Performance of a multi-marker diabetes risk score in the Insulin Resistance Atherosclerosis Study (IRAS), a multi-ethnic US cohort.

Background: This study compares a previously developed Diabetes Risk Score to commonly used clinical tools for type 2 diabetes risk evaluation in the Insulin Resistance Atherosclerosis Study (IRAS) cohort, a multi-ethnic US cohort. Available as a clinical test, the PreDx® Diabetes Risk Score uses fasting concentrations of adiponectin, C-reactive protein, ferritin, interleukin-2 receptor alpha, HbA(1c) , glucose and insulin, plus age and gender to predict 5-year risk of diabetes. It was developed in a Northern European population.

Validation of a multi-marker model for the prediction of incident type 2 diabetes mellitus: combined results of the Inter99 and Botnia studies.

Purpose: To assess performance of a biomarker-based score that predicts the five-year risk of diabetes (Diabetes Risk Score, DRS) in an independent cohort that included 15-year follow-up.

Method: DRS was developed on the Inter99 cohort, and validated on the Botnia cohort. Performance was benchmarked against other risk-assessment tools comparing calibration, time to event analysis, and net reclassification.

Biomarkers in Type 2 diabetes: improving risk stratification with the PreDx ® Diabetes Risk Score.

Type 2 diabetes is a chronic, debilitating and often deadly disease that has reached epidemic proportions. The onset of diabetes can be delayed or prevented in high-risk individuals by diet and lifestyle changes and medications, and hence a key element for addressing the diabetes epidemic is to identify those most at risk of developing diabetes so that preventative measures can be effectively focused. The PreDx(®) Diabetes Risk Score is a multimarker tool for assessing a patient's risk of developing diabetes within the next 5 years.

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

Background: 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.

Cost-effectiveness of risk stratification for preventing type 2 diabetes using a multi-marker diabetes risk score.

Background: Personalized medicine requires diagnostic tests that stratify patients into distinct groups that may differentially benefit from targeted treatment approaches. This study compared the costs and benefits of two approaches for identifying those at high risk of developing type 2 diabetes for entry into a diabetes prevention program. The first approach identified high risk patients using impaired fasting glucose (IFG).

Biomarkers in fasting serum to estimate glucose tolerance, insulin sensitivity, and insulin secretion.

Background: Biomarkers for estimating reduced glucose tolerance, insulin sensitivity, or impaired insulin secretion would be clinically useful, since these physiologic measures are important in the pathogenesis of type 2 diabetes mellitus.

Methods: We conducted a cross-sectional study in which 94 individuals, of whom 84 had 1 or more risk factors and 10 had no known risk factors for diabetes, underwent oral glucose tolerance testing. We measured 34 protein biomarkers associated with diabetes risk in 250-μL fasting serum samples.

Validation of a multimarker model for assessing risk of type 2 diabetes from a five-year prospective study of 6784 Danish people (Inter99).

Background: Improved identification of subjects at high risk for development of type 2 diabetes would allow preventive interventions to be targeted toward individuals most likely to benefit. In previous research, predictive biomarkers were identified and used to develop multivariate models to assess an individual's risk of developing diabetes. Here we describe the training and validation of the PreDx Diabetes Risk Score (DRS) model in a clinical laboratory setting using baseline serum samples from subjects in the Inter99 cohort, a population-based primary prevention study of cardiovascular disease.

Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort.

Objective: The purpose of this study was to develop a model for assessing the 5-year risk of developing type 2 diabetes from a panel of 64 circulating candidate biomarkers.

Research Design And Methods: Subjects were selected from the Inter99 cohort, a longitudinal population-based study of approximately 6,600 Danes in a nested case-control design with the primary outcome of 5-year conversion to type 2 diabetes. Nondiabetic subjects, aged >or=39 years, with BMI >or=25 kg/m(2) at baseline were selected.

Sustained high proportion of zidovudine-resistant HIV variants despite prolonged substitution of zidovudine by other nucleoside reverse transcriptase inhibitors.

The consequences of zidovudine (ZDV) replacement by other nucleoside reverse transcriptase inhibitors on the expression of resistance mutations at codons 215 and 41 of the reverse transcriptase (RT) gene was investigated prospectively in 66 patients harboring mutant genotypes who were changed to an effective two- or three-drug combination antiretroviral regimen. Quantitation of mutant (MUT) viral populations at codon 215 by means of RT-PCR with differential hybridization of amplicons specific for MUT and wild (WT) variants revealed no difference in the proportion of 215 MUT variants prior to (93.5 +/- 2.

Detection of viral infection and gene expression in clinical tissue specimens using branched DNA (bDNA) in situ hybridization.

In situ hybridization (ISH) methods for detection of nucleic acid sequences have proved especially powerful for revealing genetic markers and gene expression in a morphological context. Although target and signal amplification technologies have enabled researchers to detect relatively low-abundance molecules in cell extracts, the sensitive detection of nucleic acid sequences in tissue specimens has proved more challenging. We recently reported the development of a branched DNA (bDNA) ISH method for detection of DNA and mRNA in whole cells.