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.

doi:10.1089/dia.2013.0051

Cited by 307Diabetes Management and ResearchDiabetes Management and EducationDiabetes Treatment and Management

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. +16 more·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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The standardized AGP report incorporates all the core CGM metrics and targets along with a 14-day composite glucose profile as an integral component of clinical decision making.
“Originally created by Mazze et. al. (68), the standardized AGP report was further developed by the International Diabetes Center and now incorporates all the core CGM metrics and targets along with a 14-day composite glucose profile as an integral component of clinical decision making (24).”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [24]
CGM use helps people with T1D achieve target time in range and avoid hyper- and hypoglycemia.
“The key benefits of CGM include real-time data monitoring for people with T1D and access for them and their HCPs to complete glucose datasets17; both of which help people to achieve their target time spent within the acceptable range (time in range [TIR]: 3.9–10.0 mmol/L [70–180 mg/dL]) and avoid hyper- and hypoglycemia.18”
From Increased Time in Range and Fewer Missed Bolus Injections After Introduction of a Smart Connected Insulin Pen (2020) · cites this paper as [18]

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Method
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BackgroundMethod
Increased Time in Range and Fewer Missed Bolus Injections After Introduction of a Smart Connected Insulin Pen
Adolfsson, P., Hartvig, N. V., Kaas, A., Møller, J. B., & Hellman, J. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0411 · Cited by 139 · see citing works
“0 mmol/L [70–180 mg/dL]) and avoid hyper- and hypoglycemia.(18) Reductions in HbA1c have been reported with CGM use in people on multiple daily injection (MDI) therapy,(19) and continuous subcutaneous insulin infusion therapy,(20) with reduced time spent in hypoglycemia,(20) and improved hypoglycemia awareness.”
Background
Improving the clinical value and utility of CGM systems: issues and recommendations
Petrie, J. R., Peters, A. L., Bergenstal, R. M., Holl, R. W., Fleming, G. A., & Heinemann, L. (2017) · Diabetologia · doi:10.1007/s00125-017-4463-4 · Cited by 140 · see citing works
Continuous glucose monitoring: The achievement of 100 years of innovation in diabetes technology
Galindo, R. J., & Aleppo, G. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108502 · Cited by 138 · see citing works
“[83, 84] (Figure 1) The AGP report facilitates rapid assessment of TIR, TBR and TAR, as well as other metrics, such as average glucose and the glucose management indicator (GMI) calculation.”
Background
Improving the Clinical Value and Utility of CGM Systems: Issues and Recommendations
Petrie, J. R., Peters, A. L., Bergenstal, R. M., Holl, R. W., Fleming, G. A., & Heinemann, L. (2017) · Diabetes Care · doi:10.2337/dci17-0043 · Cited by 135 · see citing works
“consensus on definitions of the various levels of time in range and hypoglycemia (in clinical practice and by CGM) is urgently required among payers and providers (21,43).”
BackgroundMethodSelf-citation
Glucose Profiles in Healthy Term Infants in the First 5 Days: The Glucose in Well Babies (GLOW) Study
Harris, D. L., Weston, P. J., Gamble, G. D., & Harding, J. E. (2020) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2020.02.079 · Cited by 126 · see citing works
American Association Of Clinical Endocrinologists And American College Of Endocrinology 2016 Outpatient Glucose Monitoring Consensus Statement
Bailey, T. S., Grunberger, G., Bode, B. W., Handelsman, Y., Hirsch, I. B., Jovanovič, L., Roberts, V. L., Rodbard, D., Tamborlane, W. V., & Walsh, J. S. A. (2016) · Endocrine Practice · doi:10.4158/ep151124.cs · Cited by 126 · see citing works
Comparison of Insulin Glargine 300 Units/mL and 100 Units/mL in Adults With Type 1 Diabetes: Continuous Glucose Monitoring Profiles and Variability Using Morning or Evening Injections
Bergenstal, R. M., Bailey, T. S., Rodbard, D., Ziemen, M., Guo, H., Muehlen-Bartmer, I., & Ahmann, A. (2017) · Diabetes Care · doi:10.2337/dc16-0684 · Cited by 122 · see citing works
“longer-term pattern of glucose levels (17).”
