Browsing by Subject "Continuous glucose monitoring"

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    Cystic fibrosis-related diabetes: Prevalence, screening, and diagnosis
    (Elsevier, 2021-12-07) Khare, Swapnil; Desimone, Marisa; Kasim, Nader; Chan, Christine L.; Medicine, School of Medicine
    Cystic fibrosis-related diabetes (CFRD) is the most common comorbidity in patients with cystic fibrosis (CF). Prevalence of CFRD increases with age and is greater with severe mutations. Other risk factors associated with CFRD are female sex, pancreatic insufficiency, liver disease, need for gastrostomy tube feedings, history of bronchopulmonary aspergillosis, and poor pulmonary function. CFRD is related to worse clinical outcomes and increased mortality. Early diagnosis and treatment have been shown to improve clinical outcomes. Screening for CFRD is recommended with an annual oral glucose tolerance test (OGTT) starting at age 10 years. Diagnosis of CFRD is made by standard American Diabetes Association (ADA) criteria during baseline health. CFRD can also be diagnosed in individuals with CF during acute illness, while on enteral feeds, and after transplant. In this review we will discuss the epidemiology of CFRD and provide an overview of the advantages and pitfalls of current screening and diagnostic tests for CFRD.
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    Effect of iGlarLixi on continuous glucose monitoring—measured time in range in insulin‐naive adults with suboptimally controlled type 2 diabetes
    (Wiley, 2025) Frías, Juan P.; Ratzki‐Leewing, Alexandria; Dex, Terry; Meneghini, Luigi; Rodrigues, Amélie; Shah, Viral N.; Medicine, School of Medicine
    Aims: People with type 2 diabetes (T2D) and glycated haemoglobin (HbA1c) ≥9% may benefit from fixed-ratio combination therapies such as iGlarLixi (insulin glargine 100 U/mL and lixisenatide 33 μg/mL). Use of continuous glucose monitoring (CGM) is recommended, but data are lacking to assess the impact of iGlarLixi in individuals with HbA1c ≥9%. Materials and methods: Soli-CGM (NCT05114590) was a 16-week, multicentre, open-label study evaluating the efficacy of once-daily iGlarLixi using blinded CGM-based metrics in insulin-naive adults with HbA1c ≥9%-13% who were receiving ≥2 oral antihyperglycaemic agents (OADs) ± glucagon-like peptide-1 receptor agonists (GLP-1 RAs). The primary outcome was the change from baseline to week 16 in percent time in range (TIR; 70-180 mg/dL). Secondary outcomes included change in mean daily blood glucose (BG), maximum postprandial glucose 4 h post-breakfast (PPG-4 h), and time above range (TAR; >180 mg/dL). On-treatment hypoglycaemia was assessed. Results: The study enrolled 124 participants (mean age, 55.6 years; HbA1c, 10.2%). Sixteen weeks of treatment with iGlarLixi improved TIR (+26.2%), mean BG (-52.5 mg/dL), maximum PPG-4 h (-73.7 mg/dL), and TAR (-28.7%); all p < 0.001. Rates of American Diabetes Association level 1 (BG <70 but ≥54 mg/dL) and level 2 (BG <54 mg/dL) hypoglycaemia were reported as 1.4 and 0.6 events per person-year, respectively. No level 3 events (requiring assistance) were reported. Conclusions: In people with T2D suboptimally controlled on ≥2 OADs ± GLP-1 RAs, 16 weeks of treatment with iGlarLixi significantly improved TIR and reduced TAR without severe hypoglycaemia.
