Journal of Diabetes Science and Technology

Meal lipids (LIP) and proteins (PRO) may influence the effect of insulin doses based on carbohydrate (CHO) counting in patients with type 1 diabetes (T1D). We developed a smartphone application for CHO, LIP, and PRO counting in daily food and assessed its usability in real-life conditions and potential usefulness.

Ten T1D patients used the android application for 1 week to collect their food intakes. Data included meal composition, premeal and 2-hour postmeal blood glucose, corrections for hypo- or hyperglycemia after meals, and time for entering meals in the application. Meal insulin doses were based on patients’ CHO counting (application in blinded mode). Linear mixed models were used to assess the statistical differences.

In all, 187 meals were analyzed. Average computed CHO amount was 74.37 ± 31.78 grams; LIP amount: 20.26 ± 14.28 grams and PRO amount: 25.68 ± 16.68 grams. Average CHO, LIP, and PRO contents were significantly different between breakfast and lunch/dinner. The average time for meal entry in the application moved from 3-4 minutes to 2.5 minutes during the week. No significant impact of LIP and PRO was found on available blood glucose values.

Our study shows CHO, LIP, and PRO intakes can be easily captured by an application on smartphone for meal entry used by T1D patients. Although LIP and PRO meal contents did not influence glucose levels when insulin doses were based on CHO in this pilot study, this application could be used for further investigation of this topic, including in closed-loop conditions.

Physical activity is recommended for patients with type 1 diabetes (T1D). However, without proper management, it can lead to higher risk for hypoglycemia and impaired glycemic control. In this work, we identify the main factors explaining the blood glucose dynamics during exercise in T1D. We then propose a prediction model to quantify the glycemic drop induced by a mild to moderate physical activity.

A meta-data analysis was conducted over 59 T1D patients from 4 different studies in the United States and France (37 men and 22 women; 47 adults; weight, 71.4 ± 10.6 kg; age, 42 ± 10 years; 12 adolescents: weight, 60.7 ± 12.5 kg; age, 14.0 ± 1.4 years). All participants had physical activity between 3 and 5 pm at a mild to moderate intensity for approximately 30 to 45 min. A multiple linear regression analysis was applied to the data to identify the main parameters explaining the glucose dynamics during such physical activity.

The blood glucose at the beginning of exercise ( B G s t a r t ), the ratio of insulin on board over total daily insulin ( IOB TDI ) and the age as a categorical variable (1 for adult, 0 for adolescents) were significant factors involved in glucose evolution at exercise (all P < .05). The multiple linear regression model has an R-squared of .6.

The main factors explaining glucose dynamics in the presence of mild-to-moderate exercise in T1D have been identified. The clinical parameters are formally quantified using real data collected during clinical trials. The multiple linear regression model used to predict blood glucose during exercise can be applied in closed-loop control algorithms developed for artificial pancreas.

Factors influencing glycemic variability in type 1 diabetes (T1D) may play a significant role in the refinement of closed loop insulin administration. Phase of menstrual cycle is one such factor that has been inadequately investigated. We propose that unique individual patterns can be constructed and used as parameters of closed loop systems.

Women with T1D on continuous subcutaneous insulin infusion and continuous glucose monitoring were studied for 3 consecutive menstrual cycles. Ovulation prediction kits and labs were used to confirm phase of menstrual cycle. Glycemic risks were assessed using the low- and high blood glucose indices (LBGI and HBGI). Insulin sensitivity (SI) was estimated using a Kalman filtering method from meal and insulin data. Overall change significance for glycemic risks was assessed by repeated measures ANOVA, with specific phases emphasized using contrasts.

Ovulation was confirmed in 33/36 cycles studied in 12 subjects (age = 33.1 ± 7.0 years, BMI = 25.7 ± 2.9 kg/m², A1c = 6.8 ± 0.7%). Risk for hyperglycemia changed significantly during the cycle (P = .023), with HBGI increasing until early luteal phase and returning to initial levels thereafter. LBGI was steady in the follicular phase, decreasing thereafter but not significantly. SI was depressed during the luteal phase when compared to the early follicular phase (P ≤ .05). Total daily insulin, carbohydrates, or calories did not show any significant fluctuations.

