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  • Ellulu, M. S. et al. Atherosclerotic cardiovascular disease: A review of initiators and protective factors. Inflammopharmacology 24, 1–10. https://doi.org/10.1007/s10787-015-0255-y (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Sanz, J. & Fayad, Z. A. Imaging of atherosclerotic cardiovascular disease. Nature 451(7181), 953–957 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Gavrilenko, A. V. et al. Correlation between morphological and biomechanical features and carotid atherosclerosis. Sci. Innov. Med. 7(3), 160–163. https://doi.org/10.35693/2500-1388-2022-7-3-160-163 (2022).

    Article 

    Google Scholar 

  • Soomro, M. K. et al. Assessment of the cardiovascular medication adherence and its related factors in patients with coronary artery angioplasty at Pmc Hospital Nawabshah. J. Peoples Univ. Med. Health Sci. Nawabshah 10(4), 18–21. https://doi.org/10.35693/2500-1388-2022-7-3-160-163 (2020).

    Article 

    Google Scholar 

  • Zardawi, F., Gul, S., Abdulkareem, A., Sha, A. & Yates, J. Association between periodontal disease and atherosclerotic cardiovascular diseases: Revisited. Front. Cardiovasc. Med. 7, 625579. https://doi.org/10.3389/fcvm.2020.625579 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Barquera, S. et al. Global overview of the epidemiology of atherosclerotic cardiovascular disease. Arch. Med. Res. 328(5), 46. https://doi.org/10.1016/j.arcmed.2015.06.006 (2015).

    Article 

    Google Scholar 

  • Perk, J. 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(13), 1635–1701. https://doi.org/10.1093/eurheartj/ehs092 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Gao, R. & Liu, L. Summary of China cardiovascular disease report 2017. Chin. Circ. J. 33(1), 1–8 (2018).

    Google Scholar 

  • Ford, E. S., Roger, V. L., Dunlay, S. M., Go, A. S. & Rosamond, W. D. Challenges of ascertaining national trends in the incidence of coronary heart disease in the United States. J. Am. Heart Assoc. 3(6), e001097. https://doi.org/10.1161/JAHA.114.001097 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mehta, R. H. et al. Acute myocardial infarction in the elderly: Differences by age. J. Am. Coll. Cardiol. 38, 736–741. https://doi.org/10.1016/S0735-1097(01)01432-2 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Sarrafzadegan, N. & Mohammmadifard, N. Cardiovascular disease in Iran in the last 40 years: Prevalence, mortality, morbidity, challenges and strategies for cardiovascular prevention. Arch. Iran. Med. 22(4), 204–210 (2019).

    PubMed 

    Google Scholar 

  • Zibaeenejad, F., Mohammadi, S. S., Sayadi, M., Safari, F. & Zibaeenezhad, M. J. Ten-year atherosclerosis cardiovascular disease (ASCVD) risk score and its components among an Iranian population: A cohort-based cross-sectional study. BMC Cardiovasc. Disord. 22(1), 1–8. https://doi.org/10.1186/s12872-022-02601-0 (2022).

    Article 

    Google Scholar 

  • Roth, G. A. et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J. Am. Coll. Cardiol. 70(1), 1–25. https://doi.org/10.1016/j.jacc.2017.04.052 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ghosh, J. K. & Valtorta, M. Building a Bayesian network model of heart disease. In Proceedings of the 38th Annual on Southeast Regional Conference. https://doi.org/10.1145/1127716.1127770 (2000).

  • Frenk, J., Bobadilla, J. L., Stern, C., Frejka, T. & Lozano, R. Elements for a theory of transition in health. Salud Publ. Mex 33, 448–462 (1991).

    CAS 

    Google Scholar 

  • Jaiswal, S. et al. Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease. N. Engl. J. Med. 377(2), 111–121. https://doi.org/10.1056/NEJMoa1701719 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rodriguez, F. et al. Atherosclerotic cardiovascular disease risk prediction in disaggregated Asian and Hispanic subgroups using electronic health records. J. Am. Heart Assoc. 8(14), e011874. https://doi.org/10.1161/JAHA.118.011874 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jamialahmadi, T. et al. The effects of statin dose, lipophilicity, and combination of statins plus ezetimibe on circulating oxidized low-density lipoprotein levels: A systematic review and meta-analysis of randomized controlled trials. Mediat. Inflamm. 2021, 12. https://doi.org/10.1155/2021/9661752 (2021).

    Article 
    CAS 

    Google Scholar 

  • Graham, I., Cooney, M.-T., Bradley, D., Dudina, A. & Reiner, Z. Dyslipidemias in the prevention of cardiovascular disease: Risks and causality. Curr. Cardiol. Rep. 14(6), 709–720. https://doi.org/10.1007/s11886-012-0313-7 (2012).

