Associations between amyloid β and white matter hyperintensities: A systematic review

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Review Article

Associations between b-amyloid and white matter hyperintensities: A systematic review Austyn Roseborougha, Joel Ramireza,b, Sandra E. Blacka,b,c,1, Jodi D. Edwardsa,b,*,1 a

LC Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Canada b Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Canada c Faculty of Medicine, Neurology, University of Toronto and Sunnybrook Health Sciences Centre, Toronto, Canada

Abstract

Introduction: This systematic review synthesizes current evidence for associations between cortical b-amyloid, visualized on amyloid positron emission tomography imaging, and white matter hyperintensity (WMH) burden on magnetic resonance imaging in healthy elderly adults and individuals with cognitive impairment and dementia. Methods: Following PRISMA systematic review guidelines, we systematically searched MEDLINE, Embase, Cochrane, and PsycINFO databases from January 2000 to September 2015. Results: Our search returned 492 articles, 34 of which met criteria for inclusion in the final selection. Most studies reported no significant relationships between b-amyloid and WMH burden across diagnostic groups. Discussion: Findings of this systematic review suggest that amyloid accumulation and WMH are independent but additive processes. The limited number of independent cohorts, lack of longitudinal data, and exclusion of individuals with mixed dementia limit the generalizability of these findings. Further studies are required to elucidate the putative contributions of vascular processes to neurodegenerative pathology. Ó 2017 Published by Elsevier Inc. on behalf of the Alzheimer’s Association.

Keywords:

Alzheimer’s disease; Dementia; Amyloid; White matter disease; Systematic review

1. Introduction Alzheimer’s disease (AD) is the most common form of dementia, affecting approximately 44 million people worldwide. With population aging, the prevalence of dementia is estimated to triple to 135 million by 2050, with annual global costs increasing to a projected $1 trillion [1]. AD is hallmarked by the formation of extracellular b-amyloid (Ab) plaques and neurofibrillary tangles of hyperphosphorylated tau in the cerebral cortex, resulting in neurodegeneration and cognitive decline. Cerebral small vessel disease (SVD) often coexists with AD and is associated

1

These authors are cosupervising authors of this research. *Corresponding author. Tel.: 11-416-480-6100x85420; Fax: 11-416480-4223. E-mail address: [email protected]

with an increased risk of cortical atrophy and cognitive impairment. SVD is the primary cause of vascular dementia and is also prevalent in aging [2–6]. There is increasing evidence to indicate the potential importance of vascular contributions to the presentation and clinical course of dementia [7]. The putative contributions of amyloidogenic and vasculopathic processes to cognitive decline and dementia are poorly understood and are complicated by the frequent comorbidity of these pathologies, often appearing as mixed dementia [8,9]. On magnetic resonance imaging (MRI), SVD is commonly visualized as white matter hyperintensities (WMHs) of presumed vascular origin, lacunes, microbleeds, and enlarged perivascular spaces [10]. Previous clinical studies have shown that the accumulation of Ab plaques is not limited to individuals with AD, as individuals with vascular dementia can show AD pathology postmortem [11]. Similarly, WMHs, which are commonly

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observed in vascular dementia, may also be increased in individuals with AD [12–14], further highlighting the comorbidity of vascular and amyloid processes. The prevalence of these copathologies leads to uncertainty regarding the precise role of SVD in the development of cognitive decline and dementia. Although the visualization of AD pathology was previously limited to autopsy [15], recent advances in positron emission tomography (PET) imaging now enable the in vivo quantification of cortical Ab and tau. This technique opens new opportunities to quantify biomarkers of AD and examines potential associations with markers of SVD [16]. Several recent clinical imaging studies have evaluated Ab burden in patients with cognitive decline and dementia and, whereas some have shown that Ab deposition independently predicts AD diagnosis, others suggest that significant Ab burden is not necessary for neurodegeneration and that impairment may occur via non-Ab pathways. Thus, the potential interactive or synergistic effects of comorbid vascular pathology remain unclear [17–19]. The purpose of this systematic review was to evaluate current evidence for associations between markers of SVD on MRI and amyloid burden on amyloid PET imaging in normal elderly patients and patients with cognitive impairment and dementia. As secondary objectives, we also sought to (1) describe the prevalence of amyloid positivity; (2) summarize reported effects of WMH and amyloid uptake on cognition; (3) evaluate moderating effects of apolipoprotein E (APOE) status or vascular risk factors; and (4) characterize potential differences in cross-sectional versus longitudinal relationships between WMH and amyloid burden. Findings of this systematic review are discussed in the context of current conceptual models of vascular and amyloid burden, with specific reference to the advantages, disadvantages, and potential limitations of these models. We also highlight areas of future inquiry that are required to address outstanding knowledge gaps regarding the relationships between these pathogenic processes.

2015. Search terms and medical subject headings are listed in Table 1. After the data extract, relevant journals were subsequently searched for additional recent publications that met the selection criteria. 2.2. Article selection criteria 2.2.1. Study design All articles selected for inclusion were primary research articles, written in English, and were cross-sectional, longitudinal, observational, or experimental in design. Where multiple studies involved analyses of the same study cohort, only those with independent reported analyses were selected for inclusion. 2.2.2. Population Study populations may have included normal elderly individuals, individuals with mild cognitive impairment (MCI), AD, subcortical vascular mild cognitive impairment (svMCI), and/or subcortical vascular dementia (sVaD). All articles that included patients with cortical stroke were excluded unless results for these patients were reported as a separate subgroup. 2.2.3. Exposure The imaging evaluation of WMH was required for inclusion, and all studies that assessed WMH either quantitatively or qualitatively using MRI were included. 2.2.4. Outcome Studies assessing measures of cortical amyloid using amyloid PET imaging were included. All studies using cerebrospinal measures of amyloid or pathologic-histologic measures of amyloid at autopsy were excluded. 2.3. Study selection methodology

2. Methods This systematic review was conducted in accordance with the PRISMA guidelines for systematic reviews [20]. 2.1. Search strategy MEDLINE, Embase, Cochrane, and PsycINFO databases were searched for entries from January 2000 to September

An overview of the selection methodology is presented in Fig. 1. Results of the search were examined and all duplicated citations, nonhuman, and non-English studies were excluded. Two primary raters (A.R. and J.D.E.) conducted a title and an abstract screen of the remaining articles to assess eligibility based on the selection criteria outlined previously. For the articles remaining after the first screen, a full text review was conducted to assess eligibility.

