Identification of Gut Microbiome Signatures in Patients With Post-stroke Cognitive Impairment and Affective Disorder.
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ABSTRACT:
SUBMITTER: Huang Y
PROVIDER: S-EPMC8417941 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Post-stroke cognitive impairment (PSCI) is a common neuropsychiatric complication of stroke. Mounting evidence has demonstrated a connection between gut microbiota (GM) and neuropsychiatric disease. Our previous study revealed the changes in the GM in a mouse model of vascular dementia. However, the characteristic GM of PSCI remains unclear. This study aimed to characterize the GM of PSCI and explored the potential of GM as PSCI biomarkers. A total of 93 patients with ischemic stroke were enrolled in this study. The patients were divided into two groups according to their MoCA scores 3 months after stroke onset. Clinical data and biological variables were recorded. GM composition was analyzed using 16S ribosomal RNA sequencing, and the characteristic GM was identified by linear discriminant analysis Effect Size (Lefse). Our results showed that Proteobacteria was highly increased in the PSCI group compared with the post-stroke non-cognitive impairment (PSNCI) group, the similar alterations were also observed at the class, order, family, and genus levels of Proteobacteria. After age adjustments, the abundance of Firmicutes, and its members, including Clostridia, Clostridiales, Lachnospiraceae, and Lachnospiraceae_other, were significantly decreased in the age-matched PSCI group compared with the PSNCI group. Besides, the GM was closely associated with MoCA scores and the risk factors for PSCI, including higher baseline National Institute of Health Stroke Scale score, higher homocysteine (Hcy) level, higher prevalence of stroke recurrence, leukoaraiosis, and brain atrophy. The KEGG results showed the enriched module for folding, sorting and degradation (chaperones and folding catalysts) and the decreased modules related to metabolisms of cofactors and vitamins, amino acid, and lipid in PSCI patients. A significant correlation was observed between PSCI and the abundance of Enterobacteriaceae after adjustments (P = 0.035). Moreover, the receiver operating characteristic (ROC) models based on the characteristic GM and Enterobacteriaceae could distinguish PSCI patients from PSNCI patients [area under the curve (AUC) = 0.840, 0.629, respectively]. Our findings demonstrated that the characteristic GM, especially Enterobacteriaceae, might have the ability to predict PSCI in post-stroke patients, which are expected to be used as clinical biomarkers of PSCI.
| S-EPMC7645221 | biostudies-literature
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.
| S-EPMC10408811 | biostudies-literature
Project description:There is ample scientific and clinical evidence of the effects of gut microbiota on the brain but no definitive evidence that the brain can affect changes in gut microbiota under the bi-directional gut-brain axis concept. As there is no pharmacotherapeutic intervention for the early stages of cognitive decline, research has focused on cognitive stimulation in reversing or slowing the impairment. Elderly patients diagnosed with mild cognitive impairment underwent a randomized-control trial of mindful awareness practice. Neuropsychological assessments, inflammatory markers, and gut microbiota profiles were tested. Here, we report that their cognitive impairment was improved and associated with changes in gut bacterial profile. A cognition-score-dependent-abundance was observed in Ruminococcus vs Recognition Trials (RT), Digit Span Backward (DSB), Semantic Fluency Span (SFS) and Memory Domain (MD); Coprococcus vs DSB, Color Trails Test 2 (CTT2) and Block Design (BD); Parabacteroides vs DSB and SFS; Fusobacterium vs DSB and CTT2; Enterobacteriaceae vs BD and SFS; Ruminococcaceae vs DSB; Phascolarctobacterium vs MD. The study showed for the first-time, alteration in the cognitive capacity leading to the corresponding changes in microbiota profiles. This strongly suggests that signals from the different segments of brain could dictate directly or indirectly the abundances of specific gut microbes.
| S-EPMC7762482 | biostudies-literature
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.
