Journal of Diabetes Science and Technology

The Diabetes Control and Complications Trial (DCCT) clearly documented long-term beneficial effects on both micro- and macro-vascular complications associated with type 1 diabetes (T1D) by using intensive insulin therapy (IIT) via multiple daily injections (MDIs) or insulin pumps more than 30 year ago. IIT, both during the DCCT and with translation into clinical practice, has been demonstrated to increase the risk of severe hypoglycemia and weight gain. Automated insulin delivery (AID) systems have become the standard of care in T1D management in the developed countries.

We reviewed the registration and real-life studies for different AID systems reported to date. Many of the registration studies were sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). A systematic literature search was conducted using the MEDLINE (PubMed) database. Studies with the longest duration and/or with the largest number of participants were included.

In the last decade, the introduction of a many AID systems for patients with T1D has shown improvements in glycemic metrics as documented by HbA1c values, time in range (TIR), time below range (TBR), and quality of life. Most of the registration and real-life studies have shown safe and effective use of AID systems for all age groups living with T1D.

In this review, we summarize the registration and real-life studies of US Food and Drug Administration (FDA)-approved AID systems. Real-life studies confirmed the glycemic outcomes of AID systems reported from registration studies.

Since the discovery of the life-saving hormone insulin in 1921 by Dr Frederick Banting in 1921, there have been many critical discoveries and technical breakthroughs that have enabled people living with type 1 diabetes (T1D) to live longer, healthier lives. The development of insulin pumps, continuous glucose monitoring (CGM) systems, and automated insulin delivery (AID) systems have enabled people living with T1D to safely manage their glucose, reduce their HbA1c, and improve their overall health and quality of life. Nevertheless, AID systems are not yet designed for all people with T1D, and they perform best during the overnight period when meals and exercise are not occurring. AID systems are not fully automated in that they require the person using the system to announce meals and exercise to the system to avoid dangerous hyper- or hypoglycemia, respectively.

In this review, which is one of a collection of manuscripts to commemorate the 75th anniversary of the National Institute for Diabetes and Digestive and Kidney Diseases, we celebrate the commercialization of the AID and discuss the major challenges and research gaps that remain to be solved to enable single- and multi-hormone AID systems to more fully support glucose management in people living with T1D.

More research is required to design and evaluate more intelligent AID systems that do not require accurate carbohydrate estimations or announcements for meals and exercise. Current AID systems are also not designed to be used by older adults or pregnant people. Results are presented on new AID systems that can automatically respond to meals and exercise. Results are also presented on evaluations of AID systems in older adults and pregnant people.

Next-generation AID systems will need to support all people, including older adults, people during pregnancy, athletes, and people who may be too busy to announce carbohydrates or exercise to the system. Solutions are now becoming available that will enable AID systems to support a broader range of people living with T1D by leveraging the latest technologies in artificial intelligence and adaptive control.

To evaluate factors affecting within-participant reproducibility in glycemic response to different forms of exercise.

Structured exercise sessions ~30 minutes in length from the Type 1 Diabetes Exercise Initiative (T1DEXI) study were used to assess within-participant glycemic variability during and after exercise. The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated.

Data from 476 adults with type 1 diabetes were analyzed. A participant’s change in glucose during exercise was reproducible within 15 mg/dL of the participant’s other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose (P < .001), lower glucose coefficient of variation (CV) (P < .001), and lower % time <70 mg/dL (P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose.

Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participant’s overall glycemic control than other modifiable factors.

Reliable blood glucose (BG) measurements are important for people with diabetes to manage their therapy as well as in point-of-care testing (POCT) performed by health care professionals to monitor BG of patients or even to diagnose diabetes. Among other factors, endogenous and exogenous substances present in blood samples can impact the measurement results. To ensure and prove that blood glucose monitoring systems (BGMSs) are robust in terms of potential interferents, manufacturers have to perform extensive evaluations.

An interference screening test was performed for three reagent system lots of a POCT system and of a BGMS for self-monitoring of BG. A paired-difference approach based on ISO 15197:2013 and CLSI guideline EP07 was used with venous whole blood samples at two different glucose concentrations. Seventy potential interferents expected to be common in people with diabetes were evaluated.

The interference effects were determined as normalized biases between test samples and corresponding control samples. For 69 of the 70 investigated potential interferents, both systems met the predefined acceptance criteria, with the normalized biases falling within ±10 mg/dL or ±10% at glucose concentrations ≤100 mg/dL or >100 mg/dL, respectively, for each of the three evaluated reagent system lots.

