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      Longitudinal serum S100β and brain aging in the Lothian Birth Cohort 1936

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          Abstract

          Elevated serum and cerebrospinal fluid concentrations of S100β, a protein predominantly found in glia, are associated with intracranial injury and neurodegeneration, although concentrations are also influenced by several other factors. The longitudinal association between serum S100β concentrations and brain health in nonpathological aging is unknown. In a large group (baseline N = 593; longitudinal N = 414) of community-dwelling older adults at ages 73 and 76 years, we examined cross-sectional and parallel longitudinal changes between serum S100β and brain MRI parameters: white matter hyperintensities, perivascular space visibility, white matter fractional anisotropy and mean diffusivity (MD), global atrophy, and gray matter volume. Using bivariate change score structural equation models, correcting for age, sex, diabetes, and hypertension, higher S100β was cross-sectionally associated with poorer general fractional anisotropy ( r = −0.150, p = 0.001), which was strongest in the anterior thalamic ( r = −0.155, p < 0.001) and cingulum bundles ( r = −0.111, p = 0.005), and survived false discovery rate correction. Longitudinally, there were no significant associations between changes in brain imaging parameters and S100β after false discovery rate correction. These data provide some weak evidence that S100β may be an informative biomarker of brain white matter aging.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Missing data: our view of the state of the art.

            Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
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              Biological Age Predictors

              The search for reliable indicators of biological age, rather than chronological age, has been ongoing for over three decades, and until recently, largely without success. Advances in the fields of molecular biology have increased the variety of potential candidate biomarkers that may be considered as biological age predictors. In this review, we summarize current state-of-the-art findings considering six potential types of biological age predictors: epigenetic clocks, telomere length, transcriptomic predictors, proteomic predictors, metabolomics-based predictors, and composite biomarker predictors. Promising developments consider multiple combinations of these various types of predictors, which may shed light on the aging process and provide further understanding of what contributes to healthy aging. Thus far, the most promising, new biological age predictor is the epigenetic clock; however its true value as a biomarker of aging requires longitudinal confirmation.
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                Author and article information

                Contributors
                Journal
                Neurobiol Aging
                Neurobiol. Aging
                Neurobiology of Aging
                Elsevier
                0197-4580
                1558-1497
                1 September 2018
                September 2018
                : 69
                : 274-282
                Affiliations
                [a ]Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, UK
                [b ]Department of Psychology, University of Edinburgh, Edinburgh, Scotland, UK
                [c ]Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, UK
                [d ]UK Dementia Research Institute at The University of Edinburgh, Edinburgh, UK
                [e ]Medical Genetics Section, University of Edinburgh Centre for Genomic and Experimental Medicine and MRC Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, UK
                [f ]Institute of Cardiovascular and Medical Sciences College of Medical, Veterinary & Life Sciences University of Glasgow, UK
                [g ]Department of Computer Science, Lagos State University, Lagos, Nigeria
                [h ]Department of Clinical Biochemistry, King's College Hospital NHS Foundation Trust, London, UK
                [i ]Institute of Pharmaceutical Science, King's College London, London, UK
                [j ]Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, Scotland, UK
                Author notes
                []Corresponding author at: Department of Psychology, The University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK. Tel.: 0131 650 8493; fax: 0131 651 1771. simon.cox@ 123456ed.ac.uk
                Article
                S0197-4580(18)30197-0
                10.1016/j.neurobiolaging.2018.05.029
                6075468
                29933100
                f5a8474c-a393-45c5-b7a7-0ed39867f393
                © 2018 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 8 January 2018
                : 22 May 2018
                : 23 May 2018
                Categories
                Article

                Neurosciences
                s100β,white matter,small vessel disease,aging,longitudinal
                Neurosciences
                s100β, white matter, small vessel disease, aging, longitudinal

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