Inpatient Glycemic Variability and Long-Term Mortality in Hospitalized Patients with Type 2 Diabetes

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.

Assessing the temporal within-day glycemic variability during hospitalization in patients with type 2 diabetes patients using continuous glucose monitoring: a retrospective observational study

AIMS

Frequent and extensive within-day glycemic variability (GV) in blood glucose levels may increase the risk of hypoglycemia and long-term mortality in hospitalized patients with diabetes. We aimed to assess the amplitude and frequency of within-day GV in inpatients with type 2 diabetes and to explore the factors influencing within-day GV.

METHODS

We conducted a single-center, retrospective observational study by analyzing hospital records and 10-day real-time continuous glucose monitoring data. Within-day GV was assessed using the coefficient of variation (%CV). The primary outcome was the amplitude and frequency of within-day GV. The frequency of within-day GV was assessed by the consecutive days (CD) of maintaining within the target %CV range after first reaching it (CD after first reaching the target) and the maximum consecutive days of maintaining within the target %CV range (Max-CD). The target %CV range was less than 24.4%. We evaluated the factors influencing within-day GV using COX regression and Poisson regression models.

RESULTS

A total of 1050 cases were analyzed, of whom 86.57% reduced the amplitude of within-day GV before the sixth day of hospitalization. Of the 1050 hospitalized patients, 66.57% stayed within the target %CV range for less than two days after first reaching the target and 69.71% experienced a Max-CD of fewer than four days. Reducing the average postprandial glucose excursion (hazard ratio [HR]: 0.81, 95% confidence interval [CI]: 0.77-0.85; incidence rate ratios [IRR]: 0.72, 95% CI: 0.69-0.74) and the use of α-glucosidase inhibitors (IRR: 1.1, 95% CI: 1.01-1.18) and glucagon-like peptide-1 agonist (IRR: 1.30, 95% CI: 1.02-1.65) contributed to reducing the amplitude and decreasing the frequency of within-day GV. However, the use of insulin (HR: 0.64, 95% CI: 0.55-0.75; IRR: 0.86, 95% CI: 0.79-0.93) and glinide (HR: 0.47, 95% CI: 0.31-0.73; IRR: 0.84, 95% CI: 0.73-0.97) may lead to an increased frequency of within-day GV.

CONCLUSIONS

An increasing frequency of within-day GV was observed during the hospitalization in patients with type 2 diabetes, despite the effective reduction in the amplitude of within-day GV. Using medications designed to lower postprandial blood glucose could contribute to minimize the risk of frequent within-day GV.

Continuous glucose monitoring (CGM) in a non-Icu hospital setting: The patient's journey

AIMS

Although consistent data support the outpatient use of continuous glucose monitoring (CGM) to improve glycemic control and reduce hypoglycemic burden, and clinical outcomes, there are limited data regarding its use in the hospital setting, particularly in the non-intensive care unit (non-ICU) setting. The emerging use of CGM in the non-critical care setting may be useful in increasing the efficiency of hospital care and reducing the length of stay for patients with diabetes while improving glycemic control.

DATA SYNTHESIS

The purpose of this Expert Opinion paper was to evaluate the state of the art and provide a practical model of how CGM can be implemented in the hospital.

SETTING

A patient's CGM journey from admission to the ward to the application of the sensor, from patient education on the device during hospitalization until discharge of the patient to maintain remote control.

CONCLUSIONS

This practical approach for the implementation and management of CGM in patients with diabetes admitted to non-ICUs could guide hospitals in their diabetes management initiatives using CGM, helping to identify patients most likely to benefit and suggesting how this technology can be implemented to maximize clinical benefits.

What is Glycaemic Variability and which Pharmacological Treatment Options are Effective? A Narrative Review

Glycated haemoglobin is currently used for diagnosis and follow-up of diabetes mellitus. However, it has important limitations; as it only reflects the average glycaemia over the last 3 months, it does not allow the identification of crucial events, such as episodes of hypoglycaemia and hyperglycaemia. Strict control of hyperglycaemia can result in severe hypoglycaemia that can be life threatening and can have important sequelae. Recently, the concept of glycaemic variability has been developed to provide information about the magnitude of glycaemic excursions and the duration of these fluctuations. This new approach has the potential to improve outcomes, decrease the risk of hypoglycaemia, and decrease cardiovascular risk. This review describes the most commonly prescribed non-insulin anti-diabetic drugs for diabetes management, their mechanism of action, and the existing evidence about their effectiveness in improving glycaemic variability and diabetes control.

