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      Heart rate, intelligence in adolescence, and Parkinson’s disease later in life

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          Abstract

          To investigate whether physical and cognitive fitness measured in late adolescence was associated with future risk of Parkinson’s disease (PD). The cohort included 1,259,485 Swedish men with physical fitness, body mass index (BMI), resting heart rate (RHR), blood pressure, intelligence quotient (IQ), and stress resilience measured at the age of 17–20 in relation to conscription. Incident cases of PD were ascertained from the Swedish Patient Register. Hazard ratios were estimated from Cox models, after controlling for multiple confounders. We further performed Mendelian randomization (MR) analyses to assess the causality of the associations, using GWAS summary statistics with > 800,000 individuals. During follow-up, we identified 1,034 cases of PD (mean age at diagnosis = 53). Men with an RHR > 100 beats per minute had a higher risk of PD compared to men with an RHR of 60–100 beats per minute (HR = 1.47; 95% CI = 1.08–1.99). Men with IQ above the highest tertile had a higher risk of PD compared to men with an IQ below the lowest tertile (HR = 1.46; 95% CI = 1.19–1.79). We found no association for physical fitness, BMI, blood pressure, or stress resilience. A causal relationship was suggested by the MR analysis between IQ and PD, but not between RHR and PD. RHR and IQ in late adolescence were associated with a higher risk of PD diagnosed at relatively young age. The association of IQ with PD is likely causal, whereas the association of RHR with PD suggests that altered cardiac autonomic function might start before 20 years of age in PD.

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          The online version contains supplementary material available at 10.1007/s10654-021-00730-y.

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          Most cited references48

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          Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

          ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.
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            The MR-Base platform supports systematic causal inference across the human phenome

            Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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              External review and validation of the Swedish national inpatient register

              Background The Swedish National Inpatient Register (IPR), also called the Hospital Discharge Register, is a principal source of data for numerous research projects. The IPR is part of the National Patient Register. The Swedish IPR was launched in 1964 (psychiatric diagnoses from 1973) but complete coverage did not begin until 1987. Currently, more than 99% of all somatic (including surgery) and psychiatric hospital discharges are registered in the IPR. A previous validation of the IPR by the National Board of Health and Welfare showed that 85-95% of all diagnoses in the IPR are valid. The current paper describes the history, structure, coverage and quality of the Swedish IPR. Methods and results In January 2010, we searched the medical databases, Medline and HighWire, using the search algorithm "validat* (inpatient or hospital discharge) Sweden". We also contacted 218 members of the Swedish Society of Epidemiology and an additional 201 medical researchers to identify papers that had validated the IPR. In total, 132 papers were reviewed. The positive predictive value (PPV) was found to differ between diagnoses in the IPR, but is generally 85-95%. Conclusions In conclusion, the validity of the Swedish IPR is high for many but not all diagnoses. The long follow-up makes the register particularly suitable for large-scale population-based research, but for certain research areas the use of other health registers, such as the Swedish Cancer Register, may be more suitable.
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                Author and article information

                Contributors
                yiqiang.zhan@ki.se
                Journal
                Eur J Epidemiol
                Eur J Epidemiol
                European Journal of Epidemiology
                Springer Netherlands (Dordrecht )
                0393-2990
                1573-7284
                6 March 2021
                6 March 2021
                2021
                : 36
                : 10
                : 1055-1064
                Affiliations
                [1 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Medical Epidemiology and Biostatistics, , Karolinska Institutet, ; Stockholm, Sweden
                [2 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Institute of Environmental Medicine, , Karolinska Institutet, ; Box 210, 171 77 Stockholm, Sweden
                [3 ]GRID grid.424247.3, ISNI 0000 0004 0438 0426, German Center for Neurodegenerative Diseases, ; Ulm, Germany
                [4 ]GRID grid.26091.3c, ISNI 0000 0004 1936 9959, Department of Preventive Medicine and Public Health, , Keio University School of Medicine, ; Tokyo, Japan
                [5 ]GRID grid.136593.b, ISNI 0000 0004 0373 3971, Public Health, Department of Social Medicine, , Osaka University Graduate School of Medicine, ; Suita, Japan
                [6 ]GRID grid.20515.33, ISNI 0000 0001 2369 4728, Department of Public Health Medicine, Faculty of Medicine, and Health Services Research and Development Center, , University of Tsukuba, ; Tsukuba, Japan
                [7 ]GRID grid.15895.30, ISNI 0000 0001 0738 8966, Faculty of Medical Sciences, , Örebro University, ; Örebro, Sweden
                [8 ]GRID grid.411377.7, ISNI 0000 0001 0790 959X, Department of Psychological and Brain Sciences, , Indiana University, ; Bloomington, USA
                [9 ]GRID grid.465198.7, Department of Clinical Neuroscience, , Karolinska Institutet, ; Solna, Sweden
                Author information
                http://orcid.org/0000-0001-6016-5665
                Article
                730
                10.1007/s10654-021-00730-y
                8542538
                33675447
                1a1193af-773e-41bd-b970-d6b8b2030887
                © The Author(s) 2021

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 April 2020
                : 10 February 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Funded by: Karolinska Institute
                Categories
                Neuro-Epidemiology
                Custom metadata
                © Springer Nature B.V. 2021

                Public health
                parkinson’s disease,epidemiology,risk factor,early-life exposure,cohort study
                Public health
                parkinson’s disease, epidemiology, risk factor, early-life exposure, cohort study

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