RESUMEN
Established life course approaches suggest that health status in adulthood can be influenced by events that occurred during the prenatal developmental period. Yet, interventions such as diet and lifestyle changes performed during pregnancy have had a small impact on both maternal and offspring health outcomes. Currently, there is a growing body of literature that highlights the importance of maternal health before conception (months or years before pregnancy occurs) for the future health of offspring. While some studies have explored factors such as maternal body composition, nutrition, and lifestyle in this area, location-based environmental and socioeconomic exposures before conception may also contribute to future offspring health. In this line, the study of a patient's geographic history presents a promising avenue. To foster research in this direction, the integration of geospatial health, medical informatics and artificial intelligence techniques offers great potential. Importantly, novel sources of big health data sets such as electronic health records registered at the primary care level provide a unique framework due to its inherent longitudinal nature. Nonetheless, a number of privacy, ethical, and technical challenges need to be overcome for this kind of longitudinal analysis to mature and succeed. In the long-term, we support the vision of incorporating a patient's geographic history into her clinical history to equip clinicians with useful contextual information to explore.
Asunto(s)
Inteligencia Artificial , Atención Primaria de Salud , Humanos , Femenino , Atención Preconceptiva , Informática Médica , Embarazo , Registros Electrónicos de SaludRESUMEN
Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we tested the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of covarying features derived from principal component analysis (PCA), the iterative signature algorithm (ISA), and averaged correlation profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. On the basis of the number and quality of the matches, we concluded that ISA and ACP can robustly identify ten and nine metabolites, respectively, half of which were shared, while PCA did not produce any signatures with robust matches.