Urinary c-peptide creatinine ratio (UCPCR) as a predictor of coronary artery disease in type 1 diabetes mellitus

BACKGROUND

Elevated C-peptide has been suggested as a risk factor for coronary artery disease (CAD). Elevated urinary C-peptide to creatinine ratio (UCPCR) as an alternative measurement is shown to be related to insulin secretion dysfunction; however, data regarding UCPCR predictive value for CAD in diabetes mellitus (DM) are scarce. Therefore, we aimed to assess the UCPCR association with CAD in type 1 DM (T1DM) patients.

METHODS

279 patients previously diagnosed with T1DM included and categorized into two groups of CAD (n = 84) and without-CAD (n = 195). Furthermore, each group was divided into obese (body mass index (BMI) ≥ 30) and non-obese (BMI < 30) groups. Four models utilizing the binary logistic regression were designed to evaluate the role of UCPCR in CAD adjusted for well-known risk factors and mediators.

RESULTS

Median level of UCPCR was higher in CAD group compared to non-CAD group (0.07 vs. 0.04, respectively). Also, the well-acknowledged risk factors including being active smoker, hypertension, duration of diabetes, and body mass index (BMI) as well as higher levels of haemoglobin A1C (HbA1C), total cholesterol (TC), low-density lipoprotein (LDL) and estimated glomeruli filtration rate (e-GFR) had more significant pervasiveness in CAD patients. Based on multiple adjustments by logistic regression, UCPCR was a strong risk factor of CAD among T1DM patients independent of hypertension, demographic variables (gender, age, smoking, alcohol consumption), diabetes-related factors (diabetes duration, FBS, HbA1C), lipid profile (TC, LDL, HDL, TG) and renal-related indicators (creatinine, e-GFR, albuminuria, uric acid) in both patients with BMI≥30 and BMI < 30.

CONCLUSION

UCPCR is associated with clinical CAD, independent of CAD classic risk factors, glycaemic control, insulin resistance and BMI in type 1 DM patients.

Glycemic Variability Within 1 Year Following Surgery for Stage II-III Colon Cancer

OBJECTIVE

To examine glycemic variability within 1 month and 1 year following surgery among adult patients, with and without Type 2 Diabetes (T2D), treated for stage II-III colon cancer.

METHOD

A retrospective analysis of electronic health record data was conducted. Glycemic variability (i.e., standard deviation [SD] and coefficient of variation [CV] of > 2 blood glucose measures) was assessed within 1 month and within 1 year following colon surgery. Chi-square (χ²), Fisher's exact, and Mann-Whitney U tests were used for the analyses.

RESULTS

Among the sample of 165 patients with stage II-III colon cancer, those with T2D had higher glycemic variability compared to patients without T2D (p < .001), with values within 1 month following surgery (SD = 44.69 mg/dL, CV = 27.4%) vs (SD = 20.55 mg/dL, CV = 17.53%); and within 1 year following surgery (SD = 45.04 mg/dL, CV = 29.04%) vs (SD = 21.36 mg/dL, CV = 18.6%). Associations were found between lower body mass index and higher glycemic variability (i.e., SD [r = -.413, p < .05] and CV [r = -.481, p < .01]) within 1 month following surgery in patients with T2D. Higher preoperative glucose was associated with higher glycemic variability (i.e., SD r = .448, p < .01) within 1 year in patients with T2D. Demographic and clinical characteristics were weakly associated with glycemic variability in patients without T2D.

CONCLUSIONS

Patients with stage II-III colon cancer with T2D experienced higher glycemic variability within 1 month and within 1 year following surgery compared to those without T2D. Associations between glycemic variability and demographic and clinical characteristics differed by T2D status. Further research in prospective studies is warranted.