Table 1 Search terms and inclusion criteria: Groupings of search terms used in database search of MEDLINE, Embase, Cochrane, and PsycINFO Group

Search terms used

1: Patient population

Dementia, mild cognitive impairment, Alzheimer’s disease, Alzheimer’s dementia, vascular dementia, vascular cognitive impairment Vascular disease, small vessel disease, white matter lesions, white matter damage, white matter disease, white matter hyperintensities, white matter changes, white matter signal abnormalities, leukoaraiosis, Leukoencephalopathy, small subcortical infarcts, lacunes, microbleeds, microinfarcts Amyloid, b-amyloid, amyloidopathy, tau, hyperphosphorylated tau, tauopathy, molecular PET imaging

2: Exposure (based on STRIVE criteria [10])

3: Outcome

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3

web 4C=FPO

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Fig. 1. Flow chart of article screening process: flow of articles from initial database search to final selection whereby 491 articles were reduced to a final 33 that were included in this review.

To validate the final article set, a secondary rater (J.R.) conducted an independent review of all selected articles and a randomly selected subset of excluded articles and assessed them for eligibility based on selection criteria. Kappa inter-rater reliability statistics were performed to assess agreement between raters for study inclusion. 2.4. Data extraction On selection of the final article set, descriptive data on cohort/case size and definition; study setting; enrollment period; length of follow-up; imaging acquisition protocols; and imaging outcome measures were extracted from included studies. For imaging outcomes, we also recorded whether visual rating scales or quantification methods were used when determining amyloid and white matter disease burden from MRI and PET images. 2.5. Quality assessment Articles were evaluated using the Studies evaluating randomised trials and meta-analyses of randomized trials (STROBE) checklist for observational studies [21]. 3. Results 3.1. Search results Results of the database search yielded 491 articles (Fig. 1). During the first screen, primary raters excluded 410 articles based on the title and abstract, with 81 retained for a fulltext screen. After the second screen, 49 articles were excluded, leaving a total of 32 articles eligible for

inclusion. Inclusion ratings of the independent secondary rater were compared with the primary raters and resulted in a k agreement score of 0.87. Of the 64 full-text articles used for inter-rater validation (including 32 selected by the primary raters and 32 randomly selected from initial database extract), only four resulted in disagreement between raters. Inter-rater disagreement was resolved via consensus among raters in the review team, yielding a total of 33 articles for inclusion. A final search of journal citations resulted in the identification of one additional eligible article, yielding a total of 34 articles selected in the final review set (Fig. 1). 3.2. Prevalence of amyloid positivity In all studies evaluated in this review, amyloid positivity was evaluated as a study outcome and not as a diagnostic criterion. Within included articles, the prevalence of amyloidpositive imaging for healthy elderly individuals was reported in 13 articles (N 5 14–430) and ranged from 21% to 52% [19,22–32]. Individuals with cognitive impairment or dementia had significantly higher ratios of amyloid uptake than normal control subjects [23,31,33], and individuals with AD had significantly higher uptake than those with MCI [31,33]. Across five studies that reported amyloid burden in those with MCI, the prevalence of amyloid positivity ranged from 56.8% to 70%, whereas six other studies reported a 57% to 100% prevalence of amyloid positivity in cohorts of patients with AD [19,23,24,31,34–36]. In the 13 articles that measured amyloid in patients described as having vascular cognitive impairment, the prevalence of amyloid positivity ranged from 33% to 39% when grouping svMCI and sVaD together, 31% to 33% for svMCI alone, and 31% to 54% for

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362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405Q8 406 407 408 409 410 411 412 413 Q9 414 415 416 417 418 419 420 421 422

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sVaD alone [18,34–44]. However, it is important to note that these 13 studies were all based on findings from a single cohort recruited from the Samsung Medical Center in Korea, which also represented the only study sample with participants classified as svMCI and sVaD in this review (Table 2). 3.3. Primary objective: associations between Ab and WMH burden Within the final article set, more than half of studies that met inclusion criteria (N 5 18/34; 53%) evaluated normal control study populations only and, of these, 13 studies explicitly examined associations between WMH and Ab uptake on PET imaging. Of these, most studies (N 5 10/13; 77%) reported no significant association between amyloid uptake and the presence of WMH in cognitively normal individuals, regardless of whether amyloid burden was dichotomized or treated as a continuous measure [19,23,26,28,30,32,33,54,55]. Among the three studies that did report an association between amyloid and WMH burden in healthy elderly individuals, two showed positive correlations between Ab uptake and WMH [22,47], whereas another showed a negative correlation between these measures [24]. However, it is important to note that one of the two studies demonstrating a strong positive correlation between WMH volume and amyloid positivity in normal elderly was limited by a small sample (N 5 3) [22]. Of the 10 studies (N 5 10/34; 29%) that included MCI subjects, only five studies explicitly compared amyloid uptake and WMH burden and, of these, three studies (N 5 3/ 5; 60%) reported no significant relationship between these imaging measures [19,23,49]. Results of our search indicated that 12 studies that met selection criteria included individuals with AD in their study sample (N 5 12/34; 35%). Of these, only five studies examined associations between amyloid uptake and WMH burden, with three of the five (60%) reporting no significant relationships between these measures [17,19,33]. An additional two studies that evaluated patients with AD as part of mixed cohorts including normal control subjects and patients with MCI and AD reported significant but contradictory associations between amyloid uptake and WMH. Although one study showed a positive correlation between WMH and average amyloid uptake in a cohort of healthy elderly, MCI, and AD subjects, an important limitation was the lack of adjustment for age in this analysis [47]. The second study, examining a mixed cohort identified from the ADNI database, reported that WMH volume and amyloid uptake were negatively correlated with each other, although WMH and amyloid positivity were not correlated [24]. In this study, amyloid positivity and WMH both independently discriminated between normal control subjects and patients with MCI and were independently associated with AD [19,24]. A single study of patients with cerebral amyloid angiopathy showed a significant positive association between amy-

