Q7

Q1

Q2

Alzheimer’s & Dementia - (2015) 1-8

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

Perspective

White matter hyperintensity burden in elderly cohort studies. TheSunnybrook Dementia Study, Alzheimer Disease Neuroimaging

Initiative, and Three-City Study

65

66

67

68

69

70

71

72

73

74

Joel Ramireza,b,*,1, Alicia A. McNeelya,b,1, Christopher J. M. Scotta,b, Mario Masellisa,b,c,d,e,Sandra E. Blacka,b,c,e, for the Alzheimer’s Disease Neuroimaging Initiative2

aLC Campbell Cognitive Neurology Research Unit, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, CanadabHeart & Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Health Sciences Centre (SHSC), Toronto, Canada

cInstitute of Medical Science, University of Toronto (UT), Toronto, CanadadNeurogenetics Section, Centre for Addiction and Mental Health, Toronto, Canada

eDepartment of Medicine, Neurology, SHSC and UT, Toronto, Canada

75

76

77

Abstract Given the recent acknowledgement of the complex mixed pathologies that contribute to the clin-

J.R., A.A.M., and

SEB from the Brill C

brook Health Science1These authors co2Data used in pre

heimer’s Disease Neu

cla.edu). As such, th

design and implement

ipate in analysis or wr

tigators can be foun

how_to_apply/ADNI_

*Corresponding au

E-mail address: jo

http://dx.doi.org/10.10

1552-5260/� 2015 Th

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

ical expression of dementia, various cohort studies have aimed to examine Alzheimer’s disease andcerebrovascular disease as comorbid pathologies, with neuroimaging playing a central role in thesestudies. Using white matter hyperintensities (WMH) as a biomarker of cerebrovascular disease, wecompared WMH burden between the Sunnybrook Dementia Study, the Alzheimer’s Disease Neuro-imaging Initiative (ADNI1), the Three-City Study, and various other studies around the world. Basedon our findings, it was evident that ADNI1 had minimal WMH burden relative to other large studiesthat examine aging and dementia. This low WMH burden in ADNI1 may be considered as both anadvantage, representing a relatively “pure” sample with little confounding vasculopathy, and a disad-vantage, as it limits generalizability to “real-world” patient populations with mixed pathologies andto nondemented groups with baseline vascular disease. We explore possible reasons for this distinc-tion, including management of vascular risk factors, gaps in diagnostic criteria, and future directionsfor clinical research.� 2015 The Alzheimer’s Association. Published by Elsevier Inc. All rights reserved.

94

95

Keywords: Alzheimer’s disease; Cerebrovascular disease; Aging; White matter hyperintensities; Dementia; Elderly cohort;

Population studies

96

97

C.J.M.S. receive salary support from HSFCPSR and

hair in Neurology Department of Medicine, Sunny-

s Centre and University of Toronto.

ntributed equally to this work.

paration of this article were obtained from the Alz-

roimaging Initiative (ADNI) database (adni.loni.u-

e investigators within the ADNI contributed to the

ation of ADNI and/or provided data but did not partic-

iting of this report. A complete listing of ADNI inves-

d at: https://adni.loni.usc.edu/wp-content/uploads/

Acknowledgement_List.pdf.

thor. Tel.: ---; Fax: ---.

elr@sri.utoronto.ca

16/j.jalz.2015.06.1886

e Alzheimer’s Association. Published by Elsevier Inc. All r

FLA 5.2.0 DTD � JALZ2049_proof

98

99

100

101

102

103

104

105

106

107

108

109

1. Introduction

Dementia affects approximately 44 million people world-wide according to current estimates, a number that is pre-dicted to more than triple to 135 million by 2050 [1]. AsAlzheimer’s disease (AD) and vascular cognitive disordersare the top two leading primary causes of dementia [2],recent studies examining the contribution of modifiablerisk factors for dementia have acknowledged cerebrovascu-lar pathology as a primary concern [3–6], with neuroimagingplaying a central role in many of these studies [7]. As mostdementia cases are mixed pathologies with some vascularcomponent [8], many present studies have increased their

ights reserved.

� 21 July 2015 � 6:40 pm � ce

J. Ramirez et al. / Alzheimer’s & Dementia - (2015) 1-82

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

focus toward understanding the role of vasculopathy,vascular brain injury, and the management of vascularrisk factors [9,10], in the context of AD pathophysiology[11–13].

As recently defined by an international consensus pro-cess, white matter hyperintensities (WMH) of presumedvascular origin, visible on structural magnetic resonance im-aging (MRI), are commonly used markers of cerebrovascu-lar disease [7]. Clinicopathologic correlations suggestWMHto be indicative of cerebral small vessel disease [7,14],potentially originating from ischemic tissue damagecaused by arteriosclerosis [15,16], vasogenic edemainduced by periventricular venous collagenosis [17,18],and cerebral amyloid angiopathy [19–21]. These imaging-based biomarkers of cerebral small vessel disease havebeen associated with increased age, vascular risk factors,mild cognitive impairment (MCI), and AD [22–24].

In this article, we chose to examine imaging markers ofsmall vessel disease within three large neuroimagingstudies: the Sunnybrook Dementia Study (SDS: Canada),the Alzheimer’s Disease Neuroimaging Initiative Phase 1(ADNI1: mainly US), and the Three-City Study (3C:France). We examined these studies because (1) they wererelatively contemporary, having been conducted aroundthe same time, (2) the populations were sampled primarilyfrom different countries, (3) the imaging acquisition proto-cols (at 1.5 tesla) were comparable, (4) WMH volumeswere quantified using proton density and T2-weighted (T2)MRI sequences (i.e., non-FLAIR based), and (5) study sam-ples were elderly, aged 50–90 years.

