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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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. / Bruhn, Lea; Vistisen, Dorte; Amadid, Hanan; Clemmensen, Kim K.B.; Karstoft, Kristian; Ried-Larsen, Mathias; Persson, Frederik; Jørgensen, Marit E.; Møller, Cathrine Laustrup; Stallknecht, Bente; Færch, Kristine; Blond, Martin B.
In: Endocrine, Vol. 81, 2023, p. 67–76.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Predicting the HbA1c level following glucose-lowering interventions in individuals with HbA1c-defined prediabetes
T2 - a post-hoc analysis from the randomized controlled PRE-D trial
AU - Bruhn, Lea
AU - Vistisen, Dorte
AU - Amadid, Hanan
AU - Clemmensen, Kim K.B.
AU - Karstoft, Kristian
AU - Ried-Larsen, Mathias
AU - Persson, Frederik
AU - Jørgensen, Marit E.
AU - Møller, Cathrine Laustrup
AU - Stallknecht, Bente
AU - Færch, Kristine
AU - Blond, Martin B.
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Glycemia
KW - HbA
KW - Prediabetes
KW - Prediction
KW - Stratified medicine
KW - Treatment response
UR - http://www.scopus.com/inward/record.url?scp=85159602544&partnerID=8YFLogxK
U2 - 10.1007/s12020-023-03384-w
DO - 10.1007/s12020-023-03384-w
M3 - Journal article
C2 - 37198379
AN - SCOPUS:85159602544
VL - 81
SP - 67
EP - 76
JO - Endocrine
JF - Endocrine
SN - 1355-008X
ER -
ID: 351035056