Physical activity associates with subarachnoid hemorrhage risk- a population-based long-term cohort study

Adult-Onset Neuroepidemiology in Finland: Lessons to Learn and Work to Do

Finland is a relatively small genetic isolate with a genetically non-homogenous population. Available Finnish data on neuroepidemiology of adult-onset disorders are limited, and this paper describes the conclusions that can be drawn and their implications. Apparently, Finnish people have a (relatively) high risk of developing Unverricht-Lundborg disease (EPM1), Multiple Sclerosis (MS), Amyotrophic Lateral Sclerosis (ALS), Spinal muscular atrophy, Jokela type (SMAJ) and adult-onset dystonia. On the other hand, some disorders, such as Friedreich's ataxia (FRDA) and Wilson's disease (WD), are almost absent or completely absent in the population. Valid and timely data concerning even many common disorders, such as stroke, migraine, neuropathy, Alzheimer's disease and Parkinson's disease, are unavailable, and there are virtually no data on many less-common neurological disorders, such as neurosarcoidosis or autoimmune encephalitides. There also appear to be marked regional differences in the incidence and prevalence of many diseases, suggesting that non-granular nationwide data may be misleading in many cases. Concentrated efforts to advance neuroepidemiological research in the country would be of clinical, administrative and scientific benefit, but currently, all progress is blocked by administrative and financial obstacles.

A two-sample Mendelian randomization analysis of modifiable risk factors and intracranial aneurysms

We aimed to investigate the causality between potentially modifiable risk factors and the risk of intracranial aneurysm. Genetic instruments for 51 modifiable factors and intracranial aneurysm data were obtained from recently published genome-wide association studies. We applied two-sample Mendelian randomization methods to investigate their causal relationships. Genetically predicted cigarettes per day, smoking initiation, systolic blood pressure, hypertension and body fat percentage were significantly associated with an increased risk of intracranial aneurysm [odds ratios (OR) 2.67, 95% confidence interval (CI) 1.75–4.07, p = 5.36 × 10^–6, OR 1.53, 95% CI 1.32–1.77, p = 9.58 × 10^–9, OR 1.05, 95% CI 1.02–1.08, p = 1.18 × 10^–3, OR 1.65, 95% CI 1.19–2.28, p = 2.56 × 10^–3 and OR 1.29, 95% CI 1.11–1.52, p = 1.33 × 10^–3, respectively]. Type 2 diabetes mellitus was significantly associated with a decreased risk of intracranial aneurysm (OR 0.89, 95% CI 0.83–0.95, p = 8.54 × 10^–4). Body fat percentage was significantly associated with subarachnoid haemorrhage (p = 5.70 × 10^–5). This study provided genetic evidence of causal effects of smoking, blood pressure, type 2 diabetes mellitus and obesity on the risk of intracranial aneurysm.

Modifiable Risk Factors for Intracranial Aneurysm and Aneurysmal Subarachnoid Hemorrhage: A Mendelian Randomization Study

Background

The aim of this study was to assess the associations of modifiable lifestyle factors (smoking, coffee consumption, sleep, and physical activity) and cardiometabolic factors (body mass index, glycemic traits, type 2 diabetes, systolic and diastolic blood pressure, lipids, and inflammation and kidney function markers) with risks of any (ruptured or unruptured) intracranial aneurysm and aneurysmal subarachnoid hemorrhage using Mendelian randomization.

Methods and Results

Summary statistical data for the genetic associations with the modifiable risk factors and the outcomes were obtained from meta‐analyses of genome‐wide association studies. The inverse‐variance weighted method was used as the main Mendelian randomization analysis, with additional sensitivity analyses conducted using methods more robust to horizontal pleiotropy. Genetic predisposition to smoking, insomnia, and higher blood pressure was associated with an increased risk of both intracranial aneurysm and aneurysmal subarachnoid hemorrhage. For intracranial aneurysm, the odds ratios were 3.20 (95% CI, 1.93–5.29) per SD increase in smoking index, 1.24 (95% CI, 1.10–1.40) per unit increase in log‐odds of insomnia, and 2.92 (95% CI, 2.49–3.43) per 10 mm Hg increase in diastolic blood pressure. In addition, there was weak evidence for associations of genetically predicted decreased physical activity, higher triglyceride levels, higher body mass index, and lower low‐density lipoprotein cholesterol levels with higher risk of intracranial aneurysm and aneurysmal subarachnoid hemorrhage, with 95% CI overlapping the null for at least 1 of the outcomes. All results were consistent in sensitivity analyses.

