Development and Validation of a Method to Estimate Insulin Sensitivity in Patients With and Without Type 1 Diabetes

ORIGINAL ARTICLE Development and Validation of a Method to Estimate Insulin Sensitivity in Patients With and Without Type 1 Diabetes Lindsey M. Duca, David M. Maahs, Irene E. Schauer, Bryan C. Bergman, Kristen J. Nadeau, Petter Bjornstad, Marian Rewers, and Janet K. Snell-Bergeon Barbara Davis Center for Diabetes (L.M.D., D.M.M., P.B., M.R., J.K.S.-B.), School of Medicine, University of Colorado, Aurora, Colorado 80045; Colorado School of Public Health (L.M.D., D.M.M., J.K.S.-B.), University of Colorado, Aurora, Colorado 80045; Division of Endocrinology, Metabolism, and Diabetes (I.E.S., B.C.B.), Department of Medicine, School of Medicine, University of Colorado, Aurora, Colorado 80045; Division of Pediatric Endocrinology (K.J.N.), Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado 80045; and Denver VA Medical Center (I.E.S.), Denver, Colorado 80220 Context: People with type 1 diabetes () have markedly reduced insulin sensitivity (IS) compared to their nondiabetic counterparts, and reduced IS is linked to higher cardiovascular risk. Objective: This study aimed to develop and validate an improved method for estimating IS in people with. Design: Prospective cohort. Setting: Adults (36 with, 41 nondiabetic) were recruited from the Coronary Artery Calcification in Type 1 Diabetes (CACTI) study for measurement of IS by hyperinsulinemic-euglycemic clamp to develop a clinically useful IS prediction equation (eis) for and nondiabetic individuals. These equations were then compared with previously published equations from the SEARCH and Pittsburgh Epidemiology of Diabetes Complications studies for the ability to predict measured IS in test sets of adults and adolescents from independent clamp studies. Intervention: None. Main Outcome Measure: Comparison of clamp-measured IS to estimated IS. Results: The best-fit prediction model (eis) differed by diabetes status and included waist circumference, triglycerides, adiponectin, and diastolic blood pressure in all CACTI adults and insulin dose in adults with (adjusted R 2 0.64) or fasting glucose and hemoglobin A1c (HbA1c) in nondiabetic adults (adjusted R 2 0.63). The eis highly correlated with clamp-measured IS in all of the non-cacti comparison populations (r 0.83, P.0002 in adults; r 0.71, P.01 in nondiabetic adults; r 0.44, P.008 in adolescents; r 0.44, P.006 in nondiabetic adolescents). Conclusions: eis performed better than previous equations for estimating IS in individuals with and without. These equations could simplify point-of-care assessment of IS to identify patients who could benefit from targeted intervention. (J Clin Endocrinol Metab 101: 686 695, 2016) ISSN Print 0021-972X ISSN Online 1945-7197 Printed in USA Copyright 2016 by the Endocrine Society Received August 26, 2015. Accepted December 9, 2015. First Published Online December 16, 2015 Abbreviations: BMI, body mass index; BP, blood pressure; CVD, cardiovascular disease; DBP, diastolic BP; egdr, estimated glucose disposal rate; eis, IS prediction equation; eis-exa, eis excluding adiponectin (model 3); eis-nf, eis nonfasting (model 2); FFA, fat free mass; GIR, glucose infusion rate; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; HOMA-IR, homeostasis model of assessment for insulin resistance; IS, insulin sensitivity; LDL, low-density lipoprotein; Quicki, quantitative IS check index;, type 1 diabetes; WHR, waist:hip ratio. 686 press.endocrine.org/journal/jcem J Clin Endocrinol Metab, February 2016, 101(2):686 695 doi: 10.1210/jc.2015-3272

doi: 10.1210/jc.2015-3272 press.endocrine.org/journal/jcem 687 Insulin resistance is associated with dyslipidemia (1), hypertension, and hyperglycemia and is a recognized risk factor for cardiovascular disease (CVD) (2 5) and diabetic nephropathy (6 8). In individuals with type 1 diabetes (), insulin resistance is associated with coronary artery calcification (6) and hard cardiovascular events (9), demonstrating that it is an important factor in CVD development. Furthermore, insulin resistance is an important outcome in research on insulin-sensitizing agents (10, 11). Although insulin resistance is a known feature of type 2 diabetes, it is less recognized in patients with, who have fewer classical manifestations of insulin resistance such as obesity and dyslipidemia. However, clear evidence, both historic and recent, exists showing that insulin sensitivity (IS) is reduced by approximately half in both adults and adolescents with (3, 12 14). The euglycemic-hyperinsulinemic clamp is the gold standard for measurement of IS (15), but it is expensive, time-consuming, technically cumbersome, and, therefore, impractical for large epidemiological studies and for clinical