Abstract
The work presented in this paper demonstrates how different data mining approaches can be applied to extend conventional combinations of variables determining the Metabolic Syndrome with new influential variables, which are easily available in the everyday physician‘s practice. The results have important consequences: patients with the Metabolic Syndrome can be recognized by using only some, one, or none of the conventional variables, when replaced with some other surrogate variables, available in patient health records, making diagnosis feasible in different work environments and at different time points of patient care. In addition, the results showed that there is a large diversity of patient groups, much larger than it was supposed earlier on when their identification was based on the conventional variables approach, indicating the underlying complexity of this syndrome. Finally, the discovered novel variables, indicating yet unknown pathogenetic pathways can be used to inspire future research.
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References
Eckel, R.A., Grundy, S.M., Zimmet, P.Z.: The metabolic syndrome. Lancet 365, 1415–1428 (2005)
Festa, A., D’Agostino, R., Howard, G., et al.: Chronic subclinical inflammation as part of the insulin resistance syndrome. Circulation 102, 42–47 (2000)
Goodwill, H.G., Frisbee, J.C.: Oxidant stress and skeletal muscle microvasculopathy in the metabolic syndrome. Vascul. Pharmacol. 57(5-6), 150–159 (2012), doi:1016/j.vph.2012. 07.002. Epub July 11, 2012
Oron-Herman, M., Rosenthal, T., Sela, B.A.: Hyperhomocysteinemia as a component of syndrome X. Metabolism 52, 1491–1495 (2003) [PubMed: 14624412]
Hjemdahl, P.: Stress and the Metabolic syndrome: an interesting but enigmatic association. Circulation 106, 2634–2636 (2002), doi:10.1161/01.CIR.0000041502.43564.79
Onat, A., Hergenc, G., Keles, T., et al.: Sex difference in development of diabetes and cardiovascular disease on the way from obesity and metabolic syndrome. Metabolism 54(6), 800–808 (2005)
Lopey-Raton, M., Rodriguez-Alvarez, M.X.: R Package, “OptimalCutpoints” (2013)
Lerner, D.J., Kannel, W.B.: Patterns of coronary heart disease morbidity and mortality in the sexes: a 26-year follow-up of the Framingham population. Am. Heart J., 383–390 (February 1986)
The MONICA, risk, genetics, archiving and monograph (MORGAM) biomarker project, Contribution of 30 biomarkers to 10-year cardiovascular risk estimation in 2 population cohorts. Circulation 121, 2388–2397 (2010)
Engstrom, G., Jerntrop, I., Pessah-Rasmussen, H., et al.: Geographic distribution of stroke incidence within an urban population: relations to socioeconomic circumstances and prevalence of cardiovascular risk factors. Stroke 32(5), 1098–1103 (2001)
Ajani, U.A., Ford, E.S.: Has the risk for coronary heart disease changed among U.S. adults? J. Am. Coll. Cardiol. 48(6), 1177–1182 (2006)
Holzinger, A., Jurisica, I.: Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 1–18. Springer, Heidelberg (2014)
Han-Saem, P., Sung-Bae, C.: Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome. Expert Systems with Applications 39(4), 4240–4249 (2012)
Worachartcheewan, A., Nantasenamat, C., Prasertsrithong, P., Amranan, J., Monnor, T., Chaisatit, T., Nuchpramool, W., Prachayasittikul, V.: Machine Learning Approaches for discerning intercorrelation of Hematological Parameters and Glucose Level for identification of diabetes mellitus. EXCLI Journal 12, 885–893 (2013)
Holzinger, A., Dehmer, M., Jurisica, I.: Knowledge Discovery and Interactive Data Mining in Bioinformatics – State-of-the-Art, Future challenges and Research Directions. BMC Bioinformatics 15(suppl. 6), I1 (2014)
Huppertz, B., Holzinger, A.: Biobanks – A Source of Large Biological Data Sets: Open Problems and Future Challenges. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 317–330. Springer, Heidelberg (2014)
Cima, I., Schiess, R., Wild, P., Kaelin, M., Schuffler, P., Lange, V., Picotti, P., Ossola, R., Templeton, A., Schubert, O., Fuchs, T., Leippold, T., Wyler, S., Zehetner, J., Jochum, W., Buhmann, J., Cerny, T., Moch, H., Gillessen, S., Aebersold, R., Krek, W.: Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer. Proc. Natl. Acad. Sci. U. S. A. 108, 3342–3347 (2011)
