Association of real-time continuous glucose monitoring with glycemic control and acute metabolic events among patients with insulin-treated diabetes
Open accessAbstract
Importance: Continuous glucose monitoring (CGM) is recommended for patients with type 1 diabetes; observational evidence for CGM in patients with insulin-treated type 2 diabetes is lacking. Objective: To estimate clinical outcomes of real-time CGM initiation. Design, Setting, and Participants: Exploratory retrospective cohort study of changes in outcomes associated with real-time CGM initiation, estimated using a difference-in-differences analysis. A total of 41 753 participants with insulin-treated diabetes (5673 type 1; 36 080 type 2) receiving care from a Northern California integrated health care delivery system (2014-2019), being treated with insulin, self-monitoring their blood glucose levels, and having no prior CGM use were included. Exposures: Initiation vs noninitiation of real-time CGM (reference group). Main Outcomes and Measures: Ten end points measured during the 12 months before and 12 months after baseline: hemoglobin A1c (HbA1c); hypoglycemia (emergency department or hospital utilization); hyperglycemia (emergency department or hospital utilization); HbA1c levels lower than 7%, lower than 8%, and higher than 9%; 1 emergency department encounter or more for any reason; 1 hospitalization or more for any reason; and number of outpatient visits and telephone visits. Results: The real-time CGM initiators included 3806 patients (mean age, 42.4 years [SD, 19.9 years]; 51% female; 91% type 1, 9% type 2); the noninitiators included 37 947 patients (mean age, 63.4 years [SD, 13.4 years]; 49% female; 6% type 1, 94% type 2). The prebaseline mean HbA1c was lower among real-time CGM initiators than among noninitiators, but real-time CGM initiators had higher prebaseline rates of hypoglycemia and hyperglycemia. Mean HbA1c declined among real-time CGM initiators from 8.17% to 7.76% and from 8.28% to 8.19% among noninitiators (adjusted difference-in-differences estimate, -0.40%; 95% CI, -0.48% to -0.32%; P < .001). Hypoglycemia rates declined among real-time CGM initiators from 5.1% to 3.0% and increased among noninitiators from 1.9% to 2.3% (difference-in-differences estimate, -2.7%; 95% CI, -4.4% to -1.1%; P = .001). There were also statistically significant differences in the adjusted net changes in the proportion of patients with HbA1c lower than 7% (adjusted difference-in-differences estimate, 9.6%; 95% CI, 7.1% to 12.2%; P < .001), lower than 8% (adjusted difference-in-differences estimate, 13.1%; 95% CI, 10.2% to 16.1%; P < .001), and higher than 9% (adjusted difference-in-differences estimate, -7.1%; 95% CI, -9.5% to -4.6%; P < .001) and in the number of outpatient visits (adjusted difference-in-differences estimate, -0.4; 95% CI, -0.6 to -0.2; P < .001) and telephone visits (adjusted difference-in-differences estimate, 1.1; 95% CI, 0.8 to 1.4; P < .001). Initiation of real-time CGM was not associated with statistically significant changes in rates of hyperglycemia, emergency department visits for any reason, or hospitalizations for any reason. Conclusions and Relevance: In this retrospective cohort study, insulin-treated patients with diabetes selected by physicians for real-time continuous glucose monitoring compared with noninitiators had significant improvements in hemoglobin A1c and reductions in emergency department visits and hospitalizations for hypoglycemia, but no significant change in emergency department visits or hospitalizations for hyperglycemia or for any reason. Because of the observational study design, findings may have been susceptible to selection bias.
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Basal insulin analogs reduce hypoglycemia risk compared with NPH insulin.
“Basal insulin analogs are preferred over NPH insulin because of a reduced risk of hypoglycemia.1066–1071”
From American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update. (2022) · cites this paper as [1069]
The Hypoglycemia section compares people who initiated real-time continuous glucose monitoring with those who did not. A baseline table lists long-acting and NPH insulin use, but does not compare their hypoglycemia risks. Searches for basal insulin analogs, NPH, and hypoglycemia found no passage supporting the claimed comparison; page numbers were unavailable. (LLM, conf=0.76)
Experimental — accuracy not yet measuredIn adults with type 1 or type 2 diabetes treated with insulin, real-time CGM significantly lowers A1C and reduces emergency department visits or hospitalizations for hypoglycemia, but does not significantly lower overall rates of emergency department visits, hospitalizations, or hyperglycemia.
“Retrospective data from rtCGM use in adults (87) with type 1 or type 2 diabetes treated with insulin showed that the use of rtCGM significantly lowered A1C levels and reduced rates of emergency department visits or hospitalizations for hypoglycemia but did not significantly lower overall rates of emergency department visits, hospitalizations, or hyperglycemia.”
