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

BackgroundGiven 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.Methodology/Principal FindingsDRS 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.Conclusions/SignificanceDRS 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.

Liver Function Tests and Risk Prediction of Incident Type 2 Diabetes: Evaluation in Two Independent Cohorts

“…To calculate the NRI, cut-off values for risk categories have to be defined. In previous studies, a number of risk categories for the 10-year risk of cardiovascular disease [23], [24] or type 2 diabetes [25], [26] have been reported. In the present study, we slightly modified these cut-off values according to the shorter time period (and hence the lower average observed risk) [25], [26], using cut-off values of <4% for low-risk, 4%–8% for intermediate-risk and ≥8% for high-risk.…”

‘Omics’‐driven discoveries in prevention and treatment of type 2 diabetes

“…In a recent study, a model (Diabetes Risk Score = DRS) for assessing 5 years risk of conversion to T2D was developed in a population based sample of 3032 Danish individuals (the Inter99 cohort) at increased risk as defined by age > 39 years and with body mass index (BMI) ≥ 25 kg/m 2 [29–31]. Sixty‐four circulating biomarkers were selected based upon a thorough search of the current literature on T2D pathogenesis and pathophysiology.…”

Performance of a multi‐marker Diabetes Risk Score in the Insulin Resistance Atherosclerosis Study (IRAS), a multi‐ethnic US cohort

“…The cut point for ‘low’ risk was chosen such that the risk was 3–4 times lower than the population risk. In a recent publication , cut points for DRS were chosen to match the population fraction defined by the number of metabolic syndrome risk factors. Again, DRS showed a significant NRI compared with metabolic risk factor counting in a population with either metabolic syndrome or IFG.…”