Evaluation of Amyloid Protective Factors and Alzheimer Disease Neurodegeneration Protective Factors in Elderly Individuals | Dementia and Cognitive Impairment | JAMA Neurology | JAMA Network
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Figure 1.  Summary of the Study Findings
Summary of the Study Findings

The outcome variables (amyloid deposition and neurodegeneration in Alzheimer disease [AD] signature regions) are shown in the boxes. Solid arrows are shown from the significant predictor variables to the outcomes based on the study data. The dashed line indicates the widely accepted relationship between the 2 outcome variables.

Figure 2.  Trajectory Models for Amyloid and Neurodegeneration in Alzheimer Disease Signature Regions
Trajectory Models for Amyloid and Neurodegeneration in Alzheimer Disease Signature Regions

A, Average trajectories for Alzheimer disease pathophysiology without protective factors. The horizontal line indicates biomarker positivity. B, High protection against amyloid and average/low/high protection against neurodegeneration in Alzheimer disease signature regions. The horizontal line indicates biomarker positivity.

Table 1.  Characteristics by Sex for Continuous Variables and Categorical Variables
Characteristics by Sex for Continuous Variables and Categorical Variables
Table 2.  Characteristics by Amyloid and Neurodegeneration Status for Continuous Variables and Categorical Variables
Characteristics by Amyloid and Neurodegeneration Status for Continuous Variables and Categorical Variables
Table 3.  Results of Multivariate Linear Regressions Adjusting for Age
Results of Multivariate Linear Regressions Adjusting for Age
Table 4.  Characteristics of Individuals 85 Years and Older With A−N− Results vs Those With A+ and/or N+ Results for Continuous Variables and for Categorical Variables
Characteristics of Individuals 85 Years and Older With A−N− Results vs Those With A+ and/or N+ Results for Continuous Variables and for Categorical Variables
1.
Ingelsson  M, Fukumoto  H, Newell  KL,  et al.  Early Abeta accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain.  Neurology. 2004;62(6):925-931.PubMedGoogle ScholarCrossref
2.
Jack  CR  Jr, Knopman  DS, Jagust  WJ,  et al.  Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers.  Lancet Neurol. 2013;12(2):207-216.PubMedGoogle ScholarCrossref
3.
Scacchi  R, De Bernardini  L, Mantuano  E, Donini  LM, Vilardo  T, Corbo  RM.  Apolipoprotein E (APOE) allele frequencies in late-onset sporadic Alzheimer’s disease (AD), mixed dementia and vascular dementia: lack of association of epsilon 4 allele with AD in Italian octogenarian patients.  Neurosci Lett. 1995;201(3):231-234.PubMedGoogle ScholarCrossref
4.
Sobel  E, Louhija  J, Sulkava  R,  et al.  Lack of association of apolipoprotein E allele epsilon 4 with late-onset Alzheimer’s disease among Finnish centenarians.  Neurology. 1995;45(5):903-907.PubMedGoogle ScholarCrossref
5.
Farrer  LA, Cupples  LA, Haines  JL,  et al; APOE and Alzheimer Disease Meta Analysis Consortium.  Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. a meta-analysis.  JAMA. 1997;278(16):1349-1356.PubMedGoogle ScholarCrossref
6.
Nelson  PT, Head  E, Schmitt  FA,  et al.  Alzheimer’s disease is not “brain aging”: neuropathological, genetic, and epidemiological human studies.  Acta Neuropathol. 2011;121(5):571-587.PubMedGoogle ScholarCrossref
7.
Jack  CR  Jr, Wiste  HJ, Weigand  SD,  et al.  Age-specific population frequencies of cerebral β-amyloidosis and neurodegeneration among people with normal cognitive function aged 50-89 years: a cross-sectional study.  Lancet Neurol. 2014;13(10):997-1005.PubMedGoogle ScholarCrossref
8.
Vemuri  P, Knopman  DS.  The role of cerebrovascular disease when there is concomitant Alzheimer disease.  Biochim Biophys Acta. 2015;1862(5):952-956.PubMedGoogle ScholarCrossref
9.
Jansen  WJ, Ossenkoppele  R, Knol  DL,  et al; Amyloid Biomarker Study Group.  Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis.  JAMA. 2015;313(19):1924-1938.PubMedGoogle ScholarCrossref
10.
Jack  CR  Jr, Wiste  HJ, Weigand  SD,  et al.  Age, sex, and APOE ε4 effects on memory, brain structure, and β-amyloid across the adult life span.  JAMA Neurol. 2015;72(5):511-519.PubMedGoogle ScholarCrossref
11.
Wilson  RS, Boyle  PA, Yu  L, Barnes  LL, Schneider  JA, Bennett  DA.  Life-span cognitive activity, neuropathologic burden, and cognitive aging.  Neurology. 2013;81(4):314-321.PubMedGoogle ScholarCrossref
12.
Vemuri  P, Lesnick  TG, Przybelski  SA,  et al.  Association of lifetime intellectual enrichment with cognitive decline in the older population.  JAMA Neurol. 2014;71(8):1017-1024.PubMedGoogle ScholarCrossref
13.
Luchsinger  JA, Tang  M-X, Stern  Y, Shea  S, Mayeux  R.  Diabetes mellitus and risk of Alzheimer’s disease and dementia with stroke in a multiethnic cohort.  Am J Epidemiol. 2001;154(7):635-641.PubMedGoogle ScholarCrossref
14.
Craft  S.  The role of metabolic disorders in Alzheimer disease and vascular dementia: two roads converged.  Arch Neurol. 2009;66(3):300-305.PubMedGoogle ScholarCrossref
15.
Kivipelto  M, Ngandu  T, Fratiglioni  L,  et al.  Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease.  Arch Neurol. 2005;62(10):1556-1560.PubMedGoogle ScholarCrossref
16.
Zhong  G, Wang  Y, Zhang  Y, Guo  JJ, Zhao  Y.  Smoking is associated with an increased risk of dementia: a meta-analysis of prospective cohort studies with investigation of potential effect modifiers.  PLoS One. 2015;10(3):e0118333.PubMedGoogle ScholarCrossref
17.
Akinyemi  RO, Mukaetova-Ladinska  EB, Attems  J, Ihara  M, Kalaria  RN.  Vascular risk factors and neurodegeneration in ageing related dementias: Alzheimer’s disease and vascular dementia.  Curr Alzheimer Res. 2013;10(6):642-653.PubMedGoogle ScholarCrossref
18.
Enzinger  C, Fazekas  F, Matthews  PM,  et al.  Risk factors for progression of brain atrophy in aging: six-year follow-up of normal subjects.  Neurology. 2005;64(10):1704-1711.PubMedGoogle ScholarCrossref
19.
Roberts  RO, Knopman  DS, Cha  RH,  et al.  Diabetes and elevated hemoglobin A1c levels are associated with brain hypometabolism but not amyloid accumulation.  J Nucl Med. 2014;55(5):759-764.PubMedGoogle ScholarCrossref
20.
Vassilaki  M, Aakre  JA, Cha  RH,  et al.  Multimorbidity and risk of mild cognitive impairment.  J Am Geriatr Soc. 2015;63(9):1783-1790.PubMedGoogle ScholarCrossref
21.
Vassilaki  M, Aakre  JA, Mielke  MM,  et al.  Multimorbidity and neuroimaging biomarkers among cognitively normal persons.  Neurology. 2016;86(22):2077-2084.PubMedGoogle ScholarCrossref
22.
Rocca  WA, Yawn  BP, St Sauver  JL, Grossardt  BR, Melton  LJ  III.  History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population.  Mayo Clin Proc. 2012;87(12):1202-1213.PubMedGoogle ScholarCrossref
23.
St Sauver  JL, Grossardt  BR, Leibson  CL, Yawn  BP, Melton  LJ  III, Rocca  WA.  Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project.  Mayo Clin Proc. 2012;87(2):151-160.PubMedGoogle ScholarCrossref
24.
St Sauver  JL, Grossardt  BR, Yawn  BP,  et al.  Data resource profile: the Rochester Epidemiology Project (REP) medical records-linkage system.  Int J Epidemiol. 2012;41(6):1614-1624.PubMedGoogle ScholarCrossref
25.
St Sauver  JL, Grossardt  BR, Yawn  BP, Melton  LJ  III, Rocca  WA.  Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester epidemiology project.  Am J Epidemiol. 2011;173(9):1059-1068.PubMedGoogle ScholarCrossref
26.
Petersen  RC, Roberts  RO, Knopman  DS,  et al; The Mayo Clinic Study of Aging.  Prevalence of mild cognitive impairment is higher in men.  Neurology. 2010;75(10):889-897.PubMedGoogle ScholarCrossref
27.
Roberts  RO, Geda  YE, Knopman  DS,  et al.  The incidence of MCI differs by subtype and is higher in men: the Mayo Clinic Study of Aging.  Neurology. 2012;78(5):342-351.PubMedGoogle ScholarCrossref
28.
Vemuri  P, Lesnick  TG, Przybelski  SA,  et al.  Effect of lifestyle activities on Alzheimer disease biomarkers and cognition.  Ann Neurol. 2012;72(5):730-738.PubMedGoogle ScholarCrossref
29.
Geda  YE, Topazian  HM, Roberts  LA,  et al.  Engaging in cognitive activities, aging, and mild cognitive impairment: a population-based study.  J Neuropsychiatry Clin Neurosci. 2011;23(2):149-154.PubMedGoogle ScholarCrossref