    Article 
    PubMed 

    Google Scholar 

  • ElSayed, N. A. et al. 10. Cardiovascular disease and risk management: Standards of care in diabetes-2023. Diabetes Care 46(Suppl 1), S158–S190. https://doi.org/10.2337/dc16-S011 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Barquera, S. et al. Global overview of the epidemiology of atherosclerotic cardiovascular disease. Arch. Med. Res. 46(5), 328–338. https://doi.org/10.1016/j.arcmed.2015.06.006 (2015).

    Article 
    PubMed 

    Google Scholar 

  • Esmaeili, P. et al. Machine learning framework for atherosclerotic cardiovascular disease risk assessment. J. Diabetes Metabol. Disord. 2022, 1–8. https://doi.org/10.1007/s40200-022-01160-7 (2022).

    Article 

    Google Scholar 

  • Berry, J. D. et al. Lifetime risks of cardiovascular disease. N. Engl. J. Med. 366(4), 321–329. https://doi.org/10.1056/NEJMoa1012848 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hong, Y. M. Atherosclerotic cardiovascular disease beginning in childhood. Korean Circ. J. 40(1), 1–9. https://doi.org/10.4070/kcj.2010.40.1.1 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kavey, R.-E.W. et al. American Heart Association guidelines for primary prevention of atherosclerotic cardiovascular disease beginning in childhood. Circulation 107(11), 1562–1566. https://doi.org/10.1161/01.CIR.0000061521.15730.6E (2003).

    Article 
    PubMed 

    Google Scholar 

  • Gæde, P. et al. Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes. N. Engl. J. Med. 348(5), 383–393. https://doi.org/10.1056/NEJMoa021778 (2003).

    Article 
    PubMed 

    Google Scholar 

  • Bahiru, E. et al. Fixed-dose combination therapy for the prevention of atherosclerotic cardiovascular diseases. Cochrane Database Syst. Rev. 2017, 3. https://doi.org/10.1002/14651858.CD009868.pub3 (2017).

    Article 

    Google Scholar 

  • Butz, C. J., Hua, S., Chen, J. & Yao, H. A simple graphical approach for understanding probabilistic inference in Bayesian networks. Inf. Sci. 179(6), 699–716. https://doi.org/10.1016/j.ins.2008.10.036 (2009).

    Article 
    MathSciNet 

    Google Scholar 

  • Fuster-Parra, P. et al. Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk. Comput. Methods Programs Biomed. 126, 128–142. https://doi.org/10.1016/j.cmpb.2015.12.010 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Badawi, A., Di Giuseppe, G., Gupta, A., Poirier, A. & Arora, P. Bayesian network modelling study to identify factors influencing the risk of cardiovascular disease in Canadian adults with hepatitis C virus infection. BMJ Open 10(5), e035867. https://doi.org/10.1136/bmjopen-2019-035867 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Shafer, G. Probabilistic Expert Systems (SIAM, 1996).

    Book 

    Google Scholar 

  • Ordovas, J. et al. A Bayesian network model for predicting cardiovascular risk. Comput. Methods Programs Biomed. 2023, 107405. https://doi.org/10.1016/j.cmpb.2023.107405 (2023).

    Article 

    Google Scholar 

  • Kyrimi, E. et al. Bayesian networks in healthcare: What is preventing their adoption?. Artif. Intell. Med. 116, 102079. https://doi.org/10.1016/j.artmed.2021.102079 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Farooq, K. et al. An ontology driven and bayesian network based cardiovascular decision support framework. In Advances in Brain Inspired Cognitive Systems: 5th International Conference, BICS 2012, Shenyang, China, July 11–14, 2012 Proceedings (Springer, 2012). https://doi.org/10.1007/978-3-642-31561-9_4.

  • Twardy, C. R., Nicholson, A. E., Korb, K. & McNeil, J. Knowledge engineering cardiovascular Bayesian networks from the literature. In School of Computer Science & Software Engineering (2005).

  • Tylman, W. et al. Real-Time prediction of acute cardiovascular events using hardware-implemented Bayesian networks. Comput. Biol. Med. 69, 245–253. https://doi.org/10.1016/j.compbiomed.2015.08.015 (2016).

    Article 
    PubMed 

    Google Scholar 

  • Orphanou, K. et al. Risk assessment for primary coronary heart disease event using dynamic Bayesian networks. In Artificial Intelligence in Medicine AIME Lecture Notes In Computer Science 2016 (eds Holmes, J., Bellazzi, R., Sacchi, L. et al.) 161–165 (Springer, 2015). https://doi.org/10.1007/978-3-319-19551-3_20.

    Chapter 

    Google Scholar 

  • Gomathi, K. & Priyaa, D. S. An efficient coronary heart disease prediction by semi parametric extended dynamic Bayesian network with optimized cut points. ARPN J. Eng. Appl. Sci. 13, 1539–1544 (2018).

