Background and Aims

DIABETER delivers value-based T1D care resulting in better glycaemic control (vs. the Dutch average) among paediatric patients who were treated at DIABETER from diagnosis onwards (primary patients: 31%). However, 69% of patients received treatment in other clinics before they transferred to DIABETER (secondary patients). Recent studies show tracking of life-time HbA1c values and clinical inertia. We assess if our care model improves glycaemic control of secondary patients or if tracking prevents improvement toward glycaemic levels comparable to the primary patient group.

Methods

HbA1c values extracted from our disease management system Vcare were included for patients treated ≥1 year at DIABETER (n= 2014). Secondary patients were only included if they had received ≥1 year of previous care in another clinic. HbA1c changes, determined cross-sectionally per year from 2006-2018, were analysed descriptively for primary and secondary patients. Three hospitals (H1-3) discontinued T1D care and transferred their T1D patients to DIABETER, allowing study of both ‘en bloc’ and individual patient transfers from >40 other referring centers.

Results

HbA1c levels from primary patients (all age groups) fluctuate around 8.0 % over the years (figure). Secondary patients had higher HbA1c at the time they transferred to DIABETER, but the group gradually improved (over months to years) to HbA1c levels comparable with those of primary patients.

Conclusions

Transition to DIABETER results in improved glycaemic control comparable with our primary patients, showing value in our comprehensive care model which may also overcome tracking in (secondary) patients. Additional studies, including treatment, care methods, use of technology and patient-related factors, are needed.

Background and Aims

Prevalence of Diabetes mellitus (DM)has reached epidemic proportions globally of which developing countries like India are likely to bear maximum burnt in 21^st century, Diabetic nephropathy is a chronic micro vascular complication , leading to end stage renal disease (ESRD). Control of DM is monitored by HbA1c. There are two important early markers to asses renal impairment , glomerular filtration rate (GFR) & microalbuminuria . Microalbuminria is better reflected by spot urine albumin-creatinine ratio (Urinary ACR) and estimated GFR can be calculated by equations .Objective of the study was to evaluate the association of HbA1c with urinary ACR and eGFR in Type 2DM. patients.

Methods

A cross sectional study carried out in the department of Pathology and Clinical biochemistry of Rajeev Gandhi College, including 50 known type 2 DM patients of 40-75 years age were evaluated dividing them on the basis of HbA1c (<8%,>8%), duration of DM (>5 years, <5 years), Blood Glucose, Serum Creatinine, Urinary Albumin & Creatinine were estimated. eGFR and urinary ACR were calculated. Results were expressed as mean ± SD. Data were analyzed with SPSS. Pearson’s correlation tests were performed to assess level of significance.

Results

Study shows that, HbA1c has significant positive correlation with Urinary ACR & with S. Creatinine and there is significant negative correlation of HbA1c with eGFR more with HbA1c >8%. Duration of DM has less significant correlation with renal functional parameters.

Conclusions

Raised HbA1c is associated with urinary ACR. ACR should be estimated in monitoring risk assessment of Type 2DM in patients with raised HbA1c.

Background and Aims

Previous studies reported that oxidative stress was associated with glucose variability measured by continuous glucose monitoring (CGM) in T2DM. The use of CGM, however, remains limited at present. We investigated the relationship between glycemic markers and oxidative stress in type 2 diabetes mellitus (T2DM).

Methods

Oxidative stress, hemoglobin A1c (HbA1c), and glycated albumin (GA) and 1,5-anhydro-D-glucitol (1.5-AG) were measured in 234 patients with T2DM. The oxidative stress was estimated using the diacron-reactive oxygen metabolites (d-ROMs) test. The associations of d-ROMs with GA, 1.5-AG, blood glucose, lipid metabolism markers, blood pressure, and clinical factors were examined.

Results

Fasting plasma glucose (FPG), HbA1c, GA, TG, and LDL-C were correlated with d-ROMs in all of the T2DM patients. HbA1c and TG were correlated with d-ROMs in T2DM patients with HbA1c < 8.0 %. FPG, HbA1c, and GA were correlated with d-ROMs in T2DM patients with HbA1c ≧ 8.0 %. An inverse correlation was found between 1.5-AG and d-ROMs in all of the patients and in patients with HbA1c < 8.0 %. The following factors were found to be independent of d-ROMs by stepwise multiple regression analysis: 1.5-AG, sex, GA, TG, and the use of metformin in all patients; 1.5-AG, sex, HbA1c, and the use of metformin in patients with HbA1c < 8.0 %; and GA, sex, and FPG in patients with HbA1c ≧ 8%.

