Risk Factors for Stroke Based on the National Health and Nutrition Examination Survey

Association between advanced lung cancer inflammation index and all-cause and cardiovascular mortality among stroke patients: NHANES, 1999-2018

Background

Stroke was a major global public health challenge, and its prognosis was remarkably associated with inflammation levels and nutritional status. The advanced lung cancer inflammation index (ALI) was a comprehensive indicator that combined inflammation and nutritional status. Currently, the relationship between ALI and the prognosis of stroke patients was not yet known. The purpose of the current study was to estimate their relationship.

Methods

Cohort data from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were collected. The association between ALI and all-cause and cardiovascular disease (CVD) mortality in stroke patients was estimated using a multivariable adjusted Cox model. Their non-linear relationship was analyzed by restricted cubic spline analysis. Sensitivity analysis was constructed through stratified analysis and interaction analysis.

Results

1,440 stroke patients were included in this study. An elevated ALI was significantly related to a reduced risk of all-cause mortality in stroke patients but not related to CVD mortality. A reverse J-shaped non-linear association between ALI and all-cause mortality in stroke patients, with an inflection point at 83.76 (the lowest of the mortality risk). On the left side of the inflection point, for each 10 U increase in ALI, there was a 16% reduction in the risk of all-cause mortality. However, on the right side, the risk increased by 6%. There was no remarkable interaction between stratified variables and ALI.

Conclusion

This was the first study on the relationship between ALI and all-cause and CVD mortality in stroke patients. Elevated ALI was closely associated with a reduced risk of all-cause mortality. A reverse J-shaped non-linear relationship existed between the two, with an inflection point at 83.76. These findings implied that controlling the ALI of stroke patients within an appropriate range was crucial for their prognosis (such as weight management, albumin supplementation, anti-inflammatory treatment). The dynamic variation in ALI was also advantageous for clinicians in establishing personalized ALI criteria to maximize the long-term survival of stroke patients.

Construction of a depression risk prediction model for type 2 diabetes mellitus patients based on NHANES 2007-2014

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a prevalent global health issue that has been linked to an increased risk of depression. The objective of this study was to construct a nomogram model for predicting depression in T2DM patients.

METHODS

A total of 4280 patients with T2DM were included in this study from the 2007-2014 NHANES. The entire dataset was split randomly into training set comprising 70 % of the data and a validation set comprising 30 % of the data. LASSO and multivariate logistic regression analyses identified predictors significantly associated with depression, and the nomogram was constructed with these predictors. The model was assessed by C-index, calibration curve, the hosmer-lemeshow test and decision curve analysis (DCA).

RESULTS

The nomogram model comprised of 9 predictors, namely age, gender, PIR, BMI, education attainment, smoking status, LDL-C, sleep duration and sleep disorder. The C-index of the training set was 0.780, while that of the validation set was 0.752, indicating favorable discrimination for the model. The model exhibited excellent clinical applicability and calibration in both the training and validation datasets. Moreover, the cut-off value of the nomogram is 223.

LIMITATIONS

This study has shortcomings in data collection, lack of external validation, and results non-extrapolation.

CONCLUSIONS

Our nomogram exhibits high clinical predictability, enabling clinicians to utilize this tool in identifying high-risk depressed patients with T2DM. It has the potential to decrease the incidence of depression and significantly improve the prognosis of patients with T2DM.

The effect of the interaction of sleep onset latency and age on ischemic stroke severity via inflammatory chemokines

Objective

Prolonged sleep onset latency (PSOL) and age have been linked to ischemic stroke (IS) severity and the production of chemokines and inflammation, both of which contribute to IS development. This study aimed to explore the relationship between chemokines, inflammation, and the interplay between sleep onset latency (SOL) and age in influencing stroke severity.

Methods

A cohort of 281 participants with mild to moderate IS was enrolled. Stroke severity was assessed using the National Institutes of Health Stroke Scale (NIHSS), and SOL was recorded. Serum levels of macrophage inflammatory protein-1alpha (MIP-1α), macrophage inflammatory protein-1beta (MIP-1β), monocyte chemoattractant protein-1 (MCP-1), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) were measured.