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

2. The Sunnybrook Dementia Study, Alzheimer’sDisease Neuroimaging Initiative, and Three-City Study

The SDS [25] is a prospective cohort study (1994–2014)conducted at the Sunnybrook Heath Sciences Centre–Uni-versity of Toronto, in Toronto, Canada (ClinicalTrials.govNCT01800214). One goal of the SDS was to examine areal-world cohort of dementia patients and normal elderly(50–90 years old) and the potential impact of comorbid ce-rebral small vessel disease manifested primarily as covert la-cunes and white matter lesions.

The ADNI1 [26] is a large multisite longitudinal brainimaging study based in the United States (53 sites) and Can-ada (5 sites). The first phase, ADNI1 (2004–2010), exam-ined patients with AD, MCI, and normal elderly controls(NC), aged 55–90 years. The study’s primary objectivesincluded the identification of biomarkers to identify AD atthe earliest stage so that intervention, prevention, and treat-ment of dementia could bemore effective (See Supplement 1for additional details).

The 3C [27] is a multicenter, longitudinal population–based cohort study (1999–2012) conducted in three citiesin France: Bordeaux, Dijon, and Montpellier. The goal ofthe 3C study was to examine the associations of vascularrisk with dementia and cognitive impairment. Participants

FLA 5.2.0 DTD � JALZ2049_proof

were randomly sampled from electoral rolls and asidefrom age (65–80 years), there were no exclusion criteria.The subsample examined in the present study included1701 nondemented elderly with a mean mini-mental stateexamination (MMSE) of 28, suggesting a relatively normalsample. Unfortunately, stratification by cognitive statuswas not possible because diagnostic criteria for MCI werenot implemented on entry into the 3C study.

3. WMH findings in dementia and the elderly: ADNI1,SDS, and 3C

To compare WMH volumes between SDS and ADNI1,we plotted head-size corrected WMH volumes by age tovisually examine the distributions across the diagnoses(Dx; Fig. 1). To account for differences in disease severityfor the AD groups, we only included patients with MMSEscores �20 (based on ADNI1 inclusion criteria). On visualinspection of the graphs displayed in Fig. 1, it was evidentthat there were very obvious differences in the distributionof WMH in these two cohort studies. Additionally, similardifferences were demonstrated for all Dx groups withineach sample, with the SDS samples exhibiting greater age-related WMH volumes compared with the ADNI1 samples.

As further demonstrated in Table 1, these differences canalso be seen with group average and variability statistics,whereby the SDS sample displayed more variability andhigher average WMH volumes across all Dx groups whencompared with ADNI1 (all significant, P , .001, Table 1).Additionally, population-based data recently reported bythe 3C group [28] were also included for relative comparison(Table 1). Based on these results, the vascular burden, indi-cated by WMH volumes, was much greater in the SDS and3C samples than in the ADNI1 sample.

Additionally, because WMH volumes typically exhibit anonnormal, often highly skewed distribution, the reportingof standard statistical measures for central tendency andspread may not be appropriate for proper visualization ofthe data. Given this phenomenon, we have also provided abreakdown of the proportional distributions by range ofWMH in the SDS and ADNI1 samples. As shown inFig. 2, compared with 22% in the SDS sample, 83% of theADNI1 sample presented with less than 1 cc of WMH(dark green) across all Dx groups. Conversely, althoughover a third of the SDS sample had over 5 cc of WMH(warm colors: yellow, orange, and red), less than 3% of theADNI1 sample had significant volumes of WMH. Althoughthis could be due to a difference in the proportional represen-tation of AD and MCI patients between the two studies,similar patterns are observed in the NC samples (albeit toa lesser degree). Interestingly, only the MCI and NC groupsin ADNI1 had any subjects with WMHs exceeding the 20-ccmark (red), a proportional representation made up of threeindividuals (MCI: n 5 2, NC: n 5 1) who would be consid-ered as statistical outliers for both groups. Overall, incontrast to the positively skewed distribution of WMH

� 21 July 2015 � 6:40 pm � ce

web4C=FPO

web4C=FPO

Fig. 1. Scatterplots showing the distribution of WMH volume by age for AD patients, MCI, and NC, comparing the ADNI1 (red circles) and the SDS (blue

triangles). Head-size corrected WMH volumes are reported in cubic centimeters (cc). AD patients were also matched for disease severity using the MMSE.

Dotted line represents the 10-cc cognitive threshold for WMHs originally proposed by Boone et al. [29]. Abbreviations: WMH, white matter hyperintensities;

AD, Alzheimer’s disease; MCI, mild cognitive impairment; NC, normal elderly controls; ADNI1, Alzheimer’s Disease Neuroimaging Initiative; SDS, Sunny-

brook Dementia Study; MMSE, mini-mental state examination.

J. Ramirez et al. / Alzheimer’s & Dementia - (2015) 1-8 3

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

load in the SDS sample, the ADNI1 exhibited less of a skew,with a greater representation of subjects with minimalWMHburden across all Dx groups.

