Dataset Information

The impact of early-life intelligence quotient on post stroke cognitive impairment.

ABSTRACT:

Background

Cognitive impairment can complicate minor stroke, but there is limited information on risk factors including peak cognitive ability earlier in life.

Methods

We recruited patients with clinically-evident lacunar or minor non-lacunar ischaemic stroke, recorded clinical features, vascular risk factors, magnetic resonance imaging-detected stroke sub-type and small vessel disease burden. At 1-3 and 12 months after stroke, we assessed educational attainment (years of education), current cognition (Addenbrooke's Cognitive Examination-Revised), pre-morbid intelligence (National Adult Reading Test) and dependency (modified Rankin Scale).

Results

We recruited 157 patients (87 lacunar, 64 non-lacunar ischaemic strokes), median age 66 (inter-quartile range 56-74) years, 36/157 (23%) patients had a Addenbrooke's Cognitive Examination-Revised score < 82 at one to three months, 29/151 (19%) had a Addenbrooke's Cognitive Examination-Revised < 82 at one year. Lower National Adult Reading Test score (cognitive impairment per point on National Adult Reading Test odds ratio 0.91, 95% confidence interval 0.87, 0.95) and older age (per year of age odds ratio 1.04 (95% confidence interval 1.01, 1.08) predicted one-year cognitive impairment more than stroke severity (per point on National Institute of Health Stroke Scale odds ratio 0.96 (95% confidence interval 0.0.68, 1.31)) or vascular risk factors e.g. hypertension (odds ratio for diagnosis of hypertension 0.52 (95% confidence interval 0.24, 1.15). Cognitive impairment was associated with having more white matter hyper-intensities (odds ratio per point increase in Fazekas score 1.42, 95% confidence interval 1.11, 1.83).

Discussion

This observational study provides evidence that pre-morbid intelligence quotient and education predict cognition after stroke, and confirms the association between cognitive impairment and small vessel disease.

Conclusion

Pre-morbid intelligence should be considered in future studies of post-stroke cognition.

SUBMITTER: Makin SD

PROVIDER: S-EPMC6460404 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

The impact of early-life intelligence quotient on post stroke cognitive impairment.

Makin Stephen Dj SD Doubal Fergus N FN Shuler Kirsten K Chappell Francesca M FM Staals Julie J Dennis Martin S MS Wardlaw Joanna M JM

European stroke journal 20180108 2

<h4>Background</h4>Cognitive impairment can complicate minor stroke, but there is limited information on risk factors including peak cognitive ability earlier in life.<h4>Methods</h4>We recruited patients with clinically-evident lacunar or minor non-lacunar ischaemic stroke, recorded clinical features, vascular risk factors, magnetic resonance imaging-detected stroke sub-type and small vessel disease burden. At 1-3 and 12 months after stroke, we assessed educational attainment (years of educatio ...[more]

PMID: 31008346

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Project description:Background: Post-stroke cognitive impairment (PSCI) is common, but evidence of cognitive symptom profiles, course over time, and pathogenesis is scarce. We investigated the significance of time and etiologic stroke subtype for the probability of PSCI, severity, and cognitive profile. Methods: Stroke survivors (n = 617) underwent cognitive assessments of attention, executive function, memory, language, perceptual-motor function, and the Montreal Cognitive Assessment (MoCA) after 3 and/or 18 months. PSCI was classified according to DSM-5 criteria. Stroke severity was assessed with the National Institutes of Health Stroke Scale (NIHSS). Stroke subtype was categorized as intracerebral hemorrhage (ICH), large artery disease (LAD), cardioembolic stroke (CE), small vessel disease (SVD), or un-/other determined strokes (UD). Mixed-effects logistic or linear regression was applied with PSCI, MoCA, and z-scores of the cognitive domains as dependent variables. Independent variables were time as well as stroke subtype, time, and interaction between these. The analyses were adjusted for age, education, and sex. The effects of time and stroke subtype were analyzed by likelihood ratio tests (LR). Results: Mean age was 72 years (SD 12), 42% were females, and mean NIHSS score at admittance was 3.8 (SD 4.8). Probability (95% CI) for PSCI after 3 and 18 months was 0.59 (0.51-0.66) and 0.51 (0.52-0.60), respectively and remained constant over time. Global measures and most cognitive domains were assessed as impaired for the entire stroke population and for most stroke subtypes. Executive function and language improved for the entire stroke population (LR) = 9.05, p = 0.003, and LR = 10.38, p = 0.001, respectively). After dividing the sample according to stroke subtypes, language improved for ICH patients (LR = 18.02, p = 0.003). No significant differences were found in the severity of impairment between stroke subtypes except for attention, which was impaired for LAD and CE in contrast to no impairment for SVD (LR = 56.58, p < 0.001). Conclusions: In this study including mainly minor strokes, PSCI is common for all subtypes, both early and long-term after stroke, while executive function and language improve over time. The findings might contribute to personalizing follow-up and offer new insights into underlying mechanisms. Further research is needed on underlying mechanisms, PSCI prevention and treatment, and relevance for rehabilitation.

