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BACKGROUND The QEPS-growth-model, developed and validated in GrowUp-Gothenburg cohorts, used for developing growth references and investigating healthy/pathological growth, lacks external validation from other longitudinal cohorts of healthy individuals. AIM To investigate if the QEPS-model can fit the longitudinal Edinburgh growth study of another design than GrowUp-Gothenburg cohorts, and to compare growth patterns in the individuals born in mid-1970s in North-Western Europe. METHODS Longitudinal growth data were obtained from the Edinburgh and the GrowUp1974Gothenburg cohorts. The QEPS-model was used to describe length/height from birth to adult height with confidence interval, and the multivariable regression model for estimating the contribution of the different QEPS-functions to adult height. RESULTS The QEPS-model fitted the Edinburgh cohort well, with high accuracy, and low confidence intervals indicating high precision. Despite 3 cm shorter stature (less QE-function growth) in Scottish children, the growth patterns of the cohorts were similar, especially for specific pubertal growth. The contribution to adult height from different QEPS functions was similar. CONCLUSION The QEPS-model is validated for the first time in a longitudinal study of healthy individuals of another design and found to fit with high accuracy and precision. The Scottish and Western-Swedish cohorts born in mid-1970s showed similar growth patterns for both sexes, especially pubertal growth. IMPACT For the first time, the QEPS height model was used and found to fit another longitudinal cohort of healthy individuals other than the Swedish longitudinal cohorts. With large numbers of individual measurements in each growth phase, the QEPS model calculates growth estimates with narrow confidence intervals (high precision) and high accuracy. The two different cohorts born in the mid-1970s from Scotland and Western Sweden have similar growth patterns, despite a 3 cm difference in adult height.

AIM To update the Swedish references for weight, weight-for-height and body mass index (BMI) considering the secular trend for height but not including that for weight. METHODS Longitudinal measures of height and weight were obtained (0-18 years) from 1418 (698 girls) healthy children from the GrowUp 1990 Gothenburg cohort born at term to non-smoking mothers and Nordic parents. A total of 145 individuals with extreme BMI value vs GrowUp 1974 BMI SDS reference were excluded (0-2 years: ±4SDS, 2

Aim We aimed to develop up‐to‐date references with standard deviation scores (SDS) for prepubertal and total height. Methods Longitudinal length/height measures from 1572 healthy children (51.5% boys) born at term in 1989‐1991 to non‐smoking mothers and Nordic parents were obtained from the GrowUp 1990 Gothenburg cohort. A total height SDS reference from birth to adult height was constructed from Quadratic‐Exponential‐Pubertal‐Stop (QEPS) function estimated heights based on individual growth curves. A prepubertal height SDS reference, showing growth trajectory in the absence of puberty, was constructed using the QE functions. Results The total height reference showed taller prepubertal mean heights (for boys 1‐2 cm; for girls 0.5‐1.0 cm) with a narrower normal within ± 2SDS range vs the GrowUp 1974 Gothenburg reference. Adult height was increased by + 0.9 cm for women (168.6 cm) and by + 1.6 cm for men (182.0 cm). Height in children growing at −2SDS (the cut‐off used for referrals) differed up to 2 cm vs the GrowUp 1974 Gothenburg reference, 3 cm vs Swedish 1981 references and World Health Organisation (WHO) 0‐5 years standard, and 6‐8 cm vs the WHO 5‐19 years reference. Conclusion Up‐to‐date total and prepubertal height references offer promise of improved growth monitoring compared with the references used in Sweden today.

