cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data

doi:10.1371/journal.pone.0216851

. 2019 Oct 11;14(10):e0216851.

doi: 10.1371/journal.pone.0216851. eCollection 2019.

cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data

Tim Vigers^{1

2

3}, Christine L Chan¹, Janet Snell-Bergeon², Petter Bjornstad¹, Philip S Zeitler¹, Gregory Forlenza², Laura Pyle^{1

2

3}

Affiliations

¹ Section of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, Colorado, United States of America.
² Barbara Davis Center, University of Colorado School of Medicine, Aurora, Colorado, United States of America.
³ Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, United States of America.

PMID: 31603912
PMCID: PMC6788735
DOI: 10.1371/journal.pone.0216851

Free PMC article

cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data

Tim Vigers et al. PLoS One. 2019.

Free PMC article

. 2019 Oct 11;14(10):e0216851.

doi: 10.1371/journal.pone.0216851. eCollection 2019.

Authors

Tim Vigers^{1

2

3}, Christine L Chan¹, Janet Snell-Bergeon², Petter Bjornstad¹, Philip S Zeitler¹, Gregory Forlenza², Laura Pyle^{1

2

3}

Affiliations

¹ Section of Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, Colorado, United States of America.
² Barbara Davis Center, University of Colorado School of Medicine, Aurora, Colorado, United States of America.
³ Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, United States of America.

PMID: 31603912
PMCID: PMC6788735
DOI: 10.1371/journal.pone.0216851

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Abstract

Continuous glucose monitoring (CGM) is an essential part of diabetes care. Real-time CGM data are beneficial to patients for daily glucose management, and aggregate summary statistics of CGM measures are valuable to direct insulin dosing and as a tool for researchers in clinical trials. Yet, the various commercial systems still report CGM data in disparate, non-standard ways. Accordingly, there is a need for a standardized, free, open-source approach to CGM data management and analysis. A package titled cgmanalysis was developed in the free programming language R to provide a rapid, easy, and consistent methodology for CGM data management, summary measure calculation, and descriptive analysis. Variables calculated by our package compare well to those generated by various CGM software, and our functions provide a more comprehensive list of summary measures available to clinicians and researchers. Consistent handling of CGM data using our R package may facilitate collaboration between research groups and contribute to a better understanding of free-living glucose patterns.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

**Fig 1. Aggregate Daily Overlay (Tukey Smoothing).**

**Fig 2. Aggregate Daily Overlay (Loess Smoothing).**

**Fig 3. Daily Overlay per Subject (LOESS Smoothing).**

**Fig 4**
**“cgmanalysis” Package Plots Compared to iPro 2 Daily Overlay** Clockwise from top left: Aggregate Daily Overlay (Tukey Smoothing), Aggregate Daily Overlay (Loess Smoothing), iPro 2 Daily Overlay.

**Fig 5**
**“cgmanalysis” Package Plots Compared to Carelink 670G Daily Overlay** Clockwise from top left: Aggregate Daily Overlay (Tukey Smoothing), Aggregate Daily Overlay (Loess Smoothing), Carelink 670G Daily Overlay.

**Fig 6**
**“cgmanalysis” Package Plots Compared to Dexcom Clarity Daily Overlay** Clockwise from top left: Aggregate Daily Overlay (Tukey Smoothing), Aggregate Daily Overlay (Loess Smoothing), Dexcom Daily Overlay.

**Fig 7**
**“cgmanalysis” Package Plots Compared to Diasend Daily Overlay** Clockwise from top left: Aggregate Daily Overlay (Tukey Smoothing), Aggregate Daily Overlay (Loess Smoothing), Diasend Daily Overlay.

