Dozio, E. and Vianello, E. and Grossi, E. and Menicanti, L. and Schmitz, G. and Romanelli, M. M. Corsi (2018) PLASMA FATTY ACID PROFILE AS BIOMARKER OF CORONARY ARTERY DISEASE: A PILOT STUDY USING FOURTH GENERATION ARTIFICIAL NEURAL NETWORKS. JOURNAL OF BIOLOGICAL REGULATORS AND HOMEOSTATIC AGENTS, 32 (4). pp. 1007-1013. ISSN 0393-974X, 1724-6083
Full text not available from this repository. (Request a copy)Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | SIMVASTATIN; |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Klinische Chemie und Laboratoriumsmedizin |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 13 Feb 2020 08:23 |
| Last Modified: | 13 Feb 2020 08:23 |
| URI: | https://pred.uni-regensburg.de/id/eprint/14294 |
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