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ORIGINAL RESEARCH

Simple Method to Predict Insulin Resistance in Children Aged 6–12 Years by Using Machine Learning

ORCID Icon & ORCID Icon
Pages 2963-2975 | Received 08 Jul 2022, Accepted 13 Sep 2022, Published online: 27 Sep 2022

References

  • Afshin A, Forouzanfar MH, Reitsma MB, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):13–27. doi:10.1056/NEJMoa1614362
  • Song P, Yu J, Chang X, Wang M, An L. Prevalence and correlates of metabolic syndrome in Chinese children: the China health and nutrition survey. Nutrients. 2017;9(1). doi:10.3390/nu9010079
  • Weiss R, Santoro N, Giannini C, Galderisi A, Umano GR, Caprio S. Prediabetes in youth - mechanisms and biomarkers. Lancet Child Adolesc Health. 2017;1(3):240–248. doi:10.1016/s2352-4642(17)30044-5
  • Esquivel Zuniga R, DeBoer MD. Prediabetes in adolescents: prevalence, management and diabetes prevention strategies. Diabetes Metab Syndr Obes. 2021;14:4609–4619. doi:10.2147/DMSO.S284401
  • Abbott DH, Bacha F. Ontogeny of polycystic ovary syndrome and insulin resistance in utero and early childhood. Fertil Steril. 2013;100(1):2–11. doi:10.1016/j.fertnstert.2013.05.023
  • Soleimani M. Insulin resistance and hypertension: new insights. Kidney Int. 2015;87(3):497–499. doi:10.1038/ki.2014.392
  • Bethel MA, Hyland KA, Chacra AR, et al. Updated risk factors should be used to predict development of diabetes. J Diabetes Complications. 2017;31(5):859–863. doi:10.1016/j.jdiacomp.2017.02.012
  • Alias-Hernandez I, Galera-Martinez R, Garcia-Garcia E, et al. Insulinaemia and insulin resistance in Caucasian general paediatric population aged 2 to 10 years: associated risk factors. Pediatr Diabetes. 2018;19(1):45–52. doi:10.1111/pedi.12533
  • Medrano M, Arenaza L, Migueles JH, Rodriguez-Vigil B, Ruiz JR, Labayen I. Associations of physical activity and fitness with hepatic steatosis, liver enzymes, and insulin resistance in children with overweight/obesity. Pediatr Diabetes. 2020;21(4):565–574. doi:10.1111/pedi.13011
  • Krisnamurti DGB, Purwaningsih EH, Tarigan TJE, Soetikno V, Louisa M. Hematological indices and their correlation with glucose control parameters in a prediabetic rat model. Vet World. 2022;15(3):672–678. doi:10.14202/vetworld.2022.672-678
  • Park JM, Lee DC, Lee YJ. Relationship between high white blood cell count and insulin resistance (HOMA-IR) in Korean children and adolescents: Korean national health and nutrition examination survey 2008–2010. Nutr Metab Cardiovasc Dis. 2017;27(5):456–461. doi:10.1016/j.numecd.2017.03.002
  • Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262–e273. doi:10.1016/s1470-2045(19)30149-4
  • Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–731. doi:10.1038/s41551-018-0305-z
  • Ksiazek W, Gandor M, Plawiak P. Comparison of various approaches to combine logistic regression with genetic algorithms in survival prediction of hepatocellular carcinoma. Comput Biol Med. 2021;134:104431. doi:10.1016/j.compbiomed.2021.104431
  • Rehman A, Kashif M, Abunadi I, Ayesha N. Lung cancer detection and classification from chest CT scans using machine learning techniques. 2021.
  • Kim J, Mun S, Lee S, Jeong K, Baek Y. Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea. BMC Public Health. 2022;22(1):664. doi:10.1186/s12889-022-13131-x
  • Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H. Predicting diabetes mellitus with machine learning techniques. Front Genet. 2018;9:515. doi:10.3389/fgene.2018.00515
  • Park S, Kim C, Wu X. Development and validation of an insulin resistance predicting model using a machine-learning approach in a population-based cohort in Korea. Diagnostics. 2022;12(1). doi:10.3390/diagnostics12010212
  • Lee CL, Liu WJ, Tsai SF. Development and validation of an insulin resistance model for a population with chronic kidney disease using a machine learning approach. Nutrients. 2022;14(14). doi:10.3390/nu14142832
  • Tagi VM, Giannini C, Chiarelli F. Insulin resistance in children. Front Endocrinol. 2019;10:342. doi:10.3389/fendo.2019.00342
  • Han R, Zhang Y, Jiang X. Relationship between four non-insulin-based indexes of insulin resistance and serum uric acid in patients with type 2 diabetes: a cross-sectional study. Diabetes Metab Syndr Obes. 2022;15:1461–1471. doi:10.2147/DMSO.S362248
