# METACOHORTS for the study of vascular disease and its contribution to cognitive decline and neurodegeneration: An initiative of the Joint Programme for Neurodegenerative Disease Research

Martin Dichgans, Joanna Wardlaw, Eric Smith, Vera Zietemann, Sudha Seshadri, Perminder Sachdev, Geert Jan Biessels, Franz Fazekas, Oscar Benavente, Leonardo Pantoni, et al.

## ► To cite this version:

Martin Dichgans, Joanna Wardlaw, Eric Smith, Vera Zietemann, Sudha Seshadri, et al.. METACOHORTS for the study of vascular disease and its contribution to cognitive decline and neurodegeneration: An initiative of the Joint Programme for Neurodegenerative Disease Research. Alzheimer's & Dementia: the Journal of the Alzheimer's Association, 2016, 10.1016/j.jalz.2016.06.004. hal-01382655

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Submitted on 20 Oct 2016

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ELSEVIER

Alzheimer's & Dementia ■ (2016) 1-14

Alzheimer's
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Dementia

Perspective

# METACOHORTS for the study of vascular disease and its contribution to cognitive decline and neurodegeneration: An initiative of the Joint Programme for Neurodegenerative Disease Research

METACOHORTS Consortium<fnref n="1" />,*

Abstract

Dementia is a global problem and major target for health care providers. Although up to 45% of cases are primarily or partly due to cerebrovascular disease, little is known of these mechanisms or treatments because most dementia research still focuses on pure Alzheimer's disease. An improved understanding of the vascular contributions to neurodegeneration and dementia, particularly by small vessel disease, is hampered by imprecise data, including the incidence and prevalence of symptomatic and clinically "silent" cerebrovascular disease, long-term outcomes (cognitive, stroke, or functional), and risk factors. New large collaborative studies with long follow-up are expensive and time consuming, yet substantial data to advance the field are available. In an initiative funded by the Joint Programme for Neurodegenerative Disease Research, 55 international experts surveyed and assessed available data, starting with European cohorts, to promote data sharing to advance understanding of how vascular disease affects brain structure and function, optimize methods for cerebrovascular disease in neurodegeneration research, and focus future research on gaps in knowledge. Here, we summarize the results and recommendations from this initiative. We identified data from over 90 studies, including over 660,000 participants, many being additional to neurodegeneration data initiatives. The enthusiastic response means that cohorts from North America, Australasia, and the Asia Pacific Region are included, creating a truly global, collaborative, data sharing platform, linked to major na-

Pharmaceutical Companies, outside the submitted work. M Ewers received grants from the European Commission, Alzheimer Forschung Initiative and LMU. R Frayne reports grants from CIHR, during the conduct of the study; grants from CIHR, outside the submitted work. J O'Brien reports personal fees from GE Healthcare, TauRx, Cytox, grants and personal fees from Avid/Lilly, outside the submitted work. T Quinn is a founding member of VISTA cognition, a not for profit resource that collates individual patient level data relating to cognition and stroke. P Sachdev reports grants from NHMRC Program Grant ID 568969, during the conduct of the study. K Shuler reports grants from JPND (Medical Research Council), grants from Scottish Funding Council PEER SINAPSE, during the conduct of the study. E Smith reports grants from Canadian Institutes of Health Research, during the conduct of the study and from the Heart and Stroke Foundation of Canada, outside the submitted work. A Thomas reports grants from NIHR BRU in Lewy Body Dementia, during the conduct of the study. A Viswanathan reports personal fees from Roche Pharmaceuticals, outside the submitted work. JM Wardlaw reports grants from JPND (Medical Research Council and Canadian Institutes of Health Research), and Scottish Funding Council PEER SINAPSE, during the conduct of the study.

<footnote>Declaration of interests: A Alonso, R Al-Shahi Salman, F Arba, H-J Bae, C Brayne, H Chabriat, C Chen, C Cordonnier, C DeCarli, M Dichgans, M Duering, F Fazekas, L Feng, S Greenberg, M Ihara, E Jouvent, R Kalaria, S-Y Kim, J-S Lim, RI Lindley, J Linn, R Malik, B Mazoyer, V Mok, B Norrving, L Pantoni, C Ritchie, R Sacco, R Schmidt, S Seshadri, L Sposato, N Sprigg, B Stephan, R Swartz, M van Boxtel, J Van der Grond, R van Oostenbrugge, M Vernooij, D Werring, W Whiteley, and V Zietemann have no disclosures. P Bath reports grants from UK Medical Research Council, NIHR Health Technology Assessment, Stroke Association, Alzheimer's Society, British Heart Foundation, during the conduct of the study. Outside the submitted work, S Black reports institutional grants from Pfizer, GE Healthcare, Eli Lilly, Elan/Transition Therapeutics, Roche, Cognoptix, and personal fees from Pfizer, GE Healthcare, Eli Lilly, Eisai, Boehringer Ingelheim, Novartis. R Bordet reports grants from French Minister of Health, Fondation Coeur et Artère, during the conduct of the study. I Deary reports grants from Age UK, UK Medical Research Council, UK Biotechnology and Biological Sciences Research Council, during the conduct of the study. F-E de Leeuw reports grants from VIDI innovational grant (ZonMW), during the conduct of the study. F Doubal reports grants from JPND (Medical Research Council and Canadian Institutes of Health Research), The Stroke Association and Garfield Weston Foundation, during the conduct of the study. KP Ebmeier reports grants from UK Medical Research Council, The HDH Wills 1965 Charitable Trust, Gordon Edward Small's Charitable Trust, National Institute of Health Research, Alzheimer Research UK, and personal fees from Eli Lilly, GE, and grants from Various</footnote>

<footnote n="1">¹A full listing of the authors for this manuscript can be found in the appendix at the end of this article.</footnote>

<footnote>*Corresponding author. Tel.: +44-131-465-9570</footnote>

<footnote>E-mail address: joanna.wardlaw@ed.ac.uk</footnote>
tional dementia initiatives. Furthermore, the revised World Health Organization International Classification of Diseases version 11 should facilitate recognition of vascular-related brain damage by creating one category for all cerebrovascular disease presentations and thus accelerate identification of targets for dementia prevention.

© 2016 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

**Keywords:** Dementia; Cerebrovascular disease; Small vessel disease; Neurodegeneration, Cohorts, Survey

# 1. Introduction

Worldwide, nearly 36 million people are estimated to be living with dementia. This is expected to triple by 2050. Cerebrovascular disease causes up to 45% of all dementias alone or in conjunction with Alzheimer's disease (AD) [1,2]. Despite vascular risk factor reduction being an achievable target for public health intervention in many countries, and some recent evidence of success in preventing dementia [3], knowledge about vascular contributions to dementia remains modest.

Many studies, from the early 1990s onward [4], have demonstrated that cognitive impairment and dementia are both common and under-recognized after stroke [5]. The concept of “vascular cognitive impairment” was introduced in 1994 [6], covering a spectrum of cognitive impairment after stroke to cognitive impairment in association with otherwise asymptomatic cerebrovascular disease. The most common vascular contributor to dementia is cerebral small vessel disease (SVD) [7], a condition that affects perforating vessels, thence white and gray matter, and accelerates neurodegenerative processes. Vascular dementia reflects the global effects of vascular disease on the brain, not just of multiple individual infarcts. [8,9] It results in stroke, cognitive decline and dementia, plus neuropsychiatric symptoms, gait, balance [8,9], and continence problems [10], necessitating a larger framework for targeted, comprehensive studies [11].

