Large Observational Databases Research Articles

Comparing Inverse Probability of Treatment Weighting and Instrumental Variable Methods for the Evaluation of Adenosine Diphosphate Receptor Inhibitors After Percutaneous Coronary Intervention.

There is increasing interest in performing comparative effectiveness analyses in large observational databases, yet these analyses must adjust for treatment selection issues. To conduct comparative safety and efficacy analyses of prasugrel vs clopidogrel bisulfate after percutaneous coronary intervention and to evaluate inverse probability of treatment weighting (a propensity score method) and instrumental variable methods. This study used data from the Treatment With Adenosine Diphosphate Receptor Inhibitors-Longitudinal Assessment of Treatment Patterns and Events After Acute Coronary Syndrome (TRANSLATE-ACS) study. Included in the study were patients undergoing percutaneous coronary intervention for myocardial infarction, 26.0% of whom received prasugrel. The study dates were April 4, 2010, to October 31, 2012. Choice of initial antiplatelet agent (prasugrel or clopidogrel). Safety and efficacy outcomes included 1-year composite major adverse cardiovascular events, moderate to severe bleeding, and stent thrombosis. Hospitalizations for pneumonia, bone fractures, and planned percutaneous coronary intervention were used as the falsification end points. The study cohort comprised 11 784 participants (mean [SD] age, 60.0 [11.6] years, and 28.0% were female). Using inverse probability of treatment weighting adjustment, prasugrel and clopidogrel had similar major adverse cardiovascular events (hazard ratio [HR], 0.98; 95% CI, 0.83-1.16) and bleeding outcomes (1.18; 0.77-1.80), but prasugrel had a lower rate of stent thrombosis (0.51; 0.31-0.85). Using instrumental variable methods, prasugrel use was associated with a lower rate of the major adverse cardiovascular event end point (HR, 0.68; 95% CI, 0.47-1.00) but nonsignificant differences in the rates of bleeding (0.95; 0.41-2.08) and stent thrombosis (0.67; 0.16-2.00). There was no significant treatment difference noted in any of the falsification end-point rates when analyses were performed using inverse probability of treatment weighting, although the bone fracture end point approached statistical significance. Nevertheless, a lower rate of pneumonia-related hospitalizations was noted in the prasugrel-treated patients when analyses were performed using instrumental variable methods. Conclusions regarding the safety and efficacy of antiplatelet therapy varied depending on analytic technique, and none were concordant with the results from randomized trials. In addition, both statistical strategies demonstrated concerning associations when tested in the falsification analyses. A high level of scrutiny and careful attention to assumptions and validity are required when interpreting complex analyses of observational data.

Batch Sample Design from Databases for Logistic Regression

The prevalence of large observational databases offers potential for identifying predictive relationships among variables of interest, although observational data are generally far less informative and less reliable than experimental data. We consider the problem of selecting a subset of records from a large observational database, for the purpose of designing a small but powerful experiment involving the selected records. It is assumed that the database contains the predictor variables but is missing the response variable, and that the purpose is to fit a logistic regression model after the response is obtained via the experiment. Active learning methods, which treat a similar problem, usually select records sequentially and focus on the single objective of classification accuracy. In contrast, many emerging applications require batch sample designs and have a variety of objectives that may include classification accuracy or accuracy of the estimated parameters, the latter being more in line with the optimal design of experiments (DOE) paradigm. The aim of this paper is to explore batch sampling from databases from a DOE perspective, particularly regarding the configuration, performance, and robustness of the designs that result from the different criteria. Through extensive simulation, we show that DOE‐based batch sampling methods can substantially outperform random sampling and the entropy method that is popular in active learning. We also provide insight and guidelines for selecting appropriate design criteria and modeling assumptions. Copyright © 2016 John Wiley & Sons, Ltd.

Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.

