Use of Glucose, Insulin, and C-Reactive Protein to Determine Need for Glucose Tolerance Testing

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1 Use of Glucose, Insulin, and C-Reactive Protein to Determine Need for Glucose Tolerance Testing Warren G. Thompson,* Erik J. Bergstralh,* and Jeffrey M. Slezak* Abstract THOMPSON, WARREN G., ERIK J. BERGSTRALH, AND JEFFREY M. SLEZAK. Use of glucose, insulin, and C-reactive protein to determine need for glucose tolerance testing. Obes Res. 2003;11: Objective: Glucose intolerance has been shown to be a better predictor of morbidity and mortality than impaired fasting glucose. However, glucose tolerance tests are inconvenient and expensive. This study evaluated the relative frequencies of glucose intolerance and impaired fasting glucose and sought to determine if 2-hour glucose could be predicted from simple demographic and laboratory data in an obese population. Research Methods and Procedures: Eighty-nine obese subjects (median BMI 35 kg/m 2, range 30 to 40 kg/m 2 ) underwent glucose tolerance testing. Using step-wise linear and logistic regression analysis, fasting glucose, high-sensitivity C-reactive protein (hscrp), fasting insulin, high-density lipoprotein cholesterol, triglycerides, weight, height, BMI, waist circumference, hip circumference, waist-to-hip ratio, sex, and age were assessed as predictors of glucose intolerance. Results: Impaired glucose tolerance was more prevalent (27%) than impaired fasting glucose (5.6%). Only fasting glucose and hscrp were significant (p 0.05) independent predictors of impaired 2-hour glucose ( 140 mg/dl). A fasting glucose 100 mg/dl or an hscrp 0.32 mg/dl (upper quartile of the normal range) detected 81% (sensitivity) of obese subjects with impaired glucose tolerance; however, specificity was poor (46%). Fasting insulin 6 Received for review October 7, Accepted in final form June 17, *Mayo Clinic, Rochester, Minnesota. Address correspondence to Warren G. Thompson, Associate Professor of Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN Thompson.Warren@mayo.edu Copyright 2003 NAASO U/mL had better sensitivity (92%) but poorer specificity (30%). Discussion: Impaired glucose tolerance is more common than impaired fasting glucose in an obese population. Possible strategies to avoid doing glucose tolerance tests in all obese patients would be to do glucose tolerance testing only in those whose fasting glucose is 100 mg/dl or whose hscrp exceeds 0.32 mg/dl or those whose fasting insulin is 6 U/mL. Key words: impaired fasting glucose, impaired glucose tolerance, high sensitivity c-reactive protein Introduction In 1997, the American Diabetes Association recommended that the oral glucose tolerance test (GTT) 1 be abandoned (1). The rationale for this decision was reviewed recently (2). The oral GTT is more expensive, more inconvenient, and has substantially greater variability than fasting glucose measurement. However, important predictive information is lost using this approach. Glucose intolerance, defined as a 2-hour post-gtt 140 mg/dl, is a more sensitive predictor of subsequent diabetes, cardiovascular mortality, and total mortality than is impaired fasting glucose (fasting glucose 110 mg/dl) (3). The relative frequency of impaired glucose tolerance vs. impaired fasting glucose varies from study to study. However, impaired glucose tolerance is more common than impaired fasting glucose in the elderly (4) and in the obese (5,6). Multiple studies have demonstrated that the correlation between fasting glucose and glucose intolerance is poor (3). No study has evaluated whether it might be possible to use lipid parameters, high-sensitivity C-reactive protein (hscrp), or fasting insulin to better predict which obese patients are more likely to have abnormal glucose tolerance. 1 Nonstandard abbreviations: GTT, glucose tolerance test; hscrp, high-sensitivity C-reactive protein; HDL, high-density lipoprotein; ROC, receiver operating curve. OBESITY RESEARCH Vol. 11 No. 8 August