30.
Roberts  RO, Knopman  DS, Przybelski  SA,  et al.  Association of type 2 diabetes with brain atrophy and cognitive impairment.  Neurology. 2014;82(13):1132-1141.PubMedGoogle ScholarCrossref
31.
Lowe  VJ, Kemp  BJ, Jack  CR  Jr,  et al.  Comparison of 18F-FDG and PiB PET in cognitive impairment.  J Nucl Med. 2009;50(6):878-886.PubMedGoogle ScholarCrossref
32.
Jack  CR  Jr, Lowe  VJ, Senjem  ML,  et al.  11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment.  Brain. 2008;131(Pt 3):665-680.PubMedGoogle ScholarCrossref
33.
Senjem  ML, Lowe  V, Kemp  B,  et al. Automated ROI analysis of 11C Pittsburgh compound B images using structural magnetic resonance imaging atlases, Alzheimer's and Dementia. Paper presented at the 2008 Alzheimer's Association International Conference on Alzheimer's Disease.
34.
Lopresti  BJ, Klunk  WE, Mathis  CA,  et al.  Simplified quantification of Pittsburgh Compound B amyloid imaging PET studies: a comparative analysis.  J Nucl Med. 2005;46(12):1959-1972.PubMedGoogle Scholar
35.
Jack  CR  Jr, Wiste  HJ, Weigand  SD,  et al.  Different definitions of neurodegeneration produce similar amyloid/neurodegeneration biomarker group findings.  Brain. 2015;138(Pt 12):3747-3759.PubMedGoogle ScholarCrossref
36.
Villemagne  VL, Burnham  S, Bourgeat  P,  et al; Australian Imaging Biomarkers and Lifestyle (AIBL) Research Group.  Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study.  Lancet Neurol. 2013;12(4):357-367.PubMedGoogle ScholarCrossref
37.
Sojkova  J, Zhou  Y, An  Y,  et al.  Longitudinal patterns of β-amyloid deposition in nondemented older adults.  Arch Neurol. 2011;68(5):644-649.PubMedGoogle ScholarCrossref
38.
Corder  EH, Ghebremedhin  E, Taylor  MG, Thal  DR, Ohm  TG, Braak  H.  The biphasic relationship between regional brain senile plaque and neurofibrillary tangle distributions: modification by age, sex, and APOE polymorphism.  Ann N Y Acad Sci. 2004;1019:24-28.PubMedGoogle ScholarCrossref
39.
Mielke  MM, Vemuri  P, Rocca  WA.  Clinical epidemiology of Alzheimer’s disease: assessing sex and gender differences.  Clin Epidemiol. 2014;6:37-48.PubMedGoogle ScholarCrossref
40.
Fassbender  K, Simons  M, Bergmann  C,  et al.  Simvastatin strongly reduces levels of Alzheimer’s disease beta-amyloid peptides Abeta 42 and Abeta 40 in vitro and in vivo.  Proc Natl Acad Sci U S A. 2001;98(10):5856-5861.PubMedGoogle ScholarCrossref
41.
Simons  M, Keller  P, De Strooper  B, Beyreuther  K, Dotti  CG, Simons  K.  Cholesterol depletion inhibits the generation of beta-amyloid in hippocampal neurons.  Proc Natl Acad Sci U S A. 1998;95(11):6460-6464.PubMedGoogle ScholarCrossref
42.
Reitz  C.  Dyslipidemia and dementia: current epidemiology, genetic evidence, and mechanisms behind the associations.  J Alzheimers Dis. 2012;30(suppl 2):S127-S145.PubMedGoogle Scholar
43.
Kivipelto  M, Helkala  EL, Laakso  MP,  et al.  Apolipoprotein E epsilon4 allele, elevated midlife total cholesterol level, and high midlife systolic blood pressure are independent risk factors for late-life Alzheimer disease.  Ann Intern Med. 2002;137(3):149-155.PubMedGoogle ScholarCrossref
44.
Willette  AA, Johnson  SC, Birdsill  AC,  et al.  Insulin resistance predicts brain amyloid deposition in late middle-aged adults.  Alzheimers Dement. 2015;11(5):504-510.e1, e1.PubMedGoogle ScholarCrossref
45.
Thambisetty  M, Jeffrey Metter  E, Yang  A,  et al.  Glucose intolerance, insulin resistance, and pathological features of Alzheimer disease in the Baltimore Longitudinal Study of Aging.  JAMA Neurol. 2013;70(9):1167-1172.PubMedGoogle ScholarCrossref
46.
Fjell  AM, McEvoy  L, Holland  D, Dale  AM, Walhovd  KB; Alzheimer’s Disease Neuroimaging Initiative.  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.PubMedGoogle ScholarCrossref
47.
Morris  JC, Roe  CM, Xiong  C,  et al.  APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging.  Ann Neurol. 2010;67(1):122-131.PubMedGoogle ScholarCrossref
48.
Vemuri  P, Wiste  HJ, Weigand  SD,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Effect of apolipoprotein E on biomarkers of amyloid load and neuronal pathology in Alzheimer disease.  Ann Neurol. 2010;67(3):308-316.PubMedGoogle Scholar
49.
Braak  H, Braak  E.  Neuropathological stageing of Alzheimer-related changes.  Acta Neuropathol. 1991;82(4):239-259.PubMedGoogle ScholarCrossref
50.
Jack  CR  Jr, Knopman  DS, Weigand  SD,  et al.  An operational approach to NIA-AA criteria for preclinical Alzheimer’s disease.  Ann Neurol. 2012;71(6):765-775.PubMedGoogle ScholarCrossref
51.
Knopman  DS, Jack  CR  Jr, Wiste  HJ,  et al.  Brain injury biomarkers are not dependent on β-amyloid in normal elderly.  Ann Neurol. 2013;73(4):472-480.PubMedGoogle ScholarCrossref
52.
Vemuri  P, Lesnick  TG, Przybelski  SA,  et al.  Effect of intellectual enrichment on AD biomarker trajectories: longitudinal imaging study.  Neurology. 2016;86(12):1128-1135.PubMedGoogle ScholarCrossref
53.
Dosunmu  R, Wu  J, Adwan  L,  et al.  Lifespan profiles of Alzheimer’s disease-associated genes and products in monkeys and mice.  J Alzheimers Dis. 2009;18(1):211-230.PubMedGoogle ScholarCrossref
54.
Elcoroaristizabal Martín  X, Gómez Busto  F, Artaza Artabe  I,  et al.  Genetic profiles of longevity and healthy cognitive aging in nonagenarians from the Basque Country.  Rev Esp Geriatr Gerontol. 2011;46(4):217-222.PubMedGoogle ScholarCrossref
55.
Sebastiani  P, Bae  H, Sun  FX,  et al.  Meta‐analysis of genetic variants associated with human exceptional longevity.  Aging (Albany NY). 2013;5(9):653-661.PubMedGoogle ScholarCrossref
56.
Suri  S, Heise  V, Trachtenberg  AJ, Mackay  CE.  The forgotten APOE allele: a review of the evidence and suggested mechanisms for the protective effect of APOE ɛ2.  Neurosci Biobehav Rev. 2013;37(10 Pt 2):2878-2886.PubMedGoogle ScholarCrossref
57.
Bennett  DA, Arnold  SE, Valenzuela  MJ, Brayne  C, Schneider  JA.  Cognitive and social lifestyle: links with neuropathology and cognition in late life.  Acta Neuropathol. 2014;127(1):137-150.PubMedGoogle ScholarCrossref
58.
Elwood  P, Galante  J, Pickering  J,  et al.  Healthy lifestyles reduce the incidence of chronic diseases and dementia: evidence from the Caerphilly cohort study.  PLoS One. 2013;8(12):e81877.PubMedGoogle ScholarCrossref
59.
Landau  SM, Marks  SM, Mormino  EC,  et al.  Association of lifetime cognitive engagement and low β-amyloid deposition.  Arch Neurol. 2012;69(5):623-629.PubMedGoogle ScholarCrossref
60.
Mattson  MP.  Lifelong brain health is a lifelong challenge: from evolutionary principles to empirical evidence.  Ageing Res Rev. 2015;20:37-45.PubMedGoogle ScholarCrossref
61.
Norton  MC, Dew  J, Smith  H,  et al; Cache County Investigators.  Lifestyle behavior pattern is associated with different levels of risk for incident dementia and Alzheimer’s disease: the Cache County study.  J Am Geriatr Soc. 2012;60(3):405-412.PubMedGoogle ScholarCrossref
62.
Schneider  N, Yvon  C.  A review of multidomain interventions to support healthy cognitive ageing.  J Nutr Health Aging. 2013;17(3):252-257.PubMedGoogle ScholarCrossref
63.
Small  GW, Siddarth  P, Ercoli  LM, Chen  ST, Merrill  DA, Torres-Gil  F.  Healthy behavior and memory self-reports in young, middle-aged, and older adults.  Int Psychogeriatr. 2013;25(6):981-989.PubMedGoogle ScholarCrossref
64.
Khachaturian  AS, Corcoran  CD, Mayer  LS, Zandi  PP, Breitner  JC; Cache County Study Investigators.  Apolipoprotein E ε4 count affects age at onset of Alzheimer disease, but not lifetime susceptibility: the Cache County Study.  Arch Gen Psychiatry. 2004;61(5):518-524.PubMedGoogle ScholarCrossref
65.
Satizabal  CL, Beiser  AS, Chouraki  V, Chêne  G, Dufouil  C, Seshadri  S.  Incidence of dementia over three decades in the Framingham Heart Study.  N Engl J Med. 2016;374(6):523-532.PubMedGoogle ScholarCrossref
66.
Kövari  E, Herrmann  FR, Bouras  C, Gold  G.  Amyloid deposition is decreasing in aging brains: an autopsy study of 1599 older people.  Neurology. 2014;82(4):326-331.PubMedGoogle ScholarCrossref
67.
Whitwell  JL, Josephs  KA, Murray  ME,  et al.  MRI correlates of neurofibrillary tangle pathology at autopsy: a voxel-based morphometry study.  Neurology. 2008;71(10):743-749.PubMedGoogle ScholarCrossref
Original Investigation
June 2017

