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
• 公共卫生与管理学 • 上一篇
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
ZHOU Miao1,2, BIAN Weiwei1,2, LIU Xiaojuan1,2, KANG Fengling1,2, XUE Fuzhong1,2, LIU Jing1,2
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。
中图分类号:
[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] | 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. |
[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] | 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. |
[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] | 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. |
[13] | 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. |
[14] | 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. |
[15] | 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. |
|