Proteins related to lipoprotein profile were identified using a pharmaco-proteomic approach as markers for growth response to growth hormone (GH) treatment in short prepubertal children

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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).</p> <p>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.</p> <p>Conclusion</p> <p>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.</p> <p>These results support our previous findings that apolipoproteins and transthyretin may have a role in GH sensitivity.</p

A proteomic approach identified growth hormone-dependent nutrition markers in children with idiopathic short stature

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>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).</p> <p>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).</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

The first-year growth response to growth hormone treatment predicts the long-term prepubertal growth response in children

<p>Abstract</p> <p>Background</p> <p>Pretreatment auxological variables, such as birth size and parental heights, are important predictors of the growth response to GH treatment. For children with missing pretreatment data, published prediction models cannot be used.</p> <p>The objective was to construct and validate a prediction model for children with missing background data based on the observed first-year growth response to GH. The accuracy and reliability of the model should be comparable with our previously published prediction model relying on pretreatment data. The design used was mathematical curve fitting on observed growth response data from children treated with a GH dose of 33 μg/kg/d.</p> <p>Methods</p> <p>Growth response data from 162 prepubertal children born at term were used to construct the model; the group comprised of 19% girls, 80% GH-deficient and 23% born SGA. For validation, data from 205 other children fulfilling the same inclusion and treatment criteria as the model group were used. The model was also tested on data from children born prematurely, children from other continents and children receiving a GH dose of 67 μg/kg/d.</p> <p>Results</p> <p>The GH response curve was similar for all children, but with an individual amplitude. The curve SD score depends on an individual factor combining the effect of dose and growth, the 'Response Score', and time on treatment, making prediction possible when the first-year growth response is known. The prediction interval (± 2 SD_res) was ± 0.34 SDS for the second treatment year growth response, corresponding to ± 1.2 cm for a 3-year-old child and ± 1.8 cm for a 7-year-old child. For the 1–4-year prediction, the SD_reswas 0.13 SDS/year and for the 1–7-year prediction it was 0.57 SDS (i.e. < 0.1 SDS/year).</p> <p>Conclusion</p> <p>The model based on the observed first-year growth response on GH is valid worldwide for the prediction of up to 7 years of prepubertal growth in children with GHD/ISS, born AGA/SGA and born preterm/term, and can be used as an aid in medical decision making.</p

Models predicting the growth response to growth hormone treatment in short children independent of GH status, birth size and gestational age

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>Using only auxological data, the model had a standard error of the residuals (SD_res), of 0.23 SDS. The model was improved when endocrine data (GH_maxprofile, IGF-I and leptin) collected before starting GH treatment were included. Inclusion of these data resulted in a decrease of the SD_resto 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 SD_resfor both types of models. Preterm children were not included in the Model group, but predictions for this group were within the expected range.</p> <p>Conclusion</p> <p>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.</p

Models predicting the growth response to growth hormone treatment in short children independent of GH status, birth size and gestational age-0

<p><b>Copyright information:</b></p><p>Taken from "Models predicting the growth response to growth hormone treatment in short children independent of GH status, birth size and gestational age"</p><p>http://www.biomedcentral.com/1472-6947/7/40</p><p>BMC Medical Informatics and Decision Making 2007;7():40-40.</p><p>Published online 12 Dec 2007</p><p>PMCID:PMC2246105.</p><p></p>p for both models are within the confidence interval, despite the more narrow SD

Different thresholds of tissue-specific dose-responses to growth hormone in short prepubertal children

Background: In addition to stimulating linear growth in children, growth hormone (GH) influences metabolism and body composition. These effects should be considered when individualizing GH treatment as dose-dependent changes in metabolic markers have been reported. Hypothesis: There are different dose-dependent thresholds for metabolic effects in response to GH treatment. Method: A randomized, prospective, multicentre trial TRN 98-0198-003 was performed for a 2-year catch-up growth period, with two treatment regimens (a) individualized GH dose including six different dose groups ranging from 17-100 mu g/kg/day (n=87) and (b) fixed GH dose of 43 mu g/kg/day (n=41). The individualized GH dose group was used for finding dose-response effects, where the effective GH dose (ED 50%) required to achieve 50% Delta effect was calculated with piecewise linear regressions. Results: Different thresholds for the GH dose were found for the metabolic effects. The GH dose to achieve half of a given effect (ED 50%, with 90% confidence interval) was calculated as 33(+/- 24.4) mu g/kg/day for Delta left ventricular diastolic diameter (cm), 39(+/- 24.5) mu g/kg/day for Delta alkaline phosphatase (mu kat/L), 47(+/- 43.5) mu g/kg/day for Delta lean soft tissue (SDS), 48(+/- 35.7) mu g/kg/day for Delta insulin (mU/L), 51(+/- 47.6) mu g/kg/day for Delta height (SDS), and 57(+/- 52.7) mu g/kg/day for Delta insulin-like growth factor I (IGF-I) SDS. Even though lipolysis was seen in all subjects, there was no dose-response effect for Delta fat mass (SDS) or Delta leptin ng/ml in the dose range studied. None of the metabolic effects presented here were related to the dose selection procedure in the trial. Conclusions: Dose-dependent thresholds were observed for different GH effects, with cardiac tissue being the most responsive and level of IGF-I the least responsive. The level of insulin was more responsive than that of IGF-I, with the threshold effect for height in the interval between