Summary
This report details the development, validation, and utility of the Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score (DPTRS) for type 1 diabetes (T1D). Proportional hazards regression was used to develop the DPTRS model which includes the glucose and C-peptide sums from oral glucose tolerance tests at 30, 60, 90, and 120 minutes, the log fasting C-peptide, age, and the log BMI. The DPTRS was externally validated in the TrialNet Natural History Study cohort (TNNHS). In a study of the application of the DPTRS, the findings showed that it could be used to identify normoglycemic individuals who were at a similar risk for T1D as those with dysglycemia. The DPTRS could also be used to identify lower risk dysglycemic individuals. Risk estimates of individuals deemed to be at higher risk according to DPTRS values did not differ significantly between the DPT-1 and the TNNHS, whereas the risk estimates for those with dysglycemia were significantly higher in DPT-1. Individuals with very high DPTRS values were found to be at such marked risk for T1D that they could reasonably be considered to be in a pre-diabetic state. The findings indicate that the DPTRS has utility in T1D prevention trials and for identifying pre-diabetic individuals.
Determining the risk of type 1 diabetes (T1D) is essential for establishing inclusion criteria for T1D prevention trials (1-3), since individuals must be at sufficient risk to warrant exposure to the experimental interventions. This is a particularly important consideration in children. Moreover, as an indicator of the extent of progression to T1D, risk could help define stages of progression in which an intervention might be most efficacious and useful.
The risk of T1D is related to immunologic, genetic, and metabolic factors. Earlier studies have shown that pancreatic autoantibodies, HLA haplotypes, the first-phase insulin response (FPIR), and impaired glucose tolerance are predictive of T1D (4-7). More recently, Ziegler et al. (8) pooled data from several studies [Colorado Diabetes Autoimmune Study in the Young (DAISY), Finnish Type 1 Diabetes Prediction and Prevention (DIPP), BABYDIAB, and BABYDIET] to show that the risk of T1D increases according to the number of autoantibodies that develop after seroconversion in children. Children who developed multiple autoantibodies were at a very high 10-year risk of progression to T1D. These findings are consistent with a previous study of Diabetes Prevention Trial-Type 1 (DPT-1) and the TrialNet Natural History Study (TNNHS) participants (9). In the study of Ziegler et al. (8), following seroconversion, HLA genotypes were further predictive of risk. DPT-1 findings have revealed that in addition to glucose abnormalities such as impaired glucose tolerance, glycemia within the normal range is predictive of T1D (10). C-peptide indices have also been shown to be predictive of T1D (10).
Previous prevention trials (1-3) have utilized such findings, especially with regard to autoantibodies and glycemia, to define T1D risks of potential participants. Thresholds of individual risk factors, sometimes in combination within algorithms, have been chosen for this purpose. As an alternative to this approach and to provide a more accurate assessment of T1D risk, we have developed a risk score for T1D from DPT-1 data. The DPT-1 risk score (DPTRS) incorporates several predictors of T1D into one measure (11). The development, validation, and utility of the DPTRS are detailed below.
Development of the DPTRS
DPT-1 consisted of two clinical trials: the parenteral insulin (n=339) and oral insulin trials (n=372) (2,3). The main objective of each trial was to test whether the intervention could delay the development of T1D in ICA positive non-diabetic relatives of T1D patients (age range: 1-45 years). Participants in the parenteral trial either had dysglycemia [impaired fasting glucose, impaired glucose tolerance, and/or a glucose level ≥200 mg/dl at 30, 60 or 90 minutes of an oral glucose tolerance test (OGTT)] or a low FPIR. Oral insulin trial participants were required to have a normal OGTT and insulin autoantibodies (in addition to ICA). Diagnoses were made through 2-hr oral glucose tolerance test surveillance at 6-month intervals or by clinical presentation. In both trials, 92% of those included were first-degree relatives. No overall therapeutic effect was evident in either trial.
In a prior analysis of DPT-1 data (10), it was evident that the area under the curve (AUC) glucose from OGTTs was a more accurate predictor of T1D than the standard fasting and 2-hr glucose indices. Also, among DPT-1 participants within the normal range of glycemia, glucose levels strongly predicted T1D. Moreover, log fasting C-peptide values were positively predictive and AUC C-peptide values were negatively predictive of T1D.
