Prevalence-dependent decision limits for the early detection of type 2 diabetes mellitus in venous blood, venous plasma and capillary blood during glucose challenge
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Rainer Haeckel
Abstract
Background: The glycemia decision limits recommended by WHO/ADA for type 2 diabetes detection are derived from clinical signs in advanced stages of the disease. Since insulin secretion patterns and sensitivitity are impaired at the beginning of type 2 diabetes, this stage may be better suited to identify decision limits with higher diagnostic efficiency than those currently applied.
Methods: Oral glucose tolerance tests were performed in 300 subjects. Glucose concentrations were measured at 30-min intervals in venous plasma, venous blood and capillary blood. Insulin concentrations in venous plasma, an insulin sensitivity index and body mass index were used to indicate a type 2 diabetic state. A multiple logistic regression procedure was “trained” using only subjects “clearly” considered to be non-diseased or diseased based on an oral glucose tolerance test according to WHO criteria. This insulin algorithm was applied to the whole study group, leading to definitive classification into the non-diseased or the diseased group. This a posteriori classification was used to identify cutoff values with the highest diagnostic efficiency.
Results: The diagnostic efficiency was significantly higher when decision limits lower than the WHO recommendations for glucose concentrations were applied in a preselected subpopulation and in all three sample systems tested, e.g., 9.49mmol/L (171mg/dL) for venous plasma and 8.94mmol/L (161mg/dL) for capillary blood in the 2-h post-load state. The optimized and WHO 2-h cutoff values corresponded to a disease prevalence of 28% and ∼5% (20% in the fasting state), respectively. Diagnostic efficiency was higher in the 2-h post-load than in the fasting state. Combining fasting values with 2-h post-load values did not further improve the diagnostic efficiency. Glucose concentrations determined from capillary blood were as efficient as those from venous blood or plasma. The number of diabetic subjects detected differed considerably between capillary blood and venous plasma for the WHO/ADA cutoff values, but not for the optimized cutoff values.
Conclusions: The efficiency of type 2 diabetes diagnosis can be improved by optimizing cutoff values according to disease prevalence. Unexpectedly, the optimized 2-h post-load cutoff was lower for capillary blood than for venous plasma. It is proposed to identify a risk group e.g., by characteristics of the metabolic syndrome in which the 2-h post-challenge concentration is determined using lower cut-off values than presently recommended.
Clin Chem Lab Med 2006;44:1462–71.
References
1. National Diabetes Data Group: Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 1979;28:1039–57.10.2337/diab.28.12.1039Suche in Google Scholar
2. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO consultation. Diabet Med 1998; 15:539–53.10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-SSuche in Google Scholar
3. Schianca GPC, Rossi A, Sainghi PP, Maduli E, Bartoli E. The significance of impaired glucose tolerance. Diabetes Care 2003; 26:1333–7.10.2337/diacare.26.5.1333Suche in Google Scholar
4. Davies MJ, Raymond NT, Day JL, Hales CN, Burden AC. Impaired glucose tolerance and the fasting hyperglycaemia have different characteristics. Diabet Med 2000; 17:433–40.10.1046/j.1464-5491.2000.00246.xSuche in Google Scholar
5. Hanefeld M, Koehler C, Fuecker K, Henkel E, Shaper F, Temelkova-Kurktschiev T. Insulin secretion in isolated impaired glucose tolerance and impaired fasting glucose. Diabetes Care 2003; 26:868–74.10.2337/diacare.26.3.868Suche in Google Scholar