2 The present study evaluates whether any of these parameters are helpful in predicting glucose intolerance. Awareness of this information might allow practitioners to select patients for glucose tolerance testing (and, hence, better predict outcome than with fasting glucose information alone) rather than perform the GTT on every obese patient. Research Methods and Procedures Ninety obese subjects were entered into a randomized trial comparing different diets in the treatment of obesity. Only the baseline (before randomization) readings were used in this report. One patient had incomplete blood data and was excluded, leaving 89 patients for all analyses. Subjects were recruited by placing an announcement in the Mayo employee newsletter, by placing posters near employee elevators, and by discussing the study with primary care physicians at the Mayo Clinic. To be eligible for the study, subjects had to complete a 2-week run-in phase keeping daily food records and exercising four times weekly. Eligibility criteria for the trial were 25 to 70 years old with BMI of 30 to 40 kg/m 2. Each subject underwent a GTT twice within a week. Venous blood was also obtained for hscrp, fasting insulin, triglycerides, and high-density lipoprotein (HDL) cholesterol on two occasions within a week. Insulin was a two-site immunoenzymatic assay performed on the Access automated immunoassay system (Beckman Instruments, Chaska, MN). The Hitachi 912 chemistry analyzer (Roche/Hitachi, Indianapolis, IN) measured the remaining analytes: total cholesterol (Technicon cholesterol reagent, Bayer Corp., Tarrytown, NY), hscrp (latex particle-enhanced immunoturbidimetric assay, Kamiya Biomedical Corp., Seattle, WA), glucose (hexokinase reagent, Boehringer Mannheim, Indianapolis, IN), HDL cholesterol (direct HDL cholesterol plus reagent, Roche Diagnostics, Indianapolis, IN), and triglycerides (Technicon triglyceride reagent, Bayer Corp.). Waist-to-hip ratio and weight were also measured twice. Waist circumference was measured at the umbilicus, and hip circumference was measured at the widest point of the hip/buttocks area with the tape measure parallel to the floor. Impaired fasting glucose was defined as fasting glucose 110 mg/dl (average of two measures). Impaired glucose tolerance was defined as 2-hour glucose level 140 mg/dl on the GTT (average of two measures). The GTT was performed in the standard manner using 75 g of a glucose load in the morning in the fasting state. Duplicate measures of all tests were performed with the average used to reduce variability (thus, reducing misclassification). Data were summarized using medians and quartiles (25th and 75th percentiles). Spearman s rank correlation was used to assess the association of 2-hour glucose with other factors. The study has 80% power to detect correlations as small as 0.30 between quantitative factors, which corresponds to being able to detect predictors that explain as little as 9% of the variability in 2-hour glucose levels. The bivariate and multivariate association of various factors with 2-hour glucose and with 2-hour glucose impairment (defined as 140 mg/dl) were modeled using linear and logistic regression analysis, respectively. Due to excessive skewness, a log transformation was used in the modeling of hscrp, triglycerides, and insulin. Variable selection was done using step-wise backwards selection with p 0.05 required for a variable to remain in the model. Receiver operating curves (ROCs) (plots of sensitivity vs. 1 specificity) for the detection of impaired 2-hour glucose tolerance using fasting glucose, hscrp, and the combination were constructed. Area under the ROC curve was estimated with an area of 1.0 indicating perfect prediction, whereas an area of 0.5 is equivalent to chance prediction. Positive and negative predictive values and associated likelihood ratios (sensitivity/[1 specificity], specificity/[1 sensitivity]) for predicting impaired 2-hour glucose tolerance were also estimated