Rev Med UAS
Vol. 12: No. 3. Julio-Septiembre 2022
ISSN 2007-8013

La circunferencia de la cintura es la única variable antropométrica que predice el HOMA-IR: Estudio de una cohorte de mujeres jóvenes

The waist circumference is the solely anthropometric variable that HOMA-IR predicts: a study of a cohort of young women

Martha Imelda Maldonado-Cervantes1, Jesús Ramón Castillo-Hernández1,2*, María Guadalupe Martel-Gallegos1, Enrique Maldonado-Cervantes1, Nuria Patiño-Marín2, Minerva García-Rangel1, Lucina Torres-Rodríguez3

  1. Doctores en Ciencias, Laboratorio de Biomedicina de la Unidad Académica Multidisciplinaria Zona Media de la Universidad Autónoma de San Luis Potosí. S.L.P Rioverde, México
  2. Doctora en Ciencias, Departamento de Investigación Clínica de la Facultad de Estomatología de la Universidad Autónoma de San Luis Potosí, S.L.P. San Luis Potosí, México.
  3. Doctora en Ciencias, Laboratorio de Especialidades Médicas, de la Facultad de Medicina de la Universidad Autónoma de San Luis Potosí. S.L.P. San Luis Potosí, México.

* Correspondencia: Dr. Jesús Ramón Castillo-Hernández.
Carretera Rioverde-San Ciro, Km 4, El Carmen, CP.79615, Rioverde, S.L.P. México.
Móvil: 444 1156768. Correo: jesus.castillo@uaslp.mx

DOI http://dx.doi.org/10.28960/revmeduas.2007-8013.v12.n3.003

Texto Completo PDF

Recibido 11 de febrero 2022, aceptado 12 de junio 2022

RESUMEN
Objetivo. Evaluar la capacidad de medidas antropométricas para predecir RI, en una cohorte de mujeres jóvenes sin diabetes. --- Material y métodos. Estudio descriptivo, correlacional y cuantitativo de corte transversal. 60 mujeres universitarias de 17 a 20 años, sin diabetes, sin inflamación sistémica fueron estudiadas, todas las participantes firmaron consentimiento informado. Se obtuvieron 9 medidas antropométricas como: índice de masa corporal (IMC), circunferencia de la cintura (CC) y % de grasa corporal (%GC). Se determinaron las concentraciones plasmáticas de glucosa e insulina en ayuno para calcular el HOMA-IR. --- Resultados. El modelo de regresión lineal paso por paso retuvo solo CC como predictor del HOMA-IR; no retuvo al IMC ni al %GC. El modelo tuvo mayor fuerza predictiva entre los grupos de mujeres con sobrepeso/obesidad que en normopeso. Mediante ROC (receiver operating characteristic curve), mostramos que 81.5 cm de CC tuvo la mayor sensibilidad y especificidad, 80.0% y 82.2% respectivamente. --- Conclusiones. CC es una medida antropométrica poderosa y única para predecir RI, sobretodo, en los grupos de sobrepeso y obesidad; se propone como herramienta de escrutinio de RI.
Palabras clave: Antropometría, Obesidad abdominal, Resistencia a la insulina.

ABSTRACT
Objective. To evaluate the capacity of anthropometric measures to predict IR in a cohort of young women without diabetes. --- Material and methods. Descriptive, correlational and quantitative cross-sectional study. 60 university women aged 17 to 20 years, without diabetes or systemic inflammation were studied, all participants signed an informed consent. Nine anthropometric measurements were obtained such as: body mass index (BMI), waist circumference (WC) and% body fat (% BC). Fasting plasma glucose and insulin concentrations were determined to calculate HOMA-IR. --- Results. The successive step linear regression model, retained only WC as a HOMA-IR predictor. Surprisingly, it did not retain BMI or % BF. The model had a greater predictive force among overweight and obese women than those with normal weight. Using ROC (receiver operating characteristic curve) we showed that, 81.5 cm of WC, had the highest sensitivity and specificity, 80.0% and 82.2% respectively. --- Conclusions. WC is a powerful and unique anthropometric measure to predict IR, especially in overweight and obesity groups. We propose that WC be included by primary care physicians as an IR scrutiny tool.
Keywords: Anthropometry, Abdominal obesity, Insulin resistance.

