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      Risk of a first clinical diagnosis of central nervous system demyelination in relation to human herpesviruses in the context of Epstein–Barr virus

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          Abstract

          Background and purpose

          Epstein–Barr virus (EBV) is implicated in multiple sclerosis (MS) risk; evidence for other herpesviruses is inconsistent. Here, we test blood markers of infection with human herpesvirus 6 (HHV‐6), varicella zoster virus (VZV), and cytomegalovirus (CMV) as risk factors for a first clinical diagnosis of central nervous system demyelination (FCD) in the context of markers of EBV infection.

          Methods

          In the Ausimmune case–control study, cases had an FCD, and population controls were matched on age, sex, and study region. We quantified HHV‐6‐ and VZV‐DNA load in whole blood and HHV‐6, VZV, and CMV antibodies in serum. Conditional logistic regression tested associations with FCD risk, adjusting for Epstein–Barr nuclear antigen (EBNA) IgG, EBV‐DNA load, and other covariates.

          Results

          In 204 FCD cases and 215 matched controls, only HHV‐6‐DNA load (positive vs. negative) was associated with FCD risk (adjusted odds ratio = 2.20, 95% confidence interval = 1.08–4.46, p = 0.03). Only EBNA IgG and HHV‐6‐DNA positivity were retained in a predictive model of FCD risk; the combination had a stronger association than either alone. CMV‐specific IgG concentration modified the association between an MS risk‐related human leucocyte antigen gene and FCD risk. Six cases and one control had very high HHV‐6‐DNA load (>1.0 × 10 6 copies/mL).

          Conclusions

          HHV‐6‐DNA positivity and high load (possibly due to inherited HHV‐6 chromosomal integration) were associated with increased FCD risk, particularly in association with markers of EBV infection. With growing interest in prevention/management of MS through EBV‐related pathways, there should be additional consideration of the role of HHV‐6 infection.

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          Most cited references38

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          No Adjustments Are Needed for Multiple Comparisons

          Adjustments for making multiple comparisons in large bodies of data are recommended to avoid rejecting the null hypothesis too readily. Unfortunately, reducing the type I error for null associations increases the type II error for those associations that are not null. The theoretical basis for advocating a routine adjustment for multiple comparisons is the "universal null hypothesis" that "chance" serves as the first-order explanation for observed phenomena. This hypothesis undermines the basic premises of empirical research, which holds that nature follows regular laws that may be studied through observations. A policy of not making adjustments for multiple comparisons is preferable because it will lead to fewer errors of interpretation when the data under evaluation are not random numbers but actual observations on nature. Furthermore, scientists should not be so reluctant to explore leads that may turn out to be wrong that they penalize themselves by missing possibly important findings.
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            Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis

            Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system of unknown etiology. We tested the hypothesis that MS is caused by Epstein-Barr virus (EBV) in a cohort comprising more than 10 million young adults on active duty in the US military, 955 of whom were diagnosed with MS during their period of service. Risk of MS increased 32-fold after infection with EBV but was not increased after infection with other viruses, including the similarly transmitted cytomegalovirus. Serum levels of neurofilament light chain, a biomarker of neuroaxonal degeneration, increased only after EBV seroconversion. These findings cannot be explained by any known risk factor for MS and suggest EBV as the leading cause of MS.
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              Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations

              Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so—and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                European Journal of Neurology
                Euro J of Neurology
                Wiley
                1351-5101
                1468-1331
                September 2023
                June 23 2023
                September 2023
                : 30
                : 9
                : 2752-2760
                Affiliations
                [1 ] National Centre for Epidemiology and Population Health Australian National University Canberra Australian Capital Territory Australia
                [2 ] Clinical Virology Department, Centre for Infectious Diseases & Microbiology Laboratory Services, Institute of Clinical Pathology & Medical Research Westmead Hospital Westmead New South Wales Australia
                [3 ] ANU Medical School Australian National University Canberra Australian Capital Territory Australia
                [4 ] University of Adelaide Adelaide South Australia Australia
                [5 ] Menzies Research Institute Tasmania Hobart Tasmania Australia
                [6 ] Howard Florey Institute Melbourne Victoria Australia
                Article
                10.1111/ene.15919
                46abb31b-5eab-4bf1-aa68-fbc1de187973
                © 2023

                http://creativecommons.org/licenses/by/4.0/

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