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Six-Month Randomized, Multicenter Trial of Closed-Loop Control in Type 1 Diabetes
Open accessAbstract
BACKGROUND: Closed-loop systems that automate insulin delivery may improve glycemic outcomes in patients with type 1 diabetes. METHODS: In this 6-month randomized, multicenter trial, patients with type 1 diabetes were assigned in a 2:1 ratio to receive treatment with a closed-loop system (closed-loop group) or a sensor-augmented pump (control group). The primary outcome was the percentage of time that the blood glucose level was within the target range of 70 to 180 mg per deciliter (3.9 to 10.0 mmol per liter), as measured by continuous glucose monitoring. RESULTS: A total of 168 patients underwent randomization; 112 were assigned to the closed-loop group, and 56 were assigned to the control group. The age range of the patients was 14 to 71 years, and the glycated hemoglobin level ranged from 5.4 to 10.6%. All 168 patients completed the trial. The mean (±SD) percentage of time that the glucose level was within the target range increased in the closed-loop group from 61±17% at baseline to 71±12% during the 6 months and remained unchanged at 59±14% in the control group (mean adjusted difference, 11 percentage points; 95% confidence interval [CI], 9 to 14; P<0.001). The results with regard to the main secondary outcomes (percentage of time that the glucose level was >180 mg per deciliter, mean glucose level, glycated hemoglobin level, and percentage of time that the glucose level was <70 mg per deciliter or <54 mg per deciliter [3.0 mmol per liter]) all met the prespecified hierarchical criterion for significance, favoring the closed-loop system. The mean difference (closed loop minus control) in the percentage of time that the blood glucose level was lower than 70 mg per deciliter was -0.88 percentage points (95% CI, -1.19 to -0.57; P<0.001). The mean adjusted difference in glycated hemoglobin level after 6 months was -0.33 percentage points (95% CI, -0.53 to -0.13; P = 0.001). In the closed-loop group, the median percentage of time that the system was in closed-loop mode was 90% over 6 months. No serious hypoglycemic events occurred in either group; one episode of diabetic ketoacidosis occurred in the closed-loop group. CONCLUSIONS: In this 6-month trial involving patients with type 1 diabetes, the use of a closed-loop system was associated with a greater percentage of time spent in a target glycemic range than the use of a sensor-augmented insulin pump. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases; iDCL ClinicalTrials.gov number, NCT03563313.).
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“The largest trial with this class in T1D was with liraglutide, which showed a reduction of A1C of 0.4% with the highest 1.8 mg dose in addition to a 5 kg weight loss and insulin dose reductions.1177”
From American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update. (2022) · cites this paper as [1177]
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“Moreover, the introduction of CGM devices that alarm with low blood glucose concentrations and semiautomated pumps that suspend insulin infusion as glucose concentrations decrease have facilitated achieving target HbA 1c levels with less risk for hypoglycemia (332).”
From Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus (2023) · cites this paper as [332]
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In the citing paper
“Hybrid closedloop systems are the most effective means of maintaining glucose in the normal range in people with type 1 diabetes (90,91).”
From The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) (2021) · cites this paper as [91]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“The use of insulin analog regimens are associated with less hypoglycemia, while hybrid closed-loop systems result in both improvement in TIR and reduction in TBR (91).”
From The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) (2021) · cites this paper as [91]
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“These prestigious publications reported pivotal trials of the commercial descendant of DiAs-a new AID system (Control-IQ by Tandem Diabetes Care)-in different age groups: 14 years old and older, 132 6 to 13 years old, 133 and 2 to 6 years old.134”
From Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility (2023) · cites this paper as [132]
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“This large-scale study and its continuation with young children ages 2 to 6 years old completed several clinical protocols, three of which were published in the New England Journal of Medicine.[132][133][134]”
From Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility (2023) · cites this paper as [132]
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“In particular, glucose time in range (70 -180 mg/dl [3.9 -10.0 mmol/l]) and time in hypoglycemia ( < 70 mg/dl [3.9 mmol/l] and < 54 mg/dl [3.0 mmol/l]) have been used as outcomes for clinical trials 259,260 and have been endorsed as appropriate metrics for clinical care. 229”
From KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease (2022) · cites this paper as [260]
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“Evidence suggests that an AID hybrid closed-loop system is superior to AID sensor-augmented pump therapy for increased percentage of time in range and reduction of hypoglycemia (34,35).”
