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      Risk Factors for Cardiovascular Disease in Type 1 Diabetes

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      The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Research Group * ,
      Diabetes
      American Diabetes Association

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          Abstract

          Risk factors for cardiovascular disease (CVD) are well-established in type 2 but not type 1 diabetes (T1DM). We assessed risk factors in the long-term (mean 27 years) follow-up of the Diabetes Control and Complications Trial (DCCT) cohort with T1DM. Cox proportional hazards multivariate models assessed the association of traditional and novel risk factors, including HbA 1c, with major atherosclerotic cardiovascular events (MACE) (fatal or nonfatal myocardial infarction [MI] or stroke) and any-CVD (MACE plus confirmed angina, silent MI, revascularization, or congestive heart failure). Age and mean HbA 1c were strongly associated with any-CVD and with MACE. For each percentage point increase in mean HbA 1c, the risk for any-CVD and for MACE increased by 31 and 42%, respectively. CVD and MACE were associated with seven other conventional factors, such as blood pressure, lipids, and lack of ACE inhibitor use, but not with sex. The areas under the receiver operating characteristics curves for the association of age and HbA 1c, taken together with any-CVD and for MACE, were 0.70 and 0.77, respectively, and for the final models, including all significant risk factors, were 0.75 and 0.82. Although many conventional CVD risk factors apply in T1DM, hyperglycemia is an important risk factor second only to age.

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          Most cited references32

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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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            Modeling Survival Data: Extending the Cox Model

            This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area. The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided. Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions. Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals.
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              Mortality from heart disease in a cohort of 23,000 patients with insulin-treated diabetes.

              Although ischaemic heart disease is the predominant cause of mortality in older people with diabetes, age-specific mortality rates have not been published for patients with Type 1 diabetes. The Diabetes UK cohort, essentially one of patients with Type 1 diabetes, now has sufficient follow-up to report all heart disease, and specifically ischaemic heart disease, mortality rates by age. A cohort of 23,751 patients with insulin-treated diabetes, diagnosed under the age of 30 years and from throughout the United Kingdom, was identified during the period 1972 to 1993 and followed for mortality until December 2000. Age- and sex-specific heart disease mortality rates and standardised mortality ratios were calculated. There were 1437 deaths during the follow-up, 536 from cardiovascular disease, and of those, 369 from ischaemic heart disease. At all ages the ischaemic heart disease mortality rates in the cohort were higher than in the general population. Mortality rates within the cohort were similar for men and women under the age of 40. The standardised mortality ratios were higher in women than men at all ages, and in women were 44.8 (95%CI 20.5-85.0) at ages 20-29 and 41.6 (26.7-61.9) at ages 30-39. The risk of mortality from ischaemic heart disease is exceptionally high in young adult women with Type 1 diabetes, with rates similar to those in men with Type 1 diabetes under the age of 40. These observations emphasise the need to identify and treat coronary risk factors in these young patients.
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                Author and article information

                Journal
                Diabetes
                Diabetes
                diabetes
                diabetes
                Diabetes
                Diabetes
                American Diabetes Association
                0012-1797
                1939-327X
                May 2016
                19 February 2016
                : 65
                : 5
                : 1370-1379
                Author notes
                Corresponding author: John M. Lachin, jml@ 123456bsc.gwu.edu .
                Article
                1517
                10.2337/db15-1517
                4839209
                26895792
                f871aa56-61e2-4793-b267-9f827f57aec9
                © 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.
                History
                : 03 November 2015
                : 13 February 2016
                Page count
                Figures: 1, Tables: 4, Equations: 0, References: 43, Pages: 10
                Funding
                Funded by: National Institute of Diabetes and Digestive and Kidney Disease http://dx.doi.org/10.13039/100000062
                Award ID: U01- DK-094176
                Award ID: U01-DK-094157
                Categories
                Pathophysiology

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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