bims-glumda 2026-04-19 papers

bims-glumda

Biomed News

on CGM data in management of diabetes

Issue of 2026–04–19
fifteen papers selected by
Mott Given

Non-enzymatic sweat sensors for pediatric continuous glucose monitoring: a systematic review of engineering readiness and the cost-accuracy trade-off.
Continuous Glucose Monitoring in Insulin-Treated Type 2 Diabetes Mellitus in Sweden: A Scoping Review and Policy Landscape Analysis.
A Digital Diabetes Self-Management Education and Support Program Integrated With Continuous Glucose Monitoring Improves Glycemic Control: A Randomized Controlled Trial.
Impact of Continuous Glucose Monitoring on Clinical Outcomes and Productivity Losses Among People Living With Type 2 Diabetes Not Using Insulin From an Employer's Perspective.
A Nurse-Led Educational Intervention for Patients With Type 1 Diabetes Using Continuous Glucose Monitoring.
DPA-Net: A dual-path attention neural network for estimating glycemic metrics from self-monitored blood glucose data.
Operational and Clinical Insights from a Fully Decentralized Clinical Study Evaluating Protocol Adherence, Participant Satisfaction, and Connected Device Data in Individuals with Type 2 Diabetes on Different Treatment Regimens.
Use of Continuous Glucose Monitoring With Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes.
Does faster aspart improve time in range in children with type 1 diabetes with glycaemia close to target on insulin pump therapy?
Diabetes Technology: An Update.
Nutritional considerations for athletes with diabetes: optimizing performance and glycemic control.
A multicenter prospective evaluation of the benefits of Dexcom ONE + in subjects with type 2 diabetes on insulin therapy beyond glycemic control: results from the LIFE-ONE study.
A Practical Approach to Insulin Pump, CGM, and Automated Insulin Delivery in the Management of Type 1 Diabetes in Pregnancy.
Clinical characteristics and individualized treatment strategies for children with type 1 diabetes and exogenous insulin antibody syndrome: a retrospective case series and literature review.
Does Resistant Starch Formed by Cooling Pasta Decrease the Postprandial Glycemic Response in Type 1 Diabetes? A Randomized Single-Blind Crossover Study.

BMC Biomed Eng. 2026 Apr 15.

Non-enzymatic sweat sensors for pediatric continuous glucose monitoring: a systematic review of engineering readiness and the cost-accuracy trade-off.

Oluwanifemi Temiloluwa Bolaji, Emmanuel Dominic Ephraim, Olumhense Benedict Adoghe, Deborah Daramola, Emmanuel Francis Kibuebu.



Keywords:  Biomedical engineering; Continuous Glucose Monitoring (CGM); Global health; Non-enzymatic sensing; Pediatric diabetes; Sweat biosensor

DOI:  https://doi.org/10.1186/s42490-026-00109-8
Diabetes Technol Ther. 2026 Apr 14. 15209156261442987

Continuous Glucose Monitoring in Insulin-Treated Type 2 Diabetes Mellitus in Sweden: A Scoping Review and Policy Landscape Analysis.

Johan Jendle, Peter Adolfsson, Jarl Hellman, Boris Klanger, Neda Rajamand Ekberg.

