Timing of Insulin with Meals in the Hospital: a Systems Improvement Approach.

PURPOSE OF REVIEW: Coordination of glucose monitoring, mealtimes, and insulin delivery in the hospital is complex, involving interactions between multiple key agents and overlapping workflows. The purpose of this review is to evaluate the scope of the problem as well as to assess evidence for interventions. RECENT FINDINGS: In recent years, there has been an emphasis on systems-based approaches which address multiple contributing components of the problem at once in an effort to more seamlessly integrate workflows. Technological advances, such as decision support systems and advances in automated insulin delivery, and strategies that minimize the need for complex insulin regimens hold promise for future study. Evaluation of the coordination of insulin delivery is limited by a lack of standardized metrics and systematically collected mealtimes. Nevertheless, successful efforts include system-wide multicomponent interventions, though advances in therapeutic approaches may be of value.

Adjustment of insulin doses when switching from glargine 100 U/ml or detemir to degludec: an observational study.

BACKGROUND: Degludec is a long-acting insulin with a longer duration of action and a greater day-to-day reproducibility of absorption in comparison with previous long-acting insulin formulations. The aim is the definition of the change in insulin needs in patients switching from detemir/glargine to degludec in real-life conditions. METHODS: In this retrospective cohort observational study, all outpatients with either type 1 or type 2 diabetes, starting therapy with degludec insulin-after a prior treatment with either detemir or glargine insulin for at least 6 months-were included. RESULTS: The analysis was performed on 266 patients, 172 and 96 with type 1 and type 2 diabetes, respectively. The equations describing the relationship between baseline and follow-up doses of basal insulin (6 months) were Y = 3.39 + 0.78X and Y = 0.44 + 0.69X, in patients receiving detemir/glargine either once or twice daily, respectively (Y = degludec dose at 6 months and X = basal insulin dose at switch). The corresponding equations for prandial insulin doses were y = 1.83 + 0.83*x and y = 2.85 + 0.80*x for those on pre-switch once or twice-daily basal insulin, respectively. In type 2 diabetes, the switch was associated with a reduction of basal insulin doses only in those with a prior twice-daily treatment with basal insulin. The reduction of prandial insulin reached statistical significance only in patients previously treated with basal insulin once daily. CONCLUSIONS: The present results provide a suggestion for a simple method for the adjustment of basal and prandial insulin doses in type 1 diabetic patients, switching from glargine or detemir to degludec.

[Faster aspart insulin (FIASP®)]. / Le médicament du mois. L'insuline «faster aspart¼ (Fiasp®).

Fast-acting insulin aspart (faster aspart), commercialized under the trade name of Fiasp®, is insulin aspart in a new formulation aiming to mimic the physiologic prandial insulin release more closely than currently available rapid-acting insulin products. Fiasp® is insulin aspart (NovoRapid®) in which two excipients (L-arginine and niacinamide) have been added, L-arginine serving as a stabilising agent, while niacinamide being responsible for accelerated initial absorption after subcutaneous administration. The pharmacokinetic characteristics of insulin faster aspart have the potential to better reproduce the fast endogenous prandial insulin secretion and thereby to improve postprandial glucose control compared with insulin aspart. The onset phase 3 programme compares head-to-head insulin faster aspart to insulin aspart. Studies showed significant reductions in postprandial glucose increment (in type 1 and type 2 diabetic patients), and glycated haemoglobin (HbA1C, in type 1 diabetes), without markedly increasing the risk of hypoglycaemia. A post hoc analysis of pooled data from six clinical trials conducted in patients with type 1 diabetes confirmed the beneficial pharmacokinetic and pharmacodynamic profiles of insulin faster aspart (earlier plasma insulin appearance, early insulin exposure two times greater and earlier offset of exposure of insulin faster aspart versus insulin aspart).