Results

NIHSS scores of middle-aged participants with PSOL were significantly higher than those with normal sleep onset latency (NSOL) (p = 0.046). This difference was also observed when compared to both the elderly with NSOL (p = 0.022), and PSOL (p < 0.001). Among middle-aged adults with PSOL, MIP-1β exhibited a protective effect on NIHSS scores (β = -0.01, t = -2.11, p = 0.039, R2 = 0.13). MIP-1α demonstrated a protective effect on NIHSS scores in the elderly with NSOL (β = -0.03, t = -2.27, p = 0.027, R2 = 0.12).

Conclusion

This study reveals a hitherto undocumented association between PSOL and IS severity, along with the potential protective effects of MIP-1β in mitigating stroke severity, especially among middle-aged patients.

Associations between socioeconomic status and stroke in American adults: A population-based study

Stroke is an acute cerebrovascular disease that can lead to disability and death. This study aimed to investigate the relationship between socioeconomic status (SES) and stroke. SES was evaluated by two variables: poverty to income ratio (PIR) and education level. In this multi-subject study, we collected data from the National Health and Nutrition Examination Survey (NHANES) database between 2009 and 2018, and finally 22,792 adults (≥20 years old) were included in the study. We proceeded with weighted multivariate logistic regression analysis as well as subgroup analysis. When analyzing the effect of PIR on stroke alone, the results showed that an increase in PIR levels was associated with a decrease in stroke incidence (OR = 0.764 95% CI: (0.711, 0.820), p < 0.001). The multivariate analysis presented a decline in stroke incidence in the highest quartile PIR group compared to the lowest quartile PIR group (OR = 0.296 95% CI: (0.214, 0.409), P＜0.001). Our results indicated that PIR is a protective factor for stroke, but there are exceptions in this relationship among different people. Hence, it is imperative that policymakers, healthcare providers, and clinicians take into account the inequality distribution of SES among adults while developing and executing stroke prevention and treatment strategies.

Association between urinary caffeine and caffeine metabolites and stroke in American adults: a cross-sectional study from the NHANES, 2009-2014

This study investigates the potential correlation between urinary caffeine levels and the occurrence of stroke, a serious cerebrovascular disease that can lead to disability or death. The data used in this study was obtained from the National Health and Nutrition Examination Survey conducted between 2009 and 2014. The study analyzed a total of 5,339 individuals, divided into a control group (n = 5,135) and a stroke group (n = 162). The researchers utilized multiple logistic regression and smoothed curve fitting to examine the relationship between urinary caffeine and caffeine metabolites and the incidence of stroke. The study found that higher urinary caffeine levels were associated with a lower risk of stroke in Mexican American participants (odds ratio [OR] = 0.886, 95% confidence interval [CI]: (0.791, 0.993), P = 0.037). After adjusting for certain participant characteristics, it was also found that higher urinary paraxanthine levels were associated with a lower risk of stroke incidence (OR = 0.991, 95% CI (0.984, 0.999), P = 0.027). Meanwhile, the highest urinary paraxanthine levels group had 43.7% fewer strokes than the lowest level group (OR = 0.563, 95% CI (0.341, 0.929), P = 0.025). In this study, we showed a negative link between urine paraxanthine levels and the risk of stroke. Meanwhile, urinary caffeine levels were negatively associated with the incidence of stroke in Mexican Americans, but no correlation in other populations. Our findings may have predictive and diagnostic implications in clinical practice. Further extensive prospective investigations are still needed to validate our conclusions.

Machine learning algorithms identify demographics, dietary features, and blood biomarkers associated with stroke records

OBJECTIVE

We conducted a comprehensive evaluation of features associated with stroke records.