   BACKGROUND: Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases in Sweden. Continuous glucose monitoring (CGM) is an increasingly important alternative to self-monitoring of blood glucose (SMBG), particularly for people receiving insulin.
OBJECTIVES: To summarize the evidence for CGM in insulin-treated T2DM and conduct a policy landscape analysis of national and local guidelines to explore access to CGM in Sweden relative to other countries.
METHODS: A scoping review was conducted to identify evidence on the clinical, patient, and economic value of CGM, supplemented with searches of Swedish and European guidelines. Current recommendations for CGM in T2DM in Sweden were compared with European-wide and country-specific recommendations. Regional recommendations and funding processes within Sweden were reviewed to examine heterogeneity in local access to CGM.
RESULTS: Across international and Swedish studies, CGM was associated with improved clinical outcomes compared with SMBG, both for people with intensive insulin-treated T2DM and those on basal insulin only. The clinical benefits likely translate into fewer long-term diabetes complications and reduced resource utilization and budget impact versus SMBG. Health economic evaluations show that CGM can be considered a cost-effective intervention for all individuals treated with insulin in Sweden. European-wide guidance supports consideration of CGM for all insulin-treated individuals, but access in Sweden remains limited outside of the high-risk intensive insulin-treated population. This is exacerbated by regional heterogeneity in access, which partly stems from the attribution of budget responsibility to individual primary care units.
CONCLUSIONS: The benefits associated with CGM show that an expansion of the Swedish recommendations to cover all insulin-treated people with T2DM is warranted. Persistent regional disparities must be addressed to ensure equitable access to care for people with T2DM in Sweden. The establishment of dedicated funding mechanisms within primary care should be considered to promote more equitable and sustainable access.

Keywords:  Sweden; access; continuous glucose monitoring; glycemic management; medical device; type 2 diabetes

DOI:  https://doi.org/10.1177/15209156261442987
J Med Internet Res. 2026 Apr 12.

A Digital Diabetes Self-Management Education and Support Program Integrated With Continuous Glucose Monitoring Improves Glycemic Control: A Randomized Controlled Trial.

Ashley Berthoumieux, Jeanean B Naqvi, Sean Zion, Jenna Napoleone, Amanda McGuill, Christian J Cerrada, Hyun Jung Lee, Timothy C Dunn, David Kerr, Carolyn Bradner Jasik, Sarah Linke.

BACKGROUND: Previous research has demonstrated that continuous glucose monitoring (CGM) use can improve glycemic control in people with type 2 diabetes when used regularly alongside an in-person digital diabetes self-management and education support (DSMES) program. However, to date there is limited evidence showing the benefits of a digitally-delivered DSMES program combined with real-time CGM for adults with type 2 diabetes.
OBJECTIVE: To evaluate the impact of a DSMES program coupled with CGM on hemoglobin A1c (HbA1c) and CGM-derived glycemic measures compared to usual care for adults with type 2 diabetes over 6 months.
METHODS: Participants with type 2 diabetes and HbA1c ≥8% (64 mmol/mol) not using mealtime bolus insulin (26-83 years old; mean HbA1c: 9.6% [81.2 mmol/mol]) were randomly assigned to a digital DSMES + CGM integrated solution (n=51) or usual care (n=49) for 6 months. The primary outcome was HbA1c. Secondary outcomes were CGM-derived glycemic measures, including glucose management indicator (GMI), percent time in range 70-180 mg/dL (TIR), above range (TAR; >180 mg/dL), and below range (TBR; <70 mg/dL), and mean glucose. Linear mixed effects models were used for intention-to-treat analyses.
RESULTS: HbA1c was lower among intervention versus usual care at 3 months (difference=-0.7% [-8.1 mmol/mol]; P=.03) and at 6 months (difference=-0.6% [-6.9 mmol/mol]; P=.12) but only reached statistical significance at 3 months. CGM-derived glycemic measures, including GMI (difference=-0.9%; P=.03), TIR (difference=14.6%; P=.04), and TAR (difference=-14.9%; P=.04), and mean glucose (difference=-36.4 mg/dL; P=.03) were also significantly improved for intervention vs usual care at 6 months.
CONCLUSIONS: The combination of digital DSMES + CGM is effective for supporting adults with type 2 diabetes in managing their condition and has potential to reduce the risk of long-term health complications.
CLINICALTRIAL: The trial was registered on ClinicalTrials.gov (NCT05368454).

DOI: https://doi.org/10.2196/78321
J Diabetes Sci Technol. 2026 Apr 14. 19322968261428484

Impact of Continuous Glucose Monitoring on Clinical Outcomes and Productivity Losses Among People Living With Type 2 Diabetes Not Using Insulin From an Employer's Perspective.