In 2006, the National Institute for Neurological Disorders and Stroke and the Canadian Stroke Network convened a multidisciplinary research group to recommend standards for the study of vascular cognitive impairment [11]. In 2013 the Alzheimer's Association convened an expert working group, which summarized the state of vascular cognitive impairment science and identified areas where new knowledge is needed [12]. However, despite strong and unanimous evidence for the major burden of vascular cognitive impairment on both patients and their caregivers [13], most dementia research largely overlooks vascular disease as a cause. In part, this reflects that clinicians and researchers working on dementia, stroke, physical, or psychiatric manifestations are still too often segregated. “Stroke” and “dementia” (both syndromes, not pathological diagnoses) present to different clinical specialists (Fig. 1); stroke specialists underrecognize the cognitive impact of stroke, whereas dementia specialists underrecognize vascular inputs to dementia pathophysiology. This separation also affects research and

funding initiatives, for example, vascular disease was rarely mentioned in a report on 169 European studies considered relevant to neurodegenerative disease research [14]. Better diagnostic criteria for the different cognitive profiles of vascular and AD are also needed [15].

The recognition of an important role for cerebrovascular disease in dementia opens major therapeutic opportunities. Vascular risk factor reduction and stroke prevention may already be reducing dementia incidence [3,16]. Increased government and public concern about dementia, as well as better grouping of codes for different cerebrovascular disease presentations in the revised International Classification of Disease (ICD) codes version 11 (ICD-11, release 2018, http://www.who.int/classifications/icd/revision/en/), will help advance understanding of cerebrovascular disease and its impact on neurodegeneration.

Here, we report on an initiative funded by the JPND to promote efficient use of available data in which we identified information, relevant to vascular disease, available in different types of studies that could provide large, statistically robust, generalizable data sets, and create platforms

<figure><img src="image_3.png" /><figcaption>Fig. 1. Approaches to tackling vascular factors in neurodegenerative disease. The challenge is to integrate the different clinical presentations when attempting to recognize more completely the interactions between vascular disease and neurodegeneration and thence improve prevention and treatment.</figcaption></figure>
for future mechanistic, epidemiological research, or clinical
trials [17]. We used the exemplar survey data to identify ma-
jor gaps in knowledge, methodological issues and suggest
priority actions to advance the field.

## 2. Method

We convened a group of experts in stroke and cerebrovas-
cular disease, AD, epidemiology, psychology, neuroimaging,
and clinical trials (Appendix). We designed a survey aiming
to capture information about data available in cohorts relevant
to vascular contributions to neurodegeneration. A "relevant
cohort" could comprise patients with stroke/transient ischaec-
mic attack (TIA), or suspected cognitive impairment or de-
mentia, or healthy subjects, from a hospital or geographical
population, and would have information on vascular disease
and/or risk factors, and one or more of the following: cogni-
tive data, long-term outcomes (including physical function
and mood), neuroimaging including biomarkers of vascular
disease and/or neurodegeneration, physiological measures
(e.g., blood pressure, vascular stiffness), or biomarker sam-
ples with a relevant vascular link. The study could be
completed or ongoing, cross-sectional or longitudinal, obser-
vational, or a clinical trial. The minimum sample for inclusion
was 50 participants, with no age or geographical limits.

The survey (Supplementary Material), designed for online
delivery, sought information on whether participants were
healthy or recruited from a stroke or memory or other relevant
clinic or participated in a clinical trial. Data were collected on
demographics, medical history, risk factors, cognition, brain
imaging, physiological measures, comorbidities at inclusion,
duration and frequency of follow-up, follow-up assessments,
outcome events, whether the study was completed or ongoing,
availability of bio- or genetic samples for further analysis, in-
terest of investigators in data sharing, and whether approvals
for sharing were already in place.

The survey was piloted by three members of the expert
group (JMW, MD, and ES), before being distributed widely.
It ran from November 15, 2014 until August 31, 2015 with
updates for ongoing studies to November 2015. We initially
invited participation from investigators of studies in Europe
through the JPND Vascular Disease group, relevant studies
listed in the JPND report [14], networks, and studies known
to them, for example, Dementia Platform UK, the German
Center for Neurodegenerative Diseases (DZNE), and "Con-
stances" in France; however, investigators based in North
America, Australasia, and the Asia Pacific Region also ex-
pressed interest and were included. We recognized that the
survey was unlikely to capture all studies but aimed to cap-
ture a broad sample, particularly from the vascular disease
perspective (including clinical trials), as these are under-
represented in other dementia initiatives [14,18-20].

We performed descriptive statistics and meta-analyses
(random effects methods) [21]. For detailed analyses of
cognition in relation to stroke, we used studies that collected
data on stroke and cognition from subjects without prestroke

dementia in community-based studies or in subjects with
stroke (hospital-based post-stroke cognitive impairment co-
horts). Responses were received from 68 investigators (some
provided data on several studies). Samples were updated to
include ongoing recruitment to December 01, 2015. Five
incomplete responses were removed from further analysis
(for full details, see Supplementary Material).

Fifty-five experts (Appendix) discussed the data to identify
knowledge gaps requiring new data, implications of ICD-11
disease codes (http://www.who.int/classifications/icd/revision/
en/), agree early targets for shared data analysis, and plan
future analyses of existing data and new research initiatives.

## 3. Results

The survey collected data on a total of 96 studies, including
167,064 participants, or 667,064 with UK Biobank [22]
(186,000 and 686,000, respectively including target samples
in ongoing studies; Table 1; Supplementary Table 1). The
sample size ranged from 41 to 29,852 (excluding UK Bio-
bank). The mean age was 72, range 15 to 106 years. There
were 84 observational studies (11 cross sectional and 73 lon-
gitudinal) and 12 randomized clinical trials. The main types of
studies overlapped (some recruited from several sources by
various methods, Table 2) but, in general, most studies could
be attributed to the following categories: community-based
cohorts including population studies (32 studies, of which
28 were suitable for analyzing incident post-stroke dementia,
sample size >600,000 subjects), hospital-based stroke clinics
(i.e., stroke and TIA services, 26 studies, 12 suitable for
analyzing incident post-stroke dementia, ~4700 subjects),
hospital-based memory clinics (15 studies, ~20,000 sub-
jects), and randomized clinical trials (12 trials, ~20,000 sub-
jects); 38 studies were ongoing (recruiting or long-term
follow-up, Table 2). Some studies recruiting from mixed sour-
ces (Table 2, Supplementary Table 1) were not included in
detailed analysis in the following section (or in Table 1), yet
provide other relevant information (details in
Supplementary Material). The longest duration of follow-up
so far was more than 5 years (Fig. 2). Sixty-seven studies
were based in Europe, 17 in North America, and 12 in the
Asia Pacific Region (excluding 2 incomplete entries).