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    Expert Clinical Interpretation of Continuous Glucose Monitor Reports From Individuals Without Diabetes
    (Sage, 2025-02-12) Spartano, Nicole L.; Prescott, Brenton; Walker, Maura E.; Shi, Eleanor; Venkatesan, Guhan; Fei, David; Lin, Honghuang; Murabito, Joanne M.; Ahn, David; Battelino, Tadej; Edelman, Steven V.; Fleming, G. Alexander; Freckmann, Guido; Galindo, Rodolfo J.; Joubert, Michael; Lansang, M. Cecilia; Mader, Julia K.; Mankovsky, Boris; Mathioudakis, Nestoras N.; Mohan, Viswanathan; Peters, Anne L.; Shah, Viral N.; Spanakis, Elias K.; Waki, Kayo; Wright, Eugene E.; Zilbermint, Mihail; Wolpert, Howard A.; Steenkamp, Devin W.; Medicine, School of Medicine
    Background: Clinical interpretation of continuous glucose monitoring (CGM) data for people without diabetes has not been well established. This study aimed to investigate concordance among CGM experts in recommending clinical follow-up for individuals without diabetes, based upon their independent review of CGM data. Methods: We sent a survey out to expert clinicians (n = 18) and asked them to evaluate 20 potentially challenging Dexcom G6 Pro CGM reports (and hemoglobin A1c [HbA1c] and fasting venous blood glucose levels) from individuals without diabetes. Clinicians reported whether they would recommend follow-up and the reasoning for their decision. We performed Fleiss Kappa interrater reliability to determine agreement among clinicians. Results: More than half of expert clinicians (56-100%, but no clear consensus) recommended follow-up to individuals who spent >2% time above range (>180 mg/dL), even if HbA1c <5.7% and fasting glucose <100 mg/dL. There were no observed trends for recommending follow-up based on mean glucose or glucose management indicator. Overall, we observed poor agreement in recommendations for who should receive follow-up based on their CGM report (Fleiss Kappa = 0.36). Conclusions: High discordance among expert clinicians when interpreting potentially challenging CGM reports for people without diabetes highlights the need for more research in developing normative data for people without diabetes. Future work is required to develop CGM criteria for identifying potentially high-risk individuals who may progress to prediabetes or type 2 diabetes.
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    Improving Care for People Living With Dementia and Diabetes: Applying the Human-Centered Design Process to Continuous Glucose Monitoring
    (Sage, 2024) Savoy, April; Holden, Richard J.; de Groot, Mary; Clark, Daniel O.; Sachs, Greg A.; Klonoff, David; Fellow AIMBE; Weiner, Michael; Medicine, School of Medicine
    People with Alzheimer's disease or related dementias and diabetes mellitus (ADRD-DM) are at high risk for hypoglycemic events. Their cognitive impairment and psychosocial situation often hinder detection of hypoglycemia. Extending use and benefits of continuous glucose monitoring (CGM) to people with ADRD-DM could improve hypoglycemia detection, inform care, and reduce adverse events. However, cognitive impairment associated with ADRD presents unique challenges for CGM use. This commentary proposes applying the human-centered design process to CGM, investigating design solutions or interventions needed to integrate CGM into the health care of patients with ADRD-DM. With this process, we can identify and inform CGM designs for people with ADRD-DM, broadening CGM access, increasing detection and treatment of the silent threat posed by hypoglycemia.