Evaluation of Amyloid Protective Factors and Alzheimer Disease Neurodegeneration Protective Factors in Elderly Individuals

Author Affiliations
  • 1Department of Radiology, Mayo Clinic–Rochester, Rochester, Minnesota
  • 2Department of Neurology, Mayo Clinic–Rochester, Rochester, Minnesota
  • 3Department of Health Sciences Research, Mayo Clinic–Rochester, Rochester, Minnesota
  • 4Department of Neuroscience, Mayo Clinic–Jacksonville, Jacksonville, Florida
  • 5Department of Psychology, Mayo Clinic–Rochester, Rochester, Minnesota
JAMA Neurol. 2017;74(6):718-726. doi:10.1001/jamaneurol.2017.0244
Key Points

Question  Do factors that influence amyloid- and Alzheimer disease–pattern neurodegeneration differ?

Findings  In this cohort study of 942 elderly individuals enrolled in the Mayo Clinic Study of Aging with magnetic resonance imaging and amyloid scans and, protective factors that influence amyloid- and Alzheimer disease-pattern neurodegeneration differed.

Meaning  Investigating independent and combined protective factors for amyloid and neurodegeneration will enable better prevention strategies to delay the onset and progression of Alzheimer disease.

Abstract

Importance  While amyloid and neurodegeneration are viewed together as Alzheimer disease pathophysiology (ADP), the factors that influence amyloid and AD-pattern neurodegeneration may be considerably different. Protection from these ADP factors may be important for aging without significant ADP.

Objective  To identify the combined and independent protective factors for amyloid and AD-pattern neurodegeneration in a population-based sample and to test the hypothesis that “exceptional agers” with advanced ages do not have significant ADP because they have protective factors for amyloid and neurodegeneration.

Design, Setting, and Participants  This cohort study conducted a prospective analysis of 942 elderly individuals (70-≥90 years) with magnetic resonance imaging and Pittsburgh compound B–positron emission tomography scans enrolled in the Mayo Clinic Study of Aging, a longitudinal population-based study of cognitive aging in Olmsted County, Minnesota. We operationalized “exceptional aging” without ADP by considering individuals 85 years or older to be without significant evidence of ADP.

Main Outcomes and Measures  We evaluated predictors including demographics, APOE, intellectual enrichment, midlife risk factors (physical inactivity, obesity, smoking, diabetes, hypertension, and dyslipidemia), and the total number of late-life cardiac and metabolic conditions. We used multivariate linear regression models to identify the combined and independent protective factors for amyloid and AD-pattern neurodegeneration. Using a subsample of the cohort 85 years of age or older, we computed Cohen d–based effect size estimations to compare the quantitative strength of each predictor variable in their contribution with exceptional aging without ADP.

Results  The study participants included 423 (45%) women and the average age of participants was 79.7 (5.9) years. Apart from demographics and the APOE genotype, only midlife dyslipidemia was associated with amyloid deposition. Obesity, smoking, diabetes, hypertension, and cardiac and metabolic conditions, but not intellectual enrichment, were associated with greater AD-pattern neurodegeneration. In the 85 years or older cohort, the Cohen d results showed small to moderate effects (effect sizes > 0.2) of several variables except job score and midlife hypertension in predicting exceptional aging without ADP.