use. In nondiabetic individuals and those with noninsulin-dependent diabetes, methods have been developed to estimate IS based on fasting levels of glucose and insulin, including the homeostasis model of assessment for insulin resistance (HOMA-IR) (16) and the quantitative IS check index (Quicki) equation (17). However, estimations of IS among patients with insulin-dependent diabetes using these equations can be problematic because fasting concentrations of glucose and insulin reflect exogenous treatment rather than underlying insulin and glucose metabolism and therefore can yield unreliable estimates of IS based on changes in insulin regimen or diet. Moreover, insulin-deficient patients with are unable to produce insulin in response to glucose challenges in oral glucose tolerance tests and iv glucose tolerance tests, or the insulinmodified frequently sampled iv glucose test and minimal model (18). Thus, there is a need for a reliable, validated, and clinically accessible method for estimating IS in patients with. Several equations for estimating IS in patients with have been developed. One equation was created by the landmark Pittsburgh Epidemiology of Diabetes Complications (EDC) study (13). The EDC equation estimates glucose disposal rate (Pittsburgh egdr) and was developed in adults in the 1990s, before many of the contemporary improvements in diabetes treatment. Also, the Pittsburgh egdr uses the older HbA1 method for estimating chronic glycemia, rather than hemoglobin A1c (HbA1c), the current standard measure (13). A similar equation was developed in the SEARCH for Diabetes in Youth study (19), with the goal of creating a single equation that was applicable to adolescents with, type 2 diabetes, and those without diabetes (SEARCH IS score). However, in light of the apparent differences in the presentation of insulin resistance in type 1 and type 2 diabetes, combining participants with type 1 and type 2 diabetes into a single equation may also be less than ideal for studies focused on alone. Therefore, an IS prediction equation (eis) was developed from and validated in the Coronary Artery Calcification in Type 1 Diabetes study and was then applied to independent populations of youth and adults with clampmeasured IS to assess its performance. In addition, the performance of eis was compared to the previously published SEARCH IS score and the Pittsburgh egdr prediction equations. Subjects and Methods CACTI clamp study population Adults were recruited from the Coronary Artery Calcification in Type 1 Diabetes (CACTI) cohort as previously reported in detail (1, 12, 20 22). Inclusion criteria for this study included ages of 27 61 years at the current visit and, in participants with, insulin requirement within a year of diagnosis, current insulin therapy, diagnosed before age 30 or a physician diagnosis of, and long-standing diabetes (mean duration, 23 8 y). We collected data from 87 adults (40 with and 47 nondiabetic controls, frequency matched for age, gender, and weight) recruited between 2005 and 2008 who underwent a three-stage euglycemic-hyperinsulinemic clamp as previously described (12). Further inclusion criteria for the clamp study included body mass index (BMI) between 18 and 40 kg/m 2, HbA1c 9.5%, blood pressure (BP) 160/100 mm Hg, albumin excretion rate 200 g/min, and triglycerides 400 mg/dl. For the development of the prediction equation, only study participants with an average final steady-state glucose concentration between 85 and 95 mg/dl were included, resulting in 77 study participants (36 with and 41 nondiabetic). The Colorado Multiple Institutional Review Board approved the protocol, and written informed consent was obtained from all study participants. CACTI euglycemic-hyperinsulinemic clamp visit Participants were maintained on a standardized macronutrient composition diet (50% carbohydrate, 20% protein, 30% fat) provided by the Clinical Translational Research Center (CTRC) with total calories based on dual-energy x-ray absorptiometry fat free mass (FFM) for 3 days before their clamp. Participants were admitted to the inpatient CTRC unit before dinner on the evening before their clamp study and fasted overnight and through the clamp protocol. In individuals with, sc insulin was withheld and replaced overnight with an insulin infusion as previously described (12). Blood samples for determination of baseline insulin and glucose concentrations were drawn over 30 minutes before initiation of the clamp protocol. A three-stage euglycemic-hyperinsulinemic clamp was then initiated using the method described previously (12). Briefly, a