6. Saad MF, Knowler WC, Pettitt DJ, Nelson RG, Mott DM, Bennett PH. The natural history of impaired glucose tolerance in the Pima Indians. N Engl J Med 1988; 319:1500–6.10.1056/NEJM198812083192302Suche in Google Scholar
7. Stern MP, Williams K, Haffner SM. Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med 2002; 136:575–81.10.7326/0003-4819-136-8-200204160-00006Suche in Google Scholar
8. Haeckel R, Wosniok W, Raber R, Janka HU. Detecting type 2 diabetes by a single post-challenge blood sample. Clin Chem Lab Med 2003; 41:1251–8.10.1515/CCLM.2003.192Suche in Google Scholar
9. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003;26:3160–7.10.2337/diacare.26.11.3160Suche in Google Scholar
10. Hosmer DW, Lemeshow ST. Applied logistic regression, 2nd ed. New York: John Wiley & Sons, 2000:1–382.10.1002/0471722146Suche in Google Scholar
11. Cederholm J, Wibell L. Evaluation of insulin release and relative peripheral resistance with use of the oral glucose tolerance test. Scand J Clin Lab Invest 1985; 45:741–55.10.3109/00365518509155289Suche in Google Scholar
12. Cederholm J, Wibell L. Insulin release and peripheral sensitivity at the oral glucose tolerance test. Diabetes Res Clin Pract 1990; 10:167–75.10.1016/0168-8227(90)90040-ZSuche in Google Scholar
13. Haeckel R, Raber R, Wosniok W. Comparability of indices for insulin resistance and insulin sensitivity determined during oral glucose tolerance tests. Clin Chem Lab Med 2006; 44:817–23.10.1515/CCLM.2006.153Suche in Google Scholar PubMed
14. Santner TJ, Duffy DE. A note on A. Altert and J. Anderson's condition for the existence of maximum likelihood estimation in logistic regression models. Biometrika 1986; 73:755–8.10.1093/biomet/73.3.755Suche in Google Scholar
15. Kairisto V, Poola A. Software for illustrative presentation of basic clinical characteristics of laboratory tests. Scand J Lab Invest 1995; 55(Suppl 222):43–60.10.3109/00365519509088450Suche in Google Scholar PubMed
16. Efron B, Tibshirani RJ. An introduction to the bootstrap. New York: Chapman and Hall, 1993.10.1007/978-1-4899-4541-9Suche in Google Scholar
17. Passing H, Bablok W. A new biometrical procedure for testing the equality of measurements from two different analytical methods. J Clin Chem Clin Biochem 1983; 21:709–20.Suche in Google Scholar
18. Haeckel R, Colic D, Wosniok W. Reference interval of serum insulin concentration. Clin Chem Lab Med 2003; 42:1069–70.Suche in Google Scholar
19. Tai MM. A mathematical model for the determination of total area under glucose tolerance and other metabolic curves. Diabetes Care 1994; 17:152–4.10.2337/diacare.17.2.152Suche in Google Scholar PubMed
20. Godsland IF, Jeffs JA, Fohnston DG. Loss of beta cell function as fasting glucose increases in the non-diabetic range. Diabetologia 2004; 47:1157–66.10.1007/s00125-004-1454-zSuche in Google Scholar PubMed
21. Bartoli E, Castello L, Sainaghi PP, Schianca GP. Progression from hidden to overt type 2 diabetes mellitus. Clin Lab 2005; 51:613–24.Suche in Google Scholar
22. Kallner A, Waldenström J. Does the uncertainty of commonly performed glucose measurements allow identification of individuals at high risk for diabetes? Clin Chem Lab Med 1993; 37:907–12.10.1515/CCLM.1999.134Suche in Google Scholar PubMed
23. Stahl M, Brandslund I, Joergensen LG, Hylthoft Petersen P, Borch-Johnsen K, de Fine Olivarius N. Can capillary whole blood and venous plasma glucose measurements be used interchangeably in diagnosis of diabetes mellitus? Scand J Clin Lab Invest 2002; 62:159–66.10.1080/003655102753611799Suche in Google Scholar PubMed
24. Haeckel R, Brinck U, Colic D, Janka HU, Püntmann I, Schneider J, et al. Comparability of blood glucose concentrations measured in different sample systems for detecting glucose intolerance. Clin Chem 2002; 48:936–9.10.1093/clinchem/48.6.936Suche in Google Scholar
