Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data

Continuous glucose monitoring‑derived time in range and CV are associated with elevated risk of adverse kidney outcomes for patients with type 2 diabetes

Current guidelines recommend assessing glycemic control using continuous glucose monitoring (CGM), which provides a comprehensive glycemic profile to supplement HbA1c measurement. However, the association between CGM-derived metrics and risk of adverse kidney outcomes is not entirely clear. This retrospective cohort study included 1274 patients with type 2 diabetes hospitalized from July 2020 to December 2022, with a median follow-up time of 923 days. Monitor using CGM at baseline and evaluate renal function indicators of participants at baseline and end of follow-up. Multiple CGM-derived metrics, particularly time in range (TIR) and glucose coefficient of variation (CV), were calculated from 3-day glucose profiles obtained from CGM. Relevant clinical data was collected from clinical records and/or patient interviews. The primary outcome was chronic-kidney-disease (CKD) progression. Secondary outcomes included worsening of albuminuria and, all-cause mortality and major-adverse-cardiac-events(MACE). Multivariate regression models were employed to analyze the association between CGM-derived indices, particularly TIR and CV, and the risk of adverse kidney outcomes. We demonstrated that the lower TIR categories had a remarkably increased risk of CKD progression, with a HR per 10 % increment of 0.90 (95 %CI:0.83-0.91). Conversely, higher CV was positively related to the subsequent risk of CKD progression, with an HR per 10 % increment of 1.30 (95 %CI:1.07-1.59). These results were consistent across various subgroups and sensitivity analyses. This study found that TIR and CV are significantly associated with CKD progression, proteinuria deterioration, all-cause mortality, and the risk of MACE. These findings have elasticity in adjusting for multiple covariates and have been confirmed in different subgroups and sensitivity analyses.

Are standardized conditions needed for correct CGM data interpretation in subjects at early stages of glucose intolerance?

AIM

The present study comparatively evaluated glucose variability (GV) parameters derived from both continuous glucose monitoring (CGM) performed under standard conditions for a 24-h period and under usual everyday conditions for a 14-day period in a high-risk population without diabetes.

METHODS AND RESULTS

Seventy five subjects: 14 with normal glucose tolerance (NGT; mean age 43.6 ± 10.7 years; BMI 30.5 ± 6.9 kg/m2), 19 with high 1-h postload glucose > 8.6 mmol/l (1hrOGTT; mean age 45.6 ± 8.9 years; BMI 33.7 ± 6.9 kg/m2), and 42 with isolated impaired glucose tolerance (iIGT; mean age 47.6 ± 11.8 years; BMI 31.0 ± 6.5 kg/m2), were enrolled. An OGTT was performed. CGM was performed with blinded FreeStyleLibrePro for 24 h under standard conditions and for the rest of the 14-day period under usual everyday conditions. GV parameters derived from both periods were compared. There was a significant increase in GV with worsening of glucose tolerance from NGT, to 1hrOGTT and iIGT, independently of the conditions. Our findings showed moderate to strong correlations among GV indices between the studied periods in the cohort and in the 1hrOGTT and iIGT groups. However, a significant difference was found in some of the GV parameters between the analyzed periods.

CONCLUSION

The trend in GV is independent of the conditions, under which CGM is performed, in subjects at early stages of glucose intolerance. Although its measurements to some extend differ in standard and everyday conditions, there is no need of standardized conditions for correct interpretation of GV indices in this population.

Soluble receptors for advanced glycation endproducts are predictors of insulin sensitivity and affected by weight loss

BACKGROUND

Mice experiments have underscored the efficacy of pharmacological inhibition of advanced glycation endproducts (AGEs) through the use of soluble receptors for advanced glycation endproducts (sRAGE) in mitigating obesity-linked metabolic disruptions and insulin resistance. However, human studies have presented conflicting findings regarding the correlation between circulating sRAGE levels and insulin resistance, as well as glucose tolerance. Here, we aimed to delve deeper into the relationship between sRAGE levels and systemic insulin sensitivity.

METHODS

Plasma sRAGE levels, hyperinsulinemic-euglycemic clamp, and continuous glucose monitoring were measured in two independent cross-sectional case-control studies [cohort 1 (n = 180) and cohort 2 (n = 124)]. In addition, a subgroup of 42 participants with obesity were followed for 12 months. In 14 of these participants, weight loss was achieved through bariatric surgery intervention.

RESULTS

Our results revealed a significant association between plasma sRAGE levels and both insulin sensitivity and glycemic control parameters, even after adjustments for age, sex, and BMI. Furthermore, longitudinal analysis demonstrated that interventions aimed at weight loss led to reductions in fasting glucose and HbA1c levels, concurrently with increases in sRAGE levels.

CONCLUSIONS

These findings underscore that sRAGE levels were strongly associated with insulin sensitivity and glycemic control, suggesting a possible role of sRAGE in preserving insulin sensitivity and maintaining glycemic control, which should be confirmed in further studies.

Consequences of Data Loss on Clinical Decision-Making in Continuous Glucose Monitoring: Retrospective Cohort Study

BACKGROUND

The impact of missing data on individual continuous glucose monitoring (CGM) data is unknown but can influence clinical decision-making for patients.

OBJECTIVE

We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making.

METHODS

The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated.

RESULTS

A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss.

CONCLUSIONS

With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293.

Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients - a pilot study

BACKGROUND

Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose management and long-term metabolic target setting for home care and outpatient treatment follow established procedures, the approach for short-term targets during hospitalization varies due to a lack of clinical consensus. Our study aims to elucidate the impact of pre-hospitalization and intra-hospitalization glycemic indexes on in-hospital survival rates in individuals with T2DM, addressing this notable gap in the current literature.

METHODS

In this pilot study involving 120 hospitalized diabetic patients, we used advanced machine learning and classical statistical methods to identify variables for predicting hospitalization outcomes. We first developed a 30-day mortality risk classifier leveraging AdaBoost-FAS, a state-of-the-art ensemble machine learning method for tabular data. We then analyzed the feature relevance to identify the key predictive variables among the glycemic and routine clinical variables the model bases its predictions on. Next, we conducted detailed statistical analyses to shed light on the relationship between such variables and mortality risk. Finally, based on such analyses, we introduced a novel index, the ratio of intra-hospital glycemic variability to pre-hospitalization glycemic mean, to better characterize and stratify the diabetic population.

RESULTS

Our findings underscore the importance of personalized approaches to glycemic management during hospitalization. The introduced index, alongside advanced predictive modeling, provides valuable insights for optimizing patient care. In particular, together with in-hospital glycemic variability, it is able to discriminate between patients with higher and lower mortality rates, highlighting the importance of tightly controlling not only pre-hospital but also in-hospital glycemic levels.

CONCLUSIONS

Despite the pilot nature and modest sample size, this study marks the beginning of exploration into personalized glycemic control for hospitalized patients with T2DM. Pre-hospital blood glucose levels and related variables derived from it can serve as biomarkers for all-cause mortality during hospitalization.

Short-term glycemic variability in non-diabetic, non-obese dogs assessed by common glycemic variability indices

Glycemic variability (GV) refers to swings in blood glucose levels and is an emerging measure of glycemic control in clinical practice. It is associated with micro- and macrovascular complications and poor clinical outcomes in diabetic humans. Although an integral part of patient assessment in human patients, it is to a large extent neglected in insulin-treated diabetic dogs. This prospective pilot study was performed to describe canine within-day GV in non-diabetic dogs with the aim to provide a basis for the interpretation of daily glucose profiles, and to promote GV as an accessible tool for future studies in veterinary medicine. Interstitial glucose concentrations of ten non-diabetic, non-obese beagles were continuously measured over a 48-h period using a flash glucose monitoring system. GV was assessed using the common indices MAGE (mean amplitude of glycemic excursion), GVP (Glycemic variability percentage) and CV (coefficient of variation). A total of 2260 sensor measurements were obtained, ranging from 3.7 mmol/L (67 mg/dL) to 8.5 mmol/L (153 mg/dL). Glucose profiles suggested a meal-dependent circadian rhythmicity with small but significant surges during the feeding periods. No differences in GV indices were observed between day and night periods (p > 0.05). The MAGE (mmol/L), GVP (%) and CV (%) were 0.86 (± 0.19), 7.37 (± 1.65), 6.72 (± 0.89) on day one, and 0.83 (± 0.18), 6.95 (± 1.52), 6.72 (± 1.53) on day two, respectively. The results of this study suggest that GV is low in non-diabetic dogs and that glucose concentrations are kept within narrow ranges.

Exploring the risk of glycemic variability in non-diabetic depressive individuals: a cross-sectional GlyDep pilot study

Background

Data on the correlation between glycemic variability and depression in nondiabetic patients remain limited. Considering the link between increased glycemic variability and cardiovascular risks, this relationship could be significant in depressed patients.

Methods

In this single-center pilot study, we utilized Flash Glucose Monitoring (Abbott Libre Pro) to study glycemic variability. The CES-D (Center for Epidemiological Studies- Depression) scale was employed to measure depression levels. Based on CES-D scores, patients were classified into two groups: those with scores ≥ 33 and those with scores < 33. We analyzed various glycemic variability indices, including HBGI, CONGA, ADDR, MAGE, MAG, LI, and J-Index, employing the EasyGV version 9.0 software. SPSS (version 28) facilitated the data analysis.

