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Comparison of insulin sensitivity indices for detection of double diabetes in Indian adolescents with type 1 diabetes

  • Chirantap Oza , Anuradha Khadilkar ORCID logo EMAIL logo , Madhura Karguppikar , Ketan Gondhalekar and Vaman Khadilkar

Abstract

Objectives

The role of insulin sensitivity (IS) in the development and progression of metabolic syndrome (MS) in subjects with type-1 diabetes (T1D) is being increasingly recognized. As patients with T1D lack endogenous insulin secretion, measurement of insulin concentration by immunoassay or by indices such as homeostasis model of assessment for insulin resistance (HOMA-IR) is not helpful in assessing IS. Hence, some equations have been developed and validated against data from euglycemic-hyper-insulinemic clamp tests (the gold standard) to estimate IS. 1) To assess IS using available equations (EDC, SEARCH and CACTI) and relationship of IS with MS and microalbuminuria in adolescents with T1D, (2) To compare the predictive value of these equations for detection of MS and derive a cut-off to predict the future risk of development of MS and microalbuminuria and (3) To identify the most accurate non-invasive and easy-to-use equation for detecting patients with double diabetes (DD) in a clinical setting.

Methods

This cross-sectional study included 181 adolescents aged 12–18 years with T1D. Demographic data and laboratory measurements were performed using standard protocols. IS was calculated using following equations:(1) EDC=24.31−12.22×(WHR)−3.29×(hypertension)−0.57×(HbA1c), (2) SEARCH=exp(4.64725−0.02032(waist)−0.09779(HbA1c)−0.00235(Triglycerides), (3)CACTI-exA=exp(4.1075–0.01299×(waist)−1.05819×(insulin dose)−0.00354×(Triglycerides)−0.00802×(DBP)).

Results

IS determined by all three methods had significant negative correlation (p<0.05) with MS as well as with microalbuminuria. The cut-off value of 5.485 mg/kg/min by SEARCH method for determining IS had the highest sensitivity and specificity in identifying MS.

Conclusions

IS by SEARCH equation may be used in routine clinical practice to detect DD in Indian adolescents with T1D at risk of developing metabolic as well as microvascular complications.


Corresponding author: Dr. Anuradha Khadilkar, Deputy Director and Consultant Pediatrician, Hirabai Cowasji Jehangir Medical Research Institute, Block V Lower Basement Jehangir Hospital, 32 Sassoon Road 411001, Pune, India; Senior Pediatric Endocrinologist, Jehangir Hospital, Pune and Bombay Hospital, Pune, India; and Department of Health Sciences, Savitribai Phule Pune University, Pune, Maharashtra, India, Phone: +91 0206057004, E-mail:

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethics approval: The local Institutional Review Board has approved the study.

  6. Availability of data and material: Yes.

  7. Code availability: None.

  8. Consent for publication: The participants have consented to the submission of the article to the journal.

References

1. International Diabetes, Federation. IDF Diabetes Atlas, 9th ed. Brussels, Belgium: International Diabetes Federation; 2019. Available from: https://www.diabetesatlas.org [Accessed Feb 2022].Search in Google Scholar

2. Forlenza, GP, Rewers, M. The epidemic of type 1 diabetes: what is it telling us? Curr Opin Endocrinol Diabetes Obes 2011;18:248–51. https://doi.org/10.1097/med.0b013e32834872ce.Search in Google Scholar

3. Snell-Bergeon, JK, Hokanson, JE, Jensen, L, MacKenzie, T, Kinney, G, Dabelea, D, et al.. Progression of coronary artery calcification in type 1 diabetes: the importance of glycemic control. Diabetes Care 2003;26:2923–8. https://doi.org/10.2337/diacare.26.10.2923.Search in Google Scholar PubMed

4. Cleland, SJ, Fisher, BM, Colhoun, HM, Sattar, N, Petrie, JR. Insulin resistance in type 1 diabetes: what is ‘double diabetes’ and what are the risks? Diabetologia 2013;56:1462–70. https://doi.org/10.1007/s00125-013-2904-2.Search in Google Scholar PubMed PubMed Central

5. Kilpatrick, ES, Rigby, AS, Atkin, SL. Insulin resistance, the metabolic syndrome, and complication risk in type 1 diabetes: “double diabetes” in the diabetes control and complications trial. Diabetes Care 2007;30:707–12. https://doi.org/10.2337/dc06-1982.Search in Google Scholar PubMed

6. Bjornstad, P, Snell-Bergeon, JK, Nadeau, KJ, Maahs, DM. Insulin sensitivity and complications in type 1 diabetes: new insights. World J Diabetes 2015;6:8–16. https://doi.org/10.4239/wjd.v6.i1.8.Search in Google Scholar PubMed PubMed Central

7. Martin, FI, Stocks, AE. Insulin sensitivity and vascular disease in insulin-dependent diabetics. Br Med J 1968;2:81–2. https://doi.org/10.1136/bmj.2.5597.81.Search in Google Scholar PubMed PubMed Central