Women with T1D have glycemic variability changes that are specific to the individual and are linked to phase of cycle. An increased risk of hyperglycemia was observed during periovulation and early luteal phases compared to the early follicular phase; these changes appear to be associated with decreased insulin sensitivity during the luteal phase.

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

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

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

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

Early detection of exercise in individuals with type 1 diabetes mellitus (T1DM) may allow changes in therapy to prevent hypoglycemia. Currently there is limited experience with automated methods that detect the onset and end of exercise in this population. We sought to develop a novel method to quickly and reliably detect the onset and end of exercise in these individuals before significant changes in blood glucose (BG) occur.

Sixteen adults with T1DM were studied as outpatients using a diary, accelerometer, heart rate monitor, and continuous glucose monitor for 2 days. These data were used to develop a principal component analysis based exercise detection method. Subjects also performed 60 and 30 minute exercise sessions at 30% and 50% predicted heart rate reserve (HRR), respectively. The detection method was applied to the exercise sessions to determine how quickly the detection of start and end of exercise occurred relative to change in BG.

Mild 30% HRR and moderate 50% HRR exercise onset was identified in 6 ± 3 and 5 ± 2 (mean ± SD) minutes, while completion was detected in 3 ± 8 and 6 ± 5 minutes, respectively. BG change from start of exercise to detection time was 1 ± 6 and −1 ± 3 mg/dL, and, from the end of exercise to detection time was 6 ± 4 and −17 ± 13 mg/dL, respectively, for the 2 exercise sessions. False positive and negative ratios were 4 ± 2% and 21 ± 22%.

The novel method for exercise detection identified the onset and end of exercise in approximately 5 minutes, with an average BG change of only −6 mg/dL.

Under controlled conditions, the Dose Safety artificial pancreas (AP) system controller, which utilizes “fuzzy logic” (FL) methodology to calculate and deliver appropriate insulin dosages based on changes in blood glucose, successfully managed glycemic excursions. The aim of this study was to show whether stressing the system with pizza (high carbohydrate/high fat) meals and exercise would reveal deficits in the performance of the Dose Safety FL controller (FLC) and lead to improvements in the dosing matrix.

Ten subjects with type 1 diabetes (T1D) were enrolled and participated in 30 studies (17 meal, 13 exercise) using 2 versions of the FLC. After conducting 13 studies with the first version (FLC v2.0), interim results were evaluated and the FLC insulin-dosing matrix was modified to create a new controller version (FLC v2.1) that was validated through regression testing using v2.0 CGM datasets prior to its use in clinical studies. The subsequent 17 studies were performed using FLC v2.1.

Use of FLC v2.1 vs FLC v2.0 in the pizza meal tests showed improvements in mean blood glucose (205 mg/dL vs 232 mg/dL, P = .04). FLC v2.1 versus FLC v2.0 in exercise tests showed improvements in mean blood glucose (146 mg/dL vs 201 mg/dL, P = .004), percentage time spent >180 mg/dL (19.3% vs 46.7%, P = .001), and percentage time spent 70-180 mg/dL (80.0% vs 53.3%, P = .002).

Stress testing the AP system revealed deficits in the FLC performance, which led to adjustments to the dosing matrix followed by improved FLC performance when retested.

Exposure to extreme temperature can affect the performance of blood glucose monitoring systems. The aim was to determine the non-equilibrated performance of these systems at extreme high and low temperatures that can occur in daily life.

The performances of 5 test systems, (1) Abbott FreeStyle Freedom Lite, (2) Roche AccuChek Aviva, (3) Bayer Contour, (4) LifeScan OneTouch Verio, and (5) Sanofi BG Star, were compared after “cooking” (50°C for 1 hour) or “chilling” (−5°C for 1 hour) with room temperature controls (23°C) using whole blood with glucose concentrations of 50, 100, and 200 mg/dl.

