The DOI matches its Crossref record; 27 of the 29 references it cites were found in a registry or an index.
Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials
Open accessAbstract
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 (P < 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 A1c 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In particular, glucose time in range (70 -180 mg/dl [3.9 -10.0 mmol/l]) and time in hypoglycemia ( < 70 mg/dl [3.9 mmol/l] and < 54 mg/dl [3.0 mmol/l]) have been used as outcomes for clinical trials 259,260 and have been endorsed as appropriate metrics for clinical care. 229”
From KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease (2022) · cites this paper as [259]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Although the evidence of HbA 1c reduction remains the most robust measure associated with chronic diabetes complications and is the only measure that is prospectively validated, more recent studies have begun to examine the relationship between time that glucose is within the target range and long-term complications and have provided the basis for glycemic targets with newer glucose monitoring”
From The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) (2021) · cites this paper as [45]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“GMI is calculated based on the average sensor glucose over the last 14 days and provides an approximation of a laboratory-measured HbA 1c in some individuals, but it may be higher or lower than actual HbA 1c in others (45).”
From The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) (2021) · cites this paper as [45]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“TIR is associated with microvascular complications (45,46), and a TIR of 70% roughly corresponds to an HbA 1c of 53 mmol/mol (7.0%).”
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Most recently, the beneficial effects of new medications, such as sodium glucose cotransporter-2 (SGLT-2) agents have helped individuals with type 1 diabetes increase TIR (49-51).”
From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [50]
Experimental — accuracy not yet measured
In the citing paper
“TIR has been found to be strongly associated with the development or progression of diabetic retinopathy and microalbuminuria in type 1 and type 2 DM.16 17”
From Glucose time in range and peripheral neuropathy in type 2 diabetes mellitus and chronic kidney disease (2020) · cites this paper as [16]
In the citing paper
“In a post hoc analysis from the Diabetes Control and Complications Trial (DCCT), Beck et al. (8) calculated TIR with seven-point fingerstick glucose values and demonstrated significant associations of TIR with the development of diabetic retinopathy and microalbuminuria.”
From Time in Range in Relation to All-Cause and Cardiovascular Mortality in Patients With Type 2 Diabetes: A Prospective Cohort Study (2021) · cites this paper as [8]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1993) · New England Journal of Medicine · doi:10.1056/nejm199309303291401 · Cited by 24,667
International 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
Self-citationSeaquist, E. R., Anderson, J., Childs, B., Cryer, P., Dagogo-Jack, S., Fish, L., Heller, S. R., Rodriguez, H., Rosenzweig, J., & Vigersky, R. (2013) · Diabetes Care · doi:10.2337/dc12-2480 · Cited by 1,389
International Hypoglycaemia Study Group (2017) · Diabetes Care · doi:10.2337/dc16-2215 · Cited by 608
The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading
Beck, R. W., Connor, C. G., Mullen, D. M., Wesley, D. M., & Bergenstal, R. M. (2017) · Diabetes Care · doi:10.2337/dc17-0636 · Cited by 477
Self-citationThe DCCT Research Group (1986) · Diabetes · doi:10.2337/diabetes.35.5.530 · Cited by 466
Agiostratidou, G., Anhalt, H., Ball, D., Blonde, L., Gourgari, E., Harriman, K. N., Kowalski, A. J., Madden, P., McAuliffe-Fogarty, A. H., McElwee-Malloy, M., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1624 · Cited by 397
The Effect of Glucose Variability on the Risk of Microvascular Complications in Type 1 Diabetes
Kilpatrick, E. S., Rigby, A. S., & Atkin, S. L. (2006) · Diabetes Care · doi:10.2337/dc06-0293 · Cited by 376
The International Hypoglycaemia Study Group (2016) · Diabetologia · doi:10.1007/s00125-016-4146-6 · Cited by 367
The impact of non-severe hypoglycemic events on work productivity and diabetes management
Brod, M., Christensen, T., Thomsen, T. L., & Bushnell, D. M. (2011) · Value in Health · doi:10.1016/j.jval.2011.02.001 · Cited by 284
Metrics for glycaemic control - from HbA1c to continuous glucose monitoring
Kovatchev, B. P. (2017) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2017.3 · Cited by 272
Nathan, D. M., McGee, P., Steffes, M. W., Lachin, J. M., & the DCCT/EDIC Research Group (2014) · Diabetes · doi:10.2337/db13-0782 · Cited by 214
Welsh, K. J., Kirkman, M. S., & Sacks, D. B. (2016) · Diabetes Care · doi:10.2337/dc15-2727 · Cited by 212
Lachin, J. M., Bebu, I., Bergenstal, R. M., Pop‐Busui, R., Service, F. J., Zinman, B., & Nathan, D. M. (2017) · Diabetes Care · doi:10.2337/dc16-2426 · Cited by 192
Self-citationUnexplained variability of glycated haemoglobin in non-diabetic subjects not related to glycaemia
