Physical factors including motor and balance functions are associated with resumption of oral intake of the stroke patients receiving nasogastric tube feeding. However, association of non-physical factors remains unclear. The aims of this study are to explore non-physical factors associated with resumption of oral intake after stroke and to clarify how they affect the time course of the resumption.

A total of 116 patients receiving nasogastric tube feeding after stroke were retrospectively analyzed from a single center observational cohort database. Associations of physical and non-physical factors with resumption of oral intake at discharge from the stroke rehabilitation facility were investigated. We also evaluated how the specific combination of non-physical prognostic factors affected the time course of resumption using survival analyses.

Among 116 patients, 59 (50.4 %) resumed oral intake. History of diabetes mellitus (DM) (P < 0.001) and higher cognitive functional independence scores (C-FIM) (P = 0.03) were significantly associated with the resumption of oral intake at discharge. These non-physical factors had an additive value on conventional risk factors to predict resumption of oral intake. Patients with a history of DM were significantly associated with improved resumption of oral intake than with non-DM patients with lower C-FIM scores (< 18) by survival analysis (P = 0.001), and this difference became prominent after 90 days post-stroke.

A history of DM and better cognitive function as non-physical factors were significantly associated with the resumption of oral intake in patients receiving nasogastric tube feeding after stroke.

The present study explores the prognostic relevance of triglyceride-glucose-based indices in assessing post-stroke survival among affected individuals.

This study utilized a multifaceted analytical approach to assess how triglyceride-glucose-based indicators relate to death risk in stroke patients. This study was analyzed using a multivariate Cox proportional risk regression model incorporating sampling weights, while a restricted cubic spline function was introduced to assess trends in non-linear associations between exposure variables and outcomes. In addition, interaction terms were set and stratified analyses were conducted to verify the robustness and heterogeneity of the model results.

This research ultimately included 796 individuals diagnosed with stroke. When adjusting for a wide range of potential confounders, those in the top TyG-BMI quartile exhibited the most pronounced reduction in mortality risk compared to individuals in the lowest category, with a hazard ratio of 0.20 (95% CI: 0.08-0.50), highlighting its protective potential across TyG-BMI. In contrast, individuals falling within the fourth quartile of the TyG-WHtR index demonstrated the strongest positive correlation with the risk of all-cause mortality (Hazard Ratio = 4.61, 95% CI: 1.77-12.00). Moreover, analysis using restricted cubic splines indicated a significant non-linear association between TyG-BMI levels and mortality outcomes (p < 0.05). No statistical interactions were observed between mortality outcomes and demographic or clinical variables including age, sex, smoking, asthma, coronary artery disease, diabetes, or hypertension across any TyG-related indices (p > 0.05).

The study outcomes suggest that stroke patients with reduced TyG-BMI and elevated TyG-WHtR levels tend to face increased mortality risks. Nonetheless, addressing obesity may be crucial in exploring potential causal pathways.

Type 2 diabetes mellitus (T2DM) is associated with impaired leptomeningeal collateral compensation and poor stroke outcome. Neutrophils tethering and rolling on endothelium after stroke can also independently reduce flow velocity. However, the chronology and topological changes in collateral circulation in T2DM is not yet defined. Here, we describe the spatial and temporal blood flow dynamics and vessel diameter changes in pial arteries and veins and leukocyte-endothelial adhesion following middle cerebral artery (MCA) stroke using two-photon microscopy in awake control and T2DM mice. Relative to control mice, T2DM mice already exhibited smaller pial vessels with reduced flow velocity prior to stroke. Following stroke, T2DM mice displayed persistently reduced blood flow in pial arteries and veins, resulting in a poor recovery of downstream penetrating arterial flow and a sustained deficit in microvascular flow. There was also persistent increase of leukocyte adhesion to the endothelium of veins, coincided with elevated neutrophils infiltration into brain parenchyma in T2DM mice compared to control mice after stroke. Our data suggest that T2DM-induced increase in inflammation and chronic remodeling of leptomeningeal vessels may contribute to the observed hemodynamics deficiency after stroke and subsequent poor stroke outcome.

To establish and compare the prediction effect of SES and SARIMA model, and select the best prediction model to predict the number of patients in neurology department. The data came from HIS and medical record management system of a Grade-A hospital in Zhejiang Province. The number of inpatients from January 2019 to September 2023 was selected to establish SES and SARIMA model, respectively. Compare the fitting parameters, The larger the R2_adjusted, R2, the smaller the RMSE, MAPE, MAE and standardized BIC, The better model is selected. Finally, the established model was used to predict the number of hospital admissions from October to December 2023, and the prediction effect of the MRE judgment model was compared. The number of admissions to the department of neurology shows a cyclical change, and drops sharply in January-February each year and rises rapidly in March. The best fitting models of SES model and SARIMA model were Winters addition model and SARIMA(0,1,1)(0,1,1)12 model, respectively. The two models were selected to predict the number of admissions in the Department of neurology from October to December 2023, and the average relative error was 0.04 and 0.03, respectively. The prediction effect of SARIMA(0,1,1)(0,1,1)12 model was better. Age and Spring Festival may be the factors that affect the periodic change of the number of admissions in neurology department. Both SES and SARIMA model can be used to predict the number of admissions in the department of neurology, and the SARIMA model may be better.

Diabetes is a well-established risk factor for stroke. Understanding the pathophysiology of this connection is crucial to implementing appropriate prevention strategies. Lately, there has been a paradigm shift in the care of individuals with diabetes toward the use of glucose-lowering medications with potential cardiovascular, cerebrovascular or cardiorenal benefits. The aim of this article is to provide a critical analysis of the role of diabetes in cerebrovascular disease and current evidence and recommendations for the use of glucose-lowering medication with particular focus on the sodium glucose cotransporter-2 inhibitor (SGLT2i) class.

Intensive glycemic control in individuals with diabetes reduces the risk of microvascular complications, but there is less clear evidence for decreasing risk of macrovascular events (e.g., stroke). A multifaceted management of diabetes addressing healthy lifestyle practices, glycemic control, and optimization of other cardiovascular risk factors is highly recommended. SGLT2i are the latest class of antihyperglycemic agents available for diabetes management. Canagliflozin and empagliflozin are associated with reduction in major adverse cardiovascular events (MACE). Dapagliflozin did not reduce the rate of MACE but is associated with reduction in heart-failure related death and hospitalization and has the potential to decrease dementia risk. Ertugliflozin decreases rates of hospitalization related to heart failure however it was non-inferior to placebo in reducing MACE. There is increasing evidence that the use of SGLT2i may reduce the risk of stroke, particularly hemorrhagic stroke, in individuals with type 2 diabetes and a high risk of cardiovascular events, and that SGLT2i may also be beneficial for brain health by decreasing risk of cognitive decline and dementia. Antihyperglycemic therapy should be tailored to patients' circumstances. SGLT2i treatment should be considered in patients with type 2 diabetes and established or high-risk cardiovascular disease, heart failure, or chronic kidney disease, to reduce the overall cerebro-cardiovascular and renal risks.