loid uptake and WMH [49]. In a series of 12 articles evaluating relationships between WMH and amyloid deposition in sVaD and svMCI populations from the Samsung Medical Center in Korea, six of these studies (N 5 6/12; 50%) compared amyloid and WMH burden and reported no significant relationship between WMH and amyloid uptake [37–40]. However, in one sample of sVaD patients from this cohort, although amyloid positivity did not affect the WMH volume, it was negatively associated with the number of lacunes [37,40]. 3.4. Cognition Of the 34 articles in the final selection, 26 studies (N 5 26/34; 76%) reported scores on measures of cognitive performance for study participants. Across studies, the effects of amyloid deposition on cognition were inconsistent, with one study of healthy elderly participants reporting a negative association between cognition and amyloid positivity [26] and another using a path analysis methodology reporting that amyloid uptake was indirectly (but not directly) associated with cognitive functioning in healthy elderly individuals [54]. In healthy elderly individuals from the Aging Brain and Berkeley Aging cohorts, SVD was a predictor of executive dysfunction but not memory, whereas amyloid positivity was not associated with either executive or memory scores [28]. Notably, in two studies, individuals with co-occurring WMH and amyloid uptake displayed the poorest performance on tests of executive function and the most rapid cognitive decline [26,28]. However, in a cohort of individuals with both AD and vascular dementia/cognitive impairment, amyloid deposition was negatively associated with memory and executive function [35], whereas WMH was associated with memory and executive function only when mediated by frontal thickness [35]. In studies of individuals with svMCI and sVaD, amyloid-negative individuals performed worse than normal control subjects on tests of language, memory, and executive dysfunction [53]. However, among patients with sVaD, amyloid negativity was associated with better performance on tests of visual and verbal memory and worse executive function scores than those positive for amyloid uptake [18,34]. An additional two studies also reported that cognitive reserve helped in minimizing the effects of both amyloid and vascular injury by increasing the baseline cognition score [26] and that lifetime cognitive activity was associated with lower amyloid uptake [54]. 3.5. Effect modification Across studies in the final selection, 22 studies (N 5 22/ 34; 65%) both reported and adjusted for APOE4 status in their analysis. In one study that stratified amyloidpositive svMCI and amyloid-positive sVaD subjects by APOE4 carrier versus noncarrier status, the authors

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484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 Table 2 Prevalence and definition of amyloid positivity: reported prevalence of amyloid positivity, definition used in determining positivity, population source, and method of WMH calculation for each diagnostic group in all articles Data acquisition period

AD (11) AD (20)

Unknown Unknown

AD (22)

Unknown

AD (7)

Unknown

AD (40)

MCI (37)

Late 2006–August 2008 Unknown (2011?) September 2008–May 20112 July 2007–20112 September 2008– August 20112 October 2010–August 2013 Unknown October 2005– December 2011 2009–2011

MCI (20)

Unknown

Cross-sectional

MCI (37)

Late 2006–August 2008 July 2007–20117 Unknown October 2010–August 2013 Unknown

Longitudinal (36 mo)

Population based

Cross-sectional Cross-sectional Cross-sectional

Hospital based Community based Multisite hospital based

Longitudinal (29.73 mo average) Cross-sectional

Multisite hospital based

AD (60) AD (68) AD (69) AD (70) AD (13) AD/MCI (20) AD/MCI (43)

MCI (45) MCI (51) MCI (17) MCI (59) AD/MCI (111) NC (14) NC (146)

September 2008–May 20117 Unknown 2009–2011

NC (18) NC (21)

Unknown Unknown

Study design

Sample setting

Cross-sectional Longitudinal (29.73 mo average) Longitudinal (28 mo average) Cross-sectional

Community based Multisite hospital based

% PIB1 82 85

Measurement of PIB1

Measurement of WMH

Reference

0.23 MCBP .1.5 SUVR

Fazekas Rating Scale Volumetric

Gordon et al. 2015 [19] Provenzano et al. 2013 [24]

Q15

Clinic based

n/a

n/a

Scheltens Rating Scale

Grimmer et al. 2012 [17]

Multisite community and clinic based

n/a

Volumetric

Marchant et al. 2013 [45]

Longitudinal (36 mo)

Population based

100

Values 2 SD greater than the average global index .1.5 SUVR

Fazekas Rating Scale

Yates et al. 2014 [31]

Cross-sectional Cross-sectional

Clinic based Hospital based

n/a 89.7

n/a 2 SD from mean of NC

Scheltens Rating Scale Visual assessment

Ortner et al. 2015 [46] Yoon et al. 2013 [34]

