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      Biological subtypes of Alzheimer disease : A systematic review and meta-analysis

      review-article
      , PhD , , MD, PhD, , PhD
      Neurology
      Lippincott Williams & Wilkins

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          Abstract

          Objective

          To test the hypothesis that distinct subtypes of Alzheimer disease (AD) exist and underlie the heterogeneity within AD, we conducted a systematic review and meta-analysis on AD subtype studies based on postmortem and neuroimaging data.

          Methods

          EMBASE, PubMed, and Web of Science databases were consulted until July 2019.

          Results

          Neuropathology and neuroimaging studies have consistently identified 3 subtypes of AD based on the distribution of tau-related pathology and regional brain atrophy: typical, limbic-predominant, and hippocampal-sparing AD. A fourth subtype, minimal atrophy AD, has been identified in several neuroimaging studies. Typical AD displays tau-related pathology and atrophy both in hippocampus and association cortex and has a pooled frequency of 55%. Limbic-predominant, hippocampal-sparing, and minimal atrophy AD had a pooled frequency of 21%, 17%, and 15%, respectively. Between-subtype differences were found in age at onset, age at assessment, sex distribution, years of education, global cognitive status, disease duration, APOE ε4 genotype, and CSF biomarker levels.

          Conclusion

          We identified 2 core dimensions of heterogeneity: typicality and severity. We propose that these 2 dimensions determine individuals' belonging to one of the AD subtypes based on the combination of protective factors, risk factors, and concomitant non-AD brain pathologies. This model is envisioned to aid with framing hypotheses, study design, interpretation of results, and understanding mechanisms in future subtype studies. Our model can be used along the A/T/N classification scheme for AD biomarkers. Unraveling the heterogeneity within AD is critical for implementing precision medicine approaches and for ultimately developing successful disease-modifying drugs for AD.

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          Most cited references36

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          Age, APOE and sex: Triad of risk of Alzheimer's disease.

          Age, apolipoprotein E ε4 (APOE) and chromosomal sex are well-established risk factors for late-onset Alzheimer's disease (LOAD; AD). Over 60% of persons with AD harbor at least one APOE-ε4 allele. The sex-based prevalence of AD is well documented with over 60% of persons with AD being female. Evidence indicates that the APOE-ε4 risk for AD is greater in women than men, which is particularly evident in heterozygous women carrying one APOE-ε4 allele. Paradoxically, men homozygous for APOE-ε4 are reported to be at greater risk for mild cognitive impairment and AD. Herein, we discuss the complex interplay between the three greatest risk factors for Alzheimer's disease, age, APOE-ε4 genotype and chromosomal sex. We propose that the convergence of these three risk factors, and specifically the bioenergetic aging perimenopause to menopause transition unique to the female, creates a risk profile for AD unique to the female. Further, we discuss the specific risk of the APOE-ε4 positive male which appears to emerge early in the aging process. Evidence for impact of the triad of AD risk factors is most evident in the temporal trajectory of AD progression and burden of pathology in relation to APOE genotype, age and sex. Collectively, the data indicate complex interactions between age, APOE genotype and gender that belies a one size fits all approach and argues for a precision medicine approach that integrates across the three main risk factors for Alzheimer's disease.
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            Neuroimaging correlates of pathologically defined subtypes of Alzheimer's disease: a case-control study.

