Transcriptomics and machine learning predict diagnosis and severity of growth hormone deficiency.

What Was Tested

The paper reports a study of Exogenous HGH, sermorelin & older GHRPs (why they're passed over), published as a phase 4 clinical trial.

The effect of gene expression data on diagnosis remains limited.

Here, the researchers show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and random forest analysis.

Who Took Part

By PubMed's age tags, the people studied were young children, children and teenagers, both men and women.

Prepubertal treatment-naive children with GHD (n = 98) were enrolled from the PREDICT study, and controls (n = 26) were acquired from online data sets.

How the Study Was Run

Performance was assessed using area under the receiver operating characteristic curve (AUC-ROC).

What Was Measured

The subject headings attached to it are Biomarkers, Case-Control Studies, Datasets as Topic, Gene Expression Profiling, Gene Regulatory Networks, Growth Disorders and Healthy Volunteers.

The authors tagged it with the keywords Endocrinology and growth factors.

What This Means in Plain Language

This is a phase 4 clinical trial of Exogenous HGH, sermorelin & older GHRPs (why they're passed over).

Results are reported in around 3 sentences of the abstract; we leave them to the paper itself rather than summarise them.

The publication is from 2018 (JCI insight), a journal based in United States.

More context: the Exogenous HGH, sermorelin & older GHRPs (why they're passed over) compound page, all human trials.