Dataset Information

Comparison of accuracy of diabetes risk score and components of the metabolic syndrome in assessing risk of incident type 2 diabetes in Inter99 cohort.

ABSTRACT: Given the increasing worldwide incidence of diabetes, methods to assess diabetes risk which would identify those at highest risk are needed. We compared two risk-stratification approaches for incident type 2 diabetes mellitus (T2DM); factors of metabolic syndrome (MetS) and a previously developed diabetes risk score, PreDx® Diabetes Risk Score (DRS). DRS assesses 5 yr risk of incident T2DM based on the measurement of 7 biomarkers in fasting blood.DRS was evaluated in baseline serum samples from 4,128 non-diabetic subjects in the Inter99 cohort (Danes aged 30-60) for whom diabetes outcomes at 5 years were known. Subjects were classified as having MetS based on the presence of at least 3 MetS risk factors in baseline clinical data. The sensitivity and false positive rate for predicting diabetes using MetS was compared to DRS. When the sensitivity was fixed to match MetS, DRS had a significantly lower false positive rate. Similarly, when the false positive rate was fixed to match MetS, DRS had a significantly higher specificity. In further analyses, subjects were classified by presence of 0-2, 3 or 4-5 risk factors with matching proportions of subjects distributed among three DRS groups. Comparison between the two risk stratification schemes, MetS risk factors and DRS, were evaluated using Net Reclassification Improvement (NRI). Comparing risk stratification by DRS to MetS factors in the total population, the NRI was 0.146 (p?=?0.008) demonstrating DRS provides significantly improved stratification. Additionally, the relative risk of T2DM differed by 15 fold between the low and high DRS risk groups, but only 8-fold between the low and high risk MetS groups.DRS provides a more accurate assessment of risk for diabetes than MetS. This improved performance may allow clinicians to focus preventive strategies on those most in need of urgent intervention.

SUBMITTER: Shafizadeh TB

PROVIDER: S-EPMC3146499 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparison of accuracy of diabetes risk score and components of the metabolic syndrome in assessing risk of incident type 2 diabetes in Inter99 cohort.

Shafizadeh Tracy B TB Moler Edward J EJ Kolberg Janice A JA Nguyen Uyen Thao UT Hansen Torben T Jorgensen Torben T Pedersen Oluf O Borch-Johnsen Knut K

PloS one 20110729 7

<h4>Background</h4>Given the increasing worldwide incidence of diabetes, methods to assess diabetes risk which would identify those at highest risk are needed. We compared two risk-stratification approaches for incident type 2 diabetes mellitus (T2DM); factors of metabolic syndrome (MetS) and a previously developed diabetes risk score, PreDx® Diabetes Risk Score (DRS). DRS assesses 5 yr risk of incident T2DM based on the measurement of 7 biomarkers in fasting blood.<h4>Methodology/principal find ...[more]