            Three subtypes of Alzheimer's disease (AD) have been pathologically defined on the basis of the distribution of neurofibrillary tangles: typical AD, hippocampal-sparing AD, and limbic-predominant AD. Compared with typical AD, hippocampal-sparing AD has more neurofibrillary tangles in the cortex and fewer in the hippocampus, whereas the opposite pattern is seen in limbic-predominant AD. We aimed to determine whether MRI patterns of atrophy differ between these subtypes and whether structural neuroimaging could be a useful predictor of pathological subtype at autopsy. We identified patients who had been followed up in the Mayo Clinic Alzheimer's Disease Research Center (Rochester, MN, USA) or in the Alzheimer's Disease Patient Registry (Rochester, MN, USA) between 1992 and 2005. To be eligible for inclusion, participants had to have had dementia, AD pathology at autopsy (Braak stage ≥IV and intermediate to high probability of AD), and an ante-mortem MRI. Cases were assigned to one of three pathological subtypes--hippocampal-sparing, limbic-predominant, and typical AD--on the basis of neurofibrillary tangle counts in hippocampus and cortex and ratio of hippocampal to cortical burden, without reference to neuronal loss. Voxel-based morphometry and atlas-based parcellation were used to compare patterns of grey matter loss between groups and with age-matched control individuals. Neuroimaging was obtained at the time of first presentation. To summarise pair-wise group differences, we report the area under the receiver operator characteristic curve (AUROC). Of 177 eligible patients, 125 (71%) were classified as having typical AD, 33 (19%) as having limbic-predominant AD, and 19 (11%) as having hippocampal-sparing AD. Most patients with typical (98 [78%]) and limbic-predominant AD (31 [94%]) initially presented with an amnestic syndrome, but fewer patients with hippocampal-sparing AD (eight [42%]) did. The most severe medial temporal atrophy was recorded in patients with limbic-predominant AD, followed by those with typical disease, and then those with hippocampal-sparing AD. Conversely, the most severe cortical atrophy was noted in patients with hippocampal-sparing AD, followed by those with typical disease, and then limbic-predominant AD. The ratio of hippocampal to cortical volumes allowed the best discrimination between subtypes (p<0·0001; three-way AUROC 0·52 [95% CI 0·47-0·52]; ratio of AUROC to chance classification 3·1 [2·8-3·1]). Patients with typical AD and non-amnesic initial presentation had a significantly higher ratio of hippocampal to cortical volumes (median 0·045 [IQR 0·035-0·056]) than did those with an amnesic presentation (0·041 [0·031-0·057]; p=0·001). Patterns of atrophy on MRI differ across the pathological subtypes of AD. MRI regional volumetric analysis can reliably track the distribution of neurofibrillary tangle pathology and can predict pathological subtype of AD at autopsy. US National Institutes of Health (National Institute on Aging). Copyright © 2012 Elsevier Ltd. All rights reserved.
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              Resistance vs resilience to Alzheimer disease: Clarifying terminology for preclinical studies.

              Preventing or delaying Alzheimer disease (AD) through lifestyle interventions will come from a better understanding of the mechanistic underpinnings of (1) why a significant proportion of elderly remain cognitively normal with AD pathologies (ADP), i.e., amyloid or tau; and (2) why some elderly individuals do not have significant ADP. In the last decades, concepts such as brain reserve, cognitive reserve, and more recently brain maintenance have been proposed along with more general notions such as (neuro)protection and compensation. It is currently unclear how to effectively apply these concepts in the new field of preclinical AD specifically separating the 2 distinct mechanisms of coping with pathology vs avoiding pathology. We propose a simplistic conceptual framework that builds on existing concepts using the nomenclature of resistance in the context of avoiding pathology, i.e., remaining cognitively normal without significant ADP, and resilience in the context of coping with pathology, i.e., remaining cognitively normal despite significant ADP. In the context of preclinical AD studies, we (1) define these concepts and provide recommendations (and common scenarios) for their use; (2) discuss how to employ this terminology in the context of investigating mechanisms and factors; (3) highlight the complementarity and clarity they provide to existing concepts; and (4) discuss different study designs and methodologies. The application of the proposed framework for framing hypotheses, study design, and interpretation of results and mechanisms can provide a consistent framework and nomenclature for researchers to reach consensus on identifying factors that may prevent ADP or delay the onset of cognitive impairment.
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                Author and article information

                Journal
                Neurology
                Neurology
                neurology
                neur
                neurology
                NEUROLOGY
                Neurology
                Lippincott Williams & Wilkins (Hagerstown, MD )
                0028-3878
                1526-632X
                10 March 2020
                10 March 2020
                : 94
                : 10
                : 436-448
                Affiliations
                From the Division of Clinical Geriatrics (D.F., A.N., E.W.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden; Theme Aging (A.N.), Karolinska University Hospital, Huddinge, Sweden; and Department of Neuroimaging (E.W.), Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
                Author notes
                Correspondence Dr. Daniel Ferreira daniel.ferreira.padilla@ 123456ki.se

                Go to Neurology.org/N for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.

                The Article Processing Charge was funded by Swedish Research Council.

                Author information
                http://orcid.org/0000-0001-9522-4338
                http://orcid.org/0000-0001-7345-5151
                http://orcid.org/0000-0002-3115-2977
                Article
                NEUROLOGY2019021873 00018
                10.1212/WNL.0000000000009058
                7238917
                32047067
                6140c9b4-4700-49ea-aad7-80b633af3f6e
                Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

                This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 August 2019
                : 17 December 2019
                Funding
                Funded by: The Swedish Research Council
                Award ID: VR, 2016-02282
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