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Background Mathematical models can be used to predict individual growth responses to growth hormone (GH) therapy. The aim of this study was to construct and validate high-precision models to predict the growth response to GH treatment of short children, independent of their GH status, birth size and gestational age. As the GH doses are included, these models can be used to individualize treatment. Methods Growth data from 415 short prepubertal children were used to construct models for predicting the growth response during the first years of GH therapy. The performance of the models was validated with data from a separate cohort of 112 children using the same inclusion criteria. Results Using only auxological data, the model had a standard error of the residuals (SDres), of 0.23 SDS. The model was improved when endocrine data (GHmax profile, IGF-I and leptin) collected before starting GH treatment were included. Inclusion of these data resulted in a decrease of the SDres to 0.15 SDS (corresponding to 1.1 cm in a 3-year-old child and 1.6 cm in a 7-year old). Validation of these models with a separate cohort, showed similar SDres for both types of models. Preterm children were not included in the Model group, but predictions for this group were within the expected range. Conclusion These prediction models can with high accuracy be used to identify short children who will benefit from GH treatment. They are clinically useful as they are constructed using data from short children with a broad range of GH secretory status, birth size and gestational age.

Background The broad range in growth observed in short prepubertal children receiving the same growth hormone (GH) dose is due to individual variation in GH responsiveness. This study used a pharmaco-proteomic approach in order to identify novel biomarkers that discriminate between short non-GH-deficient (GHD) children who show a good or poor growth response to GH treatment. A group of 32 prepubertal children with idiopathic short stature (ISS) were included in the study. Children were classified on the basis of their first year growth velocity as either good (high responders, n = 13; range, 0.9–1.3 standard deviation score (SDS) or poor (low responders, n = 19; range, 0.3–0.5 SDS) responders to GH treatment (33 μg/kg daily). Serum protein expression profiles before, and after 1 year of GH treatment, were analyzed on a weak cationic exchange array (CM10) using surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS). Results Changes in the intensity of two protein peaks (13.788 kDa and 17.139 kD) during the study period allowed the correct classification of 82% of children as high and low responders, respectively. The 13.788 kD peak, transthyretin, decreased in the high-responder group and increased in the low-responder group during 1 year of GH treatment, whereas the 17.139 kDa peak, apolipoprotein A-II (Apo A-II) decreased in the high-responder group and remained unchanged in the low-responder group. These peaks were identified by the consistency of peak pattern in the spectra, serum depletion experiments using specific antibodies and mass spectrometry. Conclusion Our results suggest that transthyretin and apolipoprotein A-II may have a role in GH sensitivity and could be used as markers to predict which short prepubertal children with ISS will show a good or poor response to GH treatment.

Background The broad range in growth observed in response to growth hormone (GH) treatment is mainly caused by individual variations in both GH secretion and GH sensitivity. Individual GH responsiveness can be estimated using evidence-based models that predict the response to GH treatment; however, these models can be improved. High-throughput proteomics techniques can be used to identify proteins that may potentially be used as variables in such models in order to improve their predictive ability. Previously we have reported that proteomic analyses can identify biomarkers that discriminate between short prepubertal children with idiopathic short stature (ISS) who show good or poor growth in response to GH treatment. In this study we used a pharmaco-proteomic approach to identify novel factors that correlate with the growth response to GH treatment in prepubertal children who are short due to GH deficiency or ISS. The study included 128 short prepubertal children receiving GH treatment, of whom 39 were GH-deficient and 89 had ISS. Serum protein expression profiles at study start and after 1 year of GH treatment were analyzed using SELDI-TOF. Cross-validated regression and random permutation analyses were performed to identify significant correlations between protein expression patterns and the 2-year growth response to GH treatment. Results At start of treatment we identified a combination of seven protein peaks that correlated with the 2-year growth response in the GH-deficient group (R2 = 0.73). After 1 year of treatment, a combination of four peaks in the GH-deficient group (R2 = 0.64), eight peaks in the ISS group R2 = 0.47) and eight peaks in the total study group correlated with the 2-year growth response R2 = 0.38). The peaks identified corresponded to apolipoproteins A-I, A-II, C-I, C-III, transthyretin and serum amyloid A 4, which are all part of the high-density lipoprotein. Conclusion Using a proteomic approach we identified biomarkers related to the lipoprotein profile that could be used to predict growth response to GH treatment in prepubertal children who are short as a result of GH-deficiency or who have ISS. These results support our previous findings that apolipoproteins and transthyretin may have a role in GH sensitivity.