Validation of Time in Range As an Outcome Measure for Diabetes Clinical Trials

Overview

Journal Diabetes Care

Specialty Endocrinology

Date 2018 Oct 25

PMID 30352896

Citations 281

Authors

Roy W Beck

Richard M Bergenstal

Tonya D Riddlesworth

Craig Kollman

Zhaomian Li

Adam S Brown

Kelly L Close

Affiliations

Soon will be listed here.

Abstract

Objective: This study evaluated the association of time in range (TIR) of 70-180 mg/dL (3.9-10 mmol/L) with the development or progression of retinopathy and development of microalbuminuria using the Diabetes Control and Complications Trial (DCCT) data set in order to validate the use of TIR as an outcome measure for clinical trials.

Research Design And Methods: In the DCCT, blood glucose concentrations were measured at a central laboratory from seven fingerstick samples (seven-point testing: pre- and 90-min postmeals and at bedtime) collected during 1 day every 3 months. Retinopathy progression was assessed every 6 months and urinary microalbuminuria development every 12 months. Proportional hazards models were used to assess the association of TIR and other glycemic metrics, computed from the seven-point fingerstick data, with the rate of development of microvascular complications.

Results: Mean TIR of seven-point profiles for the 1,440 participants was 41 ± 16%. The hazard rate of development of retinopathy progression was increased by 64% (95% CI 51-78), and development of the microalbuminuria outcome was increased by 40% (95% CI 25-56), for each 10 percentage points lower TIR ( < 0.001 for each). Results were similar for mean glucose and hyperglycemia metrics.

Conclusions: Based on these results, a compelling case can be made that TIR is strongly associated with the risk of microvascular complications and should be an acceptable end point for clinical trials. Although hemoglobin A remains a valuable outcome metric in clinical trials, TIR and other glycemic metrics-especially when measured with continuous glucose monitoring-add value as outcome measures in many studies.

Citing Articles

Evaluation of glycemia risk index and continuous glucose monitoring-derived metrics in type 1 diabetes: a real-world observational study.

Al Hayek A, Al Mashali M, Al Dawish M J Diabetes Metab Disord. 2025; 24(1):59.

PMID: 39902092 PMC: 11787086. DOI: 10.1007/s40200-025-01569-w.

International Society for Pediatric and Adolescent Diabetes Clinical Practice Consensus Guidelines 2024 Diabetes Technologies: Glucose Monitoring.

Tauschman M, Cardona-Hernandez R, DeSalvo D, Hood K, Laptev D, Lindholm Olinder A Horm Res Paediatr. 2025; 97(6):615-635.

PMID: 39884260 PMC: 11854985. DOI: 10.1159/000543156.

Effect of Imeglimin, a Novel Anti-Diabetic Agent, on Insulin Secretion and Glycemic Variability in Type 2 Diabetes Treated with DPP-4 Inhibitor: A 16-Week, Open Label, Pilot Study.

Itsukaichi A, Yoshikawa F, Fuchigami A, Iwata Y, Sato G, Miyagi M Diabetes Metab Syndr Obes. 2025; 18():101-111.

PMID: 39807125 PMC: 11727693. DOI: 10.2147/DMSO.S495930.

Do continuous glucose monitoring (CGM) metrics predict macrovascular and microvascular complications in diabetes? The FACULTY protocol of a retrospective real-world cohort study.

Ajjan R, Battelino T, Seufert J, Blin P, de Pouvourville G, Vicaut E BMJ Open. 2025; 15(1):e085961.

PMID: 39779259 PMC: 11749619. DOI: 10.1136/bmjopen-2024-085961.

Effectiveness and safety of iGlarLixi in people with type 2 diabetes not at target on basal insulin and oral antidiabetic therapy in a prospective observational trial.

Seufert J, Wiesner T, Pegelow K, Kenzler J, Pfohl M Diabetes Obes Metab. 2025; 27(3):1526-1535.

PMID: 39757952 PMC: 11802399. DOI: 10.1111/dom.16161.

References

Nathan D, Genuth S, Lachin J, Cleary P, Crofford O, Davis M . The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993; 329(14):977-86. DOI: 10.1056/NEJM199309303291401. View

. The Diabetes Control and Complications Trial (DCCT). Design and methodologic considerations for the feasibility phase. The DCCT Research Group. Diabetes. 1986; 35(5):530-45. View

Novodvorsky P, Bernjak A, Chow E, Iqbal A, Sellors L, Williams S . Diurnal Differences in Risk of Cardiac Arrhythmias During Spontaneous Hypoglycemia in Young People With Type 1 Diabetes. Diabetes Care. 2017; 40(5):655-662. DOI: 10.2337/dc16-2177. View

Service F, OBrien P . The relation of glycaemia to the risk of development and progression of retinopathy in the Diabetic Control and Complications Trial. Diabetologia. 2001; 44(10):1215-20. DOI: 10.1007/s001250100635. View

