Skip to main content
Download PDF

Exploring vascular contributions to cognitive impairment and dementia (ENIGMA): protocol for a prospective observational study

BMC Neurology volume 24, Article number: 110 (2024) Cite this article

Abstract

Background

Post-stroke cognitive impairment (PSCI) is common. However, the underlying pathophysiology remains largely unknown. Understanding the role of microvascular changes and finding markers that can predict PSCI, could be a first step towards better screening and management of PSCI. Capillary dysfunction is a pathological feature of cerebral small vessel disease and may play a role in the mechanisms underlying PSCI. Extracellular vesicles (EVs) are secreted from cells and may act as disease biomarkers. We aim to investigate the role of capillary dysfunction in PSCI and the associations between EV characteristics and cognitive function one year after acute ischemic stroke (AIS) and transient ischemic attack (TIA).

Methods

The ENIGMA study is a single-centre prospective clinical observational study conducted at Aarhus University Hospital, Denmark. Consecutive patients with AIS and TIA are included and followed for one year with follow-up visits at three and 12 months. An MRI is performed at 24 h and 12 months follow-up. EV characteristics will be characterised from blood samples drawn at 24 h and three months follow-up. Cognitive function is assessed three and 12 months after AIS and TIA using the Repeatable Battery for the Assessment of Neuropsychological Status.

Discussion

Using novel imaging and molecular biological techniques the ENIGMA study will provide new knowledge about the vascular contributions to cognitive decline and dementia.

Trial registration

The study is retrospectively registered as an ongoing observational study at ClinicalTrials.gov with the identifier NCT06257823.

Peer Review reports

Introduction and rationale

We continue to live longer, and the impact of age-related diseases such as stroke and dementia is growing [1]. In addition to the functional disability a stroke may cause, cognitive impairment after stroke is common. Post-stroke cognitive impairment (PSCI) is observed in up to 50% of stroke survivors, and a stroke doubles the risk of dementia [2,3,4]. Persistent cognitive impairment has also been found in patients with transient ischemic attack (TIA) [5], a disorder that is considered reversible by definition.

PSCI and dementia are associated with increased morbidity and mortality [6,7,8], however no specific disease modifying management exist. This has urged stakeholders to call for more research into the epidemiology, risk-factors, biomarkers, and mechanisms underlying PSCI [9, 10].

Many risk factors for PSCI have been identified including stroke severity, lesion size and location and common vascular risk factors such as age, hypertension, diabetes, atrial fibrillation, and smoking [5, 11,12,13]. Further, some magnetic resonance imaging (MRI) markers have been associated with PSCI including brain atrophy and markers of cerebral small vessel disease (cSVD) such as white matter hyperintensities and cerebral microbleeds [14, 15]. In addition, several blood-derived biomarkers have also shown promise as predictors of PSCI [16,17,18]. Despite these advances in the field, the exact underlying pathophysiology behind PSCI is not well understood. Identification of early pathologic changes associated with PSCI could be a first step in the development of a prognostic aid and in identifying future therapeutic targets.

Cerebral capillary dysfunction is characterized by limited oxygen extraction from the brain capillaries due to age- and risk factor-related capillary flow heterogeneity [19]. Capillary dysfunction is a pathophysiological feature of cSVD and may play an important role in the vascular mechanisms underlying PSCI [20, 21]. Advanced perfusion magnetic resonance imaging (MRI) techniques can detect and quantify capillary dysfunction and may help identify markers of PSCI [22,23,24].

Extracellular vesicles (EVs) are cell-derived membrane-enclosed vesicles (40-1000 nm diameter) secreted by most cell types including neurons and endothelial cells. They are involved in cell-to-cell communication and can cross the blood brain barrier [25]. The content, surface markers and release of EVs (hereafter called EV characteristics) are altered during disease processes. Because of their potential as diagnostic and prognostic biomarkers, EVs have recently become a field of interest [26]. Studies suggest that EV characteristics may change during acute stroke, in the chronic stroke phase and according to the level of cSVD [27,28,29]. However, associations between EV characteristics and post-stroke cognition are largely unstudied.

With this prospective clinical observational study, we aim to investigate the role of capillary dysfunction in PSCI and to examine the associations between EV characteristics and cognitive function after acute ischemic stroke (AIS) and TIA.

Methods

Design

The ENIGMA study is a single-centre prospective clinical observational study.

Patient population

Patients aged ≥ 60 years admitted with AIS or TIA within 24 h of symptom onset and with a clinically relevant diffusion restriction identified on MRI diffusion weighted-imaging (DWI) are eligible for inclusion. Patients are enrolled from the comprehensive stroke center at Aarhus University Hospital in Denmark from April 2021 to June 2024, with last follow-up expected in June 2025. Detailed inclusion and exclusion criteria are summarized in Table 1.

