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

Selecting a target population for type 2 diabetes lifestyle prevention programs: A cost-effectiveness perspective

AIMS

Cost-effectiveness (CE) of lifestyle change programs (LCP) for type 2 diabetes (T2D) prevention is influenced by a participant's risk. We identified the risk threshold of developing T2D in the intervention population that was cost-effective for three formats of the LCP: delivered in-person individually or in groups, or delivered virtually. We compared the cost-effectiveness across program formats when there were more than one cost-effective formats.

METHODS

Using the CDC-RTI T2D CE Simulation model, we estimated CEs associated with 3 program formats in 8 population groups with an annual T2D incidence of 1% to 8%. We generated a nationally representative simulation population for each risk level using the 2011-2016 National Health and Nutrition Examination Survey data. We used an incremental cost-effectiveness ratio (ICER), cost per quality-adjusted life year (QALY) gained in 25-years, to measure the CEs of the programs. We took a health care system perspective.

RESULTS

To achieve an ICER of $50,000/QALY or lower, the annual T2D incidence of the program participant needed to be ≥5% for the in-person individual program, ≥4% for the digital individual program, and ≥3% for the in-person group program. For those with T2D risk of ≥4%, the in-person group program always dominated the digital individual program. The in-person individual program was cost-effective compared with the in-person group program only among persons with T2D risk of ≥8%.

CONCLUSIONS

Our findings could assist decision-makers in selecting the most appropriate target population for different formats of lifestyle intervention programs to prevent T2D.

Targeting of the diabetes prevention program leads to substantial benefits when capacity is constrained

OBJECTIVE

Approximately 84 million people in the USA have pre-diabetes, but only a fraction of them receive proven effective therapies to prevent type 2 diabetes. We estimated the value of prioritizing individuals at highest risk of progression to diabetes for treatment, compared to non-targeted treatment of individuals meeting inclusion criteria for the Diabetes Prevention Program (DPP).

METHODS

Using microsimulation to project outcomes in the DPP trial population, we compared two interventions to usual care: (1) lifestyle modification and (2) metformin administration. For each intervention, we compared targeted and non-targeted strategies, assuming either limited or unlimited program capacity. We modeled the individualized risk of developing diabetes and projected diabetic outcomes to yield lifetime costs and quality-adjusted life expectancy, from which we estimated net monetary benefits (NMB) for both lifestyle and metformin versus usual care.

RESULTS

Compared to usual care, lifestyle modification conferred positive benefits and reduced lifetime costs for all eligible individuals. Metformin's NMB was negative for the lowest population risk quintile. By avoiding use when costs outweighed benefits, targeted administration of metformin conferred a benefit of $500 per person. If only 20% of the population could receive treatment, when prioritizing individuals based on diabetes risk, rather than treating a 20% random sample, the difference in NMB ranged from $14,000 to $20,000 per person.

CONCLUSIONS

Targeting active diabetes prevention to patients at highest risk could improve health outcomes and reduce costs compared to providing the same intervention to a similar number of patients with pre-diabetes without targeted selection.

Multiple Biomarkers Improved Prediction for the Risk of Type 2 Diabetes Mellitus in Singapore Chinese Men and Women

BACKGROUND

Multiple biomarkers have performed well in predicting type 2 diabetes mellitus (T2DM) risk in Western populations. However, evidence is scarce among Asian populations.

METHODS

Plasma triglyceride-to-high density lipoprotein (TG-to-HDL) ratio, alanine transaminase (ALT), high-sensitivity C-reactive protein (hs-CRP), ferritin, adiponectin, fetuin-A, and retinol-binding protein 4 were measured in 485 T2DM cases and 485 age-and-sex matched controls nested within the prospective Singapore Chinese Health Study cohort. Participants were free of T2DM at blood collection (1999 to 2004), and T2DM cases were identified at the subsequent follow-up interviews (2006 to 2010). A weighted biomarker score was created based on the strengths of associations between these biomarkers and T2DM risks. The predictive utility of the biomarker score was assessed by the area under receiver operating characteristics curve (AUC).