METHODS

We screened the dietary nutrients, blood biomarkers, and clinical information from the National Health and Nutrition Examination Survey (NHANES) 2015-16 database to assess a self-reported history of all strokes (136 strokes, n = 4381). We computed feature importance, built machine learning (ML) models, developed a nomogram, and validated the nomogram on NHANES 2007-08, 2017-18, and the baseline UK Biobank. We calculated the odds ratios with/without adjusting sampling weights (OR/OR_w).

RESULTS

The clinical features have the best predictive power compared to dietary nutrients and blood biomarkers, with 22.8% increased average area under the receiver operating characteristic curves (AUROC) in ML models. We further modeled with ten most important clinical features without compromising the predictive performance. The key features positively associated with stroke include age, cigarette smoking, tobacco smoking, Caucasian or African American race, hypertension, diabetes mellitus, asthma history; the negatively associated feature is the family income. The nomogram based on these key features achieved good performances (AUROC between 0.753 and 0.822) on the test set, the NHANES 2007-08, 2017-18, and the UK Biobank. Key features from the nomogram model include age (OR = 1.05, OR_w = 1.06), Caucasian/African American (OR = 2.68, OR_w = 2.67), diabetes mellitus (OR = 2.30, OR_w = 1.99), asthma (OR = 2.10, OR_w = 2.41), hypertension (OR = 1.86, OR_w = 2.10), and income (OR = 0.83, OR_w = 0.81).

CONCLUSIONS

We identified clinical key features and built predictive models for assessing stroke records with high performance. A nomogram consisting of questionnaire-based variables would help identify stroke survivors and evaluate the potential risk of stroke.

Relationship between circadian syndrome and stroke: A cross-sectional study of the national health and nutrition examination survey

Aim

The aim of this study was to assess the relationship of circadian syndrome and stroke.

Methods

We performed a cross-sectional analysis of 11,855 participants from the National Health and Nutrition Examination Survey (NHANES) database between 2005 and 2018, and collected the baseline characteristics. Multivariate logistic regression models were developed to explore the association between circadian syndrome and stroke. Simultaneously, subgroup analyses based on the difference of gender, race, and components associated with circadian syndrome also were performed. The odds ratio (OR) and 95% CI were calculated in this study.

Results

All the participants were divided into the non-stroke group and the stroke group. There were approximately 3.48% patients exclusively with stroke and 19.03% patients exclusively with circadian syndrome in our study. The results suggested that the risk of stroke in patients with circadian syndrome was higher than that in patients without circadian syndrome (OR = 1.322, 95 CI%: 1.020–1.713). Similar associations were found in women with circadian syndrome (OR = 1.515, 95 CI%: 1.086–2.114), non-Hispanic whites with circadian syndrome (OR = 1.544, 95 CI%: 1.124–2.122), participants with circadian syndrome who had elevated waist circumference (OR = 1.395, 95 CI%: 1.070–1.819) or short sleep (OR = 1.763, 95 CI%: 1.033–3.009).

Conclusion

Circadian syndrome was associated with the risk of stroke. Particularly, we should pay more close attention to the risk of stroke in those populations who were female, non-Hispanic whites, had the symptoms of elevated waist circumference or short sleep.

Molecular mechanisms underlying some major common risk factors of stroke

Ischemic and hemorrhagic strokes are the most common known cerebrovascular disease which can be induced by modifiable and non-modifiable risk factors. Age and race are the most common non-modifiable risk factors of stroke. However, hypertension, diabetes, obesity, dyslipidemia, physical inactivity, and cardiovascular disorders are major modifiable risk factors. Understanding the molecular mechanism mediating each of these risk factors is expected to contribute significantly to reducing the risk of stroke, preventing neural damage, enhancing rehabilitation, and designing suitable treatments. Abnormalities in the structure of the blood-brain barrier and blood vessels, thrombosis, vasoconstriction, atherosclerosis, reduced cerebral blood flow, neural oxidative stress, inflammation, and apoptosis, impaired synaptic transmission, excitotoxicity, altered expression/activities of many channels and signaling proteins are the most knows mechanisms responsible for stroke induction. However, the molecular role of risk factors in each of these mechanisms is not well understood and requires a lot of search and reading. This review was designed to provide the reader with a single source of information that discusses the current update of the prevalence, pathophysiology, and all possible molecular mechanisms underlying some major risk factors of stroke namely, hypertension, diabetes mellitus, dyslipidemia, and lipid fraction, and physical inactivity. This provides a full resource for understanding the molecular effect of each of these risk factors in stroke.