| S-EPMC10863024 | biostudies-literature
Project description:BackgroundThe aim of this study is to investigate the associations between post-stroke cognitive impairment (PSCI) severity and reactive astrogliosis (RA) extent on normalized 18F-THK-5351 positron-emission tomography (PET) imaging in amyloid-negative patients with first-ever stroke.MethodsWe prospectively enrolled 63 amyloid-negative patients with first-ever stroke. Neurocognitive evaluation, MRI, 18F-THK-5351, and 18F-florbetapir PET were performed around 3 months after stroke. The 18F-THK-5351 uptake intensity was normalized using a signal distribution template to obtain the Z-SUM scores as the RA extent in the whole brain and cerebral hemisphere ipsilateral to stroke lesion. We evaluated stroke volume, leukoaraiosis, and brain atrophy on MRI. We used a comprehensive neurocognitive battery to obtain composite cognitive scores, and defined PSCI as a general cognitive function score < - 1. We analyzed the influence of Z-SUM scores on PSCI severity after adjusting for demographic, vascular, and neurodegenerative variables.ResultsTwenty-five of 63 stroke patients had PSCI. Patients with PSCI had older age, lower education, and more severe cortical atrophy and total Z-SUM scores. Total Z-SUM scores were significantly associated with general cognitive and executive functions at multiple regression models. Path analyses showed that stroke can exert cognitive influence directly by stroke itself as well as indirectly through RA, including total and ipsilateral Z-SUM scores, in patients with either right or left hemisphere stroke.ConclusionThe patterns and intensity of 18F-THK-5351 uptake in amyloid-negative patients with first-ever stroke were associated with PSCI manifestations, which suggests that RA presents a modulating effect in PSCI development.
| S-EPMC7568828 | biostudies-literature
Project description:Background: Together with cerebral small vessel disease (CSVD), large vessel atherosclerosis is considered to be an equally important risk factor in the progression of vascular cognitive impairment. This article aims to investigate whether carotid atherosclerotic calcification is associated with the increased risk of post-stroke cognitive impairment (PSCI). Methods: A total of 128 patients (mean age: 62.1 ± 12.2 years, 37 women) suffering from ischemic stroke underwent brain/neck computer tomography angiography examination. The presence and characteristic of carotid calcification (size, number and location) were analyzed on computer tomography angiography. White matter hyperintensity (WMH) was assessed using Fazekas scales. PSCI was diagnosed based on a battery of neuropsychological assessments implemented 6-12 months after stroke. Results: Among 128 patients, 26 developed post-stroke dementia and 96 had carotid calcification. Logistic regression found carotid calcification (odds ratio [OR] = 7.15, 95% confidence interval [CI]: 1.07-47.69) and carotid artery stenosis (OR = 6.42, 95% CI: 1.03-40.15) both significantly increased the risk for post-stroke dementia. Moreover, multiple, thick/mixed, and surface calcifications exhibited an increasing trend in PSCI (P trend = 0.004, 0.016, 0.045, respectively). The prediction model for post-stroke dementia including carotid calcification (area under curve = 0.67), WMH (area under curve = 0.67) and other covariates yielded an area under curve (AUC) of 0.90 (95% CI: 0.82-0.99). Conclusion: Our findings demonstrated that the quantity and location of carotid calcifications were independent indicators for PSCI. The significant role of large vessel atherosclerosis in PSCI should be concerned in future study.
| S-EPMC8185032 | biostudies-literature
Project description:Background and objectivesPost-stroke cognitive impairment (PSCI) occurs in up to 50% of patients with acute ischemic stroke (AIS). Thus, the prediction of cognitive outcomes in AIS may be useful for treatment decisions. This PSCI cohort study aimed to determine the applicability of a machine learning approach for predicting PSCI after stroke.MethodsThis retrospective study used a prospective PSCI cohort of patients with AIS. Demographic features, clinical characteristics, and brain imaging variables previously known to be associated with PSCI were included in the analysis. The primary outcome was PSCI at 3-6 months, defined as an adjusted z-score of less than - 2.0 standard deviation in at least one of the four cognitive domains (memory, executive/frontal, visuospatial, and language), using the Korean version of the Vascular Cognitive Impairment Harmonization Standards-Neuropsychological Protocol (VCIHS-NP). We developed four machine learning models (logistic regression, support vector machine, extreme gradient boost, and artificial neural network) and compared their accuracies for outcome variables.ResultsA total of 951 patients (mean age 65.7 ± 11.9; male 61.5%) with AIS were included in this study. The area under the curve for the extreme gradient boost and the artificial neural network was the highest (0.7919 and 0.7365, respectively) among the four models for predicting PSCI according to the VCIHS-NP definition. The most important features for predicting PSCI include the presence of cortical infarcts, mesial temporal lobe atrophy, initial stroke severity, stroke history, and strategic lesion infarcts.ConclusionOur findings indicate that machine-learning algorithms, particularly the extreme gradient boost and the artificial neural network models, can best predict cognitive outcomes after ischemic stroke.
| S-EPMC10468853 | biostudies-literature
Project description:We investigate the single cell transcriptome of hippocampus in both groups. A total of 27,069 cells were identified as 13 cell types, belong to neuronal, glial and vascular lineages. Then the cell-specific molecular changes and function in PSCI were analyzed.