RESULTS

The biomarker score that comprised of four biomarkers (TG-to-HDL ratio, ALT, ferritin, and adiponectin) was positively associated with T2DM risk (P trend <0.001). Compared to the lowest quartile of the score, the odds ratio was 12.0 (95% confidence interval [CI], 5.43 to 26.6) for those in the highest quartile. Adding the biomarker score to a base model that included smoking, history of hypertension, body mass index, and levels of random glucose and insulin improved AUC significantly from 0.81 (95% CI, 0.78 to 0.83) to 0.83 (95% CI, 0.81 to 0.86; P=0.002). When substituting the random glucose levels with glycosylated hemoglobin in the base model, adding the biomarker score improved AUC from 0.85 (95% CI, 0.83 to 0.88) to 0.86 (95% CI, 0.84 to 0.89; P=0.032).

CONCLUSION

A composite score of blood biomarkers improved T2DM risk prediction among Chinese.

Selecting the optimal risk threshold of diabetes risk scores to identify high-risk individuals for diabetes prevention: a cost-effectiveness analysis

AIMS

Although risk scores to predict type 2 diabetes exist, cost-effectiveness of risk thresholds to target prevention interventions are unknown. We applied cost-effectiveness analysis to identify optimal thresholds of predicted risk to target a low-cost community-based intervention in the USA.

METHODS

We used a validated Markov-based type 2 diabetes simulation model to evaluate the lifetime cost-effectiveness of alternative thresholds of diabetes risk. Population characteristics for the model were obtained from NHANES 2001-2004 and incidence rates and performance of two noninvasive diabetes risk scores (German diabetes risk score, GDRS, and ARIC 2009 score) were determined in the ARIC and Cardiovascular Health Study (CHS). Incremental cost-effectiveness ratios (ICERs) were calculated for increasing risk score thresholds. Two scenarios were assumed: 1-stage (risk score only) and 2-stage (risk score plus fasting plasma glucose (FPG) test (threshold 100 mg/dl) in the high-risk group).

RESULTS

In ARIC and CHS combined, the area under the receiver operating characteristic curve for the GDRS and the ARIC 2009 score were 0.691 (0.677-0.704) and 0.720 (0.707-0.732), respectively. The optimal threshold of predicted diabetes risk (ICER < $50,000/QALY gained in case of intervention in those above the threshold) was 7% for the GDRS and 9% for the ARIC 2009 score. In the 2-stage scenario, ICERs for all cutoffs ≥ 5% were below $50,000/QALY gained.

CONCLUSIONS

Intervening in those with ≥ 7% diabetes risk based on the GDRS or ≥ 9% on the ARIC 2009 score would be cost-effective. A risk score threshold ≥ 5% together with elevated FPG would also allow targeting interventions cost-effectively.

Decision models of prediabetes populations: A systematic review

AIMS

With evidence supporting the use of preventive interventions for prediabetes populations and the use of novel biomarkers to stratify the risk of progression, there is a need to evaluate their cost-effectiveness across jurisdictions. Our aim is to summarize and assess the quality and validity of decision models and model-based economic evaluations of populations with prediabetes, to evaluate their potential use for the assessment of novel prevention strategies and to discuss the knowledge gaps, challenges and opportunities.

MATERIALS AND METHODS

We searched Medline, Embase, EconLit and NHS EED between 2000 and 2018 for studies reporting computer simulation models of the natural history of individuals with prediabetes and/or we used decision models to evaluate the impact of treatment strategies on these populations. Data were extracted following PRISMA guidelines and assessed using modelling checklists. Two reviewers independently assessed 50% of the titles and abstracts to determine whether a full text review was needed. Of these, 10% was assessed by each reviewer to cross-reference the decision to proceed to full review. Using a standardized form and double extraction, each of four reviewers extracted 50% of the identified studies.

RESULTS

A total of 29 published decision models that simulate prediabetes populations were identified. Studies showed large variations in the definition of prediabetes and model structure. The inclusion of complications in prediabetes (n = 8) and type 2 diabetes (n = 17) health states also varied. A minority of studies simulated annual changes in risk factors (glycaemia, HbA1c, blood pressure, BMI, lipids) as individuals progressed in the models (n = 7) and accounted for heterogeneity among individuals with prediabetes (n = 7).