Cross-sectional Cross-sectional

Hospital based Hospital based

.1.5 SUVR

Volumetric Visual assessment

Ye et al. 2015 [35] Kim et al. 2015 [36]

Cross-sectional

Multisite hospital based

n/a

n/a

Volumetric

Zhou et al. 2015 [47]

Cross-sectional Cross-sectional

Hospital based Hospital based

n/a n/a

n/a n/a

ARIC score Volumetric

Zazulia et al. 2010 [48] Gurol et al. 2013 [49]

Longitudinal (2 y)

Multisite community based Multisite community and clinic based

.1.57 SUVR in 5 regions

Volumetric

Lopez et al. 2014 [23]

Values 2 SD greater than the average global index .1.5 SUVR

Volumetric

Marchant et al. 2013 [50]

Fazekas Rating Scale

Yates et al. 2014 [31]

.1.5 SUVR 0.23 MCBP n/a

Volumetric Fazekas Rating Scale Volumetric

Ye et al. 2015 [35] Gordon et al. 2015 [19] Zhou et al. 2015 [47]

70

.1.5 SUVR

Volumetric

Provenzano et al. 2013 [24]

Hospital based

77.40

.1.5 SUVR

Volumetric

Kim et al. 2013 [44]

Community based Multisite community based Hospital based Multisite hospital based

21.40 51

Visual rating .1.57 SUVR

Volumetric Volumetric

Brickman et al. 2015 [22] Lopez et al. 2014 [23]

n/a .1.5 SUVR

Fazekas Rating Scale Volumetric

Madsen et al. 2012 [51] Provenzano et al. 2013 [24]

Cross-sectional Longitudinal (2 y) Cross-sectional Longitudinal (29.73 mo average)

89.90 57.10

67.50 n/a

56.80 62.20 71 n/a

n/a 52

Q16

Q17

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Population (N)

(Continued ) 5

545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605

606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 Data acquisition period

Study design

Sample setting

NC (27)

Unknown

Cross-sectional

Population based

35.70

NC (393) NC (430) NC (67)

Unknown Unknown Unknown

Longitudinal (2.7 y) Cross-sectional Cross-sectional

34 32 32.8

NC (50)

Cross-sectional

NC (54)

October 2005– December 2011 Unknown

Community based Community based Multisite community based Hospital based

Cross-sectional

Multisite community based

NC (30)

Unknown

Cross-sectional

Multisite community and clinic based

n/a

NC (6) NC (72)

Unknown Unknown

Cross-sectional Cross-sectional

Hospital based Community based

n/a 35

NC (91)

2009–2011

Cross-sectional

NC (75)

September 2008– August 20118 Unknown Late 2006–August 2008 October 2010–August 2013 Unknown Unknown

Cross-sectional

Multisite community based Hospital based

n/a

Cross-sectional Longitudinal (36 mo)

Community based Population based

n/a 29.90

Cross-sectional

Multisite hospital based

n/a

Cross-sectional Cross-sectional

21 33.33

Cross-sectional

Community based Multisite community based Hospital based

Cross-sectional

Hospital based

Cross-sectional

NC (92) NC (97) NC (14) NC (397) NC/MCI (66) sVaD (45) sVaD (67) sVaD (68) sVaD (70) sVaD (70) sVaD (77) svMCI and sVaD (136)

September 2008– August 20118 September 2008–May 20118 September 2008– August 20118 September 2008– August 20118 July 2007–20118 September 2008– 20118 September 2008– August 20118,9

% PIB1

n/a 38.90

47

31.10

Measurement of PIB1

Measurement of WMH

Reference

Cluster analysis to define high vs. low groups .1.5 SUVR .1.5 SUVR 1.16 cutoff

Rating Scale

Sojkova et al. 2008 [25]

Volumetric Volumetric Volumetric

Vemuri et al. 2015 [26] Knopman et al. 2013 [27] Villeneuve et al. 2014 [32]

n/a

Volumetric

Gurol et al. 2013 [49]

Values 2 SD greater than the average global index Values 2 SD greater than the average global index n/a 2 SD greater than mean (.1.08) 1.57 SUVR cutoff

Fazekas Rating Scale

Marchant et al. 2012 [28]

Volumetric

Marchant et al. 2013 [50]

Fazekas Rating Scale Volumetric

Huang et al. 2012 [52] Wirth et al. 2013 [29]

Volumetric

Hughes et al. 2013 [30]

Volumetric

Kim et al. 2014 [53]

Volumetric Fazekas Rating Scale

Wirth et al. 2014 [54] Yates et al. 2014 [31]

Volumetric

Zhou et al. 2015 [47]

Fazekas Rating Scale Volumetric

Gordon et al. 2015 [19] Villeneuve et al. 2014 [32]

SUVR 2 SD greater than mean n/a .1.5 SUVR Exception: AV45 ligand used 0.23 MCBP 1.16 cutoff

Visual assessment

Lee et al. 2011 [18]

34.30

SUVR 2 SD greater than mean 2 SD from mean of NC

Visual assessment

Yoon et al. 2013 [34]

Hospital based

33.80

.1.5 SUVR

Volumetric

Kim et al. 2013 [37]

Cross-sectional

Hospital based

54.30

Visual assessment

Kim et al. 2015 [36]

Cross-sectional Cross-sectional

Hospital based Hospital based

32.90 36.40

Volumetric Volumetric

Ye et al. 2015 [35] Kim et al. 2014 [38]

Cross-sectional

Hospital based

39

Volumetric

Noh et al. 2014 [39]

