- Open Access
“Exceptional brain aging” without Alzheimer’s disease: triggers, accelerators, and the net sum game
© The Author(s). 2018
- Published: 1 June 2018
As human longevity increases and Alzheimer’s disease (AD) increasingly becomes a significant societal burden, finding pathways or protective factors that facilitate exceptional brain aging without AD pathophysiologies (ADP) will be critical. The goal of this viewpoint is two-fold: 1) to present evidence for “exceptional brain aging” without ADP; and 2) to bring together ideas and observations from the literature and present them as testable hypotheses for biomarker studies to discover protective factors for “exceptional brain aging” without ADP and AD dementia.
Discovering pathways to exceptional aging
There are three testable hypotheses. First, discovering and quantifying links between risk factor(s) and early ADP changes in midlife using longitudinal biomarker studies will be fundamental to understanding why the majority of individuals deviate from normal aging to the AD pathway. Second, a risk factor may have quantifiably greater impact as a trigger and/or accelerator on a specific component of the biomarker cascade (amyloid, tau, neurodegeneration). Finally, and most importantly, while each risk factor may have a different mechanism of action on AD biomarkers, “exceptional aging” and protection against AD dementia will come from “net sum” protection against all components of the biomarker cascade. The knowledge of the mechanism of action of risk factor(s) from hypotheses 1 and 2 will aid in better characterization of their effect on outcomes, identification of subpopulations that would benefit, and the timing at which the risk factor(s) would have the maximal impact. Additionally, hypothesis 3 highlights the importance of multifactorial or multi-domain approaches to “exceptional aging” as well as prevention of AD dementia.
While important strides have been made in identifying risk factors for AD dementia incidence, further efforts are needed to translate these into effective preventive strategies. Using biomarker studies for understanding the mechanism of action, effect size estimation, selection of appropriate end-points, and better subject recruitment based on subpopulation effects are fundamental for better design and success of prevention trials.
- Exceptional Aging
- AD prevention
- Biomarker cascade
In this view point, the main goal is to bring together ideas and observations from the literature and present them as testable hypotheses or frameworks that can be employed in biomarker studies to discover protective factors or pathways to “exceptional brain aging”. In the context of the terminology we recently proposed, for hypotheses 1 and 2 the focus is on “resistance to ADP”  and for hypothesis 3 the focus is on both resistance to ADP and prevention of AD dementia.
These concepts are presented in the context of the primary AD pathophysiological processes in the biomarker cascade (amyloid, tau, and neurodegeneration due to AD pathologies). The focus is on primary prevention in midlife, designing effective trials by understanding the mechanisms of action on the biomarker cascade, and looking at the net sum protection against all components of the biomarker cascade. Although additional AD processes are not explicitly addressed, such as inflammation, synaptic and microglial dysfunction that are relevant to aging and AD dementia, the concepts here can also be extended to other measurable biomarkers that are mechanistically relevant to AD.
Several pathology and observational studies have provided evidence for aging without ADP [4, 5] and have focused on optimal or successful aging without cognitive decline [6–8] in the oldest old. In addition, specific evidence for “exceptional brain aging” without ADP comes from these three different lines of investigation.
Prevalence of AD pathologies
Nelson et al.  published an amalgamation of neuropathological literature showing that each added year of life does not lead to an increased prevalence of AD pathologies, unlike hippocampal sclerosis and cerebrovascular disease. Neuroimaging studies in the Mayo Clinic Study of Aging (MCSA) have also found non-monotonicity in the frequency of amyloid positivity in clinically unimpaired individuals [10, 11]. The data from our previous work  were consolidated to plot the prevalence of elevated amyloid versus excess cerebrovascular disease burden in clinically unimpaired individuals (Fig. 2a). These curves are reminiscent of two types of growth curve models in population ecology: exponential, or J-shaped, and logistic, or S-shaped, models. While exponential models have uninhibited growth in numbers, logistic growth models exhibit a slowing in growth as the population reaches its carrying capacity. Vascular pathologies show a steady increase in the prevalence or rate of growth representing an exponential model over an age range of 50–100 years. On the contrary, the amyloid elevation curves exhibit a slow saturation alluding to the fact that there may be a proportion of the population that will never develop elevated levels of amyloid, supported by evidence from Khachaturian et al. . Amyloid data collected from 55 studies by Jansen et al. also showed that a logistic model was the best fit for amyloid prevalence .