for selected combinations of predictors. All tests were two-sided with p 0.05 said to be statistically significant. Results Eighty-nine subjects entered the study (Table 1). As expected in an obese population, the hscrp levels were high. Forty-six subjects (52%) had hscrp in the upper quartile of the normal range ( 0.32 mg/dl) for our laboratory (7). In bivariate analysis, only fasting glucose, insulin, trigylcerides, HDL, and hscrp were found to be significantly (p 0.05) correlated with 2-hour glucose tolerance levels, whereas BMI was of borderline significance (p 0.087) (Table 1). Step-wise multiple regression analysis found only fasting glucose and hscrp to be selected as significant (p for both) independent predictors of 2-hour glucose (model R 2 28%). The effects [regression coefficient (SE)] of fasting glucose [1.6 (0.35)] and hscrp [6.6(2.5)] remained significant (p 0.01 for both) on adjustment for age, gender, and BMI. Impaired glucose metabolism was common in this population as diagnosed by 2-hour glucose 140 mg/dl but was uncommon as diagnosed by fasting glucose (Table 2). Five subjects had impaired fasting glucose (5.6%), whereas 24 subjects had impaired glucose tolerance (27.0%). No subject was diabetic as classified by fasting glucose 126 mg/dl, but two subjects had 2-hour glucose levels 200 mg/dl. (One-hour blood glucose levels were also 200 in these two subjects.) One of these two subjects had impaired fasting glucose; the other had normal fasting glucose. The variables in Table 1 were evaluated individually to see if they would be useful in predicting 2-hour glucose impairment ( 140) using logistic regression. Insulin (p 0.046), fasting glucose (p 0.043), and hscrp (p 0.063) were the only factors with p values 0.10 (Table 3). ROC 1028 OBESITY RESEARCH Vol. 11 No. 8 August 2003

3 Table 1. Descriptive statistics and associations with 2-hour GTT test in 89 obese subjects Variable Median (25th and 75th) Spearman correlation with 2-hour glucose Female gender [0 no, 1 yes (84%)] 0.01 Age (years) 41 (35, 47) 0.02 Weight (kg) 97 (87, 107) 0.07 Height (cm) 168 (162, 173) 0.08 BMI (kg/m 2 ) 34.5 (31.8, 37.5) 0.18 Waist circumference (cm) 106 (101, 115) 0.14 Hip circumference (cm) 123 (116, 128) 0.09 Waist-to-hip ratio.87 (.85,.93) Hour glucose (mg/dl) 125 (103, 148) NA Fasting glucose (mg/dl) 92 (87, 97) 0.35** Fasting insulin ( U/mL) 8.3 (6.1, 11.2) 0.35** Triglycerides (mg/dl) 133 (101, 173) 0.25* HDL-cholesterol (mg/dl) 41 (35, 47) 0.25* hscrp (mg/dl) 0.35 (0.20, 0.67) 0.26* NA, not applicable. * 0.01 p 0.05 for test of true correlation significantly different from zero. p 0.01 for test of true correlation significantly different from zero. curve area was for insulin, for fasting glucose, and for hscrp. In multivariate step-wise logistic regression, fasting glucose (p 0.030) and hscrp (p 0.044) were found to be significant independent predictors of 2-hour glucose impairment. The combination of fasting glucose and hscrp (based on the logistic regression coefficients) had ROC area of The next best two-variable model included fasting glucose and insulin as predictors (ROC area 0.649). Fasting triglycerides, HDL cholesterol, age, sex, waist-to-hip ratio, weight, and BMI were not significant independent predictors in multivariate analysis in this population with a high and relatively narrow range of BMI (30 to 40 kg/m 2 ). The sensitivities and specificities for selected cut-off points of insulin, hscrp, fasting glucose, or the combination of the latter two for predicting 2-hour glucose levels are given in Table 4. Fasting glucose alone is an inadequate predictor of glucose intolerance. hscrp provides additional information, but the sensitivities and specificities are poor. The combination of an hscrp 0.32 mg/dl or a fasting glucose 100 mg/dl (present in 62% of this obese population) detected 81% of the subjects with glucose intolerance, although specificity was poor (46%) (Table 4). Positive and negative predictive values for this combination