Conclusions

Our data suggest that 1.5-AG reliably estimates oxidative stress in well-controlled T2DM and that GA reliably estimates oxidative stress in poorly controlled T2DM.

Background and Aims

We report the case of a woman with T2DM who presented hemolytic anemia (HA). The treatment with corticosteroids led to a great variability of her BG, as well as difficulty to manage her BG and falsely low A1c. A FGM was installed to allow better evaluation of her glucose profile.

Methods

HA treatment was made with Prednisone and a pulse of 3 days of Methylprednisolone. Her BG had great variations and her A1c was falsely low. We put a FGM system to evaluate the behaviour of glucose levels in her hospitalization and compared them with the BG results.

Results

Her A1c was 4.5%, but considering HA, it was considered unreal. After 3 days with the FGM, she had an estimated A1c of 9.6% (mean glucose of 229mg/dl) and time in-range (TIR) of 37%, while the mean BG was of 253mg/dl in the same period. After 3 days of insulin management, she had an estimated A1c of 7.5% (mean glucose of 163mg/dl) and TIR of 64%, with the mean of BG of 151mg/dl.

Conclusions

The case is the first one we know in which the hemoglobinopathy was acquired and treated with corticosteroids hindering T2DM management. It is one of the few cases in which the FGM was used in hospitalar environment for optimal diabetes management. The FGM is little studied in intra-hospitalar environments, but, the success of our case suggests that further studies should be conducted in order to evaluate the feasibility of this tool to help the management of challenging cases, not only outside, but even inside the hospital.

Background and Aims

This study compared the effects of multiple daily injection therapy (MDI) on glycemic variability in a real fasting with CGM technology in real- life condition of patients with type 1 diabetes (T1D).

Methods

This was a paralell trial in 47 T1D patients, with a mean age of 34 years, and glycosilated haemoglobin (HbA1c) level of 8.50%; 31 patients were treated MDI with glargina-100, 10 with detemir, 6 with degludec). We analyzed the patients in three groups of HbA1c, <7.5% (N=17), 7.5-8-8.8% (N=15) and >8.8% (N=15). We used CGM by evaluated the coefficient of variation (CV) and mean amplitude of glycemic excursions (MAGE), we defined real fasting before three hours to breakfast and real posprandial state by rest of day.

The primary end point was the change of CV and MAGE in real fasting. Secondary end points included CV and MAGE in posprandial state. We compared different subgroups of basal HbA1c and insulin basal therapy.

Results

the HbA1c was different (6.86±0.14 vs 8.24±0.10 vs 10.28±0.30; p=0.001).However we didn’t found differences between the groups respect to CV-fasting (28.65±2.53 vs 37.39±4.17 vs 30.81±2.73; p=0.14) and MAGE-fasting (162.70±24.25 vs 158.93±23.40 vs 135.60±22.84; p=0.68). We found the only significant differences in analysis of subgroups with MAGE-posprandial state (77.58±12.92 vs 83.66±13.27 vs 128.93±17.36; p=0.034). We didn’t found any differences in analysis of subgroups with different insulin basal therapy

Conclusions

In T1D patients under clinical practice conditions, subgroups Of HbA1c or insulin basal therapy were not different regarding glycemic variability in a real fasting period.

Background and Aims

To study if motivational interviewing (MI) added to standard educational care (SE) improves vascular health in adolescents with poorly controlled type 1 diabetes.

Methods

47 adolescents with type 1 diabetes of at least 2 years duration and HbA1c > 75 mmol/mol (> 9.0%) on two visits were randomized to MI+SE or SE, clinicaltrials.gov; NCT02637154.

