Reference curves for pediatric endocrinology: leveraging biomarker z-scores for clinical classifications

Context: Hormone reference intervals in pediatric endocrinology are traditionally partitioned by age and lack the framework for benchmarking individual blood test results as normalized z-scores and plotting sequential measurements onto a chart. Reference curve modeling is applicable to endocrine variables and represents a standardized method to account for variation with gender and age. Objective: We aimed to establish gender-specifc biomarker reference curves for clinical use and benchmark associations between hormones, pubertal phenotype, and body mass index (BMI). Methods: Using cross-sectional population sample data from 2139 healthy Norwegian children and adolescents, we analyzed the pubertal status, ultrasound measures of glandular breast tissue (girls) and testicular volume (boys), BMI, and laboratory measurements of 17 clinical biomarkers modeled using the established “LMS” growth chart algorithm in R. Results: Reference curves for puberty hormones and pertinent biomarkers were modeled to adjust for age and gender. Z-score equivalents of biomarker levels and anthropometric measurements were compiled in a comprehensive beta coeffcient matrix for each gender. Excerpted from this analysis and independently of age, BMI was positively associated with female glandular breast volume (β = 0.5, P < 0.001) and leptin (β = 0.6, P < 0.001), and inversely correlated with serum levels of sex hormone-binding globulin (SHBG) (β = −0.4, P < 0.001). Biomarker z-score profles differed signifcantly between cohort subgroups stratifed by puberty phenotype and BMI weight class. <p<Conclusion: Biomarker reference curves and corresponding z-scores provide an intuitive framework for clinical implementation in pediatric endocrinology and facilitate the application of machine learning classifcation and covariate precision medicine for pediatric patients

Higher vitamin B12 levels in neurodevelopmental disorders than in healthy controls and schizophrenia: A comparison among participants between 2 and 53 years

Author´s accepted manuscript.This is the peer reviewed version of the following article: Hope, S., Nærland, T., Høyland, A. L., Torske, T., Malt, E., Abrahamsen, T. G., Nerhus, M., Wedervang-Resell, K., Lonning, V. L. H., Johannessen, J., Steen, N. E., Agartz, I., Stenberg, N., Hundhausen, T. E., Mørkrid, L. & Andreassen, O. A. (2020). Higher vitamin B12 levels in neurodevelopmental disorders than in healthy controls and schizophrenia : A comparison among participants between 2 and 53 years. The FASEB Journal, 34(6), 8114-8124, which has been published in final form at https://doi.org/10.1096/fj.201900855RRR. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Recent studies suggest that both high and low levels of vitamin B12 (vitB12) may have negative health impacts. We measured VitB12 in patients with the Neurodevelopmental disorders (ND) (n = 222), comprised of Autism Spectrum Disorders, specific Developmental disorders, and Intellectual Disability (aged 2-53 years), schizophrenia (n = 401), and healthy controls (HC) (n = 483). Age-and gender-adjusted vitB12 z-scores were calculated by comparisons with a reference population (n = 76 148). We found higher vitB12 in ND (median 420 pmol/L, mean z-score: 0.30) than in HC (316 pmol/L, z-score: 0.06, P < .01) and schizophrenia (306 pmol/L, z-score: −0.02, P < .001), which was significant after adjusting for age, gender, vitB12 supplement, folate, hemoglobin, leukocytes, liver, and kidney function (P < .02). In ND, 20% (n = 44) had vitB12 above 650 pmol/L, and 1% (n = 3) had below 150 pmol/L (common reference limits). In 6.3% (n = 14) of ND, vitB12 was above 2SD of mean in the age-and gender-adjusted reference population, which was more frequent than in HC (n = 8, 1.6%), OR: 4.0, P = .001. Low vitB12 was equally frequent as in HC, and vitB12 z-scores were equal across the age groups. To conclude, vitB12 was higher in ND than in HC and schizophrenia, suggesting a specific feature of ND, which warrants further studies to investigate the underlying mechanisms.acceptedVersio

Reference curves for pediatric endocrinology: leveraging biomarker z-scores for clinical classifications

Context Hormone reference intervals in pediatric endocrinology are traditionally partitioned by age and lack the framework for benchmarking individual blood test results as normalized z-scores and plotting sequential measurements onto a chart. Reference curve modeling is applicable to endocrine variables and represents a standardized method to account for variation with gender and age. Objective We aimed to establish gender-specific biomarker reference curves for clinical use and benchmark associations between hormones, pubertal phenotype, and body mass index (BMI). Methods Using cross-sectional population sample data from 2139 healthy Norwegian children and adolescents, we analyzed the pubertal status, ultrasound measures of glandular breast tissue (girls) and testicular volume (boys), BMI, and laboratory measurements of 17 clinical biomarkers modeled using the established “LMS” growth chart algorithm in R. Results Reference curves for puberty hormones and pertinent biomarkers were modeled to adjust for age and gender. Z-score equivalents of biomarker levels and anthropometric measurements were compiled in a comprehensive beta coefficient matrix for each gender. Excerpted from this analysis and independently of age, BMI was positively associated with female glandular breast volume (β = 0.5, P < 0.001) and leptin (β = 0.6, P < 0.001), and inversely correlated with serum levels of sex hormone-binding globulin (SHBG) (β = −0.4, P < 0.001). Biomarker z-score profiles differed significantly between cohort subgroups stratified by puberty phenotype and BMI weight class. Conclusion Biomarker reference curves and corresponding z-scores provide an intuitive framework for clinical implementation in pediatric endocrinology and facilitate the application of machine learning classification and covariate precision medicine for pediatric patients