Associations of blood biomarkers with glomerular filtration rate in patients with TIA and stroke: population-based study

The Kidney-Immune-Brain Axis: The Role of Inflammation in the Pathogenesis and Treatment of Stroke in Chronic Kidney Disease

Cardiovascular diseases such as stroke are a major cause of morbidity and mortality for patients with chronic kidney disease (CKD). The underlying mechanisms connecting CKD and cardiovascular disease are yet to be fully elucidated, but inflammation is proposed to play an important role based on genetic association studies, studies of inflammatory biomarkers, and clinical trials of anti-inflammatory drug targets. There are multiple sources of both endogenous and exogenous inflammation in CKD, including increased production and decreased clearance of proinflammatory cytokines, oxidative stress, metabolic acidosis, chronic and recurrent infections, dialysis access, changes in adipose tissue metabolism, and disruptions in intestinal microbiota. This review focuses on the mechanisms of inflammation in CKD, dialysis and associated therapies, its proposed impact on stroke pathogenesis and prognosis, and the potential role of anti-inflammatory agents in the prevention and treatment of stroke in patients with CKD.

Impact of age on clinical characteristics and 1-year outcomes of non-disabling ischemic cerebrovascular events: A multicenter prospective cohort study

BACKGROUND

The exploration of age-related clinical features and adverse outcomes of non-disabling ischemic cerebrovascular disease (NICE) has been largely unaddressed in current research. This study aimed to analyze the differences in clinical characteristics and prognostic outcomes of NICE across various age groups, utilizing data from the Xi'an Stroke Registry Study in China.

METHODS

The age distribution of NICE was categorized into four groups: age ≤ 54 years, age 55-64 years, age 65-74 years, and age ≥ 75 years. Multivariate Cox logistic regression analysis was employed to evaluate the 1-year risk of outcome events in each age group of patients with NICE. A subgroup analysis was conducted to explore interaction factors influencing age-dependent outcomes in patients with NICE.

RESULTS

This study included 1,121 patients with NICE aged between 23 and 96 years, with an average age of 63.7 ± 12.2 years. Patients aged ≥ 75 years had a higher proportion of women, lower education levels, and a greater likelihood of having urban employee medical insurance. Those aged < 55 years had a higher prevalence of smoking, while individuals aged > 65 years showed a higher prevalence of comorbidities. Furthermore, there was a significant decrease in body mass index among patients aged ≥ 75 years. Laboratory tests indicated well-controlled blood lipids, liver function, and inflammation across all age groups, but renal function was notably reduced in patients with NICE aged ≥ 75 years. Adjusting for potential confounding factors revealed a significant increase in the one-year risk of all-cause mortality and poor prognosis among patients aged ≥ 75 years compared to those aged < 55 years, with no significant gender difference observed. Subgroup analysis indicated that patients with NICE who consumed alcohol were more prone to experience all-cause mortality with advancing age.

CONCLUSIONS

Age significantly influences the clinical characteristics and prognostic outcomes of NICE patients. Clinicians should consider age-specific characteristics when diagnosing, treating, and developing prevention strategies. Tailored prevention and treatment strategies for different age groups can enhance prognosis and reduce adverse outcomes in NICE patients.

Prognostic value of anoikis-related genes revealed using multi-omics analysis and machine learning based on lower-grade glioma features and tumor immune microenvironment

Background

The investigation explores the involvement of anoikis-related genes (ARGs) in lower-grade glioma (LGG), seeking to provide fresh insights into the disease's underlying mechanisms and to identify potential targets for therapy.

Methods

We applied unsupervised clustering techniques to categorize LGG patients into distinct molecular subtypes based on ARGs with prognostic significance. Additionally, various machine learning algorithms were employed to pinpoint genes most strongly correlated with patient outcomes, which were then used to develop and assess risk profiles.

