Variation in Glucose and A1c Measurement: Which Diabetes Test is Best?

Transcription

1 Variation in Glucose and A1c Measurement: Which Diabetes Test is Best? Martha E Lyon, PhD, DABCC, FAACC Division of Clinical Biochemistry Department of Pathology & Laboratory Medicine Saskatchewan Health Authority

2 Disclosures Speaking Honoraria Radiometer (Canada) Nova Biomedical, Draeger Roche Diagnostics (Canada) HemoCue Research Support (Reagents, Instrumentation, Travel) Nova Biomedical Abbott Laboratories (Canada) Roche Diagnostics (Canada) Radiometer (Canada) Instrumentation Laboratories (Canada) ALOL Biomedical Inc. Clinical laboratory consulting company 2 Nov 11, 2018

3 Guidelines for diagnosing diabetes keep changing

4 WHY? Guidelines for diagnosing diabetes keep changing

5 Healthy Disease access Nov 11, accessed Nov 11, Nov 11, 2018

6 Nov 11, 2018 Who is diabetic?

7 Who is diabetic? There is some ambiguity when determining whether a patient is diabetic Nov 11, 2018

8 Diabetes is a group of metabolic disorders associated with hyperglycemia

9 Diabetes is a group of metabolic disorders associated with hyperglycemia

10 Diabetes is a group of metabolic disorders associated with hyperglycemia Diagnosis partly depends on a lab test that is NOT perfect

11 Diabetes is a group of metabolic disorders associated with hyperglycemia Diagnosis partly depends on a lab test that is NOT perfect Some patients will move between healthy and diabetic status (depending upon definitions)

12 Diabetes is a group of metabolic disorders associated with hyperglycemia Diagnosis partly depends on a lab test that is NOT perfect Some patients will move between healthy and diabetic status (depending upon definitions) Patient populations have NOT been constant (obesity epidemic with higher prevalence of disorders like diabetes)

13 Diabetes Societies Adapt and change diagnostic criteria to best serve patients

14 Diabetes Societies Adapt and change diagnostic criteria to best serve patients Changes will likely continue for decades

15 And In the Clinical Laboratory

16 And In the Clinical Laboratory We need to surf the waves of change Adapt and rides the waves Continue to improve the laboratory tests to avoid inconsistencies to provide better patient care accessed Nov 11, 2018

17 The Diabetic Climate is Changing Definition of who is diabetic? (and why) will continue to change accessed Nov 11, 2018

18 Objectives 1) Briefly review the pathophysiology of type 1 and type 2 diabetes and describe the ADA criteria for the diagnosis of diabetes and pre-diabetes using either FPG, A1c or OGTT test results. 2) Describe the agreement amongst FPG, A1c and OGTT to categorize patients using the CDC-NHANES datasets. 3) Describe the effect of individual biological variation and glucose measurement analytical error or A1c analytical error on the categorization of patients using the NHANES datasets and ADA diagnostic criteria.

19 Type 1 Diabetes (Insulin dependent diabetes; Juvenile onset diabetes) Account for 5 to 10% of diabetes Due to autoimmune B cell destruction Environmental factors, genetics and some viruses may contribute to type 1 diabetes Usually leads to absolute insulin deficiency Nov 11, &biw=1440&bih=850 accessed Nov 11, j0i67k1l6j0l j c.1.64.img i8i30k1j35i39k1.0.etlyj7_jpdu#imgrc=d WL4lq9G2aiCGM: accessed Nov 11, 2018

20 Islet Cell Autoantibodies GAD 65 Insulin (IAA) Tyrosine phosphatases IA-2, IA-2β Zinc transporter 8

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22 Type 2 Diabetes (Non-Insulin dependent diabetes; Adult onset diabetes) Account for 90 to 95% of diabetes Progressive loss of β cell function and frequently see insulin resistance. NOT due to autoimmune B cell destruction DKA is seldom seen accessed Nov 11, 2018

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29 What do you do if different tests give different results?