The BGMS investigated in this study were found to be robust with respect to the 70 evaluated potential interferents. Interference effects were observed only for N-Acetyl-L-cysteine. Extensive evaluations of potential interfering factors can make an important contribution to ensure reliability of BGMS.

The main objective of this study is to evaluate the incremental cost-effectiveness (ICER) of the Cambridge hybrid closed-loop automated insulin delivery (AID) algorithm versus usual care for children and adolescents with type 1 diabetes (T1D).

This multicenter, binational, parallel-controlled trial randomized 133 insulin pump using participants aged 6 to 18 years to either AID (n = 65) or usual care (n = 68) for 6 months. Both within-trial and lifetime cost-effectiveness were analyzed. Analysis focused on the treatment subgroup (n = 21) who received the much more reliable CamAPS FX hardware iteration and their contemporaneous control group (n = 24). Lifetime complications and costs were simulated via an updated Sheffield T1D policy model.

Within-trial, both groups had indistinguishable and statistically unchanged health-related quality of life, and statistically similar hypoglycemia, severe hypoglycemia, and diabetic ketoacidosis (DKA) event rates. Total health care utilization was higher in the treatment group. Both the overall treatment group and CamAPS FX subgroup exhibited improved HbA_1C (−0.32%, 95% CI: −0.59 to −0.04; P = .02, and −1.05%, 95% CI: −1.43 to −0.67; P < .001, respectively). Modeling projected increased expected lifespan of 5.36 years and discounted quality-adjusted life years (QALYs) of 1.16 (U.K. tariffs) and 1.52 (U.S. tariffs) in the CamAPS FX subgroup. Estimated ICERs for the subgroup were £19 324/QALY (United Kingdom) and −$3917/QALY (United States). For subgroup patients already using continuous glucose monitors (CGM), ICERs were £10 096/QALY (United Kingdom) and −$33 616/QALY (United States). Probabilistic sensitivity analysis generated mean ICERs of £19 342/QALY (95% CI: £15 903/QALY to £22 929/QALY) (United Kingdom) and −$28 283/QALY (95% CI: −$59 607/QALY to $1858/QALY) (United States).

For children and adolescents with T1D on insulin pump therapy, AID using the Cambridge algorithm appears cost-effective below a £20 000/QALY threshold (United Kingdom) and cost saving (United States).

Painful diabetic neuropathy (PDN) can result in the loss of protective sensation, in which people are at twice the likelihood of foot ulceration and three times the risk of lower extremity amputation. Here, we evaluated the long-term effects of high-frequency (10 kHz) paresthesia-independent spinal cord stimulation (SCS) on protective sensation in the feet and the associated risk of foot ulceration for individuals with PDN.

The SENZA-PDN clinical study was a randomized, controlled trial in which 216 participants with PDN were randomized to receive either conventional medical management (CMM) alone or 10 kHz SCS plus CMM, with optional treatment crossover after 6 months. At study visits (baseline through 24 months), 10-g monofilament sensory assessments were conducted at 10 locations per foot. Two published methods were used to evaluate protective sensation via classifying risk of foot ulceration.

Participants in the 10 kHz SCS group reported increased numbers of sensate locations as compared to CMM alone (P < .001) and to preimplantation (P < .01) and were significantly more likely to be at low risk of foot ulceration using both classification methods. The proportion of low-risk participants approximately doubled from preimplantation to 3 months postimplantation and remained stable through 24 months (P ≤ .01).

Significant improvements were observed in protective sensation from preimplantation to 24 months postimplantation for the 10 kHz SCS group. With this unique, disease-modifying improvement in sensory function, 10 kHz SCS provides the potential to reduce ulceration, amputation, and other severe sequelae of PDN.

The SENZA-PDN study is registered on ClinicalTrials.gov with identifier NCT03228420.

Studies have demonstrated that 50% to 80% of patients do not receive an International Classification of Diseases (ICD) code assigned to their medical encounter or condition. For these patients, their clinical information is mostly recorded as unstructured free-text narrative data in the medical record without standardized coding or extraction of structured data elements. Leumit Health Services (LHS) in collaboration with the Israeli Ministry of Health (MoH) conducted this study using electronic medical records (EMRs) to systematically extract meaningful clinical information about people with diabetes from the unstructured free-text notes.

