OBJECTIVEThe 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 METHODSSubjects were selected from the Inter99 cohort, a longitudinal population-based study of ∼6,600 Danes in a nested case-control design with the primary outcome of 5-year conversion to type 2 diabetes. Nondiabetic subjects, aged ≥39 years, with BMI ≥25 kg/m2 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.RESULTSA 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.CONCLUSIONSA 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.

Assays of drug action typically evaluate biochemical activity. However, accurately matching therapeutic efficacy with biochemical activity is a challenge. High-content cellular assays seek to bridge this gap by capturing broad information about the cellular physiology of drug action. Here, we present a method of predicting the general therapeutic classes into which various psychoactive drugs fall, based on high-content statistical categorization of gene expression profiles induced by these drugs. When we used the classification tree and random forest supervised classification algorithms to analyze microarray data, we derived general ''efficacy profiles'' of biomarker gene expression that correlate with antidepressant, antipsychotic and opioid drug action on primary human neurons in vitro. These profiles were used as predictive models to classify naïve in vitro drug treatments with 83.3% (random forest) and 88.9% (classification tree) accuracy. Thus, the detailed information contained in genomic expression data is sufficient to match the physiological effect of a novel drug at the cellular level with its clinical relevance. This capacity to identify therapeutic efficacy on the basis of gene expression signatures in vitro has potential utility in drug discovery and drug target validation.pharmacogenomics ͉ predictive efficacy ͉ drug screening M icroarray-based gene expression patterns can be used as fingerprints of cellular physiology. The variety of cellular physiologies discernable by gene expression profile fingerprinting is expanding as an increasing range of cell types and cellular manipulations are investigated, and statistical methods of expression profile classification are refined. In yeast, distinctive profiles of genomic expression have been used to characterize cellular responses to diverse environmental transitions (1), functionally classify genetic manipulations, and discover a novel target for a drug of partially characterized function (2). In cancer studies, microarray data has been used to classify solid tumors (3), correlate tumor characteristics to clinical outcome (4), and cluster cell lines on the basis of their tissue of origin and response to drugs (5-9). In the area of toxicogenomics, large-scale gene expression analysis of toxin-treated cells and animals has yielded a highly accurate capacity to recognize the toxic potential of novel drug candidates (10-14), resulting in an increase in the efficiency of drug triage in the pharmaceutical development pipeline.Multiple statistical methods have been applied to classification and recognition of expression profiles. Supervised classification analysis methods, which can classify patterns of novel data based on prior knowledge of sample classes, include linear discriminant analysis and genetic algorithm/K-nearest neighbors (11, 15), Fisher discriminant analysis (16), support vector machines (17), neural networks (18), and tree-based analysis (19). Here, we use human primary neurons treated with multiple members of multiple classes of antidepressan...

The PreDx DRS provides the absolute risk of diabetes conversion in five years for subjects identified to be "at risk" using the clinical factors.

Short-term anti-tumor effects and long-term effects (complete regression) were observed with CR011-vcMMAE, but not with the reference agents. These results suggest that CR011-vcMMAE may provide therapeutic benefit in malignant melanoma.

Soluble ST2 is an established biomarker of heart failure (HF) progression. Data about its prognostic implications in patients with mildly symptomatic HF eligible to receive cardiac resynchronization therapy defibrillators (CRT-D) are limited. In a cohort of 684 patients enrolled in Multicenter Automated Defibrillator Implantation Trial (MADIT)-CRT, levels of soluble ST2 (sST2) were serially assessed at baseline and 1 year (n = 410). In multivariable-adjusted models, elevated baseline sST2 was associated with an increased risk of death, death or HF, and death or ventricular arrhythmia (VA) even when adjusting for baseline brain natriuretic protein (BNP) levels. In addition, patients with lower baseline sST2 levels had greater risk reduction with CRT-D (p = 0.006). Serial assessment revealed increased risk of VA and death or VA (HR per 10 % increase in sST2 1.11 (1.04-1.20), p = 0.004). Among patients with mildly symptomatic HF and eligibility for CRT-D, baseline and serial assessments sST2 may provide important information for risk stratification.