The adoption of continuous glucose monitoring (CGM) results in vast amounts of data, but their interpretation is still more art than exact science. The International Consensus on Time in Range (TIR) proposed the widely accepted TIR system of metrics, which we now take forward by introducing a finite and fixed set of clinically similar clusters (CSCs), such that the TIR metrics of daily CGM profiles within a cluster are homogeneous.
Methods:
CSC definition and validation used 204,710 daily CGM profiles in health, and types 1 and 2 diabetes (T1D and T2D) on different treatments. The CSCs were defined using 23,916 daily CGM profiles (Training set), and the final fixed set of CSCs was obtained using another 37,758 profiles (Validation set). The Testing set (143,036 profiles) was used to establish the robustness and generalizability of CSCs.
Results:
The final set of CSCs contains 32 clusters. Any daily CGM profile was classifiable to a single CSC, which approximated common glycemic metrics of the daily CGM profile, as evidenced by regression analyses with 0 intercept (R-squares ≥0.83, e.g., correlation ≥0.91), for all TIR and several other metrics. The CSCs distinguished CGM profiles in health, T2D, and T1D on different treatments, and allowed tracking of daily changes in a person's glycemic control over time.
Conclusion:
Daily CGM profiles can be classified into one of 32 prefixed CSCs, which enables a host of applications, for example, tabulated data interpretation and algorithmic approaches to treatment, database indexing, pattern recognition, and tracking disease progression.
Clinical Trial Registration:
N/A—not a clinical trial.
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References
1.
BretonMD, KovatchevBP. One year real-world use of the control-IQ advanced hybrid closed-loop technology. Diabetes Technol Ther, 2021; 23(9):601–608; doi: 10.1089/dia.2021.0097
2.
KovatchevBP, SinghH, MuellerL, Gonder-FrederickLA. Biobehavioral changes following transition to automated insulin delivery: A large real-life database analysis. Diabetes Care, 2022; 45(11):2636–2643; doi: 10.2337/dc22-1217
3.
BaghurstPA. Calculating the mean amplitude of glycemic excursion from continuous glucose monitoring data: An automated algorithm. Diabetes Technol Ther, 2011; 13(3):296–302; doi: 10.1089/dia.2010.0090
4.
FabrisC, PatekSD, BretonMD. Are risk indices derived from CGM interchangeable with SMBG-based indices?. J Diabetes Sci Technol, 2015; 10(1):50–59; doi: 10.1177/1932296815599177
KovatchevBP, ClarkeWL, BretonM, et al.Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: Mathematical methods and clinical application. Diabetes Technol Ther, 2005; 7(6):849–862; doi: 10.1089/dia.2005.7.849
7.
RodbardD. New and improved methods to characterize glycemic variability using continuous glucose monitoring. Diabetes Technol Ther, 2009; 11(9):551–565; doi: 10.1089/dia.2009.0015
8.
RodbardD. Interpretation of continuous glucose monitoring data: Glycemic variability and quality of glycemic control. Diabetes Technol Ther, 2009; 11(Suppl. 1):S55–S67; doi: 10.1089/dia.2008.0132;Erratumin:DiabetesTechnolTher2018;20(4):320.
MagniL, RaimondoDM, ManCD, et al.Evaluating the efficacy of closed-loop glucose regulation via control-variability grid analysis. J Diabetes Sci Technol, 2008; 2(4):630–635; doi: 10.1177/193229680800200414
11.
CobelliC, ManCD, SparacinoG, et al.Diabetes: Models, signals, and control. IEEE Rev Biomed Eng, 2009; 2:54–96; doi: 10.1109/RBME.2009.2036073
12.
KovatchevB, CobelliC. Glucose variability: Timing, risk analysis, and relationship to hypoglycemia in diabetes. Diabetes Care, 2016; 39(4):502–510; doi: 10.2337/dc15-2035
13.
