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      Forecasting levels of serum 25-hydroxyvitamin D based on dietary intake, lifestyle and personal determinants in a sample of Southern Europeans

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          Abstract

          Vitamin D is an essential nutrient to be consumed in the habitual dietary intake, whose deficiency is associated with various disturbances. This study represents a validation of vitamin D status estimation using a semi-quantitative FFQ, together with data from additional physical activity and lifestyle questionnaires. This information was combined to forecast the serum vitamin D status. Different statistical methods were applied to estimate the vitamin D status using predictors based on diet and lifestyle. Serum vitamin D was predicted using linear regression (with leave-one-out cross-validation) and random forest models. Intraclass correlation coefficients, Lin’s agreement coefficients, Bland–Altman plots and other methods were used to assess the accuracy of the predicted v. observed serum values. Data were collected in Spain. A total of 220 healthy volunteers aged between 18 and 78 years were included in this study. They completed validated questionnaires and agreed to provide blood samples to measure serum 25-hydroxyvitamin D (25(OH)D) levels. The common final predictors in both models were age, sex, sunlight exposure, vitamin D dietary intake (as assessed by the FFQ), BMI, time spent walking, physical activity and skin reaction after sun exposure. The intraclass correlation coefficient for the prediction was 0·60 (95 % CI: 0·52, 0·67; P < 0·001) using the random forest model. The magnitude of the correlation was moderate, which means that our estimation could be useful in future epidemiological studies to establish a link between the predicted 25(OH)D values and the occurrence of several clinical outcomes in larger cohorts.

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          Bias in random forest variable importance measures: Illustrations, sources and a solution

          Background Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories. Results Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. Conclusion We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and documented thoroughly in an application re-analyzing data from a study on RNA editing. Therefore the suggested method can be applied straightforwardly by scientists in bioinformatics research.
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            Vitamin D deficiency: a worldwide problem with health consequences.

            Vitamin D deficiency is now recognized as a pandemic. The major cause of vitamin D deficiency is the lack of appreciation that sun exposure in moderation is the major source of vitamin D for most humans. Very few foods naturally contain vitamin D, and foods that are fortified with vitamin D are often inadequate to satisfy either a child's or an adult's vitamin D requirement. Vitamin D deficiency causes rickets in children and will precipitate and exacerbate osteopenia, osteoporosis, and fractures in adults. Vitamin D deficiency has been associated with increased risk of common cancers, autoimmune diseases, hypertension, and infectious diseases. A circulating level of 25-hydroxyvitamin D of >75 nmol/L, or 30 ng/mL, is required to maximize vitamin D's beneficial effects for health. In the absence of adequate sun exposure, at least 800-1000 IU vitamin D3/d may be needed to achieve this in children and adults. Vitamin D2 may be equally effective for maintaining circulating concentrations of 25-hydroxyvitamin D when given in physiologic concentrations.
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              Worldwide vitamin D status.

              The aim of the present study is to summarize existing literature on vitamin D levels in adults in different continents and different countries worldwide. The best determinant of vitamin D status is the serum concentration of 25-hydroxyvitamin D (25(OH)D). Most investigators agree that serum 25(OH)D should be higher than 50 nmol/l, but some recommend higher serum levels. Traditional risk groups for vitamin D deficiency include pregnant women, children, older persons, the institutionalized, and non-western immigrants. This chapter shows that serum 25(OH)D levels are not only suboptimal in specific risk groups, but also in adults in many countries. Especially, in the Middle-East and Asia, vitamin D deficiency in adults is highly prevalent. Copyright © 2011 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: MethodologyRole: ValidationRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: VisualizationRole: Writing – review & editing
                Role: Data curationRole: Project administration
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: Writing – review & editing
                Role: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: Writing – review & editing
                Journal
                Br J Nutr
                Br J Nutr
                BJN
                The British Journal of Nutrition
                Cambridge University Press (Cambridge, UK )
                0007-1145
                1475-2662
                28 November 2023
                11 April 2023
                : 130
                : 10
                : 1814-1822
                Affiliations
                [ 1 ] University of Navarra , Department of Preventive Medicine and Public Health, Pamplona, Spain
                [ 2 ]Department of Family Medicine, Aragon Health Service (SALUD) , Zaragoza, Spain
                [ 3 ]CIBERobn, Instituto de Salud Carlos III , Madrid, Spain
                [ 4 ]IdiSNa, Navarra Institute for Health Research , Pamplona, Spain
                [ 5 ]Navarra Public Health Institute , Pamplona, Spain
                [ 6 ] Institute IMDEA Food , Madrid, Spain
                [ 7 ] University of Navarra , Department of Nutrition, Food Science and Physiology, Centre for Nutrition Research, Pamplona, Spain
                [ 8 ]Department of Medicine and Psychiatry, University of Zaragoza , Instituto de Investigación Sanitaria Aragón (IIS Aragón), Zaragoza, Spain
                [ 9 ]Harvard T.H. Chan School of Public Health , Boston, MA, USA
                Author notes
                [* ] Corresponding author: Carmen Sayón-Orea, email msayon@ 123456unav.es
                Author information
                https://orcid.org/0000-0001-6961-8149
                https://orcid.org/0000-0002-4137-3263
                https://orcid.org/0000-0002-3917-9808
                Article
                S0007114523000946
                10.1017/S0007114523000946
                10587381
                37039468
                4bf97dc1-f93a-4bd7-9025-d0dcb6bfcf58
                © The Author(s) 2023

                This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.

                History
                : 21 September 2022
                : 18 March 2023
                : 30 March 2023
                Page count
                Figures: 3, Tables: 2, References: 40, Pages: 9
                Categories
                Research Article
                Dietary Surveys and Nutritional Epidemiology

                Nutrition & Dietetics
                vitamin d,predictive model,lifestyle,predictors
                Nutrition & Dietetics
                vitamin d, predictive model, lifestyle, predictors

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