30 What do you do if different tests give different results? First specimen: A1c 6.8% FPG 116 mg/dl

31 What do you do if different tests give different results? First specimen: A1c 6.8% FPG 116 mg/dl Diabetic

32 What do you do if different tests give different results? First specimen: A1c 6.8% FPG 116 mg/dl Diabetic Repeat the A1c measurement

33 What do you do if different tests give different results? First specimen: A1c 6.8% FPG 116 mg/dl Diabetic Repeat the A1c measurement Second specimen: A1c 6.7% This patient should be considered to be diabetic

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35 Diagnostic Tests for Diabetes Fasting plasma glucose 2 hour plasma glucose (75 g oral glucose tolerance test) Hemoglobin A1c accessed Nov 11, accessed Nov 11, 2018

36 Diagnostic Tests for Diabetes Fasting plasma glucose 2 hour plasma glucose (75 g oral glucose tolerance test) Hemoglobin A1c How were the current threshold values determined? accessed Nov 11, accessed Nov 11, 2018

37 Current thresholds are glycemic levels associated with a substantial increased risk of diabetes associated complications (retinopathy) First assessed in 1997 DETECT-2 evaluated newer datasets and re-evaluated the relationship between retinopathy and the three glycemic measures

38 Diabetes specific retinopathy is virtually absent (<0.4%) at low levels of each glycemic measure Optimal threshold for FPG ( mmol/l)m A1c ( %) and 2 hr OGTT ( mmol/l); observed an increase in prevalence

39 How well do the FPG, 2hr post 75 g OGTT and Hemoglobin A1c agree with respect to the categorization of patients?

40 Program of studies designed to assess health and nutritional status of adults in the US Population based survey designed to be representative of the US civilian noninstitutionalized population National Center for Health Statistics release public data sets from the NHANES studies

41 NHANES Biospecimens Glycohemoglobin, fasting plasma glucose and OGTT results obtained on the same subject were analyzed Subjects 12 yo who completed a 9 h fast were asked to participate in OGTT (the same morning) After the initial fasting blood draw, subjects drank 75g dextrose (10 min) and 2 hr later a second blood specimen was collected Glucose measured using the Roche/Hitachi hexokinase method Glycohemoglobin was measured using the Tosoh G8 analyzer Analysis conducted at the Univeristy of Missouri/ Columbia (Dr. Randie Little)

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43 Passing Bablok Regression Hemoglobin A1c (%) with Fasting Plasma Glucose (mg/dl) A1 c (%) Passing-Bablok fit (y = x) 95% CI Correlation - r Parameter Estimate Bootstrap 95% CI Intercept to Slope to FPG (mg/dl)

44 Passing Bablok Regression Hemoglobin A1c (%) with 2 hr OGTT (mg/dl) Correlation - r A1c (%) OGTT (mg/dl) Passing-Bablok fit (y = x) Parameter Estimate Bootstrap 95% CI Intercept to Slope to

45 Passing Bablok Regression Fasting Plasma Glucose (mg/dl) with 2 hr OGTT (mg/dl) Correlation - r FPG (mg/dl) Parameter Estimate Bootstrap 95% CI Intercept to Slope to Passing-Bablok fit (y = x) OGTT (mg/dl)

46 2 hr post 75g OGTT Diabetes 200 mg/dl Hemoglobin A1c 6.5% FPG 126 mg/dl

47 FPG ( 126 mg/dl) n= 106 Diabetic A1c Diabetic = 38 Healthy = 22 Prediabetic = 46 OGTT Diabetic = 58 Healthy = 20 Prediabetic = 28

48 Diagnostic Test and Criteria (Diabetic) Glycemic Test Sensitivity Specificity PPV NPV FPG ( 126 mg/dl) A1c ( 6.5%) 38.0% 99.3% 78.1% 96.5% FPG ( 126 mg/dl) OGTT ( 200 mg/dl) 53.8% 99.0% 49.1% 97.5%

49 ROC Analysis: Diabetes Defined As FPG 126 mg/dl TPF (Sensitivity) FPF (1 - Specificity) No discrimination A1c (0.838) OGTT (0.882) ogtta1c (0.886) Comparison AUC 95% CI SE A1c to OGTT to ogtta1c to Contrast Difference 95% CI SE ogtta1c - A1c to OGTT - A1c to ogtta1c - OGTT to

50 accessed Nov 11, 2018 Alluvial Plot of Titanic Passengers

51 Alluvial Plot FPG and OGTT

52 Alluvial Plot FPG and OGTT 20 (19%) (55%) 28 (26%)

53 Alluvial Plot FPG and OGTT 45% of diabetic patients (FPG) were misclassified as either healthy or prediabetic (OGTT) 20 (19%) (55%) 28 (26%)

54 Alluvial Plot FPG and A1c 22 (21%) (36%) 46 (43%)

55 Alluvial Plot FPG and A1c 64% of diabetic patients (FPG) were misclassified as either healthy or prediabetic (A1c) 22 (21%) (36%) 46 (43%)

