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
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
The relationships between time in range, hyperglycemia metrics, and HbA1c
Beck, R. W., Bergenstal, R. M., Cheng, P., Kollman, C., Carlson, A. L., Johnson, M. L., & Rodbard, D. (2019) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818822496 · Cited by 479
Self-citationThe relationship of hemoglobin A1C to time-in-range in patients with diabetes
Vigersky, R. A., & McMahon, C. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0310 · Cited by 464
Holt, R. I. G., DeVries, J. H., Fischl, A. H., Hirsch, I. B., Kirkman, M. S., Klupa, T., Ludwig, B., Nørgaard, K., Pettus, J., Renard, É., et al. (2021) · Diabetologia · doi:10.1007/s00125-021-05568-3 · Cited by 470
Maiorino, M. I., Signoriello, S., Maio, A., Chiodini, P., Bellastella, G., Scappaticcio, L., Longo, M., Giugliano, D., & Esposito, K. (2020) · Diabetes Care · doi:10.2337/dc19-1459 · Cited by 364
Bergenstal, R. M., Nimri, R., Beck, R. W., Criego, A., Laffel, L., Schatz, D., Battelino, T., Danne, T., Weinzimer, S. A., Sibayan, J., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(20)32514-9 · Cited by 344
Self-citationYapanis, M., James, S., Craig, M. E., O'Neal, D. N., & Ekinci, E. I. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgac034 · Cited by 319
Grunberger, G., Sherr, J., Allende, M., Blevins, T., Bode, B., Handelsman, Y., Hellman, R., Lajara, R., Roberts, V. L., Rodbard, D., et al. (2021) · Endocrine Practice · doi:10.1016/j.eprac.2021.04.008 · Cited by 288
Wang, S., Du, Y., Zhang, B., Meng, G., Liu, Z., Liew, S. Y., Liang, R., Zhang, Z., Cai, X., Wu, S., et al. (2024) · Cell · doi:10.1016/j.cell.2024.09.004 · Cited by 281
Lu, J., Wang, C., Shen, Y., Chen, L., Zhang, L., Cai, J., Lu, W., Zhu, W., Hu, G., Xia, T., et al. (2021) · Diabetes Care · doi:10.2337/dc20-1862 · Cited by 282
de Bock, M. I., Ethel Codner, D., Craig, M. E., Huynh, T., Maahs, D. M., Mahmud, F. H., Marcovecchio, L., & DiMeglio, L. A. (2022) · Pediatric Diabetes · doi:10.1111/pedi.13455 · Cited by 282
Time in Range Is Associated with Carotid Intima-Media Thickness in Type 2 Diabetes
Lu, J., Ma, X., Shen, Y., Wu, Q. E., Wang, R., Zhang, L., Mo, Y., Lu, W., Zhu, W., Bao, Y., et al. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0251 · Cited by 230
Benhamou, P.-Y., Franc, S., Reznik, Y., Thivolet, C., Schaepelynck, P., Renard, E., Guerci, B., Chaillous, L., Lukas-Croisier, C., Jeandidier, N., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30003-2 · Cited by 217
Glycemic Monitoring and Management in Advanced Chronic Kidney Disease
Galindo, R. J., Beck, R. W., Scioscia, M. F., Umpierrez, G. E., & Tuttle, K. R. (2020) · Endocrine Reviews · doi:10.1210/endrev/bnaa017 · Cited by 211
Self-citationOnce-Weekly Insulin for Type 2 Diabetes without Previous Insulin Treatment
