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      Determinants of elevated chemerin as a novel biomarker of immunometabolism: data from a large population-based cohort

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          Abstract

          Objective

          Chemerin is a novel inflammatory biomarker suggested to play a role in the development of metabolic disorders, providing new avenues for treatment and prevention. Little is known about the factors that predispose elevated chemerin concentrations. We therefore aimed to explore a range of lifestyle-associated, dietary, and metabolic factors as potential determinants of elevated chemerin concentrations in asymptomatic adults.

          Design

          We used cross-sectional data from a random subsample of 2433 participants (1494 women and 939 men) aged 42–58 years of the European Prospective Investigation into Cancer and Nutrition-Potsdam cohort.

          Methods

          Random forest regression (RFR) was applied to explore the relative importance of 32 variables as statistical predictors of elevated chemerin concentrations overall and by sex. Multivariable-adjusted linear regression was applied to evaluate associations between selected predictors and chemerin concentrations.

          Results

          Results from RFR suggested BMI, waist circumference, C-reactive protein, fatty liver index, and estimated glomerular filtration rate as the strongest predictors of chemerin concentrations. Additional predictors included sleeping duration, alcohol, red and processed meat, fruits, sugar-sweetened beverages (SSB), vegetables, dairy, and refined grains. Collectively, these factors explained 32.9% variation of circulating chemerin. Multivariable-adjusted analyses revealed linear associations of elevated chemerin with metabolic parameters, obesity, longer sleep, higher intakes of red meat and SSB, and lower intakes of dairy.

          Conclusions

          These findings come in support of the role of chemerin as a biomarker characterizing inflammatory and metabolic phenotypes in asymptomatic adults. Modifiable dietary and lifestyle-associated determinants of elevated chemerin concentrations require further evaluation in a prospective study setting.

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          Most cited references59

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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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            Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.

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              MissForest--non-parametric missing value imputation for mixed-type data.

              Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. The package missForest is freely available from http://stat.ethz.ch/CRAN/. stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch
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                Author and article information

                Journal
                Endocr Connect
                Endocr Connect
                EC
                Endocrine Connections
                Bioscientifica Ltd (Bristol )
                2049-3614
                25 August 2021
                01 September 2021
                : 10
                : 9
                : 1200-1211
                Affiliations
                [1 ]Department of Molecular Epidemiology , German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany
                [2 ]Institute of Nutritional Science , University of Potsdam, Potsdam, Germany
                [3 ]Department of Epidemiological Methods and Etiological Research , Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany
                [4 ]Department of Biometry and Data Management , Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany
                [5 ]German Center for Diabetes Research (DZD) , Neuherberg, Germany
                [6 ]Department of Food Safety , German Federal Institute for Risk Assessment, Berlin, Germany
                [7 ]Institute for Social Medicine , Epidemiology and Health Economics, Charité University Medical Center, Berlin, Germany
                [8 ]Institute of Laboratory Medicine , Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
                [9 ]Division of Endocrinology , Diabetology, Nephrology, Vascular Disease and Clinical Chemistry, Department of Internal Medicine, University of Tübingen, Tübingen, Germany
                [10 ]Faculty of Human and Health Sciences , University of Bremen, Bremen, Germany
                Author notes
                Correspondence should be addressed to K Aleksandrova: aleksandrova@ 123456leibniz-bips.de
                Article
                EC-21-0273
                10.1530/EC-21-0273
                8494416
                34431786
                20bebb23-97dc-47fa-b22f-506b0d3b98c1
                © The authors

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

                History
                : 03 August 2021
                : 25 August 2021
                Categories
                Research

                chemerin,inflammation,biomarkers,lifestyle determinants
                chemerin, inflammation, biomarkers, lifestyle determinants

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