8. Bjornstad, P, Maahs, DM, Johnson, RJ, Rewers, M, Snell-Bergeon, JK. Estimated insulin sensitivity predicts regression of albuminuria in Type 1 diabetes. Diabet Med 2015;32:257–61. https://doi.org/10.1111/dme.12572.Search in Google Scholar PubMed PubMed Central

9. Cherney, DZ, Sochett, EB. Evolution of renal hyperfiltration and arterial stiffness from adolescence into early adulthood in type 1 diabetes. Diabetes Care 2011;34:1821–6. https://doi.org/10.2337/dc11-0167.Search in Google Scholar PubMed PubMed Central

10. Aronoff, SL, Berkowitz, K, Shreiner, B, Want, L. Glucose metabolism and regulation: beyond insulin and glucagon. Diabetes Spectr 2004;17:183–90. https://doi.org/10.2337/diaspect.17.3.183.Search in Google Scholar

11. 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;28:412–9. https://doi.org/10.1007/bf00280883.Search in Google Scholar PubMed

12. DeFronzo, RA, Tobin, JD, Andres, R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol 1979;237:E214–23. https://doi.org/10.1152/ajpendo.1979.237.3.e214.Search in Google Scholar PubMed

13. Williams, KV, Erbey, JR, Becker, D, Arslanian, S, Orchard, TJ. Can clinical factors estimate insulin resistance in type 1 diabetes? Diabetes 2000;49:626–32. https://doi.org/10.2337/diabetes.49.4.626.Search in Google Scholar PubMed

14. Dabelea, D, D’agostino, RB, Mason, CC, West, N, Hamman, RF, Mayer-Davis, EJ, et al.. Development, validation and use of an insulin sensitivity score in youths with diabetes: the SEARCH for diabetes in youth study. Diabetologia 2011;54:78–86. https://doi.org/10.1007/s00125-010-1911-9.Search in Google Scholar PubMed PubMed Central

15. Duca, LM, Maahs, DM, Schauer, IE, Bergman, BC, Nadeau, KJ, Bjornstad, P, et al.. Development and validation of a method to estimate insulin sensitivity in patients with and without type 1 diabetes. J Clin Endocrinol Metab 2016;101:686–95. https://doi.org/10.1210/jc.2015-3272.Search in Google Scholar PubMed PubMed Central

16. Billow, A, Anjana, RM, Ngai, M, Amutha, A, Pradeepa, R, Jebarani, S, et al.. Prevalence and clinical profile of metabolic syndrome among type 1 diabetes mellitus patients in southern India. J Diabet Complicat 2015;29:659–64. https://doi.org/10.1016/j.jdiacomp.2015.03.014.Search in Google Scholar PubMed

17. Oza, C, Khadilkar, V, Karguppikar, M, Ladkat, D, Gondhalekar, K, Shah, N, et al.. Prevalence of metabolic syndrome and predictors of metabolic risk in Indian children, adolescents and youth with type 1 diabetes mellitus. Endocrine 2022;75:794–803.10.1007/s12020-021-02924-6Search in Google Scholar PubMed

18. Khadilkar, VV, Khadilkar, AV. Revised Indian Academy of Pediatrics 2015 growth charts for height, weight and body mass index for 5-18-year-old Indian children. Indian J Endocrinol Metab 2015;19:470–6. https://doi.org/10.4103/2230-8210.159028.Search in Google Scholar PubMed PubMed Central

19. Warnick, GR, Knopp, RH, Fitzpatrick, V, Branson, L. Estimating low-density lipoprotein cholesterol by the Friedewald equation is adequate for classifying patients on the basis of nationally recommended cutpoints. Clin Chem 1990;36:15–9. https://doi.org/10.1093/clinchem/36.1.15.Search in Google Scholar

20. Alberti, G, Zimmet, P, Kaufman, F, Tajima, N, Silink, M, Arslanian, S, Wong, G, Bennett, P, Shaw, J, Caprio, S. The IDF consensus definition of the metabolic syndrome in children and adolescents. International Diabetes Federation 2007;24:2–930229.Search in Google Scholar

21. Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents; National Heart, Lung, and Blood Institute. Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: summary report. Pediatrics 2011;128(5 Suppl):S213-56.10.1542/peds.2009-2107CSearch in Google Scholar PubMed PubMed Central

22. Unnikrishnan, RI, Rema, M, Pradeepa, R, Deepa, M, Shanthirani, CS, Deepa, R, et al.. Prevalence and risk factors of diabetic nephropathy in an urban South Indian population: the Chennai Urban Rural Epidemiology Study (CURES 45). Diabetes Care 2007;30:2019–24. https://doi.org/10.2337/dc06-2554.Search in Google Scholar PubMed

23. Akobeng, AK. Understanding diagnostic tests 3: receiver operating characteristic curves. Acta Paediatr 2007;96:644–7. https://doi.org/10.1111/j.1651-2227.2006.00178.x.Search in Google Scholar PubMed

24. Zhou, XH, McClish, DK, Obuchowski, NA. Statistical methods in diagnostic medicine. New York: John Wiley & Sons; 2009, 25.Search in Google Scholar