From 9. Pharmacologic Approaches to Glycemic Treatment: Standards of Care in Diabetes—2025 (2025) · cites this paper as [34]
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“HbA 1c , TIR, and TBR are improved further when physiological MDI or pump regimens are augmented with CGM usage (89), with the greatest benefits seen with algorithm-driven automated basal (and in some systems correction) insulin delivery, which is commonly called hybrid closed-loop therapy (90,91).”
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“Although this agent was more effective with those participants still producing endogenous insulin, the added hypoglycemia and ketosis noted has resulted in no attempt for FDA approval in T1D.”
From American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update. (2022) · cites this paper as [1177]
Experimental — accuracy not yet measuredIn the citing paper
“all AID system pivotal trials that examined individuals 2 years old or older, including older adults, have consistently demonstrated superiority to either standard insulin delivery (or manual mode for the single-arm studies) or SAP and/or usual care (for the randomized trials)”
From 7. Diabetes Technology: Standards of Care in Diabetes—2025 (2025) · cites this paper as [208]
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“with consistent improvement in A1C, increase in TIR, especially overnight, as well as reduction of time spent in hypoglycemia (207-219).”
From 7. Diabetes Technology: Standards of Care in Diabetes—2025 (2025) · cites this paper as [208]
In the citing paper
“with consistent improvement in A1C, increase in TIR, especially overnight, as well as reduction of time spent in hypoglycemia (207-219).”
From 7. Diabetes Technology: Standards of Care in Diabetes—2025 (2025) · cites this paper as [208]
In the citing paper
“with consistent improvement in A1C, increase in TIR, especially overnight, as well as reduction of time spent in hypoglycemia (207-219).”
From 7. Diabetes Technology: Standards of Care in Diabetes—2025 (2025) · cites this paper as [208]
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Cites
(1993) · New England Journal of Medicine · doi:10.1056/nejm199309303291401 · Cited by 24,667
State of Type 1 Diabetes Management and Outcomes from the T1D Exchange in 2016–2018
Foster, N. C., Beck, R. W., Miller, K. M., Clements, M. A., Rickels, M. R., DiMeglio, L. A., Maahs, D. M., Tamborlane, W. V., Bergenstal, R., Smith, E., et al. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0384 · Cited by 1,907
Self-citation6. Glycemic Targets: Standards of Medical Care in Diabetes—2019
American Diabetes Association (2019) · Diabetes Care · doi:10.2337/dc19-s006 · Cited by 655
Safety of a hybrid closed-loop insulin delivery system in patients with type 1 diabetes
Bergenstal, R. M., Garg, S., Weinzimer, S. A., Buckingham, B. A., Bode, B. W., Tamborlane, W. V., & Kaufman, F. R. (2016) · JAMA · doi:10.1001/jama.2016.11708 · Cited by 644
Self-citationWeisman, A., Bai, J.-W., Cardinez, M., Kramer, C. K., & Perkins, B. A. (2017) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(17)30167-5 · Cited by 463
Tauschmann, M., Thabit, H., Bally, L., Allen, J. M., Hartnell, S., Wilinska, M. E., Ruan, Y., Sibayan, J., Kollman, C., Cheng, P., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31947-0 · Cited by 440
Self-citationBekiari, E., Kitsios, K., Thabit, H., Tauschmann, M., Athanasiadou, E., Karagiannis, T., Haidich, A.-B., Hovorka, R., & Tsapas, A. (2018) · BMJ · doi:10.1136/bmj.k1310 · Cited by 437
Kleinman, L. C., & Norton, E. C. (2008) · Health Services Research · doi:10.1111/j.1475-6773.2008.00900.x · Cited by 287
Forlenza, G. P., Li, Z., Buckingham, B. A., Pinsker, J. E., Cengiz, E., Wadwa, R. P., Ekhlaspour, L., Church, M. M., Weinzimer, S. A., Jost, E., et al. (2018) · Diabetes Care · doi:10.2337/dc18-0771 · Cited by 246