Due to the absence of direct visualization during the operative field in stereotactic surgery for sICH, there exists individual variability in hematoma evacuation (HE) rates, with about 42% of patients not attaining the expected threshold for HE. The aim of this study was to find the relevant factors affecting HE during the treatment of sICH with stereotactic surgery. We pooled individual data from our prospective ICH database, encompassing patients who underwent stereotactic aspiration and catheter drainage. The primary outcome was HE rates prior to extubation. Multivariate logistic regression using the stepwise forward method to identify the independent risk factors. A predictive scoring model was developed based on regression coefficients. To evaluate its discrimination performance, we conducted ROC curve analysis and calculated the AUC. Additionally, we employed calibration curves as an indicator of concordance. The bootstrap internal validation was used to ascertain the model's stability. DCA was performed to determine the clinical utility. The study included 90 patients, of whom 43 (47.8%) patients achieved HE rates ≥ 70%. The multivariate logistic analysis showed that blend sign (OR 7.003, 95% CI 2.118-23.161, P = 0.001), irregular shape (OR 0.235, 95% CI 0.067-0.821, P = 0.023), two drainage tubes (OR 28.643, 95% CI 1.872-438.181, P = 0.016), diabetes (OR 0.078, 95% CI 0.006-0.948, P = 0.045), and hematoma edge linked to ventricle (OR 0.145, 95% CI 0.032-0.659, P = 0.012) were independent risk factors. For clinical use, the Model-score was established, with a total score ranging from -6 to 5. The AUC values of the Model-logit and Model-score were 0.820 (95% CI 0.733-0.906) and 0.822 (95% CI 0.737-0.908) respectively. The cutoff values were 0.275 and -0.5. Calibration curves demonstrated excellent agreement between predicted probabilities and observed outcomes in both models. Utilizing the bootstrap method for internal validation, the mean AUC values were determined to be 0.819 (95% CI 0.736-0.903) for Model-logit and 0.823 (95% CI 0.742-0.903) for Model-score, demonstrating stability across the resampled datasets. The DCA confirmed that both models provide superior net benefit for predicting HE rates ≥ 70% when the individualized threshold ranges from 10 to 82%. The predictive model of HE rates ≥ 70% prior to extuation has demonstrated predictive capability, holds the potential to assist clinicians in optimizing surgical efficiency.

Studies on the association between multimorbidity and mortality in large populations have mainly been conducted in European and North American populations. This study aimed to identify the association between cardiometabolic multimorbidity and all-cause and cardiovascular disease (CVD) mortality in the Asia Cohort Consortium.

In this prospective cohort study, pooled analysis was performed to evaluate the association between cardiometabolic diseases (hypertension, diabetes, ischemic heart disease, and stroke), multimorbidity, and all-cause and CVD mortality, including premature mortality, among participants from 11 Asian cohort studies. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox hazard regression.

A total of 483,532 participants were followed for a median of 14.3 years. Compared with participants without any disease, those with stroke and diabetes had higher age- and sex-adjusted HRs for all-cause mortality (HR 3.9; 95% CI, 3.28-4.56). Moreover, the age- and sex-adjusted HRs for CVD mortality were highest in participants with stroke, ischemic heart disease, and diabetes (HR 10.6; 95% CI, 6.16-18.25). These patterns remained consistent after additional adjustments for smoking status and body mass index. The risk of premature mortality followed similar trends but was more pronounced.

These findings highlight the differential impacts of individual cardiometabolic diseases and their combinations on mortality risks. Stroke and diabetes were associated with the highest risks for all-cause and cardiovascular mortality, underscoring the need for targeted prevention and personalized management strategies tailored to these high-risk conditions in Asian populations.

Acute ischemic stroke (AIS) is one of the leading causes of mortality and morbidity. This study aimed to identify risk factors, subtypes, and associated outcomes of AIS in the West Bank based on sex. We retrospectively analyzed medical records from 2018 to 2022 of stroke patients from four main hospitals in the West Bank (N = 711). The Modified Rankin Scale (mRS) assessed post-stroke disability on presentation day and thirty days later based on patient history and physical examination findings. An adjusted multinomial logistic regression model was implemented to calculate the adjusted odds ratios (OR) and the 95% confidence interval (CI). The significance level was set at P < 0.05. Out of 711 records, 118 were excluded. The final analysis included 593 AIS patients, with a median age of 63 and an interquartile range of 15. The majority of the patients (60.37%) were males and most of them (62.1%) were smokers. Males were less likely to have diabetes mellitus (DM) (P = 0.037, OR = 0.691) and atrial fibrillation (P = 0.039, OR = 0.627) compared to females. Small-volume strokes accounted for the majority of cases (60.7%). AIS had a thrombotic cause in (81.9%) of patients. On presentation, (40.4%) and (37.2%) of patients had more severe symptoms with mRS scores of 4 and 3, respectively. Males were more likely than females (P = 0.018, OR = 2.03) to present with more severe symptoms (mRS 3-4-5) on day one. An increase of one year in age resulted in a 9.8% higher risk of death (mRS 6) on day one (P = 0.016, OR = 1.098). Smoking history was associated with a seven-fold increase in mortality on day one (P = 0.049, OR = 7.396). Males developed AIS at a younger age while DM and atrial fibrillation were significantly more common in females. The majority of patients reported more severe symptoms on presentation, with notable differences observed between sexes. Male patients exhibited a higher prevalence of severe symptoms compared to female patients. Additionally, key risk factors such as smoking was significantly associated with the severity of symptoms at presentation, with variations observed across sexes. Prevention of risk factors (e.g., HTN, DM, atrial fibrillation, and smoking) is crucial, and further research is required.

Head and neck cancer (HNC) due to its nature and proximity to essential vasculature, along with different treatments, can lead to stroke, significantly contributing to morbidity and mortality. Our aim is to systematically evaluate the association of stroke incidence, mortality, and predictors with HNC treatment.

Pubmed, Web of Science, Embase, and ScienceDirect were searched from inception to July 2024 for articles reporting stroke incidences, mortality, or associated risk factors following treatment in HNC patients. A random-effects meta-analysis assessed cumulative incidence and mortality rates with proportional analysis and risk factors using hazard ratios (HRs) associated with HNC treatment. Subgroup analyses of incidence and mortality were conducted for pre- and post-2010 periods, reflecting changes in stroke protocols.

