Purpose: To investigate whether the prediction of post-treatment HbA_1c levels can be improved by adding an additional biomarker of the glucose metabolism in addition to baseline HbA_1c. Methods: We performed an exploratory analysis based on data from 112 individuals with prediabetes (HbA_1c 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 HbA_1c as the sole glucometabolic marker and six models each containing one additional glucometabolic biomarker in addition to baseline HbA_1c. 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 HbA_1c in individuals with HbA_1c-defined prediabetes.