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. METHODS: The Diabetes Risk Score was measured using archived fasting plasma specimens from 722 non-diabetic IRAS participants, 17.6% of whom developed diabetes during 5.2 years median follow-up (inter-quartile range: 5.1-5.4 years). The study included non-Hispanic whites (41.8%), Hispanics (34.5%) and African Americans (23.7%). Performance of the algorithm was evaluated by area under the receiver operating characteristic curve (AROC) and risk reclassification against other tools. RESULTS: The Diabetes Risk Score discriminates participants who developed diabetes from those who did not significantly better than fasting glucose (AROC = 0.763 versus 0.710, p = 0.003). The Diabetes Risk Score performed equally well in subpopulations defined by race/ethnicity or gender. The Diabetes Risk Score provided a significant net reclassification improvement of 0.24 (p = 0.01) when comparing predefined low/moderate/high Diabetes Risk Score categories to metabolic syndrome risk factor counting. The Diabetes Risk Score complemented the use of the oral glucose tolerance test by identifying high risk patients with impaired fasting glucose but normal glucose tolerance, 33% of whom converted. CONCLUSIONS: Measuring the Diabetes Risk Score of elevated-risk US patients could help physicians decide which patients warrant more intensive intervention. The Diabetes Risk Score performed equally well across the ethnic subpopulations present in this cohort.

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. We applied multiple regression selection techniques to identify the most informative biomarkers and develop multivariate models to estimate glucose tolerance, insulin sensitivity, and insulin secretion. The ability of the glucose tolerance model to discriminate between diabetic individuals and those with impaired or normal glucose tolerance was evaluated by area under the ROC curve (AUC) analysis. RESULTS: Of the at-risk participants, 25 (30%) were found to have impaired glucose tolerance, and 11 (13%) diabetes. Using molecular counting technology, we assessed multiple biomarkers with high accuracy in small volume samples. Multivariate biomarker models derived from fasting samples correlated strongly with 2-h postload glucose tolerance (R(2) = 0.45, P < 0.0001), composite insulin sensitivity index (R(2) = 0.91, P < 0.0001), and insulin secretion (R(2) = 0.45, P < 0.0001). Additionally, the glucose tolerance model provided strong discrimination between diabetes vs impaired or normal glucose tolerance (AUC 0.89) and between diabetes and impaired glucose tolerance vs normal tolerance (AUC 0.78). CONCLUSIONS: Biomarkers in fasting blood samples may be useful in estimating glucose tolerance, insulin sensitivity, and insulin secretion.

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. Based on bDNA signal amplification technology, bDNA ISH is highly sensitive and can detect one or two copies of DNA per cell. In this study we evaluated bDNA ISH for detection of nucleic acid sequences in tissue specimens. Using normal and human papillomavirus (HPV)-infected cervical biopsy specimens, we explored the cell type-specific distribution of HPV DNA and mRNA by bDNA ISH. We found that bDNA ISH allowed rapid, sensitive detection of nucleic acids with high specificity while preserving tissue morphology. As an adjunct to conventional histopathology, bDNA ISH may improve diagnostic accuracy and prognosis for viral and neoplastic diseases.

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. RESULTS: The area under the receiver-operating characteristic curve (AUC) was 0.84 for the Inter99 cohort and 0.78 for the Botnia cohort. In the Botnia cohort, DRS provided better discrimination than fasting plasma glucose (FPG), homeostasis model assessment of insulin resistance, oral glucose tolerance test or risk scores derived from Framingham or San Antonio Study cohorts. Overall reclassification with DRS was significantly better than using FPG and glucose tolerance status (p < 0.0001). In time to event analysis, rates of conversion to diabetes in low, moderate, and high DRS groups were significantly different (p < 0.001). CONCLUSION: This study validates DRS performance in an independent population, and provides a more accurate assessment of T2DM risk than other methods.