Out of 1561 studies, 69 studies with 258 850 HNC patients were included. The global cumulative incidence of stroke in HNC was 4.1% (95% CI: 3.3%-5.0%), with similar rates before and after 2010 (4.4% vs. 4.0%). In patients undergoing chemoradiotherapy (CRT), stroke incidence was 4.9% (95% CI: 3.5%-6.7%) with a median time to first stroke of 45 months (range: 14-51.7 months). Following radiation therapy (RT), stroke incidence was 3.8% (95% CI: 2.7%-5.3%) with a median time to stroke of 36 months (range: 6.8-130 months). The incidence rates of stroke in HNC patients were higher compared to the general population (HR: 1.69, 95% CI: 1.24-2.31, p = 0.001). Stroke mortality decreased from 28.5% (95% CI: 11.6%-54.9%) pre-2010 to 14.5% (95% CI: 11.6%-17.9%) 2010-2024. Stroke mortality was 39.3% (95% CI: 17.8%-66.0%) post-CRT and 21% (95% CI: 7.2%-47.7%) post-RT. Hypertension (HR = 1.75), diabetes (HR = 1.71), and age > 65 (HR = 2.17) increased stroke risk (p < 0.0001 for all). Geographically, South Korea (6.6%) had the highest incidence of stroke.

This is the first systematic review to analyze the association between stroke and HNC treatment. Stroke mortality decreased from 28.5% to 14.5% (pre-2010 vs. 2010-2024), with the highest mortality in the CRT group (39.3%). Given that stroke occurs 36-45 months after CRT, a screening protocol within 3-4 years is crucial.

Hyperglycemia aggravates cerebral ischemic reperfusion injury (CIRI). Neuroprotective drugs that are effective in reducing CIRI in animals with normoglycemic condition are ineffective in ameliorating CIRI under hyperglycemic condition. This study investigated whether quercetin alleviates hyperglycemic CIRI by inhibiting endoplasmic reticulum stress (ERS) through modulating the SIRT1 signaling pathway. A middle cerebral artery occlusion/reperfusion (MCAO/R) model was induced in STZ-injected hyperglycemic rats. High glucose and oxygen glucose deprivation/reoxygenation (OGD/R) models were established in HT22 cells. The results demonstrated that hyperglycemia exacerbated CIRI, and quercetin pretreatment decreased the neurological deficit score and cerebral infarct volume, and alleviated neuron damage in the cortex of the penumbra in hyperglycemic MCAO/R rats, indicating that quercetin could be a candidate for treating hyperglycemic CIRI. Moreover, quercetin pretreatment reduced apoptosis, inhibited the expression of the ERS marker proteins GRP78 and ATF6, and mitigated the expression of the ERS-mediated proapoptotic protein CHOP in hyperglycemic MCAO/R rats, suggesting that quercetin alleviated hyperglycemic CIRI by inhibiting ERS and ERS-mediated apoptosis. Furthermore, quercetin upregulated Sirt1 expression in HG+OGD/R treated HT22 cells and inhibited PERK, p-eIF2α, ATF4, and CHOP expression. In contrast, the SIRT1 selective inhibitor EX-527 blocked the effect of quercetin on protein expression in the SIRT1/PERK pathway and aggravated HT22 cell injury. These findings indicate that quercetin inhibits ERS-mediated apoptosis through modulating the SIRT1 and PERK pathway. In conclusion, quercetin alleviates hyperglycemic CIRI by inhibiting ERS-mediated apoptosis through activating SIRT1 that consequently suppressed ERS signaling.

Diabetes significantly increases the risk of cardiovascular events, including stroke. Although the association with ischemic stroke is well established, the relationship with hemorrhagic stroke remains unclear. This study aimed to evaluate the risk of ischemic and hemorrhagic stroke in individuals with type 1 and type 2 diabetes compared with diabetes-free controls from the general population.

This cohort study included individuals with type 1 or type 2 diabetes from the Swedish National Diabetes Register between 2005 and 2019, matched to diabetes-free controls by age and sex. Data on baseline characteristics, comorbidities, medications, and outcomes were collected from multiple national registers. Stroke incidence rates and adjusted hazard ratios were estimated using Cox proportional hazard models, stratified by diabetes type, for ischemic and hemorrhagic stroke.

The study included 47,720 individuals with type 1 diabetes (mean age 34.4, 44.8% female) and 686,158 with type 2 diabetes (mean age 65.3, 43.3% female), matched to 143,160 and 2,058,474 controls, respectively. In individuals with type 1 diabetes, the ischemic stroke risk was 2.54 times higher (95% CI 2.36-2.73) and the hemorrhagic stroke risk was 1.88 times higher (95% CI 1.57-2.26) compared with controls. In individuals with type 2 diabetes, the ischemic stroke risk was 1.37 times higher (95% CI 1.35-1.38) while the hemorrhagic stroke risk was not significantly increased (HR: 0.99, 95% CI 0.96-1.02). Higher HbA1c levels were associated with increased ischemic stroke risk for both diabetes types. For hemorrhagic stroke, individuals with type 1 diabetes had significantly higher risk starting at HbA1c > 52 mmol/mol while in those with type 2 diabetes, a modest risk increase was observed only at HbA1c > 72 mmol/mol.

The risk of ischemic stroke was higher for both diabetes types. Individuals with type 1 diabetes also exhibited a higher risk of hemorrhagic stroke compared with diabetes-free controls while type 2 diabetes was significantly associated with risk of hemorrhagic stroke only when HbA1c was higher than 72 mmol/mol. These findings highlight the increased stroke risk in diabetes, with distinct patterns by stroke subtype and diabetes type. Tailored prevention strategies are essential to address these differences.

This study aimed to investigate the shared molecular mechanisms underlying cardioembolic stroke (CS) and ischemic stroke (IS) using integrated bioinformatics analysis.

Microarray datasets for the CS (GSE58294, blood samples from CS and controls) and IS (GSE16561, blood from IS and controls; GSE22255, peripheral blood mononuclear cells from IS and matched controls) were acquired from the Gene Expression Omnibus database. Differential expression analysis and weighted gene co-expression network analysis were utilized to identify shared genes between the two diseases. Protein-protein interaction (PPI) network and topology analyses were conducted to identify the core shared genes. Three machine learning algorithms were employed to detect biomarkers from the core shared genes, and the diagnostic value of the hub genes was evaluated by establishing a predictive nomogram. Immune infiltration was evaluated using single-sample gene set enrichment analysis (ssGSEA), and pathways were analyzed with gene set enrichment analysis.

There were 125 shared up-regulated genes and 2 shared down-regulated between CS and IS, which were mainly involved in immune inflammatory response-related biological functions. The Maximum Clique Centrality algorithm identified 25 core shared genes in the PPI network constructed using the shared genes. ABCA1, CLEC4E, and IRS2 were identified as biomarkers for both CS and IS and performed well in predicting the onset risk of CS and IS. All three biomarkers were highly expressed in both CS and IS compared to their corresponding controls. These biomarkers significantly correlated with neutrophil infiltration and autophagy activation in both CS and IS. Particularly, all three biomarkers were associated with the activation of neutrophil extracellular trap formation, but only in the IS.

