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Figure 1.
Adjusted Odds Ratios for Global Cortex Florbetapir SUVRs >1.2 by Number of Vascular Risk Factors, Midlife Through Late Life
Adjusted Odds Ratios for Global Cortex Florbetapir SUVRs >1.2 by Number of Vascular Risk Factors, Midlife Through Late Life

Adjusted odds ratios (with 95% CIs as error bars) are shown for number of vascular risk factors for visits 1 through 5 for standardized uptake value ratios (SUVRs) >1.2. Models are adjusted for age (at visit 5, 2011-2013), sex, race, education level, and APOE ε4 genotype. Vascular risk factors include body mass index ≥30, current smoking, hypertension, diabetes, and total cholesterol level ≥200 mg/dL.

Figure 2.
Locally Weighted Scatterplot Smoothing Curves Demonstrating Associations Between Midlife Vascular Risk Factors and Global Cortex Florbetapir SUVRs >1.2 by APOE Status
Locally Weighted Scatterplot Smoothing Curves Demonstrating Associations Between Midlife Vascular Risk Factors and Global Cortex Florbetapir SUVRs >1.2 by APOE Status

Curves show smoothed associations between vascular risk factors in midlife (visit 1, 1987-1989) and continuous global cortex florbetapir standardized uptake value ratios (SUVRs) by APOE status (0 APOE ε4 alleles [n = 222] vs 1 or 2 APOE ε4 alleles [n = 100]). Body mass index was calculated as weight in kilograms divided by height in meters squared.

Table 1.  
Participant Characteristics Overall and by Race (N = 322)
Participant Characteristics Overall and by Race (N = 322)
Table 2.  
Adjusted Odds Ratios for the Association of Midlife and Late-Life Vascular Risk Factors With Global Cortex SUVR >1.2 (N = 322)
Adjusted Odds Ratios for the Association of Midlife and Late-Life Vascular Risk Factors With Global Cortex SUVR >1.2 (N = 322)
Table 3.  
Adjusted Odds Ratios for the Association of Midlife and Late-Life Number of Vascular Risk Factors With Global Cortex SUVR >1.2 Overall and Stratified by APOE ε4 Genotype (N = 322)
Adjusted Odds Ratios for the Association of Midlife and Late-Life Number of Vascular Risk Factors With Global Cortex SUVR >1.2 Overall and Stratified by APOE ε4 Genotype (N = 322)
Supplement.

eFigure. Flowchart of Participants Through the ARIC, ARIC Neurocognitive, and ARIC PET Studies

eTable 1. Participant Characteristics Comparing Those Included in Analysis and Those Excluded for Missing Data From Analysis, Among Those Who Underwent a PET Scan and Were Eligible for Analysis (of Black or White Race, Without Dementia, N=343)

eTable 2. Adjusted Odds Ratios (95% Confidence Intervals) for the Association of Midlife and Late-Life Number of Vascular Risk Factors With Elevated SUVR (Global Cortex SUVR>1.2) Overall and Stratified by Race, N=322

eTable 3. Adjusted Odds Ratios (95% Confidence Intervals) for the Association of Midlife and Late-Life Number of Vascular Risk Factors With Elevated SUVR (Global Cortex SUVR>1.2) Overall and Stratified by Sex, N=322

eTable 4. Adjusted Odds Ratios (95% Confidence Intervals) for the Association of Midlife and Late-Life Vascular Risk Factors With Elevated SUVR (Global Cortex SUVR>1.2) Stratified by Cognitive Status (Normal [n=235] Versus Mild Cognitive Impairment [n=87])

eTable 5. Adjusted Odds Ratios (95% Confidence Intervals) for the Association of Midlife and Late-Life Number of Vascular Risk Factors With Elevated SUVR (Global Cortex SUVR>1.2) Stratified by Cognitive Status (Normal [N=235] Versus Mild Cognitive Impairment [n=87])

eTable 6. Adjusted Odds Ratios (95% Confidence Intervals) for the Association of Midlife and Late-Life Number of Vascular Risk Factors With Elevated SUVR (Defined by Different Cut-points) Overall, N=322

eTable 7. Adjusted Odds Ratios (95% Confidence Intervals) for the Association of Midlife and Late-Life Number of Vascular Risk Factors With Elevated SUVR >1.2 by Region of Interest, Overall Population, N=322

