Diagnostic performance of three anthropometric indices of weight and height for metabolic syndrome in workers
Keywords:
metabolic syndrome, body mass index, early diagnosis, sensitivity and specificity, area under curve.Abstract
Introduction: Metabolic syndrome is associated with long-term chronic diseases, which is why different ways of obtaining an early diagnosis are sought.
Objective: To determine the diagnostic yield of 3 anthropometric indices of weight and height for metabolic syndrome in a sample of Peruvian workers.
Methods: The population are workers from 18 to 65 years old, both sexes, occupation operators and administrators; the studied variables were: age, sex, occupation, weight, height, waist circumference, history of type 2 diabetes mellitus, pressure systolic and diastolic blood pressure, fasting glucose, triglycerides, and high-density lipoprotein; 370 workers were included, receiver operating characteristic curves (ROC) were created with their respective area under the curve, obtaining the sensitivity and specificity of each of the indices.
Results: Of the total number of workers, 20% presented Metabolic Syndrome; 46.76% were women, 60% drank alcohol at some time, and 5.14% reported having smoked. The Body Mass Index the greatest ROC= 0.73; cutoff= 26.04; sensitivity= 78.4 and specificity= 67.9) followed by the New Body Mass Index (ROC= 0.70; cutoff= 27.85; sensitivity= 68.9 and specificity= 70.6), the last place was occupied by the Triponderal Index (ROC= 0.66; cutoff= 16.67; sensitivity= 67.6 and specificity= 64.5); the parameters for metabolic syndrome showed a statistically significant association.
Conclusion: Body Mass Index is the best diagnostic yield for Metabolic Syndrome and could be a useful predictor to detect this syndrome.
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References
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23. Van Haute M, Rondilla E, Vitug JL, Batin KD, Abrugar RE, Quitoriano F, et al. Assessment of a proposed BMI formula in predicting body fat percentage among Filipino young adults. Sci Rep. 2020; 10(1):21988. DOI: 10.1038/s41598-020-79041-3
2. Rojas M, Chávez-Castillo M, Pirela D, Parra H, Nava M, Chacín M, et al. Metabolic Syndrome: Is It Time to Add the Central Nervous System? Nutrients. 2021; 13(7):2254. DOI: 10.3390/nu13072254
3. Bangen KJ, Armstrong NM, Au R, Gross AL. Metabolic Syndrome and Cognitive Trajectories in the Framingham Offspring Study. Journal of Alzheimer's Disease. 2019; 71(3):931-43. DOI: 10.3233/JAD-190261
4. Uzunlulu M, Caklili OT, Oguz A. Association between Metabolic Syndrome and Cancer. ANM. 2016; 68(3):173-9. DOI: 10.1159/000443743
5. Kachur S, Morera R, De Schutter A, Lavie CJ. Cardiovascular Risk in Patients with Prehypertension and the Metabolic Syndrome. Curr Hypertens Rep. 2018; 20(2):15. DOI: 10.1007/s11906-018-0801-2
6. Wu S, Zhou K, Misra-Hebert A, Bena J, Kashyap SR. Impact of Metabolic Syndrome on Severity of COVID-19 Illness. Metabolic Syndrome and Related Disorders. 2022; 20(4):191-8. DOI: 10.1089/met.2021.0102
7. Yanai H. Metabolic Syndrome and COVID-19. Cardiol Res. 2020; 11(6):360-5. DOI: 10.14740/cr1181
8. Saklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep. 2018; 20(2):12. DOI: 10.1007/s11906-018-0812-z
9. Díaz-Ortega JL, Quispe-Tácunan A, Gallo-Ancajima M, Castro-Caracholi L, Yupari-Azabache I, Díaz-Ortega JL, et al. Indicadores de aterogenicidad en la predicción del síndrome metabólico en adultos, Trujillo-Perú. Revista chilena de nutrición. 2021; 48(4):586-94. DOI: 10.4067/S0717-75182021000400586
10. Diaz A, Espeche W, March C, Flores R, Parodi R, Genesio MA, et al. Prevalencia del síndrome metabólico en Argentina en los últimos 25 años: revisión sistemática de estudios observacionales poblacionales. Hipertensión y Riesgo Vascular. 2018; 35(2):64-9. DOI: 10.1016/j.hipert.2017.08.003
