Thyroid Function Affects the Risk of Post-stroke Depression in Patients With Acute Lacunar Stroke

Exploring the causal factor effects of hypothyroidism on ischemic stroke: a two-sample Mendelian randomization study

Background

Observational studies have suggested a possible association between hypothyroidism and increased risk of ischemic stroke. However, a causal relationship remains unclear.

Methods

Data on single nucleotide polymorphisms (SNPs) associated with hypothyroidism and ischemic stroke were sourced from the FinnGens database and the UK Biobank of European descent. Both databases underwent separate two-sample Mendelian randomization (MR) analyses. A subsequent meta-analysis of MR results using a random-effects model was conducted to determine the causal relationship between hypothyroidism and ischemic stroke.

Results

All five analyses indicated a positive causal relationship between hypothyroidism and ischemic stroke. MR analysis of the association between hypothyroidism and ischemic stroke yielded a result of the inverse variance weighted (IVW) method at 4.7411 (1.3598-16.5308), p = 0.0146. The analysis of ischemic stroke (without excluding controls) yielded a result of the IVW method of 4.5713 (1.3570-15.3986), p = 0.0142. MR analysis with cerebral infarction yielded a result of the IVW method at 1.0110 (1.0006-1.0215), p = 0.0373. The MR analysis with cerebrovascular disease sequelae yielded an IVW method result of 2.4556 (1.0291-5.8595), p = 0.0429. Analysis for the sequelae of cerebrovascular disease (without excluding controls) yielded an IVW method result of 2.4217 (1.0217-5.7402), p = 0.0446. No evidence of heterogeneity or horizontal pleiotropy was found. The meta-analysis of the five MR results was 2.24 (1.18-4.26), p = 0.025.

Conclusion

Our two-sample Mendelian randomization study suggested a causal relationship between hypothyroidism and ischemic stroke, indicating that hypothyroidism could be a risk factor for ischemic stroke. However, further studies are required to elucidate the underlying biological mechanisms.

Association between thyroid hormone levels in the acute stage of stroke and risk of poststroke depression: A meta-analysis

BACKGROUND

Thyroid hormones have been indicated to be associated with depression, but their relationship with poststroke depression (PSD) remains controversial. Therefore, we performed a meta-analysis to explore the correlation between thyroid hormone levels in acute stroke and PSD.

METHODS

We searched databases for eligible studies. Standard mean differences (SMD) and 95% confidence intervals (CI) were applied to evaluate the association among levels of thyroid hormones, including thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4), in acute stroke patients and the risk of PSD.

RESULTS

A total of 13 studies were included in the analysis. Compared to non-PSD patients, PSD patients had remarkably lower serum TSH and FT3 levels (TSH: SMD = -0.59, 95%CI = -1.04 to -.15, p = .009; FT3: SMD = -0.40, 95%CI = -.51 to -.30, p = .000) and higher serum FT4 levels (SMD = 0.33, 95%CI = .07-.59, p = .013). Subgroup analysis showed that there may be a more statistically significant association between FT3 and the risk of PSD compared to TSH and FT4.

CONCLUSIONS

Our results suggested that patients with lower serum TSH and FT3 levels as well as higher serum FT4 levels in the acute stage of stroke may be more susceptible to PSD.

Liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study

BACKGROUND

Post-stroke depression (PSD) was one of the most prevalent and serious neuropsychiatric effects after stroke. Nevertheless, the association between liver function test indices and PSD remains elusive, and there is a lack of effective prediction tools. The purpose of this study was to explore the relationship between the liver function test indices and PSD, and construct a prediction model for PSD.

METHODS

All patients were selected from seven medical institutions of Chongqing Medical University from 2015 to 2021. Variables including demographic characteristics and liver function test indices were collected from the hospital electronic medical record system. Univariate analysis, least absolute shrinkage and selection operator (LASSO) and logistic regression analysis were used to screen the predictors. Subsequently, logistic regression, random forest (RF), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), categorical boosting (CatBoost) and support vector machine (SVM) were adopted to build the prediction model. Furthermore, a series of evaluation indicators such as area under curve (AUC), sensitivity, specificity, F1 were used to assess the performance of the prediction model.

RESULTS

A total of 464 PSD and 1621 stroke patients met the inclusion criteria. Six liver function test items, namely AST, ALT, TBA, TBil, TP, ALB/GLB, were closely associated with PSD, and included for the construction of the prediction model. In the test set, logistic regression model owns the AUC of 0.697. Compared with the other four machine learning models, the GBDT model has the best predictive performance (F1 = 0.498, AUC = 0.761) and was chosen to establish the prediction tool.

CONCLUSIONS

The prediction model constructed using these six predictors with GBDT algorithm displayed a promising prediction ability, which could be used for the participating hospital units or individuals by mobile phone or computer.

An updated review on prediction and preventive treatment of post-stroke depression

INTRODUCTION

Post-stroke depression (PSD), one of the most common complications following stroke, affects approximately one-third of stroke patients and is significantly associated with increased disability and mortality as well as decreased quality of life, which makes it an important public health concern. Treatment of PSD significantly ameliorates depressive symptoms and improves the prognosis of stroke.

