Journal of diabetes science and technology

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ISSN 1932-2968

Publications in this journal

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    ABSTRACT: Complications of diabetes mellitus, namely diabetic retinopathy and diabetic maculopathy, are the leading cause of blindness in working aged people. Sufferers can avoid blindness if identified early via retinal imaging. Systematic screening of the diabetic population has been shown to greatly reduce the prevalence and incidence of blindness within the population. Many national screening programs have digital fundus photography as their basis. In the past 5 years several techniques and adapters have been developed that allow digital fundus photography to be performed using smartphones. We review recent progress in smartphone-based fundus imaging and discuss its potential for integration into national systematic diabetic retinopathy screening programs. Some systems have produced promising initial results with respect to their agreement with reference standards. However further multisite trialling of such systems' use within implementable screening workflows is required if an evidence base strong enough to affect policy change is to be established. If this were to occur national diabetic retinopathy screening would, for the first time, become possible in low- and middle-income settings where cost and availability of trained eye care personnel are currently key barriers to implementation. As diabetes prevalence and incidence is increasing sharply in these settings, the impact on global blindness could be profound.
    Journal of diabetes science and technology 11/2015; DOI:10.1177/1932296815617969
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    ABSTRACT: Background: Closed-loop artificial pancreas device (APD) systems are externally worn medical devices that are being developed to enable people with type 1 diabetes to regulate their blood glucose levels in a more automated way. The innovative concept of this emerging technology is that hands-free, continuous, glycemic control can be achieved by using digital communication technology and advanced computer algorithms. Methods: A horizon scanning review of this field was conducted using online sources of intelligence to identify systems in development. The systems were classified into subtypes according to their level of automation, the hormonal and glycemic control approaches used, and their research setting. Results: Eighteen closed-loop APD systems were identified. All were being tested in clinical trials prior to potential commercialization. Six were being studied in the home setting, 5 in outpatient settings, and 7 in inpatient settings. It is estimated that 2 systems may become commercially available in the EU by the end of 2016, 1 during 2017, and 2 more in 2018. Conclusions: There are around 18 closed-loop APD systems progressing through early stages of clinical development. Only a few of these are currently in phase 3 trials and in settings that replicate real life.
    Journal of diabetes science and technology 11/2015; DOI:10.1177/1932296815617968

  • Journal of diabetes science and technology 11/2015; DOI:10.1177/1932296815616135
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    ABSTRACT: Background: About 10% of patients with diabetes discontinue treatment, resulting in the progression of diabetes-related complications and reduced quality of life. Objective: The objective was to predict a missed clinical appointment (MA), which can lead to discontinued treatment for diabetes patients. Methods: A machine-learning algorithm was used to build a logistic regression model for MA predictions, with L2-norm regularization used to avoid over-fitting and 10-fold cross validation used to evaluate prediction performance. Data associated with patient MAs were extracted from electronic medical records and classified into two groups: one related to patients' clinical condition (X1) and the other related to previous findings (X2). The records used were those of the University of Tokyo Hospital, and they included the history of 16 026 clinical appointments scheduled by 879 patients whose initial clinical visit had been made after January 1, 2004, who had diagnostic codes indicating diabetes, and whose HbA1c had been tested within 3 months after their initial visit. Records between April 1, 2011, and June 30, 2014, were inspected for a history of MAs. Results: The best predictor of MAs proved to be X1 + X2 (AUC = 0.958); precision and recall rates were, respectively, 0.757 and 0.659. Among all the appointment data, the day of the week when an appointment was made was most strongly associated with MA predictions (weight = 2.22). Conclusions: Our findings may provide information to help clinicians make timely interventions to avoid MAs.
    Journal of diabetes science and technology 11/2015; DOI:10.1177/1932296815614866
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    ABSTRACT: Background: Previously we have introduced the eA1c-a new approach to real-time tracking of average glycemia and estimation of HbA1c from infrequent self-monitoring (SMBG) data, which was developed and tested in type 2 diabetes. We now test eA1c in type 1 diabetes and assess its relationship to the hemoglobin glycation index (HGI)-an established predictor of complications and treatment effect. Methods: Reanalysis of previously published 12-month data from 120 patients with type 1 diabetes, age 39.15 (14.35) years, 51/69 males/females, baseline HbA1c = 7.99% (1.48), duration of diabetes 20.28 (12.92) years, number SMBG/day = 4.69 (1.84). Surrogate fasting BG and 7-point daily profiles were derived from these unstructured SMBG data and the previously reported eA1c method was applied without any changes. Following the literature, we calculated HGI = HbA1c - (0.009 × Fasting BG + 6.8). Results: The correlation of eA1c with reference HbA1c was r = .75, and its deviation from reference was MARD = 7.98%; 95% of all eA1c values fell within ±20% from reference. The HGI was well approximated by a linear combination of the eA1c calibration factors: HGI = 0.007552*θ1 + 0.007645*θ2 - 3.154 (P < .0001); 73% of low versus moderate-high HGIs were correctly classified by the same factors as well. Conclusions: The eA1c procedure developed in type 2 diabetes to track in real-time changes in average glycemia and present the results in HbA1c-equivalent units has shown similar performance in type 1 diabetes. The eA1c calibration factors are highly predictive of the HGI, thereby explaining partially the biological variation causing discrepancies between HbA1c and its linear estimates from SMBG data.
    Journal of diabetes science and technology 11/2015; DOI:10.1177/1932296815608870