In-Hospital Mortality and Glycemic Control in Patients with Hospital Hyperglycemia

BACKGROUND

Stress-induced hyperglycemia is a phenomenon that occurs typically in patients hospitalized for acute disease and resolves spontaneously after regression of the acute illness. However, it can also occur in diabetes patients, a fact that is sometimes overlooked. It is thus important to make a proper diabetes diagnosis if hospitalized patients with episodes of hyperglycemia with and without diabetes are studied.

AIMS

To estimate the extent of the association between stress-induced hyperglycemia and in-hospital mortality in patients with hospital hyperglycemia (HH), and to explore potential differences between patients diagnosed with diabetes (HH-DBT) and those with stress-induced hyperglycemia (SH), but not diagnosed with diabetes.

METHODS

A cohort of adults with hospital hyperglycemia admitted to a tertiary, university hospital in Buenos Aires, Argentina, was analyzed retrospectively.

RESULTS

In the study, 2,955 patients were included and classified for analysis as 1,579 SH and 1,376 HH-DBT. Significant differences were observed in glycemic goal (35.53% SH versus 25.80% HH-DBT, p < 0.01), insulin use rate (26.66% SH versus 46.58% HH-DBT, p < 0.01), and severe hypoglycemia rate (1.32% SH versus 1.74% HH-DBT, p < 0.01). There were no differences in hypoglycemia rate (8.23% SH versus 10.53% HH-DBT) and hospital mortality. There was no increase in risk of mortality in the SH group adjusted for age, non-scheduled hospitalization, major surgical intervention, critical care, hypoglycemia, oncological disease, cardiovascular comorbidity, and prolonged hospitalization.

CONCLUSIONS

In this study, we observed better glycemic control in patients with SH than in those with HH-DBT, and there was no difference in hospital mortality.

Glycemic Variability With Insulin Glargine Versus Detemir in Hospitalized Patients With Diabetes

BACKGROUND

Prior research has demonstrated increased mortality with increasing glycemic variability (GV) in hospitalized patients with diabetes.

OBJECTIVE

We aimed to compare glycemic variability (GV) of insulin glargine to detemir in the inpatient setting.

METHODS

This single-center, retrospective, cohort study evaluated noncritically ill patients with diabetes on long-acting insulin at a large academic medical institution between 2010 and 2017. This study was reviewed and approved by the Institutional Review Board. The formulary transitioned from insulin glargine to detemir in December 2013; therefore, patients were compared before and after transition. The primary endpoint was to compare coefficient of variation (CV), a measure of GV, between detemir and glargine. Secondary endpoints included GV measured by standard deviation (SD), CV within 72 hours of long-acting insulin initiation, length-of-stay (LOS), in-hospital mortality, and comparison between subgroups.

RESULTS

2334 patients were included in the study, and there were 1167 in each group. CV was significantly less variable with detemir compared to glargine (33.7% versus 34.8%, difference = 1.09, p = 0.02) and remained significant after controlling for confounders. Similarly, SD was significantly less with detemir (p = 0.048). CV within 72 hours, LOS, and in-hospital mortality were not statistically different. Lastly, GV was higher in medical patients compared to surgical.

CONCLUSION

Insulin detemir exhibited less GV than insulin glargine, although the small difference is unlikely to be clinically significant. Application of this data will aid in formulary decisions and support the use of either agent within the hospital setting.

Safe care for people with diabetes in hospital

Diabetes is the most prevalent long-term condition, occurring in approximately 6.5% of the UK population. However, an average of 18% of all acute hospital beds are occupied by someone with diabetes. Having diabetes in hospital is associated with increased harm - however that may be defined. Over the last few years the groups such as the Joint British Diabetes Societies for Inpatient Care have produced guidelines to help medical and nursing staff manage inpatients with diabetes. These guidelines have been rapidly adopted across the UK. The National Diabetes Inpatient Audit has shown that over the last few years the care for people with diabetes has slowly improved, but there remain challenges in terms of providing appropriate staffing and education. Patient safety is paramount, and thus there remains a lot to do to ensure this vulnerable group of people are not at increased risk of harm.