1.
Freitag  MH, Peila  R, Masaki  K,  et al.  Midlife pulse pressure and incidence of dementia: the Honolulu-Asia Aging Study.  Stroke. 2006;37(1):33-37.PubMedGoogle ScholarCrossref
2.
Launer  LJ, Masaki  K, Petrovitch  H, Foley  D, Havlik  RJ.  The association between midlife blood pressure levels and late-life cognitive function: the Honolulu-Asia Aging Study.  JAMA. 1995;274(23):1846-1851.PubMedGoogle ScholarCrossref
3.
Alonso  A, Mosley  TH  Jr, Gottesman  RF, Catellier  D, Sharrett  AR, Coresh  J.  Risk of dementia hospitalisation associated with cardiovascular risk factors in midlife and older age: the Atherosclerosis Risk in Communities (ARIC) study.  J Neurol Neurosurg Psychiatry. 2009;80(11):1194-1201.PubMedGoogle ScholarCrossref
4.
Kivipelto  M, Helkala  EL, Laakso  MP,  et al.  Midlife vascular risk factors and Alzheimer’s disease in later life: longitudinal, population based study.  BMJ. 2001;322(7300):1447-1451.PubMedGoogle ScholarCrossref
5.
Ott  A, Stolk  RP, van Harskamp  F, Pols  HA, Hofman  A, Breteler  MM.  Diabetes mellitus and the risk of dementia: the Rotterdam Study.  Neurology. 1999;53(9):1937-1942.PubMedGoogle ScholarCrossref
6.
Hernán  MA, Alonso  A, Logroscino  G.  Cigarette smoking and dementia: potential selection bias in the elderly.  Epidemiology. 2008;19(3):448-450.PubMedGoogle ScholarCrossref
7.
Solomon  A, Kivipelto  M, Wolozin  B, Zhou  J, Whitmer  RA.  Midlife serum cholesterol and increased risk of Alzheimer’s and vascular dementia three decades later.  Dement Geriatr Cogn Disord. 2009;28(1):75-80.PubMedGoogle ScholarCrossref
8.
Corder  EH, Saunders  AM, Strittmatter  WJ,  et al.  Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families.  Science. 1993;261(5123):921-923.PubMedGoogle ScholarCrossref
9.
Zlokovic  BV.  Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders.  Nat Rev Neurosci. 2011;12(12):723-738.PubMedGoogle Scholar
10.
Song  Y, Stampfer  MJ, Liu  S.  Meta-analysis: apolipoprotein E genotypes and risk for coronary heart disease.  Ann Intern Med. 2004;141(2):137-147.PubMedGoogle ScholarCrossref
11.
ARIC Investigators.  The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives.  Am J Epidemiol. 1989;129(4):687-702.PubMedGoogle ScholarCrossref
12.
Knopman  DS, Griswold  ME, Lirette  ST,  et al; ARIC Neurocognitive Investigators.  Vascular imaging abnormalities and cognition: mediation by cortical volume in nondemented individuals: Atherosclerosis Risk in Communities-Neurocognitive Study.  Stroke. 2015;46(2):433-440.PubMedGoogle ScholarCrossref
13.
Knopman  DS, Gottesman  RF, Sharrett  AR,  et al.  Mild cognitive impairment and dementia prevalence: the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS).  Alzheimers Dement (Amst). 2016;2:1-11. PubMedGoogle Scholar
14.
Gottesman  RF, Schneider  ALC, Zhou  Y,  et al.  The ARIC-PET amyloid imaging study: brain amyloid differences by age, race, sex, and APOE Neurology. 2016;87(5):473-480.PubMedGoogle ScholarCrossref
15.
La Joie  R, Perrotin  A, Barré  L,  et al.  Region-specific hierarchy between atrophy, hypometabolism, and β-amyloid (Aβ) load in Alzheimer’s disease dementia.  J Neurosci. 2012;32(46):16265-16273.PubMedGoogle ScholarCrossref
16.
Saint-Aubert  L, Nemmi  F, Péran  P,  et al.  Comparison between PET template-based method and MRI-based method for cortical quantification of florbetapir (AV-45) uptake in vivo.  Eur J Nucl Med Mol Imaging. 2014;41(5):836-843.PubMedGoogle ScholarCrossref
17.
Wong  DF, Rosenberg  PB, Zhou  Y,  et al.  In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (florbetapir [corrected] F 18).  J Nucl Med. 2010;51(6):913-920.PubMedGoogle ScholarCrossref
18.
Landau  SM, Mintun  MA, Joshi  AD,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Amyloid deposition, hypometabolism, and longitudinal cognitive decline.  Ann Neurol. 2012;72(4):578-586.PubMedGoogle ScholarCrossref
19.
Joshi  AD, Pontecorvo  MJ, Clark  CM,  et al; Florbetapir F 18 Study Investigators.  Performance characteristics of amyloid PET with florbetapir F 18 in patients with Alzheimer’s disease and cognitively normal subjects.  J Nucl Med. 2012;53(3):378-384.PubMedGoogle ScholarCrossref
20.
Plassman  BL, Langa  KM, Fisher  GG,  et al.  Prevalence of dementia in the United States: the Aging, Demographics, and Memory Study.  Neuroepidemiology. 2007;29(1-2):125-132.PubMedGoogle ScholarCrossref
21.
Chambless  LE, Heiss  G, Shahar  E, Earp  MJ, Toole  J.  Prediction of ischemic stroke risk in the Atherosclerosis Risk in Communities Study.  Am J Epidemiol. 2004;160(3):259-269.PubMedGoogle ScholarCrossref
22.
Sharrett  AR, Patsch  W, Sorlie  PD, Heiss  G, Bond  MG, Davis  CE.  Associations of lipoprotein cholesterols, apolipoproteins A-I and B, and triglycerides with carotid atherosclerosis and coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) Study.  Arterioscler Thromb. 1994;14(7):1098-1104.PubMedGoogle ScholarCrossref
23.
Siedel  J, Hägele  EO, Ziegenhorn  J, Wahlefeld  AW.  Reagent for the enzymatic determination of serum total cholesterol with improved lipolytic efficiency.  Clin Chem. 1983;29(6):1075-1080.PubMedGoogle Scholar
24.
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
25.
Moran  C, Beare  R, Phan  TG, Bruce  DG, Callisaya  ML, Srikanth  V; Alzheimer’s Disease Neuroimaging Initiative.  Type 2 diabetes mellitus and biomarkers of neurodegeneration.  Neurology. 2015;85(13):1123-1130.PubMedGoogle ScholarCrossref
26.
Reed  BR, Marchant  NL, Jagust  WJ, DeCarli  CC, Mack  W, Chui  HC.  Coronary risk correlates with cerebral amyloid deposition.  Neurobiol Aging. 2012;33(9):1979-1987.PubMedGoogle ScholarCrossref
27.
Cifuentes  D, Poittevin  M, Dere  E,  et al.  Hypertension accelerates the progression of Alzheimer-like pathology in a mouse model of the disease.  Hypertension. 2015;65(1):218-224.PubMedGoogle ScholarCrossref
28.
Faraco  G, Park  L, Zhou  P,  et al.  Hypertension enhances Aβ-induced neurovascular dysfunction, promotes β-secretase activity, and leads to amyloidogenic processing of APP.  J Cereb Blood Flow Metab. 2016;36(1):241-252.PubMedGoogle ScholarCrossref
29.
Rodrigue  KM, Rieck  JR, Kennedy  KM, Devous  MD  Sr, Diaz-Arrastia  R, Park  DC.  Risk factors for β-amyloid deposition in healthy aging: vascular and genetic effects.  JAMA Neurol. 2013;70(5):600-606.PubMedGoogle ScholarCrossref
30.
Malek-Ahmadi  M, Beach  T, Obradov  A,  et al.  Increased Alzheimer’s disease neuropathology is associated with type 2 diabetes and ApoE ε.4 carrier status.  Curr Alzheimer Res. 2013;10(6):654-659.PubMedGoogle ScholarCrossref
31.
Hohman  TJ, Samuels  LR, Liu  D,  et al; Alzheimer’s Neuroimaging Initiative.  Stroke risk interacts with Alzheimer’s disease biomarkers on brain aging outcomes.  Neurobiol Aging. 2015;36(9):2501-2508.PubMedGoogle ScholarCrossref
32.
Vemuri  P, Lesnick  TG, Przybelski  SA,  et al.  Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly.  Brain. 2015;138(pt 3):761-771.PubMedGoogle ScholarCrossref
33.
Marchant  NL, Reed  BR, Sanossian  N,  et al.  The aging brain and cognition: contribution of vascular injury and aβ to mild cognitive dysfunction.  JAMA Neurol. 2013;70(4):488-495.PubMedGoogle ScholarCrossref
34.
Gutierrez  J, Honig  L, Elkind  MSV,  et al.  Brain arterial aging and its relationship to Alzheimer dementia.  Neurology. 2016;86(16):1507-1515.PubMedGoogle ScholarCrossref
35.
Gupta  A, Iadecola  C.  Impaired Aβ clearance: a potential link between atherosclerosis and Alzheimer’s disease.  Front Aging Neurosci. 2015;7(115):115.PubMedGoogle Scholar
36.
Marín-Padilla  M, Knopman  DS.  Developmental aspects of the intracerebral microvasculature and perivascular spaces: insights into brain response to late-life diseases.  J Neuropathol Exp Neurol. 2011;70(12):1060-1069.PubMedGoogle ScholarCrossref
37.
Selvin  E, Parrinello  CM, Sacks  DB, Coresh  J.  Trends in prevalence and control of diabetes in the United States, 1988-1994 and 1999-2010.  Ann Intern Med. 2014;160(8):517-525.PubMedGoogle ScholarCrossref
38.
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
Original Investigation
April 11, 2017

