Obesity surgery elicits complex changes in glucose metabolism that are difficult to observe with discontinuous glucose measurements. We aimed to evaluate glucose variability after gastric bypass by ...continuous glucose monitoring (CGM) in a real-life setting.
CGM was performed for 4.2 ± 1.3 days in three groups of 10 subjects each: patients who had undergone gastric bypass and who were referred for postprandial symptoms compatible with mild hypoglycemia, nonoperated diabetes controls, and healthy controls.
The maximum interstitial glucose (IG), SD of IG values, and mean amplitude of glucose excursions (MAGE) were significantly higher in operated patients and in diabetes controls than in healthy controls. The time to the postprandial peak IG was significantly shorter in operated patients (42.8 ± 6.0 min) than in diabetes controls (82.2 ± 11.1 min, P = 0.0002), as were the rates of glucose increase to the peak (2.4 ± 1.6 vs. 1.2 ± 0.3 mg/mL/min; P = 0.041). True hypoglycemia (glucose <60 mg/dL) was rare: the symptoms were probably more related to the speed of IG decrease than to the glucose level achieved. Half of the operated patients, mostly those with a diabetes background before surgery, had postprandial glucose concentrations above 200 mg/dL (maximum IG, 306 ± 59 mg/dL), in contrast to the normal glucose concentrations in the fasting state and 2 h postmeal.
Glucose variability is exaggerated after gastric bypass, combining unusually high and early hyperglycemic peaks and rapid IG decreases. This might account for postprandial symptoms mimicking hypoglycemia but often seen without true hypoglycemia. Early postprandial hyperglycemia might be underestimated if glucose measurements are done 2 h postmeal.
Performance criteria have been established for in vitro blood glucose monitoring, particularly for the self-monitoring of blood glucose using glucose meters. Devices intended for use in the future, ...such as the continuous glucose monitoring system (CGMS), should satisfy similar criteria, particularly in diabetic patients under intensive therapy.
The analysis was conducted on 18 type 1 diabetic patients (not controlled, HbA(1c) >7.5%) treated by external pump using insulin analogs. Each patient received a glucose sensor for 3 days during his/her hospitalization and was instructed in its operation. Medtronic criteria were used to determine the accuracy of the CGMS. In addition, the data were analyzed according to American Diabetes Association (ADA) criteria, Clarke Error Grid analysis, and method of residuals, with the glucose oxidase method using a Beckman analyzer used as the reference method. Specificity and sensitivity were evaluated from the viewpoint of accuracy in the detection of hypoglycemia. For nine patients, two glucose sensors were simultaneously inserted into an abdominal site to determine the reproducibility of the system. RESULTS-Among the 33 glucose sensors inserted, 6 (18%) were nonfunctional. The mean duration of CGMS recording was 63 +/- 12 h. From all of the 692 sets of data that paired glucose readings and CGMS, the coefficients of correlation ranged from 0.87 to 0.92 and the mean absolute error ranged from 12.8 to 15.7%. The time experienced in hypoglycemia (<55 mg/dl) was reported at 86 +/- 62 min/day. Only 39% of the CGMS values satisfied the ADA precision criteria to within +/-10%, and 19% of these values satisfied the future ADA precision criteria of accuracy to within +/-5%. The means of difference method showed that the CGMS slightly underestimated the plasma glucose values (mean = -12 mg/dl). Error grid analysis showed only 77% of the glucose sensor values were in zone A, and 98.9% were in zones A and B. Two values fell in zone C and a single value fell in zone D. The sensitivity and specificity of the CGMS to detect hypoglycemia were 33 and 96%, respectively. A total of 6666 paired sensor values were recorded with a coefficient of correlation of 0.84 with a coefficient of variation of 8.25%.
CGMS could be useful in routine clinical practice to provide much more information on the glucose profile than intermittent self-monitoring of blood glucose (SMBG). However, CGMS cannot be used as a replacement for glucose meters because it does not satisfy the conventional performance goals set down for in vitro glucose measurements and could therefore lead to clinically incorrect treatment decisions.
Summary
Aims
To assess the effect of duration of hyperglycaemia before basal insulin (BI) initiation on clinical outcomes in type 2 diabetes (T2D).
Materials and methods
Patients with T2D who ...initiated BI during 2009‐2013, had continuous enrolment for ≥2 years preceding and ≥1 year following BI initiation (“index date”), and had ≥1 glycated haemoglobin (A1C) measure not at target (ie, ≥7.0%) within 6 months preindex date were included in the study. Patients were stratified by preindex‐date duration of A1C ≥7.0%. Longitudinal A1C, weight, BMI, and diabetes medication were compared between cohorts for up to 15‐month follow‐up.
