The biological variation of insulin resistance markers: data from the European Biological Variation Study (EuBIVAS).
HOMA
QUICKI index
analytical performance specifications
biological variation
insulin resistance
reference change values
Journal
Clinical chemistry and laboratory medicine
ISSN: 1437-4331
Titre abrégé: Clin Chem Lab Med
Pays: Germany
ID NLM: 9806306
Informations de publication
Date de publication:
12 Jul 2024
12 Jul 2024
Historique:
received:
07
06
2024
accepted:
18
06
2024
medline:
11
7
2024
pubmed:
11
7
2024
entrez:
10
7
2024
Statut:
aheadofprint
Résumé
An insulin resistant state is characteristic of patients with type 2 diabetes, polycystic ovary syndrome, and metabolic syndrome. Identification of insulin resistance (IR) is most readily achievable using formulae combining plasma insulin and glucose results. In this study, we have used data from the European Biological Variation Study (EuBIVAS) to examine the biological variability (BV) of IR using the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and the Quantitative Insulin sensitivity Check Index (QUICKI). Ninety EuBIVAS non-diabetic subjects (52F, 38M) from five countries had fasting HOMA-IR and QUICKI calculated from plasma glucose and insulin samples collected concurrently on 10 weekly occasions. The within-subject (CV The CV The EuBIVAS, by utilising a rigorous experimental protocol, has produced robust BV estimates for two of the most commonly used markers of insulin resistance in non-diabetic subjects. This has shown that HOMA-IR, in particular, is highly variable in the same individual which limits the value of single measurements.
Identifiants
pubmed: 38987271
pii: cclm-2024-0672
doi: 10.1515/cclm-2024-0672
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 Walter de Gruyter GmbH, Berlin/Boston.
Références
Yalow, RS, Berson, SA. Plasma insulin concentrations in nondiabetic and early diabetic subjects: determinations by a new sensitive immuno-assay technic. Diabetes 1960;9:254–60. https://doi.org/10.2337/diab.9.4.254 .
doi: 10.2337/diab.9.4.254
Reaven, G. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 1988;37:1595–607. https://doi.org/10.2337/diab.37.12.1595 .
doi: 10.2337/diab.37.12.1595
DeFronzo, RA, Hendler, R, Simonson, D. Insulin resistance is a prominent feature of insulin-dependent diabetes. Diabetes 1982;31:795–801. https://doi.org/10.2337/diab.31.9.795 .
doi: 10.2337/diab.31.9.795
Eckel, RH, Grundy, SM, Zimmet, PZ. The metabolic syndrome. Lancet 2005;365:1415–28. https://doi.org/10.1016/S0140-6736(05)66378-7 .
doi: 10.1016/S0140-6736(05)66378-7
Ehrmann, DA. Polycystic ovary syndrome. N Engl J Med 2005;352:1223–36. https://doi.org/10.1056/NEJMra041536 .
doi: 10.1056/NEJMra041536
Kahn, SE, Hull, RL, Utzschneider, KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 2006;444:840–6. https://doi.org/10.1038/nature05482 .
doi: 10.1038/nature05482
Seidell, JC. Obesity, insulin resistance and diabetes – a worldwide epidemic. Br J Nutr 2000;83:S5–8. https://doi.org/10.1017/s000711450000088x .
doi: 10.1017/s000711450000088x
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 .
doi: 10.1152/ajpendo.1979.237.3.E214
Bergman, RN. Toward physiological understanding of glucose tolerance: minimal-model approach. Diabetes 1989;38:1512–27. https://doi.org/10.2337/diab.38.12.1512 .
doi: 10.2337/diab.38.12.1512
Matthews, DR, Hosker, JP, Rudenski, AS, Naylor, BA, Treacher, DF, Turner, RC. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412–9. https://doi.org/10.1007/BF00280883 .
doi: 10.1007/BF00280883
Katz, A, Nambi, SS, Mather, K, Baron, AD, Follmann, DA, Sullivan, G, et al.. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 2000;85:2402–10. https://doi.org/10.1210/jcem.85.7.6661 .
doi: 10.1210/jcem.85.7.6661
Fraser, CG. The nature of biological variation. In: Biological variation: from principles to practice . Washington, DC: AACC Press; 2001:1–27 pp.