L'insuline aspart ultra-rapide («faster aspart¼), commercialisée sous le nom de Fiasp®, est de l'insuline aspart modifiée dans le but de reproduire, de manière plus proche que ne le font les préparations d'insuline à action rapide actuellement disponibles, la sécrétion physiologique prandiale d'insuline. Fiasp® est constituée d'insuline aspart (commercialisée sous le nom de Novorapid®) à laquelle deux excipients (L-arginine et nicotinamide) ont été ajoutés, la L-arginine comme agent stabilisateur de la préparation et la nicotinamide pour favoriser une absorption initiale accélérée après administration sous-cutanée. Les caractéristiques pharmacocinétiques de l'insuline faster aspart présentent le potentiel de mieux mimer la sécrétion précoce d'insuline qui suit le repas et, de ce fait, de contrôler, de manière plus efficace, l'hyperglycémie postprandiale en comparaison à ce que l'on observe avec de l'insuline aspart. Le programme onset d'études cliniques de phase 3 vise à comparer directement l'insuline faster aspart à l'insuline aspart. Ces études ont démontré une réduction significative des augmentations de la glycémie postprandiale (chez les sujets diabétiques de type 1 et de type 2) et de l'hémoglobine glyquée (HbA1C, dans le diabète de type 1), sans augmenter sensiblement le risque d'hypoglycémie. Une analyse post hoc de données rassemblées à partir de six études cliniques réalisées dans le diabète de type 1 a confirmé le bénéfice du profil pharmacocinétique et pharmacodynamique de l'insuline faster aspart par rapport à l'insuline aspart (apparition plasmatique plus précoce de l'insuline et exposition initiale à l'insuline deux fois plus importante, avec une durée d'exposition raccourcie).

The A1C Prandial Product: An objective tool to decision making in diabetes.

This brief article describes a numerical index designed to assess the relative contribution of fasting and postprandial glucose to hyperglycaemia. This helps plan appropriate insulin and oral glucose-lowering therapy in an objective manner. It also reviews three similar indices described earlier in literature. Such indices need to be validated in large, multicentric trials, and have the potential to bring objectivity to choice of treatment of diabetes.

The gluco-phenotype.

This article describes the concept of gluco-phenotype, i.e., the clinical and biochemical attributes, which allow characterization of the glycaemic status, understanding of the etio-pathogenesis of dysglycaemia, and planning of therapeutic strategies, in an individual. It emphasizes the need to take a detailed history, conduct a comprehensive physical examination, and assess various glycaemic parameters, including fasting glucose, postprandial glucose, and HbA1c, while planning diabetes management.

Pharmacologic treatment of type 2 diabetes: oral medications.

OBJECTIVE: To review the oral and injectable pharmacologic treatment options for type 2 diabetes. DATA SOURCES: A literature search was conducted using PubMed electronic database for studies published in English between 1993 and September 2014. Search terms included diabetes mellitus, type 2 diabetes, and the individual name for each antidiabetic medication reviewed. In addition, manual searches were performed for cross-references from publications. Package inserts, United States Food and Drug Administration (FDA) Web site, Institute for Safe Medication Practices Web site, American Diabetes Association Web site and scientific session poster presentations, and individual drug company Web pages were also reviewed. STUDY SELECTION AND DATA EXTRACTION: This review focused on information elucidated over the past 10 years to assist prescribers in choosing optimal therapy based on individual patient characteristics. Studies leading to the approval of or raising safety concerns for the antidiabetic medications reviewed in this article were included. DATA SYNTHESIS: In the past 10 years, there have been 4 novel oral antidiabetic medication classes and 9 new injectable agents and insulin products approved by the FDA for the treatment of type 2 diabetes as well as new information regarding the safety and use of several older antidiabetic medication classes. The distinctions were reviewed for each individual agent, and a comparison was completed if there was more than one agent in a particular therapeutic class. Using current information available, select investigational agents in phase III trials or those with a pending new drug application were highlighted. CONCLUSION: There are now 9 distinct oral pharmacologic classes and a variety of insulin and noninsulin injectable medications available for the treatment of type 2 diabetes. Metformin remains the first-line treatment option for most patients. When considering options for alternative or additional treatment, prescribers must weigh the benefits and risks using individual patient characteristics.

Pharmacologic treatment of type 2 diabetes: injectable medications.