A subset of patients participated in the clinical randomized controlled trial, Remote Ischemic Conditioning for Acute Stroke (RESIST) [30].

Table 1 Eligibility criteria
Full size table

Protocol approvals and patient consent

The study is approved by the Danish regional research ethics committee (ID: VEK 1-10-72-253-20). The study is conducted according to the declaration of Helsinki. All patients provide written informed consent before enrollment.

Study objectives

Primary objectives.

  • To investigate associations between capillary dysfunction and PSCI.

  • To investigate the associations between EV characteristics and PSCI.

Secondary objectives.

  • To investigate associations between capillary dysfunction and functional outcome, well-being, and depression after stroke.

  • To investigate the associations between EV characteristics and functional outcome, well-being, and depression after stroke.

Study procedures and follow-up

Eligible patients are included within the first 24 h of admission. Baseline measurements are carried out within 48 h from admission. In-person follow-up visits are conducted three months (+/- 1 week) and 12 months (+/- 2 weeks) after inclusion. An MRI is performed in extension to the 12-month follow-up visit. If patients decline to participate in the MRI or if they have incurred contraindications to undergo MRI, they are still eligible for the 12-month follow-up visit. In addition, long-term register-based follow-up is planned. It will include follow-up for recurrent stroke, other vascular events, vascular death, and dementia. Study procedures and their timings are listed in Table 2. Figure 1 illustrates anticipated patient inclusion and follow-up.

Table 2 Study assessments and registrations
Full size table
Fig. 1
figure 1

Anticipated inclusion and patient flow in the ENIGMA study

Full size image

Magnetic resonance imaging

An MRI is performed at 24 h (+/- 6 h) after the acute MRI on admission and again at the 12-month follow-up visit. MRI sequences include T1-weighted 3D images for co-registration, DWI, kurtosis diffusion imaging, 3D fluid-attenuated inversion recovery (FLAIR), susceptibility weighted imaging (SWI), and gradient echo (GRE) and spin echo (SE) perfusion imaging.

White matter hyperintensities, visible on FLAIR images, are semi-automatically outlined [31]. Capillary function is characterized by capillary transit time heterogeneity (CTH), parameterized as the standard deviation of blood transit times within each image voxel using SE perfusion imaging [19, 22, 23]. Finally, grey and white matter microstructural integrity, including dendrite density, axonal density and dispersion, and intra- and extra-axonal water diffusivity are characterized based on DWI and kurtosis diffusion imagning [32,33,34,35]. All MRI scans are performed on site with a 3T Siemens Magnetom Vida scanner (Siemens Healthcare, Erlangen, Germany).

The burden of cSVD is assessed on the 24-hour and the 12-month MRI scans. A total cSVD score is calculated based on the STRIVE-1 criteria with local specifications [36]. The cSVD score includes number and location of microbleeds, superficial siderosis, number of lacunes, grade of white matter hyperintensities, grade of global cortical atrophy, and enlarged perivascular spaces. Scoring is independently performed by a trained neurologist and a neuroradiologist. At scoring disagreement, consensus is achieved. Table 3 presents an overview of the used MRI sequences.

Table 3 MRI sequences and characteristics at 24 h and 12 months
Full size table

Blood samples, EV characterization, and genotyping

At 24 h (+/- 8 h) after admission and at the three-month follow-up visit, a blood sample consisting of three 3.5 mL citrate collection tubes is drawn by study personnel and centrifuged to yield plasma, which is stored at -80 °C. EV size and concentration will be determined after EV purification using size exclusion chromatography (Izon, Addington, New Zealand) and nanoparticle tracking analysis (NanoSight 300, Malvern Panalytica, Worcestershire, United Kingdom) [37]. Changes in EV surface markers will be studied using the EV Array platform that simultaneous assesses up to 60 EV surface markers [38]. In addition, changes in EV content (microRNA and proteins) will be assessed using nCounter microRNA panels (NanoString, Seattle, WA, USA) and Orbitrap mass spectrometers (Thermo Fisher Scientific, Waltham, MA, USA), respectively.

At the three-month visit, an additional blood sample in a 3 mL EDTA collection tube is drawn and stored at -80 °C without centrifugation. This full-blood sample will undergo DNA extraction and APOE genotyping.

