Biochemical identification of prepubertal boys with Klinefelter syndrome by combined reproductive hormone profiling using machine learning

Abstract

Objective: Klinefelter syndrome (KS) is the most common sex chromosome disorder and genetic cause of infertility in males. A highly variable phenotype contributes to the fact that a large proportion of cases are never diagnosed. Typical hallmarks in adults include small testes and azoospermia which may prompt biochemical evaluation that typically shows extremely high follicle-stimulating hormone and low/undetectable inhibin B serum concentrations. However, in prepubertal KS individuals, biochemical parameters are largely overlapping those of prepubertal controls. We aimed to characterize clinical profiles of prepubertal boys with KS in relation to controls and to develop a novel biochemical classification model to identify KS before puberty. Methods: Retrospective, longitudinal data from 15 prepubertal boys with KS and data from 1475 controls were used to calculate age- and sex-adjusted standard deviation scores (SDS) for height and serum concentrations of reproductive hormones and used to infer a decision tree classification model for KS. Results: Individual reproductive hormones were low but within reference ranges and did not discriminate KS from controls. Clinical and biochemical pro files including age- and sex-adjusted SDS from multiple reference curves provided input data to train a ‘random forest’ machine learning (ML) model for the detection of KS. Applied to unseen data, the ML model achieved a classification accuracy of 78% (95% CI, 61–9 4%). Conclusions: Supervised ML applied to clinically relevant variables enabled computational classification of control and KS profiles. The application of age - and sex-adjusted SDS provided robust predictions irrespective of age. Specialized ML models applied to combined reproductive hormone concentrations may be useful diagnostic tools to improve the identification of prepubertal boys with KS.

Keywords

• klinefelter syndrome
• early diagnosis
• classification
• machine learning