CONCLUSIONS

Current prediabetes decision models have considerable limitations in terms of their quality and validity and do not allow evaluation of stratified strategies using novel biomarkers, highlighting a clear need for more comprehensive prediabetes decision models.

Identification of novel population clusters with different susceptibilities to type 2 diabetes and their impact on the prediction of diabetes

Type 2 diabetes is one of the subtypes of diabetes. However, previous studies have revealed its heterogeneous features. Here, we hypothesized that there would be heterogeneity in its development, resulting in higher susceptibility in some populations. We performed risk-factor based clustering (RFC), which is a hierarchical clustering of the population with profiles of five known risk factors for type 2 diabetes (age, gender, body mass index, hypertension, and family history of diabetes). The RFC identified six population clusters with significantly different prevalence rates of type 2 diabetes in the discovery data (N = 10,023), ranging from 0.09 to 0.44 (Chi-square test, P < 0.001). The machine learning method identified six clusters in the validation data (N = 215,083), which also showed the heterogeneity of prevalence between the clusters (P < 0.001). In addition to the prevalence of type 2 diabetes, the clusters showed different clinical features including biochemical profiles and prediction performance with the risk factors. SOur results seem to implicate a heterogeneous mechanism in the development of type 2 diabetes. These results will provide new insights for the development of more precise management strategy for type 2 diabetes.

A systematic review of economic evaluations of screening programmes for cardiometabolic diseases

Background

The early detection and adequate management of cardiometabolic diseases (CMD) is becoming a priority to prevent future health problems and related healthcare costs.

Aim

This study systematically reviewed the economic evaluations of screening programmes for the early detection of persons at risk for CMD.

Methods

A systematic review was conducted using MEDLINE, Web of Science, NHSEED and the CEA registry to identify relevant articles published between 1 January 2005 and 1 May 2015. Two reviewers independently selected articles, systematically extracted data and critically appraised the study quality using the Extended Consensus on Health Economic Criteria (CHEC) List.

Results

From the initial 2820 studies identified, 17 were included. Six studies assessed whether screening would be cost-effective, seven aimed to determine the most efficient screening programme and four assessed the cost-effectiveness of existing programmes. There were 11 cost-utility analyses using quality-adjusted life years (QALYs) or disability-adjusted life years. Decision-analytic modelling (e.g. Markov model) was most frequently used (n = 10), followed by simulation models (n = 4), observational (n = 2) and trial-based (n = 1) studies. All studies assessing the cost per QALY gained of screening for cardiovascular diseases and diabetes mellitus (n = 8) were below a threshold of £30 000, while those assessing chronic kidney diseases (n = 2) were above the threshold. Conclusions: In view of the heterogeneity in study objectives, country setting, screening programmes, comparators, methodology and outcomes, it is not possible to make clear recommendations about the economic value of screening programmes for CMD. Developing further screening programmes and conducting thorough economic analysis, including usual care, is needed.

Lab-on-a-chip electrical multiplexing techniques for cellular and molecular biomarker detection

Signal multiplexing is vital to develop lab-on-a-chip devices that can detect and quantify multiple cellular and molecular biomarkers with high throughput, short analysis time, and low cost. Electrical detection of biomarkers has been widely used in lab-on-a-chip devices because it requires less external equipment and simple signal processing and provides higher scalability. Various electrical multiplexing for lab-on-a-chip devices have been developed for comprehensive, high throughput, and rapid analysis of biomarkers. In this paper, we first briefly introduce the widely used electrochemical and electrical impedance sensing methods. Next, we focus on reviewing various electrical multiplexing techniques that had achieved certain successes on rapid cellular and molecular biomarker detection, including direct methods (spatial and time multiplexing), and emerging technologies (frequency, codes, particle-based multiplexing). Lastly, the future opportunities and challenges on electrical multiplexing techniques are also discussed.