Ideally, questions about comparative effectiveness or safety would be answered using an appropriately designed and conducted randomized experiment. When we cannot conduct a randomized experiment, we analyze observational data. Causal inference from large observational databases (big data) can be viewed as an attempt to emulate a randomized experiment-the target experiment or target trial-that would answer the question of interest. When the goal is to guide decisions among several strategies, causal analyses of observational data need to be evaluated with respect to how well they emulate a particular target trial. We outline a framework for comparative effectiveness research using big data that makes the target trial explicit. This framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls.

PDB4 - Utilization of Negative Controls to Examine association between Rare Genetic Disorders and Type 2 Diabetes in four Large Observational Databases

PDB4 - Utilization of Negative Controls to Examine association between Rare Genetic Disorders and Type 2 Diabetes in four Large Observational Databases

PDB49 - A Cost-effectiveness Analysis of Alogliptin In Comparison to Saxagliptin

The association between rare genetic disorders, hereditary fructose intolerance (HFI) or alpha-1 antitrypsin deficiency (A1AT), and type 2 diabetes (T2D) has not yet been investigated. Therefore, the objective of this undertaking was to evaluate the association between both genetic disorders and T2D using four large observational databases and adjust for ascertainment bias. Patients with a HFI diagnosis (ICD-9: 271.2) or A1AT diagnosis (ICD-9: 273.4) and T2D diagnosis (ICD-9: 250.x0 or 250.x2) were identified in the Truven MarketScan Claims Database (2007-2012), Optum Claims Database (2002-2012), Humedica Electronic Health Records (EHR) Database (2007-2012), and GE Centricity EHR Database (1995-2012). The association between both genetic disorders and T2D was compared to the association between T2D and seven negative control chronic diseases with no established relationship with T2D. The unadjusted association between both genetic disorders and T2D was positive and heterogeneous (p<0.001) in all four databases. The unadjusted pooled odds ratio (OR) calculated using a random-effects model meta-analysis was 3.48 (95% CI: 2.21-5.46) for HFI and 2.71 for A1AT (95% CI: 1.75-4.20). After pooling all patients and adjusting for the negative controls using a random-effects model meta-analysis, it was found that HFI patients have a 73% increased odds of T2D (ratio of odds ratios [ROR]=1.73, 95% CI: 1.08-2.75) compared to patients with negative control diseases; the association was stronger when utilizing a fixed-effects model meta-analysis (ROR=2.19, 95% CI: 2.07-2.31). The adjusted association between A1AT and T2D was statistically significant in the fixed-effects (ROR=1.33, 95% CI: 1.27-1.40) model meta-analysis but not the random-effects model meta-analysis (ROR=1.35, 95% CI: 0.86-2.12). HFI and T2D were positively associated after adjustment for negative control chronic diseases in both meta-analysis models. Rare disease researchers using observational data to conduct comorbidity analyses can utilize negative controls and multiple datasets to account for ascertainment bias and database heterogeneity, respectively.

Methods and Issues to Consider for Detection of Safety Signals From Spontaneous Reporting Databases: A Report of the DIA Bayesian Safety Signal Detection Working Group.

Spontaneous reporting (SR) adverse event system databases, large observational databases, large clinical trials, and large health records databases comprise repositories of information that may be useful for early detection of potential harms associated with drugs, devices, and vaccines. All of the data sources include many different adverse events and many medical products, so that any approach designed to detect "important" signals of potential harm must have adequate specificity to protect against false alarms yet provide satisfactory sensitivity for detecting issues that really need further investigation. Algorithms for evaluating potential risks using information from these sources, especially SR databases, have been described in the literature. The algorithms may seek to identify potential product-event associations without any prior specifications, to identify events associated with a particular product or set of products, or to identify products associated with a particular event or set of events. This article provides recommendations for using information from postmarketing spontaneous adverse event reporting databases to provide insight into risks of potential harm expressed by safety signals and offers guidance regarding appropriate methods, both frequentist and Bayesian, to use in various situations as a function of the objective of the analysis.