Martha E Shepherd, Jon Harris-Shapiro, David W Hines, Marie Steele-Adjognon, Abraham Lee, Yeesha Poon.

   BACKGROUND: This study evaluated the impact of continuous glucose monitoring (CGM) on health outcomes and workplace absenteeism among people living with type 2 diabetes (T2D) who are not using insulin.
METHODS: This was a pre-post observational study using real-world data from a large employer health plan. Glycated hemoglobin (HbA1c), body mass index (BMI), and sick days were measured 360 days before (baseline period) and 360 days after (follow-up period) CGM initiation. Persistence was defined as refilling CGM supplies at least once every 90 days.
RESULTS: In total, 71 patients were included (mean age, 52.3 years; 56.3% female); 33 (46.5%) were persistent to CGM. Overall, mean HbA1c levels decreased from 7.6% to 7.1% after the initiation of CGM (mean reduction, 0.52%; N = 55; P = .042), while mean BMI decreased by 0.95 kg/m² (N = 34; P = .075). Compared with the nonpersistent group, persistent patients achieved larger mean reductions in HbA1c levels (-0.89% vs. -0.17%; mean difference, 0.72%; P = .152) and BMI (-2.26 kg/m² vs. +0.37 kg/m²; mean difference, 2.62 kg/m2; P = .009). Absenteeism decreased in the persistent group but increased in the nonpersistent cohort (-8.41 vs. +6.95 mean sick days; mean difference, 15.36 days, P = .002).
CONCLUSIONS: In this population, CGM was associated with improvements in clinical outcomes and absenteeism, although the economic implications require further evaluation. BMI and absenteeism were statistically significantly improved among persistent patients, compared with those of the nonpersistent cohort. Employers could benefit from broader coverage of CGM in this population.

Keywords:  absenteeism; continuous glucose monitoring; glycemic control; persistence; real-world evidence; type 2 diabetes mellitus

DOI:  https://doi.org/10.1177/19322968261428484
Worldviews Evid Based Nurs. 2026 Apr;23(2): e70139

A Nurse-Led Educational Intervention for Patients With Type 1 Diabetes Using Continuous Glucose Monitoring.

Marcos Pazos-Couselo, Verónica Izquierdo, Mercedes Muñoz Martinez, Francisco Javier Gómez Romero, José-Ramón Muñoz-Rodríguez, Antía Fernández-Pombo, Jesus Moreno-Fernandez.

   BACKGROUND: The primary barrier to maximizing the benefits of intermittently scanned continuous glucose monitoring (isCGM) is low scan frequency. Higher daily scan frequency correlates with better glycemic control.
AIM: To evaluate the effect of a nurse-led educational intervention on scan frequency and behavioral change in adults with type 1 diabetes (T1D) showing low scanning frequency.
METHODS: In this 12-week quasi-experimental study, adults with T1D using isCGM and low scan frequency participated in a single, individualized, direct education session led by a diabetes nurse educator. The intervention focused on increasing daily scan frequency and supporting patient engagement in self-management.
RESULTS: Thirty-four patients using isCGM participated. Mean daily scan frequency increased from 3.1 to 6.1 scans/day following the intervention. This rise was associated with an 8.0% improvement in TIR. A positive correlation was observed between scan frequency and TIR, with each additional daily scan associated with a 0.51% increase in TIR.
LINKING EVIDENCE TO ACTION: A single, targeted educational session can significantly improve isCGM adherence and glycemic control in adults with T1D and low adherence, supporting its value as a practical strategy in routine clinical care.
TRIAL REGISTRATION: The protocol was publicly registered at ClinicalTrials.gov (NCT05570162).

Keywords:  continuous glucose monitoring; diabetes education; diabetes technology; type 1 diabetes

DOI:  https://doi.org/10.1111/wvn.70139
Comput Biol Med. 2026 Apr 11. pii: S0010-4825(26)00237-4. [Epub ahead of print]208 111673

DPA-Net: A dual-path attention neural network for estimating glycemic metrics from self-monitored blood glucose data.