Cognitive assessments

Post-stroke cognitive function is assessed at three and 12-month follow-up visits using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). To reduce learning bias, version A is administered at the three-month visit and version B is administered at the 12-month visit. All assessments are done by trained personnel. Training and scoring are overseen by a neuropsychologist and a neurologist specialized in dementia. All RBANS measures are administered and scored using standard procedures detailed in the Danish manual. The RBANS is a validated repeatable assessment of cognitive function [39, 40]. With 12 subtests it covers the five cognitive domains: immediate memory, visuospatial function, language, attention, and delayed memory. Based on norm data, the RBANS provides sub scores and age-adjusted index scores for each domain and combine them to a total index score.

The Montreal Cognitive Assessment (MoCA) is administered at baseline and at three- and 12-month follow-up. The MoCA is a brief cognitive screening tool validated for post-stroke cognitive impairment [41].

Pre-stroke cognition is assessed by the short version of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). The IQCODE is a validated 16-item questionnaire assessing change in cognition over the course of 10 years [42]. It assesses changes in working, short-term and long-term memory, learning and attention and the ability to perform daily activities. The questionnaire is completed by the nearest relative either in person or by telephone interview. The respondent is asked to rate the change in the patient’s cognitive ability before the AIS or TIA.

Questionnaires: well-being, depressive symptoms, and physical activity level

Subjective well-being is assessed by the short 5-item World Health Organization Well-Being Index (WHO-5) [43]. Depressive symptoms are measured by the Major Depression Inventory (MDI). The MDI is a 10-item questionnaire covering the symptoms of major depressive disorder [44]. Both the WHO-5 and the MDI are administered at baseline, and at three- and 12-month follow-up. They are answered by patients themselves or with assistance from study personnel.

The Physical Activity Scale for the Elderly (PASE) is used to assess self-reported physical activity level during previous seven days. It is a validated 12-item questionnaire on overall physical activity and it includes work, leisure time-, household- and sports activities to provide a total physical activity score [45]. The PASE is completed by patient interview at baseline and at the follow-up visits.

Additional assessments

Information about symptom onset, admission time, prior medical history, demographics, alcohol consumption, medication use, admission National Institutes of Health Stroke Scale (NIHSS) score, routine blood chemistry, height, weight, blood pressure, history of smoking, and educational attainment is collected at baseline. Pre-stroke functional ability and post-stroke functional outcome are assessed on the modified Rankin Scale (mRS). Based on information from hospital charts and routine acute MRI, the primary stroke is classified using the Oxfordshire Community Stroke Project classification [46], while the etiological subtype is classified according to the Trial of Org 10,172 in Acute Stroke Treatment (TOAST) criteria [47]. At each follow-up visit, height, weight, and blood pressure are measured. In addition, new clinically verified cardiovascular events are recorded. Cardiovascular events are defined as fatal or non-fatal acute myocardial infarction, AIS, hemorrhagic stroke, or TIA.

Statistical analysis

The role of capillary dysfunction on PSCI will be assessed by the association between CTH at baseline and PSCI. The primary outcome will be the difference in total RBANS score from three to 12 months follow-up. Secondary outcomes include total RBANS score at 12 months follow-up, difference in MoCA score from three to 12 months follow-up, and MoCA score at 12 months follow-up. All associations will be assessed using linear mixed effects models adjusting for age, sex, educational level, baseline NIHSS, smoking, and diabetes. Analyses will be stratified by APOE genotype (ε4 carriers vs. non-carriers) and pre-stroke cognitive decline (IQCODE > 3.48 vs. IQCODE ≤ 3.48) to assess for effect modification by these factors. Level of cSVD will be assessed as a mediator.

Associations between EV characteristics and cognitive function after stroke will be assessed primarily by exploring differences in EV characteristics at baseline between patients with normal cognition, impaired cognitive function (total RBANS index score 70–85) and reduced cognitive function (total RBANS index score < 70) at 12-month follow-up using analysis of differential expression. Secondary analyses include exploring differences in EV characteristics at baseline among patients with a decline (≥ 6 points) versus patients with no decline in total RBANS score from three to 12 months, between patients with decline (≥ 2 points) versus no decline in MoCA score from three to 12 months follow-up, and between patients above and below MoCA score of 22 and 26 at 12-months follow-up. Analyses will be adjusted for age, sex, educational level, baseline NIHSS, diabetes, and hypertension. In addition, analyses stratified by APOE genotype, pre-stroke cognitive decline, and level of cSVD will be performed.

Sample size calculation

A previous study could show a correlation between capillary dysfunction and cognitive status in 32 patients with Alzheimer’s disease [48]. To find a similar effect size in a linear model with 10 predictor variables, a sample size of 71 patients is needed (90% power, significance level 0.05). This calculation was performed using the pwr-package in R version 4.3.2 [49]. Previous studies of EV characteristics were able to distinguish between stroke severity, lesion volume and outcome when comparing groups of 21 and 20 stroke patients [50]. A sample size of 100 patients with complete follow-up is therefore considered adequate.