Fraser, CG, Kallner, A, Kenny, D, Petersen, PH. Introduction: strategies to set global quality specifications in laboratory medicine. Scand J Clin Lab Invest 1999;59:477–8. https://doi.org/10.1080/00365519950185184 .
doi: 10.1080/00365519950185184
Fraser, CG. Reference change values: the way forward in monitoring. Ann Clin Biochem 2009;46:264–5. https://doi.org/10.1258/acb.2009.009006 .
doi: 10.1258/acb.2009.009006
Coskun, A, Sandberg, S, Unsal, I, Cavusoglu, C, Serteser, M, Kilercik, M, et al.. Personalized reference intervals in laboratory medicine: a new model based on within-subjects biological variation. Clin Chem 2021;67:374–84. https://doi.org/10.1093/clinchem/hvaa233 .
doi: 10.1093/clinchem/hvaa233
Carobene, A, Banfi, G, Locatelli, M, Vidali, M. Within-person biological variation estimates from the European Biological Variation Study (EuBIVAS) for serum potassium and creatinine used to obtain personalized reference intervals. Clin Chim Acta 2021;523:205–7. https://doi.org/10.1016/j.cca.2021.09.018 .
doi: 10.1016/j.cca.2021.09.018
Sandberg, S, Carobene, A, Bartlett, B, Coskun, A, Fernandez-Calle, P, Jonker, N, et al.. Biological variation: recent development and future challenges. Clin Chem Lab Med 2022;61:741–50. https://doi.org/10.1515/cclm-2022-1255 .
doi: 10.1515/cclm-2022-1255
Carobene, A, Strollo, M, Jonker, N, Barla, G, Bartlett, WA, Sandberg, S, et al.. Sample collections from healthy volunteers for biological variation estimates’ update: a new project undertaken by the Working Group on Biological Variation established by the European Federation of Clinical Chemistry and Laboratory Medicine. Clin Chem Lab Med 2016;54:1599–608. https://doi.org/10.1515/cclm-2016-0035 .
doi: 10.1515/cclm-2016-0035
Carobene, A, Aarsand, AK, Bartlett, WA, Coskun, A, Diaz-Garzon, J, Fernandez-Calle, P, et al.. The European Biological Variation Study (EuBIVAS): a summary report. Clin Chem Lab Med 2022;60:505–17. https://doi.org/10.1515/cclm-2021-0370 .
doi: 10.1515/cclm-2021-0370
Aarsand, AK, Díaz-Garzón, J, Fernandez-Calle, P, Guerra, E, Locatelli, M, Bartlett, WA, et al.. The EuBIVAS: within- and between-subject biological variation data for electrolytes, lipids, urea, uric acid, total protein, total bilirubin, direct bilirubin, and glucose. Clin Chem 2018;64:1380–93. https://doi.org/10.1373/clinchem.2018.288415 .
doi: 10.1373/clinchem.2018.288415
Carobene, A, Lao, EG, Simon, M, Locatelli, M, Coşkun, A, Díaz-Garzón, J, et al.. Biological variation of serum insulin: updated estimates from the European Biological Variation Study (EuBIVAS) and meta-analysis. Clin Chem Lab Med 2022;60:518–22. https://doi.org/10.1515/cclm-2020-1490 .
doi: 10.1515/cclm-2020-1490
Aarsand, AK, Røraas, T, Fernandez-Calle, P, Ricos, C, Díaz-Garzón, J, Jonker, N, et al.. The biological variation data critical appraisal checklist: a standard for evaluating studies on biological variation. Clin Chem 2018;64:501–14. https://doi.org/10.1373/clinchem.2017.281808 .
doi: 10.1373/clinchem.2017.281808
Bartlett, WA, Braga, F, Carobene, A, Coşkun, A, Prusa, R, Fernandez-Calle, P, et al.. A checklist for critical appraisal of studies of biological variation. Clin Chem Lab Med 2015;53:879–85. https://doi.org/10.1515/cclm-2014-1127 .
doi: 10.1515/cclm-2014-1127
Clouet-Foraison, N, Marcovina, SM, Guerra, E, Aarsand, AK, Coşkun, A, Díaz-Garzón, J, et al.. Analytical performance specifications for lipoprotein(a), Apolipoprotein B-100, and Apolipoprotein A-I using the biological variation model in the EuBIVAS population. Clin Chem 2020;66:727–36. https://doi.org/10.1093/clinchem/hvaa054 .
doi: 10.1093/clinchem/hvaa054
Røraas, T, Støve, B, Petersen, PH, Sandberg, S. Biological variation: the effect of different distributions on estimated within-person variation and reference change values. Clin Chem 2016;62:725–36. https://doi.org/10.1373/clinchem.2015.252296 .
doi: 10.1373/clinchem.2015.252296
Jayagopal, V, Kilpatrick, ES, Jennings, PE, Hepburn, DA, Atkin, SL. Biological variation of homeostasis model assessment-derived insulin resistance in type 2 diabetes. Diabetes Care 2002;25:2022–5. https://doi.org/10.2337/diacare.25.11.2022 .
