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Szmuilowicz ED, Barbour L, Brown FM, Durnwald C, Feig DS, O'Malley G, Polsky S, Aleppo G. Continuous Glucose Monitoring Metrics for Pregnancies Complicated by Diabetes: Critical Appraisal of Current Evidence. J Diabetes Sci Technol 2024; 18:819-834. [PMID: 38606830 DOI: 10.1177/19322968241239341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Ascertaining the utility of continuous glucose monitoring (CGM) in pregnancy complicated by diabetes is a rapidly evolving area, as the prevalence of type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes mellitus (GDM) escalates. The seminal randomized controlled trial (RCT) evaluating CGM use added to standard care in pregnancy in T1D demonstrated significant improvements in maternal glycemia and neonatal health outcomes. Current clinical guidance recommends targets for percentage time in range (TIR), time above range (TAR), and time below range (TBR) during pregnancy complicated by T1D that are widely used in clinical practice. However, the superiority of CGM over blood glucose monitoring (BGM) is still questioned in both T2D and GDM, and whether glucose targets should be different than in T1D is unknown. Questions requiring additional research include which CGM metrics are superior in predicting clinical outcomes, how should pregnancy-specific CGM targets be defined, whether CGM targets should differ according to gestational age, and if CGM metrics during pregnancy should be similar across all types of diabetes. Limiting the potential for CGM to improve pregnancy outcomes may be our inability to maintain TIR > 70% throughout gestation, a goal achieved in the minority of patients studied. Adverse pregnancy outcomes remain high in women with T1D and T2D in pregnancy despite CGM technology, and this review explores the potential reasons and questions yet to be investigated.
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Affiliation(s)
| | - Linda Barbour
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | | | | | | | - Sarit Polsky
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Grazia Aleppo
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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Battarbee AN, Sauer SM, Sanusi A, Fulcher I. Discrete glucose profiles identified using continuous glucose monitoring data and their association with adverse pregnancy outcomes. Am J Obstet Gynecol 2024; 231:122.e1-122.e9. [PMID: 38527606 PMCID: PMC11194156 DOI: 10.1016/j.ajog.2024.03.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND Continuous glucose monitoring has facilitated the evaluation of dynamic changes in glucose throughout the day and their effect on fetal growth abnormalities in pregnancy. However, studies of multiple continuous glucose monitoring metrics combined and their association with other adverse pregnancy outcomes are limited. OBJECTIVE This study aimed to (1) use machine learning techniques to identify discrete glucose profiles based on weekly continuous glucose monitoring metrics in pregnant individuals with pregestational diabetes mellitus and (2) investigate their association with adverse pregnancy outcomes. STUDY DESIGN This study analyzed data from a retrospective cohort study of pregnant patients with type 1 or 2 diabetes mellitus who used Dexcom G6 continuous glucose monitoring and delivered a nonanomalous, singleton pregnancy at a tertiary center between 2019 and 2023. Continuous glucose monitoring data were collapsed into 39 weekly glycemic measures related to centrality, spread, excursions, and circadian cycle patterns. Principal component analysis and k-means clustering were used to identify 4 discrete groups, and patients were assigned to the group that best represented their continuous glucose monitoring patterns during pregnancy. Finally, the association between glucose profile groups and outcomes (preterm birth, cesarean delivery, preeclampsia, large-for-gestational-age neonate, neonatal hypoglycemia, and neonatal intensive care unit admission) was estimated using multivariate logistic regression adjusted for diabetes mellitus type, maternal age, insurance, continuous glucose monitoring use before pregnancy, and parity. RESULTS Of 177 included patients, 90 (50.8%) had type 1 diabetes mellitus, and 85 (48.3%) had type 2 diabetes mellitus. This study identified 4 glucose profiles: (1) well controlled; (2) suboptimally controlled with high variability, fasting hypoglycemia, and daytime hyperglycemia; (3) suboptimally controlled with minimal circadian variation; and (4) poorly controlled with peak hyperglycemia overnight. Compared with the well-controlled profile, the suboptimally controlled profile with high variability had higher odds of a large-for-gestational-age neonate (adjusted odds ratio, 3.34; 95% confidence interval, 1.15-9.89). The suboptimally controlled with minimal circadian variation profile had higher odds of preterm birth (adjusted odds ratio, 2.59; 95% confidence interval, 1.10-6.24), cesarean delivery (adjusted odds ratio, 2.76; 95% confidence interval, 1.09-7.46), and neonatal intensive care unit admission (adjusted odds ratio, 4.08; 95% confidence interval, 1.58-11.40). The poorly controlled profile with peak hyperglycemia overnight had higher odds of preeclampsia (adjusted odds ratio, 2.54; 95% confidence interval, 1.02-6.52), large-for-gestational-age neonate (adjusted odds ratio, 3.72; 95% confidence interval, 1.37-10.4), neonatal hypoglycemia (adjusted odds ratio, 3.53; 95% confidence interval, 1.37-9.71), and neonatal intensive care unit admission (adjusted odds ratio, 3.15; 95% confidence interval, 1.20-9.09). CONCLUSION Discrete glucose profiles of pregnant individuals with pregestational diabetes mellitus were identified through joint consideration of multiple continuous glucose monitoring metrics. Prolonged exposure to maternal hyperglycemia may be associated with a higher risk of adverse pregnancy outcomes than suboptimal glycemic control characterized by high glucose variability and intermittent hyperglycemia.
