bims-glumda Biomed News
on CGM data in management of diabetes
Issue of 2025–09–14
sixteen papers selected by
Mott Given
  1. Editorial: Continuous glucose monitoring: beyond diabetes management.
  2. Impact of transitioning from conventional blood glucose monitoring to continuous glucose monitoring on glycemic control and self-management in adults with type 2 diabetes on oral glucose-lowering medications.
  3. Achievement of time in range goals among patients with diabetes using continuous glucose monitoring.
  4. Hypoglycaemia in Older Adults with Diabetes: Pathophysiology, Prevention, and Personalized Care in an Aging Population.
  5. Impact of Omnipod 5 automated insulin delivery on continuous glucose monitoring metrics and predictors of improvement in time in range.
  6. Efficacy of Real-Time Continuous Glucose Monitoring in Improving Glycemic Outcomes Among Adults With Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials.
  7. Digital Innovation and Integrated Care in People With Diabetes in Western Sydney: Retrospective Cohort Study.
  8. Comment on: Editor's Note on Clin-STAR Corner: Practice-Changing Advances in Diabetes Care.
  9. Glycemic Analysis and Stratification of Pediatric Patients with Type 1 Diabetes Using isCGM in Southern Spain: Insights from the Andiacare Digital Platform.
  10. Time below range alone is insufficient to identify severe hypoglycaemia risk in type 1 diabetes-the critical role of hypoglycaemia awareness: results from the SFDT1 study.
  11. Hypoglycemia incidence and behavioural adjustments during free-living unstructured physical activity in adults with type 1 diabetes using AID systems: Results from the RAPPID study.
  12. Implantable biosensors: Advancements and applications.
  13. Real-time converter of intermittent-scanned glucose sensors to continuous glucose monitors with potential future applications for insulin delivery.
  14. Glucose interpretation meaning and action: enhancing type 1 diabetes decision-making with textual descriptions.
  15. LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet.
  16. Goodhart's Law in Diabetes Care: The Hidden Drawbacks of Overoptimizing CGM Metrics.
  1. Front Endocrinol (Lausanne). 2025 ;16 1678600
    Editorial: Continuous glucose monitoring: beyond diabetes management.
    Jianzhong Xiao.
      
    Keywords:  continuous glucose monitoring; diabetes; diabetes management; glycemic variability; metabolic health; personalized nutrition
    DOI:  https://doi.org/10.3389/fendo.2025.1678600
  2. Diabetes Res Clin Pract. 2025 Sep 05. pii: S0168-8227(25)00472-3. [Epub ahead of print]229 112458
    Impact of transitioning from conventional blood glucose monitoring to continuous glucose monitoring on glycemic control and self-management in adults with type 2 diabetes on oral glucose-lowering medications.
    Ayman Al Hayek, David C Klonoff, Wael M Al Zahrani, Abdulghani H Al Saeed, Mohammed A Al Dawish.
       BACKGROUND: Despite advances, glycemic control in people with type 2 diabetes (PwT2D) treated with oral antidiabetic medications (ADMs) often remains suboptimal. Continuous glucose monitoring (CGM) has shown promise in diabetes management, offering real-time insights into glucose trends. This study evaluates the impact of transitioning from conventional self-monitoring of blood glucose (SMBG) to CGM on glycemic outcomes and self-management in PwT2D receiving oral ADMs.
    METHODS: In this prospective study, 108 PwT2D managed with oral ADMs were enrolled. Participants transitioned from conventional SMBG to the FreeStyle Libre 2 CGM system for 12 weeks. Clinical, metabolic, and glycemic parameters, alongside patient-reported outcomes assessed by the Impact of Glucose Monitoring on Self-Management Scale (IGMSS), were recorded at baseline and study endpoint.
