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Nigi L, Simon Batzibal MDLA, Cataldo D, Dotta F. 12-Month Time in Tight Range Improvement with Advanced Hybrid-Closed Loop System in Adults with Type 1 Diabetes. Diabetes Ther 2024:10.1007/s13300-024-01656-w. [PMID: 39347899 DOI: 10.1007/s13300-024-01656-w] [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: 08/03/2024] [Accepted: 09/10/2024] [Indexed: 10/01/2024] Open
Abstract
INTRODUCTION Time in tight range (TITR) is an emerging and valuable metric for assessing normoglycemia. The latest advancement in automated insulin delivery (AID) systems, the advanced hybrid closed-loop (AHCL) systems, are particularly noteworthy for managing type 1 diabetes (T1D) and enhancing glycemic control. METHODS In a real-world clinical setting, we carried out a retrospective evaluation of TITR in 42 adult subjects with T1D using the AHCL Minimed™ 780G system over a 12-month period. RESULTS Within just 14 days of activating the automatic mode, the AHCL Minimed™ 780G system showed rapid improvement in TITR, and in the other continuous glucose monitoring (CGM) metrics. This improvement persisted over 12 months, achieving the proposed 45-50% range for effective glycemic control. CONCLUSION The AHCL Minimed™ 780G system significantly enhances TITR, demonstrating continuous improvement throughout a 12-month follow-up period.
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Affiliation(s)
- Laura Nigi
- Department of Medical Sciences, Surgery and Neurosciences, University of Siena, Siena, Italy.
- Diabetes and Metabolic Diseases Unit, Azienda Ospedaliera Universitaria Senese, Siena, Italy.
| | | | - Dorica Cataldo
- Diabetes and Metabolic Diseases Unit, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Francesco Dotta
- Department of Medical Sciences, Surgery and Neurosciences, University of Siena, Siena, Italy
- Diabetes and Metabolic Diseases Unit, Azienda Ospedaliera Universitaria Senese, Siena, Italy
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2
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Castañeda J, de Galan BE, van Kuijk SMJ, Arrieta A, van den Heuvel T, Cohen O. The interdependence of targets for continuous glucose monitoring outcomes in type 1 diabetes with automated insulin delivery. Diabetes Obes Metab 2024. [PMID: 39323365 DOI: 10.1111/dom.15955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 08/23/2024] [Accepted: 08/31/2024] [Indexed: 09/27/2024]
Abstract
AIM The aim was to determine the interdependence of targets for glucose management indicator (GMI), time within the ranges of 70-180 mg/dL (TIR) and 70-140 mg/dL (time in tight glucose range [TITR]), time above 180 mg/dL (TA180) and 250 mg/dL (TA250) and time below 70 mg/dL (TB70) and 54 mg/dL (TB54) and its implications for setting targets in automated insulin delivery (AID). MATERIALS AND METHODS Real-world data from individuals with type 1 diabetes using the 780G system were used to calculate the receiver operating characteristic curves and establish interdependent targets for time in ranges based on several GMI benchmarks. Correlation, regression and principal component analysis were used to determine their association and dimensionality. RESULTS In individuals aged >15 years (n = 41 692), a GMI <6.5% required targets of >81%, >58%, <15% and <1.9% for TIR, TITR, TA180 and TA250, respectively, with high sensitivity, specificity and accuracy (>90%), whereas these values were poor for time in hypoglycaemia and GMI, which had a modest correlation (-0.21 to -0.43). Two dimensions emerged: (1) GMI, TIR, TITR, TA180 and TA250, and (2) TB70 and TB54, explaining 95% of total variability. GMI (or TIR) and TB70 explained >81% of the variability in the remaining continuous glucose monitoring (CGM) metrics, providing accurate predictions. Individuals aged ≤15 years (n = 14 459) showed similar results. CONCLUSION We developed a methodology to establish interdependent CGM targets for therapies with CGM data outputs. In AID with the 780G system, a GMI <7% requires time in ranges close to consensus targets. Targets for GMI, TIR, TITR, TA180 and TA250 could be reduced to targets for GMI or TIR, whereas targets for time in hypoglycaemia are not inherently tied to GMI/TIR targets.
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Affiliation(s)
| | - Bastiaan E de Galan
- Department of Internal Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Department of Internal Medicine, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Arcelia Arrieta
- Medtronic International Trading Sàrl, Tolochenaz, Switzerland
| | | | - Ohad Cohen
- Medtronic International Trading Sàrl, Tolochenaz, Switzerland
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Thomas A, Haak T, Tombek A, Kulzer B, Ehrmann D, Kordonouri O, Kröger J, Schubert-Olesen O, Kolassa R, Siegmund T, Haller N, Heinemann L. How to Use Continuous Glucose Monitoring Efficiently in Diabetes Management: Opinions and Recommendations by German Experts on the Status and Open Questions. J Diabetes Sci Technol 2024:19322968241267768. [PMID: 39129243 DOI: 10.1177/19322968241267768] [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] [Indexed: 08/13/2024]
Abstract
Today, continuous glucose monitoring (CGM) is a standard diagnostic option for patients with diabetes, at least for those with type 1 diabetes and those with type 2 diabetes on insulin therapy, according to international guidelines. The switch from spot capillary blood glucose measurement to CGM was driven by the extensive and immediate support and facilitation of diabetes management CGM offers. In patients not using insulin, the benefits of CGM are not so well studied/obvious. In such patients, factors like well-being and biofeedback are driving CGM uptake and outcome. Apps can combine CGM data with data about physical activity and meal consumption for therapy adjustments. Personalized data management and coaching is also more feasible with CGM data. The same holds true for digitalization and telemedicine intervention ("virtual diabetes clinic"). Combining CGM data with Smart Pens ("patient decision support") helps to avoid missing insulin boluses or insulin miscalculation. Continuous glucose monitoring is a major pillar of all automated insulin delivery systems, which helps substantially to avoid acute complications and achieve more time in the glycemic target range. These options were discussed by a group of German experts to identify concrete gaps in the care structure, with a view to the necessary structural adjustments of the health care system.