The impact of a structured education and treatment programme (FLASH) for people with diabetes using a flash sensor-based glucose monitoring system: Results of a randomized controlled trial
Hermanns, N., Ehrmann, D., Schipfer, M., Kröger, J., Haak, T., & Kulzer, B. (2019) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2019.03.003 · Cited by 113 · see citing works
Utilizing the Ambulatory Glucose Profile to Standardize and Implement Continuous Glucose Monitoring in Clinical Practice
Johnson, M. L., Martens, T. W., Criego, A. B., Carlson, A. L., Simonson, G. D., & Bergenstal, R. M. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0034 · Cited by 104 · see citing works
“47 This recommendation was later endorsed in an international consensus conference convened in Paris in 2017 18 and is now referenced as an example of a standardized CGM report in the ADA 2019 Standards of Care (page 75) 16 and an update to the AACE consensus on use of CGM. 48 Most of the CGM…”
Association Between Continuous Glucose Monitoring-Derived Time in Range, Other Core Metrics, and Albuminuria in Type 2 Diabetes
Yoo, J. H., Choi, M. S., Ahn, J., Park, S. W., Kim, Y., Hur, K. Y., Jin, S., Kim, G., & Kim, J. H. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0499 · Cited by 102 · see citing works
Association between blood glucose variability and coronary plaque instability in patients with acute coronary syndromes
Okada, K., Hibi, K., Gohbara, M., Kataoka, S., Takano, K., Akiyama, E., Matsuzawa, Y., Saka, K., Maejima, N., Endo, M., et al. (2015) · Cardiovascular Diabetology · doi:10.1186/s12933-015-0275-3 · Cited by 98 · see citing works
“In this study, significant hyperglycemia and significant hypoglycemia were defined as a blood glucose value of ≥200 and <60 mg/dl, respectively [14, 15].”
Background
How Can We Realize the Clinical Benefits of Continuous Glucose Monitoring?
Ajjan, R. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0021 · Cited by 100 · see citing works
“54 Retrospective or real-time CGM?”
Approach to Using Trend Arrows in the FreeStyle Libre Flash Glucose Monitoring Systems in Adults
Kudva, Y. C., Ahmann, A., Bergenstal, R. M., Gavin, J. R., Kruger, D. F., Midyett, L. K., Miller, E. M., & Harris, D. R. (2018) · Journal of the Endocrine Society · doi:10.1210/js.2018-00294 · Cited by 99 · see citing works
Utility of different glycemic control metrics for optimizing management of diabetes
Kohnert, K. (2015) · World Journal of Diabetes · doi:10.4239/wjd.v6.i1.17 · Cited by 95 · see citing works
Time in Range from Continuous Glucose Monitoring: A Novel Metric for Glycemic Control
Yoo, J. H., & Kim, J. H. (2020) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2020.0257 · Cited by 95 · see citing works
The Effect of Canagliflozin, a Sodium Glucose Cotransporter 2 Inhibitor, on Glycemic End Points Assessed by Continuous Glucose Monitoring and Patient-Reported Outcomes Among People With Type 1 Diabetes
Rodbard, H. W., Peters, A. L., Slee, A., Cao, A., Traina, S. B., & Alba, M. (2017) · Diabetes Care · doi:10.2337/dc16-1353 · Cited by 94 · see citing works
A Comparison of Continuous Glucose Monitoring-Measured Time-in-Range 70–180 mg/dL Versus Time-in-Tight-Range 70–140 mg/dL
Beck, R. W., Raghinaru, D., Calhoun, P. M., & Bergenstal, R. M. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0380 · Cited by 86 · see citing works
Evolution of Diabetes Technology
Dovč, K., & Battelino, T. (2019) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2019.10.009 · Cited by 85 · see citing works
A Glucose Model Based on Support Vector Regression for the Prediction of Hypoglycemic Events Under Free-Living Conditions
Georga, E. I., Protopappas, V. C., Ardigò, D., Polyzos, D. K., & Fotiadis, D. I. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2012.0285 · Cited by 79 · see citing works
Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning
Vehí, J., Contreras, I., Oviedo, S., Biagi, L., & Bertachi, A. (2020) · Health Informatics Journal · doi:10.1177/1460458219850682 · Cited by 76 · see citing works
“25 In both cases, an adverse glycaemic event was predicted whenever three consecutive BG measurements (15 min) were detected above or below their corresponding levels.”
