The purpose of this study was to investigate the relationship between statin eligibility and the degree of renal dysfunction using the Adult Treatment Panel (ATP) III and the American College of Cardiology (ACC)/American Heart Association (AHA) guidelines in Korean adults.
Renal function was assessed in 18,746 participants of the Kangbuk Samsung Health Study from January 2011 to December 2012. Subjects were divided into three groups according to estimated glomerular filtration rate (eGFR): stage 1, eGFR ≥90 mL/min/1.73 m2; stage 2, eGFR 60 to 89 mL/min/1.73 m2; and stages 3 to 5, eGFR <60 mL/min/1.73 m2. Statin eligibility in these groups was determined using the ATP III and ACC/AHA guidelines, and the risk for 10-year atherosclerotic cardiovascular disease (ASCVD) was calculated using the Framingham Risk Score (FRS) and Pooled Cohort Equation (PCE).
There were 3,546 (18.9%) and 4,048 (21.5%) statin-eligible subjects according to ATP III and ACC/AHA guidelines, respectively. The proportion of statin-eligible subjects increased as renal function deteriorated. Statin eligibility by the ACC/AHA guidelines showed better agreement with the Kidney Disease Improving Global Outcomes (KDIGO) recommendations compared to the ATP III guidelines in subjects with stage 3 to 5 chronic kidney disease (CKD) (κ value, 0.689 vs. 0.531). When the 10-year ASCVD risk was assessed using the FRS and PCE, the mean risk calculated by both equations significantly increased as renal function declined.
The proportion of statin-eligible subjects significantly increased according to worsening renal function in this Korean cohort. ACC/AHA guideline showed better agreement for statin eligibility with that recommended by KDIGO guideline compared to ATP III in subjects with CKD.
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The term "omics" refers to any type of specific study that provides collective information on a biological system. Representative omics includes genomics, proteomics, and metabolomics, and new omics is constantly being added, such as lipidomics or glycomics. Each omics technique is crucial to the understanding of various biological systems and complements the information provided by the other approaches. The main strengths of metabolomics are that metabolites are closely related to the phenotypes of living organisms and provide information on biochemical activities by reflecting the substrates and products of cellular metabolism. The transcriptome does not always correlate with the proteome, and the translated proteome might not be functionally active. Therefore, their changes do not always result in phenotypic alterations. Unlike the genome or proteome, the metabolome is often called the molecular phenotype of living organisms and is easily translated into biological conditions and disease states. Here, we review the general strategies of mass spectrometry-based metabolomics. Targeted metabolome or lipidome analysis is discussed, as well as nontargeted approaches, with a brief explanation of the advantages and disadvantages of each platform. Biomedical applications that use mass spectrometry-based metabolomics are briefly introduced.
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