A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

David C. Klonoff; Jing Wang; David Rodbard; Michael A. Kohn; Chengdong Li; Dorian Liepmann; David Kerr; David Ahn; Anne L. Peters; Guillermo E. Umpierrez; Jane Jeffrie Seley; Nicole Y. Xu; Kevin T. Nguyen; Gregg Simonson; Michael S.D. Agus; Mohammed E. Al-Sofiani; Gustavo Armaiz-Pena; Timothy S. Bailey; Ananda Basu; Tadej Battelino; Sewagegn Yeshiwas Bekele; Pierre Yves Benhamou; B. Wayne Bequette; Thomas Blevins; Marc D. Breton; Jessica R. Castle; James Geoffrey Chase; Kong Y. Chen; Pratik Choudhary; Mark A. Clements; Kelly L. Close; Curtiss B. Cook; Thomas Danne; Francis J. Doyle; Angela Drincic; Kathleen M. Dungan; Steven V. Edelman; Niels Ejskjaer; Juan C. Espinoza; G. Alexander Fleming; Gregory P. Forlenza; Guido Freckmann; Rodolfo J. Galindo; Ana Maria Gomez; Hanna A. Gutow; Lutz Heinemann; Irl B. Hirsch; Thanh D. Hoang; Roman Hovorka; Johan H. Jendle; Linong Ji; Shashank R. Joshi; Michael Joubert; Suneil K. Koliwad; Rayhan A. Lal; M. Cecilia Lansang; Wei An Lee; Lalantha Leelarathna; Lawrence A. Leiter; Marcus Lind; Michelle L. Litchman; Julia K. Mader; Katherine M. Mahoney; Boris Mankovsky; Umesh Masharani; Nestoras N. Mathioudakis; Alexander Mayorov; Jordan Messler; Joshua D. Miller; Viswanathan Mohan; James H. Nichols; Kirsten Nørgaard; David N. O’Neal; Francisco J. Pasquel; Athena Philis-Tsimikas; Thomas Pieber; Moshe Phillip; William H. Polonsky; Rodica Pop-Busui; Gerry Rayman; Eun Jung Rhee; Steven J. Russell; Viral N. Shah; Jennifer L. Sherr; Koji Sode; Elias K. Spanakis; Deborah J. Wake; Kayo Waki; Amisha Wallia; Melissa E. Weinberg; Howard Wolpert; Eugene E. Wright; Mihail Zilbermint; Boris Kovatchev

doi:10.1177/19322968221085273

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

David C. Klonoff, Jing Wang, David Rodbard, Michael A. Kohn, Chengdong Li, Dorian Liepmann, David Kerr, David Ahn, Anne L. Peters, Guillermo E. Umpierrez, Jane Jeffrie Seley, Nicole Y. Xu, Kevin T. Nguyen, Gregg Simonson, Michael S.D. Agus, Mohammed E. Al-Sofiani, Gustavo Armaiz-Pena, Timothy S. Bailey, Ananda Basu, Tadej BattelinoSewagegn Yeshiwas Bekele, Pierre Yves Benhamou, B. Wayne Bequette, Thomas Blevins, Marc D. Breton, Jessica R. Castle, James Geoffrey Chase, Kong Y. Chen, Pratik Choudhary, Mark A. Clements, Kelly L. Close, Curtiss B. Cook, Thomas Danne, Francis J. Doyle, Angela Drincic, Kathleen M. Dungan, Steven V. Edelman, Niels Ejskjaer, Juan C. Espinoza, G. Alexander Fleming, Gregory P. Forlenza, Guido Freckmann, Rodolfo J. Galindo, Ana Maria Gomez, Hanna A. Gutow, Lutz Heinemann, Irl B. Hirsch, Thanh D. Hoang, Roman Hovorka, Johan H. Jendle, Linong Ji, Shashank R. Joshi, Michael Joubert, Suneil K. Koliwad, Rayhan A. Lal, M. Cecilia Lansang, Wei An Lee, Lalantha Leelarathna, Lawrence A. Leiter, Marcus Lind, Michelle L. Litchman, Julia K. Mader, Katherine M. Mahoney, Boris Mankovsky, Umesh Masharani, Nestoras N. Mathioudakis, Alexander Mayorov, Jordan Messler, Joshua D. Miller, Viswanathan Mohan, James H. Nichols, Kirsten Nørgaard, David N. O’Neal, Francisco J. Pasquel, Athena Philis-Tsimikas, Thomas Pieber, Moshe Phillip, William H. Polonsky, Rodica Pop-Busui, Gerry Rayman, Eun Jung Rhee, Steven J. Russell, Viral N. Shah, Jennifer L. Sherr, Koji Sode, Elias K. Spanakis, Deborah J. Wake, Kayo Waki, Amisha Wallia, Melissa E. Weinberg, Howard Wolpert, Eugene E. Wright, Mihail Zilbermint, Boris Kovatchev

