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Metabolic syndrome screening in adolescents: New scores AI_METS based on artificial intelligence techniques

      Highlights

      • Explainable Artificial Intelligence methods are used to extract the learned function.
      • Gini importance techniques were used to build new scores for the screening of metabolic syndrome in adolescents without using percentiles.
      • These scores are based on age, waist circumference, mean blood pressure and TyG index.
      • The accuracy of these scores, is effective for the detection of metabolic syndrome.
      • Mean blood pressure offers a better specificity and sensitivity than TyG index to detect metabolic syndrome.

      Abstract

      Background and aims

      Metabolic syndrome (MetS) definitions in adolescents based on the percentiles of its components are rather complicated to use in clinical practice. The aim of this study was to test the validity of artificial intelligence (AI)-based scores (AI_METS) that do not use these percentiles for MetS screening for adolescents.

      Methods and results

      This study included 1086 adolescents aged 12 to 18. The cohort underwent anthropometric measurements and blood tests. Mean blood pressure (MBP), and triglyceride glucose index (TyG) were calculated. Explainable AI methods are used to extract the learned function. Gini importance techniques were tested and used to build new scores for the screening of MetS. IDF, Cook, De Ferranti, Viner, and Weiss definitions of MetS were used to test the validity of these scores.
      MetS prevalence was 0.4%–4.7% according to these definitions. AI_METS used age, waist circumference, MBP, and TyG index. They offer area under the curves (AUCs) 0.91, 0.93, 0.89, 0.93, and 0.98; specificity 81%, 75%, 72%, 80%, and 97%; and sensitivity 90%, 100%, 90%, 100%, and 100%, respectively, for the detection of MetS according to these definitions. Considering only MBP offers a better specificity and sensitivity to detect MetS than considering only TyG index. MBP offers slightly lower performance than AI_METS.

      Conclusion

      AI techniques have proven their ability to extract knowledge from data. They allowed us to generate new scores for MetS detection in adolescents without using specific percentiles for each component. Although these scores are less intuitive than the percentile-based definition, their accuracy is rather effective for the detection of MetS.