Results
Of 37 053 patients who initiated BI, 40.7%, 15.3%, 16.0%, and 28.0%, respectively, had uncontrolled A1C for <6, 6‐<12, 12‐<18 and 18‐24 months preindex date. Baseline characteristics were similar between cohorts. Baseline A1C values were similar across cohorts (9.2%‐9.6%). Mean follow‐up A1C values were higher with longer preindex‐date duration of uncontrolled A1C (8.0 ± 1.7%, 8.2 ± 1.6%, 8.5 ± 1.7%, and 8.6 ± 1.7% for <6, 6‐<12, 12‐<18, and 18‐24 months); attainment of A1C <7.0% worsened with increasing preindex‐date duration of A1C ≥7.0% (29.6%, 20.0%, 14.6%, and 11.5% for <6, 6‐<12, 12‐<18, and 18‐24 months).
Conclusions
These data suggest that longer duration of uncontrolled A1C before BI initiation increases the risk of not reaching glycaemic targets. However, target attainment was poor in all cohorts, highlighting inadequate glycaemic control as an important unmet need in US patients with T2D.
Real‐world data to illustrate the effect of duration of hyperglycaemia and the extent of clinical inertia, including the effects of glycaemic control, in patients with T2D would be useful. Our retrospective, real‐world data analyses suggest that delaying treatment intensification increases the risk of not meeting glycaemic targets. Such patients would benefit from initiating treatments that can improve glycaemic control without a high risk of hypoglycaemia or weight gain.
This study was designed to test the accuracy of capillary ketonemia for diagnosis of ketosis after interruption of insulin infusion.
A total of 18 patients with type 1 diabetes treated by external ...pump were studied during pump stop for 5 h. Plasma and capillary ketonemia and ketonuria were determined every hour from 7:00 A.M. (time 0 min = T0) to 12:00 P.M. (time 300 min = T300). Plasma beta-hydroxybutyrate (beta-OHB) levels were measured by an enzymatic end point spectrophotometric method, and capillary beta-OHB levels were measured by an electrochemical method (MediSense Optium meter). Ketonuria was measured by a semiquantitative test (Ketodiastix). Positive ketosis was defined by a value of >/=0.5 mmol/l for ketonemia and >/=4 mmol/l (moderate) for ketonuria.
After stopping the pump, concentrations of beta-OHB in both plasma and capillary blood increased significantly at time 60 min (T60) compared with T0 (P < 0.001), reaching maximum levels at T300 (1.30 +/- 0.49 and 1.23 +/- 0.78 mmol/l, respectively). Plasma and capillary beta-OHB values were highly correlated (r = 0.94, P < 0.0001). For diagnosis of ketosis, capillary ketonemia has a higher sensitivity and negative predictive value (80.4 and 82.5%, respectively) than ketonuria (63 and 71.8%, respectively). For plasma glucose levels >/=250 mg/dl, plasma and capillary ketonemia were found to be more frequently positive (85 and 78%, respectively) than ketonuria (59%) (P = 0.017). The time delay to diagnosis of ketosis was significantly higher for ketonuria than for plasma ketonemia (212 +/- 67 vs. 140 +/- 54 min, P = 0.0023), whereas no difference was noted between plasma and capillary ketonemia.
The frequency of screening for ketosis and the efficiency of detection of ketosis definitely may be improved by the use of capillary blood ketone determination in clinical practice.
The recent results of Cardiovascular Outcomes Trials (CVOTs) in type 2 diabetes have clearly established the cardiovascular (CV) safety or even the benefit of two therapeutic classes, Glucagon-Like ...Peptide-1 receptor agonists (GLP-1 RA) and Sodium-Glucose Co-Transporter-2 inhibitors (SGLT-2i). Publication of the latest CVOTs for these therapeutic classes also led to an update of ESC guidelines and ADA/EASD consensus report in 2019, which considers using GLP-1 RA or SGLT-2i with proven cardiovascular benefit early in the management of type 2 diabetic patient with established cardiovascular disease (CVD) or at high risk of atherosclerotic CVD. The main beneficial results of these time-to event studies are supported by conventional statistical measures attesting the effectiveness of GLP-1 RA or SGLT2i on cardiovascular events (absolute risk, absolute risk difference, relative risk, relative risk reduction, odds ratio, hazard ratio). In addition, another measure whose clinical meaning appears to be easier, the Number Needed to Treat (NNT), is often mentioned while discussing the results of CVOTs, in order to estimating the clinical utility of each drug or sometimes trying to establish a power ranking. While the value of the measure is admittedly of interest, the subtleties of its computation in time-to-event studies are little known. We provide in this article a clear and practical explanation on NNT computation methods that should be used in order to estimate its value, according to the type of study design and variables available to describe the event of interest, in any randomized controlled trial. More specifically, a focus is made on time-to-event studies of which CVOTs are part, first to describe in detail an appropriate and adjusted method of NNT computation and second to help properly interpreting NNTs with the example of CVOTs conducted with GLP-1 RA and SGLT-2i. We particularly discuss the risk of misunderstanding of NNT values in CVOTs when some specific parameters inherent in each study are not taken into account, and the following risk of erroneous comparison between NNTs across studies. The present paper highlights the importance of understanding rightfully NNTs from CVOTs and their clinical impact to get the full picture of a drug's effectiveness.