Conclusions and Relevance  The protective factors that influence amyloid and AD-pattern neurodegeneration are different. “Exceptional aging” without ADP may be possible with a greater number of protective factors across the lifespan but warrants further investigation.

Introduction

The 2 important processes of Alzheimer disease pathophysiology (ADP) are amyloid and neurodegeneration. The widely accepted AD model posits that amyloid accelerates downstream neurodegeneration, which is the underlying substrate of cognitive impairment.1,2 While amyloid and neurodegeneration are viewed together as composing ADP, we aimed to test whether protective factors that influence amyloid and AD-pattern neurodegeneration differ. Investigating these factors will enable the development of better prevention strategies to delay the onset and progression of ADP.

Although the main AD risk factors are age and APOE genotype, there is no association between APOE genotype and the risk of AD among the oldest elderly individuals.3-5 Nelson et al6 showed that each added year of life does not lead to an increased prevalence of ADP, unlike cerebrovascular disease. Mayo Clinic Study of Aging (MCSA) imaging studies also found nonmonotonicity in the prevalence of amyloid positivity among cognitively normal individuals while the prevalence of cerebrovascular disease monotonically increased with age.7,8 Is it possible that a combination of genetic, environmental, and lifestyle factors trigger the onset of ADP among most individuals and that a fraction of the oldest elderly individuals have protection or resistance against ADP despite the aging process? Our second aim was to test whether certain individuals do not have significant ADP in advanced ages because they have protective factors for both amyloid and neurodegeneration.

Protective factors against AD dementia can be because of several factors: demographics, APOE,5,9,10 intellectual enrichment,11,12 midlife risk factors,13-19 and late-life chronic conditions.20,21 This work’s strength is the investigation of all of these protective factors in the context of underlying ADP in a population-based sample to test our hypotheses.

Methods
Selection of Participants

The MCSA is an epidemiological study among Olmsted County, Minnesota residents ages 70 to 89 years. The MCSA enumeration is based on the Rochester Epidemiology Project medical records linkage system.22-25 We included 942 elderly individuals with the APOE genotype, intellectual enrichment variables, chronic conditions, risk factors (discussed later), and concurrent Pittsburgh compound B (PiB) and magnetic resonance imaging (MRI) scans. We included the last available PiB and MRI results when individuals had multiple scans. At the time of the scan, 737 patients were cognitively normal, 174 had mild cognitive impairment, 24 received a diagnosis of a neurodegenerative disorder (18 AD, 1 Parkinson disease, 1 AD with vascular dementia, 1 progressive supranuclear palsy, and 3 dementia hard to classify), and 7 had a missing clinical diagnosis because of incomplete data. Peterson et al26 and Roberts et al27 describe the MCSA design and diagnosis processes. This study was approved by the Mayo Clinic and Olmsted Medical Center institutional review boards. Written informed consent was obtained from all participants or their surrogates.

Predictor Variables
Demographics and APOE

The age and sex of participants were obtained at the clinical visit. We used age at the time of the MRI scan. The presence of the APOE genotype (the presence of APOE ε4 and APOE ε2) was determined from blood collected at clinical visit.

Intellectual Enrichment

We included 3 intellectual enrichment variables: (1) education, (2) job score based on the participant’s primary occupation throughout his or her life (intellectually challenging jobs are rated high on a scale of 1-5),28 and (3) midlife weekly cognitive activity that was based on a cognitive activity questionnaire29 in which participants are asked how often a certain cognitive task (such as reading or crafts) was performed on average in midlife (50-65 years) and summarized.28

Midlife Risk Factors

A history of midlife (40-64 years) vascular risk factors was assessed by trained nurses using the Rochester Epidemiology Project medical records linkage system. Roberts et al30 explain the criteria for determining type 2 diabetes, hypertension, dyslipidemia, and obesity. We assessed smoking (ever smoked, formerly smoked, or currently smoked) from self-reported data. We considered the midlife physical inactivity measure defined by 21 minus the midlife physical activity measure, which is the sum of 6 components that ask the participant how often a certain physical task was performed on average during midlife (50-65 years).28

Identifying Cardiac and Metabolic Chronic Conditions

In addition to the aforementioned midlife risk factors, we included the standard definition of chronic conditions in a 5-year capture frame (late-life).20 From the 2010 US Department of Health and Human Services list for studying multimorbidity, we limited the scope to 7 cardiac and metabolic conditions (CMCs) as a composite score. This score includes the sum of the presence or absence of these conditions: hypertension, hyperlipidemia, cardiac arrhythmias, coronary artery disease, congestive heart failure, diabetes, and stroke. Although CMC includes the 3 prime vascular risk factors in its definition, we did not have problems with multicollinearity because we used single predictor models.

Imaging Biomarkers
Amyloid Pathology Assessment From PiB–Positron Emission Tomography Scans

Lowe et al,31 Jack et al,32 and Senjem et al33 describe PiB–positron emission tomography (PET) acquisition and processing. The global cortical PiB-PET retention ratio was computed for each participant by calculating the median uptake in the prefrontal, orbitofrontal, parietal, temporal, anterior cingulate, and posterior cingulate/precuneus regions of interest divided by the median uptake in the cerebellar gray matter regions of interest.34 We used amyloid as a continuous measure for regression analyses and dichotomized participants as amyloid negative/positive (A−/A+) using a cutoff of a 1.4 standard uptake value ratio for descriptive tables.35

AD-Pattern Neurodegeneration Assessment From Structural MRI

Magnetic resonance imaging results were acquired at 3T (General Electric Company) and FreeSurfer, version 5.3 (FreeSurfer) was used to estimate cortical thickness in AD signature regions (the entorhinal cortex, inferior temporal, middle temporal, and fusiform) into a single measure.35 We used it as a continuous measure for regression and dichotomized participants as AD-pattern neurodegeneration-negative/positive (N−/N+) using a cutoff of 2.74 mm35 for descriptive tables.

Statistical Methods

Standard summary measures were used to describe characteristics for all participants, strata determined by sex, and strata determined by joint categorized amyloid and AD-pattern neurodegeneration status. The results by sex were compared using t tests for continuous variables and χ2 tests for categorical variables. The amyloid/AD-pattern neurodegeneration results were compared using analyses of covariance (A− vs A+ and N− vs N+) for continuous variables or logistic regressions for categorical variables with an adjustment for age. In sensitivity analyses, we also performed analyses of covariance, allowing for nonlinear age adjustments from spline curves with knots at 75, 80, and 85 years.

We used multivariate linear regressions, adjusting for age at MRI, to determine associations of individual predictor variables with continuous measures of amyloid burden and cortical thickness. Amyloid burden was log transformed and subtracted from 0 to meet regression assumptions and to keep the direction of associations consistent with neurodegeneration (positive regression associations being protective). Both outcomes were standardized to z-scores. We used Pillai trace to test if the regression coefficients for the 2 outcomes were simultaneously equal to 0 (no association with both outcomes) for each predictor. We then performed linear regressions, adjusting for age, for each predictor with each outcome. We summarized these results using regression coefficients with their associated standard errors and P values, and statistical significance was set at P < .05. Finally, because amyloid might mediate the effect of the risk factors on cortical thickness, we performed linear regressions, adjusting for age and amyloid, to test for direct effects of the risk factors on neurodegeneration.

Testing the “Exceptional Aging” Hypothesis

To test the “exceptional aging” hypothesis, we considered a subsample of our cohort older than 85 years. Among those individuals, we operationalized exceptional aging without ADP as a subsample of individuals without significant evidence of ADP (ie, A−N−). The key group of interest was the group with A−N− results (ie, the group free of both disease processes). Therefore, we compared the sample of the 85-year-old individuals with A−N− results with those who had either A+, N+, or A+N+ results using t tests for continuous variables and χ2 tests for categorical variables. Because the sample sizes were small, we also computed Cohen d–based effect size estimations to compare the quantitative strength of each measure in their contribution with exceptional aging.