Finally, as changes to the brain’s white matter are histor-ically believed to be an age-related phenomenon, the ques-tion regarding the clinical meaningfulness of WMH oftenarises. Based on the threshold theory originally proposedby Boone et al. (1992) [29–31], we examined the datausing a threshold value of 10 cc, above which the effect ofWMH can be measured clinically (see dotted line onFig. 1 and Supplement 3). Using this threshold as a bench-

Table 1

MMSE, age, and WMH volumetrics by Dx group and study sample

Study

MMSE (/30) Age (y) WMH (cc)

Mean

(SD)

Mean

(SD) Range

Mean

(SD) P

AD

SDS, n 5 212 24.5 (2.7) 72.2 (8.9) 50.4–88.9 7.6 (9.4) ***

ADNI, n 5 161 23.0 (2.1)* 74.9 (7.6) 55.0–91.0 1.0 (1.9)

MCI

SDS, n 5 70 26.8 (2.2) 71.6 (7.8) 51.5–87.3 5.2 (7.1) ***

ADNI, n 5 347 27.0 (1.8)* 74.6 (7.5) 55.0–90.0 0.8 (2.4)

NC

SDS, n 5 105 28.9 (1.0) 69.5 (8.1) 50.5–89.6 5.0 (8.4) ***

ADNI, n 5 216 29.0 (1.0)* 75.9 (5.0) 60.0–90.0 0.7 (2.2)

3C, n 5 1701 27.7 (1.7) 72.3 (4.1) 65–80y 5.5 (4.9) —

Abbreviations: MMSE, mini-mental state examination; WMH, white

matter hyperintensities; Dx, diagnostic group; SD, standard deviation;

AD, Alzheimer’s disease; MCI, mild cognitive impairment; NC, normal

elderly controls; ADNI1, Alzheimer’s Disease Neuroimaging Initiative;

SDS, Sunnybrook Dementia Study.

NOTE. ***P , .001 (Mann-Whitney U test).

*Mean (SD) reported by Carmichael et al. [32].yRange based on reported group inclusion criteria.

FLA 5.2.0 DTD � JALZ2049_proof

mark, it is apparent that a quarter of the AD patients in theSDS presented with significant white matter disease. Moreimportantly, the presence of clinically meaningful WMHin the SDS was not exclusive to AD patients, as both theMCI and NC groups had 17% and 13% of the samples,respectively, exceeding this threshold. In contrast, theADNI1 had fewer subjects with WMH volumes beyondthis threshold, providing a cleaner sample through whichcognitive decline can be attributed primarily to AD pathol-ogy without confounding comorbid vasculopathy.

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

4. Comparisons with other studies around the world

These graphs and descriptive statistics demonstrate thatthe ADNI1 multisite sample, obtained primarily from USsites, has significantly less burden of WMH on MRIcompared with those obtained from the SDS tertiary mem-ory clinic in Canada, and the 3C elderly population-basedsample in France. Although these graphs and tables weregenerated from volumetrics obtained directly from theADNI1 and SDS database tables, it would be prudent tolook at how these results differ from some of the publishedreports from ADNI1, SDS, and 3C. Furthermore, an exami-nation of publications from other similar elderly cohortstudies around the world may provide additional insightinto the visible small vessel disease burden in elderly andneurodegenerative clinical populations.

As shown in Table 2, the WMH volumes reported inrecent publications of these first three groups were similarto our current results, with ADNI1 demonstrating the lowestWMH volumes compared with SDS and 3C publications.Specifically, ADNI1 published averages around �1 cc forall groups [32,33]; the SDS publications report averagevolumes in the range of 5–8 cc for dementia patients

� 21 July 2015 � 6:40 pm � ce

Q3

web4C=FPO

web4C=FPO

Fig. 2. Pie chart showing WMH volume ranges for the Sunnybrook Dementia Study (SDS) sample (left) and the Alzheimer’s Disease Neuroimaging Initiative

(ADNI1) sample (right). Abbreviations: WMH, white matter hyperintensities; ADNI1, Alzheimer’s Disease Neuroimaging Initiative; SDS, Sunnybrook De-

mentia Study; AD, Alzheimer’s disease; MCI, mild cognitive impairment; NC, normal elderly controls; Dx, diagnostic group.

J. Ramirez et al. / Alzheimer’s & Dementia - (2015) 1-84

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

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

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

[24,34], and the 3C publications report volumes in the 4–5 ccrange for its nondemented population-based sample[28,35,36].

Although we specifically selected these three studiesbased on their aforementioned similarities, there are severalother large studies that have quantified WMH volumetricswhich we can use for additional comparison (Table 2). Find-ings from the Leukoaraiosis and Disability Study [37], a Eu-ropean multicenter study which examined nondisabledelderly subjects with evidence of age-related white matterchanges on MRI, report an average WMH volume of 20.2cc 6 21.0 for their entire group and 6.4 cc 6 5.0 for thelowest grade group (i.e. Fazekas) [38]. Similarly, the Rotter-dam Study, a Dutch population-based cohort study whichexamined stroke-free nondemented elderly subjects,recently reported an average white matter lesion volume of

FLA 5.2.0 DTD � JALZ2049_proof

3.7 cc 6 4.6 [39]. The Personality & Total Health ThroughLife longitudinal cohort study based in Australia, recentlyreported an average WMH volume of 4.8 cc 6 4.7 for theirrelatively young elderly cohort (60–64 years) [40]. The Clin-ical Research Center for Dementia of South Korea, a SouthKorean multisite elderly cohort study which examined pa-tients with MCI or dementia, recently reported an averageWMH volume of 10.8 cc 6 18.4 [41]. Thus, despite manyof the potential differences in study protocols and image ac-quisitions between these studies, it would be reasonable toconclude that the visible small vessel disease burden in theSDS and 3C samples are within the range of typical real-world values.

