In Frontiers in endocrinology ; h5-index 55.0
BACKGROUND : Gene expression (GE) data have shown promise as a novel tool to aid in the diagnosis of childhood growth hormone deficiency (GHD) when comparing GHD children to normal children. The aim of this study was to assess the utility of GE data in the diagnosis of GHD in childhood and adolescence using non-GHD short stature children as a control group.
METHODS : GE data was obtained from patients undergoing growth hormone stimulation testing. Data were taken for the 271 genes whose expression was utilized in our previous study. The synthetic minority oversampling technique was used to balance the dataset and a random forest algorithm applied to predict GHD status.
RESULTS : 24 patients were recruited to the study and eight subsequently diagnosed with GHD. There were no significant differences in gender, age, auxology (height SDS, weight SDS, BMI SDS) or biochemistry (IGF-I SDS, IGFBP-3 SDS) between the GHD and non-GHD subjects. A random forest algorithm gave an AUC of 0.97 (95% CI 0.93 - 1.0) for the diagnosis of GHD.
CONCLUSION : This study demonstrates highly accurate diagnosis of childhood GHD using a combination of GE data and random forest analysis.
Garner Terence, Wangsaputra Ivan, Whatmore Andrew, Clayton Peter Ellis, Stevens Adam, Murray Philip George
2023
growth hormone, growth hormone deficiency, machine learning, random forest - ensemble classifier, transcriptome (RNA-seq)