Most cohorts (~86/96) did neither appear in the JPND
report of 169 cohort studies [14] nor overlap by more than
20% with other recent initiatives, for example, Cohort
Studies of Memory in an International Consortium
(COSMIC) [18], Virtual International Stroke Trials Archive
(VISTA) Cognition [22], or the Consortium of Studies of
Post-Stroke Cognitive Decline and Dementia (STROKOG;
[23]). This lack of overlap indicates a gap in information
about vascular disease in neurodegeneration when viewed
from "traditional" neurodegenerative perspectives and shows
that there is a large amount of data available for sharing and
meta-analyses if it could be brought together efficiently and
effectively.
<table><caption>Table 1
Summary of types and amount of data available in cohorts recorded in the survey that were analyzed in detail</caption><thead><tr><th></th><th>Community-based<br>cohorts</th><th>Hospital-based<br>cohorts from<br>stroke services</th><th>Hospital-based<br>cohorts from<br>memory clinics</th><th>Clinical trials</th><th>All</th></tr></thead><tbody><tr><td>Number of studies</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total/completed recruitment/<br>completed follow-up</td><td>28/22/15</td><td>12/8/5</td><td>16/8/4</td><td>12/7/5</td><td>68/45/29</td></tr><tr><td>Current number of patients</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total/with imaging*</td><td>583.851/23.388</td><td>4.134/3.529</td><td>19.144/5.982</td><td>20.035/12.050</td><td>627.164/44.949</td></tr><tr><td>Planned sample size</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total/with imaging data</td><td>&gt;600.000/&gt;150.000</td><td>4.702/4.289</td><td>21.353/8.153</td><td>22.314/12.439</td><td>~655.000/~172.000</td></tr><tr><td>Mean age (y)</td><td>71</td><td>70</td><td>73</td><td>71.6</td><td>72</td></tr><tr><td>Male sex (%)</td><td>46</td><td>56</td><td>51</td><td>58.5</td><td>46</td></tr><tr><td>Number of studies with</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Clinical diagnosis of stroke supported<br>by neuroimaging</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>MRI/CT/MRI or CT</td><td>6/1/4</td><td>5/4/3</td><td>4/0/2</td><td>3/2/6</td><td>18/7/15</td></tr><tr><td>Baseline information on risk factors</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Hypertension/diabetes mellitus/<br>hypercholesterolemia/smoking/<br>medication/education</td><td>26/26/22/26/27/27</td><td>12/12/12/12/12/11</td><td>16/16/16/16/15/16</td><td>10/10/10/9/8/4</td><td>64/64/60/63/62/58</td></tr><tr><td>Follow-up assessment</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>After months 3/6/12/24/36/48/60</td><td>1/2/6/10/10/5/6</td><td>9/6/9/3/6/3/6</td><td>0/4/14/9/7/1/0</td><td>7/4/5/2/2/0/0</td><td>17/16/34/24/25/9/12</td></tr><tr><td>Functional outcomes</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>mRS/BI/SIS/EuroQol/SF36/ADL or<br>IADL</td><td>3/2/0/1/2/5</td><td>7/7/1/2/2/2</td><td>4/4/1/3/0/4</td><td>8/3/1/7/1/0</td><td>22/16/3/13/5/11</td></tr><tr><td>Vascular outcomes</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Stroke/TIA/MI/Vascular death</td><td>17/14/16/15</td><td>10/7/7/8</td><td>14/12/10/9</td><td>9/8/9/7</td><td>50/41/42/39</td></tr><tr><td>Cognitive outcomes</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>MCI/Dementia/MoCA or ACE-R/<br>MMSE/TICS/Memory/Executive/<br>Reaction time/Visuospatial</td><td>7/15/4/17/16/18/15/16</td><td>7/9/7/10/8/7/4/7</td><td>8/11/6/12/14/14/10/14</td><td>1/2/1/8/6/4/4/4/4</td><td>23/37/18/47/44/43/33/41</td></tr><tr><td>Psychiatric outcomes</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Depression/anxiety</td><td>18/12</td><td>9/7</td><td>14/7</td><td>7/0</td><td>48/26</td></tr><tr><td>Mobility outcomes</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Gait/balance/manual dexterity</td><td>1/0/0</td><td>1/0/0</td><td>1/0/0</td><td>0/0/0</td><td>3/0/0</td></tr><tr><td>Criteria used for a diagnosis of MCI</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DSM-V/AHA or ASA/Petersen/<br>NIA-AA/other</td><td>1/1/4/3/2</td><td>2/1/3/0/1</td><td>0/2/2/6/3</td><td>0/0/0/0/6</td><td>3/4/9/9/12</td></tr><tr><td>Criteria used for a diagnosis of<br>dementia</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DSM-IV/DSM-V/ICD-10/other</td><td>7/3/3/3</td><td>5/1/1/0</td><td>10/0/0/3</td><td>1/0/0/6</td><td>23/4/4/12</td></tr><tr><td>Criteria used for diagnosis of<br>vascular CI</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NINDS-AIREN/AHA or ASA/other</td><td>8/4/2</td><td>1/1/1</td><td>7/4/1</td><td>0/0/0</td><td>16/9/4</td></tr><tr><td>Criteria used for a diagnosis of AD</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NIA-AA clinical/DSM-V/DSM-V/<br>NINCDS-ADRDA/other</td><td>5/5/0/2/6</td><td>1/4/1/0/0</td><td>8/2/1/4/2</td><td>0/0/0/0/0</td><td>14/11/2/6/8</td></tr><tr><td>Stored sample</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DNA/blood</td><td>26/20</td><td>7/6</td><td>13/14</td><td>3/3</td><td>49/43</td></tr></tbody></table>

Abbreviations: ACE-R, revised Addenbrooke's cognitive examination; AD, Alzheimer's disease; AHA, American Heart Association; AIREN, Association Internationale pour la Recherché et l'Enseignement en Neurosciences; ASA, American Stroke Association; CT, computed tomography; DSM, Diagnostic and Statistical Manual of Mental Disorders; ICD, International Classification of Disease; MoCA, Montreal Cognitive Assessment; MCI, mild cognitive impairment; MI, myocardial infarction; MMSE, mini-mental state examination; MRI, magnetic resonance imaging; NIA-AA, National Institutes of Aging-Alzheimer's Association; NINDS, National Institute for Neurological Disorders and Stroke; TIA, transient ischaemic attack; TICS, Telephone Interview of Cognitive Assessment.

NOTE. For studies not included in detailed analysis and full details of all studies, see Supplementary Table 1.

*Number includes UK Biobank of 500,000 recruited, 100,000 expected to have brain and other imaging. For 11 cross-sectional and 17 longitudinal studies not listed in Table 1, see Supplementary Material.
<table><caption>Table 2
Summary of 84 observational cohort studies by study setting</caption><thead><tr><th>Setting</th><th>n</th></tr></thead><tbody><tr><td>Observational studies</td><td>84</td></tr><tr><td>Cross sectional</td><td>11</td></tr><tr><td>Longitudinal</td><td>73</td></tr><tr><td>Community based</td><td>32</td></tr><tr><td>Via advertising</td><td>9</td></tr><tr><td>Population based</td><td>23</td></tr><tr><td>Hospital based for some recruits and community based for others</td><td>4</td></tr><tr><td>Stroke or TIA clinic</td><td>1</td></tr><tr><td>Stroke or TIA clinic and memory clinic</td><td>1</td></tr><tr><td>Memory clinic and general geriatric clinic</td><td>1</td></tr><tr><td>Other</td><td>1</td></tr><tr><td>Hospital/clinical based</td><td>37</td></tr><tr><td>Stroke or TIA clinic</td><td>19</td></tr><tr><td>Stroke or TIA clinic and memory clinic</td><td>2</td></tr><tr><td>Stroke or TIA clinic and memory clinic and general geriatric clinic</td><td>3</td></tr><tr><td>Stroke or TIA clinic and memory clinic and general geriatric clinic and any healthy volunteers</td><td>1</td></tr><tr><td>Stroke or TIA clinic and memory clinic and any healthy volunteers</td><td>1</td></tr><tr><td>Memory clinic</td><td>5</td></tr><tr><td>Memory clinic and general geriatric clinic</td><td>1</td></tr><tr><td>Other</td><td>5</td></tr></tbody></table>

Abbreviation: TIA, transient ischaemic attack

NOTE. Of 84 observational studies, 11 are cross sectional, and 73 are longitudinal. Data on 12 clinical trials are not included.