Enhancing Glycemic Control via Detection of Insulin Using Electrochemical Impedance Spectroscopy

BACKGROUND

Currently, glycemic management for individuals with diabetes mellitus involves monitoring glucose only, which is insufficient as glucose metabolism involves other biomarkers such as insulin. Monitoring additional biomarkers alongside glucose has been proposed to improve glycemic control. In this work, the development of a rapid and label-free insulin biosensor with high sensitivity and accuracy is presented. The insulin sensor prototype also serves as a prior study for a multimarker sensing platform technology that can further improve glycemic control in the future.

METHODS

Electrochemical impedance spectroscopy was used to identify an optimal frequency specific to insulin detection on a gold disk electrode with insulin antibody immobilized, which was accomplished by conjugating the primary amines of insulin antibody to the carboxylic bond of the self-assembling monolayer on the gold surface. After blocking with ethanolamine, the insulin physiological concentration gradient was tested. The imaginary impedance was correlated to insulin concentration and the results were compared with standard equivalent circuit analysis and correlation of charge transfer resistance to target concentration.

RESULTS

The optimal frequency of insulin is 810.5 Hz, which is characterized by having the highest sensitivity and sufficient specificity. The lower limit of detection was 2.26 [Formula: see text] which is comparable to a standard and better than traditional approaches.

CONCLUSION

An insulin biosensor prototype capable of detecting insulin in physiological range without complex data normalization was developed. This prototype will be the ground works of a multimarker platform sensor technology for future all-in-one glycemic management sensors.

A systematic review and checklist presenting the main challenges for health economic modeling in personalized medicine: towards implementing patient-level models

INTRODUCTION

The ongoing development of genomic medicine and the use of molecular and imaging markers in personalized medicine (PM) has arguably challenged the field of health economic modeling (HEM). This study aims to provide detailed insights into the current status of HEM in PM, in order to identify if and how modeling methods are used to address the challenges described in literature. Areas covered: A review was performed on studies that simulate health economic outcomes for personalized clinical pathways. Decision tree modeling and Markov modeling were the most observed methods. Not all identified challenges were frequently found, challenges regarding companion diagnostics, diagnostic performance, and evidence gaps were most often found. However, the extent to which challenges were addressed varied considerably between studies. Expert commentary: Challenges for HEM in PM are not yet routinely addressed which may indicate that either (1) their impact is less severe than expected, (2) they are hard to address and therefore not managed appropriately, or (3) HEM in PM is still in an early stage. As evidence on the impact of these challenges is still lacking, we believe that more concrete examples are needed to illustrate the identified challenges and to demonstrate methods to handle them.

Feasibility in the development of a multi-marker detection platform

A feasibility study for a label-free, multi-marker single sensor using electrochemical impedance spectroscopy (EIS), imaginary impedance, and a signal decoupling technique is reported. To our knowledge, this is the first reported attempt of using imaginary impedance for biomarker detection and multi-marker detection. The electrochemical responses of purified low and high density lipoproteins (LDL and HDL, respectively) were first individually characterized through the immobilization of their molecular recognition elements (MREs) onto gold disk electrodes (GDEs). The co-immobilization was performed by immobilizing the MREs of both LDL and HDL on the same GDE, which was then used to detect LDL and HDL simultaneously in mixed solution. Previous individual purified responses were then used to de-convolute the mixed response, when the two biomarkers were detected in mixed solutions. The optimal frequencies of LDL and HDL were found to be 81.38Hz and 5.49Hz, respectively, which shifted to 175.8Hz and 3.74Hz under co-immobilized conditions. After comparing the electrochemical signal in complex and imaginary impedance, imaginary impedance was found to be more suitable for multi-marker detection purposes. Since imaginary impedance is related to capacitance, electric displacement, relative permittivity, and effective capacitance were derived to elucidate the theory of optimal frequency. This work shows that EIS has the potential for multi-marker detection and can be extended to monitor other complex diseases such as diabetes mellitus for better management and diagnostic purposes.

Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts

Background

Detection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening.

Methods

Plasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia – the AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D.

Results

The LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76 %. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention.

Conclusions

Considering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.

Electronic supplementary material

The online version of this article (doi:10.1186/s12944-016-0234-3) contains supplementary material, which is available to authorized users.