  • Journal of diabetes science and technology 11/2015; DOI:10.1177/1932296815613803
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    ABSTRACT: Background: Monitoring of HbA1c is the standard of care to assess diabetes control. In Trinidad & Tobago (T&T) there are no existing data on the quality of HbA1c measurement. Our study examined the precision and accuracy of HbA1c testing in T&T. Methods: Sets of 10 samples containing blinded duplicates were shipped to laboratories in T&T. This exercise was repeated 6 months later. Precision and accuracy were estimated for each laboratory/method. Results: T&T methods included immunoassay, capillary electrophoresis, and boronate affinity binding. Most, but not all, laboratories demonstrated acceptable precision and accuracy. Conclusions: Continuous oversight of HbA1c testing (eg, through proficiency testing) in T&T is recommended. These results highlight the lack of oversight of HbA1c testing in some developing countries.
    Journal of diabetes science and technology 11/2015; DOI:10.1177/1932296815609620

  • Journal of diabetes science and technology 11/2015; 9(6):1167-1169. DOI:10.1177/1932296815608980
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    ABSTRACT: Background: Current electrochemical glucose sensors use a single electrode. Multiple electrodes (redundancy) may enhance sensor performance. We evaluated an electrochemical redundant sensor (ERS) incorporating 2 working electrodes (WE1 and WE2) onto a single subcutaneous insertion platform with a processing algorithm providing a single real-time continuous glucose measure. Methods: Twenty-three adults with type 1 diabetes each wore 2 ERSs concurrently for 168 hours. Post-insertion a frequent sampling test (FST) was performed with ERS benchmarked against a glucose meter (Bayer Contour Link). Day 4 and 8 FSTs were performed with a standard meal and venous blood collected for reference glucose measurements (YSI and meter). Between visits, ERS was worn with capillary-blood glucose testing >7 times/day. Sensor glucose data were processed prospectively. Results: Mean absolute relative deviation (MARD) for ERS day 1-7 (3,297 paired points with glucose meter) was (mean [SD]) 10.1 [11.5]% versus 11.4 [11.9]% for WE1 and 12.0 [11.9]% for WE2; P < .0001. ERS Clarke A and A+B was 90.2% and 99.8% respectively. Day 4 and day 7 ERS MARD (1,237 pairs with YSI) was 9.4 [9.5]% versus 9.6 [9.7]% for WE1 and 9.9 [9.7]% for WE2; P = ns. ERS day 1-7 precision absolute relative deviation (PARD) was 9.9 [3.6]% versus 11.5 [6.2]% for WE1 and 10.1 [4.4]% for WE2; P = ns. ERS sensor display time was 97.8 [6.0]% versus 91.0 [22.3]% for WE1 and 94.1 [14.3]% for WE2; P < .05. Conclusions: Electrochemical redundancy enhances glucose sensor accuracy and display time compared with each individual sensing element alone. ERS performance compares favorably with "best-in-class" of nonredundant sensors.
    Journal of diabetes science and technology 10/2015; DOI:10.1177/1932296815612096
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    ABSTRACT: Physical activity is an important determinant of glucose variability in type 1 diabetes (T1D). It has been incorporated as a nonglucose input into closed-loop control (CLC) protocols for T1D during the last 4 years mainly by 3 research groups in single center based controlled clinical trials involving a maximum of 18 subjects in any 1 study. Although physical activity data capture may have clinical benefit in patients with T1D by impacting cardiovascular fitness and optimal body weight achievement and maintenance, limited number of such studies have been conducted to date. Clinical trial registries provide information about a single small sample size 2 center prospective study incorporating physical activity data input to modulate closed-loop control in T1D that are seeking to build on prior studies. We expect an increase in such studies especially since the NIH has expanded support of this type of research with additional grants starting in the second half of 2015. Studies (1) involving patients with other disorders that have lasted 12 weeks or longer and tracked physical activity and (2) including both aerobic and resistance activity may offer insights about the user experience and device optimization even as single input CLC heads into real-world clinical trials over the next few years and nonglucose input is introduced as the next advance.