ABCA1, CLEC4E, and IRS2 were identified as potential key biomarkers and therapeutic targets for CS and IS. Autophagy and neutrophil infiltration may represent the common mechanisms linking these two diseases.

The evidence surrounding the connection between the Prognostic Nutritional Index (PNI) and the prognosis of patients with Acute Ischemic Stroke (AIS) remains insufficient. Therefore, this study is designed to examine how PNI relates to short-term outcomes in individuals affected by AIS.

This study is a single-center, prospective cohort investigation. The study sample comprised 1,697 patients with AIS who received treatment at Shenzhen Second People's Hospital between January 2022 and June 2024. To evaluate the association between the PNI and the risk of at 90-day unfavorable outcomes, as well as 90-day mortality, a binary logistic regression model was employed. Furthermore, a logistic regression model incorporating cubic spline functions was utilized to explore the potential non-linear relationship between PNI and 90-day unfavorable outcomes. Additionally, a series of sensitivity analyses and subgroup analyses were performed to enhance the robustness of the findings.

Following the adjustment for covariates, the binary logistic regression analysis demonstrated a notable inverse connection between PNI and the occurrence of unfavorable outcomes at 90 days among patients diagnosed with AIS (OR = 0.951, 95% CI: 0.925-0.979). A similarly significant negative relationship was found between PNI and 90-day mortality (OR = 0.868, 95% CI: 0.806-0.934). Additionally, the study revealed a non-linear association between PNI and 90-day, identifying an inflection point at PNI = 49.3. To the left of this inflection point, the OR for the risk of 90-day unfavorable outcomes in AIS patients was 0.910 (95% CI: 0.880-0.942). Conversely, to the right of the inflection point, the OR was 1.149 (95% CI: 0.998-1.249), although this finding was not statistically significant. The findings were further supported by sensitivity analyses, which reinforced the reliability of these results.

This study reveals a significant negative association between the PNI and 90-day unfavorable outcomes as well as 90-day mortality in patients with AIS. A non-linear relationship between PNI and 90-day unfavorable outcomes was observed. Specifically, a significant inverse association between them was evident when PNI values were below 49.3. These findings offer valuable insights for refining rehabilitation strategies and improving the clinical management of AIS patients.

Hospital readmissions following spontaneous intracerebral hemorrhage (sICH) are a significant concern in Ghana. The paper explores readmission rates and influencing factors. Adult patients with sICH between 2021 and 2023 were reviewed to evaluate readmission rates and associated factors. Statistical analyses were performed using Wilcoxon rank sum and chi-square tests comparing non-readmitted and readmitted groups. The study included 102 patients, of which 82.4% (84) were not readmitted and 17.6% (18) were re-admitted. The median age of patients in the non-readmitted group was 50 years (Interquartile range [IQR]: 44-60) vs. 60.5 years (IQR: 50-71.5). Regarding gender, 61 patients (72.6%) in the non-readmitted group were male, while the readmitted group had an equal number of males and females (9 each, 50.0%). Hypertension was the most prevalent comorbidity in both groups, observed in 71 patients (85.5%) vs. 15 patients (83.3%). Most patients were managed conservatively, with 88.1% of non-readmitted patients and 77.8% of readmitted patients receiving conservative treatment (p = 0.265). The median hospital length of stay was 8.5 days (IQR: 4-14.8) vs. 5 days (IQR: 3-11, p = 0.094). The median modified rankin scale (mRS) score at 1-month post-discharge was 2.0 (IQR: 0.5-4) for the non-readmitted group and 3.5 (IQR: 3-4) for the readmitted group (p = 0.225). At 6 months post-discharge, the median mRS score was 1 (IQR: 0-3) in the non-readmitted group and 3.5 (IQR: 3-4) in the readmitted group (p = 0.072). Readmitted patients had a higher median age, greater prevalence of diabetes, and worse functional outcomes at 1- and 6-month follow-ups. These findings highlight the need for comprehensive discharge planning and post-discharge support for sICH patients, particularly those with diabetes and higher mRS scores. Tailored interventions and follow-up protocols can potentially reduce readmission rates and improve outcomes, warranting further research in this area.

Diabetes is a key risk factor for ischemic stroke and negatively impacts long-term outcomes post-stroke. However, genomic studies on diabetic stroke remain insufficient. This study aims to investigate the interaction between diabetes and stroke from the acute phase to the early recovery phase by establishing a diabetic stroke animal model and comparing transcriptome sequencing results with those of non-diabetic stroke models. The study identified a greater number of downregulated genes in the diabetic stroke group compared to the non-diabetic group at different stages post-stroke. Functional enrichment analysis revealed an enhanced immune response and a relatively lower neurodegeneration potential in the diabetic group. Post-stroke, a higher presence of CD4 + T cells, eosinophils, and M1 macrophages was observed in the diabetic group. Additionally, time-series analysis identified a set of genes with time-specific expression patterns following diabetic stroke. This study underscores the role of inflammation and immune responses as potential factors exacerbating ischemic stroke in diabetes while also identifying gene regulatory networks at different stages post-stroke. These findings provide new insights into the role of diabetes in stroke and suggest potential therapeutic targets for improving outcomes in diabetic patients.

A comprehensive epidemiological investigation of the coexistence between diabetes and stroke/TIA in China is urged.

Data from the Chinese Stroke Center Alliance program, a nationwide multi-center registry study, were used to detect the prevalence, awareness, treatment, and control of diabetes among stroke/TIA. The distribution of diagnosed and undiagnosed diabetes and prediabetes among stroke/TIA patients was investigated, the medical care around diabetes and their respective risk predictors were analyzed, and the association of all above diabetes characteristics with in-hospital death was evaluated using multi-variable Cox regression models.

Of 980 625 patients included, 308 426 (31.5%) had prediabetes, while 365 052 (37.2%) had diabetes, with nearly a third of them undiagnosed (112 969, 30.9%). Of residual aware diabetic patients, 59.0% were treated, with 27.3% controlled. Compared to Han ethnicity, Zhuang ethnicity had a lower prevalence of diabetes (37.3% vs. 35.1%) but were less aware (69.4% vs. 56.5%), treated (59.4% vs. 47.8%), and controlled (27.4% vs. 26.0%). Patients with prediabetes, diagnosed, and undiagnosed diabetes had increasingly higher risks of in-hospital death (adjusted HR [95% CI]: 1.47 [1.35-1.60]; 2.15 [1.97-2.34]; 4.20 [3.87-4.56], all p < 0.001). Unaware and untreated diabetes were independently associated with in-hospital death (adjusted HR [95% CI]: 1.99 [1.85-2.14]; 2.84 [2.63-3.07, both p < 0.001]). Compared with controlled diabetes, those with uncontrolled diabetes had a lower risk of in-hospital death (adjusted HR [95% CI]: 0.77[0.68-0.88], p < 0.001).

The findings indicate that over two-thirds of stroke/TIA patients are exposed to diabetes in China, causing higher in-hospital mortality, which should be screened and intervened early.