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Affiliation(s)
- Ashley N Battarbee
- Center for Women's Reproductive Health, University of Alabama at Birmingham, Birmingham, AL; Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, AL.
| | - Sara M Sauer
- Delfina Care, San Francisco, CA; Department of Global Health and Social Medicine, Harvard Medical School; Boston, MA
| | - Ayodeji Sanusi
- Center for Women's Reproductive Health, University of Alabama at Birmingham, Birmingham, AL; Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, AL
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Liarakos AL, Lim JZM, Leelarathna L, Wilmot EG. The use of technology in type 2 diabetes and prediabetes: a narrative review. Diabetologia 2024:10.1007/s00125-024-06203-7. [PMID: 38951212 DOI: 10.1007/s00125-024-06203-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/09/2024] [Indexed: 07/03/2024]
Abstract
The increasing incidence of type 2 diabetes, which represents 90% of diabetes cases globally, is a major public health concern. Improved glucose management reduces the risk of vascular complications and mortality; however, only a small proportion of the type 2 diabetes population have blood glucose levels within the recommended treatment targets. In recent years, diabetes technologies have revolutionised the care of people with type 1 diabetes, and it is becoming increasingly evident that people with type 2 diabetes can also benefit from these advances. In this review, we describe the current knowledge regarding the role of technologies for people living with type 2 diabetes and the evidence supporting their use in clinical practice. We conclude that continuous glucose monitoring systems deliver glycaemic benefits for individuals with type 2 diabetes, whether treated with insulin or non-insulin therapy; further data are required to evaluate the role of these systems in those with prediabetes (defined as impaired glucose tolerance and/or impaired fasting glucose and/or HbA1c levels between 39 mmol/mol [5.7%] and 47 mmol/mol [6.4%]). The use of insulin pumps seems to be safe and effective in people with type 2 diabetes, especially in those with an HbA1c significantly above target. Initial results from studies exploring the impact of closed-loop systems in type 2 diabetes are promising. We discuss directions for future research to fully understand the potential benefits of integrating evidence-based technology into care for people living with type 2 diabetes and prediabetes.
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Affiliation(s)
- Alexandros L Liarakos
- Department of Diabetes and Endocrinology, University Hospitals of Derby and Burton NHS Foundation Trust, Royal Derby Hospital, Derby, UK
- School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Jonathan Z M Lim
- Diabetes, Endocrinology and Metabolism Centre, Manchester University NHS Foundation Trust, Manchester Royal Infirmary, Manchester, UK
| | - Lalantha Leelarathna
- Diabetes, Endocrinology and Metabolism Centre, Manchester University NHS Foundation Trust, Manchester Royal Infirmary, Manchester, UK
- Department of Diabetes, Imperial College Healthcare NHS Trust, London, UK
- Faculty of Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Emma G Wilmot
- Department of Diabetes and Endocrinology, University Hospitals of Derby and Burton NHS Foundation Trust, Royal Derby Hospital, Derby, UK.
- School of Medicine, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK.