Results

We screened patients with depression visiting the department of psychiatry, FGM was inserted in eligible patients of both the groups which yielded a data of 196 patient-days (98 patient-days for CES-D ≥ 33 and 98 patient-days for CES-D < 33). The glycemic variability indices CONGA (mg/dl), (76.48 ± 11.9 vs. 65.08 ± 7.12) (p = 0.048), MAGE (mg/dl) (262.50 ± 25.65 vs. 227.54 ± 17.72) (p = 0.012), MODD (mg/dl) (18.59 ± 2.77 vs. 13.14 ± 2.39) (p = 0.002), MAG(mg/dl) (92.07 ± 6.24vs. 63.86 ± 9.38) (p = <0.001) were found to be significantly higher in the CES-D ≥ 33 group.

Conclusion

Patients with more severe depressive symptoms, as suggested by CES-D ≥ 33, had higher glycemic variability.

The Relationship Between Dietary Patterns and Glycemic Variability in People with Impaired Glucose Tolerance

BACKGROUND

Diurnal glucose fluctuations are increased in prediabetes and might be affected by specific dietary patterns.

OBJECTIVES

The present study assessed the relationship between glycemic variability (GV) and dietary regimen in people with normal glucose tolerance (NGT) and impaired glucose tolerance (IGT).

METHODS

Forty-one NGT (mean age: 45.0 ± 9.0 y, mean BMI: 32.0 ± 7.0 kg/m2) and 53 IGT (mean age: 48.4 ± 11.2 y, mean BMI: 31.3 ± 5.9 kg/m2) subjects were enrolled in this cross-sectional study. The FreeStyleLibre Pro sensor was used for 14 d, and several parameters of GV were calculated. The participants were provided with a diet diary to record all meals. ANOVA analysis, Pearson correlation, and stepwise forward regression were performed.

RESULTS

Despite no difference in diet patterns between the 2 groups, GV parameters were higher in IGT than in NGT. GV worsened with an increase in overall daily carbohydrate and refined grain consumption and improved with the increase in whole grain intake in IGT. GV parameters were positively related [r = 0.14-0.53; all P < 0.02 for SD, continuous overall net glycemic action 1 (CONGA1), J-index, lability index (LI), glycemic risk assessment diabetes equation, M-value, and mean absolute glucose (MAG)], and low blood glucose index (LBGI) inversely (r = -0.37, P = 0.006) related to the total percentage of carbohydrate, but not to the distribution of carbohydrate between the main meals in the IGT group. A negative relationship existed between total protein consumption and GV indices (r = -0.27 to -0.52; P < 0.05 for SD, CONGA1, J-index, LI, M-value, and MAG). The total EI was related to GV parameters (r = 0.27-0.32; P < 0.05 for CONGA1, J-index, LI, and M-value; and r = -0.30, P = 0.028 for LBGI).

CONCLUSIONS

The primary outcome results showed that insulin sensitivity, calories, and carbohydrate content are predictors of GV in individuals with IGT. Overall, the secondary analyses suggested that carbohydrate and daily consumption of refined grains might be associated with higher GV, whereas whole grains and daily protein intake were related to lower GV in people with IGT.

A New Analysis Tool for Continuous Glucose Monitor Data

BACKGROUND

With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools.

METHODS

In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual's CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies.

RESULTS

In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability.

CONCLUSIONS

We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

Insulin secretion and action affect glucose variability in the early stages of glucose intolerance

AIMS

Since it is unknown whether glucose variability (GV) is increased and whether this is related to worsening of insulin secretion and action in prediabetes, we have assessed insulin secretion and sensitivity, and daily GV in early stages of dysglycemia.

MATERIALS AND METHODS

Twenty subjects with normal glucose tolerance (NGT; age 45.0 ± 9.5 years; BMI 31.1 ± 6.4 kg/m2), 25 with NGT and 1hrOGTT>8.6 mmol/L (1hrOGTT; 45.7 ± 8.5 years; 32.4 ± 7.0 kg/m2), and 59 with isolated impaired glucose tolerance (iIGT; 47.7 ± 11.2 years; 31.3 ± 6.1 kg/m2) underwent OGTT and MMTT. CGM was performed with blinded FreeStyle Libre Pro for 24 h under standard conditions. Parameters of beta-cell function, insulin sensitivity and GV were calculated.

RESULTS

Overall insulin secretion and action as well as GV progressively worsened across glucose tolerance categories. On a matrix analysis, GV parameters were inversely related to ISSI-2; r = -0.37 to -0.52; p < 0.0001; and IGI; r = -0.28 to -0.48; p < 0.0001 for CV, SD, J-index, LI, HBGI and MAGE. Insulin secretion (IGI) and b-cell function (ISSI-2) emerged as independent contributors to GV in early stage of dysglycemia accounting for about 16%-38% of its variability.

CONCLUSIONS

Our results show that daily GV worsens already with mild impairment of glucose tolerance. The increase in GV is inversely related to insulin secretion and action.

Risk Factor Analysis for Type 2 Diabetes Patients About Hypoglycemia Using Continuous Glucose Monitoring: Results from a Prospective Observational Study

Introduction: To determine the relationship between hypoglycemia and glucose variability in outpatients with type 2 diabetes mellitus (T2DM). Materials and Methods: The study participants were 999 outpatients with T2DM who used the FreeStyle Libre Pro for continuous glucose monitoring (FLP-CGM). Hypoglycemia was defined as glucose level of <3.0 mM, and the frequency of episodes and duration of hypoglycemia were evaluated by comparing patients who did or did not achieve time-below-range <3.0 mM (TBR^<3.0) of <1% of the time. The association of TBR^<3.0 and long% coefficient of variation (%CV) with medications used was examined using multivariate analysis with a proportional odds model. Results: The average TBR^<3.0 was 0.33% (4.75 min). The TBR^<3.0 >1% group comprised 71/999 patients. Patients of the TBR^<3.0 >1% group had lower body mass index, longer disease duration, and poorer renal function. For the TBR^<3.0 >1% group, the predicted cutoff values were 7.19 mM average glucose (AG), and 30.30% for %CV. When AG <7.19 mM and %CV >30.30% were considered as hypoglycemic risk factors, the frequency and duration of hypoglycemia increased as the risk factor values increased. In multivariate analysis, sulfonylurea (SU) use, insulin use, and low blood glucose index correlated significantly with increased length of TBR^<3.0 and %CV, even after adjustment for concomitant diabetes medications. Conclusion: In T2DM, maintaining TBR^<3.0 <1% requires to keep AG >7.2 mM and %CV <30%, in addition to comprehensive management of CGM metrics. Since SU and insulin use is associated with prolonged TBR^<3.0 and increased %CV, their doses should be adjusted to avoid excessive fall in AG and raising %CV.

Associations between continuous glucose monitoring-derived metrics and HbA1c in patients with type 2 diabetes mellitus

AIMS

The aim of this study was to define the relationship between time in range (TIR) and hemoglobin A1c (HbA1c) levels in patients with type 2 diabetes mellitus (T2DM).

METHODS

The glycemic profile of 999 Japanese patients was analyzed with FreeStyle Libre Pro Continuous Glucose Monitoring (FLP-CGM) while they continued their prescribed glucose-lowering medications. FLP-CGM data recorded over 8 consecutive days were analyzed.

RESULTS

The regression model for HbA1c on TIR was HbA1c = 9.4966-0.0309 × TIR. The predicted HbA1c level for TIR of 70% was 7.33% and is higher than reports subjecting mostly T1DM. The TIR corresponding to HbA1c 7.0% was 80.64%. The patients with low TIR tended to have long duration of diabetes, used high dose of daily insulin, high body mass index, high HbA1c, liver dysfunction and high triglyceride. Relatively higher percentages of patients of this group used sulfonylureas, glucagon like peptide-1 receptor agonists and insulin.

CONCLUSIONS

Our data showed predicted HbA1c corresponding to TIR is largely depends on study population, thus is not uniform. Our results provide new insights on the management of T2DM. However, caution should be exercised in extending the HbA1C-TIR relationship using FLP-CGM to any other sensors since there could be a risk of hypoglycemia in doing so.

Relationship between blood glucose variability in ambulatory glucose profile and standardized continuous glucose monitoring metrics: Subanalysis of a prospective cohort study

AIM

To clarify the relationship between ambulatory glucose profile (AGP) indexes and standardized continuous glucose monitoring (CGM) metrics in patients with type 2 diabetes (T2D).

METHODS

This is an exploratory, cross-sectional analysis of baseline data collected from a prospective, multicentre, 5-year follow-up observational study conducted and published previously by our group. The study participants were 999 outpatients with T2D who used CGM at baseline, and had no apparent history of cardiovascular disease. We investigated the relationship between average interquartile range (IQR) and time in range (TIR). We also calculated, for the first time, the cutoff values to achieve the TIR target values.

RESULTS

In both the TIR more than 70% and TIR more than 90% achievement groups, the average IQR was notably small compared with the non-achievement groups. Particularly in comparison of the TIR quartiles, the average IQR became significantly smaller as the TIR became larger. The average IQR correlated negatively with TIR, and the cutoff values for TIR of more than 70% achievement and TIR of more than 90% achievement were an average IQR (>70%/>90%) of 2.13/1.85 mmol/L.

CONCLUSION

Our results showed a negative correlation between TIR and the range of blood glucose variations visually represented in AGP. The results also showed that the range of blood glucose variations in AGP is associated with indices of intraday and interday blood glucose variations and also with hypoglycaemia. Our results may provide new perspectives in the assessment and application of AGP in the clinical setting.