To develop and validate natural language processing (NLP) algorithms to identify diabetes-related complications in the free-text medical records of patients who have LHS membership.

The study data included 2.3 million records of 41 469 patients with diabetes aged 35 or older between the years 2012 and 2017. The diabetes related complications included cardiovascular disease, diabetic neuropathy, nephropathy, retinopathy, diabetic foot, cognitive impairments, mood disorders and hypoglycemia. A vocabulary list of terms was determined and adjudicated by two physicians who are experienced in diabetes care board certified diabetes specialist in endocrinology or family medicine. Two independent registered nurses with PhDs reviewed the free-text medical records. Both rule-based and machine learning techniques were used for the NLP algorithm development. Precision, recall, and F-score were calculated to compare the performance of (1) the NLP algorithm with the reviewers’ comments and (2) the ICD codes with the reviewers’ comments for each complication.

The NLP algorithm versus the reviewers (gold standard) achieved an overall good performance with a mean F-score of 86%. This was better than the ICD codes which achieved a mean F-score of only 51%.

NLP algorithms and machine learning processes may enable more accurate identification of diabetes complications in EMR data.

Diabetic foot ulcers (DFUs) are serious complications of diabetes which can lead to lower extremity amputations (LEAs). Risk prediction models can identify high-risk patients who can benefit from early intervention. Machine learning (ML) methods have shown promising utility in medical applications. Explainable modeling can help its integration and acceptance. This study aims to develop a risk prediction model using ML algorithms with explainability for LEA in DFU patients.

This study is a retrospective review of 2559 inpatient DFU episodes in a tertiary institution from 2012 to 2017. Fifty-one features including patient demographics, comorbidities, medication, wound characteristics, and laboratory results were reviewed. Outcome measures were the risk of major LEA, minor LEA and any LEA. Machine learning models were developed for each outcome, with model performance evaluated using receiver operating characteristic (ROC) curves, balanced-accuracy and F1-score. SHapley Additive exPlanations (SHAP) was applied to interpret the model for explainability.

Model performance for prediction of major, minor, and any LEA event achieved ROC of 0.820, 0.637, and 0.756, respectively, with XGBoost, XGBoost, and Gradient Boosted Trees algorithms demonstrating best results for each model, respectively. Using SHAP, key features that contributed to the predictions were identified for explainability. Total white cell (TWC) count, comorbidity score and red blood cell count contributed highest weightage to major LEA event. Total white cell, eosinophils, and necrotic eschar in the wound contributed most to any LEA event.

Machine learning algorithms performed well in predicting the risk of LEA in a patient with DFU. Explainability can help provide clinical insights and identify at-risk patients for early intervention.

To evaluate the use of intermittently scanned continuous glucose monitoring (isCGM) in patients with liver cirrhosis (LC).

Observational study including 30 outpatients with LC (Child-Pugh B/C): 10 without diabetes (DM) (G1), 10 with newly diagnosed DM by oral glucose tolerance test (G2), and 10 with a previous DM diagnosis (G3). isCGM (FreeStyle Libre Pro) was used for 56 days (four sensors/patient). Blood tests were performed at baseline and after 28 and 56 days.

No differences were found in the baseline characteristics, except for higher age in G3. There were significant differences between G1, G2 and G3 in glucose management indicator (GMI) (5.28 ± 0.17, 6.03 ± 0.59, 6.86 ± 1.08%, P < .001), HbA1c (4.82 ± 0.39, 5.34 ± 1.26, 6.97 ± 1.47%, P < .001), average glucose (82.79 ± 7.06, 113.39 ± 24.32, 149.14 ± 45.31mg/dL, P < .001), time in range (TIR) (70.89 ± 9.76, 80.2 ± 13.55, 57.96 ± 17.96%, P = .006), and glucose variability (26.1 ± 5.0, 28.21 ± 5.39, 35.31 ± 6.85%, P = .004). There was discordance between GMI and HbA1c when all groups were considered together, with a mean difference of 0.35% (95% SD 0.17, 0.63). In G1, the mean difference was 0.46% (95% SD 0.19, 0.73) and in G2 0.69% (95% SD 0.45, 1.33). GMI and HbA1c were concordant in G3, with a mean difference of −0.10 % (95% SD [−0.59, 0.38]).