    Journal of diabetes science and technology 10/2015; DOI:10.1177/1932296815609949
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    ABSTRACT: Background: 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. Methods: 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. Results: The blood glucose at the beginning of exercise ([Formula: see text]), the ratio of insulin on board over total daily insulin ([Formula: see text]) 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. Conclusions: 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.
    Journal of diabetes science and technology 10/2015; DOI:10.1177/1932296815607864
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    ABSTRACT: The MyStar Extra self-monitoring blood glucose (SMBG) system provides moving estimates of the patient's hemoglobin A1c (HbA1c). There is a treasure trove of highly accurate glucose data available from highly accurate SMBG, CGM and FGM along with highly accurate HPLC HbA1c. If Nathan's criteria are used to select subjects whose glucoses can be correlated to the HbA1c, then algorithms can be developed for robustly transforming glucose into HbA1c. These algorithms can then be implemented in any SMBG or with the CGM and FGM software. This calculated HbA1c would even be accurate with Nathan's excluded population thus reducing the use of fructosamine and glycated protein. Finally, the developer of these new algorithms is advised to use a specific approach for testing her algorithm.
    Journal of diabetes science and technology 10/2015; DOI:10.1177/1932296815610779
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    ABSTRACT: Background: 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. Method: 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. Results: Ovulation was confirmed in 33/36 cycles studied in 12 subjects (age = 33.1 ± 7.0 years, BMI = 25.7 ± 2.9 kg/m(2), 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. Conclusions: 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.
    Journal of diabetes science and technology 10/2015; DOI:10.1177/1932296815608400

  • Journal of diabetes science and technology 10/2015; DOI:10.1177/1932296815611425
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    ABSTRACT: Background: Results from accuracy assessments of systems for self-monitoring of blood glucose (SMBG) are often visualized in difference or regression plots. These approaches become more difficult to read as the number of data points displayed increases, thus limiting their use. In the recently presented rectangle target plot (RTP) approach, data from each reagent system lot or product are displayed graphically as a single rectangle, thus allowing the plot to remain comprehensible even when displaying system accuracy data from multiple reagent system lots or products. Methods: The RTP illustrates the accuracy of SMBG systems. Each rectangle shows the mean bias and the variability of a system. By use of statistical tolerance intervals, each rectangle most closely approximates the total error for lower (<100 mg/dL) and upper (≥100 mg/dL) glucose concentrations. RTPs were created for data from 8 different manufacturers of systems for SMBG. In total, the accuracy data of 87 different reagent system lots of 50 different SMBG systems were displayed in RTPs. Results: The RTP approach was suitable for 81 of the 87 reagent system lots analyzed. In the remaining cases, outliers caused excessive skewness of the distribution of measurements. The reagent system lots analyzed were grouped according to manufacturer in RTPs. Data from 3 to 15 different reagent system lots were displayed in each RTP. Conclusion: Applying the RTP approach to a large number of reagent system lots showed that it was suitable in more than 93% of cases analyzed. The display of system accuracy data in RTPs enables lot-to-lot variability within specific products and product reliability of specific manufacturers to be visualized in a comprehensible manner.
    Journal of diabetes science and technology 10/2015; DOI:10.1177/1932296815612496