Switching from Insulin Degludec plus Dipeptidyl Peptidase-4 Inhibitor to Insulin Degludec/Liraglutide Improves Glycemic Variability in Patients with Type 2 Diabetes: A Preliminary Prospective Observation Study

Incretins reduce glycemic variability (GV) in patients with type 2 diabetes, but it is unknown whether switching from a combination of basal insulin and a DPP-4 inhibitor to insulin degludec/liraglutide (IDegLira) improves GV. We performed an exploratory prospective observational study to compare the effect of IDegLira and the combination on GV. We recruited hospitalized patients with type 2 diabetes who had stable glycemic control with insulin degludec (≤16 units/day) and taking a DPP-4 inhibitor. GV was analyzed using continuous glucose monitoring (CGM) before and after switching the medication to IDegLira. The principal endpoint was the change in mean amplitude of glycemic excursions (MAGE). Other indices of GV and CGM parameters were analyzed as the secondary endpoints. Fifteen participants were enrolled and 12 completed the study. In these participants, the DPP-4 inhibitor and insulin degludec were discontinued, and the equivalent dose of IDegLira was commenced. Switching to IDegLira significantly improved MAGE from 74.9 (60.3, 97.7) mg/dL to 64.8 (52.0, 78.2) mg/dL (P < 0.05), as well as other indices of GV and 24-hour mean blood glucose concentration. Analysis of the ambulatory glucose profile showed marked reductions in postprandial glucose concentration. Nocturnal glucose concentration was similar under the two treatment regimens. IDegLira improved GV as well as the mean and the postprandial glucose concentration by switching from insulin degludec plus DPP-4 inhibitor combination. IDegLira might be beneficial for patients being treated with low-dose basal insulin.

Impact of Comorbidities on Beneficial Effect of Lactated Ringers vs. Saline in Sepsis Patients

Background: Lactated Ringers reduced mortality more than saline in sepsis patients but increased mortality more than saline in traumatic brain injury patients.

Method: This prospective cohort study was conducted in a medical intensive care unit (ICU) in central Taiwan. We applied standard sepsis evaluation protocol and identified heart, lung, liver, kidney, and endocrine comorbidities. We also evaluated resuscitation response with central venous pressure, central venous oxygen saturation, and serum lactate level simultaneously. Propensity-score matching and Cox regression were used to estimate mortality. The competing risk model compared the lengths of hospital stays with the subdistribution hazard ratio (SHR).

Results: Overall, 938 patients were included in the analysis. The lactated Ringers group had a lower mortality rate (adjusted hazard ratio, 0.59; 95% CI 0.43-0.81) and shorter lengths of hospital stay (SHR, 1.39; 95% C.I. 1.15-1.67) than the saline group; the differences were greater in patients with chronic pulmonary disease and small and non-significant in those with chronic kidney disease, moderate to severe liver disease and cerebral vascular disease. The resuscitation efficacy was the same between fluid types, but serum lactate levels were significantly higher in the lactated Ringers group than in the saline group (0.12 mg/dl/h; 95% C.I.: 0.03, 0.21), especially in chronic liver disease patients. Compared to the saline group, the lactated Ringers group achieved target glucose level earlier in both diabetes and non-diabetes patients.

Conclusion: Lactate Ringer's solution provides greater benefits to patients with chronic pulmonary disease than to those with chronic kidney disease, or with moderate to severe liver disease. Comorbidities are important in choosing resuscitation fluid types.

Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions

Discrete Markov chains are frequently used to analyse transition behaviour in sequential data. Here, the transition probabilities can be estimated using varying order Markov chains, where order k specifies the length of the sequence history that is used to model these probabilities. Generally, such a model is fitted to the entire dataset, but in practice it is likely that some heterogeneity in the data exists and that some sequences would be better modelled with alternative parameter values, or with a Markov chain of a different order. We use the framework of Exceptional Model Mining (EMM) to discover these exceptionally behaving sequences. In particular, we propose an EMM model class that allows for discovering subgroups with transition behaviour of varying order. To that end, we propose three new quality measures based on information-theoretic scoring functions. Our findings from controlled experiments show that all three quality measures find exceptional transition behaviour of varying order and are reasonably sensitive. The quality measure based on Akaike’s Information Criterion is most robust for the number of observations. We furthermore add to existing work by seeking for subgroups of sequences, as opposite to subgroups of transitions. Since we use sequence-level descriptive attributes, we form subgroups of entire sequences, which is practically relevant in situations where you want to identify the originators of exceptional sequences, such as patients. We show this relevance by analysing sequences of blood glucose values of adult persons with diabetes type 2. In the experiments, we find subgroups of patients based on age and glycated haemoglobin (HbA1c), a measure known to correlate with average blood glucose values. Clinicians and domain experts confirmed the transition behaviour as estimated by the fitted Markov chain models.

Retrospective Evaluation on the Use of a New Polysaccharide Complex in Managing Paediatric Type 1 Diabetes with Metabolic Syndrome (MetS)

Background: Children and adolescents affected by type 1 diabetes have an increased risk of being overweight or obese and of suffering from cardiometabolic symptoms. Aims: To retrospectively evaluate the effects of a new complex of polysaccharide macromolecules, Policaptil Gel Retard^® (PGR), on auxological and metabolic parameters, glycaemic variability and control parameters in paediatric patients with type 1 diabetes and metabolic syndrome (MetS). Patients and Methods: Data for 27 paediatric patients with a diagnosis of type 1 diabetes in conjunction with obesity and MetS of at least 5 years’ standing were collected and retrospectively studied. Of these, 16 (median age 12.9, range 9.5–15.8 years) had been adjunctively treated with PGR and 11 (median age 12.6, range 9.4–15.6 years) had not been treated with PGR. Auxological, metabolic and glycaemic control and variability parameters and insulin dosing were compared after 6 months in the two groups. Results: PGR significantly reduced BMI standard deviation score (SDS) (p < 0.005), waist SDS (p < 0.005), HbA1c (p < 0.05) and daily mean insulin dose requirement (p < 0.005). A significant improvement was also observed in the metabolic and glycaemic variability parameters of mean daily blood glucose (BG) levels (p < 0.005), SD of daily BG levels (p < 0.0001), mean coefficient of variation (p < 0.05), LBGI (p < 0.0001), HBGI (p < 0.0001), J-index (p < 0.005), total cholesterol (p < 0.005), HDL-cholesterol (p < 0.005) and LDL-cholesterol (p < 0.005) and triglycerides (p < 0.05). Conclusions: PGR produces a good auxological and metabolic response in obese patients with MetS who are affected by type 1 diabetes. It led to a significant reduction in BMI SDS, waist SDS and an improvement in glucose control and variability as well as in other MetS parameters. The use of polysaccharide compounds, especially if associated with appropriate dietary changes, may help achieve treatment targets in type 1 diabetes and reduce the risk that patients develop metabolic syndrome.

Glycemic variability of acute stroke patients and clinical outcomes: a continuous glucose monitoring study

Introduction:

Glycemic variability (GV) has been associated with worse prognosis in critically ill patients. We sought to evaluate the potential association between GV indices and clinical outcomes in acute stroke patients.

Methods:

Consecutive diabetic and nondiabetic, acute ischemic or hemorrhagic stroke patients underwent regular, standard-of-care finger-prick measurements and continuous glucose monitoring (CGM) for up to 96 h. Thirteen GV indices were obtained from CGM data. Clinical outcomes during hospitalization and follow-up period (90 days) were recorded. Hypoglycemic episodes disclosed by CGM but missed by finger-prick measurements were also documented.

Results:

A total of 62 acute stroke patients [48 ischemic and 14 hemorrhagic, median NIHSS score: 9 (IQR: 3–16) points, mean age: 65 ± 10 years, women: 47%, nondiabetic: 79%] were enrolled. GV expressed by higher mean absolute glucose (MAG) values was associated with a lower likelihood of neurological improvement during hospitalization before and after adjusting for potential confounders (OR: 0.135, 95% CI: 0.024–0.751, p = 0.022). There was no association of GV indices with 3-month clinical outcomes. During CGM recording, 32 hypoglycemic episodes were detected in 17 nondiabetic patients. None of these episodes were identified by the periodic blood glucose measurements and therefore they were not treated.

Conclusions:

Greater GV of acute stroke patients may be related to lower odds of neurological improvement during hospitalization. No association was disclosed between GV indices and 3-month clinical outcomes.

Integrating an Islet-Based Biosensor in the Artificial Pancreas: In Silico Proof-of-Concept

OBJECTIVE

Current treatment of type 1 diabetes by closed-loop approaches depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore glucose homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurement. This work aims at investigating the performance of the biosensor in a blood glucose control loop, to establish an in silico proof-of-concept.

METHODS

Two islet algorithm models were identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inner regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Then, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients.

RESULTS

With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as performing as standard treatments, and outperformed them under challenging conditions.

CONCLUSION

This work validates the concept of using pancreatic islets algorithms in an artificial pancreas in silico.

SIGNIFICANCE

Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.

Glucose Regulation Beyond HbA1c in Type 2 Diabetes Treated With Insulin: Real-World Evidence From the DIALECT-2 Cohort

OBJECTIVE

To investigate glucose variations associated with glycated hemoglobin (HbA1c) in insulin-treated patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Patients included in Diabetes and Lifestyle Cohort Twente (DIALECT)-2 (n = 79) were grouped into three HbA1c categories: low, intermediate, and high (≤53, 54-62, and ≥63 mmol/mol or ≤7, 7.1-7.8, and ≥7.9%, respectively). Blood glucose time in range (TIR), time below range (TBR), time above range (TAR), glucose variability parameters, day and night duration, and frequency of TBR and TAR episodes were determined by continuous glucose monitoring (CGM) using the FreeStyle Libre sensor and compared between HbA1c categories.

