Steroid profiling in adrenal disease

The measurement of steroid hormones in blood and urine, which reflects steroid biosynthesis and metabolism, has been recognized as a valuable tool for identifying and distinguishing steroidogenic disorders. The application of mass spectrometry enables the reliable and simultaneous analysis of large panels of steroids, ushering in a new era for diagnosing adrenal diseases. However, the interpretation of complex hormone results necessitates the expertise and experience of skilled clinicians. In this scenario, machine learning techniques are gaining worldwide attention within healthcare fields. The clinical values of combining mass spectrometry-based steroid profiles analysis with machine learning models, also known as steroid metabolomics, have been investigated for identifying and discriminating adrenal disorders such as adrenocortical carcinomas, adrenocortical adenomas, and congenital adrenal hyperplasia. This promising approach is expected to lead to enhanced clinical decision-making in the field of adrenal diseases. This review will focus on the clinical performances of steroid profiling, which is measured using mass spectrometry and analyzed by machine learning techniques, in the realm of decision-making for adrenal diseases.

Novel methods in adrenal research: a metabolomics approach

Metabolic alterations have implications in a spectrum of tissue functions and disease. Aided by novel molecular biological and computational tools, our understanding of physiological and pathological processes underpinning endocrine and endocrine-related disease has significantly expanded over the last decade. Herein, we focus on novel metabolomics-related methodologies in adrenal research: in situ metabolomics by mass spectrometry imaging, steroid metabolomics by gas and liquid chromatography-mass spectrometry, energy pathway metabologenomics by liquid chromatography-mass spectrometry-based metabolomics of Krebs cycle intermediates, and cellular reprogramming to generate functional steroidogenic cells and hence to modulate the steroid metabolome. All four techniques to assess and/or modulate the metabolome in biological systems provide tremendous opportunities to manage neoplastic and non-neoplastic disease of the adrenal glands in the era of precision medicine. In this context, we discuss emerging clinical applications and/or promising metabolic-driven research towards diagnostic, prognostic, predictive and therapeutic biomarkers in tumours arising from the adrenal gland and extra-adrenal paraganglia as well as modern approaches to delineate and reprogram adrenal metabolism.

Peer group normalization and urine to blood context in steroid metabolomics: the case of CAH and obesity

Traditional interpretation of GC-MS output involved the semi-quantitative estimation of outstanding low or high specific metabolites and the ratio between metabolites. Here, we utilize a systems biology approach to steroid metabolomics of a complex steroid-related disorder, using an all-inclusive analysis of the steroidal pathway in the form of a subject steroidal fingerprint and disease signature, providing novel methods of normalization and visualization. The study compares 324 normal children to pure enzymatic deficiency in 27 untreated 21-hydroxylase CAH patients and to complex disease in 70 children with obesity. Steroid profiles were created by quantitative data generated by GC-MS analyses. A novel peer-group normalization method defined each individual subject's control group in a multi-dimensional space of metadata parameters. Classical steroid pathway visualization was enhanced by adding urinary end-product sub-nodes and by color coding of semi-quantitative metabolic concentrations and enzymatic activities. Unbiased automated data analysis confirmed the common knowledge for CAH - the inferred 17-hydroxyprogesterone was up-regulated and the inferred 21-hydroxylase enzyme activity was down-regulated. In childhood obesity, we observe a general decrease of both glucocorticoid and mineralocorticoid metabolites, increased androgens, up-regulation of 17,20-lyase, 17-OHase and 11β-HSD1 activity and down-regulation of 21-OHase enzymatic activity. Our study proved novel normalization and visualization techniques are to be useful in identifying subject fingerprint and disease signature in enzymatic deficiency and insufficiency, while demonstrating hypothesis generation in a complex disease such as childhood obesity.