Background Growth references are traditionally constructed relative to chronological age, despite inter-individual variations in pubertal timing. A new type of height reference was recently developed allowing growth to be aligned based on onset of pubertal height growth. We here aim to develop a corresponding reference for pubertal weight. Methods To model QEPS-weight, 3595 subjects (1779 girls) from GrowUp1974Gothenburg and GrowUp1990Gothenburg were used. The QEPS-height-model was transformed to a corresponding QEPS-weight-model; thereafter, QEPS-weight was modified by an individual, constitutional weight-height-factor. Longitudinal weight and length/height measurements from 1418 individuals (698 girls) from GrowUp1990Gothenburg were then used to create weight references aligned for height at pubertal onset (the age at 5% of P-function growth, AgeP5). GrowUp1974Gothenburg subgroups based on pubertal timing, stature at pubertal onset, and childhood body composition were assessed using the references. Results References (median, SDS) for total weight (QEPS-functions), weight specific to puberty (P-function), and weight gain in the absence of specific pubertal growth (basic weight, QES-functions), allowing alignment of individual growth based on age at pubertal onset. For both sexes, basic weight was greater than average for late maturing, tall and high-BMI subgroups. The P-function-related weight was greater than average in short and lower than average in tall children, in those with high BMI, and in girls but not boys with low BMI. Conclusions New pubertal weight references allow individual variations in pubertal timing to be taken into consideration when evaluating growth. When used together with the comparable pubertal height reference, this will improve growth monitoring in clinical practice for identifying abnormal growth and serve as a valuable research tool providing insight into human growth.

Over the past 150 years, humans have become taller, and puberty has begun earlier. It is unclear if these changes are continuing in Sweden, and how longitudinal growth patterns are involved. We aimed to evaluate the underlying changes in growth patterns from birth to adulthood by QEPS estimates in two Swedish cohorts born in 1974 and 1990. Growth characteristics of the longitudinal 1974 and 1990-birth cohorts (n = 4181) were compared using the QEPS model together with adult heights. There was more rapid fetal/infancy growth in girls/boys born in 1990 compared to 1974, as shown by a faster Etimescale and they were heavier at birth. The laterborn were taller also in childhood as shown by a higher Q-function. Girls born in 1990 had earlier and more pronounced growth during puberty than girls born in 1974. Individuals in the 1990 cohort attained greater adult heights than those in the 1974 cohort; 6 mm taller for females and 10 mm for males. A positive change in adult height was attributed to more growth during childhood in both sexes and during puberty for girls. The QEPS model proved to be effective detecting small changes of growth patterns, between two longitudinal growth cohorts born only 16 years apart.

Computerized mathematical models describing absolute and relative individual growth during puberty in both cm and standard deviation (SD)-scores are lacking. The present study aimed to fill this gap, by applying the QEPS-model that delineates mathematically the specific pubertal functions of the total growth curve. Study population used was the individual growth curves of the longitudinally followed cohort GrowUp1974 Gothenburg (n = 2280). The QEPS-model describes total height as (T)otal-function: a combination of four shape-invariant growth functions, modified by time-scale and height-scale parameters: a (Q)uadratic-function for the continuous growth from fetal life to adulthood; a negative (E)xponential-function adds the rapid, declining fetal/infancy growth; a (P)ubertal-function the specific pubertal growth spurt; a (S)top-function the declining growth until adult height. A constructed variable, MathSelect, was developed for assessing data-quality. CIs and SD-scores for growth estimates were calculated for each individual. QEPS-model estimates used for pubertal growth; from the T-function: onset of puberty as minimal height velocity (AgeT ONSET ); mid-puberty as peak height velocity (AgeT PHV ); end of puberty as height velocity decreased to 1 cm/year (AgeT END ); duration of different intervals and gain (AgeT ONSET–END and Tpubgain); from the P-function: onset of puberty, estimated as growth at 1% or 5% (AgeP1 , AgeP5); mid-puberty as 50% (AgeP50) and PHV (AgeP PHV ); end of pubertal growth at 95 or 99% (AgeP95, AgeP99); duration of different intervals and pubertal gain (Ppubgain; P max ); from the QES-function: gain (QESpubgain) . Application of these mathematical estimates for onset, middle and end of puberty of P-function, QES-function, and T-function during puberty showed: the later the onset of puberty, the greater the adult height; pubertal gain due to the P-function growth was independent of age at onset of puberty; boys had higher total gain during puberty due to P-function growth than to QES-function growth; for girls it was reversed. QEPS is the first growth model to provide individualized estimates of both the specific pubertal growth function and the total growth during puberty, with accompanying SD-scores and Cis for each individual. These QEPS-derived estimates enable more in-depth analysis of different aspects of pubertal growth than previously possible.

