Analysis Of Influencing Factors And Construction Of Diagnosis Model For Cardiometabolic Risk Among Undergraduate Students In Sports University

BackgroundThe prevalence of cardiovascular disease in China is continuously rising,and the incidence of cardiovascular disease has increased among younger individuals.It is urgent to strengthen the management of cardiovascular diseases in order to help achieve"Healthy China"2030 goal.The advancement of prevention and control gates needs to start with the screening and management of risk factors.The transition period from adolescents to youths in colleges and universities is a stage where healthy behaviors and body shapes gradually mature,and their cardiovascular health has a profound impact on future cardiovascular diseases.PurposeBased on Shanghai University of Sport Alumni Health Cohort Study—the first large sports health cohort in China,this study aims to:1.To describe the epidemic characteristics of cardiometabolic risk factor clustering among sports students and academic students in different majors;2.To analysis the factors that affecting the cardiometabolic risk of university students from the aspects of health behavior,dietary habits,physical activity,body composition,and physical fitness;3.To explore the correlation and associations between parameters of body fatness and physical fitness with cardiometabolic risk among university students;4.To determine the optimal cut-off of body fatness and physical fitness for detecting cardiometabolic risk factor clustering among university students,and construct the diagnostic model of cardiometabolic risk factor clustering of university students in different majors.MethodsPart 1:A total of 805 subjects（male=421,female=384）with complete data from the Shanghai University of Sport Alumni Health Study were selected for physical examination and tests of cardiometabolic risk factors including blood pressure,fasting blood glucose,and lipid metabolism.Lean mass and fat mass of the whole body,android region,and gynoid region were assessed using dual energy x-ray absorptiometry.Cardiometabolic risk factor clustering was defined having as two or more abnormal cardiometabolic risk factors:elevated blood pressure,elevated triglycerides level,decreased high-density lipoprotein cholesterol level,and elevated fasting glucose level.Describe the epidemic characteristics of cardiometabolic risk factor clustering among academic students,and sport students in different majors.Part 2:In addition to the variables of Part 1,the health behavior,dietary habits of participants were assessed by questionnaires.Physical activity was objectively measured over 7 days by using Acti Graph accelerometers:sedentary time,light physical activity,moderate physical activity,vigorous physical activity.Using a Physical-Fitness Test System assessed the physical fitness of participants including handgrip strength,forced vital capacity,whole-body reaction time,sit and reach,standing on one leg with eyes closed,maximal oxygen consumption（VO₂max）,sit-ups,and standing long jump.Univariate and multivariate logistic regression was used to examine the influencing factors of cardiometabolic risk among university students.Part 3:The correlations between body composition,physical fitness and cardiometabolic risk factors were analyzed by Spearman grade correlation.The dose-response relationships between body fat percentage,physical fitness and cardiometabolic risk factors were analyzed by linear regression with body fat percentage and physical fitness as independent variables and cardiometabolic risk score as dependent variable.The cardiometabolic risk score was the sum of the sex-specific z-score of the following metabolic traits:mean arterial pressure（[（2×diastolic blood pressure）+systolic blood pressure]/3）,triglycerides,high-density lipoprotein cholesterol*-1,and fasting blood glucose.The fitness score was the sum of the sex-specific z-score for physical fitness test items（normalized handgrip strength,and VO₂max）.Physical fitness score was categorized as“fit”(1^st quartile)and“unfit”(4^thquartile).Body fat percentage was categorized as“high fat”and“low fat”using specific cut-off each parameter for Chinese individuals as previously described.Four groups were used:（i）low fat and fit;（ii）low fat and unfit;（iii）high fat and fit;（iv）high fat and unfit.To examine the cardiometabolic risk score differences between groups by using Kruskal-Wallis H test.To examine whether the association between physical fitness and cardiometabolic risk factor clustering was mediated by fatness parameters,linear regression models were fitted using bootstrapped mediation procedures included in the PROCESS SPSS script.Part 4:Taking the cardiometabolic risk factor clustering of university students as the gold standard,the software Medcalc was used to draw receiver operating characteristic curve（ROC）to evaluate the effect of parameters of body fatness and physical fitness in the diagnosis of cardiometabolic risk of university students.At the same time,the area under curve（AUC）was calculated,and found the optimal thresholds of body fatness and physical fitness for diagnosing the cardiometabolic risk factor clustering.Logical regression analysis was used to calculate the joint predictors of body fat percentage,normalized grip strength and VO₂max,and Z-test was used to compare various parameters of body fatness,physical fitness and combined test.P<0.05 showed a significant level.Results1.The total prevalence of cardiometabolic risk factor clustering was 14.9%among undergraduate students in sports university.There was no gender difference in the prevalence of cardiometabolic risk factor clustering.The prevalence of cardiometabolic risk factor clustering increases with the increase of BMI,which are underweight（0.0%）,normal weight（11.1%）,overweight（25.8%）,and obesity（47.2%）.Compared with their peers with normal BMI,university students with normal BMI but higher body fat percentage had a higher prevalence of cardiometabolic risk factor clustering,and their metabolic disorders tended to be obese.The prevalence of cardiometabolic risk factor clustering in academic students（19.1%）was higher than sports