Association Between Midlife Vascular Risk Factors and Estimated Brain Amyloid Deposition

Author Affiliations
  • 1Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • 2Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
  • 3Department of Radiology, Section of High Resolution Brain PET Imaging, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • 4Department of Radiology, University of Mississippi Medical Center, Jackson
  • 5Hagerstown Imaging, Hagerstown, Maryland
  • 6Department of Neurology, Mayo Clinic, Rochester, Minnesota
  • 7Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina
  • 8Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
  • 9Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • 10Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • 11Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
  • 12Department of Medicine, University of Mississippi Medical Center, Jackson
JAMA. 2017;317(14):1443-1450. doi:10.1001/jama.2017.3090
Key Points

Question  Are midlife vascular risk factors associated with late-life brain amyloid deposition?

Findings  In a prospective cohort study of 346 members of the community-based Atherosclerosis Risk in Communities (ARIC)–PET cohort without dementia, having 2 or more midlife vascular risk factors compared with none was significantly associated with elevated amyloid deposition in the brain (61.2% vs 30.8%). There was no significant association for late-life risk factors.

Meaning  These findings are consistent with a role of vascular disease in the development of Alzheimer disease.

Abstract

Importance  Midlife vascular risk factors have been associated with late-life dementia. Whether these risk factors directly contribute to brain amyloid deposition is less well understood.

Objective  To determine if midlife vascular risk factors are associated with late-life brain amyloid deposition, measured using florbetapir positron emission tomography (PET).

Design, Setting, and Participants  The Atherosclerosis Risk in Communities (ARIC)–PET Amyloid Imaging Study, a prospective cohort study among 346 participants without dementia in 3 US communities (Washington County, Maryland; Forsyth County, North Carolina; and Jackson, Mississippi) who have been evaluated for vascular risk factors and markers since 1987-1989 with florbetapir PET scans in 2011-2013. Positron emission tomography image analysis was completed in 2015.

Exposures  Vascular risk factors at ARIC baseline (age 45-64 years; risk factors included body mass index ≥30, current smoking, hypertension, diabetes, and total cholesterol ≥200 mg/dL) were evaluated in multivariable models including age, sex, race, APOE genotype, and educational level.

Main Outcomes and Measures  Standardized uptake value ratios (SUVRs) were calculated from PET scans and a mean global cortical SUVR was calculated. Elevated florbetapir (defined as a SUVR >1.2) was the dependent variable.

Results  Among 322 participants without dementia and with nonmissing midlife vascular risk factors at baseline (mean age, 52 years; 58% female; 43% black), the SUVR (elevated in 164 [50.9%] participants) was measured more than 20 years later (median follow-up, 23.5 years; interquartile range, 23.0-24.3 years) when participants were between 67 and 88 (mean, 76) years old. Elevated body mass index in midlife was associated with elevated SUVR (odds ratio [OR], 2.06; 95% CI, 1.16-3.65). At baseline, 65 participants had no vascular risk factors, 123 had 1, and 134 had 2 or more; a higher number of midlife risk factors was associated with elevated amyloid SUVR at follow-up (30.8% [n = 20], 50.4% [n = 62], and 61.2% [n = 82], respectively). In adjusted models, compared with 0 midlife vascular risk factors, the OR for elevated SUVR associated with 1 vascular risk factor was 1.88 (95% CI, 0.95-3.72) and for 2 or more vascular risk factors was 2.88 (95% CI, 1.46-5.69). No significant race × risk factor interactions were found. Late-life vascular risk factors were not associated with late-life brain amyloid deposition (for ≥2 late-life vascular risk factors vs 0: OR, 1.66; 95% CI, 0.75-3.69).

Conclusions and Relevance  An increasing number of midlife vascular risk factors was significantly associated with elevated amyloid SUVR; this association was not significant for late-life risk factors. These findings are consistent with a role of vascular disease in the development of Alzheimer disease.