See this image and copyright information in PMC

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References

1. DeSalvo DJ, Miller KM, Hermann JM, Maahs DM, Hofer SE, Clements MA, et al. Continuous glucose monitoring and glycemic control among youth with type 1 diabetes: International comparison from the T1D Exchange and DPV Initiative. Pediatr Diabetes 2018; 19(7): 1271–1275. 10.1111/pedi.12711 - DOI - PMC - PubMed
1. Beck RW, Bergenstal RM, Riddlesworth TD, Kollman C, Li Z, Brown AS, et al. Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials. Diabetes Care 2019; 42(3): 400–405. 10.2337/dc18-1444 - DOI - PMC - PubMed
1. Bergenstal RM, Beck RW, Close KL, Grunberger G, Sacks DB, Kowalski A, et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care 2018; 41(11): 2275–2280. 10.2337/dc18-1581 - DOI - PMC - PubMed
1. Hernandez TL, Barbour LA. A standard approach to continuous glucose monitor data in pregnancy for the study of fetal growth and infant outcomes. Diabetes Technol Ther 2013; 15(2): 172–9. 10.1089/dia.2012.0223 - DOI - PMC - PubMed
1. Hill NR, Oliver NS, Choudhary P, Levy JC, Hindmarsh P, Matthews DR. Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups. Diabetes Technol Ther 2011; 13(9): 921–8. 10.1089/dia.2010.0247 - DOI - PMC - PubMed

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[1] DeSalvo DJ, Miller KM, Hermann JM, Maahs DM, Hofer SE, Clements MA, et al. Continuous glucose monitoring and glycemic control among youth with type 1 diabetes: International comparison from the T1D Exchange and DPV Initiative. Pediatr Diabetes 2018; 19(7): 1271–1275. 10.1111/pedi.12711 - DOI - PMC - PubMed

[2] DeSalvo DJ, Miller KM, Hermann JM, Maahs DM, Hofer SE, Clements MA, et al. Continuous glucose monitoring and glycemic control among youth with type 1 diabetes: International comparison from the T1D Exchange and DPV Initiative. Pediatr Diabetes 2018; 19(7): 1271–1275. 10.1111/pedi.12711 - DOI - PMC - PubMed

[3] Beck RW, Bergenstal RM, Riddlesworth TD, Kollman C, Li Z, Brown AS, et al. Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials. Diabetes Care 2019; 42(3): 400–405. 10.2337/dc18-1444 - DOI - PMC - PubMed

[4] Beck RW, Bergenstal RM, Riddlesworth TD, Kollman C, Li Z, Brown AS, et al. Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials. Diabetes Care 2019; 42(3): 400–405. 10.2337/dc18-1444 - DOI - PMC - PubMed

[5] Bergenstal RM, Beck RW, Close KL, Grunberger G, Sacks DB, Kowalski A, et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care 2018; 41(11): 2275–2280. 10.2337/dc18-1581 - DOI - PMC - PubMed

[6] Bergenstal RM, Beck RW, Close KL, Grunberger G, Sacks DB, Kowalski A, et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care 2018; 41(11): 2275–2280. 10.2337/dc18-1581 - DOI - PMC - PubMed

[7] Hernandez TL, Barbour LA. A standard approach to continuous glucose monitor data in pregnancy for the study of fetal growth and infant outcomes. Diabetes Technol Ther 2013; 15(2): 172–9. 10.1089/dia.2012.0223 - DOI - PMC - PubMed

[8] Hernandez TL, Barbour LA. A standard approach to continuous glucose monitor data in pregnancy for the study of fetal growth and infant outcomes. Diabetes Technol Ther 2013; 15(2): 172–9. 10.1089/dia.2012.0223 - DOI - PMC - PubMed

[9] Hill NR, Oliver NS, Choudhary P, Levy JC, Hindmarsh P, Matthews DR. Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups. Diabetes Technol Ther 2011; 13(9): 921–8. 10.1089/dia.2010.0247 - DOI - PMC - PubMed

[10] Hill NR, Oliver NS, Choudhary P, Levy JC, Hindmarsh P, Matthews DR. Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups. Diabetes Technol Ther 2011; 13(9): 921–8. 10.1089/dia.2010.0247 - DOI - PMC - PubMed

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