Alberti, K. G. M. M., Zimmet, P. & Shaw, J. The metabolic syndrome—a new worldwide definition. Lancet 366, 1059–1062. https://doi.org/10.1016/S0140-6736(05)67402-8 (2005).
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, 2735–2752. https://doi.org/10.1161/CIRCULATIONAHA.105.169404 (2005).
Google Scholar
den Engelsen, C. et al. High-sensitivity C-reactive protein to detect metabolic syndrome in a centrally obese population: a cross-sectional analysis. Cardiovasc. Diabetol. 11, 1–7. https://doi.org/10.1186/1475-2840-11-25 (2012).
Google Scholar
Saklayen, M. G. The global epidemic of the metabolic syndrome. Curr. Hypertens. Rep. 20, 1–8. https://doi.org/10.1007/s11906-018-0812-z (2018).
Google Scholar
Farmanfarma, K. K. et al. Prevalence of metabolic syndrome in iran: A meta-analysis of 69 studies. Diabetes Metabolic Syndrome: Clin. Res. Reviews. 13, 792–799. https://doi.org/10.1016/j.dsx.2018.11.055 (2019).
Google Scholar
Fatahi, A., Doosti-Irani, A. & Cheraghi, Z. Prevalence and incidence of metabolic syndrome in iran: a systematic review and meta-analysis. Int. J. Prev. Med. 11, 64. https://doi.org/10.4103/ijpvm.IJPVM_489_18 (2020).
Google Scholar
Lu, J. et al. Metabolic syndrome among adults in china: the 2010 China noncommunicable disease surveillance. J. Clin. Endocrinol. Metab. 102, 507–515. https://doi.org/10.1210/jc.2016-2477 (2017).
Google Scholar
Kastorini, C. M. et al. Metabolic syndrome and 10-year cardiovascular disease incidence: the ATTICA study. Nutr. Metabolism Cardiovasc. Dis. 26, 223–231. https://doi.org/10.1016/j.numecd.2015.12.010 (2016).
Google Scholar
Wu, X., Zhu, X., Wu, G. Q. & Ding, W. Data mining with big data. IEEE Trans. Knowl. Data Eng. 26, 97–107. https://doi.org/10.1109/TKDE.2013.109 (2013).
Google Scholar
Ibrahim, M., Beneyto, A., Contreras, I. & Vehi, J. An ensemble machine learning approach for the detection of unannounced meals to enhance postprandial glucose control. Comput. Biol. Med. 108154. https://doi.org/10.1016/j.compbiomed.2024.108154 (2024).
Ahari, R. K. et al. Association of atherosclerosis indices, serum uric acid to high‐density lipoprotein cholesterol ratio and triglycerides‐glucose index with hypertension: A gender‐disaggregated analysis. J. Clin. Hypertens. https://doi.org/10.1016/j.compbiomed.2024.108154 (2024).
Google Scholar
Kolahi Ahari, R. et al. Association of three novel inflammatory markers: lymphocyte to HDL‐C ratio, High‐Sensitivity C‐Reactive protein to HDL‐C ratio and High‐Sensitivity C‐Reactive protein to lymphocyte ratio with metabolic syndrome. Endocrinol. Diabetes Metab. 7, e00479. https://doi.org/10.1002/edm2.479 (2024).
Google Scholar
Bloch, L., Friedrich, C. M. & Initiative, A. D. N. Systematic comparison of 3D deep learning and classical machine learning explanations for alzheimer’s disease detection. Comput. Biol. Med. 170, 108029. https://doi.org/10.1016/j.compbiomed.2024.108029 (2024).
Google Scholar
Jablonka, K. M., Ongari, D., Moosavi, S. M. & Smit, B. Big-data science in porous materials: materials genomics and machine learning. Chem. Rev. 120, 8066–8129. https://doi.org/10.1021/acs.chemrev.0c00004 (2020).
Google Scholar
Kakudi, H. A., Loo, C. K. & Moy, F. M. Diagnosis of metabolic syndrome using machine learning, statistical and risk quantification techniques: A systematic literature review. MedRxiv 2020.06.01.20119339 https://doi.org/10.1101/2020.06.01.20119339 (2020).
Hosseini-Esfahani, F. et al. Using machine learning techniques to predict factors contributing to the incidence of metabolic syndrome in tehran: cohort study. JMIR Public. Health Surveill. 7, e27304. https://doi.org/10.2196/27304 (2021). (accessed August 28, 2024).