The prognosis for stroke patients with type 2 diabetes mellitus (T2DM) remains poorly understood. We examined the risk of mortality and stroke recurrence in stroke patients with T2DM and stroke patients without diabetes.

We conducted a population-based cohort study including all patients diagnosed with a first-time ischemic stroke (n = 131,594) or intracerebral hemorrhage (ICH, n = 15,492) in Denmark, 2005-2021. Patients with T2DM were identified using hospital diagnosis codes and glucose-lowering drug prescriptions. We calculated risks, risk differences, and risk ratios, standardized by age, sex, and calendar year of stroke admission.

Following ischemic stroke, the 5-year standardized mortality was 46.1% for patients with T2DM and 35.4% for patients without diabetes (standardized risk difference: 10.7% [95% CI 9.9-11.6]; risk ratio: 1.3 [95% CI 1.3-1.3]). The 5-year risk of recurrence following ischemic stroke was 12.7% for patients with T2DM and 11.3% for those without diabetes (risk difference: 1.4% [95% CI 0.9-2.0]; risk ratio: 1.1 [95% CI 1.1-1.2]). Following ICH, the 5-year mortality was 62.8% for patients with T2DM and 53.0% for patients without diabetes (risk difference: 9.8% [95% CI 7.2-12.4)]; risk ratio: 1.2 [95% CI 1.1-1.2]). The 5-year risk of recurrence after ICH was 9.1% for patients with T2DM and 9.7% for patients without diabetes.

Stroke patients with T2DM were at increased risk of mortality. The risk of stroke recurrence was slightly higher for ischemic stroke patients with T2DM than patients without diabetes, while no difference was observed among ICH patients.

Statin therapy serves a crucial role as a primary preventive strategy against atherosclerotic cardiovascular disease (ASCVD) in patients with type 2 diabetes mellitus (T2DM). Even though diabetes poses a significant and growing health concern in Sri Lanka, there is a lack of information regarding the prevalence and intensity of statin prescriptions and the achievement of recommended LDL-C targets in diabetic patients for the primary prevention of ASCVD within the nation. We aimed to assess the prevalence and intensity of statin prescriptions, target LDL-C achievement, and factors associated with target LDL-C achievement for the primary prevention of ASCVD in T2DM patients across several tertiary care facilities in Sri Lanka.

A multi-centered, cross-sectional study was conducted among T2DM patients without clinical ASCVD attending six tertiary care medical clinics in Sri Lanka. Data on ASCVD risk factors and statin prescription were collected using an interviewer-administered questionnaire. ASCVD risk was calculated using the WHO charts. Atorvastatin 20 mg/ rosuvastatin 10 mg was defined as high-intensity statins and target LDL-C was defined as < 70 mg/dL for moderate to high and < 100 mg/dL for low-risk groups of ASCVD according to the NICE guideline. The independent sample t test, one-way ANOVA and chi-square test were used for data analysis as appropriate. Factors linked to achieving LDL-C targets were determined through multiple logistic regression analysis. Level of significance was considered as 0.05.

Of the 2013 participants studied, 46.7% were at moderate-high risk and the rest were at low risk of ASCVD. All were eligible for statin therapy, and 84.1% were prescribed statins. High-intensity statins had been prescribed only for 38.5% of moderate-high-risk patients. Nonetheless, high-intensity statins have also been prescribed for 30.7% of low-risk patients. LDL-C target achievement was studied in a randomly selected subsample of 683 and 65.4% (70.7% in low-risk patients and 60.3% in moderate-high-risk patients) achieved LDL-C targets. Of moderate-high-risk patients, 46.3% had not achieved target LDL-C even with high-intensity statin therapy. Female gender (OR = 1.52, 95% CI 1.03-2.24, p = 0.036), poor adherence to statins (OR = 1.67, 95% CI 1.18-2.37, p = 0.004), poor glycemic control (OR = 2.27, 95% CI 1.41-3.65, p = 0.001), and inadequate physical activity (OR = 1.48, 95% CI 1.04-2.10, p = 0.031) were significantly associated with failing to achieve LDL-C targets.

Only about one third of diabetes patients with moderate-high ASCVD risk received high-intensity statins. Even with high-intensity statin therapy, nearly half of the treated patients failed to meet recommended LDL-C targets.

Up to 80% of intensive care unit (ICU) patients suffer from neuromuscular disorders like critical illness polyneuropathy/myopathy (CIP/CIM), requiring further rehabilitation.

This study investigates differences in the clinical course between Coronavirus Disease 2019 (COVID-19) and non-COVID-19 patients with CIP/CIM undergoing post-acute neurological rehabilitation (PANR) and the impact of COVID-19 on rehabilitation outcomes.

A multicenter observational study was conducted in nine German PANR facilities. The clinical course and outcome were compared between COVID-19 and non-COVID-19 patients at rehabilitation admission and discharge. Functional outcomes were measured with the modified Ranking Scale (mRS) and the Barthel Index (BI). Functional independence was defined as either mRS Score < 3 or BI > 75 at discharge, discharged home without nursing support with a BI > 65, or discharged to further rehabilitation (phase D). To analyze outcome predictors, Cox regression was used.

A total of 323 patients (COVID-19 n = 166) after an ICU stay were enrolled, and outcome measures were available for 298. 56 of them achieved functional independence at discharge. COVID-19 patients had better functional scores at admission and discharge and a shorter length of stay as the non-COVID group. Pre-existing diabetes was significantly negatively associated with functioning at discharge.

This analysis provides the first evidence for differences and predictors of the clinical course and outcome for COVID-19 and non-COVID-19 patients with CIP/CIM in the phase of post-acute rehabilitation. Our results offer valuable impulses for rehabilitation strategies for these patients. https://drks.de/search/de , German Clinical Trials Register, ID DRKS00022845, registered on September 1, 2020.

The high costs associated with diabetes management, coupled with the increasing prevalence of comorbidities, present a significant challenge to China's healthcare system, with cardiovascular complications being particularly prominent. The purpose of this study was to evaluate the comorbidities of diabetic patients with stroke in Beijing from 2016 to 2018 and the impact on treatment options and associated costs.

This retrospective cohort study included diabetic patients enrolled in Beijing's medical insurance with outpatient medical records. We compared comorbidities, medications, and related treatment costs between stroke and non-stroke patients.

A representative sample of 2,853,036 patients with diabetes was identified from the data collected from 2016 to 2018, of which an average of 21.18% of patients reported stroke. A higher percentage of diabetic patients with stroke reported other comorbidities including hypertension, coronary artery disease, dyslipidemia, chronic respiratory disease, and osteoporosis as compared to those without stroke(all p's < 0.0001). The costs increased dramatically if diabetic patients developed comorbidities, including hypertension, CAD, dyslipidemia, CRD, and osteoporosis. Annual costs of medications were higher for diabetic patients with stroke and any types of comorbidity compared to diabetic patients without stroke (p <.0001, respectively). More types of drugs were used for diabetic patients with stroke and any types of comorbidity.