    RESULTS: CGM use was associated with significant improvement across multiple glycemic and self-management metrics. Over 12 weeks, HbA1c decreased from 7.93 % to 7.31 % (p < 0.001), and mean capillary glucose levels decreased from 191 mg/dL to 180 mg/dL (p < 0.001). Time in range reached 70 % (SD = 6.86). Self-management, as measured by the IGMSS, improved-with Capability scores rising from 16.1 to 30.3, Opportunity from 19.8 to 35, and Motivation from 12.3 to 23.3 (all p < 0.001). The scale score increased from 42 to 75.6 (p < 0.001). Additionally, longer diabetes duration (p = 0.024) and more frequent SMBG (p = 0.012) were associated with lower endpoint IGMSS scores, whereas the presence of co-morbidities was linked to higher scores (p = 0.03).
    CONCLUSION: Switching from conventional SMBG to CGM significantly improved glycemic control and self-management in PwT2D treated with oral ADMs. This observation highlights the potential for broader integration of CGM into this population.
    Keywords:  Bloodglucose self-monitoring; Diabetes mellitus, Type 2; Glucosemonitoring, continuous; Hypoglycemicagents; Patient-reported outcome measures
    DOI:  https://doi.org/10.1016/j.diabres.2025.112458
  3. J Am Pharm Assoc (2003). 2025 Sep 08. pii: S1544-3191(25)00603-X. [Epub ahead of print] 102924
    Achievement of time in range goals among patients with diabetes using continuous glucose monitoring.
    Faezeh Azizi, Katherine Diehl, Lauren Enser, Samantha Heacock.
       AIMS: Despite robust evidence supporting continuous glucose monitoring (CGM) use, successful utilization remains relatively low. This study aimed to determine percentage of patients with a baseline time in range (TIR) <70% who achieved TIR ≥ 70% when using CGM and identify patient variables associated with achievement and/or maintenance of TIR goal within our institution.
    MATERIALS AND METHODS: This was a retrospective, observational review of adult patients with diabetes using CGM for at least 6-months consecutively. Key exclusion criteria were gestational diabetes, pregnancy, non-adherence, and controlled baseline TIR. The primary outcome was the percentage of patients achieving TIR ≥ 70% within 6-months of starting CGM. Secondary outcomes included identifying patient variables associated with achievement and/or maintenance of TIR goals and describing the relationship between TIR and hemoglobin A1c.
    RESULTS: A total of 71 patients were included; 54% were male and the median age was 57 (IQR: 39-75) years. Sixty-two percent of patients had type 2 diabetes. Fifty four percent of patients achieved TIR ≥70% at any time during the study period, with a median time to achievement of 42 (IQR: 28-84) days. Among patients who achieved TIR goal, 58% maintained this at 6-months and 67% also reached an A1c of < 7%. Type 2 diabetes, use of metformin, and GLP-1 receptor antagonists (GLP-1RA) were positively associated with achievement of TIR goal (p = 0.008, 0.026, 0.004 respectively).
    CONCLUSION: Among patients with diabetes using CGM consecutively for 6-months, the majority reached and maintained TIR goals. Larger studies are needed to confirm these findings.
    Keywords:  Continuous Glucose Monitoring; Diabetes; Time In Range
    DOI:  https://doi.org/10.1016/j.japh.2025.102924
  4. Drugs Aging. 2025 Sep 10.
    Hypoglycaemia in Older Adults with Diabetes: Pathophysiology, Prevention, and Personalized Care in an Aging Population.
    Virginia Boccardi, Alan J Sinclair.
      Managing diabetes in older adults requires balancing long-term glycaemic control with the prevention of hypoglycaemia, to which this population is particularly vulnerable owing to frailty, multimorbidity and cognitive decline. Guidelines recommend individualized glucose targets for older adults, particularly those with multimorbidity or increased hypoglycaemia risk. For individuals with frailty or cognitive impairment, relaxed HbA1c targets are often appropriate to reduce the risk of adverse events. While HbA1c is widely used, it has important limitations in this population due to its inability to reflect daily glucose fluctuations. Continuous glucose monitoring (CGM) or self-monitoring of blood glucose provide more granular data to guide therapy. This review explores the pathophysiology, complications, and management of hypoglycaemia in older adults, emphasizing individualized care, safer pharmacotherapies (e.g. DPP-4 inhibitors, GLP-1 receptor agonists, ultra-long-acting insulins), and emerging technologies (continuous glucose monitoring, artificial Intelligence-guided insulin delivery and telehealth).