56 Pre-Diabetes 2 hr post 75g OGTT 140 mg/dl 199 mg/dl Hemoglobin A1c 5.7% 6.4% FPG 100 mg/dl 125 mg/dl

57 FPG ( mg/dl) n= 943 Prediabetic A1c Diabetic = 15 Healthy = 557 Prediabetic = 371 OGTT Diabetic = 52 Healthy = 649 Prediabetic = 242

58 Alluvial Plot FPG and OGTT (69%) 242 (26%) 52 (5%)

59 Alluvial Plot FPG and OGTT 74% of prediabetic patients (FPG) were 943 misclassified as either healthy or diabetic (OGTT) 649 (69%) 242 (26%) 52 (5%)

60 Alluvial Plot FPG and A1c 557 (59%) (39%) 15 (2%)

61 Alluvial Plot FPG and A1c 61% of prediabetic patients (FPG) were misclassified as either 557 (59%) 943 healthy or diabetic (A1c) 371 (39%) 15 (2%)

62 Healthy 2 hr post 75g OGTT < 140 mg/dl Hemoglobin A1c < 5.7% FPG < 100 mg/dl

63 FPG (<100 mg/dl) n= 1035 Healthy A1c Diabetic = 0 Healthy = 874 Prediabetic = 161 OGTT Diabetic = 11 Healthy = 941 Prediabetic = 83

64 Alluvial Plot FPG and OGTT (91%) 83 (8%) 11 (1%)

65 Alluvial Plot FPG and OGTT (91%) 9% of healthy patients (FPG) were misclassified as either prediabetic and diabetic (OGTT) 83 (8%) 11 (1%)

66 Alluvial Plot FPG and A1c (84%) 161 (16%)

67 Alluvial Plot FPG and A1c (84%) 16% of healthy patients (FPG) were misclassified as prediabetic (A1c) 161 (16%)

68 ROC Analysis: Diabetes Defined As OGTT 200 mg/dl TPF (Sensitivity) No discrimination A1c (0.871) FPG (0.875) fpga1c (0.896) FPF (1 - Specificity) Comparison AUC 95% CI SE A1c to FPG to fpga1c to Contrast Difference 95% CI SE fpga1c - A1c to fpga1c - FPG to FPG - A1c to

69 ROC Analysis: Diabetes Defined As A1c 6.5% TPF (Sensitivity) No discrimination FPG (0.753) Comparison AUC 95% CI SE FPG to OGTT to fpgogtt to OGTT (0.712) fpgogtt (0.743) Contrast Difference 95% CI SE FPG - OGTT to fpgogtt - OGTT to FPG - fpgogtt to FPF (1 - Specificity)

70 Diabetes Diagnostic Test FPG FPG FPG A1c A1c A1c OGTT OGTT OGTT Status Healthy (n=1035) Pre-diabetic (n=943) Diabetic (n=106) Healthy (n=1453) Pre-diabetic (n=578) Diabetic (n=53) Healthy (n=1610) Pre-diabetic (n=353) Diabetic (n=121) OGTT Misclassification A1c Misclassification 9% 16% 74% 61% 45% 64% FPG Misclassification 14% 40% 39% 36% 15% 28% 23% 42% 52% 31% 63% 52%

71 Diabetes Diagnostic Test FPG FPG FPG A1c A1c A1c OGTT OGTT OGTT Status Healthy (n=1035) Pre-diabetic (n=943) Diabetic (n=106) Healthy (n=1453) Pre-diabetic (n=578) Diabetic (n=53) Healthy (n=1610) Pre-diabetic (n=353) Diabetic (n=121) OGTT Misclassification A1c Misclassification 9% 16% 74% 61% 45% 64% FPG Misclassification 14% 40% 39% 36% 15% 28% 23% 42% 52% 31% 63% 52%

72 How will analytical error in glucose and A1c measurement influence the categorization of patients using the NHANES datasets and ADA diagnostic criteria?