## 3.1. Data sources

### 3.1.1. Community cohorts

Community-based (including population-based) cohorts are the best way to determine the relative contribution of vascular and other pathologies to the development of dementia and cognitive aging. They permit assessment of risk factor levels *before* the development of cognitive impairment or dementia, and hence, risk factor measurement is less likely to be affected by disease or its treatment. These cohorts often have repeated measures, starting in midlife, so, the impact of cumulative exposures, including during specific ages, can be explored. In contrast, clinical studies usually recruit from one extreme of the population distribution of vascular susceptibility; these studies may generate hypotheses that can be tested in population-based cohorts.

We gathered data from a representative sample of 32 community-based cohorts (Supplementary Table 1, Supplementary Fig. 1; 28 suitable for analysis) that defined the extent of vascular compromise, and structural brain injury in each person using serial brain imaging, and also measured global and domain-specific cognitive performance, mood, and physical function. Most studies prospectively followed their participants using health record linkage, questionnaires, or repeated examinations, to detect progression to cognitive impairment, stroke, or dementia. Most studies include biobanking, and a few have prospective postmortem brain banks. The community-based studies

were either geographically population-based or recruited through advertisement and other strategies.

Most studies were conducted in the past three decades in North America and Europe, studied older participants (mean age 70 years), and used brain magnetic resonance imaging (MRI). Sample size ranged from around 100 to 500,000 participants (median: 1400; Q1–Q3: 400–9500), with imaging planned for ~150,000. However, there are limitations. The imaging protocols, hence sensitivity to vascular pathology, varied, particularly in the older studies. Vascular risk factor assessment covered common risk factors, but interim TIA, clinical stroke, and stroke subtype were ascertained with varying degrees of rigor ranging from surveillance for incident events with direct participant examination and consensus review by study investigators, through medical records linkage, to self-reported events. Data on lipids, inflammatory markers, and renal function were missing from about half the studies. There was considerable variation in methods and timing used to assess vascular and cognitive outcomes. Data from a wider age span starting in youth to mid adulthood, diverse race, ethnic, and geographic origins, but using common protocols for cognition, physical function, minimum standard physiological measures, and MRI, are needed.

### 3.1.2. Post-stroke cognitive impairment cohorts

In hospital-based series, about 20% of stroke patients have dementia after stroke, and the cumulative incidence of dementia after the first year is about 3% pa [24]. The survey identified 26 hospital-based cohorts recruiting from stroke/TIA ± other services (Table 2). Twelve had data suitable for analysis comprising studies that recruited patients presenting to hospital with ischemic stroke or TIA, followed longitudinally with cognitive and other measures. These studies collectively included >4134 subjects currently (planned >4700), and nearly 90% have structural neuroimaging. Most assessed educational attainment (although few assessed premorbid intelligence quotient), most used the Montreal Cognitive Assessment (MoCA) and all collected details of vascular disease and vascular risk factors (Supplementary Fig. 2). Of note, several cohorts include amyloid-positron emission tomography imaging, thus enabling assessment of the interactions between vascular and amyloid pathology in cognitive decline.

These data, if combined, would help overcome many gaps in knowledge remaining from previous post-stroke cognitive impairment studies [5,25]: poor generalizability due to small sample and inclusion of high-functioning ischemic, nondysphasic, stroke patients, with an informant; or various entry restrictions, for example, inclusion of TIAs only, or first-only strokes, rather than any stroke. The variety of cognitive, physical, and physiological assessment tools and lack of prestroke cognition or contemporaneous mood data restrict comparisons. There is little information on concomitant AD pathology, or on biomarkers of vascular dementia, or pathology specimens to determine true proportions of vascular disease, and imaging acquisitions vary.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Duration of follow-up by study type and available information. Note some community-based studies have >5 years of follow-up.</figcaption></figure>

As with population studies, agreed standards for future studies would increase research efficiency.

### 3.1.3. Memory clinics

Patients attending a memory clinic represent a highly relevant population to study vascular contributions to cognitive decline and dementia. Most patients attending memory clinics have vascular lesions co-occurring with other pathologies, in particular, Alzheimer-type processes. However, there is limited evidence from longitudinal studies and randomized controlled trials (RCTs) in memory clinic-based cohorts to determine how much the vascular lesions contribute to cognitive profiles, predict prognosis, or should influence treatment. Extrapolation of observations from, for example, population-based studies, may not be valid because disease predictive factors in a relatively healthy population

may not necessarily predict disease progression among individuals affected by the disease, or vice versa.

We identified 15 cohorts that included memory clinic patients (Table 2, Supplementary Fig. 3), some providing data only on memory [26], whereas others included other clinical presentations [27]. Cohort sizes were generally modest, with 8 of 15 cohorts including ≤200 patients, although baseline data are available on 19,144 patients of mean age of 73 years. All studies collected data on demographics, risk factors for vascular disease including education. Cognitive test results and neuroimaging (mostly MRI or computed tomography) were available from most patients in all cohorts. However, diagnostic criteria for mild cognitive impairment (MCI) and dementia, and its subtypes, varied across cohorts. Moreover, although most of the studies collected longitudinal data, both timing and content of follow-up varied substantially.
Unfortunately, substantial gaps in knowledge remain, such as the extent to which different pathologies may have differential prognostic impact in different stages of the dementia process. For example, larger studies at different disease stages across multiple clinics would help to determine if white matter hyperintensity (WMH) burden is indeed a stronger predictor of progressive brain atrophy in people with MCI and early AD than in later stages [28,29]. Further studies are needed to determine how much co-occurring pathologies affect risk-benefit ratios of treatments that are typically used to reduce vascular risk, such as antithrombotic drugs. For example, randomized trials of aspirin in patients with AD observed rates of intracerebral hemorrhage that were much higher than those in people without AD [30].

### 3.1.4. Physical function

Gait and balance disorders are common in elderly people, increasing rapidly from around 15% at the age of 60 years to >50% at age of ≥80 years [31–33]. They are often multifactorial and increase falls, institutionalization, and mortality [31,32,34]. For example, Parkinson's disease, impairs gait, and balance [35], but vascular cerebral disorders also disturb gait and may contribute to Parkinsonian symptoms [36,37], particularly in SVD where gait is the second commonest symptom after cognitive disturbance [38,39]. The Leukoaraiosis and Disability in the Elderly Study (LADIS) showed that gait and balance were correlated with WMH severity [8]. As gait represents a complex higher order form of motor functioning, impaired gait and cognition are closely intertwined [31].

However, gait and balance were rarely assessed in population-based or hospital-based post-stroke or memory studies. The survey found very limited information, but such details were poorly captured in the questionnaire. For example, none of five studies from general geriatric clinics mentioned recording gait, walking, or movement. Under “other,” no study mentioned these words either. Seven other studies mentioned “gait” (Supplementary Table 1, ABC1936, ABC1921, CASPER, ONDRI, PURE-MIND, RUN DMC, STRIDE) although we recognize that some studies that did not specifically mention gait do collect such data. This suggests that problems of gait and balance are under-recognized compared with other features of vascular neurodegeneration and hence are poorly assessed in vascular-focused clinics for older people, despite representing a major problem for older people, their families, hospitals, and social services.

