Michelle C. Johansen

BACKGROUND: As life expectancy rises, identifying causes and risk factors for incident acute ischemic stroke (AIS) among the oldest-old (≥80 years) is increasingly important. We examined whether the effect of age at stroke on AIS subtype is mediated by embolic risk factors and whether these factors improve AIS prediction. METHODS: Stroke-free participants from the ARIC study (Atherosclerosis Risk in Communities) who developed AIS between visit 5 (2011–2013) and visit 10 (2023) were included for causal analysis; Stroke-free participants at visit 5 were included for prediction analysis. In logistics regression models, the association between age at stroke-onset (≥80 versus 2 DS ₂ -VASc) and compared preinclusion and postinclusion of embolic risk factors. RESULTS: Of 6213 stroke-free participants at visit 5, 277 (4.4%) developed AIS during a median (Q1–Q3) of 5.1 (2.6–7.1) years (median [Q1–Q3] age: 76 [72–80] years; median [Q1–Q3] age at AIS: 81 [77–86] years; 62% female; 99 embolic ischemic stroke and 178 thrombotic ischemic stroke). Individuals with AIS ≥80 years had higher odds of embolic ischemic stroke (versus thrombotic ischemic stroke) compared with those aged P =0.03), an abnormal left atrium volume index (45%; P =0.048), or an abnormal P-wave axis (43%; P =0.04). The predictive performance for AIS ≥80 years using the Predicting Risk of Cardiovascular Disease Events equation (N=5702, C statistic, 0.49 [95% CI, 0.45–0.53]), or CHA ₂ DS ₂ -VASc score (N=5739, C statistic, 0.57 [95% CI, 0.55–0.59]) was poor, but inclusion of embolic risk factors improved the performance (Predicting Risk of Cardiovascular Disease Events: C statistics, 0.77 [95% CI, 0.74–0.80]; CHA ₂ DS ₂ -VASc: C statistics, 0.63 [95% CI, 0.59–0.67]). CONCLUSIONS: These findings suggest that identification and control of embolic risk factors are critical to reduce stroke risk as people age, and better stroke-specific prediction tools are needed.

BACKGROUND: Prior studies have demonstrated an association between oral infection and both incident stroke and atrial fibrillation (AF), a major cause of cardioembolic stroke. However, the impact of preventive oral behaviors such as dental flossing on incident stroke and AF remains unclear. We evaluated the relationship between flossing, incident stroke subtypes, and AF from the ARIC study (Atherosclerosis Risk in Communities). METHODS: We conducted a prospective cohort study using data from the ARIC study. Flossing was assessed via a structured questionnaire at visit 4 (1996–1998). Participants were censored at the first stroke, death, or loss-to-follow-up through December 31, 2021. Incident ischemic stroke and subtypes were evaluated using Cox proportional hazards models, adjusting for cardiovascular risk factors, education, regular brushing, dental care utilization, periodontal disease, and dental caries. RESULTS: Among 6200 dentate participants without prior stroke, 65% (n=4049) reported regular flossing. Flossers had significantly lower prevalence of vascular risk factors, periodontal disease, and dental caries at baseline. Flossers had lower IL (interleukin)-6 levels in crude analysis, but this difference was explained by confounding from age, body mass index, and smoking. CRP (C-reactive protein) levels did not differ between groups. During a median 23.7 years follow-up, 434 ischemic strokes occurred, including 146 thrombotic, 102 cardioembolic, 95 lacunar, and 91 other ischemic subtypes. Nonflossers had higher rates of both ischemic stroke (8.7% versus 6.1%) and nonstroke mortality (40.8% versus 34.5%). Flossing was associated with lower risk of ischemic stroke (adjusted hazard ratio, 0.77 [95% CI, 0.64–0.96]), cardioembolic stroke (adjusted hazard ratio, 0.60 [95% CI, 0.38–0.94]), and AF (adjusted hazard ratio, 0.88 [95% CI, 0.77–0.99]) with dose-response relationship ( P trend=0.001) 6.6% of the effect mediated by lower AF incidence. Results remained robust in competing risk analysis (adjusted hazard ratio, 0.79 [95% CI, 0.63–0.98]) with an E value of 1.87. CONCLUSIONS: Regular flossing was associated with a lower risk of ischemic stroke, primarily driven by reductions in cardioembolic stroke and AF, independent of other oral health behaviors. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT00005131.