International Diabetes Federation. The IDF consensus worldwide definition of the Metabolic Syndrome (2006), http://www.idf.org/webdata/does/IDF_Meta_def_final.pdf
Holzinger, A., Zupan, M.: KNODWAT: A scientific framework application for testing knowledge discovery methods for the biomedical domain. BMC Bioinformatics 14, 191 (2013)
Youden, W.J.: Index for rating diagnostic tests. Cancer 3, 32–35 (1950)
Yin, J., Tian, L.: Optimal linear combinations of multiple diagnostic biomarkers based on Youden index. Statistics in Medicine (2013)
Lai, C.-Y., Tian, L., Schisterman, E.F.: Exact confidence interval estimation for the Youden index and its corresponding optimal cut-point. Computational Statistics & Data Analysis 56, 1103–1114 (2012)
Regitz-Zagrosek, V., Lehmkuhl, E., Weickert, M.O.: Gender differences in the metabolic syndrome and their role for cardiovascular disease. Clin. Res. Cardiol. 95(3), 136–147 (2006)
Monnier, L., Colette, C.: Glycemic variability. Diabetes Care 31(suppl. 2), S150–S154 (2008)
Franceschi, C., Bonafe, M., Valensin, S., et al.: Human immunosenescence: the prevailing of innate immunity, the failing of clonotypic immunity and the filling of immunological space. Vaccine 18(16), 1717–1720 (2000)
Hung-Chih, H., Jeng-Fong, C., Yu-Huei, W., et al.: Folate deficiency triggers an oxidative-nitrosative stress-mediated apoptotic cell death and impedes insulin biosynthesis in RNm5F pancreatic islet β-cells: relevant to the pathogenesis of Diabetes. PLoS ONE 8(11), e77931 (2013), doi:10.1371/journal.pone.0077931
Schneider, M.P., Schlaich, M.P., Harazy, J.M., et al.: Folic acid treatment normalizes NOS-dependence of vascular tone in the metabolic syndrome Obesity (Silver Spring) 19(5), 960–967 (2011), doi:10.1038/oby.2010.210. Epub September 23, 2010
Franceschi, C., Motta, L., Valensin, S., et al.: Do men and women follow different trajectories to reach extreme longevity? Italian Multicenter Study on Centenarians (IMUSCE) Aging (Milano) 12(2), 77–84 (2000)
Sluik, D., Boeing, H., Montonen, J., et al.: HbA1c measured in stored erythrocytes is positively linearly associated with mortality in individuals with Diabetes mellitus. PLoS ONE 7(6), e38877 (2012), doi:10.1371/journal.pone.0038877
The Task Force on Diabetes and Cardiovascular Diseases of the European Society of Cardiology and the European Association for the Study of Diabetes: Guidelines on diabetes, pre-diabetes and cardiovascular diseases. Eur. Heart J. (2007), doi:10.1093/eurheartj/ehl261
Nebert, D.N., McKinnon, R.A., Puga, A.: Human drug-metabolizing enzyme polymorphisms: effects on risk of toxicity and cancer. DNA Cell Biol. 15(4), 273–280 (1996)
Cavagno, L., Boffini, N., Cagnotto, G., et al.: Atherosclerosis and rheumatoid arthritis: more than a simple association. Mediators of Inflammation, Article ID 147354 (2012), doi:10.1155/2012/147354
Waring, A.C., Rodondi, N., Harrison, S., et al.: Thyroid function and prevalent and incident metabolic syndrome in older adults: the health, aging and body composition study. Clin. Endocrinol (Oxf.) 76(6), 911–918 (2012), doi:10.1111/i.1365-226.2011.03428.x
Ruhla, S., Weickert, M.O., Arafat, A.M., et al.: A high normal TSH is associated with the metabolic syndrome. Clin. Endocrinol (Oxf.) 72(5), 696–701 (2010), doi:10.1111/j.1365-2265.20090369.x
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Babič, F., Majnarić, L., Lukáčová, A., Paralič, J., Holzinger, A. (2014). On Patient’s Characteristics Extraction for Metabolic Syndrome Diagnosis: Predictive Modelling Based on Machine Learning. In: Bursa, M., Khuri, S., Renda, M.E. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2014. Lecture Notes in Computer Science, vol 8649. Springer, Cham. https://doi.org/10.1007/978-3-319-10265-8_11
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DOI: https://doi.org/10.1007/978-3-319-10265-8_11
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