Metabonomics and Metabolomics of the exposome by NMR

• Grant number: 24/21942-9
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: March 01, 2025
• End date: January 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Chemistry - Organic Chemistry
• Principal Investigator: Ljubica Tasic
• Grantee: Glenda Santos de Oliveira
• Host Institution: Instituto de Química (IQ). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 18/24069-3 - ReSEARCH: Recognizing Signatures of the Exposome to Anticipate the Risks for a Continuous Health, AP.TEM

Abstract

The exposome covers all exposures an individual is subjected to from conception to death. This work aims to identify metabolic profiles of populations submitted to various types of exposures, relating to the increase of health risks and regional differences. Metabolomic andmetabolomic analyses based on serum and urine NMR and subsequent application of multivariate statistical analysis techniques will be used. Metabolomics proposes that changes in concentrations of endogenous metabolites in response to disturbances in an organism can be identified as a fingerprintof the disorder. Metabolomics seeks an analytical description of complex biological samples and aims to characterize and quantify all small molecules in a sample. As the response of NMR analysis is a very complex spectrum, due to the number of substances present in the biofluids, to observe the differences between samples it is necessary the apply chemometric techniques to the datamatrix, to identify standards, and to classify the samples. Exploratory data analysis will be performed to observe the natural behavior of the data; recognize anomalous samples, and verify if the samples have a natural tendency of grouping in the classes of interest. Classificatory analysis techniques will be used, to construct models capable of differentiating the samples of the different groups. Cross-validation and external validation will be carried out, to evaluate: the representative character of the data used to produce the model, the number of variables necessary to characterize the data set, and the ability of the model to predict unknown samples. Finally, the biomarkers will be identified by interpreting the crucial signs for the discrimination of the groups of samples, based on the literature and databases.