Childhood BMI may influence subsequent growth in height as well as the timing of puberty. The aim of the present study was to investigate associations between BMI in childhood and subsequent height gain/pubertal growth. Longitudinal growth data were used (GrowUp1990Gothenburg cohort, n = 1,901). The QEPS growth-model was used to characterize height gain in relation to the highest BMISDS value between 3.5 and 8 y of age. Children were defined as overweight/obese (OwOb) or normal weight/underweight (NwUw), using the 2012 International Obesity Task Force criteria. A negative association between childhood BMISDS and pubertal height gain was observed. Already at birth, OwOb children were heavier than NwUw children, and had a greater height velocity during childhood. Onset of puberty was 3.5/3.0 mo earlier in OwOb girls/boys, and they had 2.3/3.1 cm less pubertal height gain from the QEPS-models specific P-function than NwUw children. Adult height was not related to childhood BMI. We found that pubertal height gain was inversely related to peak BMI in childhood. Higher childhood BMISDS was associated with more growth before onset of puberty, earlier puberty, and less pubertal height gain, resulting in similar adult heights for OwOb and NwUw children.

Background Only one mathematical model to date describes human growth and its different phases from fetal life until adult height. Aim To develop a model describing growth from fetal life to adult height taking maturation/biological tempo into consideration. Methods Subjects: The model was developed based on longitudinal mean height values obtained from published growth references for a cohort of 3650 healthy Swedish children followed from birth circa 1974 until adult height combined with birth-length for circa 400 000 healthy infants born 1990-1995. Results The QEPS-model for individual growth was constructed with a combination of four basic shape-invariant growth functions: a quadratic Q-function and a negative exponential E-function, both started during fetal life, 8 months before birth; the E-function levelled off after birth, whereas the Q-function continued until end of growth. A specific nonlinear pubertal P-function started at onset of puberty, and a stop S-function ended growth according to both the Q-function continuing during puberty and the specific P-function. For each function, an individual height-scale parameter was defined, and for the E- and P-functions, a time-scale parameter; giving six modifying parameters in total. In addition standardized proportional scores were used for biological interpretations. The QEPS-model was used to fit and generate mathematical functions suitable to describe the growth of the healthy population of Swedish children; thereafter, the model was modified using four height-scale parameters to model individual height in cm, and two time-scale parameters to adjust for the individual tempo of growth. Individual confidence intervals were calculated for all parameters. Conclusions A new shape-invariant growth model, QEPS, was developed, that requires only four basic growth functions to describe the total pattern of growth in height from fetal life to adult height, with addition of height- and time-scale parameters describing individual growth. The model can describe a wide variety of growth curves. Moreover, it is the first model to provide confidence intervals which enable us to describe the precision/quality of individual parameters.

BACKGROUND Intensification of organic dairy production leads to the question of whether the implementation of intensive feeding incorporating maize silage and concentrates is altering milk quality. Therefore the fatty acid (FA) and antioxidant (AO) profiles of milk on 24 farms divided into four system groups in three replications (n = 71) during the outdoor period were analyzed. In this system comparison, a differentiation of the system groups and the effects of the main system factors ‘intensification level’ (high-input versus low-input) and ‘origin’ (organic versus conventional) were evaluated in a multivariate statistical approach. RESULTS Consistent differentiation of milk from the system groups due to feeding-related impacts was possible in general and on the basis of 15 markers. The prediction of the main system factors was based on four or five markers. The prediction of ‘intensification level’ was based mainly on CLA c9,t11 and C18:1 t11, whereas that of ‘origin’ was based on n-3 PUFA. CONCLUSION It was possible to demonstrate consistent differences in the FA and AO profiles of organic and standard conventional milk samples. Highest concentrations of nutritionally beneficial compounds were found in the low-input organic system. Adapted grass-based feeding strategies including pasture offer the potential to produce a distinguishable organic milk product quality. © 2014 Society of Chemical Industry