      Keywords

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      References

        • Eckel R.H.
        • Grundy S.M.
        • Zimmet P.Z.
        The metabolic syndrome.
        Lancet. 2005; 365: 1415-1428
        • Reaven G.M.
        Banting lecture 1988. Role of insulin resistance in human disease.
        Diabetes. 1988; 37: 1595-1607
        • Zimmet P.
        • Alberti G.
        • Kaufman F.
        • et al.
        The metabolic syndrome in children and adolescents.
        Lancet. 2007; 369: 2059-2061
        • Bitew Z.W.
        • Alemu A.
        • Ayele E.G.
        • et al.
        Metabolic syndrome among children and adolescents in low and middle income countries: a systematic review and meta-analysis.
        Diabetol Metab Syndrome. 2020; 12: 93
        • Reisinger C.
        • Nkeh-Chungag B.N.
        • Fredriksen P.M.
        • et al.
        The prevalence of pediatric metabolic syndrome—a critical look on the discrepancies between definitions and its clinical importance.
        Int J Obes. 2021; 45: 12-24
        • Wilson P.W.
        • D'Agostino R.B.
        • Parise H.
        • et al.
        Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus.
        Circulation. 2005; 112: 3066-3072
        • Magge S.N.
        • Goodman E.
        • Armstrong S.C.
        AAP committee on nutrition, section on endocrinology, section on obesity. The metabolic syndrome in children and adolescents: shifting the focus to cardiometabolic risk factor clustering.
        Pediatrics. 2017; 140e20171603
        • Liu C.
        • Wu S.
        • Pan X.
        Clustering of cardio-metabolic risk factors and pre-diabetes among U.S. adolescents.
        Sci Rep. 2021; 11: 5015
        • Koskinen J.
        • Magnussen C.G.
        • Sinaiko A.
        • et al.
        Childhood age and associations between childhood metabolic syndrome and adult risk for metabolic syndrome, type 2 diabetes mellitus and carotid intima media thickness: the international childhood cardiovascular cohort consortium.
        J Am Heart Assoc. 2017; : 6e005632
        • Ford E.S.
        • Li C.
        Defining the metabolic syndrome in children and adolescents: will the real definition please stand up?.
        J Pediatr. 2008; 152: 160e4
        • Cook S.
        • Weitzman M.
        • Auinger P.
        • et al.
        Prevalence of metabolic syndrome phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 1988–1994.
        Arch Pediatr Adolesc Med. 2003; 157: 821-827
        • De Ferranti S.D.
        • Gauvreau K.
        • Ludwig D.S.
        • et al.
        Inflammation and changes in metabolic syndrome abnormalities in US adolescents: findings from the 1988–1994 and 1999–2000 National Health and Nutrition Examination Surveys.
        Clin Chem. 2006; 52: 1325-1330
        • Viner R.M.
        • Segal T.Y.
        • Lichtarowicz-Krynska E.
        • et al.
        Prevalence of the insulin resistance syndrome in obesity.
        Arch Dis Child. 2005; 90: 10-14
        • Weiss R.
        • Dziura J.
        • Burgert T.S.
        • et al.
        Obesity and the metabolic syndrome in children and adolescents.
        N Engl J Med. 2004; 350: 2362-2374
        • Yu C.S.
        • Lin Y.J.
        • Lin C.H.
        • et al.
        Predicting metabolic syndrome with machine learning models using a decision tree algorithm: retrospective cohort study.
        JMIR Med Inform. 2020; 8e17110
        • Hirose H.
        • Takayama T.
        • Hozawa S.
        • et al.
        Prediction of metabolic syndrome using artificial neural network system based on clinical data including insulin resistance index and serum adiponectin.
        Comput Biol Med. 2011; 41: 1051-1056
        • Benmohammed K.
        • Valensi P.
        • Benlatreche M.
        • et al.
        Anthropometric markers for detection of the metabolic syndrome in adolescents.
        Diabetes Metab. 2015; 41: 138-144
      1. National high blood pressure education program working group on high blood pressure in children adolescents. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents.
        Pediatrics. 2004; 114: 555-576
        • Kodama S.
        • Horikawa C.
        • Fujihara K.
        • et al.
        Meta-analysis of the quantitative relation between pulse pressure and mean arterial pressure and cardiovascular risk in patients with diabetes mellitus.
        Am J Cardiol. 2014; 113: 1058-1065
        • Cole T.J.
        The LMS method for constructing normalized growth standards.
        Eur J Clin Nutr. 1990; 44: 45-60
        • Omri N.
        • Al Masry Z.
        • Mairot N.
        • et al.
        X-PHM: Prognostics and health management knowledge-based framework for SME.
        Procedia CIRP. 2021; 104: 1595-1600