Google Scholar
Karimi-Alavijeh, F., Jalili, S. & Sadeghi, M. Predicting metabolic syndrome using decision tree and support vector machine methods. ARYA Atheroscler. 12, 146 (2016). PMID: 27752272; PMCID: PMC5055373.
Google Scholar
Eyvazlou, M. et al. Prediction of metabolic syndrome based on sleep and work-related risk factors using an artificial neural network. BMC Endocr. Disord. 20, 1–11. https://doi.org/10.1186/s12902-020-00645-x (2020).
Google Scholar
Liu, J. et al. Integrating artificial intelligence in the diagnosis and management of metabolic syndrome: A comprehensive review. Diabetes Metab. Res. Rev. 41, e70039. https://doi.org/10.1002/dmrr.70039 (2025).
Google Scholar
Churpek, M. M. et al. Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit. Care Med. 44, 368–374. https://doi.org/10.1097/CCM.0000000000001571 (2016).
Google Scholar
Hanley, A. J. G. et al. Elevations in markers of liver injury and risk of type 2 diabetes: the insulin resistance atherosclerosis study. Diabetes 53, 2623–2632. https://doi.org/10.2337/diabetes.53.10.2623 (2004).
Google Scholar
Yokoyama, M. et al. Association of the aspartate aminotransferase to Alanine aminotransferase ratio with BNP level and cardiovascular mortality in the general population: the Yamagata study 10-year follow-up. Dis. Markers. 2016, 4857917 (2016).
Google Scholar
Klein, M. et al. Alanine transferase: an independent indicator of adiposity related comorbidity risk in youth: 丙氨酸转移酶: 一个年轻人肥胖相关合并症风险的独立指标. J. Diabetes. 7, 649–656 (2015).
Google Scholar
Hanley, A. J. G., Wagenknecht, L. E., Festa, A., D’Agostino, R. B. Jr & Haffner, S. M. Alanine aminotransferase and directly measured insulin sensitivity in a multiethnic cohort: the insulin resistance atherosclerosis study. Diabetes Care. 30, 1819–1827 (2007).
Google Scholar
Ballestri, S. et al. Nonalcoholic fatty liver disease is associated with an almost twofold increased risk of incident type 2 diabetes and metabolic syndrome. Evidence from a systematic review and meta-analysis. J. Gastroenterol. Hepatol. 31, 936–944 (2016).
Google Scholar
Bekkelund, S. I. Serum Alanine aminotransferase activity and risk factors for cardiovascular disease in a Caucasian population: the Tromsø study. BMC Cardiovasc. Disord. 21, 1–7. https://doi.org/10.1186/s12872-020-01826-1 (2021).
Google Scholar
Jalilian, M., Rasad, R. & Rotbeh, A. Fatty liver disease in overweight and obese Iranian children: comprehensive systematic review and meta-analysis. Obes. Med. 100455. https://doi.org/10.1016/j.obmed.2022.100455 (2022).
Rinaldi, L. et al. Mechanisms of non-alcoholic fatty liver disease in the metabolic syndrome. A narrative review. Antioxidants 10, 270. https://doi.org/10.3390/antiox10020270 (2021).
Google Scholar
Yki-Järvinen, H. Non-alcoholic fatty liver disease as a cause and a consequence of metabolic syndrome. Lancet Diabetes Endocrinol. 2, 901–910. https://doi.org/10.1016/S2213-8587(14)70032-4 (2014).
Google Scholar
Ghotbi, S. et al. Evaluation of elevated serum liver enzymes and metabolic syndrome in the PERSIAN Guilan cohort study population. Heliyon 10(11), e32449 (2024).
Makri, E., Goulas, A. & Polyzos, S. A. Epidemiology, pathogenesis, diagnosis and emerging treatment of nonalcoholic fatty liver disease. Arch. Med. Res. 52, 25–37 (2021).
Google Scholar
Devaraj, S., Singh, U. & Jialal, I. Human C-reactive protein and the metabolic syndrome. Curr. Opin. Lipidol. 20, 182–189 (2009).
Google Scholar
Zheng, H., Sechi, L. A., Navarese, E. P., Casu, G. & Vidili, G. Metabolic dysfunction-associated steatotic liver disease and cardiovascular risk: a comprehensive review. Cardiovasc. Diabetol. 23, 346 (2024).