OBJECTIVE: To review the oral and injectable pharmacologic treatment options for type 2 diabetes. DATA SOURCES: A literature search was conducted using PubMed electronic database for studies published in English between 1993 and September 2014. Search terms included diabetes mellitus, type 2 diabetes, and the individual name for each antidiabetic medication reviewed. In addition, manual searches were performed for cross-references from publications. Package inserts, United States Food and Drug Administration (FDA) Web site, Institute for Safe Medication Practices Web site, American Diabetes Association Web site and scientific session poster presentations, and individual drug company Web pages were also reviewed. STUDY SELECTION AND DATA EXTRACTION: This review focused on information elucidated over the past 10 years to assist prescribers in choosing optimal therapy based on individual patient characteristics. Studies leading to the approval of or raising safety concerns for the antidiabetic medications reviewed in this article were included. DATA SYNTHESIS: In the past 10 years, there have been 4 novel oral antidiabetic medication classes and 10 new injectable agents and insulin products approved by the FDA for the treatment of type 2 diabetes as well as new information regarding the safety and use of several older antidiabetic medication classes. The distinctions were reviewed for each individual agent, and a comparison was completed if there was more than one agent in a particular therapeutic class. Using current information available, select investigational agents in phase III trials or with a pending new drug application were highlighted. CONCLUSION: There are now 9 distinct oral pharmacologic classes and a variety of insulin and noninsulin injectable medications available for the treatment of type 2 diabetes. Metformin remains the first-line treatment option for most patients. When considering options for alternative or additional treatment, prescribers must weigh the benefits and risks using individual patient characteristics.

Bolus calculator and wirelessly communicated blood glucose measurement effectively reduce hypoglycaemia in type 1 diabetic children - randomized controlled trial.

BACKGROUND: The aim of this study was to assess the effect of bolus calculator function and wireless communication between insulin pump and blood glucose metre on metabolic control in children with type 1 diabetes, treated with insulin pumps. METHODS: In this randomized, controlled, 12-week trial, 156 patients, aged 12.9 ± 2.6 years, with a history of diabetes of 5.1 ± 3.3 years and glycated haemoglobin values of 7.3 ± 1.2% (56.3 ± 13.44 mmol/mol) were included. Children were assigned to one of three arms: group A, subjects using bolus calculator and wireless communication between insulin pump and blood glucose metre; group B, subjects using bolus calculator without communication between the devices and group C, control group. Devices were downloaded at 0, 6 and 12 weeks. RESULTS: There were statistically fewer episodes of hypoglycaemia in children using bolus calculator compared with the control group: A versus C (3.8 ± 3.1 versus 7.8 ± 5.13 episodes/2 weeks, respectively, p < 0.0001); B versus C (3.6 ± 3.3 versus 7.8 ± 5.1 episodes/2 weeks, respectively, p < 0.0001). Patients in group A used bolus calculator function significantly more frequently than patients in group B (4.9 ± 3.4 versus 2.5 ± 2.9 times/24 h, respectively, p = 0.0006). No significant differences in glycated haemoglobin levels were found between the experimental and the control groups: group A versus C (p = 0.699). The use of bolus calculator did not influence post-prandial glycaemia, body mass index-SD score or insulin/kg/24 h. CONCLUSIONS: Bolus calculator use reduces hypoglycaemic episodes independently of communication between insulin pump and blood glucose metre. Wireless communication between devices results in more frequent bolus calculator use.

Delayed prandial insulin boluses are an important determinant of blood glucose control and relate to fear of hypoglycemia in people with type 1 diabetes on advanced technologies.

AIMS: Automated insulin delivery systems improve blood glucose control in patients with type 1 diabetes (T1D). However, optimizing their performance requires patient's proper compliance to meal insulin bolus administration. We explored real-life prevalence of delayed prandial boluses (DBs) in adults with T1D on advanced technologies, and their association with glycemic control and fear of hypoglycemia (FH). METHODS: In the last two-week web-based reports of 152 adults with T1D on Hybrid Closed Loop Systems (HCLS) or Sensor Augmented Pump (SAP), DBs were identified when a steep increase in blood glucose occurred at CGM before the prandial bolus, and CGM metrics were evaluated. All participants completed an online questionnaire on FH. RESULTS: Mean DBs over two weeks were 10.2 ± 4.7 (M ± SD, range 1-23) and more frequent in women than men (11.0 ± 4.6 vs. 9.4 ± 4.7, p = 0.036). Participants with more DBs (>12) showed significantly lower Time-In-Range (62.4 ± 13.8 vs. 76.6 ± 9.0 %) than those with less DBs (<7.7), along with higher Time-Above-Range, GMI, and Coefficient-of-Variation (ANOVA, p < 0.001 for all). Participants with higher FH score showed more DBs (11.6 ± 5.0) than those in lower tertiles (9.57 ± 4.59 and 9.47 ± 4.45, ANOVA p = 0.045). CONCLUSIONS: In patients on advanced technologies, delayed boluses are extremely common, and associate with significantly worse glycemic control. Utmost attention is needed to bolus timing, mainly tackling fear of hypoglycemia.