.1.5 SUVR SUVR 2 SD greater than mean .1.5 SUVR

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Population (N)

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Table 2 Prevalence and definition of amyloid positivity: reported prevalence of amyloid positivity, definition used in determining positivity, population source, and method of WMH calculation for each diagnostic group in all articles (Continued )

667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727

svMCI (67) svMCI and sVaD (127) svMCI and sVaD (134) svMCI and sVaD (91)

Abbreviations: AD, Alzheimer’s disease; AV45, florbetapir F 18; MCI, mild cognitive impairment; NC, normal control subjects; PIB, Pittsburgh compound B; SD, standard deviation; SUVR, standard uptake Q18 value ratio; sVaD, subcortical vascular dementia; svMCI, subcortical vascular mild cognitive impairment; WMH, white matter hyperintensity. NOTE. Studies were considered hospital based if they recruited from a specific research hospital, clinic based if recruitment occurred through a memory/stroke clinic, community based if they recruited from a specific community, and population based if the recruitment was targeted at the larger public population.

Kim et al. 2014 [53] Volumetric n/a Hospital based Cross-sectional

SUVR 2 SD greater than mean

Kim et al. 2013 [44] Volumetric 33.50 Hospital based Cross-sectional

.1.5 SUVR

Ye et al. 2015 [35] Kim et al. 2013 [43] 31.30 33.10 Hospital based Hospital based Cross-sectional Cross-sectional

Volumetric Volumetric

Noh et al. 2014 [42] Volumetric 32.80

October 2009–May 20119 July 2007–20119 September 2008– August 20118,9 September 2008–May 20118,9 September 2008– August 20118,9

Cross-sectional

Hospital based

.1.5 SUVR (2 SD greater than mean for normal) .1.5 SUVR .1.5 SUVR

Park et al. 2013 [41] Volumetric .1.5 SUVR 32.80 Hospital based Cross-sectional

Volumetric .1.5 SUVR 33.10 Hospital based Cross-sectional

September 2008– August 20118,9 Unknown

svMCI and sVaD (136) svMCI and sVaD (137) svMCI (67)

728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788

Park et al. 2014 [40]

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7

reported a positive correlation between WMH and amyloid uptake in APOE4 noncarriers but not in carriers [39]. This correlation was strongest in posterior brain regions including the cuneus, superior cerebellum, and medial occipitotemporal gyrus [39]. In another study of amyloidpositive svMCI subjects from the same cohort, APOE4 significantly modified the relationship between blood viscosity and amyloid positivity [42]. Although most studies analyzed in this review did not adjust for vascular risk, 22 of the 34 included studies (65%) reported some measure of vascular risk in characterizing the study population. Of these, five studies (N 5 5/ 22; 23%) reported no significant differences in vascular risk factors among healthy elderly individuals who were amyloid positive compared with those who were amyloid negative [27,28,30,33,55]. In one study of healthy elderly individuals, measures of vascular risk modified the effect of amyloid on cortical thinning, whereas WMH did not [55]. In another study examining arterial stiffness in healthy elderly individuals, increased stiffness was differentially associated with both amyloid positivity and WMH load, with amyloid burden associated with mixed stiffness and WMH associated with central stiffness [30]. Although four studies reported no difference in vascular risk factors between amyloid-positive and amyloidnegative individuals with svMCI and sVaD [18,37,42,53], it is important to note that participants included in these analyses were drawn from the same cohort and may overlap. 3.6. Longitudinal associations Of the articles that met our selection criteria, only five studies (N 5 5/34; 15%) were longitudinal in design, with four of these evaluating the relationship between WMH burden and amyloid deposition over time [17,23,24,26,31]. In one study, conversion to dementia was not correlated with amyloid positivity but was correlated with mean amyloid uptake values, and this study also reported an association between WMH volume and progression to dementia over time [23]. In this study, among those that progressed to dementia, 49% had both high WMH volume and amyloid positivity, in comparison to only 8% with high WMH in the absence of amyloid positivity and 6.5% with amyloid positivity and low WMH [23]. Another three studies reported an increased likelihood of conversion to dementia in those with both amyloid and WMH burden [23,24,26]. In a sample from the ADNI database, individuals with amyloid positivity and significant WMH represented the largest proportion converting from MCI to AD over a 34-month interval [24]. In a sample of individuals with AD followed longitudinally, baseline WMH was correlated with an increase in amyloid uptake over a mean follow-up of 28 months, but amyloid uptake at baseline was not associated with longitudinal WMH change [17].