Results

39 adolescents (20 MI + SE) completed the study. At 12 months, vascular health parameter changes were not statistically significantly different between MI + SE and SE (carotid-femoral pulse-wave velocity (PWV): mean difference 0.052 m/s (95% CI -0.395 – 0.500, p=0.81); carotid-radial PWV: 0.118 m/s (95% -0.478 – 0.713, p=0.69), carotid intima-media thickness (IMT): 0.002 mm (95% CI -0.37 – 0.40, p=0.93), systolic blood pressure (SBP) z-score: 0.495 (95% CI -0.099 – 1.09, p=0.10). At baseline, duration of type 1 diabetes was associated with radial IMT (r=0.430, p=0.007) and cfPWV (r=0.373, p=0.018), and carotid, femoral and brachial IMT were correlated with CGM-SD (r=0.440, p=0.017; r=0.377, p=0.048; r=0.387, p=0.038). There was an inverse association between CGM time-in-range (3.9-10.0 mmol/L) and crPWV (r=-0.476, p=0.022) changes. SBP change was associated with BMI change (r=0.374, p=0.019) and IMT change (r=0.461, p=0.016 for carotid IMT; r=0.498, p=0.010 for femoral IMT).

Conclusions

There was no effect of MI added to SE on vascular health parameters. Although disease duration and glycemic control were associated with vascular health at baseline, there were only limited associations between glycemic control and vascular health parameter changes. Vascular health parameter changes were interrelated suggesting clustering of cardiovascular risk.

Background and Aims

The objective of this study was to assess the cost-effectiveness of retrospective CGM (rCGM) in people with type 2 diabetes (T2D).

Methods

The IQVIA CORE Diabetes model was used to perform cost-effectiveness analyses over patient lifetimes. Clinical data were sourced from the single-arm before/after ADJUST study. Type 2 patients already on insulin were equipped with a rCGM device. The use of the rCGM was associated with a reduction in HbA1c of -1.3%, from 9.4% (79 mmol/mol) at baseline to 8.1% (65 mmol/mol) at 12 months. Cost data, expressed in 2018 euros (EUR), were obtained from Portuguese reference prices and the published literature. A 5% discount rate was applied to both clinical and economic outcomes.

Results

rCGM was associated with a quality-adjusted life-year (QALY) gain of 0.09 per patient based on their remaining life expectancy (ca 24 years) but with higher overall costs 616 EUR, due to the costs of rCGM and related visits. This led to an incremental cost-effectiveness ratio (ICER) of EUR 6,765 per QALY gained. Use of rCGM would lower the cumulative incidence of diabetes-related complications. Higher rCGM acquisition costs were partially offset by reduced complication costs. Extensive sensitivity analysis on key drivers confirmed the robustness of results.

Conclusions

rCGM was associated with improved glycemic control and quality of life in peoples with T2D with elevated HbA1c and already on insulin. rCGM is a cost-effective management tool for people with T2D in Portugal.

Background and Aims

The relationship between intermediate-term glycemic variability and hypoglycemia is well unknown, therefore, we analyzed that relationship using continuous glucose monitor (CGM) data.

Methods

We cross-sectionally analyzed CGM (FreeStyle Libre Pro) data for 97 patients with type 2 diabetes whose 24 h glucose levels were measured continuously for 13 days during hospitalization for type 2 diabetes treatment. Values over a span of 13 days for all glycemic variability and hypoglycemia metrics were evaluated. We have proposed novel glycemic variability metrics as follows: mean of daily difference 1 (MODD1) ÷ mean glucose level × 100 (MODD1/mean), mean absolute glucose (MAG) ÷ mean glucose level × 100 (MAG/mean), and glycemic variability percentage (GVP) ÷ mean glucose level × 100 (GVP/mean).

Results

The standard deviation (SD), MODD1, MAG, GVP, and the mean glucose level significantly negatively correlated with the percentage of time in the hypoglycemic range (< 70 mg/dL) [TIR < 70] (r = -0.32 – -0.75, p = 0.002 ~ < 0.001). Coefficient of variation (CV) tended to correlate with TIR < 70 positively. MODD1/mean, MAG/mean, and GVP/mean significantly positively correlated with TIR < 70. CV, MODD1/mean, MAG/mean, and GVP/mean significantly positively correlated with the percentage of time in the hypoglycemic range (< 54 mg/dL) [TIR < 54] (Table).

Conclusions

Intermediate-term glycemic variability which is divided by the mean glucose level may predict hypoglycemia.

Background and Aims

We analysed data obtained from electronic patient records from inpatients with diabetes admitted to a large university hospital.

Methods

The study was conducted using electronic patient record data from Oxford University Hospitals NHS Foundation Trust. The dataset contains hospital admission data for patients with diabetes. We define a biochemical hypoglycemic episode as any blood glucose measurement < 4mmol/l and a clinically significant hypoglycemic episode as any blood glucose measurement <3mmol/l. Any two or more than two consecutive low blood glucose within a 4-hour time window are considered as one hypoglycemic episode.