25. Butler AE, Janson J, Bonner-Weir S, Ritzel R, Rizza RA, Butler PC. β-Cell deficit and increased β-cell apoptosis in humans with type 2 diabetes. Diabetes 2003; 52:102–10.10.2337/diabetes.52.1.102Suche in Google Scholar PubMed
26. Weyer Ch, Bogardus C, Mott DM, Pratley RE. The natural history of insulin secretory dysfunction and insulin resistance in the pathogenesis of type 2 diabetes mellitus. J Clin Invest 1999; 104:787–94.10.1172/JCI7231Suche in Google Scholar PubMed PubMed Central
27. Piché ME, Arcand-Bossé JF, Desprès JP, Pérusse L, Lemieux S, Weisnagel SJ. What is a normal glucose value? Diabetes Care 2004; 27:2470–7.10.2337/diacare.27.10.2470Suche in Google Scholar PubMed
28. The DECODE study group. Is the current definition for diabetes relevant to mortality risk from all causes and cardiovascular and noncardiovascular diseases? Diabetes Care 2003;26:688–96.10.2337/diacare.26.3.688Suche in Google Scholar PubMed
29. Stern MP, Rosenthal M, Haffner SM. A new concept of impaired glucose tolerance: relation to cardiovascular risk. Arteriosclerosis 1985; 5:311–4.10.1161/01.ATV.5.4.311Suche in Google Scholar
30. Ceriello A, Quagliaro L, Piconi L, Assaloni R, Da Ros R, Maier A, Eposito K, Giugliano D. Effect of postprandial hypertriglyceridemia and hyperglycemia on circulating adhesion molecules and oxidative stress generation and the possible role of simvastatin treatment. Diabetes 2004; 53:701–10.10.2337/diabetes.53.3.701Suche in Google Scholar PubMed
31. Heine RJ, Dekker JM. Beyond postprandial hyperglycaemia: metabolic factors associated with cardiac vascular disease. Diabetes Care 2002; 45:461–75.Suche in Google Scholar
32. Singleton JR, Smith AG, Russell JW, Feldman EL. Microvascular complications of impaired glucose tolerance. Diabetes 2003; 52:2867–73.10.2337/diabetes.52.12.2867Suche in Google Scholar PubMed
33. Schousboe K, Henriksen JE, Kyvik KO, Soerensen TI, Hylthoft Petersen P. Reproducibility of S-insulin and B-glucose responses in two identical glucose tolerance tests. Scand J Clin Invest 2002; 62:623–30.10.1080/003655102764654358Suche in Google Scholar PubMed
34. Qiao Q, Eriksson J, Jousilahti P, Tuomilehto J. Predictive properties of impaired glucose tolerance for cardiovascular risk are not explained by the development of overt diabetes during follow-up. Diabetes Care 2003; 26:2910–4.10.2337/diacare.26.10.2910Suche in Google Scholar PubMed
35. Rathman W, Haastert B, Icks A, Löwel H, Meisinger C, Holle R, et al. High prevalence of undiagnosed diabetes mellitus in Southern Germany: target populations for efficient screening, The KORA survey 2000. Diabetologia 2003; 46:182–9.10.1007/s00125-002-1025-0Suche in Google Scholar
36. Ricos C, Caia F, Garcia-Lario JV, Hernandez A, Iglesias N, Jimenez CV, et al. The reference change value: a proposal to interpret laboratory reports in serial testing based on biological variation. Scand J Clin Lab Invest 2004; 64:175–84.10.1080/00365510410004885Suche in Google Scholar
37. Genuth S, Alberti KG, Bennett P, Buse J, Defronzo R, Kahn R, Kitzmiller J, Knowler WC, Lebowitz H, Lernmark A, Nathan D, Palmer J, Rizza R, Saudek C, Shaw J, Steffes M, Stern M, Tuomiletho J, Zimmet P, the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003; 26:3160–7.10.2337/diacare.26.11.3160Suche in Google Scholar
38. Galton F. Regression towards mediocrity in hereditary stature. J Anthropol Inst 1885; 15:246–65.Suche in Google Scholar
39. Haeckel R, Wosniok W. The phenomenon of regression-towards-the-mean. Clin Chem Lab Med 2004; 42:241–2.10.1515/CCLM.2004.044Suche in Google Scholar
40. Joergensen LG, Stahl M, Brandslund I, Hylthoft Petersen P, Borch-Johnson K, de Fine Olivarius N. Plasma glucose reference interval in a low-risk population. 2. Impact of the new WHO and ADA recommendations on the diagnosis of diabetes mellitus. Scand L Clin Lab Invest 2001; 61:181–90.10.1080/003655101300133621Suche in Google Scholar