Canyu Lei, Benjamin Lobo, Jianxin Xie.

  Continuous glucose monitoring (CGM) provides dense and dynamic glucose profiles that enable reliable estimation of glycemic metrics, such as time-above-range (TAR), time-in-range (TIR), and time-below-range (TBR). However, the cost and limited accessibility of CGM restrict its widespread adoption, particularly in low- and middle-income countries. In contrast, self-monitoring of blood glucose (SMBG) is inexpensive and widely available, but produces sparse and irregular measurements that are typically event-driven: patients often check glucose levels when feeling unwell (e.g., dizziness, fatigue, or discomfort). Such behaviorally triggered sampling leads to biased estimates of TAR, TIR, and TBR. To address this challenge, we propose a Dual-Path Attention Neural Network (DPA-Net) that generates unbiased time-in-ranges estimates from SMBG data by leveraging generalizable knowledge learned from large collections of paired SMBG-CGM data. DPA-Net integrates two complementary paths: (1) a spatial-channel attention path that reconstructs a CGM-like continuous glucose trajectory from sparse SMBG inputs, and (2) a multi-scale residual network path that directly predicts glycemic metrics. An inter-path alignment mechanism enforces consistency between the reconstructed trajectory and the predicted metrics, thereby reducing bias and mitigating overfitting. Furthermore, to overcome the scarcity of real-world paired SMBG-CGM datasets, we develop an Active Point Selector (APS) that models behavioral patterns underlying SMBG measurements. Utilizing large-scale CGM recordings, APS identifies the most probable temporal instances at which users would self-monitor their glucose levels and formulates a synthetic SMBG-CGM paired dataset. Experimental results demonstrate that DPA-Net achieves robust accuracy with low estimation errors and minimal systematic bias. To the best of our knowledge, this is the first machine learning framework that utilizes the knowledge of a vast amount of CGM data designed to infer key glycemic metrics from SMBG data, offering a practical framework to enhance SMBG-based glycemic assessment in settings where CGM is unavailable or unaffordable.

Keywords:  Deep learning; Diabetes management; Glycemic control; Self-monitored blood glucose (SMBG); Time in range (TIR)

DOI:  https://doi.org/10.1016/j.compbiomed.2026.111673
Diabetes Technol Ther. 2026 Apr 17. 15209156261437512

Operational and Clinical Insights from a Fully Decentralized Clinical Study Evaluating Protocol Adherence, Participant Satisfaction, and Connected Device Data in Individuals with Type 2 Diabetes on Different Treatment Regimens.

Kirsten Nørgaard, Uffe Christian Braae, Anne Kaas, Sarah Amandine Caroline Voisin, Lise Sylvest Helledi.

   OBJECTIVE: Traditional clinical trials place a substantial burden on participants owing to the need for frequent, tightly controlled site visits, which can limit access and participation. Advances in digital health allow for decentralized clinical trials (DCTs), with site visits replaced by virtual contact and data collection, potentially reducing burden and broadening access. This proof-of-concept study assessed the operational feasibility of a fully remote, digitalized DCT.
METHODS: This was a 12-week observational study conducted in Denmark; adults with type 2 diabetes were recruited via social media. Following e-consent, participants were provided with continuous glucose monitoring (CGM) devices and an activity tracker, with data collected via smartphone apps. All visits and support were conducted remotely. Primary endpoints were: proportion consenting via e-signature (weeks -2 to 0), proportion with ≥ 70% unblinded CGM coverage (weeks 2-12), and percentage of scheduled telemedicine visits and questionnaires completed (weeks 0-12). Safety was monitored remotely through adverse event (AE) reporting during telemedicine visits.
RESULTS: Most study places were filled within 2 days of advertisement. Of 166 invited individuals, 100% consented electronically; 156 were enrolled, and 87% completed the study. Among participants starting unblinded CGM, 95% achieved ≥70% data coverage, meeting international completeness standards. Adherence to remote interactions was high: 97% attended all telemedicine visits, and 77% completed all questionnaires; 72% completed all scheduled remote interactions. Satisfaction with the study and CGM devices was very high, although adherence to the activity tracker was low (12% with ≥70% coverage) owing to technical issues. Safety monitoring via remote AE reporting revealed no unexpected findings.
CONCLUSIONS: A fully remote study was feasible to set up and conduct in Denmark, with rapid recruitment, high retention, and high compliance with CGM and telemedicine visits. DCTs have the potential to reduce participant burden, improve recruitment, and increase the representativeness of clinical trial populations.