Discussion

With the ENIGMA study we aim to study vascular contributions to cognitive decline and dementia. Using advanced MRI techniques and studying EV characteristics, we aim to investigate capillary dysfunction and EV characteristics as predictors of cognitive decline and cognitive function one year after AIS and TIA.

It is well established that ischemic stroke and cSVD increase risk of cognitive impairment and dementia [5, 51]. However, mechanisms behind vascular cognitive impairment are largely unknown. For this reason, numerous prospective observational studies are examining predictors of post-stroke cognitive decline [52,53,54,55,56,57,58] and consortia have been established to drive the research into vascular cognitive impairment forward [59, 60]. With ENIGMA we hope to provide novel knowledge about the microvascular mechanisms behind PSCI and the potential of EVs as biomarkers of the vascular brain reserve and consequently post-stroke cognitive function.

Traditionally, we consider stroke and TIA flow-limiting conditions. However, increasing evidence suggests that we should also consider dysfunction of the capillaries, primarily limiting oxygen extraction rather than blood supply, as an important mechanism of cerebrovascular disease [20, 61]. Capillary dysfunction has been shown to contribute to tissue injury after acute stroke and to cognitive impairment in both vascular dementia and Alzheimer’s disease [21, 48, 61, 62]. New imaging methods, developed by researchers in our group, now enable us to characterize microvascular flow patterns on MRI with the potential to detect and quantify the preclinical manifestations of this contribution to cerebrovascular disease [22, 24]. In addition, advanced diffusion-weighted MRI methods such as diffusion kurtosis imaging may contribute to a better understanding of the microstructure and functional integrity of brain tissue.

EVs may represent biomarkers indicating the current cerebrovascular disease state [27]. The diagnostic and prognostic use of EVs is appealing as they have unique surface markers. The release, content, and composition of these markers are altered during disease in the central nervous system. Further, these changes are detectable in the blood as EVs naturally cross the blood brain barrier [63]. The EV-array technology makes it possible to detect up to 60 different EV surface markers directly from plasma using antibodies as detection agents [38]. EVs derived from e.g. endothelial cells and leucocytes could be of particular interest, as they may reflect vascular dysfunction and contribute to post-stroke neuroinflammation and PSCI. In addition, profiling EV content, such as microRNAs and proteins, can be done using molecular biological techniques including proteomics and next generation sequencing. Specifically, miRNA upregulated in cSVD would be of interest. EVs thus represent a novel reservoir of potential biomarkers of stroke outcomes.

Study results will be published in peer-reviewed journals and reported according to STROBE guidelines. The results will be generalizable to most stroke populations. However, as patients aged < 60 years, patients with known neurodegenerative disease, and patients with pre-stroke mRS > 2 were excluded, the study might not elucidate the associations between PSCI and capillary dysfunction or EV characteristics in patients < 60 years and in patients with more severe pre-stroke disability. Further, although the RBANS has the advantage of different versions for repeated testing, it may not sufficiently test executive function and psychomotor speed, which are cognitive functions often affected in PSCI. Different additional tests could have been performed, e.g. the Trail Making Test (TMT). As this test cannot be scored if the participant takes more than 5 min to complete, the MoCA test, which, among other subtests, contains a short version of the TMT-B was chosen to be performed at baseline as well as at 3- and 12-months follow-up.

Summary and conclusions.

In conclusion, the ENIGMA study is a single-centre prospective clinical observational study with the aim to investigate vascular contributions to cognitive decline and dementia. By studying MRI-defined capillary dysfunction and EV characteristics, the ENIGMA study links novel imaging and basic research techniques to a clinical cohort of stroke patients. With this study we hope to enhance the understanding of the mechanisms behind post-stroke cognitive decline and dementia.

Data availability

All investigators will have access to the entire dataset. Trial metadata, including, but not limited to codebooks, data dictionaries and analysis code will be shared on a repository under a permissive license. Upon completing a collaboration agreement, individual patient trial data may be shared upon reasonable request. Patient data will not be made publicly available.