Project description:Introduction: Post-stroke cognitive impairment (PSCI) is common, but evidence on the impact of vascular risk factors is lacking. We explored the association between pre-stroke vascular risk factors and PSCI and studied the course of PSCI. Materials and Methods: Vascular risk factors were collected at baseline in stroke survivors (n = 635). Cognitive assessments of attention, executive function, memory, language, and the Montreal Cognitive Assessment (MoCA) were performed at 3 and/or 18 months post-stroke. Stroke severity was assessed with the National Institutes of Health Stroke Scale (NIHSS). PSCI was measured with global z; MoCA z-score; and z-score of the four assessed cognitive domains. Mixed-effect linear regression was applied with global z, MoCA z-score, and z-scores of the cognitive domains as dependent variables. Independent variables were the vascular risk factors (hypertension, hypercholesterolemia, smoking, diabetes mellitus, atrial fibrillation, coronary heart disease, previous stroke), time, and the interaction between these. The analyses were adjusted for age, education, and sex. There were between 5 and 25% missing data for the variables for PSCI. Results: Mean age was 71.6 years (SD 11.7); 42% were females; and the mean NIHSS score at admittance was 3.8 (SD 4.8). Regardless of vascular risk factors, global z, MoCA, and all the assessed cognitive domains were impaired at 3 and 18 months, with MoCA being the most severely impaired. Atrial fibrillation (AF) was associated with poorer language at 18 months and coronary heart disease (CHD) with poorer MoCA at 18 months (LR = 12.80, p = 0.002, and LR = 8.32, p = 0.004, respectively). Previous stroke was associated with poorer global z and attention at 3 and 18 months (LR = 15.46, p < 0.001, and LR = 16.20, p < 0.001). In patients without AF, attention improved from 3 to 18 months, and in patients without CHD, executive function improved from 3 to 18 months (LR = 10.42, p < 0.001, and LR = 9.33, p = 0.009, respectively). Discussion: Our findings indicate that a focal stroke lesion might be related to pathophysiological processes leading to global cognitive impairment. The poorer prognosis of PSCI in patients with vascular risk factors emphasizes the need for further research on complex vascular risk factor interventions to prevent PSCI.

