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      Meta-Analysis of Early Nonmotor Features and Risk Factors for Parkinson Disease

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          Abstract

          Objective

          To evaluate the association between diagnosis of Parkinson disease (PD) and risk factors or early symptoms amenable to population-based screening.

          Methods

          A systematic review and meta-analysis of risk factors for PD.

          Results

          The strongest associations with later diagnosis of PD were found for having a first-degree or any relative with PD (odds ratio [OR], 3.23; 95% confidence interval [CI], 2.65–3.93 and OR, 4.45; 95% CI, 3.39–5.83) or any relative with tremor (OR, 2.74; 95% CI, 2.10–3.57), constipation (relative risk [RR], 2.34; 95% CI, 1.55–3.53), or lack of smoking history (current vs never: RR, 0.44; 95% CI, 0.39–0.50), each at least doubling the risk of PD. Further positive significant associations were found for history of anxiety or depression, pesticide exposure, head injury, rural living, beta-blockers, farming occupation, and well-water drinking, and negative significant associations were found for coffee drinking, hypertension, nonsteroidal anti-inflammatory drugs, calcium channel blockers, and alcohol, but not for diabetes mellitus, cancer, oral contraceptive pill use, surgical menopause, hormone replacement therapy, statins, acetaminophen/paracetamol, aspirin, tea drinking, history of general anesthesia, or gastric ulcers. In the systematic review, additional associations included negative associations with raised serum urate, and single studies or studies with conflicting results.

          Interpretation

          The strongest risk factors associated with later PD diagnosis are having a family history of PD or tremor, a history of constipation, and lack of smoking history. Further factors also but less strongly contribute to risk of PD diagnosis or, as some premotor symptoms, require further standardized studies to demonstrate the magnitude of risk associated with them. ANN NEUROL 2012

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          Most cited references68

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          Measuring inconsistency in meta-analyses.

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            Quantifying heterogeneity in a meta-analysis.

            The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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              Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

              David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses
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                Author and article information

                Journal
                Ann Neurol
                Ann. Neurol
                ana
                Annals of Neurology
                Wiley Subscription Services, Inc., A Wiley Company (Hoboken )
                0364-5134
                1531-8249
                December 2012
                15 October 2012
                : 72
                : 6
                : 893-901
                Affiliations
                [1 ]Institute of Neurology, University College London London, United Kingdom
                [2 ]Blizard Institute of Cell and Molecular Science, Barts and the London School of Medicine and Dentistry, Queen Mary University of London London, United Kingdom
                [3 ]Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London London, United Kingdom
                [4 ]Department of Neurology, State University of Campinas Campinas, Brazil
                Author notes
                Address correspondence to Dr Schrag, Department of Clinical Neurosciences, Institute of Neurology, Royal Free Campus, University College London, London NW3 2PF, United Kingdom. E-mail: a.schrag@ 123456ucl.ac.uk

                The data contained in this paper are from published studies only. There are no additional data available.

                Additional supporting information can be found in the online version of this article.

                Article
                10.1002/ana.23687
                3556649
                23071076
                025e8c09-1726-4af8-a3ef-9a7f57f283d2
                Copyright © 2012 American Neurological Association

                Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

                History
                : 11 May 2012
                : 19 June 2012
                : 06 July 2012
                Categories
                Original Articles

                Neurology
                Neurology

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