Insulin Titration Guidelines for Patients With Type 1 Diabetes: It Is About Time!

PURPOSE: A proposal that an Insulin Advisory Committee develop insulin titration guidelines 100 years after its discovery. FINDINGS: Glucose control metrics remain poor despite significant advances in diabetes technology. SUMMARY: A century after the introduction of insulin, health care providers and patients with type 1 diabetes have worldwide access to a variety of insulin delivery devices (IDDs), glucose monitors, bolus calculators (BCs), continuous glucose monitors (CGMs), and automated insulin delivery (AID) systems. However, these advances have not enabled most patients to achieve today's clear A1c and time-in-range goals. Much of this failure arises from the lack of clear insulin titration guidelines for determining appropriate insulin doses. The lack of dosing clarity results in local physicians, clinics, and individual patients managing insulin titrations as they see fit, creating significant inefficiencies for reaching recommended glycemic goals. This review (1) details the widespread problems generated by nonphysiological dose settings in today's BCs, insulin pumps, and AID systems; (2) presents a method to develop and implement optimized total daily doses of insulin to correct the most common problem of hyperglycemia; (3) discusses using large device databases to provide clear insulin titration guidelines that optimize BC settings from an optimized total daily dose (TDD) of insulin for patients with T1D; and (4) recommends the formation of an Insulin Advisory Committee to clarify the steps to take toward universal insulin titration guidelines, optimized BC settings, and a systematic logic for their use in insulin delivery devices.

Significance of Postprandial Insulin and Triglycerides to Evaluate the Metabolic Response of Composite Meals Differing in Nutrient Composition - A Randomized Cross-Over Trial.

Background and aims: GlucoTRIG, based on postprandial plasma insulin and triglyceride concentrations, has been recently developed as a novel index to determine the postprandial metabolic response to the meals. This study aimed to test GlucoTRIG as a measure for ranking composite meals for their metabolic effects. Methods: In a randomized cross-over trial, healthy adult volunteers (both males and females; n = 10 for each meal) consumed three is caloric (2000 kj) test meals (meal 1, meal 2, meal 3) of varying macronutrient composition. Postmeal consumption, venous blood samples were collected to determine plasma insulin and plasma triglycerides for estimating the GlucoTRIG value using (Triglycerides180min × Insulin180min) - (Triglycerides0min × Insulin0min). Results: The GlucoTRIG values differed significantly (p = 0.0085) across meals. The statistical significance remains even after adjusting for confounding variables such as baseline diet, insulin, and triglycerides. The meal (M3) with a high fiber, low total fat content and containing less refined foods (fruits, beans, vegetables, plain yogurt) exhibited a significantly (p = 0.007) lower GlucoTRIG value (10 ± 7.7) compared to the other two meals, M1 (77 ± 19.8) and M2 (38 ± 12.1) which contained low processed foods, and were relatively high in fat and low in fiber meals. No statistically significant differences were observed between M1 and M2 meal. Conclusions: GlucoTRIG is a physiologically based index that may be useful to rank composite meals for reducing the risk of metabolic diseases. Further research focusing on the application of GlucoTRIG to foods, meals, and diets is warranted.ACTRN12619000973112 (Australian New Zealand Clinical Trials Registry, ANZCTR).

One-hundred year evolution of prandial insulin preparations: From animal pancreas extracts to rapid-acting analogs.

