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Recommendations for Standardizing Glucose Reporting and Analysis to Optimize Clinical Decision Making in Diabetes: The Ambulatory Glucose Profile (AGP)

Bergenstal, Richard M., Ahmann, Andrew J., Bailey, Timothy, Beck, Roy W., Bissen, Joan, Buckingham, Bruce, Deeb, Larry, Dolin, Robert H., Garg, Satish K., Goland, Robin, Hirsch, Irl B., Klonoff, David C., Kruger, Davida F., Matfin, Glenn, Mazze, Roger S., Olson, Beth A., Parkin, Christopher, Peters, Anne, Powers, Margaret A., Rodriguez, Henry, Southerland, Phil, Strock, Ellie S., Tamborlane, William, Wesley, David M.·2013·Diabetes Technology & Therapeutics

DOI 10.1089/dia.2013.0051

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Abstract

Abstract Underutilization of glucose data and lack of easy and standardized glucose data collection, analysis, visualization, and guided clinical decision making are key contributors to poor glycemic control among individuals with type 1 diabetes. An expert panel of diabetes specialists, facilitated by the International Diabetes Center and sponsored by the Helmsley Charitable Trust, met in 2012 to discuss recommendations for standardization of analysis and presentation of glucose monitoring data, with the initial focus on data derived from CGM systems. The panel members were introduced to a universal software report, the Ambulatory Glucose Profile (AGP), and asked to provide feedback on its content and functionality, both as a research tool and in clinical settings. This paper provides a summary of the topics and issues discussed during the meeting and presents recommendations from the expert panel regarding the need to standardize glucose profile summary metrics and the value of a uniform glucose report to aid clinicians, researchers, and patients.