Highlights? An algorithm is developed to characterize additive-specific crystallization structures. ? The structures are quantified by computing 15 width, and length parameters. ? Output generates a non-arbitrary zero point and an equidistant scale permitting all statistical measures. ? The monotonic dependency between the morphology and influencing parameters is shown. Crystallization patterns emerge when an aqueous dihydrate Copper chloride (CuCl2?2H2O) solution in the presence of organic additives (juices/extracts) is crystallized on a glass plate. The emerging patterns are additive-specific and reflect physiological processes like maturation and ageing, the effect of processing, feeding regime and production system in a broad range of agricultural products. The patterns and their underlying structures are evaluated visually by means of defined morphological criteria and by means of computerized image analysis, respectively. The currently applied texture analysis algorithm reflects the spatial linear relationships between grey-scale values of the scanned crystallization structures, rendering the zero point arbitrary and constraining data analysis to the ordinal scale. Furthermore the algorithm is non-consistent with the physically defined geometric properties of the crystallization structures.In this article the development of a structure analysis algorithm is described and discussed which allows a quantification of the crystallization structures by computing 15 width-, and length-parameters, introducing a non-arbitrary zero-point and an equidistant scale which permits all statistical measures. The algorithm is applied to crystallization structures produced from carrot samples which shows it reflects the monotonic relation between physically defined geometric properties of the crystallization structures and laboratory procedure parameters influencing the overall morphological features of the crystallization structures. For instance the nucleation time, which is the time elapsed prior to initial nucleation of the crystallization structure, and the circular region of interest (ROI) around the geometric center of the glass plate used in image analysis evaluation. It is concluded that this structure analysis algorithm is a valuable addition to the image evaluation tools applicable for crystallization investigations of agricultural products, augmenting the image analysis with a non-arbitrary zero point and an equidistant scale which permits all statistical measures.

Context Few studies have evaluated the metabolic outcomes of growth hormone (GH) treatment in idiopathic short stature (ISS). Moreover, children with ISS appear to need higher GH doses than childr ...

To establish new reference values for measurements of waist circumference and waist-to-height ratio in preschool children.A population-based, cross-sectional study of 4502 children aged 0-5 years derived from child health care in a Swedish county. Measurements of weight, height and waist circumference were recorded using a standardized procedure.New reference values for waist circumference and waist-to-height ratio for preschool children are presented. Reference charts were constructed and are presented. Waist circumference increased with age (r = 0.80, p0.001). After adjustment to the individual height, expressed as waist-to-height ratio, there was an inverse correlation to age during the first 5 years of age (r = -0.87, p0.001).The new reference values for waist circumference and waist-to-height ratio for Swedish preschool children enable future identification of new risk populations for childhood obesity. For clinicians, new reference charts for these two variables are provided for practical use.

There is an increasing interest in science and medicine in the systems approach. Instead of the reductionist approach that focuses on the physical and chemical properties of the individual components, systems biology aims to describe, understand, and explain from the complex biological systems that are studied: All levels of structural and functional complexity, explicitly including the supracellular domain; their systems behavior or phenotypes; their networks with relationships that interact with the genome, the environment, and the phenotype; and their multifactorial processes involved in maintaining homeostasis and the breakdown of homeostasis within the system. This shift from a more reductionist to a more holistic approach on both the epistemological (theoretical) and the methodological level is also important for the conceptualization and the development of biomarkers. Based on the dataset of a randomized controlled trial on the effects of a treatment of seasonal allergic rhinitis, using five different methods of permuted stepwise regression, three systems biology-oriented immunological pattern variables (biomarkers) were developed that demonstrated larger CV correct values than the separate cytokines with regard to the classification of cytokine samples in baseline (before treatment) and post-baseline (after treatment). This example demonstrates that a systems biological approach in both the conceptualization and development of biomarkers is promising. However, more empirical studies with larger datasets are necessary to confirm the positive results of the presented study.

The aim of the study was to develop and validate models that could predict the growth responses to GH therapy of individual children. Models for prediction of the initial one and 2-y growth response were constructed from a cohort of 269 prepubertal children (Model group) with isolated GH deficiency or idiopathic short stature, using a nonlinear multivariate data fitting technique. Five sets of clinical information were used. The "Basic model" was created using auxological data from the year before the start of GH treatment and parental heights. In addition to Basic model data, the other four models included growth data from the first 2 y of life, or IGF-I, or GH secretion estimated during a provocation test (AITT) or a spontaneous GH secretion profile. The performance of the models was validated by calculating the differences between predicted and observed growth responses in 149 new GH treated children (Validation group) who fulfilled the inclusion criteria used in the original cohort. The SD of these differences (SD(res)) in the validation group was compared with the SD(res) for the model group. For the 1st y, the SD(res) for the Basic model was 0.28 SDscores. The lowest SD(res) (0.19 SDscores), giving the most narrow prediction interval, was achieved adding the 24h GH profile and data on growth from the first 2 y of life to the Basic model. The models presented permit estimation of GH responsiveness in children over a broad range in GH secretion, and with an accuracy of the models substantially better than when using maximal GH response during an provocation test. The predicted individual growth response, calculated using a computer program, can serve as a guide for evidence-based decisions when selecting children to GH treatment.