Progression from impaired fasting glucose to type 2 diabetes mellitus among Chinese subjects with and without hypertension in a primary care setting

BACKGROUND

The progression from impaired fasting glucose (IFG) to type 2 diabetes mellitus (T2DM) in Chinese subjects, with and without hypertension, in a primary care setting was unknown.

METHODS

The present retrospective multicenter 5-year (2002-2007) cohort study was performed on IFG subjects attending 23 general outpatient clinics who were identified by their elevated fasting blood glucose laboratory results. Development of T2DM was determined by physician diagnosis of T2DM or starting of oral antidiabetic drugs within 5 years. The relationship between the time of T2DM diagnosis and subject characteristics was assessed by adjusted hazard ratios (aHR) from Cox hazards model.

RESULTS

Of the 9161 IFG subjects, 4080 (45%) were men and 5081 (55%) were women. There were 1998 subjects who developed T2DM. The 5-year cumulative incidence was 0.218, whereas the overall annual incidence rate was 5.981/100 person-years. Subjects were more likely to develop T2DM if they were hypertensive (aHR = 1.44; 95% confidence interval [CI] 1.28-1.62; P < 0.001), aged <60 years (aHR = 1.36, 95% CI 1.24-1.49; P < 0.001), female (aHR = 1.18, 95% CI 1.08-1.29; P < 0.001), and had higher fasting glucose levels (6.39 ± 0.49 vs 6.24 ± 0.43 mmol/L in the group that developed T2DM vs the group without T2DM, respectively; aHR = 2.01, 95% CI 1.83-2.20; P < 0.001).

CONCLUSION

Overall, more than one-fifth of IFG subjects in the primary care setting developed T2DM within 5 years. Health care professionals can target interventions to patients with risk factors for disease progression.

Preventing type 2 diabetes mellitus: a call for personalized intervention

In parallel with the rising prevalence of obesity worldwide, especially in younger people, there has been a dramatic increase in recent decades in the incidence and prevalence of metabolic consequences of obesity, in particular prediabetes and type 2 diabetes mellitus (DM2). Although approximately one-third of US adults now meet one or more diagnostic criteria for prediabetes, only a minority of those so identified as being at risk for DM2 actually progress to diabetes, and some may regress to normal status. Given the uncertain prognosis of prediabetes, it is not clear who is most likely to benefit from lifestyle change or medication interventions that are known to reduce DM2 risk. We review the many factors known to influence risk of developing DM2 and summarize treatment trials demonstrating the possibility of preventing DM2. Applying the concepts of personalized medicine and the potential of "big data" approaches to analysis of massive amounts of routinely gathered clinical and laboratory data from large populations, we call for the development of tools to more precisely estimate individual risk of DM2.

Modelling the economics of type 2 diabetes mellitus prevention: a literature review of methods

Our objective was to review modelling methods for type 2 diabetes mellitus prevention cost-effectiveness studies. The review was conducted to inform the design of a policy analysis model capable of assisting resource allocation decisions across a spectrum of prevention strategies. We identified recent systematic reviews of economic evaluations in diabetes prevention and management of obesity. We extracted studies from two existing systematic reviews of economic evaluations for the prevention of diabetes. We extracted studies evaluating interventions in a non-diabetic population with type 2 diabetes as a modelled outcome, from two systematic reviews of obesity intervention economic evaluations. Databases were searched for studies published between 2008 and 2013. For each study, we reviewed details of the model type, structure, and methods for predicting diabetes and cardiovascular disease. Our review identified 46 articles and found variation in modelling approaches for cost-effectiveness evaluations for the prevention of type 2 diabetes. Investigation of the variables used to estimate the risk of type 2 diabetes suggested that impaired glucose regulation, and body mass index were used as the primary risk factors for type 2 diabetes. A minority of cost-effectiveness models for diabetes prevention accounted for the multivariate impacts of interventions on risk factors for type 2 diabetes. Twenty-eight cost-effectiveness models included cardiovascular events in addition to type 2 diabetes. Few cost-effectiveness models have flexibility to evaluate different intervention types. We conclude that to compare a range of prevention interventions it is necessary to incorporate multiple risk factors for diabetes, diabetes-related complications and obesity-related co-morbidity outcomes.