    DOI:  https://doi.org/10.1007/s40266-025-01236-y
  5. Diabet Med. 2025 Sep 09. e70137
    Impact of Omnipod 5 automated insulin delivery on continuous glucose monitoring metrics and predictors of improvement in time in range.
    Roland H Stimson, Mark W J Strachan, Shareen Forbes, Rohana J Wright, Scott D Mackenzie, Gayle McRobert, Emily M McMurray, Marcus J Lyall, Anna R Dover, Fraser W Gibb.
       AIMS: This study aimed to assess the impact of the Omnipod 5 automated insulin delivery (AID) system on continuous glucose monitoring (CGM) metrics, HbA1c, and weight in a real-world setting. Additionally, independent predictors of glycaemic response were assessed.
    METHODS: Observational analysis of adults with type 1 diabetes using Omnipod 5 (n = 353). Paired data on CGM metrics (n = 268), HbA1c (n = 193), and weight (n = 173) were collected at baseline and compared after median of 191, 120, and 221 days, respectively. Independent predictors of TIR response (≥5%) and HbA1c (≥5 mmol/mol) were assessed.
    RESULTS: Omnipod 5 use was associated with improved TIR (+16%, p < 0.001) and a reduction in HbA1c (-3 mmol/mol, p < 0.001). The greatest improvements (-7 mmol/mol, p < 0.001) were observed in individuals with elevated baseline HbA1c (≥58 mmol/mol). Sensor choice (Dexcom G6 vs. Freestyle Libre 2 Plus) influenced time in full auto mode (94% vs. 96%, p < 0.001) but did not affect the likelihood of improved TIR or HbA1c. Logistic regression identified baseline HbA1c (OR 1.24 per mmol/mol, p < 0.001) as the main association with improved HbA1c. Similarly, baseline TIR was associated with improvement in TIR (OR 0.83 per %, p < 0.001). Greater time in automation and using the lowest glucose target were also associated with improved outcomes.
    CONCLUSIONS: Omnipod 5 is associated with significant and sustained improvements in CGM metrics and HbA1c, particularly in individuals with higher baseline HbA1c. The results suggest the potential benefits of prioritizing AID for individuals at greatest risk of complications.
    Keywords:  CSII; continuous glucose monitoring; insulin therapy; type 1 diabetes
    DOI:  https://doi.org/10.1111/dme.70137
  6. Cureus. 2025 Aug;17(8): e89534
    Efficacy of Real-Time Continuous Glucose Monitoring in Improving Glycemic Outcomes Among Adults With Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials.
    Shaza Awad Ali Badawi, Ali Hadi M Alhajri, Nisreen Mohamed Osman Altayb, Enas Mohmmed, Rayan Kamil Elamin Hassabelrasoul, Shahd Abdullahi Sidahmed Mohammed, Wamda Hussein Altahir Mohammed, Jihad A El Ghali.
      Type 2 diabetes (T2D) requires rigorous glycemic control to prevent complications, but traditional self-monitoring of blood glucose (SMBG) offers limited insights. Real-time continuous glucose monitoring (RT-CGM) provides dynamic data to optimize management, although its efficacy in T2D remains debated. This systematic review synthesizes evidence from randomized controlled trials (RCTs) to evaluate RT-CGM's impact on glycemic outcomes in adults with T2D. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines, we searched PubMed, Scopus, Web of Science, and ClinicalTrials.gov (2015-2025) for RCTs comparing RT-CGM to SMBG/usual care in non-pregnant adults with T2D. Eleven studies met the inclusion criteria. Data were extracted for HbA1c, time in range (TIR), hypoglycemia, and safety. Risk of bias was assessed using Cochrane RoB 2. RT-CGM significantly improved TIR and reduced HbA1c in insulin-treated patients, although benefits varied by intervention frequency and patient subgroup. Episodic use required multiple sessions for sustained HbA1c reduction. Non-insulin-treated cohorts saw smaller HbA1c changes but improved glycemic variability. Treatment satisfaction was consistently higher with RT-CGM. Safety profiles were favorable, with no severe device-related adverse events. RT-CGM enhances glycemic control in T2D, particularly for insulin-treated patients, with structured use yielding the greatest benefits. Clinicians should prioritize individualized protocols and patient education. Future research should address long-term efficacy and cost-effectiveness.