RESULTS

CGM was performed for a median (interquartile range) of 10 (7-12) days/patient. TIR was not different for low and intermediate HbA1c categories (76.8% [68.3-88.2] vs. 76.0% [72.5.0-80.1]), whereas in the low category, TBR was higher and TAR lower (7.7% [2.4-19.1] vs. 0.7% [0.3-6.1] and 8.2% [5.7-17.6] vs. 20.4% [11.6-27.0], respectively, P < 0.05). Patients in the highest HbA1c category had lower TIR (52.7% [40.9-67.3]) and higher TAR (44.1% [27.8-57.0]) than the other HbA1c categories (P < 0.05), but did not have less TBR during the night. All patients had more (0.06 ± 0.06/h vs. 0.03 ± 0.03/h; P = 0.002) and longer (88.0 [45.0-195.5] vs. 53.4 [34.4-82.8] minutes; P < 0.001) TBR episodes during the night than during the day.

CONCLUSIONS

In this study, a high HbA1c did not reduce the occurrence of nocturnal hypoglycemia, and low HbA1c was not associated with the highest TIR. Optimal personalization of glycemic control requires the use of newer tools, including CGM-derived parameters.

Associations between continuous glucose monitoring-derived metrics and diabetic retinopathy and albuminuria in patients with type 2 diabetes

INTRODUCTION

Preventing the development and progression of diabetic microvascular complications through optimal blood glucose control remains an important challenge. Whether metrics based on continuous glucose monitoring are useful for the management of diabetic microvascular complications is not entirely clear.

RESEARCH DESIGN AND METHODS

This is an exploratory analysis of an ongoing prospective, multicenter, 5-year follow-up observational study. Study participants included 999 outpatients with type 2 diabetes who underwent continuous glucose monitoring at baseline. Associations between continuous glucose monitoring-derived metrics and the severity of diabetic retinopathy or albuminuria were investigated using multivariable proportional odds models.

RESULTS

The overall prevalence of diabetic retinopathy was 22.2%. Multivariate analysis with proportional odds models demonstrated that continuous glucose monitoring-derived metrics related to intraday and interday glucose variability are significantly associated with the severity of diabetic retinopathy, even after adjusting for various possible risk factors. However, significant relationships were not observed after adjusting for hemoglobin A1c (HbA1c) levels. The prevalence of microalbuminuria and macroalbuminuria was 20.3% and 6.7%, respectively. Similarly, multivariate analysis demonstrated that those metrics are significantly associated with the severity of albuminuria. These relationships remained significant even after further adjusting for HbA1c levels.

CONCLUSIONS

Continuous glucose monitoring-derived metrics related to intraday and interday glucose variability are significantly associated with the severity of diabetic retinopathy or albuminuria in patients with type 2 diabetes. Thus, evaluating these metrics might possibly be useful for risk assessment of diabetic microvascular complications.Trial registration number UMIN000032325.

The Differential and Combined Action of Insulin Glargine and Lixisenatide on the Fasting and Postprandial Components of Glucose Control

BACKGROUND

iGlarLixi is an injectable combination of long acting insulin glargine (iGlar) and glucagon-like peptide 1 receptor agonist lixisenatide in a fixed ratio, which was proven safe and effective for the treatment of type 2 diabetes. Lixisenatide and iGlar act differently on fasting and postprandial plasma glucose (fasting plasma glucose [FPG] and postprandial glucose [PPG]). Here, we deconstruct quantitatively their respective FPG and PPG effects.

METHOD

This post hoc study analyzes data from the Lixilan-O trial, where 1170 subjects with type 2 diabetes were randomly assigned to 30 weeks of once daily injections of lixisenatide, iGlar, and iGlarLixi (1:2:2). The FPG and PPG components of glucose control were assessed in terms of mean glucose (fasting mean plasma glucose [FMPG] and prandial mean plasma glucose [PMPG], respectively). The MPGP was computed across all meals as a delta between post- and premeal glucose; glucose variability was measured by the high blood glucose index (HBGI) (fasting HBGI and prandial HBGI [PHBGI], respectively), and glycemic exposure measured by area under the curve (AUC) computed overall. All metrics were derived from seven-point self-monitoring glucose profiles.

RESULTS

Insulin glargine lowered significantly FMPG by 15.3 mg/dL (P < .01) without any significant change in PMPG. Lixisenatide, when added to iGlar, reduced PMPG by 9.7 mg/dL (P < .01), AUC by 96.3 mg∙h/dL (P < .01), and PHBGI by 2.4 (P < .01), primarily due to attenuation of PPG and without significant change in mean FPG.

CONCLUSION

Insulin glargine and lixisenatide act selectively on FPG and PPG. Their combination iGlarLixi offers more effective glucose control than its components due to the cumulative effect on FPG and PPG, which is evidenced by reduced average glycemia, glycemic exposure, and glucose variability.

Associations between continuous glucose monitoring-derived metrics and arterial stiffness in Japanese patients with type 2 diabetes

BACKGROUND

Previous studies have suggested that high mean glucose levels and glycemic abnormalities such as glucose fluctuation and hypoglycemia accelerate the progression of atherosclerosis in patients with type 2 diabetes. Although continuous glucose monitoring (CGM) that could evaluate such glycemic abnormalities has been rapidly adopted, the associations between CGM-derived metrics and arterial stiffness are not entirely clear.

METHODS

This exploratory cross-sectional study used baseline data from an ongoing prospective, multicenter, observational study with 5 years of follow-up. Study participants included 445 outpatients with type 2 diabetes and no history of apparent cardiovascular disease who underwent CGM and brachial-ankle pulse wave velocity (baPWV) measurement at baseline. Associations between CGM-derived metrics and baPWV were analyzed using multivariate regression models.

RESULTS

In a linear regression model, all CGM-derived metrics were significantly associated with baPWV, but HbA1c was not. Some CGM-derived metrics related to intra-day glucose variability, hyperglycemia, and hypoglycemia remained significantly associated with baPWV after adjusting for possible atherosclerotic risk factors, including HbA1c. Based on baPWV ≥ 1800 cm/s as indicative of high arterial stiffness, multivariate logistic regression found that some CGM-derived metrics related to intra-day glucose variability and hyperglycemia are significantly associated with high arterial stiffness even after adjusting for possible atherosclerotic risk factors, including HbA1c.

CONCLUSIONS

Multiple CGM-derived metrics are significantly associated with baPWV and high arterial stiffness in patients with type 2 diabetes who have no history of apparent cardiovascular disease. These metrics might be useful for identifying patients at high risk of developing cardiovascular disease.

The Diabits App for Smartphone-Assisted Predictive Monitoring of Glycemia in Patients With Diabetes: Retrospective Observational Study

BACKGROUND

Diabetes mellitus, which causes dysregulation of blood glucose in humans, is a major public health challenge. Patients with diabetes must monitor their glycemic levels to keep them in a healthy range. This task is made easier by using continuous glucose monitoring (CGM) devices and relaying their output to smartphone apps, thus providing users with real-time information on their glycemic fluctuations and possibly predicting future trends.

OBJECTIVE

This study aims to discuss various challenges of predictive monitoring of glycemia and examines the accuracy and blood glucose control effects of Diabits, a smartphone app that helps patients with diabetes monitor and manage their blood glucose levels in real time.

METHODS

Using data from CGM devices and user input, Diabits applies machine learning techniques to create personalized patient models and predict blood glucose fluctuations up to 60 min in advance. These predictions give patients an opportunity to take pre-emptive action to maintain their blood glucose values within the reference range. In this retrospective observational cohort study, the predictive accuracy of Diabits and the correlation between daily use of the app and blood glucose control metrics were examined based on real app users' data. Moreover, the accuracy of predictions on the 2018 Ohio T1DM (type 1 diabetes mellitus) data set was calculated and compared against other published results.

RESULTS

On the basis of more than 6.8 million data points, 30-min Diabits predictions evaluated using Parkes Error Grid were found to be 86.89% (5,963,930/6,864,130) clinically accurate (zone A) and 99.56% (6,833,625/6,864,130) clinically acceptable (zones A and B), whereas 60-min predictions were 70.56% (4,843,605/6,864,130) clinically accurate and 97.49% (6,692,165/6,864,130) clinically acceptable. By analyzing daily use statistics and CGM data for the 280 most long-standing users of Diabits, it was established that under free-living conditions, many common blood glucose control metrics improved with increased frequency of app use. For instance, the average blood glucose for the days these users did not interact with the app was 154.0 (SD 47.2) mg/dL, with 67.52% of the time spent in the healthy 70 to 180 mg/dL range. For days with 10 or more Diabits sessions, the average blood glucose decreased to 141.6 (SD 42.0) mg/dL (P<.001), whereas the time in euglycemic range increased to 74.28% (P<.001). On the Ohio T1DM data set of 6 patients with type 1 diabetes, 30-min predictions of the base Diabits model had an average root mean square error of 18.68 (SD 2.19) mg/dL, which is an improvement over the published state-of-the-art results for this data set.

CONCLUSIONS

Diabits accurately predicts future glycemic fluctuations, potentially making it easier for patients with diabetes to maintain their blood glucose in the reference range. Furthermore, an improvement in glucose control was observed on days with more frequent Diabits use.

Low Blood Glucose Index and Hypoglycaemia Risk: Insulin Glargine 300 U/mL Versus Insulin Glargine 100 U/mL in Type 2 Diabetes

INTRODUCTION

We examined differences in hypoglycaemia risk between insulin glargine 300 U/mL (Gla-300) and insulin glargine 100 U/mL (Gla-100) in individuals with type 2 diabetes (T2DM) using the low blood glucose index (LBGI).

METHODS

Daily profiles of self-monitored plasma glucose (SMPG) from the EDITION 2, EDITION 3 and SENIOR treat-to-target trials of Gla-300 versus Gla-100 were used to compute the LBGI, which is an established metric of hypoglycaemia risk. The analysis also examined documented (blood glucose readings < 3.0 mmol/L [54 mg/dL]) symptomatic hypoglycaemia (DSH).