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Linear Time Series Models of Glucose Concentration
Georga, E. I., Fotiadis, D. I., & Tigas, S. K. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-804831-3.00005-4
An mhealth Platform to Evaluate Glycaemic Variability in Type 1 Diabetes
Gogolos, G. I., Georga, E. I., Rizos, E. C., & Fotiadis, D. I. (2017) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2017.00-42
“Recommendations for standardization of analysis and report of blood glucose data including measures for GV and glucose exposure (GE) have been introduced in the Ambulatory Glucose Profile (AGP) [3].”
BackgroundMethodInfluential
11th Annual Symposium on Self-Monitoring of Blood Glucose: April 12–14, 2018, Oslo, Norway
Parkin, C. G., Homberg, A., & Hinzmann, R. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0284
Effects of High Intensity Interval Exercise versus Moderate Intensity Continuous Exercise on Blood Glucose Profiles of Individuals with Type 2 Diabetes
Terada, T. (2014) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r30m4k
Software in Diabetes
Sonnet, E. (2019) · Springer eBooks · doi:10.1007/978-3-319-98119-2_8
CGM-Parameter für das Diabetesmanagement – ein Update
Siegmund, T., Kolassa, R., & Thomas, A. (2019) · Diabetes aktuell · doi:10.1055/a-0918-0483
Digitalisierung im Therapiealltag: Softwarelösungen für das kontinuierliche Glukosemonitoring
Thomas, A., & Sahm, C. (2020) · Diabetes aktuell · doi:10.1055/a-1284-4415
“Eine ebenfalls in den internationalen Konsensus‑Statements vorgeschlagene Darstellung ist das ambulante Glukoseprofil (AGP), ursprünglich entwickelt für die stan‑ dardisierte Aufbereitung und Auswertung von SMBG‑Daten [13], nun erweitert für CGM‑Kurven [14] (▶Abb.”
BackgroundInfluential
Improved Nocturnal Glycaemia and Reduced Insulin Use Following Clinical Exercise Trial Participation in Individuals With Type 1 Diabetes
McCarthy, O. M., Deere, R., Eckstein, M. L., Pitt, J. P., Wellman, B., Bain, S. C., Moser, O., & Bracken, R. M. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2020.568832
Flash Glucose Monitoring（FGM）時代の血糖値の分類 ―低血糖・血糖値スパイクを中心に―
勝太郎, 永., 悟子, 志., 大槻, 千., & 喜山, 克. (2021) · doi:10.32183/ifcm.19.1_21
Das Ambulante Glukoseprofil
(2021) · Diabetes aktuell · doi:10.1055/a-1627-4413
mHealth Environments for Chronic Disease Management
Georga, E. I., Papadopoulos, A. N., & Fotiadis, D. I. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-2237-9.ch038
mHealth Environments for Chronic Disease Management
Georga, E. I., Papadopoulos, A. N., & Fotiadis, D. I. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-6915-2.ch030
Ambulantes Glukoseprofil (AGP) und relevante Daten aus CGM-Messungen
Kröger, J., & Thomas, A. (2022) · Diabetes aktuell · doi:10.1055/a-1779-2222
Diabetes Technology for Precision Therapy in Children, Adults, and Pregnancy
Mazze, R. S., Kong, A. P. S., Petrovski, G., & Basu, R. (2022) · Springer eBooks · doi:10.1007/978-3-030-98927-9_14 · Cited by 1
„Time in range“ (TIR) vs. Glykohämoglobin Typ A1c (HbA1c): was zählt für unsere Patienten?
Harer, C., & Mader, J. K. (2022) · Die Diabetologie · doi:10.1007/s11428-022-00963-9
Variabilidad glucémica por medio de automonitoreo glicémico escalonado en sujetos con diabetes tipo 2
Pérez-Gualdrón, C. E., Ordoñez, J. S. F., Bernal, N. B., Pacheco, A. F. P., Chaparro, N. J. M., & Cadena, J. D. C. (2023) · Revista Colombiana de Endocrinología Diabetes & Metabolismo · doi:10.53853/encr.10.2.793
“[13] Bergenstal RM, Ahmann AJ, Bailey T, Beck RW, Bissen J, Buckingham B, et al.”
Background
New Technologies for Glucose Monitoring
Danne, T., & Kordonouri, O. (2024) · Wiley eBooks · doi:10.1002/9781119697473.ch32
Treatment and prevention of type 1 diabetes in the 21st century
Baidal, D. A., Sosenko, J. M., & Skyler, J. S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91707-0.00007-3
Association between perioperative glucose proﬁles assessed by the continuous glucose monitoring (CGM) system and prognosis in patients with ST-segment elevation myocardial infarction (STEMI): protocol for a cohort study
Shi, J., Wang, X., Zhang, H., Ding, Y., Wu, J., Luo, S., Hu, H., & Zheng, X. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-079659
“The availability of monitoring data in a simple and standardised format, such as the ambulatory glucose profile, 8–10 can facilitate their use in routine clinical practice, enhancing treatment adjustments and improving patient education.”
Background
Consenso mexicano de expertos sobre el uso del sistema flash de monitoreo de glucosa en diabetes mellitus tipo 2
Yépez-Rodríguez, A. E., Garduño-Pérez, Á. A., Martínez-Abundis, E., Lavalle-González, F. J., Canto, J. A. C., Garnica-Cuéllar, J. C., Vera-Zertuche, J. M., la Garza-Hernández, N. E. D., Almeda‐Valdés, P., & Faradji, R. N. (2024) · Revista Mexicana de Endocrinología Metabolismo y Nutrición · doi:10.24875/rme.m24000032
“La HbA1c tiene va-rias limitaciones: representa solo un promedio de dos a tres meses; no detecta hipoglucemia o hiper-glucemia; es poco confiable en anemia, hemoglo-binopatías, deficiencia de hierro y embarazo; no refleja cambios rápidos y no ayuda en el ajuste de tratamiento en tiempo real 9,10 .”
Interpretation of Meters, Pumps, and Continuous Glucose Monitoring Downloads
Weaver, K., & Hirsch, I. B. (2019) · doi:10.2337/9781580406932.part1_ch08
Glycemic Goals and Assessment of Glycemia
(2022) · American Diabetes Association eBooks · doi:10.2337/9781580407700.ch02
Inter- and intra-physician variability in insulin injection adjustments compared with Bayesian algorithm recommendations in type 1 diabetes
Kobayati, A., Tsoukas, M. A., Garfield, N., Legault, L., Pasqua, M., Yale, J., Meltzer, S. J., Wing, S. S., Michaud, S., Tardio, V., et al. (2025) · Diabetologia · doi:10.1007/s00125-025-06627-9
“…data were presented in the platform as: (1) summaries of insulin and glucose metrics; (2) a detailed graph displaying intermittently scanned CGM data and logged meal and insulin data; and (3) an ambulatory glucose profile graph displaying trends across a 24 h period [12] (ESM Figs 2–3).”
Additional file 2 of Relationship between interstitial glucose variability in ambulatory glucose profile and standardized continuous glucose monitoring metrics; a pilot study
Tokutsu, A., Okada, Y., Torimoto, K., & Tanaka, Y. (2020) · Figshare · doi:10.6084/m9.figshare.12799284
Adaptation of the Glucose Monitoring System Satisfaction Scale for Patients With Type 1 Diabetes Into Turkish: A Study on Validity and Reliability
Demirbaş, M., Ergün, U., & Pamuk, B. Ö. (2026) · International Journal of Clinical Practice · doi:10.1155/ijcp/4857821
Enhancing diabetes monitoring systems’ reports: A novel integrated diabetes report (IDR)
Aldaghi, T., Bém, R., & Mužík, J. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106288
Time to redefine success in continuous glucose monitoring
Albert, L., Capel, I., & Rigla, M. (2026) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2026.02.007
Temporal Discontinuity in Continuous Glucose Monitoring: How Midnight Segmentation Generates Systematic Measurement Artifact
Paull, M. E. (2026) · Diabetes Technology & Therapeutics · doi:10.1177/15209156261435242
“10,11 The human endocrine system operates through coupled circadian oscillators — clock genes, autonomic tone, and counter-regulatory hormone secretion — following approximately 24-h cycles without reset points.”
Automated Insulin Delivery System Use in Pediatric Patients After Total Pancreatectomy With Islet Autotransplantation
Gandhi, K. K., Friesner-Gephart, K., Hoffman, R. S., Okafor, M. J., Freeman, A. J., Heinzman, C., Rasmussen, S. K., Nathan, J. D., & Ladd, J. M. (2026) · Endocrine Practice · doi:10.1016/j.eprac.2026.03.102
Domain-based Basal and Daytime Ambulatory Glycemic Exposure Metrics Derived from Continuous Glucose Monitoring: A Real-world Clinic-based Study
Shinde, S. N., Shinde, R. S., & Bhangaaley, S. Y. (2026) · International Journal of Diabetes and Technology · doi:10.64898/2026.05.24.26353983
Digital biomarkers in non-communicable diseases
Agha-Mir-Salim, L., Grünewald, E., Balzer, F., & Braune, K. (2026) · Biomarkers · doi:10.1080/1354750x.2026.2682425
“…in diverse populations. the standardized Ambulatory Glucose Profile (AGP) has become a key tool for visualizing and interpreting cGM data in home, clinical and research settings, facilitating the detection of glucose patterns, variability, and time in target ranges (Bergenstal et al. 2013).”
GLP-1 receptor agonist adjunct therapy stabilises Ramadan dysglycaemia in insulin-treated diabetes: a CGM-based study
Ashraf, T., & Lessan, N. G. (2026) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2026.113369
“How Do I Want to Live with Type 1 Diabetes?”: Understanding Self-Management Styles to Inform the Design of T1D Technologies
Barth, C.-M., Fedosov, A., Wacharamanotham, C., Bernard, J., & Huang, E. M. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3813059
Use of Continuous Glucose Monitors in Exercise Research Studies—A Scoping Review on Study Characteristics and Common Practices
Schwensfeier, L., & Brinkmann, C. K. (2026) · Sports · doi:10.3390/sports14070274
“In this context, standardized CGM outcomes and reporting frameworks have already been established, such as the “Ambulatory Glucose Profile” (AGP) [13] or the International Consensus Statement on CGM and Metrics for Clinical Trials [14].”