Characteristics, Management, and Outcomes of Elderly Patients with Diabetes in a Covid-19 Unit: Lessons Learned from a Pilot Study

Background and objectives: Diabetes may affect in-hospital mortality of patients with Coronavirus disease 2019 (COVID-19). We have retrospectively evaluated clinical characteristics, diabetes management, and outcomes in a sample of COVID-19 patients with diabetes admitted to our hospital. Materials and Methods: All patients admitted to the Infectious Diseases Unit from 28 March 2020, to 16 June 2020, were enrolled. Clinical information and biochemical parameters were collected at the time of admission. Patients were ranked according to diabetes and death. Results: Sixty-one patients with COVID-19 were analyzed. Most of them were from a long-term health care facility. Mean age was 77 ± 16 years, and 19 had type 2 diabetes (T2D). Eighteen patients died, including 8 with T2D and 10 without T2D (p = 0.15). Patients with diabetes were significantly older, had a higher prevalence of cardiovascular diseases, and a significantly lower lymphocyte count. No significant relationship was found between diabetes and in-hospital mortality (Odds Ratio OR 2.3; Confidence Interval CI 0.73–7.38, p = 0.15). Patients with diabetes were treated with insulin titration algorithm. Severe hypoglycemic events, ketoacidosis and hyperosmolar hyperglycemias did not occur during hospitalization. Mean pre-meal capillary blood glucose was 157 ± 45 mg/dL, and the coefficient of variation of glycaemia was 29%. Conclusions: Our study, albeit limited by the small number of subjects, did not describe any significant association between T2D diabetes and mortality. Clinical characteristics of patients, and acceptable glucose control prior and during hospitalization may have influenced the result. The use of an insulin titration algorithm should be pursued during hospitalization.

Affinity-based protein profiling to reveal targets of puerarin involved in its protective effect on cardiomyocytes

Natural products are an important source of new drugs. Some of them may be used directly in clinical settings without further structural modification. One of these directly used natural products is puerarin (Pue), which protects cardiomyocytes against oxidative stress and high glucose stress. Although Pue has been used in clinics for many years, its direct binding targets involved in the protection of cardiomyocytes are not yet fully understood. Here, we reported that Pue could prevent cardiomyocytes from apoptosis under H₂O₂ and high glucose conditions. Based on affinity-based protein profiling methods, we synthesized an active Pue probe (Pue-DA) with a photosensitive crosslinker to initiate a biological orthogonal reaction. Because of the steric hindrance of Pue-DA, two conformational isomers (syn and anti) unequivocally existed in the probe, and these transformed into one isomer when the probe was heated at 60 °C. We confirmed that the alkylation was on the 7-position phenol group of Pue. Mass spectroscopy revealed that Pue-DA can bind with three proteins, namely CHAF1B, UBE2C, and UBE2T. Finally, cellular thermal shift assay showed that Pue has the ability to stabilize CHAF1B stabilization. The knock-down of CHAF1B reduced the protective effect of Pue on cardiomyocytes. In conclusion, Pue protects cardiomyocytes from apoptosis through binding with CHAF1B.

A systematic review considering risk factors for mortality of patients discharged from hospital with a diagnosis of diabetes

AIM

To identify known risk factors for mortality for adult patients, discharged from hospital with diabetes.

METHOD

The systematic review was based on the PRISMA protocol. Studies were identified through EMBASE & MEDLINE databases. The inclusion criteria were papers that were published over the last 6 years, in English language, and focused on risk factors of mortality in adult patients with diabetes, after they were discharged from hospitals. This was followed by data extraction "with quality assessment and semi-quantitative synthesis according to PRISMA guidelines".

RESULTS

There were 35 studies identified, considering risk factors relating to mortality for patients, discharged from hospital with diabetes. These studies are distributed internationally. 48 distinct statistically significant risk factors for mortality can be identified. Risk factors can be grouped into the following categories; demographic, socioeconomic, lifestyle, patient medical, inpatient stay, medication related, laboratory results, and gylcaemic status. These risk factors can be further divided into risk factors identified in generalized populations of patients with diabetes, compared to specific sub-populations of people with diabetes.

CONCLUSION

A relatively small number of studies have considered risk factors relating to mortality for patients, discharged from hospital with a diagnosis of diabetes. Mortality is an important outcome, when considering discharge from hospital with diabetes. However, there has only been limited consideration within the research literature.