688 Duca et al New Method to Estimate Insulin Sensitivity J Clin Endocrinol Metab, February 2016, 101(2):686 695 primed continuous infusion of insulin was administered at 4 mu/m 2 /min for 1.5 hours, 8 mu/m 2 /min for 1.5 hours, and then 40 mu/m 2 /min for the final 1.5 hours. A variable amount of 20% dextrose was infused to maintain blood glucose at a target of 90 mg/dl. Glucose infusion rate (GIR) was determined from the mean steady-state iv dextrose infusion in the final 30 minutes of the clamp and is reported per kilogram of FFM (mg/kg/ffm/ min). GIR was used as the gold-standard method for measured whole-body IS. Independent adult and adolescent comparison populations The adult comparison population consisted of 25 premenopausal women (, n 12; nondiabetic, n 13) from the Women, Insulin and Sex Hormones (WISH) study who completed a three-stage (4, 8, and 40 mu/m 2 /min) hyperinsulinemic-euglycemic clamp using the same methods described for the CACTI cohort (12). The adolescent comparison group consisted of 75 adolescents (, n 36; nondiabetic, n 39) from the RESistance to InSulin in type 1 ANd type 2 diabetes (RESISTANT) and the Effects of Metformin on CaRdiovascular Function in AdoLescents with type 1 Diabetes (EMERALD) studies who completed a three-stage (8, 16, and 80 mu/m 2 /min) hyperinsulinemic-euglycemic clamp (3). Laboratory assays Adiponectin was measured using the RIA methodology (Millipore). Statistical analyses Development of the eis Baseline characteristics were compared by sex and diabetes status using t tests for continuous variables and a 2 test for categorical variables. Test of normality was conducted with the Kolmogorov-Smirnov and Shapiro-Wilks tests. Variables that were positively skewed, not normally distributed, were natural log-transformed for analysis. An eis was first modeled in a block randomized training set of 26 adults with and 32 nondiabetic adults from the CACTI study, and the findings were then applied to and confirmed in a validation set of 10 adults with and nine nondiabetic adults from the CACTI study, based on an a priori power calculation. There were no significant differences between the test and validation sets for age, HbA1c, waist circumference, triglycerides, diabetes duration, or insulin dose. GIR was natural log-transformed for analysis. Easily measured clinical parameters, known to influence IS, that were considered for inclusion in the CACTI model included age, diabetes duration (in individuals), BMI, waist circumference, waist:hip ratio (WHR), systolic and diastolic BP, hypertension (defined as BP 140/90 mm Hg or antihypertensive treatment), total cholesterol, low-density lipoprotein (LDL)-cholesterol, high-density lipoprotein (HDL)-cholesterol, triglycerides, adiponectin, family history of diabetes, fasting free fatty acids, HbA1c, fasting glucose (in nondiabetics), and insulin dose (in participants). Most CACTI study participants were non-hispanic white (95% of and 83% of nondiabetic individuals). There was no difference in measured IS by race in the cohort (P.74 in, P.97 in nondiabetics), so race was not further considered in the modeling. Scatterplots were used to assess whether the independent predictors were linearly associated with GIR, and there was no evidence of a nonlinear relationship. Next, a rigorous process was used to assess the models developed using the clinical parameters above. More specifically, multivariable linear regression models were fit separately for the test set of study participants with and those without diabetes to select the 20 models with the highest adjusted R 2. The best-fit models for each group ( and nondiabetic) were selected from among the models that maximized the adjusted R 2 based on the models best predicting measured IS in the validation set. For variables that were highly collinear (ie, waist circumference and BMI), the variable with the strongest univariate association was used. Interactions by both sex and diabetes were also considered for all variables, and the selected model resulted in different intercepts and factors by diabetes status. The eis equation developed in the training set was tested in the validation set, and the 95% limits of agreement of IS difference by average IS in the validation set were calculated as described by Bland and Altman (23). The correlation of the eis and the clamp-measured IS was examined using Spearman correlation coefficients. An additional step was then performed excluding fasting measures and variables not commonly measured in clinical practice (adiponectin). Evaluating the performance of eis in independent cohorts Once the eis was developed and validated within the CACTI cohort, it was then tested in independent adult and adolescent populations in whom hyperinsulinemic-euglycemic clamp studies were performed. The performance of the three prediction equations (eis, SEARCH IS score, and Pittsburgh egdr) was compared to the GIR from the independent adult and adolescent studies. Clamp-measured