This information was taken into account when the DPTRS (11) was developed with proportional hazards modeling. We used the metabolic measures from the prior study (sums from baseline OGTT values at 30, 60, 90, and 120 minutes were used instead of the AUC glucose, since they correlated almost perfectly with the AUC measures and were easier to calculate.) The entirety of the non-diabetic range of glycemia was used rather than categories of glycemia. Age (negatively predictive) and the log BMI (positively predictive) were also included in the final proportional hazards model, since they were strongly predictive of T1D when the log fasting C-peptide (positively predictive), and the glucose (positively predictive) and C-peptide sums (negatively predictive) were also present in the model. The full proportional hazards model for the DPTRS is shown in Table 1. The DPTRS can be calculated in the following manner:
Table 1. Associations of T1D with the variables in the DPTRS model in DPT-1 participants*.
| Variable | Estimate±SE** | Chi-Square | p-Value |
|---|---|---|---|
| Log BMI (kg/m2 | 1.57±0.35 | 20.3 | <0.001 |
| Age (Years) | -0.06±0.01 | 23.5 | <0.001 |
| Glucose Sum/100 (mg/dl) | 0.81±0.06 | 197.1 | <0.001 |
| C-peptide Sum/10 (ng/ml) | -0.85±0.15 | 33.7 | <0.001 |
| Log Fasting C-peptide (ng/ml) | 0.48±0.12 | 15.1 | <0.001 |
n=670
Based on proportional hazards models
[Follow-up was 2.5±1.5 years for those who were diagnosed with T1D (n=241) and 3.6±1.7 years for those who were not diagnosed (n=539)]
DPTRS = (1.57 x log BMI) − (0.06 x age) + (0.81 x glucose sum from 30 to 120 min/100) − (0.85 x C-peptide sum from 30 to 120 min/10) + (0.48 x log fasting C-peptide)
Although the FPIR was also a predictor of T1D, it was not included in the final DPTRS model for two reasons. Firstly, the FPIR had little impact on prediction when the other variables were included in the DPTRS model, and secondly, it would have necessitated the performance of intravenous glucose tolerance tests.
Validation of the DPTRS
An internal split-sample validation within the DPT-1 data showed that the DPTRS model, developed in one sample of the DPT-1 participants, was accurately predictive of T1D in another sample. The final DPTRS (11) was based on the same variables, but derived from all of the individuals studied.
An external validation was performed using the TrialNet Natural History Study (TNNHS) cohort (12). The TNNHS is the central core of TrialNet, an international consortium of researchers studying the prevention of T1D. As in DPT-1, TNNHS participants are also autoantibody positive relatives (91% first-degree) of T1D patients (age range: 1-45 years). However, in the TNNHS, entry is based on positivity of GAD65, IA2A, and mIAA autoantibodies. More recently, ZnT8A autoantibodies have also been measured. TNNHS participants who qualify can participate in prevention trials; otherwise, TNNHS participants did not receive experimental interventions. The diagnostic surveillance of T1D was similar to DPT-1.
Figure 1 shows that when the DPTRS was used for prediction in the TNNHS cohort (mean±SD age=18.5±13.3 years), the 2-year and 3-year risks for DPTRS values were similar to those in the DPT-1 cohort (age=13.9±9.6 years)(12). There were no significant differences in the cumulative incidence curves between DPT-1 and the TNNHS at any of the DPTRS intervals. Since the autoantibody criteria for entry into the TNNHS differed from DPT-1, the findings suggested that the DPTRS could be accurately predictive across autoantibody positive populations.
Figure 1.
(12) Shown are the 2-year (A) and 3-year (B) risk estimates for T1D according to intervals of the DPTRS in DPT-1 and the TNNHS. There were no significant differences in the cumulative incidence curves between DPT-1 and the TNNHS at any of the DPTRS intervals.