Rosenstock, J., BAJAJ, H. S., Janež, A., Silver, R., Begtrup, K., Hansen, M. V., Jia, T., & Goldenberg, R. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2022474 · Cited by 206
Continuous glucose monitoring: A review of the evidence in type 1 and 2 diabetes mellitus
Lin, R., Brown, F., James, S., Jones, J., & Ekinci, E. (2021) · Diabetic Medicine · doi:10.1111/dme.14528 · Cited by 197
Galindo, R. J., Migdal, A. L., Davis, G. M., Urrutia, M. A., Albury, B., Zambrano, C., Vellanki, P., Pasquel, F. J., Fayfman, M., Peng, L., et al. (2020) · Diabetes Care · doi:10.2337/dc19-2073 · Cited by 181
Positioning time in range in diabetes management
Advani, A. (2020) · Diabetologia · doi:10.1007/s00125-019-05027-0 · Cited by 170
Insulin Therapy in Adults with Type 1 Diabetes Mellitus: a Narrative Review
Janež, A., Guja, C., Mitrakou, A., Lalic, N., Tankova, T., Czupryniak, L., Tabák, A. G., Prazny, M., Martinka, E., & Smircic-Duvnjak, L. (2020) · Diabetes Therapy · doi:10.1007/s13300-019-00743-7 · Cited by 177
Disease Monitoring in Inflammatory Bowel Disease: Evolving Principles and Possibilities
Plevris, N., & Lees, C. W. (2022) · Gastroenterology · doi:10.1053/j.gastro.2022.01.024 · Cited by 178
Visser, M. M., Charleer, S., Fieuws, S., De Block, C., Hilbrands, R., Van Huffel, L., Maes, T., Vanhaverbeke, G., Dirinck, E., Myngheer, N., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(21)00789-3 · Cited by 165
Mayeda, L., Katz, R., Ahmad, I., Bansal, N., Batacchi, Z., Hirsch, I. B., Robinson, N., Trence, D. L., Zelnick, L., & de Boer, I. H. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-000991 · Cited by 162
Riddell, M. C., Li, Z., Gal, R. L., Calhoun, P. M., Jacobs, P. G., Clements, M. A., Martin, C. K., Doyle, F. J., Patton, S. R., Castle, J. R., et al. (2023) · Diabetes Care · doi:10.2337/dc22-1721 · Cited by 158
Self-citationISPAD Clinical Practice Consensus Guidelines 2022: Diabetes technologies: Insulin delivery
Sherr, J. L., Schoelwer, M., Dos Santos, T. J., Reddy, L., Biester, T., Galderisi, A., van Dyk, J. C., Hilliard, M. E., Berget, C., & DiMeglio, L. A. (2022) · Pediatric Diabetes · doi:10.1111/pedi.13421 · Cited by 152
Current and future therapies for type 1 diabetes
von Scholten, B. J., Kreiner, F. F., Gough, S. C. L., & von Herrath, M. (2021) · Diabetologia · doi:10.1007/s00125-021-05398-3 · Cited by 159
Glycaemic management in diabetes: old and new approaches
Ceriello, A., Prattichizzo, F., Phillip, M., Hirsch, I. B., Mathieu, C., & Battelino, T. (2021) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(21)00245-x · Cited by 149
Continuous glucose monitoring: The achievement of 100 years of innovation in diabetes technology
Galindo, R. J., & Aleppo, G. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108502 · Cited by 144
Is HbA1c an ideal biomarker of well-controlled diabetes?