Keywords:  adherence to study procedures; continuous glucose monitoring; fully decentralized clinical study; remote data collection; type 2 diabetes

DOI:  https://doi.org/10.1177/15209156261437512
J Diabetes Sci Technol. 2026 Apr 14. 19322968261431860

Use of Continuous Glucose Monitoring With Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes.

Ahmed A Metwally, Heyjun Park, Yue Wu, Tracey McLaughlin, Michael P Snyder.

  The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity, primarily driven by insulin resistance (IR), β-cell dysfunction, and incretin deficiency. This review demonstrates that continuous glucose monitoring (CGM) and wearable technologies enable a paradigm shift toward non-invasive, dynamic metabolic phenotyping. We show evidence that machine learning models can leverage high-resolution glucose data from at-home, CGM-enabled oral glucose tolerance tests to accurately predict gold-standard measures of muscle IR and β-cell function. This personalized characterization extends to real-world nutrition, where an individual's unique postprandial glycemic response (PPGR) to standardized meals, such as the relative glucose spike to potatoes versus grapes, could serve as a biomarker for their metabolic subtype. Moreover, integrating wearable data reveals that habitual diet, sleep, and physical activity patterns, particularly their timing, are uniquely associated with specific metabolic dysfunctions, informing precision lifestyle interventions. The efficacy of dietary mitigators in attenuating PPGR is also shown to be phenotype-dependent. Collectively, this evidence demonstrates that CGM can deconstruct the complexity of early dysglycemia into distinct, actionable subphenotypes. This approach moves beyond simple glycemic control, paving the way for targeted nutritional, behavioral, and pharmacological strategies tailored to an individual's core metabolic defects, thereby paving the way for a new era of precision diabetes prevention.

Keywords:  CGM; artificial intelligence; diabetes; wearables

DOI:  https://doi.org/10.1177/19322968261431860
Diabet Med. 2026 Apr 12. e70330

Does faster aspart improve time in range in children with type 1 diabetes with glycaemia close to target on insulin pump therapy?

Kowalczyk-Korcz Emilia, Dymińska Magdalena, Groele Lidia, Szypowska Agnieszka.

   AIMS: To evaluate whether faster insulin aspart (FIA) improves time in range (TIR) compared with standard insulin aspart (SIA) in children and adolescents with type 1 diabetes achieving glycaemia close to target treated with continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM).
METHODS: This prospective, open-label, randomized, 1:1 crossover trial included participants aged 6-17 years with T1D duration of ≥1 year, CSII use ≥3 months, CGM use ≥1 month, and HbA1c 64 mmol/mol (<8%). After a 2-week run-in period, they then crossed over to the alternate insulin for another 4 weeks. All participants used the same CGM system. Assessments were performed at the end of each treatment phase. The primary endpoint was the between-treatment difference in TIR (3.9-10.0 mmol/L, 70-180 mg/dL).
RESULTS: Seventy-seven children were enrolled (mean T1D duration approximately 7 years; 66% male; mean HbA1c 53 mmol/mol, 7%). Mean TIR was 68.5% (SD 12.3%) with SIA and 67.6% (SD 12.1%) with FIA, with no statistically significant difference (mean difference -0.9%; 95% CI -2.60 to 0.86; P = 0.322). Similar patterns were observed for additional glycaemic metrics. Time in tight range was also similar between treatments: 46.3% for SIA versus 45.4% for FIA (P = 0.674).
CONCLUSIONS: In this randomised crossover study of children and adolescents with T1D achieving glycaemia close to target on CSII, switching from SIA to FIA does not improve TIR. The absence of improvement across CGM-derived metrics suggests that FIA does not meaningfully enhance glycaemic outcomes in this clinical setting.