IS was compared to estimated IS using Spearman correlation coefficients. Statistical analyses were performed using SAS software, version 9.3, of the SAS System for Windows. P values.05 were considered statistically significant. Results Characteristics of CACTI study participants used to develop the eis are shown in Table 1. Similar to previously reported findings, CACTI adults with and without did not differ in terms of age, BMI, waist circumference, WHR, BP, or prevalence of hypertension (12). Measured IS was significantly lower in both men and women with compared to men and women without diabetes. Spearman coefficients for correlations of IS with the clinical factors used to develop the eis are shown in Appendix Table 1 by diabetes status. The best-fit model (eis) for the CACTI adult participants with (Table 2) included waist circumference, daily insulin dose per kilogram body weight, adiponectin, triglycerides, and diastolic BP (DBP). The

doi: 10.1210/jc.2015-3272 press.endocrine.org/journal/jcem 689 Table 1. Characteristics of CACTI Study Participants CACTI Study (n 36) Controls (n 41) Men (n 19) Women (n 17) Men (n 18) Women (n 23) Age, y 48 10 43 9 47 6 45 8 Diabetes duration, y 24 8 23 9 N/A N/A HbA1c, % 7.5 0.8 b 7.6 1.0 b 5.4 0.4 5.5 0.3 HbA1c, mmol/mol 58 8.7 b 60 10.9 b 36 4.4 37 3.3 Fasting glucose, mg/dl 124 52 110 18 a 100 9 93 6 c Fasting insulin, U/mL 27.4 15.7 37.1 38.3 10.5 5.2 7.6 2.2 c Daily insulin dose, U/kg body weight 0.55 0.14 0.59 0.19 N/A N/A BMI, kg/m 2 28.1 4.2 25.1 4.2 c 27.5 3.7 25.3 4.5 Weight category, % Normal weight, BMI 25 kg/m 2 32 41 17 44 Overweight, BMI 25 29 kg/m 2 26 47 61 48 Obese, BMI 30 kg/m 2 42 12 22 9 Waist circumference, cm 95.1 9.1 81.5 10.5 d 96.5 10.6 79.4 8.6 d WHR 0.89 0.04 0.80 0.07 d 0.92 0.05 0.79 0.05 d Total cholesterol, mg/dl 145 31 a 132 25 b 173 29 172 33 LDL-cholesterol, mg/dl 70 25 64 22 b 103 28 95 27 b HDL-cholesterol, mg/dl 61 30 a 53 10 44 9 59 15 d Triglycerides, mg/dl 70 22 a 73 46 132 76 95 27 Adiponectin, g/ml 11.6 5.4 a 13.1 6.0 7.4 4.3 11.1 5.5 c Systolic BP, mm Hg 118 12 113 9 118 9 110 10 c DBP, mm Hg 75 7 a 73 7 82 10 73 7 c Hypertension, % [n] 65 [12] a,c 29 [5] 22 [4] 13 [3] Steady-state glucose concentration, mg/dl 4.9 0.1 5.0 0.2 5.0 0.1 5.0 0.1 c Steady-state insulin concentration, U/mL 723 217 744 304 612 164 758 217 GIR, mg/kg/ffm/min 5.3 3.7 a 6.1 3.5 b 9.5 4.8 16.1 4.3 d Abbreviation: N/A, not available. Data are presented as mean SD, unless stated otherwise. a P.05, b P.001 for comparison by diabetes status within gender. c P.05, d P.001 for comparison by gender within diabetes group. formula to calculate eis is: exp (4.06154 0.01317 waist [cm] 1.09615 insulin dose [daily units per kg] 0.02027 adiponectin [ g/ml] 0.27168 triglycerides [mmol/l ( 0.00307 for mg/dl)] 0.00733 DBP [mm Hg]). Because patients are not always able to fast for clinical visits, we next removed fasting measures from the best model to fit an additional nonfasting model (eis-nf) ( model 2), which included waist circumference and daily insulin dose. Similarly, because adiponectin is not routinely measured, we then fit an additional model excluding adiponectin (eis-exa) ( model 3), which included waist circumference, daily insulin dose per kilogram body weight, triglycerides, and DBP. Adjusted R 2 for models 1, 2, and 3 were 0.67, 0.61, and 0.67, respectively. Table 2 similarly shows the best-fit model (eis) among participants without diabetes (nondiabetic model 1), which included waist circumference, adiponectin, triglycerides, and DBP as well as both fasting glucose and HbA1c. The equation for this model was: exp (7.47237 0.01275 waist [cm] 0.24990 HbA1c [%] 0.35730 fasting glucose [mmol/l ( 0.01983 for mg/dl)] 0.01905 adiponectin [ g/ml] 0.28673 triglycerides [mmol/l ( 0.00324 for mg/dl)] 0.00588 DBP [mm Hg]). In addition, Table 2 shows the best-fit model not including fasting variables (eis-nf) (nondiabetic model 2) and not including adiponectin (eis-exa) (nondiabetic model 3). Adjusted R 2 for models 1, 2, and 3 were 0.68, 0.65, and 0.68, respectively. We also included a model with the HOMA-IR equation, based on fasting glucose and insulin (nondiabetic model 4) because HOMA-IR was more strongly correlated with measured IS than Quicki (HOMA-IR, r 0.55, P.0001; Quicki, r 0.40, P.05). A file to estimate IS using eis is included (Supplemental Data). The best-fit models developed in the training set were then validated in a separate, a priori, randomly selected group of CACTI clamp study participants. Estimated IS was calculated for each study participant in the validation group and was compared to measured IS from the clamp study. Correlation coefficients were significant for all models (Appendix Table 2). Interactions by sex were