Differences in Clinical Outcomes between Patients with and without Hypoglycemia during Hospitalization: A Retrospective Study Using Real-World Evidence

BACKGROUND

Some patients admitted to hospitals for glycemic control experience hypoglycemia despite regular meals and despite adhering to standard blood glucose control protocols. Different factors can have a negative impact on blood glucose control and prognosis after discharge. This study investigated risk factors for hypoglycemia and its effects on glycemic control during the hospitalization of patients in the general ward.

METHODS

This retrospective study included patients who were admitted between 2009 and 2018. Patients were provided regular meals at fixed times according to ideal body weights during hospitalization. We categorized the patients into two groups: those with and those without hypoglycemia during hospitalization.

RESULTS

Of the 3,031 patients, 379 experienced at least one episode of hypoglycemia during hospitalization (HYPO group). Hypoglycemia occurred more frequently particularly in cases of premixed insulin therapy. Compared with the control group, the HYPO group was older (61.0±16.8 years vs. 59.1±16.5 years, P=0.035), with more females (60.4% vs. 49.6%, P<0.001), lower body mass index (BMI) (23.5±4.2 kg/m² vs. 25.1±4.4 kg/m², P<0.001), and higher prevalence of type 1 diabetes mellitus (6.1% vs. 2.6%, P<0.001), They had longer hospital stay (11.1±13.5 days vs. 7.6±4.6 days, P<0.001). After discharge the HYPO group had lower glycosylated hemoglobin reduction rate (-2.0%±0.2% vs. -2.5%±0.1%, P=0.003) and tended to have more frequent cases of cardiovascular disease.

CONCLUSION

Hypoglycemia occurred more frequently in older female patients with lower BMI and was associated with longer hospital stay and poorer glycemic control after discharge. Therefore, clinicians must carefully ensure that patients do not experience hypoglycemia during hospitalization.

Glycemic variability: adverse clinical outcomes and how to improve it?

Glycemic variability (GV), defined as an integral component of glucose homoeostasis, is emerging as an important metric to consider when assessing glycemic control in clinical practice. Although it remains yet no consensus, accumulating evidence has suggested that GV, representing either short-term (with-day and between-day variability) or long-term GV, was associated with an increased risk of diabetic macrovascular and microvascular complications, hypoglycemia, mortality rates and other adverse clinical outcomes. In this review, we summarize the adverse clinical outcomes of GV and discuss the beneficial measures, including continuous glucose monitoring, drugs, dietary interventions and exercise training, to improve it, aiming at better addressing the challenging aspect of blood glucose management.

Impact of Glycemic Variability and Hypoglycemia on the Mortality and Length of Hospital Stay among Elderly Patients in Brazil

BACKGROUND

Glycemic variability (GV) is an alternative diabetes-related parameter that has been associated with mortality and longer hospitalization periods. There is no ideal method for calculating GV. In this study, we used standard deviation and coefficient of variation due to their suitability for this sample and ease of use in daily clinical practice.

OBJECTIVE

This study aimed to investigate the association between GV, hypoglycemia, and the 90-day mortality and length of hospital stay (LOS) among non-critically ill hospitalized elderly patients.

METHODS

The medical records of 2,237 elderly patients admitted to the Zilda Arns Elderly Hospital over a 2.5-year period were reviewed. Hypoglycemia was defined as a glucose level <70 mg/dL (hypoglycemia alert value) and represented by the proportion of days in which the patient presented with this condition relative to the LOS. The Charlson comorbidity index was used to evaluate prognosis. Data were analyzed using multiple linear and logistic multivariate regression analyses.

RESULTS

Adjusted analysis of 687 patients (305 men [44.4%] and 382 women [55.6%], mean age of 77.86±9.25 years) revealed that GV was associated with a longer LOS (p=0.048). Mortality was associated with hypoglycemia (p=0.005) and mean patient-day blood glucose level (p=0.036). Variables such as age (p<0.001), Charlson score (p<0.001), enteral diet (p<0.001), and corticosteroid use (p=0.007) were also independently associated with 90-day mortality.