Keywords:  Glycaemic control; Glycaemic variability; continuous blood glucose monitoring; insulin Aspart; rapid‐acting insulin; treatment outcome; type 1 diabetes mellitus

DOI:  https://doi.org/10.1111/dme.70330
J Clin Endocrinol Metab. 2026 Apr 13. pii: dgag164. [Epub ahead of print]

Diabetes Technology: An Update.

Priya Prahalad, Dessi Zaharieva, David Maahs.

  Diabetes is one of the most prevalent chronic diseases worldwide, with rates that continue to increase. Over 30 years ago, the Diabetes Control and Complications Trial demonstrated that intensive glucose management decreased long term vascular complications. Unfortunately, many people with diabetes still struggle to meet glycemic goals. In this mini-review, we highlight advances in diabetes technology that are associated with improvements in glycemic management. Continuous glucose monitoring (CGM) and automated insulin delivery systems are associated with significant improvements in HbA1c, time-in-range (70-180 mg/dL), and decreases in severe hypoglycemia and diabetic ketoacidosis in people with both type 1 and type 2 diabetes. Recent data shows that these technologies improve outcomes early in the course of type 1 diabetes. CGM is also being explored as a tool to monitor progression through early-stage type 1 diabetes (2 antibodies positive) to identify individuals who may benefit from disease-modifying therapies as well as to prevent the onset of diabetic ketoacidosis at onset of stage 3 (insulin requiring) type 1 diabetes. Adjunctive pharmacologic therapies and artificial intelligence may further expand and improve therapies, offering potential synergistic benefits. However, there continue to be significant disparities in access to diabetes technologies and access to insulin worldwide. This mini-review summarizes recently published data, highlights emerging applications, and underscores the need to pair technological innovation with strategies that promote equitable access and support for diabetes care to improve outcomes for all people with diabetes.

Keywords:  automated insulin delivery; continuous glucose monitoring; diabetes technology; type 1 diabetes; type 2 diabetes

DOI:  https://doi.org/10.1210/clinem/dgag164
Front Nutr. 2026 ;13 1737219

Nutritional considerations for athletes with diabetes: optimizing performance and glycemic control.

Liguang Xu, Wei Zhang.

  Athletes with diabetes encounter the intricate physiological challenge of harmonizing optimal physical performance with meticulous glycemic control. Although nutritional management is crucial for success, generic guidelines often do not provide the required differentiation for various diabetes causes and contemporary insulin delivery methods. This narrative review aims to consolidate the latest research on optimizing macronutrient intake, hydration, and micronutrient support specifically for athletes with Type 1 (T1D) and Type 2 (T2D) diabetes. A structured search of the literature was conducted on Google Scholar (2015-2025) to identify relevant peer-reviewed clinical trials, meta-analyses, and expert consensus statements. The identified nutritional strategies were then analyzed and classified based on an evidence-grading framework: Level A (Strong evidence/Meta-analyses), Level B (Moderate evidence/Single RCTs), and Level C (Expert consensus). Carbohydrate timing and dosing are crucial factors in maintaining normal blood sugar levels during exercise, and they need to be adjusted based on the intensity and duration of the activity, as well as the type of insulin therapy being used (e.g., multiple daily injections vs. automated insulin delivery systems). This review presents structured guidelines for managing carbohydrate intake before, during, and after exercise, highlighting the importance of protein for muscle recovery and the influence of micronutrients like magnesium and vitamin D on metabolic function. Additionally, the use of Continuous Glucose Monitoring (CGM) data is discussed as a valuable tool for reducing fluctuations in blood sugar levels and preventing exercise-induced hypoglycemia. Optimizing athletic performance in individuals with diabetes necessitates a comprehensive, multidisciplinary strategy. By coordinating dietary choices with appropriate treatment modalities and utilizing evidence-based assessments, healthcare providers can offer more secure and efficient recommendations for both competitive and recreational athletes.