### 3.1.5. Clinical trials

Many acute stroke and stroke prevention RCTs have not collected cognitive data because of the following: (1) they focused on the physical consequences of stroke; (2) cognitive testing was considered too laborious and not applicable to participants whose vision, speech, or hand function were impaired; or (3) there was no informant, thus excluding significant proportions of patients from testing [40].

The survey collected data from 12 RCTs, testing treatments for acute ischemic or hemorrhagic stroke or secondary prevention of ischemic stroke (Supplementary Table 1). The sample size ranged from 41 to 4750, total current sample 20,035 (planned sample 22,314), of which 12,439 will have detailed neuroimaging. The mean participant age is 71.6 years, and 58.5% are men. Most trials consistently recorded baseline vascular risk factors including blood pressure, and outcomes such as recurrent vascular events and functional outcome assessed using the modified Rankin Scale. However, cognitive tests varied (eight used the mini-mental state examination [MMSE], one the MoCA, five the Telephone Interview of Cognitive Assessment (TICS), and two a more detailed assessment), none tested premorbid intelligence quotient although four trials recorded educational attainment, few assessed mood, and none corrected for imaging features such as WMH burden (a predictor of post-stroke cognitive impairment). The latest follow-up was at 12 months in all but two trials (latest assessment 24 and 36 months in one trial each), substantially shorter than that in the other study types (Fig. 2).

An individual patient data meta-analysis of cognition after stroke in these trials, which are typical of many stroke treatment or prevention trials, would be hampered by lack of consistency in cognitive measures and of long-term data, despite providing exemplary vascular risk factor and vascular outcome assessments. Inadequate attention has been given to assessing cognition after stroke in RCTs to date. Agreement on pragmatic and rapid ways to assess important cognitive domains such as executive function and processing speed, not just memory, and to correct for premorbid cognition, depression, and WMH burden on imaging, are essential to advance understanding of cognitive trajectories after stroke. There is an opportunity to progress by including cognitive tests in ongoing multicentre stroke trials where feasible as pragmatic methods are becoming available.

## 3.2. Methods for assessing vascular effects on cognition and neurodegeneration

Vascular disease requires different methods compared with other types of neurodegeneration or dementia research. Two key methods, which differ substantially in their requirements for vascular disease and neurodegenerative pathologies such as AD, are the assessment of neuroimaging and cognition. Integrated cerebrovascular disease codes are essential to bridge clinical presentations. All are discussed here.

### 3.2.1. Imaging, protocols, and analysis methods

In the 1990s, landmark neuroepidemiological studies showed that clinically silent cerebrovascular lesions detected only on MRI, including lacunes and WMH of presumed vascular origin, were associated with cognitive impairment and an increased risk of future stroke and
dementia [41,42]. More recent studies have incorporated advanced imaging modalities that interrogate physiological and molecular changes—such as structural and functional connectivity with diffusion tensor imaging and functional MRI, cerebral perfusion, and molecular markers such as amyloid deposition—with larger sample sizes to increase statistical power for subgroup analyses and to predict clinical events.

Our synthesis of cohort studies identified many participants in community-dwelling settings and clinical studies on stroke who have cognitive data and undergone or will undergo neuroimaging, predominantly brain MRI (Table 1, Supplementary Table 1). We identified five major areas where there are currently limitations, gaps in knowledge, or unrealized opportunities for harmonization and collaboration of neuroimaging methods.

First, vascular lesion definitions and terminology require standardization, to enable cross-cohort comparisons and meta-analysis. This need has largely been met by the recent STandards for ReportIng Vascular ChangEs Neuroimaging (STRIVE) [44]; however, updates will become necessary for new neuroimaging methods that bring new imaging markers or increased sensitivity of known markers.

Second, to harmonize and compare findings across studies, full details of neuroimaging acquisition and analysis methods should be reported. New studies could adopt successful, validated methods recommended by STandards for ReportIng Vascular ChangEs Neuroimaging or used previously if the full methodological details were available. Imaging protocols could be shared through a single, publicly available website.

Third, reliability and accuracy of lesion classification would be improved through sharing of (exemplary) MRI data across cohorts with appropriate anonymization. For visual rating, a shared MRI repository could be used to train new raters using expert consensus as the gold standard. For computational analysis, for example, of MRI WMH, a repository could allow developers access to MRI images showing a range of lesions, from different vendors and field strengths, to derive or validate processing methods.

Fourth, there is great need for integrating information on vascular and neurodegenerative pathology from cohorts recruited through different settings, leveraging the expertise of stroke and dementia specialists in vascular and neurodegenerative disease. Integrating data across geographic and race/ethnic backgrounds would also help to more reliably identify and explore differences in subclinical vascular brain injury.

Fifth, more collaboration would enhance innovative methods for neuroimaging post-processing and data analysis. One example is the increasing emphasis on integrated data analysis to determine total SVD burden and effects on brain connectivity, neurodegeneration, and cognition (Fig. 3). Advances in machine learning and graph theory–based network analysis provide new opportunities to accelerate image analysis for large-scale studies. A multidiscipl

<figure><img src="image_5.png" /><figcaption>Fig. 3. Dynamic effects of small vessel disease on the brain: (A) Periventricular and deep white matter hyperintensity (WMH; orange) can increase in size (red), occasionally shrink, and lead to atrophy of white matter. (B) Acute small subcortical infarcts (dashed black line) may cavitate and shrink (black area), develop into a WMH (orange) or disappear. Distant effects (blue) involve thinning of connected cortex and degeneration of projection fibers.</figcaption></figure>

plinary approach including neuroepidemiologists and clinical researchers on the one hand, and computer scientists, mathematicians and biomedical engineers on the other hand, is required.

### 3.2.2. Cognition

There are particular challenges in the assessment of cognition in patients with cerebrovascular diseases, rendering detailed neuropsychological approaches (typically used in studies of dementia) impractical. Fatigue is common after stroke, limiting patient tolerance of prolonged tests. Patients may have dysphasia, impaired hand function, or visual deficits, making some tests impossible, even if comprehension is preserved. Depression and apathy are common in SVD and affect cognitive test performance. Many brief cognitive screening tests focus on memory, although vascular disease, in particular SVD, typically results in subcortical cognitive impairments, for example, loss of frontal and executive function [7,43], and may occur in a stepwise manner reflecting sudden vascular events.

The survey shows huge variability in cognitive outcome measures in stroke/TIA patients (Table 1, Supplementary Material). About 25% of studies used a diagnosis of
dementia or MCI, another 25% used a single cognitive test (most commonly MoCA, MMSE, or the revised Addenbrooke's cognitive examination); less than 10% assessed the premorbid (i.e., optimal early adulthood) cognitive status; and few assessed cognition immediately before the stroke. Of cognitive domains assessed in these studies, there was an almost equal distribution among memory, executive functions, reaction time, visuospatial function, with many accounting also for depression and anxiety.

Longitudinal observational studies included patients with different cognitive impairment or dementia diagnoses, but of note, about half the studies did not assign a specific dementia type and the diagnostic criteria for MCI, AD, vascular cognitive impairment (VCI), and dementia varied. The most commonly used criteria were the National Institutes of Aging-Alzheimer's Association for MCI and AD, *Diagnostic and Statistical Manual of Mental Disorders, IV Edition* for dementia, and the American Heart Association/American Stroke Association for VCI. However, with the exception of the widely used *Diagnostic and Statistical Manual of Mental Disorders, IV Edition* criteria for dementia, most studies used widely differing criteria for other categories of cognitive disorders, perhaps reflecting the difficulties and complexity of cognitive assessment in vascular disease. Most community-based and longitudinal observational studies used the MMSE, which is insensitive to vascular cognitive decline. A stepwise approach to determining the cognitive test approach to vascular disease is suggested in Table 3.

