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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.

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Introduction Direct measurement oflipogenesis in humans has not been possible previously owing to a methodologic constraint. In order to measure the synthesis of FA (or any macromolecule) using tracers, one needs to know the labeling intensity (specific activity or enrichment) of the true biosynthetic precursor which, for lipogenesis, is cytosolic acetyl-CoA. In contrast to glycogen and protein synthesis, which occur in numerous tissues, the majority of lipogenesis probably occurs in liver in humans (9-1 1) and newly synthesized FA then enter the circulation in the form of very low density lipoprotein-triglyceride (VLDL-TG) and phospholipids. Accordingly, isotopic measurement of total lipogenesis in humans could in theory be achieved by sampling only blood if hepatic cytosolic acetylCoA were experimentally accessible.We (12)(13)(14)(15)(16)(17)(18)(19)(20) and others (21-23) have previously sampled intrahepatic metabolites using the xenobiotic "probe" technique (Fig. 1). One such probe is secreted acetaminophenglucuronide (GlcUA). This is derived from hepatic uridine di- (Fig. 2). We chose the 7-10-mg/kg per min feeding rates for carbohydrate because these are well above the maximal capacity for glucose oxidation in normal humans (-4

Liquid chromatography tandem mass spectrometry (LC/MS/MS) is replacing classical methods for steroid hormone analysis. It requires small sample volumes and has given rise to improved specificity and short analysis times. Its growth has been fueled by criticism of the validity of steroid analysis by older techniques, testosterone measurements being a prime example. While this approach is the gold-standard for measurement of individual steroids, and panels of such compounds, LC/MS/MS is of limited use in defining novel metabolomes. GC/MS, in contrast, is unsuited to rapid high-sensitivity analysis of specific compounds, but remains the most powerful discovery tool for defining steroid disorder metabolomes. Since the 1930s almost all inborn errors in steroidogenesis have been first defined through their urinary steroid excretion. In the last 30 years, this has been exclusively carried out by GC/MS and has defined conditions such as AME syndrome, glucocorticoid remediable aldosteronism (GRA) and Smith–Lemli–Opitz syndrome. Our recent foci have been on P450 oxidoreductase deficiency (ORD) and apparent cortisone reductase deficiency (ACRD).In contrast to LC/MS/MS methodology, a particular benefit of GC/MS is its non-selective nature; a scanned run will contain every steroid excreted, providing an integrated picture of an individual's metabolome. The “Achilles heel” of clinical GC/MS profiling may be data presentation. There is lack of familiarity with the multiple hormone metabolites excreted and diagnostic data are difficult for endocrinologists to comprehend. While several conditions are defined by the absolute concentration of steroid metabolites, many are readily diagnosed by ratios between steroid metabolites (precursor metabolite/product metabolite). Our work has led us to develop a simplified graphical representation of quantitative urinary steroid hormone profiles and diagnostic ratios.