were 38% and 85%, respectively. Fasting insulin 6 U/mL had a better sensitivity (92%) but a poorer specificity (30%). Positive and negative predictive values for this strategy were 35% and 90%, respectively. Discussion In an obese population, we found that impaired glucose tolerance (2-hour glucose 140 mg/dl) was much more common (29% vs. 5.6%) than impaired fasting glucose (fasting glucose 110 mg/dl). Similarly, Richard et al. found a prevalence of 22.4% for glucose intolerance vs. only 6% for impaired fasting glucose in 1167 French patients with BMI 30 kg/m 2 (mean 36.6 kg/m 2 ) (6). Mannucci et al. found nearly 3 times as many subjects with impaired glucose tolerance and normal fasting glucose as subjects with impaired fasting glucose and normal glucose tolerance in 528 Italian patients with BMI 30 kg/m 2 (mean 35.9 kg/m 2 ) (5). Thus, in an obese population, reliance on impaired fasting glucose alone would result in missing a substantial fraction of patients at risk for subsequent diabetes and cardiovascular mortality. Table 2. Relationship of fasting glucose with GTT GTT at 2 hours N (row %) Fasting glucose (mg/dl) N Normal (<140 mg/dl) Impaired (141 to 200 mg/dl) Diabetic (>200 mg/dl) Normal ( 110) 84 (94) 61 (73) 22 (26) 1 (1) Impaired (110 to 125) 5 (6) 2 (40) 2 (40) 1 (20) Diabetic (126 ) 0 (0) 0 (0) 0 (0) 0 (0) Total 89 (100) 63 (71) 24 (27) 2 (2) OBESITY RESEARCH Vol. 11 No. 8 August

4 Table 3. Bivariate relationship of insulin, fasting glucose, and C-reactive protein with impaired ( 140 mg/dl) 2-hour GTT Factor Total patients (N) Cumulative %ofn (N 89) Impaired GTT (N) Cumulative %of impaired GTT (N 26) Cumulative % with impaired GTT/cumulative (N) p value* Fasting insulin ( U/mL) % (2/21) 6 to % (10/42) 8 to % (14/61) 10 to % (19/72) % (26/89) Fasting glucose (mg/dl) % (7/31) 90 to % (19/72) 100 to % (23/84) % (26/89) hscrp (mg/dl) % (3/22) 0.20 to % (12/51) 0.40 to % (15/62) 0.60 to % (21/76) % (26/89) % (8/43) % (26/89) * Test for linear trend from bivariate logistic regression model predicting impairment at the 2-hour GTT. One strategy for avoiding missing persons at risk would be to perform GTTs on all obese patients. However, this is expensive, time-consuming, and inconvenient, which explains why such tests are rarely performed in the U.S. anymore. Nevertheless, failure to perform glucose tolerance testing in obese patients will result in missing many highrisk patients, who should receive intensive counseling because diabetes can be prevented (8,9). Therefore, we sought to determine if glucose intolerance could be predicted from more easily measured parameters. The only parameters that predicted 2-hour glucose in bivariate analyses were fasting glucose, fasting insulin, and hscrp. (Other parameters, particularly BMI, would be predictive in populations with a broader range of weights.) It is also possible that the association between these other parameters and 2-hour glucose may be weaker in an obese population than in a population of normal and overweight subjects. Table 4 demonstrates that all have limitations as individual tests (in that either sensitivity or specificity is poor). A study of 100 postmenopausal women in Turkey (10) evaluated the same parameters as the present study with the exception of hscrp. Fasting glucose and BMI were found to be the only predictors of impaired glucose tolerance in that study. A fasting glucose of 98 mg/dl had a sensitivity of 71% and a specificity of 76% in that study. Differences in age and weight most likely account for higher sensitivity of fasting glucose in their study. Several studies have shown that hscrp levels are higher in obese subjects and in subjects with insulin resistance (11 14). Elevated hscrp predicts the subsequent development of both cardiovascular disease (15) and diabetes (16). The present study confirms that hscrp tends to be elevated in obese patients. Forty-six percent of our patients had hscrp levels 0.32 mg/dl (upper quartile of the normal distribution). Our study demonstrates for the first time, to our knowledge, that hscrp is a significant predictor of impaired glucose tolerance. Using hscrp 0.32 mg/dl would detect 69% of those with impaired fasting glucose. However, this sensitivity (69%) is likely too low to be of use clinically OBESITY RESEARCH Vol. 11 No. 8 August 2003

