Abstract
The development of trends and practice-oriented approaches to personalized programs for the diagnosis and correction depending on the clinical and phenotypic variants of the person is relevant. A software application was created for data mining from respondent profiles in a semi-automatic mode; libraries with data preprocessing were analyzed. The anthropometric measurements and serum lipoprotein spectrum of 2131 volunteers (average age 45.75 ± 11.7 years) were studied. To estimate the association of blood pressure and cardiovascular events markers was carried out by means of multivariate analysis of data by the methods of selection and classification significant signs. The machine learning was used to predict cardiovascular events. Depends on gender there was found the significant difference in atherogenic index of plasma (AIP) (F < 0.05). In young women (20–30 y.o.), the lipoproteins did not correlate with the presence of hypertension, whereas for older women the statistically significant markers were higher, such as cholesterol (CH, F = 0.03), low-density lipoproteins (LDL, F = 0.03) and AIP (F = 0.02). In men for identifying the risk of hypertension developing lipoproteins should be considered depending on age. Accuracy of the risk recognition for the cardiovascular disease (CVD) model was more than 89% with an average confidence of the model in each forecasted case of 90%. The markers for diagnosing the risk of CVD, the following indicators can be used according to their degree of significance: AIP, CH and LDL. Thus, the data obtained indicate the importance of risk factor phenotyping using anthropometric markers and biochemical profile for determining their significance in the top 17 predictors of CVD. The machine learning provides CVD prediction according to standard risk assessments.
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References
Boersma, E., Pieper, K.S., Steyerberg, E.W., Wilcox, R.G., Chang, W., Lee, K.L., Akkerhuis, K.M., Harrington, R.A., Deckers, J.W., Armstrong, P.W. et al.: Predictors of outcome in patients with acute coronary syndromes without persistent St-segment elevation. Results from an international trial of 9461 patients. Circulation 101(22), 2557–2567 (2000)
Pollack Jr., C.V., Sites, F.D., Shofer, F.S., Sease, K.L., Hollander, J.E.: Application of the TIMI risk score for unstable angina and non-St elevation acute coronary syndrome to an unselected emergency department chest pain population. Academic. Emergency Med. 13(1), 13–18 (2006)
Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Biomarkers definitions working group. Downing GJ, ed. Clin. Pharmacol. Ther. 69, 89–95 (2001)
Rosenfeld, L.: Clinical chemistry since 1800: growth and development. Clin. Chem. 48, 186–197 (2002)
Wilson, P.W.F., D’Agostino, R.B., Levy, D., Belanger, A.M., Silbershatz, H., Kannel, W.B.: Prediction of coronary heart disease using risk factor categories. Circulation 97(18), 1837 (1998)
Thompson, P.D., Buchner, D., Pina, I.L., Balady, G.J., Williams, M.A., Marcus, B.H., et al.: Exercise and physical activity in the prevention and treatment of cardiovascular disease. Circulation 107, 3109–3116 (2003)
Stone, N.J., Robinson, J.G., Lichtenstein, A.H., Bairey Merz, C.N., Blum, C.B., Eckel, R.H., et al.: American college of cardiology/American heart. Circulation 25(Suppl 2), S1–45 (2014)
Superko, H.R., King, S. Lipid management to reduce cardiovascular risk: a new strategy is required. 3rd. Circulation 117(4), 560–568 (2008)
Dobiásová, M.: AIP-atherogenic index of plasma as a significant predictor of cardiovascular risk: From research to practice. Vnitr. Lek. 52, 64–71 (2006)
Pearson-Stuttard, J., Bandosz, P., Rehm, C.D., Afshin, A., Peñalvo, J.L., Whitsel, L., Danaei, G., Micha, R., Gaziano, T., Lloyd-Williams, F., et al.: Comparing the effectiveness of mass media campaigns with price reductions targeting fruit and vegetable intake on US cardiovascular disease mortality and race disparities. Am. J. Clin. Nutr. 106, 199–206 (2017)
Perk, J., De Backer, G., Gohlke, H., Graham, I., Reiner, Z., Verschuren, M., Albus, C., Benlian, P., Boysen, G., Cifkova, R., et al.: European guidelines on cardiovascular disease prevention in clinical practice (version 2012). The fifth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts). Eur. Heart J. 33, 1635–1701 (2012)
Roth, G.A., Johnson, C., Abajobir, A., Abd-Allah, F., Abera, S.F., Abyu, G., Ahmed, M., Aksut, B., Alam, T., Alam, K., et al.: Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J. Am. Coll. Cardiol. 70, 1–25 (2017)