RESULTS

Overall LBGI in EDITION 2 and SENIOR and night-time LBGI in all three trials were significantly (p < 0.05) lower with Gla-300 versus Gla-100. The largest differences between Gla-300 and Gla-100 were observed during the night. In all three trials, individual LBGI results correlated with the observed number of DSH episodes per participant (EDITION 2 [r = 0.35, p < 0.001]; EDITION 3 [r = 0.26, p < 0.001]; SENIOR [r = 0.30, p < 0.001]). Participants at moderate risk of experiencing hypoglycaemia (defined as LBGI > 1.1) reported 4- to 8-fold more frequent DSH events than those at minimal risk (LBGI ≤ 1.1) (p ≤  0.009).

CONCLUSIONS

The LBGI identified individuals with T2DM at risk for hypoglycaemia using SMPG data and correlated with the number of DSH events. Using the LBGI metric, a lower risk of hypoglycaemia with Gla-300 than Gla-100 was observed in all three trials. The finding that differences in LBGI are greater at night is consistent with previously published differences in the pharmacokinetic profiles of Gla-300 and Gla-100, which provides the physiological foundation for the presented results.

Different amounts of moderate to vigorous physical activity and change in glycemic variability in adolescents with type 1 diabetes: is there dose-response relationship?

OBJECTIVE

To identify the level of physical activity and glycemic variability of adolescents with type 1 diabetes mellitus and to compare glycemic variability on days with different amounts of moderate to vigorous physical activity (MVPA).

SUBJECTS AND METHODS

A sample of 34 subjects aged 10 to 15 years, 18 (52.94%) female; age: 13.04 ± 1.94; HbA1c: 9.76 ± 1.51. Physical activity was measured by wGT3X accelerometer. The glucose data were obtained using continuous glucose monitoring, and the following glycemic variability measures were calculated: standard deviation (SD), low blood glucose index (LBGI), high blood glucose index (HBGI), mean amplitude of glycemic excursions (MAGE), glycemic risk assessment in diabetes equation (GRADE) and coefficient of variation (CV). The most and least active days (the days with greater and lesser time dedicated to physical activities of moderate to vigorous intensity, respectively) were identified. In addition, based on the whole period of accelerometer use, daily means of time spent in MVPA were identified among participants, who were then divided into three groups: up to 100 minutes; from 101 to 200 minutes and above 201 minutes. Then, the measures of glycemic variability were compared among the most and least active days and among the groups too.

RESULTS

The amount of MVPA was significantly different between the days evaluated (237.49 ± 93.29 vs. 125.21 ± 58.10 minutes), but glycemic variability measures did not present a significant difference.

CONCLUSION

Despite the significant differences in the amount of MVPA between the two days evaluated, the glycemic variability did not change significantly. Arch Endocrinol Metab. 2020;64(3):312-8.

Comparison of Glycemic Variability Indices: Blood Glucose, Risk Index, and Coefficient of Variation in Predicting Adverse Outcomes for Patients Undergoing Cardiac Surgery

OBJECTIVES

Fluctuations in blood glucose (glycemic variability) increase the risk of adverse outcomes. No universally accepted tool for glycemic variability exists during the perioperative period. The authors compared 2 measures of glycemic variability-(1) coefficient of variation (CV) and (2) the Blood Glucose Risk Index (BGRI)-in predicting adverse outcomes after cardiac surgery.

DESIGN

Prospective, observational study.

SETTING

Single-center, teaching hospital.

PARTICIPANTS

A total of 1,963 adult patients undergoing cardiac surgery.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Postoperative blood glucose levels were measured hourly for the first 24 hours and averaged every 4 hours (4, 8, 12, 16, 20, and 24 hours). Glycemic variability was measured by CV and the BGRI. The primary outcome, major adverse events (MAEs), was a predefined composite of postoperative complications (death, reoperation, deep sternal infection, stroke, pneumonia, renal failure, tamponade, and myocardial infarction). Logistic regression models were constructed to evaluate the association. Predictive ability was measured using C-statistics. Major adverse events were seen in 170 (8.7%) patients. Only the fourth quartile of CV showed association (odds ratio [OR] 1.91; 95% confidence interval [CI] [1.19-3.14]; p = 0.01), whereas BGRI was related significantly to MAE (OR 1.20; 95% CI [1.10-1.32]; p < 0.0001). The predictive ability of CV and BGRI increased on adding the standard Society of Thoracic Surgeons (STS) risk index. The C-statistic for STS was 0.68, whereas STS + CV was 0.70 (p = 0.012) and STS + BGRI was 0.70 (p = 0.012).

CONCLUSION

Both CV and the BGRI had good predictive ability. The BGRI being a continuous variable could be a preferred measure of glycemic variability in predicting adverse outcomes (cutoff value 2.24) after cardiac surgery.

Impact of Carbohydrate on Glucose Variability in Patients with Type 1 Diabetes Assessed Through Professional Continuous Glucose Monitoring: A Retrospective Study

INTRODUCTION

The aim of this study was to objectively analyze the correlation between dietary components and blood glucose variation by means of continuous glucose monitoring (CGM).

METHODS

Patients with type 1 diabetes mellitus (T1DM) who received CGM to manage their blood glucose levels were enrolled into the study, and the components of their total caloric intake were analyzed. Glycemic variation parameters were calculated, and dietary components, including percentages of carbohydrate, protein and fat in the total dietary intake, were analyzed by a dietitian. The interaction between parameters of glycemic variability and dietary components was analyzed.

RESULTS

Sixty-one patients with T1DM (33 females, 28 males) were enrolled. The mean age of the participants was 34.7 years, and the average duration of diabetes was 14 years. Glycated hemoglobin before CGM was 8.54%. Participants with a carbohydrate intake that accounted for < 50% of their total caloric intake had a longer DM duration and a higher protein and fat intake than did those with a carbohydrate intake that accounted for ≥ 50% of total caloric intake, but there was no between-group difference in total caloric intake per day. The group with a carbohydrate intake that accounted for < 50% of their total caloric intake also had lower nocturnal continuous overlapping net glycemic action (CONGA) 1, - 2 and - 4 values. The percentage of protein intake had a slightly negative correlation with mean amplitude of glycemic excursions (MAGE) (r = - 0.286, p < 0.05) and a moderately negative correlation with coefficient of variation (CV) (r = 0.289, p < 0.05). One additional percentage of protein calories of total calories per day decreased the MAGE to 4.25 mg/dL and CV to 0.012 (p < 0.05). The optimal dietary protein percentage for MAGE < 140 mg/dL was 15.13%. The performance of predictive models revealed the beneficial effect of adequate carbohydrate intake on glucose variation when combined with protein consumption.

CONCLUSIONS

Adequate carbohydrate consumption-but not more than half the daily total calories-combined with protein calories that amount to approximately 15% of the daily caloric intake is important for glucose stability and beneficial for patients with T1DM.

Optimizing Postprandial Glucose Management in Adults With Insulin-Requiring Diabetes: Report and Recommendations

Faster-acting insulins, new noninsulin drug classes, more flexible insulin-delivery systems, and improved continuous glucose monitoring devices offer unprecedented opportunities to improve postprandial glucose (PPG) management and overall care for adults with insulin-treated diabetes. These developments led the Endocrine Society to convene a working panel of diabetes experts in December 2018 to assess the current state of PPG management, identify innovative ways to improve self-management and quality of life, and align best practices to current and emerging treatment and monitoring options. Drawing on current research and collective clinical experience, we considered the following issues for the ∼200 million adults worldwide with type 1 and insulin-requiring type 2 diabetes: (i) the role of PPG management in reducing the risk of diabetes complications; (ii) barriers preventing effective PPG management; (iii) strategies to reduce PPG excursions and improve patient quality of life; and (iv) education and clinical tools to support endocrinologists in improving PPG management. We concluded that managing PPG to minimize or prevent diabetes-related complications will require elucidating fundamental questions about optimal ways to quantify and clinically assess the metabolic dysregulation and consequences of the abnormal postprandial state in diabetes and recommend research strategies to address these questions. We also identified practical strategies and tools that are already available to reduce barriers to effective PPG management, optimize use of new and emerging clinical tools, and improve patient self-management and quality of life.

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.

Associations of serum glucagon levels with glycemic variability in type 1 diabetes with different disease durations

PURPOSE

Glucagon has been recognized as a pivotal factor implicated in the pathophysiology ofdiabetes. The purpose of this study is to investigate the dynamic secretion levels of serum glucagon (GLA) in patients with type 1 diabetes mellitus (T1DM) with different courses of disease, and to analyze its correlation with blood glucose fluctuation.

METHODS

This observational study included 55 T1DM patients and divided into 3 groups according to the courses of disease. Group 1(the disease duration <1 year), Group 2(1≤the disease durations≤5), 3(the disease durations >5 years). All patients underwent a 100g standard steamed buns meal test,measuring the levels of serum glucose, glucagon, insulin, C-peptide in different points of time, and 48 of the total patients used continuous glucose monitoring system (CGMS) to monitor blood glucose.

RESULTS

The fasting glucagon level in Group 1 was significantly higher than it in Group 2. Furthermore, the GLA1h, the GLA3h and the AUCGLA0-3h in Group 1 were greatly larger than those in Group 3. Referring to glycemic variability, the LBGI, AUC of hypoglycemia, the percentage of hypoglycemia time andthe times of nocturnal hypoglycemia in Group 1 were significantly lower than those in Group 3. Moreover,the fasting glucagon level was the independent factors to SD and MAGE. The AUCGLA0-3h were negatively correlated with MODD, LBGI, GRADE-hypo and AUC of nocturnal hypoglycemia.