Given these various reports from Asia and Australia toEurope and North America that have examined large elderlypopulations with and without dementia, the ADNI1 study

� 21 July 2015 � 6:40 pm � ce

Table 2

Comparison of WMH volumetric reports from the SDS, ADNI1, 3C, and elderly cohort studies

Study Location

Study

duration Publication Sample (n) Age, y

White matter hyperintensity (cc) by Dx

Various NC MCI AD

SDS Canada 1994–2014 Current findings NC (105), MCI (70), AD (212) 71.3 (8.6) — 5.0 (8.4) 5.2 (7.1) 7.6 (9.4)

McNeely et al. 2015 AD (234) 72.0 (9.0) — — — 7.3 (9.2)

Ramirez et al. 2014 NC (100), AD (265) 69.5 (8.0)* — 2.5 (3.3)y — 5.4 (11.0)y

ADNI1 N. America 2004–2010 Current findings NC (216), MCI (347), AD (161) 75.0 (6.9) — 0.7 (2.2) 0.8 (2.4) 1.0 (1.9)

Carmichael et al. 2010 NC (224), MCI (391), AD (189) 76.0 (6.9) — 0.5 (1.1) 0.7 (1.2) 1.1 (2.0)

Barnes et al. 2013 NC (197), MCI (331), AD (146) 76.0 (5.1)* — 0.3 (0.5)y 0.3 (0.5)y 0.4 (1.0)y

3C France 1999–2012 Godin et al. 2010 Nondemented elderly (1701) 72.3 (4.1) 5.5 (4.9) — — —

Godin et al. 2011 Nondemented elderly (1319) 72.0 (0.1)z 5.4 (0.1)z — — —

Satizabal et al. 2012 Nondemented elderly (1771) 72.5 (4.1) 4.1 (3.7)y — — —

LADIS Europe From 2001 Schmidt et al. 2010 Nondisabled elderly (340) 73.9 (5.1) 20.2 (21.0) — — —

Rotterdam Netherlands 2005–2009 Verlinden et al. 2014 Nondemented elderly (2025) 59.9 (7.0) 3.7 (4.6) — — —

PATH Australia 2001–2010 Chen et al. 2009 Community elderly (477) 62.6 (1.5) 4.9 (4.7) — — —

CREDOS S. Korea 2000–2008 Noh et al. 2014 MCI and Dementia (352) 72.1 (8.0) 10.8 (18.4) — — —

Abbreviations: WMH, white matter hyperintensities; SDS, Sunnybrook Dementia Study; ADNI1, Alzheimer’s Disease Neuroimaging Initiative; 3C, Three-

City Study; Dx, diagnostic group; NC, normal elderly controls; MCI, mild cognitive impairment; AD, Alzheimer’s disease; LADIS, Leukoaraiosis and

Disability Study; PATH, Personality & Total Health; CREDOS, Clinical Research Center for Dementia of South Korea; SD, standard deviation; IQR, interquar-

tile range; SE, standard error.

NOTE. All data reported as mean (SD), unless otherwise marked.

*Based on reported NC data.yMedian (IQR).zMean (SE).

J. Ramirez et al. / Alzheimer’s & Dementia - (2015) 1-8 5

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

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

appears to provide a rare opportunity to examine progressionof early prodromalMCI to ADwithout the typical real-worldconfound of comorbid vasculopathy. As ADNI1 is the onlyelderly cohort study that reports an average WMH volumearound�1 cc for both normal elderly and dementia patients,this may have important implications for the insights gainedfrom ADNI1 results.

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

5. Gaps in the current diagnostic criteria?

When ADNI1 began in 2004, there were no guidelines inthe National Institute of Neurological and CommunicativeDisorders and Stroke and the Alzheimer’s Disease andRelated Disorders Association clinical diagnostic criteriaregarding WMH burden as observed on neuroimaging[42]. To account for significant vascular burden, ADNI1applied an exclusion criteria threshold of �4 on the Hachin-ski ischemia scale [43]. Interestingly, the more recent Na-tional Institute on Aging-Alzheimer’s Association (NIA-AA) workgroups diagnostic criteria acknowledge the poten-tial use of neuroimaging to evaluate the presence of WMHburden [44]. In contrast to the diagnosis of probable AD de-mentia, Section 5.2 of the NIA-AA criteria proposed a diag-nosis of possible AD dementia in circumstances wherepatients have an etiologically mixed presentation. In partic-ular, evidence of concomitant cerebrovascular disease, asindicated by “severe WMH burden,” would necessitate adiagnosis of possible AD. The primary difficulty with theetiologically mixed presentation criteria is that “severe” isa relative term, with no quantitative threshold for cliniciansand researchers to follow (Fig. 3). In other words, at what

FLA 5.2.0 DTD � JALZ2049_proof

point on the continuum does WMH burden transition frommoderate to severe and consequently change a patient’sdiagnosis from probable to possible AD dementia? Shouldthis reliance on structural MRI for patient diagnostics bebased on visual rating of WMH (e.g. Fazekas scale), orshould it be based on a volumetric quantification method?Should there be a threshold for WMH burden before cogni-tive deficits are observed, as originally proposed by Booneand others in the 1990s [29,31]?

Conversely, a recent proposal by the International Societyfor Vascular Behavioural and Cognitive Disorders outlinedsome recommendations regarding the diagnostic criteriafor vascular cognitive disorders [45]. As the second mostcommon single cause of dementia after AD, the diagnosisof vascular dementia (VaD) presents a significant challenge,particularly in light of the issues regarding the diagnosis ofAD dementia with an etiologically mixed presentation.The publication outlines some of the issues related to theinteraction between vascular and neurodegenerative pro-cesses and the complications when discerning AD-typepathology from VaD, as these two pathologies often co-exist. Furthermore, as the term “dementia” has becomeincreasingly synonymous with the term “Alzheimer’s dis-ease,” when a patient presents with significant WMHburden, that patient is more likely to be diagnosed as mixedAD, limiting the potential early detection of cognitiveimpairment due to vascular pathology.