In the Land of the Blind, the One-eyed Man Is King

In the Land of the Blind, the One-eyed Man Is King

Likelihood ratio based tests for longitudinal drug safety data.

This article presents longitudinal likelihood ratio test (LongLRT) methods for large databases with exposure information. These methods are applied to a pooled large longitudinal clinical trial dataset for drugs treating osteoporosis with concomitant use of proton pump inhibitors (PPIs). When the interest is in the evaluation of a signal of an adverse event for a particular drug compared with placebo or a comparator, the special case of the LongLRT, referred to as sequential LRT (SeqLRT), is also presented. The results show that there is some possible evidence of concomitant use of PPIs leading to more adverse events associated with osteoporosis. The performance of the proposed LongLRT and SeqLRT methods is evaluated using simulated datasets and shown to be good in terms of (conditional) power and control of type I error over time. The proposed methods can also be applied to large observational databases with exposure information under the US Food and Drug Administration Sentinel Initiative for active surveillance. Published 2014. This article is a US Government work and is in the public domain in the USA.

Local control for identifying subgroups of interest in observational research: persistence of treatment for major depressive disorder

Caregivers are regularly faced with decisions between competing treatments. Large observational health care databases provide a golden opportunity for research on heterogeneity in patient response to guide caregiver decisions, due to their sample size, diverse populations, and real-world setting. Local control is a promising tool for using observational data to detect patient subgroups with differential response on one treatment relative to another. While standard data mining approaches find subgroups with optimal responses for a particular population, detecting subgroups that reveal treatment differences while also adjusting for confounding in observational data is challenging. Local control utilizes unsupervised clustering to form non-parametric patient-level counterfactual treatment differences and displays them as an observed distribution of effect-size estimates. Classification and regression trees (CART) then find the factors that drive the greatest outcome differentiation between treatments. In this manuscript, we demonstrate the use of this two-step strategy using local control plus CART to identify depression patients most (least) likely to benefit from treatment with duloxetine relative to extended-release venlafaxine. Prior medication costs and age were found to be factors most associated with differential outcome, with prior medication costs remaining as an important factor after sensitivity analyses using a second dataset.

Validation sampling can reduce bias in health care database studies: an illustration using influenza vaccination effectiveness

ObjectivesEstimates of treatment effectiveness in epidemiologic studies using large observational health care databases may be biased owing to inaccurate or incomplete information on important confounders. Study methods that collect and incorporate more comprehensive confounder data on a validation cohort may reduce confounding bias. Study Design and SettingWe applied two such methods, namely imputation and reweighting, to Group Health administrative data (full sample) supplemented by more detailed confounder data from the Adult Changes in Thought study (validation sample). We used influenza vaccination effectiveness (with an unexposed comparator group) as an example and evaluated each method's ability to reduce bias using the control time period before influenza circulation. ResultsBoth methods reduced, but did not completely eliminate, the bias compared with traditional effectiveness estimates that do not use the validation sample confounders. ConclusionAlthough these results support the use of validation sampling methods to improve the accuracy of comparative effectiveness findings from health care database studies, they also illustrate that the success of such methods depends on many factors, including the ability to measure important confounders in a representative and large enough validation sample, the comparability of the full sample and validation sample, and the accuracy with which the data can be imputed or reweighted using the additional validation sample information.

