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The covariates included in the model were time (random), lipid levels, and time �� lipid levels (interaction term; to assess the difference in renal function decline between the lipid level categories). All analyses were adjusted for the potential baseline confounders age, sex, primary kidney disease, smoking status, cardiovascular disease, diabetes mellitus, body mass index, systolic blood pressure, Oxacillin and proteinuria. In an additional model, we further adjusted for available malnutrition-inflammation complex system-related factors (albumin, C-reactive protein, and subjective global assessment). To maintain power and avoid bias, missing baseline confounder values were imputed (using 10 repetitions) with the method of multiple imputations in PASW/SPSS version 20.0. This is a recommended technique where missing data for a patient are imputed by a value that is predicted by other known characteristics of this patient [31,32]. All characteristics in tables ?tables11 and ?and2,2, follow-up time, and the outcome (dialysis, transplantation, death, or censoring) were included in the imputation model, because missing baseline characteristics are often related to the outcome [33]. For each baseline characteristic, the number of patients with an available value is given in tables ?tables11 and ?and2.2. Skewed distributed continuous variables, including follow-up time, were logarithmically selleck products transformed before being entered into the model. Table 1 Baseline patient characteristics for the total population and stratified by the LDL cholesterol target goal Table 2 Baseline lipid levels, treatment, and malnutrition-inflammation LY2109761 molecular weight characteristics for the total population and stratified by the LDL cholesterol target goal We performed several additional analyses. First, we excluded patients who were prescribed lipid-lowering medication, defined as either a statin, a fibrate, or a cholesterol absorption inhibitor. Second, we analyzed the abnormal lipid levels continuously to investigate whether there was a similar association throughout the entire range of lipid levels. Third, we compared the effect of LDL cholesterol levels on renal function decline between the centres who estimated LDL cholesterol levels with the Friedewald equation and the centres who directly measured LDL cholesterol. Fourth, we excluded eGFR values measured in the 2 weeks before starting dialysis, receiving a kidney transplant, death, or censoring, because there can be a large variation between eGFR measurements during this period. Fifth, we repeated all analyses after excluding the patients with only one eGFR measurement, to check whether the results from our linear mixed model were robust. Finally, we stratified our results by sex, because female hormones could influence the association between lipid levels and renal function decline [34]. p values