From 7. Diabetes Technology: Standards of Care in Diabetes—2025 (2025) · cites this paper as [87]
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Cites
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.568786 · Cited by 12,242 · see citing works
Correspondence - Tranexamic acid for traumatic brain injury
(2005) · London School of Economics and Political Science Theses Online (London School of Economics and Political Science) · doi:10.1016/s0140-6736 · Cited by 11,894 · see citing works
Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases
DEYO, R. (1992) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(92)90133-8 · Cited by 10,452 · see citing works
Marginal Structural Models and Causal Inference in Epidemiology
Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581 · see citing works
Causal Diagrams for Epidemiologic Research
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00008 · Cited by 3,728 · see citing works
Estimating Causal Effects from Large Data Sets Using Propensity Scores
Rubin, D. V. (1997) · Annals of Internal Medicine · doi:10.7326/0003-4819-127-8_part_2-199710151-00064 · Cited by 3,104 · see citing works
Air Pollution and Hospital Admissions for Heart Disease in Eight U.S. Counties
Schwartz, J. D. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00005 · Cited by 3,044 · see citing works
The statistical power of abnormal-social psychological research: A review.
Cohen, J. (1962) · The Journal of Abnormal and Social Psychology · doi:10.1037/h0045186 · Cited by 1,824 · see citing works
Maternal Pesticide Exposure from Multiple Sources and Selected Congenital Anomalies
Shaw, G. M., Wasserman, C. R., O’Malley, C. D., Nelson, V. E., & Jackson, R. J. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00011 · Cited by 1,783 · see citing works
Methods for Evaluating Changes in Health Care Policy: The Difference-in-Differences Approach
Dimick, J. B., & Ryan, A. M. (2014) · JAMA · doi:10.1001/jama.2014.16153 · Cited by 1,193 · see citing works
Doubly robust estimation of causal effects
Funk, M. J., Westreich, D., Wiesen, C., Stürmer, T., Brookhart, M. A., & Davidian, M. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwq439 · Cited by 1,158 · see citing works
Schneeweiss, S., Rassen, J. A., Glynn, R. J., Avorn, J., Mogun, H., & Brookhart, M. A. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181a663cc · Cited by 1,128 · see citing works
Balancing Covariates via Propensity Score Weighting
Li, F., Morgan, K. L., & Zaslavsky, A. M. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1260466 · Cited by 1,033 · see citing works
Ethnic disparities in diabetic complications in an insured population
Karter, A. J. (2002) · JAMA · doi:10.1001/jama.287.19.2519 · Cited by 840 · see citing works
Self-citationThomas (2020) · doi:10.1001/jama.2020.7819 · Cited by 835 · see citing works
Adjust for multiple comparisons? It’s not that simple
Althouse, A. D. (2016) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2015.11.024 · Cited by 825 · see citing works
Addressing extreme propensity scores via the overlap weights
Li, F., & Thomas, L. E. (2018) · American Journal of Epidemiology · doi:10.1093/aje/kwy201 · Cited by 694 · see citing works
Beck, R. W., Riddlesworth, T. D., Ruedy, K., Ahmann, A., Haller, S., Kruger, D., McGill, J. B., Polonsky, W., Price, D., Aronoff, S., et al. (2017) · Annals of Internal Medicine · doi:10.7326/m16-2855 · Cited by 635 · see citing works
Heinemann, L., Freckmann, G., Ehrmann, D., Faber-Heinemann, G., Guerra, S., Waldenmaier, D., & Hermanns, N. (2018) · The Lancet · doi:10.1016/s0140-6736(18)30297-6 · Cited by 522 · see citing works
Bjørge, T., Tretli, S., & Engeland, A. (2004) · American Journal of Epidemiology · doi:10.1093/aje/ · Cited by 492 · see citing works
Pratley, R. E., Kanapka, L. G., Rickels, M. R., Ahmann, A., Aleppo, G., Beck, R., Bhargava, A., Bode, B. W., Carlson, A., Chaytor, N. S., et al. (2020) · JAMA · doi:10.1001/jama.2020.6928 · Cited by 375 · see citing works
Maiorino, M. I., Signoriello, S., Maio, A., Chiodini, P., Bellastella, G., Scappaticcio, L., Longo, M., Giugliano, D., & Esposito, K. (2020) · Diabetes Care · doi:10.2337/dc19-1459 · Cited by 364 · see citing works
Assessment and Control for Confounding by Indication in Observational Studies
Psaty, B. M., Koepsell, T. D., Lin, D., Weiss, N. S., Siscovick, D. S., Rosendaal, F. R., Pahor, M., & Furberg, C. D. (1999) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.1999.tb01603.x · Cited by 363 · see citing works
