Therefore, the impact of OSA in patients with MetS may be even greater. Second, our patients were middle-aged and without a history of coronary disease and stroke or intake of statins, fibrates and hypoglycemic drugs. Hence, our results may not be applicable to other age groups, or patients with established cardiovascular disease. On the contrary, the exclusion of drugs that directly affect the metabolic and inflammatory profile in MetS may be an advantage for studying metabolic and pro-inflammatory effects of OSA. Third, we were unable to exclude patients on anti-hypertensive treatment, since more than 50% of patients were on medications, which could not be discontinued for ethical reasons. Finally, the cross sectional nature of the study does not prove cause-effect relationships between OSA and metabolic and inflammatory markers. In conclusion, we have shown that OSA is highly common in patients with MetS. OSA is independently associated with increased prevalence and severity of hypertriglyceridemia and hyperglycemia, as well as with several other markers of metabolic and inflammatory dysregulation. Our data strongly suggest that patients with MetS need to be evaluated for OSA regardless of daytime sleepiness. Cancers are a complex set of proliferative diseases whose progression, in most cases, is driven in part by an accumulation of genetic changes, including copy number aberrations of large or small genomic regions which may for example lead to amplification of oncogenes or loss of tumor suppressor genes. However, cancer progression is also often characterized by increasing genomic instability, potentially generating many ”passenger” CNAs that do not confer clonal growth advantage. These processes give rise to a complicated landscape of genomic alterations within an individual tumor and great diversity of these CNAs across tumor samples, making it difficult to identify driver mutations associated with cancer progression. In recent years, array-based comparative genomic hybridization and single nucleotide polymorphism arrays have been used to analyze the CNAs of tumor samples at a genomic scale and at progressively higher WY 14643 molecular weight resolutions. Moreover, numerous large-scale tumor profiling studies have generated copy number data sets for large cohorts of tumors. These large and complex ”cancer genome” data sets present difficult statistical challenges. Individual CNAs may be as small as a few adjacent probes or as large as a whole chromosomes and may be difficult to detect above probe-level noise; moreover, it is unclear how to make sense out of diverse CNAs from hundreds of tumors. We hypothesize that the presence of OSA can exacerbate MetS and further increase cardiovascular morbidity and mortality. Future interventional studies will demonstrate whether treatment of OSA will improve MetS and cardiovascular outcomes in these patients.