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      Life course, genetic, and neuropathological associations with brain age in the 1946 British Birth Cohort: a population-based study

      research-article
      , FRACP a , d , e , , MSc a , , PhD a , , PhD h , , PhD a , f , g , , PhD a , , FRACP a , , MBChB a , , MBChB a , , PhD a , , PhD a , i , , MSc a , , MSci a , , PhD a , b , , PhD a , , PhD a , , PhD h , n , p , , PhD h , , PhD a , , BSc a , , Prof, PhD a , , Prof, PhD k , , PhD j , , Prof, MD PhD b , c , l , m , , PhD b , c , , PhD b , c , , Prof, PhD a , c , n , o , , Prof, PhD h , , Prof, FMedSci a , b , , PhD a , n , , , Prof, FRCP a , b , , *
      The Lancet. Healthy Longevity
      Elsevier Ltd

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          Summary

          Background

          A neuroimaging-based biomarker termed the brain age is thought to reflect variability in the brain's ageing process and predict longevity. Using Insight 46, a unique narrow-age birth cohort, we aimed to examine potential drivers and correlates of brain age.

          Methods

          Participants, born in a single week in 1946 in mainland Britain, have had 24 prospective waves of data collection to date, including MRI and amyloid PET imaging at approximately 70 years old. Using MRI data from a previously defined selection of this cohort, we derived brain-predicted age from an established machine-learning model (trained on 2001 healthy adults aged 18–90 years); subtracting this from chronological age (at time of assessment) gave the brain-predicted age difference (brain-PAD). We tested associations with data from early life, midlife, and late life, as well as rates of MRI-derived brain atrophy.

          Findings

          Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. We included 456 participants (225 female), with a mean chronological age of 70·7 years (SD 0·7; range 69·2 to 71·9). The mean brain-predicted age was 67·9 years (8·2, 46·3 to 94·3). Female sex was associated with a 5·4-year (95% CI 4·1 to 6·8) younger brain-PAD than male sex. An increase in brain-PAD was associated with increased cardiovascular risk at age 36 years (β=2·3 [95% CI 1·5 to 3·0]) and 69 years (β=2·6 [1·9 to 3·3]); increased cerebrovascular disease burden (1·9 [1·3 to 2·6]); lower cognitive performance (–1·3 [–2·4 to –0·2]); and increased serum neurofilament light concentration (1·2 [0·6 to 1·9]). Higher brain-PAD was associated with future hippocampal atrophy over the subsequent 2 years (0·003 mL/year [0·000 to 0·006] per 5-year increment in brain-PAD). Early-life factors did not relate to brain-PAD. Combining 12 metrics in a hierarchical partitioning model explained 33% of the variance in brain-PAD.

          Interpretation

          Brain-PAD was associated with cardiovascular risk, and imaging and biochemical markers of neurodegeneration. These findings support brain-PAD as an integrative summary metric of brain health, reflecting multiple contributions to pathological brain ageing, and which might have prognostic utility.

          Funding

          Alzheimer's Research UK, Medical Research Council Dementia Platforms UK, Selfridges Group Foundation, Wolfson Foundation, Wellcome Trust, Brain Research UK, Alzheimer's Association.

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          Most cited references42

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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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            Neurofilament light chain as a biomarker in neurological disorders

            In the management of neurological diseases, the identification and quantification of axonal damage could allow for the improvement of diagnostic accuracy and prognostic assessment. Neurofilament light chain (NfL) is a neuronal cytoplasmic protein highly expressed in large calibre myelinated axons. Its levels increase in cerebrospinal fluid (CSF) and blood proportionally to the degree of axonal damage in a variety of neurological disorders, including inflammatory, neurodegenerative, traumatic and cerebrovascular diseases. New immunoassays able to detect biomarkers at ultralow levels have allowed for the measurement of NfL in blood, thus making it possible to easily and repeatedly measure NfL for monitoring diseases’ courses. Evidence that both CSF and blood NfL may serve as diagnostic, prognostic and monitoring biomarkers in neurological diseases is progressively increasing, and NfL is one of the most promising biomarkers to be used in clinical and research setting in the next future. Here we review the most important results on CSF and blood NfL and we discuss its potential applications and future directions.
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              Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers.

              The brain changes as we age and these changes are associated with functional deterioration and neurodegenerative disease. It is vital that we better understand individual differences in the brain ageing process; hence, techniques for making individualised predictions of brain ageing have been developed. We present evidence supporting the use of neuroimaging-based 'brain age' as a biomarker of an individual's brain health. Increasingly, research is showing how brain disease or poor physical health negatively impacts brain age. Importantly, recent evidence shows that having an 'older'-appearing brain relates to advanced physiological and cognitive ageing and the risk of mortality. We discuss controversies surrounding brain age and highlight emerging trends such as the use of multimodality neuroimaging and the employment of 'deep learning' methods.
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                Author and article information

                Contributors
                Journal
                Lancet Healthy Longev
                Lancet Healthy Longev
                The Lancet. Healthy Longevity
                Elsevier Ltd
                2666-7568
                1 September 2022
                September 2022
                : 3
                : 9
                : e607-e616
                Affiliations
                [a ]Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
                [b ]Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK
                [c ]Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK
                [d ]Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
                [e ]Neurodegeneration Biology Laboratory, The Francis Crick Institute, London, UK
                [f ]Department of Brain Sciences, Imperial College London, London, UK
                [g ]UK Dementia Research Institute Centre for Care Research and Technology, Imperial College London, London, UK
                [h ]Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK
                [i ]Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
                [j ]Dementia Research Institute, Cardiff University, Cardiff, UK
                [k ]Division of Neuroscience and Mental Health, Cardiff University, Cardiff, UK
                [l ]Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
                [m ]Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
                [n ]Department of Computer Science, Centre for Medical Imaging Computing, University College London, London, UK
                [o ]Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, Netherlands
                [p ]School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
                Author notes
                [* ]Correspondence to: Prof J M Schott, Dementia Research Centre, University College London Queen Square Institute of Neurology, London WC1N 3BG, UK j.schott@ 123456ucl.ac.uk
                [†]

                Contributed equally

                Article
                S2666-7568(22)00167-2
                10.1016/S2666-7568(22)00167-2
                10499760
                36102775
                be817616-16fe-4888-96bb-01172ef7d2bf
                © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

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

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