The first insulin preparation injected in humans in 1922 was short-acting, extracted from animal pancreas, contaminated by impurities. Ever since the insulin extracted from animal pancreas has been continuously purified, until an unlimited synthesis of regular human insulin (RHI) became possible in the '80s using the recombinant-DNA (rDNA) technique. The rDNA technique then led to the designer insulins (analogs) in the early '90s. Rapid-acting insulin analogs were developed to accelerate the slow subcutaneous (sc) absorption of RHI, thus lowering the 2-h post-prandial plasma glucose (PP-PG) and risk for late hypoglycemia as comparing with RHI. The first rapid-acting analog was lispro (in 1996), soon followed by aspart and glulisine. Rapid-acting analogs are more convenient than RHI: they improve early PP-PG, and 24-h PG and A1C as long as basal insulin is also optimized; they lower the risk of late PP hypoglycemia and they allow a shorter time-interval between injection and meal. Today rapid-acting analogs are the gold standard prandial insulins. Recently, even faster analogs have become available (faster aspart, ultra-rapid lispro) or are being studied (Biochaperone lispro), making additional gains in lowering PP-PG. Rapid-acting analogs are recommended in all those with type 1 and type 2 diabetes who need prandial insulin replacement.

Continuous Glucose Monitoring Sensor Glucose Levels and Insulin Pump Infusion Set Wear-Time During Treatment with Fast-Acting Insulin Aspart: A Post Hoc Analysis of Onset 5.

Background: In the onset 5 trial, fast-acting insulin aspart (faster aspart) was noninferior to insulin aspart (IAsp) for change from baseline glycated hemoglobin at 16 weeks, when used in continuous subcutaneous insulin infusion by participants with type 1 diabetes. The aim of this post hoc analysis was to investigate whether infusion set wear-time was associated with changes in sensor glucose, measured using continuous glucose monitoring (CGM). Materials and Methods: This was a post hoc analysis of onset 5 data. Mean infusion set wear-time and duration of CGM-wearing period were assessed. Mean CGM sensor glucose 24 h before and 24 h after were used to calculate the before-after difference (CGM sensor glucose drift). Results: Mean infusion set wear-time was 2.9 and 3.0 days in the faster aspart and IAsp arms, respectively. At 16 weeks, the average duration of the CGM wearing period was 13.7 and 13.8 days, respectively. Mean CGM sensor glucose before versus after an infusion set change, at week 16, was 10.14 versus 9.39 mmol/L with faster aspart and 9.48 versus 9.47 mmol/L with IAsp. The estimated treatment difference in CGM sensor glucose drift at 16 weeks for faster aspart versus IAsp was +0.72 mmol/L (95% confidence interval: 0.48-0.96, P < 0.001). Conclusions: Mean infusion set wear-time and duration of CGM-wearing period were similar for faster aspart and IAsp. A significantly greater upward drift in CGM sensor glucose values measured during an infusion set wearing period was observed with faster aspart versus IAsp. Clinical trial registration: NCT02825251.

A Difference Between Bedtime and Pre-Breakfast Plasma Glucose Levels Indicates the Need for Prandial Insulin in Basal Insulin-Treated Type 2 Diabetic Patients with Normal Fasting Glucose.

AIM: In the present analysis, we characterised the efficacy and safety of adding a single daily injection of insulin glulisine to optimised basal-supported oral therapy (BOT) in patients with a high BeAM value, defined as a more than 50 mg/dl difference between bedtime and pre-breakfast blood glucose. METHODS: The BeAM value was retrospectively calculated for patients pooled from two clinical trials that supplemented BOT with glulisine. Data regarding changes in HbA1c, fasting plasma glucose (FPG), and postprandial glucose (PPG) levels from observation periods of 3 to 6 months were assessed. RESULTS: Out of 358 patients that received BOT/glulisine, 182 had a high BeAM value. Patients with a high BeAM value were older and had a longer diabetes duration than patients with a medium BeAM value. Significant reductions in HbA1c (7.5% to 7.2% [59 to 55 mmol/mol], p<0.0001) and PPG (202 to 143 mg/dl, p<0.0001) levels were documented. The proportion of patients with a high BeAM value achieving an HbA1c <7% [53 mmol/mol], alone or in combination with no hypoglycaemia, was lower than that of patients with a medium BeAM value. CONCLUSION: The analysis indicates that the supplementation of BOT with a single daily injection of prandial insulin is safe and effective for reducing HbA1c and PPG levels in patients with a high BeAM value (more than 50 mg/dl). However, patients with a medium BeAM value also responded well, which suggests that they should also be considered candidates for this change in therapy.

