A machine-learning approach to the prediction of oxidative stress in chronic inflammatory disease.
Oxidative stress is implicated in the development of a wide range of chronic human diseases, ranging from cardiovascular to neurodegenerative and inflammatory disorders. As oxidative stress results from a complex cascade of biochemical reactions, its quantitative prediction remains incomplete. Here, we describe a machine-learning approach to the prediction of levels of oxidative stress in human subjects. From a database of biochemical analyses of oxidative stress biomarkers in blood, plasma and urine, non-linear models have been designed, with a statistical methodology that includes variable selection, model training and model selection. Our data demonstrate that, despite a large inter- and intra-individual variability, levels of biomarkers of oxidative damage in biological fluids can be predicted quantitatively from measured concentrations of a limited number of exogenous and endogenous antioxidants.
de la Villehuchet
A Magon
AM
Ecole Supérieure de Physique et de Chimie Industrielles, ESPCI-Paristech, Laboratoire d'Electronique (CNRS UMR 7084), Paris, France.
Brack
M
M
Dreyfus
G
G
Oussar
Y
Y
Bonnefont-Rousselot
D
D
Chapman
M J
MJ
Kontush
A
A
D000975:Antioxidants / Q000032:analysis*; D001185:Artificial Intelligence; D015415:Biomarkers / Q000097:blood*; D002318:Cardiovascular Diseases / Q000097:blood / Q000473:pathology; D002908:Chronic Disease; D005260:Female; D006801:Humans; D007249:Inflammation / Q000097:blood / Q000473:pathology; D008297:Male; D008954:Models, Biological; D019636:Neurodegenerative Diseases / Q000097:blood / Q000473:pathology; D018384:Oxidative Stress
D000975:Antioxidants; D015415:Biomarkers