Keywords:  athletes; carbohydrate timing; continuous glucose monitoring (CGM); glycemic control; nutrition; performance optimization; type 1 diabetes; type 2 diabetes

DOI:  https://doi.org/10.3389/fnut.2026.1737219
Acta Diabetol. 2026 Apr 11.

A multicenter prospective evaluation of the benefits of Dexcom ONE + in subjects with type 2 diabetes on insulin therapy beyond glycemic control: results from the LIFE-ONE study.

Ana Chico, Eva Aguilera-Hurtado, Mercè Albareda, Jesús Moreno-Fernández, Pilar Beato-Vibora, Elsa Fernández-Rubio, Martín Cuesta, Amparo Bartual, Francisco Javier Ampudia-Blasco, Orosia Bandrés, Jessica Ares, María Teresa Rivero, María José Picón-César.



Keywords:  Continuous glucose monitoring; Dexcom; Glucometric data; Insulin; Patient-reported outcomes; Type 2 diabetes

DOI:  https://doi.org/10.1007/s00592-026-02682-9
Am J Perinatol. 2026 Apr 16.

A Practical Approach to Insulin Pump, CGM, and Automated Insulin Delivery in the Management of Type 1 Diabetes in Pregnancy.

Maggie Wong, Marwan Maayeh, George Saade, Antonio F Saad.

OBJECTIVE: To provide clinicians with practical, pregnancy-specific guidance for initiating and optimizing continuous subcutaneous insulin infusion (CSII), continuous glucose monitoring (CGM), and automated insulin delivery (AID) in pregnancies complicated by type 1 diabetes (T1D).
STUDY DESIGN: Narrative review of key trials and device labeling, paired with pragmatic algorithms for antepartum titration, intrapartum management, and postpartum dose reduction.
RESULTS: CGM improves glycemic metrics and neonatal outcomes in T1D pregnancy. Continuation of CSII during labor is safe and achieves similar or improved glycemic control compared with intravenous insulin strategies. Hybrid closed-loop AID increases time in the pregnancy target range of 60-140 mg/dL and reduces time above range without increasing severe hypoglycemia. Because the majority of AID systems are used off-label in pregnancy , clinicians need explicit protocols for pump failure, ketone monitoring, steroid exposure, and prevention of euglycemic diabetic ketoacidosis.
CONCLUSION: With frequent review and clear escalation pathways, diabetes technology can help achieve pregnancy glycemic targets across gestation, labor, and the early postpartum period.

DOI: https://doi.org/10.1055/a-2854-5752
Front Endocrinol (Lausanne). 2026 ;17 1770560

Clinical characteristics and individualized treatment strategies for children with type 1 diabetes and exogenous insulin antibody syndrome: a retrospective case series and literature review.

Liang Zhang, Xinyuan Shen, Lulu Cui, Ying Zhang, Jiaoru Yang, Wenxin Liu, Lili Wang, Sheng Guo.