Impact of Fast-Acting Insulin Aspart on Glycemic Control in Patients with Type 1 Diabetes Using Intermittent-Scanning Continuous Glucose Monitoring Within a Real-World Setting: The GoBolus Study.

Background: The GoBolus study investigated the real-world effectiveness of faster aspart in patients with type 1 diabetes (T1D) using intermittent-scanning continuous glucose monitoring (iscCGM) systems. Methods: This 24-week, multicenter, single-arm, noninterventional study investigated adults with T1D (HbA1c, 7.5%-9.5%) receiving multiple daily injections (MDI) of insulin and using iscCGM within local healthcare settings for ≥6 months before switching to faster aspart at study start (week 0; baseline). Primary endpoint was HbA1c change from baseline to week 24. Exploratory endpoint was change in iscCGM metrics from baseline to week 24. Results: Overall, 243 patients were included (55.6% male), with mean age/diabetes duration, 49.9/18.8 years; mean HbA1c, 8.1%. By week 24, HbA1c had decreased by 0.19% (-2.1 mmol/mol, P < 0.0001) with no mean change in insulin doses or basal/bolus insulin ratios. For patients with sufficient available iscCGM data (n = 92): "time in range" (TIR; 3.9-10.0 mmol/L) increased from 46.9% to 50.1% (P = 0.01), corresponding to an increase of 46.1 min/day; time in hyperglycemia decreased from 49.1% to 46.1% (>10.0 mmol/L, P = 0.026) and 20.4% to 17.9% (>13.9 mmol/L, P = 0.013), corresponding to 43.5 (P = 0.024) and 35.6 (P = 0.015) fewer minutes per day on average spent in these ranges, respectively; no change for time in hypoglycemia (<3.9 and <3.0 mmol/L). Mean interstitial and postprandial glucose improved from 10.4 to 10.1 mmol/L (P = 0.035) and 11.9 to 11.0 mmol/L (P = 0.002), respectively. Conclusion: Real-world switching to faster aspart in adults with T1D on MDI improved HbA1c, increased TIR, and decreased time in hyperglycemia without affecting time in hypoglycemia. The GoBolus study: NCT03450863.

Basal-Bolus Insulin Regimen for Hospitalised Patients with COVID-19 and Diabetes Mellitus: A Practical Approach.

BACKGROUND AND AIM: The coronavirus disease 2019 (COVID-19) outbreak has rapidly crossed international boundaries and placed increasing demands on healthcare facilities worldwide. Patients with diabetes and uncontrolled blood glucose levels are at increased risk for poor clinical outcomes and in-hospital mortality related to COVID-19. Therefore, achieving good glycaemic control is of paramount importance among hospitalised patients with COVID-19. Basal-bolus insulin therapy is a safe and effective intervention for the management of hyperglycaemia in hospitalised patients. The aim of this article is to provide a practical guidance for the use of the basal-bolus insulin regimen in hospitalised patients with COVID-19 and diabetes mellitus. METHODS: This guidance document was formulated based on the review of available literature and the combined personal experiences of the authors. We provide a comprehensive review on the use of the basal-bolus insulin regimen, including its principles, rationale, indications, prerequisites, initiation, and dose titration, and also suggest targets for blood glucose control and different levels of capillary blood glucose monitoring. Various case scenarios are used to illustrate how optimal glucose control can be achieved, such as through adjustments in doses of prandial and basal insulin, the use of correctional insulin dosing and changes in the timing and content of major and minor meals. CONCLUSION: The practical guidance for the use of the basal-bolus insulin regimen in hospitalised patients with COVID-19 and diabetes mellitus presented here can be used for patients admitted to hospital for indications other than COVID-19 and for those in ambulatory care.

Comprehensive Analysis of the Real Lifestyles of T1D Patients for the Purpose of Designing a Personalized Counselor for Prandial Insulin Dosing.