690 Duca et al New Method to Estimate Insulin Sensitivity J Clin Endocrinol Metab, February 2016, 101(2):686 695 Table 2. Linear Regression Models for the Estimation of IS in the CACTI Test Set R 2 R 2 P Value Adjusted Model 1: best fit for individual parameters 0.67 0.64.0001 Constant 4.06154.0001 Waist, cm 0.01317.054 Insulin dose, daily dose per kg body weight 1.09615.128 Adiponectin, g/ml 0.02027.178 Triglycerides, mmol/l (mg/dl) 0.27168 ( 0.00307).055 DBP, mm Hg 0.00733.495 Model 2: best model nonfasting 0.61 0.60.0001 Constant 4.61476.0001 Insulin dose, per daily dose per kg body weight 1.53803.062 Waist, per cm 0.02506.0001 Model 3: best model not including adiponectin 0.67 0.63.0001 Constant 4.1075.0001 Waist (per cm) 0.01299.058 Insulin dose, daily dose per kg body weight 1.05819.072 Triglycerides, mmol/l (mg/dl) 0.31327 ( 0.00354).027 DBP, mm Hg 0.00802.456 controls Model 1: best fit for individual parameters 0.68 0.63.0001 Constant 7.47237.0001 Waist, cm 0.01275.043 Adiponectin, g/ml 0.01905.205 HbA1c, % 0.24990.279 Fasting glucose, mmol/l (mg/dl) 0.35730 ( 0.01983).050 Triglycerides, mmol/l (mg/dl) 0.28673 ( 0.00324).043 DBP, mm Hg 0.00588.586 Model 2: best model nonfasting 0.65 0.61.0001 Constant 6.10604.0001 Male gender 0.21170.044 HbA1c, % 0.28233.237 Waist, cm 0.02293.001 Model 3: Best model not including adiponectin 0.68 0.63.0001 Constant 7.19138.0001 Male gender 0.10173.279 Waist, cm 0.01414.080 HbA1c, % 0.33308.157 Fasting glucose, mmol/l (mg/dl) 0.23243 ( 0.01290).228 Triglycerides, mmol/l (mg/dl) 0.27965 ( 0.00316).054 Model 4: best model including HOMA-IR 0.66 0.63.0001 Constant 4.08207.0001 Male gender 0.03170.842 Waist, cm 0.01673.034 HOMA-IR 0.17438.026 Adiponectin, g/ml 0.02231.238 tested for the variables examined, and none were significant. Characteristics of the independent adult and adolescent populations, used to compare the three IS prediction equations, are shown in Table 3. As expected, the individuals with in both groups had higher HbA1c, fasting glucose, and insulin levels. In the adult population from the WISH Study, women with were slightly older than the nondiabetic women, and they had a significantly higher BMI and waist circumference but lower fasting triglyceride levels. In the adolescents, there was no difference in BMI or waist circumference between the two groups, but WHR was greater in the nondiabetic girls compared to the diabetic girls. Among adolescent girls, total cholesterol and triglycerides were lower in the participants with compared to those without diabetes. As shown in Table 4, the eis was strongly correlated with clamp-measured IS in all participants in the adult and adolescent cohorts. The SEARCH IS score was positively correlated with measured IS in the CACTI study adults and the adolescents but was not significantly correlated with measured IS in the women from the WISH cohort. Pittsburgh egdr correlated with the measured IS only in the nondiabetic CACTI study adults. In the nondiabetic individuals, HOMA-IR was negatively correlated with measured IS in both the CACTI adult and adolescent cohorts, but not in the WISH adult comparison population. eis had stronger correlation coefficients compared to all of

doi: 10.1210/jc.2015-3272 press.endocrine.org/journal/jcem 691 Table 3. Sex Characteristics of Study Participants Used to Compare the Prediction Equations by Diabetes Status and Adolescent Studies WISH Study (n 36) Controls (n 39) (n 12) controls (n 13) Boys (n 17) Girls (n 19) Boys (n 14) Girls (n 25) Women (n 12) Women (n 13) Age, y 16 2 16 2 15 2 15 2 36 8 a 30 7 a Diabetes duration, y 7 4 9 4 N/A N/A 21 11 N/A HbA1c, % 8.7 1.5 b 8.3 1.2 b 5.0 0.4 b 5.2 0.3 b 7.1 1.0 b 5.0 0.3 b HbA1c, mmol/mol 72 16.4 b 67 13.1 b 31 4.4 b 33 3.3 b 54 10.9 b 31 3.3 b Fasting glucose, mg/dl 107 16 b 105 20 b 88 10 b 85 5 b 104 18 a 89 4 a Fasting insulin, U/mL 59 43 a 57 50 a 23 34 a 17 8 a 27.4 25.5 a 10.2 5.0 a Daily insulin dose, U/kg body weight 0.89 0.27 0.86 0.27 N/A N/A 0.51 0.11 N/A BMI, kg/m 2 23.5 4.7 25.5 4.1 25.4 8.9 26.9 6.1 26.9 4.8 a 22.8 2.9 a Waist circumference, cm 82.9 14.3 77.5 9.8 84.5 20.1 88.6 25.6 80.3 9.4 a 73.0 5.8 a WHR 0.87 0.09 c 0.80 0.07 a,c 0.89 0.07 0.87 0.07 a 0.73 0.03 0.71 0.02 Total cholesterol, mg/dl 148 33 135 22 a 154 39 166 32 a 149 20 157 28 LDL-cholesterol, mg/dl 83 19 76 28 82 24 99 26 69 12 68 19 HDL-cholesterol, mg/dl 45 9 49 11 45 7 42 9 46 11 39 9 Triglycerides, mg/dl 83 24 c 66 23 a,c 136 149 132 86 a 51 18 76 26 a Adiponectin, g/ml 10.9 5.5 12.0 3.8 a 11.2 4.0 c 8.7 2.7 a,c 14.3 5.9 13.2 7.3 Systolic BP, mm Hg 122 7 a 117 10 116 9 a 113 8 109 6 106 9 DBP, mm Hg 68 7 70 8 71 10 67 6 71 5 70 7 Hypertension, % [n] e 6 [1] 15 [3] 13 [2] 4 [1] 8 [1] 0 [0] GIR, mg/kg/ffm/min 10.5 4.4 b 11.7 4.3 b 17.0 6.4 b 17.1 5.6 b 8.3 5.1 b 18.3 8.7 b Standardized GIR f 5.3 2.2 b 5.9 2.2 