Introduction

Increasing evidence supports a role of vascular risk factors and markers in the development and etiology of Alzheimer disease (AD). Most major vascular risk factors, including hypertension,1-4 diabetes,5 smoking,3,6 and hypercholesterolemia,4,7 particularly when measured in midlife, have been associated with risk of dementia generally and AD specifically. Whether these risk factors directly increase the neurodegeneration specifically associated with AD (such as through increasing amyloid deposition) or lead to other cerebral changes that, in conjunction with ongoing neurodegeneration, might worsen cognitive performance is not yet known.

The role of the APOE ε4 allele as a genetic risk factor for AD is well established,8 but its role as a modifier of the relationship between vascular disease and AD is less well understood. APOE ε4 carriage in combination with vascular disease may work synergistically to increase risk of AD,9 with worse cognitive outcomes in persons with both increased genetic and vascular risks. These relationships are further complicated by the direct association between APOE and vascular disease: the ε4 allele is proatherogenic,10 so evaluation of the interactive effects of APOE and vascular risk factors on AD neuropathology must also consider the independent contribution of APOE to vascular disease.

The availability of imaging biomarkers for brain amyloid allows the study of individuals before the development of dementia and thereby allows consideration of the relative contributions of vascular disease and amyloid to cognition, as well as the contribution of vascular disease to amyloid deposition. In the current study, vascular risk factors were collected for more than 25 years in participants from the Atherosclerosis Risk in Communities (ARIC) Study, with brain amyloid positron emission tomography (PET) imaging obtained in late life, to evaluate the associations among vascular risk factors, APOE genotype, and brain amyloid deposition.

Methods

The ARIC-PET Study is an ancillary to the ongoing ARIC-Neurocognitive Study, which itself is a major ancillary of the ARIC study. The study was approved by each institution’s institutional review board. All participants provided written informed consent.

Participant Inclusion

Participants underwent a baseline visit in 1987-1989, when 15 792 individuals were recruited from 4 US communities,11 with 4 additional in-person visits, most recently in 2011-2013 and with annual/semiannual telephone calls throughout the study duration. Each visit (including baseline) has included in-person assessment of vascular risk factors, with a shorter cognitive evaluation at the second and fourth visits and a more extensive neuropsychological assessment including informant interview at visit 5 (2011-2013).

Nearly 2000 participants without contraindication to magnetic resonance imaging (MRI) were invited for brain MRI based on any of the following12: (1) prior research brain MRI as part of the cohort; (2) low cognitive scores or cognitive decline at visit 5; and (3) a random age-stratified sample of cognitively healthy participants. This study recruited from this subset, with the additional exclusion criteria of heavy current alcohol use, renal dysfunction, prolonged (>450 milliseconds) QTc interval, or neuropsychological results consistent with dementia. Methods used to adjudicate mild cognitive impairment and dementia research diagnoses are described elsewhere,13 but for the purposes of this study, participants were excluded if they had an already adjudicated research diagnosis of dementia, a Clinical Dementia Rating sum-of-boxes score of greater than 3, a Functional Activities Questionnaire score of greater than 5, or a Mini-Mental State Examination score of less than 19 (among black participants) or less than 21 (among white participants).13

Brain MRI and PET

Brain MRI scans, obtained as research studies at a 3T MRI facility near each field center, were read centrally at the Mayo Clinic.12 Positron emission tomographic images were co-registered using magnetization prepared rapid acquisition gradient-echo sequences.

The details of PET image processing and coregistration with MRI, carried out at the Johns Hopkins University reading center, were described previously.14 A global cortical measure of florbetapir uptake was used as a weighted average (based on region of interest size) of the orbitofrontal, prefrontal, and superior frontal cortices; the lateral temporal, parietal, and occipital lobes; the precuneus, the anterior cingulate, and the posterior cingulate. An automated region for cerebellum gray was used as a reference.14-17 Standardized uptake value ratios (SUVRs) were dichotomized at the sample median of an SUVR of greater than 1.2,14 although other cut points (1.1118 and 1.1019) were explored in sensitivity analyses. Positron emission tomographic scans were obtained within 1 year of MRI scans, ideally within 6 months.

Positron emission tomography image analysis took place from 2012-2015, and statistical analysis for the current study was completed in 2016.

Vascular Risk Factors and Other Covariates

Vascular risk factors were evaluated at all in-person visits. Analyses in this study focused on risk factor status in midlife (visit 1 at age 45-64 years) as well as concurrent with PET (visit 5 at age 67-88 years). For demographic factors, date of birth, queried at visit 1, was used to calculate age at each visit, and sex and educational level were self-reported in visit 1. Race was self-selected from several fixed categories (Asian, black, American Indian/Alaskan Indian, or white); race was evaluated given previously reported differences in dementia rates20 and baseline differences in amyloid SUVR in this study.14 Blood pressure was measured 2 to 3 times per visit; hypertension was present if the mean of the last 2 measurements was greater than 140 mm Hg (systolic) or greater than 90 mm Hg (diastolic) or if the participant was taking an antihypertensive medication at that visit. Diabetes was defined as a fasting glucose level of at least 126 mg/dL, a nonfasting glucose of at least 200 mg/dL, self-report of physician-diagnosed diabetes, or use of oral diabetes medications or insulin. Estimated 10-year stroke risk, calculated at visits 1 and 5, was based on a previously published algorithm for stroke risk,21 and smoking history was self-reported as current/former/never. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Fasting lipids were measured at each visit22; plasma total cholesterol was measured using enzymatic methods.23APOE was genotyped previously and defined based on the number of ε4 alleles (0,1, or 2).

To evaluate cumulative burden of vascular risk factors, the number of vascular risk factors present in midlife (visit 1) and in late life (visit 5) was tallied, up to 5 maximum, including current smoking, hypertension, diabetes, obesity (BMI ≥30), and elevated total cholesterol (≥200 mg/dL). Because very few individuals had 3 or 4 risk factors and none who were included in our study had 5 risk factors, the number of midlife risk factors was categorized as 0, 1, or 2 or more.