    Keywords:  glycemic control; hba1c; real-time continuous glucose monitoring; rt-cgm; systematic review; time in range; type 2 diabetes
    DOI:  https://doi.org/10.7759/cureus.89534
  7. JMIR Diabetes. 2025 Sep 11. 10 e64832
    Digital Innovation and Integrated Care in People With Diabetes in Western Sydney: Retrospective Cohort Study.
    Ummul Mahfuza, Gideon Meyerowitz-Katz, Riham Abdulla Rasheed, Helen Dick, Glen Maberly, Rajini Jayaballa.
       BACKGROUND: The COVID-19 pandemic catalyzed the adoption of digital technologies in health care. This study assesses a digital-first integrated care model for type 2 diabetes management in Western Sydney, using continuous glucose monitoring (CGM) and virtual Diabetes Case Conferences (DCC) involving the patient, general practitioner (GP), diabetes specialist, and diabetes educator at the same time.
    OBJECTIVE: This study aims to assess the effectiveness of the innovative diabetes clinics in Western Sydney.
    METHODS: In 2020, a total of 833 new patients with type 2 diabetes were seen at Western Sydney Diabetes (WSD) clinics. An early cohort of 103 patients was evaluated before and after participation in virtual DCC, incorporating CGM data analysis, digital educational resources, and remote consultations with a diabetes multidisciplinary team. Assessments were conducted at baseline and 3-4 months post DCC.
    RESULTS: The integration of CGM and virtual consultations significantly improved glycemic control. Hemoglobin A1c (HbA1c) levels decreased notably from 9.6% to 8.2% (average reduction of 1.4%; 95% CI 1.03-1.82; P<.001). Time in range (TIR) as measured by CGM increased substantially from 46% to 73% (95% CI 20-32; P<.001), and the glucose management indicator (GMI) improved from 7.9% to 7% (average reduction of 0.9%; 95% CI 0.55-1.2; P<.001). Despite no significant change in the total daily insulin dose, the proportion of patients on insulin therapy rose from 27% to 39% (P<.001), indicating more targeted and effective diabetes management.
    CONCLUSIONS: Our findings demonstrate the effectiveness of a digitally enabled integrated care model in managing type 2 diabetes. The use of CGM technology, complemented by virtual DCCs and digital educational tools, not only facilitated better disease management and patient engagement but also empowered primary care providers with advanced management capabilities. This digital approach addresses traditional barriers in diabetes care, highlighting the potential for scalable, technology-driven solutions in chronic disease management.
    Keywords:  Australia; CGM; T2DM; adoption; chronic disease management; cohort; continuous glucose monitoring; daily insulin dose; diabetes; diabetes educator; diabetes management; diabetes specialist; digital innovation; digital technologies; disease management; effectiveness; glucose monitoring; insulin; integrated care; patient engagement; primary care; type 2 diabetes; type 2 diabetes care; virtual consultations
    DOI:  https://doi.org/10.2196/64832
  8. J Am Geriatr Soc. 2025 Sep 10.
    Comment on: Editor's Note on Clin-STAR Corner: Practice-Changing Advances in Diabetes Care.
    Anna R Kahkoska, Richard E Pratley.
      
    Keywords:  continuous glucose monitoring; older adults; technology; type 1 diabetes; type 2 diabetes
    DOI:  https://doi.org/10.1111/jgs.70077
  9. J Clin Med. 2025 Sep 04. pii: 6243. [Epub ahead of print]14(17):
    Glycemic Analysis and Stratification of Pediatric Patients with Type 1 Diabetes Using isCGM in Southern Spain: Insights from the Andiacare Digital Platform.