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Affiliation(s)
| | - Thomas Haak
- Diabetes consulting, Mergentheim Diabetes Center, Bad Mergentheim, Germany
| | - Astrid Tombek
- Diabetes consulting, Mergentheim Diabetes Center, Bad Mergentheim, Germany
| | - Bernhard Kulzer
- Diabetes consulting, Mergentheim Diabetes Center, Bad Mergentheim, Germany
- FIDAM, Forschungsinstitut Diabetes-Akademie Mergentheim (Diabetes Academy Mergentheim Research Institute), Bad Mergentheim, Germany
| | - Dominic Ehrmann
- FIDAM, Forschungsinstitut Diabetes-Akademie Mergentheim (Diabetes Academy Mergentheim Research Institute), Bad Mergentheim, Germany
| | - Olga Kordonouri
- AUF DER BULT Hospital, Diabetes Center for Children and Adolescents, Hannover, Germany
| | - Jens Kröger
- Diabetes, Hamburg City Diabetes Center, Hamburg, Germany
| | | | - Ralf Kolassa
- Diabetes, Diabetes Focus Practice Bergheim/Erft, Bergheim/Erft, Germany
| | | | - Nicola Haller
- Diabetes, Diabetes & Metabolic Center Starnberg, Starnberg, Germany
| | - Lutz Heinemann
- Science Consulting in Diabetes GmbH, Düsseldorf, Germany
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4
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Fagherazzi G, Aguayo GA, Zhang L, Hanaire H, Picard S, Sablone L, Vergès B, Hamamouche N, Detournay B, Joubert M, Delemer B, Guilhem I, Vambergue A, Gourdy P, Hadjadj S, Velayoudom FL, Guerci B, Larger E, Jeandidier N, Gautier JF, Renard E, Potier L, Benhamou PY, Sola A, Bordier L, Bismuth E, Prévost G, Kessler L, Cosson E, Riveline JP. Heterogeneity of glycaemic phenotypes in type 1 diabetes. Diabetologia 2024; 67:1567-1581. [PMID: 38780786 PMCID: PMC11343912 DOI: 10.1007/s00125-024-06179-4] [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] [Received: 10/05/2023] [Accepted: 04/08/2024] [Indexed: 05/25/2024]
Abstract
AIMS/HYPOTHESIS Our study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes. METHODS In the Study of the French-speaking Society of Type 1 Diabetes (SFDT1), we characterised glycaemic heterogeneity thanks to a set of complementary metrics: HbA1c, time in range (TIR), time below range (TBR), CV, Gold score and glycaemia risk index (GRI). Applying the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm, we created a phenotypic tree, i.e. a 2D visual mapping. We also carried out a clustering analysis for comparison. RESULTS We included 618 participants with type 1 diabetes (52.9% men, mean age 40.6 years [SD 14.1]). Our phenotypic tree identified seven glycaemic phenotypes. The 2D phenotypic tree comprised a main branch in the proximal region and glycaemic phenotypes in the distal areas. Dimension 1, the horizontal dimension, was positively associated with GRI (coefficient [95% CI]) (0.54 [0.52, 0.57]), HbA1c (0.39 [0.35, 0.42]), CV (0.24 [0.19, 0.28]) and TBR (0.11 [0.06, 0.15]), and negatively with TIR (-0.52 [-0.54, -0.49]). The vertical dimension was positively associated with TBR (0.41 [0.38, 0.44]), CV (0.40 [0.37, 0.43]), TIR (0.16 [0.12, 0.20]), Gold score (0.10 [0.06, 0.15]) and GRI (0.06 [0.02, 0.11]), and negatively with HbA1c (-0.21 [-0.25, -0.17]). Notably, socioeconomic factors, cardiovascular risk indicators, retinopathy and treatment strategy were significant determinants of glycaemic phenotype diversity. The phenotypic tree enabled more granularity than traditional clustering in revealing clinically relevant subgroups of people with type 1 diabetes. CONCLUSIONS/INTERPRETATION Our study advances the current understanding of the complex glycaemic profile in people with type 1 diabetes and suggests that strategies based on isolated glycaemic metrics might not capture the complexity of the glycaemic phenotypes in real life. Relying on these phenotypes could improve patient stratification in type 1 diabetes care and personalise disease management.