Declining AD incidence and amyloid levels
Recent evidence of age-specific decline in both incidence of dementia [14, 15] and amyloid levels  in aging brains provides compelling evidence for the possibility of aging without AD pathologies. With the strong possibility that better medical care and increasing education levels may have contributed to these declining trends , investigation into the underlying mechanisms may lead us closer to understanding the differences between normal aging and developing ADP.
APOE4 carriers without AD dementia and AD pathologies in the oldest old
Age and the apolipoprotein (APO)E4 genotype are the two well-established risk factors for AD . Therefore, one would expect that, as people age, the odds of an APOE4 individual developing AD dementia would increase with age. However, there have been several observations showing that the association between APOE4 genotype and development of AD dementia is weak in the oldest old, i.e., there are some APOE4 carriers who live into their 90s without AD dementia [12, 18–20]. While these studies have proven the presence of very old APOE4 carriers without AD dementia, one may argue that protection against AD dementia primarily comes from “resilience to ADP, i.e., coping with pathology”. However, the presence of amyloid-negative APOE4 cognitively normal individuals at 85 years of age (~ 25%) in a large meta-analysis  supports the idea of “resistance to ADP” in the oldest old APOE4 carriers.
While the observed evidence can be attributed to excess mortality early in life in those at risk (for example, for APOE4 carriers), it is important to study and understand how some individuals are able to age without ADP.
Given the possibility of “exceptional aging”, how does one discover the important protective factors. Three inter-related ideas or hypotheses are presented here that, when taken together, can aid in discovering protective pathways and help design effective preventive strategies.
Hypothesis 1 (primary prevention in midlife)
Discovering and quantifying links between risk factors and early ADP changes in midlife using longitudinal biomarker studies is fundamental to understanding why the majority of individuals deviate from normal aging to the AD pathway.
Normal aging versus pathological aging
There is well-established literature supporting that midlife conditions have a significant impact on late-life dementia, especially cardiorespiratory fitness  and vascular risk factors . The relationship between several risk factors (obesity, hypertension, dyslipidemia) and dementia incidence has been observed to be U-shaped in nature with the greatest association during midlife [31–33]. Additionally, the prevalence of amyloid curves (as mentioned above) follows a logistic growth curve model with the greatest rate of amyloid accumulation in the population during late midlife. The first hypothesis proposes that greater focus needs to be placed on longitudinal biomarker studies that can discover and quantify links between risk factors in midlife and increased ADP accumulation in late midlife to understand why individuals deviate from the normal aging process.
One may argue that there has been extensive literature already supporting the hypothesis that midlife risk factors such as vascular risk factors increase late life dementia incidence. However, the results from intervention studies based on a reduction of vascular risk factors  highlights the need for longitudinal biomarker studies in midlife that focus on understanding the mechanisms of action of the suggested risk factors as early ADP changes evolve. This is especially important for risk or protective factors that are highly debated in the literature [35–37]. Understanding how the risk factors or combination of risk factors impact early ADP changes (whether it is amyloid, tau, or neurodegeneration) using longitudinal studies will facilitate a better understanding of how protective factors can be employed for primary prevention [38, 39]. While significant focus has been placed on amyloid imaging since it has been available from the mid-2000s, the same concepts can be extended to tau-related studies as longitudinal tau data become available .
Hypothesis 2 (designing effective trials)
A specific risk factor may have quantifiably greater impact as a trigger and/or accelerator on a specific component of the biomarker cascade (amyloid, tau, or neurodegeneration).
The biomarker cascade framework and quantifying the impact of each risk/protective factor
Although amyloid and tau deposition can be initiated independently, there is sufficient recent evidence supporting the hypothesis that amyloid deposition accelerates tau deposition which, in turn, is closely associated with cognitive decline [41–44]. Autosomal dominant AD studies that represent younger-onset pure AD cases have confirmed the sequence of amyloid followed by tau, followed by cognitive decline [45, 46]. The biomarker model presented and refined based on the literature by Jack et al.  synthesized AD processes into a set of testable hypotheses. Amyloid, tau, neurodegeneration, and cognitive decline form the biomarker cascade and this framework has helped significantly improve our understanding of disease onset and progression [41, 47–49].
