您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(医学版)》

山东大学学报(医学版) ›› 2018, Vol. 56 ›› Issue (3): 85-90.doi: 10.6040/j.issn.1671-7554.0.2017.1040

• 临床医学 • 上一篇    下一篇

嗜碱性粒细胞百分比与慢性肾脏病关系的回顾性队列研究

周苗1,2,卞伟玮1,2,柳晓涓1,2,康凤玲1,2,薛付忠1,2,刘静1,2   

  1. 山东大学 1.公共卫生学院生物统计学系;2.齐鲁生物医学大数据研究中心, 山东 济南 250012
  • 收稿日期:2017-10-26 出版日期:2018-03-10 发布日期:2018-03-10
  • 通讯作者: 刘静. E-mail:liujing@sdu.edu.cn E-mail:liujing@sdu.edu.cn
  • 基金资助:
    国家自然科学基金(81273177)

Association between basophils percentage and chronic kidney disease: a retrospective cohort study

ZHOU Miao1,2, BIAN Weiwei1,2, LIU Xiaojuan1,2, KANG Fengling1,2, XUE Fuzhong1,2, LIU Jing1,2   

  1. 1. Department of Biostatistics, School of Public Health;
    2. Cheeloo Research Center for Biomedical Big Data, Shandong University, Jinan 250012, Shandong, China
  • Received:2017-10-26 Online:2018-03-10 Published:2018-03-10

摘要: 目的 探讨嗜碱性粒细胞百分比对慢性肾脏病(CKD)发病的影响。 方法 从“山东多中心健康管理纵向观察队列”中抽取年龄20岁以上、至少接受2次健康体检、初次体检未患CKD且无重要指标缺失者建立回顾性队列。将研究对象按基线嗜碱性粒细胞百分比的四分位数分为4组(Q1、Q2、Q3、Q4),应用Cox回归模型分析嗜碱性粒细胞百分比与CKD发生的关联。 结果 研究队列包含17 173人,男10 614人,女6 559人。研究期间共随访42 204.04人年,新发CKD 737例,发病密度为17.46/1 000人年。多元Cox回归模型结果显示,在调整年龄和性别后,以Q1为参照组,Q2、Q3、Q4三组嗜碱性粒细胞百分比的HR(95%CI)分别为0.990(0.776~1.263)、1.235(1.011~1.509)、1.352(1.099~1.663);进一步调整体质量指数、高血压、糖尿病、血尿酸、血肌酐、血尿素氮、总胆固醇、低密度脂蛋白胆固醇、甘油三酯后,以Q1组为参照组,Q2、Q3、Q4的HR(95%CI)分别为0.966(0.740~1.262)、1.225(0.985~1.525)、1.355(1.077~1.705)。 结论 嗜碱性粒细胞百分比升高是CKD发生的独立危险因素。

关键词: 嗜碱性粒细胞百分比, 队列, Cox回归, 慢性肾脏病, 健康管理人群

Abstract: Objective To explore the effect of basophils percentage on the incidence of chronic kidney disease(CKD). Methods A retrospective cohort was conducted using the data from Shandong Multi-center Longitudinal Cohort for Health Management. All subjects ≥20 years old who were free of CKD at baseline and accepted at least two annually physical examinations were selected. The participants were divided into four groups(denoted by Q1, Q2, Q3, Q4)according to quartiles of basophils percentage at baseline. Cox regression models were used to identify the association between basophils percentage and CKD. Results The cohort consisted of 17 173 subjects, including 10 614 males and 6 559 females. There were 737 CKD cases occurring during the 42 204.04 person-years following up, resulting in an incidence of 17.46/1 000 person-year. The multivariate Cox regression model with adjusting age and gender showed that HRs(95%CI) of basophils percentage to CKD for the groups of Q2, Q3 and Q4(with Q1 as reference group)were 0.990(0.776-1.263), 1.235(1.011-1.509)and 1.352(1.099-1.663)respectively. Furthermore, after all other related variables such as body mass index, hypertension, diabetes, blood uric acid, serum creatinine, blood urea nitrogen, total cholesterol, low density lipoprotein cholesterol and triglyceride were adjusted, the HRs(95%CI) of Q2, Q3 and Q4 山 东 大 学 学 报 (医 学 版)56卷3期 -周苗,等.嗜碱性粒细胞百分比与慢性肾脏病关系的回顾性队列研究 \=-were 0.966(0.740-1.262), 1.225(0.985-1.525)and 1.355(1.077-1.705), respectively. Conclusion Increasing basophils percentage is an independent risk factor of CKD.

