Advances in technology have allowed for the sensitive, specific, and simultaneous quantitative profiling of steroid precursors, bioactive steroids and inactive metabolites, facilitating comprehensive characterization of the serum and urine steroid metabolomes. The quantification of steroid panels is therefore gaining favor over quantification of single marker metabolites in the clinical and research laboratories. However, although the biochemical pathways for the biosynthesis and metabolism of steroid hormones are now well defined, a gulf still exists between this knowledge and its application to the measured steroid profiles. In this review, we present an overview of steroid hormone biosynthesis and metabolism by the liver and peripheral tissues, specifically highlighting the pathways linking and differentiating the serum and urine steroid metabolomes. A brief overview of the methodology used in steroid profiling is also provided.

Context: Adrenal tumors have a prevalence of around 2% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2–11% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values. Objective: Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy. Design: Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19–84 yr) and 45 ACC patients (20–80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26–201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids. Results: Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88% (area under the curve = 0.96) when using only the nine most differentiating markers. Conclusions: Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.

Summary: The recent onslaught of mass spectrometry (MS) to measurements of steroid hormones, including demands that they should be the only acceptable method, has confused clinicians and scientists who have relied for more than 40 years on a variety of immunoassay (IA) methods in steroid hormone measurements. There is little doubt that MS methods with their superior specificity will be the future method of choice in many clinical and research applications of steroid hormone measurement. However, the majority of steroid measurements are currently, and will continue to be, carried out using various types of IAs for several reasons, including their technical ease, cost and availability of commercial reagents. Speedy replacement of all IAs with MS is an unrealistic and unnecessary goal, because the availability of MS measurements is limited by cost, need of expensive equipment, technical demands and lack of commercial applications. Furthermore, IAs have multiple well-known advantages that vindicate their continuing use. The purpose of this article is to elucidate the advantages and limitations of the MS and IA techniques from two angles, i.e. promotion of MS and defence of IA. The purpose of the text is to give the reader an unbiased view about the current state and future trends of steroid analysis and to help him/her choose the correct assay method to serve his/her diagnostic and research needs.

Older adults are less able to produce a protective antibody response to vaccinations. One factor that contributes to this is immune ageing. Here we examined whether diurnal variations in immune responses might extend to the antibody response to vaccination. We utilised a cluster-randomised trial design. 24 General Practices (GPs) across the West Midlands, UK who were assigned to morning (9-11am; 15 surgeries) or afternoon (3-5pm; 9 surgeries) vaccination times for the annual UK influenza vaccination programme. 276 adults (aged 65+ years and without a current infection or immune disorder or taking immunosuppressant medication). Participants were vaccinated in the morning or afternoon between 2011 and 2013. The primary outcome was the change in antibody titres to the three vaccine influenza strains from pre-vaccination to one month post-vaccination. Secondary outcomes of serum cytokines and steroid hormone concentrations were analysed at baseline to identify relationships with antibody responses. The increase in antibody levels due to vaccination differed between morning and afternoon administration; mean difference (95% CI) for H1N1 A-strain, 293.3 (30.97-555.66) p=.03, B-strain, 15.89 (3.42-28.36) p=.01, but not H3N2 A-strain, 47.0 (-52.43 to 146.46) p=.35; those vaccinated in the morning had a greater antibody response. Cytokines and steroid hormones were not related to antibody responses. No adverse events were reported. This simple manipulation in the timing of vaccine administration to favour morning vaccination may be beneficial for the influenza antibody response in older adults, with potential implications for vaccination strategies generally. This trial is registered with the ISRCTN (ISRCTN70898162).

