Dataset Information

Steroid metabolome analysis reveals prevalent glucocorticoid excess in primary aldosteronism.

ABSTRACT:

Background

Adrenal aldosterone excess is the most common cause of secondary hypertension and is associated with increased cardiovascular morbidity. However, adverse metabolic risk in primary aldosteronism extends beyond hypertension, with increased rates of insulin resistance, type 2 diabetes, and osteoporosis, which cannot be easily explained by aldosterone excess.

Methods

We performed mass spectrometry-based analysis of a 24-hour urine steroid metabolome in 174 newly diagnosed patients with primary aldosteronism (103 unilateral adenomas, 71 bilateral adrenal hyperplasias) in comparison to 162 healthy controls, 56 patients with endocrine inactive adrenal adenoma, 104 patients with mild subclinical, and 47 with clinically overt adrenal cortisol excess. We also analyzed the expression of cortisol-producing CYP11B1 and aldosterone-producing CYP11B2 enzymes in adenoma tissue from 57 patients with aldosterone-producing adenoma, employing immunohistochemistry with digital image analysis.

Results

Primary aldosteronism patients had significantly increased cortisol and total glucocorticoid metabolite excretion (all P < 0.001), only exceeded by glucocorticoid output in patients with clinically overt adrenal Cushing syndrome. Several surrogate parameters of metabolic risk correlated significantly with glucocorticoid but not mineralocorticoid output. Intratumoral CYP11B1 expression was significantly associated with the corresponding in vivo glucocorticoid excretion. Unilateral adrenalectomy resolved both mineralocorticoid and glucocorticoid excess. Postoperative evidence of adrenal insufficiency was found in 13 (29%) of 45 consecutively tested patients.

Conclusion

Our data indicate that glucocorticoid cosecretion is frequently found in primary aldosteronism and contributes to associated metabolic risk. Mineralocorticoid receptor antagonist therapy alone may not be sufficient to counteract adverse metabolic risk in medically treated patients with primary aldosteronism.

Funding

Medical Research Council UK, Wellcome Trust, European Commission.

SUBMITTER: Arlt W

PROVIDER: S-EPMC5396526 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Steroid metabolome analysis reveals prevalent glucocorticoid excess in primary aldosteronism.

Arlt Wiebke W Lang Katharina K Sitch Alice J AJ Dietz Anna S AS Rhayem Yara Y Bancos Irina I Feuchtinger Annette A Chortis Vasileios V Gilligan Lorna C LC Ludwig Philippe P Riester Anna A Asbach Evelyn E Hughes Beverly A BA O'Neil Donna M DM Bidlingmaier Martin M Tomlinson Jeremy W JW Hassan-Smith Zaki K ZK Rees D Aled DA Adolf Christian C Hahner Stefanie S Quinkler Marcus M Dekkers Tanja T Deinum Jaap J Biehl Michael M Keevil Brian G BG Shackleton Cedric Hl CH Deeks Jonathan J JJ Walch Axel K AK Beuschlein Felix F Reincke Martin M

JCI insight 20170420 8

<h4>Background</h4>Adrenal aldosterone excess is the most common cause of secondary hypertension and is associated with increased cardiovascular morbidity. However, adverse metabolic risk in primary aldosteronism extends beyond hypertension, with increased rates of insulin resistance, type 2 diabetes, and osteoporosis, which cannot be easily explained by aldosterone excess.<h4>Methods</h4>We performed mass spectrometry-based analysis of a 24-hour urine steroid metabolome in 174 newly diagnosed p ...[more]

PMID: 28422753

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Project description:ImportanceMost patients with primary aldosteronism, a major cause of secondary hypertension, are not identified or appropriately treated because of difficulties in diagnosis and subtype classification. Applications of artificial intelligence combined with mass spectrometry-based steroid profiling could address this problem.ObjectiveTo assess whether plasma steroid profiling combined with machine learning might facilitate diagnosis and treatment stratification of primary aldosteronism, particularly for patients with unilateral adenomas due to pathogenic KCNJ5 sequence variants.Design, setting, and participantsThis diagnostic study was conducted at multiple tertiary care referral centers. Steroid profiles were measured from June 2013 to March 2017 in 462 patients tested for primary aldosteronism and 201 patients with hypertension. Data analyses were performed from September 2018 to August 2019.Main outcomes and measuresThe aldosterone to renin ratio and saline infusion tests were used to diagnose primary aldosteronism. Subtyping was done by adrenal venous sampling and follow-up of patients who underwent adrenalectomy. Statistical tests and machine-learning algorithms were applied to plasma steroid profiles. Areas under receiver operating characteristic curves, sensitivity, specificity, and other diagnostic performance measures were calculated.ResultsPrimary aldosteronism was confirmed in 273 patients (165 men [60%]; mean [SD] age, 51 [10] years), including 134 with bilateral disease and 139 with unilateral adenomas (58 with and 81 without somatic KCNJ5 sequence variants). Plasma steroid profiles varied according to disease subtype and were particularly distinctive in patients with adenomas due to KCNJ5 variants, who showed better rates of biochemical cure after adrenalectomy than other patients. Among patients tested for primary aldosteronism, a selection of 8 steroids in combination with the aldosterone to renin ratio showed improved effectiveness for diagnosis over either strategy alone. In contrast, the steroid profile alone showed superior performance over the aldosterone to renin ratio for identifying unilateral disease, particularly adenomas due to KCNJ5 variants. Among 632 patients included in the analysis, machine learning-designed combinatorial marker profiles of 7 steroids alone both predicted primary aldosteronism in 1 step and subtyped patients with unilateral adenomas due to KCNJ5 variants at diagnostic sensitivities of 69% (95% CI, 68%-71%) and 85% (95% CI, 81%-88%), respectively, and at specificities of 94% (95% CI, 93%-94%) and 97% (95% CI, 97%-98%), respectively. The validation series yielded comparable diagnostic performance.Conclusions and relevanceMachine learning-designed combinatorial plasma steroid profiles may facilitate both screening for primary aldosteronism and identification of patients with unilateral adenomas due to pathogenic KCNJ5 variants, who are most likely to show benefit from surgical intervention.

Dataset Information

Steroid metabolome analysis reveals prevalent glucocorticoid excess in primary aldosteronism.

Background

Methods

Results

Conclusion

Funding

Publications

Steroid metabolome analysis reveals prevalent glucocorticoid excess in primary aldosteronism.

Similar Datasets

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