Disagreements were found between the GMI and HbA1c levels in patients with LC. isCGM was able to detect abnormalities in glycemic control that would not be detected by monitoring with HbA1c, suggesting that isCGM can be useful in assessing glycemic control in patients with LC.

A significant majority of adolescents from lower-middle-income countries do not meet recommendations for daily physical activity. THANDAV (Taking High-Intensity Interval Training [HIIT] ANd Dance to Adolescents for Victory over noncommunicable diseases [NCDs]) is a 10-minute dance intervention incorporating principles of HIIT. The present study evaluated the effect of THANDAV on cardiovascular risk factors and lifestyle behavior in Asian Indian adolescent girls.

THANDAV was delivered as a 12-week pilot cluster randomized controlled trial (cRCT) in two schools, involving 108 schoolgirls aged 13 to 15 years in Chennai, India. The primary outcome was step counts, while secondary outcomes included metabolic, clinical, and lifestyle parameters. Focus group discussions and interviews were held to assess barriers to and acceptability of THANDAV intervention.

In the intention-to-treat analysis, the intervention group had significantly favorable changes in step counts (+1073 steps/day), skeletal muscle mass (+0.9 kg), body weight (−0.7 kg), body fat percentage (−2.0%), body mass index (−0.3 kg/m²), waist circumference (−1.0 cm), systolic (−4 mm Hg) and diastolic blood pressure (−4 mm Hg), resting heart rate (−3 bpm) and body fat mass (−1.7 kg), moderate -to-vigorous physical activity (+29.5 minutes/day), sleep (+46.4 minutes/day), sedentary time (−199.7 minutes/day), Adolescence Stress Scale (−6.6), and junk food consumption (−2.7) scores compared with controls. Qualitative interviews revealed that THANDAV routines were time-efficient, enjoyable, and easily fit into adolescents’ busy schedules enabling feasible engagement in active leisure time.

THANDAV is a culturally acceptable HIIT-based dance intervention that improves leisure-time physical activity and reduces cardiometabolic risk in Asian Indian adolescent girls.

The trial is registered with the Central Trials Registry-India (CTRI/2020/02/023384; URL: https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=MzgyMTQ=&Enc=&userName=).

SAR341402 insulin aspart (SAR-Asp) is a rapid-acting insulin analog developed as an interchangeable biosimilar to the marketed insulin aspart reference product (NovoLog; NN-Asp). GEMELLI X was a randomized controlled trial to assess outcomes with a biosimilar in line with the US Food and Drug Administration requirements for designation as an interchangeable biosimilar. This report assessed whether multiple switches between SAR-Asp and NN-Asp lead to equivalent safety and efficacy compared with continuous use of NN-Asp in adults with type 1 diabetes (T1D) treated with multiple daily injections, using once-daily insulin glargine U100 (Lantus) as the basal insulin.

This open-label randomized (1:1), parallel-group, phase 3 trial compared four × four weeks of alternating use of individually titrated SAR-Asp and NN-Asp (NN-Asp for first four weeks, SAR-Asp in last four weeks; switching group) vs 16 weeks of continuous use of NN-Asp (nonswitching group). End points included pharmacokinetics, immunogenicity, adverse events, hypoglycemia, insulin dose, and change in efficacy parameters.

Of the 210 patients randomized, 200 (95.5%) completed the trial. Patients assigned to switching group (n = 104) and nonswitching group (n = 106) showed similar safety and tolerability, including anti-insulin aspart antibody responses, adverse events, and hypoglycemia. At week 16, there was no relevant difference between switching vs nonswitching groups in the change from baseline in glycated hemoglobin (least square [LS] mean difference = 0.05% [95% confidence interval [CI] = −0.13, 0.22]; 0.50 mmol/mol [−1.40, 2.39]), fasting plasma glucose (LS mean difference = 0.23 mmol/L [95% CI = −1.08, 1.53]; 4.12 mg/dL [−19.38, 27.62]), and changes in insulin dosages.

Alternating doses of SAR-Asp and NN-Asp compared with continuous use of NN-Asp showed similar safety, immunogenicity, and clinical efficacy in adults with T1D. This study supports interchangeability between SAR-Asp and NN-Asp in T1D management.

Automated insulin delivery (AID) systems have shown to improve glycemic control in a range of populations and settings. At the start of this study, only one commercial AID system had entered the Austrian market (MiniMed 670G, Medtronic). However, there is an ever-growing community of people living with type 1 diabetes (PWT1D) using open-source (OS) AID systems.