b 8.5 3.2 b 8.5 2.8 b 8.3 5.1 b 18.3 8.7 b Abbreviation: N/A, not available. Data are presented as mean SD. a P.05, b P.001 for comparison by diabetes status within gender. c P.05, d P.001 for comparison by gender within diabetes group. e Hypertension in the adolescent study was defined as systolic and diastolic values greater than the 90th percentile for the child s age, sex, and height. f GIR was standardized by the equation (GIR 80)/40 to account for adolescents receiving a higher dose of insulin compared to the adults. the other IS estimating equations, with the exception of the nondiabetic adolescents. When examining the best estimated IS model using the nonfasting clinical measures (model 2, Table 2), eis-nf was highly positively correlated with clamp-measured IS in both the comparison adult (r 0.77; P.006) and adolescent (r 0.40; P.02) populations with. Additionally, eis-nf was positively correlated with measured IS, although not significantly, in the nondiabetic adults from the WISH study (r 0.46; P.14) and a similar, but Table 4. Spearman Correlation Coefficients for Estimated IS Compared to Measured IS CACTI Study WISH Study Adolescent Studies (n 36) (n 41) (n 12) (n 13) (n 36) (n 39) eis 0.56 0.61 0.83 0.71 0.44 0.44 P value.0005.0001.002.01.008.006 SEARCH IS score 0.51 0.49 0.29 0.53 0.34 0.45 P value.002.001.40.07.04.005 Pittsburgh egdr 0.17 0.55 0.12 0.004 0.27 0.25 P value.33.0002.83.99.12.13 HOMA-IR N/A 0.40 N/A 0.07 N/A 0.49 P value.01.83.001 Abbreviation: N/A, not available. Spearman correlation coefficients are partially adjusted for age. SEARCH IS score: eis exp (4.64725 0.02032 [waist, cm] 0.09779 [HbA1c, %] 0.00235 [triglycerides, mg/dl]). Pittsburgh egdr equation: eis 24.31 12.22 (WHR) 3.29 (hypertension, 0 no; 1 yes) 0.57 (HbA1, %). eis equation: eis exp (4.06154 0.01317 [waist, cm] 1.09615 [insulin dose, daily units per kg] 0.0202 [adiponectin, g/ml] 0.00307 [triglycerides, mg/dl] 0.00733 [DBP, mm Hg]). eis equation: eis exp (7.47237 0.01275 [waist, cm] 0.24990 [HbA1c, %] 0.01983 [fasting glucose, mg/dl] 0.01905 [adiponectin, g/ml] 0.00324 [triglycerides, mg/dl] 0.00588 [DBP, mm Hg]).

692 Duca et al New Method to Estimate Insulin Sensitivity J Clin Endocrinol Metab, February 2016, 101(2):686 695 significant, relationship was observed in the adolescents without diabetes (r 0.45; P.004) (Appendix Table 3). When examining the best eis model excluding adiponectin (model 3, Table 2), eis-exa was positively correlated with measured IS in both the and nondiabetic adults from the WISH study (, r 0.79, P.004; nondiabetic, r 0.58, P.04) and adolescents (, r 0.50, P.002; nondiabetic, r 0.44, P.005) (Appendix Table 3). Discussion In the present study, we developed and tested prediction equations for IS using easily measured clinical factors that explained 61 64% of the variance in measured insulin resistance. Our study is unique in that it included adults both with and without in generating IS estimation equations for each population. Moreover, when applied to independent cohorts of adults and adolescents, eis from the CACTI study showed better agreement with clamp-measured IS in a contemporary independent cohort than the previously published prediction equations from the SEARCH and EDC studies, possibly due to differences in factors affecting IS in adolescents who are obese or have type 2 diabetes and changes in the population of individuals with over the past 20 years or more, respectively. Our data suggest that the CACTI prediction equations can be broadly applied to patients with and without using common clinical measures. Using identical clamp methods, separate prediction equations were developed for individuals with and without, allowing for differing strengths of association for parameters as well as the inclusion of factors specific to each group, such as daily insulin dose in people with and fasting insulin and glucose in nondiabetic individuals. Although elevated fasting glucose, insulin, and HbA1c all predicted insulin resistance in adults without diabetes, there was no association between glycemic control as measured by HbA1c and insulin resistance in participants with in the CACTI study (12) or in the WISH cohort (unpublished) or any of our adolescent studies (3). This is in contrast to 20th century studies that showed associations of HbA1 and insulin resistance at worse levels of glucose control than those of our study participants, potentially due to corresponding metabolic dysfunction and elevated counter-regulatory hormones in the setting of marked hyperglycemia (25, 26). Daily insulin dose was a strong predictor of the degree of insulin resistance in the CACTI, WISH, and adolescent study cohorts. In support of these findings, the pre-diabetes Complications and Control Trial clamp studies showed improved