Although the management of diabetes as a simple entity has been extensively developed, there is a dearth of evidence in elderly, frail patients with multiple comorbidities and polymedication. This ...population represents a large proportion of the residents of nursing homes (NHs). As a multidisciplinary group of French experts (geriatricians, endocrinologists, diabetologists, and general practitioners) with practical experience in this area, which is growing in magnitude throughout the world, we convened to compile pragmatic, simple advice on the management of elderly, frail diabetic patients. Given demands on NH personnel (manager, medical coordinator, nurses, and, at the front line of care provision, the undertrained and overworked carers), coupled with the quasiconstant of high staff turnover, the foundation stone of a patient's diabetes management is an Individual Care Plan (ICP) expressed in layman's language. This document that is opened on the patient's admission aims to make sure that the prescriptions established at admission are followed, notably to ensure correct treatment and adapted, regular monitoring with dates and times when examinations and tests are due. This includes monitoring of the diabetes control (HbA1c and, if necessary, blood and urine glucose) and its complications (cardiovascular disease, hypoglycemia, ocular problems, foot disorders, malnutrition, peripheral neuropathy, kidney failure). A necessary corollary is the training of staff to understand the specificities of caring for a frail patient with diabetes, on what to do in a potential emergency, and how to keep the ICP up to date for consultation by doctors and nurses.
The RELIEF study assessed rates of hospitalization for acute diabetes complications in France before and after initiation of the FreeStyle Libre system.
A total of 74,011 patients with type 1 ...diabetes or type 2 diabetes who initiated the FreeStyle Libre system were identified from the French national claims database with use of ICD-10 codes, from hospitalizations with diabetes as a contributing diagnosis, or the prescription of insulin. Patients were subclassified based on self-monitoring of blood glucose (SMBG) strip acquisition prior to starting FreeStyle Libre. Hospitalizations for diabetic ketoacidosis (DKA), severe hypoglycemia, diabetes-related coma, and hyperglycemia were recorded for the 12 months before and after initiation.
Hospitalizations for acute diabetes complications fell in type 1 diabetes (-49.0%) and in type 2 diabetes (-39.4%) following FreeStyle Libre initiation. DKA fell in type 1 diabetes (-56.2%) and in type 2 diabetes (-52.1%), as did diabetes-related comas in type 1 diabetes (-39.6%) and in type 2 diabetes (-31.9%). Hospitalizations for hypoglycemia and hyperglycemia decreased in type 2 diabetes (-10.8% and -26.5%, respectively). Before initiation, hospitalizations were most marked for people noncompliant with SMBG and for those with highest acquisition of SMBG, which fell by 54.0% and 51.2%, respectively, following FreeStyle Libre initiation. Persistence with FreeStyle Libre at 12 months was at 98.1%.
This large retrospective study on hospitalizations for acute diabetes complications shows that a significantly lower incidence of admissions for DKA and for diabetes-related coma is associated with use of flash glucose monitoring. This study has significant implications for patient-centered diabetes care and potentially for long-term health economic outcomes.
This study was designed to assess the insulin-sparing effect of oral administration of metformin along with a continuous subcutaneous insulin infusion (CSII) for the treatment of type 1 diabetic ...patients.
A total of 62 patients (25 women and 37 men) were studied in a monocenter, randomized, double-blind placebo-controlled study, comparing metformin (850 mg b.i.d.) with placebo in association with CSII during a 6-month period.
Treatment with metformin was associated with a reduction in daily insulin requirements between V0 and V6 of -4.3 +/- 9.9 units (-7.8 +/- 18%) compared with an increase with placebo treatment of 1.7 +/- 8.3 units (2.8 +/- 12.7%) (P = 0.0043). A decrease in basal requirement of insulin was also observed in patients treated with metformin of -2.6 +/- 3.2 units (-7.9 +/- 23.8%) compared with an increase with placebo treatment of 1.9 +/- 5.7 units (8.8 +/- 27.1%) (P = 0.023). HbA(1c) remained unchanged in treatment with metformin and placebo between V0 and V6. The number of hypoglycemic events (<60 mg/dl) was similar in both groups. Significant reductions of total cholesterol (P = 0.04) and LDL cholesterol (P = 0.05) were observed in patients treated with metformin. Gastrointestinal events, including diarrhea and abdominal pain, were reported in three patients in the metformin group who discontinued the trial. Mild or moderate gastrointestinal side effects were also reported in eight patients treated with metformin and two patients treated with placebo (P = 0.069).
Metformin was found to be a safe insulin-sparing agent, when used in combination with CSII for the treatment of type 1 diabetes.
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