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International Consensus on Use of Continuous Glucose Monitoring
Open accessAbstract
Measurement of glycated hemoglobin (HbA1c) has been the traditional method for assessing glycemic control. However, it does not reflect intra- and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA1c testing and self-monitoring of blood glucose. Although both provide the means to move beyond the HbA1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to the most appropriate glucose monitoring methodologies, as well as standardized advice about how best to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.
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“In many countries, the benefits and utility of CGM are now recognized by national and international medical organizations for individuals with insulin-requiring diabetes and/or those at risk for hypoglycemia (16-21).”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [18]
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“Correlations of fructosamine with HbA1c and mean blood glucose were examined in 4 observational studies.”
From KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease (2022) · cites this paper as [198]
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“The list of core CGM metrics has now been streamlined for use in clinical practice based on the expert opinion of this International Consensus Group (18).”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [18]
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“This recommendation was later endorsed at the aforementioned international consensus conference on CGM metrics (18) and is referenced as an example in the American Diabetes Association (ADA) 2019 Standards of Care (16) and in an update to the American Association of Clinical Endocrinologists (AACE) consensus on use of CGM (69).”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [18]
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“CGM helps characterize the timing, amplitude, and frequency of excursions [133–139], and provides quantitative metrics such as TIR, time in hypoglycemia (TBR) and hyperglycemia (TAR), and indices of glycemic variability, which are increasingly used as clinically meaningful targets in diabetes management [140–144].”
From Diabetic gastroparesis: pathophysiology and impact on insulin timing choices (2026) · cites this paper as [133]
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“In this context, CGM plays a central role by enabling visualization of glycemic trends and revealing patterns that are missed with capillary glucose testing [132, 133].”
From Diabetic gastroparesis: pathophysiology and impact on insulin timing choices (2026) · cites this paper as [133]
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“Our findings support the recent consensus conference recommendation (12, 13) that sensor glucose values ,54 mg/dL represent a more meaningful cut-off than 70 mg/dL to define clinically important hypoglycemia because CGM glucose levels below 54 mg/dL were uncommon, whereas most participants had some glucose levels of 55 to 69 mg/dL.”
From Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study (2019) · cites this paper as [12]
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“The current consensus conferences have somewhat arbitrarily set time in ranges for CGM sensor glucose levels for patients with T1D, with time in target identified as being between 70 to 180 mg/dL (12,13).”
From Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study (2019) · cites this paper as [12]
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“Core glucose metrics determined by CGM, including TIR and GMI, provide more precise real-time and retrospective information about glycemic control.34”
From Glucose time in range and peripheral neuropathy in type 2 diabetes mellitus and chronic kidney disease (2020) · cites this paper as [34]
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Hermanns, N., Ehrmann, D., Heinemann, L., Freckmann, G., Waldenmaier, D., & Calhoun, P. M. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0130 · Cited by 18
Self-citationGriggs, S. A., Pignatiello, G. A., & Hickman, R. L. (2022) · Journal of Sleep Research · doi:10.1111/jsr.13784 · Cited by 17
Edge-Based Temporal Fusion Transformer for Multi-Horizon Blood Glucose Prediction
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Bergenstal, R. M. (2023) · Diabetes Spectrum · doi:10.2337/dsi23-0005 · Cited by 19
Self-citationReinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes
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Bergenstal, R. M., Ásbjörnsdóttir, B., Watt, S. K., Lingvay, I., Mader, J. K., Nishida, T., & Rosenstock, J. (2024) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(24)00220-1 · Cited by 18
Self-citationBermingham, K. M., Smith, H., Duncan, E. L., Gonzalez, J. T., Valdes, A. M., Franks, P. W., Delahanty, L. M., Dashti, H. S., Davies, R., Hadjigeorgiou, G. M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-70308-3 · Cited by 15
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Demir, G., Özen, S., Çetin, H., Darcan, Ş., & Gökşen, D. (2019) · Journal of Clinical Research in Pediatric Endocrinology · doi:10.4274/jcrpe.galenos.2019.2019.0009 · Cited by 17
Ekhlaspour, L., Tabatabai, I., & Buckingham, B. A. (2019) · Journal of Diabetes Science and Technology · doi:10.1177/1932296819851790 · Cited by 16
Self-citationSenior, P. A., Lam, A. P., Farnsworth, K., Perkins, B. A., & RABASA-LHORET, R. P. R. (2020) · Current Diabetes Reports · doi:10.1007/s11892-020-01339-3 · Cited by 17
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Long-term complications of type 1 diabetes: what do we know and what do we need to understand?
