Abstract

Metabolic syndrome (MS) is a complex clinical condition, initially described as ‘metabolic syndrome X’, characterised by the presence in the same individual of multiple metabolic and cardiovascular traits, typically high blood pressure, abdominal obesity, lipid profile abnormalities, insulin resistance and glucose intolerance. More recently, other features have been proposed; in particular, chronic pro-inflammatory and pro-thrombotic states seem to further characterise MS. Because of its multifactorial pathogenesis, a unitary clinical management of MS is still debated and physicians tend to make individual choices. In particular, the threshold for treatment initiation is rather heterogeneous. However, a large body of observational studies have demonstrated that patients with MS have an increased incidence of major cardiovascular events, such as acute myocardial infarction and ischemic stroke, particularly in those patients with a high-risk profile (hypertensive patients with target organ damage or diabetes mellitus). An aggressive therapeutic approach for reducing global cardiovascular risk is effective in these subgroups. On the other hand, the lack of specific international guidelines, consisting of a diagnostic and therapeutic algorithm with accurate cardiovascular risk stratification, makes the clinical management of patients with MS by primary physicians difficult and not uniform. In particular, while there is convincing evidence that the progressive addition of components of MS increases cardiovascular risk, there are no consistent indications regarding treatment priorities and strategies. In this article we propose a global risk-based stratification, aimed at identifying ‘low-risk’ and ‘high-risk’ patients within those presenting the MS. This approach may prove to be of particular efficacy in clinical practice to identify patients with MS with different degrees of cardiovascular disease risk profile. This approach may also be helpful to develop therapeutic strategies based on global cardiovascular risk profile.

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