Detecting Potential Safety Issues in Large Clinical or Observational Trials by Bayesian Screening When Event Counts Arise from Poisson Distributions

Patients in large clinical trials and in studies employing large observational databases report many different adverse events, most of which will not have been anticipated at the outset. Conventional hypothesis testing of between group differences for each adverse event can be problematic: Lack of significance does not mean lack of risk, the tests usually are not adjusted for multiplicity, and the data determine which hypotheses are tested. This article describes a Bayesian screening approach that does not test hypotheses, is self-adjusting for multiplicity, provides a direct assessment of the likelihood of no material drug–event association, and quantifies the strength of the observed association. The criteria for assessing drug-event associations can be determined by clinical or regulatory considerations. In contrast to conventional approaches, the diagnostic properties of this new approach can be evaluated analytically. Application of the method to findings from a vaccine trial yields results similar to those found by methods using a false discovery rate argument or a hierarchical Bayes approach. [Supplemental materials are available for this article. Go to the publisher's online edition of Journal of Biopharmaceutical Statistics for the following free supplemental resource: Appendix R: Code for calculations.]

Assessment of Case Definitions for Identifying Acute Liver Injury in Large Observational Databases

Determining the aetiology of acute liver injury (ALI) may be challenging to both clinicians and researchers. Observational research is particularly useful in studying rare medical outcomes such as ALI; however, case definitions for ALI in previous observational studies lack consistency and sensitivity. ALI is a clinically important condition with various aetiologies, including drug exposure. The aim of this study was to evaluate four distinct case definitions for ALI across a diverse set of large observational databases, providing a better understanding of ALI prevalence and natural history. Seven healthcare databases: GE Healthcare, MarketScan(®) Lab Database, Humana Inc., Partners HealthCare System, Regenstrief Institute, SDI Health (now IMS Health, Inc.), and the National Patient Care Database of the Veterans Health Administration. We evaluated prevalence of ALI through the application of four distinct case definitions across seven observational healthcare databases. We described how laboratory and clinical characteristics of identified case populations varied across definitions and examined the prevalence of other hepatobiliary disorders among identified ALI cases that may decrease suspicion of drug-induced liver injury (DILI) in particular. This study demonstrated that increasing the restrictiveness of the case definition resulted in fewer cases, but greater prevalence of ALI clinical features. Considerable heterogeneity in the frequency of laboratory testing and results observed among cases meeting the most restrictive definition suggests that the clinical features, monitoring patterns and suspicion of ALI are highly variable among patients. Creation of four distinct case definitions and application across a disparate set of observational databases resulted in significant variation in the prevalence of ALI. A greater understanding of the natural history of ALI through examination of electronic healthcare data can facilitate development of reliable and valid ALI case definitions that may enhance the ability to accurately identify associations between ALI and drug exposures. Considerable heterogeneity in laboratory values and frequency of laboratory testing among individuals meeting the criteria for ALI suggests that the evaluation of ALI is highly variable.

Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic

Background: Drug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance. Methods: Using data from nine health plans, we conducted a pilot study to evaluate the implementation and findings of the GPS approach for two antifungal drugs, terbinafine and itraconazole, and two diabetes drugs, pioglitazone and rosiglitazone. We evaluated 1676 diagnosis codes grouped into 183 different clinical concepts and four levels of granularity. Several signaling thresholds were assessed. GPS results were compared to findings from a companion study using the identical analytic dataset but an alternative statistical method—the tree-based scan statistic (TreeScan). Results: We identified 71 statistical signals across two signaling thresholds and two methods, including closely-related signals of overlapping diagnosis definitions. Initial review found that most signals represented known adverse drug reactions or confounding. About 31% of signals met the highest signaling threshold. Conclusions: The GPS method was successfully applied to observational health plan data in a distributed data environment as a drug safety data mining method. There was substantial concordance between the GPS and TreeScan approaches. Key method implementation decisions relate to defining exposures and outcomes and informed choice of signaling thresholds.