  • Yin J, Li M, Xu L, et al. Insulin resistance determined by Homeostasis Model Assessment (HOMA) and associations with metabolic syndrome among Chinese children and teenagers. Diabetol Metab Syndr. 2013;5(1):71. doi:10.1186/1758-5996-5-71
  • Pan SY, de Groh M, Aziz A, Morrison H. Relation of insulin resistance with social-demographics, adiposity and behavioral factors in non-diabetic adult Canadians. J Diabetes Metab Disord. 2015;15:31. doi:10.1186/s40200-016-0253-7
  • Koren D, Taveras EM. Association of sleep disturbances with obesity, insulin resistance and the metabolic syndrome. Metabolism. 2018;84:67–75. doi:10.1016/j.metabol.2018.04.001
  • Myers J, Kokkinos P, Nyelin E. Physical activity, cardiorespiratory fitness, and the metabolic syndrome. Nutrients. 2019;11(7):1652. doi:10.3390/nu11071652
  • Lee CT, Harris SB, Retnakaran R, et al. White blood cell subtypes, insulin resistance and β-cell dysfunction in high-risk individuals–the PROMISE cohort. Clin Endocrinol. 2014;81(4):536–541. doi:10.1111/cen.12390
  • Park JM, Lee JW, Shim JY, Lee YJ. Relationship between platelet count and insulin resistance in Korean adolescents: a nationwide population-based study. Metab Syndr Relat Disord. 2018;16(9):470–476. doi:10.1089/met.2018.0016
  • Ferreira D, Severo M, Araújo J, Barros H, Guimarães JT, Ramos E. Association between insulin resistance and haematological parameters: a cohort study from adolescence to adulthood. Diabetes Metab Res Rev. 2019;35(8):e3194. doi:10.1002/dmrr.3194
  • Cruz-Pineda WD, Garibay-Cerdenares OL, Rodriguez-Ruiz HA, et al. Changes in the expression of insulin pathway, neutrophil elastase and alpha 1 antitrypsin genes from leukocytes of young individuals with insulin resistance. Diabetes Metab Syndr Obes. 2022;15:1865–1876. doi:10.2147/DMSO.S362881
  • Flores-Viveros KL, Aguilar-Galarza BA, Ordonez-Sanchez ML, et al. Contribution of genetic, biochemical and environmental factors on insulin resistance and obesity in Mexican young adults. Obes Res Clin Pract. 2019;13(6):533–540. doi:10.1016/j.orcp.2019.10.012
  • Bonneau GA, Pedrozo WR, Berg G. Adiponectin and waist circumference as predictors of insulin-resistance in women. Diabetes Metab Syndr. 2014;8(1):3–7. doi:10.1016/j.dsx.2013.10.005
  • Xu W, Zhang J, Zhang Q, Wei X. Risk Prediction of Type II Diabetes Based on Random Forest Model. IEEE. 2017:382–386.
  • Karimi-Alavijeh F, Jalili S, Sadeghi M. Predicting metabolic syndrome using decision tree and support vector machine methods. ARYA Atheroscler. 2016;12(3):146–152.
  • Choe EK, Rhee H, Lee S, et al. Metabolic syndrome prediction using machine learning models with genetic and clinical information from a nonobese healthy population. Genomics Inform. 2018;16(4):e31. doi:10.5808/GI.2018.16.4.e31
  • Stawiski K, Pietrzak I, Mlynarski W, Fendler W, Szadkowska A. NIRCa: an artificial neural network-based insulin resistance calculator. Pediatr Diabetes. 2018;19(2):231–235. doi:10.1111/pedi.12551
  • Moreira SR, Ferreira AP, Lima RM, et al. Predicting insulin resistance in children: anthropometric and metabolic indicators. J Pediatr. 2008;84(1):47–52. doi:10.2223/JPED.1740
  • Lin H, Tas E, Borsheim E, Mercer KE. Circulating miRNA signatures associated with insulin resistance in adolescents with obesity. Diabetes Metab Syndr Obes. 2020;13:4929–4939. doi:10.2147/DMSO.S273908
  • Svedberg J, Strömblad G, Wirth A, Smith U, Björntorp P. Fatty acids in the portal vein of the rat regulate hepatic insulin clearance. J Clin Invest. 1991;88(6):2054–2058. doi:10.1172/jci115534
  • Velásquez-Rodríguez CM, Velásquez-Villa M, Gómez-Ocampo L, Bermúdez-Cardona J. Abdominal obesity and low physical activity are associated with insulin resistance in overweight adolescents: a cross-sectional study. BMC Pediatr. 2014;14:258. doi:10.1186/1471-2431-14-258
  • Petersen MC, Shulman GI. Mechanisms of insulin action and insulin resistance. Physiol Rev. 2018;98(4):2133–2223. doi:10.1152/physrev.00063.2017
  • Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform biomedicine. Cell. 2020;181(1):92–101. doi:10.1016/j.cell.2020.03.022
  • Saberi-Karimian M, Khorasanchi Z, Ghazizadeh H, et al. Potential value and impact of data mining and machine learning in clinical diagnostics. Crit Rev Clin Lab Sci. 2021;58(4):275–296. doi:10.1080/10408363.2020.1857681