Edward Moler

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 ∼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. RESULTS A model using six biomarkers (adiponectin, C-reactive protein, ferritin, interleukin-2 receptor A, glucose, and insulin) was developed for assessing an individuals 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.

CHIR-124, a Novel Potent Inhibitor of Chk1, Potentiates the Cytotoxicity of Topoisomerase I Poisons In vitro and In vivo

Purpose: Chk1 kinase is a critical regulator of both S and G2-M phase cell cycle checkpoints in response to DNA damage. This study aimed to evaluate the biochemical, cellular, and antitumor effects of a novel Chk1 inhibitor, CHIR124. Experimental Design: CHIR-124 was evaluated for its ability to abrogate cell cycle checkpoints, to potentiate cytotoxicity, and to inhibit Chk1-mediated signaling induced by topoisomerase I poisons in human tumor cell line and xenograft models. Results: CHIR-124 is a quinolone-based small molecule that is structurally unrelated to other known inhibitors of Chk1. It potently and selectively inhibits Chk1 in vitro (IC50 = 0.0003 μmol/L). CHIR-124 interacts synergistically with topoisomerase poisons (e.g., camptothecin or SN-38) in causing growth inhibition in several p53-mutant solid tumor cell lines as determined by isobologram or response surface analysis. CHIR-124 abrogates the SN-38–induced S and G2-M checkpoints and potentiates apoptosis in MDA-MD-435 breast cancer cells. The abrogation of the G2-M checkpoint and induction of apoptosis by CHIR-124 are enhanced by the loss of p53. We have also shown that CHIR-124 treatment can restore the level of cdc25A protein, which is normally targeted by Chk1 for degradation following DNA damage, indicating that Chk1 signaling is suppressed in the presence of CHIR-124. Finally, in an orthotopic breast cancer xenograft model, CHIR-124 potentiates the growth inhibitory effects of irinotecan by abrogating the G2-M checkpoint and increasing tumor apoptosis. Conclusions: CHIR-124 is a novel and potent Chk1 inhibitor with promising antitumor activities when used in combination with topoisomerase I poisons.

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 individuals 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.

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

Abstract 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

Comparison of Accuracy of Diabetes Risk Score and Components of the Metabolic Syndrome in Assessing Risk of Incident Type 2 Diabetes in Inter99 Cohort

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

Development of a Screening Assay for Surrogate Markers of Chk1 Inhibitor-Induced Cell Cycle Release

235,500/QALY gained at 5 years and

Development of a Type 2 Diabetes Risk Model From a Panel of Serum Biomarkers From the Inter99 Cohort Response to Rathmann, Kowall, and Schulze

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.

CACNA1E in cancer diagnosis detection and treatment

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.

Effects of inhibitors of fgfr3 on gene transcription

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.

Method for determining risk of diabetes

Rathmann, Kowall, and Schulze (1) suggest that the diabetes risk score (DRS) model is no better than simple clinical models and thus is of limited utility. To support this contention, they compare our area under the receiver operating characteristic curve (AROC) with those reported for different models. However, the AROC of a test is population specific; therefore, comparisons between populations with different baseline risks are problematic because sensitivity and specificity are subject to alteration by disease prevalence (2). In general, the AROC decreases as prevalence increases as it is increasingly difficult to differentiate outcomes in less healthy populations. The Inter99 subpopulation with age >39 years and BMI ≥25 kg/m2 that we used had a 5-year risk of 5.7%, nearly 2.5-fold higher …