Cardiovascular diseases (CVDs). [https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)].
Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, et al. Global Burden of Cardiovascular diseases and Risk factors, 1990–2019: Update from the GBD 2019 study. J Am Coll Cardiol. 2020;76(25):2982–3021.
Google Scholar
Sofogianni A, Stalikas N, Antza C, Tziomalos K. Cardiovascular Risk Prediction Models and Scores in the Era of Personalized Medicine. J Pers Med. 2022;12(7):1180.
Google Scholar
SCORE2 risk prediction. Algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021;42(25):2439–54.
Google Scholar
Khan SS, Coresh J, Pencina MJ, Ndumele CE, Rangaswami J, Chow SL, Palaniappan LP, Sperling LS, Virani SS, Ho JE, et al. Novel prediction equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: a Scientific Statement from the American Heart Association. Circulation. 2023;148(24):1982–2004.
Google Scholar
Rocha VZ, Libby P. Obesity, inflammation, and atherosclerosis. Nat Rev Cardiol. 2009;6(6):399–409.
Google Scholar
Chen L, Ding XH, Fan KJ, Gao MX, Yu WY, Liu HL, Yu Y. Association between triglyceride-glucose index and 2-Year adverse Cardiovascular and cerebrovascular events in patients with type 2 diabetes Mellitus who underwent off-pump coronary artery bypass grafting. Diabetes Metab Syndr Obes. 2022;15:439–50.
Google Scholar
Hill MA, Yang Y, Zhang L, Sun Z, Jia G, Parrish AR, Sowers JR. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. 2021;119:154766.
Google Scholar
da Silva AA, do Carmo JM, Li X, Wang Z, Mouton AJ, Hall JE. Role of Hyperinsulinemia and Insulin Resistance in hypertension: metabolic syndrome revisited. Can J Cardiol. 2020;36(5):671–82.
Google Scholar
Studziński K, Tomasik T, Krzysztoń J, Jóźwiak J, Windak A. Effect of using cardiovascular risk scoring in routine risk assessment in primary prevention of cardiovascular disease: an overview of systematic reviews. BMC Cardiovasc Disord. 2019;19(1):11.
Google Scholar
Tao L-C, Xu J-n, Wang T-t, Hua F, Li J-J. Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovasc Diabetol. 2022;21(1):1–17.
Google Scholar
Cersosimo E, Solis-Herrera C, Trautmann ME, Malloy J, Triplitt CL. Assessment of pancreatic β-cell function: review of methods and clinical applications. Curr Diabetes Rev. 2014;10(1):2–42.
Google Scholar
Minh HV, Tien HA, Sinh CT, Thang DC, Chen CH, Tay JC, Siddique S, Wang TD, Sogunuru GP, Chia YC, Kario K. Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension. J Clin Hypertens (Greenwich). 2021;23(3):529–37.
Google Scholar
Pan L, Zou H, Meng X, Li D, Li W, Chen X, Yang Y, Yu X. Predictive values of metabolic score for insulin resistance on risk of major adverse cardiovascular events and comparison with other insulin resistance indices among Chinese with and without diabetes mellitus: Results from the 4 C cohort study. J Diabetes Invest. 2023;14(8):961–72.
Google Scholar
Zhang X, Ye R, Yu C, Liu T, Chen X. Correlation between non-insulin-based insulin resistance indices and increased arterial stiffness measured by the Cardio–Ankle Vascular Index in non-hypertensive Chinese subjects: a cross-sectional study. Front Cardiovasc Med. 2022;9:903307.
Google Scholar
Nakamura Y, Otaki S, Tanaka Y, Adachi A, Wada N, Tajiri Y. Insulin Resistance Is Better Estimated by Using Fasting Glucose, Lipid Profile, and Body Fat Percent Than by HOMA-IR in Japanese Patients with Type 2 Diabetes and Impaired Glucose Tolerance: An Exploratory Study. Metab Syndr Relat Disord. 2024. 22(3):199–206.
Google Scholar
Bello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A, Sánchez-Lázaro D, Meza-Oviedo D, Vargas-Vázquez A, Campos OA. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur J Endocrinol. 2018;178(5):533–44.
Google Scholar
Rattanatham R, Tangpong J, Chatatikun M, Sun D, Kawakami F, Imai M, Klangbud WK. Assessment of eight insulin resistance surrogate indexes for predicting metabolic syndrome and hypertension in Thai law enforcement officers. PeerJ. 2023;11:e15463.