The needs of individuals with diabetes and stroke, including their comorbidity patterns and medical burdens, must be carefully taken into account. Health systems will need to address the increasing demand for diagnosing and managing comorbidities in individuals with diabetes and stroke.

The triglyceride-glucose (TyG) index has been proposed as a reliable marker of insulin resistance. However, its value in patients with carotid plaque stability remains unclear. This study investigated the association between the TyG index and unstable carotid plaque.

A total of 12 068 participants were enrolled. Carotid ultrasound was used to determine the stability of carotid plaque. Logistic regression was used to analyze the relationship between the TyG index and unstable carotid plaque. The relationship between the TyG index and unstable carotid plaque was evaluated according to sex, age, and glucose metabolism states. Further, the dose-response relationship between the TyG index and unstable carotid plaque was also determined by restrictive cubic splines.

Of the 12 068 participants, 11 601 had stable carotid plaque and 467 had unstable carotid plaque. In several different adjustment models, the TyG index is significantly related to the risk of unstable carotid plaque. The association between the TyG index and an unstable carotid plaque was similar between men and women, despite the fact that the odds ratio (OR) tended to be higher in men (OR, 2.80 [95% CI, 2.04-3.83]) than women (OR, 2.07 [95% CI, 1.51-2.82]), and higher in older patients (aged >60 years; (OR, 3.59 [95% CI, 2.74-4.70]) than middle-aged patients (aged ≤60 years) (OR, 2.00 [95% CI, 1.36-2.95]). The TyG index of patients with different glycemic status was significantly correlated with the risk of unstable carotid plaque, among which the OR value of diabetes (OR, 2.51 [95% CI, 1.87-3.36]) was the highest. The restrictive cubic spline analysis indicated a nonlinear relationship between the TyG index and unstable carotid plaque, with TyG index >8.63 identified as an independent risk factor for unstable carotid plaque.

The TyG index has a significant association with unstable carotid plaque. The association between the TyG index and unstable carotid plaque is similar between men and women, and the association in older patients is higher than that in middle-aged patients. In different glycemic status, the association between the TyG index and unstable carotid plaque is highest in patients with diabetes.

Recurrent ischemic stroke (RIS) induces additional functional limitations in patients. Prognosticating globally functional outcome (GFO) in RIS patients is thereby important to plan a suitable rehabilitation programme. This study sought to investigate the ability of baseline features for classifying the patients with and without improving GFO (task 1) and identifying patients with poor GFO (task 2) at the third month after discharging from RIS.

A total of 86 RIS patients were recruited and divided into the training set and testing set (50:50). The clinical and pre-clinical data were recorded. The outcome was the changes in Modified Rankin Scale (mRS) (task 1) and the mRS score at the third month (mRS 0-2: good GFO, mRS >2: poor GFO) (task 2). The permutation importance ranking method selected features. Four algorithms were trained on the training set with five-fold cross-validation. The best model was tested on the testing set.

In task 1, the support vector machine (SVM) model outperformed the other models, with the high performance matrix on the training set (sensitivity = 0.80; specificity = 1.00) and the testing set (sensitivity = 0.80; specificity = 0.95). In task 2, the SVM model with selected features also performed well on both datasets (training set: sensitivity = 0.76; specificity = 0.92; testing set: sensitivity = 0.72; specificity = 0.88).

A machine learning model could be used to classify GFO responses to treatment and identify the third-month poor GFO in RIS patients, supporting physicians in clinical practice.

To determine whether an association exists between health insurance and diabetic retinopathy (DR) prevalence in adults with diabetes.

An analytical cross-sectional study was conducted utilizing the National Health and Nutrition Examination Survey database. 4530 Patients aged ≥ 18 with diabetes from 2011 to 2020 with various insurance types (no insurance, private, Medicare, Medicaid, or other) were evaluated for prevalence of DR, including covariates. Unadjusted and adjusted logistic regression analysis were conducted to calculate odds ratios (OR) and 95 % confidence intervals (CI).

There was no significant association between insurance status and DR prevalence when adjusting for confounders. OR for DR in patients without insurance, Medicare, Medicaid, or other insurance compared to those with private insurance were 1.13 (95 % CI 0.74-1.71), 0.78 (95 % CI 0.54-1.13), 1.20 (95 % CI 0.80-1.81), and 0.81 (95 % CI 0.47-1.37) respectively. However, factors like age ≥ 65 and use of diabetes medication were associated with reduced DR prevalence.

Although insurance status alone does not have an association with the prevalence of DR, this study highlights several confounding variables that potentially influence previously reported associations between insurance status and DR.

This cohort study aimed to evaluate the prognostic outcomes of patients with acute ischemic stroke (AIS) and diabetes mellitus following intravenous thrombolysis, utilizing machine learning techniques. The analysis was conducted using data from Shenyang First People's Hospital, involving 3,478 AIS patients with diabetes who received thrombolytic therapy from January 2018 to December 2023, ultimately focusing on 1,314 patients after screening. The primary outcome measured was the 90-day Modified Rankin Scale (MRS). An 80/20 train-test split was implemented for model development and validation, employing various machine learning classifiers, including artificial neural networks (ANN), random forest (RF), XGBoost (XGB), and LASSO regression. Results indicated that the average accuracy of the XGB model was 0.7355 (±0.0307), outperforming the other models. Key predictors for prognosis post-thrombolysis included the National Institutes of Health Stroke Scale (NIHSS) and blood platelet count. The findings underscore the effectiveness of machine learning algorithms, particularly XGB, in predicting functional outcomes in diabetic AIS patients, providing clinicians with a valuable tool for treatment planning and improving patient outcome predictions based on receiver operating characteristic (ROC) analysis and accuracy assessments.

Although studies have investigated the association between adverse childhood experiences and chronic health outcomes including stroke, few studies have investigated the association between parental divorce and stroke among adults with no history of childhood abuse. The objectives of this study were to investigate the association between parental divorce in childhood and stroke in older adulthood among those who did not experience child abuse and to examine whether this association differs between men and women. This study utilized population-based data from the 2022 Behavioral Risk Factor Surveillance System. An analytic sample of 13,205 adults aged 65 and above (56.6% female) who have never experienced childhood physical nor sexual abuse were analyzed using binary logistic regression. The outcome variable investigated was self-report of a physician-diagnosis of stroke, and the main exposure of interest was parental divorce. In this sample of older adults, 7.3% reported having stroke, while 13.9% reported that their parents had divorced before the respondent was 18 years old. Controlling for the effects of other factors, respondents who experienced parental divorce had 1.61 times higher odds of having a stroke when compared to their counterparts who did not experience parental divorce (AOR = 1.61, 95% CI = 1.15-2.24). The association between parental divorce and stroke was not dependent on sex; however, compared to females, males had 1.47 times higher odds of having a stroke (AOR = 1.47, 95% CI = 1.11-1.93). The findings of this study suggest that individuals in this cohort whose parents divorced as children were at greater risk for stroke later in life. Potentially moderating variables were hypothesized, including childhood poverty, sleep hygiene, and hypertension.