The equilibration period (time from the end of incubation to when the test system is operational) was between 1 and 8 minutes, and each test system took between 15 and 30 minutes after incubation to obtain stable measurements at room temperature. Incubating the strips at −5°C or 50°C had little effect on the glucose measurement, whereas incubating the meters introduced bias in performance between 0 and 15 minutes but not subsequently, compared to room temperature controls and at all 3 glucose levels.

Compensating technologies embedded within blood glucose monitoring systems studied here perform well at extreme temperatures. People with diabetes need to be alerted to this feature to avoid perceptions of malperformance of their devices and the possible inability to get blood glucose readings on short notice (eg, during time of suspected rapid change or before an unplanned meal).

Using self-measurements of blood glucose (SMBG) is daily routine for patients with insulin-treated diabetes, however measuring ß-ketones in blood is not widespread. How the use of a combined device, which can measure both, is accepted in daily routine by patients and will lead to better glycemic control is not well studied.

This multicenter, prospective, noninterventional study assessed the impact of routine use of the GlucoMen® LX Plus on patient acceptance, usage and glycemic control among insulin-treated patients with diabetes mellitus type 1 and type 2. A1c and self-reported frequency of SMBG were evaluated at baseline and also postprandial SMBG, ß-ketone measurements, and use of reminders after 3 and 6 months of use. A total of 631 patients, 254 type 1/350 type 2 (27 no type specified), with mean (SD) baseline A1c 8.5% (1.5), age 54.6 (15.6) years, and 47.3% female were studied.

Frequent use of SMBG at baseline led to a higher decrease in A1c at 6 month (V3): –0.3% if SMBG measured up to 1/day versus −0.9% in 4-6/day. Increase of SMBG frequency during the study showed also a negative correlation to A1c, 9.2% at V1 versus 7.6% at V3. Postprandial SMBG was done by 77.7% and ß-ketone measurements by 45.5% of all patients; the reminders were used by 33.4% and led to an increased frequency of SMBG at 6 months.

A combined device for SMBG and ß-ketone measurements is well accepted by patients with insulin-treated diabetes and can lead through the avoidance or detection of ketoacidosis/increased frequency of SMBG and increased awareness of the patients to an improved glycemic outcome.

The prevalence of diabetes is increasing in low-resource settings; however, accessing glucose monitoring is extremely difficult and expensive in these regions. Work is being done to address the multitude of issues surrounding diabetes care in low-resource settings, but an affordable glucose monitoring solution has yet to be presented. An inkjet-printed test strip solution is being proposed as a solution to this problem.

The use of a standard inkjet printer is being proposed as a manufacturing method for low-cost glucose monitoring test strips. The printer cartridges are filled with enzyme and dye solutions that are printed onto filter paper. The result is a colorimetric strip that turns a blue/green color in the presence of blood glucose.

Using a light-based spectroscopic reading, the strips show a linear color change with an R² = .99 using glucose standards and an R² = .93 with bovine blood. Initial testing with bovine blood indicates that the strip accuracy is comparable to the International Organization for Standardization (ISO) standard 15197 for glucose testing in the 0-350 mg/dL range. However, further testing with human blood will be required to confirm this. A visible color gradient was observed with both the glucose standard and bovine blood experiment, which could be used as a visual indicator in cases where an electronic glucose meter was unavailable.

These results indicate that an inkjet-printed filter paper test strip is a feasible method for monitoring blood glucose levels. The use of inkjet printers would allow for local manufacturing to increase supply in remote regions. This system has the potential to address the dire need for glucose monitoring in low-resource settings.

This study sought to assess the function and delivery reliability of intradermal (ID) infusion sets used with commercial insulin pumps.

Healthy subjects (n = 43) were randomized to either ID or subcutaneous (SC) arms, and received basal/bolus placebo delivery for 24 hours. Subjects received 4 of 8 infusion set combinations (ID: microneedle design A or B, with 2 pump brands [Animas or MiniMed]; SC: Teflon Quickset or steel Rapid-D, Animas pump only, with or without overtaping) and were evaluated for pump occlusion alarms, fluid leakage, pain, and tissue tolerability. A novel algorithm was developed to determine flow consistency based on fluid pressure, and the duration and occurrence rate for periods of unalarmed but interrupted flow (“silent occlusions’”) were compared.