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4. Discussion 4.1. Associations between Ab and WMH burden Overall, the studies evaluated in this systematic review indicated that, although positivity for amyloid accumulation appears prevalent in elderly individuals, including both healthy elderly and those with MCI and AD, most studies showed no relationship between amyloid uptake and WMH volume. This finding suggests that amyloid and vascular burden represent independent pathogenic processes in aging populations with and without cognitive impairment. This finding is consistent with recent models of AD pathogenesis suggesting that the development of these processes may be biphasic where biomarkers of AD amyloid pathology are distinct from markers of neurodegeneration but modified by comorbid conditions, such as cerebrovascular disease [56]. However, it should be stressed that the question of a potential relationship between WMH and amyloid uptake cannot be answered definitively because of the limitations and outstanding questions, which are discussed in Section 4.4. In addition, a recent study published after the literature search for this review reported a relationship between amyloid PET data and WMH in ADNI, using florbetapir instead of Pittsburgh compound B, further supporting the need for more investigations of these complex pathophysiologies [57]. Although most studies included in the present review reported that amyloid accumulation and vascular injury did not appear to enhance one another [26], there was also evidence that, despite appearing as independent processes, increased amyloid and WMH burden may have additive effects [19]. Specifically, among individuals who were positive for amyloid uptake across studies, increased WMH was associated with a diagnosis of AD [24]. However, whether the relationship between amyloid uptake and WMH is consistent with other measures of amyloid burden remains unclear. Interpreting potential associations between WMH and cortical levels of Ab in comparison to relationships with amyloid at autopsy is challenging, as there is variability across studies in the threshold for amyloid positivity and uptake ratio values considered to yield a positive amyloid PET scan have not been well validated. Although it has previously been reported that individuals with both SVD and amyloid pathology at autopsy are more likely to have been demented [58], the relationship between WMH and parenchymal or vascular amyloid deposition is further complicated by the fact that both the Pittsburgh compound B and florbetapir F 18 (AV45) ligands label both fibrillary and vascular amyloid [16,59,60]. Interestingly, amyloid uptake is correlated with WMH in cerebral amyloid angiopathy patients, suggesting that associations between WMH and amyloid PET may be stronger when considering subjects with high vascular amyloid burden [33]. Further studies are needed to reconcile amyloid PET data with other measures of amyloid and with the various forms of amyloid (vascular vs. parenchymal) when considering potential associations with SVD.

4.2. Longitudinal investigations of Ab and WMH relationships The additive effects of amyloid burden and SVD may be best studied longitudinally, as these data will offer insight into the combined effects of WMH and amyloid deposition over time and reveal the temporality of both processes. Of our selected articles, only five studies were longitudinal in design, but findings of these studies offered support for the notion that amyloid burden and SVD may have additive effects. In these studies, healthy elderly individuals who were both amyloid positive and had SVD showed lower cognitive scores and experienced more rapid cognitive decline and progression to MCI or AD [26]. In a sample of AD individuals, longitudinal baseline WMH was correlated with an increase in amyloid uptake at follow-up; however, the reverse was not true, amyloid uptake at baseline was not associated with longitudinal WMH change [17]. This seemingly discordant result could indicate that earlier vascular injury sets the stage for further amyloid accumulation over time, perhaps through mechanisms involving reduced amyloid clearance [61]. This is consistent with current models of amyloid clearance whereby amyloid and other metabolites are cleared from the brain through interstitial fluid and/or cerebrospinal fluid mechanisms and with the biphasic model of disease Q10 progression mentioned previously where vascular changes influence amyloid accumulation [62]. Although these represent possible mechanisms to understand disease progression, there is a need for more longitudinal studies assessing this relationship. More specifically, studies that account for varying rates of disease progression and varying stages of pathology at study entry [63]. Longitudinal data may also be informative to empirically evaluate whether vascular pathology precedes amyloid burden and whether this timeline varies based on other confounding variables. Future studies should also consider studying other biomarkers longitudinally in addition to amyloid. As an example, investigating the interactive effects of tau and vascular pathology with the use of new tau-imaging techniques in addition to amyloid imaging could provide insight into other pathogenic relationships of characteristic AD biomarkers. 4.3. Modifying factors: Vascular risk and APOE genotype The modification of potential associations between Ab and markers of SVD, including WMH burden, by vascular risk factors or genotype also remains unclear. Previous literature suggests that vascular risk factors are significantly associated with amyloid uptake on PET imaging and may be representative of a common underlying dysfunction that leads to both amyloid accumulation and SVD [64]. However, in this review, there were no significant associations between vascular risk factors and amyloid PET in healthy elderly individuals or those with svMCI/ sVaD. In one study from our selection, increased arterial stiffness was associated with both amyloid positivity and

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WMH load [65], suggesting that systematic vascular dysfunction may be associated with the accumulation of amyloid burden and WMH. Although underlying drivers of WMH and amyloid deposition may be overlapping, subtle pathophysiologic changes may be overshadowed by the time enough amyloid accumulates to yield a positive PET scan. Of studies that evaluated the effects of vascular risk factors on amyloid positivity, most were in populations of normal and svMCI or sVaD. Further work in AD samples or individuals with mixed dementia is required to more clearly characterize whether vascular risk factors represent common underlying mechanisms of WMH and amyloid deposition. In addition to vascular risk, the APOE E4 variant of the APOE gene locus is recognized as a strong contributing factor to the risk of developing AD [66]. Understanding any moderating effects of APOE4 is of particular importance in understanding the accumulation of amyloid, as prior work has shown that parieto-occipital increases in WMH volume are apparent 22 years before symptom onset and amyloid is seen on PET imaging up to 15 years before the onset of symptoms [67,68]. The relationship between WMH and Ab burden may vary in individuals positive for the APOE4 allele. Although many studies reported the prevalence of APOE4 carriers in their samples, few studies stratified their analyses by genotype, with the exception of one study, which showed a positive association between WMH and amyloid deposition in APOE4 noncarriers but not in carriers [39]. Although the APOE E4 variant is a wellestablished risk factor for AD, any potential association with WMH remains unclear. There are contradicting reports characterizing the potential relationship between APOE E4 status and the presence of WMH, with some articles showing an increased risk for VaD with APOE e4 [69–72], and others reporting that APOE e4 was not a significant factor for WMH in AD and non-AD [72–74]. Therefore, further work elucidating the potential involvement of the APOE E4 allele with the development of WMH in addition to amyloid uptake is required. An interesting approach could be to study amyloid uptake and APOE genetic variability of individuals with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy caused by mutations of the Notch 3 gene on chromosome 19. This may provide insight into amyloid clearance in this pure form of SVD and aid in the understanding of the potential roles of APOE alleles in vascular disease [45]. Previous literature suggests an association of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy with the APOE E2 variant [75], and the future work may explore whether this extends to other forms of the APOE locus. Stratification of genetic variants of WMH represents a useful addition to studies in the future and may provide valuable information to elucidate discrepancies between clinical presentation and levels of vascular or amyloid burden on MRI or PET imaging.