The presence of suspected non-AD pathophysiology (SNAP; neurodegeneration in the absence of amyloid)  and primary age-related tauopathy (PART) in the absence of amyloid  illustrate the heterogeneity in the age-related neurodegenerative processes and share some pathophysiological aspects (neurodegeneration or tau) of the AD biomarker cascade. Since each of these pathophysiologies plays a role in the development of AD dementia, as discussed further in hypothesis 3 below, studying independent triggers and accelerators for each component of the AD biomarker cascade will be important. In the second hypothesis, it is proposed that looking at each individual component of the biomarker cascade (amyloid, tau, neurodegeneration) to explore the impact of the risk factor of interest will aid in understanding the mechanisms through which the specific risk factor impacts AD processes.
Importance of knowing the mechanisms
Although a vast amount of literature has provided evidence for the impact of risk factors on dementia incidence, less has been published on the impact of each individual risk factor on the primary disease mechanisms. Discerning the disease stage at which the reduction of a specific risk factor would be helpful will be important for designing effective preventive strategies. A recent example was the failure of the TOMORROW trial, which targeted diabetes medications for reduction of dementia . While there has been substantial evidence that diabetes is associated with AD dementia incidence, the primary mechanism of action may be through neurodegeneration (discussed further below) . Therefore, with diabetes as a preventive strategy, the focus should be on measuring the reduction in neurodegeneration and not on reduction in amyloid deposition. Another example is that of sleep as a preventive strategy. While poor sleep has been shown to impact amyloid deposition through poor clearance of amyloid [53, 54], and thus could mechanistically be linked to greater dementia incidence  and brain atrophy , improving sleep quality as a preventive strategy for AD dementia may fail in individuals who have high levels of amyloid. Therefore, quantifying the effect size of risk factors on each component of the biomarker cascade will aid in choosing appropriate outcomes and the sample sizes required. In addition, determining the effect modifiers (main biological and disease-related factors that may influence the treatment response such as additional interactions of the risk factors with age and APOE4 status) will aid in better enrichment strategies and intervention optimization.
Hypothesis 3 (net sum game)
“Exceptional aging” as well as protection against AD dementia will come from “net sum” protection against all the components of the AD biomarker cascade.
If protection against AD pathology in each individual were viewed as a “net sum” of effects from all triggers and accelerators (lifestyle, midlife risk factors, chronic conditions, net difference between protective and risk genes) as well as additive and interactive non-AD processes, then “exceptional aging” without ADP and ultimately without AD dementia would be possible if a large positive “net sum” were present. This hypothesis highlights the importance for multifactorial or multidomain approaches to “exceptional aging” without ADP and AD dementia.
The presence of non-AD processes such as cerebrovascular disease, TDP-43, Lewy bodies (often alongside AD processes) and their contribution to cognitive impairment are important to consider in this context since non-AD neurodegenerative pathologies reduce the threshold to AD dementia when present along with ADP [57, 64]. This concept can be observed in Fig. 4b, which illustrates two subsets of individuals: the first have cognitive decline or neurodegeneration only due to ADP, and the second have a greater rate of neurodegeneration or cognitive decline due to other non-AD neurodegenerative processes along with ADP. A clear difference can be observed in the levels of ADP at which the same level of cognitive impairment would be expected for both groups. The second group would need a much lower level of amyloid to experience the same level of cognitive impairment as the first group. This figure illustrates the importance of viewing protection against AD dementia as protection against all components of the AD biomarker cascade.
A major limitation of this work was limiting the scope to the three main AD biomarkers for simplicity. However the concepts illustrated in Figs. 3 and 4 can be extended after inclusion of additional measurable AD-specific processes such as inflammation as well as non-AD processes and pathologies.
While important strides have been made in identifying risk factors for AD dementia incidence, future efforts need to be directed towards discovering the timing and mechanism of action of each of these risk factors on AD processes. In this work, three inter-related ideas are presented that are important to consider while studying risk factors and may help us move towards developing effective preventive strategies to maneuver individuals away from the AD pathway towards the pathway of “exceptional brain aging” without ADP.
The author would like to thank David S. Knopman, MD, Eider M. Arenaza-Urquijo, PhD, and the reviewers for their excellent comments, as well as Heather Wiste and Timothy Lesnick for their help generating Fig. 2. For the images used, we would like to thank AVID Radiopharmaceuticals for the provision of AV-1451 precursor, chemistry production advice and oversight, and FDA regulatory cross-filing permission and documentation.
The author was funded by NIH grants (R01 NS097495 and R01 AG056366).
The author read and approved the final manuscript.