Self-citationBenhamou, P.-Y., Franc, S., Reznik, Y., Thivolet, C., Schaepelynck, P., Renard, E., Guerci, B., Chaillous, L., Lukas-Croisier, C., Jeandidier, N., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30003-2 · Cited by 217
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Hallström, S., Wijkman, M. O., Ludvigsson, J., Ekman, P., Pfeffer, M. A., Wedel, H., Rosengren, A., & Lind, M. (2022) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2022.100469 · Cited by 30
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Lehmann, V. F., Züger, T., Zeder, A., Scott, S. N., Bally, L., Laimer, M. W., & Stettler, C. (2020) · Diabetes Care · doi:10.2337/dc20-1560 · Cited by 26
Malone, S. K., Peleckis, A. J., Grunin, L., Yu, G., Jang, S., Weimer, J. E., Lee, I., Rickels, M. R., & Goel, N. (2021) · Journal of Diabetes Research · doi:10.1155/2021/6611064 · Cited by 25
Liu, J. J., Spakowicz, D. J., Ash, G. I., Hoyd, R., Ahluwalia, R., Zhang, A., Lou, S., Lee, D., Zhang, J., Presley, C. J., et al. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009303 · Cited by 26
Recent advances in closed-loop insulin delivery
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Mewes, D., Wäldchen, M., Knoll, C., Raile, K., & Braune, K. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221080199 · Cited by 25
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Pasqua, M., Jafar, A., Kobayati, A., Tsoukas, M. A., & Haidar, A. (2022) · Diabetes Care · doi:10.2337/dc22-0490 · Cited by 25
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Alotaibi, A., Khalifah, R. A. A., & McAssey, K. (2020) · Pediatric Diabetes · doi:10.1111/pedi.13088 · Cited by 27
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Liu, W., Zhang, D., Wang, R., Chen, J., Zhang, J., Tao, D., & Liu, C. (2023) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15349 · Cited by 24
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Nallicheri, A., Close Concerns, S. F., Mahoney, K. M., Close Concerns, S. F., Gutow, H. A., Close Concerns, S. F., Bellini, N. J., & Isaacs, D. M. (2022) · touchREVIEWS in Endocrinology · doi:10.17925/ee.2022.18.1.27 · Cited by 24
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Pinsker, J. E., LEAS, S. A., Müller, L., & HABIF, S. (2020) · Endocrine Practice · doi:10.4158/ep-2019-0554 · Cited by 23
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Self-citation
Metadata
- Authors
- Brown, S. A., Kovatchev, B. P., Raghinaru, D., Lum, J. W., Buckingham, B. A., Kudva, Y. C., Laffel, L. M., Levy, C. J., Pinsker, J. E., Wadwa, R. P., Dassau, E., Doyle, F. J., Anderson, S. M., Church, M. M., Dadlani, V., Ekhlaspour, L., Forlenza, G. P., Isganaitis, E., Lam, D. W., Kollman, C., & Beck, R. W.
- Affiliations
- Sansum Diabetes Research Institute (US)
- Mayo Clinic (US)
- Joslin Diabetes Center (US)
- Harvard University (US)
- ORCID
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2019-10-31
- Volume
- 381
- Issue
- 18
- Pages
- 1707-1717
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC7076915
- OpenAlex
- W2981310284
- Semantic Scholar
- a8aee7f3c717498afd65ea39bd87dd1c1ea5d693
- Cited by
- 1,030
- References
- 17
- Citations by year
- 2026: 128 · 2025: 134 · 2024: 140 · 2023: 180 · 2022: 158 · 2021: 156 · 2020: 125 · 2019: 8
- FWCI
- 90.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (UC4 DK108483)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Keywords
- type 1 diabetes, artificial pancreas, sensor-augmented pump, continuous glucose monitoring, glycemic control, HbA1c, hypoglycemia, hyperglycemia, insulin pump therapy, glycemic variability, HbA1c reduction, hypoglycemia risk reduction, HbA1c thresholds, hypoglycemia risk
- MeSH terms
- Adolescent, Adult, Aged, Blood Glucose, Diabetes Mellitus, Type 1, Equipment Design, Female, Glycated Hemoglobin, Humans, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Male, Middle Aged, Pancreas, Artificial, Young Adult
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