Given the issues with the diagnostic criteria for AD,and the problems related to the overlapping neurodegenera-tive and vascular contributions to dementia, there areno agreed-on guidelines for how neuroimaging-based

� 21 July 2015 � 6:40 pm � ce

Q4

Fig. 3. Structural MRI (left5 T1, middle5 PD, and right5T2) of a 71-year-old woman living with Alzheimer’s disease. Lesion analysis [58] revealed that she

had 16 cc of WMH. Should this be considered moderate or severe WMH burden? Should this patient’s diagnosis be probable or possible AD dementia? (See

Supplement 2 for complete proton density images). Abbreviations: MRI, magnetic resonance imaging; PD, proton density; WMH, white matter hyperinten-

sities; AD, Alzheimer’s disease.

J. Ramirez et al. / Alzheimer’s & Dementia - (2015) 1-86

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664

665

666

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

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

biomarkers should be used in dementia diagnostics. The lackof consensus-based criteria that specifically classify a mixedpathology dementia [46] may help to explain the differencesbetween ADNI1 and other studies. Thus, in contrast to othermore heterogeneous “real-world” clinical samples with awider range of vascular burden in both demented and normalelderly populations, ADNI1 may represent a controlled“pure” sample, with little to no vascular comorbidity. Inter-estingly, despite the low vascular burden in the ADNI1 sam-ple, baselineWMH burden was found to be associated with adecline in cognition, executive function, and semantic mem-ory [32,47], suggesting that visible WMHmay indeed be thetip of the iceberg of a more diffuse disease that is clinicallyrelevant.

Although varying interpretations of the relevance ofsmall vessel disease in applying diagnostic criteria couldexplain the low WMH burden in ADNI1’s AD and MCIgroups, the low burden in the NC sample is less clearly ex-plained. Although differences in education levels, imbalancein gender representation, or a larger proportion of apoE ε4carriers could also partially explain the findings in ADNI1’sAD and MCI groups, these demographic and genetic vari-ables did not account for the low WMH’s in the NC sample.Alternatively, the low WMH burden across the ADNI1groups could represent a selection bias toward normalelderly and dementia volunteers who have very well-managed vascular risk factors when they came to academiccenters for participation in such studies.

Results from a recent report by the Rotterdam Study [48],as well as similar reports from population-based studies inthe United States [49–51], and the United Kingdom [52],suggest that recent increases in the administration of anti-thrombotics, anti-hypertensives, and lipid-lowering drugshave recently improved management of hypertension,obesity, and overall vascular health. Additional support forthis comes from the Rotterdam Study’s neuroimaging re-sults, where participants in the more recent subcohort

FLA 5.2.0 DTD � JALZ2049_proof

(2005–2006) had significantly less WMH burden than thosein the earlier subcohort (1995–1996), suggesting a decreasein cerebrovascular injury in the more recent sample. Thisdecline in WMH burden was believed to be related toincreased prosperity, education, and, more importantly,improved management of vascular risk factors [48,51–53].

In light of these recent positive reports, the low cerebro-vascular burden in the ADNI1 sample may be evidence of acontrolled sample with little to no overlapping vascular pa-thology and/or individuals with properly controlled manage-ment of their overall vascular health. Future studiesexamining the use of lipid lowering and anti-hypertensivemedications in ADNI1 may provide further insight intothis possibility. Most importantly, ADNI1, which is freelyavailable, can be regarded as an important reference sampleof clinically “pure” AD in a highly educated population,which can be used for comparison with other more represen-tative “real-world” memory clinic samples and populationstudies.

6. Conclusion

Using WMH as a neuroimaging marker of cerebral smallvessel disease, we found that the ADNI1 sample had asignificantly lower burden relative to those reported in theSDS, the 3C, and various other elderly and dementia cohortstudies around the world. Although this could be explainedby uncertainty regarding what constitutes “severe” WMHburden in the diagnostic criteria, inclusion of participantswhose vascular risk factors are well-controlled before andduring the study, it is our view that ADNI1’s sample canbe considered a relatively “pure,” filtered cohort ofdemented and nondemented elderly with little to no vascularburden—possibly the “cleanest” deeply endophenotypedelderly cohort acquired to date. There is no doubt thatADNI1 has already and will continue to yield critically

� 21 July 2015 � 6:40 pm � ce

Q5

Q6

J. Ramirez et al. / Alzheimer’s & Dementia - (2015) 1-8 7

780

781

782

783

784

785

786

787

788

789

790

791

792

793

794

795

796

797

798

799

800

801

802

803

804

805

806

807

808

809

810

811

812

813

814

815

816

817

818

819

820

821

822

823

824

825

826

827

828

829

830

831

832

833

834

835

836

837

838

839

840

841

842

843

844

845

846

847

848

849

850

851

852

853

854

855

856

857

858

859

860

861

862

863

864

865

866

867

868

869

870

871

872

873

874

875

876

877

878

879

880

881

882

883

884

885

886

887

888

889

890

891

892

893

894

895

896

897

898

899

900

901

902

903

904

905

906

907

908

909

910

911

912

913

important insight into genetics, clinical, and progression pat-terns of sporadic AD that is relatively free of SVD.

