Longitudinal Study on the Risk Factors of Stroke in Check-up Population Based on Bayesian Multivariate Joint Model

Abstract

Background Stroke is one of the major public health problems affecting human health in current. Longitudinal check up data has accumulated a large amount of health information. However, the utilization rate of the longitudinal check up data is low and important information has not been fully extracted due to many problems such as missing data and small sample size, which brings difficulties to the effective prevention and control of common chronic diseases. Objective To explore the risk factors of stroke in check-up population based on Bayesian multivariate joint model, so as to provide a new approach for the analysis of risk factors for chronic diseases. Methods In this study, the data were collected from the Center for Health and Medicine, Xijing Hospital, Air Force Military Medical University from 2008 to 2015. Follow-up status: the follow up was conducted with the first occurrence of stroke as the outcome event and stopped at the occurrence of outcome event or ended when the collection of medical examination information was completed by 2015 if the outcome event did not occur. The interval between physical examinations was 1 year. The participants were divided into the stroke group and the non-stroke group according to whether stroke occurred during follow-up. Longitudinal variables observed in this study included total cholesterol (TC) , triglyceride (TG) , low density lipoprotein cholesterol (LDL-C) , high density lipoprotein cholesterol (HDL-C) , body mass index (BMI) and systolic blood pressure (SBP) . Multivariate Cox regression model was used to analyze the influence of baseline conditions on stroke outcome events. Bayesian multivariate joint model was used for analyzing the effect of longitudinal trajectory of TC, TG, LDL-C, HDL-C, BMI and SBP on the incidence of stroke during follow-up. Results A total of 234 subjects with 1 581 longitudinal follow-up records were included in this study, with the mean follow-up time of (7.4±1.2) years, of which 70 cases (29.9%) developed stroke during the follow-up. The results of multivariate Cox proportional hazards model showed that there was no effect of baseline values including TC, TG, LDL-C, HDL-C, BMI and SBP on the incidence of stroke (P>0.05) . The results of Bayesian multivariate joint model showed that the risk of stroke was 1.863 times higher for per longitudinal increase of 1 mmol/L TG level 〔95%CI (1.018, 3.294) , P=0.042〕 and 1.347 times higher for per longitudinal increase of 1 mmol/L LDL-C level〔95%CI (1.045, 1.863) , P=0.046〕. Conclusion The longitudinal increase of TG and LDL-C levels over time is a risk factor for stroke in check-up population. Bayesian multivariate joint model can be used to explore the risk factors of chronic diseases in check-up population.

Keywords

• stroke|physical examination|bayesian joint model|cox regression model|dyslipidemias|risk factors