students（11.6%）.The differences in prevalence of cardiometabolic risk factor clustering among sports were found,and the prevalence of cardiometabolic risk factor clustering in confrontational sports,such as basketball and football,was higher（23.3%）,while the prevalence of cardiometabolic risk factor clustering in racket sports,such as table tennis and badminton,was lower（6.3%）.2.Among the four aspects of health behavior and dietary habits,sedentary time and physical activity,body composition,and physical fitness,the latter two are the main factors affecting the cardiometabolic risk in university students.Higher BMI（males:OR=1.277,95%CI=1.152-1.416;females:OR=1.239,95%CI=1.128-1.361）;body fat percentage（males:OR=1.107,95%CI=1.063-1.153;females:OR=1.095,95%CI=1.044-1.147）;and Android fat percentage（males:OR=1.083,95%CI=1.050-1.117;females:OR=1.094,95%CI=1.052-1.139）were risk factors for cardiometabolic risk factor clustering in university students.And physical fitness such as higher normalized grip strength（males:OR=0.524,95%CI=0.356-0.769;females:OR=0.587,95%CI=0.391-0.880）;and maximal oxygen consumption（males:OR=0.910,95%CI=0.866-0.957;females:OR=0.867,95%CI=0.810-0.928）were protective factors for cardiometabolic risk factor clustering in university students.Multivariate logistic regression showed that body fat and physical fitness influenced cardiometabolic risk mutually.3.Body fat content was significantly positively correlated with cardiometabolic risk factors,while parameters of physical fitness were significantly negatively correlated with cardiometabolic risk factors.An increase in body fat percentage would cause an increase in cardiometabolic risk factors including total cholesterol,triglycerides and fasting blood glucose levels,while an increase in VO₂max will cause a decrease in cardiometabolic risk factors.Parameters of body fatness and physical fitness had both independent and combined effects on cardiometabolic risk.Compared with the high fat and unfit group,the low fat and fit,low fat and unfit group had lower cardiometabolic risk score.In addition,there was a mediation of body fat percentage in the association of fitness with cardiometabolic risk factor clustering.The contribution of body fat percentage was 85.7%in males and 56.7%in females.And the mediation of physical fitness in the association of body fat percentage with cardiometabolic risk factor clustering was not found.4.ROC analysis indicated that fatness and fitness parameters can be used to identify the cardiometabolic risk factor clustering in university students.BMI cut-offs for cardiometabolic risk factor clustering were 23.2 kg/m²（sport male students）,22.0kg/m²（sport female students）,25.7 kg/m²（academic male students）,and 23.3 kg/m²（academic female students）,respectively.Body fat percentage cut-offs measured by DXA for cardiometabolic risk factor clustering were 16.3%（sport male students）,22.7%（academic male students）,and 35.2%（academic female students）,respectively.Android fat percentage cut-offs for cardiometabolic risk factor clustering were 18.7%（sport male students）,33.8%（sport female students）,30.2%（academic male students）and 42.1%（academic female students）,respectively.Physical fitness parameters including normalized grip strength and VO₂max can significantly identify cardiometabolic risk factor clustering in university students.The identify of cardiometabolic risk in university students by normalized grip strength and VO₂max may vary by major and gender.The optimal cut-off of normalized grip strength was 0.53 for the identify of cardiometabolic risk in academic male students,and the optimal cut-offs of VO₂max for the identify of cardiometabolic risk sport male students and academic female students were 41.2 m L/kg/min and 28.3 m L/kg/min,respectively.With regard to body fat percentage,normalized grip strength,VO₂max,and fatness combined fitness predictors,there were major differences in the identify of cardio metabolic risks.Among sport male students,the combined predictors of fatness and fitness is more effective in identifying cardiometabolic risk than the individual physical fitness parameters（VO₂max）;while in academic male and female students,body fat percentage and physical fitness（boys:normalized grip strength,girls:VO₂max）and the combined predictors of fatness and fitness can be used as effective predictors for identifying cardiometabolic risks,and there is no significant difference in diagnostic value.Conclusions1.The total prevalence of cardiometabolic risk factor clustering was 14.9%among undergraduate students in sports university.The prevalence of cardiometabolic risk factor clustering increases with the increase of BMI.The prevalence of cardiometabolic risk factor clustering in academic students was higher than sports students.There were differences in sports events between the prevalence of cardiometabolic risk factor clustering.Racket sports such as badminton,table tennis,and tennis may be suitable sports for reducing cardiometabolic risks.2.Body fatness and physical fitness are the main factors affecting cardiometabolic risk of university students.University students with high fat and unfit had the highest cardiometabolic risk.Suggested that in university students,we should pay more attention to the cardiometabolic risk caused by the increase of body fat percentage and the decline of physical fitness.3.We determined the optimal cut-offs of fatness and fitness parameters for identifying the cardiometabolic risk factor clustering in university students,including BMI（sport male students:23.2 kg/m²,sport female students:22.0 kg/m²,academic male students:25.7 kg/m²,academic female students:23.3 kg/m²）;body fat percentage（sport male students:16.3%,academic male students:22.7%,academic female students:35.2%）;Android fat percentage（sport male students:18.7%,sport female students:33.8%,academic male students:30.2%,academic female students:42.1%）;normalized grip strength（academic male students:0.53）;and VO₂max（sport male students:41.2m L/kg/min,academic female students:28.3 m L/kg/min）.It provides guidance for the application of body fat percentage and physical fitness in clinical cardiometabolic risk assessment.