SummaryObjective A high-throughput pharmaco-proteomic approach has previously been successfully used to identify lipoprotein biomarkers related to changes in longitudinal growth and bone mass in response to growth hormone (GH) treatment. The aim of this study was to identify protein markers involved in the diverse anabolic and lipolytic remodelling of body composition during GH treatment. Design, patients and measurements The study population consisted of 128 prepubertal children receiving GH treatment. Thirty-nine were short as a result of GH deficiency, and 89 had idiopathic short stature (ISS). Serum protein expression profiles at study start and after 1 year of GH treatment were analysed using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Body composition was analysed by dual-energy X-ray absorptiometry (DXA), reliably estimating muscle mass from appendicular (arms and legs) lean soft tissue mass (LST). DXA was also used to estimate appendicular bone mineral content (BMC) and fat mass for the total body. Results Specific protein expression patterns associated with GH response in different body compartments were identified. Among identified proteins, different isoforms of nutrition markers such as apolipoproteins (Apo) were recognized: Apo C-I, Apo A-II, serum amyloid A4 (SAA4) and transthyretin (TTR). In addition, unidentified peaks were associated with GH effects on specific body compartments. Conclusions Our results suggest that unique protein markers are associated with remodelling of different body compartments during GH treatment, which in the future might be useful to optimize GH treatment not only with regard to longitudinal growth.

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 mug/kg/day (n=87) and (b) fixed GH dose of 43 mug/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) mug/kg/day for [increment] left ventricular diastolic diameter (cm), 39(+/-24.5) mug/kg/day for [increment] alkaline phosphatase (mukat/L), 47(+/-43.5) mug/kg/day for [increment] lean soft tissue (SDS), 48(+/-35.7) mug/kg/day for [increment] insulin (mU/L), 51(+/-47.6) mug/kg/day for [increment] height (SDS), and 57(+/-52.7) mug/kg/day for [increment] 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.

This paper examined the effects of the combined product,Citrus e fructibus/Cydonia e fructibus(Citrus/Cydonia; Citrus and Cydonia: each 0.01 g/mL), and separate products of Citrus (0.01 g/mL) and Cydonia (0.01 g/mL) on the immunological pathways involved in seasonal allergic rhinitis (SAR). Peripheral blood mononuclear cells (PBMCs) from five healthy and five grass pollen-allergic donors were isolated and analyzedin vitroafter polyclonal and allergen-specific stimulation of T cells in the presence of the three extracts. The analyses demonstrated acceptable cell survival with no signs of toxicity. Citrus mainly had a selective effect on reducing allergen-specific chronic inflammatory (TNF-α; Citrus compared to Cydonia and Citrus/Cydonia: −87.4 (P<0.001) and −68.0 (P<0.05), resp.) and Th2 pathway activity (IL-5; Citrus compared to Cydonia: −217.8 (P<0.01); while, both Cydonia and Citrus/Cydonia mainly affected the induction of the allergen-specific Th1 pathway (IFN-γ; Cydonia and Citrus/Cydonia compared to Citrus: 3.8 (P<0.01) and 3.0 (P<0.01), resp.). Citrus and Cydonia demonstrated different working mechanisms in the treatment of SAR and the combination product did not demonstrate larger effects than the separate preparations. Further effectiveness and efficacy studies comparing the effects of the products on SARin vivoare indicated.

