Plasma fatty acid profile as biomarker of coronary artery disease: a pilot study using fourth generation artificial neural networks.
- 1 Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy.
- 2 Villa Santa Maria Institute, Tavernerio, Como, Italy.
- 3 Department of Cardiac Surgery, I.R.C.C.S. Policlinico San Donato, San Donato Milanese, Milan, Italy.
- 4 Institute for Clinical Chemistry and Laboratory Medicine, University of Regensburg, Regensburg, Germany.
- 5 Service of Laboratory Medicine 1-Clinical Pathology, I.R.C.C.S. Policlinico San Donato, San Donato Milanese, Milan, Italy.
Many studies, focused on identifying new biomarkers for coronary artery disease (CAD) risk computation and monitoring, suggested a potential diagnostic role for fatty acids (FA). In the present study, we explored the potential diagnostic role of FA by using a data mining approach based on fourth generation artificial neural networks (ANN). Forty-one male subjects were enrolled. According to coronary angiography, 31 displayed CAD and 10 did not (non-CAD, control group). FA analysis was performed on plasma samples using a gas chromatography-mass spectrometry system and analyses were performed by an ANN method. The variables most closely related to CAD were low levels of alpha-linolenic acid, eicosapentaenoic acid, eicosatetraenoic and docosahexaenoic acids. High levels of 1,1-dimethoxyhexadecane, total dimethyl acetals and docosatetraenoic acid were related to non-CAD condition. This subset of variables, which were most closely correlated to the target diagnosis, achieved a consistent predictive rate. The average accuracy obtained was 76.5%, with 93% of sensitivity and 60% of specificity. The area under the ROC curve was equal to 0.79. In conclusion, our study highlighted the association between different plasma FA species, CAD and non-CAD conditions. The specific subset of variables could be of interest as a new diagnostic tool for CAD management.
- Journal Article