    Isabel Leiva-Gea, Fernando Moreno-Jabato, Ana Belén Ariza-Jiménez, Alfonso Lendínez-Jurado, Ana Gómez-Perea, María Del Mar Romero-Pérez, Emilio García-García, María Ángeles Santos Mata, Gabriela Martínez-Moya, Jerónimo Momblan, Alfonso María Lechuga-Sancho, José María Gómez-Vida, Mercedes Mier-Palacios, María Del Pilar Ranchal-Pérez, Gustavo Vivas-González, Patricia Calleja Cabeza, Eugenio Fernández-Hernández, Ana Pilar Jiménez-Martín, Jessica Guarino-Narváez, Pablo Rodríguez de Vera-Gómez, María Asunción Martínez-Brocca.
      Background/Objectives: Type 1 diabetes mellitus (T1D) is the most common metabolic disorder in children, with significant physical and emotional impacts. Achieving optimal glucometric control is challenging due to the complex management and limitations of insulin therapy. Advances in pharmacology and technology, including continuous glucose monitoring (CGM) systems, offer new options for diabetes management. We developed Andiacare, an open-source platform for macro/micro-management of diabetes and analyzed its application in a pediatric T1D cohort to evaluate glucometric control patterns. Methods: A retrospective cohort study was conducted in a pediatric population (<18 years old) in Andalusia, Spain. Patients treated with Multiple Daily Injections of Insulin (MDI) and FreeStyle Libre 2 System (Abbott, Spain) were included. The patient data were analyzed using the Andiacare platform, which categorizes patients based on the Advanced Technologies and Treatments for Diabetes (ATTD) panel's targets for glucometric control. Results: The study included 2215 patients from 18 pediatric hospitals. The Andiacare platform categorized patients into four groups based on glucometric control parameters, enabling patient stratification based on their glucometric control. Only 25.8% of the cohort achieved the recommended Time in Range (TIR), and 9.5% of the patients achieved all target parameters of glucometric control. Age is a determinant factor in adherence and achievement of set goals. Conclusions: This study offers insights into glucometric control in a large pediatric population with T1D in Andalusia. Few patients achieved the recommended glucometric control targets, highlighting the need for improved management strategies. The use of digital platforms such as Andiacare might contribute to facilitating the management of large pediatric cohorts. New algorithms integrating glucometric and non-glucometric parameters are required for improved individual and cohort categorization to optimize therapeutic interventions.
    Keywords:  advanced technologies and treatments for diabetes (AATD); continuous glucose monitoring (CGM); multiple daily injections of insulin (MDI); pediatric population; therapeutic scalability; time in range (TIR); type 1 diabetes mellitus
    DOI:  https://doi.org/10.3390/jcm14176243
  10. Diabetologia. 2025 Sep 09.
    Time below range alone is insufficient to identify severe hypoglycaemia risk in type 1 diabetes-the critical role of hypoglycaemia awareness: results from the SFDT1 study.
    Dulce Canha, Pratik Choudhary, Emmanuel Cosson, Isabela Banu, Sara Barraud, René Valéro, Nathalie Ronci, Blandine Delenne, Lise Dufaitre, Tiphaine Vidal-Trecan, Pauline Schaepelynck, Caroline Sanz, Sopio Tatulashvili, Gloria A Aguayo, Guy Fagherazzi, Jean-Pierre Riveline.
       AIMS/HYPOTHESIS: Severe hypoglycaemia events (SHE) remain frequent in people with type 1 diabetes despite advanced diabetes technologies. We examined whether time below range (TBR) 3.9 mmol/l (70 mg/dl; TBR70) or 3.0 mmol/l (54 mg/dl; TBR54) is associated with future SHE risk and whether impaired awareness of hypoglycaemia (IAH) modifies this relationship.
    METHODS: We analysed data from participants in the Study of the French-speaking Society of Type 1 Diabetes (SFDT1) who used continuous glucose monitoring. IAH was assessed using the Gold Score (≤2, no IAH; 3, undetermined; ≥4, IAH). SHE frequency was self-reported 12 months after inclusion. We analysed associations between TBR and SHE using logistic regression models adjusted for age, sex, social vulnerability and insulin treatment, including TBR-IAH interactions. We performed spline analyses to explore non-linear patterns.