Abbreviations

AIS:

acute ischemic stroke

APOE:

apolipoprotein E

BP:

blood pressure

cSVD:

cerebral small vessel disease

CTH:

capillary transit time heterogeneity

DWI:

diffusion-weighted imaging

eGFR:

estimated glomerular filtration rate

EVs:

extracellular vesicles

FLAIR:

fluid-attenuated inversion recovery

GRE:

gradient echo

IQCODE:

informant Questionnaire on Cognitive Decline in the Elderly

MDI:

Major Depression Inventory

MoCA:

Montreal Cognitive Assessment

MRI:

magnetic resonance imaging

mRS:

modified Rankin Scale

NIHSS:

National Institute of Health Stroke Scale

OCSP:

Oxfordshire Community Stroke Project Classification

PASE:

Physical Activity Scale for the Elderly

PSCI:

post-stroke cognitive impairment

RBANS:

Repeatable Battery for the Assessment of Neuropsychological Status

SE:

spin echo

STRIVE-1:

Standards for Reporting Vascular Changes on Neuroimaging-1

SWI:

susceptibility weighted imaging

TIA:

transient ischemic attack

TOAST:

trial of Org. 10172 in Acute Stroke Treatment

WHO-5:

World Health Organization Five Well-Being Index

References

  1. Feigin VL, Stark BA, Johnson CO, et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the global burden of Disease Study 2019. Lancet Neurol. 2021;20:795.

    Article  CAS  Google Scholar 

  2. Sexton E, McLoughlin A, Williams DJ, et al. Systematic review and meta-analysis of the prevalence of cognitive impairment no dementia in the first year post-stroke. Eur Stroke J. 2019;4:160–71.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kuźma E, Lourida I, Moore SF, et al. Stroke and dementia risk: a systematic review and meta-analysis. Alzheimers Dement. 2018;14:1416–26.

    Article  PubMed  Google Scholar 

  4. Koton S, Pike JR, Johansen M, et al. Association of ischemic stroke incidence, severity, and recurrence with dementia in the atherosclerosis risk in communities Cohort Study. JAMA Neurol. 2022;79:271–80.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Pendlebury ST, Rothwell PM. Incidence and prevalence of dementia associated with transient ischaemic attack and stroke: analysis of the population-based Oxford Vascular Study. Lancet Neurol. 2019;18:248–58.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Harnod T, Lin CL, Hsu CY, et al. Post-stroke dementia is associated with increased subsequent all-cause mortality: a population-based cohort study. Atherosclerosis. 2019;284:148–52.

    Article  CAS  PubMed  Google Scholar 

  7. Pasquini M, Leys D, Rousseaux M, et al. Influence of cognitive impairment on the institutionalisation rate 3 years after a stroke. J Neurol Neurosurg Psychiatry. 2007;78:56.

    Article  CAS  PubMed  Google Scholar 

  8. Kuo LM, Tsai WC, Chiu MJ, et al. Cognitive dysfunction predicts worse health-related quality of life for older stroke survivors: a nationwide population-based survey in Taiwan. Aging Ment Health. 2019;23:305–10.

    Article  PubMed  Google Scholar 

  9. Quinn TJ, Richard E, Teuschl Y, et al. European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment. Eur Stroke J. 2021;6:I–XXXVIII.

    Article  PubMed  PubMed Central  Google Scholar 

  10. El Husseini N, Katzan IL, Rost NS, et al. Cognitive impairment after ischemic and hemorrhagic stroke: a Scientific Statement from the American Heart Association/American Stroke Association. Stroke. 2023;54:e272–91.

    Article  PubMed  Google Scholar 

  11. Iadecola C, Duering M, Hachinski V, et al. Vascular cognitive impairment and dementia: JACC Scientific Expert Panel. J Am Coll Cardiol. 2019;73:3326–44.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hachinski V, Einhäupl K, Ganten D, et al. Preventing dementia by preventing stroke: the Berlin Manifesto. Alzheimers Dement. 2019;15:961–84.

    Article  PubMed  Google Scholar 

  13. Weaver NA, Kuijf HJ, Aben HP, et al. Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute ischaemic stroke cohorts. Lancet Neurol. 2021;20:448–59.

    Article  PubMed  Google Scholar 

  14. Rost NS, Brodtmann A, Pase MP, et al. Post-stroke cognitive impairment and dementia. Circ Res. 2022;130:1252–71.

    Article  CAS  PubMed  Google Scholar 

  15. Ball EL, Shah M, Ross E, et al. Predictors of post-stroke cognitive impairment using acute structural MRI neuroimaging: a systematic review and meta-analysis. Int J Stroke. 2023;18:543–54.

    Article  PubMed  Google Scholar 

  16. Kim KY, Shin KY, Chang K-A. Potential biomarkers for Post-stroke Cognitive Impairment: a systematic review and Meta-analysis. Int J Mol Sci. 2022;23:602.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Casolla B, Caparros F, Cordonnier C, et al. Biological and imaging predictors of cognitive impairment after stroke: a systematic review. J Neurol. 2019;266:2593–604.

