ArticleEndocrine2025
Beyond cortisol: multiplex steroid profiling after overnight dexamethasone suppression to optimize Cushing's syndrome screening.
Article in Endocrine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeCushing’s syndrome (CS) is a life-threatening endocrine disorder challenging to diagnose. The 1 mg Dexamethasone Suppression Test (DST) is highly sensitive but limited by specificity. This study evaluated the diagnostic value of liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based post-DST serum steroid profiling in improving the discrimination between overt CS and non-CS conditions, providing enhanced diagnostic resolution for mild autonomous cortisol secretion (MACS).
methodsWe prospectively analyzed paired pre- and post-DST serum from 232 patients (79 CS, 76 MACS, 77 controls) using an LC-MS/MS steroid panel. Individual and comparative diagnostic performance were assessed by receiver operating characteristic (ROC) analysis (AUROC, sensitivity, specificity); optimal cut-offs were determined by Youden’s index. We compared pre- versus post-DST metrics and examined subgroup (adrenal CS vs. pituitary CS) patterns.
resultsPost-DST profiling materially improved discrimination of overt CS. The six key analytes—cortisol (F), cortisone (E), 11-deoxycortisol (11-DF), 11β-hydroxytestosterone (11β-OHT), corticosterone (CORT), and 18-hydroxycorticosterone (18-OHF)—each showed strong diagnostic performance, with most AUROCs ≥ 0.90. A multivariable logistic model integrating these six post-DST markers achieved similarly high AUROCs (≥ 0.90) across all primary comparisons. However, discrimination against MACS was attenuated, and the panel did not reliably distinguish pituitary from adrenal etiologies.
conclusionsLC-MS/MS-based multi-hormone analysis of post-DST serum substantially enhances DST diagnostic accuracy for overt CS compared with baseline measures. Multi-hormone dynamics mitigate single F assessment limitations, offering a novel approach for diagnostic optimization and subclassification.
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