Google Scholar
Rawal, R. et al. A comprehensive review of bilirubin determination methods with special emphasis on biosensors. Process Biochem. 89, 165–174. https://doi.org/10.1016/j.procbio.2019.10.034 (2020).
Google Scholar
Nano, J. et al. Association of Circulating total bilirubin with the metabolic syndrome and type 2 diabetes: a systematic review and meta-analysis of observational evidence. Diabetes Metab. 42, 389–397. https://doi.org/10.1016/j.diabet.2016.06.002 (2016).
Google Scholar
Liu, J. et al. Bilirubin increases insulin sensitivity by regulating cholesterol metabolism, adipokines and PPARγ levels. Sci. Rep. 5, 1–12. https://doi.org/10.1038/srep09886 (2015).
Google Scholar
Dong, H. et al. Bilirubin increases insulin sensitivity in leptin-receptor deficient and diet-induced obese mice through suppression of ER stress and chronic inflammation. Endocrinology 155, 818–828 (2014).
Google Scholar
Li, M. et al. Interdiction of the diabetic state in NOD mice by sustained induction of Heme oxygenase: possible role of carbon monoxide and bilirubin. Antioxid. Redox Signal. 9, 855–863 (2007).
Google Scholar
Nicolai, A. et al. Heme oxygenase-1 induction remodels adipose tissue and improves insulin sensitivity in obesity-induced diabetic rats. Hypertension 53, 508–515 (2009).
Google Scholar
Shakeri-Manesch, S. et al. Diminished upregulation of visceral adipose Heme oxygenase-1 correlates with waist-to-hip ratio and insulin resistance. Int. J. Obes. 33, 1257–1264 (2009).
Google Scholar
Jeong, H. et al. C reactive protein level as a marker for dyslipidaemia, diabetes and metabolic syndrome: results from the Korea National health and nutrition examination survey. BMJ Open. 9, e029861. https://doi.org/10.1136/bmjopen-2019-029861 (2019).
Google Scholar
Xue, Q. et al. Association between baseline and changes in high-sensitive C-reactive protein and metabolic syndrome: a nationwide cohort study and meta-analysis. Nutr. Metab. (Lond). 19, 1–12. https://doi.org/10.1186/s12986-021-00632-6 (2022).
Google Scholar
Maury, E. & Brichard, S. M. Adipokine dysregulation, adipose tissue inflammation and metabolic syndrome. Mol. Cell. Endocrinol. 314, 1–16 (2010).
Google Scholar
McCracken, E., Monaghan, M. & Sreenivasan, S. Pathophysiology of the metabolic syndrome. Clin. Dermatol. 36, 14–20 (2018).
Google Scholar
D’Alessandris, C., Lauro, R., Presta, I. & Sesti, G. C-reactive protein induces phosphorylation of insulin receptor substrate-1 on Ser 307 and Ser 612 in L6 myocytes, thereby impairing the insulin signalling pathway that promotes glucose transport. Diabetologia 50, 840–849 (2007).
Google Scholar
Hong, G. et al. High-sensitivity C-reactive protein leads to increased incident metabolic syndrome in women but not in men: a five-year follow-up study in a Chinese population. Diabetes Metab. Syndr. Obes. 13, 581. https://doi.org/10.2147/DMSO.S241774 (2020).
Google Scholar
Gao, X. et al. C-reactive protein as a moderator and insulin resistance as a mediator for the association between Endothelin-1 and dysglycemia among African americans: Jackson heart study. Obes. Med. 33, 100435. https://doi.org/10.1016/j.obmed.2022.100435 (2022).
Google Scholar
Jayedi, A. et al. Inflammation markers and risk of developing hypertension: a meta-analysis of cohort studies. Heart 105, 686–692. https://doi.org/10.1136/heartjnl-2018-314216 (2019).
Google Scholar
Mogharnasi, M., TaheriChadorneshin, H. & Abbasi-Deloei, N. Effect of exercise training type on plasma levels of vaspin, nesfatin-1, and high-sensitivity C-reactive protein in overweight and obese women. Obes. Med. 13, 34–38. https://doi.org/10.1016/j.obmed.2018.12.006 (2019).
