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      Insulin Resistance and Risk of Incident Cardiovascular Events in Adults without Diabetes: Meta-Analysis

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          Abstract

          Background

          Glucose, insulin and Homeostasis Model Assessment Insulin Resistance (HOMA-IR) are markers of insulin resistance. The objective of this study is to compare fasting glucose, fasting insulin concentrations and HOMA-IR in strength of association with incident cardiovascular disease.

          Methods

          We searched the PubMed, MEDLINE, EMBASE, Web of Science, ScienceDirect and Cochrane Library databases from inception to March, 2011, and screened reference lists. Cohort studies or nested case-control studies that investigated the association between fasting glucose, fasting insulin or HOMA-IR and incident cardiovascular disease, were eligible. Two investigators independently performed the article selection, data extraction and risk of bias assessment. Cardiovascular endpoints were coronary heart disease (CHD), stroke or combined cardiovascular disease. We used fixed and random-effect meta-analyses to calculate the pooled relative risk for CHD, stroke and combined cardiovascular disease, comparing high to low concentrations of glucose, insulin or HOMA-IR. Study heterogeneity was calculated with the I 2 statistic. To enable a comparison between cardiovascular disease risks for glucose, insulin and HOMA-IR, we calculated pooled relative risks per increase of one standard deviation.

          Results

          We included 65 studies (involving 516,325 participants) in this meta-analysis. In a random-effect meta-analysis the pooled relative risk of CHD (95% CI; I 2) comparing high to low concentrations was 1.52 (1.31, 1.76; 62.4%) for glucose, 1.12 (0.92, 1.37; 41.0%) for insulin and 1.64 (1.35, 2.00; 0%) for HOMA-IR. The pooled relative risk of CHD per one standard deviation increase was 1.21 (1.13, 1.30; 64.9%) for glucose, 1.04 (0.96, 1.12; 43.0%) for insulin and 1.46 (1.26, 1.69; 0.0%) for HOMA-IR.

          Conclusions

          The relative risk of cardiovascular disease was higher for an increase of one standard deviation in HOMA-IR compared to an increase of one standard deviation in fasting glucose or fasting insulin concentration. It may be useful to add HOMA-IR to a cardiovascular risk prediction model.

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          Most cited references14

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          Is nondiabetic hyperglycemia a risk factor for cardiovascular disease? A meta-analysis of prospective studies.

          Although hyperglycemia increases the risk of cardiovascular disease (CVD) in diabetic patients, the risk associated with blood glucose levels in the nondiabetic range remains unsettled. We identified 38 reports in which CVD incidence or mortality was an end point, blood glucose levels were measured prospectively, and the relative risk (RR) and information necessary to calculate the variance were reported comparing groups of nondiabetic people. These reports were prospective studies, published in English-language journals. First author, publication year, participant age and sex, study duration, CVD end points, glucose assessment methods, control for confounding, range of blood glucose levels, RR, and confidence intervals (CIs) or P values were extracted. Using a random effects model, we calculated pooled RRs and 95% CIs. The group with the highest postchallenge blood glucose level (midpoint range, 150-194 mg/dL [8.3-10.8 mmol/L]) had a 27% greater risk for CVD compared with the group with the lowest level (midpoint range, 69-107 mg/dL [3.8-5.9 mmol/L]) (RR, 1.27 [95% CI, 1.09-1.48]). The results were similar when combining studies regardless of type of blood glucose assessment (RR, 1.36 [95% CI, 1.23-1.52]) and when using strict criteria for exclusion of diabetic subjects (RR, 1.26 [95% CI, 1.11-1.43]). Adjustment for CVD risk factors attenuated but did not abolish this relationship (RR, 1.19 [95% CI, 1.07-1.32]). The RR was greater in cohorts including women than in cohorts of men (RR, 1.56 vs 1.24 [P = .03]). Blood glucose level is a risk marker for CVD among apparently healthy individuals without diabetes.
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            World Health Organization definition of myocardial infarction: 2008-09 revision.