In line with the continuing story of vascular contribu-tions to dementia, there are studies currently underway inCanada which aim to directly assess the clinical impactof WMH burden in the context of neurodegeneration, ag-ing, atherosclerosis, stroke, and dementia. The Medical Im-aging Trial Network of Canada C6 (ClinicalTrials.govNCT02330510) is a nationwide study measuring baselineamyloid uptake progression in patients with significantWMH burden to determine relationships with clinical,structural, and functional brain measures [54]. The Cana-dian Atherosclerosis Imaging Network is a pan-Canadianstudy examining carotid stenosis using 3D in vivo neck im-aging, and end-organ brain disease through structural MRI,with the final goal of developing novel therapeutic inter-ventions aimed at atherosclerosis [55,56]. The OntarioNeurodegenerative Disease Research Initiative is amultimodal observational study which is examining theinteractions between various neurodegenerative diagnosesand contributions from small vessel disease copathologyby looking for the early indicators, commonalities, anddistinguishing characteristics in these diseases [57]. Inaddition, imaging analysis pipelines optimized for quanti-fying subtypes of small vessel disease have been developedfor application in a number of these studies [58]. Thesemultisite Canadian studies currently underway may provideadditional insight into the complex neurodegenerative andvascular processes that lead to the clinical expression ofdementia.

On this world stage of large, longitudinal, multicentre,multinational studies that are designed to help us understandthe various neurologic disorders that plague our aging pop-ulation, this balancing act between controlled scientificstudies and those representing more real-world clinical pop-ulations presents a unique analytical challenge to the knowl-edge translation of “big data,” which has yet to be resolved.Understanding similarities and differences between pure andmixed vascular-AD dementia “big data” cohorts shouldyield important information that may eventually aid in thedevelopment of personalized therapeutics for AD dementia.

Acknowledgments

The authors gratefully acknowledge financial support fromthe Canadian Institutes of Health Research (MOP#13129),the Alzheimer Society of Canada and Alzheimer Associa-tion (US), the Heart and Stroke Foundation Canadian Part-nership for Stroke Recovery (HSFCPSR), Brain SciencesResearch program at Sunnybrook Research Institute, andthe Linda C. Campbell Foundation.

Supplementary data

Supplementary data related to this article can be found athttp://dx.doi.org/10.1016/j.jalz.2015.06.1886.

FLA 5.2.0 DTD � JALZ2049_proof

References

[1] Alzheimer’s Disease International.World Alzheimer Report 2014: De-

mentia and risk reduction: An analysis of protective and modifiable

factors. London, UK: 2014.

[2] Fratiglioni L, Launer LJ, Andersen K, Breteler MM, Copeland JR,

Dartigues JF, et al. Incidence of dementia and major subtypes in Eu-

rope: A collaborative study of population-based cohorts. Neurologic

Diseases in the Elderly Research Group. Neurology 2000;54:S10–5.

[3] Black S, Iadecola C. Vascular cognitive impairment: Small vessels, big

toll: Introduction. Stroke 2009;40:S38–9.

[4] Knopman DS. Cerebrovascular pathology in cognitive impairment:

New (in)sights. Neurology 2012;78:1032–3.

[5] De la Torre JC. Detection, prevention, and pre-clinical treatment of

Alzheimer’s disease. J Alzheimers Dis 2014;:64–5.

[6] Portet F, Brickman AM, Stern Y, Scarmeas N, Muraskin J,

Provenzano FA, et al. Metabolic syndrome and localization of white

matter hyperintensities in the elderly population. Alzheimers Dement

2012;8:S88–951.

[7] Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F,

Frayne R, et al. Neuroimaging standards for research into small vessel

disease and its contribution to ageing and neurodegeneration. Lancet

Neurol 2013;12:822–38.

[8] Schneider JA, Arvanitakis Z, Bang W, Bennett DA. Mixed brain pa-

thologies account for most dementia cases in community-dwelling

older persons. Neurology 2007;69:2197–204.

[9] Viswanathan A, Rocca WA, Tzourio C. Vascular risk factors and de-

mentia: How to move forward? Neurology 2009;72:368–74.

[10] Mielke MM, Montine T, Khachaturian AS. Vascular diseases: one

pathway toward new conceptual models of dementia. Alzheimers De-

ment 2012;8:S69–70.

[11] Deramecourt V, Slade JY, Oakley AE, Perry RH, Ince PG,

Maurage CA, et al. Staging and natural history of cerebrovascular pa-

thology in dementia. Neurology 2012;78:1043–50.

[12] Toledo JB, Arnold SE, Raible K, Brettschneider J, Xie SX,

GrossmanM, et al. Contribution of cerebrovascular disease in autopsy

confirmed neurodegenerative disease cases in the National Alz-

heimer’s Coordinating Centre. Brain 2013;136:2697–706.

[13] Moghekar A, Kraut M, Elkins W, Troncoso J, Zonderman AB,

Resnick SM, et al. Cerebral white matter disease is associated with

Alzheimer pathology in a prospective cohort. Alzheimers Dement

2012;8:S71–7.

[14] Pantoni L. Cerebral small vessel disease: from pathogenesis and clin-

ical characteristics to therapeutic challenges. Lancet Neurol 2010;

9:689–701.

[15] GouwAA, SeewannA, van der FlierWM,Barkhof F, Rozemuller AM,

Scheltens P, et al. Heterogeneity of small vessel disease: A systematic

review of MRI and histopathology correlations. J Neurol Neurosurg

Psychiatry 2011;82:126–35.