Dietary Copper Intake and Risk of Stroke in Adults: A Case-Control Study Based on National Health and Nutrition Examination Survey 2013-2018

The association between dietary copper intake and the risk of stroke is unknown. We included a total of 10,550 participants from the National Health and Nutrition Examination Survey (NHANES) 2013−2018. Two 24-h dietary recalls and a standard questionnaire were used to determine copper intake and stroke, respectively. We used logistic regression models to estimate the associations between dietary copper intake and the risk of stroke. The nearest-neighbor propensity score matching (PSM) with a ratio of 1:2 was used to reduce selection bias. The non-linear relationship was explored with restricted cubic splines (RCS). The correlation between copper intake and baseline characteristics was detected by the Pearson correlation coefficient. The median dietary copper intake was 1.072 mg/day (IQR = 1.42−0.799). Approximately 3.8% (399) of the participants had a history of stroke. A multivariate logistic regression analysis before and after matching showed that subjects in the higher quartile had significantly lower odds of stroke compared with subjects in the first quartile of copper intake. A stratified analysis showed that copper intake was a significant protective factor for women, individuals <65 years old, individuals with hypertension, individuals who smoke, and diabetic stroke patients. The RCS models showed an L-shaped nonlinear relationship (p for nonlinear < 0.001) between copper intake and stroke. Our results suggested that increased dietary copper intake was associated with a lower risk of stroke.

Myeloperoxidase: a new target for the treatment of stroke?

Myeloperoxidase is an important inflammatory factor in the myeloid system, primarily expressed in neutrophils and microglia. Myeloperoxidase and its active products participate in the occurrence and development of hemorrhagic and ischemic stroke, including damage to the blood-brain barrier and brain. As a specific inflammatory marker, myeloperoxidase can be used in the evaluation of vascular disease occurrence and development in stroke, and a large amount of experimental and clinical data has indicated that the inhibition or lack of myeloperoxidase has positive impacts on stroke prognosis. Many studies have also shown that there is a correlation between the overexpression of myeloperoxidase and the risk of stroke. The occurrence of stroke not only refers to the first occurrence but also includes recurrence. Therefore, myeloperoxidase is significant for the clinical evaluation and prognosis of stroke. This paper reviews the potential role played by myeloperoxidase in the development of vascular injury and secondary brain injury after stroke and explores the effects of inhibiting myeloperoxidase on stroke prognosis. This paper also analyzes the significance of myeloperoxidase etiology in the occurrence and development of stroke and discusses whether myeloperoxidase can be used as a target for the treatment and prediction of stroke.

Effect of the Interaction between Depression and Sleep Disorders on the Stroke Occurrence: An Analysis Based on National Health and Nutritional Examination Survey

OBJECTIVE

To investigate the effect of the interaction between depression and sleep disorders on the stroke occurrence based on the data from the National Health and Nutritional Examination Survey (NHANES).

METHODS

Seven cycles of 2-year NHANES data (2005-2018) were analyzed in this study. Univariate analysis was first performed between the stroke and nonstroke patients, and then, multivariate logistic regression models were conducted to analyze the association of depression, sleep disorders, and their interactions with stroke occurrence.

RESULTS

A total of 30473 eligible participants were included in this study, including 1138 (3.73%) with stroke and 29335 (96.27%) with nonstroke. Except sex, the differences were all significant between the stroke and nonstroke patients in baseline information (all P < 0.001). Depression (odds ratio (OR): 2.494, 95% confidence interval (CI): 2.098-2.964), depression severity (moderate, OR: 2.013, 95% CI: 1.612-2.514; moderately severe, OR: 2.598, 95% CI: 1.930-3.496; severe, OR: 5.588, 95% CI: 3.883-8.043), and sleep disorders (OR: 1.677, 95% CI: 1.472-1.910) were presented to be associated with an increased risk of stroke after correcting all the confounders. The logistic regression analysis showed that there was a synergic, additive interaction between depression and sleep disorders on the stroke occurrence, and the proportion of stroke patients caused by this interaction accounted for 27.1% of all the stroke patients.