Results

We analyzed data obtained from 17,658 inpatients with diabetes [1,696 type 1 diabetes, 14,006 type 2 diabetes, 9,277 males, age 66(18) years, mean(SD)] who underwent 32,758 hospital admissions between 2014 and 2018. We identified all the biochemical and clinically significant hypoglycemic episodes during these admissions. The incidence of biochemical hypoglycemia was 21.5% and that of clinically significant hypoglycemia was 9.6%. Major findings from the data analysis include: Recurrent biochemical and clinically significant hypoglycemia happened during 50% and 39% of hospital admissions with at least one hypoglycemic episode; Patients on metformin alone had the lowest incidence of hypoglycemia(8%) comparing to those on rapid analogue, long analogue and human rapid insulin at the same time, with the highest incidence (53%); Incidence of biochemical hypoglycaemia in type 1 diabetes(37%) doubles that in type 2 diabetes(18%).

Conclusions

Retrospective analysis of data from electronic patient records helps gain clinical understanding about inpatient hypoglycaemia and may improve inpatient glycaemic control through targeting high-risk hypo-prone inpatients.

Background and Aims

Aim: to assess the variability of glycemia (GV) in patients with type 2 diabetes on the background of a low-calorie diet with the inclusion of a specialized food (SF) with a modified carbohydrate profile.

Methods

Materials and methods: 38 women with type 2 diabetes and obesity (BMI on average 38.1 ± 0.89 kg/m²) aged 37 to 69 years were examined. All patients receiving standard hypoglycemic therapy were assessed for GV using the continuous glucose monitoring system from Medtronic for 6 days: 3 days against the background of a low-calorie diet (1,500 kcal / day) and 3 days against the background of a low-calorie diet with the inclusion of a SF for medical nutrition. SF was included in the hypocaloric diet in the form of a drink in the amount of 200 ml for a second breakfast instead of a carbohydrate-containing dish.

Results

Results: It is shown that the inclusion of SF in the hypocaloric diet was accompanied by a statistically significant decrease in the level of maximum and average glycemia. For the majority of patients over the entire observation period, the average glycemia in the afternoon and in the evening was higher than at night, reflecting the natural effect of meals on glycemia.

Conclusions

Conclusions: modification of the hypocaloric diet due to the inclusion of SF with a modified carbohydrate profile helps to reduce some indicators of hepatitis B in patients with type 2 diabetes.

Background and Aims

CGM-data is essential in both clinical practice and diabetes studies for evaluating glucose control. To understand what glucose profiles should be judged as normal and for target values in persons with diabetes, we examined the glucose profile in healthy individuals.

Methods

Persons without known diabetes or prediabetes were included after passing a normal oral glucose tolerance test, 2-hour value <8.9 mmol/l, fasting glucose <6.1 mmol/l, HbA1c <42 mmol/mol (6.0%). During days 1-8 they wore masked CGM (DexCom G4). During days 8-14 they had an open CGM-system with an alarm at 4.0 mmol/l to regularly confirm with a HemoCue capillary meter the low glucose-levels.

Results

In total 60 persons were included, mean age was 43 years, 70% women, mean HbA1c 34 mmol/mol (5.3%) and mean BMI 25.7 kg/m2. Mean glucose level days 1-7 was 5.83 mmol/l and mean time with hypoglycaemia <4.0 mmol/l /24h was 51 minutes (mean 3.54%, median 1.92 % [range 0.0-25.5%]) and mean time with <3.0 mmol/l /24h was 7.10 minutes (mean 0.49%, median 0.0% [range 0.0-9.5%]). The mean SD was 1.15 mmol/l, CV 0.20 and MAGE 2.63 mmol/l. Mean time with glucose levels >10 mmol/l /24h was 20.8 minutes (mean 1.44%, median 0.25% [range 0.0-19.7%]).

Conclusions

CGM-profiles in persons without diabetes or prediabetes show around 2% of time with glucose levels <4.0 mmol/l and 0%-0.5% <3.0 mmol/l. An SD and CV close to 1.15 mmol/l and 20% respectively should be viewed as a glucose variability close to that of persons without prediabetes or diabetes.