Kaiafa, G. D., Veneti, S., Polychronopoulos, G., Pilalas, D., Daios, S., Kanellos, I., Didangelos, T., Pagoni, S., & Savopoulos, C. G. (2020) · Postgraduate Medical Journal · doi:10.1136/postgradmedj-2020-138756 · Cited by 151
Time in range: a new parameter to evaluate blood glucose control in patients with diabetes
Gabbay, M. A. L., Rodacki, M., Calliari, L. E., Vianna, A. G. D., Krakauer, M., Pinto, M. S., Reis, J. S., Puñales, M. K., Miranda, L. G., Ramalho, A. C., et al. (2020) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-020-00529-z · Cited by 143
Abraham, M. B., de Bock, M. I., Smith, G. J., Dart, J., Fairchild, J. M., King, B. R., Ambler, G. R., Cameron, F. J., McAuley, S. A., KEECH, A. C., et al. (2021) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2021.3965 · Cited by 128
A Systematic Review of Commercial Hybrid Closed-Loop Automated Insulin Delivery Systems
Peacock, S., Frizelle, I., & Hussain, S. (2023) · Diabetes Therapy · doi:10.1007/s13300-023-01394-5 · Cited by 122
BAJAJ, H. S., Bergenstal, R. M., Christoffersen, A., Davies, M. J., Gowda, A., Isendahl, J., Lingvay, I., Senior, P. A., Silver, R., Trevisan, R., et al. (2021) · Diabetes Care · doi:10.2337/dc20-2877 · Cited by 124
Self-citationBraune, K., Lal, R. A., Petruželková, L., Scheiner, G., Winterdijk, P., Schmidt, S., Raimond, L., Hood, K. K., Riddell, M. C., Skinner, T. C., et al. (2022) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(21)00267-9 · Cited by 130
Shamanna, P., Saboo, B., Damodharan, S., Mohammed, J., Mohamed, M., Poon, T., Kleinman, N. L., & Thajudeen, M. (2020) · Diabetes Therapy · doi:10.1007/s13300-020-00931-w · Cited by 121
ISPAD Clinical Practice Consensus Guidelines 2022: Diabetes education in children and adolescents
Olinder, A. L., Deabreu, M., Greene, S. A., Haugstvedt, A., Lange, K., Majaliwa, E., Pais, V., Pélicand, J., Town, M., & Mahmud, F. H. (2022) · Pediatric Diabetes · doi:10.1111/pedi.13418 · Cited by 142
Peralta, F. G., Choudhary, P., Cosson, E., Irace, C., Rami-Merhar, B. M., & Seibold, A. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14638 · Cited by 120
Malahi, A. E., Elsen, M. V., Charleer, S., Dirinck, E. L., Ledeganck, K. J., Keymeulen, B., Crenier, L., Radermecker, R. P., Taes, Y., Vercammen, C., et al. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab688 · Cited by 115
Johnson, M. L., Martens, T. W., Criego, A. B., Carlson, A. L., Simonson, G. D., & Bergenstal, R. M. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0034 · Cited by 106
Self-citationRaj, R., Mishra, R., Jha, N., Joshi, V. R., Correa, R. R., & Kern, P. A. (2022) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2021-002573 · Cited by 110
ISPAD Clinical Practice Consensus Guidelines 2022: Diabetes technologies: Glucose monitoring
Tauschmann, M., Forlenza, G. P., Hood, K. K., Cardona‐Hernandez, R., Giani, E., Hendrieckx, C., DeSalvo, D. J., Laffel, L. M., Saboo, B., Wheeler, B. J., et al. (2022) · Pediatric Diabetes · doi:10.1111/pedi.13451 · Cited by 123
Yoo, J. H., Choi, M. S., Ahn, J., Park, S. W., Kim, Y., Hur, K. Y., Jin, S., Kim, G., & Kim, J. H. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0499 · Cited by 102
HbA1c and Glucose Management Indicator Discordance: A Real-World Analysis
Perlman, J. E., Gooley, T. A., MCNULTY, B. K., Meyers, J., & Hirsch, I. B. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0501 · Cited by 101