Results

Participant characteristics are shown in Table 1. Men had significantly higher education and job scores and women reported higher midlife cognitive activities. A greater percentage of men reported smoking and men had higher CMC scores compared with women. In Table 2, participants were dichotomized into 4 biomarker groups: A−N−, A+N−, A−N+, and A+N+. Because the 4 groups significantly differed by age, we adjusted the group comparisons for age. Amyloid levels differed by APOE and sex. Midlife dyslipidemia approached statistical significance in predicting amyloid differences by groups (P = .07). Alzheimer disease-pattern neurodegeneration differed by sex, APOE, midlife obesity, smoking, and CMC. Midlife hypertension and dyslipidemia were close to a significant association with neurodegeneration (P > .05 to P = .10). Individuals in the groups with N+ results had higher CMC compared with the groups with N− results (median values, 2.8-2.9 [N+] vs 2.3-2.4 [N−]). The more general analyses allowing for a nonlinear age adjustment produced very similar results, except that the P value for AD-pattern neurodegeneration and APOE became marginally greater than .05 (P = .052). Also, eTable 1 in the Supplement shows these differences among cognitively normal individuals to corroborate that the findings were not driven by diagnoses.

Combined and Independent Protective Factors for Amyloid and Neurodegeneration

The results of the multivariate linear regression models are shown in Table 3. Figure 1 summarizes these findings. The Pillai trace P values determine if the regression coefficients for the 2 outcomes are simultaneously equal to 0 (ie, no association with both outcomes) for each predictor. If significant, then regressions for each outcome can be interpreted. We found that age, sex, APOE genotype, several risk factors, and CMCs were predictors of at least 1 of the outcomes. Older age and the presence of the APOE ε4 allele were associated with higher amyloid deposition. Being male and the presence of the APOE ε2 allele were associated with lower amyloid deposition. Older age, being male, and the presence of an APOE ε4 allele were associated with greater neurodegeneration. However, APOE ε4 association with cortical thinning was nonsignificant (P = .58) after adjusting for amyloid (eTable 2 in the Supplement). If amyloid lies between APOE ε4 and cortical thinning on a causal pathway, this would be consistent with an indirect effect of APOE ε4 through amyloid deposition rather than a direct effect on AD-pattern neurodegeneration.

Other than demographics, the protective factors for amyloid and AD-pattern neurodegeneration did not overlap. Increased CMC, midlife diabetes, midlife obesity, and smoking had a significant Pillai trace with cortical thickness as an outcome. On the other hand, midlife dyslipidemia was the only risk factor significantly associated with greater amyloid. Although midlife dyslipidemia approached significance in predicting AD signature thickness (Table 3), this association was nonsignificant after adjusting for amyloid (eTable 2 in the Supplement).

Exceptional Aging Hypothesis

Table 4 describes the characteristics of individuals older than 85 years, dichotomized into [A−N−] 85-year-old individuals with A−N− results without ADP (ie, the exceptional agers) vs those who had A+, or N+, or A+N+ results. The 2 groups differed significantly on the frequency of the presence of the APOE ε4 allele. Midlife dyslipidemia and physical inactivity approached statistical significance between the groups (P < .10). This lack of statistical significance could be because of a lack of differences or a lack of power from the small sample size. The Cohen d results showed small to moderate effects (effect sizes >0.2) for several variables except job score and midlife hypertension. We tested for participation bias among those volunteering for the imaging studies and found some differences (eTable 3 in the Supplement).

Discussion

Our main findings were: (1) the protective factors that influence amyloid and AD-pattern neurodegeneration are different; (2) apart from demographics and APOE genotype, only midlife dyslipidemia was associated with amyloid deposition; and (3) fewer midlife risk factors and chronic conditions, but not intellectual enrichment, may provide significant protection against AD-pattern neurodegeneration.

Protection Against Amyloid Deposition: Age, Sex, APOE, and Midlife Dyslipidemia

Age and the APOE genotype are, to our knowledge, the strongest known amyloid risk factors.9,10,36,37 Our age and APOE findings are consistent with the current literature. There is neuropathological evidence of greater amyloid burden among women compared with men.38 In Table 1, the variables that were significantly higher among men compared with women were education level, job score, smoking history, and CMC, but none of these variables were predictors of amyloid (Table 3), suggesting that sex differences in amyloid accumulation may be because of unidentified sex-specific mechanisms.39

Experimental studies have reported that cholesterol accelerates β-amyloid production.40,41 Epidemiological studies have asserted that dyslipidemia increases dementia risk.42 Midlife cholesterol levels are independent AD risk factors in addition to APOE ε4.43 The association we found here is consistent with these studies. The lack of association after adjusting for amyloid suggests that cholesterol plays an important role in AD pathogenesis via amyloid and not AD-pattern neurodegeneration (eTable 2 in the Supplement).

We found that CMC and the other midlife risk factors were not significant predictors of amyloid deposition. While there is some evidence that insulin resistance may predict amyloid deposition,44 we did not find an association of diabetes and amyloid similar to previous studies.19,45 Each of these risk factors has been shown to increase AD dementia risk, which is the sum of the risk for amyloidosis and neurodegeneration. Therefore, the increased risk could be derived from the fact that these midlife risk factors and CMC are catalysts for the neuronal processes but not amyloid deposition, which to our knowledge has not been previously studied.

In Figure 2, we illustrate that an individual with protection against accumulation of amyloid (eg, absence of the APOE ε4 allele and healthy cholesterol levels) may experience a later elevation in amyloid levels (Figure 2B) compared with the average trajectories seen among the population (Figure 2A). There are 2 components to amyloid trajectories: age at onset and rate of accumulation. While the absence of the APOE ε4 allele may largely contribute to a later age of onset,9 the protective factors to amyloid can act either by delaying the age at onset or slowing the rate of accumulation.

Protection Against Neurodegeneration: Age, Sex, Midlife Risk Factors, and Chronic Conditions

Age is a known risk factor for neurodegeneration.46 It has been shown that the APOE ε4 is related to amyloid but not related to tau pathology47 or AD-pattern neurodegeneration after accounting for an individual’s clinical status.48 Therefore, the lack of association between the APOE ε4 genotype and neurodegeneration, after accounting for amyloid, is consistent with literature (eTable 2 in the Supplement). Men have a higher frequency of AD-pattern neurodegeneration.10 The measure for neurodegeneration we used was based on cortical thickness, which does not differ by head size.46 Therefore, head size is not a possible confounder here.35

Midlife risk factors (midlife diabetes, hypertension, adiposity, and smoking) and CMCs are associated with an increased risk of AD13-16 as well as neurodegeneration.17-19 Medial temporal lobe atrophy on MRI is seen in aging (those at low risk of AD) and AD,46 and these brain structures have increased vulnerability to tau pathology.49 Because chronic conditions represent a model of accelerated aging, we expect AD-pattern neurodegeneration because of CMC and related risk factors to be present in the same vulnerable regions.

In Figure 2B, we illustrate an individual with low protection against neurodegeneration using the dashed lines (eg, a greater number of risk factors and CMC). These individuals may have neurodegenerative changes earlier than amyloid and their results for AD-neurodegeneration may be positive while amyloid biomarkers are still under the detection threshold. Recently, individuals with biomarker evidence of neurodegeneration and no amyloidosis were designated with the terminology suspected non-AD pathophysiology, and these individuals who have atrophy in the AD signature regions are more likely to be classified as such.50,51 These individuals may either be on a separate pathway (eg, vascular or non-AD tauopathy pathway) or may have positive results for amyloid later in life. If they go onto the AD pathway, the time to cognitive impairment will be short and at much lower amyloid levels because of mixed etiologies.

