In addition to utility and convenience, A1C could help identify persons at increased risk of developing diabetes. This is an important public health priority since a structured lifestyle program or the drug metformin can reduce the incidence of diabetes by at least 50 and 30%, respectively (8). Ideally, selection of diagnostic cut points for pre-diabetes would be based on evidence that intervention, when applied to the high-risk group of interest, results not only in the prevention of diabetes but also later complications. However, currently there are no trials that can provide data to determine the ideal method for defining cut points. In the absence of such data, expert committees had to rely on information about the shape of risk curves for complications such as retinopathy. Previous expert committees assembled to address this issue have noted that there is no clear difference in retinopathy risk between different levels of impaired glucose tolerance (7). We are unaware of published prospective studies of adequate sample size or duration that have followed people in various pre-diabetic categories across the full span of time until complications developed. In the absence of informative trials (as well as prospective studies), the studies that measure A1C at baseline and incident diabetes may provide the definitions of high-risk states.

To better define A1C ranges that might identify persons who would benefit from interventions to prevent or delay type 2 diabetes, we carried out a systematic review of published prospective studies that have examined the relationship of A1C to future diabetes incidence.

In light of recent interest in adopting A1C for the diagnosis of diabetes, these findings may be useful to guide policies related to the classification and diagnosis of persons at high risk of developing diabetes prior to preventive intervention. The progression of risk of diabetes with A1C is similar in magnitude and shape as previously described for fasting plasma glucose and 2-h glucose and suggests that A1C may have a similar application as an indicator of future risk (27). The ideal decision about what A1C cut point is used for intervention should ultimately be based on the capacity for benefit as shown in clinical trials. Our findings suggest that A1C range of 5.5 and 6.5% will capture a large portion of people at high risk, and if interventions can be employed to this target population, it may bring about significant absolute risk reduction. Given the current science and evidence of the cost-effectiveness of intensive interventions conducted in clinical trials (28,29), the use of a threshold somewhere between 5.5 and 6.0% is likely to ensure that persons who will truly benefit from preventive interventions are efficiently identified. It is also reassuring that the mean A1C values of the populations from the Diabetes Prevention Program, the Finnish Diabetes Prevention Study, and the Indian Diabetes Prevention Program, wherein the mean A1C was 5.8 to 6.2% and SDs of at least 0.5 percentage points, span the range from 5.5 to 6.5% (28–30).

There was considerable variation in the estimates of relative risk and absolute incidence across studies stemming from several factors. First, there was considerable variation in the populations studied ranging from relatively young women (15) to older men (23). Second, the magnitude of relative risk is highly dependent upon the overall risk of the population and the selection of the referent group; studies with low absolute risk and the selection of a particularly low-risk referent group will have very high relative risks across the spectrum of A1C. Third, there was variation in the outcome definition with almost all studies using fasting glucose of 7.0 mmol/l as the definition of diabetes, but only approximately half of the studies using the oral glucose tolerance test. Fourth, there is likely to be some variation in relative risk because of variation in the calculation of risk statistics; studies reported relative risks, odds ratios, and incidence ratios, and simple presentations of incidence. Since we lacked original data, we were unable to optimally convert and standardize risk estimates across groups. Fifth, A1C assays vary across laboratories. As indicated above, A1C measurement was standardized by NGSP only in three studies (11,24,25), and only one study (24) reported both standardized and unstandardized A1C values. When we conducted a sensitivity analysis in our modeling A1C as a function of incidence using both standardized and unstandardized A1C values from one study (24), there was the maximum likelihood that continuous curves did not show any significant difference. Finally, there was variation in the choice of cutoff points that may have influenced the conclusions. Several studies presented in our review were not suitable for modeling because they did not examine incidence of diabetes across a broad range of A1C values. However, the conclusions from these additional studies were generally consistent with those that examined multiple A1C categories. For example, studies by Ko et al. (15), Inoue et al. (14), and Little et al. (18) used dichotomous cut points of 5.8, 6.1, and 6.0, respectively, and found that persons above the threshold had roughly three times the incidence of those below the cutoff point.

Several studies found that A1C is particularly predictive of future diabetes after prior stratification of fasting plasma glucose (11,14,21,24,26). This is consistent with prior observations that elevated fasting and 2-h glucose in combination indicates greater risk than either fasting plasma glucose or A1C alone. This improved predictability may be a function of reducing error variance; in other words, conducting a follow-up test clarifies the group with more stable hyperglycemia, and is the main reason that a second test is recommended for a full clinical diagnosis.

Our most important limitation was the lack of original data to model the continuous association between A1C values and incidence. This lack of original data required us to use a modeling approach with which many readers are unfamiliar. Nevertheless, our modeling of average studies resulted in an average incidence value of roughly 1% per year for persons with normal A1C values, an incidence estimate that is consistent with numerous other estimates of the general population. The lack of access to raw data also prevented us from conducting formal ROC analyses of A1C cut-off points to distinguish between eventual cases/noncases or to quantitatively assess the impact of variation in population characteristics on the relationship between A1C and incidence. Our findings could also be influenced by the choice of outcome definition. A1C is more apt to predict diabetes if the outcome is also A1C-based. We did not detect major differences in the A1C/diabetes incidence association according to the choice of glycemic test. Since identifying A1C to predict diabetes defined by glycemic indicators is ultimately circular, future studies should examine the relationship of glycemic markers and later diabetes risk by using several glycemic markers to define incident diabetes, as well as to consider morbidity outcomes.