PMID: 21829540

Similar Datasets

Project description:ImportancePolygenic risk scores comprising millions of single-nucleotide polymorphisms (SNPs) could be useful for population-wide coronary heart disease (CHD) screening.ObjectiveTo determine whether a polygenic risk score improves prediction of CHD compared with a guideline-recommended clinical risk equation.Design, setting, and participantsA retrospective cohort study of the predictive accuracy of a previously validated polygenic risk score was assessed among 4847 adults of white European ancestry, aged 45 through 79 years, participating in the Atherosclerosis Risk in Communities (ARIC) study and 2390 participating in the Multi-Ethnic Study of Atherosclerosis (MESA) from 1996 through December 31, 2015, the final day of follow-up. The performance of the polygenic risk score was compared with that of the 2013 American College of Cardiology and American Heart Association pooled cohort equations.ExposuresGenetic risk was computed for each participant by summing the product of the weights and allele dosage across 6 630 149 SNPs. Weights were based on an international genome-wide association study.Main outcomes and measuresPrediction of 10-year first CHD events (including myocardial infarctions, fatal coronary events, silent infarctions, revascularization procedures, or resuscitated cardiac arrest) assessed using measures of model discrimination, calibration, and net reclassification improvement (NRI).ResultsThe study population included 4847 adults from the ARIC study (mean [SD] age, 62.9 [5.6] years; 56.4% women) and 2390 adults from the MESA cohort (mean [SD] age, 61.8 [9.6] years; 52.2% women). Incident CHD events occurred in 696 participants (14.4%) and 227 participants (9.5%), respectively, over median follow-up of 15.5 years (interquartile range [IQR], 6.3 years) and 14.2 (IQR, 2.5 years) years. The polygenic risk score was significantly associated with 10-year CHD incidence in ARIC with hazard ratios per SD increment of 1.24 (95% CI, 1.15 to 1.34) and in MESA, 1.38 (95% CI, 1.21 to 1.58). Addition of the polygenic risk score to the pooled cohort equations did not significantly increase the C statistic in either cohort (ARIC, change in C statistic, -0.001; 95% CI, -0.009 to 0.006; MESA, 0.021; 95% CI, -0.0004 to 0.043). At the 10-year risk threshold of 7.5%, the addition of the polygenic risk score to the pooled cohort equations did not provide significant improvement in reclassification in either ARIC (NRI, 0.018, 95% CI, -0.012 to 0.036) or MESA (NRI, 0.001, 95% CI, -0.038 to 0.076). The polygenic risk score did not significantly improve calibration in either cohort.Conclusions and relevanceIn this analysis of 2 cohorts of US adults, the polygenic risk score was associated with incident coronary heart disease events but did not significantly improve discrimination, calibration, or risk reclassification compared with conventional predictors. These findings suggest that a polygenic risk score may not enhance risk prediction in a general, white middle-aged population.

Project description:BackgroundStrong evidence supports that dietary modifications may decrease incident type 2 diabetes mellitus (T2DM). Numerous diabetes risk models/scores have been developed, but most do not rely specifically on dietary variables or do not fully capture the overall dietary pattern. We prospectively assessed the association of a dietary-based diabetes-risk score (DDS), which integrates optimal food patterns, with the risk of developing T2DM in the SUN ("Seguimiento Universidad de Navarra") longitudinal study.MethodsWe assessed 17,292 participants initially free of diabetes, followed-up for a mean of 9.2 years. A validated 136-item FFQ was administered at baseline. Taking into account previous literature, the DDS positively weighted vegetables, fruit, whole cereals, nuts, coffee, low-fat dairy, fiber, PUFA, and alcohol in moderate amounts; while it negatively weighted red meat, processed meats and sugar-sweetened beverages. Energy-adjusted quintiles of each item (with exception of moderate alcohol consumption that received either 0 or 5 points) were used to build the DDS (maximum: 60 points). Incident T2DM was confirmed through additional detailed questionnaires and review of medical records of participants. We used Cox proportional hazards models adjusted for socio-demographic and anthropometric parameters, health-related habits, and clinical variables to estimate hazard ratios (HR) of T2DM.ResultsWe observed 143 T2DM confirmed cases during follow-up. Better baseline conformity with the DDS was associated with lower incidence of T2DM (multivariable-adjusted HR for intermediate (25-39 points) vs. low (11-24) category 0.43 [95% confidence interval (CI) 0.21, 0.89]; and for high (40-60) vs. low category 0.32 [95% CI: 0.14, 0.69]; p for linear trend: 0.019).ConclusionsThe DDS, a simple score exclusively based on dietary components, showed a strong inverse association with incident T2DM. This score may be applicable in clinical practice to improve dietary habits of subjects at high risk of T2DM and also as an educational tool for laypeople to help them in self-assessing their future risk for developing diabetes.

Dataset Information

Comparison of accuracy of diabetes risk score and components of the metabolic syndrome in assessing risk of incident type 2 diabetes in Inter99 cohort.

Publications

Comparison of accuracy of diabetes risk score and components of the metabolic syndrome in assessing risk of incident type 2 diabetes in Inter99 cohort.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets