학술논문
Reference Curves for Pediatric Endocrinology: Leveraging Biomarker Z-Scores for Clinical Classifications
Clinical Research Article
Clinical Research Article
Document Type
Academic Journal
Author
Source
Journal of Clinical Endocrinology & Metabolism. July 2022, Vol. 107 Issue 7, p2004, 12 p.
Subject
Language
English
ISSN
0021-972X
Abstract
Reproductive hormone references for evaluating blood test results in pediatric patients are essential during clinical investigations of a wide range of conditions including hypo/hypergonadism, differences of sex development (DSDs), and [...]
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 puberta status, ultrasound measures of glandular breast tissue (girls) and testicular volume (boys), BMI, and laboratory measurements of 17 clinical bio-markers 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 ([beta] = 0.5, P < 0.001) and leptin ([beta] = 0.6, P < 0.001), and inversely correlated with serum levels of sex hormone-binding globulin (SHBG) ([beta] = -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. Key Words: pediatric endocrinology, biomarker, references, machine learning Abbreviations: AI, artificial intelligence; BGS2, Bergen Growth Study 2; BMI, body mass index; CLSI, Clinical and Laboratory Standards Institute; CV, coefficient of variation; [E.sub.2], estradiol; FSH, follicle-stimulating hormone; IGF1, insulin-like growth factor 1; LC-MS/MS, liquid chromatography-tandem mass spectrometry; LH, luteinizing hormone; LLOQ, lower limit of quantitation; ML, machine learning; PCA, principal component analysis; RCV, reference change value; ROC, receiver operating characteristics curve; SHBG, sex hormone-binding globulin.
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 puberta status, ultrasound measures of glandular breast tissue (girls) and testicular volume (boys), BMI, and laboratory measurements of 17 clinical bio-markers 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 ([beta] = 0.5, P < 0.001) and leptin ([beta] = 0.6, P < 0.001), and inversely correlated with serum levels of sex hormone-binding globulin (SHBG) ([beta] = -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. Key Words: pediatric endocrinology, biomarker, references, machine learning Abbreviations: AI, artificial intelligence; BGS2, Bergen Growth Study 2; BMI, body mass index; CLSI, Clinical and Laboratory Standards Institute; CV, coefficient of variation; [E.sub.2], estradiol; FSH, follicle-stimulating hormone; IGF1, insulin-like growth factor 1; LC-MS/MS, liquid chromatography-tandem mass spectrometry; LH, luteinizing hormone; LLOQ, lower limit of quantitation; ML, machine learning; PCA, principal component analysis; RCV, reference change value; ROC, receiver operating characteristics curve; SHBG, sex hormone-binding globulin.