Lingvay, I., Buse, J. B., Franek, E., Hansen, M. V., Koefoed, M. M., Mathieu, C., Pettus, J., Stachlewska, K., & Rosenstock, J. (2021) · Diabetes Care · doi:10.2337/dc20-2878 · Cited by 99
Time in Range from Continuous Glucose Monitoring: A Novel Metric for Glycemic Control
Yoo, J. H., & Kim, J. H. (2020) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2020.0257 · Cited by 98
The effect of elexacaftor/tezacaftor/ivacaftor (ETI) on glycemia in adults with cystic fibrosis
Scully, K. J., Marchetti, P., Sawicki, G. S., Uluer, A., Cernadas, M., Cagnina, R. E., Kennedy, J. C., & Putman, M. S. (2021) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2021.09.001 · Cited by 95
Bergenstal, R. M., Mullen, D. M., Strock, E., Johnson, M. L., & Xi, M. X. (2022) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2021.108106 · Cited by 87
Self-citationAleppo, G., Beck, R. W., Bailey, R., Ruedy, K. J., Calhoun, P., Peters, A. L., Pop-Busui, R., Philis-Tsimikas, A., Bao, S., Umpierrez, G., et al. (2021) · Diabetes Care · doi:10.2337/dc21-1304 · Cited by 82
Self-citationEvolution of Diabetes Technology
Dovč, K., & Battelino, T. (2019) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2019.10.009 · Cited by 93
Closed-loop control in insulin pumps for type-1 diabetes mellitus: safety and efficacy
Fuchs, J., & Hovorka, R. (2020) · Expert Review of Medical Devices · doi:10.1080/17434440.2020.1784724 · Cited by 91
cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data
Vigers, T., Chan, C. L., Snell-Bergeon, J., Bjornstad, P., Zeitler, P., Forlenza, G. P., & Pyle, L. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216851 · Cited by 78
The trials and tribulations of determining HbA1c targets for diabetes mellitus
Klein, K. R., & Buse, J. B. (2020) · Nature Reviews Endocrinology · doi:10.1038/s41574-020-00425-6 · Cited by 91
Forlenza, G. P., Buckingham, B. A., Brown, S. A., Bode, B. W., Levy, C. J., Criego, A. B., Wadwa, R. P., Cobry, E. C., Slover, R., Messer, L. H., et al. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0546 · Cited by 79
Uhl, S., Choure, A., Rouse, B., Loblack, A., & Reaven, P. (2024) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgad652 · Cited by 76
Guo, Q., Zang, P., Xu, S.-Y., Song, W., Zhang, Z., Liu, C., Guo, Z., Chen, J., Lu, B., Gu, P., et al. (2020) · Journal of Diabetes Research · doi:10.1155/2020/5817074 · Cited by 75
Digital Diabetes Management: A Literature Review of Smart Insulin Pens
Heinemann, L., Schnell, O., Gehr, B., Schloot, N. C., Görgens, S. W., & Görgen, C. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820983863 · Cited by 77
Karges, B., Tittel, S. R., Bey, A., Freiberg, C., Klinkert, C., Kordonouri, O., Thiele‐Schmitz, S., Schröder, C., Steigleder‐Schweiger, C., & Holl, R. W. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(23)00061-x · Cited by 77
Jiao, X., Shen, Y., & Chen, Y. (2022) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2021-002633 · Cited by 76
Rodbard, D. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0440 · Cited by 75
Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes
Emerson, H., Guy, M., & McConville, R. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104376 · Cited by 78
Meulemeester, J. D., Charleer, S., Visser, M. M., Block, C. E. M. D., Mathieu, C., & Gillard, P. (2024) · Diabetologia · doi:10.1007/s00125-024-06171-y · Cited by 82
Ranjan, A. G., Rosenlund, S. V., Hansen, T. W., Rossing, P., Andersen, S., & Nørgaard, K. (2020) · Diabetes Care · doi:10.2337/dc20-0909 · Cited by 72