IS and insulin dose without changing HbA1 (27). The performance of all of the CACTI models (eis, eis-nf, and eis-exa) was similar, and these results demonstrate that they are robust and capable of estimating IS using a variety of easily obtainable clinical measures. Furthermore, in comparison to the published SEARCH IS score and Pittsburgh egdr equations, the eis performed better in adults with and without, adolescents with, and similarly to the SEARCH IS score equation, in nondiabetic adolescents. When the Pittsburgh egdr equation was tested in the independent comparison groups, it did not correlate significantly with the clamp-measured IS among adults or adolescents with, but it did correlate with measured IS in the nondiabetic CACTI study participants, most likely due to the stronger relationship between HbA1c and IS in these groups. Also in contrast to the results from the egdr equation (13), HbA1c was not included in the eis best-fit model among patients with ; instead, insulin dose and triglycerides were included. Furthermore, in CACTI, waist circumference and DBP were stronger predictors than WHR and hypertension, respectively. There are several potential explanations for these differences. The CACTI study population was 10 years older than the EDC study population, although age alone is not likely the main explanation because eis also performed better in adolescents. CACTI participants on average, had relatively good glycemic control, with a mean HbA1c of 7.6% compared to 9.5% in the EDC study, although again our adolescents were not as well controlled (mean HbA1c, 8.5%; upper limit, 12%). However, it should be noted that both our model and the Pittsburgh egdr model demonstrated that insulin resistance in relates to abdominal adiposity and BP. Additionally, the participants in the EDC study were selected based on predetermined cutoffs of triglycerides, HDL-cholesterol, WHR, and HbA1 to obtain a balanced group of study participants in each tertile of predicted IS, assumptions that may have influenced the results. Our study participants, in contrast, were not screened on variables thought to influence IS. Notably, triglyceride levels were dramatically lower in our study, which better reflects the broad population of patients seen today (28). Overall, our study was able to improve on the adjusted R 2 reported in the EDC study (R 2 0.571) in both the (R 2 0.64) and nondiabetic (R 2 0.63) participants. More importantly, eis more

doi: 10.1210/jc.2015-3272 press.endocrine.org/journal/jcem 693 accurately estimated clamp-measured IS when compared to the Pittsburgh egdr equation. In adolescents with type 1 and type 2 diabetes combined, we recently reported in the SEARCH study that waist circumference, triglycerides, and HbA1c predict IS as measured by an 80 U/m 2 euglycemic-hyperinsulinemic clamp (19). It is likely that HbA1c is a more important factor predicting IS in adolescents with type 2 diabetes, perhaps accounting for these different findings. Moreover, of the variables in the SEARCH model, HbA1c explained the least variance in measured IS. Despite the inclusion of HbA1c, the SEARCH IS score equation still correlated with measured IS in both the adolescents and CACTI adults with and without, most likely due to the strong influence of waist circumference and triglycerides. However, the strength of association for the SEARCH IS score was significantly less than that for the eis equation when tested in the independent adult comparison cohort and similar in magnitude in the adolescent comparison cohort. Although we report the largest study to date to develop a prediction equation for IS in adults both with and without and then compare it to other published equations in independent cohorts of adults and adolescents, there are several important limitations to our study. The current study focused on simple clinical measurements potentially related to insulin resistance, and other variables that were not assessed in the current study might better predict IS. Moreover, adiponectin Appendix Table 1. Spearman Correlation Coefficients of Clinical Parameters and log GIR, as a Measure of IS, in CACTI (n 36) Controls (n 41) Age 0.12 0.22 Diabetes duration 0.22 N/A HbA1c 0.008 0.15 Fasting glucose 0.28 0.49 a Fasting insulin 0.41 a 0.34 a Daily insulin dose, per kg body 0.32 a N/A weight BMI 0.53 a 0.33 a Waist circumference 0.48 a 0.55 b WHR 0.36 a 0.62 b Total cholesterol 0.02 0.004 LDL-cholesterol 0.13 0.13 HDL-cholesterol 0.28 0.49 a Triglycerides 0.48 a 0.30 Adiponectin 0.33 a 0.35 a Systolic BP 0.02 0.30 DBP 0.29 0.32 a Abbreviation: N/A, not available. a P.05. b P.001. Appendix Table 2. Spearman Correlation Coefficients of Estimated IS Compared to Measured IS from the CACTI Clamp Study, in the CACTI Validation Group (n 10) Model 