Diagnostic Accuracy of Insulin Resistance in Diabetes and Prediabetes

Authors

  • Fiorella Elvira Zuzunaga Montoya Instituto de Investigación de Enfermedades Tropicales, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú. Facultad de Medicina (FAMED), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú https://orcid.org/0000-0002-2354-273X
  • Luisa Erika Milagros Vásquez Romero Instituto de Investigación de Enfermedades Tropicales, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú. Facultad de Medicina (FAMED), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú https://orcid.org/0000-0003-2981-3526
  • Joan Loayza Castro Instituto de Investigación de Enfermedades Tropicales, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú. Facultad de Medicina (FAMED), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú https://orcid.org/0000-0001-6495-6501
  • Carmen Inés Gutierrez De Carrillo Instituto de Investigación de Enfermedades Tropicales, Universidad Nacional Toribio Rodríguez deMendoza de Amazonas (UNTRM), Amazonas, Perú. Facultad de Medicina (FAMED), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú https://orcid.org/0000-0002-4711-7201
  • Enrique Vigil Ventura Instituto de Investigación de Enfermedades Tropicales, Universidad Nacional Toribio Rodríguez deMendoza de Amazonas (UNTRM), Amazonas, Perú. Facultad de Medicina (FAMED), Universidad Nacional Toribio Rodríguez de Mendoza deAmazonas (UNTRM), Amazonas, Perú https://orcid.org/0000-0003-2727-0476
  • Victor Juan Vera Ponce Universidad Tecnológica del Perú, Lima, Perú. https://orcid.org/0000-0003-4075-9049

Keywords:

diabetes mellitus, prediabetic state, hyperglycemia, insulin resistance, public health

Abstract

Introduction: It has been suggested that the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) could serve as an additional test to support and complement existing tests for hyperglycemic states.

Objective: To determine the diagnostic capacity of HOMA-IR for Type 2 diabetes mellitus (T2DM) and prediabetes.

Methods: Diagnostic test study. Both were measured according to elevated fasting; postprandial, and glycosylated hemoglobin levels. The receiver operating characteristic curve (ROC) along with its corresponding area under the curve (AUC) was used to evaluate diagnostic efficacy. Sensitivity and specificity metric were calculated.

Results: The prevalence of prediabetes and T2DM were 18.82% and 10.52%, respectively. The median HOMA-IR was 1.33. HOMA-IR had an AUC of 0.843 for prediabetes in the total population, with a cutoff point of 1.51, a sensitivity of 88.37%, and a specificity of 73.05%. In the case of T2DM, the AUC was 0.907, with a cutoff point of 2.02, a sensitivity of 90.91%, and a specificity of 77.99%.

Conclusions: HOMA-IR demonstrates good diagnostic capability for the detection of prediabetes and T2DM.

Downloads

Download data is not yet available.

Author Biographies

Fiorella Elvira Zuzunaga Montoya, Instituto de Investigación de Enfermedades Tropicales, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú. Facultad de Medicina (FAMED), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú

Médica Investigadora en el área de enfermedades metabólicas y salud pública

Luisa Erika Milagros Vásquez Romero, Instituto de Investigación de Enfermedades Tropicales, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú. Facultad de Medicina (FAMED), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú

Médica investigadora en enfermedades metabólicas

Joan Loayza Castro, Instituto de Investigación de Enfermedades Tropicales, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú. Facultad de Medicina (FAMED), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú

Investigador médico en el área de enfermedades metabólicas

Carmen Inés Gutierrez De Carrillo, Instituto de Investigación de Enfermedades Tropicales, Universidad Nacional Toribio Rodríguez deMendoza de Amazonas (UNTRM), Amazonas, Perú. Facultad de Medicina (FAMED), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Amazonas, Perú

Médico Investigador en áreas de salud publica y enfermedades metabólicas

Enrique Vigil Ventura, Instituto de Investigación de Enfermedades Tropicales, Universidad Nacional Toribio Rodríguez deMendoza de Amazonas (UNTRM), Amazonas, Perú. Facultad de Medicina (FAMED), Universidad Nacional Toribio Rodríguez de Mendoza deAmazonas (UNTRM), Amazonas, Perú

Médico especialista; investiga temas relacionados con enfermedades metabólicas

Victor Juan Vera Ponce, Universidad Tecnológica del Perú, Lima, Perú.

