Relationship between white matter hyperintensities and chronic kidney disease in patients with acute lacunar stroke

Preliminary study of the interactive effects of coronary heart disease and lacunar infarction on renal function in patients with type 2 diabetes mellitus by gender

BACKGROUND

Coronary heart disease (CHD) and lacunar infarction (LI) are the most common cardio- cerebrovascular complications of type 2 diabetes mellitus (T2DM) and a recognized risk factor for renal injury. Although a unidirectional association of CHD or LI with T2DM or the kidney has been demonstrated, however, it remains unknown whether there is an interactive effect of the coexistence of CHD and LI on renal function in T2DM patients. The aim of our study was to investigate the interaction between CHD and LI on renal function in gender-specific patients with T2DM and the association between cardio-cerebrovascular disease-related conventional serum markers and the estimated glomerular filtration rate (eGFR).

METHODS

We conducted a cross-sectional study in Beijing and Tianjin from April 2019 to August 2021. Participants with T2DM aged ≥18 years were asked to complete a one-to-one questionnaire and physical examination.

RESULTS

In this study, 389 eligible patients with T2DM were included, with a mean age of 63.04 ± 9.41 years, of whom 200 (51.41 %) were male. The proportions of patients with CHD, LI, and both CHD and LI were 28.53 %, 24.42 %, and 11.05 %, respectively. Compared to T2DM patients without either CHD or LI, those with both CHD and LI were found to have a significantly greater risk of reduced eGFR (OR: 12.82, 95 % CI 5.06-32.52, P < 0.001) than those with CHD alone (OR: 2.42, 95 % CI 1.37-3.00, P = 0.004) or LI alone (OR: 1.15, 95 % CI 0.61-2.18, P = 0.664). The combined presence of CHD and LI is associated with a significantly greater risk of decreased eGFR in female T2DM patients compared to their male counterparts. We found both multiplicative and additive effects in all T2DM patients; however, when stratified by sex, only multiplicative effects were observed. After controlling for interference from CHD, LI, and age, we found that total cholesterol (TC) was negatively correlated with eGFR in females (r = -0.156, P = 0.034), and low-density lipoprotein cholesterol (LDL-C) was negatively correlated with eGFR in males (r = -0.229, P = 0.001).

CONCLUSION

This study provides novel evidence that the synergistic effect of CHD and LI on renal injury in patients with T2DM is significantly greater than their individual effects. Women with T2DM who have both CHD and LI are at a 4.85-fold higher risk of decreased eGFR than men. Therefore, increased clinical attention should be given to preventing and treating vascular complications in T2DM patients, as well as aggressively reducing lipid levels, particularly TC and LDL-C, to delay or prevent renal dysfunction in T2DM patients.

Application of machine learning model to predict lacunar cerebral infarction in elderly patients with femoral neck fracture before surgery

BACKGROUND

Femoral neck fracture and lacunar cerebral infarction (LCI) are the most common diseases in the elderly. When LCI patients undergo a series of traumas such as surgery, their postoperative recovery results are often poor. Moreover, few studies have explored the relationship between LCI and femoral neck fracture in the elderly. Therefore, this study will develop a ML (machine learning)-based model to predict LCI before surgery in elderly patients with a femoral neck fracture.

METHODS

Professional medical staff retrospectively collected the data of 161 patients with unilateral femoral neck fracture who underwent surgery in the Second Affiliated Hospital of Wenzhou Medical University database from January 1, 2015, to January 1, 2020. Patients were divided into two groups based on LCI (diagnosis based on cranial CT image): the LCI group and the non-LCI group. Preoperative clinical characteristics and preoperative laboratory data were collected for all patients. Features were selected by univariate and multivariate logistic regression analysis, with age, white blood cell (WBC), prealbumin, aspartate aminotransferase (AST), total protein, globulin, serum creatinine (Scr), blood urea nitrogen (Bun)/Scr, lactate dehydrogenase (LDH), serum sodium and fibrinogen as the features of the ML model. Five machine learning algorithms, Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Decision tree (DT), were used in combination with preoperative clinical characteristics and laboratory data to establish a predictive model of LCI in patients with a femoral neck fracture. Furthermore, indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance.

RESULTS

The AUROC of 5 ML models ranged from 0.76 to 0.95. It turned out that the RF model demonstrated the highest performance in predicting LCI for femoral neck fracture patients before surgery, whose AUROC was 0.95, sensitivity 1.00, specificity 0.81, and accuracy 0.90 in validation sets. Furthermore, the top 4 high-ranking variables in the RF model were prealbumin, fibrinogen, globulin and Scr, in descending order of importance.

CONCLUSION

In this study, 5 ML models were developed and validated for patients with femoral neck fracture to predict preoperative LCI. RF model provides an excellent predictive value with an AUROC of 0.95. Clinicians can better conduct multidisciplinary perioperative management for patients with femoral neck fractures through this model and accelerate the postoperative recovery of patients.

Covert vascular brain injury in chronic kidney disease

Chronic kidney disease (CKD) contributes to the increased risk of stroke and dementia. Accumulating evidence indicates that structural brain abnormalities, such as cerebral small vessel disease, including white matter hyperintensities, lacunes, perivascular spaces, and cerebral microbleeds, as well as brain atrophy, are common in patients with CKD. All of these imaging findings have been implicated in the development of stroke and dementia. The brain and kidney exhibit similar impairments and promote structural brain abnormalities due to shared vascular risk factors and similar anatomical and physiological susceptibility to vascular injury in patients with CKD. This indicates that kidney function has a significant effect on brain aging. However, as most results are derived from cross-sectional observational studies, the exact pathophysiology of structural brain abnormalities in CKD remains unclear. The early detection of structural brain abnormalities in CKD in the asymptomatic or subclinical phase (covert) should enable stroke risk prediction and guide clinicians on more targeted interventions to prevent stroke in patients with CKD. This article summarizes the currently available clinical evidence linking covert vascular brain injuries with CKD.

Association Between White Matter Hyperintensities and Chronic Kidney Disease: A Systematic Review and Meta-Analysis

Objectives

Previous studies of the associations between white matter hyperintensities (WMH) and chronic kidney disease (CKD) were still conflicting; therefore, our study aimed to conduct a systematic review of all of the available research on this topic and a meta-analysis of the association between WMH and CKD among observational studies.

Setting and Design

Systematic review and meta-analysis.

Outcome Measures

Severity of WMH.

Methods and Participants

All relevant studies in public databases were examined until 15 November 2020. Two independent reviewers assessed all the included studies using the Cross-Sectional/Prevalence Study Quality (CSSQ) scale, and then literature review and meta-analyses were undertaken.

Results

We pooled the odds ratio (OR) for the presence of WMH, periventricular hyperintensities (PVH), and deep subcortical white matter hyperintensities (DWMH) of patients with CKD vs. non-CKD patients by subgroup analysis, and the results obtained were WMH OR 2.07, 95% CI [1.58, 2.70], PVH OR 2.41, 95% CI [1.90, 3.05], and DWMH OR 2.11, 95% CI [1.60, 2.80], respectively. The main outcome showed that patients with CKD were more likely to have WMH in the brain compared to the normal controls. Another meta-analysis showed a statistically significant decline in renal function in patients with moderate to severe WMH compared with those with no to mild WMH.

Conclusions

The findings indicated that patients with CKD were more likely to experience WMH than demographically matched controls. On the other hand, patients with moderate to severe WMH in the brain had poor renal function more frequently than those with no to mild WMH.