CONCLUSION

Increased GV during hospitalization is independently associated with a longer LOS and hypoglycemia in non-critically ill elderly patients, while the mean patient-day blood glucose is associated with increased mortality.

Hypoglycemia and hyperglycemia are risk factors for falls in the hospital population

OBJECTIVE

To determine the role of hypoglycemia, hyperglycemia or the combination of both as independent risk factors for falls in a hospital population. Secondary objectives included evaluation of other risk factors for falling and their relationships with glucose levels.

RESEARCH DESIGN AND METHODS

Retrospective cohort study over 2 years on hospitalized subjects (N = 57411) analyzing in-hospital-falls and capillary glucose values. Bivariate analysis (χ² test) and multivariate analysis (logistic regression) were performed to test for correlation of glucose values, age, sex, Charlson index, service of care, diagnosis at discharge and diabetes treatment with risk of in-hospital-falls.

RESULTS

The comparison of patients who experienced a fall (fall population) with the non-fall population suggested that: glucose determinations were significantly more frequent in the fall population (OR 3.45; CI 2.98-3.99; p < 0.0001); values of glucose below 70 mg/dl and over 200 mg/dl were significantly associated to falls during hospitalization (OR 1.76; CI 1.42-2.19; p < 0.001) as compared to glycemic values between 70 and 200 mg/dl; diabetes treatment was significantly correlated to risk of fall (OR 2.97; CI 2.54-3.49; p < 0.001); the frequency of glycemia measurements below 70 mg/dl and over 200 mg/dl in the same subject was significantly associated to falls during hospitalization (OR 1.01; CI 1.01-1.02; p < 0.001).

CONCLUSION

Hypoglycemia and hyperglycemia during hospital stays are correlated with an increased risk for falls in the hospitalized population. Presence of diabetes, use of insulin or glucose variability could potentially constitute risk factors for falls inside the hospital as well.

Admission Glucose Number (AGN): A Point of Admission Score Associated With Inpatient Glucose Variability, Hypoglycemia, and Mortality

AIMS

We investigated a point of admission metric of glycemia, the Admission Glucose Number (AGN), and its relationship with both high risk inpatient glucose patterns and mortality in hospital inpatients with type 2 diabetes (T2DM).

METHODS

Inpatient capillary blood glucose (CBG) data for patients with T2DM in our health board were identified for a 5-year period and associated with most recent preadmission HbA1c. AGN was calculated as first CBG measured during admission (mmol/L), subtracted from most recent preadmission HbA1c (converted to estimated median glucose mmol/l) within 15 months preadmission. The association between AGN and CBG variability (interquartile range), hypoglycemia free survival (HR) and both inpatient and 100-day mortality (HR) were investigated.

RESULTS

A total of 21 045 first admissions with available HbA1c data were identified. A positive correlation between AGN and glycemic variability was described (partial correlation coefficient 0.25, P < .001), which was stronger than the correlation of either of AGNs' individual components: adjusted CBG1 = 0.07 ( P < .001), eAG = 0.08 ( P < .001). The hazard ratio for time to first recorded CBG < 3 mmol/L for high AGN versus low AGN was 1.74 (95% CI 1.55-1.96), P < .001. A high AGN was associated with increased 100-day mortality (HR 1.26, P = .005), however not with in-hospital mortality (HR = 1.31, P = .08).

CONCLUSION

AGN is a simple metric that combines 2 readily available measures associated with adverse outcome in T2DM. AGN may be a useful tool to stratify patients for risk of hypoglycemia and postdischarge death.

Glycaemic variation is a predictor of all-cause mortality in the Veteran Affairs Diabetes Trial