    RESULTS: One-year incidence of SHE was 11.7% among 848 participants (mean ± SD age 41.6 ± 13.3 years; 53.8% female sex, HbA1c 57.2 ± 10.9 mmol/mol [7.4 ± 1.0%]). Incidence by TBR70 was 12.1% for ≤1%, 10.2% for 1.1-3.9%, 10.6% for 4-6%, and 14.6% for >6%. Only those with TBR70 >6% and IAH had a significantly higher SHE risk (OR 3.32 [95% CI 1.40, 7.82]) compared with TBR70 ≤1% and no IAH. For TBR54, SHE incidence was 11.0% and 13.3% for categories <1% and ≥1%, respectively. Similarly, only individuals with TBR54≥1% and IAH had increased SHE risk (OR 2.99 [95% CI 1.46, 5.92]). Spline analysis showed low, stable SHE risk across TBR70 values in participants without IAH, with a non-linear pattern only in those with IAH.
    CONCLUSIONS/INTERPRETATION: TBR alone is not discriminative for high-risk SHE but combining TBR with hypoglycaemia awareness status identifies those at the highest risk for both TBR70 and TBR54.
    TRIAL REGISTRATION: ClinicalTrials.gov NCT04657783.
    Keywords:  Continuous glucose monitoring; Impaired awareness of hypoglycaemia; Severe hypoglycaemia; Time below range; Type 1 diabetes
    DOI:  https://doi.org/10.1007/s00125-025-06536-x
  11. Diabetes Obes Metab. 2025 Sep 09.
    Hypoglycemia incidence and behavioural adjustments during free-living unstructured physical activity in adults with type 1 diabetes using AID systems: Results from the RAPPID study.
    Michael Joubert, Laurent Meyer, Said Bekka, Luc Rakotoarisoa, Nicolas Scheyer, Bleuenn Dreves, Bruno Guerci.
       AIMS: To assess the frequency and management of hypoglycaemia during unstructured physical activity (PA) in adults with type 1 diabetes (T1D) using automated insulin delivery (AID) systems in real-life settings.
    MATERIALS AND METHODS: RAPPID is a prospective, multicenter, observational study conducted over 1 month in four French tertiary care centres. Adults with T1D using one of three AID systems (MiniMed 780G, Tandem t:slim X2 with Control-IQ, or Ypsopump with CamAPS FX) and performing ≥2 unstructured PA sessions per week were included. Participants completed paper logbooks documenting each PA session (type, intensity, hypoglycemia, adjustments). Continuous glucose monitoring (CGM) and pump data were downloaded. Glycaemic control was assessed using CGM-derived metrics within predefined peri-exercise time windows.
    RESULTS: Eighty-six participants (mean age 42.5 ± 14.3 years; 43% women; diabetes duration 23.6 ± 13.1 years; mean HbA1c 52 ± 7 mmol/mol (6.9% ± 0.6%)) reported 954 PA sessions (73% aerobic; 61% moderate intensity). TBR (<70 mg/dL) increased from 1% pre-exercise to 6% during and 5% post-exercise (early recovery phase). Clinical hypoglycaemia occurred in 20% of sessions (one-third of episodes were asymptomatic); 38% of participants experienced at least 1 level 2 event (<54 mg/dL). Anaerobic or high-intensity sessions were associated with lower hypoglycaemia risk. Temporary targets were used in 73% of sessions but initiated ≥1 h before PA in only 27%. Carbohydrate intake before and during PA was frequent but often suboptimally timed or dosed.
    CONCLUSIONS: Hypoglycemia remains common during and after PA in AID users. Suboptimal adjustment strategies and impaired symptom awareness contribute to risk. Individualised education remains essential to enhance safety.