Stroke is a major cause of death and disability globally, especially among diabetic patients. In this study, we aim to scrutinise the effects of metformin on the clinical outcomes of stroke in diabetic patients.

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

PubMed, Embase and Web of Science databases were searched between their inception and 5 December 2023.

Studies investigating the effect of metformin on the clinical outcomes of stroke in patients with diabetes were included.

The effect of metformin on the clinical outcomes of stroke in patients with diabetes was identified using combined ORs and 95% CIs.

A total of 11 studies involving 18 525 participants were included in this review. Pooled analysis has demonstrated that prestroke metformin use could reduce the probability of poor course after stroke by 34% in diabetes mellitus (DM) patients (OR=0.66, 95% CI: 0.61 to 0.72) and reduce the probability of death by 43% (OR=0.57, 95% CI: 0.51 to 0.64).

Prestroke metformin use is beneficial for the improvement of clinical outcomes in patients who had a stroke with DM, although the potential bias should be carefully considered.

CRD42024496056.

The progression of diabetes status in post-stroke patients remains under-investigated, particularly regarding new treatments for type II diabetes mellitus (DM II), like glucagon-like peptide 1 receptor agonists (GLP-1-RA) and sodium-glucose co-transporter-2 (SGLT-2) inhibitors, which have not been studied in the post-stroke setting.

Eight hundred eighty-four consecutive ischemic stroke patients recruited to our prospective STROKE-CARD Registry were assessed concerning their glycemic status at baseline (normoglycemia, prediabetes, DM II) and change over time within 1 year follow-up. Multivariate logistic regression was performed to identify factors associated with transitioning from normoglycemia to prediabetes or DM II. Additionally, we reviewed ongoing clinical trials for GLP-1-RA and SGLT-2 inhibitors in the context of acute ischemic stroke.

At baseline, 44.6% (n = 394) of individuals had normoglycemia, 33.9% (n = 300) were prediabetic, and 21.5% had DM II (n = 190). After 1 year, normoglycemia decreased by 12.1 percentage points (n = 107), whereas prediabetes and DM II increased by 10.2 percentage (n = 90) points and 1.9 percentage points (n = 17), respectively. Statin therapy was the only significant risk factor for progression. 23.4% (n = 207) of our cohort would have met eligibility criteria for a recent trial on semaglutide in obese non-diabetics with prior cardiovascular disease. However, only one ongoing trial aims at evaluating short-term cardiovascular risk reduction in stroke patients.

GPrediabetes and DM II are frequent in ischemic stroke patients. Even within an intensified post-stroke disease management setting, a considerable amount of stroke survivors convert to prediabetes or DM II within the first year. Our results demonstrate a notable proportion of patients qualifying inclusion in studies examining the efficacy of GLP-1-RA agonists and SGLT-2 inhibitors in secondary prevention.

Given the high prevalence and progression of prediabetes and DM II in stroke survivors, there is a need for clinical trials evaluating the use of GLP-1-RA and SGLT-2 inhibitors in this population.

Breakdown of blood-brain barrier (BBB) represents a key pathology in hyperglycemia-mediated cerebrovascular damage after an ischemic stroke. As changes in the level and nature of vasoactive agents released by endothelial cells (ECs) may contribute to BBB dysfunction, this study first explored the specific impact of hyperglycemia on EC characteristics and secretome. It then assessed whether secretome obtained from ECs subjected to normoglycaemia or hyperglycemia might regulate pericytic cytokine profile differently. Using a triple cell culture model of human BBB, composed of brain microvascular EC (BMEC), astrocytes and pericytes, this study showed that exposure to hyperglycemia (25 mM D-glucose) for 72 h impaired the BBB integrity and function as evidenced by decreases in transendothelial electrical resistance and increases in paracellular flux of sodium fluorescein. Dissolution of zonula occludens-1, a tight junction protein, and appearance of stress fibers appeared to play a key role in this pathology. Despite elevations in angiogenin, endothelin-1, interleukin-8 and basic fibroblast growth factor levels and a decrease in placental growth factor levels in BMEC subjected to hyperglycemia vs normoglycaemia (5.5 mM D-glucose), tubulogenic capacity of BMECs remained similar in both settings. Similarly, pericytes subjected to secretome obtained from hyperglycemic BMEC released higher quantities of macrophage migration inhibitory factor and serpin and lower quantities of monocyte chemoattractant protein-1, intercellular adhesion molecule, interleukin-6 and interleukin-8. Taken together these findings indicate the complexity of the mechanisms leading to BBB disruption in hyperglycemic settings and emphasize the importance of endothelial cell-pericyte axis in the development of novel therapeutic strategies.

The prevalence of multimorbidity is socially patterned, but little is known about how socioeconomic inequality might affect the long-term progression of multimorbidity. This study aimed to identify multimorbidity trajectories and to examine their association with socioeconomic status (SES) among middle-aged and older Chinese adults.

A total of 3837 middle-aged and older participants were included from the dynamic cohort of the China Health and Retirement Longitudinal Study, 2011-2018. Multimorbidity trajectories were assessed using the Chinese Multimorbidity-Weighted Index (CMWI), which covers 14 chronic conditions. Group-based trajectory modeling was used to identify multimorbidity developmental trajectories. Education, working status, and total household income were used to construct SES scores. The associations between SES and CMWI trajectories were estimated using multinomial logistic regression models adjusting for sociodemographic and lifestyle factors.

Four distinct CMWI trajectories were identified: 'no multimorbidity' (16.8 %), 'new-onset multimorbidity' (48.7 %), 'slowly increasing multimorbidity' (24.3 %), and 'rapidly increasing multimorbidity' (10.2 %). Lower SES was associated with higher odds of experiencing the 'rapidly increasing' trajectory (P trend < 0.01); for example, compared with the 'no multimorbidity' group, participants with low SES had a 96 % (OR, 1.96; 95 % CI, 1.29 to 2.98) increased odds of belonging to the 'rapidly increasing' group.

Socioeconomic inequalities were observed in the CMWI trajectories of multimorbidity in middle-aged and older Chinese adults. The findings suggest effective strategies for preventing and controlling multimorbidity should be made from a long-term perspective, especially for those of lower SES.

Brain diseases are a leading cause of disability and death worldwide. Early detection can lead to earlier intervention and better outcomes for patients. In recent years, optical coherence tomography (OCT) and OCT angiography (OCTA) imaging have been widely used in stroke, traumatic brain injury (TBI), and brain cancer due to their advantages of in vivo, unlabeled, and high-resolution 3D microvessel imaging at the capillary resolution level. This review summarizes recent advances and challenges in living brain imaging using OCT/OCTA, including technique modality, types of diseases, and theoretical approach. Although there may still be many limitations, with the development of lasers and the advances in artificial intelligence are expected to enable accurate detection of deep cerebral hemodynamics and guide intraoperative tumor resection in vivo in the future.