BackgroundMethod
Monitoring Technologies- Continuous Glucose Monitoring, Mobile Technology, Biomarkers of Glycemic Control
Reddy, N., Verma, N., & Dungan, K. M. (2020) · MDText.com, Inc. eBooks · Cited by 68 · see citing works
Practical implementation, education and interpretation guidelines for continuous glucose monitoring: A French position statement
Borot, S., Benhamou, P., Atlan, C., Bismuth, É., Bonnemaison, É., Catargi, B., Charpentier, G., Farret, A., Filhol, N., Franc, S., et al. (2017) · Diabetes & Metabolism · doi:10.1016/j.diabet.2017.10.009 · Cited by 69 · see citing works
“The working group agrees with the recent attempts to standardize glycaemic data expression [46], which is helpful for categorizing patients and evaluating therapeutic impact.”
A case study in open source innovation: Developing the Tidepool Platform for interoperability in type 1 diabetes management
Neinstein, A., Wong, J., Look, H., Arbiter, B., Quirk, K., McCanne, S., Sun, Y., Blum, M., & Adi, S. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv104 · Cited by 66 · see citing works
Lowest Glucose Variability and Hypoglycemia Are Observed With the Combination of a GLP-1 Receptor Agonist and Basal Insulin (VARIATION Study)
BAJAJ, H. S., Venn, K., Ye, C., Patrick, A., Kalra, S., Khandwala, H. M., Aslam, N., Twum-Barima, D., & Aronson, R. (2016) · Diabetes Care · doi:10.2337/dc16-1582 · Cited by 65 · see citing works
“SD was chosen because of its general familiarity and validation history in quantifying GV from CGM (16,17), especially in our setting where two contributing variables, A1C and mean glucose, were expected to be controlled among all four cohorts.”
Method
Time in range centered diabetes care
Dovč, K., & Battelino, T. (2021) · Clinical Pediatric Endocrinology · doi:10.1297/cpe.30.1 · Cited by 64 · see citing works
“Bergenstal RM, Ahmann AJ, Bailey T, Beck RW, Bissen J, Buckingham B, et al. Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: the Ambulatory Glucose Profile (AGP).”
BackgroundInfluential
Digital Technology for Diabetes
Hughes, M. S., Addala, A., & Buckingham, B. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2215899 · Cited by 59 · see citing works
Self-citation
Metrics to Evaluate Quality of Glycemic Control: Comparison of Time in Target, Hypoglycemic, and Hyperglycemic Ranges with “Risk Indices”
Rodbard, D. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0416 · Cited by 58 · see citing works
Poor sleep quality is associated with nocturnal glycaemic variability and fear of hypoglycaemia in adults with type 1 diabetes
Martyn-Nemeth, P. A., Phillips, S. A., Mihailescu, D. V., Farabi, S. S., Park, C., Lipton, R. B., Idemudia, E., & Quinn, L. (2018) · Journal of Advanced Nursing · doi:10.1111/jan.13765 · Cited by 58 · see citing works
“This procedure is recommended by experts on the standardization of glucose reporting (Bergenstal et al., 2013; Rodbard, 2009).”