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Affiliation(s)
- Guy Fagherazzi
- Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
| | - Gloria A Aguayo
- Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Lu Zhang
- Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Hélène Hanaire
- Department of Diabetology, Metabolic Diseases and Nutrition, CHU Toulouse, University of Toulouse, Toulouse, France
- Francophone Foundation for Diabetes Research, Paris, France
| | - Sylvie Picard
- Endocrinology and Diabetes, Point Medical, Dijon, France
| | - Laura Sablone
- Francophone Foundation for Diabetes Research, Paris, France
| | - Bruno Vergès
- Department of Endocrinology-Diabetology, Inserm LNC UMR1231, University of Burgundy, Dijon, France
| | | | | | - Michael Joubert
- Service d'Endocrinologie-Diabétologie (Endocrinology/Diabetes Unit), Centre Hospitalier Universitaire de Caen, Caen, France
| | - Brigitte Delemer
- Endocrinology, Diabetology and Nutrition Department, Robert Debré University Hospital, Reims, France
| | - Isabelle Guilhem
- Department of Endocrinology, Diabetes and Nutrition, University Hospital of Rennes, Rennes, France
| | - Anne Vambergue
- Endocrinology, Diabetology, Metabolism and Nutrition Department, Lille University Hospital, Lille, France
| | - Pierre Gourdy
- Department of Diabetology, Metabolic Diseases and Nutrition, CHU Toulouse, University of Toulouse, Toulouse, France
- Institute of Metabolic and Cardiovascular Diseases, UMR1297 Inserm/UPS, Toulouse University, Toulouse, France
| | - Samy Hadjadj
- Institut du thorax, INSERM, CNRS, Université Nantes, CHU Nantes, Nantes, France
| | - Fritz-Line Velayoudom
- Department of Endocrinology-Diabetology, University Hospital of Guadeloupe, Pointe-À-Pitre, France
- Inserm UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Lille, France
| | - Bruno Guerci
- Department of Endocrinology, Diabetology, and Nutrition, Brabois Adult Hospital, University of Lorraine, Vandoeuvre-Lès-Nancy, France
| | - Etienne Larger
- University Paris Cité, Institut Cochin, U1016, Inserm, Paris, France
- Diabetology Department, Cochin Hospital, AP-HP, Paris, France
| | - Nathalie Jeandidier
- Department of Endocrinology, Diabetes and Nutrition, Hôpitaux Universitaires de Strasbourg, Université de Strasbourg, Strasbourg, France
| | - Jean-François Gautier
- Institut Necker Enfants Malades, Inserm U1151, CNRS UMR 8253, IMMEDIAB Laboratory, Paris, France
- Centre Universitaire de Diabétologie et de ses Complications, AP-HP, Hôpital Lariboisière, Paris, France
| | - Eric Renard
- Institute of Functional Genomics, University of Montpellier, CNRS, Inserm, Montpellier, France
- Department of Endocrinology, Diabetes, Nutrition, Montpellier University Hospital, Montpellier, France
| | - Louis Potier
- Institut Necker Enfants Malades, Inserm U1151, CNRS UMR 8253, IMMEDIAB Laboratory, Paris, France
- Department of Diabetology, Endocrinology and Nutrition, AP-HP, Bichat Hospital, Paris, France
| | | | - Agnès Sola
- Diabetology Department, Cochin Hospital, AP-HP, Paris, France
| | - Lyse Bordier
- Service d'Endocrinologie, Hôpital Bégin, Saint Mandé, France
| | - Elise Bismuth
- Robert-Debré University Hospital, Department of Paediatric Endocrinology and Diabetology, AP-HP, University of Paris, Paris, France
| | - Gaëtan Prévost
- Department of Endocrinology, Diabetes and Metabolic Diseases, Normandie Université, UNIROUEN, Rouen University Hospital, Centre d'Investigation Clinique (CIC-CRB)-Inserm 1404, Rouen University Hospital, Rouen, France
| | - Laurence Kessler
- Department of Endocrinology, Diabetes and Nutrition, Hôpitaux Universitaires de Strasbourg, Université de Strasbourg, Strasbourg, France
| | - Emmanuel Cosson
- Department of Endocrinology-Diabetology-Nutrition, AP-HP, Avicenne Hospital, Paris 13 University, Sorbonne Paris Cité, CRNH-IdF, CINFO, Bobigny, France
- Equipe de Recherche en Epidémiologie Nutritionnelle (EREN), Université Sorbonne Paris Nord and Université Paris CitéInserm, INRAE, CNAM, Centre of Research in Epidemiology and StatisticS (CRESS), Bobigny, France
| | - Jean-Pierre Riveline
- Institut Necker Enfants Malades, Inserm U1151, CNRS UMR 8253, IMMEDIAB Laboratory, Paris, France
- Centre Universitaire de Diabétologie et de ses Complications, AP-HP, Hôpital Lariboisière, Paris, France
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Lee MH, Vogrin S, Jones TW, O’Neal DN. Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index. J Diabetes Sci Technol 2024; 18:764-770. [PMID: 38372246 PMCID: PMC11307212 DOI: 10.1177/19322968241231307] [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: 02/20/2024]
Abstract
BACKGROUND Glycemia risk index (GRI) is a novel composite metric assessing overall glycemic risk, accounting for both hypoglycemia and hyperglycemia and weighted toward extremes. Data assessing GRI as an outcome measure in closed-loop studies and its relation with conventional key continuous glucose monitoring (CGM) metrics are limited. METHODS A post hoc analysis was performed to evaluate the sensitivity of GRI in assessing glycemic quality in adults with type 1 diabetes randomized to 26 weeks hybrid closed-loop (HCL) or manual insulin delivery (control). The primary outcome was GRI comparing HCL with control. Comparisons were made with changes in other CGM metrics including time in range (TIR), time above range (TAR), time below range (TBR), and glycemic variability (standard deviation [SD] and coefficient of variation [CV]). RESULTS GRI with HCL (N = 61) compared with control (N = 59) was significantly lower (mean [SD] 33.5 [11.7] vs 56.1 [14.4], respectively; mean difference -22.8 [-27.2, -18.3], P = .001). The mean increase in TIR was +14.8 (11.0, 18.5)%. GRI negatively correlated with TIR for combined arms (r = -.954; P = .001), and positively with TAR >250 mg/dL (r = .901; P = .001), TBR < 54 mg/dL (r = .416; P = .001), and glycemic variability (SD [r = .916] and CV [r = .732]; P = .001 for both). CONCLUSIONS Twenty-six weeks of HCL improved GRI, in addition to other CGM metrics, compared with standard insulin therapy. The improvement in GRI was proportionally greater than the change in TIR, and GRI correlated with all CGM metrics. We suggest that GRI may be an appropriate primary outcome for closed-loop trials.