A total of 144 PWT1D who used either the MiniMed 670G (670G) or OS-AID systems routinely for a period of at least three to a maximum of six months, between February 18, 2020 and January 15, 2023, were retrospectively analyzed (116 670G aged from 2.6 to 71.8 years and 28 OS-AID aged from 3.4 to 53.5 years). The goal is to evaluate and compare the quality of glycemic control of commercially available AID and OS-AID systems and to present all data by an in-depth descriptive analysis of the population. No statistical tests were performed.

The PWT1D using OS-AID systems spent more time in range (TIR)_{70-180 mg/dL} (81.7% vs 73.9%), less time above range (TAR)_{181-250 mg/dL} (11.1% vs 19.6%), less TAR_{>250 mg/dL} (2.5% vs 4.3%), and more time below range (TBR)_{54-69 mg/dL} (2.2% vs 1.7%) than PWT1D using the 670G system. The TBR_{<54 mg/dL} was comparable in both groups (0.3% vs 0.4%). In the OS-AID group, median glucose level and glycated hemoglobin (HbA1c) were lower than in the 670G system group (130 vs 150 mg/dL; 6.2% vs 7.0%).

In conclusion, both groups were able to achieve satisfactory glycemic outcomes independent of age, gender, and diabetes duration. However, the PWT1D using OS-AID systems attained an even better glycemic control with no clinical safety concerns.

We compared the performance of three currently available laboratory benchtop glucose analyzers with the outgoing YSI 2300 Stat Plus.

Plasma samples (100), across a wide glucose concentration range were analysed on the YSI 2500, Randox daytona+ (glucose oxidase) and EKF Biosen in a single laboratory and compared to the YSI 2300 Stat Plus.

All three analyzers showed good agreement with the YSI 2300 Stat Plus, and only a small bias (≤1% YSI 2500 and Randox daytona+, 4.6% EKF Biosen) was observed for each analyzer. None of the three comparator analyzers were affected by either proportional or constant bias, thus no significant differences between the YSI 2300 Stat Plus and the comparator methods were identified.

The results from this study suggest all could be considered as suitable reference laboratory glucose analyzers and replacements for the recently withdrawn YSI 2300 Stat Plus.

Individuals with intellectual disabilities (IDs) are at risk of diabetes mellitus (DM) and diabetic peripheral neuropathy (DPN), which can lead to foot ulcers and lower-extremity amputations. However, cognitive differences and communication barriers may impede some methods for screening and prevention of DPN. Wearable and mobile technologies—such as smartphone apps and pressure-sensitive insoles—could help to offset these barriers, yet little is known about the effectiveness of these technologies among individuals with ID.

We conducted a scoping review of the databases Embase, PubMed, and Web of Science using search terms for DM, DPN, ID, and technology to diagnose or monitor DPN. Finding a lack of research in this area, we broadened our search terms to include any literature on technology to diagnose or monitor DPN and then applied these findings within the context of ID.

We identified 88 articles; 43 of 88 (48.9%) articles were concerned with gait mechanics or foot pressures. No articles explicitly included individuals with ID as the target population, although three articles involved individuals with other cognitive impairments (two among patients with a history of stroke, one among patients with hemodialysis-related cognitive changes).

Individuals with ID are not represented in studies using technology to diagnose or monitor DPN. This is a concern given the risk of DM complications among patients with ID and the potential for added benefit of such technologies to reduce barriers to screening and prevention. More studies should investigate how wearable devices can be used among patients with ID.

Glucose is vital for biological processes, requiring blood sugar levels to be maintained between 3.88 and 6.1 mmol/L, especially during fasting. Elevated levels signal diabetes, a global concern affecting 537 million people, necessitating effective glucose-monitoring devices.

Enzyme-based sensors, though selective, are sensitive to environmental factors. Nonenzymatic sensors, especially those with nanoparticles, offer stability, high surface area, and cost-effectiveness. Existing literature supports their immediate glucose oxidation, showcasing exceptional sensitivity.

This review details nonenzymatic sensors, highlighting materials, detection limits, and the promise of nanoparticle-based designs, which exhibit enhanced sensitivity and selectivity in glucose detection.

Nanoparticle-based sensors, as reviewed, show potential for glucose monitoring, overcoming enzyme-based limitations. The conclusion suggests future directions for advancing these sensors, emphasizing ongoing innovation in this critical research area.