BergenstalRM, BeckRW, CloseKL, et al.Glucose management indicator (GMI): A new term for estimating A1C from continuous glucose monitoring. Diabetes Care, 2018; 41(11):2275–2280; doi: 10.2337/dc18-1581
14.
KlonoffDC, WangJ, RodbardD, et al. A Glycemia Risk Index (GRI) of hypoglycemia and hyperglycemia for continuous glucose monitoring validated by clinician ratings. J Diabetes Sci Technol, 2022:19322968221085273; doi: 10.1177/19322968221085273
15.
KovatchevBP. Metrics for glycaemic control: From HbA1c to continuous glucose monitoring. Nat Rev Endocrinol, 2017; 13:425–436.
16.
MontaserE, FabrisC, KovatchevB. Essential continuous glucose monitoring metrics: The principal dimensions of glycemic control in diabetes. Diabetes Technol Ther, 2022; 24(11):797–804; doi: 10.1089/dia.2022.0104
17.
BattelinoT, DanneT, BergenstalRM, et al.Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the international consensus on time in range. Diabetes Care, 2019; 42:1593–1603.
18.
MazzeRS, LucidoD, LangerO, et al.Ambulatory glucose profile: Representation of verified self-monitored blood glucose data. Diabetes Care, 1987; 10(1):111–117.
19.
BergenstalRM, AhmannAJ, BaileyT, et al.Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: The Ambulatory Glucose Profile (AGP). Diabetes Technol Ther, 2013; 15(3):198–211.
20.
American Diabetes Association. 7. Diabetes Technology: Standards of Care in Diabetes—2023. Diabetes Care, 2023; 46(Suppl. 1):S111–S127; doi: 10.2337/dc23-S007
21.
FonsecaV, GrunbergerG. Letter to the editor: standard glucose reporting: Follow-up to the February 2016 AACE CGM Consensus Conference. Endocr Pract, 2017; 23:629–632.
22.
PhillipM, NimriR, BergenstalRM, et al.Consensus recommendations for the use of automated insulin delivery (AID) technologies in clinical practice. Endocr Rev, 2023; 44(2):254–280; doi: 10.1210/endrev/bnac022
23.
BattelinoT, AlexanderCM, AmielSA, et al.Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol, 2023; 11(1):42–57; doi: 10.1016/S2213-8587(22)00319-9
24.
LaffelLM, KanapkaLG, BeckRW, et al; CGM intervention in teens and young adults with T1D (CITY) study group; CDE10. Effect of continuous glucose monitoring on glycemic control in adolescents and young adults with type 1 diabetes: A randomized clinical trial. JAMA, 2020; 323(23):2388–2396; doi: 10.1001/jama.2020.6940
25.
KovatchevB, AndersonSM, RaghinaruD, et al.Randomized controlled trial of mobile closed-loop control. Diabetes Care, 2020; 43(3):607–615; doi: 10.2337/dc19-1310;Erratumin:DiabetesCare2020;43(6):1366.
26.
BrownSA, KovatchevBP, RaghinaruD, et al.Six-month randomized, multicenter trial of closed-loop control in type 1 diabetes. N Engl J Med, 2019; 381(18):1707–1717; doi: 10.1056/NEJMoa1907863
27.
BretonMD, KanapkaLG, BeckRW, et al.A randomized trial of closed-loop control in children with type 1 diabetes. N Engl J Med, 2020; 383(9):836–845; doi: 10.1056/NEJMoa2004736
28.
BeckRW, RiddlesworthT, RuedyK, et al.Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections: The DIAMOND randomized clinical trial. JAMA, 2017; 317(4):371–378; doi: 10.1001/jama.2016.19975
29.
BeckRW, RiddlesworthTD, RuedyK, et al.Continuous glucose monitoring versus usual care in patients with type 2 diabetes receiving multiple daily insulin injections: A randomized trial. Ann Intern Med, 2017; 167(6):365–374; doi: 10.7326/M16-2855
30.