Project description:ObjectiveTo observe the changes in the composition of gut microbiota in stroke patients showing cognitive impairment within one month after the stroke, and to explore the correlation between bacteria presenting dissimilarity and cognitive functions and other clinical indicators.MethodsA cross-sectional study was conducted, involving 12 patients with post-stroke cognitive impairment (PSCI group), 12 stroke patients without cognitive impairment (Non-PSCI group), and 12 healthy volunteers in a normal control group (NC group). The demographic and clinical data were gathered. The abundance, diversity and dissimilarity of gut bacterial communities were determined by 16S rRNA gene sequencing. Then, we studied the correlation between gut microbiota and clinical characteristics and the effectiveness of using microbiome markers to identify cognitive decline.ResultsThe Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores of the PSCI group were significantly lower than those the Non-PSCI group ( P<0.001). There was no significant intergroup difference in the demographic data, the clinical data, and the Alpha diversity of gut microbiota among the three groups ( P>0.05). Microbial composition analysis of the three groups revealed proportion alternations at the phylum, genus and species levels. At the phylum level, linear discriminant analysis (LDA) effect size (LEfSe) analysis suggested that the Actinomycetes had significantly increased relative abundance in the PSCI group (LDA score>2). At the genus and species levels, Firmicutes had the highest diversity among the top 10 bacteria in the three groups, while the relative abundance of Verrucomicrophyla presented an increasing trend in the Non-PSCI group and that of Actinobacteria showed an increasing trend in the PSCI group. Further LEfSe analysis revealed that there were different microbiome markers in each group, among which the Bifidobacterium, Alloscardovia, and Alloscardovia omnicolens of the phylum Actinomycetes and Lactobacillus gasseri and Anaerostipes hadrus of the phylum Firmicutes in the PSCI group increased significantly (LDA score>2). Correlation analysis indicated that Anaerostipes hadrus was negatively correlated with the MoCA scores, while Bifidobacterium was positively correlated with blood uric acid (UA). Bifidobacterium, Lactobacillus gasseri and Anaerostipes hadrus could be used to distinguish PSCI patients from Non-PSCI patients, presenting an area under the curve of 0.785, 0.792 and 0.750, respectively ( P<0.05).ConclusionStroke patients with cognitive impairment in the early stage showed composition changes in their gut microbiota, and the bacteria exhibiting dissimilarity were correlated, to some degree, with cognitive function and related risk factors, which could provide new clues for the early management of PSCI.

Project description:BackgroundCognitive impairment after stroke is associated with poorer health outcomes and increased need for long-term care. The aim of this study was to determine the impact of stroke, cognitive function and post-stroke cognitive impairment (PSCI) on healthcare utilisation in older adults in Ireland.MethodsThis cross-sectional study involved secondary data analysis of 8,175 community-dwelling adults (50 + years), from wave 1 of The Irish Longitudinal Study on Ageing (TILDA). Participants who had been diagnosed with stroke by a doctor were identified through self-report in wave 1. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA). The main outcome of the study was healthcare utilisation, including General Practitioner (GP) visits, emergency department visits, outpatient clinic visits, number of nights admitted to hospital, and use of rehabilitation services. The data were analysed using multivariate adjusted negative binomial regression and logistic regression. Incidence-rate ratios (IRR), odds ratios (OR) and 95% confidence intervals (CI) are presented.ResultsThe adjusted regression analyses were based on 5,859 participants who completed a cognitive assessment. After adjusting for demographic and clinical covariates, stroke was independently associated with an increase in GP visits [IRR (95% CI): 1.27 (1.07, 1.50)], and outpatient service utilisation [IRR: 1.49 (1.05, 2.12)]. Although participants with poor cognitive function also visited the GP more frequently than participants with normal cognitive function [IRR: 1.07 (1.04, 1.09)], utilisation of outpatient services was lower in this population [IRR: 0.92 (0.88, 0.97)]. PSCI was also associated with a significant decrease in outpatient service utilisation [IRR: 0.75 (0.57, 0.99)].ConclusionsStroke was associated with higher utilisation of GP and outpatient services. While poor cognitive function was also associated with more frequent GP visits, outpatient service utilisation was lower in participants with poor cognitive function, indicating that cognitive impairment may be a barrier to outpatient care. In Ireland, the lack of appropriate neurological or cognitive rehabilitation services appears to result in significant unaddressed need among individuals with cognitive impairment, regardless of stroke status.

Project description:ImportanceSecondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.ObjectiveTo develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.DesignCohort study with patients enrolled between 2003-2016 with follow-up through 2022.SettingPrimary care practices affiliated with two academic medical centers.ParticipantsIndividuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort).ExposuresPredictors of PSCI were ascertained from the electronic health record.Main outcomeThe outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included.ResultsThe analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.Conclusions and relevanceFive-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.

Dataset Information

The impact of early-life intelligence quotient on post stroke cognitive impairment.

Background

Methods

Results

Discussion

Conclusion

Publications

The impact of early-life intelligence quotient on post stroke cognitive impairment.

Similar Datasets

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