Shah, V. N., Kanapka, L. G., Aktürk, H. K., Polsky, S., Forlenza, G. P., Kollman, C., Beck, R. W., & Snell‐Bergeon, J. K. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0486 · Cited by 75
Self-citationDehghani Zahedani, A., Shariat Torbaghan, S., Rahili, S., Karlin, K., Scilley, D., Thakkar, R., Saberi, M., Hashemi, N., Perelman, D., Aghaeepour, N., et al. (2021) · Diabetes Therapy · doi:10.1007/s13300-021-01081-3 · Cited by 62
Li, F., Zhang, Y., Li, H., Lu, J., Jiang, L., Vigersky, R. A., Zhou, J., Wang, C., Bao, Y., & Jia, W. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108289 · Cited by 69
Reddy, N., Verma, N., & Dungan, K. M. (2020) · MDText.com, Inc. eBooks · Cited by 68
JENDLE, J. H., Ericsson, Å., Gundgaard, J., Møller, J. B., Valentine, W. J., & Hunt, B. (2020) · Diabetes Therapy · doi:10.1007/s13300-020-00980-1 · Cited by 68
Godoi, A., Marques, I. R., Padrão, E. M. H., Mahesh, A., Hespanhol, L. C., Júnior, J. E. R. L., de Souza, I. A. F., Moreira, V. C. S., Silva, C. H., Miyawaki, I. A., et al. (2023) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01144-4 · Cited by 79
Battelino, T., Bergenstal, R. M., Rodríguez, A., Landó, L. F., Bray, R., Tong, Z., & Brown, K. (2022) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(22)00077-8 · Cited by 65
Self-citationExpanding the Role of Continuous Glucose Monitoring in Modern Diabetes Care Beyond Type 1 Disease
Klupa, T., Czupryniak, L., Dzida, G. J., Fichna, P., Jarosz-Chobot, P. A., Gumprecht, J., Myśliwiec, M., Szadkowska, A., Bomba-Opon, D. A., Czajkowski, K., et al. (2023) · Diabetes Therapy · doi:10.1007/s13300-023-01431-3 · Cited by 89
Time in range centered diabetes care
Dovč, K., & Battelino, T. (2021) · Clinical Pediatric Endocrinology · doi:10.1297/cpe.30.1 · Cited by 66
Precision medicine in type 1 diabetes
Carr, A. L. J., Evans‐Molina, C., & Oram, R. A. (2022) · Diabetologia · doi:10.1007/s00125-022-05778-3 · Cited by 69
Glycaemic variability: The under‐recognized therapeutic target in type 1 diabetes care
Wilmot, E. G., Choudhary, P., Leelarathna, L., & Baxter, M. (2019) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13842 · Cited by 67
Biester, T., Muller, I., Berge, T. V. D., Atlas, E., Nimri, R., Phillip, M., Battelino, T., Bratina, N., Dovč, K., Scheerer, M. F., et al. (2020) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14258 · Cited by 61
Monnier, L., Wojtusciszyn, A., Molinari, N., Colette, C., Renard, É., & Owens, D. R. (2020) · Diabetes Care · doi:10.2337/dc19-1549 · Cited by 62
Fabris, C., Heinemann, L., Beck, R., Cobelli, C., & Kovatchev, B. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0236 · Cited by 58
Self-citationNovel Biochemical Markers of Glycemia to Predict Pregnancy Outcomes in Women With Type 1 Diabetes
Meek, C. L., Tundidor, D., Feig, D. S., Yamamoto, J. M., SCOTT, E. M., Ma, D.-D., Halperin, J. A., Murphy, H. R., & Corcoy, R. (2021) · Diabetes Care · doi:10.2337/dc20-2360 · Cited by 59
Knoll, C., Peacock, S., Wäldchen, M., Cooper, D., Aulakh, S. K., Raile, K., Hussain, S., & Braune, K. (2021) · Diabetic Medicine · doi:10.1111/dme.14741 · Cited by 62
Powell, D. R., Zambrowicz, B., Morrow, L., Beysen, C., Hompesch, M., Turner, S., Hellerstein, M. K., Banks, P. L., Strumph, P., & Lapuerta, P. (2019) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgz258 · Cited by 60
Pease, A., Lo, C., Earnest, A., Kiriakova, V., Liew, D., & Zoungas, S. (2020) · Diabetes Care · doi:10.2337/dc19-1785 · Cited by 65