[16] O’Sullivan M. Leukoaraiosis. Pract Neurol 2008;8:26–38.

[17] Black SE, Gao FQ, Bilbao J. Understanding white matter disease:

Imaging-pathological correlations in vascular cognitive impairment.

Stroke 2009;40:S48–52.

[18] Moody DM, Brown WR, Challa VR, Anderson RL. Periventricular

venous collagenosis: Association with leukoaraiosis. Radiology

1995;194:469–76.

[19] HaglundM, Englund E. Cerebral amyloid angiopathy, white matter le-

sions and Alzheimer encephalopathy—a histopathological assess-

ment. Dement Geriatr Cogn Disord 2002;14:161–6.

[20] Viswanathan A, Chabriat H. Cerebral microhemorrhage. Stroke 2006;

37:550–5.

[21] Smith EE, Eichler F. Cerebral amyloid angiopathy and lobar intracere-

bral hemorrhage. Arch Neurol 2006;63:148–51.

[22] Yoshita M, Fletcher E, Harvey D, OrtegaM,Martinez O, Mungas DM,

et al. Extent and distribution of white matter hyperintensities in normal

aging, MCI, and AD. Neurology 2006;67:2192–8.

� 21 July 2015 � 6:40 pm � ce

J. Ramirez et al. / Alzheimer’s & Dementia - (2015) 1-88

914

915

916

917

918

919

920

921

922

923

924

925

926

927

928

929

930

931

932

933

934

935

936

937

938

939

940

941

942

943

944

945

946

947

948

949

950

951

952

953

954

955

956

957

958

959

960

961

962

963

964

965

966

967

968

969

970

971

972

973

974

975

976

977

978

979

980

981

982

983

984

985

986

987

988

989

990

991

992

993

994

995

996

997

998

999

1000

1001

1002

1003

1004

1005

1006

1007

1008

1009

1010

1011

1012

1013

1014

1015

1016

1017

1018

1019

1020

1021

1022

1023

1024

1025

1026

1027

1028

1029

1030

1031

1032

1033

1034

1035

1036

1037

1038

1039

1040

1041

1042

1043

[23] DeCarli C, Miller BL, Swan GE, Reed T, Wolf PA, Carmelli D. Cere-

brovascular and brain morphologic correlates of mild cognitive

impairment in the National Heart, Lung, and Blood Institute Twin

Study. Arch Neurol 2001;58:643–7.

[24] Ramirez J, McNeely AA, Scott CJ, Stuss DT, Black SE. Subcortical

hyperintensity volumetrics in Alzheimer’s disease and normal elderly

in the Sunnybrook Dementia Study: Correlations with atrophy, execu-

tive function, mental processing speed, and verbal memory. Alz-

heimers Res Ther 2014;6:49.

[25] The Sunnybrook Dementia Study n.d. Available from: www.brainlab.

ca/sunnybrookdementiastudy (accessed March 19, 2015).

[26] Alzheimer’s Disease Neuroimaging Initiative n.d. Available from:

www.adni.loni.usc.edu (accessed March 19, 2015).

[27] The Three-City Study n.d. Available from: www.three-city-study.com

(accessed March 19, 2015).

[28] Godin O, Tzourio C, Rouaud O, Zhu Y, Maillard P, Pasquier F, et al.

Joint effect of white matter lesions and hippocampal volumes on

severity of cognitive decline: The 3C-Dijon MRI study. J Alzheimers

Dis 2010;20:453–63.

[29] Boone KB, Miller BL, Lesser IM, Mehringer CM, Hill-Gutierrez E,

GoldbergMA, et al. Neuropsychological correlates of white-matter le-

sions in healthy elderly subjects. A threshold effect. Arch Neurol 1992;

49:549–54.

[30] Albert M, Massaro J, Decarli C, Beiser A, Seshadri S, Wolf PA, et al.

Profiles by sex of brainMRI and cognitive function in the Framingham

Offspring Study. Alzheimer Dis Assoc Disord 2010;24:190–3.

[31] Decarli C, Murphy DG, Tranh M, Grady CL, Haxby JV, Gillette JA,

et al. The effect of white matter hyperintensity volume on brain struc-

ture, cognitive performance, and cerebral metabolism of glucose in 51

healthy adults. Neurology 1995;45:2077–84.

[32] Carmichael O, Schwarz C, Drucker D, Fletcher E, Harvey D,

Beckett L, et al. Longitudinal changes in white matter disease and

cognition in the first year of the Alzheimer disease neuroimaging

initiative. Arch Neurol 2010;67:1370–8.

[33] Barnes J, Carmichael OT, Leung KK, Schwarz C, Ridgway GR,

Bartlett JW, et al. Vascular and Alzheimer’s disease markers indepen-

dently predict brain atrophy rate in Alzheimer’s Disease Neuroimag-

ing Initiative controls. Neurobiol Aging 2013;34:1996–2002.

[34] McNeely AA, Ramirez J, Nestor SM, Zhao J, Gao F, Kiss A, et al.

Cholinergic subcortical hyperintensities in Alzheimer’s disease pa-

tients from the Sunnybrook Dementia Study: Relationships with

cognitive dysfunction and hippocampal atrophy. J Alzheimers Dis

2015;43:785–96.

[35] Godin O, Tzourio C, Maillard P, Mazoyer B, Dufouil C. Antihyperten-

sive treatment and change in blood pressure are associatedwith the pro-

gression of white matter lesion volumes: The Three-City (3C)-Dijon

Magnetic Resonance Imaging Study. Circulation 2011;123:266–73.