Background The immune system, a complex set of integrated responses, often cannot be explained, predicted, or monitored by examining its separate components as biomarkers. Combining different components may therefore be a suitable approach to develop relevant biomarkers reflecting immune system functioning in an appropriate way. Methods Here we compute and test pattern variables that should reflect immune system functioning on the systems level. Computation was based on a dataset (from a randomized controlled trial comparing two routes of administration) of allergen-specifically induced expression levels of cytokines (IL-1β, IL-5, IL-10, IL-12, IL-13, IL-17, IFN-γ and TNF-α) and symptom severity scores from 22 seasonal allergic rhinitis (SAR) patients measured before and after six weeks of treatment with medicinal products containing Citrus and Cydonia. By means of stepwise regression analyses we explored and tested pattern variables of the immunological data using permuted stepwise regression (PStR) to distinguish optimally between (immunological) baseline and post-baseline data for the whole treatment group (22 patients) and the two separate treatment groups (11 patients in each group). The validity of the stepwise selection method for the computed pattern variables was tested by means of random permutation tests and evaluated with the cross-validated correct rate of classification (CV correct). Results For the total group a pattern variable was computed with three variables: IL-10 (day 7), TNF-α (day 1) and IL-10 (day 1) (CV correct: 0.91; p 2 = 0.66), demonstrating a small improvement from the model with IL-10 (day 7) only (CV correct: 0.84; p 2 = 0.47). For the subcutaneous injection group a pattern variable was computed with four variables: IL-10 (day 7), IL-10 (day 1), IL-17 (day 7) and IFN-γ (day 7) (CV correct: 0.90; p 2 = 0.78), demonstrating a very small improvement from the model with IL-10 (day 7) only (CV correct: 0.86; p 2 = 0.58). For the nasal spray group a pattern variable was computed with three variables: IL-10 (day 7), TNF-α (day 1) and IL-10 (day 1) (CV correct: 0.95; p 2 = 0.79), demonstrating a moderate improvement from the model with IL-10 (day 7) only (CV correct: 0.79; p 2 = 0.37). Conclusion/discussion In this study three robust systems biology-oriented biomarkers for the monitoring of SAR were computed that demonstrated small to moderate improvement compared to monitoring of a single cytokine (IL-10 (day 7)) (CV correct improvement: 0.07 (total group), 0.04 (subcutaneous injection group), 0.16 (nasal spray group)). Further computation and biomarker validation with larger datasets, including data from healthy persons and SAR patients, are indicated.

Background. Clinical experiences in vitro and clinical studies have demonstrated the curative potency and safety of Citrus/Cydonia compositum in seasonal allergic rhinitis treatment. Objectives. To compare the efficacy and safety of two routes of administration (nasal spray versus subcutaneous injections). Methodology: Design. a national, randomised, comparative clinical trial with two parallel groups. Participants. 23 patients fulfilled the study requirements. Intervention. after a one- or two-week wash-out period, 23 patients were randomized, to a 6-week treatment period. Outcomes. immunological and symptom severity changes and safety. Immunologic outcome assessments were blinded to group assignment. 23 patients were randomized and from 22/23 patients (11 in each group) blood samples were analyzed before and after treatment. Conclusion. Both routes of administration demonstrate immunological and clinical effects, with larger inflammatory and innate immunological effects of the nasal spray route and larger allergen-specific clinical effects of the subcutaneous route, and are safe.

Growth hormone (GH) promotes longitudinal growth and bone mineralization. In this study, a proteomic approach was used to analyze the association between serum protein expression pattern and height-adjusted bone mineralization in short prepubertal children receiving GH treatment. Patterns of protein expression were compared with those associated with longitudinal bone growth. Specific protein expression patterns associated with changes in height-adjusted bone mineralization in response to GH treatment were identified. Out of the 37 peaks found in significant regression models, 27 were uniquely present in models correlated with changes in bone mineralization and 7 peaks were uniquely present in models correlated with changes in height. The peaks identified corresponded to apolipoproteins, transthyretin, serum amyloid A4 and hemoglobin beta. We conclude that a proteomic approach could be used to identify specific protein expression patterns associated with bone mineralization in response to GH treatment and that height-adjusted bone mineralization and longitudinal bone growth are regulated partly by the same and partly by different mechanisms. Protein isoforms with different post-translational modifications might be of importance in the regulation of these processes. However, further validation is needed to assess the clinical significance of the results.

Background 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. 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. Methods 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. Results 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 SDres) 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 SDres was 0.13 SDS/year and for the 1–7-year prediction it was 0.57 SDS (i.e. < 0.1 SDS/year). Conclusion 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.

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.