Conclusions

This Mendelian randomization study suggests that smoking, insomnia, and high blood pressure are major risk factors for intracranial aneurysm and aneurysmal subarachnoid hemorrhage.

Hydrocephalus Following Experimental Subarachnoid Hemorrhage in Rats with Different Aerobic Capacity

Low aerobic capacity is considered to be a risk factor for stroke, while the mechanisms underlying the phenomenon are still unclear. The current study looked into the impacts of different aerobic capacities on early brain injury in a subarachnoid hemorrhage (SAH) model using rats bred for high and low aerobic capacity (high-capacity runners, HCR; low-capacity runners, LCR). SAH was modeled with endovascular perforation in HCR and LCR rats. Twenty-four hours after SAH, the rats underwent behavioral testing and MRI, and were then euthanized. The brains were used to investigate ventricular wall damage, blood-brain barrier breakdown, oxidative stress, and hemoglobin scavenging. The LCR rats had worse SAH grades (p < 0.01), ventricular dilatation (p < 0.01), ventricular wall damage (p < 0.01), and behavioral scores (p < 0.01). The periventricular expression of HO-1 and CD163 was significantly increased in LCR rats (p < 0.01 each). CD163-positive cells were co-localized with HO-1-positive cells. The LCR rats had greater early brain injuries than HCR rats. The LCR rats had more serious SAH and extensive ventricular wall damage that evolved more frequently into hydrocephalus. This may reflect changes in iron handling and neuroinflammation.

Rupture Risk Assessment for Cerebral Aneurysm Using Interpretable Machine Learning on Multidimensional Data

Background: Assessment of cerebral aneurysm rupture risk is an important task, but it remains challenging. Recent works applying machine learning to rupture risk evaluation presented positive results. Yet they were based on limited aspects of data, and lack of interpretability may limit their use in clinical setting. We aimed to develop interpretable machine learning models on multidimensional data for aneurysm rupture risk assessment. Methods: Three hundred seventy-four aneurysms were included in the study. Demographic, medical history, lifestyle behaviors, lipid profile, and morphologies were collected for each patient. Prediction models were derived using machine learning methods (support vector machine, artificial neural network, and XGBoost) and conventional logistic regression. The derived models were compared with the PHASES score method. The Shapley Additive Explanations (SHAP) analysis was applied to improve the interpretability of the best machine learning model and reveal the reasoning behind the predictions made by the model. Results: The best machine learning model (XGBoost) achieved an area under the receiver operating characteristic curve of 0.882 [95% confidence interval (CI) = 0.838-0.927], significantly better than the logistic regression model (0.779; 95% CI = 0.729-0.829; P = 0.002) and the PHASES score method (0.758; 95% CI = 0.713-0.800; P = 0.001). Location, size ratio, and triglyceride level were the three most important features in predicting rupture. Two typical cases were analyzed to demonstrate the interpretability of the model. Conclusions: This study demonstrated the potential of using machine learning for aneurysm rupture risk assessment. Machine learning models performed better than conventional statistical model and the PHASES score method. The SHAP analysis can improve the interpretability of machine learning models and facilitate their use in a clinical setting.

Smoking Causes Fatal Subarachnoid Hemorrhage

Background and Purpose:

One of the largest twin studies to date suggested that subarachnoid hemorrhage (SAH) is mainly of nongenetic origin, but the causal effect of environmental factors on SAH is yet unknown. We hypothesized that if only one of the twins experience fatal SAH, they do not share the most important environmental risk factor for SAH, namely smoking. If true, such finding would suggest that smoking causes SAH.

Methods:

Through the nationwide cause-of-death register, we followed 16 282 same-sex twin pairs of Finnish origin from the older Finnish Twin Cohort between 1976 and 2018 and identified all participants who died from SAH. For the baseline, we collected risk factor information about smoking, hypertension, physical activity, body mass index, alcohol consumption, and education. We classified the pairs as monozygotic, dizygotic, or of unknown zygosity. We examined the within-pair risk factor differences in the pairs discordant for SAH, that is, where one twin died from SAH and the other did not. We computed both individual (whole cohort) and pairwise (discordant pair) hazard ratios and 95% CIs.