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    ISPAD Clinical Practice Consensus Guidelines 2022: Editorial
    (Wiley, 2022) Craig, Maria E.; Codner, Ethel; Mahmud, Farid H.; Marcovecchio, M. Loredana; DiMeglio, Linda A.; Priyambada, Leena; Wolfsdorf, Joseph I.; Pediatrics, School of Medicine
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    Long-term Continuous Glucose Monitor Use in Very Young Children With Type 1 Diabetes: One-Year Results From the SENCE Study
    (Sage, 2023) Van Name, Michelle A.; Kanapka, Lauren G.; DiMeglio, Linda A.; Miller, Kellee M.; Albanese-O’Neill, Anastasia; Commissariat, Persis; Corathers, Sarah D.; Harrington, Kara R.; Hilliard, Marisa E.; Anderson, Barbara J.; Kelley, Jennifer C.; Laffel, Lori M.; MacLeish, Sarah A.; Nathan, Brandon M.; Tamborlane, William V.; Wadwa, R. Paul; Willi, Steven M.; Williams, Kristen M.; Wintergerst, Kupper A.; Woerner, Stephanie; Wong, Jenise C.; DeSalvo, Daniel J.; Pediatrics, School of Medicine
    Objectives: Achieving optimal glycemic outcomes in young children with type 1 diabetes (T1D) is challenging. This study examined the durability of continuous glucose monitoring (CGM) coupled with a family behavioral intervention (FBI) to improve glycemia. Study design: This one-year study included an initial 26-week randomized controlled trial of CGM with FBI (CGM+FBI) and CGM alone (Standard-CGM) compared with blood glucose monitoring (BGM), followed by a 26-week extension phase wherein the BGM Group received the CGM+FBI (BGM-Crossover) and both original CGM groups continued this technology. Results: Time in range (70-180 mg/dL) did not improve with CGM use (CGM+FBI: baseline 37%, 52 weeks 41%; Standard-CGM: baseline 41%, 52 weeks 44%; BGM-Crossover: 26 weeks 38%, 52 weeks 40%). All three groups sustained decreases in hypoglycemia (<70 mg/dL) with CGM use (CGM+FBI: baseline 3.4%, 52 weeks 2.0%; Standard-CGM: baseline 4.1%, 52 weeks 2.1%; BGM-Crossover: 26 weeks 4.5%, 52 weeks 1.7%, P-values <.001). Hemoglobin A1c was unchanged with CGM use (CGM+FBI: baseline 8.3%, 52 weeks 8.2%; Standard-CGM: baseline 8.2%, 52 weeks 8.0%; BGM-Crossover: 26 weeks 8.1%, 52 weeks 8.3%). Sensor use remained high (52-week study visit: CGM+FBI 91%, Standard-CGM 92%, BGM-Crossover 88%). Conclusion: Over 12 months young children with T1D using newer CGM technology sustained reductions in hypoglycemia and, in contrast to prior studies, persistently wore CGM. However, pervasive hyperglycemia remained unmitigated. This indicates an urgent need for further advances in diabetes technology, behavioral support, and diabetes management educational approaches to optimize glycemia in young children.
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    Navigating Automated Insulin Delivery for Type 1 Diabetes Management During Pregnancy
    (Sage, 2025-04-17) Scifres, Christina M.; Cleary, Erin M.; Sheerer, Madilyn; Bowdler, Marissa; Shah, Viral N.; Obstetrics and Gynecology, School of Medicine
    Achieving pregnancy-specific glucose targets is difficult in pregnant individuals with type 1 diabetes (T1D), and the rates of complications for mothers and their infants remain high. Currently marketed automated insulin delivery (AID) systems are hybrid closed-loop (HCL) systems in which basal insulin delivery (with or without automated correction boluses) is driven by algorithms, and users are required to initiate meal boluses. For non-pregnant people with T1D, HCL therapy has established benefits for glycemic outcomes and quality of life. While none of the currently available HCL systems were designed for pregnancy-specific glucose targets and outcomes, preliminary data suggest that the use of HCL systems may result in improved glycemia during pregnancy. There is an accumulating body of literature examining HCL systems in pregnancy, although there are still limited data regarding the impact of HCL systems on perinatal outcomes. Many individuals conceive while using clinically available HCL systems and may be hesitant to discontinue use during pregnancy, and clinicians may consider HCL therapy for pregnant individuals who are struggling to meet recommended glycemic levels during pregnancy. We therefore offer guidance on how to counsel patients on the risks and benefits of HCL therapy in pregnancy, how to identify appropriate candidates for HCL therapy in pregnancy, and how to manage commercially available HCL systems off-label throughout gestation.
