本文選題：高血壓 + 紅細胞計數��； 參考：《山東大學》2017年碩士論文

【摘要】：高血壓是心腦血管疾病最重要和最常見的危險因素,中國高血壓的發(fā)病和患病形勢嚴峻。識別高血壓危險因素,構建高血壓發(fā)病風險預測模型,評估高血壓發(fā)病風險,發(fā)現(xiàn)高危人群,對高危人群進行干預可延緩甚至阻止高血壓的發(fā)生。目前多個國家和地區(qū)建立了高血壓發(fā)病風險預測模型,但以往的高血壓發(fā)病風險預測模型通常采用傳統(tǒng)的預測參數(年齡、收縮壓、舒張壓、體質指數、吸煙、飲酒和高血壓家族史),缺乏新的預測因子,使得模型預測能力受限。近些年來多項研究發(fā)現(xiàn)紅細胞參數(紅細胞計數、血紅蛋白含量、血細胞比容)可能是高血壓的預測因子,有望助于提高模型預測能力。為此,本文基于隊列探討紅細胞參數對高血壓的影響,確定其是否可以作為高血壓預測因子,如果可以,則在考慮紅細胞參數的基礎上構建高血壓發(fā)病風險預測模型。資料與方法:本研究基于"山東多中心縱向健康管理隊列",采用隊列中2005年至2010年期間參加健康查體的體檢者構建隊列,體檢三次及以上,排除首次體檢時有高血壓、心血管疾病、腦卒中、年齡小于18歲的體檢者,最終有12497人(男7537人、女4960人)進入隊列。描述性分析的基礎上,控制其他影響因素,分性別采用Cox比例回歸分析方法研究紅細胞參數(紅細胞計數、血紅蛋白含量、血細胞比容)對高血壓的影響;分性別納入紅細胞參數的基礎上,構建高血壓Cox風險回歸預測模型,并用ROC曲線下面積AUC及O/E進行評價。結果:1.該健康管理隊列12497人共隨訪了 38958人年,其中有2785人(男2021、女764人)發(fā)生高血壓,高血壓的發(fā)病密度為71.48/1000人年。2.將紅細胞參數按照四分位數分為四類(Q1,Q2,Q3,Q4),則紅細胞參數與其他基線變量之間的關系如下:無論男女,多數基線變量隨紅細胞參數的增大而增高,但有統(tǒng)計學意義的基線變量在不同紅細胞參數中略有不同。Cochran-Armitage趨勢性檢驗顯示,對于男性,僅有血細胞比容與高血壓發(fā)生率間存在趨勢性(Z=-3.1628,P0.0001);而女性,三個紅細胞參數均與高血壓發(fā)生率間存在趨勢性(紅細胞計數,Z=-4.2950,P0.0001;血紅蛋白含量,Z=-5.8120,P0.0001;血細胞比容,Z=-6.5504,P0.0001)。3.紅細胞參數與高血壓發(fā)生風險的Cox比例回歸分析:對于男性,僅調整年齡時三個紅細胞參數Q4的相對危險度(RR,以Q1為參照組)、紅細胞參數四分類模型趨勢性檢驗及紅細胞參數每增加1個標準差的RR值有統(tǒng)計學意義,調整更多協(xié)變量時無統(tǒng)計學意義。對于女性,模型調整不同協(xié)變量時,Q3和Q4的RR值、紅細胞參數四分類模型趨勢性檢驗及紅細胞參數每增加1個標準差的RR值均有統(tǒng)計學意義(P0.05);調整年齡、吸煙、飲酒、規(guī)律鍛煉、體質指數、收縮壓、空腹血糖、高密度脂蛋白后,紅細胞計數Q2、Q3、Q4的RR值分別是1.140、1.285、1.240,血紅蛋白Q2、Q3、Q4的RR值分別是1.069、1.309、1.311,血細胞比容Q2、Q3、Q4的 R值分別是 1.019、1.263、1.234。4.多因素Cox比例回歸分析構建高血壓發(fā)病風險預測模型:采用后退法進行變量篩選,經多因素Cox比例回歸分析構建分性別的高血壓發(fā)病風險預測模型,納入男性模型的有年齡、體質指數、收縮壓、舒張壓、γ-谷氨酰轉移酶、空腹血糖、飲酒、年齡與體質指數的交互項及年齡與舒張壓的交互項。納入女性模型的有年齡、體質指數、收縮壓、舒張壓、空腹血糖、血細胞比容、飲酒和吸煙。5.男性高血壓發(fā)病風險預測模型的ROC曲線下面積AUC(95%CI)為0.761(0.752,0.771),十折交叉驗證后 AUC(95%CI)為 0.760(0.751,0.770),O/E為0.9561。女性高血壓發(fā)病風險預測模型的AUC(95%CI)為0.750(0.738,0.762),十折交叉驗證后 AUC(95%CI)為 0.747(0.735,0.759),O/E 為 0.9707。結論:1.紅細胞計數、血紅蛋白含量、血細胞比容升高將增加高血壓發(fā)病的風險,這種關聯(lián)在女性尤為明顯。2.血細胞比容最終納入女性高血壓發(fā)病風險預測模型,血細胞比容是女性高血壓發(fā)生的預測因子。3.分性別構建的高血壓發(fā)病風險預測模型判別能力和校準能力效果良好,可用于評估高血壓的發(fā)病風險。
[Abstract]:Hypertension is the most important and most common risk factor for cardiovascular and cerebrovascular diseases. The incidence and incidence of hypertension in China are severe. Identifying the risk factors of hypertension, constructing the prediction model of hypertension risk, assessing the risk of hypertension, finding high-risk groups and intervening in high-risk groups can delay or even prevent the occurrence of hypertension. Many countries and regions have established a predictive model for the risk of hypertension, but the previous prediction models of hypertension risk usually adopt traditional predictive parameters (age, systolic pressure, diastolic pressure, body mass index, smoking, drinking and family history of hypertension), lack of new pretest factors and limited prediction ability. In recent years, many studies have been made. It is found that red blood cell parameters (red blood cell count, hemoglobin content, hematocyte specific volume) may be a predictor of hypertension and may help improve model prediction. Therefore, this paper is based on a cohort study to determine the effect of red cell parameters on hypertension and determine whether it can be used as a predictor of hypertension. If possible, the red blood cell is considered. Based on the parameters, a model for predicting the risk of hypertension was constructed. Data and methods: Based on the "Shandong multi center longitudinal health management queue", a cohort of health checkup participants from 2005 to 2010 in the cohort was constructed and examined for three times and above, excluding hypertension, cardiovascular disease, stroke, and the first physical examination. At the age of 18 years of age, 12497 people (7537 men and 4960 women) entered the cohort. On the basis of descriptive analysis, other factors were controlled and the Cox proportional regression analysis was used to study