CONCLUSIONS

It is concluded that glucagon secretory function impairs with duration of type 1 diabetes extended and correlates to glycemic fluctuation, especially hypoglycemia.

Continuous Glucose Monitoring After Gastric Bypass to Evaluate the Glucose Variability After a Low-Carbohydrate Diet and to Determine Hypoglycemia

BACKGROUND

Roux-en-Y gastric bypass (RYGB) alters glucose metabolism and can cause postprandial hypoglycemia. Continuous glucose monitoring (CGM) has been proposed as an evaluation tool in hypoglycemic RYGB individuals. The objective of this study is to investigate the use of CGM in clinical decision-making including diagnosing hypoglycemia and evaluating treatment effects. Furthermore, we aim to assess its accuracy in RYGB-operated individuals.

METHODS

Thirteen RYGB individuals with symptomatic hypoglycemia and 13 asymptomatic RYGB individuals underwent CGM for 5 days. During this period, a mixed-meal test with concomitant plasma glucose (PG) measurements was performed. Furthermore, the RYGB individuals followed a low-carbohydrate diet (LCD) for 1 day and maintained their ordinary diet (OD) for the rest of the period.

RESULTS

LCD reduced the CGM-determined glycemic variability of the mean interstitial fluid glucose (IFG) significantly compared to OD (p < 0.0001). Receiver operating characteristic analysis confirmed that low blood glucose index (e.g., the frequency and amplitude of hypoglycemic events) is the most reliable parameter related to the development of symptomatic hypoglycemia, with a sensitivity of 0.91 (confidence interval [CI] 0.59; 1.00) and a specificity of 0.77 (CI 0.46; 0.95). However, CGM, measuring the IFG in the subcutaneous adipose tissue, overestimated the minimum glucose levels by 1.1 ± 0.9 mmol/l compared with PG.

CONCLUSIONS

CGM was a good method for demonstrating increased glycemic variability among RYGB individuals and for displaying dietary effects on reducing this glycemic variability, including hypoglycemic events. In RYGB individuals, CGM-measured IFG overestimated the real glucose value by about 1 mmol/l in the hypoglycemic range. This should be taken into consideration if CGM is used to diagnose hypoglycemia after RYGB.

Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data

BACKGROUND

High levels of glycemic variability are still observed in most patients with diabetes with severe insulin deficiency. Glycemic variability may be an important risk factor for acute and chronic complications. Despite its clinical importance, there is no consensus on the optimum method for characterizing glycemic variability.

METHOD

We developed a simple new metric, the glycemic variability percentage (GVP), to assess glycemic variability by analyzing the length of the continuous glucose monitoring (CGM) temporal trace normalized to the duration under evaluation. The GVP is similar to other recently proposed glycemic variability metrics, the distance traveled, and the mean absolute glucose (MAG) change. We compared results from distance traveled, MAG, GVP, standard deviation (SD), and coefficient of variation (CV) applied to simulated CGM traces accentuating the difference between amplitude and frequency of oscillations. The GVP metric was also applied to data from clinical studies for the Dexcom G4 Platinum CGM in subjects without diabetes, with type 2 diabetes, and with type 1 diabetes (adults, adolescents, and children).

RESULTS

In contrast to other metrics, such as CV and SD, the distance traveled, MAG, and GVP all captured both the amplitude and frequency of glucose oscillations. The GVP metric was also able to differentiate between diabetic and nondiabetic subjects and between subjects with diabetes with low, moderate, and high glycemic variability based on interquartile analysis.

CONCLUSION

A new metric for the assessment of glycemic variability has been shown to capture glycemic variability due to fluctuations in both the amplitude and frequency of glucose given by CGM data.

High Glucose Variability in Hospitalized Patients with Type 1 Diabetes Mellitus

BACKGROUND

Glucose variability (GV) has been increasingly (or more extensively) studied in patients with type 1 diabetes (T1D) in the ambulatory setting; limited data exist on GV in hospitalized patients with T1D.

MATERIALS AND METHODS

Retrospective single center cohort study, we analyzed in-hospital glucose measurements to assess GV in 736 hospitalized patients in different units over a consecutive 5-year period of time. GV was assessed by mean blood glucose (BG), Average daily risk range (ADRR), high BG index, and low BG index. To place our findings in context, we conducted a systematic review using Cochrane collaboration methodology to critically analyze current published literature on GV in hospitalized T1D patients.

RESULTS

Overall, glycemic control was suboptimal with mean BG 183 ± 51.5 mg/dL and mean ADRR 35 with only 16% patients being categorized as low risk (ADRR <20) for hypo or hyperglycemia. Patients admitted in medical units had mean BG of 194.4 ± 42.8 mg/dL (95% CI = 101.2-346.6) and ADRR of 39.4 ± 16 (95% CI = 1.3-118.7), which were higher than the patients admitted in the surgical units (mean BG 168.1 ± 35.7 (95% CI = 74.8-301.8) and mean ADRR (28.8 ± 13.1 [95% CI = 0.3-93.1]). For the systematic review, initial search yielded 2336 studies for screening, however, none of them had data specific for T1D.

CONCLUSION

GV is high in hospitalized T1D patients admitted at our tertiary care center. Review of the literature shows paucity of data regarding GV in hospitalized patients with T1D.

Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models

In patients with type 1 diabetes mellitus (T1DM), glucose dynamics are influenced by insulin reactions, diet, lifestyle, etc., and characterized by instability and nonlinearity. With the objective of a dependable decision support system for T1DM self-management, we aim to model glucose dynamics using their nonlinear chaotic properties. A group of patients was monitored via continuous glucose monitoring (CGM) sensors for several days under free-living conditions. We assessed the glycemic variability (GV) and chaotic properties of each time series. Time series were subsequently transformed into the phase-space and individual autoregressive (AR) models were applied to predict glucose values over 30-minute and 60-minute prediction horizons (PH). The logistic smooth transition AR (LSTAR) model provided the best prediction accuracy for patients with high GV. For a PH of 30 minutes, the average values of root mean squared error (RMSE) and mean absolute error (MAE) for the LSTAR model in the case of patients in the hypoglycemia range were 5.83 ( ± 1.95) mg/dL and 5.18 ( ± 1.64) mg/dL, respectively. For a PH of 60 minutes, the average values of RMSE and MAE were 7.43 ( ± 1.87) mg/dL and 6.54 ( ± 1.6) mg/dL, respectively. Without the burden of measuring exogenous information, nonlinear regime-switching AR models provided fast and accurate results for glucose prediction.

Metrics for glycaemic control - from HbA1c to continuous glucose monitoring

As intensive treatment to lower levels of HbA_1c characteristically results in an increased risk of hypoglycaemia, patients with diabetes mellitus face a life-long optimization problem to reduce average levels of glycaemia and postprandial hyperglycaemia while simultaneously avoiding hypoglycaemia. This optimization can only be achieved in the context of lowering glucose variability. In this Review, I discuss topics that are related to the assessment, quantification and optimal control of glucose fluctuations in diabetes mellitus. I focus on markers of average glycaemia and the utility and/or shortcomings of HbA_1c as a 'gold-standard' metric of glycaemic control; the notion that glucose variability is characterized by two principal dimensions, amplitude and time; measures of glucose variability that are based on either self-monitoring of blood glucose data or continuous glucose monitoring (CGM); and the control of average glycaemia and glucose variability through the use of pharmacological agents or closed-loop control systems commonly referred to as the 'artificial pancreas'. I conclude that HbA_1c and the various available metrics of glucose variability reflect the management of diabetes mellitus on different timescales, ranging from months (for HbA_1c) to minutes (for CGM). Comprehensive assessment of the dynamics of glycaemic fluctuations is therefore crucial for providing accurate and complete information to the patient, physician, automated decision-support or artificial-pancreas system.

Predicting and Reducing Driving Mishaps Among Drivers With Type 1 Diabetes

OBJECTIVE

Two aims of this study were to develop and validate A) a metric to identify drivers with type 1 diabetes at high risk of future driving mishaps and B) an online intervention to reduce mishaps among high-risk drivers.

RESEARCH DESIGN AND METHODS

To achieve aim A, in study 1, 371 drivers with type 1 diabetes from three U.S. regions completed a series of established questionnaires about diabetes and driving. They recorded their driving mishaps over the next 12 months. Questionnaire items that uniquely discriminated drivers who did and did not have subsequent driving mishaps were assembled into the Risk Assessment of Diabetic Drivers (RADD) scale. In study 2, 1,737 drivers with type 1 diabetes from all 50 states completed the RADD online. Among these, 118 low-risk (LR) and 372 high-risk (HR) drivers qualified for and consented to participate in a 2-month treatment period followed by 12 monthly recordings of driving mishaps. To address aim B, HR participants were randomized to receive either routine care (RC) or the online intervention "DiabetesDriving.com" (DD.com). Half of the DD.com participants received a motivational interview (MI) at the beginning and end of the treatment period to boost participation and efficacy. All of the LR participants were assigned to RC. In both studies, the primary outcome variable was driving mishaps.

RESULTS

Related to aim A, in study 1, the RADD demonstrated 61% sensitivity and 75% specificity. Participants in the upper third of the RADD distribution (HR), compared with those in the lower third (LR), reported 3.03 vs. 0.87 mishaps/driver/year, respectively (P < 0.001). In study 2, HR and LR participants receiving RC reported 4.3 and 1.6 mishaps/driver/year, respectively (P < 0.001). Related to aim B, in study 2, MIs did not enhance participation or efficacy, so the DD.com and DD.com + MI groups were combined. DD.com participants reported fewer hypoglycemia-related driving mishaps than HR participants receiving RC (P = 0.01), but more than LR participants receiving RC, reducing the difference between the HR and LR participants receiving RC by 63%. HR drivers differed from LR drivers at baseline across a variety of hypoglycemia and driving parameters.