Balancing Covariates via Propensity Score WeightingPreprint
Li, F., Lock, K., Alan, M., & Zaslavsky, M. (2014) · Cited by 313 · see citing works
Type 2 Diabetes in the Real World: The Elusive Nature of Glycemic Control
Edelman, S. V., & Polonsky, W. H. (2017) · Diabetes Care · doi:10.2337/dc16-1974 · Cited by 289 · see citing works
7. Diabetes Technology: <i>Standards of Medical Care in Diabetes—2021</i>
American Diabetes Association (2021) · Diabetes Care · doi:10.2337/dc21-s007 · Cited by 202 · see citing works
Parker, M. M., Fernández, A., Moffet, H. H., Grant, R. W., Torreblanca, A., & Karter, A. J. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.8648 · Cited by 201 · see citing works
Self-citationClinicoEconomics and Outcomes Research
(2020) · ClinicoEconomics and Outcomes Research · doi:10.2147/ceor · Cited by 190 · see citing works
Karter, A. J., Warton, E. M., Lipska, K. J., Ralston, J. D., Moffet, H. H., Jackson, G. G., Huang, E. S., & Miller, D. R. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.3844 · Cited by 150 · see citing works
Self-citationLaraia, B. A., Karter, A. J., Warton, E. M., Schillinger, D., Moffet, H. H., & Adler, N. E. (2012) · Social Science & Medicine · doi:10.1016/j.socscimed.2011.11.036 · Cited by 140 · see citing works
Self-citationElectronic medical records can be used to emulate target trials of sustained treatment strategies
Danaei, G., Rodríguez, L. A. G., Cantero, O. F., Logan, R. W., & Hernán, M. Á. (2017) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2017.11.021 · Cited by 135 · see citing works
Zhou, H., Taber, C. R., Arcona, S., & Li, Y. (2016) · Applied Health Economics and Health Policy · doi:10.1007/s40258-016-0249-y · Cited by 126 · see citing works
Why prefer double robust estimators in causal inference?
Neugebauer, R. S., & van der Laan, M. J. (2004) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2004.06.060 · Cited by 125 · see citing works
The Cost-Effectiveness of Continuous Glucose Monitoring in Type 1 Diabetes
Huang, E. S., O’Grady, M. J., Basu, A., Winn, A. N., John, P. M., Lee, J. M., Meltzer, D. O., Kollman, C., Laffel, L. M., Tamborlane, W. V., et al. (2010) · Diabetes Care · doi:10.2337/dc09-2042 · Cited by 117 · see citing works
Lai, C. W., Lipman, T. H., Willi, S. M., & Hawkes, C. P. (2021) · Diabetes Care · doi:10.2337/dc20-1663 · Cited by 114 · see citing works
Ida, S., Kaneko, R., & Murata, K. (2019) · Journal of Diabetes Research · doi:10.1155/2019/4684815 · Cited by 86 · see citing works
Surveillance of hypoglycemia-limitations of emergency department and hospital utilization data
Karter, A. J., Moffet, H. H., Liu, J. Y., & Lipska, K. J. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.1014 · Cited by 73 · see citing works
Self-citationKainz, K., Greifer, N., Givens, A., Swietek, K., Lombardi, B. M., Zietz, S., & Kohn, J. L. (2017) · Journal of the Society for Social Work and Research · doi:10.1086/691464 · Cited by 64 · see citing works
Ahmadi, S. S., Westman, K., Pivodic, A., Ólafsdóttir, A. F., Dahlqvist, S., Hirsch, I. B., Hellman, J., Ekelund, M., Heise, T., Polonsky, W. H., et al. (2020) · Diabetes Care · doi:10.2337/dc19-2606 · Cited by 52 · see citing works
Dicembrini, I., Mannucci, E., Monami, M., & Pala, L. (2019) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13845 · Cited by 46 · see citing works
Roze, S., Isitt, J. J., Smith‐Palmer, J., Javanbakht, M., & Lynch, P. M. (2020) · Diabetes Care · doi:10.2337/dc19-2213 · Cited by 46 · see citing works
Charleer, S., Block, C. E. M. D., Nobels, F. R. E., Radermecker, R. P., Lowyck, I., Mullens, A., Scarnière, D., Spincemaille, K., Strivay, M., Weber, E., et al. (2020) · Diabetes Care · doi:10.2337/dc20-1531 · Cited by 39 · see citing works
Lind, M., Ólafsdóttir, A. F., Hirsch, I. B., Bolinder, J., Dahlqvist, S., Pivodic, A., Hellman, J., Wijkman, M. O., Schwarcz, E., Albrektsson, H., et al. (2020) · Diabetes Care · doi:10.2337/dc20-1468 · Cited by 35 · see citing works
Interesting Conflicts and Conflicting Interests
Whitehouse, P. J. (1999) · Journal of the American Geriatrics Society · doi:10.1111/jgs.1999.47.issue-6 · Cited by 7 · see citing works
The number needed to treat and relevant between-trial comparisons of competing interventions
Jansen, J. P., Khalid, J. M., Smyth, M. D. L., & Patel, H. (2018) · ClinicoEconomics and Outcomes Research · doi:10.2147/ceor.s180491 · Cited by 4 · see citing works