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    Novel Detection and Progression Markers for Diabetes Based on Continuous Glucose Monitoring Data Dynamics
    (Oxford University Press, 2024) Montaser, Eslam; Farhy, Leon S.; Kovatchev, Boris P.; Medicine, School of Medicine
    Context: Static measures of continuous glucose monitoring (CGM) data, such as time spent in specific glucose ranges (70-180 mg/dL or 70-140 mg/dL), do not fully capture the dynamic nature of blood glucose, particularly the subtle gradual deterioration of glycemic control over time in individuals with early-stage type 1 diabetes. Objective: Develop a diabetes diagnostic tool based on 2 markers of CGM dynamics: CGM entropy rate (ER) and Poincaré plot (PP) ellipse area (S). Methods: A total of 5754 daily CGM profiles from 843 individuals with type 1, type 2 diabetes, or healthy individuals with or without islet autoantibody status were used to compute 2 individual dynamic markers: ER (in bits per transition; BPT) of daily probability matrices describing CGM transitions between 8 glycemic states, and the area S (mg2/dL2) of individual CGM PP ellipses using standard PP descriptors. The Youden index was used to determine "optimal" cut-points for ER and S for health vs diabetes (case 1); type 1 vs type 2 (case 2); and low vs high type 1 immunological risk (case 3). The markers' discriminative power was assessed through the area under the receiver operating characteristics curves (AUC). Results: Optimal cutoff points were determined for ER and S for each of the 3 cases. ER and S discriminated case 1 with AUC = 0.98 (95% CI, 0.97-0.99) and AUC = 0.99 (95% CI, 0.99-1.00), respectively (cutoffs ERcase1 = 0.76 BPT, Scase1 = 1993.91 mg2/dL2), case 2 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase2 = 1.00 BPT, Scase2 = 5112.98 mg2/dL2), and case 3 with AUC = 0.72 (95% CI, 0.58-0.86), and AUC = 0.66 (95% CI, 0.47-0.86), respectively (ERcase3 = 0.52 BPT, Scase3 = 923.65 mg2/dL2). Conclusion: CGM dynamics markers can be an alternative to fasting plasma glucose or glucose tolerance testing to identify individuals at higher immunological risk of progressing to type 1 diabetes.
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    Precision medicine in type 1 diabetes
    (Springer, 2022) Carr, Alice L.J.; Evans-Molina, Carmella; Oram, Richard A.; Pediatrics, School of Medicine
    First envisioned by early diabetes clinicians, a person-centred approach to care was an aspirational goal that aimed to match insulin therapy to each individual's unique requirements. In the 100 years since the discovery of insulin, this goal has evolved to include personalised approaches to type 1 diabetes diagnosis, treatment, prevention and prediction. These advances have been facilitated by the recognition of type 1 diabetes as an autoimmune disease and by advances in our understanding of diabetes pathophysiology, genetics and natural history, which have occurred in parallel with advancements in insulin delivery, glucose monitoring and tools for self-management. In this review, we discuss how these personalised approaches have improved diabetes care and how improved understanding of pathogenesis and human biology might inform precision medicine in the future.
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    Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study
    (Sage, 2024-10-28) Montaser, Eslam; Shah, Viral N.; Medicine, School of Medicine
    Background: Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T1D). This exploratory study uses machine learning on continuous glucose monitoring (CGM) data to identify factors influencing DR and predict high-risk individuals for timely intervention. Methods: Between June 2018 and March 2022, adults with T1D with incident DR or no retinopathy (control) were identified. The CGM data were collected retrospectively for up to seven years before the date of defining incident DR or no retinopathy. A mixture of three machine learning algorithms was trained and evaluated in two different scenarios, using different glycemic features extracted from CGM traces (scenario 1), and the two principal components (two PCs; exposure to hyperglycemia and hypoglycemia risk) of those features (scenario 2). Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. Results: The CGM data of 30 adults with incident DR (mean±SD age of 21.2±9.4 years, glycated hemoglobin [HbA1c] of 8.6%±1.0%, and body mass index [BMI] of 24.5±4.8 kg/m2) and 30 adults without DR (age of 41.8±14.7 years, HbA1c of 7.0%±0.9%, and BMI of 26.2±3.6 kg/m2) were included in this analysis. In scenario 2, classifiers outperformed scenario 1, resulting in an average AUC-ROC increase to 0.92 for two of three models, indicating that the two PCs captured vital classification data, representing the most discriminative aspects and enhancing model performance. Conclusion: Machine learning approaches using CGM data may have potential to aid in identifying adults with T1D at risk of DR.