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Affiliation(s)
- Melissa H. Lee
- Department of Medicine, The University
of Melbourne, Melbourne, VIC, Australia
- Department of Endocrinology and
Diabetes, St Vincent’s Hospital Melbourne, Melbourne, VIC, Australia
| | - Sara Vogrin
- Department of Medicine, The University
of Melbourne, Melbourne, VIC, Australia
| | - Timothy W. Jones
- Department of Endocrinology and
Diabetes, Perth Children’s Hospital, Perth, WA, Australia
- Telethon Kids Institute, The University
of Western Australia, Perth, WA, Australia
- School of Paediatrics and Child Health,
The University of Western Australia, Perth, WA, Australia
| | - David N. O’Neal
- Department of Medicine, The University
of Melbourne, Melbourne, VIC, Australia
- Department of Endocrinology and
Diabetes, St Vincent’s Hospital Melbourne, Melbourne, VIC, Australia
- The Australian Centre for Accelerating
Diabetes Innovations, Melbourne, VIC, Australia
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6
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Toschi E, O’Neal D, Munshi M, Jenkins A. Glucose Targets Using Continuous Glucose Monitoring Metrics in Older Adults With Diabetes: Are We There Yet? J Diabetes Sci Technol 2024; 18:808-818. [PMID: 38715259 PMCID: PMC11307211 DOI: 10.1177/19322968241247568] [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: 07/02/2024]
Abstract
The older population is increasing worldwide and up to 30% of older adults have diabetes. Older adults with diabetes are at risk of glucose-related acute and chronic complications. Recently, mostly in type 1 diabetes (T1D), continuous glucose monitoring (CGM) devices have proven beneficial in improving time in range (TIR glucose, 70-180 mg/dL or glucose 3.9-10 mmol/L), glycated hemoglobin (HbA1c), and in lowering hypoglycemia (time below range [TBR] glucose <70 mg/dL or glucose <3.9 mmol/L). The international consensus group formulated CGM glycemic targets relating to older adults with diabetes based on very limited data. Their recommendations, based on expert opinion, were aimed at mitigating hypoglycemia in all older adults. However, older adults with diabetes are a heterogeneous group, ranging from healthy to very complex frail individuals based on chronological, biological, and functional aging. Recent clinical trial and real-world data, mostly from healthy older adults with T1D, demonstrated that older adults often achieve CGM targets, including TIR recommended for non-vulnerable groups, but less often meet the recommended TBR <1%. Existing data also support that hypoglycemia avoidance may be more strongly related to minimization of glucose variability (coefficient of variation [CV]) rather than lower TIR. Very limited data are available for glucose goals in older adults adjusted for the complexity of their health status. Herein, we review the bidirectional associations between glucose and health status in older adults with diabetes; use of diabetes technologies, and their impact on glucose control; discuss current guidelines; and propose a new set of CGM targets for older adults with insulin-treated diabetes that are individualized for health and living status.
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Affiliation(s)
- Elena Toschi
- Joslin Diabetes Center, Harvard Medical
School, Boston, MA, USA
| | - David O’Neal
- Department of Medicine, St Vincent’s
Hospital, The University of Melbourne, Melbourne, VIC, Australia
- Department of Diabetes and
Endocrinology, St Vincent’s Hospital, Melbourne, VIC, Australia
- Australian Centre for Accelerating
Diabetes Innovations, The University of Melbourne, Melbourne, VIC, Australia
| | - Medha Munshi
- Joslin Diabetes Center, Harvard Medical
School, Boston, MA, USA
| | - Alicia Jenkins
- Department of Medicine, St Vincent’s
Hospital, The University of Melbourne, Melbourne, VIC, Australia
- Department of Diabetes and
Endocrinology, St Vincent’s Hospital, Melbourne, VIC, Australia
- Australian Centre for Accelerating
Diabetes Innovations, The University of Melbourne, Melbourne, VIC, Australia
- Baker Heart & Diabetes Institute,
Melbourne, VIC, Australia
- Faculty of Medicine, Monash University,
Melbourne, VIC, Australia
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7
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Castañeda J, Arrieta A, van den Heuvel T, Battelino T, Cohen O. Time in Tight Glucose Range in Type 1 Diabetes: Predictive Factors and Achievable Targets in Real-World Users of the MiniMed 780G System. Diabetes Care 2024; 47:790-797. [PMID: 38113453 PMCID: PMC11043222 DOI: 10.2337/dc23-1581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/13/2023] [Indexed: 12/21/2023]
Abstract
OBJECTIVE We studied time in tight range (TITR) (70-140 mg/dL) in real-world users of the MiniMed 780G system (MM780G). RESEARCH DESIGN AND METHODS CareLink Personal data were extracted (August 2020 to December 2022) to examine TITR and its relationship with time in range (TIR; 70-180 mg/dL), factors predicting higher TITR, and which TITR target is a reasonable treatment goal. RESULTS The 13,461 users (3,762 age ≤15 years and 9,699 age >15 years) showed an average TITR of 48.9% in those age ≤15 years and 48.8% in the older group (vs. TIR 71.2% and 73.9%, respectively). Consistent use of a glucose target (GT) of 100 mg/dL and active insulin time (AIT) of 2 h were the most relevant factors predicting higher TITR (P < 0.0001). In users consistently applying these optimal settings, TITR was 56.7% in those age ≤15 years and 57.0% in the older group, and the relative impact of these settings on TITR was 60% and 86% greater than that on TIR, respectively. TITRs of ∼45% (age ≤15 years 46.3% and older group 45.4%), ∼50% (50.7% and 50.7%) and ∼55% (56.4% and 58.0%) were best associated with glucose management indicators <7.0%, <6.8%, and <6.5%, respectively. TITRs of >45%, >50%, and >55% were achieved in 91%, 74%, and 55% of those age ≤15 years and 93%, 81%, and 57% of older group users, respectively, at optimal settings. CONCLUSIONS This study demonstrates that 1) mean TIR is high with a high mean TITR in MM780G users (>48%), 2) consistent use of optimal GT/AIT improves TITR (>56%), 3) the impact of these settings on TITR is larger than on TIR, and 4) a TITR target >50% is our suggested treatment goal.