Improving valuation sampling of EQ-5D health states

BackgroundThe original valuation exercise which formed the basis of the UK EQ-5D time trade-off social tariff of health states, employed a sampling scheme involving 43 health states. Neither that study, nor other published international valuations studies have used explicit quantifiable criteria to justify the choice of sampled states. New criteria are proposed and methods described to aid researchers in designing improved sampling schemes for future EQ-5D sampling exercises.MethodFour such criteria are described, and applied to assess the merits of four sampling schemes previously reported, using three large observational databases to quantify relative performance. An alternative sampling design conforming to these criteria is described, which aims to generate improved performance.ResultsPrevious published approaches are shown to perform poorly against the measured criteria. The alternative sampling design is demonstrated to provide superior performance on all measures.ConclusionFuture valuation exercises using sampled health states based on this approach may be expected to offer benefits in terms of greater precision, avoidance of bias in favour of less severe states, and a higher proportion of research observations valued directly rather than dependent on extrapolation modelling.

Computationally Efficient Marginal Models for Clustered Recurrent Event Data

Large observational databases derived from disease registries and retrospective cohort studies have proven very useful for the study of health services utilization. However, the use of large databases may introduce computational difficulties, particularly when the event of interest is recurrent. In such settings, grouping the recurrent event data into prespecified intervals leads to a flexible event rate model and a data reduction that remedies the computational issues. We propose a possibly stratified marginal proportional rates model with a piecewise-constant baseline event rate for recurrent event data. Both the absence and the presence of a terminal event are considered. Large-sample distributions are derived for the proposed estimators. Simulation studies are conducted under various data configurations, including settings in which the model is misspecified. Guidelines for interval selection are provided and assessed using numerical studies. We then show that the proposed procedures can be carried out using standard statistical software (e.g., SAS, R). An application based on national hospitalization data for end-stage renal disease patients is provided.

Mechanical circulatory support devices for acute heart failure syndromes: considerations for clinical trial design

Mechanical circulatory support (MCS) devices are a guideline-recommended treatment option for a small subset of advanced heart failure patients. MCS has the potential to become more prominent in the management of Acute Heart Failure Syndromes (AHFS) as device technology advances and as clinical trials consistently discover neutral or harmful effects with pharmacologic therapies hypothesized to be beneficial in this population. While it is now possible to identify AHFS patients who are at high risk of death, the therapeutic options available to improve their long-term outcomes are limited. MCS therapy in this population offers a "bridge to recovery" strategy; these patients may have viable myocardium that responds favorably to the influence of MCS on neurohormones, cytokines, and/or reverse remodeling. Patients at high risk for mortality who have a substantial likelihood of benefiting from MCS can be easily identified using standard clinical criteria developed from large observational databases. MCS technology is rapidly evolving, and risks related to implantation are declining. It is evident that rigorous clinical trial testing of the potential risks, benefits, and economic implications of MCS in patients with AHFS will need to be conducted before the "routine" application of this aggressive therapy. This paper examines the rationale for conducting trials of MCS devices in patients with AHFS, and it explores considerations for patient selection and appropriate endpoints. This manuscript was generated from discussions on this issue during the third international meeting of the International Working Group on AHFS held in Washington, DC, April 8-9, 2006.

Risk of cardiovascular events in patients at optimal values for combined lipid parameters