Role of Continuous Glucose Monitoring in Clinical Trials: Recommendations on Reporting
Schnell, O., Barnard, K. D., Bergenstal, R. M., Bosi, E., Garg, S. K., Guerci, B., Haak, T., Hirsch, I. B., Ji, L., Joshi, S., et al. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0054 · Cited by 56 · see citing works
“Despite suggestions for standardization of endpoints, no consistency has been reached, limiting comparability between CGM systems and publications using them.(51,52) Thus, some studies use historical parameters such as the MAGE, standard deviation (SD) or coefficient of variation about mean plasma glucose level, or th…”
Method
Improvement in Glucose Control in Difficult-to-Control Patients With Diabetes Using a Novel Flash Glucose Monitoring Device
Ish‐Shalom, M., Wainstein, J., Raz, I., & Mosenzon, O. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816653412 · Cited by 52 · see citing works
Treating type 1 diabetes: from strategies for insulin delivery to dual hormonal control.
McCall, A. L., & Farhy, L. S. (2013) · PubMed · Cited by 52 · see citing works
Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies
Augstein, P., Heinke, P., Vogt, L., Vogt, R., Rackow, C., Kohnert, K., & Salzsieder, E. (2015) · BMC Endocrine Disorders · doi:10.1186/s12902-015-0019-0 · Cited by 51 · see citing works
“We performed the first published factor analysis [29,30] of CGM-variables [4-7,9,10].”
Influential
Comparator Data Characteristics and Testing Procedures for the Clinical Performance Evaluation of Continuous Glucose Monitoring Systems
Eichenlaub, M., Pleus, S., Rothenbühler, M., Bailey, T. S., Bally, L., Brazg, R., Bruttomesso, D., Diem, P., Boija, E. E., Fokkert, M. J., et al. (2024) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0465 · Cited by 51 · see citing works
Continuous glucose monitoring in cystic fibrosis – A practical guide
Chan, C. L., Ode, K. L., Granados, A., Moheet, A. A., Moran, A., & Hameed, S. A. (2019) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2019.08.025 · Cited by 50 · see citing works
Glucodensities: A new representation of glucose profiles using distributional data analysis
Matabuena, M., Petersen, A., Vidal, J. C., & Gude, F. (2021) · Statistical Methods in Medical Research · doi:10.1177/0962280221998064 · Cited by 47 · see citing works
“In 2012, a panel of experts discussed how to represent CGM data in an “easy to view format”.(19) They also analyzed the convenience of using glycemic variability measures and other summary measures such as time in range to extract the CGM’s recorded information.”
Background
Taking a Closer Look—Continuous Glucose Monitoring in Non-Critically Ill Hospitalized Patients with Type 2 Diabetes Mellitus Under Basal-Bolus Insulin Therapy
Schaupp, L., Donsa, K., Neubauer, K., Mader, J., Aberer, F., Höll, B., Spat, S., Augustin, T., Beck, P., Pieber, T., et al. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2014.0343 · Cited by 46 · see citing works
Reference Guide for Integrating Continuous Glucose Monitoring Into Clinical Practice
Kruger, D. F., Edelman, S. V., Hinnen, D. A., & Parkin, C. G. (2019) · The Diabetes Educator · doi:10.1177/0145721718818066 · Cited by 55 · see citing works
Wearable systems and mobile applications for diabetes disease management
Georga, E. I., Protopappas, V. C., Bellos, C. V., & Fotiadis, D. I. (2014) · Health and Technology · doi:10.1007/s12553-014-0082-y · Cited by 46 · see citing works
“sured [14, 15].”
Method
Time-in-range for monitoring glucose control: Is it time for a change?
Bellido, V., Pinés-Corrales, P. J., Villar‐Taibo, R., & Ampudia‐Blasco, F. J. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108917 · Cited by 46 · see citing works
First Clinical Experience with Retrospective Flash Glucose Monitoring (FGM) Analysis in South Africa
Distiller, L. A., CRANSTON, I. C., & Mazze, R. S. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816648165 · Cited by 45 · see citing works
“However, the adoption of CGM for clinical decision making and research remains elusive.(3,4) Scheiner recently reviewed the advantages of, and problems with retrospective analysis of CGM data, suggesting that a structured approach to data interpretation may hold the key to future success.”