Ethics approval and consent to participate
The data reported here are from Mayo Clinic Study of Aging and from publications by the author. These studies were approved by the Mayo Clinic and Olmsted Medical Center institutional review board. Informed consent was obtained from all participants or their surrogates.
The author declares that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Braak H, Braak E. Neuropathological staging of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239–59.View ArticlePubMedGoogle Scholar
- Stern Y. Cognitive reserve in ageing and Alzheimer's disease. Lancet Neurol. 2012;11(11):1006–12.View ArticlePubMedPubMed CentralGoogle Scholar
- Arenaza-Urquijo EM, Vemuri P. Resistance vs resilience to Alzheimer disease: clarifying terminology for preclinical studies. Neurology. 2018;90(15):695–703. https://doi.org/10.1212/WNL.0000000000005303.View ArticlePubMedGoogle Scholar
- Bennett DA, et al. Relation of neuropathology to cognition in persons without cognitive impairment. Ann Neurol. 2012;72(4):599–609.View ArticlePubMedPubMed CentralGoogle Scholar
- Besser LM, et al. Late-life vascular risk factors and Alzheimer disease neuropathology in individuals with normal cognition. J Neuropathol Exp Neurol. 2016;75(10):955–62.View ArticlePubMedPubMed CentralGoogle Scholar
- Goveas JS, et al. Predictors of optimal cognitive aging in 80+ women: the Women’s Health Initiative Memory Study. J Gerontol A Biol Sci Med Sci. 2016;71 Suppl 1:S62–71.View ArticlePubMedGoogle Scholar
- Hayden KM, et al. Cognitive decline in the elderly: an analysis of population heterogeneity. Age Ageing. 2011;40(6):684–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Rogalski E, et al. Cognitive trajectories and spectrum of neuropathology in SuperAgers: the first 10 cases. Hippocampus. 2018. https://doi.org/10.1002/hipo.22828. [Epub ahead of print].
- Nelson PT, et al. Alzheimer’s disease is not “brain aging”: neuropathological, genetic, and epidemiological human studies. Acta Neuropathol. 2011;121(5):571–87.View ArticlePubMedPubMed CentralGoogle Scholar
- Jack CR Jr, et al. Age-specific population frequencies of cerebral beta-amyloidosis and neurodegeneration among people with normal cognitive function aged 50-89 years: a cross-sectional study. Lancet Neurol. 2014;13(10):997–1005.View ArticlePubMedPubMed CentralGoogle Scholar
- Vemuri P, Knopman DS. The role of cerebrovascular disease when there is concomitant Alzheimer disease. Biochim Biophys Acta. 2016;1862(5):952–6.View ArticlePubMedGoogle Scholar
- Khachaturian AS, et al. Apolipoprotein E epsilon4 count affects age at onset of Alzheimer disease, but not lifetime susceptibility: the Cache County Study. Arch Gen Psychiatry. 2004;61(5):518–24.View ArticlePubMedGoogle Scholar
- Jansen WJ, et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA. 2015;313(19):1924–38.View ArticlePubMedPubMed CentralGoogle Scholar
- Satizabal CL, et al. Incidence of dementia over three decades in the Framingham Heart Study. N Engl J Med. 2016;374(6):523–32.View ArticlePubMedPubMed CentralGoogle Scholar
- Matthews FE, et al. A two decade dementia incidence comparison from the Cognitive Function and Ageing Studies I and II. Nat Commun. 2016;7:11398.View ArticlePubMedPubMed CentralGoogle Scholar
- Kovari E, et al. Amyloid deposition is decreasing in aging brains: an autopsy study of 1,599 older people. Neurology. 2014;82(4):326–31.View ArticlePubMedGoogle Scholar
- Norton S, et al. Potential for primary prevention of Alzheimer's disease: an analysis of population-based data. Lancet Neurol. 2014;13(8):788–94.View ArticlePubMedGoogle Scholar
- Sobel E, et al. Lack of association of apolipoprotein E allele epsilon 4 with late-onset Alzheimer's disease among Finnish centenarians. Neurology. 1995;45(5):903–7.View ArticlePubMedGoogle Scholar