Endocrinology, Diabetes and Clinical Nutrition

Research output: Contribution to journal › Article › peer-review

71 Scopus citations

Abstract

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Original language	English (US)
Pages (from-to)	1226-1242
Number of pages	17
Journal	Journal of Diabetes Science and Technology
Volume	17
Issue number	5
DOIs	https://doi.org/10.1177/19322968221085273
State	Published - Sep 2023

Keywords

ambulatory glucose profile
composite metric
continuous glucose monitor
diabetes
glycemia risk index
hyperglycemia
hypoglycemia
time in range

ASJC Scopus subject areas

Internal Medicine
Endocrinology, Diabetes and Metabolism
Bioengineering
Biomedical Engineering

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10.1177/19322968221085273

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Klonoff, D. C., Wang, J., Rodbard, D., Kohn, M. A., Li, C., Liepmann, D., Kerr, D., Ahn, D., Peters, A. L., Umpierrez, G. E., Seley, J. J., Xu, N. Y., Nguyen, K. T., Simonson, G., Agus, M. S. D., Al-Sofiani, M. E., Armaiz-Pena, G., Bailey, T. S., Basu, A., ... Kovatchev, B. (2023). A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings. Journal of Diabetes Science and Technology, 17(5), 1226-1242. https://doi.org/10.1177/19322968221085273

Klonoff, DC, Wang, J, Rodbard, D, Kohn, MA, Li, C, Liepmann, D, Kerr, D, Ahn, D, Peters, AL, Umpierrez, GE, Seley, JJ, Xu, NY, Nguyen, KT, Simonson, G, Agus, MSD, Al-Sofiani, ME, Armaiz-Pena, G, Bailey, TS, Basu, A, Battelino, T, Bekele, SY, Benhamou, PY, Bequette, BW, Blevins, T, Breton, MD, Castle, JR, Chase, JG, Chen, KY, Choudhary, P, Clements, MA, Close, KL, Cook, CB, Danne, T, Doyle, FJ, Drincic, A, Dungan, KM, Edelman, SV, Ejskjaer, N, Espinoza, JC, Fleming, GA, Forlenza, GP, Freckmann, G, Galindo, RJ, Gomez, AM, Gutow, HA, Heinemann, L, Hirsch, IB, Hoang, TD, Hovorka, R, Jendle, JH, Ji, L, Joshi, SR, Joubert, M, Koliwad, SK, Lal, RA, Lansang, MC, Lee, WA, Leelarathna, L, Leiter, LA, Lind, M, Litchman, ML, Mader, JK, Mahoney, KM, Mankovsky, B, Masharani, U, Mathioudakis, NN, Mayorov, A, Messler, J, Miller, JD, Mohan, V, Nichols, JH, Nørgaard, K, O’Neal, DN, Pasquel, FJ, Philis-Tsimikas, A, Pieber, T, Phillip, M, Polonsky, WH, Pop-Busui, R, Rayman, G, Rhee, EJ, Russell, SJ, Shah, VN, Sherr, JL, Sode, K, Spanakis, EK, Wake, DJ, Waki, K, Wallia, A, Weinberg, ME, Wolpert, H, Wright, EE, Zilbermint, M & Kovatchev, B 2023, 'A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings', Journal of Diabetes Science and Technology, vol. 17, no. 5, pp. 1226-1242. https://doi.org/10.1177/19322968221085273

