Rate of hypoglycemia in insulin-treated patients with type 2 diabetes can be predicted from glycemic variability data
Abstract
BACKGROUND: We set out to study the relationship between different measures of glycemic variability and the rate of hypoglycemia in patients with type 2 diabetes. SUBJECTS AND METHODS: Data were pooled from three 24-week insulin trials including patients on twice-daily (BID) insulin lispro mix 75/25 (75% insulin lispro protamine suspension, 25% insulin lispro) (n=805), daily (QD) insulin glargine (n=1,019), insulin lispro protamine suspension (n=353) (QD or BID), and insulin detemir (n=166) (QD or BID), all with continuation of prestudy oral antihyperglycemic medications. Glycemic variability measures were derived from seven-point self-monitored blood glucose profiles. RESULTS: At baseline, mean (±SD) age was 56.9±9.7 years, duration of type 2 diabetes was 9.5±6.1 years, hemoglobin A1c (HbA1c) was 8.9±1.1%, and 51.9% were male. Intra-day glucose coefficient of variation (CV), fasting blood glucose, intra-day minimum glucose and inter-day glucose CV at 24 weeks, and intra-day glucose CV at baseline were significantly correlated with the rate of hypoglycemia events between Weeks 12 to 24 (P<0.05 for all measures). CONCLUSIONS: Intra-day and inter-day glycemic variability is significantly associated with the risk of hypoglycemia in insulin-treated patients with type 2 diabetes, even after adjusting for mean glucose and HbA1c. The intra-day glycemic variability before starting insulin is significantly associated with the risk of hypoglycemia during insulin treatment, which points at treatment- and patient-related factors mediating this relationship.
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Certain conditions such as anemia, hemoglobinopathies, iron deficiency, and pregnancy can confound A1C measurements.
“Moreover, certain conditions such as anemia (36), hemoglobinopathies (37), iron deficiency (38), and pregnancy (39) can confound A1C measurements.”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [36]
Glycemic variability is associated with increased risk of hypoglycemia in patients with diabetes, independently of glycated hemoglobin level.
“Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level.”
From Glycemic variability and cardiovascular disease in patients with type 2 diabetes (2021) · cites this paper as [14]
Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067 · see citing works
Mean Amplitude of Glycemic Excursions, a Measure of Diabetic Instability
Service, F. J., Molnar, G. D., Rosevear, J. W., Ackerman, E., Gatewood, L. C., & Taylor, W. F. (1970) · Diabetes · doi:10.2337/diab.19.9.644 · Cited by 1,039 · see citing works
Glucose variability is associated with intensive care unit mortality*
Hermanides, J., Vriesendorp, T. M., Bosman, R. J., Zandstra, D. F., Hoekstra, J. B. L., & DeVries, J. H. (2010) · Critical Care Medicine · doi:10.1097/ccm.0b013e3181cc4be9 · Cited by 436 · see citing works
Self-citationGlucose Variability; Does It Matter?
Siegelaar, S. E., Holleman, F., Hoekstra, J. B. L., & DeVries, J. H. (2010) · Endocrine Reviews · doi:10.1210/er.2009-0021 · Cited by 409 · see citing works
Self-citationEvaluation of a New Measure of Blood Glucose Variability in Diabetes
Kovatchev, B. P., Otto, E., Cox, D. J., Gonder-Frederick, L. A., & Clarke, W. L. (2006) · Diabetes Care · doi:10.2337/dc06-1085 · Cited by 333 · see citing works
Kilpatrick, E. S., Rigby, A. S., Goode, K. M., & Atkin, S. L. (2007) · Diabetologia · doi:10.1007/s00125-007-0820-z · Cited by 208 · see citing works
The Contribution of Glucose Variability to Asymptomatic Hypoglycemia in Persons with Type 2 Diabetes
Monnier, L., Wojtusciszyn, A., Colette, C., & Owens, D. R. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0049 · Cited by 168 · see citing works
Measurements of Glucose Control
Service, F. J., O’Brien, P. C., & Rizza, R. A. (1987) · Diabetes Care · doi:10.2337/diacare.10.2.225 · Cited by 140 · see citing works
Cox, D. J., Kovatchev, B. P., Julian, D. M., Gonder-Frederick, L. A., Polonsky, W. H., Schlundt, D. G., & Clarke, W. L. (1994) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.79.6.7989471 · Cited by 137 · see citing works
DURAbility of Basal Versus Lispro Mix 75/25 Insulin Efficacy (DURABLE) Trial 24-Week Results
Buse, J. B., Wolffenbuttel, B. H. R., Herman, W. H., Shemonsky, N. K., Jiang, H. H., Fahrbach, J. L., Scism-Bacon, J. L., & Martin, S. (2009) · Diabetes Care · doi:10.2337/dc08-2117 · Cited by 126 · see citing works
Siegelaar, S. E., Kerr, L., Jacober, S. J., & Devries, J. H. (2011) · Diabetes Care · doi:10.2337/dc10-1684 · Cited by 89 · see citing works
Self-citationA Probabilistic Model for Predicting Hypoglycemia in Type 2 Diabetes Mellitus