Intellectual Enrichment: Protection Against Cognitive Decline

We found that intellectual enrichment was not a significant predictor of amyloid or AD-pattern neurodegeneration. Our work previously showed that the effect of intellectual enrichment on AD biomarkers may be minimal but that it has a larger effect on delaying the onset of cognitive impairment,12,52 suggesting that it is mainly protective against cognitive decline.

Is “Exceptional Aging” Without ADP Possible?

The main risk factors for AD are age, genetics, lifestyle, midlife risk factors, and CMC. The absence of ADP among “exceptional aging” individuals may be explained by survival bias and genetic profiles.53-56 However, recent literature supported the fact that risk factors and behaviors throughout the lifespan may influence AD pathology,57-63 which is supported by neuropathological literature that found that some individuals with APOE ε4 will not develop AD dementia even over the lifespan.3,4,64 Although we did not detect clear differences in Table 4, the small- to moderate-effect sizes of several predictors suggests that protection against both amyloid and AD-pattern neurodegeneration is important.

If protection against AD pathology in each individual were to be viewed as a “net sum” of effects from aging, genetics (net difference between protective and risk genes), lifestyle, midlife risk factors, and CMC, then “exceptional aging” without ADP in individuals is possible if a large positive “net sum” were present. The absence of midlife risk factors and lower CMC may be important for a positive “net sum.” Steadily falling dementia incidence65 and declining amyloid deposition in aging brains66 provide evidence for the possibility of “exceptional aging.”

Strengths and Limitations

A strength of this study was our large population-based sample with imaging biomarkers. Our study also had several limitations. First, we focused on a limited number of protective factors. Second, the group with A−N− results had a lower mean age than the groups with A+ and N+ results. It is possible that some participants with A−N− results would have A+ or N+ results within a few years. The groups were thus not as distinct as in a non-population based sample in which the groups were age matched. Third, neurodegeneration in AD-susceptible regions is a surrogate of AD tau pathology67 and thus was used in this study. However, future studies will benefit from using tau-PET imaging assessment for the direct assessment of tau burden. Finally, while we found evidence for exceptional aging without ADP, future studies will need to further investigate how the cumulative sum of positive and negative factors across the lifespan may contribute to the risk of amyloidosis and neurodegeneration.

Conclusions

We found that the protective factors that influence amyloid and AD pattern neurodegeneration are different. We found the “exceptional aging” without ADP may be possible with a greater number of protective factors across the lifespan, but this warrants further investigation.

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Article Information

Corresponding Author: Prashanthi Vemuri, PhD, Mayo Clinic and Foundation, 200 First St SW, Rochester, MN 55905 (vemuri.prashanthi@mayo.edu).

Accepted for Publication: February 28, 2017.

Published Online: April 17, 2017. doi:10.1001/jamaneurol.2017.0244

Author Contributions: Dr Vemuri had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Vemuri, Lesnick, Murray.

Acquisition, analysis, or interpretation of data: Vemuri, Knopman, Lesnick, Przybelski, Mielke, Graff-Radford, Roberts, Vassilaki, Lowe, Machulda, Jones, Petersen, Jack.

Drafting of the manuscript: Vemuri, Lesnick.

Critical revision of the manuscript for important intellectual content: Knopman, Lesnick, Przybelski, Mielke, Graff-Radford, Murray, Roberts, Vassilaki, Lowe, Machulda, Jones, Petersen, Jack.

Statistical analysis: Lesnick, Przybelski.

Obtained funding: Vemuri, Roberts, Lowe, Jack.

Administrative, technical, or material support: Vemuri, Murray, Roberts, Lowe, Jones, Petersen, Jack.

Supervision: Vemuri, Graff-Radford, Lowe, Petersen.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by the National Institutes of Health grants R01 NS097495 (principal investigator [PI], Dr Vemuri), R00 AG37573 (PI, Dr Vemuri), U01 AG06786 (PI, Dr Petersen), P50 AG16574/P1 (PI, Dr Vemuri), P50 AG16574 (PI, Dr Petersen), R01 AG034676 (PI, Walter A. Rocca, MD, Mayo Clinic–Rochester), R01 AG11378 (PI, Dr Jack), R01 AG041851 (PIs, Drs Jack and Knopman), and Opus building grant C06 RR018898. This work also received grant support from the Gerald and Henrietta Rauenhorst Foundation, the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Foundation, and the Elsie and Marvin Dekelboum Family Foundation, U.S.A.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We thank all of the study participants and staff in the Mayo Clinic Study of Aging, Mayo Alzheimer’s Disease Research Center, and Aging Dementia Imaging Research laboratory at the Mayo Clinic. We thank Dennis W. Dickson, MD, Mayo Clinic, and Eider M. Arenaza-Urquijo, PhD, University of Barcelona, for their insightful discussions. These individuals were not compensated for their contributions. We also thank the Nelson Family Genomics Research Fund and Gerstner Family Career Development Award.