Background
International consensus on use of continuous glucose monitoring
Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S. K., Heinemann, L., Hirsch, I. B., Amiel, S. A., et al. (2018) · Yearbook of pediatric endocrinology · doi:10.1530/ey.15.10.10 · Cited by 44 · see citing works
Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter?
Mo, Y., Lu, J., & Zhou, J. (2023) · Journal of Diabetes Investigation · doi:10.1111/jdi.14112 · Cited by 48 · see citing works
“Ambulatory glucose pro ﬁ le condenses CGM data into a 24-h representation, and IQR, represented by the 25th to 75th percentile trend lines on ambulatory glucose pro ﬁ le, serves as a powerful visual tool for assessing GV 19,20 .”
Method
Essential Continuous Glucose Monitoring Metrics: The Principal Dimensions of Glycemic Control in Diabetes
Montaser, E., Fabris, C., & Kovatchev, B. P. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0104 · Cited by 42 · see citing works
Emerging Diabetes Technologies: Continuous Glucose Monitors/Artificial Pancreases
ALMURASHI, A. M., Rodriguez, E., & Garg, S. K. (2023) · Journal of the Indian Institute of Science · doi:10.1007/s41745-022-00348-3 · Cited by 48 · see citing works
Basal Glucose Can Be Controlled, but the Prandial Problem Persists—It’s the Next Target!
Riddle, M. C. (2017) · Diabetes Care · doi:10.2337/dc16-2380 · Cited by 42 · see citing works
“0 mmol/L) as a summary indicator of glycemic control (34).”
Background
Design of FLAT-SUGAR: Randomized Trial of Prandial Insulin Versus Prandial GLP-1 Receptor Agonist Together With Basal Insulin and Metformin for High-Risk Type 2 Diabetes
The FLAT-SUGAR Trial Investigators (2015) · Diabetes Care · doi:10.2337/dc14-2689 · Cited by 38 · see citing works
Consensus recommendations for the use of Ambulatory Glucose Profile in clinical practice
Matthaei, S., Antuna, R., Bosi, E., Evans, M. L., Geelhoed-Duijvestijn, N., & Joubert, M. (2014) · The British Journal of Diabetes · doi:10.15277/bjdvd.2014.046 · Cited by 38 · see citing works
A New Era in Continuous Glucose Monitoring: Food and Drug Administration Creates a New Category of Factory-Calibrated Nonadjunctive, Interoperable Class II Medical Devices
Garg, S. K., & Aktürk, H. K. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0142 · Cited by 37 · see citing works
Effect of Professional CGM (pCGM) on Glucose Management in Type 2 Diabetes Patients in Primary Care
Simonson, G. D., Bergenstal, R. M., Johnson, M. L., Davidson, J. L., & Martens, T. W. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/1932296821998724 · Cited by 37 · see citing works
Self-citation
The Impact of Insulin Pump Therapy on Glycemic Profiles in Patients with Type 2 Diabetes: Data from the OpT2mise Study
Conget, I., Castañeda, J., Petrovski, G., Guerci, B., Racault, A.-S., Reznik, Y., Cohen, O., Runzis, S., de Portu, S., Aronson, R., et al. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0159 · Cited by 35 · see citing works
Ambulatory Glucose Profile (AGP) Report in Daily Care of Patients with Diabetes: Practical Tips and Recommendations
Czupryniak, L., Dzida, G. J., Fichna, P., Jarosz-Chobot, P. A., Gumprecht, J., Klupa, T., Myśliwiec, M., Szadkowska, A., Bomba-Opon, D. A., Czajkowski, K., et al. (2022) · Diabetes Therapy · doi:10.1007/s13300-022-01229-9 · Cited by 35 · see citing works
Continuous Glucose Monitoring to Optimize Management of Diabetes in Patients with Advanced CKD
Galindo, R. J., de Boer, I. H., Neumiller, J. J., & Tuttle, K. R. (2022) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.04510422 · Cited by 35 · see citing works
Key indices of glycaemic variability for application in diabetes clinical practice