Post-prandial hyperglycemia is still a challenging issue in intensified insulin therapy. Data of 35 T1D patients during a four-week period were analyzed: RT-CGM (real time continuous glucose monitoring) record, insulin doses, diet (including meal photos), energy expenditure, and other relevant conditions. Patients made significant errors in carbohydrate counting (in 56% of cooked and 44% of noncooked meals), which resulted in inadequate insulin doses. Subsequently, a mobile application was programmed to provide individualized advice on prandial insulin dose. When using the application, a patient chooses only the type of categorized situation (e.g., meals with other relevant data) without carbohydrates counting. The application significantly improved postprandial glycemia as normoglycemia was reached in 95/105 testing sessions. Other important findings of the study include: A high intake of saturated fat (median: 162% of recommended intake); a low intake of fiber and vitamin C (median: 42% and 37%, respectively, of recommended intake); an increase in overweight/obesity status (according to body fat measurement), especially in women (median of body fat: 30%); and low physical activity (in 16/35 patients). The proposed individualized approach without carbohydrate counting may help reach postprandial normoglycemia but it is necessary to pay attention to the lifestyle habits of T1D patients too.

A higher blood glucose level pre-breakfast in comparison to bedtime is a contraindication for intensification of prandial insulin therapy in patients with type 2 diabetes - The impact of a negative BeAM value.

AIMS: The BeAM value refers to the difference between a patient's blood glucose level at bedtime (Be) and the following morning before breakfast (AM). The clinical impact of a negative BeAM value (AM blood glucose reading compared to that taken at bedtime) is unknown. METHODS: T2DM patients of the OPAL and POC trials were pooled and their BeAM values calculated. RESULTS: From a total of 358 patients, 31 were calculated as having a negative BeAM value at baseline, while 182 had a high value. Patients in the negative BeAM group were younger, had shorter diabetes duration, and lower HbA1c levels. Fasting blood glucose levels were higher in the negative BeAM group, and these increased to a greater extent during the trial periods. No significant differences in hypoglycaemia occurrence were observed. Multivariate adjusted analysis indicated no association between a negative BeAM value and achievement of HbA1c < 7%, or composite endpoints that additionally included no hypoglycaemia and no weight gain. CONCLUSIONS: Supplementation of BOT with prandial insulin is not beneficial for patients who have a higher blood glucose reading before breakfast in comparison to before bedtime. Further investigation into the cause of the high morning reading in these patients is indicated.

Bolus Advisors: Sources of Error, Targets for Improvement.

Bolus advisors that are designed to improve the accuracy of individual bolus doses relative to a meal's carb content and the current glucose have not substantially changed since they were introduced 15 years ago despite an obvious need for enhancement and innovation. Although some glycemic benefits have been demonstrated, bolus advisors largely ignore the large amounts of clinical data they gather that could have a significant impact on glucose outcomes. Concerns have also been raised regarding the aggressive nature of largely unpublished or poorly explained bolus advisor algorithms. Hypoglycemia and hyperglycemia remain significant risks due to inaccurate bolus advisor settings and the absence of tracking or an inappropriate handling of bolus on board. This review covers common sources for bolus advisor error such as the selection of physiologically inappropriate bolus advisor settings, the use of short duration of insulin action times, poor algorithm logic that tends to cover all carb intake fully, and an excessive reliance on simplistic dosing algorithms. As well as discussing these areas, we provide 21 ways to improve current bolus calculators.

Evaluation of the timing and coordination of prandial insulin administration in the hospital.

AIMS: The objective of this study was to examine the relationship between measures of coordinated insulin delivery and capillary blood glucose (CBG) levels among hospitalized patients and to assess nurse perceptions of insulin administration. METHODS: Hospitalized patients (n=451) receiving rapid acting insulin analog (RAIA) using carbohydrate counting were retrospectively analyzed. Nurses (n=35) were asked to complete an 18-item anonymous survey assessing perception of RAIA dosing. RESULTS: The median time from breakfast CBG to RAIA dose was 93 (IQR 57-138) min. There was no association between timeliness measures and mean CBG at lunch or dinner. Hypoglycemia was rare (N=2). More than half (54%) of nurses were confident all of the time in determining the correct dose of RAIA, though none were confident in administering it on time. The majority of nurses perceived an electronic dosing calculator and a patient reminder to notify the nurse at the end of the meal favorably. CONCLUSIONS: The data demonstrate suboptimal coordination of CBG monitoring and insulin doses using a flexible meal insulin dosing strategy, though there was minimal impact on glycemic control. Nurses reported high confidence in the ability to calculate the correct insulin dose but not in the ability to administer it on time.