Applications of the DPTRS
Selecting Target Populations for Prevention Trials
Since autoantibody positivity occurs infrequently, even in relatives of T1D patients, it is essential to include as many individuals as possible who truly qualify for entry into prevention trials. Also, since the experimental interventions could have adverse reactions, the erroneous inclusion of individuals in trials must be avoided. Thus, it is important to minimize risk misclassification and the DPTRS can be helpful in this regard.
The utility of the DPTRS for identifying potential prevention trial participants was examined in the TNNHS cohort (13). Those with normoglycemia and DPTRS values >7.00 were found to have a similar degree of risk as those with dysglycemia (Figure 2). Moreover, the DPTRS identified a number of individuals with dysglycemia who were actually not at high risk. Those with dysglycemia and a DPTRS value <7.00 [94/221 (43%)] had an overall 3-year risk estimate of only 0.16.
Figure 2.
(13) Shown are cumulative incidence curves for T1D for those normoglycemic with DPTRS values >7.00 and those with dysglycemia. The curves were not significantly different.
The normoglycemic TNNHS participants with DPTRS values >7.00 were much younger than those with dysglycemia (8.1±4.9 years vs. 19.6±14.3 years; p<0.001). In an additional analysis, we have found that among the DPTRS participants with dysglycemia, those with DPTRS values >7.00 were much younger than those with values <7.00 (11.6±8.1 years vs. 30.4±13.8 years; p<0.001). Thus, the findings showed that the DPTRS can be used to identify those higher risk children who would be missed by using dysglycemia as an indicator of risk, and among those with dysglycemia, it can help to identify those adults who are less likely to progress to T1D. It is apparent that through the inclusion of age, the DPTRS can refine risk estimates.
The findings also showed that the DPTRS was a more consistent indicator of risk than dysglycemia across autoantibody populations. This is evident in Figure 3 (13) which shows that there was appreciably less difference between the DPT-1 and TNNHS cumulative incidence curves for T1D among those with DPTRS values >7.00 (n.s.) than for those with dysglycemia (p<0.001).
Figure 3.
(13) Shown are comparisons of cumulative incidence curves for T1D between the DPT-1 and TNNHS cohorts for individuals with DPTRS values >7.00 (A) and individuals with dysglycemia (B). The curves are more consistent between DPT-1 and the TNNHS for those with DPTRS values >7.00 than for those with dysglycemia. Whereas the curves for dysglycemia were significantly different between the cohorts, the curves for those with DPTRS values >7.00 were not significantly different.
Table 2 shows how DPTRS thresholds could be used to select potential participants for prevention trials (13). The DPTRS could define high risk populations in a more flexible and efficient manner than the use of dysglycemia alone. For example, although the risk of T1D in the TNNHS was similar between those with a DPTRS threshold >6.50 and those with dysglycemia, there were many more individuals with a DPTRS threshold >6.50 DPTRS (n=344) than those with dysglycemia (n=221).
Table 2.
| Total | Dysglycemia | Diagnosed | 2-Year Risk (with 95% CI) | 3-Year Risk (with 95% CI) | 4-Year Risk (with 95% CI) | |
|---|---|---|---|---|---|---|
| Dysglycemia | 221 | ___ | 62 | 0.30 (0.24,0.38) | 0.33 (0.26, 0.41) | 0.42 (0.32, 0.54) |
| DPTRS>6.50 | 344 | 151 | 95 | 0.27 (0.22, 0.33) | 0.35 (0.28, 0.41) | 0.42 (0.34, 0.51) |
| DPTRS>6.75 | 253 | 143 | 85 | 0.33 (0.27, 0.40) | 0.41 (0.34, 0.49) | 0.51 (0.41, 0.62) |
| DPTRS>7.00 | 191 | 127 | 71 | 0.36 (0.28, 0.44) | 0.43 (0.35, 0.52) | 0.53 (0.42, 0.64) |
| DPTRS>7.25 | 146 | 110 | 62 | 0.43 (0.34, 0.53) | 0.50 (0.41, 0.61) | 0.59 (0.47, 0.72) |
| DPTRS>7.50 | 109 | 93 | 50 | 0.49 (0.39, 0.60) | 0.56 (0.44, 0.68) | 0.62 (0.47, 0.77) |
Based on Kaplan-Meier curves
Dysglycemia= impaired fasting glucose, impaired glucose tolerance, and/or a glucose level ≥200 mg/dl at 30, 60 or 90 minutes of an oral glucose tolerance test
Identification of Individuals at Very High Risk for T1D
An inspection of Figure 1 (12) shows that the risk of T1D increases markedly with small increments in DPTRS values above a value of 7.00. This suggested that DPTRS thresholds could identify individuals who would almost inevitably progress to T1D. This possibility was examined in DPT-1 and TNNHS participants with DPTRS values >9.00 (14). The findings showed that when the 9.00 DPTRS value was exceeded during follow-up, the subsequent 2-year risk of T1D was extremely high (0.88 in DPT-1 and 0.77 in the TNNHS). This suggests that a very high DPTRS threshold could define a pre-clinical state of T1D, if not T1D itself. Thus, there appears to be a point of transition at which a predictor actually becomes an indicator of T1D.
