Association of high waist-to-height ratio with functional outcomes in patients with acute ischemic stroke: A report from the ACROSS-China study

Investigation of the Association between Smoking Behavior and Metabolic Syndrome Using Lipid Accumulation Product Index among South Korean Adults

Electronic cigarette vaping has recently been chosen as a smoking alternative for those who want to quit smoking, but some of the electronic cigarette users use both traditional and electronic cigarettes (dual smoking) without stopping smoking. This study investigated the association between smoking behavior and metabolic syndrome among Korean adults. Data from 14,607 participants (6142 males and 8465 females) were examined. They were divided into four categories: dual smoking (both conventional and e-cigarettes), single smoking (only conventional cigarettes), previously smoking, and non-smoking. Metabolic syndrome risk was calculated as a continuous variable using the lipid accumulation product (LAP) index. Multiple linear regression analyses were performed to examine the association of log-transformed LAP with smoking behavior. Among the total participants, 187 males and 35 females were dual smokers and 1850 males and 372 females were single smokers. LAP was significantly higher in male who practiced dual and single smoking than LAP of non-smokers-dual: β = 0.27, standard error (SE) = 0.06, p < 0.0001; single: β = 0.18, SE = 0.03, p < 0.0001. In female, LAP was significantly higher among those who practiced only single smoking than LAP of non-smokers (β = 0.21, SE = 0.04, p < 0.0001). Dual and single smoking were significantly associated with higher LAP, a strong predictor of metabolic syndrome. Further studies and awareness regarding the adverse effects of dual smoking are required.

Development and validation of a 2-year new-onset stroke risk prediction model for people over age 45 in China

Multiple factors, including increasing incidence, poor knowledge of stroke and lack of effective, noninvasive and convenient stroke risk prediction tools, make it more difficult for precautions against stroke in China. Effective prediction models for stroke may assist to establish better risk awareness and management, healthier lifestyle, and lower stroke incidence for people.The China Health and Retirement Longitudinal Survey was the development cohort. Logistic regression was applied to model's development, in which the candidate variables with statistically significant coefficient were included in the prediction model. The area under receiver operating characteristic curve (AUC) and 10-times cross-validation were used for internal validation. Cutoff point of high-risk group was measured by Youden index. The China Health and Nutrition Survey was the validation cohort.The development cohort and the validation cohort included 16557 and 5065 participants, and the incidence density was 358.207/100,000 person-year and 350.701/100,000 person-year, respectively. The model for 2-year new-onset stroke risk prediction included age, hypertension, diabetes, heart disease, and smoking. The AUC and cross-validation AUC were 0.707 (95% confidence interval[CI]: 0.664, 0.750) and the 0.710 (95% CI: 0.650, 0.736). The sensitivity, specificity and accuracy of the cutoff point were 0.774, 0.545, and 0.319. The AUC and cross-validation AUC were 0.800 (95% CI: 0.744, 0.856) and 0.811(95% CI:0.714, 0.847), and the sensitivity, specificity and accuracy of cutoff point being 0.857,0.569, and 0.426 in external validation.A simple prediction tool using 5 noninvasive and easily accessible factors can assist in 2-year new-onset stroke risk prediction in Chinese people over 45 years old, which is believed to be applicable in identifying high-risk individuals and health management in China.

The associations between smoking and obesity in northeast China: a quantile regression analysis

Obesity is a risk factors of chronic diseases, and smoking is associated with both chronic diseases and obesity. There were some controversies about the associations between smoking and obesity. Thus, our study aimed to explore the associations of smoking with obesity, using body mass index (BMI) and waist circumference (WC) as obesity indices in northeast China. We enrolled a sample of 16,412 participants in Jilin province aged 18-79 in this study, which were derived from a cross-sectional survey in 2012. We used quantile regression (QR) models to identify the associations of smoking with obesity in different quantiles of BMI (or WC) by genders. The differences of BMI and WC by genders were statistically significant (p < 0.05). In conclusion, compared with current non-smokers, current smokers had lower BMI but higher WC. As increasing of WC, the association of WC with smoking was getting stronger, especially in females.