Key words: Chronic kidney disease, Basophils percentage, Cohort, Cox regression, Health management population

中图分类号: 

  • R692
[1] Webster AC, Nagler EV, Morton RL, et al. Chronic kidney disease[J]. Lancet, 2017, 389(10075): 1238-1252.
[2] Hill NR, Fatoba ST, Oke JL, et al. Global prevalence of chronic kidney disease-a systematic review and meta-analysis[J]. PLoS One, 2016, 11(7): e158765. doi: 10.1371/journal.pone.0158765.
[3] Zhang L, Wang F, Wang L, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey[J]. Lancet, 2012, 379(9818): 815-822.
[4] Wang F, Zhang L, Wang H. Awareness of CKD in China: a national cross-sectional survey[J]. Am J Kidney Dis, 2014, 63(6): 1068-1070.
[5] Wang F, Ye P, Xiao WK, et al. Association of risk factors for cardiovascular disease and the rate of glomerular filtration: a cross-sectional study in the population from certain areas of Beijing[J]. Chin J Epidemiol, 2010, 31(3): 256-259.
[6] Sepanlou SG, Barahimi H, Najafi I, et al. Prevalence and determinants of chronic kidney disease in northeast of Iran: Results of the Golestan cohort study[J]. PLoS One, 2017, 12(5): e176540. doi:10.1371/journal.pone.0176540.
[7] Kang HT, Kim JK, Shim JY, et al. Low-grade inflammation, metabolic syndrome and the risk of chronic kidney disease: the 2005 Korean National Health and Nutrition Examination Survey[J]. J Korean Med Sci, 2012, 27(6): 630-635.
[8] Shankar A, Sun L, Klein BE, et al. Markers of inflammation predict the long-term risk of developing chronic kidney disease: a population-based cohort study [J]. Kidney Int, 2011, 80(11): 1231-1238.
[9] Miyake K, Karasuyama H. Emerging roles of basophils in allergic inflammation[J]. Allergol Int, 2017, 66(3): 382-391.
[10] Yamanishi Y, Karasuyama H. Basophil-derived IL-4 plays versatile roles in immunity[J]. Semin Immunopathol, 2016, 38(5): 615-622.
[11] Carrero JJ, Stenvinkel P. Persistent inflammation as a catalyst for other risk factors in chronic kidney disease: a hypothesis proposal[J]. Clin J Am Soc Nephrol, 2009, 4(Suppl 1): S49-S55.
[12] Gungor O, Unal HU, Guclu A, et al. IL-33 and ST2 levels in chronic kidney disease: associations with inflammation, vascular abnormalities, cardiovascular events, and survival[J]. PLoS One, 2017, 12(6): e178939. doi:10.1371/journal.pone.0178939.
[13] 刘娅飞,邢娉,徐秀琴,等. 山东多中心健康管理纵向观察队列[J]. 山东大学学报(医学版),2017, 55(6): 30-36. LIU Yafei, XING Ping, XU Xiuqin, et al. Shandong multi-center longitudinal cohort for health management: a brief introduction[J]. Journal of Shandong University(Health Sciences), 2017, 54(7): 30-36.
[14] Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate[J]. Ann Intern Med, 2009, 150(9): 604-612.
[15] Kidney Disease: Improving Global Outcomes(KDIGO)CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease[J]. Kidney Int Suppl, 2013, 3(1): 1-150.
[16] 中国高血压防治指南修订委员会. 中国高血压防治指南2010[J]. 中华心血管病杂志, 2011, 39(7): 579-616. Writing Group of 2010 Chinese Guidelines for the Management of Hypertension. 2010 Chinese guidelines for the management of hypertension[J]. Chin J Cardiol, 2011, 39(7): 579-616.
[17] 中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2013年版)[J]. 中华糖尿病杂志, 2014, 6(7): 447-498.
[18] 中国成人血脂异常防治指南修订联合委员会. 中国成人血脂异常防治指南(2016年修订版)[J]. 中国循环杂志, 2016, 31(10): 937-953.
[19] Shen ZW, Xing J, Wang QL, et al. Association between serum gamma-glutamyltransferase and chronic kidney disease in urban Han Chinese: a prospective cohort study[J]. Int Urol Nephrol, 2017, 49(2): 303-312.