Médico Investigador en el área de enfermedades metabólicas

References

1. Diabetes Canada Clinical Practice Guidelines Expert Committee; Punthakee Z, Goldenberg R, Katz P. Definition, Classification and Diagnosis of Diabetes, Prediabetes and Metabolic Syndrome [Internet]. Can J Diabetes. 2018;42 (Suppl):10-15. DOI: 10.1016/j.jcjd.2017.10.003

2. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition [Internet]. Diabetes Res Clin Pract. 2019;157:107843. DOI: 10.1016/j.diabres.2019.107843

3. Carrillo-Larco R, Bernabé-Ortiz A. Diabetes mellitus tipo 2 en Perú: una revisión sistemática sobre la prevalencia e incidencia en población general. [Internet]. Rev Perú Med Exp Salud Publica. 2019;36(1):26-36. DOI:10.17843/rpmesp.2019.361.4027

4. Seclen SN, Rosas ME, Arias AJ, Huayta E, Medina CA. Prevalence of diabetes and impaired fasting glucose in Peru: report from PERUDIAB, a national urban population-based longitudinal study [Internet]. BMJ Open Diabetes Research and Care. 2015;3(1):e000110. DOI: 10.1136/bmjdrc-2015-000110

5. Sitasuwan T, Lertwattanarak R. Prediction of type 2 diabetes mellitus using fasting plasma glucose and HbA1c levels among individuals with impaired fasting plasma glucose: a cross-sectional study in Thailand. [Internet]. BMJ Open. 2020; 10(11):e041269. DOI: 10.1136/bmjopen-2020-041269

6. Joung KH, Ju SH, Kim JM, Choung S, Lee JM, Park KS, et al. Clinical Implications of Using Post-Challenge Plasma Glucose Levels for Early Diagnosis of Type 2 Diabetes Mellitus in Older Individuals [Internet]. Diabetes Metab J. 2018;42(2):147-54. DOI: 10.4093/dmj.2018.42.2.147

7. Aggarwal M, Verma G, Wahid A, Mathew S, Roat A. Visceral Fat Volume is a Better Predictor of Insulin Resistance than Abdominal Wall Fat Index in Patients with Prediabetes and Type 2 Diabetes Mellitus [Internet]. J Assoc Physicians India. 2022 [acceso: 12/08/2024];70(4):11-2. Disponible en: https://pubmed.ncbi.nlm.nih.gov/35443353/

8. Biernacka-Bartnik A, Kocelak P, Owczarek AJ. The cut-off value for HOMA-IR discriminating the insulin resistance based on the SHBG level in women with polycystic ovary syndrome [Internet]. Front Med. 2023; 10:1100547. DOI: 10.3389/fmed.2023.1100547

9. Cohen JF, Korevaar DA, Altman DG, Bruns DE, Gatsonis CA, Hooft L, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration [Internet]. BMJ Open. 2016; 6(11):e012799. DOI: 10.1136/bmjopen-2016-012799

10. Carrillo-Larco RM, Miranda JJ, Gilman RH, Checkley W, Smeeth L, Bernabe-Ortiz A, et al. The HOMA-IR Performance to Identify New Diabetes Cases by Degree of Urbanization and Altitude in Peru: The CRONICAS Cohort Study [Internet]. Journal of Diabetes Research. 2018; 2018:e7434918. DOI: 10.1155/2018/7434918

11. American Diabetes Association. American Diabetes Association Releases 2023 Standards of Care in Diabetes to Guide Prevention, Diagnosis, and Treatment for People Living with Diabetes [Internet]. Arlington: ADA; 2022. [acceso: 05/08/2023]. Disponible en: https://diabetes.org/newsroom/press-releases/2022/american-diabetes-association-2023-standards-care-diabetes-guide-for-prevention-diagnosis-treatment-people-living-with-diabetes

12. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling [Internet]. Diabetes Care. 2004;27(6):1487-95. DOI: 10.2337/diacare.27.6.1487

13. Mantilla Toloza, S. C., & Gómez-Conesa, A. (2007). El Cuestionario Internacional de Actividad Física. Un instrumento adecuado en el seguimiento de la actividad física poblacional. Revista Iberoamericana de Fisioterapia y Kinesiología, 10(1), 48-52. DOI: 10.1016/S1138-6045(07)73665-1

14. Aggarwal M, Verma G, Wahid A, Mathew S, Roat A. Visceral Fat Volume is a Better Predictor of Insulin Resistance than Abdominal Wall Fat Index in Patients with Prediabetes and Type 2 Diabetes Mellitus [Internet]. J Assoc Physicians India. 2022 [acceso: 31/07/2024];70(4):11-12. Disponible en: https://pubmed.ncbi.nlm.nih.gov/35443353

15. Paracha AI, Haroon ZH, Aamir M, Bibi A. Diagnostic Accuracy of Markers of Insulin Resistance (HOMA-IR) and Insulin Sensitivity (QUICKI) in Gestational Diabetes [Internet]. J Coll Physicians Surg Pak. 2021;31(9):1015-9. DOI: 10.29271/jcpsp.2021.09.1015

16. González-González JG, Violante-Cumpa JR, Zambrano-Lucio M, Burciaga-Jimenez E, Castillo-Morales PL, Garcia-Campa M, et al. HOMA-IR as a predictor of Health Outcomes in Patients with Metabolic Risk Factors: A Systematic Review and Meta-analysis [Internet]. High Blood Press Cardiovasc Prev. 2022;29(6):547-64. DOI:10.1007/s40292-022-00542-5

17. Sajiir H, Wong KY, Müller A, Keshvari S, Burr L, Aiello E, et al. Pancreatic beta-cell IL-22 receptor deficiency induces age-dependent dysregulation of insulin biosynthesis and systemic glucose homeostasis [Internet]. Nat Commun. 2024;15(1):4527. DOI: 10.1038/s41467-024-48320-2

18. Jamjl J. Overnutrition, Hyperinsulinemia and Ectopic Fat: It Is Time for A Paradigm Shift in the Management of Type 2 Diabetes [Internet]. International Journal of Molecular Sciences. 2024;25(10):5488. DOI: 10.3390/ijms25105488

19. Vera-Ponce VJ, Rodas-Alvarado L, Talavera JE, Cruz-Ausejo L, Torres-Malca JR, Vera-Ponce VJ, et al. Asociación entre resistencia a la insulina y proteína C reactiva en una muestra de peruanos no obesos [Internet]. Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo. 2021;14(2):124-7. DOI: 10.35434/rcmhnaaa.2021.142.1021

20. Vera-Ponce VJ, Guerra-Valencia J, Poma MÁ, Loayza-Castro JA, Zeñas-Trujillo GZ, Zuzunaga-Montoya FE, et al. Rendimiento diagnóstico de once indicadores para resistencia a la insulina en una muestra de pobladores peruanos [Internet]. Medicina Clínica y Social. 2023;7(3):168-76. DOI: 10.52379/mcs.v7i3.292

21. Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity [Internet]. Diabetes Care. 2000;23(1):57-63. DOI: 10.2337/diacare.23.1.57

Published

2025-04-23

How to Cite

1.
Zuzunaga Montoya FE, Vásquez Romero LEM, Loayza Castro J, Gutierrez De Carrillo CI, Vigil Ventura E, Vera Ponce VJ. Diagnostic Accuracy of Insulin Resistance in Diabetes and Prediabetes. Rev Cubana Med Milit [Internet]. 2025 Apr. 23 [cited 2025 May 8];54(2):e025059955. Available from: https://revmedmilitar.sld.cu/index.php/mil/article/view/59955