11. Domínguez-Reyes T, Quiroz-Vargas I, Salgado-Bernabé AB, Salgado-Goytia L, Muñoz-Valle JF, Parra-Rojas I. Las medidas antropométricas como indicadores predictivos de riesgo metabólico en una población mexicana. Nutrición Hospitalaria. 2017; 34(1):96-101. DOI: 10.20960/nh.983
12. Yeste D, Clemente M, Campos A, Fábregas A, Mogas E, Soler L, et al. Precisión diagnóstica del índice de masa triponderal (kg/m3) para identificar el fenotipo de riesgo metabólico en pacientes obesos. Anales de Pediatría. 2021; 94(2):68-74. DOI: 10.1016/j.anpedi.2020.04.004
13. Trefethen LN. BMI (Body Mass Index): Calculate your "New BMI". Oxford: Oxford University Mathematical Institute; 2013. [acceso: 29/05/2022]. Disponible en: http://people.maths.ox.ac.uk/trefethen/bmi.html
14. Ramírez-López LX, Aguilera AM, Rubio CM, Aguilar-Mateus ÁM. Síndrome metabólico: una revisión de criterios internacionales. RCCAR. 2022; 28(1):5885. DOI: 10.24875/RCCAR.M21000010
15. Ofer K, Ronit L, Ophir A. Normal body mass index (BMI) can rule out metabolic syndrome: An Israeli cohort study: Erratum. Medicine. 2019; 98(19):e15744. DOI: 10.1097/MD.0000000000015744
16. Ghachem A, Marcotte-Chénard A, Dionne IJ, Brochu M. Body mass index to predict fat mass and metabolic syndrome severity: should it really be specific to sex, age and ethnicity? A NHANES study (1999-2014). Annals of Human Biology. 2019; 46(3):215-24. DOI: 10.1080/03014460.2019.1635645
17. Radetti G, Fanolla A, Grugni G, Lupi F, Tamini S, Cicolini S, et al. The Role of Different Indexes of Adiposity and Body Composition for the Identification of Metabolic Syndrome in Women with Obesity. Journal of Clinical Medicine. 2021; 10(9):1975. DOI: 10.3390/jcm10091975
18. Peterson CM, Su H, Thomas DM, Heo M, Golnabi AH, Pietrobelli A, et al. Tri-Ponderal Mass Index vs Body Mass Index in Estimating Body Fat During Adolescence. JAMA Pediatrics. 2017; 171(7):629-36. DOI: 10.1001/jamapediatrics.2017.0460
19. Ramírez-Vélez R, Correa-Bautista JE, Carrillo HA, González-Jiménez E, Schmidt-RioValle J, Correa-Rodríguez M, et al. Tri-Ponderal Mass Index vs. Fat Mass/Height3 as a Screening Tool for Metabolic Syndrome Prediction in Colombian Children and Young People. Nutrients. 2018; 10(4):412. DOI: 10.3390/nu10040412
20. Sun J, Yang R, Zhao M, Bovet P, Xi B. Tri-Ponderal Mass Index as a Screening Tool for Identifying Body Fat and Cardiovascular Risk Factors in Children and Adolescents: A Systematic Review. Frontiers in Endocrinology. 2021; 12:694681. DOI: 10.3389/fendo.2021.694681
21. Elagizi A, Kachur S, Lavie CJ, Carbone S, Pandey A, Ortega FB, et al. An Overview and Update on Obesity and the Obesity Paradox in Cardiovascular Diseases. Progress in Cardiovascular Diseases. 2018; 61(2):142-50. DOI: 10.1016/j.pcad.2018.07.003
22. Bauce GJ. Comparación entre el IMC tradicional y el IMC Oxford y su relación con otros indicadores, en niños y adolescentes. Rev. Inst. Nac. Hig. 2019 [acceso: 22/05/2022]; 50(1-2):22-29. Disponible en: https://pesquisa.bvsalud.org/gim/resource/zh/biblio-1118371?lang=es
23. Van Haute M, Rondilla E, Vitug JL, Batin KD, Abrugar RE, Quitoriano F, et al. Assessment of a proposed BMI formula in predicting body fat percentage among Filipino young adults. Sci Rep. 2020; 10(1):21988. DOI: 10.1038/s41598-020-79041-3
Published
2023-04-26
How to Cite
1.
Zeñas -Trujillo GZ, Vera-Ponce VJ, Trujillo-Ramírez I. Diagnostic performance of three anthropometric indices of weight and height for metabolic syndrome in workers. Rev Cubana Med Milit [Internet]. 2023 Apr. 26 [cited 2025 Jan. 10];52(2):e02302556. Available from: https://revmedmilitar.sld.cu/index.php/mil/article/view/2556
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