5 Table 4. Prediction of impaired glucose tolerance (IGT) using fasting blood glucose (FBG), fasting insulin and hscrp in obese subjects Criterion for positive test Criterion prevalence Sensitivity Specificity PPV* NPV LR( ) LR( ) Fasting insulin Fasting insulin FBG FBG FBG hscrp hscrp FBG 90 and hscrp FBG 100 and hscrp FBG 100 or hscrp * PPV positive predictive value NPV negative predictive value LR( ) Likelihood ratio for a positive test sensitivity/(1 specificity). LR( ) Likelihood ratio for a negative test specificity/(1 sensitivity). Note: If p prevalence of IGT in the population, then the odds of IGT are defined as p/(1 p). For this study p 0.29 (26 of 89). In general: The odds of IGT given a positive test [ p/(1 p)] LR( ), and the odds of no IGT given a negative test [(1 p)/p] LR( ). The strategy that leads to the greatest area under the ROC is the combination of glucose ( 100 mg/dl) or hscrp ( 0.32 mg/dl). Testing all obese subjects with fasting glucose and hscrp results in a sensitivity of 81%. With a specificity of 46%, this rule results in a substantial number of patients (54%) without impaired glucose tolerance who would be identified as false positives. However, the primary risk for these patients is the time and cost of an oral GTT. In populations similar to ours, this strategy would avoid glucose tolerance testing in 38% of obese subjects (those subjects with fasting glucose 100 mg/dl and hscrp 0.32 mg/dl). It is important to emphasize that the average of two values were used for hscrp, fasting insulin, fasting glucose, and 2-hour glucose to reduce misclassification bias. Results may differ if these parameters are measured only once. The utility of other strategies in obese populations similar to ours can be derived from Table 4. For example, the strategy that would pick up the most cases of abnormal GTTs would be to test all patients with a fasting insulin 6 U/mL. This would pick up 92% (sensitivity) of all patients with glucose intolerance but would result in 76% of the population being tested. It could be argued that this is a better strategy than combining fasting glucose with hscrp. Strategies with specificities 70% (such as fasting glucose 100 mg/dl) result in missing substantial numbers of glucose-intolerant patients. The utility of the GTT continues to be debated (17,18). American studies of middle-aged subjects, such as the San Antonio Heart Study and the Framingham Offspring Study, have not shown much benefit from the oral GTT (19 21). However, studies of populations with a much higher prevalence of impaired GTT than impaired fasting glucose (such as the elderly) have found that 2-hour glucose predicts subsequent diabetes and cardiovascular disease more effectively than fasting glucose alone (22 26). Our study and others demonstrate that impaired glucose tolerance has a much higher prevalence than impaired fasting glucose in the obese and, thus, is likely to be more predictive of future events. Therefore, we believe that the current strategy of not performing GTTs in obese patients is unwise. Performing the test on all obese patients might be the best approach, but it is inconvenient and expensive. Performing the GTT on the 62% of obese patients whose fasting glucose is 100 mg/dl or whose hscrp is 0.32 mg/dl or on the 76% of patients whose fasting insulin is 6 U/mL would detect most of the patients at risk and should be considered as alternative strategies. Acknowledgments This study was funded by the National Dairy Council. Support is also acknowledged from GCRC Grant M01- RR Nicole Rostad and Denise Janzow helped to OBESITY RESEARCH Vol. 11 No. 8 August

