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Since the first description of the association between chronic kidney disease and heart disease, many epidemiological studies have confirmed and extended this finding. As chronic kidney disease progresses, kidney-specific risk factors for cardiovascular events and disease come into play. As a result, the risk for cardiovascular disease is notably increased in individuals with chronic kidney disease. When adjusted for traditional cardiovascular risk factors, impaired kidney function and raised concentrations of albumin in urine increase the risk of cardiovascular disease by two to four times. Yet, cardiovascular disease is frequently underdiagnosed and undertreated in patients with chronic kidney disease. This group of patients should, therefore, be acknowledged as having high cardiovascular risk that needs particular medical attention at an individual level. This view should be incorporated in the development of guidelines and when defining research priorities. Here, we discuss the epidemiology and pathophysiological mechanisms of cardiovascular risk in patients with chronic kidney disease, and discuss methods of prevention.
The Chronic Kidney Disease Prognosis Consortium (CKD-PC) was established in 2009 to provide comprehensive evidence about the prognostic impact of two key kidney measures that are used to define and stage CKD, estimated glomerular filtration rate (eGFR) and albuminuria, on mortality and kidney outcomes. CKD-PC currently consists of 46 cohorts with data on these kidney measures and outcomes from >2 million participants spanning across 40 countries/regions all over the world. CKD-PC published four meta-analysis articles in 2010-11, providing key evidence for an international consensus on the definition and staging of CKD and an update for CKD clinical practice guidelines. The consortium continues to work on more detailed analysis (subgroups, different eGFR equations, other exposures and outcomes, and risk prediction). CKD-PC preferably collects individual participant data but also applies a novel distributed analysis model, in which each cohort runs statistical analysis locally and shares only analysed outputs for meta-analyses. This distributed model allows inclusion of cohorts which cannot share individual participant level data. According to agreement with cohorts, CKD-PC will not share data with third parties, but is open to including further eligible cohorts. Each cohort can opt in/out for each topic. CKD-PC has established a productive and effective collaboration, allowing flexible participation and complex meta-analyses for studying CKD.