Limitations
There are limitations to the use of the DPTRS. It should be emphasized that since the DPTRS was developed and validated in autoantibody positive populations, it might not be applicable to other populations. Since there can be marked heterogeneity even among autoantibody positive populations (15), the DPTRS might not always be applicable to specific subgroups.
Implications for the Future
The utility of the DPTRS for predicting T1D should provide an impetus for the development of even more accurate risk predictors. In a recent study, combining an autoantibody risk score with the DPTRS provided a slightly more accurate prediction of T1D than the DPTRS alone (16). The autoantibody risk score accounts for the specific autoantibodies that are positive along with their levels or titers.
The very strong likelihood of being diagnosed with T1D for those above high DPTRS thresholds suggests that risk scores can also contribute to refining the definition of T1D. Even though the DPTRS is useful for present purposes, it is likely to be a forerunner of other risk scores that will be utilized for the prediction of T1D. Such risk scores could be applicable to more populations. In addition, they could more precisely identify those who will develop T1D and when it will occur.
Supplementary Material
Acknowledgments
This work was funded by the National Institutes of Health through the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, Eunice Kennedy Shriver National Institute for Child Health and Human Development and National Center for Research Resources; the Juvenile Diabetes Research Foundation International; and the American Diabetes Association. Dr. Jay Sosenko is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Funding: The sponsor of the trial was the Type 1 Diabetes TrialNet Study Group. Type 1 Diabetes TrialNet Study Group is a clinical trials network funded by the National Institutes of Health (NIH) through the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Allergy and Infectious Diseases, and The Eunice Kennedy Shriver National Institute of Child Health and Human Development, through the cooperative agreements U01 DK061010, U01 DK061016, U01 DK061034, U01 DK061036, U01 DK061040, U01 DK061041, U01 DK061042, U01 DK061055, U01 DK061058, U01 DK084565, U01 DK085453, U01 DK085461, U01 DK085463, U01 DK085466, U01 DK085499, U01 DK085505, U01 DK085509, and a contract HHSN267200800019C; the National Center for Research Resources, through Clinical Translational Science Awards UL1 RR024131, UL1 RR024139, UL1 RR024153, UL1 RR024975, UL1 RR024982, UL1 RR025744, UL1 RR025761, UL1 RR025780, UL1 RR029890, UL1 RR031986, P30 DK017047, and General Clinical Research Center Award M01 RR00400; the Juvenile Diabetes Research Foundation International (JDRF); and the American Diabetes Association (ADA). The contents of this Article are solely the responsibility of the authors and do not necessarily represent the official views of the NIH, JDRF, or ADA.
Footnotes
Author Contributions: Jay Sosenko: writing manuscript; Jay Skyler: reviewing manuscript; Jerry Palmer: reviewing manuscript.
Contributor Information
Jay M. Sosenko, Email: jsosenko@med.miami.edu, Division of Endocrinology; University of Miami; Miami, FL 33101; PO Box 016960 (D110); Miami, FL 33101; Phone: 305-243-6146; Fax: 305-243-4484.
Jay S. Skyler, Division of Endocrinology; University of Miami; Miami, Florida.
Jerry P. Palmer, VA Puget Sound Health Care System; Division of Endocrinology, Metabolism, and Nutrition; University of Washington; Seattle, Washington.
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