6 coordinate the study, and Michael Zemel provided guidance and advice about the design of the study. References 1. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care. 1997;20: Barr RG, Nathan DM, Meigs JB, Singer DE. Tests of glycemia for the diagnosis of type 2 diabetes mellitus. Ann Intern Med. 2002;137: Thompson WG. Failure to diagnose and treat the pre-diabetic state: lessons from etiologic and treatment studies of diabetes and coronary artery disease. Mayo Clin Proc. 2001;76: Barzilay JI, Spiekerman CF, Wahl PW, et al. Cardiovascular disease in older adults with glucose disorders: comparison of American Diabetes Association criteria for diabetes mellitus with WHO criteria. Lancet. 1999;354: Mannucci E, Bardini G, Ognibene A, Rotella CM. Comparison of ADA and WHO screening methods for diabetes mellitus in obese patients. Diabetes Med. 1999;16: Richard JL, Sultan A, Daures JP, Vannereau D, Parer- Richard C. Diagnosis of diabetes mellitus and intermediate glucose abnormalities in obese patients based on ADA (1997) and WHO (1985) criteria. Diabetes Med. 2002;19: McConnell JP, Branum EL, Lagerstedt SA, Katzmann JA, Jaffe AS. Gender differences in C-reactive protein measurement. Clin Chem Lab Med. 2002;40: Tuomilehto J, Lindstrom J, Eriksson JG, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344: Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346: Erenus M, Gurler AD, Elter K. Should we consider performing oral glucose tolerance tests more frequently in postmenopausal women for optimal screening of impaired glucose tolerance? Menopause. 2002;9: Visser M, Bouter LM, McQuillan GM, et al. Elevated C-reactive protein levels in overweight and obese adults. JAMA. 1999;282: Yudkin JS, Stehouwer CDA, Emeis JJ, et al. C-reactive protein in healthy subjects: associations with obesity, insulin resistance, and endothelial dysfunction: a potential role for cytokines originating from adipose tissue? Arterioscler Thromb Vasc Biol. 1999;19: Ford ES. Body mass index, diabetes, and C-reactive protein among U. S. adults. Diabetes Care. 1999;22: Hak AE, Stehouwer CDA, Bots ML, et al. Associations of C-reactive protein with measures of obesity, insulin resistance, and subclinical atherosclerosis in healthy, middleaged women. Arterioscler Thromb Vasc Biol. 1999;19: Danesh J, Whincup P, Walker M, et al. Low grade inflammation and coronary heart disease: prospective study and updated meta-analysis. BMJ. 2000;321: Pradhan AD, Manson JE, Rifai N, Buring JE, Ridker PM. C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. JAMA. 2001;286: Tuomilehto J. Point: a glucose tolerance test is important for clinical practice. Diabetes Care. 2002;25: Davidson MB. Counterpoint: the oral glucose tolerance test is superfluous. Diabetes Care. 2002;25: 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: Stern MP, Fatehi P, Williams K, Haffner SM. Predicting future cardiovascular disease: do we need the oral glucose tolerance test? Diabetes Care. 2002;25: Meigs JB, D Agostino RB, Nathan DM, Wilson PWF. Fasting and postchallenge glycemia and cardiovascular risk: The Framingham Offspring Study. Diabetes Care. 2002;25: Tominaga M, Eguchi H, Manaka H, Igarashi K, Kato T, Sekikawa A. Impaired glucose tolerance is a risk factor for cardiovascular disease, but not impaired fasting glucose: The Funagata Diabetes Study. Diabetes Care. 1999;22: The DECODE Study Group. Glucose tolerance and mortality: comparison of WHO and American Diabetes Association diagnostic criteria. Lancet. 1999;354: Smith NL, Barzilay JI, Shaffer D, et al. Fasting and 2-hour postchallenge serum glucose measures and risk of incident cardiovascular events in the elderly: The Cardiovascular Health Study. Arch Intern Med. 2002;162: Vaccaro O, Ruffa G, Imperatore G, Iovino V, Rivelles AA, Riccardi G. Risk of diabetes in the new diagnostic category of impaired fasting glucose. Diabetes Care. 1999;22: Shaw JE, Zimmet PZ, Hodge AM, et al. Impaired fasting glucose: how low should it go? Diabetes Care. 2000;23: OBESITY RESEARCH Vol. 11 No. 8 August 2003