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Diagnosis of inflammatory rheumatic diseases: preliminary approach by metabolomics
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As doenças reumáticas inflamatórias (IRDs), como a espondilite anquilosante (AS), a artrite reumatoide (RA) e o lúpus eritematoso sistémico (SLE), são caracterizadas por dor, incapacidade, comorbidade e risco de morte precoce. Cerca de 5% da população sofre de uma doença reumática inflamatória. O diagnóstico precoce e o tratamento especializado são cruciais para minimizar o impacto destas doenças nos pacientes. Embora vários avanços tenham sido feitos, o diagnóstico destas patologias ainda é um desafio hoje em dia. Tendo em conta que a metabolómica é a tecnologia pós-genómica que oferece a caracterização mais próxima do fenótipo, esta metodologia pode constituir uma ferramenta fundamental para discriminar doentes com IRDs. De facto, a análise do metaboloma (metabolómica) está a começar a fornecer novos e eficazes biomarcadores. Assim, com o objetivo final de desenvolver uma abordagem clínica viável para o diagnóstico/ discriminação de doentes com AS, RA e SLE, este estudo tem como objetivo identificar diferenças no perfil metabólico na urina de doentes com estas três IRDs e controlos saudáveis. Para isso, foi efetuado um estudo transversal de metabolómica não-direcionada, baseado em espectrometria de massa, onde foram analisadas amostras de urina de 111 indivíduos compostos por: AS (n = 27, de acordo com os critérios da ASAS), RA (n = 22, de acordo com os critérios do ACR/ EULAR para RA) e SLE (n = 23, de acordo com os critérios de classificação do ACR para SLE) e controlos saudáveis (HC; n = 39). As amostras de urina foram analisadas por espectrometria de massa de cromatografia líquida de alta resolução (LC-HRMS) e, de seguida, os dados foram pré-processados com o software MZmine. A matriz resultante foi normalizada por área total e analisada por análise multivariada com o software SIMCA. A identificação dos metabolitos baseou-se nos resultados obtidos em experiências HRMS de “tandem”. Neste estudo, foram identificadas diferenças metabólicas entre controlos saudáveis e doentes com SLE e RA e foi possível discriminar doentes com SLE e RA de doentes com AS. Além disso, foram identificados três metabolitos com funções bioquímicas relevantes. Em conclusão, os resultados obtidos no âmbito deste trabalho sugerem que o perfil metabolómico da urina pode ser útil para discriminar IRDs. No entanto, são necessários mais estudos para validar o potencial uso da análise metabolómica de urina para o diagnóstico/ prognóstico destas doenças na prática clínica.
Inflammatory rheumatic diseases (IRDs), such as ankylosing spondylitis (AS), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE), are characterized by pain, disability, comorbidity and risk of early death. Although many advances have been made, the diagnosis of these disorders is still very challenging. Taking into consideration that metabolomics is a post-genomics technology that offers the closest characterization of the phenotype, this methodology can constitute a key tool for discriminating patients with IRDs. In fact, the analysis of the metabolome (metabolomics) is starting to provide new and powerful biomarkers. With the ultimate goal of developing a feasible clinical approach for AS, RA and SLE diagnosis/ prognosis, this work was aimed at identifying differences in the urinary metabolic profile, of patients diagnosed with these three IRDs and healthy controls. A cross-sectional non-targeted mass spectrometry-based metabolomics study was performed in 111 individuals composed by: AS (n = 27, according to ASAS criteria), RA (n = 22, according to ACR/ EULAR criteria for RA) and SLE (n = 23, according to ACR classification criteria for SLE) patients and healthy controls (HC; n = 39). Urine samples were analyzed by liquid chromatography high-resolution mass spectrometry (LC-HRMS) and data was subsequently preprocessed with the open-source software MZmine. The resulting matrix was normalized by total area and analyzed by multivariate analysis with the SIMCA software. Identification of metabolites responsible for metabolic differences between groups was based on tandem-HRMS experiments. Using this approach, it was possible to identify metabolic differences between healthy controls and SLE and RA diseases, and to discriminate SLE and RA patients from AS patients. Additionally, three metabolites with relevant biochemical roles were identified. These results suggest that urine metabolomics can be a valuable technology to discriminate IRDs. Nonetheless, further studies are necessary to validate the potential use of urine metabolomics for the prognosis and/ or stratification of IRDs in clinical practice.
Inflammatory rheumatic diseases (IRDs), such as ankylosing spondylitis (AS), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE), are characterized by pain, disability, comorbidity and risk of early death. Although many advances have been made, the diagnosis of these disorders is still very challenging. Taking into consideration that metabolomics is a post-genomics technology that offers the closest characterization of the phenotype, this methodology can constitute a key tool for discriminating patients with IRDs. In fact, the analysis of the metabolome (metabolomics) is starting to provide new and powerful biomarkers. With the ultimate goal of developing a feasible clinical approach for AS, RA and SLE diagnosis/ prognosis, this work was aimed at identifying differences in the urinary metabolic profile, of patients diagnosed with these three IRDs and healthy controls. A cross-sectional non-targeted mass spectrometry-based metabolomics study was performed in 111 individuals composed by: AS (n = 27, according to ASAS criteria), RA (n = 22, according to ACR/ EULAR criteria for RA) and SLE (n = 23, according to ACR classification criteria for SLE) patients and healthy controls (HC; n = 39). Urine samples were analyzed by liquid chromatography high-resolution mass spectrometry (LC-HRMS) and data was subsequently preprocessed with the open-source software MZmine. The resulting matrix was normalized by total area and analyzed by multivariate analysis with the SIMCA software. Identification of metabolites responsible for metabolic differences between groups was based on tandem-HRMS experiments. Using this approach, it was possible to identify metabolic differences between healthy controls and SLE and RA diseases, and to discriminate SLE and RA patients from AS patients. Additionally, three metabolites with relevant biochemical roles were identified. These results suggest that urine metabolomics can be a valuable technology to discriminate IRDs. Nonetheless, further studies are necessary to validate the potential use of urine metabolomics for the prognosis and/ or stratification of IRDs in clinical practice.
Descrição
Tese de mestrado, Biologia Humana e Ambiente, Universidade de Lisboa, Faculdade de Ciências, 2019
Palavras-chave
Doenças reumáticas inflamatórias. Diagnóstico Metabolómica Biomarcadores Análise multivariada Espectrometria de massa de alta resolução Teses de mestrado - 2019