CONCLUSION

Depression, depression severity, and sleep disorders are all independently associated with a high risk of stroke. The interaction between depression and sleep disorders can synergistically increase the stroke occurrence.

Stroke Risk Factors in United States: An Analysis of the 2013-2018 National Health and Nutrition Examination Survey

Purpose

This research intended to identify significant risk factors of stroke among the elderly population in the United States using the k-means clustering method.

Patients and Methods

In this cross-sectional study, we analyzed data of 4346 subjects aged ≥60 years using the National Health and Nutrition Examination Survey (NHANES) 2013-2018 datasets. Questionnaire data, dietary data, and laboratory data were accessed to acquire measurements of the potential risk factors. A pre-defined classification method was used based on the Medical Condition Questionnaire to define the stroke group. K-means clustering analysis used all potential risk factors for differentiating both groups. A stepwise logistic regression analysis examined the association between significant risk factors and the odds of stroke.

Results

Age (OR:1.053, 95% CI:1.029-1.077), diabetes (OR: 28.019, 95% CI: 19.139-41.020), glycohemoglobin (OR: 2.309, 95% CI: 1.818-2.934), plasma fasting glucose (OR: 1.017, 95% CI: 1.010-1.024), hypertension (OR: 2.343, 95% CI: 1.602-3.426), dietary fiber consumption (OR:0.980, 95% CI:0.964-0.995), and education level (OR:0.541, 95% CI: 0.411-0.713) were identified as significant risk factor for stroke among the elderly population in the k-means clustering method. In the pre-defined grouping method, age (OR:1.093, 95% CI:1.054-1.132), diabetes (OR:2.228, 95% CI: 1.432-3.466), hypertension (OR:2.295, 95% CI:1.338-3.938), and dietary fiber consumption (OR: 0.966, 95CI%:0.947-0.985) were found to influence to the risk of stroke.

Conclusion

Age, hypertension, dietary fiber consumption, and education level are the significant risk factors of stroke among elders aged >60 years. Among all the risk factors, diabetes is the strongest predictor of stroke. Glycohemoglobin and plasma fasting glucose are also associated with stroke risks, implying that glycemic control is particularly crucial in stroke prevention and management among older adults.

CE: Acute Ischemic Stroke

ABSTRACT

Stroke, the most common form of cerebrovascular disease, is a leading cause of death and disability throughout the world. There have been no significant advances in the development of effective therapeutics for hemorrhagic stroke, and for ischemic stroke highly effective, evidence-based therapies such as alteplase and mechanical thrombectomy are widely underutilized. Improving outcomes for patients experiencing ischemic stroke requires faster recognition and appropriate intervention within the treatment window (the first 24 hours after symptom onset). This article discusses the pathophysiology underlying the various types of ischemic stroke; the risk factors for ischemic stroke; stroke presentation; and the evidence-based treatments, nursing assessments, and monitoring protocols that are critical to patient recovery.

Asthma and stroke: a narrative review

Asthma is a heterogeneous disease, usually characterized by chronic airway inflammation, bronchial reversible obstruction and hyperresponsiveness to direct or indirect stimuli. It is a severe disease causing approximately half a million deaths every year and thus possessing a significant public health burden. Stroke is the second leading cause of death and a major cause of disability worldwide. Asthma and asthma medications may be a risk factors for developing stroke. Nevertheless, since asthma is associated with a variety of comorbidities, such as cardiovascular, metabolic and respiratory, the increased incidence of stroke in asthma patients may be due to a confounding effect. The purpose of this review is to analyze the complex relationship between asthma and stroke.