Yudkin, J. S., Forrest, R. D., Jackson, C. A., Ryle, A., Davie, S. J., & GOULD, B. J. (1990) · Diabetologia · doi:10.1007/bf00404798 · Cited by 154
Kilpatrick, E. S., Rigby, A. S., & Atkin, S. L. (2009) · Diabetes Care · doi:10.2337/dc09-0109 · Cited by 144
Malka, R., Nathan, D. M., & Higgins, J. M. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aaf9304 · Cited by 134
Optimal sampling intervals to assess long-term glycemic control using continuous glucose monitoring
Xing, D., Kollman, C., Beck, R. W., Tamborlane, W. V., Laffel, L., Buckingham, B. A., Wilson, D. M., Weinzimer, S., Fiallo-Scharer, R., & Ruedy for the Juvenile Diabetes Research Foundation Continuo, K. J. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2010.0156 · Cited by 131
Novodvorsky, P., Bernjak, A., Chow, E., Iqbal, A., Sellors, L., Williams, S., Fawdry, R. A., Parekh, B., Jacques, R. M., Marques, J. L. B., et al. (2017) · Diabetes Care · doi:10.2337/dc16-2177 · Cited by 111
Service, F. J., & O'Brien, P. C. (2001) · Diabetologia · doi:10.1007/s001250100635 · Cited by 106
The Diabetes Research in Children Network (DirecNet) Study Group (2005) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2004-2510 · Cited by 88
Need for Regulatory Change to Incorporate Beyond A1C Glycemic Metrics
Beyond A1C Writing Group (2018) · Diabetes Care · doi:10.2337/dci18-0010 · Cited by 88
Runge, A. S., Kennedy, L., Brown, A. S., Dove, A. E., Levine, B. J., Koontz, S. P., Iyengar, V. S., Odeh, S. A., Close, K. L., Hirsch, I. B., et al. (2018) · Clinical Diabetes · doi:10.2337/cd17-0094 · Cited by 87
Self-citationBrod, M., Pohlman, B., Wolden, M., & Christensen, T. (2013) · Quality of Life Research · doi:10.1007/s11136-012-0234-3 · Cited by 71
Factors Predictive of Severe Hypoglycemia in Type 1 Diabetes
Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group (2011) · Diabetes Care · doi:10.2337/dc10-1111 · Cited by 65
Use of Continuous Glucose Monitoring as an Outcome Measure in Clinical Trials
Beck, R. W., Calhoun, P. M., & Kollman, C. (2012) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2012.0079 · Cited by 58
Self-citationFOSTER, N. C., MILLER, K., DIMEGLIO, L., MAAHS, D. M., TAMBORLANE, W. V., BERGENSTAL, R. M., CLEMENTS, M. A., RICKELS, M. R., SMITH, E., OLSON, B. A., et al. (2018) · Diabetes · doi:10.2337/db18-1689-p · Cited by 19
Self-citation
Flagged first, then by citations
2 of the 29 references couldn’t be matched to a known paper.
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 4,237
“0 mmol/L]) with diabetes complications (49,50) as well as a relationship between TIR and A1C (26,27).”
BackgroundMethodSelf-citationKDIGO 2020 Clinical practice guideline for diabetes management in chronic kidney disease
de Boer, I. H., Caramori, M. L., Chan, J. C. N., Heerspink, H. J. L., Hurst, C., Khunti, K., Liew, A., Michos, E. D., Navaneethan, S. D., Olowu, W. A., et al. (2020) · Kidney International · doi:10.1016/j.kint.2020.06.019 · Cited by 1,399
KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease
Rossing, P., Caramori, M. L., Chan, J. C. N., Heerspink, H. J. L., Hurst, C., Khunti, K., Liew, A., Michos, E. D., Navaneethan, S. D., Olowu, W. A., et al. (2022) · Kidney International · doi:10.1016/j.kint.2022.06.008 · Cited by 1,373
Continuous glucose monitoring and metrics for clinical trials: an international consensus statement
Battelino, T., Alexander, C. M., Amiel, S. A., Arreaza-Rubin, G., Beck, R. W., Bergenstal, R. M., Buckingham, B. A., Carroll, J., Ceriello, A., Chow, E., et al. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(22)00319-9 · Cited by 831
Self-citationHolt, R. I. G., DeVries, J. H., Hess-Fischl, A., Hirsch, I. B., Kirkman, M. S., Klupa, T., Ludwig, B., Nørgaard, K., Pettus, J., Renard, E., et al. (2021) · Diabetes Care · doi:10.2337/dci21-0043 · Cited by 699
Metadata
- Authors
- Beck, R. W., Bergenstal, R. M., Riddlesworth, T. D., Kollman, C., Li, Z., Brown, A. S., & Close, K. L.
- Affiliations
- Jaeb Center for Health Research (US)
- Park Nicollet Health Services (US)
- Close Concerns (United States) (US)
- The diaTribe Foundation, San Francisco, CA
- Corresponding author
- Beck, Roy W.
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2018-10-23
- Volume
- 42
- Issue
- 3
- Pages
- 400-405
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC6905478
- OpenAlex
- W2897457722
- Cited by
- 793
- References
- 29
- Citations by year
- 2026: 73 · 2025: 93 · 2024: 87 · 2023: 106 · 2022: 138 · 2021: 134 · 2020: 103 · 2019: 54 · 2018: 2
- FWCI
- 57.92
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes Care (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Jaeb Center for Health Research Foundation
- 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
- time in range, glycemic control, retinopathy progression, microalbuminuria, microvascular complications, outcome measure validation, Diabetes Control and Complications Trial, continuous glucose monitoring, HbA1c, glycemic metrics
- MeSH terms
- Adult, Blood Glucose, Clinical Trials as Topic, Diabetes Mellitus, Female, Glycated Hemoglobin, Humans, Longitudinal Studies, Male, Middle Aged, Patient Care Planning, Prognosis, Research Design, Time Factors, Blood Glucose Self-Monitoring, Treatment Outcome, Outcome Assessment, Health Care, Disease Progression, Diabetes Complications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review