   Objective: This study investigates the clinical characteristics, diagnostic strategies, and therapeutic outcomes of individualized treatment for children with type 1 diabetes mellitus (T1DM) complicated by exogenous insulin antibody syndrome (EIAS).
Methods: A retrospective case series study was conducted on five pediatric patients diagnosed with T1DM+EIAS at a single center between January 2016 and January 2026. Among 1,245 T1DM patients evaluated, 5 (0.4%) met EIAS diagnostic criteria. Data included demographics, clinical manifestations, laboratory findings (insulin antibodies [IA], C-peptide, continuous glucose monitoring [CGM]), treatment regimens, and outcomes. Longitudinal autoantibody profiles, thyroid function, immune parameters, and cytokines were assessed at four time points. A narrative review of published EIAS cases was conducted following PRISMA guidelines.
Results: Case 1 achieved glycemic stability using ultra-rapid insulin with closed-loop pump (6-hour active insulin duration). Case 2, a 1-year-old infant, required regular insulin every 4 hours (six doses daily). Case 3 switched from pump to conventional multiple daily injections (MDI) with 4-6 daily injections. Case 4 used 4-6 daily aspart doses plus once-daily glargine. All four achieved TIR >70%. Case 5, with refractory disease and IA titer of 33.40 COI, received mycophenolate mofetil (MMF) 600-1,000 mg/m²/day. After 3 months, TIR improved to >70%, TBR <5%, and IA titers decreased by >30%. MMF discontinuation resulted in rapid recurrence of instability within 4 weeks.
Conclusion: EIAS is a rare cause of severe glycemic dysregulation in pediatric T1DM. CGM metrics (TIR, TBR, CV) are essential for assessment, as HbA1c may not reflect glycemic variability. Individualized insulin optimization improves TIR in most patients. For refractory cases, MMF may offer a potential therapeutic option. Due to the observational nature, small sample size (n=5), absence of a control group, and lack of blinding, these findings should be considered hypothesis-generating. Causal inferences cannot be drawn, and the results require validation in prospective, multicenter, controlled studies.

Keywords:  continuous glucose monitoring; exogenous insulin antibody syndrome; glycemic variability; mycophenolate mofetil; personalized treatment; type 1 diabetes

DOI:  https://doi.org/10.3389/fendo.2026.1770560
Nutrients. 2026 Apr 03. pii: 1152. [Epub ahead of print]18(7):

Does Resistant Starch Formed by Cooling Pasta Decrease the Postprandial Glycemic Response in Type 1 Diabetes? A Randomized Single-Blind Crossover Study.

Anita Rogowicz-Frontczak, Sylwia Strozyk, Stanislaw Pilacinski, Anna Koperska, Joanna Le Thanh-Blicharz, Magdalena Tanska, Dorota Zozulinska-Ziolkiewicz.

  Background: Carbohydrate quality and culinary processing can meaningfully alter postprandial glycemia in people with type 1 diabetes (T1D). Cooling gelatinized starch promotes retrogradation and increases resistant starch (RS), potentially attenuating postprandial glucose excursions. Objectives: We investigated whether pasta cooled after cooking (24 h at 4 °C) and reheated before consumption improves postprandial glycemia in adults with T1D without increasing hypoglycemia risk under routine insulin pump bolus-calculator dosing. Methods: In this randomized, single-blind, crossover study, 32 adults with T1D treated with continuous subcutaneous insulin infusion (CSII) consumed two standardized pasta-based meals (50 g of available carbohydrate): freshly cooked pasta and cooled/reheated pasta. Participants administered rapid-acting insulin boluses calculated by their pump bolus calculator 10 min before the meal. Interstitial glucose was recorded for 180 min using flash glucose monitoring. Results: Compared with freshly cooked pasta, cooled/reheated pasta produced lower maximum glycemia (10.7 vs. 12.6 mmol/L, p = 0.0001), lower maximum glycemic rise (2.8 vs. 4.7 mmol/L, p < 0.0001), lower incremental area under the curve (iAUC; 211.9 vs. 524.8 mmol/L × 180 min, p < 0.0001), and a shorter time-to-peak (65 vs. 125 min, p = 0.014). Resistant starch content increased after cooling (12.88 ± 0.06 vs. 8.03 ± 0.08 g/100 g). The number of hypoglycemic episodes did not differ between conditions. Conclusions: Cooling and reheating pasta therefore increased RS and attenuated postprandial glycemia in adults with T1D without increasing early postprandial hypoglycemia in the studied setting.

Keywords:  continuous glucose monitoring; postprandial glycemia; resistant starch; retrogradation; type 1 diabetes

DOI:  https://doi.org/10.3390/nu18071152