ABSTRACTBackground: Current prevention guidelines support efforts to achieve optimal high-density lipoprotein (HDL‑C) and triglyceride (TG) values, in addition to low-density lipoprotein (LDL‑C) in order to reduce cardiovascular (CV) events. The study objective was to evaluate the risk of CV events in patients attaining versus not attaining combined (LDL‑C, HDL‑C, and TG) optimal lipid values.Methods/results: This retrospective cohort analysis was conducted using a 1.1 million member managed care database. Eligible patients had a full lipid panel between 10/1/99 and 9/30/00, were naive to lipid therapy, and had health plan eligibility 12 months pre- and post-index (baseline) lipid laboratory value. Optimal lipid values (LDL‑C, HDL‑C, and TG) were established using the National Cholesterol Education Program Adult Treatment Panel (NCEP ATP III) guidelines, and patients were placed into one of four groups: none, one, two, or three lipid components non-optimal at baseline. The presence of cardiovascular risk, disease, and events were determined by selected International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9 CM) and Current Procedural Terminology (CPT codes). The definition of a CV event included: diagnosis of ischemic heart disease, peripheral arterial disease, stroke/TIA, or revascularization procedure. Odds ratios (OR) for a CV event associated with attainment of each optimal lipid fraction were determined by multivariate logistic regression. The study cohort included 30 348 patients, with a mean follow-up of 27 ± 8 months. Mean age was 66 ± 12 years; 16 549 (54%) were male; and 17 289 (57%) patients had coronary heart disease (CHD) or CHD risk equivalent. There were 5955 CV events that occurred in 4059 (13%) study patients. The presence of a single non-optimal lipid value slightly increased CV event risk [OR: 1.06; 95% CI: 0.95–1.18], whereas two or all three non-optimal lipid values significantly increased the risk of a CV event [OR: 1.22; 95% CI: 1.08–1.37; and 1.45; 95% CI: 1.24–1.68, respectively].Limitations: As with all large observational databases there are potential limitations including: patient selection bias (e.g., more interventions in patients with greater illness, lack of mortality data, and frequency of lipid monitoring), unknown confounding variables, and potential coding errors.Conclusion: Not attaining optimal combined lipid values, independently and significantly, increased the risk of CV events in this large at-risk population with approximately 68 283 patient-years of follow-up. The combination of non-attainment of optimal LDL‑C with non-attainment of optimal HDL‑C or TG values, or both, increased the adjusted risk of CV events by 22–45%. Thus, therapeutic strategies should focus on assessment and management of multiple lipid abnormalities, and not on single lipid risk factor modification.

Effect of Antipsychotic Drugs on Cerebrovascular Morbidity and Mortality: A Systematic Review

Background: Since 2002, there has been contrasting evidence indicating an increased risk of adverse cerebrovascular outcomes in people treated with the two most commonly prescribed atypical antipsychotics, risperidone and olanzapine. Objective: The objective of this systematic review is to examine the evidence of the correlation between antipsychotic drug use and the risk of cerebrovascular diseases. Method: We used the Medline computer database to search the relevant papers published in English from January 1966 to October 2005. We used the following key words: antipsychotic agents, transient ischaemic attack, cerebrovascular accident, stroke, vascular diseases, risk factors and mortality. Articles that investigated the relationship between antipsychotic drug exposure and subsequent cerebrovascular morbidity and mortality were collected and reviewed. Results: We found one meta-analysis based on a systematic review of randomised controlled trials (RCTs), 4 pooled analyses of RCTs, 4 database analyses, 1 case-control study and 1 cross-sectional study. A total of 11 RCTs suggested that 48 out of 2187 (2.2%) subjects exposed to antipsychotics experienced cerebrovascular adverse events, compared with 10 out of 1190 (0.8%) placebo-treated subjects. In terms of risks, the relative risk was statistically significant for risperidone (3.2, 95% Confidence Interval (CI) 1.4 to 7.2) but not for olanzapine 1.8 (95% Confidence Interval (CI) 0.5 to 6.3). In general, data from large observational administrative databases suggested no increased risk of cerebrovascular events with the atypical antipsychotics, compared with typical antipsychotics, although the evidence seems still inconclusive. Conclusion: So far, randomised and non-randomised studies provided contrasting findings on the risk of cerebrovascular accidents in subjects exposed to antipsychotic drugs. Individual patient data meta-analyses, carried out by independent organisations, are urgently needed to systematically summarise factors associated with cerebrovascular morbidity and mortality in individuals treated with first and second-generation antipsychotic drug. Keywords: Stroke, transitory ischaemic attack, cerebrovascular adverse events, antipsychotic agents, systematic review

Modern epidemiology, prophylaxis, and diagnosis and therapy for infective endocarditis

