Long-term prognosis after myocardial infarction in men with diabetes.
ABSTRACT Men (1306) who survived a first myocardial infarction (MI) were studied. The mean follow-up time was 6.5 yr, and at the end of the follow-up period survival status was known for all patients. By the time of the MI the prevalence of diabetes was 5.6%. Patients with and without diabetes were compared. There were no differences in the estimated primary or secondary risk. The cumulative survival rate 1, 2, and 5 yr after the MI was 82, 78, and 58% among the diabetic subjects compared with 94, 92, and 82% among the nondiabetic subjects (P less than 0.001). The difference remained even after allowance for age and estimated secondary risk in a multivariate regression analysis. There were no differences in mortality rates among patients with type I diabetes compared with type II diabetes, nor among patients treated with diet alone, sulfonylurea, or insulin, but the numbers were small. The cumulative rate of reinfarctions after 1, 2, and 5 yr was 18, 28, and 46% in diabetic subjects and 12, 17, and 27% in nondiabetic subjects (P = 0.004). A history of diabetes was an independent secondary risk factor among male survivors of a first MI with respect to deaths and reinfarctions.
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ABSTRACT: The central nervous system mediates energy balance (energy intake and energy expenditure) in the body; the hypothalamus has a key role in this process. Recent evidence has demonstrated an important role for hypothalamic malonyl CoA in mediating energy balance. Malonyl CoA is generated by the carboxylation of acetyl CoA by acetyl CoA carboxylase and is then either incorporated into long-chain fatty acids by fatty acid synthase, or converted back to acetyl-CoA by malonyl CoA decarboxylase. Increased hypothalamic malonyl CoA is an indicator of energy surplus, resulting in a decrease in food intake and an increase in energy expenditure. In contrast, a decrease in hypothalamic malonyl CoA signals an energy deficit, resulting in an increased appetite and a decrease in body energy expenditure. A number of hormonal and neural orexigenic and anorexigenic signaling pathways have now been shown to be associated with changes in malonyl CoA levels in the arcuate nucleus (ARC) of the hypothalamus. Despite compelling evidence that malonyl CoA is an important mediator in the hypothalamic ARC control of food intake and regulation of energy balance, the mechanism(s) by which this occurs has not been established. Malonyl CoA inhibits carnitine palmitoyltransferase-1 (CPT-1), and it has been proposed that the substrate of CPT-1, long-chain acyl CoA(s), may act as a mediator(s) of appetite and energy balance. However, recent evidence has challenged the role of long-chain acyl CoA(s) in this process, as well as the involvement of CPT-1 in hypothalamic malonyl CoA signaling. A better understanding of how malonyl CoA regulates energy balance should provide novel approaches to targeting intermediary metabolism in the hypothalamus as a mechanism to control appetite and body weight. Here, we review the data supporting an important role for malonyl CoA in mediating hypothalamic control of energy balance, and recent evidence suggesting that targeting malonyl CoA synthesis or degradation may be a novel approach to favorably modify appetite and weight gain.Pharmacological reviews 06/2010; 62(2):237-64. · 17.00 Impact Factor
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ABSTRACT: We describe the baseline characteristics, management, and in-hospital outcomes of patients in the United Arab Emirates (UAE) with DM admitted with an acute coronary syndrome (ACS) and assess the influence of DM on in-hospital mortality. Data was analyzed from 1697 patients admitted to various hospitals in the UAE with a diagnosis of ACS in 2007 as part of the 1st Gulf RACE (Registry of Acute Coronary Events). Of 1697 patients enrolled, 668 (39.4%) were diabetics. Compared to patients without DM, diabetic patients were more likely to have a past history of coronary artery disease (49.1% versus 30.1%, P < 0.001), hypertension (67.2% versus 36%, P < 0.001), and prior revascularization (21% versus 11.4%, P < 0.001). They experienced more in-hospital recurrent ischemia (8.5% versus 5.1%; P = 0.004) and heart failure (20% versus 10%; P < 0.001). The mortality rate was 2.7% for diabetics and 1.6% for nondiabetics (P = 0.105). After age adjustment, in-hospital mortality increased by 3.5% per year of age (P = 0.016). This mortality was significantly higher in females than in males (P = 0.04). ACS patients with DM have different clinical characteristics and appear to have poorer outcomes.The Scientific World Journal 01/2012; 2012:698597. · 1.73 Impact Factor
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ABSTRACT: With the increasing burden of chronic diseases on the health care system, Markov-type models are becoming popular to predict the long-term outcomes of early intervention and to guide disease management. However, statisticians have not been actively involved in the development of these models. Typically, the models are developed by using secondary data analysis to find a single "best" study to estimate each transition in the model. However, due to the nature of secondary data analysis, there frequently are discrepancies between the theoretical model and the design of the studies being used. This paper illustrates a likelihood approach to correctly model the design of clinical studies under the conditions where 1) the theoretical model may include an instantaneous state of distinct interest to the researchers, and 2) the study design may be such that study data can not be used to estimate a single parameter in the theoretical model of interest. For example, a study may ignore intermediary stages of disease. Using our approach, not only can we accommodate the two conditions above, but more than one study may be used to estimate model parameters. In the spirit of "If life gives you lemon, make lemonade", we call this method "Lemonade Method". Simulation studies are carried out to evaluate the finite sample property of this method. In addition, the method is demonstrated through application to a model of heart disease in diabetes.Information Fusion 04/2012; 13(2):137-145. · 2.26 Impact Factor