Clinically significant novel biomarkers for prediction of first ever myocardial infarction: the Tromsø Study.

BACKGROUND: Identification of individuals with high risk for first-ever myocardial infarction (MI) can be improved. The objectives of the study were to survey multiple protein biomarkers for association with the 10-year risk of incident MI and identify a clinically significant risk model that adds information to current common risk models. METHODS AND RESULTS: We used an immunoassay platform that uses a sensitive, sample-efficient molecular counting technology to measure 51 proteins in samples from the fourth survey (1994) in the Tromsø Study, a longitudinal study of men and women in Tromsø, Norway. A case control design was used with 419 first-ever MI cases (169 females/250 males) and 398 controls (244 females/154 males). Of the proteins measured, 17 were predictors of MI when considered individually after adjustment for traditional risk factors either in men, women, or both. The 6 biomarkers adjusted for traditional risk factors that were selected in a multivariable model (odds ratios [OR] per standard deviation) using a stepwise procedure were apolipoprotein B/apolipoprotein A1 ratio (1.40), kallikrein (0.73), lipoprotein a (1.29), matrix metalloproteinase 9 (1.30), the interaction term IP-10/CXCL10×women (0.69), and the interaction term thrombospondin 4×men (1.38). The composite risk of these biomarkers added significantly to the traditional risk factor model with a net reclassification improvement of 14% (P=0.0002), whereas the receiver operating characteristic area increased from 0.757 to 0.791, P=0.0004. CONCLUSIONS: Novel protein biomarker models improve identification of 10-year MI risk above and beyond traditional risk factors with 14% better allocation to either high or low risk group.

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.

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.

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.

Development of a screening assay for surrogate markers of CHK1 inhibitor-induced cell cycle release.

Chk1 is a key regulator of the S and G2/M checkpoints and is activated following DNA damage by agents such as the topoisomerase I inhibitor camptothecin (CPT). It has been proposed that Chk1 inhibitors used in combination with such a DNA damaging agent to treat tumors would potentiate cytotoxicity and increase the therapeutic index, particularly in tumors lacking functional p53. The aim of this study was to determine whether gene expression analysis could be used to inform lead optimization of a novel series of Chk1 inhibitors. The candidate small-molecule Chk1 inhibitors were used in combination with CPT to identify potential markers of functional Chk1 inhibition, as well as resulting cell cycle progression, using cDNA-based microarrays. Differential expression of several of these putative marker genes was further validated by RT-PCR for use as a medium-throughput assay. In the presence of DNA damage, Chk1 inhibitors altered CPT-dependent effects on the expression of cell cycle and DNA repair genes in a manner consistent with a Chk1-specific mechanism of action. Furthermore, differential expression of selected marker genes, cyclin E2, EGR1, and DDIT3, was dose dependent for Chk1 inhibition. RT-PCR results for these genes following treatment with a panel of Chk1 inhibitors showed a strong correlation between marker gene response and the ability of each compound to abrogate cell cycle arrest in situ following CPT-induced DNA damage. These results demonstrate the utility of global expression analysis to identify surrogate markers, providing an alternative method for rapid compound characterization to support advancement decisions in early drug discovery.