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Ohara, M., Hiromura, M., Nagaike, H., Kohata, Y., Fujikawa, T., Goto, S., Sato, N., Kushima, H., Terasaki, M., Yamamoto, T., et al. (2019) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2019.107904 · Cited by 17
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Self-citation
Metadata
- Authors
- Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S., Heinemann, L., Hirsch, I., Amiel, S. A., Beck, R., Bosi, E., Buckingham, B., Cobelli, C., Dassau, E., Doyle, F. J., Heller, S., Hovorka, R., Jia, W., Jones, T., Kordonouri, O., Kovatchev, B., Kowalski, A., Laffel, L., Maahs, D., Murphy, H. R., Nørgaard, K., Parkin, C. G., Renard, E., Saboo, B., Scharf, M., Tamborlane, W. V., Weinzimer, S. A., & Phillip, M.
- Affiliations
- Kinderkrankenhaus auf der Bult (DE)
- Schneider Children's Medical Center (IL)
- University of Ljubljana (SI)
- Park Nicollet Health Services (US)
- Close Concerns (United States) (US)
- Amsterdam UMC Location University of Amsterdam (NL)
- University of Colorado Denver (US)
- Science & Co, Düsseldorf, Germany
- University of Washington (US)
- King's College London (GB)
- Jaeb Center for Health Research (US)
- Vita-Salute San Raffaele University (IT)
- Stanford Medicine (US)
- University of Padua (IT)
- Harvard University (US)
- University of Sheffield (GB)
- University of Cambridge (GB)
- Shanghai Jiao Tong University (CN)
- Telethon Kids Institute and School of Paediatrics and Child Health, The University of Western Australia, and Department of Endocrinology and Diabetes, Princess Margaret Hospital for Children, Perth, Australia
- University of Virginia (US)
- Breakthrough T1D (US)
- Joslin Diabetes Center (US)
- University of East Anglia (GB)
- Hvidovre Hospital (DK)
- CGParkin Communications, Boulder City, NV
- Department of Endocrinology, Diabetes, and Nutrition, Montpellier University Hospital, and Institute of Functional Genomics, University of Montpellier, and INSERM Clinical Investigation Centre, Montpellier, France
- DiaCare, Ahmedabad, Gujarat, India
- Hospital Nossa Senhora das Graças (BR)
- Yale University (US)
- Corresponding author
- Danne, Thomas
- ORCID
- Danne, Thomas
- Nimri, Revital
- Battelino, Tadej
- Bergenstal, Richard M.
- Close, Kelly L.
- DeVries, J. Hans
- Garg, Satish
- Heinemann, Lutz
- Hirsch, Irl
- Amiel, Stephanie A.
- Beck, Roy
- Bosi, Emanuele
- Buckingham, Bruce
- Cobelli, Claudio
- Dassau, Eyal
- Doyle, Francis J.
- Heller, Simon
- Hovorka, Roman
- Jia, Weiping
- Jones, Tim
- Kordonouri, Olga
- Kovatchev, Boris
- Kowalski, Aaron
- Laffel, Lori
- Maahs, David
- Murphy, Helen R.
- Nørgaard, Kirsten
- Parkin, Christopher G.
- Saboo, Banshi
- Tamborlane, William V.
- Weinzimer, Stuart A.
- Phillip, Moshe
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2017-11-10
- Volume
- 40
- Issue
- 12
- Pages
- 1631-1640
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC6467165
- OpenAlex
- W2770616601
- Cited by
- 2,069
- References
- 82
- Citations by year
- 2026: 175 · 2025: 227 · 2024: 241 · 2023: 247 · 2022: 281 · 2021: 298 · 2020: 271 · 2019: 210 · 2018: 110 · 2017: 2
- FWCI
- 128.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes Care (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- HAL (submittedVersion)
- Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- American Diabetes Association
- Abbott Laboratories
- Bristol-Myers Squibb
- Eli Lilly and Company
- AstraZeneca
- Sanofi
- Leona M. and Harry B. Helmsley Charitable Trust
- Bigfoot Biomedical
- Insulet Corporation
- Dexcom
- Jaeb Center for Health Research
- National Institute for Health and Care Research (NF-SI-0515-10079, CDF-2013-06-035)
- Bayer HealthCare
- Novo Nordisk
- Roche Diabetes Care
- Abbott Diabetes Care
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK116074)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- continuous glucose monitoring, glycemic control, HbA1c, HbA1c limitations, hypoglycemia, postprandial hyperglycemia, diabetes management, real-time CGM, diabetes complications, expert consensus recommendations
- MeSH terms
- Blood Glucose, Clinical Trials as Topic, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Glycated Hemoglobin, Humans, Hyperglycemia, Hypoglycemia, Hypoglycemic Agents, Insulin, International Agencies, Physicians, Reference Standards, Research Personnel, Blood Glucose Self-Monitoring, Consensus
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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