Pan, Z. | bioRxiv Science

bioRxiv2018

Interaction of diabetes and smoking on stroke: A population-based cross-sectional survey in China

ObjectivesDiabetes and smoking are known independent risk factors for stroke; however, their interaction concerning stroke is less clear. We aimed to explore such interaction and its influence on stroke in Chinese adults.\n\nDesignCross-sectional study.\n\nSettingCommunity-based investigation in Xuzhou, China.\n\nParticipantsA total of 39,887 Chinese adults who fulfilled the inclusion criteria were included.\n\nMethodsParticipants were selected using a multi-stage stratified cluster method, and completed self-reported questionnaires on stroke and smoking. Type 2 diabetes mellitus (DM2) was assessed by fasting blood glucose or use of antidiabetic medication. Interaction, relative excess risk owing to interaction (RERI), attributable proportion (AP), and synergy index (S) were evaluated using a logistic regression model.\n\nResultsAfter adjustment for age, sex, marital status, educational level, occupation, physical activity, body mass index, hypertension, family history of stroke, alcohol use, and blood lipids, the relationships between DM2 and stroke, and between smoking and stroke, were still significant: odds ratios were 2.75 (95% confidence interval [CI]: 2.03-3.73) and 1.70 (95% CI: 1.38-2.10), respectively. In subjects with DM2 who smoked, the RERI, AP, and S values (and 95% CIs) were 1.80 (1.24-3.83), 0.52 (0.37-0.73), and 1.50 (1.18-1.84), respectively.\n\nConclusionsThe results suggest there are additive interactions between DM2 and smoking and that these affect stroke in Chinese adults.\n\nArticle Summary: Strengths and limitations of this studyO_LIThe strengths of this study were that a large sample population was randomly selected from the general population of Xuzhou and many confounding risk factors were adjusted for.\nC_LIO_LIOwing to the cross-sectional design, we could not determine a causal combined relationship among diabetes, smoking and stroke.\nC_LIO_LIWe were not able to control for some important and well-known risk factors of diabetes, such as heart rate and cardiovascular causes.\nC_LIO_LIWe did not measure fresh fruit consumption, which is causally related to stroke.\nC_LI

epidemiology↗

bioRxiv2017

Extracting active modules from multilayer PPI network: a continuous optimization approach

Active modules identification has received much attention due to its ability to reveal regulatory and signaling mechanisms of a given cellular response. Most existing algorithms identify active modules by extracting connected nodes with high activity scores from a graph. These algorithms do not consider other topological properties such as community structure, which may correspond to functional units. In this paper, we propose an active module identification algorithm based on a novel objective function, which considers both and network topology and nodes activity. This objective is formulated as a constrained quadratic programming problem, which is convex and can be solved by iterative methods. Furthermore, the framework is extended to the multilayer dynamic PPI networks. Empirical results on the single layer and multilayer PPI networks show the effectiveness of proposed algorithms.\n\nAvailability: The package and code for reproducing all results and figures are available at https://github.com/fairmiracle/ModuleExtraction.

bioinformatics↗