[20] Tian N, Penman AD, Manning RJ, et al. Association between circulating specific leukocyte types and incident chronic kidney disease: the Atherosclerosis Risk in Communities(ARIC)study[J]. J Am Soc Hypertens, 2012, 6(2): 100-108.
[21] Agarwal R, Light RP. Patterns and prognostic value of total and differential leukocyte count in chronic kidney disease[J]. Clin J Am Soc Nephrol, 2011, 6(6): 1393-1399.
[22] Pecaric-Petkovic T, Didichenko SA, Kaempfer S, et al. Human basophils and eosinophils are the direct target leukocytes of the novel IL-1 family member IL-33[J]. Blood, 2009, 113(7): 1526-1534.
[23] MacGlashan DJ. IgE receptor and signal transduction in mast cells and basophils[J]. Curr Opin Immunol, 2008, 20(6): 717-723.
[24] Li B, Haridas B, Jackson AR, et al. Inflammation drives renal scarring in experimental pyelonephritis[J]. Am J Physiol Renal Physiol, 2017, 312(1): F43-F53.
[25] Park YS. Renal scar formation after urinary tract infection in children[J]. Korean J Pediatr, 2012, 55(10): 367-370.
[1] 曹瑾,季晓康,孙秀彬,蒋正,薛付忠. γ-谷氨酰转肽酶与高尿酸血症关系的队列分析[J]. 山东大学学报(医学版), 2017, 55(6): 124-128.
[2] 周苗,夏同耀,孙爱玲,李明,申振伟,卞伟玮,蒋正,康凤玲,柳晓涓,薛付忠,刘静. 健康管理人群慢性肾脏病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 98-103.
[3] 孙苑潆,杨亚超,曲明苓,陈雁敏,李敏,王淑康,薛付忠,刘云霞. 健康管理人群代谢综合征发病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 87-92.
[4] 苏萍,杨亚超,杨洋,季加东,阿力木·达依木,李敏,薛付忠,刘言训. 健康管理人群2型糖尿病发病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 82-86.
[5] 李江冰,宋心红,林海燕,张冬芝,李向一,许艺博,王丽,薛付忠. 健康管理人群缺血性异常心电图的影响因素[J]. 山东大学学报(医学版), 2017, 55(6): 77-81.
[6] 张光,王广银,吴红彦, 张红玉,王停停,李吉庆,李敏,康凤玲,刘言训,薛付忠. 健康管理人群高脂血症风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 72-76.
[7] 王春霞,许艺博,杨宁,夏冰,王萍,薛付忠. 基于健康管理队列的冠心病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 66-71.
[8] 于涛,刘焕乐,冯新,徐付印,陈亚飞,薛付忠,张成琪. 基于健康管理队列的高血压风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 61-65.
[9] 刘娅飞,邢娉,徐秀琴,杨淑芳,刘言训,袁中尚,薛付忠. 山东多中心健康管理纵向观察队列[J]. 山东大学学报(医学版), 2017, 55(6): 30-36.
[10] 柳晓涓,蒋正,康凤玲,周苗,林伟强,薛付忠. 中性粒细胞计数与非酒精性脂肪肝关联性的前瞻性队列研究[J]. 山东大学学报(医学版), 2017, 55(6): 119-123.
[11] 于媛媛,王春霞,苏萍,孙苑潆,薛付忠,刘言训. 健康管理队列白内障发病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 104-107.
[12] 李敏,王春霞,夏冰,朱茜,孙苑潆,王淑康,薛付忠,贾红英. 健康管理人群脑卒中风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 93-97.
[13] 柳晓涓,丁荔洁,康凤玲,周苗,薛付忠. 健康管理人群支气管哮喘风险预测模型[J]. 山东大学学报(医学版), 2017, 55(12): 56-61.
[14] 顾建华,马晓天,李吉庆,薛付忠,王家林. 健康管理队列慢性阻塞性肺疾病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(12): 62-65.
[15] 康凤玲,丁荔洁,柳晓涓,周苗,薛付忠. 多中心健康管理人群心脑血管疾病负担分析[J]. 山东大学学报(医学版), 2017, 55(12): 51-55.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 高立芬,孙汶生,马春红,张利宁,梁晓红,王晓燕,郭春,陈有海 . pcDNA3-HBV瞬时转染对树突状细胞的影响[J]. 山东大学学报(医学版), 2007, 45(4): 325 -328 .
[2] 肖水清,杜毅,韩晶 . 正畸治疗过程中龈沟液内细菌变化的研究[J]. 山东大学学报(医学版), 2006, 44(5): 508 -510 .
[3] 张纪庆,王志刚,丁璇,冀勇,沈寻,韩磊. 脑动静脉畸形癫痫的相关血管构筑学及栓塞治疗研究[J]. 山东大学学报(医学版), 2006, 44(12): 1238 -1240 .
[4] 王志刚,冀勇,丁璇. 动脉瘤性蛛网膜下腔出血患者血清垂体激素含量变化的动态研究[J]. 山东大学学报(医学版), 2006, 44(12): 1241 -1244 .
[5] 刘义庆,王来城,焦玉莲,张捷,马春燕,崔彬,高新谱,刘正敏,张雪,赵跃然,. 人IL-26的基因克隆及其真核表达载体的构建[J]. 山东大学学报(医学版), 2006, 44(6): 541 -544 .
[6] 颜军昊,陈子江,李媛,胡京美,高姗姗. 体外受精周期中来源于单原核受精卵胚胎性染色体分析[J]. 山东大学学报(医学版), 2006, 44(6): 545 -548 .
[7] 费 玲,隋树建,任满意,许复郁,刘伟华,杜贻萌 . 实验性兔动脉粥样硬化病变中TRAIL、DR5的表达及意义[J]. 山东大学学报(医学版), 2007, 45(2): 135 -138 .
[8] 喻芳,周庚寅,张翠娟,高鹏,马超,李红. 原发性肝癌中HIF-1α、P-gp的表达及相关性的研究[J]. 山东大学学报(医学版), 2007, 45(3): 246 -249 .
[9] 单宁宁,邹雄,张义,杨晓静,庄学伟,王洪春,侯明 . 小鼠LAK细胞MHC表达水平对其体外杀伤肿瘤活性的影响[J]. 山东大学学报(医学版), 2007, 45(11): 1096 -1100 .
[10] 程艳艳,王青,李笃民,崔凤玉. 多层螺旋CT支气管动脉成像在原发性肺癌中的应用[J]. 山东大学学报(医学版), 2008, 46(3): 305 -308 .