    Article  PubMed  Google Scholar 

  18. Sandvig HV, Aam S, Alme KN, et al. Plasma inflammatory biomarkers are Associated with Poststroke Cognitive Impairment: the Nor-COAST study. Stroke. 2023;54:1303–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Østergaard L, Jespersen SN, Engedahl T et al. Capillary dysfunction: its detection and causative role in dementias and stroke. Curr Neurol Neurosci Rep; 15. Epub ahead of print 1 June 2015. https://doi.org/10.1007/S11910-015-0557-X.

  20. Østergaard L, Jespersen SN, Mouridsen K, et al. The role of the cerebral capillaries in acute ischemic stroke: the extended penumbra model. J Cereb Blood Flow Metab. 2013;33:635–48.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Østergaard L, Engedal TS, Moreton F, et al. Cerebral small vessel disease: Capillary pathways to stroke and cognitive decline. J Cereb Blood Flow Metab. 2016;36:302–25.

    Article  PubMed  Google Scholar 

  22. Mouridsen K, Friston K, Hjort N, et al. Bayesian estimation of cerebral perfusion using a physiological model of microvasculature. NeuroImage. 2006;33:570–9.

    Article  PubMed  Google Scholar 

  23. Mouridsen K, Hansen MB, Østergaard L, et al. Reliable estimation of capillary transit time distributions using DSC-MRI. J Cereb Blood Flow Metab. 2014;34:1511–21.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Hansen MB, Tietze A, Kalpathy-Cramer J, et al. Reliable estimation of microvascular flow patterns in patients with disrupted blood-brain barrier using dynamic susceptibility contrast MRI. J Magn Reson Imaging JMRI. 2017;46:537–49.

    Article  PubMed  Google Scholar 

  25. Rufino-Ramos D, Albuquerque PR, Carmona V, et al. Extracellular vesicles: novel promising delivery systems for therapy of brain diseases. J Controlled Release. 2017;262:247–58.

    Article  CAS  Google Scholar 

  26. Loyer X, Vion A-C, Tedgui A, et al. Microvesicles as cell–cell messengers in Cardiovascular diseases. Circ Res. 2014;114:345–53.

    Article  CAS  PubMed  Google Scholar 

  27. Stenz KT, Just J, Blauenfeldt RA, et al. Extracellular vesicles in Acute Stroke Diagnostics. Biomedicines. 2020;8:248.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. van Kralingen JC, McFall A, Ord ENJ, et al. Altered extracellular vesicle MicroRNA expression in ischemic stroke and small Vessel Disease. Transl Stroke Res. 2019;10:495–508.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Burlacu C-C, Ciobanu D, Badulescu A-V, et al. Circulating MicroRNAs and Extracellular Vesicle-derived MicroRNAs as predictors of functional recovery in ischemic stroke patients: a systematic review and Meta-analysis. Int J Mol Sci. 2022;24:251.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Blauenfeldt RA, Hjort N, Valentin JB, et al. Remote ischemic conditioning for Acute Stroke: the RESIST Randomized Clinical Trial. JAMA. 2023;330:1236–46.

    Article  CAS  PubMed  Google Scholar 

  31. Smart SD, Firbank MJ, O’Brien JT. Validation of automated white matter hyperintensity segmentation. J Aging Res; 2011. Epub ahead of print 2011. https://doi.org/10.4061/2011/391783.

  32. Jespersen SN, Kroenke CD, Østergaard L, et al. Modeling dendrite density from magnetic resonance diffusion measurements. NeuroImage. 2007;34:1473–86.

    Article  PubMed  Google Scholar 

  33. Hansen B, Shemesh N, Jespersen SN. Fast imaging of mean, axial and radial diffusion kurtosis. NeuroImage. 2016;142:381–93.

    Article  PubMed  Google Scholar 

  34. Hansen B, Khan AR, Shemesh N et al. White matter biomarkers from fast protocols using axially symmetric diffusion kurtosis imaging. NMR Biomed; 30. Epub ahead of print 1 September 2017. https://doi.org/10.1002/NBM.3741.

  35. Hansen B, Lund TE, Sangill R, et al. Experimentally and computationally fast method for estimation of a mean kurtosis. Magn Reson Med. 2013;69:1754–60.

    Article  PubMed  Google Scholar 

  36. Wardlaw JM, Smith EE, Biessels GJ, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12:822–38.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Gu T, Just J, Stenz KT, et al. The role of plasma Extracellular vesicles in Remote Ischemic Conditioning and Exercise-Induced ischemic tolerance. Int J Mol Sci. 2022;23:3334.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Jørgensen M, Bæk R, Pedersen S, et al., et al. Extracellular vesicle (EV) array: microarray capturing of exosomes and other extracellular vesicles for multiplexed phenotyping. J Extracell Vesicles. 2013;2. https://doi.org/10.3402/jev.v2i0.20920. Epub ahead of print.

