| Objective:In this study,grouped trajectory modeling was used to explore the behavioral dynamic change patterns of high-risk cardiovascular groups in Jilin Province,and to clarify the grouping and epidemiological distribution characteristics of different behavioral trajectories among high-risk cardiovascular groups in Jilin Province.The impact of vascular events,explore the relationship between behavioral change trajectory and cardiovascular events,and provide a scientific basis for the development of effective cardiovascular disease prevention and control strategies and measures.Methods:In this study,a total of 2056 people with high cardiovascular risk who had complete initial screening and 5 follow-up records from 2014 to 2019 in Jilin Province and did not suffer from cardiovascular disease at the initial screening were selected as the research subjects.Survey methods include on-site questionnaires and laboratory examinations.IBM SPSS 24.0 statistical software was used for data processing and analysis.Measurement data conforming to normal distribution were expressed by,and t-test or ANOVA test were used for comparison between groups.Measurement data that did not conform to the normal distribution were represented by M（Q）,and comparison between groups was performed by rank sum testχ~2test was used for comparison between groups.The influencing factors were analyzed by multivariate Logistic regression analysis,and the difference was statistically significant at P<0.05.SAS 9.4 was used to construct the grouped trajectory model,which was completed through the proc traj process of SAS software.Results:1.Smoking status trajectories were divided into persistent non-smoking group and smoking cessation trend group.There were statistically significant differences in baseline age,height,weight,BMI,systolic blood pressure,total cholesterol,high-density lipoprotein cholesterol,low-density lipoprotein cholesterol,and gender between the two groups（P<0.05）.The differences between the two groups were statistically significant（P<0.05）,and the differences in the mean diastolic blood pressure and average body weight during the follow-up period were statistically significant（P<0.05）.2.Drinking trajectories were divided into persistent non-drinking group and abstinence trend group.There were statistically significant differences in baseline age,height,weight,diastolic blood pressure,gender,and abnormal blood pressure rate between the two groups（P<0.05）,and differences in baseline smoking and drinking rates were statistically significant（P<0.05）,and the differences between the mean systolic blood pressure,mean diastolic blood pressure and mean body weight during the follow-up period were statistically significant（P<0.05）.3.The trajectory of physical exercise is divided into a low-level physical exercise rising group,a high-level physical exercise declining group,and a high-level physical exercise rising group.There were statistically significant differences in baseline age,systolic blood pressure,diastolic blood pressure,triglyceride（TG）,and gender among the three groups（P<0.05）,and the baseline smoking rate was statistically significant（P<0.05）.The difference in mean diastolic blood pressure during the follow-up period was statistically significant（P<0.05）.4.The trajectory of high-salt diet was divided into a low-level high-salt diet decline group and a high-level high-salt diet decline group.There were statistically significant differences in baseline age,systolic blood pressure,body weight,BMI,triglyceride（TG）,abnormal blood pressure rate and dyslipidemia rate between the two groups（P<0.05）.The difference was statistically significant（P<0.05）,and the difference between the mean diastolic blood pressure during the follow-up period was statistically significant（P<0.05）.5.The trajectories of the risk behavior aggregation were divided into the risk behavior aggregation increasing group and the risk behavior aggregation decreasing group.There were statistically significant differences in baseline height,weight,total cholesterol（TC）,and gender between the two groups（P<0.05）.The smoking rate and drinking rate were significantly different（P<0.05）,and the mean systolic blood pressure,mean diastolic blood pressure and mean body weight during the follow-up period were statistically significant（P<0.05）.6.The results of multivariate Logistic regression analysis showed that:for the smoking status track group,age,gender,baseline smoking status,low-density lipoprotein cholesterol,baseline height,baseline weight,and baseline BMI were the influencing factors for grouping;for the drinking status track group,culture Degree,gender,baseline smoking status,baseline drinking status,and follow-up mean BMI were the influencing factors for grouping;for the physical exercise track group,total cholesterol,low-density lipoprotein cholesterol,follow-up mean diastolic blood pressure,ethnicity,baseline smoking status,age,follow-up average systolic blood pressure,follow-up average heart rate,occupation are the influencing factors of grouping;for the high-salt diet track group,age,baseline systolic blood pressure,ethnicity,follow-up average systolic blood pressure,follow-up average diastolic blood pressure are the influencing factors of grouping;For the trajectory group of risk behavior aggregation,gender,household registration type,baseline smoking status,follow-up mean diastolic blood pressure,and follow-up mean heart rate were the influencing factors of grouping.7.Cox regression analysis showed that after adjusting for possible confounding factors,the effects of smoking status trajectory grouping,drinking status trajectory grouping,physical exercise status trajectory grouping,high-salt diet status trajectory grouping,and risk behavior aggregation trajectory grouping on the occurrence of cardiovascular events were not statistically significant（P>0.05）.Conclusions:1.The construction of grouped trajectory model is feasible to apply to the trajectories of high-risk populations with cardiovascular disease,and the changes of behavior trajectories with time are dynamic and continuous.It is more representative than judging whether a certain behavior exists at a single point in time.2.Age,gender,ethnicity,baseline smoking status,baseline drinking status,blood lipids,baseline physique indicators,etc.are the main influencing factors of smoking,drinking,physical exercise,high-salt diet,and risk behavior clustering trajectories.3.None of the smoking status trajectory grouping,drinking status trajectory grouping,physical exercise status trajectory grouping,high-salt diet status trajectory grouping,and risk behavior aggregation trajectory grouping showed any association with cardiovascular events.4.Using trajectory grouping to establish Cox regression model can focus on the impact of behavioral dynamic changes on cardiovascular events. |
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