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title = "A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings",

abstract = "Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.",

keywords = "ambulatory glucose profile, composite metric, continuous glucose monitor, diabetes, glycemia risk index, hyperglycemia, hypoglycemia, time in range",

author = "Klonoff, {David C.} and Jing Wang and David Rodbard and Kohn, {Michael A.} and Chengdong Li and Dorian Liepmann and David Kerr and David Ahn and Peters, {Anne L.} and Umpierrez, {Guillermo E.} and Seley, {Jane Jeffrie} and Xu, {Nicole Y.} and Nguyen, {Kevin T.} and Gregg Simonson and Agus, {Michael S.D.} and Al-Sofiani, {Mohammed E.} and Gustavo Armaiz-Pena and Bailey, {Timothy S.} and Ananda Basu and Tadej Battelino and Bekele, {Sewagegn Yeshiwas} and Benhamou, {Pierre Yves} and Bequette, {B. Wayne} and Thomas Blevins and Breton, {Marc D.} and Castle, {Jessica R.} and Chase, {James Geoffrey} and Chen, {Kong Y.} and Pratik Choudhary and Clements, {Mark A.} and Close, {Kelly L.} and Cook, {Curtiss B.} and Thomas Danne and Doyle, {Francis J.} and Angela Drincic and Dungan, {Kathleen M.} and Edelman, {Steven V.} and Niels Ejskjaer and Espinoza, {Juan C.} and Fleming, {G. Alexander} and Forlenza, {Gregory P.} and Guido Freckmann and Galindo, {Rodolfo J.} and Gomez, {Ana Maria} and Gutow, {Hanna A.} and Lutz Heinemann and Hirsch, {Irl B.} and Hoang, {Thanh D.} and Roman Hovorka and Jendle, {Johan H.} and Linong Ji and Joshi, {Shashank R.} and Michael Joubert and Koliwad, {Suneil K.} and Lal, {Rayhan A.} and Lansang, {M. Cecilia} and Lee, {Wei An} and Lalantha Leelarathna and Leiter, {Lawrence A.} and Marcus Lind and Litchman, {Michelle L.} and Mader, {Julia K.} and Mahoney, {Katherine M.} and Boris Mankovsky and Umesh Masharani and Mathioudakis, {Nestoras N.} and Alexander Mayorov and Jordan Messler and Miller, {Joshua D.} and Viswanathan Mohan and Nichols, {James H.} and Kirsten N{\o}rgaard and O{\textquoteright}Neal, {David N.} and Pasquel, {Francisco J.} and Athena Philis-Tsimikas and Thomas Pieber and Moshe Phillip and Polonsky, {William H.} and Rodica Pop-Busui and Gerry Rayman and Rhee, {Eun Jung} and Russell, {Steven J.} and Shah, {Viral N.} and Sherr, {Jennifer L.} and Koji Sode and Spanakis, {Elias K.} and Wake, {Deborah J.} and Kayo Waki and Amisha Wallia and Weinberg, {Melissa E.} and Howard Wolpert and Wright, {Eugene E.} and Mihail Zilbermint and Boris Kovatchev",

note = "Publisher Copyright: {\textcopyright} 2022 Diabetes Technology Society.",

year = "2023",

month = sep,

doi = "10.1177/19322968221085273",

language = "English (US)",

volume = "17",

pages = "1226--1242",

journal = "Journal of Diabetes Science and Technology",

issn = "1932-2968",

publisher = "Diabetes Technology Society",

number = "5",

}

TY - JOUR

T1 - A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

AU - Klonoff, David C.

AU - Wang, Jing

AU - Rodbard, David

AU - Kohn, Michael A.