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Affiliation(s)
| | - Arcelia Arrieta
- Medtronic International Trading Sàrl, Tolochenaz, Switzerland
| | | | - Tadej Battelino
- University Medical Centre Ljubljana, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Ohad Cohen
- Medtronic International Trading Sàrl, Tolochenaz, Switzerland
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8
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Jacobs PG, Herrero P, Facchinetti A, Vehi J, Kovatchev B, Breton MD, Cinar A, Nikita KS, Doyle FJ, Bondia J, Battelino T, Castle JR, Zarkogianni K, Narayan R, Mosquera-Lopez C. Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities. IEEE Rev Biomed Eng 2024; 17:19-41. [PMID: 37943654 DOI: 10.1109/rbme.2023.3331297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
OBJECTIVE Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. METHODS Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. SIGNIFICANCE These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.
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9
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Karakus KE, Shah VN, Klonoff D, Akturk HK. Changes in the glycaemia risk index and its association with other continuous glucose monitoring metrics after initiation of an automated insulin delivery system in adults with type 1 diabetes. Diabetes Obes Metab 2023; 25:3144-3151. [PMID: 37427768 DOI: 10.1111/dom.15208] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/11/2023]
Abstract
AIM To evaluate the glycaemia risk index (GRI) and its association with other continuous glucose monitoring (CGM) metrics after initiation of an automated insulin delivery (AID) system in patients with type 1 diabetes (T1D). MATERIALS AND METHODS Up to 90 days of CGM data before and after initiation of an AID system from 185 CGM users with T1D were collected. GRI and other CGM metrics were calculated using cgmanalysis R software and were analysed for 24 hours, for both night-time and daytime. GRI values were assigned to five GRI zones: zone A (0-20), B (21-40), C (41-60), D (61-80) and E (81-100). RESULTS Compared with baseline, GRI and its components decreased significantly after AID initiation (GRI: 48.7 ± 21.8 vs. 29 ± 13; hypoglycaemia component: 2.7 ± 2.8 vs. 1.6 ± 1.7; hyperglycaemia component: 25.3 ± 14.5 vs. 15 ± 8.5; P < .001 for all). The GRI was inversely correlated with time in range before (r = -0.962) and after (r = -0.961) AID initiation (P < .001 for both). GRI was correlated with time above range (before: r = 0.906; after = 0.910; P < .001 for both), but not with time below range (P > .05). All CGM metrics improved after AID initiation during 24 hours, for both daytime and night-time (P < .001 for all). Metrics improved significantly more during night-time than daytime (P < .01). CONCLUSIONS GRI was highly correlated with various CGM metrics above, but not below target range, both before and after AID initiation.
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Affiliation(s)
- Kagan E Karakus
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, Colorado, USA
- School of Medicine, Koç University, Istanbul, Turkey
| | - Viral N Shah
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, Colorado, USA
| | - David Klonoff
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, California, USA
| | - Halis K Akturk
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, Colorado, USA
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10
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Huang J, Yeung AM, DuBord AY, Wolpert H, Jacobs PG, Lee WA, Drincic A, Spanakis EK, Sherr JL, Prahalad P, Fleming A, Hsiao VC, Kompala T, Lal RA, Fayfman M, Ginsberg BH, Galindo RJ, Stuhr A, Chase JG, Najafi B, Masharani U, Seley JJ, Klonoff DC. Diabetes Technology Meeting 2022. J Diabetes Sci Technol 2023; 17:1085-1120. [PMID: 36704821 PMCID: PMC10347991 DOI: 10.1177/19322968221148743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 3 to November 5, 2022. Meeting topics included (1) the measurement of glucose, insulin, and ketones; (2) virtual diabetes care; (3) metrics for managing diabetes and predicting outcomes; (4) integration of continuous glucose monitor data into the electronic health record; (5) regulation of diabetes technology; (6) digital health to nudge behavior; (7) estimating carbohydrates; (8) fully automated insulin delivery systems; (9) hypoglycemia; (10) novel insulins; (11) insulin delivery; (12) on-body sensors; (13) continuous glucose monitoring; (14) diabetic foot ulcers; (15) the environmental impact of diabetes technology; and (16) spinal cord stimulation for painful diabetic neuropathy. A live demonstration of a device that can allow for the recycling of used insulin pens was also presented.
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Affiliation(s)
| | | | | | | | - Peter G. Jacobs
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Wei-An Lee
- Los Angeles County+University of Southern California Medical Center, Los Angeles, CA, USA
| | | | - Elias K. Spanakis
- Baltimore Veterans Affairs Medical Center, Baltimore, MD, USA
- Division of Endocrinology, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | | | | | - Tejaswi Kompala
- University of California, San Francisco, San Francisco, CA, USA
- Teladoc Health, Purchase, NY, USA
| | | | - Maya Fayfman
- Emory University School of Medicine, Atlanta, GA, USA
| | | | | | | | | | | | - Umesh Masharani
- University of California, San Francisco, San Francisco, CA, USA
| | | | - David C. Klonoff
- Diabetes Technology Society, Burlingame, CA, USA
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA
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11
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Castañeda J, Arrieta A, van den Heuvel T, Cohen O. The significance of coefficient of variation as a measure of hypoglycaemia risk and glycaemic control in real world users of the automated insulin delivery MiniMed 780G system. Diabetes Obes Metab 2023. [PMID: 37246797 DOI: 10.1111/dom.15139] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/30/2023]
Abstract
AIM Use of the MiniMed 780G system (MM780G) can result in a reduction in mean and standard deviation (SD) of sensor glucose (SG) values. We assessed the significance of the coefficient of variation (CV) as a measure of hypoglycaemia risk and glycaemic control. MATERIALS AND METHODS Data from 10 404 MM780G users were analysed using multivariable logistic regression to assess the contribution of CV to (a) hypoglycaemia risk, measured as not reaching target <1% for time below range (TBR), and (b) achieving targets of time-in-range (TIR) >70% and glucose management indicator <7%. CV was compared with SD and low blood glucose index. To assess the relevance of CV <36% as a therapeutic threshold, we identified the CV cut-off point that optimally discriminated users at risk of hypoglycaemia. RESULTS The contribution of CV was the smallest in terms of risk of hypoglycaemia (vs. low blood glucose index and SD) and TIR and glucose management indicator targets (vs. SD). In all cases the models with SD showed the best fit. A CV <43.4% (95% CI: 42.9-43.9) was the optimal cut-off point with a correct classification rate of 87.2% (vs. 72.9% for CV <36%). CONCLUSION For MM780G users, CV is a poor marker for hypoglycaemia risk and glycaemic control. We recommend using, for the former, TBR and whether the TBR target is met (and not using CV <36% as a therapeutic threshold for hypoglycaemia); for the latter, TIR, time above range, whether targets are met and a discrete description of mean SG and SD of SG values.
