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      Lipids, Apolipoproteins, and the Risk of Parkinson Disease : A Prospective Cohort Study and a Mendelian Randomization Analysis

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          A complete picture of the associations of the most common lipid fractions, including total cholesterol (TC), LDL-C (low-density lipoprotein cholesterol), HDL-C (high-density lipoprotein cholesterol), triglycerides, and apolipoproteins, with the risk of Parkinson disease (PD), is lacking.

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          Most cited references21

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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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            Effects of smoking and smoking cessation on lipids and lipoproteins: outcomes from a randomized clinical trial.

            the effects of smoking and smoking cessation on lipoproteins have not been studied in a large contemporary group of smokers. This study was designed to determine the effects of smoking cessation on lipoproteins. this was a 1-year, prospective, double-blind, randomized, placebo-controlled clinical trial of the effects of 5 smoking cessation pharmacotherapies. Fasting nuclear magnetic resonance spectroscopy lipoprotein profiles were obtained before and 1 year after the target smoking cessation date. The effects of smoking cessation and predictors of changes in lipoproteins after 1 year were identified by multivariable regression. the 1,504 current smokers were (mean [SD]) 45.4 (11.3) years old and smoked 21.4 (8.9) cigarettes per day at baseline. Of the 923 adult smokers who returned at 1 year, 334 (36.2%) had quit smoking. Despite gaining more weight (4.6 kg [5.7] vs 0.7 kg [5.1], P < .001], abstainers had increases in high-density lipoprotein cholesterol (HDL-C) (2.4 [8.3] vs 0.1 [8.8] mg/dL, P < .001), total HDL (1.0 [4.6] vs -0.3 micromol/L [5.0], P < .001), and large HDL (0.6 [2.2] vs 0.1 [2.1] micromol/L, P = .003) particles compared with continuing smokers. Significant changes in low-density lipoprotein (LDL) cholesterol and particles were not observed. After adjustment, abstinence from smoking (P < .001) was independently associated with increases in HDL-C and total HDL particles. These effects were stronger in women. despite weight gain, smoking cessation improved HDL-C, total HDL, and large HDL particles, especially in women. Smoking cessation did not affect LDL or LDL size. Increases in HDL may mediate part of the reduced cardiovascular disease risk observed after smoking cessation.
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              Is Open Access

              Meta-analysis of brain iron levels of Parkinson’s disease patients determined by postmortem and MRI measurements

              Brain iron levels in patients of Parkinson’s disease (PD) are usually measured in postmortem samples or by MRI imaging including R2* and SWI. In this study we performed a meta-analysis to understand PD-associated iron changes in various brain regions, and to evaluate the accuracy of MRI detections comparing with postmortem results. Databases including Medline, Web of Science, CENTRAL and Embase were searched up to 19th November 2015. Ten brain regions were identified for analysis based on data extracted from thirty-three-articles. An increase in iron levels in substantia nigra of PD patients by postmortem, R2* or SWI measurements was observed. The postmortem and SWI measurements also suggested significant iron accumulation in putamen. Increased iron deposition was found in red nucleus as determined by both R2* and SWI, whereas no data were available in postmortem samples. Based on SWI, iron levels were increased significantly in the nucleus caudatus and globus pallidus. Of note, the analysis might be biased towards advanced disease and that the precise stage at which regions become involved could not be ascertained. Our analysis provides an overview of iron deposition in multiple brain regions of PD patients, and a comparison of outcomes from different methods detecting levels of iron.

                Author and article information

                Circulation Research
                Circ Res
                Ovid Technologies (Wolters Kluwer Health)
                August 30 2019
                August 30 2019
                : 125
                : 6
                : 643-652
                [1 ]From the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (F.F., Y.Z., X.S., K.W., D.M.)
                [2 ]Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden (N.H., G.W.)
                [3 ]Center for Global Health Research, Usher Institute of Population Health Sciences and Informatics, Old Medical School, University of Edinburgh, Scotland, United Kingdom (X.S.)
                [4 ]Biostatistics Group, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China (X.S.)
                [5 ]Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (K.W.).
                © 2019


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