Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their Ratios.
Metabolites
; 12(8)2022 Aug 16.
Article
en En
| MEDLINE
| ID: mdl-36005627
ABSTRACT
Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised primary aldosteronism (PA), Cushing's syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C181, C182, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
2_ODS3
Problema de salud:
2_cobertura_universal
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Aspecto:
Patient_preference
Idioma:
En
Revista:
Metabolites
Año:
2022
Tipo del documento:
Article
País de afiliación:
Reino Unido