REFERENCIAS

  1. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism 2019; 92:6-10.
  2. Abdullah A, Peeters A, Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract 2010; 89(3): 309-19.
  3. Bogardus C, Lillioja S, Mott DM, Hollenbeck C, Reaven GM. Relationship between degree of obesity and in vivo insulin action in man. Am J Physiol 1985; 248: E286-E291
    4. .
  4. Kim JK. Hyperinsulinemic–euglycemic clamp to assess insulin sensitivity in vivo. Methods Mol Biol 2009; 560: 221-238.
  5. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985; 28(7):412-9.
  6. Suárez GS, Díaz GL, Alvarez CA, López FV, Arias GMT, Prieto DM. Use of anthropometric measures to predict insulin resistance. Endocrinol Nutr 2008; 55(2):69-77.
  7. Gómez GA, Nieto AE, Gómez AC, Figueroa NB, Álvarez AC. Parámetros antropométricos como predictores de resistencia a la insulina en adultos con sobrepeso y obesidad. At Primaria 2010; 42(7):364–371.
  8. Michaelsen VL, Braaten T, Kvaløy K, Melhus M, Broderstad AR. Relationships between metabolic markers and obesity measures in two populations that differ in stature—The SAMINOR Study. Obes Sci Pract 2020; 6(3): 324-339.
  9. Faulkner JA. Physiology of swimming and diving. Res Q 1966; 37(1):41-54.
  10. Fernández RJM, Vayreda M, Casamitjana R, Saez M, Ricart W. Body mass index (BMI) and percent fat mass. A BMI > 27.5 kg/m2 could be indicative of obesity in the Spanish population. Med Clin (Barc) 2001; 117: 681-684.
  11. Wedin WK, Diaz GL, Convit AJ. Prediction of insulin resistance with anthropometric measures: lessons from a large adolescent population. Diabetes Metab Syndr Obes: Targets Therapy 2012; 5: 219–225.
  12. Ferrannini E, Vichi S, Beck NH, Laakso M, Laakso P, Smith G and European Group for the Study of Insulin Resistance. Insulin Action and Age: European Group for the Study of Insulin Resistance (EGIR). Diabetes 1996; 45 (7): 947 - 953.
  13. Srinivasan SR, Myersy L, Berenson GS. Changes in metabolic syndrome variables since childhood in prehypertensive and hypertensive subjects: The Bogalusa Heart Study. Hypertension 2006; 48:33-9.
  14. Reinehr T, Andler W. Changes in the atherogenic risk factor profile according to degree of weight loss. Arch Dis Child 2004; 89:419 – 422.
  15. Keskin M, Kurtoglu S, Kendirci M, Atabek ME, Yazici C. Homeostasis model assessment is more reliable than the fasting glucose/insulin ratio and quantitative insulin sensitivity check index for assessing insulin resistance among obese children and adolescents. Pediatrics 2005; 115(4): e500-3.
  16. Vasques AC, Rosado LE, Galfenas R, Geloneze B. Critical analysis on the use of the homeostasis model assessment (HOMA) indexes in the evaluation of the insulin resistance and the pancreatic beta cells functional capacity. Arq Bras Endocrinol Metabol 2008; 52: 32-9.
  17. Silva de Morais PR, Lima SAL, de Souza VJT, Nogueira NM, Mendonca KL, Rolim-Povoa TI et al. Correlation of Insulin Resistance with Anthropometric Measures and Blood Pressure in Adolescents. Arq Bras Cardiol 2016;106(4):319-326.
  18. Ceballos PJC, Solís MRA, Quevedo CA, López MJJD, Moreno CML. Resistencia a la insulina y su relación con alteraciones bioquímicas y antropométricas en adolescentes con prediabetes. Rev Biomed 2020; 31(1) 22-27.
  19. Niţescu M, Streinu CA, Tusaliu M, Pitigoi D, Otelea M. Correlation between the waist circumference, diastolic blood pressure and insulin resistance in non-obese young adults. Acta Endocrinol (Buc) 2016; XII (4) 493-499.
  20. Lena VG, Adams HB, Peshock R, Willet D, Shah B, Grundy S. Influence of body fat content and distribution on variation in metabolic risk. J Clin Endocrinol Metab 2006; 91(11): 4459–4466.