  39. Randolph C, Tierney MC, Mohr E, et al. The repeatable battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity. J Clin Exp Neuropsychol. 1998;20:310–9.

    Article  CAS  PubMed  Google Scholar 

  40. Larson EB, Kirschner K, Bode R, et al. Construct and predictive validity of the repeatable battery for the assessment of neuropsychological status in the evaluation of stroke patients. J Clin Exp Neuropsychol. 2005;27:16–32.

    Article  PubMed  Google Scholar 

  41. Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53:695–9.

    Article  PubMed  Google Scholar 

  42. van Nieuwkerk AC, Pendlebury ST, Rothwell PM. Accuracy of the Informant Questionnaire on Cognitive decline in the Elderly for detecting Preexisting Dementia in transient ischemic attack and stroke a Population-based study. Stroke. 2021;52:1283–90.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Topp CW, Østergaard SD, Søndergaard S, et al. The WHO-5 well-being index: a systematic review of the literature. Psychother Psychosom. 2015;84:167–76.

    Article  PubMed  Google Scholar 

  44. Bech P, Rasmussen N-A, Olsen LR, et al. The sensitivity and specificity of the Major Depression Inventory, using the Present State Examination as the index of diagnostic validity. J Affect Disord. 2001;66:159–64.

    Article  CAS  PubMed  Google Scholar 

  45. Washburn RA, McAuley E, Katula J, et al. The physical activity scale for the Elderly (PASE). J Clin Epidemiol. 1999;52:643–51.

    Article  CAS  PubMed  Google Scholar 

  46. Bamford J, Sandercock P, Dennis M, et al. Classification and natural history of clinically identifiable subtypes of cerebral infarction. Lancet. 1991;337:1521–6.

    Article  CAS  PubMed  Google Scholar 

  47. Adams HP, Bendixen BH, Kappelle LJ, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke. 1993;24:35–41.

    Article  PubMed  Google Scholar 

  48. Nielsen RB, Egefjord L, Angleys H, et al. Capillary dysfunction is associated with symptom severity and neurodegeneration in Alzheimer’s disease. Alzheimers Dement. 2017;13:1143–53.

    Article  PubMed  Google Scholar 

  49. R Core Team. R: A Language and Environment for Statistical Computing, https://www.r-project.org/ (2021).

  50. Simak J, Gelderman MP, Yu H, et al. Circulating endothelial microparticles in acute ischemic stroke: a link to severity, lesion volume and outcome. J Thromb Haemost. 2006;4:1296–302.

    Article  CAS  PubMed  Google Scholar 

  51. Rensma SP, van Sloten TT, Launer LJ, et al. Cerebral small vessel disease and risk of incident stroke, dementia and depression, and all-cause mortality: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2018;90:164–73.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Rost NS, Meschia JF, Gottesman R et al. Cognitive impairment and dementia after stroke: design and rationale for the DISCOVERY study. Stroke 2021; E499–516.

  53. Ben Assayag E, Korczyn AD, Giladi N, et al. Predictors for poststroke outcomes: the Tel Aviv Brain Acute Stroke Cohort (TABASCO) study protocol. Int J Stroke. 2012;7:341–7.

    Article  PubMed  Google Scholar 

  54. Brodtmann A, Werden E, Pardoe H, et al. Charting cognitive and volumetric trajectories after stroke: protocol for the Cognition and neocortical volume after stroke (CANVAS) study. Int J Stroke. 2014;9:824–8.

    Article  PubMed  Google Scholar 

  55. Douven E, Schievink SHJ, Verhey FRJ et al. The Cognition and Affect after Stroke - a Prospective Evaluation of Risks (CASPER) study: rationale and design. BMC Neurol; 16. Epub ahead of print 12 May 2016. https://doi.org/10.1186/S12883-016-0588-1.

  56. Aben HP, Reijmer YD, Visser-Meily JMA et al. A Role for New Brain Magnetic Resonance Imaging Modalities in Daily Clinical Practice: Protocol of the Prediction of Cognitive Recovery After Stroke (PROCRAS) Study. JMIR Res Protoc; 7. Epub ahead of print 1 May 2018. https://doi.org/10.2196/RESPROT.9431.

  57. Thingstad P, Askim T, Beyer MK et al. The Norwegian Cognitive impairment after stroke study (Nor-COAST): study protocol of a multicentre, prospective cohort study. BMC Neurol; 18. Epub ahead of print 26 November 2018. https://doi.org/10.1186/S12883-018-1198-X.

  58. Wollenweber FA, Zietemann V, Rominger A, et al. The determinants of Dementia after Stroke (DEDEMAS) study: protocol and pilot data. Int J Stroke. 2014;9:387–92.

