Adrenal Morphology as an Indicator of Long-Term Disease Control in Adults with Classic 21-Hydroxylase Deficiency

Multiplexed Serum Steroid Profiling Reveals Metabolic Signatures of Subtypes in Congenital Adrenal Hyperplasia

Context

Altered metabolic signatures on steroidogenesis may characterize individual subtypes of congenital adrenal hyperplasia (CAH), but conventional diagnostic approaches are limited to differentiate subtypes.

Objective

We explored metabolic characterizations and identified multiple diagnostic biomarkers specific to individual subtypes of CAH.

Methods

Liquid chromatography-mass spectrometry-based profiling of 33 adrenal steroids was developed and applied to serum samples obtained from 67 CAH patients and 38 healthy volunteers.

Results

Within- and between-run precisions were 95.4% to 108.3% and 94.1% to 110.0%, respectively, while all accuracies were <12% and the correlation coefficients (r2) were > 0.910. Metabolic ratios corresponding to 21-hydroxylase characterized 21-hydroxylase deficiency (21-OHD; n = 63) from healthy controls (area under the curve = 1.0, P < 1 × 10-18 for all) and other patients with CAH in addition to significantly increased serum 17α-hydroxyprogesterone (P < 1 × 10-16) and 21-deoxycortisol (P < 1 × 10-15) levels. Higher levels of mineralocorticoids, such as corticosterone (B) and 18-hydroxyB, were observed in 17α-hydroxylase deficiency (17α-OHD; N = 3), while metabolic ratio of dehydroepiandrosterone sulfate to pregnenolone sulfate was remarkably decreased against all subjects. A patient with 11β-hydroxylase deficiency (11β-OHD) demonstrated significantly elevated 11-deoxycortisol and its metabolite tetrahydroxy-11-deoxyF, with reduced metabolic ratios of 11β-hydroxytestosterone/testosterone and 11β-hydroxyandrostenedione/androstenedione. The steroid profiles resulted in significantly decreased cortisol metabolism in both 21-OHD and 17α-OHD but not in 11β-OHD.

Conclusion

The metabolic signatures with specific steroids and their corresponding metabolic ratios may reveal individual CAH subtypes. Further investigations with more substantial sample sizes should be explored to enhance the clinical validity.

Landscape of Adrenal Tumours in Patients with Congenital Adrenal Hyperplasia

Our aim is to update the topic of adrenal tumours (ATs) in congenital adrenal hyperplasia (CAH) based on a multidisciplinary, clinical perspective via an endocrine approach. This narrative review is based on a PubMed search of full-length, English articles between January 2014 and July 2023. We included 52 original papers: 9 studies, 8 case series, and 35 single case reports. Firstly, we introduce a case-based analysis of 59 CAH-ATs cases with four types of enzymatic defects (CYP21A2, CYP17A1, CYP17B1, and HSD3B2). Secondarily, we analysed prevalence studies; their sample size varied from 53 to 26,000 individuals. AT prevalence among CAH was of 13.3-20%. CAH prevalence among individuals with previous imaging diagnosis of AT was of 0.3-3.6%. Overall, this 10-year, sample-based analysis represents one of the most complex studies in the area of CAH-ATs so far. These masses should be taken into consideration. They may reach impressive sizes of up to 30-40 cm, with compressive effects. Adrenalectomy was chosen based on an individual multidisciplinary decision. Many tumours are detected in subjects with a poor disease control, or they represent the first step toward CAH identification. We noted a left lateralization with a less clear pathogenic explanation. The most frequent tumour remains myelolipoma. The risk of adrenocortical carcinoma should not be overlooked. Noting the increasing prevalence of adrenal incidentalomas, CAH testing might be indicated to identify non-classical forms of CAH.

Fully automatic volume measurement of the adrenal gland on CT using deep learning to classify adrenal hyperplasia

OBJECTIVES

To develop a fully automated deep learning model for adrenal segmentation and to evaluate its performance in classifying adrenal hyperplasia.

METHODS

This retrospective study evaluated automated adrenal segmentation in 308 abdominal CT scans from 48 patients with adrenal hyperplasia and 260 patients with normal glands from 2010 to 2021 (mean age, 42 years; 156 women). The dataset was split into training, validation, and test sets at a ratio of 6:2:2. Contrast-enhanced CT images and manually drawn adrenal gland masks were used to develop a U-Net-based segmentation model. Predicted adrenal volumes were obtained by fivefold splitting of the dataset without overlapping the test set. Adrenal volumes and anthropometric parameters (height, weight, and sex) were utilized to develop an algorithm to classify adrenal hyperplasia, using multilayer perceptron, support vector classification, a random forest classifier, and a decision tree classifier. To measure the performance of the developed model, the dice coefficient and intraclass correlation coefficient (ICC) were used for segmentation, and area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used for classification.

RESULTS

The model for segmenting adrenal glands achieved a Dice coefficient of 0.7009 for 308 cases and an ICC of 0.91 (95% CI, 0.90-0.93) for adrenal volume. The models for classifying hyperplasia had the following results: AUC, 0.98-0.99; accuracy, 0.948-0.961; sensitivity, 0.750-0.813; and specificity, 0.973-1.000.

CONCLUSION

The proposed segmentation algorithm can accurately segment the adrenal glands on CT scans and may help clinicians identify possible cases of adrenal hyperplasia.

KEY POINTS

• A deep learning segmentation method can accurately segment the adrenal gland, which is a small organ, on CT scans. • The machine learning algorithm to classify adrenal hyperplasia using adrenal volume and anthropometric parameters (height, weight, and sex) showed good performance. • The proposed segmentation algorithm may help clinicians identify possible cases of adrenal hyperplasia.

Long-Term Outcomes of Congenital Adrenal Hyperplasia

A plethora of negative long-term outcomes have been associated with congenital adrenal hyperplasia (CAH). The causes are multiple and involve supra-physiological gluco- and mineralocorticoid replacement, excess adrenal androgens both intrauterine and postnatal, elevated steroid precursor and adrenocorticotropic hormone levels, living with a congenital condition as well as the proximity of the cytochrome P450 family 21 subfamily A member 2 (CYP21A2) gene to other genes. This review aims to discuss the different long-term outcomes of CAH.