- Farrer LA, et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. JAMA. 1997;278(16):1349–56.View ArticlePubMedGoogle Scholar
- Bonham LW, et al. Age-dependent effects of APOE epsilon4 in preclinical Alzheimer's disease. Ann Clin Transl Neurol. 2016;3(9):668–77.View ArticlePubMedPubMed CentralGoogle Scholar
- Fabbri E, et al. Aging and multimorbidity: new tasks, priorities, and frontiers for integrated gerontological and clinical research. J Am Med Dir Assoc. 2015;16(8):640–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Jagust W. Vulnerable neural systems and the borderland of brain aging and neurodegeneration. Neuron. 2013;77(2):219–34.View ArticlePubMedPubMed CentralGoogle Scholar
- Fjell AM, et al. What is normal in normal aging? Effects of aging, amyloid and Alzheimer's disease on the cerebral cortex and the hippocampus. Prog Neurobiol. 2014;117:20–40.View ArticlePubMedPubMed CentralGoogle Scholar
- Berchtold NC, et al. Gene expression changes in the course of normal brain aging are sexually dimorphic. Proc Natl Acad Sci U S A. 2008;105(40):15605–10.View ArticlePubMedPubMed CentralGoogle Scholar
- Vemuri P, et al. Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly. Brain. 2015;138(Pt 3):761–71.View ArticlePubMedPubMed CentralGoogle Scholar
- Bancks MP, et al. Cardiovascular health in young adulthood and structural brain MRI in midlife: The CARDIA study. Neurology. 2017;89(7):680–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Arenaza-Urquijo EM, et al. Distinct effects of late adulthood cognitive and physical activities on gray matter volume. Brain Imaging Behav. 2017;11(2):346–56.View ArticlePubMedGoogle Scholar
- Okonkwo OC, et al. Physical activity attenuates age-related biomarker alterations in preclinical AD. Neurology. 2014;83(19):1753–60.View ArticlePubMedPubMed CentralGoogle Scholar
- Defina LF, et al. The association between midlife cardiorespiratory fitness levels and later-life dementia: a cohort study. Ann Intern Med. 2013;158(3):162–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Gottesman RF, et al. Associations between midlife vascular risk factors and 25-year incident dementia in the Atherosclerosis Risk in Communities (ARIC) cohort. JAMA Neurol. 2017;74(10):1246–54.View ArticlePubMedPubMed CentralGoogle Scholar
- Beydoun MA, Beydoun HA, Wang Y. Obesity and central obesity as risk factors for incident dementia and its subtypes: a systematic review and meta-analysis. Obes Rev. 2008;9(3):204–18.View ArticlePubMedPubMed CentralGoogle Scholar
- Walker KA, Power MC, Gottesman RF. Defining the relationship between hypertension, cognitive decline, and dementia: a review. Curr Hypertens Rep. 2017;19(3):24.View ArticlePubMedPubMed CentralGoogle Scholar
- Wendell CR, Waldstein SR, Zonderman AB. Nonlinear longitudinal trajectories of cholesterol and neuropsychological function. Neuropsychology. 2014;28(1):106–12.View ArticlePubMedGoogle Scholar
- Moll van Charante EP, et al. Effectiveness of a 6-year multidomain vascular care intervention to prevent dementia (preDIVA): a cluster-randomised controlled trial. Lancet. 2016;388(10046):797–805.View ArticlePubMedGoogle Scholar
- Kemppainen N, et al. Brain amyloid load and its associations with cognition and vascular risk factors in FINGER study. Neurology. 2018;90(3):e206–e213. https://doi.org/10.1212/WNL.0000000000004827.
- Gottesman RF, et al. Association between midlife vascular risk factors and estimated brain amyloid deposition. JAMA. 2017;317(14):1443–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Vemuri P, et al. Evaluation of amyloid protective factors and Alzheimer disease neurodegeneration protective factors in elderly individuals. JAMA Neurol. 2017;74(6):718–26.View ArticlePubMedPubMed CentralGoogle Scholar
- There’s No Tomorrow for TOMMORROW, in https://www.alzforum.org/news/research-news/theres-no-tomorrow-tommorrow. 10 Feb 2018.