Real-world evidence of automated insulin delivery system use
Considine, E. G., & Sherr, J. L. (2024) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2023.0442 · Cited by 59
Furler, J., O'Neal, D., Speight, J., Blackberry, I., Manski-Nankervis, J.-A., Thuraisingam, S., de La Rue, K., Ginnivan, L., Doyle, R., Holmes-Truscott, E., et al. (2020) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(19)30385-7 · Cited by 55
Tauschmann, M., Cardona‐Hernandez, R., DeSalvo, D. J., Hood, K. K., Лаптев, Д. Н., Olinder, A. L., Wheeler, B. J., & Smart, C. E. (2025) · Hormone Research in Paediatrics · doi:10.1159/000543156 · Cited by 69
Wilmot, E. G., Lumb, A. N., Hammond, P. J., Murphy, H. R., SCOTT, E. M., Gibb, F. W., Platts, J. K., & Choudhary, P. (2020) · Diabetic Medicine · doi:10.1111/dme.14433 · Cited by 54
Correlation Between Glycemic Variability and Diabetic Complications: A Narrative Review
Huang, L., Pan, Y., Zhou, K., Liu, H., & Zhong, S. (2023) · International Journal of General Medicine · doi:10.2147/ijgm.s418520 · Cited by 54
Cherubini, V., Bonfanti, R., Casertano, A., Nitto, E. D., Iannilli, A., Lombardo, F., Maltoni, G., Marigliano, M., Bassi, M., Minuto, N., et al. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0031 · Cited by 53
Alharthi, S. K., Alyusuf, E. Y., Alguwaihes, A. M., Alfadda, A. A., & Al‐Sofiani, M. E. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108682 · Cited by 53
Di, J., Demanuele, C., Kettermann, A. E., Karahanoğlu, F. I., Cappelleri, J. C., Potter, A., Bury, D. P., Cedarbaum, J. M., & Byrom, B. (2021) · Contemporary Clinical Trials · doi:10.1016/j.cct.2021.106661 · Cited by 57
Dementia in Diabetes: The Role of Hypoglycemia
Husain, K. H., Sarhan, S. F., AlKhalifa, H. K. A. A., Buhasan, A., Moin, A. S. M., & Butler, A. E. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24129846 · Cited by 52
Monitoring of Pediatric Type 1 Diabetes
Marks, B. E., & Wolfsdorf, J. I. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00128 · Cited by 55
Lu, J., Ma, X., Zhang, L., Mo, Y., Lu, W., Zhu, W., Bao, Y., Jia, W., & Zhou, J. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108032 · Cited by 49
de Kreutzenberg, S. V. (2022) · High Blood Pressure & Cardiovascular Prevention · doi:10.1007/s40292-022-00524-7 · Cited by 48
Dovč, K., Lanzinger, S., Cardona‐Hernandez, R., Tauschmann, M., Marigliano, M., Cherubini, V., Preikša, R. T., Schierloh, U. H. E., Clapin, H. F., Aljaser, F., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.0077 · Cited by 49
Connecting the Dots: Validation of Time in Range Metrics With Microvascular Outcomes
Hirsch, I. B., Sherr, J. L., & Hood, K. K. (2019) · Diabetes Care · doi:10.2337/dci18-0040 · Cited by 48
Glucodensities: A new representation of glucose profiles using distributional data analysis
Matabuena, M., Petersen, A., Vidal, J. C., & Gude, F. (2021) · Statistical Methods in Medical Research · doi:10.1177/0962280221998064 · Cited by 47
Mathieu, C., Dandona, P., Phillip, M., Oron, T., Lind, M., Hansen, L., THOREN, F. A., Xu, J. L., Langkilde, A. M., & on behalf of the DEPICT-1 and DEPICT-2 Investigators (2019) · Diabetes Care · doi:10.2337/dc18-1983 · Cited by 48
Diabetes education in pediatrics: How to survive diabetes
Ergun-Longmire, B., Clemente, E. G., Maravolo, P. V., Roberts, C., Buth, K., & Greydanus, D. E. (2021) · Disease-a-Month · doi:10.1016/j.disamonth.2021.101153 · Cited by 49
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