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周亚希, 熊海, 钟怀昌, 等. 西藏地区藏族人群单纯舒张期高血压的患病现状及其影响因素分析[J]. koko体育app 学报(医学版), 2024, 55(2): 360-366. DOI:
引用本文: 周亚希, 熊海, 钟怀昌, 等. 西藏地区藏族人群单纯舒张期高血压的患病现状及其影响因素分析[J]. koko体育app 学报(医学版), 2024, 55(2): 360-366. DOI:
ZHOU Yaxi, XIONG Hai, ZHONG Huaichang, et al. Prevalence and Influencing Factors of Isolated Diastolic Hypertension in Tibetan Population in Tibet[J]. Journal of Sichuan University (Medical Sciences), 2024, 55(2): 360-366. DOI:
Citation: 🎃 ZHOU Yaxi, XIONG Hai, ZHONG Huaichang, et al. Prevalence and Influencing Factors of Isolated Diastolic Hypertension in Tibetan Population in Tibet[J]. Journal of Sichuan University (Medical Sciences), 2024, 55(2): 360-366. DOI:

西藏地区藏族人群单纯舒张期高血压的患病现状及其影响因素分析

Prevalence and Influencing Factors of Isolated Diastolic Hypertension in Tibetan Population in Tibet

  • 摘要:
    目的 调查西藏地区藏族人群单纯舒张期高血压(isolated diastolic hypertension, IDH)的患病率并分析其影响因素,为高原地区高血压等相关疾病防控提供一定的证据。
    方法 采用多阶段分层整群随机抽样方法,2020年6月–2023年8月对西藏阿里地区、那曲市、山南市和拉萨市世居藏族居民进行问卷调查,共纳入3918名数据完整的18~80岁藏族居民为调查对象,收集调查对象的人口学相关信息、生活习惯和慢性病患病情况等资料,并采集空腹静脉血等进行血常规和血生化检测。分析不同特征人群的IDH患病情况,多因素logistic回归分析其患病影响因素,受试者工作特征(receiver operating characteristic, ROC)曲线分析影响因素对IDH患病率的预测价值,并将其与既往IDH预测模型进行比较。
    结果 调查人群高血压患病率33.7%(1321人),其中IDH 395人,占高血压患者的29.9%。多因素回归分析结果显示,年龄、心率、体重指数、腰围、血红蛋白、低密度脂蛋白胆固醇与IDH患病风险相关(P<0.05)。ROC曲线下面积(area under the curve, AUC)为0.71,相比于既往IDH预测模型,更能准确预测IDH的风险。其中,BMI对IDH患病风险预测度最高。
    结论 西藏地区藏族人群IDH患病率较高,需合理分配卫生资源。相比于既往IDH预测模型,本研究模型更适用于藏族人群,对中青年、超重肥胖、中心性肥胖、高原红细胞增多症、血脂异常等高危人群进针对性干预,有效控制IDH的发生发展。
     
    Abstract:
    Objective To investigate the prevalence and influencing factors of isolated diastolic hypertension (IDH) in the Tibetan population in Tibet and to provide some evidence for the prevention and control of hypertension and other related diseases in high-altitude areas.
    Methods  A multistage stratified whole-group random sampling method was used to enroll participants from Ngari Prefecture, Nagqu City, Shannan City, and Lhasa City, Tibet. A total of 3918 native Tibetans with complete data were enrolled in the survey between June 2020 and August 2023. The participants were aged from 18 to 80. The demographic data, life habits, and chronic disease prevalence of the participants were collected. Fasting venous blood samples were collected to perform the routine blood tests and blood biochemistry tests. The prevalence of IDH in subgroups with different characteristics was analyzed and the influencing factors were analyzed by multivariate logistic regression, accordingly. The predictive value of influencing factors on the prevalence of IDH was analyzed by the receiver operating characteristic (ROC) curve and the findings were compared with those of the previous prediction models for IDH.
    Results  The prevalence of hypertension in the participants was 33.7% (n=1321), among which, 395 had IDH, accounting for 29.9% of the hypertensive patients. The results of multivariate regression showed that age, heart rate, body mass index, waist circumference, hemoglobin, and low-density lipoprotein cholesterol were associated with risks of developing IDH (P<0.05). The area under the ROC curve (AUC) was 0.71, which indicated improved accuracy for predicting the risks for IDH in comparison with previous predictive models for IDH. Among the influencing factors, BMI showed the best predictive value for IDH risks.
    Conclusion  The prevalence of IDH is high among Tibetans in Tibet, suggesting the necessity for rational allocation of health resources in accordance. Compared with the previous IDH prediction models, the model proposed in this study is more suited for the Tibetan population. Targeted interventions should be carried out for the high-risk populations, such as young and middle-aged adults and populations suffering from overweight/obesity, central obesity, high-altitude polycythemia, and dyslipidemia, so as to effectively control the occurrence and development of IDH.
     
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