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The development, validation, and utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS).

ABSTRACT: 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 min, 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.

SUBMITTER: Sosenko JM

PROVIDER: S-EPMC4795007 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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The development, validation, and utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS).

Sosenko Jay M JM Skyler Jay S JS Palmer Jerry P JP

Current diabetes reports 20150801 8

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 min, 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 o ...[more]

PMID: 26077017

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Project description:BackgroundType 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.MethodsWe integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts.ResultsThe trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5-4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance.ConclusionsBy integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

Project description:The aim of this study was to develop and externally validate a score for use in dental settings to identify those at risk of undiagnosed nondiabetic hyperglycemia (NDH) or type 2 diabetes (T2D). The Studies of Health in Pomerania (SHIP) project comprises 2 representative population-based cohort studies conducted in northeast Germany. SHIP-TREND-0, 2008 to 2012 (the development data set) had 3,339 eligible participants, with 329 having undiagnosed NDH or T2D. Missing data were replaced using multiple imputation. Potential covariates were selected for inclusion in the model using backward elimination. Heuristic shrinkage was used to reduce overfitting, and the final model was adjusted for optimism. We report the full model and a simplified paper-based point-score system. External validation of the model and score employed an independent data set comprising 2,359 participants with 357 events. Predictive performance, discrimination, calibration, and clinical utility were assessed. The final model included age, sex, body mass index, smoking status, first-degree relative with diabetes, presence of a dental prosthesis, presence of mobile teeth, history of periodontal treatment, and probing pocket depths ≥5 mm as well as prespecified interaction terms. In SHIP-TREND-0, the model area under the curve (AUC) was 0.72 (95% confidence interval [CI] 0.69, 0.75), calibration in the large was -0.025. The point score AUC was 0.69 (95% CI 0.65, 0.72), with sensitivity of 77.0 (95% CI 76.8, 77.2), specificity of 51.5 (95% CI 51.4, 51.7), negative predictive value of 94.5 (95% CI 94.5, 94.6), and positive predictive value of 17.0 (95% CI 17.0, 17.1). External validation of the point score gave an AUC of 0.69 (95% CI 0.66, 0.71), sensitivity of 79.2 (95% CI 79.0, 79.4), specificity of 49.9 (95% CI 49.8, 50.00), negative predictive value 91.5 (95% CI 91.5, 91.6), and positive predictive value of 25.9 (95% CI 25.8, 26.0). A validated prediction model involving dental variables can identify NDH or undiagnosed T2DM. Further studies are required to validate the model for different European populations.

Dataset Information

The development, validation, and utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS).

Publications

The development, validation, and utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS).

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