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Affiliation(s)
| | - Arcelia Arrieta
- Medtronic Bakken Research Center, Maastricht, The Netherlands
| | | | - Ohad Cohen
- Medtronic International Trading Sàrl, Tolochenaz, Switzerland
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12
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Abstract
AIMS The aim was to investigate rebound hypoglycemic and hyperglycemic events, and describe their relation to other glycemic metrics. METHODS Data from intermittently scanned continuous glucose monitoring were downloaded for 90 days for 159 persons with type 1 diabetes. A hypoglycemic event was defined as glucose <3.9 mmol/l for at least two 15-minute periods. Rebound hypoglycemia (Rhypo) was a hypoglycemic event preceded by glucose >10.0 mmol/l within 120 minutes and rebound hyperglycemia (Rhyper) was hypoglycemia followed by glucose >10.0 mmol/l within 120 minutes. RESULTS A total of 10 977 hypoglycemic events were identified of which 3232 (29%) were Rhypo and 3653 (33%) were Rhyper, corresponding to a median frequency of 10.1, 2.5, and 3.0 events per person/14 days. For 1267 (12%) of the cases, Rhypo and Rhyper coexisted. The mean peak glucose was 13.0 ± 1.6 mmol/l before Rhypo; 12.8 ± 1.1 mmol/l in Rhyper. The frequency of Rhyper was significantly (P < .001) correlated with Rhypo (Spearman's rho 0.84), glucose coefficient of variation (0.78), and time below range (0.69) but not with time above range (0.12, P = .13). CONCLUSIONS The strong correlation between Rhyper and Rhypo suggests an individual behavioral characteristic toward intensive correction of glucose excursions.
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Affiliation(s)
- Klavs W Hansen
- University Research Clinic for Innovative Patient Pathways, Diagnostic Centre, Silkeborg Regional Hospital, Silkeborg, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus N, Denmark
| | - Bo M Bibby
- Biostatistical Advisory Service, Faculty of Health, Aarhus University, Aarhus N, Denmark
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13
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Kovatchev BP, Lobo B. Clinically-Similar Clusters of Daily CGM Profiles: Tracking the Progression of Glycemic Control Over Time. Diabetes Technol Ther 2023. [PMID: 37130300 DOI: 10.1089/dia.2023.0117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
BACKGROUND The adoption of CGM results in vast amounts of data, but their interpretation is still more art than exact science. The International Consensus on Time in Range (TIR) proposed the widely accepted TIR system of metrics, which we now take forward by introducing a finite and fixed set of clinically-similar clusters (CSCs), such that the TIR metrics of the daily CGM profiles within a cluster are homogeneous. METHODS CSC definition and validation used 204,710 daily CGM profiles in health, type 1 and type 2 diabetes (T1D, T2D), on different treatments. The CSCs were defined using 23,916 daily CGM profiles (Training set), and the final fixed set of CSCs was obtained using another 37,758 profiles (Validation set). The Testing set (143,036 profiles) was used to establish the robustness and generalizability of the CSCs. RESULTS The final set of CSCs contains 32 clusters. Any daily CGM profile was classifiable to a single CSC which approximated common glycemic metrics of the daily CGM profile, as evidenced by regression analyses with 0 intercept (R-squares≥0.83, e.g., correlation≥0.91), for all TIR and several other metrics. The CSCs distinguished CGM profiles in health, T2D, and T1D on different treatments, and allowed tracking of the daily changes in a person's glycemic control over time. CONCLUSION Daily CGM profiles can be classified into one of 32 prefixed CSCs, which enables a host of applications, e.g. tabulated data interpretation and algorithmic approaches to treatment, database indexing, pattern recognition, and tracking disease progression.
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Affiliation(s)
- Boris P Kovatchev
- University of Virginia, 2358, Center for Diabetes Technology, Charlottesville, Virginia, United States;
| | - Benjamin Lobo
- University of Virginia, 2358, School of Data Science, Charlottesville, Virginia, United States;
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14
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Donaldson LE, Vogrin S, So M, Ward GM, Krishnamurthy B, Sundararajan V, MacIsaac RJ, Kay TW, McAuley SA. Continuous glucose monitoring-based composite metrics: a review and assessment of performance in recent-onset and long-duration type 1 diabetes. Diabetes Technol Ther 2023. [PMID: 37010375 DOI: 10.1089/dia.2022.0563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
Abstract
This study examined correlations between continuous glucose monitoring (CGM)-based composite metrics and standard glucose metrics within CGM data sets from individuals with recent-onset and long-duration type 1 diabetes. First, a literature review and critique of published CGM-based composite metrics was undertaken. Second, composite metric results were calculated for the two CGM data sets and correlations with six standard glucose metrics were examined. Fourteen composite metrics met selection criteria; these metrics focused on overall glycemia (n = 8), glycemic variability (n = 4), and hypoglycemia (n = 2), respectively. Results for the two diabetes cohorts were similar. All eight metrics focusing on overall glycemia strongly correlated with glucose time in range; none strongly correlated with time below range. The eight overall glycemia-focused and two hypoglycemia-focused composite metrics were all sensitive to automated insulin delivery therapeutic intervention. Until a composite metric can adequately capture both achieved target glycemia and hypoglycemia burden, the current two-dimensional CGM assessment approach may offer greatest clinical utility.