25. Chillarón, JJ, Goday, A, Flores-Le-Roux, JA, Benaiges, D, Carrera, MJ, Puig, J, et al.. Estimated glucose disposal rate in assessment of the metabolic syndrome and microvascular complications in patients with type 1 diabetes. J Clin Endocrinol Metab 2009;94:3530–4. https://doi.org/10.1210/mend.23.9.9996.Search in Google Scholar

26. Thorn, LM, Forsblom, C, Fagerudd, J, Thomas, MC, Pettersson-Fernholm, K, Saraheimo, M, et al.. Metabolic syndrome in type 1 diabetes: association with diabetic nephropathy and glycemic control (the FinnDiane study). Diabetes Care 2005;28:2019–24. https://doi.org/10.2337/diacare.28.8.2019.Search in Google Scholar PubMed

27. Pambianco, G, Costacou, T, Orchard, TJ. The prediction of major outcomes of type 1 diabetes: a 12-year prospective evaluation of three separate definitions of the metabolic syndrome and their components and estimated glucose disposal rate: the Pittsburgh epidemiology of diabetes complications study experience. Diabetes Care 2007;30:1248–54. https://doi.org/10.2337/dc06-2053.Search in Google Scholar PubMed

28. Specht, BJ, Wadwa, RP, Snell-Bergeon, JK, Nadeau, KJ, Bishop, FK, Maahs, DM. Estimated insulin sensitivity and cardiovascular disease risk factors in adolescents with and without type 1 diabetes. J Pediatr 2013;162:297–301. https://doi.org/10.1016/j.jpeds.2012.07.036.Search in Google Scholar PubMed PubMed Central

29. Cano, A, Llauradó, G, Albert, L, Mazarico, I, Astiarraga, B, González-Sastre, M, et al.. Utility of insulin resistance in estimating cardiovascular risk in subjects with type 1 diabetes according to the scores of the steno type 1 risk engine. J Clin Med 2020;9:2192. https://doi.org/10.3390/jcm9072192.Search in Google Scholar PubMed PubMed Central

30. Ferreira-Hermosillo, A, Ibarra-Salce, R, Rodríguez-Malacara, J, Molina-Ayala, MA. Comparison of indirect markers of insulin resistance in adult patients with double diabetes. BMC Endocr Disord 2020;20:87. https://doi.org/10.1186/s12902-020-00570-z.Search in Google Scholar PubMed PubMed Central

31. Reaven, GM. Banting lecture 1988. role of insulin resistance in human disease. Diabetes 1988;37:1595–607. https://doi.org/10.2337/diabetes.37.12.1595.Search in Google Scholar

32. Palomo Atance, E, Ballester Herrera, MJ, Giralt Muiña, P, Ruiz Cano, R, León Martín, A, Giralt Muiña, J. Tasa estimada de disposición de glucosa en pacientes menores de 18 años con diabetes mellitus tipo 1 y sobrepeso-obesidad [Estimated glucose disposal rate in patients under 18 years of age with type 1 diabetes mellitus and overweight or obesity]. Endocrinol Nutr 2013;60:379–85.10.1016/j.endonu.2013.02.005Search in Google Scholar PubMed

33. Epstein, EJ, Osman, JL, Cohen, HW, Rajpathak, SN, Lewis, O, Crandall, JP. Use of the estimated glucose disposal rate as a measure of insulin resistance in an urban multiethnic population with type 1 diabetes. Diabetes Care 2013;36:2280–5. https://doi.org/10.2337/dc12-1693.Search in Google Scholar PubMed PubMed Central

34. Tam, CS, Xie, W, Johnson, WD, Cefalu, WT, Redman, LM, Ravussin, E. Defining insulin resistance from hyperinsulinemic-euglycemic clamps. Diabetes Care 2012;35:1605–10. https://doi.org/10.2337/dc11-2339.Search in Google Scholar PubMed PubMed Central

35. Bîcu, ML, Bîcu, D, Gârgavu, S, Sandu, M, Vladu, MI, Clenciu, D, et al.. 2016 estimated glucose disposal rate (egdr)–a marker for the assessment of insulin resistance in type 1 diabetes mellitus. Rom J 2016;23:177–82. https://doi.org/10.1515/rjdnmd-2016-0021.Search in Google Scholar

36. Šimonienė, D, Platūkiene, A, Prakapienė, E, Radzevičienė, L, Veličkiene, D. Insulin resistance in type 1 diabetes mellitus and its association with patient’s micro- and macrovascular complications, sex hormones, and other clinical data. Diabetes Ther 2020;11:161–74. https://doi.org/10.1007/s13300-019-00729-5.Search in Google Scholar PubMed PubMed Central

37. Teixeira, MM, Diniz Mde, F, Reis, JS, Ferrari, TC, de Castro, MG, Teixeira, BP, et al.. Insulin resistance and associated factors in patients with Type 1 diabetes. Diabetol Metab Syndrome 2014;6:131. https://doi.org/10.1186/1758-5996-6-131.Search in Google Scholar PubMed PubMed Central

Received: 2022-02-10
Accepted: 2022-05-24
Published Online: 2022-06-15
Published in Print: 2022-08-26

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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