Results:

During the 869 469 person-years of follow-up, we identified 116 discordant and 2 concordant (both died from SAH) twin pairs for fatal SAH. Overall, 25 of the discordant twin pairs were monozygotic. For the whole cohort, smoking (occasional/current) was associated with increased risk of SAH death (hazard ratio, 3.33 [CI, 2.24–4.95]) as compared with nonsmokers (never/former). In the pairwise analyses for discordant twin pairs, we found that the twin who smoked had an increased risk of fatal SAH (hazard ratio, 6.33 [CI, 1.87–21.4]) as compared with the nonsmoking twin. The association remained consistent regardless of the twin pairs’ zygosity or sex.

Conclusions:

Our results provide strong evidence for a causal, rather than associative, role of smoking in SAH.

Influence of Triggering Events on the Occurrence of Spontaneous Intracranial Hemorrhage : Comparison of Non-Lesional Spontaneous Intraparenchymal Hemorrhage and Aneurysmal Subarachnoid Hemorrhage

Objective

Spontaneous intracranial hemorrhage is a life-threatening disease, and non-lesional spontaneous intraparenchymal hemorrhage (nIPH) and aneurysmal subarachnoid hemorrhage (aSAH) are the leading causes of spontaneous intracranial hemorrhage. Only a few studies have assessed the association between prior physical activity or triggering events and the occurrence of nIPH or aSAH. The purpose of this study is to investigate the role of specific physical activities and triggering events in the occurrence of nIPH and aSAH.

Methods

We retrospectively reviewed 824 consecutive patients with spontaneous intracranial hemorrhage between January 2010 and December 2018. Among the 824 patients, 132 patients were excluded due to insufficient clinical data and other etiologies of spontaneous intracranial hemorrhage. The medical records of 692 patients were reviewed, and the following parameters were assessed : age, sex, history of hypertension, smoking, history of stroke, use of antiplatelet or anticoagulation agents, season and time of onset, physical activities performed according to the metabolic equivalents, and triggering event at onset. Events that suddenly raised the blood pressure such as sudden postural changes, defecation or urination, sexual intercourse, unexpected emotional stress, sauna bath, and medical examination were defined as triggering events. These clinical data were compared between the nIPH and aSAH groups.

Results

Both nIPH and aSAH most commonly occurred during non-strenuous physical activity, and there was no significant difference between the two groups (p=0.524). Thirty-two patients (6.6%) in the nIPH group and 39 patients (8.1%) in the aSAH group experienced triggering events at onset, and there was a significant difference between the two groups (p=0.034). The most common triggering events were defecation or urination in both groups.

Conclusion

Specific physical activity dose no affect the incidence of nIPH and aSAH. The relationship between the occurrence of intracranial hemorrhage and triggering events is higher in aSAH than nIPH.

Effects of clazosentan, cilostazol, and statins on aneurysmal subarachnoid hemorrhage: A protocol for systematic review and meta-analysis

BACKGROUND

Aneurysmal subarachnoid hemorrhage (aSAH) is a disease caused by the infiltration of blood into the subarachnoid space due to the rupture of an intracranial aneurysm. It is a serious cerebrovascular disease, with a mortality rate of about 40% worldwide, which seriously threatens human life and health. Many drugs are used to treat aSAH and its complications, and some have been tested in systematic reviews and have shown good effects. But which drug has the best effect remains unclear. This network meta-analysis (NMA) aims to assess the effectiveness and feasibility of clazosentan, cilostazol, and statins in patients with aSAH.

METHODS

We will search for EMBASE.com, PubMed, the Cochrane Library, and Web of Science from inception to December 2019. Randomized controlled trials (RCTs) reporting efficacy and safety of clazosentan, cilostazol, and statins compared with the control, or compared with each other for the treatment of aSAH will be included. Two independent reviewers will assess the risk of bias of the included RCTs with the Cochrane "Risk of bias" tool. The pairwise meta-analysis will be performed with the random-effects model. The NMA will be performed in a Bayesian hierarchical framework using Markov Chain Monte Carlo method in WinBUGS 1.4.3. Egger test and funnel plot will be used to assess the publication bias. We will evaluate the quality of evidence for each outcome according to the GRADE approach.

RESULTS

The results of this NMA will be submitted to a peer-reviewed journal for publication.

CONCLUSION

This study will summarize up-to-date evidence to compare the efficacy and safety of clazosentan, cilostazol, and statins on aSAH.PROSPERO registration number: CRD42019147523.