Patients at high risk for recurrent vascular events can be identified based on readily available clinical characteristics.

Objectives To predict treatment effects for individual patients based on data from randomised trials, taking rosuvastatin treatment in the primary prevention of cardiovascular disease as an example, and to evaluate the net benefit of making treatment decisions for individual patients based on a predicted absolute treatment effect.Setting As an example, data were used from the Justification for the Use of Statins in Prevention (JUPITER) trial, a randomised controlled trial evaluating the effect of rosuvastatin 20 mg daily versus placebo on the occurrence of cardiovascular events (myocardial infarction, stroke, arterial revascularisation, admission to hospital for unstable angina, or death from cardiovascular causes).Population 17 802 healthy men and women who had low density lipoprotein cholesterol levels of less than 3.4 mmol/L and high sensitivity C reactive protein levels of 2.0 mg/L or more.Methods Data from the Justification for the Use of Statins in Prevention trial were used to predict rosuvastatin treatment effect for individual patients based on existing risk scores (Framingham and Reynolds) and on a newly developed prediction model. We compared the net benefit of prediction based rosuvastatin treatment (selective treatment of patients whose predicted treatment effect exceeds a decision threshold) with the net benefit of treating either everyone or no one. ResultsThe median predicted 10 year absolute risk reduction for cardiovascular events was 4.4% (interquartile range 2.6-7.0%) based on the Framingham risk score, 4.2% (2.5-7.1%) based on the Reynolds score, and 3.9% (2.5-6.1%) based on the newly developed model (optimal fit model). Prediction based treatment was associated with more net benefit than treating everyone or no one, provided that the decision threshold was between 2% and 7%, and thus that the number willing to treat (NWT) to prevent one cardiovascular event over 10 years was between 15 and 50.Conclusions Data from randomised trials can be used to predict treatment effect in terms of absolute risk reduction for individual patients, based on a newly developed model or, if available, existing risk scores.The value of such prediction of treatment effect for medical decision making is conditional on the NWT to prevent one outcome event.Trial registration number Clinicaltrials.gov NCT00239681. IntroductionUsually the results of trials are implemented in clinical practice by either treating all patients (in the case of a positive trial result) or treating no one (in the case of a negative trial result), expecting the treatment effect for every patient to be similar to the average treatment effect in the original trial. Clinicians intuitively know that this idea is oversimplified because in reality some patients benefit more than average from treatment, whereas others do not or may even be harmed. 1-6The direct translation of trial results to individual patients in clinical practice is, however, complicated by some important limitations. The treatment effects of randomised trials are RESEA...

Despite criticism regarding its clinical relevance, the concept of the metabolic syndrome improves our understanding of both the pathophysiology of insulin resistance and its associated metabolic changes and vascular consequences. Free fatty acids (FFA) and tumour necrosis factor-alpha (TNF-α ) play prominent roles in the development of insulin resistance by impairing the intracellular insulin signalling transduction pathway. Obesity is an independent risk factor for cardiovascular disease and strongly related to insulin resistance. In case of obesity, FFAs and TNF-α are produced in abundance by adipocytes, whereas the production of adiponectin, an anti-inflammatory adipokine, is reduced. This imbalanced production of pro-and anti-inflammatory adipokines, as observed in adipocyte dysfunction, is thought to be the driving force behind insulin resistance. The role of several recently discovered adipokines such as resistin, visfatin and retinol-binding protein (RBP)-4 in the pathogenesis of insulin resistance is increasingly understood. Insulin resistance induces several metabolic changes, including hyperglycaemia, dyslipidaemia and hypertension, all leading to increased cardiovascular risk. In addition, the dysfunctional adipocyte, reflected largely by low adiponectin levels and a high TNF-α concentration, directly influences the vascular endothelium, causing endothelial dysfunction and atherosclerosis. Adipocyte dysfunction could therefore be regarded as the common antecedent of both insulin resistance and atherosclerosis and functions as the link between obesity and cardiovascular disease. Targeting the dysfunctional adipocyte may reduce the risk for both cardiovascular disease and the development of type 2 diabetes. Although lifestyle intervention remains the cornerstone of therapy in improving insulin sensitivity and its associated metabolic changes, medical treatment might prove to be important as well.

In patients with various manifestations of atherosclerosis, presence of NCEP and NCEP-R-defined MetS is associated with increased risk of cardiovascular events and all-cause mortality, independently of the presence of DM2. This risk is significantly higher than the risk associated with International Diabetes Federation-defined MetS. Also in patients at treatment goals for SBP (<140 mmHg) or LDL-c (<2.5 mmol/L) according to current guidelines, presence of NCEP-R-defined MetS points to a higher vascular risk.

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