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      The Rotterdam Study: 2018 update on objectives, design and main results

      research-article
      1 , 3 , , 1 , 11 , 12 , 2 , 8 , 1 , 1 , 5 , 9 , 1 , 7 , 10 , 1 , 2 , 1 , 2 , 1 , 4 , 1 , 2 , 1 , 6 , 1 , 13
      European Journal of Epidemiology
      Springer Netherlands
      Biomarkers, Cardiovascular diseases, Cohort study, Dermatological diseases, Endocrine diseases, Epidemiologic methods, Genetic epidemiology, Liver diseases, Neurological diseases, Oncology, Ophthalmic diseases, Otolaryngological diseases, Pharmacoepidemiology, Renal diseases, Psychiatric diseases, Respiratory diseases

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          Abstract

          The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1500 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

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          Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors

          Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval −0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect. Electronic supplementary material The online version of this article (doi:10.1007/s10654-015-0011-z) contains supplementary material, which is available to authorized users.
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            Demography. Broken limits to life expectancy.

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              The severity of ultrasonographic findings in nonalcoholic fatty liver disease reflects the metabolic syndrome and visceral fat accumulation.

              Nonalcoholic fatty liver disease (NAFLD) is closely associated with the metabolic syndrome. We evaluated the association among the metabolic syndrome, visceral fat accumulation, and the severity of fatty liver with a new scoring system of ultrasonographic findings in apparently healthy Japanese adults. Subjects consisted of 94 patients who received liver biopsy and 4,826 participants who were selected from the general population. Two hepatologists scored the ultrasonographic findings from 0 to 6 points. We calculated Cohen's kappa of within-observer reliability and between-observer reliability. We evaluated the predictive value of the score by the area under a conventional receiver operating characteristic curve (AUC). Within-observer reliability was 0.95 (95% CI 0.93-0.97, P<0.001) and between-observer reliability was 0.95 (95% CI 0.93-0.97, P<0.001). The AUC to diagnose NAFLD was 0.980. The sensitivity was 91.7% (95% CI 87.0-95.1, P<0.001) and the specificity was 100% (95% CI 95.4-100.0, P<0.001). The AUC to diagnose visceral obesity was 0.821. The sensitivity was 68.3% (95% CI 51.9-81.9, P=0.028) and the specificity was 95.1% (95% CI 86.3-99.0, P<0.001). Adjusted odds ratio of the score for the metabolic syndrome was 1.37 (95% CI 1.26-1.49, P<0.001). The scoring system with abdominal ultrasonography could provide accurate information about hepatic steatosis, visceral obesity, and the metabolic syndrome in apparently healthy people who do not consume alcohol.
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                Author and article information

                Contributors
                m.a.ikram@erasmusmc.nl
                Journal
                Eur J Epidemiol
                Eur. J. Epidemiol
                European Journal of Epidemiology
                Springer Netherlands (Dordrecht )
                0393-2990
                1573-7284
                24 October 2017
                24 October 2017
                2017
                : 32
                : 9
                : 807-850
                Affiliations
                [1 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Epidemiology, , Erasmus Medical Center, ; PO Box 2040, 3000 CA Rotterdam, The Netherlands
                [2 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Internal Medicine, , Erasmus Medical Center, ; Rotterdam, The Netherlands
                [3 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Neurology, , Erasmus Medical Center, ; Rotterdam, The Netherlands
                [4 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Psychiatry, , Erasmus Medical Center, ; Rotterdam, The Netherlands
                [5 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Cardiology, , Erasmus Medical Center, ; Rotterdam, The Netherlands
                [6 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Radiology and Nuclear Medicine, , Erasmus Medical Center, ; Rotterdam, The Netherlands
                [7 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Ophthalmology, , Erasmus Medical Center, ; Rotterdam, The Netherlands
                [8 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Gastro-Enterology, , Erasmus Medical Center, ; Rotterdam, The Netherlands
                [9 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Otolaryngology, , Erasmus Medical Center, ; Rotterdam, The Netherlands
                [10 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Dermatology, , Erasmus Medical Center, ; Rotterdam, The Netherlands
                [11 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Respiratory Medicine, , Erasmus Medical Center, ; Rotterdam, The Netherlands
                [12 ]ISNI 0000 0004 0626 3303, GRID grid.410566.0, Department of Respiratory Medicine, , Ghent University Hospital, ; Ghent, Belgium
                [13 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Epidemiology, , Harvard T. H. Chan School of Public Health, ; Boston, MA USA
                Article
                321
                10.1007/s10654-017-0321-4
                5662692
                29064009
                21996c9d-71ec-441c-9b43-56f5c3087dc3
                © Springer Science+Business Media B.V. 2017
                History
                : 26 June 2017
                : 6 October 2017
                Categories
                Study Update
                Custom metadata
                © Springer Science+Business Media B.V. 2017

                Public health
                biomarkers,cardiovascular diseases,cohort study,dermatological diseases,endocrine diseases,epidemiologic methods,genetic epidemiology,liver diseases,neurological diseases,oncology,ophthalmic diseases,otolaryngological diseases,pharmacoepidemiology,renal diseases,psychiatric diseases,respiratory diseases

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