Statistical Analysis

Stata SE, version 13 for Macintosh (Stata Corp), was used for all analyses. Group comparisons were evaluated visually and with descriptive statistics. Florbetapir uptake was evaluated as a dichotomous (SUVR >1.2) measure in logistic regression models; additional cut points were evaluated in sensitivity analyses. Because of the highly skewed florbetapir SUVR variable, regression analyses did not evaluate continuous florbetapir uptake. Multivariable logistic regression models evaluating the described vascular risk factors in the same model included age, sex, race, educational level, and APOE status; model goodness of fit was confirmed with the Hosmer-Lemeshow test (P = .27). Models had low residuals and were without evidence of high leverage points. The same analyses were then repeated using a single variable for the number of vascular risk factors (midlife or late life in separate models). Based on a priori hypotheses, models were then stratified by race, sex, and APOE status (1 or 2 ε4 alleles vs 0 ε4 alleles), with formal tests for interaction by race, sex, and APOE status. P<.05 was considered statistically significant; testing was 2-sided.

Sensitivity analyses stratified the sample by cognitive status, with separate analysis of individuals with normal cognition or with mild cognitive impairment, in addition to the evaluation of other florbetapir cut points. In addition, multiple imputation was used as a sensitivity analysis for the 21 individuals excluded because of missing covariate data. Amyloid deposition in distinct regions of interest was also considered.

Results

Participants were a mean age of 52 years at the time of midlife vascular risk factor assessment and 76 years at the time of PET imaging; median follow-up was 23.5 years (interquartile range, 23.0-24.3 years). Fifty-seven percent of the sample was white and 43% black (Table 1). Rates of hypertension and diabetes increased from midlife to late life (Table 1), as did BMI and the stroke risk score, and lipid levels and smoking rates decreased. No participant had 5 vascular risk factors at either visit 1 or 5, and the number of risk factors was greater at visit 5 than at visit 1. At visit 1, 65 participants (20%) had no risk factors, 123 (38%) had 1 risk factor, and 134 (42%) had 2 or more risk factors. Of the 346 participants who underwent florbetapir PET imaging, 24 were excluded, leaving 322 participants for analysis (eFigure and eTable 1 in the Supplement). Defined as positive above the sample median, 164 (50.9%) of the cohort had an elevated amyloid SUVR.

Evaluation of Vascular Risk Factors and Late-Life Brain Amyloid

In the overall sample, elevated midlife BMI was the only vascular risk factor with a statistically significant association with elevated late-life brain amyloid (odds ratio [OR], 2.06; 95% CI, 1.16-3.65) (Table 2). Risk factor–amyloid relationships did not differ by race, as indicated by non–statistical significance in interaction terms (P values between .054 and .93 for each midlife individual risk factor).

Cumulative Number of Vascular Risk Factors

A higher number of vascular risk factors in midlife, but not in late life, was associated with elevated brain amyloid. Thirty-one percent of individuals with 0 vascular risk factors in midlife had elevated amyloid in late life compared with 61% of individuals with at least 2 vascular risk factors in midlife who had elevated amyloid in late life (difference in proportions, 30.4%; 95% CI, 16.4%-44.3%). When evaluated continuously, each additional midlife vascular risk factor was associated with an increased odds of elevated SUVR (OR, 1.41; 95% CI, 1.09-1.83); similar results were seen when numbers of risk factors were categorized, and each category was compared with having 0 risk factors (Table 3). Having more risk factors was associated with higher odds of elevated SUVR, with strongest associations in midlife and with decreasing ORs associated with increasing numbers of risk factors as they were considered at older ages (Figure 1). Fewer individuals had 0 vascular risk factors in late life, but amyloid levels were elevated in 37% of this group; amyloid positivity was more frequent among people with 2 or more vascular risk factors in late life (compared with no risk factors, difference in proportions, 18.5%; 95% CI, 1.1%-53.1%) (Table 3).

The observed association between number of risk factors and elevated odds of amyloid was found only in white participants (eTable 2 in the Supplement), with an increased odds of elevated amyloid per additional vascular risk factor in midlife (OR, 1.66; 95% CI, 1.15-2.39) in white participants compared with a smaller and nonsignificant increase in black participants per additional risk factor (OR, 1.26; 95% CI, 0.85-1.88). However, the statistical test for interaction of race by number of risk factors was not significant (P = .42). Similarly, a significant association was found in men (OR, 6.77; 95% CI, 2.37-19.35) but not in women (OR, 1.71; 95% CI, 0.66-4.45), with imprecise estimates and non–statistically significant tests for interaction (P=.36) (eTable 3 in the Supplement).

Association of APOE With Vascular Risk Factor–Brain Amyloid Associations

No interaction P values for individual vascular risk factors by APOE status reached statistical significance. Odds of elevated florbetapir in participants with elevated BMI were statistically similar regardless of APOE status (OR, 2.48 [95% CI, 1.26-4.88] in APOE ε4 carriers and OR, 1.11 [95% CI, 0.85-1.44] in noncarriers; interaction P=.50). All 5 individuals with diabetes in midlife and who had an APOE ε4 allele who were included in the study had elevated SUVR, but continuous fasting glucose in visit 1 among the 299 participants who fasted was not significantly associated with florbetapir uptake in people with an ε4 allele (OR, 1.96; 95% CI, 0.74-5.19) or in those without (OR, 0.89; 95% CI, 0.61-1.32; interaction P=.12).

When continuous SUVR was evaluated visually, increasing numbers of ε4 alleles did appear to be associated with higher florbetapir in the setting of increasing vascular risk (Figure 2).

Stratified analyses by APOE status showed no significant interaction between APOE and number of risk factors. There was no statistical difference in the odds of elevated amyloid in association with a larger number of midlife risk factors among ε4 carriers (OR, 2.46; 95% CI, 1.37-4.42) vs noncarriers (OR, 1.19; 95% CI, 0.87-1.61), with similar results when number of risk factors was treated categorically (Table 3). Increasing numbers of risk factors in late life were not associated with increased amyloid regardless of APOE status.