Google Scholar
Dang K, Wang X, Hu J, Zhang Y, Cheng L, Qi X, Liu L, Ming Z, Tao X, Li Y. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003–2018. Cardiovasc Diabetol. 2024;23(1):8.
Google Scholar
Wu Z, Cui H, Li W, Zhang Y, Liu L, Liu Z, Zhang W, Zheng T, Yang J. Comparison of three non-insulin-based insulin resistance indexes in predicting the presence and severity of coronary artery disease. Front Cardiovasc Med. 2022;9:918359.
Google Scholar
Mahdavi-Roshan M, Mozafarihashjin M, Shoaibinobarian N, Ghorbani Z, Salari A, Savarrakhsh A, Hekmatdoost A. Evaluating the use of novel atherogenicity indices and insulin resistance surrogate markers in predicting the risk of coronary artery disease: a case–control investigation with comparison to traditional biomarkers. Lipids Health Dis. 2022;21(1):126.
Google Scholar
Lal TN, Chapelle O, Weston J, Elisseeff A. Embedded Methods. In: Feature Extraction: Foundations and Applications Edited by Guyon I, Nikravesh M, Gunn S, Zadeh LA. Berlin, Heidelberg: Springer Berlin Heidelberg; 2006: 137–165.
Pudjihartono N, Fadason T, Kempa-Liehr AW, O’Sullivan JM. A review of feature selection methods for machine learning-based Disease Risk Prediction. Front Bioinform. 2022;2:927312.
Google Scholar
Liu W, Laranjo L, Klimis H, Chiang J, Yue J, Marschner S, Quiroz JC, Jorm L, Chow CK. Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis. Eur Heart J Qual Care Clin Outcomes. 2023;9(4):310–22.
Google Scholar
Bi Q, Goodman KE, Kaminsky J, Lessler J. What is Machine Learning? A primer for the epidemiologist. Am J Epidemiol. 2019;188(12):2222–39.
Google Scholar
Patel B, Sengupta P. Machine learning for predicting cardiac events: what does the future hold? Expert Rev Cardiovasc Ther. 2020;18(2):77–84.
Google Scholar
Mirjalili SR, Soltani S, Heidari Meybodi Z, Marques-Vidal P, Kraemer A, Sarebanhassanabadi M. An innovative model for predicting coronary heart disease using triglyceride-glucose index: a machine learning-based cohort study. Cardiovasc Diabetol. 2023;22(1):200.
Google Scholar
Hagströmer M, Oja P, Sjöström M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 2006;9(6):755–62.
Google Scholar
Maddison R, Ni Mhurchu C, Jiang Y, Vander Hoorn S, Rodgers A, Lawes CM, Rush E. International Physical Activity Questionnaire (IPAQ) and New Zealand physical activity questionnaire (NZPAQ): a doubly labelled water validation. Int J Behav Nutr Phys Act. 2007;4:62.
Google Scholar
COOK DG, Shaper A, MacFarlane P. Using the WHO (Rose) angina questionnaire in cardiovascular epidemiology. Int J Epidemiol. 1989;18(3):607–13.
Google Scholar
López-Ratón M, Rodríguez-Álvarez MX, Cadarso-Suárez C, Gude-Sampedro F. OptimalCutpoints: an R package for selecting optimal cutpoints in diagnostic tests. J Stat Softw. 2014;61:1–36.
Google Scholar
Pauly O. Random forests for medical applications. Technische Universität München; 2012.
Kursa MB, Rudnicki WR. Feature selection with the Boruta package. J Stat Softw. 2010;36:1–13.
Google Scholar
Ranstam J, Cook JA. LASSO regression. Br J Surg. 2018;105(10):1348–1348.
Google Scholar
Ceteris-paribus Profiles [https://ema.drwhy.ai/ceterisParibus.html].
Baniecki H, Kretowicz W, PiÄ P, WiĹ J. Dalex: responsible machine learning with interactive explainability and fairness in python. J Mach Learn Res. 2021;22(214):1–7.
Low S, Khoo KCJ, Irwan B, Sum CF, Subramaniam T, Lim SC, Wong TKM. The role of triglyceride glucose index in development of type 2 diabetes mellitus. Diabetes Res Clin Pract. 2018;143:43–9.
Google Scholar
Zhang M, Hu T, Zhang S, Zhou L. Associations of different adipose tissue depots with insulin resistance: a systematic review and Meta-analysis of Observational studies. Sci Rep. 2015;5:18495.
