Models to predict changes in serum IGF1 and body composition in response to GH replacement therapy in GH-deficient adults

Objective: Clinical response to GH therapy in GH-deficient (GHD) adults varies widely. Good predictors of treatment response are lacking. The aim of the study was to develop mathematical models to predict changes in serum IGF1 and body composition (BC) in response to GH therapy in GHD adults. Design and methods: One hundred and sixty-seven GHD patients (103 men, median age 50 years) were studied before and after 12 months of GH treatment. GH dose was tailored according to serum IGF1 concentrations. Good responders (GR) and poor responders (PR) to GH therapy were defined as patients with a response O60th and !40th percentile respectively, for changes in serum IGF1 levels (adjusted for GH cumulative dose) and in BC (lean body mass (LBM) and body fat determined using dual-energy X-ray absorptiometry). A logistic regression model was used to predict the probability of being a GR or PR. Results: In the IGF1 prediction model, men (odds ratio (OR) 5.62: 95% confidence interval 2.59‐12.18) and patients with higher insulin levels (OR 1.06: 1.00‐1.12) were more likely to be GR. The accuracy of the prediction model was 70%. In the BC model, men (OR 10.72: 1.36‐84.18) and GHD patients with lower LBM (OR 0.82: 0.73‐0.92) and greater height (OR 1.23: 1.08‐1.40) at baseline were more likely to be GR. The accuracy of the prediction model was 80%. Conclusion: Accurate mathematical models to predict GH responsiveness in GHD adults were developed using gender, body height, baseline LBM, and serum insulin levels as the major clinical predictors.