RESUMO
AIMS: Metabolomics have been used to evaluate the role of small molecules in human disease. However, the cost and complexity of the methodology and interpretation of findings have limited the transference of knowledge to clinical practice. Here, we apply a targeted metabolomics approach using samples blotted in filter paper to develop clinical-metabolomics models to detect kidney dysfunction in diabetic kidney disease (DKD). METHODS: We included healthy controls and subjects with type 2 diabetes (T2D) with and without DKD and investigated the association between metabolite concentrations in blood and urine with eGFR and albuminuria. We also evaluated performance of clinical, biochemical and metabolomic models to improve kidney dysfunction prediction in DKD. RESULTS: Using clinical-metabolomics models, we identified associations of decreased eGFR with body mass index (BMI), uric acid and C10:2 levels; albuminuria was associated to years of T2D duration, A1C, uric acid, creatinine, protein intake and serum C0, C10:2 and urinary C12:1 levels. DKD was associated with age, A1C, uric acid, BMI, serum C0, C10:2, C8:1 and urinary C12:1. Inclusion of metabolomics increased the predictive and informative capacity of models composed of clinical variables by decreasing Akaike's information criterion, and was replicated both in training and validation datasets. CONCLUSIONS: Targeted metabolomics using blotted samples in filter paper is a simple, low-cost approach to identify outcomes associated with DKD; the inclusion of metabolomics improves predictive capacity of clinical models to identify kidney dysfunction and DKD-related outcomes.
Assuntos
Nefropatias Diabéticas/sangue , Metabolômica/métodos , Técnicas de Diagnóstico Molecular/métodos , Idoso , Biomarcadores/sangue , Biomarcadores/urina , Diabetes Mellitus Tipo 2/complicações , Nefropatias Diabéticas/urina , Feminino , Humanos , Masculino , Metaboloma , Metabolômica/normas , Pessoa de Meia-Idade , Técnicas de Diagnóstico Molecular/normasRESUMO
The output of metabolomics relies to a great extent upon the methods and instrumentation to identify, quantify, and access spatial information on as many metabolites as possible. However, the most modern machines and sophisticated tools for data analysis cannot compensate for inappropriate harvesting and/or sample preparation procedures that modify metabolic composition and can lead to erroneous interpretation of results. In addition, plant metabolism has a remarkable degree of complexity, and the number of identified compounds easily surpasses the number of samples in metabolomics analyses, increasing false discovery risk. These aspects pose a large challenge when carrying out plant metabolomics experiments. In this chapter, we address the importance of a proper experimental design taking into consideration preventable complications and unavoidable factors to achieve success in metabolomics analysis. We also focus on quality control and standardized procedures during the metabolomics workflow.