Effect of congenital adrenal hyperplasia treated by glucocorticoids on plasma metabolome: a machine-learning-based analysis.
Sci Rep
; 10(1): 8859, 2020 06 01.
Article
in En
| MEDLINE
| ID: mdl-32483270
BACKGROUND: Congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency leads to impaired cortisol biosynthesis. Treatment includes glucocorticoid supplementation. We studied the specific metabolomics signatures in CAH patients using two different algorithms. METHODS: In a case-control study of CAH patients matched on sex and age with healthy control subjects, two metabolomic analyses were performed: one using MetaboDiff, a validated differential metabolomic analysis tool and the other, using Predomics, a novel machine-learning algorithm. RESULTS: 168 participants were included (84 CAH patients). There was no correlation between plasma cortisol levels during glucocorticoid supplementation and metabolites in CAH patients. Indoleamine 2,3-dioxygenase enzyme activity was correlated with ACTH (rho coefficient = -0.25, p-value = 0.02), in CAH patients but not in controls subjects. Overall, 33 metabolites were significantly altered in CAH patients. Main changes came from: purine and pyrimidine metabolites, branched aminoacids, tricarboxylic acid cycle metabolites and associated pathways (urea, glucose, pentose phosphates). MetaboDiff identified 2 modules that were significantly different between both groups: aminosugar metabolism and purine metabolism. Predomics found several interpretable models which accurately discriminated the two groups (accuracy of 0.86 and AUROC of 0.9). CONCLUSION: CAH patients and healthy control subjects exhibit significant differences in plasma metabolomes, which may be explained by glucocorticoid supplementation.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Adrenal Hyperplasia, Congenital
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Metabolome
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Machine Learning
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Glucocorticoids
Type of study:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limits:
Adult
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Female
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Humans
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Male
Language:
En
Journal:
Sci Rep
Year:
2020
Document type:
Article
Affiliation country:
France
Country of publication:
United kingdom