Google Scholar
Kolahi Ahari, R. et al. Serum uric acid to high‐density lipoprotein ratio as a novel indicator of inflammation is correlated with the presence and severity of metabolic syndrome: A large‐scale study. Endocrinol. Diabetes Metab. 6, e446. https://doi.org/10.1002/edm2.446 (2023).
Google Scholar
Saberi-Karimian, M. et al. Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements. J. Clin. Lab. Anal. 37, e24798. https://doi.org/10.1002/jcla.24798 (2023).
Google Scholar
Ghayour-Mobarhan, M. et al. Mashhad stroke and heart atherosclerotic disorder (MASHAD) study: design, baseline characteristics and 10-year cardiovascular risk Estimation. Int. J. Public. Health. 60, 561–572. https://doi.org/10.1007/s00038-015-0679-6 (2015).
Google Scholar
Mansoori, A. et al. Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis. Sci. Rep. 13, 663. https://doi.org/10.1038/s41598-022-27340-2 (2023).
Google Scholar
Nohara, Y., Matsumoto, K., Soejima, H. & Nakashima, N. Explanation of machine learning models using Shapley additive explanation and application for real data in hospital. Comput. Methods Programs Biomed. 214, 106584. https://doi.org/10.1016/j.cmpb.2021.106584 (2022).
Google Scholar
Piri, S., Delen, D. & Liu, T. A synthetic informative minority over-sampling (SIMO) algorithm leveraging support vector machine to enhance learning from imbalanced datasets. Decis. Support Syst. 106, 15–29. https://doi.org/10.1016/j.dss.2017.11.006 (2018).
Google Scholar
Su, X., Yan, X. & Tsai, C. Linear regression. Wiley Interdiscip Rev. Comput. Stat. 4, 275–294. https://doi.org/10.1002/wics.1198 (2012).
Google Scholar
Swain, P. H. & Hauska, H. The decision tree classifier: design and potential. IEEE Trans. Geoscience Electron. 15, 142–147. https://doi.org/10.1109/TGE.1977.6498972 (1977).
Google Scholar
Cristianini, N. & Ricci, E. Support vector machines, in: Encyclopedia of Algorithms, Springer-, : 928–932. https://doi.org/10.1109/5254.708428. (2008).
Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).
Google Scholar
Błaszczyński, J. & Stefanowski, J. Actively balanced bagging for imbalanced data, in: Foundations of Intelligent Systems: 23rd International Symposium, ISMIS 2017, Warsaw, Poland, June 26–29, Proceedings 23, Springer, 2017: pp. 271–281. (2017). https://doi.org/10.1007/978-3-319-60438-1_27
Błaszczyński, J. & Stefanowski, J. Improving bagging ensembles for class imbalanced data by active learning. Adv. Feature Selection Data Pattern Recognit. 25–52. https://doi.org/10.1007/978-3-319-67588-6_3 (2018).
Natekin, A. & Knoll, A. Gradient boosting machines, a tutorial. Front. Neurorobot. 7, 21. https://doi.org/10.3389/fnbot.2013.00021 (2013).
Google Scholar
McCulloch, W. S. & Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133. https://doi.org/10.1007/BF02478259 (1943).
Google Scholar
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536. https://doi.org/10.1038/323533a0 (1986).
Google Scholar
Lin, J. P. et al. Association between the UGT1A1* 28 allele, bilirubin levels, and coronary heart disease in the Framingham heart study. Circulation 114, 1476–1481. https://doi.org/10.1161/CIRCULATIONAHA.106.633206 (2006).
Google Scholar
Vítek, L. & Schwertner, H. A. The Heme catabolic pathway and its protective effects on oxidative stress-mediated diseases. Adv. Clin. Chem. 43, 1–57. https://doi.org/10.1016/S0065-2423(06)43001-8 (2007).
Google Scholar
Tang, L., Huang, C. & Feng, Y. Serum total bilirubin concentration is associated with carotid atherosclerosis in patients with prehypertension. Clin. Exp. Hypertens. 41, 682–686. https://doi.org/10.1080/10641963.2018.1539094 (2019).
Google Scholar
Vítek, L. The role of bilirubin in diabetes, metabolic syndrome, and cardiovascular diseases. Front. Pharmacol. 3, 55. https://doi.org/10.3389/fphar.2012.00055 (2012).
Google Scholar
Vitek, L. Bilirubin and atherosclerotic diseases. Physiol. Res. 66, S11. https://doi.org/10.33549/physiolres.933581 (2017).