Sensitivity Analysis

When analyses were repeated comparing individuals by cognitive status, estimates for individual risk factors were not statistically significant and were imprecise (eTable 4 in the Supplement). Associations between number of vascular risk factors and amyloid positivity remained statistically significant only in the 87 individuals with mild cognitive impairment (eTable 5 in the Supplement).

Results were similar with SUVR cut points of 1.11 and 1.10 (eTable 6 in the Supplement). Analysis of separate regions of interest yielded similar results as the primary analysis.

Imputing missing covariate data (for the total N = 343) resulted in very similar results for the risk factor analysis (for 1 midlife vascular risk factor, OR, 1.81 [95% CI, 0.92-3.56], and for 2 midlife vascular risk factors, OR, 2.71 [95% CI, 1.39-5.39], compared with 0 midlife risk factors). Results were similar for evaluation of late-life vascular risk factor status when imputed data were included.

Discussion

In this study of brain florbetapir uptake in individuals without dementia from 3 US communities, a cumulative number of midlife vascular risk factors was associated with elevated brain amyloid. Relationships between vascular risk factors and brain amyloid did not differ by race, despite previous findings in this study that amyloid distribution differs by race.14 Furthermore, these results were not supportive of a significant difference in association among people who were or were not carriers of an APOE ε4 allele. These data support the concept that midlife, but not late-life, exposure to these vascular risk factors is important for amyloid deposition.

Previous studies have demonstrated inconsistent results evaluating associations between vascular risk factors and brain amyloid. In multiple studies, diabetes has not been associated with elevated amyloid measured by Pittsburgh compound B (PiB) PET,24,25 whereas others have reported elevated amyloid in association with elevated vascular risk (elevated Framingham coronary risk score was associated with elevated PiB independent of any APOE effect26). Animal data support a direct effect of vascular disease, especially hypertension, on the deposition of brain amyloid.27,28

The concept that vascular risk factors contribute to brain amyloid particularly—or even only—in the setting of an additional risk factor, namely APOE status, has been supported by previous studies. In this study, there was no statistical evidence for a difference by APOE status, but the study may have been underpowered to detect this interaction. In 118 cognitively healthy adults, hypertension and APOE interacted in the prediction of risk of florbetapir amyloid uptake,29 and in an autopsy study, diabetes was associated with AD neuropathology only among APOE ε4 carriers.30 Further evaluation of these relative pathologies on cognitive performance is needed because it is likely that vascular risk factors, APOE, AD neurodegeneration, and amyloid deposition all play a role, some of which may affect each other, in cognition. The Framingham stroke risk score has been most strongly associated with cognition in individuals without increased AD neuropathology on autopsy,31 and in the Alzheimer Disease Neuroimaging Initiative, amyloid and vascular pathology acted additively and not synergistically, and independently of one another, in increasing association with cognitive decline.32

This study focused on vascular risk factors, rather than clinical or subclinical vascular disease itself. Subclinical vascular disease might mediate the association of cumulative vascular risk factors with amyloid deposition. Although some authors have failed to find an association between radiographic vascular disease on MRI and PiB,33 arteriolar disease on autopsy has been associated with worsening AD neuropathology, independent of infarctions,34 suggesting that there may be aspects of brain vascular disease that, if adequately measured, might be important in increasing risk of amyloid deposition. An alternative hypothesis, supported by the findings of this study, is that vascular disease, particularly at the arteriolar level, might reduce vascular clearance of amyloid.35,36

Limitations

This study has several limitations. The lack of an association with individual vascular risk factors may not reflect a true null relationship but instead may reflect inadequate sample size; the same may be true for the lack of an association in black participants (inadequate power to detect these associations or to detect interactions by race) or in noncarriers of an APOE ε4 allele. This relatively modest sample size makes it difficult to make conclusions about any true interaction by APOE status. By excluding individuals with dementia, it is likely that ARIC participants seen in 1987-1989 with the highest amounts of both vascular disease and amyloid deposition were excluded from these analyses because they developed dementia or died prior to ARIC-PET. Thus, in participants reaching late life with relatively healthy cognition, it might be expected that the opposite relationship would have been observed, namely that ARIC survivors with multiple midlife risk factors would have had less brain amyloid. These APOE findings would argue against this concern about selection bias, however, because individuals with all 3 risk factors for dementia (ε4 carriage, vascular risk, and elevated amyloid deposition) survived to participate in this study. Furthermore, analysis by cognitive status demonstrated that higher vascular risk in midlife was associated with elevated amyloid among individuals with mild cognitive impairment at the time of amyloid imaging.

Relatively few people with very high vascular risk in midlife survived to the PET visit or met entry criteria for the study, supporting the likelihood that the true strength of association of vascular risk with amyloid risk may have been underestimated as a result of this survival bias. Furthermore, although representative of the community, this study has a higher prevalence of diabetes in late life than has otherwise been described,37 with very high rates of hypertension; the frequency of these risk factors might dilute their association in late life. In addition, the chronological distinction between midlife and late life is not sharp, so some participants at visits 2 and 3, when an association (although decreased) between number of risk factors and amyloid positivity was still noted, were in their 60s. In addition, prevalence of amyloid positivity is reported to be less than 20% in persons with healthy cognition in the age range of our cohort,38 but without PET at the midlife visit, it is not certain that none of our participants were amyloid positive at the time of their first visit.

Conclusions

An increasing number of midlife vascular risk factors was significantly associated with elevated amyloid SUVR; this association was not significant for late-life risk factors. These findings are consistent with a role of vascular disease in the development of AD.

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Article Information

Corresponding Author: Rebecca F. Gottesman, MD, PhD, Department of Neurology, Johns Hopkins University School of Medicine, Phipps 446D, 600 N Wolfe St, Baltimore, MD 21287 (rgottesm@jhmi.edu).

Author Contributions: Dr Gottesman, as principal investigator, had full access to all 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: Gottesman, Schneider, Zhou, Green, Wong.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Gottesman, Zhou, Rahmim, Wong.

Critical revision of the manuscript for important intellectual content: Schneider, Coresh, Green, Gupta, Knopman, Mintz, Sharrett, Wagenknecht, Wong, Mosley.