ID delivery was successfully maintained over the 24-hour infusion period. The number of silent occlusions was lower for ID microneedle cannula design B than A (P < .01) and lower for Rapid-D SC device compared to Quick-set (P = .03). There was no significant difference in the number of occlusion alarms between the ID and SC devices with the Animas pump. However, the pumps tested with ID devices had significantly different alarm rates (MiniMed 29.5%, Animas 0%, P < .001). Leakage and tissue tolerability were comparable across devices.

The ID infusion set reliably delivered diluent for an extended 24-hour period in healthy subjects and was well tolerated. Silent occlusion flow interruptions could be detected in both ID and SC infusion sets using a proprietary algorithm. This algorithm is a promising method for quantitatively evaluating infusion set flow performance.

Infusion sets for use with insulin pumps are recommended for use for 2 to 3 days to avoid local skin reactions, for example, to the insulin formulation and preservatives like meta-cresol. However, many patients use the catheters longer for economic reasons. We performed this study to investigate the tolerability of 2-day use of infusion sets in comparison to 4-day use in a real-world setting.

This prospective randomized controlled crossover study with 2 × 3-month observation periods was performed with 24 type 1 patients. At baseline, patients were trained on the use of the infusion system (Medtronic /Mio® or inset™ II) and randomized to any of the 2 treatment sequences. Observation parameters included glycemic control, frequency and nature of device-related, and procedure-related adverse events and patient preference.

The per-protocol analysis was performed with 22 patients (5 men, 17 women, age 39 ± 11 years, BMI 27.0 ± 3.5 kg/m2). The number of catheter related adverse events was 290 with 2-day use versus 495 with 4-day use (P < .05). The overall number of treatment related events was 750 with 2-day use versus 934 with 4-day use (P < .001). There was no difference in glycemic control between the treatment arms. Treatment satisfaction was higher with 2-day use (very high/high satisfaction: 90.4% versus 4 day-use: 77.3%, P < .05).

Our results demonstrate that using the infusion sets for a longer usage period of 2-3 days resulted in a clinically relevant increase in treatment-related tolerability problems. Patients should be trained and encouraged not to use insulin pump infusion sets for a longer than the recommended time period.

The objective of the analysis was to review the effectiveness of a care process model (CPM) developed to guide management of patients on insulin pump therapy undergoing elective surgical procedures.

Electronic medical records were reviewed to assess the impact of the CPM on documentation of insulin pump status, glucose monitoring, and safety during the perioperative phase of care. Post-CPM care was compared with management provided before CPM implementation.

We reviewed 45 cases on insulin pump therapy in the pre-CPM cohort and 106 in the post-CPM cohort. Demographic characteristics, categories of surgery, and perioperative times were not significantly different between the 2 groups. Recommended hemoglobin A1c monitoring occurred in 73% of cases in the pre-CPM cohort but improved to 94% in the post-CPM group (P < .01). There was a higher frequency of documentation of the insulin pump during the preoperative, intraoperative, and postanesthesia care unit segments of care in the post- vs pre-CPM periods (all P < .01). The number of cases with intraoperative glucose monitoring increased (57% pre-CPM vs 81% post-CPM; P < .01). Glycemic control was comparable between the 2 CPM periods. Hypoglycemia was rare, with only 3 episodes in the pre-CPM group and 4 in the post-CPM. No adverse events associated with perioperative insulin pump use were observed.

This analysis adds to previous data on use of insulin pump therapy during the perioperative period. Some processes require additional attention, but data continue to indicate that a standardized approach to care can lead to a successful and safe transition of insulin pump therapy throughout the perioperative period.

Elevated fasting intact proinsulin is a biomarker of late-stage ß-cell-dysfunction associated with clinically relevant insulin resistance. In this pilot investigation, we explored the potential value of measuring intact proinsulin as a functional predictor of ß-cell exhaustion during an oral glucose tolerance test (OGTT).

