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新疆地区代谢综合征患者西北燥证危险因素Logistic回归分析 被引量:3

Logistic Regression Analysis on Risk Factors of Northwest Dryness Syndrome Among Patients of Metabolic Syndrome in Xinjiang Uygur Autonomous Region
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摘要 目的分析新疆地区代谢综合征患者中西北燥证危险因素。方法在新疆喀什、哈密、乌鲁木齐地区共收集321例代谢综合征患者,共筛选15类变量,计算OR值和95%CI,并用条件Logistic回归分析方法进行单因素及多因素分析。结果单因素Logistic回归分析结果显示,饮食习惯嗜食肥甘,饮食偏嗜喜辣、喜咸,及吸烟史、高血压是西北燥证危险因素,其OR值分别为0.387、0.071、0.033、2.614、0.440,均P<0.05。多因素Logistic回归分析结果,吸烟史、饮酒史、嗜食肥甘及高血压均进入回归模型,其OR值分别为3.945、4.334、0.370、2.142,均P<0.01。结论建议代谢综合征患者中嗜食肥甘及喜辣、喜咸者适当调整饮食口味,增加酸味饮食,荤素搭配,戒烟酒,控制血压,可能有助于适应当地气候、生活环境,减少西北燥证的发生,利于疾病康复。 Objective To analyze risk factors of northwest dryness syndrome (NDS) among the patients of metabolic syndrome in Xinjiang Uygur Autonomous Region.Methods Totally 321 cases of metabolic syndrome in Kashgar, Hami and Urumqi were collected to filter 15 variables to calculate OR value and 95% CI, and evaluated by single factor and multi-factor analysis by applying conditional Logistic regression analysis.Results Single factor conditional Logistic regression analysis showed that high fat diet, addiction to hot diet, salty diet, smoking history and hypertension were the risk factors of NDS. The OR values of them were 0.387, 0.071, 0.033, 2.614 and 0.440, respectively (P&lt;0.05). Multi-factor conditional Logistic regression analysis showed that smoking history, alcohol drinking history, high fat diet and hypertension were all entered into regression model. The OR values of them were 3.945, 4.334, 0.370 and 2.142, respectively (P&lt;0.01).Conclusion Patients of metabolic syndrome with a high fat, salty and hot diet are suggested to adjust their diet habit by adding sour food, giving up smoking and drinking, and controlling their blood pressure. It will be helpful for them to adapt local climate and living environment, and reduce the attack rate of NDS.
出处 《中国中医药信息杂志》 CAS CSCD 2015年第9期15-17,共3页 Chinese Journal of Information on Traditional Chinese Medicine
基金 国家自然科学基金(81260518)
关键词 代谢综合征 西北燥证 危险因素 LOGISTIC回归分析 metabolic syndrome northwest dryness syndrome risk factor Logistic regression analysis
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