Results

Our analysis identified two distinct molecular subtypes of LGG, each with significantly different prognoses. Patients in Cluster 2 had a median survival of 2.036 years, markedly shorter than the 7.994 years observed in Cluster 1 (P < 0.001). We also constructed a six-gene ARG signature that efficiently classified patients into two risk categories, showing median survival durations of 4.084 years for the high-risk group and 10.304 years for the low-risk group (P < 0.001). Significantly, the immune profiles, tumor mutation characteristics, and drug sensitivity varied greatly among these risk groups. The high-risk group was characterized by a "cold" tumor microenvironment (TME), a lower IDH1 mutation rate (61.7 % vs. 91.4 %), a higher TP53 mutation rate (53.7 % vs. 38.9 %), and greater sensitivity to targeted therapies such as QS11 and PF-562271. Furthermore, our nomogram, integrating risk scores with clinicopathological features, demonstrated strong predictive accuracy for clinical outcomes in LGG patients, with an AUC of 0.903 for the first year. The robustness of this prognostic model was further validated through internal cross-validation and across three external cohorts.

Conclusions

The evidence from our research suggests that ARGs could potentially serve as reliable indicators for evaluating immunotherapy effectiveness and forecasting clinical results in patients with LGG.

Dibutyl phthalate induces epithelial-mesenchymal transition of renal tubular epithelial cells via the Ang II/AMPKα2/Cx43 signaling pathway

Maternal exposure to dibutyl phthalate (DBP) induces renal fibrosis in offspring. However, the specific roles of connexin 43 (Cx43) in DBP-induced renal fibrosis remain unknown. Therefore, in this study, we analysed the expression of Cx43 in renal tubular epithelial cells (RTECs) with or without DBP exposure using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting. A small interfering RNA against Cx43 was introduced to assess its role in epithelial-mesenchymal transition (EMT) of RTECs caused by 100 μmol/L DBP. Bioinformatics analysis was conducted with AMP-activated protein kinase (AMPK)-α2 and angiotensin (Ang) II inhibitors to determine the mechanisms involved in the expression of Cx43 in HK-2 cells. RT-qPCR and western blotting revealed that DBP increased the expression of Cx43 in vitro. Moreover, Cx43 knockdown significantly alleviated DBP-induced EMT caused by DBP in HK-2 cells. Bioinformatics analysis with AMPKα2 and Ang II inhibitors revealed that DBP upregulated Cx43 expression by activating the Ang II/AMPKα2 signaling pathway. Our findings indicate that DBP induces renal fibrosis by activating Ang II/AMPKα2/Cx43 signaling pathway and EMT in RETCs, suggesting a potential target for the treatment of renal fibrosis.

External Validation of the Nelson Equation for Kidney Function Decline in Patients with Acute Ischemic Stroke or Transient Ischemic Attack

Background

There is a close brain-kidney interaction following ischemic cerebrovascular disease. The new-onset kidney injury after stroke leads to severe neurological deficits and poor functional outcomes. We aimed to validate the Nelson equation for predicting the new-onset and long-term kidney function decline in patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA).

Methods

A total of 3169 patients were enrolled in the Third China National Stroke Registry, whose baseline estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m². The outcome of interest was the incident eGFR< 60 mL/min/1.73 m² at 3 months. The prediction equation of participants with or without diabetes was validated respectively. The receiver operating characteristic curve (AUC) evaluated prediction performance. The Delong test compared the Nelson equation performance with the O'Seaghdha equation and the Chien equation. Continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were determined to evaluate the incremental effect.

Results

During the 3-mo follow-up period, among 1151 patients with diabetes, there were 31 cases (2.7%) of reduced eGFR. Meanwhile, among 2018 non-diabetic patients, there were 23 cases (1.1%) of reduced eGFR. The Nelson equation showed good discrimination and was well-calibrated in patients with diabetes (AUC 0.82, Hosmer-Lemeshow test p = 0.67) or without diabetes (AUC 0.82, Hosmer-Lemeshow test p = 0.09). The performance of the Nelson equation was superior to other equation, as increased continuous NRI (diabetic, 0.64; non-diabetic, 1.13) and IDI (diabetic, 0.10; non-diabetic, 0.13) to the Chien equation.