CONCLUSIONS

The RADD identified higher-risk drivers, and identification seemed relatively stable across time, samples, and procedures. This 11-item questionnaire could inform patients at higher risk, and their clinicians, that they should take preventive steps to reduce driving mishaps, which was accomplished in aim B using DD.com.

Biochemical, Physiological and Psychological Changes During Endurance Exercise in People With Type 1 Diabetes

BACKGROUND

Increasing numbers of people with diabetes are adopting exercise programs. Fear of hypoglycemia, hypoglycemia itself, and injuries are major issues for many people with diabetes undertaking physical activity. The purpose of this study was to investigate the effects of type 1 diabetes mellitus on the risk of hypoglycemia, glycemic variability, exercise performance, changes in body composition, changes in insulin dosage, and psychosocial well-being during a multiday endurance exercise event.

METHODS

Eleven participants (7 with type 1 diabetes, 4 with normal glucose tolerance) undertook a 15-day, 2300 km cycling tour from Barcelona to Vienna. Data were prospectively collected using bike computers, continuous glucose monitors, body composition analyzers, and mood questionnaires.

RESULTS

Mean blood glucose in riders with and without diabetes significantly reduced as the event progressed. Glycemic variability and time spent in hypoglycemia did not change throughout the ride for either set of riders. Riders with diabetes in the lowest quartile of sensor glucose values had significantly reduced power output. Percentage body fat also significantly fell. Hypo- and hyperglycemia provoked feelings of anxiety and worry.

CONCLUSIONS

This is the first study to describe a real-time endurance event in type 1 diabetes, and provides important new data that cannot be studied in laboratory conditions. Hypoglycemia continues to occurs in spite of peer support and large reductions in insulin dose. Glycemic variability is shown as a potential barrier to participation in physical activity through effects on mood and psychological well-being.

An empirically derived short form of the Hypoglycaemia Fear Survey II

AIMS

To develop an empirically derived short version of the Hypoglycaemia Fear Survey II that still accurately measures fear of hypoglycaemia.

METHODS

Item response theory methods were used to generate an 11-item version of the Hypoglycaemia Fear Survey from a sample of 487 people with Type 1 or Type 2 diabetes mellitus. Subsequently, this scale was tested on a sample of 2718 people with Type 1 or insulin-treated Type 2 diabetes taking part in DIALOG, a large observational prospective study of hypoglycaemia in France.

RESULTS

The short form of the Hypoglycaemia Fear Survey II matched the factor structure of the long form for respondents with both Type 1 and Type 2 diabetes, while maintaining adequate internal reliability on the total scale and all three subscales. The two forms were highly correlated on both the total scale and each subscale (Pearson's R > 0.89).

CONCLUSIONS

The short form of the Hypoglycaemia Fear Survey II is an important first step in more efficiently measuring fear of hypoglycaemia. Future prospective studies are needed for further validity testing and exploring the survey's applicability to different populations.

Head-to-head comparison between flash and continuous glucose monitoring systems in outpatients with type 1 diabetes

PURPOSE

Continuous glucose monitoring (CGM) is being increasingly used in clinical practice. The flash glucose monitoring (FGM) and CGM are different systems of interstitial glucose recording. We aimed to determine the agreement between the factory-calibrated FGM FreeStyle Libre (FSL) and the gold-standard CGM Dexcom G4 Platinum (DG4P).

METHODS

We analyzed data from n = 8 outpatients with type 1 diabetes, who wore the FSL and DG4P for up to 14 days during their habitual life. We aligned FSL and DG4P recordings to obtain paired glucose measures. We calculated correlation coefficients, mean absolute relative difference (MARD), percentages in Clarke error grid areas, time spent in hyperglycaemia, target glycaemia, or hypoglycaemia, as well as glucose variability with both sensors. Comparison with self-monitoring of blood glucose (SMBG) was also performed.

RESULTS

Patients varied in terms of age, diabetes duration, and HbA1c (from 5.9 to 9.6 %). In the pooled analysis of 10,020 paired values, there was a good correlation between FSL and DG4P (r ² = 0.76; MARD = 18.1 ± 14.8 %) with wide variability among patients. The MARD was significantly higher during days 11-14 than in days 1-10, and during hypoglycaemia (19 %), than in normoglycaemia (16 %) or hyperglycaemia (13 %). Average glucose profiles and MARD versus SMBG were similar between the two sensors. Time spent in normo-, hyper-, or hypoglycaemia, and indexes of glucose variability was similarly estimated by the two sensors.

CONCLUSIONS

In outpatients with type 1 diabetes, we found good agreement between the FSL and DG4P. No significant difference was detected in the estimation of clinical diagnostic parameters.

Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes

Glucose control, glucose variability (GV), and risk for hypoglycemia are intimately related, and it is now evident that GV is important in both the physiology and pathophysiology of diabetes. However, its quantitative assessment is complex because blood glucose (BG) fluctuations are characterized by both amplitude and timing. Additional numerical complications arise from the asymmetry of the BG scale. In this Perspective, we focus on the acute manifestations of GV, particularly on hypoglycemia, and review measures assessing the amplitude of GV from routine self-monitored BG data, as well as its timing from continuous glucose monitoring (CGM) data. With availability of CGM, the latter is not only possible but also a requirement-we can now assess rapid glucose fluctuations in real time and relate their speed and magnitude to clinically relevant outcomes. Our primary message is that diabetes control is all about optimization and balance between two key markers-frequency of hypoglycemia and HbA1c reflecting average BG and primarily driven by the extent of hyperglycemia. GV is a primary barrier to this optimization, including to automated technologies such as the "artificial pancreas." Thus, it is time to standardize GV measurement and thereby streamline the assessment of its two most important components-amplitude and timing.

How well do glucose variability measures predict patient glycaemic outcomes during treatment intensification in type 2 diabetes?

AIM

Despite links to clinical outcomes in patients with type 2 diabetes mellitus (T2DM), the clinical utility of glycaemic variability (GV) measures is unknown. We evaluated the correlation between baseline GV, and glycated haemoglobin (HbA1c) attainment and hypoglycaemic events during treatment intensification in a large group of patients.

METHODS

Patient-level data from six 24-week clinical trials of T2DM patients undergoing treatment intensification with basal insulin or comparators (N = 1699) were pooled. Baseline GV measures included standard deviation (SD), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG), coefficient of variation (CV), high blood glucose index (HBGI), and low blood glucose index (LBGI) and were correlated with HbA1c change and hypoglycaemic events.

RESULTS

All mean GV measures, excluding CV which worsened, improved significantly from baseline to Week 24, with the largest proportional reduction obtained for HBGI (-65.5%). When assessed as mean individual percentage changes, only HBGI improved significantly. Baseline GV correlated positively with Week 24 HbA1c for SD, MAGE, and HBGI. Baseline HBGI and CV correlated negatively and positively, respectively, with Week 24 HbA1c change. Correlations also existed between most baseline GV measures and age, body mass index, Week 24 fasting plasma glucose, Week 24 postprandial plasma glucose, and hypoglycaemic events; statistical significance depended on the specific measure.

CONCLUSIONS

Pre-treatment GV is associated with glycaemic outcomes in T2DM patients undergoing treatment intensification over 24 weeks. HBGI might be the most robust predictor, warranting validation in dedicated prospective studies or randomized trials to assess the predictive value of measuring GV.

Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?

BACKGROUND

The risk of hypo- and hyperglycemia has been assessed for years by computing the well-known low blood glucose index (LBGI) and high blood glucose index (HBGI) on sparse self-monitoring blood glucose (SMBG) readings. These metrics have been shown to be predictive of future glycemic events and clinically relevant cutoff values to classify the state of a patient have been defined, but their application to continuous glucose monitoring (CGM) profiles has not been validated yet. The aim of this article is to explore the relationship between CGM-based and SMBG-based LBGI/HBGI, and provide a guideline to follow when these indices are computed on CGM time series.

METHODS

Twenty-eight subjects with type 1 diabetes mellitus (T1DM) were monitored in daily-life conditions for up to 4 weeks with both SMBG and CGM systems. Linear and nonlinear models were considered to describe the relationship between risk indices evaluated on SMBG and CGM data.

RESULTS

LBGI values obtained from CGM did not match closely SMBG-based values, with clear underestimation especially in the low risk range, and a linear transformation performed best to match CGM-based LBGI to SMBG-based LBGI. For HBGI, a linear model with unitary slope and no intercept was reliable, suggesting that no correction is needed to compute this index from CGM time series.

CONCLUSIONS

Alternate versions of LBGI and HBGI adapted to the characteristics of CGM signals have been proposed that enable extending results obtained for SMBG data and using clinically relevant cutoff values previously defined to promptly classify the glycemic condition of a patient.

Sensitivity of Traditional and Risk-Based Glycemic Variability Measures to the Effect of Glucose-Lowering Treatment in Type 2 Diabetes Mellitus

BACKGROUND

Here we assess associations between glycemic variability (GV) measures and outcomes from glucose-lowering therapy in patients with type 2 diabetes (T2DM) to identify the metrics most sensitive to treatment response.

METHODS

Data from 1699 patients in 6 previously reported studies in adults with T2DM treated with basal insulin and/or oral glucose-lowering drugs were included in a post hoc meta-analysis. Using 7-point blood glucose (BG) profiles we compared the GV metrics standard deviation (SD), mean amplitude of glycemic excursion (MAGE), mean absolute glucose (MAG), low and high BG risk indices (LBGI, HBGI), and average daily risk range (ADRR). Treatment-related changes in GV and risk status and associations between end-of-trial GV/risk metrics with treatment outcomes (end-of-trial glycated hemoglobin A1c[A1C] level ≥7.0%, hypoglycemia, and composite outcome of A1C <7.0% and no hypoglycemia), were evaluated.

