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      Serum Resistin, Cardiovascular Disease and All-Cause Mortality in Patients with Type 2 Diabetes

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          Abstract

          Background

          High serum resistin has been associated with increased risk of cardiovascular disease in the general population, Only sparse and conflicting results, limited to Asian individuals, have been reported, so far, in type 2 diabetes. We studied the role of serum resistin on coronary artery disease, major cardiovascular events and all-cause mortality in type 2 diabetes.

          Methods

          We tested the association of circulating resistin concentrations with coronary artery disease, major cardiovascular events (cardiovascular death, non-fatal myocardial infarction and non-fatal stroke) and all-cause mortality in 2,313 diabetic patients of European ancestry from two cross-sectional and two prospective studies. In addition, the expression of resistin gene ( RETN) was measured in blood cells of 68 diabetic patients and correlated with their serum resistin levels.

          Results

          In a model comprising age, sex, smoking habits, BMI, HbA1c, and insulin, antihypertensive and antidyslipidemic therapies, serum resistin was associated with coronary artery disease in both cross-sectional studies: OR (95%CI) per SD increment = 1.35 (1.10–1.64) and 1.99 (1.55–2.55). Additionally, serum resistin predicted incident major cardiovascular events (HR per SD increment = 1.31; 1.10–1.56) and all-cause mortality (HR per SD increment = 1.16; 1.06–1.26). Adjusting also for fibrinogen levels affected the association with coronary artery disease and incident cardiovascular events, but not that with all cause-mortality. Finally, serum resistin was positively correlated with RETN mRNA expression (rho = 0.343).

          Conclusions

          This is the first study showing that high serum resistin (a likely consequence, at least partly, of increased RETN expression) is a risk factor for cardiovascular disease and all-cause mortality in diabetic patients of European ancestry.

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          Most cited references30

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          General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

          Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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            The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56).

            A definitive model for predicting absolute risk of coronary heart disease (CHD) in male and female people with Type II diabetes is not yet available. This paper provides an equation for estimating the risk of new CHD events in people with Type II diabetes, based on data from 4540 U.K. Prospective Diabetes Study male and female patients. Unlike previously published risk equations, the model is diabetes-specific and incorporates glycaemia, systolic blood pressure and lipid levels as risk factors, in addition to age, sex, ethnic group, smoking status and time since diagnosis of diabetes. All variables included in the final model were statistically significant (P<0.001, except smoking for which P=0.0013) in likelihood ratio testing. This model provides the estimates of CHD risk required by current guidelines for the primary prevention of CHD in Type II diabetes.
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              Trends in cardiovascular disease risk factors in individuals with and without diabetes mellitus in the Framingham Heart Study.

              Individuals with diabetes mellitus are at 2- to 3-fold increased risk for cardiovascular disease (CVD) relative to those without diabetes. Our objective was to examine CVD risk factor level changes among individuals with and without type 2 diabetes mellitus from 1970 to 2005 in the Framingham Heart Study. We included 4195 participants (3990 with no diabetes and 205 with diabetes) 50 years of age and 3495 participants (3178 with no diabetes and 317 with diabetes) 60 years of age. Contemporaneous CVD risk factor levels were measured; linear regression models were used to assess the interaction between diabetes status and calendar year on CVD risk factor levels. Among 50-year-olds without diabetes mellitus, there was an increase in body mass index of 0.39 kg/m(2) per 10 years, whereas for those with diabetes, there was an increase of 2.52 kg/m(2) (P value for the diabetes-by-calendar year interaction [P for interaction] <0.001). For low-density lipoprotein cholesterol, the mean decrease was -7.43 mg/dL per decade (nondiabetes) and -15.5 mg/dL for diabetes (P for interaction=0.002). For systolic blood pressure, the mean decrease was -3.35 mm Hg per decade (nondiabetes) and -3.50 mm Hg for diabetes (P for interaction=0.97). The direction of the trends for those with diabetes relative to those without diabetes was similar for 60-year-olds. Compared with individuals without diabetes mellitus, individuals with diabetes experienced a greater increase in body mass index, a greater decrease in low-density lipoprotein cholesterol, and a similar magnitude of decline in systolic blood pressure. Individuals with diabetes mellitus have not experienced the necessary declines in CVD risk factors to overcome their increased risk of CVD. Further efforts are needed to aggressively control CVD risk factors among individuals with diabetes mellitus.
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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
                2013
                3 June 2013
                : 8
                : 6
                : e64729
                Affiliations
                [1 ]Research Unit of Diabetes and Endocrine Diseases, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
                [2 ]Unit of Endocrinology, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
                [3 ]Research Division, Joslin Diabetes Center, Boston, Massachusetts, United States of America
                [4 ]Unit of Biostatistics, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
                [5 ]IRCSS Casa Sollievo della Sofferenza-Mendel Laboratory, Rome, Italy
                [6 ]Department of Internal Medicine, University of Rome Tor Vergata, Rome, Italy
                [7 ]Unit of Biostatistics, Consorzio Mario Negri Sud, Santa Maria Imbaro, Italy
                [8 ]Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
                [9 ]Department of Experimental Medicine, Sapienza University of Rome, Italy
                University of Siena, Italy
                Author notes

                Competing Interests: CM received funding from the EFSD/Pfizer. MF is a PLOS ONE Editorial Board member. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: C. Menzaghi SB MF SDC AD VT. Performed the experiments: LS C. Mendonca GP CDB AM SP EM SR GF DM. Analyzed the data: C. Menzaghi SB GP AF FP AD. Contributed reagents/materials/analysis tools: SB EG AK MF SDC FP AD. Wrote the paper: C. Menzaghi AD VT.

                Article
                PONE-D-13-02553
                10.1371/journal.pone.0064729
                3670852
                23755138
                c34c08cb-795b-41be-9045-88ff81bd511d
                Copyright @ 2013

                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
                : 16 January 2013
                : 18 April 2013
                Page count
                Pages: 9
                Funding
                This research was supported by Accordo Programma Quadro in Materia di Ricerca Scientifica nella Regione Puglia-PST 2006 and PO Puglia FESR 2007–2013, Italian Ministry of Health grants RC2011, RC2012, European Foundation for the Study of Diabetes/Pfizer grant (CM) and National Institutes of Health grant HL073168 (AD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Immunology
                Immune System
                Cytokines
                Molecular Cell Biology
                Gene Expression
                Medicine
                Cardiovascular
                Myocardial Infarction
                Clinical Immunology
                Immune System
                Cytokines
                Endocrinology
                Diabetic Endocrinology
                Diabetes Mellitus Type 2
                Non-Clinical Medicine
                Health Care Policy
                Health Risk Analysis

                Uncategorized
                Uncategorized

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