References
1.
Ingelsson  M, Fukumoto  H, Newell  KL,  et al.  Early Abeta accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain.  Neurology. 2004;62(6):925-931.PubMedGoogle ScholarCrossref
2.
Jack  CR  Jr, Knopman  DS, Jagust  WJ,  et al.  Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers.  Lancet Neurol. 2013;12(2):207-216.PubMedGoogle ScholarCrossref
3.
Scacchi  R, De Bernardini  L, Mantuano  E, Donini  LM, Vilardo  T, Corbo  RM.  Apolipoprotein E (APOE) allele frequencies in late-onset sporadic Alzheimer’s disease (AD), mixed dementia and vascular dementia: lack of association of epsilon 4 allele with AD in Italian octogenarian patients.  Neurosci Lett. 1995;201(3):231-234.PubMedGoogle ScholarCrossref
4.
Sobel  E, Louhija  J, Sulkava  R,  et al.  Lack of association of apolipoprotein E allele epsilon 4 with late-onset Alzheimer’s disease among Finnish centenarians.  Neurology. 1995;45(5):903-907.PubMedGoogle ScholarCrossref
5.
Farrer  LA, Cupples  LA, Haines  JL,  et al; APOE and Alzheimer Disease Meta Analysis Consortium.  Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. a meta-analysis.  JAMA. 1997;278(16):1349-1356.PubMedGoogle ScholarCrossref
6.
Nelson  PT, Head  E, Schmitt  FA,  et al.  Alzheimer’s disease is not “brain aging”: neuropathological, genetic, and epidemiological human studies.  Acta Neuropathol. 2011;121(5):571-587.PubMedGoogle ScholarCrossref
7.
Jack  CR  Jr, Wiste  HJ, Weigand  SD,  et al.  Age-specific population frequencies of cerebral β-amyloidosis and neurodegeneration among people with normal cognitive function aged 50-89 years: a cross-sectional study.  Lancet Neurol. 2014;13(10):997-1005.PubMedGoogle ScholarCrossref
8.
Vemuri  P, Knopman  DS.  The role of cerebrovascular disease when there is concomitant Alzheimer disease.  Biochim Biophys Acta. 2015;1862(5):952-956.PubMedGoogle ScholarCrossref
9.
Jansen  WJ, Ossenkoppele  R, Knol  DL,  et al; Amyloid Biomarker Study Group.  Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis.  JAMA. 2015;313(19):1924-1938.PubMedGoogle ScholarCrossref
10.
Jack  CR  Jr, Wiste  HJ, Weigand  SD,  et al.  Age, sex, and APOE ε4 effects on memory, brain structure, and β-amyloid across the adult life span.  JAMA Neurol. 2015;72(5):511-519.PubMedGoogle ScholarCrossref
11.
Wilson  RS, Boyle  PA, Yu  L, Barnes  LL, Schneider  JA, Bennett  DA.  Life-span cognitive activity, neuropathologic burden, and cognitive aging.  Neurology. 2013;81(4):314-321.PubMedGoogle ScholarCrossref
12.
Vemuri  P, Lesnick  TG, Przybelski  SA,  et al.  Association of lifetime intellectual enrichment with cognitive decline in the older population.  JAMA Neurol. 2014;71(8):1017-1024.PubMedGoogle ScholarCrossref
13.
Luchsinger  JA, Tang  M-X, Stern  Y, Shea  S, Mayeux  R.  Diabetes mellitus and risk of Alzheimer’s disease and dementia with stroke in a multiethnic cohort.  Am J Epidemiol. 2001;154(7):635-641.PubMedGoogle ScholarCrossref
14.
Craft  S.  The role of metabolic disorders in Alzheimer disease and vascular dementia: two roads converged.  Arch Neurol. 2009;66(3):300-305.PubMedGoogle ScholarCrossref
15.
Kivipelto  M, Ngandu  T, Fratiglioni  L,  et al.  Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease.  Arch Neurol. 2005;62(10):1556-1560.PubMedGoogle ScholarCrossref
16.
Zhong  G, Wang  Y, Zhang  Y, Guo  JJ, Zhao  Y.  Smoking is associated with an increased risk of dementia: a meta-analysis of prospective cohort studies with investigation of potential effect modifiers.  PLoS One. 2015;10(3):e0118333.PubMedGoogle ScholarCrossref
17.
Akinyemi  RO, Mukaetova-Ladinska  EB, Attems  J, Ihara  M, Kalaria  RN.  Vascular risk factors and neurodegeneration in ageing related dementias: Alzheimer’s disease and vascular dementia.  Curr Alzheimer Res. 2013;10(6):642-653.PubMedGoogle ScholarCrossref
18.
Enzinger  C, Fazekas  F, Matthews  PM,  et al.  Risk factors for progression of brain atrophy in aging: six-year follow-up of normal subjects.  Neurology. 2005;64(10):1704-1711.PubMedGoogle ScholarCrossref
19.
Roberts  RO, Knopman  DS, Cha  RH,  et al.  Diabetes and elevated hemoglobin A1c levels are associated with brain hypometabolism but not amyloid accumulation.  J Nucl Med. 2014;55(5):759-764.PubMedGoogle ScholarCrossref
20.
Vassilaki  M, Aakre  JA, Cha  RH,  et al.  Multimorbidity and risk of mild cognitive impairment.  J Am Geriatr Soc. 2015;63(9):1783-1790.PubMedGoogle ScholarCrossref
21.
Vassilaki  M, Aakre  JA, Mielke  MM,  et al.  Multimorbidity and neuroimaging biomarkers among cognitively normal persons.  Neurology. 2016;86(22):2077-2084.PubMedGoogle ScholarCrossref
22.
Rocca  WA, Yawn  BP, St Sauver  JL, Grossardt  BR, Melton  LJ  III.  History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population.  Mayo Clin Proc. 2012;87(12):1202-1213.PubMedGoogle ScholarCrossref
23.
St Sauver  JL, Grossardt  BR, Leibson  CL, Yawn  BP, Melton  LJ  III, Rocca  WA.  Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project.  Mayo Clin Proc. 2012;87(2):151-160.PubMedGoogle ScholarCrossref
24.
St Sauver  JL, Grossardt  BR, Yawn  BP,  et al.  Data resource profile: the Rochester Epidemiology Project (REP) medical records-linkage system.  Int J Epidemiol. 2012;41(6):1614-1624.PubMedGoogle ScholarCrossref
25.
St Sauver  JL, Grossardt  BR, Yawn  BP, Melton  LJ  III, Rocca  WA.  Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester epidemiology project.  Am J Epidemiol. 2011;173(9):1059-1068.PubMedGoogle ScholarCrossref
26.
Petersen  RC, Roberts  RO, Knopman  DS,  et al; The Mayo Clinic Study of Aging.  Prevalence of mild cognitive impairment is higher in men.  Neurology. 2010;75(10):889-897.PubMedGoogle ScholarCrossref
27.
Roberts  RO, Geda  YE, Knopman  DS,  et al.  The incidence of MCI differs by subtype and is higher in men: the Mayo Clinic Study of Aging.  Neurology. 2012;78(5):342-351.PubMedGoogle ScholarCrossref
28.
Vemuri  P, Lesnick  TG, Przybelski  SA,  et al.  Effect of lifestyle activities on Alzheimer disease biomarkers and cognition.  Ann Neurol. 2012;72(5):730-738.PubMedGoogle ScholarCrossref
29.
Geda  YE, Topazian  HM, Roberts  LA,  et al.  Engaging in cognitive activities, aging, and mild cognitive impairment: a population-based study.  J Neuropsychiatry Clin Neurosci. 2011;23(2):149-154.PubMedGoogle ScholarCrossref
30.
Roberts  RO, Knopman  DS, Przybelski  SA,  et al.  Association of type 2 diabetes with brain atrophy and cognitive impairment.  Neurology. 2014;82(13):1132-1141.PubMedGoogle ScholarCrossref
31.
Lowe  VJ, Kemp  BJ, Jack  CR  Jr,  et al.  Comparison of 18F-FDG and PiB PET in cognitive impairment.  J Nucl Med. 2009;50(6):878-886.PubMedGoogle ScholarCrossref
32.
Jack  CR  Jr, Lowe  VJ, Senjem  ML,  et al.  11C PiB and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment.  Brain. 2008;131(Pt 3):665-680.PubMedGoogle ScholarCrossref
33.
Senjem  ML, Lowe  V, Kemp  B,  et al. Automated ROI analysis of 11C Pittsburgh compound B images using structural magnetic resonance imaging atlases, Alzheimer's and Dementia. Paper presented at the 2008 Alzheimer's Association International Conference on Alzheimer's Disease.
34.
Lopresti  BJ, Klunk  WE, Mathis  CA,  et al.  Simplified quantification of Pittsburgh Compound B amyloid imaging PET studies: a comparative analysis.  J Nucl Med. 2005;46(12):1959-1972.PubMedGoogle Scholar