1, full model 0.69 a 0.77 a Model 2, nonfasting 0.66 a 0.70 a Model 3, excluding adiponectin 0.69 a 0.75 a Model 4, including HOMA-IR N/A 0.72 a Abbreviation: N/A, not available. a P.05. Controls (n 9) currently does not have a standardized assay but remained in the best-fit model because it contributed to the prediction of IS independent of adiposity and significantly improved the model fit. Adiponectin is becoming more widely used, but we realize the limitation in the assay and thus derived models excluding adiponectin. Additionally, there may be certain populations in which eis does not perform well. However, the main strength of this study was that eis was developed for estimating IS using simple, clinically available mea- Appendix Table 3. Spearman Correlation Coefficients Estimated IS From Models 2 and 3 Compared to Measured IS WISH Study (n 12) (n 13) Adolescent Studies (n 36) Model 1, 0.83 0.71 0.44 0.44 eis P value.002.01.008.006 Model 2, 0.77 0.46 0.40 0.45 eis-nf P value.006.14.02.004 Model 3, 0.79 0.58 0.50 0.44 eis-exa P value.004.04.002.005 (n 39) Model 1 (best-fit), : eis exp (4.06154 0.01317 [waist, cm] 1.09615 [insulin dose, daily units per kg] 0.0202 [adiponectin, g/ ml] 0.00307 [triglycerides, mg/dl] 0.00733 [DBP, mm Hg]). : eis exp (7.47237 0.01275 [waist, cm] 0.24990 [HbA1c, %] 0.01983 [fasting glucose, mg/dl] 0.01905 [adiponectin, g/ml] 0.00324 [triglycerides, mg/dl] 0.00588 [DBP, mm Hg]). Model 2 (nonfasting), : eis exp (4.61476 1.53803 [insulin dose, daily units per kg] 0.02506 [waist, cm]). : eis exp (6.10604 0.21170 [male] 0.28233 [HbA1c, %] 0.02293 [waist, cm]). Model 3 (excluding adiponectin), : eis exp (4.1075 0.01299 [waist, cm] 1.05819 [insulin dose, daily units per kg] 0.00354 [triglycerides, mg/dl] 0.00802 [DBP, mm Hg]). : eis exp (7.19138 0.10173 [male] 0.01414 [waist, cm] 0.33308 [HbA1c, %] 0.01290 [fasting glucose, mg/dl] 0.00316 [triglycerides, mg/dl]).

694 Duca et al New Method to Estimate Insulin Sensitivity J Clin Endocrinol Metab, February 2016, 101(2):686 695 sures for adults with and without. Another strength is that the eis also performed well in adolescents, despite the wider range in HbA1c, greater degree of insulin resistance, shorter duration of diabetes, and other adolescent-specific factors characteristic of this population. The sample size used to develop eis was similar to that used to develop the previously published models (eis, n 36 participants; Pittsburgh egdr, n 24; SEARCH IS score, n 39). Insulin resistance is increasingly being recognized as a risk factor for CVD and other complications of diabetes, but due to the difficulty of performing clamp studies, it is not practical to measure IS directly in large epidemiological studies. The application of an equation to estimate IS using easily measured clinical factors could therefore be used to examine further the relationship of IS with complications and the impact of interventions on IS in people with. In addition, this equation could be used to identify those at highest risk of complications and allow individualization of intensified preventive measures, thus providing an immediate clinical application for a pointof-care assessment of IS. Acknowledgments Address all correspondence and requests for reprints to: Lindsey M. Duca, Barbara Davis Center for Childhood Diabetes, School of Medicine, University of Colorado Denver, Box A-140, 1775 Aurora Court, Aurora, CO, 80045. E-mail: Lindsey.Duca@ucdenver.edu. The study was performed at the Barbara Davis Center for Childhood Diabetes in Denver, Colorado, and at the Clinical Translational Research Centers (CTRC) at the University of Colorado and Children s Hospital Colorado and was supported by the National Institutes of Health (NIH) Grant M01 RR000051 and Colorado Clinical and Translational Sciences Institute (CCTSI) Grant UL1 TR000154. Support was provided by the NIH National Heart, Lung and Blood Institute Grants R01 HL61753, R01 HL079611, and R01 HL11309; the American Diabetes Association Junior Faculty Award 1-10-JF-50 and 7-13-CD-10 (to J.K.S.-B.), American Diabetes Association Grant 7-11-CD-08, the Juvenile Diabetes Research Foundation Grant 11-2010-343 and 17-2013-313, NIH Building Interdisciplinary Research Careers in Women s Health (BIRCWH) Grant K12 5K12HD057022-04, the National Center for Research Resources Grant K23 RR020038-01, NIH Grant R56 DK088971 (to K.J.N.), Office of Research in Women s Health BIRCWH K12 Program (to I.E.S.), and Diabetes Endocrinology Research Center Clinical Investigation Core Grant P30 DK57516. Author Contributions: J.K.S.-B. and L.M.D. researched data, analyzed data, and wrote the manuscript. D.M.M., P.B., and I.E.S. researched data, contributed to the discussion, and reviewed the manuscript. 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