Use of number needed to treat in cost-effectiveness analyses

OBJECTIVE

To review the use of number needed to treat (NNT) and/or number needed to harm (NNH) values to determine their relevance in helping clinicians evaluate cost-effectiveness analyses (CEAs).

DATA SOURCES

PubMed and EconLit were searched from 1966 to September 2012.

STUDY SELECTION AND DATA EXTRACTION

Reviews, editorials, non-English-language articles, and articles that did not report NNT/NNH or cost-effectiveness ratios were excluded. CEA studies reporting cost per life-year gained, per quality-adjusted life-year (QALY), or other cost per effectiveness measure were included. Full texts of all included articles were reviewed for study information, including type of journal, impact factor of the journal, focus of study, data source, publication year, how NNT/NNH values were reported, and outcome measures.

DATA SYNTHESIS

A total of 188 studies were initially identified, with 69 meeting our inclusion criteria. Most were published in clinician-practice-focused journals (78.3%) while 5.8% were in policy-focused journals, and 15.9% in health-economics-focused journals. The majority (72.4%) of the articles were published in high-impact journals (impact factor >3.0). Many articles focused on either disease treatment (40.5%) or disease prevention (40.5%). Forty-eight percent reported NNT as a part of the CEA ratio per event. Most (53.6%) articles used data from literature reviews, while 24.6% used data from randomized clinical trials, and 20.3% used data from observational studies. In addition, 10% of the studies implemented modeling to perform CEA.

CONCLUSIONS

CEA studies sometimes include NNT ratios. Although it has several limitations, clinicians often use NNT for decision-making, so including NNT information alongside CEA findings may help clinicians better understand and apply CEA results. Further research is needed to assess how NNT/NNH might meaningfully be incorporated into CEA publications.

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.

'Personalized medicine' to identify genetic risks for type 2 diabetes and focus prevention: can it fulfill its promise?

Public health measures are required to address the worldwide increase in type 2 diabetes. Proponents of personalized medicine predict a future in which disease treatment and, more important, prevention will be tailored to high-risk individuals rather than populations and will be based on genetic and other new biomarker tests. Accurate biomarker tests to identify people at risk for diabetes could allow more-targeted and perhaps individualized prevention efforts. DNA variants conferring higher risk for type 2 diabetes have been identified. However, these account for only a small fraction of genetic risk, which limits their practical predictive value. Nor has identification of these variants yet led to new, individualized prevention methods. Further research is needed to identify genomic and other types of biomarkers that could accurately predict risk and facilitate targeted prevention.

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.

A Dutch and American commentary on IT in health care: roundtable discussions on IT and innovations in health care

PURPOSE

This chapter reports on experts' perspectives on health information technology (HIT) and how it may be used to improve health care quality and to lower health care costs.

DESIGN/METHODOLOGY/APPROACH

Two roundtables were convened that focused on how to best use HIT to improve the quality of health care while ensuring it is accessible and affordable. Participants drew upon lessons learned in the Netherlands, the United States, and other countries.

FINDINGS

The first roundtable focused on the use of (1) electronic health records (EHRs) by health care providers, (2) cloud computing for EHRs and health portals for consumers, and (3) data registries and networks for public health surveillance. The second roundtable highlighted (1) the rapid growth of personalized medicine, (2) the corresponding growth and sophistication of bioinformatics and analytics, (3) the increasing presence of mobile HIT, and (4) the disruptive changes in the institutional structures of biomedical research and development.

PRACTICAL IMPLICATIONS

Governmental sponsorship of small pilot projects to solve practicable health system problems would encourage HIT innovation among key stakeholders. However, large-scale HIT solutions developed through small pilot projects--should be pursued through public-private partnerships. At the same time, governments should speed up legislative and regulatory procedures to encourage adoption of cost-effective HIT innovations.

SOCIAL IMPLICATIONS

Mobile HIT and social media are capable of fostering disease prevention and encouraging personal responsibility for improving or stabilizing chronic diseases.

ORIGINALITY/VALUE

Both health services researchers and policy makers should find this chapter of value since it highlights trends in HIT and addresses how health care quality may be improved while costs are contained.