The importance of assessing cognitive profiles (without worsening the complexity of categorization) is emphasized by recent findings in SVD patients of memory loss (a cortical dysfunction) [7,44], suggesting that SVD affects not just white and deep gray matter but also the (connected) cerebral cortex (Fig. 3) [44–46]. Lower connectivity within cerebral networks was associated with cognitive impairment and dementia [47,48]; WMHs were associated with less brain activation in the frontal cortex on fludeoxyglucose-positron emission tomography [49–51]. Mood disorders and apathy are common after stroke, affect cognitive test performance (and more fundamentally, patient participation in research), and may reflect impaired connectivity. Integrating the results from different cohorts that investigate these underlying mechanisms and consequences is essential to understand

the full impact of vascular disease on brain function and progression toward the complete spectrum of cognitive impairments.

### 3.2.3. Relevance and mapping to next generation disease coding, ICD-11

Accurate and uniformly applied diagnoses are essential in health care and research. The World Health Organization has the main responsibility for the global classification systems, a core constitutional task. ICD-10 was published about 25 years ago. Major advances in the understanding of diseases have occurred since then, making ICD-10 outdated in several areas. The revision of ICD-10 into ICD-11 is currently in its final stage.

A major change from ICD-10 to ICD-11 is that cerebrovascular diseases will no longer be split across different chapters, but will constitute “one single section” in *Diseases of the Nervous System*. ICD-11 will also, for the first time, include definitions of all cerebrovascular diagnostic codes including definitions of transient ischemic attack and the different main types of stroke. It will also encompass cerebrovascular diseases not causing acute neurological dysfunction: *silent cerebral infarcts*, *cerebral microbleeds*, and *silent white matter abnormalities associated with vascular disease*. The term “silent” denotes that these entities have not caused acute neurologic symptoms (and hence are not “strokes”) but are important for brain function, affect prognosis, and should not be regarded only as incidental imaging findings.

The final ICD-11 classification is expected to be approved for governmental use by the World Health Assembly for release in 2018, but the prefinal beta draft is officially available at the World Health Organization website [52]. For several research purposes, it is recommended that the ICD-11 terminology and definitions be considered and may be applied already at this stage.

## 4. Discussion

Our initiative identified more than 90 cohort studies, including over 660,000 participants, many outside current neurodegeneration data initiatives, most with consent for data sharing, providing substantial scope for data mining. We acknowledge that our survey is incomplete. However, we consider it sufficiently representative to draw important conclusions. The segregation of stroke and dementia remains prevalent 10 years after the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network (NINDS-CSN) standards [11], in spite of recognition that larger frameworks and better diagnostic criteria for dementias are urgently needed [1,53]. Even among the survey respondents (likely “cerebrovascular disease enthusiasts”), there was a large gap between “stroke” and “dementia,” and sparse overlap with the JPND report [14]. Although stroke clinic-based studies and RCTs were trying to collect at least some cognitive data, the

<caption>Table 3
Choosing tests to measure cognition in studies and trials dealing with vascular diseases: a step-based process</caption>

1) Decide to assess cognition (mandatory)

2) Decide whether to assess

a) diagnostic outcome measure (e.g., dementia, cognitive decline, mild cognitive impairment) and/or

b) cognitive measures (i.e., use domain-specific cognitive tests)

4) If b, decide which domains to assess, and

5) Which tests to use.
<table><caption>Table 4
Recommendations for research</caption><thead><tr><td>Recommendation</td><td>Reason 1</td><td>Reason 2</td><td>Reason 3</td></tr></thead><tbody><tr><td>General</td><td>Vascular and neurodegenerative pathologies are closely related; vascular pathology is an integral part of the pathological spectrum of AD, and vascular disease can play an important primary or secondary role to other pathology in neurodegeneration and dementia.</td><td>Secondary neurodegeneration due to vascular insults is an important contributor to accumulating structural brain damage and brain dysfunction.</td><td>Vascular damage can manifest as progressive cognitive, behavioral or sensorimotor dysfunction, that is, not just stroke.</td></tr><tr><td>Integrated approaches are needed</td><td>Vascular neurodegenerative disorders may present to many different clinics but are underpinned by a common vascular disorder.</td><td>These clinics should integrate to avoid overlooking the multifaceted effects of vascular disease on cognition, psychiatric symptoms, and physical function.</td><td>Clinical practice and research should assess risk factors, clinical, cognitive, imaging, and physical function.</td></tr><tr><td>Vascular disease is a dynamic and far-reaching process</td><td>Apparently small lesions that may precipitate clinical presentations have remote effects on other parts of the brain, which increase neurological and cognitive dysfunction.</td><td>Small lesions are also evidence of a global brain disease and should be treated as a progressive, global pathology.</td><td>Vascular lesions are not small, individually trivial lesions, without clinical meaningfulness.</td></tr><tr><td>Always assess vascular risk factors, disease burden, and outcomes</td><td>Cerebrovascular disease and AD share multiple risk factors, for example, smoking, hypertension, hyperglycemia, diabetes, and obesity. Vascular risk factors may have a greater impact in midlife than old age.</td><td>As an absolute minimum, routinely assess history of cerebrovascular, peripheral vascular, cardiovascular disease, blood pressure, smoking, exercise, occupation, and diet, blood lipids, blood glucose.</td><td>Pragmatic approaches can be suitable and avoid overburdening researchers and participants. Follow-up should continue long term.</td></tr><tr><td>Cognitive assessments should be performed in, and relevant to, vascular disease</td><td>Need to be applicable to vascular patients and the environment in which they typically present.<br>To avoid mistaking lifelong stable traits for late life change, routinely assess prior cognitive ability (or proxy measures, e.g., educational attainment) in any studies of cognition and vascular disease or other dementias.</td><td>Adapted to reflect their specific cognitive deficits and physical limitations.<br>Long-term outcome events, rates, and timings of decline in cognitive and physical function are needed to power clinical trials and inform patients and health services more effectively.</td><td>Assess executive function and processing speed in addition to memory.<br>Socioeconomic factors have major influence on vascular disease beyond that attributed to vascular risk factors alone and should be assessed routinely.</td></tr><tr><td>Assess physical function across several domains routinely</td><td>Gait, balance, and continence are often affected and should be assessed routinely in suspected vascular cognitive impairment.</td><td>Simple tests such as “timed up and go” are valuable to assess physical function.</td><td></td></tr><tr><td>Use standard, validated data collection that accounts for vascular disease</td><td>Agreed core standard data (clinical, cognitive, imaging, biomarkers, and so forth) and definitions would facilitate future data sharing and meta-analyses.</td><td>Agreed standards are available, for example, NINDS-CSN Vascular Cognitive Impairment Harmonization Standards; or STRIVE standards for neuroimaging.</td><td>Imaging can identify “silent” and symptomatic vascular disease if the right sequences are used.</td></tr><tr><td>Encourage postmortem brain tissue collection</td><td>Brain tissue from subjects well phenotyped in life, including brain regions commonly affected by vascular disease, are not widely available; storing samples frozen, and in paraffin, would facilitate protein, and gene as well as histological assessments.</td><td>More research is needed on the interaction of AD with cerebrovascular pathology, the consequences for function of brain networks, and ultimately how these pathologies evolve and combine to cause clinical consequences.</td><td>Tissue-imaging analysis of individual lesions is needed to understand pathological mechanisms.</td></tr><tr><td>Make better use of existing cohort data</td><td>Study registration and public availability of protocols would facilitate identification of novel and ongoing studies for meta-analyses.</td><td>Cohorts that assess cerebrovascular disease and include molecular imaging to assess AD pathology (e.g., by PET amyloid imaging) will help understand joint pathologies.</td><td>Open data initiatives and databanks would encourage sharing of existing cohort data.</td></tr></tbody></table>