Infective endocarditis (IE) is a rare disease. Although its incidence and bacteriology have remained relatively stable in outpatient populations without injection drug use, health care-associated infections, particularly with staphylococcus, are becoming more common. Large-scale prospective clinical trials are unavailable to guide strategies for preventing IE, timing surgical intervention, and avoiding complications. We continue to rely on new data from smaller series and large observational databases to track these changes and improve care of patients. At the present time, there are several controversies regarding best practices in IE. In this review, we address the following questions: What is the future of recommendations for antibiotic prophylaxis against IE? How should we best use echocardiography in diagnosis, management and follow up of IE patients? What are the most appropriate antibiotic regimens for different patients in the face of shifting microbiology and demographics? Lastly, how should patients be selected for early surgery to avoid the complications of these infections?

Progress and Promise of Diabetes Research

THE EPIDEMIC OF DIABETES HAS TAKEN HOLD AND IS EXtending to new populations. The prevalence of type 2 diabetes in children and in developing nations has increased dramatically, and adult-onset type 1 diabetes is now well recognized. Diabetic cardiovascular disease has become a worldwide burden. While new cases of heart disease and cancer are stable or decreasing, the incidence of diabetes has increased 6% annually in the United States and more than that in developing countries. The human and economic toll of diabetes increases correspondingly, and this requires an integrated, global response. However, policy makers, insurance companies, and the general public are just beginning to recognize the seriousness of diabetes. Opportunities for progress are virtually limitless, with some of the most fundamental questions raised by diabetes remaining unanswered: What underlies the metabolic syndrome? What exactly mediates the link between obesity and insulin resistance? Can this resistance be overcome pharmacologically? What triggers the process that destroys beta cells in type 1 diabetes? How can that process be prevented? Can the enormous potential of cell therapy and mechanical insulin delivery be realized? How can individuals with diabetes be motivated to follow self-care patterns necessary to prevent complications? How can an entire society be motivated to decrease its food consumption? How can prevention be paid for while reducing the total cost of diabetes care? The articles in this theme issue of THE JOURNAL address important questions in diabetes today, and also illustrate some of the newer methods being applied to diabetes research. Just as new techniques undergird the advances in basic science, so they also enhance clinical research. This issue includes reports from a large community observational cohort study, linked state and hospital databases, a computerbased patient record review, a cost-effectiveness analysis, 2 randomized controlled trials, and long-term follow-up of an important clinical study. There is keen interest in the events that precede diabetes, and researchers are seeking to discover diabetogenic genes. The thrifty gene hypothesis postulates that some individuals store fat more efficiently, adapting well to periods of starvation, but poorly to times of plenty. This concept is undoubtedly oversimplified—type 2 diabetes is polygenic. Moreover, it is possible that the multiple genes may express themselves through such nontraditional phenotypes as susceptibility to obesity from dysregulation of appetite. A related area of interest is the “minienvironment of the uterus,” and the role that early imprinting can have on later life. The study by Innes and colleagues examines whether a woman’s birth weight influences her risk of developing gestational diabetes, which is a marker for risk of type 2 diabetes. Linking hospital discharge data to state vital records, the authors report that women with lower birth weights as newborns were more likely to develop gestational diabetes. This finding is consistent with results obtained under extremely different circumstances— pregnant women in the Netherlands during a fixed period of Nazi-imposed famine during World War II gave birth to offspring that were predisposed to type 2 diabetes. In a longitudinal community observational study, Wong and colleagues made the surprising observation of an association between retinal arteriolar narrowing and the risk of incident diabetes. The implication is that diabetes not only causes, but may be caused by, microvascular disease. These results call to mind the work of Siperstein and colleagues in the 1960s and 1970s, which suggested capillary basement membrane thickening precedes the onset of diabetes. Studies of patients prior to the diagnosis of diabetes raise the potential for primary prevention, while improved management of diabetes could prevent complications. Davies and colleagues studied what might be considered an unlikely insulin sensitizer—alcohol. In a randomized, controlled, crossover study of healthy postmenopausal women, the authors found improved insulin sensitivity