35.
Jack  CR  Jr, Wiste  HJ, Weigand  SD,  et al.  Different definitions of neurodegeneration produce similar amyloid/neurodegeneration biomarker group findings.  Brain. 2015;138(Pt 12):3747-3759.PubMedGoogle ScholarCrossref
36.
Villemagne  VL, Burnham  S, Bourgeat  P,  et al; Australian Imaging Biomarkers and Lifestyle (AIBL) Research Group.  Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study.  Lancet Neurol. 2013;12(4):357-367.PubMedGoogle ScholarCrossref
37.
Sojkova  J, Zhou  Y, An  Y,  et al.  Longitudinal patterns of β-amyloid deposition in nondemented older adults.  Arch Neurol. 2011;68(5):644-649.PubMedGoogle ScholarCrossref
38.
Corder  EH, Ghebremedhin  E, Taylor  MG, Thal  DR, Ohm  TG, Braak  H.  The biphasic relationship between regional brain senile plaque and neurofibrillary tangle distributions: modification by age, sex, and APOE polymorphism.  Ann N Y Acad Sci. 2004;1019:24-28.PubMedGoogle ScholarCrossref
39.
Mielke  MM, Vemuri  P, Rocca  WA.  Clinical epidemiology of Alzheimer’s disease: assessing sex and gender differences.  Clin Epidemiol. 2014;6:37-48.PubMedGoogle ScholarCrossref
40.
Fassbender  K, Simons  M, Bergmann  C,  et al.  Simvastatin strongly reduces levels of Alzheimer’s disease beta-amyloid peptides Abeta 42 and Abeta 40 in vitro and in vivo.  Proc Natl Acad Sci U S A. 2001;98(10):5856-5861.PubMedGoogle ScholarCrossref
41.
Simons  M, Keller  P, De Strooper  B, Beyreuther  K, Dotti  CG, Simons  K.  Cholesterol depletion inhibits the generation of beta-amyloid in hippocampal neurons.  Proc Natl Acad Sci U S A. 1998;95(11):6460-6464.PubMedGoogle ScholarCrossref
42.
Reitz  C.  Dyslipidemia and dementia: current epidemiology, genetic evidence, and mechanisms behind the associations.  J Alzheimers Dis. 2012;30(suppl 2):S127-S145.PubMedGoogle Scholar
43.
Kivipelto  M, Helkala  EL, Laakso  MP,  et al.  Apolipoprotein E epsilon4 allele, elevated midlife total cholesterol level, and high midlife systolic blood pressure are independent risk factors for late-life Alzheimer disease.  Ann Intern Med. 2002;137(3):149-155.PubMedGoogle ScholarCrossref
44.
Willette  AA, Johnson  SC, Birdsill  AC,  et al.  Insulin resistance predicts brain amyloid deposition in late middle-aged adults.  Alzheimers Dement. 2015;11(5):504-510.e1, e1.PubMedGoogle ScholarCrossref
45.
Thambisetty  M, Jeffrey Metter  E, Yang  A,  et al.  Glucose intolerance, insulin resistance, and pathological features of Alzheimer disease in the Baltimore Longitudinal Study of Aging.  JAMA Neurol. 2013;70(9):1167-1172.PubMedGoogle ScholarCrossref
46.
Fjell  AM, McEvoy  L, Holland  D, Dale  AM, Walhovd  KB; Alzheimer’s Disease Neuroimaging Initiative.  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.PubMedGoogle ScholarCrossref
47.
Morris  JC, Roe  CM, Xiong  C,  et al.  APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging.  Ann Neurol. 2010;67(1):122-131.PubMedGoogle ScholarCrossref
48.
Vemuri  P, Wiste  HJ, Weigand  SD,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Effect of apolipoprotein E on biomarkers of amyloid load and neuronal pathology in Alzheimer disease.  Ann Neurol. 2010;67(3):308-316.PubMedGoogle Scholar
49.
Braak  H, Braak  E.  Neuropathological stageing of Alzheimer-related changes.  Acta Neuropathol. 1991;82(4):239-259.PubMedGoogle ScholarCrossref
50.
Jack  CR  Jr, Knopman  DS, Weigand  SD,  et al.  An operational approach to NIA-AA criteria for preclinical Alzheimer’s disease.  Ann Neurol. 2012;71(6):765-775.PubMedGoogle ScholarCrossref
51.
Knopman  DS, Jack  CR  Jr, Wiste  HJ,  et al.  Brain injury biomarkers are not dependent on β-amyloid in normal elderly.  Ann Neurol. 2013;73(4):472-480.PubMedGoogle ScholarCrossref
52.
Vemuri  P, Lesnick  TG, Przybelski  SA,  et al.  Effect of intellectual enrichment on AD biomarker trajectories: longitudinal imaging study.  Neurology. 2016;86(12):1128-1135.PubMedGoogle ScholarCrossref
53.
Dosunmu  R, Wu  J, Adwan  L,  et al.  Lifespan profiles of Alzheimer’s disease-associated genes and products in monkeys and mice.  J Alzheimers Dis. 2009;18(1):211-230.PubMedGoogle ScholarCrossref
54.
Elcoroaristizabal Martín  X, Gómez Busto  F, Artaza Artabe  I,  et al.  Genetic profiles of longevity and healthy cognitive aging in nonagenarians from the Basque Country.  Rev Esp Geriatr Gerontol. 2011;46(4):217-222.PubMedGoogle ScholarCrossref
55.
Sebastiani  P, Bae  H, Sun  FX,  et al.  Meta‐analysis of genetic variants associated with human exceptional longevity.  Aging (Albany NY). 2013;5(9):653-661.PubMedGoogle ScholarCrossref
56.
Suri  S, Heise  V, Trachtenberg  AJ, Mackay  CE.  The forgotten APOE allele: a review of the evidence and suggested mechanisms for the protective effect of APOE ɛ2.  Neurosci Biobehav Rev. 2013;37(10 Pt 2):2878-2886.PubMedGoogle ScholarCrossref
57.
Bennett  DA, Arnold  SE, Valenzuela  MJ, Brayne  C, Schneider  JA.  Cognitive and social lifestyle: links with neuropathology and cognition in late life.  Acta Neuropathol. 2014;127(1):137-150.PubMedGoogle ScholarCrossref
58.
Elwood  P, Galante  J, Pickering  J,  et al.  Healthy lifestyles reduce the incidence of chronic diseases and dementia: evidence from the Caerphilly cohort study.  PLoS One. 2013;8(12):e81877.PubMedGoogle ScholarCrossref
59.
Landau  SM, Marks  SM, Mormino  EC,  et al.  Association of lifetime cognitive engagement and low β-amyloid deposition.  Arch Neurol. 2012;69(5):623-629.PubMedGoogle ScholarCrossref
60.
Mattson  MP.  Lifelong brain health is a lifelong challenge: from evolutionary principles to empirical evidence.  Ageing Res Rev. 2015;20:37-45.PubMedGoogle ScholarCrossref
61.
Norton  MC, Dew  J, Smith  H,  et al; Cache County Investigators.  Lifestyle behavior pattern is associated with different levels of risk for incident dementia and Alzheimer’s disease: the Cache County study.  J Am Geriatr Soc. 2012;60(3):405-412.PubMedGoogle ScholarCrossref
62.
Schneider  N, Yvon  C.  A review of multidomain interventions to support healthy cognitive ageing.  J Nutr Health Aging. 2013;17(3):252-257.PubMedGoogle ScholarCrossref
63.
Small  GW, Siddarth  P, Ercoli  LM, Chen  ST, Merrill  DA, Torres-Gil  F.  Healthy behavior and memory self-reports in young, middle-aged, and older adults.  Int Psychogeriatr. 2013;25(6):981-989.PubMedGoogle ScholarCrossref
64.
Khachaturian  AS, Corcoran  CD, Mayer  LS, Zandi  PP, Breitner  JC; Cache County Study Investigators.  Apolipoprotein E ε4 count affects age at onset of Alzheimer disease, but not lifetime susceptibility: the Cache County Study.  Arch Gen Psychiatry. 2004;61(5):518-524.PubMedGoogle ScholarCrossref
65.
Satizabal  CL, Beiser  AS, Chouraki  V, Chêne  G, Dufouil  C, Seshadri  S.  Incidence of dementia over three decades in the Framingham Heart Study.  N Engl J Med. 2016;374(6):523-532.PubMedGoogle ScholarCrossref
66.
Kövari  E, Herrmann  FR, Bouras  C, Gold  G.  Amyloid deposition is decreasing in aging brains: an autopsy study of 1599 older people.  Neurology. 2014;82(4):326-331.PubMedGoogle ScholarCrossref
67.
Whitwell  JL, Josephs  KA, Murray  ME,  et al.  MRI correlates of neurofibrillary tangle pathology at autopsy: a voxel-based morphometry study.  Neurology. 2008;71(10):743-749.PubMedGoogle ScholarCrossref
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