41. Rihl J, Biermann E, Standl E. Insulin resistance and type 2 diabetes: the IRIS study. Diabetes Stoffwechsel 2002; 11:150–8.Suche in Google Scholar
42. Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Surveys. J Am Med Assoc 2002; 287:356–9.10.1001/jama.287.3.356Suche in Google Scholar
43. Tuomiletho J. A glucose tolerance test is important for clinical practice. Diabetes Care 2002; 25:1880–2.10.2337/diacare.25.10.1880Suche in Google Scholar
44. Chan AY, Swaminathan R, Cockram CS. Effectiveness of sodium fluoride as a preservative of glucose in blood. Clin Chem 1989; 35:315–7.10.1093/clinchem/35.2.315Suche in Google Scholar
45. Burrin JM, Alberti KG. What is blood glucose. Can it be measured? Diabet Med 1990; 7:199–206.Suche in Google Scholar
46. Morrison B, Fleck A. Plasma or whole blood glucose? Clin Chim Acta 1873; 45:293–7.10.1016/0009-8981(73)90441-5Suche in Google Scholar
47. Rainey PM. Monitoring blood glucose meters. Am J Clin Pathol 1995; 103:125–6.10.1093/ajcp/103.2.125Suche in Google Scholar PubMed
©2006 by Walter de Gruyter Berlin New York
Artikel in diesem Heft
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- National survey on the pre-analytical variability in a representative cohort of Italian laboratories
- 10% CV concentration for the fourth generation Roche cardiac troponin T assay derived from Internal Quality Control data
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- Acknowledgement
- Contents Volume 44, 2006
- Author Index
- Subject Index
Artikel in diesem Heft
- Initiation and progression of atherosclerosis – enzymatic or oxidative modification of low-density lipoprotein?
- Blood transfusions in athletes. Old dogmas, new tricks
- Molecular detection of tyrosinase transcripts in peripheral blood from patients with malignant melanoma: correlation of PCR sensitivity threshold with clinical and pathologic disease characteristics
- Increase in and clearance of cell-free plasma DNA in hemodialysis quantified by real-time PCR
- Lipoprotein lipase gene polymorphism at the PvuII locus and serum lipid levels in Guangxi Hei Yi Zhuang and Han populations
- Interpretation of cardiac troponin T behaviour in size-exclusion chromatography
- Point-of-care C-reactive protein testing in febrile children in general practice
- Improvement in HPLC separation of porphyrin isomers and application to biochemical diagnosis of porphyrias
- Measurement of late-night salivary cortisol with an automated immunoassay system
- Combining markers of nephrotoxicity and hepatotoxicity for improved monitoring and detection of chronic alcohol abuse
- Stone or stricture as a cause of extrahepatic cholestasis – do liver function tests predict the diagnosis?
- Insulin resistance and enhanced protein glycation in men with prehypertension
- Prevalence-dependent decision limits for the early detection of type 2 diabetes mellitus in venous blood, venous plasma and capillary blood during glucose challenge
- Analytical performance and clinical utility of the INNOTEST® PHOSPHO-TAU(181P) assay for discrimination between Alzheimer's disease and dementia with Lewy bodies
- Variations in assay protocol for the Dako cystatin C method may change patient results by 50% without changing the results for controls
- Approved IFCC recommendation on reporting results for blood glucose: International Federation of Clinical Chemistry and Laboratory Medicine Scientific Division, Working Group on Selective Electrodes and Point-of-Care Testing (IFCC-SD-WG-SEPOCT)
- National survey on the pre-analytical variability in a representative cohort of Italian laboratories
- 10% CV concentration for the fourth generation Roche cardiac troponin T assay derived from Internal Quality Control data
- Biological variation of non-SI traceable biological quantities: example of proteins
- Effect of tibolone therapy on lipids and coagulation indices
- Acknowledgement
- Contents Volume 44, 2006
- Author Index
- Subject Index
