Validation of CSR model to predict stroke risk after transient ischemic attack

It is essential to identify high risk transient ischemic attack (TIA) patients. The previous study reported that the CSR (comprehensive stroke recurrence) model, a neuroimaging model, had a high predictive ability of recurrent stroke. The aims of this study were to validate the predictive value of CSR model in TIA patients and compare the predictive ability with ABCD3-I score. Data were analyzed from the prospective hospital-based database of patients with TIA which defined by the World Health Organization time-based criteria. The predictive outcome was stroke occurrence at 90 days. The receiver-operating characteristic (ROC) curves were plotted and the C statistics were calculated as a measure of predictive ability. Among 1186 eligible patients, the mean age was 57.28 ± 12.17 years, and 474 (40.0%) patients had positive diffusion-weighted imaging (DWI). There were 118 (9.9%) patients who had stroke within 90 days. In 1186 TIA patients, The C statistic of CSR model (0.754; 95% confidence interval [CI] 0.729–0.778) was similar with that of ABCD3-I score (0.717; 95% CI 0.691–0.743; Z = 1.400; P = 0.1616). In 474 TIA patients with positive DWI, C statistic of CSR model (0.725; 95% CI 0.683–0.765) was statistically higher than that of ABCD3-I score (0.626; 95% CI 0.581–0.670; Z = 2.294; P = 0.0245). The CSR model had good predictive value for assessing stroke risk after TIA, and it had a higher predictive value than ABCD3-I score for assessing stroke risk for TIA patients with positive DWI.

Markers of Iron Metabolism and Stroke Risk: Cross-Sectional and Longitudinal Findings from the China Health and Nutrition Survey (CHNS)

Background: The association of iron metabolism or status with the stroke risk remains unclear. We aimed to examine the associations between markers of iron metabolism or status and stroke risk using data from the China Health and Nutrition Survey (CHNS). Methods: Overall, 8589 in the CHNS in 2009, and 7290 participants between 2009 and 2015 were included in the cross-sectional and longitudinal analyses, respectively. Markers included hemoglobin, ferritin (FET), transferrin (TRF), soluble transferrin receptor (sTRF-R), and ratio of sTRF-R/log FET (sTfR-F index). Multivariable logistic regression and Cox proportional hazards models were used to analyze the associations between those markers and risk of stroke. Age, gender, high-sensitivity CRP (hsCRP), body mass index (BMI), current smoking, drinking status, diabetes and hypertension were included as potential confounding factors. Results: We observed longitudinal associations of hemoglobin (HR: 1.54, 95% CI: 1.15 – 2.06, P = 0.004), and sTfR-F index (HR: 0.68, 95% CI: 0.46 – 0.99, P = 0.044) with stroke risk among the participants whose BMI ≤ 23 kg/m2. In addition, FET levels were significantly associated with stroke risk among female (HR: 1.45, 95% CI: 1.00 – 2.09, P = 0.049) after a median of 6.1 years follow-up. Hemoglobin, FET, TRF, sTRF-R, and sTfR-F index were not associated with the risk of stroke in overall analyses. Conclusion: FET among female, hemoglobin and sTfR-F index among those BMI ≤ 23 kg/m2 may be contributing factors for stroke.

Vascular risk factors and stroke risk across the life span: A population-representative study of half a million people

Background: The incidence of stroke in developed countries is increasing selectively in young individuals, but whether this is secondary to traditional vascular risk factors is unknown. Methods: We used the Canadian Community Health Survey from 2000 to 2016 to create a large population-representative cohort of individuals over the age of 30 and free from prior stroke. All analyses were stratified by age decile. We linked with administrative databases to determine emergency department visits or hospitalizations for acute stroke until December 2017. We calculated time trends in risk factor prevalence (hypertension, diabetes, obesity, and smoking) using meta-regression. We used Cox proportional hazard models to evaluate the association between vascular risk factors and stroke risk, adjusted for demographic, co-morbid, and social variables. We used competing risk regression to account for deaths and calculated population-attributable fractions. In a sensitivity analysis, we excluded those with prior heart disease or cancer. Results: We included 492,400 people in the analysis with 8865 stroke events over a median follow-up time of 8.3 years. Prevalence of hypertension, diabetes, and obesity increased over time while smoking decreased. Associations of diabetes, hypertension, and obesity with stroke risk were progressively stronger at younger age (adjusted hazard ratio for diabetes was 4.47, 95% confidence interval (CI) = 1.95–10.28 at age 30–39, vs 1.21, 95% CI = 0.93–1.57 at age 80+), although the obesity association was attenuated with adjustment. Smoking was associated with higher risk of stroke without a gradient across age deciles, although had the greatest population-attributable fraction at younger age. The hazard ratio for stroke with multiple concurrent risk factors was much higher at younger age (adjusted hazard ratio for 3–4 risk factors was 8.60, 95% CI = 2.97–24.9 at age 30–39 vs 1.61, 95% CI = 0.88–2.97 at age 80+) and results were consistent when accounting for the competing risk of death and excluding those with prior heart disease or cancer. Conclusions: Diabetes and hypertension were associated with progressively elevated relative risk of stroke in younger individuals and prevalence was increasing over time. The association of obesity with stroke was not significant after adjustment for other factors. Smoking had the greatest prevalence and population-attributable fraction for stroke at younger age. Our findings assist in understanding the relationship between vascular risk factors and stroke across the life span and planning public health measures to lower stroke incidence in the young.