Google Scholar
Kipp, Z. A. et al. Bilirubin levels are negatively correlated with adiposity in obese men and women, and its catabolized product, urobilin, is positively associated with insulin resistance. Antioxidants 12, 170. https://doi.org/10.3390/antiox12010170 (2023).
Google Scholar
Lee, M. J. et al. Serum bilirubin as a predictor of incident metabolic syndrome: a 4-year retrospective longitudinal study of 6205 initially healthy Korean men. Diabetes Metab. 40, 305–309. https://doi.org/10.1016/j.diabet.2014.04.006 (2014).
Google Scholar
Oda, E. & Aizawa, Y. Total bilirubin is inversely associated with metabolic syndrome but not a risk factor for metabolic syndrome in Japanese men and women. Acta Diabetol. 50, 417–422. https://doi.org/10.1007/s00592-012-0447-5 (2013).
Google Scholar
Li, X. H. et al. Direct bilirubin levels and risk of metabolic syndrome in healthy Chinese men. Biomed. Res. Int. 2017, 9621615. https://doi.org/10.1155/2017/9621615 (2017).
Google Scholar
Theodorakis, N. & Nikolaou, M. From Cardiovascular-Kidney-Metabolic syndrome to Cardiovascular-Renal-Hepatic-Metabolic syndrome: proposing an expanded framework. Biomolecules 15, 213 (2025).
Google Scholar
Shah, S. C. & Sass, D. A. Cardiac hepatopathy: a review of liver dysfunction in heart failure. Liver Res. Open. J. 1, 1–10 (2015).
Google Scholar
Liang, C. et al. Association of serum bilirubin with metabolic syndrome and non-alcoholic fatty liver disease: a systematic review and meta-analysis. Front. Endocrinol. (Lausanne). 13, 869579 (2022).
Google Scholar
Kim, A. H. et al. Sex differences in the relationship between serum total bilirubin and risk of incident metabolic syndrome in community-dwelling adults: propensity score analysis using longitudinal cohort data over 16 years. Cardiovasc. Diabetol. 23, 92 (2024).
Google Scholar
Song, Y., Yang, S. K., Kim, J. & Lee, D. C. Association between C-reactive protein and metabolic syndrome in Korean adults. Korean J. Fam Med. 40, 116. https://doi.org/10.4082/kjfm.17.0075 (2019).
Google Scholar
Oliveira, A. C. et al. RETRACTION: C-reactive protein and metabolic syndrome in youth: A strong relationship?? Obesity 16, 1094–1098. https://doi.org/10.1038/oby.2008.43 (2008).
Google Scholar
Yudkin, J. S. Cda. Stehouwer, jj. Emeis, sw. Coppack, C-reactive protein in healthy subjects: associations with obesity, insulin resistance, and endothelial dysfunction: a potential role for cytokines originating from adipose tissue? Arterioscler. Thromb. Vasc Biol. 19, 972–978. https://doi.org/10.1161/01.ATV.19.4.972 (1999).
Google Scholar
D’Alessandris, C., Lauro, R., Presta, I. & Sesti, G. C-reactive protein induces phosphorylation of insulin receptor substrate-1 on Ser 307 and Ser 612 in L6 myocytes, thereby impairing the insulin signalling pathway that promotes glucose transport. Diabetologia 50, 840–849. https://doi.org/10.1007/s00125-006-0522-y (2007).
Google Scholar
Akter, S., Shekhar, H. U. & Akhteruzzaman, S. Application of biochemical tests and machine learning techniques to diagnose and evaluate liver disease. Adv. Bioscience Biotechnol. 12, 154–172 (2021).
Google Scholar
Kwo, P. Y., Cohen, S. M. & Lim, J. K. ACG clinical guideline: evaluation of abnormal liver chemistries. Official J. Am. Coll. Gastroenterology| ACG. 112, 18–35 (2017).
Google Scholar
Chen, S. et al. Metabolic syndrome and serum liver enzymes in the general Chinese population. Int. J. Environ. Res. Public. Health. 13, 223 (2016).
Google Scholar
Zhang, H. et al. Machine learning-based prediction for 4-year risk of metabolic syndrome in adults: a retrospective cohort study. Risk Manag. Healthc. Policy 14, 4361–4368. https://doi.org/10.2147/RMHP.S328180 (2021).