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4.4. Outstanding questions and future directions 4.4.1. Limitations A limitation of this review is the difficulty associated with comparing studies that were studying different research questions. Although they all assessed WMH and amyloid PET data to meet our selection criteria, the studies addressed a variety of research questions. In addition, there was a large range of patients included in the studies and in the type of population sources, for example, community- versus hospital-based sources. Of our selected articles, 19 were hospital-based articles, four were clinic-based articles, and 11 were community/population-based articles. The high proportion of hospital- and clinic-based studies reduces the generalizability to the broader community, and the varying recruitment strategies hinder the ability to compare results between studies. Another major source of differences across studies was in regards to the differences in clinical characterization of the study samples. Variability of clinical constructs and inconsistency between study populations arose because of a lack of universally accepted, evidence-based definitions for the diseases/conditions being studied. For example, the criteria used to differentiate between Alzheimer’s dementia and vascular dementia vary between sites, which reduce the generalizability of these findings [76]. A potential solution to this issue would be to design disease-agnostic research studies, where the categorization of study participants is based on neuroimaging markers, biometrics, and/or psychometrics, rather than diagnostics based on clinical impression/opinion. This would potentially allow study sample individuals to be assessed and grouped using a range of amyloid and WMH burden levels as opposed to grouping them by clinical diagnosis. 4.4.2. Amyloid positivity Many previous clinical imaging studies using amyloid PET measures of Ab burden have dichotomized study participants into positive and negative uptake groups, with a commonly adopted definition of an amyloid-positive scan as one that has a standard uptake value ratio of two standard deviations greater than the mean for normal control subjects [18]. However, it is worth noting that there is no standardized way of defining amyloid positivity, and it does not represent all the variability in the uptake values. Limiting the variability of amyloid measurement may also make it more challenging to find significant relationships in normal populations, where positivity is often used as an outcome measure although amyloid levels vary considerably. The dichotomization of amyloid PET data poses two main problems. First, information on levels of amyloid deposition that are less than the threshold required for a positive scan is lost. These lower levels of amyloid deposition may be more informative when considering interactive effects as the amount of amyloid deposition in a positive scan may have reached high

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levels where mediating factors will not be apparent. In addition, within a longitudinal framework, the dichotomization of amyloid PET data hinders the ability to detect subtle changes over time and creates ceiling effects for the amyloid-positive group. Using continuous measures, such as standard uptake value ratio, may allow for the detection of more subtle changes. However, in populations with little variability, measurement error may account for smaller changes in variability. This could be a limitation in populations with high amyloid burden where levels reach a plateau. Future work to consolidate the threshold used when defining amyloid positivity and use of continuous measures is required to better interpret data from amyloid PET. 4.4.3. Cognition measures The reports of amyloid PET on cognition in normal control subjects are contradictory, and a clear relationship has not been established between amyloid positivity and executive function or memory scores. However, worse cognition has been reported for individuals with the burden of both WMH and amyloid, suggesting that these copathologies may have additive effects on cognitive decline. Consistent with previous literature suggesting that SVD affects executive functioning [6], in those with sVaD, amyloid deposition

correlates with worse memory scores, whereas individuals with sVaD who are amyloid negative perform worse on tests of executive functioning. Further prospective studies with domain-specific cognitive evaluations, independently evaluating changes in executive function and memory over time, are required to gain a better understanding of cognitive decline associated with amyloid positivity and the influence of comorbid vasculopathy. 4.4.4. Amyloid versus vascular pathways: What about mixed dementia? A potentially important limitation with previous studies is the under-representation of individuals with mixed dementia. Amyloid positivity has been used to categorize individuals into pure amyloid and vascular subgroups and to discriminate between AD/MCI and sVaD/svMCI pathways (Fig. 2). Amyloid positivity was used to define “pure” sVaD, where subjects had significant vascular pathology as indicated by WMH (and lacunes) and were amyloid negative [18]. Similarly, pure AD subjects were identified as those who were amyloid positive and did not have significant WMH [36]. Despite these pure phenotypes, the more common presentation is of comorbid vascular and amyloid disease, suggesting that amyloid accumulation and WMH