    Google Scholar 

  • Poustchi, H. et al. Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): Rationale, objectives, and design. Am. J. Epidemiol. 187(4), 647–655. https://doi.org/10.1093/aje/kwx314 (2018).

    Article 
    PubMed 

    Google Scholar 

  • Farhang, S. et al. Cohort profile: The AZAR cohort, a health-oriented research model in areas of major environmental change in Central Asia. Int. J. Epidemiol. 48(2), 382. https://doi.org/10.1093/ije/dyy215 (2019).

    Article 
    PubMed 

    Google Scholar 

  • Marfell-Jones, M., Olds, T., Stewart, A. & Carter, L. International Standards for Anthropometric Assessment, International Society for the Advancement of Kinanthropometry, Potchefstroom: South Africa. https://doi.org/10.4324/9780203970157 (2006).

  • National Institutes of Health. The Practical Guide to the Identification, Evaluation and Treatment of Overweight and Obesity in Adults. Bethesda, Maryland: National Institutes of Health (2000).

  • World Health Organization. Obesity: Preventing and Managing The Global Epidemic (WHO, 1998).

    Google Scholar 

  • Burgos, M. S. et al. Obesity parameters as predictors of early development of cardiometabolic risk factors. Ciencia Saude Coletiva 20, 2381–2388. https://doi.org/10.1590/1413-81232015208.11672014 (2015).

    Article 
    PubMed 

    Google Scholar 

  • Gevers Leuven, J., vd-Voort, H., Kempen, H., de-Wit, E. & Havekes, L. The effect of cyclandelate on cholesterol metabolism in patients with familial hypercholesterolaemia. Drugs 33, 131–135 (1987).

    Article 
    PubMed 

    Google Scholar 

  • Robinson, J. G. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA 285(19), 2486–2497 (2001).

    Article 

    Google Scholar 

  • Robinson, J. G. & Ray, K. Moving toward the next paradigm for cardiovascular prevention. Am. Heart Assoc. 133, 1533–1536. https://doi.org/10.1161/CIRCULATIONAHA.116.022134 (2016).

    Article 

    Google Scholar 

  • Adegoke, O. et al. The impact of sex on blood pressure and anthropometry trajectories from early adulthood in a Nigerian population: Insights into women’s cardiovascular disease risk across the lifespan. BMC Women’s Health 2022, 1–9. https://doi.org/10.1186/S12905-022-01888-7 (2022).

    Article 

    Google Scholar 

  • Engin, A. The definition and prevalence of obesity and metabolic syndrome. Obes. Lipotoxicity 2017, 1–17. https://doi.org/10.1007/978-3-319-48382-5 (2017).

    Article 

    Google Scholar 

  • Cui, Y. et al. Non–high-density lipoprotein cholesterol level as a predictor of cardiovascular disease mortality. Arch. Internal Med. 161(11), 1413–1419. https://doi.org/10.1001/archinte.161.11.1413 (2001).

    Article 
    CAS 

    Google Scholar 

  • Health, N. I. F. & Excellence, C. Type 2 diabetes: Prevention in people at high risk. NICE guideline (PH38). https://www.nice.org.uk/guidance/qs209/chapter/Quality-statement-1-Preventing-type-2-diabetes#:~:text=Many%20cases%20of%20type%202,for%20those%20at%20high%20risk (2012).

  • Geiger, D., Verma, T. & Pearl, J. d-separation: From theorems to algorithms. Mach. Intell. Pattern Recogn. 10, 139–148. https://doi.org/10.1016/B978-0-444-88738-2.50018-X (1990).

    Article 

    Google Scholar 

  • Jensen, F. V. & Nielsen, T. D. Bayesian Networks and Decision Graphs (Springer Science & Business Media, 2007). https://doi.org/10.1198/tech.2008.s543.

    Book 

    Google Scholar 

  • Korb, K. B. & Nicholson, A. E. Bayesian Artificial Intelligence 2nd edn. (Chapman and Hall/CRC Press, 2010).

    Book 

    Google Scholar 

  • Huang, H. C. & Tsai, C. W. Structure learning in bayesian networks: A comprehensive review. IEEE Trans. Knowl. Data Eng. 31(12), 2275–2293 (2019).

    Google Scholar 

  • Yanhong, B. M., Bi, Y., Che, X. & Liu, Y. A bayesian network analysis of the probabilistic relationships between various obesity phenotypes and cardiovascular disease risk in Chinese adults: Chinese population-based observational study. JMIR Med. Inf. 10(3), e33026 (2022).

