OBJECTIVE: To assess the diagnostic value of combining plasma steroid profiling with machine learning (ML) in differentiating between mild autonomous cortisol secretion (MACS) and nonfunctioning adenoma (NFA) in patients with adrenal incidentalomas.
METHODS: The plasma steroid profiles data in the laboratory information system were screened from January 2021 to December 2023. EXtreme Gradient Boosting was applied to establish diagnostic models using plasma 24-steroid panels and/or clinical characteristics of the subjects. The SHapley Additive exPlanation (SHAP) method was used for explaining the model.
RESULTS: Seventy-six patients with MACS and 86 patients with NFA were included in the development and internal validation cohort while the external validation cohort consisted of 27 MACS and 21 NFA cases. Among 5 ML models evaluated, eXtreme Gradient Boosting demonstrated superior performance with an area under the curve of 0.77 using 24 steroid hormones. The SHAP method identified 5 steroids that exhibited optimal performance in distinguishing MACS from NFA, namely dehydroepiandrosterone, 11-deoxycortisol, 11β-hydroxytestosterone, testosterone, and dehydroepiandrosteronesulfate. Upon incorporating clinical features into the model, the area under the curve increased to 0.88, with a sensitivity of 0.77 and specificity of 0.82. Furthermore, the results obtained through SHAP revealed that lower levels of testosterone, dehydroepiandrosterone, low-density lipoprotein cholesterol, body mass index, and adrenocorticotropic hormone along with higher level of 11-deoxycortisol significantly contributed to the identification of MACS in the model.
CONCLUSIONS: We have elucidated the utilization of ML-based steroid profiling to discriminate between MACS and NFA in patients with adrenal incidentalomas. This approach holds promise for distinguishing these 2 entities through a single blood collection.

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.

BACKGROUND: A rapid and accurate measurement approach for 17α-hydroxyprogesterone (17-OHP) and related steroids in amount/volume-limited clinic samples is of importance for precise newborn diagnosis of congenital adrenal hyperplasia (CAH) and its subtypes in clinic.
METHODS: Sixteen steroids (17-OHP, androstenedione, cortisol, tetrahydro-11-deoxycortisol, pregnenolone, progesterone, 11-deoxycorticosterone, corticosterone, 21-deoxycortisol, 11-deoxycortisol, dehydroepiandrosterone, testosterone, aldosterone, 17α-hydroxypregnenolone, dihydrotestosterone and 18-hydroxycorticosterone) were included in the panel of high-throughput microbore ultra-performance liquid chromatography-tandem mass spectrometry. Samples were collected from 126 normal subjects and 65 patients including different subtypes of CAH.
RESULTS: The method was validated with satisfactory analytical performance in linearity, repeatability, recovery and limit of detection. Reference intervals for 16 steroids were established by quantifying the level of steroids detected in normal infants. The applicability of the method was tested by differentiating steroid metabolic characteristics between normal infants and infants with CAH, as well as between infants with different CAH subtypes. The relevance of 17-OHP, 21-deoxycortisol, and 17-OHP/11-deoxycortisol for 21-hydroxylase deficiency screening was demonstrated. The level of 11-deoxycorticosterone, 11-deoxycortisol, progesterone and androstenedione can be used for the diagnosis of different rare subtypes of CAH.
CONCLUSIONS: This study provides a strategy for highly efficient steroid analysis of amount/volume-limited clinic samples and holds great potential for clinical diagnosis of CAH.