BACKGROUND: Cerebral microbleeds (CMBs) are associated with dementia, but the impact of specific microbleed patterns (with distinct pathophysiologies) is unclear. Lobar CMBs commonly result from cerebral amyloid angiopathy (sometimes with cortical superficial siderosis [cSS]), while subcortical microbleeds indicate a hypertensive cause. This study investigates the association of CMB presence, patterns, and frequency with dementia risk. METHODS: Participants from the ARIC-NCS (Atherosclerosis Risk in Communities-Neurocognitive Study), a community-based longitudinal cohort, with a 3T research magnetic resonance imaging scan at visit 5 (2011–2013) and without prior intracerebral hemorrhage or dementia were included. CMB and cSS presence and location were evaluated using a T2* gradient-recalled echo sequence. Each individual’s pattern of CMBs was classified as no CMBs, subcortical only, mixed (subcortical and lobar±cSS), lobar±cSS only, or cSS. Dementia adjudication was determined by blinded review of cognitive testing, informant interviews, and hospital discharge codes or death certificates (from follow-up visits and ongoing surveillance). Cox proportional hazards models assessed associations between CMBs and incident dementia through 2020, adjusted for demographics, vascular risk factors, and white matter hyperintensities. RESULTS: Among 1583 participants (mean age, 76 years; 24.7% cumulative dementia incidence over a median of 23.6 follow-up years), 361 had CMBs. Compared with individuals without CMBs, those with mixed (hazard ratio, 1.99 [95% CI, 1.40–2.83]) or lobar±cSS (hazard ratio, 1.96 [95% CI, 1.30–2.97]) CMB patterns had increased dementia risk, whereas individuals with a subcortical-only pattern did not (hazard ratio, 1.04 [95% CI, 0.78–1.39]). cSS presence (n=21; versus absence) showed an increased risk of dementia (hazard ratio, 2.57 [95% CI, 1.36–4.89]). Greater numbers of microbleeds across all CMB subtypes exhibited an increased risk of dementia. CONCLUSIONS: Lobar and mixed CMB patterns and increasing numbers of CMBs are associated with incident dementia. Certain CMB patterns have differential associations with dementia, but severe enough disease burden, regardless of CMB distribution, is associated with increased dementia risk.

Periodontal disease (PD) and dental caries are prevalent chronic oral inflammatory conditions associated with systemic health outcomes. Individually, both have been linked to an increased risk of ischemic stroke, a leading cause of morbidity and mortality. However, few studies have examined the combined effect of PD and dental caries on stroke, stroke subtypes, and major adverse cardiovascular events (MACEs) within US cohorts. We aimed to evaluate the individual and joint associations of PD and dental caries with incident ischemic stroke, stroke subtypes, and MACEs and assess the relationship between regular dental care utilization and oral disease status. We analyzed data from participants in the Atherosclerosis Risk in Communities study who completed dental assessments during Visit 4 (1996–1998). Participants with previous ischemic stroke, coronary heart disease (CHD), or dental caries without PD were excluded. Oral health status was categorized as good oral health, PD only, or PD with(+) caries. Incident ischemic stroke and MACEs (ischemic stroke, myocardial infarction, or fatal CHD) were identified using adjudicated records through 2019. Cox proportional hazard models estimated hazard ratios (HRs) for outcomes, adjusting for demographics and vascular risk factors. The association of regular dental care with oral disease status was analyzed using logistic regression. Among 5,986 participants (mean age 63 ± 5.6 years; 52% female), 1,640 had good oral health, 3,151 had PD only, and 1,195 had PD + caries. The incidence of ischemic stroke was 4.1%, 6.9%, and 10.0% across groups, respectively. In fully adjusted models, PD + caries was associated with increased risk of ischemic stroke (HR 1.86; 95% CI 1.32–2.61) and MACEs (HR 1.36; 95% CI 1.10–1.69). Elevated risk was observed for thrombotic (HR 2.27; 95% CI 1.22–4.24) and cardioembolic (HR 2.58; 95% CI 1.27–5.26) strokes. Regular dental care was associated with lower odds of PD (odds ratio [OR] 0.71; 95% CI 0.58–0.86) and PD + caries (OR 0.19; 95% CI 0.15–0.25). Co-occurring PD and dental caries were independently associated with increased risk of ischemic stroke and MACEs. Regular dental care may reduce this risk by improving oral health. Limitations include reliance on one-time dental assessment and residual confounding.

