Background: Cerebral small vessel disease (CSVD) is a cluster of microvascular disorders with unclear pathological mechanisms. The microbiota-gut-brain axis is an essential regulatory mechanism between gut microbes and their host. Therefore, the compositional and functional gut microbiota alterations lead to cerebrovascular disease pathogenesis. The current study aims to determine the alteration and clinical value of the gut microbiota in CSVD patients.Methods: Sixty-four CSVD patients and 18 matched healthy controls (HCs) were included in our study. All the participants underwent neuropsychological tests, and the multi-modal magnetic resonance imaging depicted the changes in brain structure and function. Plasma samples were collected, and the fecal samples were analyzed with 16S rRNA gene sequencing.Results: Based on the alpha diversity analysis, the CSVD group had significantly decreased Shannon and enhanced Simpson compared to the HC group. At the genus level, there was a significant increase in the relative abundances of Parasutterella, Anaeroglobus, Megasphaera, Akkermansia, Collinsella, and Veillonella in the CSVD group. Moreover, these genera with significant differences in CSVD patients revealed significant correlations with cognitive assessments, plasma levels of the blood-brain barrier-/inflammation-related indexes, and structural/functional magnetic resonance imaging changes. Functional prediction demonstrated that lipoic acid metabolism was significantly higher in CSVD patients than HCs. Additionally, a composite biomarker depending on six gut microbiota at the genus level displayed an area under the curve of 0.834 to distinguish CSVD patients from HCs using the least absolute shrinkage and selection operator (LASSO) algorithm.Conclusion: The evident changes in gut microbiota composition in CSVD patients were correlated with clinical features and pathological changes of CSVD. Combining these gut microbiota using the LASSO algorithm helped identify CSVD accurately.

No acceptable biomarker can facilitate the early identification of cognitive impairment associated with cerebral small vessel disease (CSVD) in the older persons. The neutrophil extracellular traps (NETs) in the inflammation response of circulatory and central systems are essential in destroying the blood–brain barrier. The present study aims to explore the potential associations of plasma NETs with cognitive performance in CSVD. We recruited 146 CSVD patients and 66 healthy controls (HCs), and comprehensive neuropsychological assessments and multimodal magnetic resonance imaging were conducted. Three NETs markers, namely citrullination of histone H3, neutrophil elastase–DNA, and myeloperoxidase (MPO)–DNA, and 4 oxidative stress-related indexes in plasma samples, were measured. The plasma levels of 3 NETs markers were more significantly elevated in CSVD patients than in HCs. Significant correlations of the 3 NETs markers were observed with multiple cognitive domain scores. Furthermore, higher plasma malondialdehyde and NETs levels were significantly associated with the worse Montreal Cognitive Assessment scores among CSVD patients. Moreover, plasma MPO–DNA levels significantly mediated the effect of the amplitude of low-frequency fluctuation value within the bilateral caudate and the scores of global cognitive function, executive function, and information processing speed. Additionally, a panel of 3 NETs markers had the highest area under the curve value to distinguish the cognitively impaired CSVD patients from HCs and nonimpaired ones. Therefore, plasma NETs may be potential biomarkers for early diagnosis of CSVD-related cognitive impairment. Activated lipid peroxidation in circulation and impaired caudate function support potential associations of plasma NETs in cognitively impaired CSVD patients.

Gut microbiota can regulate the metabolic and immunological aspects of ischemic stroke and modulate the treatment effects. The present study aimed to identify specific changes in gut microbiota in patients with large vessel occlusion (LVO) ischemic stroke and assess the potential association between gut microbiota and clinical features of ischemic stroke. A total of 63 CSVD patients, 64 cerebral small vessel disease (CSVD) patients, and 36 matching normal controls (NCs) were included in this study. The fecal samples were collected for all participants and analyzed for gut microbiota using 16S rRNA gene sequencing technology. The abundances of five gut microbiota, including genera Bifidobacterium, Butyricimonas, Blautia, and Dorea and species Bifidobacterium_longum, showed significant changes with high specificity in the LVO patients as compared to the NCs and CSVD patients. In LVO patients, the genera Bifidobacterium and Blautia and species Bifidobacterium_longum were significantly correlated with the National Institutes of Health Stroke Scale (NIHSS) scores at the admission and discharge of the patients. Serum triglyceride levels could significantly affect the association of the abundance of genus Bifidobacterium and species Bifidobacterium_longum with the NIHSS scores at admission and modified Rankin Scale (mRS) at discharge in LVO patients. The identification of five gut microbiota with high specificity were identified in the early stage of LVO stroke, which contributed to performed an effective clinical management for LVO ischemic stroke.

