Methods for quantifying self-monitoring blood glucose profiles exemplified by an examination of blood glucose patterns in patients with type 1 and type 2 diabetes
- PMID: 12165168
- DOI: 10.1089/152091502760098438
Methods for quantifying self-monitoring blood glucose profiles exemplified by an examination of blood glucose patterns in patients with type 1 and type 2 diabetes
Abstract
The maintenance of glycemic control in patients with type 1 or type 2 diabetes mellitus (T1DM and T2DM, respectively) is commonly assisted by devices for self-monitoring of blood glucose (SMBG) that store multiple BG determinations. However, besides average BG, no other SMBG characteristics are routinely computed. We describe several SMBG-based measures that quantify the extent and rate of patients' BG excursions into hypoglycemia and hyperglycemia and can be used as markers for patients' vulnerability to hypoglycemia and BG irregularity. These markers are applied to analyze data from patients with T1DM (n = 277) and T2DM (n = 323), all of whom used insulin. T1DM and T2DM patients were matched by HbA(1c), gender, and number of SMBG readings/day. On average, 230 SMBG readings and three HbA(1c) assays were collected for each subject over 3 months. Compared with T2DM, patients with T1DM diabetes had (1) more extreme low and high BGs, (2) greater risk for severe hypoglycemia as quantified by the Low BG Index, (3) faster descent into hypoglycemia as quantified by the risk rate of change/hour, and (4) greater BG irregularity as computed by BG rate of change/hour and BG SD (all p levels < 0.0001). SMBG data allow for computing and frequent updating of various idiosyncratic diabetes characteristics and risk factors. The use of such computations may assist in optimizing patients' glycemic control.
Similar articles
-
Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data.Diabetes Technol Ther. 2003;5(5):817-28. doi: 10.1089/152091503322527021. Diabetes Technol Ther. 2003. PMID: 14633347
-
Evaluation of blood glucose fluctuation in Japanese patients with type 1 diabetes mellitus by self-monitoring of blood glucose and continuous glucose monitoring.Diabetes Res Clin Pract. 2015 May;108(2):342-9. doi: 10.1016/j.diabres.2015.01.040. Epub 2015 Mar 4. Diabetes Res Clin Pract. 2015. PMID: 25779865 Clinical Trial.
-
Beyond HbA1c.J Diabetes. 2017 Dec;9(12):1052-1053. doi: 10.1111/1753-0407.12590. Epub 2017 Sep 13. J Diabetes. 2017. PMID: 28792665
-
Meta-analysis of the benefits of self-monitoring of blood glucose on glycemic control in type 2 diabetes patients: an update.Diabetes Technol Ther. 2009 Dec;11(12):775-84. doi: 10.1089/dia.2009.0091. Diabetes Technol Ther. 2009. PMID: 20001678
-
Value and utility of self-monitoring of blood glucose in non-insulin-treated patients with type 2 diabetes mellitus.Postgrad Med. 2013 May;125(3):191-204. doi: 10.3810/pgm.2013.05.2668. Postgrad Med. 2013. PMID: 23748520 Review.
Cited by
-
Arguments for and against the role of glucose variability in the development of diabetes complications.J Diabetes Sci Technol. 2009 Jul 1;3(4):649-55. doi: 10.1177/193229680900300405. J Diabetes Sci Technol. 2009. PMID: 20144307 Free PMC article. Review.
-
Peculiarities of the continuous glucose monitoring data stream and their impact on developing closed-loop control technology.J Diabetes Sci Technol. 2008 Jan;2(1):158-63. doi: 10.1177/193229680800200125. J Diabetes Sci Technol. 2008. PMID: 19578532 Free PMC article.
-
From data to insights: a tool for comprehensive Quantification of Continuous Glucose Monitoring (QoCGM).PeerJ. 2025 Jun 9;13:e19501. doi: 10.7717/peerj.19501. eCollection 2025. PeerJ. 2025. PMID: 40511383 Free PMC article.
-
Intensive structured self-monitoring of blood glucose and glycemic control in noninsulin-treated type 2 diabetes: the PRISMA randomized trial.Diabetes Care. 2013 Oct;36(10):2887-94. doi: 10.2337/dc13-0092. Epub 2013 Jun 4. Diabetes Care. 2013. PMID: 23735724 Free PMC article. Clinical Trial.
-
Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas.Diabetes Care. 2016 Jul;39(7):1135-42. doi: 10.2337/dc15-2344. Epub 2016 Jun 11. Diabetes Care. 2016. PMID: 27289127 Free PMC article. Clinical Trial.
Publication types
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical
Miscellaneous