Over the last 30 years, several classification systems have been proposed or used to aid in patients with subtyping ischemic stroke based on its likely etiology. Since these classification systems have been published, the field has gained more knowledge about newly reported mechanisms (eg, carotid artery web), new treatments being effective (eg, patent foramen ovale closure), and new diagnostic testing (eg, prolonged outpatient cardiac monitoring). Therefore, an updated ischemic stroke classification system is needed to integrate these advancements, enabling a more targeted workup and potentially improving treatment strategies. We propose a more comprehensive but practical and generalizable diagnostic evaluation and checklist for patients with ischemic stroke to help identify definite, probable, or possible mechanisms related to cardioembolism, large artery atherosclerosis, small vessel disease, or other determined causes (eg, hypercoagulable disorders, genetic disorders, cancer, or dissection). This expands the minimum diagnostic evaluation, leading to reclassification of patients and possibly improving secondary stroke prevention strategies. This will also create a new framework for targeted treatments and randomized trials for various ischemic stroke subtypes.

BACKGROUND: There is a strong association between vascular risk factors, particularly in midlife, and stroke risk; therefore, the co-occurrence of multiple risk factors may be especially informative. This study used a machine-learning–based cluster analysis to group individuals into clusters based on similar clinical profiles in midlife and assessed the clusters’ associations with stroke risk and severity. METHODS: Participants (N=15 404) without prevalent stroke from the ARIC study (Atherosclerosis Risk in Communities) were included. An unsupervised agglomerative hierarchical clustering approach was used to allocate participants into clusters based on the presence of clinical risk factors in midlife: hypertension, diabetes, coronary heart disease, heart failure, atrial fibrillation, renal dysfunction, and peripheral artery disease. Clusters were then characterized by their defining features. In Cox proportional hazard models, the association of the clusters with overall stroke incidence (and with ischemic stroke incidence stratified by stroke severity) was tested. Multinomial logistic regression models were used to examine the association of morbidity clusters with outcomes of no stroke, stroke before the age of 70 years, and stroke at age of ≥70 years. RESULTS: Of 1424 incident ischemic strokes diagnosed from baseline (1987–1989) to December 31, 2020, 1104 included National Institutes of Health Stroke Scale (NIHSS) grading (minor-mild stroke: NIHSS score ≤5 [n=687]; moderate-severe stroke: NIHSS score >5 [n=417]). The cluster analysis identified 9 distinct clusters in the population with defining features: cluster 1 (relatively healthy); cluster 2 (smoking); cluster 3 (cancer); cluster 4 (peripheral artery disease); cluster 5 (obesity, diabetes, hypertension, and hypertriglyceridemia); cluster 6 (coronary heart disease); cluster 7 (atrial fibrillation); cluster 8 (heart failure); and cluster 9 (renal dysfunction). Compared with cluster 1, clusters 2 to 9 were each associated with a greater stroke risk, with the largest effect estimate for cluster 9 (hazard ratio, 3.00 [95% CI, 2.00–4.50]). The association with moderate-severe stroke incidence (versus no stroke) was also strongest for cluster 9 (hazard ratio, 4.78 [95% CI, 2.62–8.74]). Except for cluster 5 (which was associated with stroke at any age), all midlife morbidity clusters were associated with greater stroke risk before the age of 70 years but not after the age of 70 years. CONCLUSIONS: The findings emphasize the importance of morbidity clusters in midlife for stroke incidence and severity.