        • Guidotti R.
        • Monreale A.
        • Ruggieri S.
        • et al.
        A survey of methods for explaining black box models.
        ACM Comput Surv. 2018; 51: 1-42
        • Morrison J.A.
        • Friedman L.A.
        • Wang P.
        • et al.
        Metabolic syndrome in childhood predicts adult metabolic syndrome and type 2 diabetes mellitus 25 to 30 years later.
        J Pediatr. 2008; 152: 201-206
        • Silva K.C.
        • Santana Paiva N.
        • Rocha de Faria F.
        • et al.
        Predictive ability of seven anthropometric indices for cardiovascular risk markers and metabolic syndrome in adolescents.
        J Adolesc Health. 2020; 66: 491e498
        • Lo K.
        • Wong M.
        • Khalechelvam P.
        • et al.
        Waist-to-height ratio, body mass index and waist circumference for screening paediatric cardio-metabolic risk factors: a meta-analysis.
        Obes Rev. 2016; 17: 1258-1275
        • Guerrero-Romero F.
        • Simental-Mendía L.E.
        • González-Ortiz M.
        • et al.
        The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperins-ulinemic clamp.
        J Clin Endocrinol Metab. 2010; 95: 3347-3351
        • Khan S.H.
        • Sobia F.
        • Niazi N.K.
        • et al.
        Metabolic clustering of risk factors: evaluation of triglyceride-glucose index (TyG index) for evaluation of insulin resistance.
        Diabetol Metab Syndrome. 2018; 10: 74
        • Sánchez-Íñigo L.
        • Navarro-González D.
        • Fernández-Montero A.
        • et al.
        The TyG index may predict the development of cardiovascular events.
        Eur J Clin Invest. 2016; 46: 189-197
        • Park G.M.
        • Cho Y.R.
        • Won K.B.
        • et al.
        Triglyceride glucose index is a useful marker for predicting subclinical coronary artery disease in the absence of traditional risk factors.
        Lipids Health Dis. 2020; 19: 7
        • Thai P.V.
        • Tien H.A.
        • Van Minh H.
        • et al.
        Triglyceride glucose index for the detection of asymptomatic coronary artery stenosis in patients with type 2 diabetes.
        Cardiovasc Diabetol. 2020; 19: 137
        • Dikaiakou E.
        • Vlachopapadopoulou E.A.
        • Paschou S.A.
        • et al.
        Τriglycerides-glucose (TyG) index is a sensitive marker of insulin resistance in Greek children and adolescents.
        Endocrine. 2020; 70: 58-64
        • Moon S.
        • Park J.S.
        • Ahn Y.
        The cut-off values of triglycerides and glucose index for metabolic syndrome in American and Korean adolescents.
        J Kor Med Sci. 2017; 32: 427-433
        • Simental-Mendia L.E.
        • Hernández-Ronquillo G.
        • Gómez-Díaz R.
        • et al.
        The triglycerides and glucose index is associated with cardiovascular risk factors in normal-weight children and adolescents.
        Pediatr Res. 2017; 82: 920-925
        • Mohd Nor N.S.
        • Lee S.
        • Bacha F.
        • Tfayli H.
        • et al.
        Triglyceride glucose index as a surrogate measure of insulin sensitivity in obese adolescents with normoglycemia, prediabetes, and type 2 diabetes mellitus: comparison with the hyperinsulinemic–euglycemic clamp.
        Pediatr Diabetes. 2016; 17: 458-465
        • Guo C.
        • Qin P.
        • Li Q.
        • et al.
        Association between mean arterial pressure and risk of type 2 diabetes mellitus: the Rural Chinese Cohort Study.
        Prim Care Diabetes. 2020; 14: 448-454
        • Mbanya V.N.
        • Mbanya J.C.
        • Kufe C.
        • et al.
        Effects of single and multiple blood pressure measurement strategies on the prediction of prevalent screen-detected diabetes mellitus: a population-based survey.
        J Clin Hypertens (Greenwich). 2016; 18: 864-870
        • Janghorbani M.
        • Amini M.
        Comparison of systolic and diastolic blood pressure with pulse pressure and mean arterial pressure for prediction of type 2 diabetes: the Isfahan Diabetes Prevention Study.
        Endokrynol Pol. 2011; 62: 324-330
        • Sesso H.D.
        • Stampfer M.J.
        • Rosner B.
        • et al.
        Systolic and diastolic blood pressure, pulse pressure, and mean arterial pressure as predictors of cardiovascular disease risk in Men.
        Hypertension. 2000; 36: 801-807
        • Hsu C.H.
        • Chang J.B.
        • Liu I.C.
        • et al.
        Mean arterial pressure is better at predicting future metabolic syndrome in the normotensive elderly: a prospective cohort study in Taiwan.
        Prev Med. 2015; 72: 76-82
        • Beam A.L.
        • Kohane I.S.
        Big data and machine learning in health care.
        JAMA. 2018; 319: 1317-1318