AREAS COVERED

The authors discuss the critical aspects of the clinical application of prediction and preventive treatment of PSD. Then, the authors update the biological factors associated with the onset of PSD. Furthermore, they summarize the recent progress in pharmacological preventive treatment in clinical trials and propose potential treatment targets. The authors also discuss the current roadblocks in the preventive treatment of PSD. Finally, the authors put postulate potential directions for future studies so as to discover accurate predictors and provide individualized preventive treatment.

EXPERT OPINION

Sorting out high-risk PSD patients using reliable predictors will greatly assist PSD management. Indeed, some predictors not only predict the incidence of PSD but also predict prognosis, which indicates that they might also aid the development of an individualized treatment scheme. Preventive application of antidepressants may also be considered.

Leukoaraiosis as a Predictor of Depression and Cognitive Impairment among Stroke Survivors: A Systematic Review

Stroke survivors are at increased risk of developing depression and cognitive decline. Thus, it is crucial for both clinicians and stroke survivors to be provided with timely and accurate prognostication of post-stroke depression (PSD) and post-stroke dementia (PSDem). Several biomarkers regarding stroke patients' propensity to develop PSD and PSDem have been implemented so far, leukoaraiosis (LA) being among them. The purpose of the present study was to review all available work published within the last decade dealing with pre-existing LA as a predictor of depression (PSD) and cognitive dysfunction (cognitive impairment or PSDem) in stroke patients. A literature search of two databases (MEDLINE and Scopus) was conducted to identify all relevant studies published between 1 January 2012 and 25 June 2022 that dealt with the clinical utility of preexisting LA as a prognostic indicator of PSD and PSDem/cognitive impairment. Only full-text articles published in the English language were included. Thirty-four articles were traced and are included in the present review. LA burden, serving as a surrogate marker of "brain frailty" among stroke patients, appears to be able to offer significant information about the possibility of developing PSD or cognitive dysfunction. Determining the extent of pre-existing white matter abnormalities can properly guide decision making in acute stroke settings, as a greater degree of such lesioning is usually coupled with neuropsychiatric aftermaths, such as PSD and PSDem.

Cardiovascular and Neuronal Consequences of Thyroid Hormones Alterations in the Ischemic Stroke

Ischemic stroke is one of the leading global causes of neurological morbidity and decease. Its etiology depends on multiple events such as cardiac embolism, brain capillaries occlusion and atherosclerosis, which ultimately culminate in blood flow interruption, incurring hypoxia and nutrient deprivation. Thyroid hormones (THs) are pleiotropic modulators of several metabolic pathways, and critically influence different aspects of tissues development. The brain is a key TH target tissue and both hypo- and hyperthyroidism, during embryonic and adult life, are associated with deranged neuronal formation and cognitive functions. Accordingly, increasing pieces of evidence are drawing attention on the consistent relationship between the THs status and the acute cerebral and cardiac diseases. However, the concrete contribution of THs systemic or local alteration to the pathology outcome still needs to be fully addressed. In this review, we aim to summarize the multiple influences that THs exert on the brain and heart patho-physiology, to deepen the reasons for the harmful effects of hypo- and hyperthyroidism on these organs and to provide insights on the intricate relationship between the THs variations and the pathological alterations that take place after the ischemic injury.

Predictors of Post-Stroke Depression: A Retrospective Cohort Study

Despite reports of a high incidence and various predictors of post-stroke depression (PSD), the underdiagnosis and undertreatment rates of PSD are still high. This study aimed to examine the incidence of depression in stroke patients and identify factors associated with PSD. This was a retrospective cohort study on ischemic stroke patients from the Geisinger Neuroscience Ischemic Stroke (GNSIS) registry. The following statistical analyses were performed to predict PSD in the studied population: a Kaplan−Meier estimator and a Cox proportional hazards model. A total of 5882 patients were included in the study. The median age at the time of an ischemic stroke was 72 years and 56% were males. A total of 294 patients were diagnosed with PSD within one year of a stroke. The cumulative incidence of depression was found to be 6.4% (95% CI 5.7−7.1%) at one year for the entire cohort. Women were found to have a higher risk of PSD than men (HR for women = 1.47, 95% CI 1.18−1.85, p = 0.001). A history of prior stroke (HR = 1.58, 95% CI 1.18−2.11, p = 0.002) and myocardial infarction (HR = 1.47, 95% CI 1.05−2.06, p = 0.025) were associated with PSD. Medicaid patients had a higher risk for PSD (HR = 2.16, 95% CI 1.5−3.12, p < 0.001) than those with commercial insurance or health maintenance organization plans. Our findings showed that women, patients with a history of prior stroke or myocardial infarction, and with Medicaid insurance were more likely to develop PSD. Through an observational study on the EHR data, we confirmed that chronic stress, including financial and health-related stress, irrespective of age, significantly increased the risk for PSD.