Lachin J, Bebu I, Bergenstal R, Pop-Busui R, Service F, Zinman B . Association of Glycemic Variability in Type 1 Diabetes With Progression of Microvascular Outcomes in the Diabetes Control and Complications Trial. Diabetes Care. 2017; 40(6):777-783. PMC: 5439414. DOI: 10.2337/dc16-2426. View

Kovatchev B . Metrics for glycaemic control - from HbA to continuous glucose monitoring. Nat Rev Endocrinol. 2017; 13(7):425-436. DOI: 10.1038/nrendo.2017.3. View

Nathan D, McGee P, Steffes M, Lachin J . Relationship of glycated albumin to blood glucose and HbA1c values and to retinopathy, nephropathy, and cardiovascular outcomes in the DCCT/EDIC study. Diabetes. 2013; 63(1):282-90. PMC: 3868040. DOI: 10.2337/db13-0782. View

Welsh K, Kirkman M, Sacks D . Role of Glycated Proteins in the Diagnosis and Management of Diabetes: Research Gaps and Future Directions. Diabetes Care. 2016; 39(8):1299-306. PMC: 4955935. DOI: 10.2337/dc15-2727. View

. Glucose Concentrations of Less Than 3.0 mmol/L (54 mg/dL) Should Be Reported in Clinical Trials: A Joint Position Statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care. 2016; 40(1):155-157. DOI: 10.2337/dc16-2215. View

10.

Yudkin J, Forrest R, Jackson C, Ryle A, Davie S, GOULD B . Unexplained variability of glycated haemoglobin in non-diabetic subjects not related to glycaemia. Diabetologia. 1990; 33(4):208-15. DOI: 10.1007/BF00404798. View

11.

Fiallo-Scharer R, Cheng J, Beck R, Buckingham B, Peter Chase H, Kollman C . Factors predictive of severe hypoglycemia in type 1 diabetes: analysis from the Juvenile Diabetes Research Foundation continuous glucose monitoring randomized control trial dataset. Diabetes Care. 2011; 34(3):586-90. PMC: 3041185. DOI: 10.2337/dc10-1111. View

12.

Agiostratidou G, Anhalt H, Ball D, Blonde L, Gourgari E, Harriman K . Standardizing Clinically Meaningful Outcome Measures Beyond HbA for Type 1 Diabetes: A Consensus Report of the American Association of Clinical Endocrinologists, the American Association of Diabetes Educators, the American Diabetes Association, the.... Diabetes Care. 2017; 40(12):1622-1630. PMC: 5864122. DOI: 10.2337/dc17-1624. View

13.

Kilpatrick E, Rigby A, Atkin S . Effect of glucose variability on the long-term risk of microvascular complications in type 1 diabetes. Diabetes Care. 2009; 32(10):1901-3. PMC: 2752912. DOI: 10.2337/dc09-0109. View

14.

Seaquist E, Anderson J, Childs B, Cryer P, Dagogo-Jack S, Fish L . Hypoglycemia and diabetes: a report of a workgroup of the American Diabetes Association and the Endocrine Society. Diabetes Care. 2013; 36(5):1384-95. PMC: 3631867. DOI: 10.2337/dc12-2480. View

15.

Malka R, Nathan D, Higgins J . Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring. Sci Transl Med. 2016; 8(359):359ra130. PMC: 5714656. DOI: 10.1126/scitranslmed.aaf9304. View

16.

Brod M, Christensen T, Thomsen T, Bushnell D . The impact of non-severe hypoglycemic events on work productivity and diabetes management. Value Health. 2011; 14(5):665-71. DOI: 10.1016/j.jval.2011.02.001. View

17.

Xing D, Kollman C, Beck R, Tamborlane W, Laffel L, Buckingham B . Optimal sampling intervals to assess long-term glycemic control using continuous glucose monitoring. Diabetes Technol Ther. 2011; 13(3):351-8. PMC: 6468940. DOI: 10.1089/dia.2010.0156. View

18.

. Need for Regulatory Change to Incorporate Beyond A1C Glycemic Metrics. Diabetes Care. 2018; 41(6):e92-e94. DOI: 10.2337/dci18-0010. View

19.

Beck R, Connor C, Mullen D, Wesley D, Bergenstal R . The Fallacy of Average: How Using HbA Alone to Assess Glycemic Control Can Be Misleading. Diabetes Care. 2017; 40(8):994-999. PMC: 5521971. DOI: 10.2337/dc17-0636. View

20.

Beck R, Calhoun P, Kollman C . Use of continuous glucose monitoring as an outcome measure in clinical trials. Diabetes Technol Ther. 2012; 14(10):877-82. PMC: 3459006. DOI: 10.1089/dia.2012.0079. View