Murata, G. H., Hoffman, R. M., Shah, J. H., Wendel, C. S., & Duckworth, W. C. (2004) · Archives of Internal Medicine · doi:10.1001/archinte.164.13.1445 · Cited by 73 · see citing works
Fogelfeld, L. A., Dharmalingam, M., Robling, K., Jones, C., Swanson, D., & Jacober, S. J. (2009) · Diabetic Medicine · doi:10.1111/j.1464-5491.2009.02899.x · Cited by 41 · see citing works
Self-citationStrojek, K., Shi, C., Carey, M. A., & Jacober, S. J. (2010) · Diabetes Obesity and Metabolism · doi:10.1111/j.1463-1326.2010.01257.x · Cited by 25 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., et al. (2019) · Diabetes Care · doi:10.2337/dci19-0028 · Cited by 4,032 · see citing works
Self-citationInternational consensus on use of continuous glucose monitoring
Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S., Heinemann, L., Hirsch, I., Amiel, S. A., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1600 · Cited by 2,069 · see citing works
Self-citationHaak, T., Hanaire, H., Ajjan, R., Hermanns, N., Riveline, J.-P., & Rayman, G. (2017) · Diabetes Therapy · doi:10.1007/s13300-016-0223-6 · Cited by 623 · see citing works
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., et al. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0051 · Cited by 307 · see citing works
Glycemic variability and cardiovascular disease in patients with type 2 diabetes
Martinez, M., Santamarina, J., Pavesi, A., Musso, C., & Umpierrez, G. E. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-002032 · Cited by 155 · see citing works
Glucose Variability: A Review of Clinical Applications and Research Developments
Rodbard, D. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0092 · Cited by 154 · see citing works
Glycemic variability: how to measure and its clinical implication for type 2 diabetes
Umpierrez, G. E., & P. Kovatchev, B. (2018) · The American Journal of the Medical Sciences · doi:10.1016/j.amjms.2018.09.010 · Cited by 148 · see citing works
Bergenstal, R. M. (2015) · Diabetes Care · doi:10.2337/dc15-0099 · Cited by 140 · see citing works
Lee, A. K., Lee, C. J., Huang, E. S., Sharrett, A. R., Coresh, J., & Selvin, E. (2017) · Diabetes Care · doi:10.2337/dc17-0819 · Cited by 119 · see citing works
Rama Chandran, S., Tay, W. L., Lye, W. K., Lim, L. L., Ratnasingam, J., Tan, A. T. B., & S.L. Gardner, D. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0388 · Cited by 114 · see citing works
Jin, S.-M., Kim, T.-H., Bae, J. C., Hur, K. Y., Lee, M.-S., Lee, M.-K., & Kim, J. H. (2014) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2014.02.003 · Cited by 74 · see citing works
Hood, R. C., Arakaki, R. F., Wysham, C., Li, Y. G., Settles, J. A., & Jackson, J. A. (2015) · Endocrine Practice · doi:10.4158/ep15612.or · Cited by 61 · see citing works
Significance of Glycemic Variability in Diabetes Mellitus
Kusunoki, Y., Konishi, K., Tsunoda, T., & Koyama, H. (2022) · Internal Medicine · doi:10.2169/internalmedicine.8424-21 · Cited by 40 · see citing works
Chow, L. S., Zmora, R., Ma, S., Seaquist, E. R., & Schreiner, P. J. (2018) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2018-000527 · Cited by 37 · see citing works
Tschöpe, D., Bramlage, P., Schneider, S., & Gitt, A. K. (2016) · Diabetes and Vascular Disease Research · doi:10.1177/1479164115610470 · Cited by 12 · see citing works
- Authors
- Qu, Y., Jacober, S. J., Zhang, Q., Wolka, L. L., & DeVries, J. H.
- Affiliations
- Eli Lilly (United States) (US)
- University of Amsterdam (NL)
- Corresponding author
- DeVries, J. Hans
- Venue
- Diabetes Technology & Therapeutics
- Publisher
- SAGE Publications
- Published
- 2012-11
- Volume
- 14
- Issue
- 11
- Pages
- 1008-1012
- ISSN
- 1520-9156 (ISSN-L)
- 1557-8593
- OpenAlex
- W2059534220
- Cited by
- 126
- References
- 15
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2023: 8 · 2022: 9 · 2021: 8 · 2020: 15 · 2019: 11 · 2018: 14 · 2017: 14 · 2016: 4 · 2015: 12 · 2014: 9 · 2013: 10 · 2012: 1
- FWCI
- 8.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Diabetes Technology & Therapeutics (publishedVersion)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Funders
- Eli Lilly and Company
- 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, Cardiovascular Risks, and Lipoproteins
- Keywords
- hypoglycemia, glycemic variability, type 2 diabetes, insulin therapy, insulin lispro, insulin glargine, insulin detemir, coefficient of variation, self-monitoring of blood glucose, hypoglycemia risk prediction, HbA1c
- MeSH terms
- Analysis of Variance, Blood Glucose, Diabetes Mellitus, Type 2, Fasting, Female, Glycated Hemoglobin, Humans, Hypoglycemia, Hypoglycemic Agents, Insulin, Male, Middle Aged, Predictive Value of Tests, Risk Assessment
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