Abbreviations: AD, Alzheimer's disease; PET, positron emission tomography.
methods for testing cognition in such environments are sub-
optimal. Meanwhile, the memory clinic-based cohorts
collected relatively little information on vascular disease.
Undoubtedly, the role of vascular disease in neurodegener-
ation remains under-recognized, and under-funded [54], a
situation that can no longer be justified: vascular risk factor
reduction prevents stroke and may also prevent dementia
[3] further evidenced by declining dementia incidence par-
alleling declining stroke incidence [16]. Fortunately, per-
spectives may be evolving. A recent call for new
conceptual formulations of AD and dementia cites the
need to account for mixed pathologies and known risk fac-
tors (many of which are also stroke risk factors) [55].
Furthermore, ICD-11 should help identify all cerebrovas-
cular disease presentations. Standardized diagnostic
workup and data collection would facilitate studies on diag-
nosis, prognosis, and treatment of vascular factors in a
memory clinic setting, and similarly, studies of cognition,
gait, and balance in a stroke clinic setting (Table 4). “Trans-
nosological” research units, integrating specialists in neu-
rovascular and neurodegenerative disorders would
facilitate a global approach to dementia prevention.

These survey data provide a framework for addressing in-
teractions between the two leading causes of cognitive
decline and dementia: vascular disease and neurodegenera-
tion. Epidemiological studies and RCTs are highly comple-
mentary when viewed as large data sets en masse.
Observational studies indicate that the most critical period
for elevated blood pressure with regard to cognitive decline
is midlife, whereas blood pressure-lowering trials mostly
included older patients with follow-up periods too short to
detect an effect. Population studies inform hospital-based
post-stroke dementia studies (and vice versa), often have
repeated measures gathered over many years, enabling the
impact of cumulative exposures, and of exposure during spe-
cific ages, to be explored. Important early life information is
present in these studies, for example, birth weight, childhood
cognition, socioeconomic data, for subjects now aged 50 to
70 years, enabling assessment of early life factors on cere-
brovascular disease and dementia risk. Capturing informa-
tion on both vascular and neurodegenerative disease from
mixed sources improves generalizability (e.g., RUN DMC;
FUTURE; Lund Stroke Registry, Supplementary Table 1).
Combining studies that focus on populations in different
epidemiological “windows” relative to the expression of dis-
ease enhances mechanisms’ discovery and can relate sys-
temic disease risk (obesity, metabolic, and cardiovascular
disease) to cerebrovascular disease and dementia. Cohort
meta-analyses would help clarify long-term event rates, their
prediction, risk factors, and variation between populations
and improve design of RCTs. Existing studies may provide
well-phenotyped “trial ready” subjects with prospective con-
sent for future research. The challenges involved in harmo-
nization and analysis of large, diverse data sets are
substantial (Table 4), but methods to overcome this are
ongoing [56], and the potential rewards are huge. Work

already ongoing as a result of this initiative is listed in
*Supplementary Table 2*.

Agreeing on a unified cognitive assessment, which can
be applied easily in cerebrovascular disease, is sensitive
to relevant domains and relevant to patients, could have
as much impact on identifying treatment to prevent VCI
as the Rankin Scale [57] has had on finding acute stroke
treatments: without the common language for functional
outcome provided by the Rankin scale, it is arguable that
stroke units, thrombolysis, hemicranectomy, and throm-
bectomy would not have become guideline acute stroke in-
terventions in as little as 20 years. Currently, most stroke
RCTs, with few exceptions [58], do not assess cognition.
The evaluation of cognition in stroke patients is complex,
difficult, with no “best cognitive test”. The 2006 collabora-
tive consensus [11] proposed three cognitive protocols with
different lengths, one being a brief test for use in large
observational studies and RCTs. Pragmatic, rapid, vali-
dated, tools sensitive to cognitive domains affected by
vascular disease early on, are required, like the MoCA
[59]. Online cognitive tests, for example, the UK Biobank
Cognitive Testing Enhancement, are too complex for
many stroke patients. In any case, a stepwise approach is
needed to assess vascular effects on neurodegeneration
(Table 3). Simple, sensitive, cognitive scales for use in tele-
phone interviews, validated in patients with cerebrovascu-
lar disease, are needed (Table 4) [60].

Several large national initiatives will address dementia
prevention in line with the 2013 G8 Dementia Summit.
These draw largely on healthy or presymptomatic disease
populations and offer new opportunities for systematic,
prospective evaluations of people at scale, and often long-
term sample biobanking, genetics, and imaging, enabling
some powerful study designs. These include the Rhineland
study (Germany, DZNE, n = 40,000), the Canadian Longi-
tudinal Study on Aging (50,000 individuals), the Canadian
Alliance for Healthy Hearts and Minds and Prospective Ur-
ban Rural Epidemiological MIND (PURE-MIND, 11,200
persons), and the UK Biobank [61] (500,000 people aged
40-70, 100,000 with detailed imaging). These long-term
initiatives are complemented by several national and
regional efforts to establish disease-based cohorts, for
example, the Canadian Consortium on Neurodegeneration
in Aging (1400 persons; AD, mixed dementia, MCI, and
VCI), or to combine existing cohorts, for example, the
DPUK [62], (29 UK community cohorts), Cohort Studies
of Memory in an International Consortium (COSMIC)
[18], Virtual International Stroke Trials Archive (VISTA)
Cognition [22], and STROKOG [23]. Other regions should
be encouraged and are creating large repositories of data—
Asia Pacific Region, Central and America [63], Russia, Af-
rica [64], and Australasia [18].

Governments, funders, and the public recognize the
importance of sharing publicly funded data. Data from
combined analyses of cohorts would provide larger sam-
ples, more robust data on individual cognitive and physical
outcomes, and the interplay between brain and body to maintain healthy, active populations into old age. The 2015 World Stroke Proclamation on preventable dementias [1] has been endorsed by several international Alzheimer's, neurological, psychiatric, and heart associations. Funding for cerebrovascular disease research should more closely match that spent on dementia or cardiac disease [54]. Researchers should work together to operationalize assessment of vascular effects on neurodegeneration; stroke should move from "stroke-related" to "anything vascular related including cognition or other effects" [52]; and dementia should move from AD to "any disorder, arising in or outside the brain, that progressively diminishes cognitive function".

## Acknowledgments

The following funders supported the work: Joint Programme for Neurodegenerative Disease (JPND) Research, specifically the UK Medical Research Council, the Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), and the Canadian Institutes of Health Research (CIHR). The Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) through the Pools Engagement in Europe (PEER) funding from the Scottish Funding Council. The Therese Pei Fong Chow Research Centre for Prevention of Dementia (in memory of Donald H K Chow). The authors thank the Siemens Foundation, Munich, Germany, for providing the workshop venue and catering. F.-E.D.L. acknowledges VIDI Innovation Grant from ZonMW ref 016.126.351. P.M.M. acknowledges generous support from the Edmond J Safra Foundation and Lily Safra and the Imperial College Health Trust BRC. P.M.W.B. is Stroke Association Professor of Stroke Medicine. C.de.C. acknowledges support from Centre Grant NIH P30 AG 010129. MD, JMW, GJB, RVO acknowledge EU Horizon 2020 SVDs@Target grant agreement No 666881.