BisioA, AndersonS, NorlanderL, et al.Impact of a novel diabetes support system on a cohort of individuals with type 1 diabetes treated with multiple daily injections: A multicenter randomized study. Diabetes Care, 2022; 45(1):186–193; doi: 10.2337/dc21-0838
31.
RickelsMR, DuBoseSN, ToschiE, et al.Mini-Dose Glucagon Exercise Study Group. Mini-dose glucagon as a novel approach to prevent exercise-induced hypoglycemia in type 1 diabetes. Diabetes Care, 2018; 41(9):1909–1916; doi: 10.2337/dc18-0051
32.
ShahVN, DuBoseSN, LiZ, et al.Continuous glucose monitoring profiles in healthy nondiabetic participants: A multicenter prospective study. J Clin Endocrinol Metab, 2019; 104(10):4356–4364; doi: 10.1210/jc.2018-02763;Erratumin:JClinEndocrinolMetab2022;107(4):e1775-e1776.
33.
KovatchevBP, KollarL, AndersonSM, et al.Evening and overnight closed-loop control versus 24/7 continuous closed-loop control for type 1 diabetes: A randomised crossover trial. Lancet Digit Health, 2020; 2(2):e64–e73; doi: 10.1016/S2589-7500(19)30218-3
34.
AleppoG, RuedyKJ, RiddlesworthTD, et al.REPLACE-BG: A randomized trial comparing continuous glucose monitoring with and without routine blood glucose monitoring in adults with well-controlled type 1 diabetes. Diabetes Care, 2017; 40(4):538–545; doi: 10.2337/dc16-2482
35.
Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group; TamborlaneWV, BeckRW, BodeBW, et al.Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med, 2008; 359(14):1464–1476; doi: 10.1056/NEJMoa0805017
36.
Strategies to Enhance New CGM Use in Early Childhood (SENCE) Study Group. A randomized clinical trial assessing continuous glucose monitoring (CGM) use with standardized education with or without a family behavioral intervention compared with fingerstick blood glucose monitoring in very young children with type 1 diabetes. Diabetes Care, 2021; 44(2):464–472; doi: 10.2337/dc20-1060
37.
WeinstockRS, DuBoseSN, BergenstalRM, et al.Risk factors associated with severe hypoglycemia in older adults with type 1 diabetes. Diabetes Care, 2016; 39(4):603–610; doi: 10.2337/dc15-1426
38.
MontaserE, VillardO, FabrisC, et al.Estimating the immunological risk for type 1 diabetes from a CGM-based one week self-administered home test. Diabetes, 2022; 71(Suppl. 1):171-LB; doi: 10.2337/db22-171-LB
39.
CarlsonAL, KanapkaLG, MillerKM, et al.Hypoglycemia and glycemic control in older adults with type 1 diabetes: Baseline results from the WISDM study. J Diabetes Sci Technol, 2021; 15(3):582–592; doi: 10.1177/1932296819894974
40.
LoboB, FarhyL, ShafieiM, KovatchevB. A data-driven approach to classifying daily continuous glucose monitoring (CGM) time series. IEEE Trans Biomed Eng, 2022; 69(2):654–665; doi: 10.1109/TBME.2021.3103127
41.
LoboBJ, KovatchevBP. External validation of a classifier of daily continuous glucose monitoring (CGM) profiles. Comput Biol Med, 2022; 143:105293; doi: 10.1016/j.compbiomed.2022.105293
42.
RodbardD. Evaluating quality of glycemic control: Graphical displays of hypo- and hyperglycemia, time in target range, and mean glucose. J Diabetes Sci Technol, 2015; 9(1):56–62; doi: 10.1177/1932296814551046
43.
VargheseJS, HoJC, AnjanaRM, et al.Profiles of intraday glucose in type 2 diabetes and their association with complications: An analysis of continuous glucose monitoring data. Diabetes Technol Ther, 2021; 23(8):555–564; doi: 10.1089/dia.2020.0672
44.
HallH, PerelmanD, BreschiA, et al.Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol, 2018; 16(7):e2005143; doi: 10.1371/journal.pbio.2005143
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