Yusuf, S., Hawken, S., Ounpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P., Varigos, J., et al.: Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART Study): Case-control study. Lancet 64, 937–952 (2004)
Perez, L., Dragicevic, S.: An agent-based approach for modeling dynamics of contagious disease spread. Int. J. Health Geogr. 8(1), 50–54 (2009)
Hernández, A.I., Le Rolle, V., Defontaine, A., Carrault, G.A.: Multiformalism and multiresolution modelling environment: application to the cardiovascular system and its regulation. Philos. Transact. Math. Phys. Eng. Sci. 367(1908), 4923–4940 (2009)
Antman, E.M., Cohen, M., Bernink, P.J., McCabe, C.H., Horacek, T., Papuchis, G., Mautner, B., Corbalan, R., Radley, D., Braunwald, E.: The TIMI risk score for unstable angina/non-St elevation Mi: a method for prognostication and therapeutic decision making. J. Am. Med. Assoc. 284(7), 835–842 (2000)
Eagle, K.A., Lim, M.J., Dabbous, O.H., Pieper, K.S., Goldberg, R.J., Van de Werf, F., Goodman, S.G., Granger, C.B., Steg, P.G., Gore, J.M.: A validated prediction model for all forms of acute coronary syndrome. Estimating the risk of 6-month postdischarge death in an international registry. J. Amer. Medical Assoc. 291(22), 2727–2733 (2004)
Conroy, R.M., Pyorala, K., Fitzgerald, A.P.: Estimation of ten-year risk cardiovascular disease in Europe: the SCORE project. Eur. Heart J. 24, 987–1003 (2003)
Sakovskaia, A., Nevzorova, V., Brodskaya, T., Chkalovec, I.: Condition aortic stiffness and content of adipokines in the serum of patients with essential hypertension in young and middle-aged. J. Hypertension 33(N e-suppl.1), 182–187 (2015)
Ni, W., Zhou, Z., Liu, T., Wang, H., Deng, J., Liu, X., Xing, G.: Gender-and lesion number-dependent difference in “atherogenic index of plasma” in Chinese people with coronary heart disease. Sci Rep. 16,7(1), 13207 (2017)
Gunay, S., Sariaydin, M., Acay, A.: New predictor of atherosclerosis in subjects with COPD: atherogenic indices. Respir Care. 61(11), 1481–1487 (2016)
Scientific and Organizing Committee of the ESSE-RF project. Epidemiology of cardiovascular diseases in various regions of Russia (ESSE - RF). Justification and design of the research. Preventive medicine. 6, pp. 25–34 (2013)
Nevzorova, V.A., Shumatov, V.B., Nastradin, O.V.: The state of the function of the vascular endothelium in people with risk factors and patients with coronary heart disease. Pacific Med. J. 2, 37–44 (2012)
Odden, M.C., Tager, I.B., Gansevoort, R.T., Bakker, S.J.L., Fried, L.F., Newman, A.B., Katz, R., Satterfield, S., Harris, T.B., Sarnak, M.J., Siscovick, D., Shlipak, M.G.: Hypertension and low HDL cholesterol were associated with reduced kidney function across the age spectrum: a collaborative study. Ann. Epidemiol. 23(3), 106–111 (2013)
Al-Naamani, N., Palevsky, H.I., Lederer, D.J., Horn, E.M., Mathai, S.C., Roberts, K.E., Tracy, R.P., Hassoun, P.M., Girgis, R.E., Shimbo, D., Post, W.S., Kawut, S.M.: Prognostic significance of biomarkers in pulmonary arterial hypertension. Ann. Am. Thorac. Soc. 13(1), 25–30 (2016)
Fowkes, F.G., Murray, G.D., Butcher, I., Heald, C.L., Lee, R.J., Chambless, L.E.: Ankle brachial index combined with Framingham risk score to predict cardiovascular events and mortality: a meta-analysis. JAMA 300, 197–208 (2008)
D’Agostino, R.B., Pencina, M.J., Massaro, J.M., Coady, S.: Cardiovascular disease risk assessment: insights from Framingham. Global Heart 8(1), 11–23 (2013)
Steyerberg, E.W., Vickers, A.J., Cook, N.R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M.J., Kattan, M.W.: Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology 21(1), 128–138 (2010)
Acknowledgments
The study was supported by a grant from the Russian Foundation for Basic Research 19-29-01077 and is part of the Ministry Health the Russian Federation state task «Clinical and phenotypic variants and molecular genetic features of vascular aging in people of different ethnic groups».
Declaration of financial and other relationships. All authors participated in the development of the concept, the design of the study and the writing of the manuscript. The final version of the manuscript was approved by all authors.
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Plekhova, N.G. et al. (2020). Association of Cardiovascular Events and Blood Pressure and Serum Lipoprotein Indicators Based on Functional Data Analysis as a Personalized Approach to the Diagnosis. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives in Intelligent Systems. CoMeSySo 2020. Advances in Intelligent Systems and Computing, vol 1295. Springer, Cham. https://doi.org/10.1007/978-3-030-63319-6_24
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