    Keywords:  automated insulin delivery; continuous glucose monitoring; hypoglycemia; physical activity; type 1 diabetes
    DOI:  https://doi.org/10.1111/dom.70122
  12. Prog Mol Biol Transl Sci. 2025 ;pii: S1877-1173(25)00087-0. [Epub ahead of print]216 279-312
    Implantable biosensors: Advancements and applications.
    Gurleen K Nirwal, Kevin Y Wu, Taanvee P Ramnawaz, Yue Xu, Marjorie Carbonneau, Bich H Nguyen, Simon D Tran.
      This chapter, "Implantable Biosensors: Advancements and Applications," provides a succinct overview of the state-of-the-art in implantable biosensor technology, highlighting both established clinical uses and promising areas of ongoing research. It begins by outlining the fundamental principles and advantages of these sensors, such as their precision in physiological monitoring and capability for real-time therapeutic interventions. A variety of implantable sensors are categorized, including biophysical and biochemical types, each designed for specific medical applications. In endocrinology, continuous glucose monitoring (CGM) systems represent a pivotal and well-established use of implantable biosensors for diabetes management. In contrast, applications in ophthalmology, such as sensors for monitoring intraocular pressure to prevent glaucoma, are still under investigation and not yet widely adopted in clinical practice, though they hold significant promise. The chapter also explores potential applications across other medical fields, including cardiology, neurology, gastroenterology, pulmonology, otolaryngology, urology, orthopedics, pharmacology, and oncology. These areas are witnessing innovative research and development efforts aimed at harnessing the potential of implantable biosensors for enhanced patient care. The integration of these sensors with drug delivery systems and their role in real-time disease biomarker monitoring underscore their transformative potential. In summary, this chapter highlights the significant advancements in implantable biosensors, emphasizing their current clinical applications and future possibilities in revolutionizing medical diagnostics and treatment.
    Keywords:  Glaucoma prevention; Implantable biosensors; Intraocular pressure monitoring; Medical diagnostics; Physiological monitoring; Real-time therapeutic interventions
    DOI:  https://doi.org/10.1016/bs.pmbts.2025.06.006
  13. HardwareX. 2025 Sep;23 e00684
    Real-time converter of intermittent-scanned glucose sensors to continuous glucose monitors with potential future applications for insulin delivery.
    Pablo S Rivadeneira, Alejandro Mira.
      This paper presents the development of a transmitter that transforms intermittent glucose sensors (isCGM) into a continuous and real-time glucose monitoring system (c-rtCGM), a key component in automated insulin delivery systems. The transmitter enhances the capabilities of conventional intermittent sensors by leveraging Near Field Communication (NFC) technology to capture raw glucose value and automatically transmit it via Bluetooth Low Energy (BLE-Bluetooth 4.2 Dual-Mode) to a smart device every five minutes. A specialized glucose monitoring application converts the raw values to blood glucose by applying a calibration based on a static linear model and a capillary blood glucose measurement. The accuracy and performance of the c-rtCGM were validated through a study involving 37 participants with type 1 diabetes, demonstrating its reliability compared to commercial transmitters. Values reported by the c-rtCGM system compared with the isCGM monitor system resulted in an overall mean average relative difference (MARD) around 9%. During the trial, the c-rtCGM system achieved a data transmission success rate of 96%, and only 2316 connection failures were recorded from the 66525 total connection attempts, indicating a high level of communication stability. The transmitter battery life lasted an average of 6.5 days, showing that it is necessary to recharge only once for the duration of the sensor (14 days). The main advantages of this customized transmitter, in contrast with the commercial versions, are reliability, cost, and the flexibility of its software, since its processor (an ESP32) can be easily programmed to fulfill other helpful tasks in managing glucose levels with automated insulin delivery systems.
    Keywords:  Automated insulin delivery systems; Glucose monitoring; Intermittent scanned sensors; Measurement accuracy
    DOI:  https://doi.org/10.1016/j.ohx.2025.e00684
  14. Ther Adv Endocrinol Metab. 2025 ;16 20420188251362089
    Glucose interpretation meaning and action: enhancing type 1 diabetes decision-making with textual descriptions.