In the short term, the main source of information about these cohorts is Supplementary Table 1, while work continues to enable a platform to improve accessibility and searching for study data.

Author contributions.

Obtaining funding: JMW, MD. Project management and administration: JMW, MD, ES. Design and testing of survey: MD, ES, JMW, KS. Survey distribution: ES, MD, JMW, KS, CC, VM. Data collection: all authors. Data analysis: VZ, KS, JMW. Manuscript preparation: JMW, MD, ES, VZ, SS, PS, GJB, FF, LP, FEdeL, BN, PM, MD, WW. Preparation of figures: MD, FEdeL, VZ, MD, KS. Critical comment and data checking: all authors. Final approval to submit: all authors.

## Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jalz.2016.06.004.

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# Appendix

## Authors

Study co-ordination, analysis and paper preparation: Martin Dichgans (1, Institute for Stroke and Dementia Research (ISD), Klinikum der Universität München, Ludwig-Maximilians-University LMU, Munich Germany; 2, German Centre for Neurodegenerative Diseases (DZNE, Munich), Munich Germany; and 3, Munich Cluster for Systems Neurology (SyNergy), Munich, Germany); Joanna Wardlaw (Centre for Clinical Brain Sciences, University of Edinburgh, UK); Eric Smith (University of Calgary, Canada); Vera Zietemann (Institute for Stroke and Dementia Research (ISD), Klinikum der Universität München, Ludwig-Maximilians-University, Munich, Germany); Sudha Se-shadri (Boston University, USA); Perminder Sachdev (University of New South Wales, Australia); Geert Jan Bies-sels (UMC Utrecht, Netherlands); Franz Fazekas (Medical University of Graz, Austria); Oscar Benavente (University of British Columbia, Canada); Leonardo Pantoni (Azienda Ospedaliero Universitaria Careggi, Florence, Italy); Frank-Erik De Leeuw (Radboud University Medical Center Nijme-gen, Netherlands); Bo Norrving (Lund University, Sweden); Paul Matthews (Imperial College, London, UK); Christo-pher Chen (National University of Singapore); Vincent Mok (The Chinese University of Hong Kong, China); Marco Düring (Institute for Stroke and Dementia Research (ISD), Klinikum der Universität München, Ludwig-Maximilians-University, Munich, Germany); Will Whiteley (University of Edinburgh, UK); Kirsten Shuler (University of Edinburgh, Edinburgh, UK).

Participants in working groups, survey, interpretation: Al-varo Alonso (University of Minnesota, USA); Sandra E Black (Sunnybrook Health Sciences Centre, University of Toronto, Canada); Carol Brayne (University of Cambridge, UK); Hugues Chabriat (Univ Paris Diderot, and DHU Neuro-Vasc, both at Sorbonne Paris Cité, UMR-S 1161 INSERM, F-75205 Paris; Hopital Lariboisiere, Dept of Neurology, F-75475 Paris, France); Charlotte Cordonnier (University Lille, Inserm, CHU, U1171 Degenerative and Cascular Cognitive Disorders, F-59000, Lille, France); Fergus Doubal (University of Edinburgh, Edinburgh, UK); Emrah Duzel (DZNE Magdeburg, Germany); Michael Ewers (Institute for Stroke and Dementia Research (ISD), Klinikum der Uni-versität München, Ludwig-Maximilians-University, Mu-nich, Germany); Richard Frayne (University of Calgary, Canada); Vladimir Hachinski (Rush University Medical Center, London, Ontario, Canada); Mohammad Arfan Ikram (Erasmus MC, Rotterdam, The Netherlands); Frank Jessen (University of Cologne, Germany); Eric Jouvent (Univ Paris Diderot, and DHU NeuroVasc, both at Sorbonne Paris Cité, UMR-S 1161 INSERM, F-75205 Paris; Hopital Lariboisiere, Dept of Neurology, F-75475 Paris, France); Jennifer Linn

(University Hospital Dresden, Germany); John O'Brien (University of Cambridge, UK); Robert van Oostenbrugge (Maastricht UMC, Netherlands); Rainer Malik (Institute for Stroke and Dementia Research (ISD), Klinikum der Uni-versität München, Ludwig-Maximilians-University, Mu-nich, Germany); Bernard Mazoyer (University of Bordeaux, France); Reinhold Schmidt (Medical University Graz, Austria); Luciano A Sposato (Western University, Lon-don, Canada); Blossom Stephan (Newcastle University, UK); Richard H Swartz (Sunnybrook Health Sciences Centre, Uni-versity of Toronto, Toronto, Canada); Meike Vernooij (Erasmus MC, Rotterdam, The Netherlands); Anand Viswanathan (Massachusetts General Hospital, Boston, USA); David Wer-ring (UCL Institute of Neurology, London, UK).

Participants in the survey: Koji Abe (Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Japan); Louise Allan ((Newcastle University, Newcastle, UK), Francesco Arba (University of Florence, Italy) on behalf of the VISTA Collaboration*; Hee-Joon Bae (Seoul National University Bundang Hospital, Republic of Korea (South Korea))); Philip MW Bath (University of Nottingham, UK); Regis Bordet (University Lille, Inserm, CHU, U1171 Degenerative and Vascular Cognitive Disor-ders, F-59000 Lille, France); Monique Breteler (German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany); Seong Choi (Inha University Hospital, Incheon, Korea); Ian Deary (University of Edinburgh, UK); Charles DeCarli (University of California, USA); Klaus Ebmeier (University of Oxford, Warneford Hospital, Oxford, UK); Lei Feng (National University of Singapore); Steven M Greenberg (Massachusetts General Hospital, Boston, USA); Masafumi Ihara (National Cerebral and Cardiovascu-lar Centre, Suita, Osaka, Japan); Rajesh Kalaria (Newcastle University, Newcastle, UK); SanYun Kim (Seoul National University Bundang Hospital, Republic of Korea (South Ko-rea))); Jae-Sung Lim (Dept of Neurology, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea (South Korea))); Richard I Lindley (University of Sydney, Australia); Gillian Mead (University of Edinburgh, UK); Alison Murray (University of Aberdeen, UK); Terry Quinn (University of Glasgow, UK); Craig Ritchie (University of Edinburgh, UK); Ralph Sacco (University of Miami, USA); Rustam Al-Shahi Salman (University of Edinburgh, UK); Nikola Sprigg (University of Nottingham, UK); Cathie Sudlow (University of Edinburgh, UK); Alan Thomas (Insi-tute of Neuroscience and Newcastle University Institute for Ageing, Newcastle University, Newcastle, UK); Martin van Boxtel (Maastricht University, Netherlands); Jeroen van der Grond (Leiden University Medical Center, Netherlands); Aad van der Lugt (Erasmus MC, Rotterdam, The Netherlands); Yuan-Han Yang (Kaohsiung Medical Univer-sity Hospital, Taiwan).

*VISTA Prevention Steering Committee: H-C Diener (Chair), S Davis, G Hankey, KR Lees, B Ovbiagele, C Weir.