2023-04-02 | GSE171393 | GEO
Project description:Background: Non-linguistic cognitive training has been suggested to improve the communication skills of patients with post-stroke aphasia (PSA). However, the association between language and non-linguistic cognitive functions is not fully understood. In this study, we used the Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) to evaluate the characteristics of non-linguistic cognitive impairments in Chinese PSA patients. Methods: A total of 86 stroke patients were recruited in this study. Language and non-linguistic cognitive impairments were evaluated by the Western Aphasia Battery (WAB) and LOTCA, respectively. The patients were divided into two groups (PSA and non-PSA), and the Chinese norm (the data came from 44 Chinese individuals without neurological disorders in a previous study) was used as the control group. The LOTCA scores were compared among the three groups. Patients in the PSA group were subdivided into the fluent aphasia group (FAG) and the non-FAG according to the Chinese aphasia fluency characteristic scale. The LOTCA scores were also compared between the PSA subdivisions. Potential confounders were adjusted in the analysis of covariance. Partial correlation analysis between the subscores of WAB and LOTCA was also performed. Results: The total LOTCA scores in the PSA group (75.11 ± 17.08) were significantly lower compared with scores in the non-PSA (96.80 ± 7.75, P < 0.001) and the control group (97.65 ± 16.24, P < 0.001). The PSA group also had lower orientation, visual perception (VP), spatial perception (SP), visuomotor organization, thinking operation, and attention scores. The total LOTCA, orientation, VP, SP, and MP scores were lower in the non-FAG (69.24 ± 18.06, 8.62 ± 5.09, 12.76 ± 2.47, 7.48 ± 3.01, and 9.62 ± 2.25, respectively) compared with the FAG (80.36 ± 14.07, 12.14 ± 3.99, 14.09 ± 1.93, 9.68 ± 3.01, 10.55 ± 1.63, respectively, P's < 0.05). The aphasia quotient was positively correlated with the total score of LOTCA and scores of orientation, VP, SP, and MP (r = 0.710, 0.744, 0.565, 0.597, and 0.616; P < 0.001). Conclusion: Compared with stroke patients without aphasia, patients with PSA often have more extensive and serious non-linguistic cognitive impairments. Patients with non-fluent aphasia often present with serious cognitive impairments than those with fluent aphasia, especially the impairments of orientation and SP. Non-linguistic cognitive impairments correlate with language impairments in aphasia.
| S-EPMC7561418 | biostudies-literature
Project description:Background and Aims: Systemic inflammation is associated with an increased risk of cognitive impairment and dementia, but the associations between them in stroke patients are less clear. We examined the impact of systemic inflammation represented as the neutrophil-lymphocyte ratio (NLR) on the development of post-stroke cognitive impairment (PSCI) and domain-specific cognitive outcomes 3-month after ischemic stroke. Methods: Using prospective stroke registry data, we consecutively enrolled 345 participants with ischemic stroke whose cognitive functions were evaluated 3-month after stroke. Their cognition was assessed with the Korean version of the Vascular Cognitive Impairment Harmonization Standards and the Korean-Mini Mental Status Examination. PSCI was defined as a z-score of < -2 standard deviations for age, sex, and education adjusted means in at least one cognitive domain. The participants were categorized into five groups according to the quintiles of NLR (lowest NLR, Q1). The cross-sectional association between NLR and PSCI was assessed using multiple logistic regression, adjusting for age, sex, education, vascular risk factors, and stroke type. Results: A total of 345 patients were enrolled. The mean age was 63.0 years and the median NIHSS score and NLR were 2 [1-4] and 2.26 [1.65-2.91], respectively. PSCI was identified in 71 (20.6%) patients. NLR was a significant predictor for PSCI both as a continuous variable (adjusted OR, 1.14; 95% CI, 1.00-1.31) and as a categorical variable (Q5, adjusted OR, 3.26; 95% CI, 1.17-9.08). Patients in the Q5 group (NLR ≥ 3.80) showed significantly worse performance in global cognition and in visuospatial and memory domains. Conclusions: NLR in the acute stage of ischemic stroke was independently associated with PSCI at 3 months after stroke, and high NLR was specifically associated with cognitive dysfunction in the memory and visuospatial domains. Thus, systemic inflammation may be a modifiable risk factor that may influence cognitive outcomes after stroke.
| S-EPMC8280279 | biostudies-literature