Barbero (2014) · doi:10.1016/j · Cited by 1 · see citing works
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Blonde, L., Umpierrez, G. E., Reddy, S. S., McGill, J. B., Berga, S. L., Bush, M., Chandrasekaran, S., DeFronzo, R. A., Einhorn, D., Galindo, R. J., et al. (2022) · Endocrine Practice · doi:10.1016/j.eprac.2022.08.002 · Cited by 507 · see citing works
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Samson, S. L., Vellanki, P., Blonde, L., Christofides, E. A., Galindo, R. J., Hirsch, I. B., Isaacs, S. D., Izuora, K. E., Low Wang, C. C., Twining, C. L., et al. (2023) · Endocrine Practice · doi:10.1016/j.eprac.2023.02.001 · Cited by 375 · see citing works
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American Diabetes Association Professional Practice Committee, ElSayed, N. A., McCoy, R. G., Aleppo, G., Balapattabi, K., Beverly, E. A., Briggs Early, K., Bruemmer, D., Echouffo-Tcheugui, J. B., Ekhlaspour, L., et al. (2025) · Diabetes Care · doi:10.2337/dc25-s007 · Cited by 348 · see citing works
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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., et al. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221085273 · Cited by 295 · see citing works
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Garg, S. K., Kipnes, M., Castorino, K., Bailey, T. S., Akturk, H. K., Welsh, J. B., Christiansen, M. P., Balo, A. K., Brown, S. A., Reid, J. L., et al. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0011 · Cited by 179 · see citing works
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Elbalshy, M., Haszard, J., Smith, H., Kuroko, S., Galland, B., Oliver, N., Shah, V., de Bock, M. I., & Wheeler, B. J. (2022) · Diabetic Medicine · doi:10.1111/dme.14854 · Cited by 104 · see citing works
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Riveline, J.-P., Roussel, R., Vicaut, E., de Pouvourville, G., Detournay, B., Emery, C., Levrat-Guillen, F., & Guerci, B. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0085 · Cited by 64 · see citing works
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Natale, P., Chen, S., Chow, C. K., Cheung, N. W., Martinez‐Martin, D., Caillaud, C., Scholes‐Robertson, N., Kelly, A., Craig, J. C., Strippoli, G., et al. (2023) · Journal of Diabetes · doi:10.1111/1753-0407.13454 · Cited by 63 · see citing works
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Lanzinger, S., Best, F., Bergmann, T., Laimer, M., Lipovsky, B., Danne, T., Zimny, S., Bramlage, P., Meyhöfer, S., & Holl, R. W. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0063 · Cited by 13 · see citing works
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- Authors
- Karter, A. J., Parker, M. M., Moffet, H. H., Gilliam, L. K., & Dlott, R.
- Affiliations
- Kaiser Permanente (US)
- Kaiser Permanente South San Francisco Medical Center (US)
- Population Care, Regional PROMPT, Regional Clinical, Thyroid Subgroup, TPMG Regional Laboratory Services, Division of Endocrinology - Diablo Service Area, Martinez, California
- Corresponding author
- Karter, Andrew J.
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2021-06-08
- Volume
- 325
- Issue
- 22
- Pages
- 2273
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC8173463
- OpenAlex
- W3164469103
- Cited by
- 191
- References
- 46
- Citations by year
- 2026: 25 · 2025: 49 · 2024: 42 · 2023: 32 · 2022: 28 · 2021: 15
- FWCI
- 21.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Dexcom
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK103721, P30DK092924)
- 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, insulin-treated diabetes, type 1 diabetes, type 2 diabetes, glycemic control, HbA1c, hypoglycemia, hyperglycemia, emergency department utilization, hospitalization, outpatient visits, telephone visits, integrated health system, diabetes technology, self-monitoring of blood glucose
- MeSH terms
- Adult, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Female, Health Services Needs and Demand, Glycated Hemoglobin, Hospitalization, Humans, Hyperglycemia, Hypoglycemia, Hypoglycemic Agents, Insulin, Male, Middle Aged, Retrospective Studies, Time Factors, Blood Glucose Self-Monitoring, Biosensing Techniques, Selection Bias, Confidence Intervals, Treatment Outcome, Propensity Score, Numbers Needed To Treat
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