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Benhalima K, Beunen K, Van Wilder N, Ballaux D, Vanhaverbeke G, Taes Y, Aers XP, Nobels F, Marlier J, Lee D, Cuypers J, Preumont V, Siegelaar SE, Painter RC, Laenen A, Gillard P, Mathieu C. Comparing advanced hybrid closed loop therapy and standard insulin therapy in pregnant women with type 1 diabetes (CRISTAL): a parallel-group, open-label, randomised controlled trial. Lancet Diabetes Endocrinol 2024; 12:390-403. [PMID: 38697182 DOI: 10.1016/s2213-8587(24)00089-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/11/2024] [Accepted: 03/11/2024] [Indexed: 05/04/2024]
Abstract
BACKGROUND Advanced hybrid closed loop (AHCL) therapy can improve glycaemic control in pregnant women with type 1 diabetes. However, data are needed on the efficacy and safety of AHCL systems as these systems, such as the MiniMed 780G, are not currently approved for use in pregnant women. We aimed to investigate whether the MiniMed 780G can improve glycaemic control with less hypoglycaemia in pregnant women with type 1 diabetes. METHODS CRISTAL was a double-arm, parallel-group, open-label, randomised controlled trial conducted in secondary and tertiary care specialist endocrinology centres at 12 hospitals (11 in Belgium and one in the Netherlands). Pregnant women aged 18-45 years with type 1 diabetes were randomly assigned (1:1) to AHCL therapy (MiniMed 780G) or standard insulin therapy (standard of care) at a median of 10·1 (IQR 8·6-11·6) weeks of gestation. Randomisation was done centrally with minimisation dependent on baseline HbA1c, insulin administration method, and centre. Participants and study teams were not masked to group allocation. The primary outcome was proportion of time spent in the pregnancy-specific target glucose range (3·5-7·8 mmol/L), measured by continuous glucose monitoring (CGM) at 14-17 weeks, 20-23 weeks, 26-29 weeks, and 33-36 weeks. Key secondary outcomes were overnight time in target range, and time below glucose range (<3·5 mmol/L) overall and overnight. Analyses were conducted on an intention-to-treat basis. This trial is registered with ClinicalTrials.gov (NCT04520971). FINDINGS Between Jan 15, 2021 and Sept 30, 2022, 101 participants were screened, and 95 were randomly assigned to AHCL therapy (n=46) or standard insulin therapy (n=49). 43 patients assigned to AHCL therapy and 46 assigned to standard insulin therapy completed the study. At baseline, 91 (95·8%) participants used insulin pumps, and the mean HbA1c was 6·5% (SD 0·6). The mean proportion of time spent in the target range (averaged over four time periods) was 66·5% (SD 10·0) in the AHCL therapy group compared with 63·2% (12·4) in the standard insulin therapy group (adjusted mean difference 1·88 percentage points [95% CI -0·82 to 4·58], p=0·17). Overnight time in the target range was higher (adjusted mean difference 6·58 percentage points [95% CI 2·31 to 10·85], p=0·0026), and time below range overall (adjusted mean difference -1·34 percentage points [95% CI, -2·19 to -0·49], p=0·0020) and overnight (adjusted mean difference -1·86 percentage points [95% CI -2·90 to -0·81], p=0·0005) were lower with AHCL therapy than with standard insulin therapy. Participants assigned to AHCL therapy reported higher treatment satisfaction. No unanticipated safety events occurred with AHCL therapy. INTERPRETATION In pregnant women starting with tighter glycaemic control, AHCL therapy did not improve overall time in target range but improved overnight time in target range, reduced time below range, and improved treatment satisfaction. These data suggest that the MiniMed 780G can be safely used in pregnancy and provides some additional benefits compared with standard insulin therapy; however, it will be important to refine the algorithm to better align with pregnancy requirements. FUNDING Diabetes Liga Research Fund and Medtronic.