- Vemuri P, et al. Effect of intellectual enrichment on AD biomarker trajectories: longitudinal imaging study. Neurology. 2016;86(12):1128–35.View ArticlePubMedPubMed CentralGoogle Scholar
- Jack CR Jr, et al. Longitudinal tau PET in ageing and Alzheimer's disease. Brain. 2018;Google Scholar
- Villemagne VL, et al. Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study. Lancet Neurol. 2013;12(4):357–67.View ArticlePubMedGoogle Scholar
- Sperling RA, et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Assocation workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):280–92.View ArticlePubMedPubMed CentralGoogle Scholar
- Jack CR Jr, et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12(2):207–16.View ArticlePubMedPubMed CentralGoogle Scholar
- Knopman DS, et al. Selective worsening of brain injury biomarker abnormalities in cognitively normal elderly persons with beta-amyloidosis. JAMA Neurol. 2013;70(8):1030–8.View ArticlePubMedGoogle Scholar
- Fleisher AS, et al. Associations between biomarkers and age in the presenilin 1 E280A autosomal dominant Alzheimer disease kindred: a cross-sectional study. JAMA Neurol. 2015;72(3):316–24.View ArticlePubMedPubMed CentralGoogle Scholar
- Bateman RJ, et al. Clinical and biomarker changes in dominantly inherited Alzheimer's disease. N Engl J Med. 2012;367(9):795–804.View ArticlePubMedPubMed CentralGoogle Scholar
- Vos SJ, et al. Preclinical Alzheimer's disease and its outcome: a longitudinal cohort study. Lancet Neurol. 2013;12(10):957–65.View ArticlePubMedPubMed CentralGoogle Scholar
- Dubois B, et al. Preclinical Alzheimer's disease: definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12(3):292–323.View ArticlePubMedGoogle Scholar
- Sperling RA, et al. The A4 study: stopping AD before symptoms begin? Sci Transl Med. 2014;6(228):228fs13.View ArticlePubMedPubMed CentralGoogle Scholar
- Jack CR Jr, et al. Suspected non-Alzheimer disease pathophysiology—concept and controversy. Nat Rev Neurol. 2016;12(2):117–24.View ArticlePubMedPubMed CentralGoogle Scholar
- Crary JF. Primary age-related tauopathy and the amyloid cascade hypothesis: the exception that proves the rule? J Neurol Neuromedicine. 2016;1(6):53–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Vemuri P, et al. Age, vascular health, and Alzheimer disease biomarkers in an elderly sample. Ann Neurol. 2017;82(5):706–18. https://doi.org/10.1002/ana.25071.
- Xie L, et al. Sleep drives metabolite clearance from the adult brain. Science. 2013;342(6156):373–7.View ArticlePubMedGoogle Scholar
- Carvalho DZ, et al. Association of excessive daytime sleepiness with longitudinal beta-amyloid accumulation in elderly persons without dementia. JAMA Neurol. 2018. https://doi.org/10.1001/jamaneurol.2018.0049. [Epub ahead of print].
- Chen JC, et al. Sleep duration, cognitive decline, and dementia risk in older women. Alzheimers Dement. 2016;12(1):21–33.View ArticlePubMedGoogle Scholar
- Carvalho DZ, et al. Excessive daytime sleepiness and fatigue may indicate accelerated brain aging in cognitively normal late middle-aged and older adults. Sleep Med. 2017;32:236–43.View ArticlePubMedGoogle Scholar
- Chui HC, et al. Vascular risk factors and Alzheimer's disease: are these risk factors for plaques and tangles or for concomitant vascular pathology that increases the likelihood of dementia? An evidence-based review. Alzheimers Res Ther. 2012;4(1):1.PubMedPubMed CentralGoogle Scholar
- Jefferson AL, et al. Lower cardiac index levels relate to lower cerebral blood flow in older adults. Neurology. 2017;89(23):2327–34.View ArticlePubMedGoogle Scholar
- Exalto LG, et al. Midlife risk score for the prediction of dementia four decades later. Alzheimers Dement. 2014;10(5):562–70.View ArticlePubMedGoogle Scholar
- Kivipelto M, et al. Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study. Lancet Neurol. 2006;5(9):735–41.View ArticlePubMedGoogle Scholar
- Vemuri P, et al. Effect of lifestyle activities on Alzheimer disease biomarkers and cognition. Ann Neurol. 2012;72(5):730–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Vemuri P, et al. Cognitive reserve and Alzheimer's disease biomarkers are independent determinants of cognition. Brain. 2011;134(Pt 5):1479–92.View ArticlePubMedPubMed CentralGoogle Scholar
- Vemuri P, et al. Association of lifetime intellectual enrichment with cognitive decline in the older population. JAMA Neurol. 2014;71(8):1017–24.View ArticlePubMedPubMed CentralGoogle Scholar
- Schneider JA, et al. The neuropathology of probable Alzheimer disease and mild cognitive impairment. Ann Neurol. 2009;66(2):200–8.View ArticlePubMedPubMed CentralGoogle Scholar