Analysis of postprandial lipemia as a Cardiovascular Disease risk factor using genetic and clinical information: An Artificial Neural Network perspective

Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"

Volumn , Issue , 2008, Pages 4609-4612

  Valavanis, Ioannis K ^a   Mougiakakou, Stavroula G ^a   Grimaldi, Keith A ^b   Nikita, Konstantina S ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

^b Sciona Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BLOOD; CARDIOLOGY; GENES; LIPIDS; NEURAL NETWORKS; RISK ASSESSMENT;

CARDIO-VASCULAR DISEASE RISK FACTORS; CARDIOVASCULAR DISEASE; CLINICAL INFORMATION; CLINICAL MEASUREMENTS; DESIGN AND DEVELOPMENT; ENVIRONMENTAL INTERACTIONS; GENETIC VARIATION; INPUT VARIABLES;

DISEASES;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; CARDIOVASCULAR DISEASE; DIET RESTRICTION; ENVIRONMENT; FEMALE; GENETIC VARIABILITY; GENETICS; HUMAN; HYPERLIPIDEMIA; MALE; POSTPRANDIAL STATE; REPRODUCIBILITY; RISK FACTOR; STATISTICAL MODEL; TIME;

CARDIOVASCULAR DISEASES; ENVIRONMENT; FASTING; FEMALE; GENETIC VARIATION; HUMANS; HYPERLIPIDEMIAS; MALE; MODELS, GENETIC; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); POSTPRANDIAL PERIOD; REPRODUCIBILITY OF RESULTS; RISK FACTORS; TIME FACTORS;

EID: 61849107062     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/iembs.2008.4650240     Document Type: Conference Paper

References (22)

1. D. B. Zilversmit, "Atherogenesis: a postprandial phenomenon," Circulation, vol. 60, pp. 473-485, 1979.
2. A. R. Sharrett et al, "Association of postprandial triglyceride and retinyl palmitate responses with asymptomatic carotid artery atherosclerosis in middle-aged men and women: The Atherosclerosis Risk in Communities (ARIC) study," Arterioscler Thromb Vasc Biol., vol. 15, pp. 2122-2129, 1995.
3. P. H. Groot et al, "Postprandial lipoprotein metabolism in normolipidemic men with and without coronary artery disease," Arterioscler Thromb., vol. 11, pp 553-562, 1991.
4. I. Stensvold, A. Tverdal, P. Urdal, S. Graff-Iversen S., "Non-fasting serum triglyceride concentration and mortality from coronary heart disease and any cause in middle aged Norwegian women," BMJ, vol. 307, pp. 1318-1322, 1993.
5. J. R. Patsch, G. Miesenböck, T. Hopferwieser, "Relation of triglyceride metabolism and coronary artery disease. Studies in the postprandial state," Arterioscler Thromb, vol. 12, pp. 1336-1345, 1992.
6. C. Dubois et al, "Effects of graded amounts (0-50g) of dietary fat on postprandial lipemia and lipoproteins in normolipidaemic adults," Am J Clin Nutr, vol. 67, pp: 31-38, 1998.
7. M. Ostos et al, "Dietary fat clearance is odulated by genetic variation in apolipoprotein A-IV gene locus," J Lip Res, vol. 39, pp. 2493-2500, 1998.
8. C. F. Sing, J. H. Stengard, S. L. Kardia, "Genes, environment and cardiovascular disease," Arterioscler Thromb Vasc Biol, vol. 23, no. 7, pp. 1190-1196, 2003.
9. F. Karpe, "Postprandial lipid metabolism in relation to coronary heart disease," Proc. Nutr. Soc., vol. 56, pp. 671-678, 1997.
10. A. G. Heidema, J. M. Boer, N. Nagelkerke, E. C. Mariman, D. L. van der A., E. J. Feskens," The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases," BMC Genet., 7:23, 2006.
11. S. Tomida, T. Hanai, Y. Suzuki, T. Kobayashi, H. Honda, "Artificial Neural Network Predictive Model for Allergic Disease using single nucleotide polymorphism data," J Biosci Bioeng, vol. 93, no. 5, 470-478, 2002.
12. N. Gueli et al "The influence of lifestyle on cardiovascular risk factors: analysis using a neural network," Arc Gerontol Geriat, vol. 40, no. 2, pp. 157-172, 2005.
13. Y. Tomita, S. Tomida, Y. Hasegawa, Y. Suzuki, T. Shirakawa, T. Kobayashi, H. Honda, "Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma," BMC Bioinformatics, 5:120, 2004.
14. A. A. Motsinger, S. L. Lee, G. Mellick, M. D. Ritchie, "GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease," BMC Bioinformatics, 7:39, 2006.
15. L. W. Hahn, M. D. Ritchie, J. H. Moore, "Mutifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions," Bionformatics, vol. 19, pp. 376-382, 2003.
16. J. Xu et al, "The interaction of four genes in the inflammation pathway significantly predicts prostate cancer risk," Cancer Epidemiol Biomarkers Prev, vol. 14, pp. 2563-2568, 2005.
17. Y. M. Cho et al, "Multifactor-dimensionality reduction shows a two locus interaction associated with Type 2 diabetes mellitus," Diabetologia, vol. 47, pp. 549-554, 2004.
18. C. S. Coffey et al, "An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene Interactions on risk of myocardial infarction: The importance of model validation", BMC Bioinformatics, 5:49, 2004.
19. M. Carstensen, C. Thomsen, and K. Hermansen, "Incremental Area Under Response Curve More Accurately Describes the Triglyceride Response to an Oral Fat Load in Both Healthy and Type 2 Diabetic Subjects", Metabolism, vol 52, no. 8, pp 1034-1037, 2003
20. S. Haykin, Neural networks: A comprehensive foundation, Prentice-Hall, 1999.
21. C. Q. Lai et al, "Influence of the APOA5 locus on plasma triglyceride, lipoprotein subclasses, and CVD risk in the Framingham Heart Study," J Lipid Res., vol. 45, no. 11, pp. 2096-2105, 2004.
22. E. Olano-Martin et al, "Influence of ApoA5 gene variants on postprandial triglyceride metabolism: Impact of gender," J Lipid Res., vol. 49, no. 5, pp. 945-953, 2008.