AU - Li, Chengdong

AU - Liepmann, Dorian

AU - Kerr, David

AU - Ahn, David

AU - Peters, Anne L.

AU - Umpierrez, Guillermo E.

AU - Seley, Jane Jeffrie

AU - Xu, Nicole Y.

AU - Nguyen, Kevin T.

AU - Simonson, Gregg

AU - Agus, Michael S.D.

AU - Al-Sofiani, Mohammed E.

AU - Armaiz-Pena, Gustavo

AU - Bailey, Timothy S.

AU - Basu, Ananda

AU - Battelino, Tadej

AU - Bekele, Sewagegn Yeshiwas

AU - Benhamou, Pierre Yves

AU - Bequette, B. Wayne

AU - Blevins, Thomas

AU - Breton, Marc D.

AU - Castle, Jessica R.

AU - Chase, James Geoffrey

AU - Chen, Kong Y.

AU - Choudhary, Pratik

AU - Clements, Mark A.

AU - Close, Kelly L.

AU - Cook, Curtiss B.

AU - Danne, Thomas

AU - Doyle, Francis J.

AU - Drincic, Angela

AU - Dungan, Kathleen M.

AU - Edelman, Steven V.

AU - Ejskjaer, Niels

AU - Espinoza, Juan C.

AU - Fleming, G. Alexander

AU - Forlenza, Gregory P.

AU - Freckmann, Guido

AU - Galindo, Rodolfo J.

AU - Gomez, Ana Maria

AU - Gutow, Hanna A.

AU - Heinemann, Lutz

AU - Hirsch, Irl B.

AU - Hoang, Thanh D.

AU - Hovorka, Roman

AU - Jendle, Johan H.

AU - Ji, Linong

AU - Joshi, Shashank R.

AU - Joubert, Michael

AU - Koliwad, Suneil K.

AU - Lal, Rayhan A.

AU - Lansang, M. Cecilia

AU - Lee, Wei An

AU - Leelarathna, Lalantha

AU - Leiter, Lawrence A.

AU - Lind, Marcus

AU - Litchman, Michelle L.

AU - Mader, Julia K.

AU - Mahoney, Katherine M.

AU - Mankovsky, Boris

AU - Masharani, Umesh

AU - Mathioudakis, Nestoras N.

AU - Mayorov, Alexander

AU - Messler, Jordan

AU - Miller, Joshua D.

AU - Mohan, Viswanathan

AU - Nichols, James H.

AU - Nørgaard, Kirsten

AU - O’Neal, David N.

AU - Pasquel, Francisco J.

AU - Philis-Tsimikas, Athena

AU - Pieber, Thomas

AU - Phillip, Moshe

AU - Polonsky, William H.

AU - Pop-Busui, Rodica

AU - Rayman, Gerry

AU - Rhee, Eun Jung

AU - Russell, Steven J.

AU - Shah, Viral N.

AU - Sherr, Jennifer L.

AU - Sode, Koji

AU - Spanakis, Elias K.

AU - Wake, Deborah J.

AU - Waki, Kayo

AU - Wallia, Amisha

AU - Weinberg, Melissa E.

AU - Wolpert, Howard

AU - Wright, Eugene E.

AU - Zilbermint, Mihail

AU - Kovatchev, Boris

PY - 2023/9

Y1 - 2023/9

N2 - Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

AB - Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

KW - ambulatory glucose profile

KW - composite metric

KW - continuous glucose monitor

KW - diabetes

KW - glycemia risk index

KW - hyperglycemia

KW - hypoglycemia

KW - time in range

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UR - http://www.scopus.com/inward/citedby.url?scp=85127832115&partnerID=8YFLogxK

U2 - 10.1177/19322968221085273

DO - 10.1177/19322968221085273

M3 - Article

C2 - 35348391

AN - SCOPUS:85127832115

SN - 1932-2968

VL - 17

SP - 1226

EP - 1242

JO - Journal of Diabetes Science and Technology

JF - Journal of Diabetes Science and Technology

IS - 5

ER -

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

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