            WHO has played a leading role in the formulation and promulgation of standard criteria for the diagnosis of coronary heart disease and myocardial infarction since early 1970s. The revised definition takes into consideration the following: well-resourced settings can use the ESC/ACC/AHA/WHF definition, which has new biomarkers as a compulsory feature; in resource-constrained settings, a typical biomarker pattern cannot be made a compulsory feature as the necessary assays may not be available; the definition must also have provision for diagnosing non-fatal events with incomplete information on cardiac biomarkers and the ECG; to facilitate epidemiologic monitoring definition must recognize fatal events with incomplete or no information on cardiac biomarkers and/or ECG and/or autopsy and/or coronary angiography. Category A definition is the same as ESC/ACC/AHA/WHF definition of MI, and can be applied to settings with no resource constraints. Category B definition of MI is to be applied whenever there is incomplete information on cardiac bio-markers together with symptoms of ischaemia and the development of unequivocal pathological Q waves. Category C definition (probable MI) is to be applied when individuals with MI may not satisfy Category A or B definitions because of delayed access to medical services and/or unavailability of electrocardiography and/or laboratory assay of cardiac biomarkers. In these situations, the term probable MI should be used when there is either ECG changes suggestive of MI or incomplete information on cardiac biomarkers in a person with symptoms of ischaemia with no evidence of a non-coronary reason. This article presents the 2008-09 revision of the World Health Organization (WHO) definition of myocardial infarction (MI) developed at a WHO expert consultation.
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              Definition, reporting, and interpretation of composite outcomes in clinical trials: systematic review

              Objective To study how composite outcomes, which have combined several components into a single measure, are defined, reported, and interpreted. Design Systematic review of parallel group randomised clinical trials published in 2008 reporting a binary composite outcome. Two independent observers extracted the data using a standardised data sheet, and two other observers, blinded to the results, selected the most important component. Results Of 40 included trials, 29 (73%) were about cardiovascular topics and 24 (60%) were entirely or partly industry funded. Composite outcomes had a median of three components (range 2–9). Death or cardiovascular death was the most important component in 33 trials (83%). Only one trial provided a good rationale for the choice of components. We judged that the components were not of similar importance in 28 trials (70%); in 20 of these, death was combined with hospital admission. Other major problems were change in the definition of the composite outcome between the abstract, methods, and results sections (13 trials); missing, ambiguous, or uninterpretable data (9 trials); and post hoc construction of composite outcomes (4 trials). Only 24 trials (60%) provided reliable estimates for both the composite and its components, and only six trials (15%) had components of similar, or possibly similar, clinical importance and provided reliable estimates. In 11 of 16 trials with a statistically significant composite, the abstract conclusion falsely implied that the effect applied also to the most important component. Conclusions The use of composite outcomes in trials is problematic. Components are often unreasonably combined, inconsistently defined, and inadequately reported. These problems will leave many readers confused, often with an exaggerated perception of how well interventions work.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                28 December 2012
                : 7
                : 12
                : e52036
                Affiliations
                [1 ]Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
                [2 ]Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
                [3 ]Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands
                [4 ]Department of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
                Universidad Peruana de Ciencias Aplicadas (UPC), Peru
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: KBG TS OMD. Performed the experiments: KBG NT. Analyzed the data: KBG TS. Contributed reagents/materials/analysis tools: OMD TS. Wrote the paper: KBG. Reviewed/edited the manuscript: NT TS JWAS OMD. Contributed to discussion and approved final document: KBG NT TS JWAS OMD.

                Article
                PONE-D-12-28580
                10.1371/journal.pone.0052036
                3532497
                23300589
                afc7e77f-539e-4d00-affb-ff863f9de627
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 September 2012
                : 13 November 2012
                Page count
                Pages: 8
                Funding
                The authors have no support or funding to report.
                Categories
                Research Article
                Medicine
                Cardiovascular
                Atherosclerosis
                Coronary Artery Disease
                Myocardial Infarction
                Stroke
                Clinical Research Design
                Meta-Analyses
                Endocrinology
                Diabetic Endocrinology
                Diabetes Mellitus Type 2
                Insulin
                Epidemiology
                Cardiovascular Disease Epidemiology
                Non-Clinical Medicine
                Health Care Policy
                Health Risk Analysis

                Uncategorized
                Uncategorized

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