A Risk Assessment Tool for Undetected Hyperglycemia Richelle J. Koopman, MD, MS** Arch G. Mainous III, PhD* Arch G. Mainous III, PhD* Charles J. Everett, PhD* Rickey E. Carter, PhD+ **Department of Family and Community Medicine, University of Missouri, Columbia *Department of Family Medicine, Medical University of South Carolina +Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina

Increasing Prevalence of Diabetes, United States

Undiagnosed Diabetes, 2005

PreDiabetes

Why Focus on Pre-Diabetes? Undiagnosed diabetes already with complications Undiagnosed diabetes already with complications Early retinopathy: 20.8% (Harris, 1992) Early retinopathy: 20.8% (Harris, 1992) Microalbuminuria: 21.5% (Koopman, 2006) Microalbuminuria: 21.5% (Koopman, 2006) Insensate feet: 24.9% (Koopman, 2006) Insensate feet: 24.9% (Koopman, 2006) Progression of IFG to DM is ~2% per year (Nichols, 2007) Progression of IFG to DM is ~2% per year (Nichols, 2007)

Why Focus on Pre-Diabetes? Pre-diabetes states associated with risk Pre-diabetes states associated with risk Urinary incontinence (Brown, 2006) Urinary incontinence (Brown, 2006) Endothelial dysfunction (Vehkavaara, 1999) Endothelial dysfunction (Vehkavaara, 1999) CAD (Nielson, 2006) CAD (Nielson, 2006) We can prevent DM with lifestyle modifications and medications (DPP, 2002; DREAM, 2006) We can prevent DM with lifestyle modifications and medications (DPP, 2002; DREAM, 2006)

Can We Assess Risk for Pre-Diabetes? Want an assessment tool that doesn’t need laboratory data Want an assessment tool that doesn’t need laboratory data Based on history and easily measured factors like blood pressure or weight Based on history and easily measured factors like blood pressure or weight Want a tool designed in younger populations to find pre-diabetes Want a tool designed in younger populations to find pre-diabetes Concentrate on IFG, rather than IGT Concentrate on IFG, rather than IGT Tool that can be used with an EHR? Tool that can be used with an EHR?

Risk Assessment Tools Synthesize effects of several risk factors Synthesize effects of several risk factors Can aid clinical decision-making Can aid clinical decision-making Help estimate pre-test probability Help estimate pre-test probability Validation Validation Use in EHR ? Use in EHR ?

What Risk Scores Exist? Danish Diabetes Risk Score Danish Diabetes Risk Score To Predict Undiagnosed Diabetes To Predict Undiagnosed Diabetes Includes Age, Gender, BMI, Hx HTN, physical activity, and Family History of Diabetes Includes Age, Gender, BMI, Hx HTN, physical activity, and Family History of Diabetes Ages Ages Cambridge Risk Score Cambridge Risk Score To Predict Undiagnosed Diabetes To Predict Undiagnosed Diabetes Includes Age, Gender, BMI, Steroid and Antihypertensive medication, Family and Smoking history Includes Age, Gender, BMI, Steroid and Antihypertensive medication, Family and Smoking history Ages Ages 40-64

Why Create a New Score? Score designed to predict presence of IFG Score designed to predict presence of IFG Younger populations Younger populations Use in EHR? Use in EHR?

Methods

Risk Factors Examined Age Age Gender Gender Fam Hx Diabetes Fam Hx Diabetes BMI BMI Waist Circumference Waist Circumference Sedentary Lifestyle Resting Heart Rate Hx Hyperlipidemia Hx or measured HTN Race/ethnicity*

What’s Not Included Acanthosis Nigricans Acanthosis Nigricans PCOS PCOS Hx gestational diabetes Hx gestational diabetes

Development Sample NHANES NHANES US Adults age years 4045 US Adults age years Fasting glucose measurement Fasting glucose measurement Without diagnosed diabetes Without diagnosed diabetes

Outcome Prediction Abnormal fasting glucose > 100 mg/dl Abnormal fasting glucose > 100 mg/dl ADA, US standard for IFG ADA, US standard for IFG ATP III definition of metabolic syndrome ATP III definition of metabolic syndrome Includes those with undiagnosed diabetes (>126 mg/dl) Includes those with undiagnosed diabetes (>126 mg/dl) Logistic Regression Modeling to Predict IFG Logistic Regression Modeling to Predict IFG Forced inclusion models Forced inclusion models SUDAAN software SUDAAN software

Model Iterations Initial model: age, gender, BMI, waist circumference, fam hx diabetes, sedentary lifestyle, hx hypertension, hx hyperlipidemia Initial model: age, gender, BMI, waist circumference, fam hx diabetes, sedentary lifestyle, hx hypertension, hx hyperlipidemia BMI vs. waist circumference BMI vs. waist circumference Hx hypertension vs. measured systolic vs. measured diastolic vs. combination hx and measured HTN Hx hypertension vs. measured systolic vs. measured diastolic vs. combination hx and measured HTN Sedentary lifestyle vs. resting heart rate Sedentary lifestyle vs. resting heart rate