Google Scholar
Yoon J, Jung D, Lee Y, Park B. The Metabolic Score for Insulin Resistance (METS-IR) as a Predictor of Incident Ischemic Heart Disease: A Longitudinal Study among Korean without Diabetes. J Pers Med. 2021;11(8):742.
Google Scholar
Liu X, Tan Z, Huang Y, Zhao H, Liu M, Yu P, Ma J, Zhao Y, Zhu W, Wang J. Relationship between the triglyceride-glucose index and risk of cardiovascular diseases and mortality in the general population: a systematic review and meta-analysis. Cardiovasc Diabetol. 2022;21(1):124.
Google Scholar
Chen Y, Chang Z, Liu Y, Zhao Y, Fu J, Zhang Y, Liu Y, Fan Z. Triglyceride to high-density lipoprotein cholesterol ratio and cardiovascular events in the general population: a systematic review and meta-analysis of cohort studies. Nutr Metabolism Cardiovasc Dis. 2022;32(2):318–29.
Google Scholar
Tian X, Chen S, Xu Q, Xia X, Zhang Y, Wang P, Wu S, Wang A. Magnitude and time course of insulin resistance accumulation with the risk of cardiovascular disease: an 11-years cohort study. Cardiovasc Diabetol. 2023;22(1):339.
Google Scholar
Wu Z, Cui H, Zhang Y, Liu L, Zhang W, Xiong W, Lu F, Peng J, Yang J. The impact of the metabolic score for insulin resistance on cardiovascular disease: a 10-year follow-up cohort study. J Endocrinol Invest. 2023;46(3):523–33.
Google Scholar
St-Pierre AC, Cantin B, Mauriège P, Bergeron J, Dagenais GR, Després JP, Lamarche B. Insulin resistance syndrome, body mass index and the risk of ischemic heart disease. CMAJ. 2005;172(10):1301–5.
Google Scholar
Meigs JB, Wilson PW, Fox CS, Vasan RS, Nathan DM, Sullivan LM, D’Agostino RB. Body mass index, metabolic syndrome, and risk of type 2 diabetes or cardiovascular disease. J Clin Endocrinol Metab. 2006;91(8):2906–12.
Google Scholar
Liu L, Peng J, Wang N, Wu Z, Zhang Y, Cui H, Zang D, Lu F, Ma X, Yang J. Comparison of seven surrogate insulin resistance indexes for prediction of incident coronary heart disease risk: a 10-year prospective cohort study. Front Endocrinol. 2024. https://doi.org/10.3389/fendo.2024.1290226.
Google Scholar
Xia MF, Chen Y, Lin HD, Ma H, Li XM, Aleteng Q, Li Q, Wang D, Hu Y, Pan BS, et al. A indicator of visceral adipose dysfunction to evaluate metabolic health in adult Chinese. Sci Rep. 2016;6:38214.
Google Scholar
Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, Galluzzo A. Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010;33(4):920–2.
Google Scholar
Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304.
Google Scholar
Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, Martínez-Abundis E, Ramos-Zavala MG, Hernández-González SO, Jacques-Camarena O, Rodríguez-Morán M. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95(7):3347–51.
Google Scholar
Vasques AC, Novaes FS, de Oliveira Mda S, Souza JR, Yamanaka A, Pareja JC, Tambascia MA, Saad MJ, Geloneze B. TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study. Diabetes Res Clin Pract. 2011;93(3):e98–100.
Google Scholar
Atlas of STEPwise approach. to noncommunicable disease (NCD) risk factor surveillance (STEPs) 2021. [https://nih.tums.ac.ir/UpFiles/Documents/3bc71b22-a5dc-4849-9d07-beede6b045e1.pdf].
Guo Y, Chung FL, Li G, Zhang L. Multi-label Bioinformatics Data classification with ensemble embedded feature selection. IEEE Access. 2019;7:103863–75.
Google Scholar
Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23(19):2507–17.
Google Scholar
Yap BW, Ibrahim NSM, Hamid HA, Rahman SA, Fong SJ. Feature selection methods: case of filter and wrapper approaches for maximising classification accuracy. pertanika J Sci Technol. 2018;26:329–40.
Pes B. Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains. Neural Comput Appl. 2020;32(10):5951–73.
Google Scholar
Rajula HSR, Verlato G, Manchia M, Antonucci N, Fanos V. Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment. Med (Kaunas). 2020;56(9):455.
Saeys Y, Abeel T, Van de Peer Y. Robust feature selection using ensemble feature selection techniques. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15–19, 2008, Proceedings, Part II 19: 2008: Springer; 2008: 313–325.