Authors: Bergenstal, R. M., Ahmann, A. J., Bailey, T., Beck, R. W., Bissen, J., Buckingham, B., Deeb, L., Dolin, R. H., Garg, S. K., Goland, R., Hirsch, I. B., Klonoff, D. C., Kruger, D. F., Matfin, G., Mazze, R. S., Olson, B. A., Parkin, C., Peters, A., Powers, M. A., Rodriguez, H., Southerland, P., Strock, E. S., Tamborlane, W., & Wesley, D. M.
Affiliations: Park Nicollet Clinic (US)
Oregon Health & Science University (US)
University of California San Diego (US)
Jaeb Center for Health Research (US)
Lucile Packard Children's Hospital (US)
Florida State University (US)
Lantana Consulting Group
University of Colorado Denver (US)
Columbia University Irving Medical Center (US)
University of Washington (US)
University of California, San Francisco (US)
Henry Ford Health (US)
CGParkin Communications, Inc.
University of Southern California (US)
University of South Florida (US)
Team Type 1, Atlanta, Georiga.
Yale University (US)
Corresponding author: Bergenstal, Richard M.
ORCID: Bergenstal, Richard M.
Ahmann, Andrew J.
Bailey, Timothy
Beck, Roy W.
Buckingham, Bruce
Dolin, Robert H.
Garg, Satish K.
Hirsch, Irl B.
Klonoff, David C.
Kruger, Davida F.
Parkin, Christopher
Peters, Anne
Rodriguez, Henry
Tamborlane, William
Venue: Diabetes Technology & Therapeutics
Publisher: SAGE Publications
Published: 2013-03
Volume: 15
Issue: 3
Pages: 198-211
ISSN: 1520-9156 (ISSN-L)
1557-8593
DOI: 10.1089/dia.2013.0051
OpenAlex: W2388765709
Cited by: 307
References: 55
Citations by year: 2026: 17 · 2025: 13 · 2024: 14 · 2023: 21 · 2022: 24 · 2021: 25 · 2020: 26 · 2019: 31 · 2018: 31 · 2017: 33 · 2016: 22 · 2015: 20 · 2014: 16 · 2013: 14
Type: conference abstract
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Diabetes Technology & Therapeutics (publishedVersion)
PubMed (publishedVersion)
Funders: Leona M. and Harry B. Helmsley Charitable Trust
U.S. Food and Drug Administration
Abbott Diabetes Care
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: ambulatory glucose profile, continuous glucose monitoring, type 1 diabetes, glycemic control, clinical decision-making, expert panel recommendations
MeSH terms: Blood Glucose, Decision Making, Diabetes Mellitus, Female, Humans, Hyperglycemia, Hypoglycemia, Male, Reference Standards, Software, United States, Blood Glucose Self-Monitoring, Monitoring, Ambulatory

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Recommendations for Standardizing Glucose Reporting and Analysis to Optimize Clinical Decision Making in Diabetes: The Ambulatory Glucose Profile (AGP)

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