None of the novel risk factors significantly improved the AUC in the total cohort or nested case-control sample. However, FEV1
did significantly, albeit modestly, improve the NRI in the total cohort. None of the risk factors statistically significantly improved the NRI in the case-control study sample; however, the addition of adiponectin, leptin, GGT, ferritin, ICAM-1, and complement C3 did statistically significantly but moderately improve the IDI. Likewise, in the total cohort, the novel risk factors WBC count, albumin, aPTT, factor VIII, magnesium, heart rate, hip circumference, and the genetic risk score exhibited significant but modest improvements in IDI. These results suggest that of these novel risk factors, only FEV1
may be helpful for type 2 diabetes risk stratification in the ARIC cohort study. Several novel risk factors did modestly improve the IDI, which indicates that the difference in average predicted probabilities between individuals with and without type 2 diabetes significantly increased when these risk factors were added to the basic model; however, critics argue that it is unclear whether a significant IDI indicates that the novel risk factor in the model is clinically useful (28
Despite the fact that many of the novel risk factors are independent risk factors for type 2 diabetes in the total cohort, none of these risk factors appeared to provide additional value to type 2 diabetes risk prediction. Previous studies that have incorporated one or more novel risk factors into a risk prediction model have been limited, and although these analyses may have found increased AUCs with the inclusion of novel risk factors, they are also often single studies in very specific populations (4
). Our own study failed to replicate the contributions of WBC count, heart rate, or alanine aminotransferase to the improvement in the AUC, as found in the aforementioned studies (4
). It is important to note that although novel risk factors may be associated with type 2 diabetes, it does not mean they will contribute to risk prediction, as these are separate issues of etiology and prediction (32
). All of the novel risk factors modeled in the total cohort and case-control analyses were significantly associated with type 2 diabetes in ARIC; however, none of them significantly contributed to improved risk prediction when C statistics were calculated with and without the novel risk factors.
It is difficult to improve upon existing risk factors for type 2 diabetes. Specifically, when a single measurement of obesity or glycemia is included in a risk model, the AUCs already range from 0.66–0.77. When obesity and glycemia measures are combined with readily available clinical variables, such as those included in the basic model, the AUC increases greatly, making it difficult to improve the risk prediction (32
). Furthermore, the correlation between novel risk factors and traditional risk factors must also be considered, as correlated risk factors provide less independent information about type 2 diabetes risk. We found this to be true in our own analysis, as many statistically significant correlations existed between traditional risk factors and novel risk factors in both the total cohort and the case-control analysis.
Recent advances in the identification of a number of genetic variants associated with type 2 diabetes have generated interest in the clinical utility of combining the loci associated with type 2 diabetes into a genetic risk score, which could be used for risk prediction. Thus far, the use of genetic risk scores in type 2 diabetes risk prediction models prior to this analysis has been limited, often involved a smaller number of genetic variants, and yielded varied results (33
Our own analysis did not find a statistically significant contribution to the AUC or NRI with the addition of a genetic risk score; however, it did moderately improve the IDI. The incorporation of a genetic risk score into future type 2 diabetes risk prediction models could be more useful, once an ideal set of variants is identified, as genes are not prone to the biological variability or measurement error that often accompanies other risk factors. Further, the genotype does not change over one’s lifetime, and this offers opportunities for earlier screening and identification of individuals at risk (34
). In fact, de Miguel-Yanes et al. (35
) found that the incorporation of a genetic score into a risk model was actually more beneficial in younger subjects. Identifying individuals at risk earlier in the disease process will allow for interventions that can either reverse the course of the disease or control its accompanying risk factors such as dyslipidemia and hypertension.
Limitations to this study include the absence of an oral glucose tolerance test or hemoglobin A1c test results to classify type 2 diabetes and the use of a single baseline value for the novel risk factors, which does not capture the variation in levels over time for risk factors. Further, not all novel risk factors are included in this analysis. We chose to only include biomarkers that had not previously been included in risk prediction analyses in ARIC and biomarkers that were measured and not self-reported.
Another limitation is the inclusion of only 30 SNPs in the genetic risk score, which account for only a small fraction of the heritability of type 2 diabetes (36
). Finally, there were 35 novel risk factors evaluated, resulting in multiple testing that may yield false positives. A strength of this analysis was the availability of a large, population-based cohort of white and African American men and women with follow-up data. Further, there were standardized data collection methods for both predictors and type 2 diabetes outcomes.
In conclusion, our modeling indicates that no novel risk factor contributed significantly to risk prediction, as measured by the AUC. There was a modest improvement in risk classification with the addition of FEV1 and a small improvement in the IDI with the addition of WBC count, aPTT, albumin, factor VIII, magnesium, heart rate, hip circumference, and the genetic risk score in the total cohort and adiponectin, leptin, GGT, ferritin, ICAM-1, and complement C3 in the case-control sample. However, these improvements are small and unlikely to motivate refinement of clinical risk reclassification or discrimination strategies. Further study by prospective, population-based cohort studies is needed to confirm the generalizability of these findings.