New screening approach for Alzheimer's disease risk assessment from urine lipid peroxidation compounds.
Carmen Peña-BautistaClaire VigorJean-Marie GalanoCamille OgerThierry DurandInés FerrerAna CuevasRogelio López-CuevasMiguel BaqueroMarina López-NoguerolesMaximo VentoAntonio José Cañada-MartínezAna García-BlancoConsuelo Cháfer-PericásPublished in: Scientific reports (2019)
Alzheimer Disease (AD) standard biological diagnosis is based on expensive or invasive procedures. Recent research has focused on some molecular mechanisms involved since early AD stages, such as lipid peroxidation. Therefore, a non-invasive screening approach based on new lipid peroxidation compounds determination would be very useful. Well-defined early AD patients and healthy participants were recruited. Lipid peroxidation compounds were determined in urine using a validated analytical method based on liquid chromatography coupled to tandem mass spectrometry. Statistical studies consisted of the evaluation of two different linear (Elastic Net) and non-linear (Random Forest) regression models to discriminate between groups of participants. The regression models fitted to the data from some lipid peroxidation biomarkers (isoprostanes, neuroprostanes, prostaglandines, dihomo-isoprostanes) in urine as potential predictors of early AD. These prediction models achieved fair validated area under the receiver operating characteristics (AUC-ROCs > 0.68) and their results corroborated each other since they are based on different analytical principles. A satisfactory early screening approach, using two complementary regression models, has been obtained from urine levels of some lipid peroxidation compounds, indicating the individual probability of suffering from early AD.
Keyphrases
- liquid chromatography
- tandem mass spectrometry
- risk assessment
- fatty acid
- mass spectrometry
- ultra high performance liquid chromatography
- high performance liquid chromatography
- solid phase extraction
- newly diagnosed
- end stage renal disease
- high resolution mass spectrometry
- machine learning
- simultaneous determination
- chronic kidney disease
- heavy metals
- cognitive decline
- big data
- neural network
- deep learning
- electronic health record
- molecularly imprinted