    Article 

    Google Scholar 

  • Badawi, A., Di-Giuseppe, G., Gupta, A., Poirier, A. & Arora, P. Bayesian network modelling study to identify factors influencing the risk of cardiovascular disease in Canadian adults with hepatitis C virus infection. BMJ Open 10(5), e035867. https://doi.org/10.1136/bmjopen-2019-035867 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nicholson, A. E., Twardy, C. R., Korb, K. B. & Hope, L. R. Decision Support for Clinical Cardiovascular Risk Assessment. Bayesian Networks: A Practical Guide to Applications 33–52 (Wiley, 2008). https://doi.org/10.1002/9780470994559.ch3.

    Book 

    Google Scholar 

  • Koller, D. & Friedman, N. Probabilistic Graphical Models: Principles and Techniques (MIT press, 2009).

    Google Scholar 

  • Pearl. Aspects of graphical models connected with causality. In Proceedings of 49th Session, International Statistical Institute: Invited papers, Florence: Italy. https://doi.org/10.1002/net.3230200507 (1993).

  • Spiegelhalter, D. J. & Lauritzen, S. L. Sequential updating of conditional probabilities on directed graphical structures. Networks 20, 579–605 (1990).

    Article 
    MathSciNet 

    Google Scholar 

  • Fuster-Parra, P., Yañez, A. M., López-González, A., Aguiló, A. & Bennasar-Veny, M. Identifying risk factors of developing type 2 diabetes from adult population with initial prediabetes using a Bayesian network. Front. Public Health 10, 5263. https://doi.org/10.3389/fpubh.2022.1035025 (2022).

    Article 

    Google Scholar 

  • Netica. In Wikipedia (2023, accessed 12 Mar 2023). https://en.wikipedia.org/wiki/Netica.

  • Lee, J. H., Kim, M., Kim, J. H., Cho, B. & Kim, J. Y. Development and validation of a Bayesian network model for coronary heart disease prediction in Korean adults. BMC Cardiovasc. Disord. 21(1), 1–9. https://doi.org/10.1186/s12872-020-01813-6 (2021).

    Article 
    CAS 

    Google Scholar 

  • Zhu, L. et al. Bayesian network analysis of risk factors for stroke in a Chinese population: A hospital-based case-control study. BMC Neurol. 20(1), 1–9 (2020).

    CAS 

    Google Scholar 

  • Tzeng, I. S. et al. Predicting major cardiovascular events in hypertensive patients: The role of the Bayesian network model. PloS one 15(7), e0236553 (2020).

    Google Scholar 

  • Jafari-Nasabian, P. et al. Predicting cardiovascular risk using a Bayesian network model: The case of a large Australian cohort. Sci. Rep. 11(1), 5552. https://doi.org/10.1038/s41598-021-84914-9 (2021).

    Article 

    Google Scholar 

  • Khot, U. N. et al. Prevalence of conventional risk factors in patients with coronary heart disease. JAMA 290(7), 898–904. https://doi.org/10.1001/jama.290.7.898 (2003).

    Article 
    PubMed 

    Google Scholar 

  • Yusuf, S. et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): Case-control study. The Lancet 364(9438), 937–952. https://doi.org/10.1016/S0140-6736(04)17018-9 (2004).

    Article 

    Google Scholar 

  • Lloyd-Jones, D. M. et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: The American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation 121(4), 586–613. https://doi.org/10.1161/CIRCULATIONAHA.109.192703 (2010).

    Article 
    PubMed 

    Google Scholar 

  • Mendelsohn, M. E. & Karas, R. H. The protective effects of estrogen on the cardiovascular system. N. Engl. J. Med. 340(23), 1801–1811. https://doi.org/10.1056/NEJM199906103402306 (2005).

    Article 

    Google Scholar 

  • Lakatta, E. G. & Levy, D. Arterial and cardiac aging: Major shareholders in cardiovascular disease enterprises: Part II: The aging heart in health: Links to heart disease. Circulation 107(2), 346–354. https://doi.org/10.1161/01.CIR.0000048893.62841.F7 (2003).

    Article 
    PubMed 

    Google Scholar 

  • Bhatt, D. L. et al. Cardiovascular risk reduction with icosapent ethyl for hypertriglyceridemia. N. Engl. J. Med. 380(1), 11–22. https://doi.org/10.1056/NEJMoa1812792 (2018).

    Article 
    PubMed 

    Google Scholar 

  • Grundy, S. M. et al. Diagnosis and management of the metabolic syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112(17), 2735–2752. https://doi.org/10.1161/CIRCULATIONAHA.105.169404 (2005).

    Article 
    PubMed 

    Google Scholar 

  • Barter, P. et al. HDL cholesterol, very low levels of LDL cholesterol, and cardiovascular events. N. Engl. J. Med. 357(13), 1301–1310. https://doi.org/10.1056/NEJMoa064278 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar 

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