    Rujiravee Kongdee, Bijan Parsia, Hood Thabit, Simon Harper.
       Background: Findings from our previous study indicate that people with type 1 diabetes mellitus (T1DM) unknowingly misinterpret data displayed on glucose monitoring systems and make inaccurate treatment decisions, which increases the risk of hospitalisation.
    Objectives: This study aims to assess the effectiveness of incorporating textual descriptions in glucose monitoring systems compared to existing systems. The main goal is to minimise the effort required in glucose data interpretation, facilitating better self-management and ultimately improving haemoglobin A1C levels.
    Methods: A two-arm and mixed-methods evaluation was conducted. Participants were randomly allocated to the control arm (existing systems) or the experimental arm (newly developed systems incorporating textual descriptions). In the first part, a task-based usability assessment was conducted to compare performance between the two arms. The second part evaluated participant preferences, agreement with textual descriptions and perceptions of the new systems.
    Results: A total of 86 participants were recruited. The experimental arm achieved an 85.15% total correctness score, compared to 74.38% in the control arm (p < 0.001). The experimental arm particularly outperformed the control arm in the ambiguous tasks, such as compression low. However, despite a higher performance and greater agreement with the textual descriptions, the experimental group exhibited a less favourable perception compared to the control group.
    Conclusion: Incorporating textual description into glucose monitoring systems enhances treatment decision-making for people with T1DM. It suggests that we are on the right path to helping them better understand their glucose data and assist their self-management. Extensive research is required to focus more on the patient-centred approach in information presentation and prioritise it in parallel with other advancements in glucose monitoring technologies.
    Keywords:  blood glucose monitoring; data visualisation; interpretation; self-management; type 1 diabetes; usability
    DOI:  https://doi.org/10.1177/20420188251362089
  15. Sensors (Basel). 2025 Aug 31. pii: 5372. [Epub ahead of print]25(17):
    LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet.
    Abdullah Mamun, Asiful Arefeen, Susan B Racette, Dorothy D Sears, Corrie M Whisner, Matthew P Buman, Hassan Ghasemzadeh.
      Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after consuming a meal, is a critical indicator of progression toward type 2 diabetes in people with prediabetes and in healthy individuals. A key metric for understanding blood glucose dynamics after eating is the postprandial Area Under the Curve (AUC). Predicting postprandial AUC in advance based on a person's lifestyle factors, such as diet and physical activity level, and explaining the factors that affect postprandial blood glucose could allow an individual to adjust their behavioral choices accordingly to maintain normal glucose levels. In this work, we develop an explainable machine learning solution, GlucoLens, that takes sensor-driven inputs and utilizes advanced data processing, large language models, and trainable machine learning models to estimate postprandial AUC and predict hyperglycemia from diet, physical activity, and recent glucose patterns. We use data obtained using wearables in a five-week clinical trial of 10 adults who worked full-time to develop and evaluate the proposed computational model that integrates wearable sensing, multimodal data, and machine learning. Our machine learning model takes multimodal data from wearable activity and glucose monitoring sensors, along with food and work logs, and provides an interpretable prediction of the postprandial glucose patterns. GlucoLens achieves a normalized root mean squared error (NRMSE) of 0.123 in its best configuration. On average, the proposed technology provides a 16% better predictive performance compared to the comparison models. Additionally, our technique predicts hyperglycemia with an accuracy of 79% and an F1 score of 0.749 and recommends different treatment options to help avoid hyperglycemia through diverse counterfactual explanations. With systematic experiments and discussion supported by established prior research, we show that our method is generalizable and consistent with clinical understanding.
    Keywords:  continuous glucose monitoring; diabetes; hyperglycemia; large language models; machine learning; metabolic health
    DOI:  https://doi.org/10.3390/s25175372
  16. Diabetes Care. 2025 Sep 09. pii: dc251985. [Epub ahead of print]
    Goodhart's Law in Diabetes Care: The Hidden Drawbacks of Overoptimizing CGM Metrics.
    Robert Richardson, Hood Thabit.
      
    DOI:  https://doi.org/10.2337/dc25-1985
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