    Article  PubMed  Google Scholar 

  59. Gladman JT, Corriveau RA, Debette S, et al. Vascular contributions to cognitive impairment and dementia: research consortia that focus on etiology and treatable targets to lessen the burden of dementia worldwide. Alzheimers Dement. 2019;5:789–96.

    Article  Google Scholar 

  60. Wilcock D, Jicha G, Blacker D, et al. MarkVCID cerebral small vessel consortium: I. Enrollment, clinical, fluid protocols. Alzheimers Dement. 2021;17:704–15.

    Article  CAS  PubMed  Google Scholar 

  61. Engedal TS, Hjort N, Hougaard KD, et al. Transit time homogenization in ischemic stroke - A novel biomarker of penumbral microvascular failure? J Cereb Blood Flow Metab. 2018;38:2006–20.

    Article  PubMed  Google Scholar 

  62. Eskildsen SF, Gyldensted L, Nagenthiraja K, et al. Increased cortical capillary transit time heterogeneity in Alzheimer’s disease: a DSC-MRI perfusion study. Neurobiol Aging. 2017;50:107–18.

    Article  PubMed  Google Scholar 

  63. Zagrean AM, Hermann DM, Opris I et al. Multicellular Crosstalk Between Exosomes and the Neurovascular Unit After Cerebral Ischemia. Therapeutic Implications. Front Neurosci; 12. Epub ahead of print 6 November 2018. https://doi.org/10.3389/FNINS.2018.00811.

Download references

Acknowledgements

Not applicable.

Funding

This study is supported by Novo Nordic Foundation (grant no. NNF20OC0060998), Aarhus University Hospital Developmental Strategy Fond, Lundbeck Foundation (grant no R310-2018-3455), and the Central Region Denmark Research Foundation.

Author information

Authors and Affiliations

  1. Department of Neurology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, Aarhus N, 8200, Denmark

    Sigrid Breinholt Vestergaard, Andreas Gammelgaard Damsbo, Katrine Zachariassen & Janne Kærgård Mortensen

  2. Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, Aarhus N, 8200, Denmark

    Sigrid Breinholt Vestergaard, Andreas Gammelgaard Damsbo, Niels Lech Pedersen, Grethe Andersen, Rikke Beese Dalby & Janne Kærgård Mortensen

  3. Department of Neuroradiology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, Aarhus N, 8200, Denmark

    Niels Lech Pedersen

  4. Department of Clinical Medicine Center of Functionally Integrative Neuroscience, Aarhus University, Universitetsbyen 3, Aarhus C, 8000, Denmark

    Kim Ryun Drasbek & Leif Østergaard

  5. Department of Radiology and Nuclear Medicine, University Hospital of Southern Denmark, Finsensgade 35, Esbjerg, 6700, Denmark

    Rikke Beese Dalby

  6. Department of Clinical Medicine, Department of Neurology, Aarhus University, Aarhus University Hospital, Palle Juul-Jensens Boulevard 165, J109, Aarhus N, 8200, Denmark

    Janne Kærgård Mortensen

Authors
  1. Sigrid Breinholt Vestergaard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Gammelgaard Damsbo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Niels Lech Pedersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Katrine Zachariassen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kim Ryun Drasbek
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Leif Østergaard
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Grethe Andersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rikke Beese Dalby
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Janne Kærgård Mortensen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JKM, GA, RBD, KRD, and LØ conceived the study and were involved in protocol development and ethical approval. SBV, JKM, AGD, and KZ are involved in patient recruitment and follow-up. SBV, AGD, NLP, KRD, LØ, GA, RBD, and JKM are involved in assessment and analysis of data related to his/her field. SBV wrote the first draft of the manuscript. SBV, AGD, NLP, KZ, KRD, LØ, GA, RBD, and JKM have revised and approved the final version of this manuscript.

Corresponding author

Correspondence to Janne Kærgård Mortensen.

Ethics declarations

Ethical approval and consent to participate

The study is approved by the Danish regional research ethics committee (ID: VEK 1-10-72-253-20). The study is conducted according to the declaration of Helsinki. All patients provide written informed consent before enrollment.

Consent for publication

Not applicable.

Competing interests

LØ is scientific advisory board member and minority shareholder in Cercare Medical. SBV, AGD, NLP, KZ, KRD, GA, RBD, and JKM declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Corresponding author:

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vestergaard, S.B., Damsbo, A.G., Pedersen, N.L. et al. Exploring vascular contributions to cognitive impairment and dementia (ENIGMA): protocol for a prospective observational study. BMC Neurol 24, 110 (2024). https://doi.org/10.1186/s12883-024-03601-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12883-024-03601-7

Keywords

BMC Neurology

ISSN: 1471-2377

Contact us