Adrenal hormonal imbalance in acute intermittent porphyria patients: results of a case control study

Abstract

BackgroundAcute Intermittent Porphyria (AIP) is a rare disease that results from a deficiency of hydroxymethylbilane synthase, the third enzyme of the heme biosynthetic pathway. AIP carriers are at risk of presenting acute life-threatening neurovisceral attacks. The disease induces overproduction of heme precursors in the liver and long-lasting deregulation of metabolic networks. The clinical history of AIP suggests a strong endocrine influence, being neurovisceral attacks more common in women than in men and very rare before puberty. To asses the hypothesis that steroidogenesis may be modified in AIP patients with biochemically active disease, we undertook a comprehensive analysis of the urinary steroid metabolome.MethodsA case–control study was performed by collecting spot morning urine from 24 AIP patients and 24 healthy controls. Steroids in urine were quantified by liquid chromatography-tandem mass spectrometry. Parent steroids (17-hydroxyprogesterone; deoxycorticosterone; corticoesterone; 11-dehydrocorticosterone; cortisol and cortisone) and a large number of metabolites (N = 55) were investigated. Correlations between the different steroids analyzed and biomarkers of porphyria biochemical status (urinary heme precursors) were also evaluated. The Mann–Whitney U test and Spearman’s correlation with a two tailed test were used for statistical analyses.ResultsForty-one steroids were found to be decreased in the urine of AIP patients (P < 0.05), the decrease being more significant for steroids with a high degree of hydroxylation. Remarkably, 13 cortisol metabolites presented lower concentrations among AIP patients (P < 0.01) whereas no significant differences were found in the main metabolites of cortisol precursors. Nine cortisol metabolites showed a significant negative correlation with heme precursors (p < 0.05). Ratios between the main metabolites of 17-hydroxyprogesterone and cortisol showed positive correlations with heme-precursors (correlation coefficient > 0.51, P < 0.01).ConclusionsComprehensive study of the urinary steroid metabolome showed that AIP patients present an imbalance in adrenal steroidogenesis, affecting the biosynthesis of cortisol and resulting in decreased out-put of cortisol and metabolites. This may result from alterations of central origin and/or may originate in specific decreased enzymatic activity in the adrenal gland. An imbalance in steroidogenesis may be related to the maintenance of an active disease state among AIP patients.

Porphyrias: A 2015 update

“…The tricarboxylic acid cycle is then unable to supply reduced cofactors for the respiratory chain. Moreover, adrenal steroidogenesis, affecting the biosynthesis of cortisol and resulting in decreased out-put of cortisol and metabolites, was imbalanced in a case-control study of the urinary steroid metabolome of AIP patients [21]. These energetic and hormonal metabolic alterations were restored after heme arginate administration.…”

A Case of Acute Inflammatory Polyneuropathy with Atypical Features / Atipik Özellikler Gösteren Akut Enflamatuvar Polinöropatili Bir Olgu

“…In our case, LD and porphyria diagnoses were suggested because of acute motor neuropathy (AMN) with abdominal pain, autonomic dysfunction, and neuropsychiatric manifestations (12,13). Facial diplegia can be seen in both of these diseases, too (14).…”

GC/MS in Recent Years Has Defined the Normal and Clinically Disordered Steroidome: Will It Soon Be Surpassed by LC/Tandem MS in This Role?

“…Heat maps allow for the visual interpretation of a large amount of related data. As an example, Pozo et al [ 41 ] used heat maps in an LC/MS/MS study of steroid excretion in acute intermittent porphyria, discussed in “C-2. Steroidomics and recently described disorders of synthesis and metabolism” ( Fig.…”

“…3C ). Additionally, the ratios between the main metabolites of 17-hydroxyprogesterone and cortisol showed a positive correlation with heme-precursors [ 41 ]. Arlt et al [ 42 ] are developing machine learning techniques to improve the diagnostic accuracy of automated data interpretation and have reported on such methods for the diagnosis of adrenal cancer.…”