Background: Reliable and individualized biomarkers are crucial for identifying early cognitive impairment in subcortical small-vessel disease (SSVD) patients. Personalized brain age prediction can effectively reflect cognitive impairment. Thus, the present study aimed to investigate the association of brain age with cognitive function in SSVD patients and assess the potential value of brain age in clinical assessment of SSVD.Materials and methods: A prediction model for brain age using the relevance vector regression algorithm was developed using 35 healthy controls. Subsequently, the prediction model was tested using 51 SSVD patients [24 subjective cognitive impairment (SCI) patients and 27 mild cognitive impairment (MCI) patients] to identify brain age-related imaging features. A support vector machine (SVM)-based classification model was constructed to differentiate MCI from SCI patients. The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers.Results: The gray matter volume (GMV) imaging features accurately predicted brain age in individual patients with SSVD (R² = 0.535, p < 0.001). The GMV features were primarily distributed across the subcortical system (e.g., thalamus) and dorsal attention network. SSVD patients with age acceleration showed significantly poorer Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) scores. The classification model based on GMV features could accurately distinguish MCI patients from SCI patients (area under the curve = 0.883). The classification outputs of the classification model exhibited significant associations with MoCA scores, Trail Making Tests A and B scores, Stroop Color and Word Test C scores, information processing speed total scores, and plasma levels of total antioxidant capacity in SSVD patients.Conclusion: Brain age can be accurately quantified using GMV imaging data and shows potential clinical value for identifying early cognitive impairment in SSVD patients.

Background: Non-valvular atrial fibrillation (NVAF) is the most common cause of cardiogenic cerebral embolism (CCE). However, the underlying mechanism between cerebral embolism and NVAF is indefinite, and there is no effective and convenient biomarker to identify potential risk of CCE in patients with NVAF in clinic. The present study aims to identify risk factors for interpreting the potential association of CCE with NVAF and providing valuable biomarkers to predict the risk of CCE for NVAF patients.Methods: 641 NVAF patients diagnosed with CCE and 284 NVAF patients without any history of stroke were recruited in the present study. Clinical data including demographic characteristics, medical history, and clinical assessments, were recorded. Meanwhile, Blood cell counts, lipid profiles, high-sensitivity C-reactive protein, and coagulation function-related indicators were measured. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized to build a composite indicator model based on the blood risk factors.Results: (1) CCE patients had significantly increased neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio (PLR), and D-dimer levels as compared with patients in the NVAF group, and these three indicators can distinguish CCE patients from ones in the NVAF group with an area under the curve (AUC) value of over 0.750, respectively. (2) Using the LASSO model, a composite indicator, i.e., the risk score, was determined based on PLR and D-dimer and displayed differential power for distinguishing CCE patients from NVAF patients with an AUC value of over 0.934. (3) The risk score was positively correlated with the National Institutes of Health Stroke Scale and CHADS2 scores in CCE patients. (4) There was a significant association between the change value of the risk score and the recurrence time of stroke in initial CCE patients.Conclusions: The PLR and D-dimer represent an aggravated process of inflammation and thrombosis in the occurrence of CCE after NVAF. The combination of these two risk factors can contribute to identifying the risk of CCE for patients with NVAF with an accuracy of 93.4%, and the greater in change of composite indicator, the shorter in the recurrence of CCE for NVAF patients.