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      Circulating Haptoglobin and Metabolic Syndrome in Renal Transplant Recipients

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          Abstract

          Haptoglobin (Hp) is an acute phase protein that has recently been linked to components of the metabolic syndrome (MetS). We aimed to evaluate Hp as marker of MetS, and to assess its association with long-term outcome in renal transplant recipients (RTR). We measured plasma Hp in a prospective cohort of 699 stable RTR and 149 healthy controls. Median plasma Hp concentration in RTR was 1.4 [interquartile range (IQR), 1.0–1.8] g/L, which was higher compared to 1.1 [0.9–1.4] g/L in controls (P < 0.001). Hp was independently associated with the MetS (β = 0.10) (P = 0.005). During follow-up of 5.4 [4.8–6.1] years, 150 (21%) recipients died, of whom 60 (9%) due to cardiovascular causes, and 83 (12%) RTR developed graft failure. High (≥2.0 g/L) and low (≤0.9 g/L) plasma Hp were associated with increased risk of mortality (HR’s 2.3 [1.3–4.1] and 1.9 [1.0–3.5], resp.), predominantly cardiovascular. The association of high Hp lost significance upon adjustment for inflammation markers (HR 1.5 [0.8–2.7]), while low Hp was independently associated with mortality (HR 2.2 [1.2–4.0]). Hp was not associated with graft failure (P = 0.49). In conclusion, plasma Hp is independently associated with MetS in RTR. Importantly, high and low Hp are associated with increased mortality risk, independent of MetS.

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          A new equation to estimate glomerular filtration rate.

          Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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            Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

            Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
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              Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

              Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them
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                Author and article information

                Contributors
                i.minovic@umcg.nl
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 October 2017
                27 October 2017
                2017
                : 7
                : 14264
                Affiliations
                [1 ]Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
                [2 ]GRID grid.420129.c, Top Institute Food and Nutrition, Nieuwe Kanaal 9-A, ; 6709 PA Wageningen, The Netherlands
                [3 ]Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
                [4 ]Department of Pathology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
                [5 ]ISNI 0000 0001 2153 9986, GRID grid.9764.c, Institute of Human Nutrition and Food Science, Christian-Albrechts-University of Kiel, ; Kiel, Germany
                [6 ]ISNI 0000000121102151, GRID grid.6451.6, Faculty of Medicine, Technion Institute of Technology, Efron Street 1, ; Haifa, Israel
                [7 ]ISNI 0000 0001 0791 5666, GRID grid.4818.5, Division of Human Nutrition, Wageningen University, Droevendaalsesteeg 4, ; 6708 PB Wageningen, The Netherlands
                [8 ]ISNI 0000 0004 0538 3477, GRID grid.420194.a, DSM Nutritional Products, Wurmisweg 576, ; 4303 Kaiseraugst, Switzerland
                Article
                14302
                10.1038/s41598-017-14302-2
                5660219
                29079835
                b594a42a-dd29-49d6-9537-5e812a6b0cbe
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 March 2017
                : 9 October 2017
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