Predicting the HbA1c level following glucose-lowering interventions in individuals with HbA1c-defined prediabetes: a post-hoc analysis from the randomized controlled PRE-D trial

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Purpose: To investigate whether the prediction of post-treatment HbA1c levels can be improved by adding an additional biomarker of the glucose metabolism in addition to baseline HbA1c. Methods: We performed an exploratory analysis based on data from 112 individuals with prediabetes (HbA1c 39–47 mmol) and overweight/obesity (BMI ≥ 25 kg/m2), who completed 13 weeks of glucose-lowering interventions (exercise, dapagliflozin, or metformin) or control (habitual living) in the PRE-D trial. Seven prediction models were tested; one basic model with baseline HbA1c as the sole glucometabolic marker and six models each containing one additional glucometabolic biomarker in addition to baseline HbA1c. The additional glucometabolic biomarkers included: 1) plasma fructosamine, 2) fasting plasma glucose, 3) fasting plasma glucose × fasting serum insulin, 4) mean glucose during a 6-day free-living period measured by a continuous glucose monitor 5) mean glucose during an oral glucose tolerance test, and 6) mean plasma glucose × mean serum insulin during the oral glucose tolerance test. The primary outcome was overall goodness of fit (R 2) from the internal validation step in bootstrap-based analysis using general linear models. Results: The prediction models explained 46–50% of the variation (R 2) in post-treatment HbA1c with standard deviations of the estimates of ~2 mmol/mol. R 2 was not statistically significantly different in the models containing an additional glucometabolic biomarker when compared to the basic model. Conclusion: Adding an additional biomarker of the glucose metabolism did not improve the prediction of post-treatment HbA1c in individuals with HbA1c-defined prediabetes.

Sider (fra-til)67–76
StatusUdgivet - 2023

Bibliografisk note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

ID: 351035056