RESULTS

Significant changes from baseline to end of treatment were observed in all measures (all P < .0001), with the largest reduction following treatment for HBGI (-65.5%) and ADRR (-43.3%). The baseline risk classification for hyperglycemia based on the risk categories of HBGI improved for 66.8%, remained unchanged for 29.8%, and deteriorated for 3.3% of patients (chi-square P < .0001), while the risk for hypoglycemia did not change. HBGI showed the strongest association with A1C ≥7.0% at the end of treatment, and LBGI showed the strongest association with symptomatic hypoglycemia.

CONCLUSIONS

During glucose-lowering therapy in T2DM, HBGI and LBGI offer insights into hyperglycemia and trends toward hypoglycemia, respectively; ADRR may be the optimal GV measure responsive to hypo- and hyperglycemic treatment effects.

Glycemic Variability Is Associated with Frequency of Blood Glucose Testing and Bolus: Post Hoc Analysis Results from the ProAct Study

INTRODUCTION

The ProAct study has shown that a pump switch to the Accu-Chek(®) Combo system (Roche Diagnostics Deutschland GmbH, Mannheim, Germany) in type 1 diabetes patients results in stable glycemic control with significant improvements in glycated hemoglobin (HbA1c) in patients with unsatisfactory baseline HbA1c and shorter pump usage time.

PATIENTS AND METHODS

In this post hoc analysis of the ProAct database, we investigated the glycemic control and glycemic variability at baseline by determination of several established parameters and scores (HbA1c, hypoglycemia frequency, J-score, Hypoglycemia and Hyperglycemia Indexes, and Index of Glycemic Control) in participants with different daily bolus and blood glucose measurement frequencies (less than four day, four or five per day, and more than five per day, in both cases). The data were derived from up to 299 patients (172 females, 127 males; age [mean±SD], 39.4±15.2 years; pump treatment duration, 7.0±5.2 years).

RESULTS

Participants with frequent glucose readings had better glycemic control than those with few readings (more than five readings per day vs. less than four readings per day: HbA1c, 7.2±1.1% vs. 8.0±0.9%; mean daily blood glucose, 151±22 mg/dL vs. 176±30 mg/dL; percentage of readings per month >300 mg/dL, 10±4% vs. 14±5%; percentage of readings in target range [80-180 mg/dL], 59% vs. 48% [P<0.05 in all cases]) and had a lower glycemic variability (J-score, 49±13 vs. 71±25 [P<0.05]; Hyperglycemia Index, 0.9±0.5 vs. 1.9±1.2 [P<0.05]; Index of Glycemic Control, 1.9±0.8 vs. 3.1±1.6 [P<0.05]; Hypoglycemia Index, 0.9±0.8 vs. 1.2±1.3 [not significant]). Frequent self-monitoring of blood glucose was associated with a higher number of bolus applications (6.1±2.2 boluses/day vs. 4.5±2.0 boluses/day [P<0.05]). Therefore, a similar but less pronounced effect on glycemic variability in favor of more daily bolus applications was observed (more than five vs. less than four bolues per day: J-score, 57±17 vs. 63±25 [not significant]; Hypoglycemia Index, 1.0±1.0 vs. 1.5±1.4 [P<0.05]; Hyperglycemia Index, 1.3±0.6 vs. 1.6±1.1 [not significant]; Index of Glycemic Control, 2.3±1.1 vs. 3.1±1.7 [P<0.05]).

CONCLUSIONS

Pump users who perform frequent daily glucose readings have a better glycemic control with lower glycemic variability.

Glycemic Variability: Assessing Glycemia Differently and the Implications for Dietary Management of Diabetes

The primary therapeutic target for diabetes management is the achievement of good glycemic control, of which glycated hemoglobin (HbA1c) remains the standard clinical marker. However, glycemic variability (GV; the amplitude, frequency, and duration of glycemic fluctuations around mean blood glucose) is an emerging target for blood glucose control. A growing body of evidence supports GV as an independent risk factor for diabetes complications. Several techniques have been developed to assess and quantify intraday and interday GV. Additionally, GV can be influenced by several nutritional factors, including carbohydrate quality, quantity; and distribution; protein intake; and fiber intake. These factors have important implications for clinical nutrition practice and for optimizing blood glucose control for diabetes management. This review discusses the available evidence for GV as a marker of glycemic control and risk factor for diabetes complications. GV quantification techniques and the influence of nutritional considerations for diabetes management are also discussed.

How well do glucose variability measures predict patient glycaemic outcomes during treatment intensification in type 2 diabetes?

AIM

Despite links to clinical outcomes in patients with type 2 diabetes mellitus (T2DM), the clinical utility of glycaemic variability (GV) measures is unknown. We evaluated the correlation between baseline GV, and glycated haemoglobin (HbA1c) attainment and hypoglycaemic events during treatment intensification in a large group of patients.

METHODS

Patient-level data from six 24-week clinical trials of T2DM patients undergoing treatment intensification with basal insulin or comparators (N=1699) were pooled. Baseline GV measures included standard deviation (SD), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG), coefficient of variation (CV), high blood glucose index (HBGI), and low blood glucose index (LBGI) were correlated with HbA1c change and hypoglycaemic events.

RESULTS

All mean GV measures, excluding CV which worsened, improved significantly from baseline to Week 24, with the largest proportional reduction obtained for HBGI (-65.5%). When assessed as mean individual percentage changes only HBGI improved significantly. Baseline GV correlated positively with Week 24 HbA1c for SD, MAGE, and HBGI. Baseline HBGI and CV correlated negatively and positively, respectively, with Week 24 HbA1c change. Correlations also existed between most baseline GV measures and age, body mass index, Week 24 fasting plasma glucose, Week 24 postprandial plasma glucose, and hypoglycaemic events; statistical significance depended on the specific measure.

CONCLUSIONS

Pre-treatment GV is associated with glycaemic outcomes in T2DM patients undergoing treatment intensification over 24 weeks. HBGI might be the most robust predictor, warranting validation in dedicated prospective studies or randomized trials to assess the predictive value of measuring GV.

Demonstration of an intrinsic relationship between endogenous C-peptide concentration and determinants of glycemic control in type 1 diabetes following islet transplantation

OBJECTIVE

Maintenance of endogenous pancreatic β-cell function could be an important goal in the management of type 1 diabetes. However, the impact of stimulated C-peptide level on overall glycemic control is unknown. The relationship between C-peptide and parameters of glucose control was therefore characterized in a cohort with rapidly changing β-cell function following islet transplantation.

RESEARCH DESIGN AND METHODS

Standardized mixed-meal tolerance test was undertaken in 12 consecutive islet recipients at 1-6-month intervals, with graft function determined by 90-min stimulated C-peptide. Continuous glucose monitoring was undertaken in the week preceding each assessment and the relationship between C-peptide and glucose control evaluated by mixed Poisson regression.

RESULTS

Recipients completed 5 (1-14) [median (range)] clinical assessments over 18 (1-51) months posttransplant encompassing a wide range of stimulated C-peptide levels (7-2,622 pmol/L). Increasing β-cell function across predefined C-peptide groups was associated with reduced insulin dose, HbA1c, mean glucose (low [<200 pmol/L] 10.7 vs. excellent [>1,000 pmol/L] 7.5 mmol/L), and glucose SD (low, 4.4 vs. excellent, 1.4 mmol/L). Highly statistically significant continuous associations between stimulated C-peptide and mean interstitial glucose (lower by 2.5% [95% CI 1.5-3.5%] per 100 pmol/L higher C-peptide), glucose SD, time outside glucose target range, and measures of hyper-/hypoglycemia risk were confirmed.

CONCLUSIONS

Repeated assessment of islet transplant recipients has enabled modeling of the relationship between endogenous β-cell function and measures of glycemic control providing quantitative estimates of likely impact of an acute change in β-cell function in individuals with type 1 diabetes.

Diet and glycaemia: the markers and their meaning. A report of the Unilever Nutrition Workshop

Consumption of carbohydrate-containing foods leads to transient postprandial rises in blood glucose concentrations that vary between food types. Higher postprandial glycaemic exposures have particularly been implicated in the development of chronic cardiometabolic diseases. Reducing such diet-related exposures may be beneficial not only for diabetic patients but also for the general population. A variety of markers have been used to track different aspects of glycaemic exposures, with most of the relevant knowledge derived from diabetic patients. The assessment of glycaemic exposures among the non-diabetic population may require other, more sensitive markers. The present report summarises key messages of presentations and related discussions from a workshop organised by Unilever intended to consider currently applied markers of glycaemic exposure. The particular focus of the meeting was to identify the potential applicability of glycaemic exposure markers for studying dietary effects in the non-diabetic population. Workshop participants concluded that markers of glycaemic exposures are sparsely used in intervention studies among non-diabetic populations. Continuous glucose monitoring remains the optimal approach to directly assess glycaemic exposure. Markers of glycaemic exposure such as glycated Hb, fructosamine, glycated albumin, 1,5-anhydroglucitol and advanced glycation end products can be preferred dependent on the aspect of interest (period of exposure and glucose variability). For all the markers of glycaemia, the responsiveness to interventions will probably be smaller among the non-diabetic than among the diabetic population. Further validation and acceptance of existing glycaemic exposure markers applied among the non-diabetic population would aid food innovation and better design of dietary interventions targeting glycaemic exposure.