Statistical analysis: Gottesman, Schneider, Zhou.

Obtained funding: Gottesman, Wagenknecht.

Administrative, technical, or material support: Gottesman, Green, Gupta, Mintz, Rahmim, Wagenknecht, Wong.

Study supervision: Gottesman, Green, Mintz, Wagenknecht, Mosley.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Gottesman reports serving as associate editor for Neurology and receiving research support from the National Institutes of Health (NIH). Dr Knopman reports previously serving as deputy editor for Neurology; serving on a data safety monitoring board for Lundbeck Pharmaceuticals and for the DIAN study; being an investigator in clinical trials sponsored by TauRX Pharmaceuticals, Lilly Pharmaceuticals, and the Alzheimer’s Disease Cooperative Study; and receiving research support from the NIH. Dr Mintz reports previously receiving an honorarium as a reader for Avid on florbetapir studies (through 2013). Dr Wong reports having had a number of contracts with Avid and Lilly administered through Johns Hopkins University and serving as a consultant during the early stages of florbetapir development and receiving and working with this radiotracer as part of research collaborations including ADNI and Biocard (NIH). He is working with all 3 US Food and Drug Administration–approved amyloid PET radiopharmaceutical companies on the Centers for Medicare & Medicaid/Medicare–funded IDEAs trial. No other disclosures were reported.

Funding/Support: ARIC is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute (NHLBI) contracts HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C. Neurocognitive data are collected with funding from grants U01 HL096812, HL096814, HL096899, HL096902, and HL096917 and the National Institute of Neurological Disorders and Stroke, with previous brain MRI examinations funded by grant R01-HL70825 from the NHLBI. The ARIC-PET study is funded by the National Institute on Aging (grant R01AG040282). Avid Radiopharmaceuticals provided the florbetapir isotope for the study.

Role of the Funder/Sponsor: Avid Radiopharmaceuticals had no role in the study design or interpretation of results. Neither Avid nor the primary sponsor (National Institute on Aging/NIH) had any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Additional Contributions: We thank the staff and participants of the ARIC study for their important contributions. We thank Andrew Crabb, MS, Johns Hopkins University, for oversight of the central data transfer and research PACS. Mr Crabb received grant funding for his role in the study.

