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铁路职工高同型半胱氨酸血症发生情况及危险因素探讨
引用本文:朱春红,张帆,雷联会,史斌,杨洋.铁路职工高同型半胱氨酸血症发生情况及危险因素探讨[J].武警医学,2019,30(10):881-884.
作者姓名:朱春红  张帆  雷联会  史斌  杨洋
作者单位:1.100038, 北京铁路局中心卫生防疫站会城门门诊部疾病预防控制科;100039 北京, 解放军总医院第三医学中心:2.重症医学二科,3.移植科
摘    要: 目的 探讨高同型半胱氨酸血症的发生情况及其相关的危险因素。方法 选取2017-03至2018-12在中心进行体检的铁路职工76 456人。依据血清同型半胱氨酸(HCY)检测结果按照排除标准与入选标准共纳入研究对象68 383人。收集包括既往史、家族史及烟酒嗜好等病史资料。测量研究对象的身高、体重、收缩压(SBP)、舒张压(DBP)并计算体质指数(BMI)。进行空腹血糖(FBG)、胆固醇(TC)、三酰甘油(TG)、低密度脂蛋白胆固醇(LDL-C)、高密度脂蛋白胆固醇(HDL-C)、HCY、尿酸(UA)等指标的检测。分析比较不同水平HCY上述指标的差异以及不同危险因素下HCY的差异,采用逐步回归法进行HCY与影响因素的多重线性回归分析,得出相关性。结果 HCY正常组42 929人,HCY增高组25 454人。两组间年龄、男性比例、心脑血管病既往史、高血压既往史、吸烟率、饮酒率、BMI、SBP、DBP、TG、LDL-C、UA等比较均有统计学意义(P<0.05),HCY增高组HDL-C(1.168±0.296)低于正常组(1.198±0.295),差异有统计学意义(P<0.05)。超重组及肥胖组血HCY水平均高于体重正常组,三组间两两比较差异均有统计学意义(P<0.05);一级高血压组、二级高血压组及三级高血压组血HCY水平均高于血压正常组,差异有统计学意义(P<0.001)。多重线性回归分析结果显示:性别、吸烟、UA升高、三级高血压、年龄(53~60岁)、二级高血压、饮酒、高血压既往史、TG升高、HDL-C降低与HCY升高存在线性关系(F=706 059.453,P<0.001)。结论 性别、吸烟、血尿酸、高血压、年龄、饮酒、TG、LDL-C是高同型半胱氨酸血症的影响因素。

关 键 词:铁路职工  同型半胱氨酸血症  危险因素  线性回归  
收稿时间:2019-07-12

Occurrence and risk factors of hyperhomocysteine in railway workers
ZHU Chunhong,ZHANG Fan,LEI Lianhui,SHI Bin,YANG Yang.Occurrence and risk factors of hyperhomocysteine in railway workers[J].Medical Journal of the Chinese People's Armed Police Forces,2019,30(10):881-884.
Authors:ZHU Chunhong  ZHANG Fan  LEI Lianhui  SHI Bin  YANG Yang
Institution:1.Department of Disease Control and Prevention, Huicheng Gate Clinic, Beijing Railway Center Health and Epidemic Prevention Station,Beijing 100038,China;2.The Third Department of ICU,3.Department of Liver Transplantation,the Third Medical Center of PLA General Hospital, Beijing 100039, China
Abstract:Objective To explore the related risk factors for the occurrence of HCY so as to provide reference for the prevention and control of cardiovascular and cerebrovascular diseases.Methods A total of 76,456 railway workers who underwent physical examination in our center from March 2017 to December 2018 were selected. A total of 68,383 subjects were included according to exclusion criteria and selection criteria. Medical data including previous disease, family history of disease, tobacco and alcohol addiction was collected. Height, weight, systolic and diastolic blood pressure were measured and body mass index was calculated. FBG, TC, TG, LDL-C, HDL-C, HCY, uric acid and other indicators were detected. The differences of the above indexes of HCY at different levels as well as the differences of HCY under different risk factors were analyzed and compared. Multiple linear regression analysis of HCY and influencing factors was conducted by using the stepwise regression method, and the correlations were determined.Results The number of employees with normal HCY was 42,929 (62.78%), compared with 25,454 (37.22%)employees with elevated HCY.There was statistically significant difference in age, percentage of males, past history of cardiovascular and cerebrovascular diseases, past history of hypertension, percentage of smokers, percentage of alcohol drinkers, BMI, SBP, DBP, TG, LDL-C and UA between the two groups (P<0.05). The mean value of HDL-C in the elevated HCY group was lower than that of the normal group (P<0.05). The serum HCY levels in overweight and obese groups were significantly higher than those in the normal weight group, and the serum HCY levels tended upward (P<0.001). Serum HCY levels in the first-grade hypertension group, the second-grade hypertension group and the third-grade hypertension group were significantly higher than those in the normal weight group (P<0.001). Multiple linear regression analysis showed that there was a linear relationship between sex, smoking, elevated UA, third-grade hypertension, age (53-60 years old), secondary hypertension, alcohol consumption, history of hypertension, elevated TG, decreased HDL-C and elevated HCY (F=706059.453, P<0.001).Conclusions Gender, smoking, blood uric acid, hypertension, age, alcohol consumption, and low-density lipoprote in cholesterol are influencing factors of hyperhomocysteinemia.
Keywords:railway worker  homocysteinemia  risk factor  linear regression  
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