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Affiliation(s)
- Laura E Donaldson
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia
- St Vincent's Hospital Melbourne Pty Ltd, 60078, Department of Endocrinology & Diabetes, Melbourne, Victoria, Australia;
| | - Sara Vogrin
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia;
| | - Michelle So
- St Vincent's Institute of Medical Research, 85092, Melbourne, Victoria, Australia
- The Royal Melbourne Hospital, 90134, Department of Diabetes and Endocrinology, Parkville, Victoria, Australia
- Northern Health NCHER, 569275, Department of Endocrinology and Diabetes, Melbourne, Victoria, Australia;
| | - Glenn M Ward
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia
- St Vincent's Hospital Melbourne Pty Ltd, 60078, Department of Endocrinology & Diabetes, Melbourne, Victoria, Australia;
| | - Balasubramanian Krishnamurthy
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia
- St Vincent's Institute of Medical Research, 85092, Melbourne, Victoria, Australia;
| | - Vijaya Sundararajan
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia;
| | - Richard J MacIsaac
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia
- St Vincent's Hospital Melbourne Pty Ltd, 60078, Department of Endocrinology & Diabetes, Melbourne, Victoria, Australia;
| | - Thomas Wh Kay
- St Vincent's Institute of Medical Research, 85092, Melbourne, Victoria, Australia;
| | - Sybil A McAuley
- The University of Melbourne, 2281, Department of Medicine, Melbourne, Victoria, Australia
- St Vincent's Hospital Melbourne Pty Ltd, 60078, Department of Endocrinology & Diabetes, Melbourne, Victoria, Australia;
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15
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Michalek DA, Onengut-Gumuscu S, Repaske DR, Rich SS. Precision Medicine in Type 1 Diabetes. J Indian Inst Sci 2023; 103:335-351. [PMID: 37538198 PMCID: PMC10393845 DOI: 10.1007/s41745-023-00356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/04/2023] [Indexed: 03/09/2023]
Abstract
Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a "staging" model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.
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Affiliation(s)
- Dominika A. Michalek
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA USA
| | - David R. Repaske
- Division of Endocrinology, Department of Pediatrics, University of Virginia, Charlottesville, VA USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA USA
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16
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O'Neal DN, Cohen O, Vogrin S, Vigersky RA, Jenkins AJ. An Assessment of Clinical Continuous Glucose Monitoring Targets for Older and High-Risk People Living with Type 1 Diabetes. Diabetes Technol Ther 2023; 25:108-115. [PMID: 36315189 DOI: 10.1089/dia.2022.0350] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Aim: To assess relationships between continuous glucose monitoring (CGM) time in range (TIR), 70-180 mg/dL, time below range (TBR), <70 mg/dL, time above range (TAR), >180 mg/dL, and glucose coefficient of variation (CV) in relation to currently recommended clinical CGM targets for older people, which recommend reduced TIR and TBR targets relative to the general type 1 diabetes population. Methods: We conducted a post hoc analysis using the JDRF Australia Adult Hybrid Closed Loop trial database examining correlations in 120 adults with type 1 diabetes of 3 weeks masked CGM (Guardian Sensor 3; Medtronic) metrics (n = 61 on multiple daily injections, 59 on non-CGM augmented pumps) using manual insulin dosing at baseline and at 26-weeks, with 50% randomized to automated insulin dosing (AID). Results: Correlations between baseline TIR and TAR were strong (r = -0.966; P < 0.0001), weak for TBR (r = 0.363; P < 0.0001), and glucose CV (r = 0.037; P = 0.687) while moderate between CV and TBR (r = 0.726; P < 0.0001). Associations were similar for participants aged >60 years (n = 15) versus younger subjects. Correlations of changes in (Δ) TIR with ΔTAR over 26 weeks were strong (r = -0.945; P < 0.001) and correlations for ΔTBR were weak (r = 0.025; P = 0.802). ΔCV did not significantly correlate with ΔTAR (r = -0.064; P = 0.526) but did with ΔTBR (r = 0.770; P = <0.001). Conclusions: Changes in TIR are not associated with changes in TBR. Thus, we recommend that for older AID users whilst TBR targets should be prioritized to reduce hypoglycemia-related risk, TBR should be addressed independently of TIR. Clinical Trial Registratrion number: (ACTRN12617000520336).