[36] Satizabal CL, Zhu YC, Mazoyer B, Dufouil C, Tzourio C. Circulating

IL-6 and CRP are associated with MRI findings in the elderly: The 3C-

Dijon Study. Neurology 2012;78:720–7.

[37] Pantoni L, Basile AM, Pracucci G, Asplund K, Bogousslavsky J,

Chabriat H, et al. Impact of age-related cerebral white matter changes

on the transition to disability—the LADIS study: Rationale, design

and methodology. Neuroepidemiology 2005;24:51–62.

[38] Schmidt R, Ropele S, Ferro J, Madureira S, Verdelho A, Petrovic K,

et al. Diffusion-weighted imaging and cognition in the leukoaraiosis

and disability in the elderly study. Stroke 2010;41:e402–8.

[39] Verlinden VJ, van der Geest JN, de Groot M, Hofman A, Niessen WJ,

van der Lugt A, et al. Structural and microstructural brain changes pre-

dict impairment in daily functioning. Am JMed 2014;127:1089–10962.

[40] Chen X, Wen W, Anstey KJ, Sachdev PS. Prevalence, incidence, and

risk factors of lacunar infarcts in a community sample. Neurology

2009;73:266–72.

FLA 5.2.0 DTD � JALZ2049_proof

[41] Noh Y, Lee Y, Seo SW, Jeong JH, Choi SH, Back JH, et al. A new clas-

sification system for ischemia using a combination of deep and peri-

ventricular white matter hyperintensities. J Stroke Cerebrovasc Dis

2014;23:636–42.

[42] McKhann G, Drachman D, Folstein M, Katzman R, Price D,

Stadlan EM. Clinical diagnosis of Alzheimer’s disease: Report of

the NINCDS- ADRDAWork Group under the auspices of Department

of Health and Human Services Task Force on Alzheimer’s disease.

Neurology 1984;34:939–44.

[43] Hachinski VC, Iliff LD, Zilhka E, DuBoulay GH, McAllister VL,

Marshall J, et al. Cerebral blood flow in dementia. Arch Neurol

1975;32:632–7.

[44] McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr,

Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s dis-

ease: recommendations from the National Institute on Aging-Alz-

heimer’s Association workgroups on diagnostic guidelines for

Alzheimer’s disease. Alzheimers Dement 2011;7:263–9.

[45] Sachdev P, Kalaria R, O’Brien J, Skoog I, Alladi S, Black SE, et al.

Diagnostic criteria for vascular cognitive disorders: AVASCOG state-

ment. Alzheimer Dis Assoc Disord 2014;28:206–18.

[46] KlingMA, Trojanowski JQ,Wolk DA, Lee VMY, Arnold SE. Vascular

disease and dementias: Paradigm shifts to drive research in new direc-

tions. Alzheimers Dement 2013;9:76–92.

[47] Carmichael O, Mungas D, Beckett L, Harvey D, Tomaszewski

Farias S, Reed B, et al. MRI predictors of cognitive change in a diverse

and carefully characterized elderly population. Neurobiol Aging 2012;

33:83–95.

[48] Larson EB, Langa KM. Aging and incidence of dementia: A critical

question. Neurology 2012;78:1452–3.

[49] Langa KM, Larson EB, Karlawish JH, Cutler DM, Kabeto MU,

Kim SY, et al. Trends in the prevalence and mortality of cogni-

tive impairment in the United States: Is there evidence of a

compression of cognitive morbidity? Alzheimers Dement 2008;

4:134–44.

[50] Manton KC, Gu XL, Ukraintseva SV. Declining prevalence of demen-

tia in the U.S. elderly population. Adv Gerontol 2005;16:30–7.

[51] Rocca WA, Petersen RC, Knopman DS, Hebert LE, Evans DA,

Hall KS, et al. Trends in the incidence and prevalence of Alzheimer’s

disease, dementia, and cognitive impairment in the United States. Alz-

heimers Dement 2011;7:80–93.

[52] Matthews FE, Arthur A, Barnes LE, Bond J, Jagger C, Robinson L,

et al. A two-decade comparison of prevalence of dementia in individ-

uals aged 65 years and older from three geographical areas of England:

Results of the Cognitive Function and Ageing Study I and II. Lancet

2013;382:1405–12.

[53] Schrijvers EMC, Verhaaren BFJ, Koudstaal PJ, Hofman A, IkramMA,

Breteler MMB. Is dementia incidence declining? Trends in dementia

incidence since 1990 in the Rotterdam Study. Neurology 2012;

78:1456–63.

[54] Medical Imaging Trial Network of Canada n.d. Available from: www.

mitnec.org (accessed March 19, 2015).

[55] Tardif JC, Spence JD, Heinonen TM,Moody A, Pressacco J, Frayne R,

et al. Atherosclerosis Imaging and the Canadian Atherosclerosis Imag-

ing Network. Can J Cardiol 2013;29:297–303.

[56] Canadian Atherosclerosis Imaging Network n.d. Available from:

www.canadianimagingnetwork.org (accessed March 19, 2015).

[57] Ontario Neurodegenerative Disease Research Initiative n.d. Available

from: www.ondri.ca (accessed March 19, 2015).

[58] Ramirez J, Gibson E, Quddus A, Lobaugh NJ, Feinstein A,

Levine B, et al. Lesion Explorer: A comprehensive segmentation

and parcellation package to obtain regional volumetrics for subcor-

tical hyperintensities and intracranial tissue. Neuroimage 2011;

54:963–73.

� 21 July 2015 � 6:40 pm � ce

1044

1045

1046

1047