Context: Androgen excess is a defining feature of polycystic ovary syndrome (PCOS), but the exact origin of hyperandrogenemia remains a matter of debate. Recent studies have highlighted the importance of the 11-oxygenated C19 steroid pathway to androgen metabolism in humans. In this study, we analyzed the contribution of 11-oxygenated androgens to androgen excess in women with PCOS. Methods: One hundred fourteen women with PCOS and 49 healthy control subjects underwent measurement of serum androgens by liquid chromatography-tandem mass spectrometry. Twenty-four–hour urinary androgen excretion was analyzed by gas chromatography-mass spectrometry. Fasting plasma insulin and glucose were measured for homeostatic model assessment of insulin resistance. Baseline demographic data, including body mass index, were recorded. Results: As expected, serum concentrations of the classic androgens testosterone (P < 0.001), androstenedione (P < 0.001), and dehydroepiandrosterone (P < 0.01) were significantly increased in PCOS. Mirroring this, serum 11-oxygenated androgens 11β-hydroxyandrostenedione, 11-ketoandrostenedione, 11β-hydroxytestosterone, and 11-ketotestosterone were significantly higher in PCOS than in control subjects, as was the urinary 11-oxygenated androgen metabolite 11β-hydroxyandrosterone. The proportionate contribution of 11-oxygenated to total serum androgens was significantly higher in patients with PCOS compared with control subjects [53.0% (interquartile range, 48.7 to 60.3) vs 44.0% (interquartile range, 32.9 to 54.9); P < 0.0001]. Obese (n = 51) and nonobese (n = 63) patients with PCOS had significantly increased 11-oxygenated androgens. Serum 11β-hydroxyandrostenedione and 11-ketoandrostenedione correlated significantly with markers of insulin resistance. Conclusions: We show that 11-oxygenated androgens represent the majority of circulating androgens in women with PCOS, with close correlation to markers of metabolic risk.

Cross-sectional imaging regularly results in incidental discovery of adrenal tumours, requiring exclusion of adrenocortical carcinoma (ACC). However, differentiation is hampered by poor specificity of imaging characteristics. We aimed to validate a urine steroid metabolomics approach, using steroid profiling as the diagnostic basis for ACC. We did a prospective multicentre study in adult participants (age ≥18 years) with newly diagnosed adrenal masses. We assessed the accuracy of diagnostic imaging strategies based on maximum tumour diameter (≥4 cm vs <4 cm), imaging characteristics (positive vs negative), and urine steroid metabolomics (low, medium, or high risk of ACC), separately and in combination, using a reference standard of histopathology and follow-up investigations. With respect to imaging characteristics, we also assessed the diagnostic utility of increasing the unenhanced CT tumour attenuation threshold from the recommended 10 Hounsfield units (HU) to 20 HU. Of 2169 participants recruited between Jan 17, 2011, and July 15, 2016, we included 2017 from 14 specialist centres in 11 countries in the final analysis. 98 (4·9%) had histopathologically or clinically and biochemically confirmed ACC. Tumours with diameters of 4 cm or larger were identified in 488 participants (24·2%), including 96 of the 98 with ACC (positive predictive value [PPV] 19·7%, 95% CI 16·2–23·5). For imaging characteristics, increasing the unenhanced CT tumour attenuation threshold to 20 HU from the recommended 10 HU increased specificity for ACC (80·0% [95% CI 77·9–82·0] vs 64·0% [61·4–66.4]) while maintaining sensitivity (99·0% [94·4–100·0] vs 100·0% [96·3–100·0]; PPV 19·7%, 16·3–23·5). A urine steroid metabolomics result indicating high risk of ACC had a PPV of 34·6% (95% CI 28·6–41·0). When the three tests were combined, in the order of tumour diameter, positive imaging characteristics, and urine steroid metabolomics, 106 (5·3%) participants had the result maximum tumour diameter of 4 cm or larger, positive imaging characteristics (with the 20 HU cutoff), and urine steroid metabolomics indicating high risk of ACC, for which the PPV was 76·4% (95% CI 67·2–84·1). 70 (3·5%) were classified as being at moderate risk of ACC and 1841 (91·3%) at low risk (negative predictive value 99·7%, 99·4–100·0). An unenhanced CT tumour attenuation cutoff of 20 HU should replace that of 10 HU for exclusion of ACC. A triple test strategy of tumour diameter, imaging characteristics, and urine steroid metabolomics improves detection of ACC, which could shorten time to surgery for patients with ACC and help to avoid unnecessary surgery in patients with benign tumours. European Commission, UK Medical Research Council, Wellcome Trust, and UK National Institute for Health Research, US National Institutes of Health, the Claire Khan Trust Fund at University Hospitals Birmingham Charities, and the Mayo Clinic Foundation for Medical Education and Research.