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). The second approach used the PreDx Diabetes Risk Score (DRS) to further stratify IFG patients into high-risk and moderate-risk groups. METHODS: A Markov model was developed to simulate the incidence and disease progression of diabetes and consequent costs and quality-adjusted life expectancy (QALY), comparing alternative approaches for identifying high-risk patients. We modeled direct medical costs, including the costs of the stratification testing, over a 10-year time horizon from a US payer perspective. RESULTS: Stratification of IFG patients by the DRS method leads to improved identification and prevention among those at highest risk. At 5 years, the number needed to treat (NNT) in the IFG-only approach was 39 patients to prevent one case of diabetes compared to an NNT of 15 in the IFG + DRS approach. When compared to IFG alone, the IFG + DRS approach results in an incremental cost-effectiveness ratio (ICER) of $17,100/QALY gained at 5 years and would become cost saving in 10 years. In contrast and as compared to no stratification, the IFG-only approach would produce an ICER of $235,500/QALY gained at 5 years and $94,600/QALY gained at 10 years. The study findings are limited by the generalizability of the DRS validation study and uncertainty regarding the long-term effectiveness of diabetes prevention. CONCLUSIONS: The analysis indicates that the cost-effectiveness of diabetes prevention can be improved by better identification of patients at highest risk for diabetes using the DRS.

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. METHODOLOGY/PRINCIPAL FINDINGS: 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. CONCLUSIONS/SIGNIFICANCE: 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.

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. Requiring a simple blood draw using standard sample collection and handling procedures, the PreDx Diabetes Risk Score is easily implemented in clinical practice and provides an assessment of diabetes risk that is superior to other measures, including fasting plasma glucose, glycated hemoglobin, measures of insulin resistance and other clinical measures. In this article, we provide an overview of the PreDx Diabetes Risk Score.

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. METHODS: Among 6784 subjects free of diabetes at baseline, 215 subjects progressed to diabetes (converters) during five years of follow-up. A nested case-control study was performed using serum samples from 202 converters and 597 randomly selected nonconverters. Samples were randomly assigned to equally sized training and validation sets. Seven biomarkers were measured using assays developed for use in a clinical reference laboratory. RESULTS: The PreDx DRS model performed better on the training set (area under the curve [AUC] = 0.837) than fasting plasma glucose alone (AUC = 0.779). When applied to the sequestered validation set, the PreDx DRS showed the same performance (AUC = 0.838), thus validating the model. This model had a better AUC than any other single measure from a fasting sample. Moreover, the model provided further risk stratification among high-risk subpopulations with impaired fasting glucose or metabolic syndrome. CONCLUSIONS: The PreDx DRS provides the absolute risk of diabetes conversion in five years for subjects identified to be "at risk" using the clinical factors.

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. Baseline fasting serum samples from 160 individuals who developed type 2 diabetes and from 472 who did not were tested. An ultrasensitive immunoassay was used to measure of 58 candidate biomarkers in multiple diabetes-associated pathways, along with six routine clinical variables. Statistical learning methods and permutation testing were used to select the most informative biomarkers. Risk model performance was estimated using a validated bootstrap bias-correction procedure. RESULTS: A model using six biomarkers (adiponectin, C-reactive protein, ferritin, interleukin-2 receptor A, glucose, and insulin) was developed for assessing an individual's 5-year risk of developing type 2 diabetes. This model has a bootstrap-estimated area under the curve of 0.76, which is greater than that for A1C, fasting plasma glucose, fasting serum insulin, BMI, sex-adjusted waist circumference, a model using fasting glucose and insulin, and a noninvasive clinical model. CONCLUSIONS: A model incorporating six circulating biomarkers provides an objective and quantitative estimate of the 5-year risk of developing type 2 diabetes, performs better than single risk indicators and a noninvasive clinical model, and provides better stratification than fasting plasma glucose alone.

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 +/- 2.4%) and 12 to 20 months after (96.9 +/- 1.9%) ZDV replacement, independently of a therapeutic change for stavudine. The fitness of the variants harboring the ZDV-resistant MUT 215 genotype following drug withdrawal was calculated to be 96 to 99% of that of the variants harboring the WT 215 genotype. The apparent stability of ZDV-resistant variants in the study population may have two main complementary explanations: persistent selective pressure secondary to partial cross-resistance due to the new regimens given after the therapeutic alteration and suppression of viral replication after the therapeutic alteration that could have hampered the replacement of less fit variants by fitter variants. These findings indicate that, at least within 15 months following discontinuation of ZDV, an effective antiretroviral therapy is insufficient to allow for ZDV-resistant strains to disappear, and thus to allow for the safe re-introduction of the drug.