Monnier, L., Bonnet, F., Colette, C., Renard, É., & Owens, D. R. (2023) · Diabetes & Metabolism · doi:10.1016/j.diabet.2023.101488 · Cited by 34 · see citing works
Technological advances shaping diabetes care
Aktürk, H. K., & Garg, S. K. (2019) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0000000000000467 · Cited by 33 · see citing works
Impact of errors in paper-based and computerized diabetes management with decision support for hospitalized patients with type 2 diabetes. A post-hoc analysis of a before and after study
Donsa, K., Beck, P., Höll, B., Mader, J. K., Schaupp, L. A., Plank, J., Neubauer, K. M., Baumgärtner, C., & Pieber, T. R. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.03.007 · Cited by 33 · see citing works
Diurnal Glycemic Patterns during an 8-Week Open-Label Proof-of-Concept Trial of Empagliflozin in Type 1 Diabetes
Perkins, B. A., Cherney, D. Z. I., Soleymanlou, N., Lee, J. A., Partridge, H., Menezes, H. T., Zinman, B., Mazze, R., Fagan, N. M., Kaspers, S., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141085 · Cited by 32 · see citing works
“CGM results were analyzed and averaged over 2-week AGP assessment periods to provide modal day estimates of pre-specified conventional parameters of glycemic exposure, glycemic variability, and glycemic stability during the 2-week placebo runin period (baseline), weeks 7 and 8 (end-of-treatment) and the two week perio…”
Method
Clinical Application of Time in Range and Other Metrics
Aleppo, G. (2021) · Diabetes Spectrum · doi:10.2337/ds20-0093 · Cited by 31 · see citing works
Consistency of Quantitative Scores of Hypoglycemia Severity and Glycemic Lability and Comparison with Continuous Glucose Monitoring System Measures in Long-Standing Type 1 Diabetes
Senior, P. A., Bellin, M. D., Alejandro, R., Yankey, J. W., Clarke, W. R., Qidwai, J. C., Schwieger, T., Eggerman, T. L., Robien, M. A., & Rickels, M. R. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2014.0289 · Cited by 32 · see citing works
“We used the software-generated CGMS SD and CGMS CV available at the start of the CIT trials as measures of glycemic variability for this analysis as there is no consensus for how best to use CGMS to quantify glucose variability.(20) If we had had access to raw sensor data, it would have been possible to calculate othe…”
Method
The clinical importance of measuring glycaemic variability: Utilising new metrics to optimise glycaemic control
Ajjan, R. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16098 · Cited by 35 · see citing works
Understanding Continuous Glucose Monitoring Data
Bergenstal, R. (2018) · Role of Continuous Glucose Monitoring in Diabetes Treatment · doi:10.2337/db20181-20 · Cited by 37 · see citing works
Development and Assessment of a Mobile Health Application for Monitoring the Linkage Among Treatment Factors of Type 1 Diabetes Mellitus
Bellei, E. A., Biduski, D., Lisboa, H. R. K., & Marchi, A. C. B. D. (2019) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2018.0329 · Cited by 32 · see citing works
“65–71 The interactive visualization within Soins DM aims to facilitate the identification of patterns and treatment failures, the understanding of impact food have on glycemia, and how food interacts with exercise and insulin to prevent hypo-and hyperglycemia.”
HbA1c
Carlson, A. L., Criego, A. B., Martens, T. W., & Bergenstal, R. M. (2019) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2019.10.010 · Cited by 31 · see citing works
Reexamining the Hypoglycemia Fear Survey for Parents of Young Children in a Sample of Children Using Insulin Pumps
Patton, S. R., Noser, A. E., Clements, M. A., Dolan, L. M., & Powers, S. W. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2016.0389 · Cited by 29 · see citing works
“Mild dysglycemia” in type 2 diabetes: to be neglected or not?