BACKGROUND: Newborn screening for congenital adrenal hyperplasia (CAH) using 17-hydroxyprogesterone dissociation-enhanced, lanthanide fluorescence immunoassay (DELFIA) generates a large number of false-positive results. The present study aimed to improve the sensitivity of the CAH neonatal screening by including second-tier steroid profiling in dried blood spots (DBS) using liquid chromatography tandem mass spectrometry (LC-MS/MS).
METHODS: We developed and validated a LC-MS/MS method for simultaneous determination of six steroids in DBS, including androstenedione, testosterone, 17-hydroxyprogesterone, 11-deoxycortisol, 21-deoxycortisol, and cortisol. Two 5-mm blood spots were eluted by internal standard working solution. We analyzed 1170 DBS samples from neonates to determine gestational age-specific reference intervals. In order to test the specificity of the second-tier method, we analyzed 707 cards with a positive screening by DELFIA.
RESULTS: Values of intra- and inter-day precision coefficients of variance and accuracy were 2.0%-13.3% and 85.8%-114.5%, respectively. Recovery ranged from 85.0% to 106.9%. The lower limit of quantification was 0.5 ng/mL for 21-deoxycortisol, 0.25 ng/mL for 17-hydroxyprogesterone and cortisol, and 0.1 ng/mL for testosterone, androstenedione, and 11-deoxycortisol. In addition, the linearity range was 0.25-50 ng/mL (R2 > 0.99). According to the 17-hydroxyprogesterone levels and ratios of (androstenedione + 17-hydroxyprogesterone)/cortisol in the 707 positive screening samples, 77 neonates should receive recall visit. The number of false-positive results reduced by 89.1%. Totally, 18 newborns were diagnosed with 21-hydroxylase deficiency, one with P450 oxidoreductase deficiency and one with 11β-hydroxylase deficiency. With two-tier screening, the positive predictive value increased to 26.0%.
CONCLUSIONS: The second-tier steroid profiling by LC-MS/MS reduced the false-positive rate and improved the positive predictive value of CAH screening. We suggest applying this steroid profiling assay as a second-tier test for CAH screening in China.

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with frequent metastatic spread and poor prognosis. The disease can occur at any age with unexpected biological behavior. Recent genome-wide studies of ACC have contributed to our understanding of the disease, but diagnosis of ACC remains a challenge, even for multidisciplinary expert teams. Patients with ACC are frequently diagnosed in advanced stages and have limited therapeutic options. Therefore, for earlier diagnosis and better clinical management of adrenocortical carcinoma, specific, sensitive, and minimal invasive markers are urgently needed. Over several decades, great efforts have been made in discovering novel and reliable diagnostic and prognostic biomarkers including microRNAs, steroid profilings, circulating tumor cells, circulating tumor DNAs and radiomics. In this review, we will summarize these novel noninvasive biomarkers and analyze their values for diagnosis, predicting prognosis, and disease monitoring. Current problems and possible future application of these non-invasive biomarkers will also be discussed.

Previous studies have demonstrated that steroids were associated with gestational diabetes mellitus (GDM). However, results from different studies remained inconsistent, and only a limited range of steroids were investigated in these studies. Therefore, we aimed to analyze comprehensive steroid profiling in Chinese women with GDM during third-trimester pregnancy. In 97 Chinese pregnant women, we measured steroid profile using a LC-MS/MS method, and calculated product-to-precursor ratios in metabolic pathways of steroids. Then sixteen genetic variants of genes encoding steroidogenic enzymes were genotyped by MassARRAY system. There were significant differences (P < 0.05) and obvious changes (fold change <0.67 or>1.5) in steroids (testosterone, estriol, pregnenolone and dehydroepiandrosterone) and product-to-precursor ratios (E2/T and T/AD) between GDM and control groups. After adjusting for maternal age, the TT genotype and T allele of CYP19A1 rs10046 were associated with an increased risk of GDM. And the CC genotype and C allele of HSD17B3 rs2257157 were also associated with an increased risk of GDM. Besides, pregnant women carrying TT genotype of CYP19A1 rs10046 and CC genotype of HSD17B3 rs2257157 had a lower E2/T ratio and higher T/AD ratio respectively comparing with those carrying other genotypes. In conclusion, our study suggested that testosterone, estriol, pregnenolone and dehydroepiandrosterone might be differential metabolites for gestational diabetes mellitus. The genetic variants rs10046 of CYP19A1 and rs2257157 of HSD17B3 could predispose to GDM in Chinese women.