insulin. CGM was performed with the iPro(®)2 system (Medtronic MiniMed, Northridge, CA) and calibrated retrospectively.

RESULTS

A remarkable consistency between CGM and BG measurements and therapy improvement was shown over the study period of 501 patient-days. The number of CGM and BG measurements (CGM/BG) in the range from 3.9-10 mmol/L increased from 67.7%/67.2% (on Day 1) to 77.5%/78.6% (on the last day) (P<0.04). The number of low glycemic episodes (3.3 to <3.9 mmol/L) during nighttime detected by CGM was 15-fold higher, and the number of episodes >13.9 mmol/L detected by CGM during night was 12.5-fold higher than the values from the BG measurements. Ninety-nine percent of data points were in the clinically accurate or acceptable Clarke Error Grid Zones A+B, and the relative numbers of correctly identified episodes of <3.9 and >13.9 mmol/L detected by CGM (sensitivity) were 47.3% and 81.5%, respectively.

CONCLUSIONS

Our data exhibit a good agreement between overall CGM and BG measurements, but there were a high number of missed hypo- and hyperglycemic episodes with BG measurements, particularly during nighttime. Overall assessment of glycemic control using CGM is feasible, whereas the use of CGM for individualized therapy decisions needs further improvement.

Glycemic Variability: How Do We Measure It and Why Is It Important?

Chronic hyperglycemia is the primary risk factor for the development of complications in diabetes mellitus (DM); however, it is believed that frequent or large glucose fluctuations may independently contribute to diabetes-related complications. Postprandial spikes in blood glucose, as well as hypoglycemic events, are blamed for increased cardiovascular events in DM. Glycemic variability (GV) includes both of these events; hence, minimizing GV can prevent future cardiovascular events. Correcting GV emerges as a target to be pursued in clinical practice to safely reduce the mean blood glucose and to determine its direct effects on vascular complications in diabetes. Modern diabetes management modalities, including glucagon-related peptide-1-based therapy, newer insulins, modern insulin pumps and bariatric surgery, significantly reduce GV. However, defining GV remains a challenge primarily due to the difficulty of measuring it and the lack of consensus regarding the optimal approach for its management. The purpose of this manuscript was not only to review the most recent evidence on GV but also to help readers better understand the available measurement options and how the various definitions relate differently to the development of diabetic complications.

Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies

BACKGROUND

Continuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic 'weak points'. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored recommendations has not yet been developed. We aimed to develop a new patient-tailored approach for the routine clinical evaluation of CGM profiles. We developed a metric allowing screening for profiles that require therapeutic action and a method to identify the individual CGM parameters with improvement potential.

METHODS

Fifteen parameters frequently used to assess CGM profiles were calculated for 1,562 historic CGM profiles from subjects with type 1 or type 2 diabetes. Factor analysis and varimax rotation was performed to identify factors that accounted for the quality of the profiles.

RESULTS

We identified five primary factors that determined CGM profiles (central tendency, hyperglycaemia, hypoglycaemia, intra- and inter-daily variations). One parameter from each factor was selected for constructing the formula for the screening metric, (the 'Q-Score'). To derive Q-Score classifications, three diabetes specialists independently categorised 766 CGM profiles into groups of 'very good', 'good', 'satisfactory', 'fair', and 'poor' metabolic control. The Q-Score was then calculated for all profiles, and limits were defined based on the categorised groups (<4.0, very good; 4.0-5.9, good; 6.0-8.4, satisfactory; 8.5-11.9, fair; and ≥12.0, poor). Q-Scores increased significantly (P <0.01) with increasing antihyperglycaemic therapy complexity. Accordingly, the percentage of fair and poor profiles was higher in insulin-treated compared with diet-treated subjects (58.4% vs. 9.3%). In total, 90% of profiles categorised as fair or poor had at least three parameters that could potentially be optimised. The improvement potential of those parameters can be categorised as 'low', 'moderate' and 'high'.

CONCLUSIONS

The Q-Score is a new metric suitable to screen for CGM profiles that require therapeutic action. Moreover, because single components of the Q-Score formula respond to individual weak points in glycaemic control, parameters with improvement potential can be identified and used as targets for optimising patient-tailored therapies.