Conclusion

The Nelson equation reliably predicted the risks of the new-onset and long-term kidney function decline in patients with AIS or TIA, which could help clinicians screen high-risk patients and improve clinical care.

Anoikis resistance in diffuse glioma: The potential therapeutic targets in the future

Glioma is the most common malignant intracranial tumor and exhibits diffuse metastasis and a high recurrence rate. The invasive property of glioma results from cell detachment. Anoikis is a special form of apoptosis that is activated upon cell detachment. Resistance to anoikis has proven to be a protumor factor. Therefore, it is suggested that anoikis resistance commonly occurs in glioma and promotes diffuse invasion. Several factors, such as integrin, E-cadherin, EGFR, IGFR, Trk, TGF-β, the Hippo pathway, NF-κB, eEF-2 kinase, MOB2, hypoxia, acidosis, ROS, Hsp and protective autophagy, have been shown to induce anoikis resistance in glioma. In our present review, we aim to summarize the underlying mechanism of resistance and the therapeutic potential of these molecules.

Does previous stroke modify the relationship between inflammatory biomarkers and clinical endpoints in CKD patients?

Background

Chronic kidney disease (CKD) is an independent risk factor for stroke. Stroke is also an independent risk factor for worse CKD outcomes and inflammation may contribute to this bidirectional relationship. This study aims to investigate inflammatory biomarkers in patients with non-dialysis CKD (ND-CKD) with and without stroke.

Methods

A propensity matched sample from > 3000 Salford Kidney Study (SKS) patients, differentiated by previous stroke at study recruitment, had stored plasma analyzed for interleukin- 6 (IL-6), Von Willebrand Factor (VWF) and C-reactive protein (CRP). Multivariable cox regression analysis investigated associations between inflammation and death, end-stage renal disease (ESRD) and future non-fatal cardiovascular events (NFCVE).

Results

A total of 157 previous stroke patients were compared against 162 non-stroke patients. There were no significant differences in inflammatory biomarkers between the two groups. Previous stroke was associated with greater mortality risk, hazard ratio (HR) (95% CI) was 1.45 (1.07–1.97). Higher inflammatory biomarker concentrations were independently associated with death but not ESRD or NFCVE in the total population. For each 1 standard deviation (SD) increase in log IL-6, VWF and CRP, the HR for all-cause mortality were 1.35 (1.10–1.70), 1.26 (1.05–1.51) and 1.34 (1.12–1.61), respectively. CRP retained its independent association (HR 1.47 (1.15–1.87)) with death in the stroke population.

Conclusion

Previous stroke is an important determinant of mortality. However, the adverse combination of stroke and ND-CKD does not seem to be driven by higher levels of inflammation detected after the stroke event. Biomarkers of inflammation were associated with worse outcome in both stroke and non-stroke ND-CKD patients.

Trial registration

15/NW/0818.

Impact of multimorbidity on risk and outcome of stroke: Lessons from chronic kidney disease

With both an aging population and greater post-stroke survival, multimorbidity is a growing healthcare challenge, affecting over 40% of stroke patients, and rising rapidly and predictably with increasing age. Commonly defined as the co-occurrence of two or more chronic conditions, multimorbidity burden is a strong adverse prognostic factor, associated with greater short- and long-term stroke mortality, worse rehabilitation outcomes, and reduced use of secondary prevention. Chronic kidney disease can be considered as the archetypal comorbidity, being age-dependent and also affecting about 40% of stroke patients. Chronic kidney disease and stroke share very similar traditional cardiovascular risk factor profiles such as hypertension and diabetes, though novel chronic kidney disease-specific risk factors such as inflammation and oxidative stress have also been proposed. Using chronic kidney disease as an exemplar condition, we explore the mechanisms of risk in multimorbidity, implications for management, impact on stroke severity, and downstream consequences such as post-stroke cognitive impairment and dementia.