Diabetes is associated with substantially increased mortality. Classic risk factors explain a portion of the excess of mortality in type 2 diabetes. The aim of this study was to examine whether visit-to-visit variation in fasting glucose and haemoglobin A1c values in the Veteran Affairs Diabetes Trial were associated with all-cause mortality in patients with type 2 diabetes in addition to other comorbidity conditions, hypoglycaemic events and adverse lifestyle behaviours. The Veteran Affairs Diabetes Trial was a randomized trial that enrolled 1791 military veterans who had a suboptimal response to therapy for type 2 diabetes to receive either intensive or standard glucose control. During the Veteran Affairs Diabetes Trial, fasting glucose and haemoglobin A1c were measured quarterly for up to 84 months. Variability measures included coefficient of variation and average real variability. We found that variability measures (coefficient of variation and average real variability) of fasting glucose were predictors of all-cause mortality, even after adjusting for comorbidity index, mean fasting glucose and adverse lifestyle behaviour during the study. Accounting for severe hypoglycaemia did not weaken this association. Our analysis indicates that in the Veteran Affairs Diabetes Trial, longitudinal variation in fasting glucose was associated with all-cause mortality, even when accounting for standard measures of glucose control as well as comorbidity and lifestyle factors.

The impact of dapagliflozin on glucose excursions related to early proatherogenic derangement in the aortic wall

INTRODUCTION

Cardiovascular risk in the course of diabetes depends greatly on glycemic variability which is even more significant than chronic hyperglycemia. Optimal management of diabetes involves a multidisciplinary approach focused in particular on decreasing the risk of atherosclerosis. Therefore, our purpose was to evaluate the impact of dapagliflozin on glucose excursions and related proatherogenic changes in the aortic wall.

METHODS AND MATERIALS

Animal model of type 2 diabetes rich-fat/STZ rats was used. Wistar rats were randomized into 3 groups: dapagliflozin-treated with glucose excursions, placebo-treated with glucose excursions and placebo-treated with stable diabetes. Dapagliflozin was administered once a day, 1 mg/kg, for 8 consecutive weeks. Glucose levels were measured twice a week at fasting and postprandially. The samples of aortas were taken for histopathological and immunochemistry examinations at the end of the experiment. The derangement in the aortic wall and the distribution of CD68+ cells in the aorta were considered early signs of atherosclerosis.

RESULTS

Dapagliflozin reduced glucose excursion to the level characteristic for stable, well-controlled diabetes. It was related to a significant decrease in histopathological changes which were observed in the placebo-treated rats with glucose variability. Dapagliflozin significantly reduced also the accumulation of CD68+ macrophages in the aortic adventitia.

CONCLUSION

Dapagliflozin provides not only mere beneficial regulation of metabolic status with the depletion of glucose variability, but is also helpful in the prevention of early atherosclerosis related to the course of diabetes type 2.

Structured education using Dose Adjustment for Normal Eating (DAFNE) reduces long-term HbA1c and HbA1c variability

AIMS

Previous evidence has demonstrated that participation in the Dose Adjustment for Normal Eating (DAFNE) education programme can reduce HbA_1c and severe hypoglycaemia in people with Type 1 diabetes. In a number of studies, increased HbA_1c variability has been associated with higher diabetic morbidity and mortality. No studies have examined the impact of structured education on HbA_1c variability in Type 1 diabetes.

METHODS

People with Type 1 diabetes who had attended DAFNE were identified for inclusion from the Scottish Care Information-Diabetes dataset. HbA_1c median and variability, expressed as coefficient of variation (CV) before and after DAFNE was calculated.

RESULTS

Some 1061 individuals participated in DAFNE education and 687 met the inclusion criteria. A significant median reduction in HbA_1c [-3.5 mmol/mol (-0.3%)] was seen at 12 months with a significant reduction [-1.5 mmol/mol (-0.1%)] still seen at 60 months of follow-up. HbA_1c variability as measured by CV was significantly lower during the post-DAFNE period: 0.08 (IQR 0.05-0.12) reduced to 0.07 (IQR 0.05-0.10); P = 0.002.

CONCLUSION

The data confirm that DAFNE participation improves glycaemic control in Type 1 diabetes with benefits being sustained for 5 years. This study is the first to demonstrate reduced HbA_1c variability after completion of structured education. This is new evidence of the beneficial impact of DAFNE on glycaemic profile.

Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data

BACKGROUND

Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach.

METHODS

The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D.

RESULTS

Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy.

CONCLUSIONS

Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.

Relationship of Hyperglycaemia, Hypoglycaemia, and Glucose Variability to Atherosclerotic Disease in Type 2 Diabetes

OBJECTIVE

Type 2 diabetes mellitus (T2DM) is known to be associated with increased cardiovascular risk. The aim of this study was therefore to investigate the independent effects of hyperglycaemia, hypoglycaemia, and glucose variability on microvascular and macrovascular disease in T2DM.