Monnier, L., Colette, C., Dejager, S., & Owens, D. R. (2014) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2014.12.004 · Cited by 28 · see citing works
“Such an observation therefore indicates that, in persons with diabetes mellitus, there exist large inter-individual variations in blood glucose patterns (Bergenstal et al., 2013).”
Background
Use of Technology When Assessing Adherence to Diabetes Self-Management Behaviors
Driscoll, K. A., & Young-Hyman, D. L. (2014) · Current Diabetes Reports · doi:10.1007/s11892-014-0521-1 · Cited by 27 · see citing works
Continuous Glucose Monitoring Integration for Remote Diabetes Management: Virtual Diabetes Care with Case Studies
Carlson, A. L., Martens, T. W., Johnson, L., & Criego, A. B. (2021) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2021.0241 · Cited by 27 · see citing works
Continuous glucose monitoring in persons with type 2 diabetes not using insulin
Cowart, K., Updike, W. H., & Franks, R. B. (2021) · Expert Review of Medical Devices · doi:10.1080/17434440.2021.1992274 · Cited by 27 · see citing works
“CGM allows the PWD and clinician to analyze the impact that diet, exercise, stress, and medication can have on the management of T2D[10].”
BackgroundInfluential
Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data
Piersanti, A., Giurato, F., Göbl, C. S., Burattini, L., Tura, A., & Morettini, M. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0237 · Cited by 29 · see citing works
“16 Such template is currently adopted by the majority of CGM device manufacturers; however, international consensus on CGM data analysis and standard metrics that should be computed for clinical care has been reached only recently.”
Assessing the value of the Ambulatory Glucose Profile in clinical practice
Matthaei, S. (2014) · The British Journal of Diabetes · doi:10.15277/bjdvd.2014.045 · Cited by 27 · see citing works
Accuracy Evaluation of CONTOUR®PLUS Compared With Four Blood Glucose Monitoring Systems
Dunne, N., Viggiani, M. T., Pardo, S. A., Robinson, C., & Parkes, J. L. (2015) · Diabetes Therapy · doi:10.1007/s13300-015-0121-3 · Cited by 27 · see citing works
Update on the American Diabetes Association Standards of Medical Care
Zitkus, B. S. (2014) · The Nurse Practitioner · doi:10.1097/01.npr.0000451880.48790.50 · Cited by 25 · see citing works
Short‐term exercise training reduces glycaemic variability and lowers circulating endothelial microparticles in overweight and obese women at elevated risk of type 2 diabetes
Rafiei, H., Robinson, E., Barry, J., Jung, M. E., & Little, J. P. (2019) · European Journal of Sport Science · doi:10.1080/17461391.2019.1576772 · Cited by 25 · see citing works
“…can best be measured using continuous glucose monitoring (CGM) that is more informative than fasting plasma glucose or HbA1C as it allows for monitoring minute by minute changes in glucose levels in response to a short-term intervention and under free-living conditions (Bergenstal et al., 2013).”
Background
Practical guidance for using the FreeStyle Libre flash continuous glucose monitoring in primary care
Unger, J., Kushner, P. R., & Anderson, J. E. (2020) · Postgraduate Medicine · doi:10.1080/00325481.2020.1744393 · Cited by 27 · see citing works
“When reviewing the glucose data, we suggest you start with the AGP report [47], which is common to all download programs (Figure 2).”
