Metabolic syndrome traits, such as waist circumference, blood glucose (BG), blood pressure (BP), and lipid profile, are all important risk factors for type 2 diabetes; however, each trait may have different prediction power for future diabetes. Therefore, these traits cannot be viewed as all equal when evaluating the patient’s risk for diabetes. In addition, the impact of insulin resistance on the metabolic profile can differ by gender and racial group, which shows that gender- and race-specific prediction methods for diabetes are needed.
The authors developed a weighted scoring system that is based on the risk factor components in the Cardiometabolic Disease Staging (CMDS) system to predict the 15-year risk for diabetes. The CMDS normally includes 6 components, which include fasting glucose, 2-hour glucose, waist circumference, BP, HDL-cholesterol, and triglycerides.
To figure out the weight of each risk factor, the authors based the CMDS score based on verified incident of diabetes cases from the Coronary Artery Risk Development in Young Adults (CARDIA) study and then validating their scoring system on subjects from the Atherosclerosis Risk in Communities (ARIC) study. They rounded the scores so that the maximum score of all the risk factors added together would be 100. They also developed several scoring systems, such as the gender-race specific CMDS scores, as well as the modified CMDS scoring system that does not include the 2h-ppBG component because OGTTs are not routinely performed in the clinical settings.
The CARDIA study had 2,857 subjects with a mean age of 35 years. The ARIC study had 6,425 participants with a mean age of 64.3 years. The subjects that were included in both studies were composed of White and Black adults.
The weighted scores and the strongest predictor of future diabetes differ from male and female and also differ for different racial groups. When the scores were ≤ 50, the Whites had lower risks for future diabetes than the Blacks at the same score. However, when the scores were above 60, there was similar risks for future diabetes with both racial groups.
They compared the weighted CMDS scoring system with the Framingham diabetes risk score. They calculate the prediction power by calculating the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. When validating the score with the subjects from the ARIC study, they found that the AUC for the ROC for the CMDS scoring system was 0.7158, which shows that it was a better diabetes predictor than the Framingham diabetes score (0.7053). The modified CMDS score with 2h-ppBG component removed had an AUC of 0.7013. Meanwhile, the gender-race specific CMDS score system showed a high power of predictor with an AUC of 0.7199. The unweighted CMDS scoring system had an AUC of 0.6981, showing that it did not perform as well as the weighted CMDS scoring system.
Although the modified CMDS system does not have the 2h-ppBG component, it’s important to note that for high-risk patients, an OGTT should be performed. One of the limitations to this study was that the scoring method cannot be generalized to other ethnic groups since the patient population that was studied was only in Black and White participants. The authors also did not consider other risk factors for diabetes such as age, patient history, dietary factors, physical activity, or sleeping patterns in the scoring system. However, the authors concluded that this weighted CMDS scoring system can be used to help with the selection of treatment options for obesity management in the clinical setting and that it is important to treat those high-risk patients with more aggressive therapy to optimize their care.
- Gender- and race-specific CMDS scoring system has the highest predictor power for future diabetes.
- This weighted CMDS scoring system cannot be generalized for other ethnic groups such as Asians and Hispanics.
- High-risk patients require more aggressive therapy to optimize treatment.
Guo F and Garvey WT. “Development of a Weighted Cardiometabolic Disease Staging (CMDS) System for the Prediction of Future Diabetes.” Journal of Clinical Endocrinology & Metabolism, 29 July 2015. Web. 7 Sep 2015.