the effects of red cell parameters (red blood cell count, hemoglobin content, blood cell specific volume) on hypertension. On the basis of the number, the Cox risk regression model of hypertension was constructed, and the area AUC and O/E under the ROC curve were evaluated. Results: 1. the 12497 people of the health management queue were followed up for 38958 years, of which 2785 people (2021 men and 764 women) had hypertension, and the density of hypertension was 71.48/1000 person year.2. and the red blood cell parameters were according to four points. The number is divided into four categories (Q1, Q2, Q3, Q4), and the relationship between red blood cell parameters and other baseline variables is as follows: the majority of baseline variables increase with the increase of red cell parameters in both men and women, but a statistically significant baseline variable has a slightly different.Cochran-Armitage trend test in different red cell parameters. For men, only blood is thin. There was a tendency (Z=-3.1628, P0.0001) between the cell specific volume and the incidence of hypertension, while in women, the three red blood cell parameters were all with the incidence of hypertension (red blood cell count, Z=-4.2950, P0.0001; hemoglobin content, Z=-5.8120, P0.0001; blood cell specific volume, Z=-6.5504, P0.0001) the Cox ratio of the.3. red blood cell parameters to the risk of hypertension Regression analysis: for men, the relative risk degree of three red blood cell parameters (RR, Q1 as reference group) was adjusted only for age (RR, Q1 as reference group). The trend test of red cell parameter four classification model and 1 standard deviation of erythrocyte parameters were statistically significant. There was no statistical significance in adjusting more covariant quantity. For women, the model adjustment was different. When covariate, the RR value of Q3 and Q4, the trend test of the red cell parameter four classification model and the RR value of the red blood cell parameters every 1 standard deviations were statistically significant (P0.05); the adjustment of age, smoking, drinking, regular exercise, body mass index, systolic blood pressure, fasting blood glucose, high density lipoprotein, Q2, Q3, Q4 were 1.140,1.28, RR value of Q4, respectively 1.140,1.28. 5,1.240, the RR values of hemoglobin Q2, Q3, and Q4 were 1.069,1.309,1.311, the R values of blood cell specific volume Q2, Q3, and Q4 were 1.019,1.263,1.234.4. multifactor Cox proportional regression analysis to predict the risk of hypertension. Risk prediction model, including age, body mass index, systolic pressure, diastolic pressure, gamma glutamyl transferase, fasting blood glucose, drinking, interaction between age and body mass index, age and diastolic pressure, including age, body mass index, systolic blood pressure, diastolic pressure, fasting blood glucose, blood cell specific volume, drinking and smoking.5 The area AUC (95%CI) under the ROC curve of the risk prediction model for male hypertension was 0.761 (0.752,0.771), AUC (95%CI) was 0.760 (0.751,0.770) after ten fold cross validation, O/E was the AUC (95%CI) of the 0.9561. female hypertension risk prediction model (0.738,0.762), 0.747 after ten fold cross validation, 0, 0. .9707. conclusion: 1. red cell count, hemoglobin content and increased blood cell specific volume will increase the risk of hypertension. This association is particularly evident in women's.2. blood cell specific volume eventually incorporated into the prediction model of the risk of hypertension in women. Blood cell specific volume is a predictor of female hypertension.3., a gender based hypertension. The risk prediction model is effective in discriminating ability and calibrating ability, and can be used to assess the risk of hypertension.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R544.1

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