An exploratory retrospective assessment of a quantitative measure of diabetes risk: medical management and patient impact in a primary care setting.

BACKGROUND: Primary care providers with limited time and resources bear a heavy responsibility for chronic disease prevention or progression. Reliable clinical tools are needed to risk stratify patients for more targeted care. This exploratory study examined the care of patients who had been risk stratified regarding their likelihood of clinically progressing to type 2 diabetes. METHODS: This was a retrospective chart review pilot study conducted to assess a primary care provider's use of a risk screening test. In this quality improvement project, the result of the risk screening was examined in relation to its influence on medical management and clinical impact on patients at risk for diabetes. All providers were board certified in family medicine and had more than 10 years clinical experience in managing diabetes and prediabetes. No specific clinical practice guidelines were mandated for patient care in this pilot study. Physicians in the practice group received an orientation to the diabetes risk measure and its availability for use in a pilot study to be conducted over a 6-month period. We identified the 696 nondiabetic adults in family practices who received a risk screening test (PreDx(®), a multi-marker blood test that estimates the 5-year likelihood of conversion to type 2 diabetes) between June and November 2011 for a 6-month sample. A comparison group of 2,002 patients from a total database of 3.2 million patients who did not receive the risk test was randomly selected from the same clinical database after matching for age, sex, selected diagnoses, and metabolic risk factors. Patient groups were compared for intensity of care provided and clinical impact. RESULTS: Compared to patients with a similar demographic and diagnostic profile, patients who had the risk test received more intensive primary care and had better clinical outcome than comparison patients. Risk-tested patients were more likely to return for follow-up visits, be monitored for relevant cardio-metabolic risk factors, and receive prescription medications with P<0.001. Further, intensity of care was associated with the level of risk test result: patients with moderate or high scores were more likely to return for follow-up visits and receive prescription medications than patients with low scores. All P-values for comparison patients between the low and moderate groups, low and high groups, and moderate and high groups resulted in P<0.001. Risk-tested patients were more likely than their comparison group counterparts to achieve weight reduction, lowered blood pressure, and improved blood glucose and cholesterol as demonstrated by P-values of <0.001. CONCLUSION: Use of a risk stratification test in primary care may help providers to more effectively identify high risk patients, manage diabetes risk, increase patient involvement in diabetes risk management, and improve clinical outcomes. A randomized controlled study is the next step to investigate the impact of diabetes risk stratification in primary care.

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.

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.

Metabolic syndrome components are associated with future medical costs independent of cardiovascular hospitalization and incident diabetes.

BACKGROUND: Higher medical care costs have been associated with the number of metabolic syndrome components present, but the association with future medical costs has not been described. Furthermore, the independent cost contribution of each component alone and in combination with other components is unknown. METHODS: We identified 57,420 nondiabetic adults aged ≥30 with all metabolic syndrome components measured in 2003-2004 and with 5 years of follow-up data available. We calculated and compared total annualized direct medical costs across the number of metabolic syndrome components present and for all possible combinations of metabolic syndrome components. The independent contribution to costs of each component was isolated by adjusting for age, sex, the other metabolic syndrome components, incident diabetes, number of years with diabetes, cardiovascular (CVD) hospitalization, and years after hospitalization. RESULTS: Annualized age- and sex-adjusted medical costs incurred over follow-up increased with each additional metabolic syndrome component present. After full adjustment, hypertension ($550), obesity ($366), low high-density lipoprotein (HDL) ($363), and high triglycerides ($317) were significantly associated with higher annual costs (P < 0.001 for all), but impaired fasting glucose was not. Further analysis indicated that costs were significantly elevated for each of these components only among those who did not develop diabetes or were not hospitalized for CVD. CONCLUSIONS: Incident diabetes or CVD hospitalizations accounted for the association between each metabolic syndrome component and future costs when these events occurred, but the elevated costs associated with metabolic syndrome components were observed even when these events did not occur. Further research is needed to understand the underlying morbidity that is driving the increased costs.

Diabetes incidence for all possible combinations of metabolic syndrome components.

AIMS: Because metabolic syndrome (MetS) is defined as any three of five criteria, not all persons with MetS have the same risk factors. Whether the combinations of criteria confer equal diabetes risk is unknown. METHODS: We identified 58,056 non-diabetic adults age >=30 with all MetS components measured in 2003-2004. We estimated age- and sex-adjusted diabetes incidence over 5 years for all possible combinations of MetS components. RESULTS: The overall incidence rate of diabetes was 12.5/1000 person-years (95% CI 12.1-12.9). Although incidence increased with the MetS factor count, incidence varied by >9-fold in patients with 3 risk factors, >5-fold in patients with 4 factors, and >54-fold in patients with <3 factors. All two-factor combinations that included hyperglycemia had higher incidence rates than three- or four-factor combinations that did not. For example, incidence in patients with only hyperglycemia and obesity was 21.7/1000 person-years (95% CI 17.4-27.1), compared to 11.4 (9.8-13.4) among those with four components without hyperglycemia. CONCLUSIONS: Diabetes risk increases exponentially with MetS factor count, but varies substantially depending upon which factors are present. Hyperglycemia, regardless of the presence of MetS, is a much stronger predictor of incident diabetes than MetS without hyperglycemia.

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 G(2)-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 (IC(50) = 0.0003 micromol/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 G(2)-M checkpoints and potentiates apoptosis in MDA-MD-435 breast cancer cells. The abrogation of the G(2)-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 G(2)-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.

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.