References
1.
Freitag  MH, Peila  R, Masaki  K,  et al.  Midlife pulse pressure and incidence of dementia: the Honolulu-Asia Aging Study.  Stroke. 2006;37(1):33-37.PubMedGoogle ScholarCrossref
2.
Launer  LJ, Masaki  K, Petrovitch  H, Foley  D, Havlik  RJ.  The association between midlife blood pressure levels and late-life cognitive function: the Honolulu-Asia Aging Study.  JAMA. 1995;274(23):1846-1851.PubMedGoogle ScholarCrossref
3.
Alonso  A, Mosley  TH  Jr, Gottesman  RF, Catellier  D, Sharrett  AR, Coresh  J.  Risk of dementia hospitalisation associated with cardiovascular risk factors in midlife and older age: the Atherosclerosis Risk in Communities (ARIC) study.  J Neurol Neurosurg Psychiatry. 2009;80(11):1194-1201.PubMedGoogle ScholarCrossref
4.
Kivipelto  M, Helkala  EL, Laakso  MP,  et al.  Midlife vascular risk factors and Alzheimer’s disease in later life: longitudinal, population based study.  BMJ. 2001;322(7300):1447-1451.PubMedGoogle ScholarCrossref
5.
Ott  A, Stolk  RP, van Harskamp  F, Pols  HA, Hofman  A, Breteler  MM.  Diabetes mellitus and the risk of dementia: the Rotterdam Study.  Neurology. 1999;53(9):1937-1942.PubMedGoogle ScholarCrossref
6.
Hernán  MA, Alonso  A, Logroscino  G.  Cigarette smoking and dementia: potential selection bias in the elderly.  Epidemiology. 2008;19(3):448-450.PubMedGoogle ScholarCrossref
7.
Solomon  A, Kivipelto  M, Wolozin  B, Zhou  J, Whitmer  RA.  Midlife serum cholesterol and increased risk of Alzheimer’s and vascular dementia three decades later.  Dement Geriatr Cogn Disord. 2009;28(1):75-80.PubMedGoogle ScholarCrossref
8.
Corder  EH, Saunders  AM, Strittmatter  WJ,  et al.  Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families.  Science. 1993;261(5123):921-923.PubMedGoogle ScholarCrossref
9.
Zlokovic  BV.  Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders.  Nat Rev Neurosci. 2011;12(12):723-738.PubMedGoogle Scholar
10.
Song  Y, Stampfer  MJ, Liu  S.  Meta-analysis: apolipoprotein E genotypes and risk for coronary heart disease.  Ann Intern Med. 2004;141(2):137-147.PubMedGoogle ScholarCrossref
11.
ARIC Investigators.  The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives.  Am J Epidemiol. 1989;129(4):687-702.PubMedGoogle ScholarCrossref
12.
Knopman  DS, Griswold  ME, Lirette  ST,  et al; ARIC Neurocognitive Investigators.  Vascular imaging abnormalities and cognition: mediation by cortical volume in nondemented individuals: Atherosclerosis Risk in Communities-Neurocognitive Study.  Stroke. 2015;46(2):433-440.PubMedGoogle ScholarCrossref
13.
Knopman  DS, Gottesman  RF, Sharrett  AR,  et al.  Mild cognitive impairment and dementia prevalence: the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS).  Alzheimers Dement (Amst). 2016;2:1-11. PubMedGoogle Scholar
14.
Gottesman  RF, Schneider  ALC, Zhou  Y,  et al.  The ARIC-PET amyloid imaging study: brain amyloid differences by age, race, sex, and APOE Neurology. 2016;87(5):473-480.PubMedGoogle ScholarCrossref
15.
La Joie  R, Perrotin  A, Barré  L,  et al.  Region-specific hierarchy between atrophy, hypometabolism, and β-amyloid (Aβ) load in Alzheimer’s disease dementia.  J Neurosci. 2012;32(46):16265-16273.PubMedGoogle ScholarCrossref
16.
Saint-Aubert  L, Nemmi  F, Péran  P,  et al.  Comparison between PET template-based method and MRI-based method for cortical quantification of florbetapir (AV-45) uptake in vivo.  Eur J Nucl Med Mol Imaging. 2014;41(5):836-843.PubMedGoogle ScholarCrossref
17.
Wong  DF, Rosenberg  PB, Zhou  Y,  et al.  In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (florbetapir [corrected] F 18).  J Nucl Med. 2010;51(6):913-920.PubMedGoogle ScholarCrossref
18.
Landau  SM, Mintun  MA, Joshi  AD,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Amyloid deposition, hypometabolism, and longitudinal cognitive decline.  Ann Neurol. 2012;72(4):578-586.PubMedGoogle ScholarCrossref
19.
Joshi  AD, Pontecorvo  MJ, Clark  CM,  et al; Florbetapir F 18 Study Investigators.  Performance characteristics of amyloid PET with florbetapir F 18 in patients with Alzheimer’s disease and cognitively normal subjects.  J Nucl Med. 2012;53(3):378-384.PubMedGoogle ScholarCrossref
20.
Plassman  BL, Langa  KM, Fisher  GG,  et al.  Prevalence of dementia in the United States: the Aging, Demographics, and Memory Study.  Neuroepidemiology. 2007;29(1-2):125-132.PubMedGoogle ScholarCrossref
21.
Chambless  LE, Heiss  G, Shahar  E, Earp  MJ, Toole  J.  Prediction of ischemic stroke risk in the Atherosclerosis Risk in Communities Study.  Am J Epidemiol. 2004;160(3):259-269.PubMedGoogle ScholarCrossref
22.
Sharrett  AR, Patsch  W, Sorlie  PD, Heiss  G, Bond  MG, Davis  CE.  Associations of lipoprotein cholesterols, apolipoproteins A-I and B, and triglycerides with carotid atherosclerosis and coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) Study.  Arterioscler Thromb. 1994;14(7):1098-1104.PubMedGoogle ScholarCrossref
23.
Siedel  J, Hägele  EO, Ziegenhorn  J, Wahlefeld  AW.  Reagent for the enzymatic determination of serum total cholesterol with improved lipolytic efficiency.  Clin Chem. 1983;29(6):1075-1080.PubMedGoogle Scholar
24.
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
25.
Moran  C, Beare  R, Phan  TG, Bruce  DG, Callisaya  ML, Srikanth  V; Alzheimer’s Disease Neuroimaging Initiative.  Type 2 diabetes mellitus and biomarkers of neurodegeneration.  Neurology. 2015;85(13):1123-1130.PubMedGoogle ScholarCrossref
26.
Reed  BR, Marchant  NL, Jagust  WJ, DeCarli  CC, Mack  W, Chui  HC.  Coronary risk correlates with cerebral amyloid deposition.  Neurobiol Aging. 2012;33(9):1979-1987.PubMedGoogle ScholarCrossref
27.
Cifuentes  D, Poittevin  M, Dere  E,  et al.  Hypertension accelerates the progression of Alzheimer-like pathology in a mouse model of the disease.  Hypertension. 2015;65(1):218-224.PubMedGoogle ScholarCrossref
28.
Faraco  G, Park  L, Zhou  P,  et al.  Hypertension enhances Aβ-induced neurovascular dysfunction, promotes β-secretase activity, and leads to amyloidogenic processing of APP.  J Cereb Blood Flow Metab. 2016;36(1):241-252.PubMedGoogle ScholarCrossref
29.
Rodrigue  KM, Rieck  JR, Kennedy  KM, Devous  MD  Sr, Diaz-Arrastia  R, Park  DC.  Risk factors for β-amyloid deposition in healthy aging: vascular and genetic effects.  JAMA Neurol. 2013;70(5):600-606.PubMedGoogle ScholarCrossref
30.
Malek-Ahmadi  M, Beach  T, Obradov  A,  et al.  Increased Alzheimer’s disease neuropathology is associated with type 2 diabetes and ApoE ε.4 carrier status.  Curr Alzheimer Res. 2013;10(6):654-659.PubMedGoogle ScholarCrossref
31.
Hohman  TJ, Samuels  LR, Liu  D,  et al; Alzheimer’s Neuroimaging Initiative.  Stroke risk interacts with Alzheimer’s disease biomarkers on brain aging outcomes.  Neurobiol Aging. 2015;36(9):2501-2508.PubMedGoogle ScholarCrossref
32.
Vemuri  P, Lesnick  TG, Przybelski  SA,  et al.  Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly.  Brain. 2015;138(pt 3):761-771.PubMedGoogle ScholarCrossref
33.
Marchant  NL, Reed  BR, Sanossian  N,  et al.  The aging brain and cognition: contribution of vascular injury and aβ to mild cognitive dysfunction.  JAMA Neurol. 2013;70(4):488-495.PubMedGoogle ScholarCrossref
34.
Gutierrez  J, Honig  L, Elkind  MSV,  et al.  Brain arterial aging and its relationship to Alzheimer dementia.  Neurology. 2016;86(16):1507-1515.PubMedGoogle ScholarCrossref
35.
Gupta  A, Iadecola  C.  Impaired Aβ clearance: a potential link between atherosclerosis and Alzheimer’s disease.  Front Aging Neurosci. 2015;7(115):115.PubMedGoogle Scholar
36.
Marín-Padilla  M, Knopman  DS.  Developmental aspects of the intracerebral microvasculature and perivascular spaces: insights into brain response to late-life diseases.  J Neuropathol Exp Neurol. 2011;70(12):1060-1069.PubMedGoogle ScholarCrossref
37.
Selvin  E, Parrinello  CM, Sacks  DB, Coresh  J.  Trends in prevalence and control of diabetes in the United States, 1988-1994 and 1999-2010.  Ann Intern Med. 2014;160(8):517-525.PubMedGoogle ScholarCrossref
38.
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
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