Method
Time-in-range and frequency of continuous glucose monitoring: Recommendations for South Asia
Kesavadev, J., Misra, A., Saboo, B., Agarwal, S., Sosale, A., Joshi, S., Hussain, A., Somasundaram, N. P., Basit, A., Choudhary, P., et al. (2021) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2021.102345 · Cited by 24 · see citing works
Utilizing continuous glucose monitoring in primary care practice: What the numbers mean
Edelman, S. V., Cavaiola, T. S., BOEDER, S. C., & Pettus, J. (2020) · Primary care diabetes · doi:10.1016/j.pcd.2020.10.013 · Cited by 25 · see citing works
Chinese clinical guidelines for continuous glucose monitoring (2018 edition)
Bao, Y., Chen, L., Chen, L., Dou, J., Gao, Z., Gao, L., Guo, L., Guo, X., Ji, L., Ji, Q., et al. (2019) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3152 · Cited by 23 · see citing works
“Therefore, standardization of CGM reports is extremely important.(114) Currently, CGM monitoring reports generally include three parts: (a) general items: basic information about the subject, clinical diagnosis, inspection date, medical staff signature and date; (b) CGM data; and (c) clinical implications of the resul…”
Background
Glucose Variability
Floch, J. P. L., & Kessler, L. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816632003 · Cited by 23 · see citing works
“However, indices of glucose variability could be especially useful to analyze ambulatory glucose profiles and optimize decision making in diabetes.(16) For the clinician, it is important to get simple indices, poorly dependent of the mean glucose value commonly estimated with HbA1c and associated with glucose fluctuat…”
Background
Indication, organization, practical implementation and interpretation guidelines for retrospective CGM recording: A French position statement
Joubert, M., Baillot-Rudoni, S., Catargi, B., Charpentier, G., Esvant, A., Franc, S., Guerci, B., Guilhem, I., Melki, V., Merlen, E., et al. (2015) · Diabetes & Metabolism · doi:10.1016/j.diabet.2015.07.001 · Cited by 22 · see citing works
Recommendations for self-monitoring in pediatric diabetes: a consensus statement by the ISPED
Diabetes Study Group of the Italian Society for Pediatric Endocrinology and Diabetology, Scaramuzza, A. E., Cherubini, V., Tumini, S., Bonfanti, R., Buono, P., Cardella, F., d’Annunzio, G., Frongia, A. P., Lombardo, F., et al. (2013) · Acta Diabetologica · doi:10.1007/s00592-013-0521-7 · Cited by 22 · see citing works
“It is our belief that the National Health Service should provide all necessary resources to provide SMBG and possibly CGM to all children and adolescents with type 1 diabetes, according to the standards of care provided by these recommendations [1] and internationally [14, 17, 18, 71–74].”
Method

Recommendations for Standardizing Glucose Reporting and Analysis to Optimize Clinical Decision Making in Diabetes: The Ambulatory Glucose Profile
Bergenstal, R. M., Ahmann, A., Bailey, T. S., Beck, R. W., Bissen, J., Buckingham, B. A., Deeb, L. C., Dolin, R. H., Garg, S. K., Goland, R., et al. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700234 · Cited by 167 · see citing works
Expert Panel Recommendations for Use of Standardized Glucose Reporting System Based on Standardized Glucometrics Plus Visual Ambulatory Glucose Profile (AGP) Data in Clinical Practice
Dağdelen, S., Deyneli, O., Dınççağ, N., ILKOVA, H. M., SİVA, Z. O., Yetkin, İ., & Yilmaz, T. M. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.663222 · Cited by 8 · see citing works
Consensus recommendations for the use of Ambulatory Glucose Profile in clinical practice
Matthaei, S., Antuna, R., Bosi, E., Evans, M. L., Geelhoed-Duijvestijn, N., & Joubert, M. (2014) · The British Journal of Diabetes · doi:10.15277/bjdvd.2014.046 · Cited by 38 · see citing works
Ambulatory Glucose Profile (AGP) Report in Daily Care of Patients with Diabetes: Practical Tips and Recommendations
Czupryniak, L., Dzida, G. J., Fichna, P., Jarosz-Chobot, P. A., Gumprecht, J., Klupa, T., Myśliwiec, M., Szadkowska, A., Bomba-Opon, D. A., Czajkowski, K., et al. (2022) · Diabetes Therapy · doi:10.1007/s13300-022-01229-9 · Cited by 35 · see citing works
Utilizing the Ambulatory Glucose Profile to Standardize and Implement Continuous Glucose Monitoring in Clinical Practice
Johnson, M. L., Martens, T. W., Criego, A. B., Carlson, A. L., Simonson, G. D., & Bergenstal, R. M. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0034 · Cited by 104 · see citing works

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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