ABSTRACT
Objective@#To investigate the effect of biofeedback therapy on metabolic syndrome (MS) and the effect of different levels of job stress on the outcome.@*Methods@#The physicians in tertiary hospitals who were diagnosed with MS from January to December, 2016 were divided into biofeedback group and health education group according to different intervention methods, and moderate group, medium group, and high group according to different levels of job stress. A 6-month intervention was implemented from May to October, 2017. A two-way factorial design was used to analyze the main effect of biofeedback on physical and biochemical parameters and the interaction of biofeedback and job stress.@*Results@#After 6 months of intervention, the patients in both the biofeedback group and the moderate group had significantly decreased waist circumference, body mass index, systolic blood pressure (SBP) , diastolic blood pressure (DBP) , and levels of triglyceride (TG) , total cholesterol, low-density lipoprotein cholesterol (LDL-C) , and fasting plasma glucose (FPG) and a significantly increased level of high-density lipoprotein cholesterol (HDL-C) (all P<0.05) ; the patients in both the health education group and the moderate group had significantly decreased SBP, DBP, and levels of TG, LDL-C, and FPG (P<0.05) . The factorial analysis of variance showed that there was a synergistic interaction between the intervention method and job stress level in SBP, DBP, TG, HDL-C, LDL-C, and FPG among MS patients (P<0.05) . The high group had significantly more improvements in all indices compared with the medium group and the moderate group (P<0.05) .@*Conclusion@#Biofeedback therapy can effectively improve blood pressure, blood lipids, and blood glucose in MS patients, and is more effective for patients with high job stress level.
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Objective To explore the predictive value of obesity-related index for metabolic syndrome(MS)among Tianjin adults. Methods A total of 522 adults who participated in a physical examination were included in this cross-sectional study. Participants 'gender and age were record-ed,height,weight,waist circumference(WC)and hip circumference were measured,and TC,TG,HDL-C and FPG were tested. The receiver op-erating characteristic(ROC)curves were generated to identify the optimal measurement of obesity for the prediction of metabolic risk in this popu-lation. Results The area under the ROC curve(AUC)value for waist-to-height ratio(WHtR)predicted the maximum value of MS,followed by WC and BMI,and WHR was the lowest. The optimal cut off value of WHtR was 0.509. There was significant difference in the ability of BMI be-tween different age groups and WC to predict MS. In the groups of<40 years old and≥60 years old,BMI had higher predictive value for MS than WC. Conclusion WHtR has the best predictive value for evaluating the MS risk compared to BMI ,WC or WHR alone among Tianjin adults. The predictive value of BMI and WC for evaluating the MS risk can be improved by the method of age stratification.
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Objective To analyse the risk factors of vulnerable plaque biomarker and to construct an early warning system. Methods Ninety patients with suspected acute coronary syndrome (ACS) hospitalized during December 2012 and December 2013 were selected. The coronary artery lesions were divided into type I, II and III plaque groups by the morphology of atherosclerotic plaque. Serum SAA, PLGF, sCD40L and Npt were measured. The results of SAA, PLGF, sCD40L and Npt were compared. Logistic regression model was fitted to explore the main influencing factors of the vulnerable plaque. Results SAA, PLGF, sCD40L, and Npt were main influencing factors of the vulnerable plaques, and the ORs were 1.61, 1.88, 1.96 and 1.79 respectively. Conclusion The detection of SAA, PLGF, sCD40L and Npt biochemical markers in patients with chest pain is important for predicting the vulnerable plaque and guiding clinical treatment.