Integer Risk Points Converted Odds Ratios to Integers Converted Odds Ratios to Integers Method defined by Charlson (1987) Method defined by Charlson (1987) Significant odds ratios 1 – 1.19  0 points Significant odds ratios 1 – 1.19  0 points 1.2 – 1.49  1 point 1.2 – 1.49  1 point 1.5 – 2.49  2 points 1.5 – 2.49  2 points 2.5 – 3.49  3 points 2.5 – 3.49  3 points

Validation Sample from NHANES III ( ) Sample from NHANES III ( ) Risk assessment tool applied to each member of NHANES III sample Risk assessment tool applied to each member of NHANES III sample Compared to actual glucose status Compared to actual glucose status Generates true +, true -, false +, false – Generates true +, true -, false +, false – Receiver Operating Characteristic curve Receiver Operating Characteristic curve Compare Area Under Curve for validation sample to derivation sample Compare Area Under Curve for validation sample to derivation sample (MedCalc statistical software) (MedCalc statistical software)

Performance in Race/Ethnicity Subgroups Non-Hispanic White Non-Hispanic White Non-Hispanic Black Non-Hispanic Black Hispanic Hispanic Other – not examined – too heterogeneous Other – not examined – too heterogeneous Generated ROC curves, compared AUCs Generated ROC curves, compared AUCs

Comparison to Other Predictive Measures BMI alone BMI alone Danish Diabetes Risk Score (Glümer, 2004) Danish Diabetes Risk Score (Glümer, 2004) Designed to identify those who should be screened for undiagnosed diabetes Designed to identify those who should be screened for undiagnosed diabetes Danish population-based Inter-99 Study Danish population-based Inter-99 Study Generated ROC curves and compared AUCs Generated ROC curves and compared AUCs

Results

IFG and Undiagnosed Diabetes Unweighted n Weighted n IFG ( mg/dl) million Undiagnosed DM ( > 126 mg/dl) million Total million

Final Model Components Variable Odds Ratio 95% CI Score Age (years) Female Male

Final Model Components Variable Odds Ratio 95% CI Score Hypertension (hx or measured) No No Yes Yes BMI (kg/m 2 ) <25 < ≥ 30 ≥

Final Model Components Variable Odds Ratio 95% CI Score Heart Rate <60 < ≥ 100 ≥ Family Hx DM No No Yes Yes

Performance Comparison: Derivation Sample (NHANES ) IFG AUC Danish AUC BMI AUC 0.644

Performance in Race/Ethnicity Subsets: Derivation Sample (NHANES ) AUC Whole Sample Non-Hispanic White Non-Hispanic Black Hispanic 0.724

Comparison, Validation Hyperglycemia Risk Tool Danish Risk Score BMI NHANES *0.644* NHANES III ( ) * * Significantly different from Hyperglycemia Risk Tool (p < 0.05)

Value of Different Cut-Off Levels Criterion Sensitivity (%) Specificity (%) + Likelihood Ratio 5 or higher or higher or higher or higher or higher

Conclusions

Undetected Hyperglycemia Risk tool can predict who might have IFG or undiagnosed diabetes Risk tool can predict who might have IFG or undiagnosed diabetes Compares favorably with existing risk tools Compares favorably with existing risk tools Validated in second population-based data set Validated in second population-based data set

Potential Uses for Risk Assessment Tool Clinical Pre-screening Clinical Pre-screening Research Case-finding Research Case-finding Raise public awareness Raise public awareness Use in conjunction with promotion of beneficial lifestyle habits Use in conjunction with promotion of beneficial lifestyle habits

Limitations Cross-sectional – does not predict risk of developing disease Cross-sectional – does not predict risk of developing disease Does not assess for presence of IGT Does not assess for presence of IGT Developed from single FPG measurement Developed from single FPG measurement Does not include a cost analysis Does not include a cost analysis

Questions Remain Should we be screening people for pre-diabetes states? What is the potential harm? Should we be screening people for pre-diabetes states? What is the potential harm? If we choose to screen for IFG, what is the right approach to a screening strategy? If we choose to screen for IFG, what is the right approach to a screening strategy? Once we know that someone has IFG, what should we do with them? Once we know that someone has IFG, what should we do with them? If someone with risk screens negative for IFG, when should we screen again? If someone with risk screens negative for IFG, when should we screen again? What real value does earlier identification serve? What real value does earlier identification serve?

Questions ? Department of Family and Community Medicine, University of Missouri-Columbia