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Affiliation(s)
- David N O'Neal
- Department of Medicine, University of Melbourne, Parkville, Australia
- Department of Endocrinology, St. Vincent's Hospital Melbourne, Fitzroy, Australia
- The Australian Centre for Accelerating Diabetes Innovations, Parkville, Australia
| | - Ohad Cohen
- Institute of Endocrinology, Ch. Sheba Medical Center, Tel-Aviv, Israel
| | - Sara Vogrin
- Department of Medicine, University of Melbourne, Parkville, Australia
| | - Robert A Vigersky
- Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Alicia J Jenkins
- Department of Medicine, University of Melbourne, Parkville, Australia
- Department of Endocrinology, St. Vincent's Hospital Melbourne, Fitzroy, Australia
- The Australian Centre for Accelerating Diabetes Innovations, Parkville, Australia
- NHMRC Clinical Trials Centre, University of Sydney, Camperdown, Australia
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17
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Battelino T, Alexander CM, Amiel SA, Arreaza-Rubin G, Beck RW, Bergenstal RM, Buckingham BA, Carroll J, Ceriello A, Chow E, Choudhary P, Close K, Danne T, Dutta S, Gabbay R, Garg S, Heverly J, Hirsch IB, Kader T, Kenney J, Kovatchev B, Laffel L, Maahs D, Mathieu C, Mauricio D, Nimri R, Nishimura R, Scharf M, Del Prato S, Renard E, Rosenstock J, Saboo B, Ueki K, Umpierrez GE, Weinzimer SA, Phillip M. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol 2023; 11:42-57. [PMID: 36493795 DOI: 10.1016/s2213-8587(22)00319-9] [Citation(s) in RCA: 197] [Impact Index Per Article: 197.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 12/12/2022]
Abstract
Randomised controlled trials and other prospective clinical studies for novel medical interventions in people with diabetes have traditionally reported HbA1c as the measure of average blood glucose levels for the 3 months preceding the HbA1c test date. The use of this measure highlights the long-established correlation between HbA1c and relative risk of diabetes complications; the change in the measure, before and after the therapeutic intervention, is used by regulators for the approval of medications for diabetes. However, with the increasing use of continuous glucose monitoring (CGM) in clinical practice, prospective clinical studies are also increasingly using CGM devices to collect data and evaluate glucose profiles among study participants, complementing HbA1c findings, and further assess the effects of therapeutic interventions on HbA1c. Data is collected by CGM devices at 1-5 min intervals, which obtains data on glycaemic excursions and periods of asymptomatic hypoglycaemia or hyperglycaemia (ie, details of glycaemic control that are not provided by HbA1c concentrations alone that are measured continuously and can be analysed in daily, weekly, or monthly timeframes). These CGM-derived metrics are the subject of standardised, internationally agreed reporting formats and should, therefore, be considered for use in all clinical studies in diabetes. The purpose of this consensus statement is to recommend the ways CGM data might be used in prospective clinical studies, either as a specified study endpoint or as supportive complementary glucose metrics, to provide clinical information that can be considered by investigators, regulators, companies, clinicians, and individuals with diabetes who are stakeholders in trial outcomes. In this consensus statement, we provide recommendations on how to optimise CGM-derived glucose data collection in clinical studies, including the specific glucose metrics and specific glucose metrics that should be evaluated. These recommendations have been endorsed by the American Association of Clinical Endocrinologists, the American Diabetes Association, the Association of Diabetes Care and Education Specialists, DiabetesIndia, the European Association for the Study of Diabetes, the International Society for Pediatric and Adolescent Diabetes, the Japanese Diabetes Society, and the Juvenile Diabetes Research Foundation. A standardised approach to CGM data collection and reporting in clinical trials will encourage the use of these metrics and enhance the interpretability of CGM data, which could provide useful information other than HbA1c for informing therapeutic and treatment decisions, particularly related to hypoglycaemia, postprandial hyperglycaemia, and glucose variability.
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Affiliation(s)
- Tadej Battelino
- Department of Pediatric Endocrinology, Diabetes and Metabolism, University Children's Hospital, University Medical Centre Ljubljana, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
| | | | | | - Guillermo Arreaza-Rubin
- Division of Diabetes, Endocrinology and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Roy W Beck
- Jaeb Center for Health Research, Tampa, FL, USA
| | | | - Bruce A Buckingham
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford Medical Center, Stanford, CA, USA
| | | | | | - Elaine Chow
- Phase 1 Clinical Trial Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Pratik Choudhary
- Leicester Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Kelly Close
- diaTribe Foundation, San Francisco, CA, USA; Close Concerns, San Francisco, CA, USA
| | - Thomas Danne
- Diabetes Centre for Children and Adolescents, Auf der Bult, Hanover, Germany
| | | | - Robert Gabbay
- American Diabetes Association, Arlington, VA, USA; Harvard Medical School, Harvard University, Boston, MA, USA
| | - Satish Garg
- Barbara Davis Centre for Diabetes, University of Colorado Denver, Aurora, CO, USA
| | | | - Irl B Hirsch
- Division of Metabolism, Endocrinology and Nutrition, University of Washington School of Medicine, University of Washington, Seattle, WA, USA
| | - Tina Kader
- Jewish General Hospital, Montreal, QC, Canada
| | | | - Boris Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Lori Laffel
- Pediatric, Adolescent and Young Adult Section, Joslin Diabetes Center, Harvard Medical School, Harvard University, Boston, MA, USA
| | - David Maahs
- Department of Pediatrics, Stanford Diabetes Research Center, Stanford, CA, USA
| | - Chantal Mathieu
- Clinical and Experimental Endocrinology, KU Leuven, Leuven, Belgium
| | - Dídac Mauricio
- Department of Endocrinology and Nutrition, CIBERDEM (Instituto de Salud Carlos III), Hospital de la Santa Creu i Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Revital Nimri
- National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
| | - Rimei Nishimura
- The Jikei University School of Medicine, Jikei University, Tokyo, Japan
| | - Mauro Scharf
- Centro de Diabetes Curitiba and Division of Pediatric Endocrinology, Hospital Nossa Senhora das Graças, Curitiba, Brazil
| | - Stefano Del Prato
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Eric Renard
- Department of Endocrinology, Diabetes and Nutrition, Montpellier University Hospital, Montpellier, France; Institute of Functional Genomics, University of Montpellier, Montpellier, France; INSERM Clinical Investigation Centre, Montpellier, France
| | - Julio Rosenstock
- Velocity Clinical Research, Medical City, Dallas, TX; University of Texas Southwestern Medical Center, University of Texas, Dallas, TX, USA
| | - Banshi Saboo
- Dia Care, Diabetes Care and Hormone Clinic, Ahmedabad, India
| | - Kohjiro Ueki
- Diabetes Research Center, National Center for Global Health and Medicine, Tokyo, Japan
| | | | - Stuart A Weinzimer
- Department of Pediatrics, Yale University School of Medicine, Yale University, New Haven, CT, USA
| | - Moshe Phillip
- National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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