The study was performed with 31 participants, 11 of whom were healthy subjects (7 female, age: 59 ± 20 years), 10 had impaired glucose tolerance (IGT, 6 female, 62 ± 10 years), and 10 had known type 2 diabetes (T2DM, 5 female, 53 ± 11 years, HbA1c: 7.0 ± 0.6%, disease duration: 8 ± 5 years). During OGTT, blood was drawn after 0 hours, 1 hour, and 2 hours for determination of glucose and intact proinsulin. Five years later, patients were again contacted to assess their diabetes status and the association to the previous OGTT results was analyzed.

The OGTT (0 hours/1 hour/2 hours) results were as follows: healthy subjects: glucose: 94 ± 8 mg/dL/140 ± 29 mg/dL/90 ± 24 mg/dL, intact proinsulin: 3 ± 2 pmol/L/10 ± 7 pmol/L/10 ± 5 pmol/L); IGT: glucose: 102 ± 9 mg/dL/158 ± 57 mg/dL/149 ± 34 mg/dL, intact proinsulin: 7 ± 4 pmol/L/23 ± 8 pmol/L/28 ± 6 pmol/L; T2DM: glucose: 121 ± 20 mg/dL/230 ± 51 mg/dL/213 ± 34 mg/dL; intact proinsulin: 7 ± 7 pmol/L/26 ± 9 pmol/L/27 ± 10 pmol/L). Five years later, all of the IGT and 2 of the healthy subjects had developed T2DM and one had devloped IGT. All of them had elevated 2-hour proinsulin values in the initial OGTT, while patients with normal intact proinsulin results did not develop diabetes.

Elevated 2-hour intact proinsulin levels during OGTT were predictive for later type 2 diabetes development. Further studies need to confirm our findings in larger populations.

Mobile health (mHealth) is an expanding field which includes the use of social media and mobile applications (apps). Apps are used in diabetes self-management but it is unclear whether these are being used to support safe drinking of alcohol by people with type 1 diabetes (T1DM). Alcohol health literacy is poor among young adults with T1DM despite specific associated risks.

Systematic literature review followed by critical appraisal of commercially available apps. An eSurvey investigating access to mHealth technology, attitudes toward apps for diabetes management and their use to improve alcohol health literacy was completed by participants.

Of 315 articles identified in the literature search, 7 met the inclusion criteria. Ten diabetes apps were available, most of which lacked the educational features recommended by clinical guidelines. In all, 27 women and 8 men with T1DM, aged 19-31 years were surveyed. Of them, 32 had access to a smartphone/tablet; 29 used apps; 20 used/had used diabetes apps; 3 had used apps related to alcohol and diabetes; 11 had discussed apps with their health care team; 22 felt more communication with their health care team would increase awareness of alcohol-associated risks.

Use of mobile apps is commonplace but the use of apps to support safe drinking in this population was rare. Most participants expressed a preference for direct communication with their health care teams about this subject. Further research is needed to determine the preferences of health care professionals and how they can best support young adults in safe drinking.

Restaurant eating while optimizing nutrition and maintaining a healthy weight is challenging. Even when nutritional information is available, consumers often consider only calories. A quick and easy method to rate both caloric density and nutrition is an unmet need. A food rating system created to address that need is assessed in this study.

The food rating system categorizes food items into 3 color-coded categories: most healthy (green), medium healthy (yellow), or least healthy (red) based on calorie density and general nutritional quality from national guidelines. Nutritional information was downloaded from 20 popular fast-food chains. Nutritional assessments and the 3 color coded categories were compared using the Wilcoxon and Median tests to demonstrate the significance of nutrition differences.

Green foods were significantly lower than yellow foods, which in turn were significantly lower than red foods, for calories and calories from fat, in addition to content of total fat, saturated fat and carbohydrates per 100 g serving weight (all P < .02). The green foods had significantly lower cholesterol than the yellow (P = .0006) and red (P < .0001) foods. Yellow foods had less sugar than red foods (P < .0001). Yellow foods were significantly higher in dietary fiber than red foods (P = .001).

The food rating color-coded system identifies food items with superior nutrition, and lower caloric density. The smartphone app, incorporating the system, has the potential to improve nutrition; reduce the risk of developing diabetes, hypertension, heart disease, and stroke; and improve public health.