Summary Background Cerebral microbleeds are a neuroimaging biomarker of stroke risk. A crucial clinical question is whether cerebral microbleeds indicate patients with recent ischaemic stroke or transient ischaemic attack in whom the rate of future intracranial haemorrhage is likely to exceed that of recurrent ischaemic stroke when treated with antithrombotic drugs. We therefore aimed to establish whether a large burden of cerebral microbleeds or particular anatomical patterns of cerebral microbleeds can identify ischaemic stroke or transient ischaemic attack patients at higher absolute risk of intracranial haemorrhage than ischaemic stroke. Methods We did a pooled analysis of individual patient data from cohort studies in adults with recent ischaemic stroke or transient ischaemic attack. Cohorts were eligible for inclusion if they prospectively recruited adult participants with ischaemic stroke or transient ischaemic attack; included at least 50 participants; collected data on stroke events over at least 3 months follow-up; used an appropriate MRI sequence that is sensitive to magnetic susceptibility; and documented the number and anatomical distribution of cerebral microbleeds reliably using consensus criteria and validated scales. Our prespecified primary outcomes were a composite of any symptomatic intracranial haemorrhage or ischaemic stroke, symptomatic intracranial haemorrhage, and symptomatic ischaemic stroke. We registered this study with the PROSPERO international prospective register of systematic reviews, number CRD42016036602. Findings Between Jan 1, 1996, and Dec 1, 2018, we identified 344 studies. After exclusions for ineligibility or declined requests for inclusion, 20 322 patients from 38 cohorts (over 35 225 patient-years of follow-up; median 1·34 years [IQR 0·19–2·44]) were included in our analyses. The adjusted hazard ratio [aHR] comparing patients with cerebral microbleeds to those without was 1·35 (95% CI 1·20–1·50) for the composite outcome of intracranial haemorrhage and ischaemic stroke; 2·45 (1·82–3·29) for intracranial haemorrhage and 1·23 (1·08–1·40) for ischaemic stroke. The aHR increased with increasing cerebral microbleed burden for intracranial haemorrhage but this effect was less marked for ischaemic stroke (for five or more cerebral microbleeds, aHR 4·55 [95% CI 3·08–6·72] for intracranial haemorrhage vs 1·47 [1·19–1·80] for ischaemic stroke; for ten or more cerebral microbleeds, aHR 5·52 [3·36–9·05] vs 1·43 [1·07–1·91]; and for ≥20 cerebral microbleeds, aHR 8·61 [4·69–15·81] vs 1·86 [1·23–2·82]). However, irrespective of cerebral microbleed anatomical distribution or burden, the rate of ischaemic stroke exceeded that of intracranial haemorrhage (for ten or more cerebral microbleeds, 64 ischaemic strokes [95% CI 48–84] per 1000 patient-years vs 27 intracranial haemorrhages [17–41] per 10...

Research in context panel: 445Identifying people at highest risk of ICH may facilitate timely and accurate prognostication to allow mitigation of reversible risk factors for bleeding (e.g. intensive blood pressure control), and selection of participants for clinical trials. While more complex combinations of clinical, biochemical, and radiological markers might further improve stroke risk prediction, balancing accuracy with simplicity will remain important.

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Background: Small vessel disease (SVD) and Alzheimer’s disease (AD) frequently coexist; however, it remains unclear how they collectively affect cognition. Objective: We investigated associations between SVD and AD biomarkers, namely amyloid, tau, and neurodegeneration (ATN) in young onset dementia (YOD) and explored how SVD and ATN interact to affect cognition. Methods: 80 YOD individuals were recruited from a memory clinic. SVD burden (SVD+) was operationalized as a score >1 on the Staals scale and ATN was measured using cerebrospinal fluid (CSF). Results: SVD+ was associated with lower CSF Aβ1–42 (B = –0.20, 95% CI: –0.32 to –0.08) and greater neurodegeneration, indexed as hippocampal atrophy (B = –0.24, 95% CI: –0.40 to –0.04). SVD+ was not associated with tau. Cognitive impairment was associated with CSF Aβ1–42 (B = –0.35, 95% CI: –0.55 to –0.18) but not SVD. Rather, SVD was indirectly associated with cognition via reduced CSF Aβ1–42, specifically with global cognition (B = –0.03, 95% CI: –0.09 to –0.01) and memory (B = 0.08, 95% CI: –.01 to .21). SVD was indirectly associated with cognition via increased neurodegeneration in grey matter (Global cognition: B = –0.06, 95% CI: –0.17 to –0.03; Memory: B = 0.05, 95% CI: 0.01 to 0.18) and the hippocampus (Global cognition: B = –0.05, 95% CI: –0.11 to –0.01; Memory: B = 0.06, 95% CI: 0.01 to 0.17). Conclusion: In YOD, SVD burden was associated with AD pathology, namely CSF Aβ1–42. SVD indirectly contributed to cognitive impairment via reducing CSF Aβ1–42 and increasing neurodegeneration.

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