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Affiliation(s)
- Katrien Benhalima
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium; Department of Endocrinology, UZ Leuven, Leuven, Belgium.
| | - Kaat Beunen
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Nancy Van Wilder
- Department of Endocrinology, University Hospital Brussels, Jette, Belgium
| | - Dominique Ballaux
- Department of Endocrinology, Vitaz Campus Sint-Niklaas Moerland, Sint-Niklaas, Belgium
| | - Gerd Vanhaverbeke
- Department of Endocrinology, General Hospital Groeninge Kortrijk, Kortrijk, Belgium
| | - Youri Taes
- Department of Endocrinology, General Hospital Sint-Jan Brugge, Brugge, Belgium
| | - Xavier-Philippe Aers
- Department of Endocrinology, General Hospital Delta Campus Rumbeke, Roeselare, Belgium
| | - Frank Nobels
- Department of Endocrinology, OLV Hospital Aalst, Aalst, Belgium
| | - Joke Marlier
- Department of Endocrinology, Ghent University Hospital, Gent, Belgium
| | - Dahae Lee
- Department of Endocrinology, Imelda Hospital Bonheiden, Bonheiden, Belgium
| | - Joke Cuypers
- Department of Endocrinology, General Hospital Turnhout Campus Sint-Jozef, Turnhout, Belgium
| | - Vanessa Preumont
- Department of Endocrinology, University Hospital Saint-Luc, Brussel, Belgium
| | - Sarah E Siegelaar
- Department of Endocrinologyand Metabolism, Amsterdam UMC location University of Amsterdam, Amsterdam, Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam University Medical Centres, Amsterdam, Netherlands
| | - Rebecca C Painter
- Department of Obstetrics & Gynecology, Amsterdam University Medical Centres, Amsterdam, Netherlands; Amsterdam Reproduction and Development, Amsterdam University Medical Centres, Amsterdam, Netherlands
| | - Annouschka Laenen
- Center of Biostatics and Statistical bioinformatics, KU Leuven, Leuven, Belgium
| | - Pieter Gillard
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium; Department of Endocrinology, UZ Leuven, Leuven, Belgium
| | - Chantal Mathieu
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium; Department of Endocrinology, UZ Leuven, Leuven, Belgium
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Mittal R, Koutras N, Maya J, Lemos JRN, Hirani K. Blood glucose monitoring devices for type 1 diabetes: a journey from the food and drug administration approval to market availability. Front Endocrinol (Lausanne) 2024; 15:1352302. [PMID: 38559693 PMCID: PMC10978642 DOI: 10.3389/fendo.2024.1352302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/22/2024] [Indexed: 04/04/2024] Open
Abstract
Blood glucose monitoring constitutes a pivotal element in the clinical management of Type 1 diabetes (T1D), a globally escalating metabolic disorder. Continuous glucose monitoring (CGM) devices have demonstrated efficacy in optimizing glycemic control, mitigating adverse health outcomes, and augmenting the overall quality of life for individuals afflicted with T1D. Recent progress in the field encompasses the refinement of electrochemical sensors, which enhances the effectiveness of blood glucose monitoring. This progress empowers patients to assume greater control over their health, alleviating the burdens associated with their condition, and contributing to the overall alleviation of the healthcare system. The introduction of novel medical devices, whether derived from existing prototypes or originating as innovative creations, necessitates adherence to a rigorous approval process regulated by the Food and Drug Administration (FDA). Diverse device classifications, stratified by their associated risks, dictate distinct approval pathways, each characterized by varying timelines. This review underscores recent advancements in blood glucose monitoring devices primarily based on electrochemical sensors and elucidates their regulatory journey towards FDA approval. The advent of innovative, non-invasive blood glucose monitoring devices holds promise for maintaining stringent glycemic control, thereby preventing T1D-associated comorbidities, and extending the life expectancy of affected individuals.
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Affiliation(s)
- Rahul Mittal
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Nicole Koutras
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL, United States
| | - Jonathan Maya
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL, United States
| | - Joana R. N. Lemos
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Khemraj Hirani
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL, United States
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Feig DS, Scott EM. Optimizing Patient Outcomes in Pregnancy: What Continuous Glucose Monitoring Metrics Should We Be Aiming For? Diabetes Care 2024; 47:54-55. [PMID: 38117998 DOI: 10.2337/dci23-0079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 09/30/2023] [Indexed: 12/22/2023]
Affiliation(s)
- Denice S Feig
- Department of Medicine, University of Toronto, Sinai Health System, Mount Sinai Hospital, Toronto, Canada
| | - Eleanor M Scott
- Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, U.K
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