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Fig. 2. Flow chart of amyloid and vascular pathways as described in the literature: for each diagnostic group, the range of average amyloid positivity on amyloid PET, WMH volumetrics, and the number of publications reporting on the association between WMH and amyloid uptake are reported. Abbreviations: AD, Alzheimer’s disease; MCI, mild cognitive impairment; PIB, Pittsburgh compound B; svMCI, subcortical vascular mild cognitive impairment; sVaD, subcortical vascular dementia; WMH, white matter hyperintensity. REV 5.4.0 DTD
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burden are not limited to a single pathogenic process, as outlined in Fig. 2, but instead reflect a highly prevalent “mixed dementia” phenotype that overlaps in normal, cognitively impaired, and demented elderly. Even in sVaD, a significant proportion of individuals are amyloid positive, with positivity occurring in those that are older, have significantly fewer lacunes, and increased medial temporal atrophy [18,38]. These individuals clearly illustrate the overlap of vascular disease with other features typically characteristic of AD, such as medial temporal atrophy and amyloid deposition, and highlight an important methodological limitation of selecting study cohorts as pure AD or pure sVaD, which does not adequately address individuals with mixed dementia, where the overlap of vascular and amyloid pathologies is the greatest and may be the most likely population to reveal interactive relationships. Future studies would benefit from the inclusion of individuals with overlapping disease burden, or mixed dementia, in particular, those with severe phenotypes where there is evidence of significant vascular disease in addition to amyloid positivity, which may be most revealing in terms of elucidating the potential interactive effects of vascular and amyloid pathologies. A Canadian multicenter longitudinal prospective amyloid PET imaging study is currently underway to evaluate this precise phenotype (ClinicalTrials.gov NCT02330510). Specifically, this study will compare progression of amyloid burden, as measured by uptake of florbetapir F18, with metabolic function, indexed via the uptake of 18F fludeoxyglucose PET, and volumetric findings from MRI imaging from baseline to 2 years of follow-up. In addition, this study will examine associated decline in cognitive performance on standardized neuropsychological assessments, over this study period. Importantly, the design of this study includes the recruitment of a unique cohort of real-world patients from stroke and dementia clinics, who also demonstrate the presence of moderate/extensive white matter disease. This cohort will then be compared with a cohort of cognitively normal control subjects and those with MCI and AD with minimal WMH burden identified from ADNI to explicitly examine associations between these imaging biomarkers and measures of cognitive function between those with severe versus minimal comorbid vascular disease. 4.4.5. Unique cohorts with amyloid PET data A significant limitation for interpreting the relationships between SVD and amyloid pathologies in this review related to the small number of independent cohorts that have previously been studied using amyloid PET imaging. Our findings indicated that, across studies, there were very few unique study cohorts and that many of the studies in this review conducted multiple analyses on varying subgroups from the same study cohort. Specifically, of the 34 articles included in the final selection, only 14 had independent study populations. The number of studies involving patients with AD/ MCI and sVaD/svMCI that measured both amyloid burden

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on PET imaging and WMH on MRI were also limited. Within our article set, the largest sample of sVaD subjects was N 5 77 and only N 5 67 for svMCI subjects [35,38], and, critically, the only source of data on participants characterized as sVaD or svMCI was the Samsung Medical Center cohort. Small sample sizes were also a limitation for studies evaluating MCI/AD populations, with the largest AD sample consisting of N 5 70 participants and the largest MCI sample including N 5 59 patients [24,36]. There is, thus, a need for further studies using multimodal imaging techniques, including amyloid PET and MRI, in unique cohorts of individuals with cognitive impairment and dementia to better characterize the relationships between measures of amyloid uptake and markers of SVD. 4.5. Conclusions In vivo imaging of amyloid pathology is an important tool, as amyloid often accumulates in prodromal stages before symptoms appear [67,77], and the exact mechanisms leading to its deposition are not fully understood. One-third of healthy elderly individuals are amyloid positive on PET imaging, and amyloid positivity increases with age and cognitive impairment [78,79]. Although the findings of this systematic review suggest that amyloid uptake on PET and WMH on MRI represent independent pathologic processes affecting brain health, early vascular insults may set the stage for future amyloid accumulation over time, potentially through the disruption of perivascular clearance systems in the central nervous system. In general, amyloid deposition is associated with memory impairment, whereas cerebral SVD is associated with executive dysfunction, and the presence of both may have additive effects on cognitive decline. However, the precise impact of these copathologies on cognitive function remains unclear. Additional gaps remain in our knowledge of patients with mixed dementia, limiting the generalizability of the studies in this review to real-world patient populations. The role of vascular risk factors and genomics is complex and also not clearly understood. Future studies using larger, independent cohorts that better represent those with mixed dementia, longitudinal designs, and stratification for vascular risk factors and genotype are required to advance our understanding of the associations between amyloidogenic and vasculopathic processes. As our understanding of these imaging biomarkers matures, we will develop a greater appreciation for the mechanisms underlying WMH and amyloid uptake, as well as how these biomarkers relate to the onset and progression of neurodegenerative and neurovascular disease. Acknowledgments The authors gratefully acknowledge financial and salary support (J.R., A.R., J.D.E.) from the Heart and Stroke Foundation of Canada, the Canadian Institutes of Health Research

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(MT#13129), the Linda C. Campbell Foundation, the Heart & Stroke Foundation Canadian Partnership for Stroke Recovery. J.R. received partial funding from the Canadian Vascular Network. In addition, S.E.B. would like to thank the Sunnybrook Research Institute, the Brill Chair in Neurology, and the Department of Medicine, the Sunnybrook Health Sciences Centre and University of Toronto for salary support. Disclosures: S.E.B. Ad hoc Consulting (personal): GE Healthcare, Eli Lilly, Boehringer Ingelheim, Novartis, Merck. S.E.B. Contract research: Elan, Roche, GE Healthcare, Eli Lilly, Pfizer, Lundbeck, Transition Therapeutics, Cognopix, Biogen Idec. S.E.B. CME Lectures (personal): Novartis, Eisai. All other coauthors report no conflicts of interest or financial disclosures.

[5]

[6]

[7]

[8] [9]

[10]

RESEARCH IN CONTEXT [11]

1. Systematic review: The literature was reviewed using systematic search methods to advance the understanding of reported interactions between amyloid positron emission tomography and magnetic resonance imaging of markers of small vessel disease, specifically white matter hyperintensity burden. 2. Interpretation: Findings of this study provide an overview of the research examining relationships between b-amyloid and white matter hyperintensities and contextualize them within current models of amyloidogenic pathophysiology. 3. Future directions: This systematic review describes several limitations with the design and population sources used in these studies and highlights gaps that remain in our knowledge of the mechanisms underlying vascular and neurodegenerative copathologies in aging. We specifically outline areas of future study that are required to advance this field.

[12]

[13]

[14] [15] [16]

[17]

[18]

[19]

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