doi: 10.2337/diacare.25.11.2022
Mather, KJ, Hunt, AE, Steinberg, HO, Paradisi, G, Hook, G, Katz, A, et al.. Repeatability characteristics of simple indices of insulin resistance: implications for research applications. J Clin Endocrinol Metab 2001;86:5457–64. https://doi.org/10.1210/jcem.86.11.7880 .
doi: 10.1210/jcem.86.11.7880
Antuna-Puente, B, Faraj, M, Karelis, AD, Garrel, D, Prud’homme, D, Rabasa-Lhoret, R, et al.. HOMA or QUICKI: is it useful to test the reproducibility of formulas? Diabetes Metab 2008;34:294–6. https://doi.org/10.1016/j.diabet.2008.02.001 .
doi: 10.1016/j.diabet.2008.02.001
Borai, A, Livingstone, C, Farzal, A, Kholeif, M, Wang, T, Ferns, G. Reproducibility of HOMA and QUICKI among individuals with variable glucose tolerance. Diabetes Metab 2010;36:247–9. https://doi.org/10.1016/j.diabet.2010.01.006 .
doi: 10.1016/j.diabet.2010.01.006
Carobene, A, Marino, I, Coşkun, A, Serteser, M, Unsal, I, Guerra, E, et al.. The EuBIVAS project: within- and between-subject biological variation data for serum creatinine using enzymatic and alkaline picrate methods and implications for monitoring. Clin Chem 2017;63:1527–36. https://doi.org/10.1373/clinchem.2017.275115 .
doi: 10.1373/clinchem.2017.275115
Carobene, A, Aarsand, AK, Guerra, E, Bartlett, WA, Coşkun, A, Díaz-Garzón, J, et al.. European Biological Variation Study (EuBIVAS): within- and between-subject biological variation data for 15 frequently measured proteins. Clin Chem 2019;65:1031–41. https://doi.org/10.1373/clinchem.2019.304618 .
doi: 10.1373/clinchem.2019.304618
Bottani, M, Aarsand, AK, Banfi, G, Locatelli, M, Coşkun, A, Díaz-Garzón, J, et al.. European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates for serum thyroid biomarkers based on weekly samplings from 91 healthy participants. Clin Chem Lab Med 2022;60:523–32. https://doi.org/10.1515/cclm-2020-1885 .
doi: 10.1515/cclm-2020-1885
Carobene, A, Aarsand, AK, Coşkun, A, Díaz-Garzón, J, Locatelli, M, Fernandez-Calle, P, et al.. Biological variation of serum iron from the European biological variation study (EuBIVAS). Clin Chem Lab Med 2023;61:e57–60. https://doi.org/10.1515/cclm-2022-1091 .
doi: 10.1515/cclm-2022-1091
Bottani, M, Banfi, G, Guerra, E, Locatelli, M, Aarsand, AK, Coşkun, A, et al.. European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates for serum biointact parathyroid hormone based on weekly samplings from 91 healthy participants. Ann Transl Med 2020;8:855. https://doi.org/10.21037/atm-19-4498 .
doi: 10.21037/atm-19-4498
Sarafidis, PA, Lasaridis, AN, Nilsson, PM, Pikilidou, MI, Stafilas, PC, Kanaki, A, et al.. Validity and reproducibility of HOMA-IR, 1/HOMA-IR, QUICKI and McAuley’s indices in patients with hypertension and type II diabetes. J Hum Hypertens 2007;21:709–16. https://doi.org/10.1038/sj.jhh.1002201 .
doi: 10.1038/sj.jhh.1002201
Harris, EK. Effects of intra-and interindividual variation on the appropriate use of normal ranges. Clin Chem. 1974;20:1535–42. https://doi.org/10.1093/clinchem/20.12.1535 .
doi: 10.1093/clinchem/20.12.1535
Petersen, PH, Fraser, CG, Sandberg, S, Goldschmidt, H. The index of individuality is often a misinterpreted quantity characteristic. Clin Chem Lab Med 1999;37:655–61. https://doi.org/10.1515/CCLM.1999.102 .
doi: 10.1515/CCLM.1999.102
Jayagopal, V, Kilpatrick, ES, Holding, S, Jennings, PE, Atkin, SL. The biological variation of insulin resistance in polycystic ovarian syndrome. J Clin Endocrinol Metab 2002;87:1560–2. https://doi.org/10.1210/jcem.87.4.8404 .
doi: 10.1210/jcem.87.4.8404
Cho, LW, Kilpatrick, ES, Keevil, BG, Coady, AM, Atkin, SL. Effect of metformin, orlistat and pioglitazone treatment on mean insulin resistance and its biological variability in polycystic ovary syndrome. Clin Endocrinol 2009;70:233–7. https://doi.org/10.1111/j.1365-2265.2008.03309.x .
doi: 10.1111/j.1365-2265.2008.03309.x