The Association of Time-in-Range and Time-in-Tight-Range with Retinopathy Progression in the Virtual Diabetes Control and Complications Trial Continuous Glucose Monitoring Dataset

<b><i>Background:</i></b> In a prior work, a virtual continuous glucose monitoring (CGM) trace was generated for each of the 1441 participants in the landmark Diabetes Control and Complications trial (DCCT). These new data allow us to compare whether time-in-tight-range (TITR) is a better predictor of diabetic microvascular complications (specifically retinopathy development or progression) than time-in-range (TIR). <b><i>Methods:</i></b> Discrete Cox proportional hazard models were used to calculate the hazard ratios (HRs) for the development/progression of retinopathy. <b><i>Results:</i></b> For a 1.0 standard deviation (SD) change, the adjusted HR (95% confidence interval) was 2.67 (2.33-3.06) for TIR, 2.74 (2.36-3.18) for TITR, and 2.37 (2.13-2.65) for HbA1c; a similar pattern of results was obtained for a 0.5 SD change. Computing Harrell's C-statistic showed that a survival model adjusted for TIR, TITR, or HbA1c had similar predictive performance. <b><i>Conclusion:</i></b> The associations of TIR and TITR with retinopathy development or progression were similar to HbA1c in the virtual DCCT CGM dataset.

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Publication Details

Journal
Diabetes Technology & Therapeutics
Published
2025-02-24
DOI
https://doi.org/10.1089/dia.2025.0033
Citations
7
Primary Topic
Diabetes Management and Research
Type
article
Field-Weighted Citation Impact
9.06
Controls
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article

The Association of Time-in-Range and Time-in-Tight-Range with Retinopathy Progression in the Virtual Diabetes Control and Complications Trial Continuous Glucose Monitoring Dataset

Benjamin J. Lobo, Lauren Kanapka, Boris P. Kovatchev, Roy W. Beck et al.
7 citations
Diabetes Technology & Therapeutics
Diabetes Management and Research
9.06
article

The Association of Time-in-Range and Time-in-Tight-Range with Retinopathy Progression in the Virtual Diabetes Control and Complications Trial Continuous Glucose Monitoring Dataset

Benjamin J. Lobo, Lauren Kanapka, Boris P. Kovatchev, Roy W. Beck, Craig Kollman
article en
7 citations

Abstract

<b><i>Background:</i></b> In a prior work, a virtual continuous glucose monitoring (CGM) trace was generated for each of the 1441 participants in the landmark Diabetes Control and Complications trial (DCCT). These new data allow us to compare whether time-in-tight-range (TITR) is a better predictor of diabetic microvascular complications (specifically retinopathy development or progression) than time-in-range (TIR). <b><i>Methods:</i></b> Discrete Cox proportional hazard models were used to calculate the hazard ratios (HRs) for the development/progression of retinopathy. <b><i>Results:</i></b> For a 1.0 standard deviation (SD) change, the adjusted HR (95% confidence interval) was 2.67 (2.33-3.06) for TIR, 2.74 (2.36-3.18) for TITR, and 2.37 (2.13-2.65) for HbA1c; a similar pattern of results was obtained for a 0.5 SD change. Computing Harrell's C-statistic showed that a survival model adjusted for TIR, TITR, or HbA1c had similar predictive performance. <b><i>Conclusion:</i></b> The associations of TIR and TITR with retinopathy development or progression were similar to HbA1c in the virtual DCCT CGM dataset.

Diabetes Technology & TherapeuticsVol. 27(7)
Jaeb Center for Health Research (US), University of Virginia (US)
Good health and well-being
Openalex Percentile: Top 2%
Diabetes Management and Research
9.06
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The Association of Time-in-Range and Time-in-Tight-Range with Retinopathy Progression in the Virtual Diabetes Control and Complications Trial Continuous Glucose Monitoring Dataset — Benjamin J. Lobo, Lauren Kanapka, et al. · Diabetes Technology & Therapeutics (2025) | TGRS Research Map | TGRS