Neural activation induced by upper extremity robot-assisted training (UE-RAT) helps characterize adaptive changes in the brains of poststroke patients, revealing differences in recovery potential among patients. However, it remains unclear whether these task-related neural activities can effectively predict rehabilitation outcomes. In this study, we utilized functional near-infrared spectroscopy (fNIRS) to measure participants' neural activity profiles during resting and UE-RAT tasks and developed models via machine learning to verify whether task-related functional brain responses can predict the recovery of upper limb motor function.

Cortical activation and brain network functional connectivity (FC) in brain regions such as the superior frontal cortex, premotor cortex, and primary motor cortex were measured using fNIRS in 82 subacute stroke patients in the resting state and during UE-RAT. The Fugl-Meyer Upper Extremity Assessment Scale (FMA-UE) was chosen as the index for assessing upper extremity motor function, and clinical information such as demographic and neurophysiological data was also collected. Robust features were screened in 100 randomly divided training sets using the least absolute shrinkage and selection operator (LASSO) method. Based on the selected robust features, machine learning algorithms were used to develop clinical models, fNIRS models, and combined models that integrated both clinical and fNIRS features. Finally, Shapley Additive Explanations (SHAP) was applied to interpret the prediction process and analyze key predictive factors.

Compared to the resting state, task-related FC is a more robust feature for modeling, with screening frequencies above 90%. The combined models built using artificial neural networks (ANNs) and support vector machines (SVMs) significantly outperformed the other algorithms, with an average AUC of 0.861 (± 0.087) for the ANN and an average correlation coefficient (r) of 0.860 (± 0.069) for the SVM. Furthermore, predictive factor analysis of the models revealed that FC measured during tasks is the most important factor for predicting upper limb motor function.

This study confirmed that UE-RAT-induced FC can serve as an important predictor of rehabilitation, especially when combined with clinical information, further enhancing the accuracy of model predictions. These findings provide new insights for the early prediction of patients' recovery potential, which may contribute to personalized rehabilitation decisions.

Insulin resistance (IR) is linked to both the vulnerable plaque and the stroke risk. However, the precise extent of this correlation and its impact on stroke risk in carotid artery stenosis patients remain unclear. Therefore, this study aims to investigate the relationship between vulnerable plaque and IR and stroke risk and the mediating role of vulnerable plaque in patients with carotid artery stenosis. This study included 505 patients with carotid artery stenosis. IR was assessed using the triglyceride-glucose (TyG) index. The association of the TyG index and vulnerable plaque with stroke risk was investigated using the restricted cubic splines (RCS)and adjusted Logistic regression. Additionally, the mediation analysis was used to explore the mediating impact of the vulnerable plaque on the association between the TyG index and stroke risk. A total of 184 (36.4%) stroke events were recorded. The RCS curves revealed a positive linear association between TyG index and risk events among patients with carotid artery stenosis (P-value < 0.001 and P for nonlinear = 0.860). After fully adjusting for covariates, both the TyG index and vulnerable plaque emerged as significant predictors of stroke events. Mediation analysis indicated that the vulnerable plaque mediated 18.3%, 15.8%, 13.9%, and 11.6% of the correlation between the TyG index and stroke risk in different adjusted models, respectively. TyG index and vulnerable plaque are associated with a higher risk of stroke in patients with carotid artery stenosis. In addition, vulnerable plaques partially mediated the relationship between TyG index and stroke risk.

Post-stroke morbidity is common, but little is known about the burden on patients' lives from their own perspective. Understanding morbidity from the point of view of the patient may support targeted intervention in post-stroke recovery. This study used a stroke specific Patient Reported Outcome Measure (PROM) containing Mental health (MH) and Physical Health (PH) domains and 5 stroke specific questions. We aimed to consider trends over a 6-month period and further assess the association between the MH and PH measures and common clinical measures.

A multicenter prospective cohort study was conducted at 19 hospital sites across England and Wales. Patients were enrolled from August 2018 to September 2019. Clinical measures and PROMs were assessed at three timepoints: acutely following the index stroke, at 3 and 6-months post-stroke. Clinical measures and PROMs were assessed in each of these points.

Physical health PROM domains show significant gradual improvement across the study period (χ2 42.6312, p<0.0001), whereas cognitive function domains (χ2 3.7849, p<0.875) did not echo this trend. All clinical measures (GAD-7, PHQ9, MoCA, MRS) were associated with poorer PROM MH outcomes, (aMD -4.4, CI -0.59, -0.29, p≤0.001, aMD -0.45, CI -0.59, -0.32, p=<0.001, aMD 0.75, CI 0.56, 0.95, aMD -1.91, CI -2.41, -1.47, p≤0.001). Clinical measures of disability, as per the MRS, are associated with poor PROM PH scores (aMD -0.57, 95% CI -0.94, -0.20, p=0.003).

This research indicates there is unmet cognitive burden in stroke survivors. PROMs may be able to measure unmet more discretely than common clinical tools that are used post-stroke. Further research and guidance on how to integrate PROMs into current clinical frameworks is essential.

Cerebrovascular accidents (CVA) represent a major complication in diabetes (DM). Real-life evidence as to whether modern management of CVA and DM have softened this relationship is limited. Therefore, we estimated prevalence and impact of DM on in-hospital survival and complications in a contemporary cohort of subjects with CVA.

We retrospectively evaluated the records of 937 patients admitted for CVA at the Stroke Unit of Verona University Hospital during a 3-year period. Pre-existing or de novo DM was ascertained by prior diagnosis, glucose-lowering therapy at admission/discharge or admittance plasma glucose ≥ 200 mg/dL. Multiple regressions were applied to test DM as predictor of in-hospital mortality, complications (composite of infections, cardio- and cerebrovascular complications, major bleeding and pulmonary complications), duration and costs of hospitalization.

Diabetes prevalence was 21%, of which 22% de novo diagnoses. Compared to non-DM, diabetic individuals were older and carried an increased burden of cardiovascular risk factors. Compared to known DM, de novo DM individuals were younger, had higher admittance plasma glucose and poorer cardiovascular comorbidities. Overall, DM versus non-DM individuals did not show significantly increased risk of death (14.0 vs. 9.3%; crude-OR 1.59 95% CI 0.99-2.56). Controlling for confounders did not improve significance. DM resulted independent predictor for in-hospital complications (36.2% vs. 26.9%; adj-OR 1.49, 1.04-2.13), but not for duration and costs of hospitalization.

DM frequently occurs in patients admitted for stroke and carries an excess burden of adverse in-hospital complications, urgently calling for strategies to anticipate DM diagnosis and tailored treatment in high-risk individuals.