73 Monte Carlo Simulation Models Mathematical technique that can provide a range of probabilities for a specific outcome while accounting for error differences in the quantitative analysis

74 Monte Carlo Simulation Models Mathematical technique that can provide a range of probabilities for a specific outcome while accounting for error differences in the quantitative analysis Boyd & Bruns (2001) demonstrated the theoretical (did not use actual patient data) utility of Monte Carlo simulation to estimate the influence of glucose meter bias and imprecision with the clinical risk of administering either too much or too little insulin

75 NHANES Glucose and A1c Analytical Variation Glucose and A1c concentrations (from the NHANES datasets) were modified by the addition of analytical error (bias and imprecision) and biological variation

76 NHANES Glucose and A1c Analytical Variation Glucose and A1c concentrations (from the NHANES datasets) were modified by the addition of analytical error (bias and imprecision) and biological variation After the different combinations of error were introduced the new values were recategorized using the ADA criteria for FPG and then compared with the original value (before error)

77 NHANES Glucose and A1c Analytical Variation Glucose and A1c concentrations (from the NHANES datasets) were modified by the addition of analytical error (bias and imprecision) and biological variation After the different combinations of error were introduced the new values were recategorized using the ADA criteria for FPG and then compared with the original value (before error) Miscategorization rates between healthy, pre-diabetic and diabetic as related to analytical error and biological variation were assessed.

78 NHANES Glucose and A1c Analytical Variation Glucose and A1c concentrations (from the NHANES datasets) were modified by the addition of analytical error (bias and imprecision) and biological variation After the different combinations of error were introduced the new values were recategorized using the ADA criteria for FPG and then compared with the original value (before error) Miscategorization rates between healthy, pre-diabetic and diabetic as related to analytical error and biological variation were assessed. Biological variation: A1c 1.8% and glucose was 5.7%

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80 5.7% CV

81 20% misclassified Glucose Biological Variation 5.7% CV

82 5.7% + 2% CV

83 5% Bias -5% Bias 5.7% + 2% CV

84 10% misclassified 5% Bias -5% Bias 43% misclassified 5.7% + 2% CV

85 Effect of Analytical Variation and Biological Variation on Patient Categorization according to fasting plasma glucose levels Condition Biological Variation (5.7%) Biological Variation + 2% Imprecision & -5% Bias Biological Variation + 2% Imprecision and +5% Bias Healthy to Pre-Diabetic 12% <1% 35% Pre-Diabetic to Diabetic 3% <1% 5% Pre-Diabetic to Healthy 20% 43% 10% Diabetic to Pre-Diabetic 4% 8% <1% Healthy to Diabetic <1% <1% <1% Diabetic to Healthy <1% <1% <1%

86 Effect of Analytical Variation and Biological Variation on Patient Categorization according to fasting plasma glucose levels Condition Biological Variation (5.7%) Biological Variation + 2% Imprecision & -5% Bias Biological Variation + 2% Imprecision and +5% Bias Healthy to Pre-Diabetic 12% <1% 35% Pre-Diabetic to Diabetic 3% <1% 5% Pre-Diabetic to Healthy 20% 43% 10% Diabetic to Pre-Diabetic 4% 8% <1% Healthy to Diabetic <1% <1% <1% Diabetic to Healthy <1% <1% <1%

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88 12% misclassified Hemoglobin A1c Biological Variation 1.8% CV

89 2% misclassified 5% Bias -5% Bias 61% misclassified 1.8% + 2% CV

90 Effect of Analytical Variation and Biological Variation on Patient Categorization according to A1c levels Condition Biological Variation (1.8%) Biological Variation + 2% Imprecision & -5% Bias Biological Variation + 2% Imprecision and +5% Bias Healthy to Pre-Diabetic 7% 8% 25% Pre-Diabetic to Diabetic 1% <1% 17% Pre-Diabetic to Healthy 12% 61% 2% Diabetic to Pre-Diabetic 2% 16% <1% Healthy to Diabetic <1% <1% <1% Diabetic to Healthy <1% <1% <1%

91 Effect of Analytical Variation and Biological Variation on Patient Categorization according to A1c levels Condition Biological Variation (1.8%) Biological Variation + 2% Imprecision & -5% Bias Biological Variation + 2% Imprecision and +5% Bias Healthy to Pre-Diabetic 7% 8% 25% Pre-Diabetic to Diabetic 1% <1% 17% Pre-Diabetic to Healthy 12% 61% 2% Diabetic to Pre-Diabetic 2% 16% <1% Healthy to Diabetic <1% <1% <1% Diabetic to Healthy <1% <1% <1%

92 Conclusions 1)The diagnostic criteria and thresholds used to identify diabetic and pre-diabetic patients has and will continue to evolve over time. 2)Hemoglobin A1c diagnostic thresholds provided a more consistent categorization of pre-diabetic and diabetic patients than OGTT and FPG criteria. 3)Variations in glucose and A1c levels attributed to a combination of individual biological variation and analytical error (bias and CV) can significantly influence the categorization of patients. The highest rates of misclassification occurred from pre-diabetic to healthy and healthy to pre-diabetic.

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