METHODS

Subjects with T2DM of <10 years duration and on stable antiglycaemic treatment underwent carotid intima-media thickness (CIMT), ankle-brachial index (ABI), albumin-creatinine ratio (ACR), and HbA_1c measurement, as well as 72-hour continuous glucose monitoring. Macrovascular disease was defined as one or more of the following: history of ischaemic heart disease (IHD), cerebrovascular accident (CVA), ABI < 0.9, or abnormal CIMT.

RESULTS

The study population comprised 121 subjects with T2DM (89 males : 32 females). The mean age was 62.6 years, and the mean DM duration was 3.7 years. Macrovascular disease was present in 71 patients (58.7%). In multivariate logistic regression analysis, body surface area (BSA) (OR 18.88 (95% CI 2.20-156.69), p = 0.006) and duration of blood glucose (BG) < 3.9 mmol/L (OR 1.12 (95% CI 1.014-1.228), p = 0.024) were independent predictors of macrovascular disease. BSA (OR 12.6 (95% CI 1.70-93.54), p = 0.013) and duration of BG < 3.9 mmol/L (OR 1.09 (95% CI 1.003-1.187), p = 0.041) were independent predictors of abnormal CIMT. Area under the curve for BG > 7.8 mmol/L (β = 15.83, p = 0.005) was the sole independent predictor of albuminuria in generalised linear regression.

CONCLUSIONS

This study demonstrates that hypoglycaemia is associated with the occurrence of atherosclerotic disease while hyperglycaemia is associated with microvascular disease in a Caucasian population with T2DM of recent duration.

A Practical Review of C-Peptide Testing in Diabetes

C-peptide is a widely used measure of pancreatic beta cell function. It is produced in equimolar amounts to endogenous insulin but is excreted at a more constant rate over a longer time. Methods of estimation include urinary and unstimulated and stimulated serum sampling. Modern assays detect levels of c-peptide which can be used to guide diabetes diagnosis and management. We explore the evidence behind the various tests available. We recommend the glucagon stimulation c-peptide testing owing to its balance of sensitivity and practicality. C-peptide levels are associated with diabetes type and duration of disease. Specifically a c-peptide level of less than 0.2 nmol/l is associated with a diagnosis of type 1 diabetes mellitus (T1DM). C-peptide level may correlate with microvascular and macrovascular complications and future use of insulin therapy, as well as likely response to other individual therapies. We explore the potential uses of c-peptide measurement in clinical practice.

Patterns and Impact of Hypoglycemia, Hyperglycemia, and Glucose Variability on Inpatients with Insulin-Treated Cystic Fibrosis-Related Diabetes

INTRODUCTION

Mortality in patients with cystic fibrosis-related diabetes (CFRD) is higher than that in patients with cystic fibrosis without diabetes. Hypoglycemia, hyperglycemia, and glucose variability confer excess mortality and morbidity in the general inpatient population with diabetes.

METHODS

We investigated patterns of hypoglycemia and the association of hypoglycemia, hyperglycemia, and glucose variability with mortality and readmission rate in inpatients with CFRD. All capillary blood glucose (CBG) readings (measured using the Abbott Precision web system) of patients with insulin-treated CFRD measured within our health board between January 2009 and January 2015 were. Frequency and timing of hypoglycemia (<4 mmol/L) and was recorded. The effect of dysglycemia on readmission and mortality was investigated with survival analysis.

RESULTS

Sixty-six patients were included. A total of 22,711 CBG results were included in the initial analysis. Hypoglycemia was common with 1433 episodes (6.3%). Hypoglycemia ascertainment was highest between 2400 and 0600 h. Hypoglycemia was associated with a significantly higher rate of readmission or death over the 3.5-year follow-up period (P = 0.03). There was no significant association between hyperglycemia or glucose variability and the rate of readmission and mortality.

CONCLUSION

Among inpatients with CFRD hypoglycemia is common and is associated with an increased composite endpoint of readmission and death. As with previously reported trends in general inpatient population this group shows a peak incidence of hypoglycemic during the night.