肺纤维化风险预测的临床生物化学模型

doi:10.3969/j.issn.1006-7795.2019.06.013

首都医科大学学报 ›› 2019, Vol. 40 ›› Issue (6): 875-880.doi: 10.3969/j.issn.1006-7795.2019.06.013

肺纤维化风险预测的临床生物化学模型

冷冬¹, 李桂芹², 王颖¹, 缪冉³, 陈铎⁴, 黄骁舾³

1. 首都医科大学附属北京朝阳医院检验科, 北京 100020;
2. 首都医科大学附属北京朝阳医院疾病预防控制处, 北京 100020;
3. 首都医科大学附属北京朝阳医院医学研究中心, 北京 100020;
4. 首都医科大学附属北京朝阳医院呼吸与危重症医学科, 北京 100020

收稿日期:2019-04-24 出版日期:2019-11-21 发布日期:2019-12-18
通讯作者: 黄骁舾 E-mail:xxhuang@126.com
基金资助:
国家自然科学基金青年项目（81700061，81300049），北京市医院管理局临床医学发展专项经费（ZYLX201811），国家临床重点专科建设项目。

A functional laboratory biochemical model for the prediction of pulmonary fibrosis risk

Leng Dong¹, Li Guiqin², Wang Ying¹, Miao Ran³, Chen Duo⁴, Huang Xiaoxi³

1. Clinical Laboratory, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China;
2. Office for Disease Prevention and Control, Chaoyang Hospital, Capital Medical University, Beijing 100020, China;
3. Medical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China;
4. Department of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China

Received:2019-04-24 Online:2019-11-21 Published:2019-12-18
Supported by:
This study was supported by National Natural Science Foundation of China (81700061,81300049), Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (ZYLX201811), Key Subject Construction Project of China.

摘要/Abstract

摘要： 目的应用数据挖掘技术分析肺纤维化（pulmonary fibrosis，PF）患者及对照组血清生物化学数据，为疾病的早期诊断提供有益线索。方法本研究收集PF患者（n=29）和健康对照（n=55）的生物化学检测数据，采用Z-计分和Log2转换方式将数据进行归一化处理，用主成分分析与贝叶斯回归分析方法提取特征指标，构建PF患者血清生物化学指标的风险预测模型，并进行受试者操作特征分析。结果与正常对照相比，PF患者血清中的α-羟丁酸脱氢酶、乳酸脱氢酶、白蛋白、白蛋白与球蛋白比值、前白蛋白及钙浓度差异有统计学意义（P<0.05）。其中α-羟丁酸脱氢酶是一种有效预测潜在PF风险的生物化学检测指标。结论本研究对PF血清生物化学数据进行挖掘分析，成功构建了PF血清生物化学预测模型。

关键词: 肺纤维化, 临床生物化学, 数据建模

Abstract: Objective To determine promising indices for early diagnosis of pulmonary fibrosis (PF) by data extraction and analysis. Methods Biochemical data from patients with PF (n=29) and healthy controls (n=55) were collected and normalized by Z-score indexation and Log2 transformation,followed by principal component and Bayesian probit regression analyses. Signature parameters for PF were identified and used for discriminative function modeling and receiver operating characteristic analysis. Results The α-hydroxybutyric dehydrogenase (HBDH),lactic dehydrogenase,albumin,albumin:globulin ratio,prealbumin,and calcium parameters were significantly different between PF and healthy control samples (P<0.05),and discriminant functions of PF and health were constructed. HBDH was found to be the efficient parameter that could indicate the potential risk of PF. Conclusion This study is attempt to investigate PF serum biochemical characteristics by mining of clinical biochemical data. Discriminant functional model for predicting PF with signature biochemical parameters were constructed successfully.

Key words: pulmonary fibrosis, clinical biochemistry, data modeling

中图分类号:

R563

冷冬, 李桂芹, 王颖, 缪冉, 陈铎, 黄骁舾. 肺纤维化风险预测的临床生物化学模型[J]. 首都医科大学学报, 2019, 40(6): 875-880.

Leng Dong, Li Guiqin, Wang Ying, Miao Ran, Chen Duo, Huang Xiaoxi. A functional laboratory biochemical model for the prediction of pulmonary fibrosis risk[J]. Journal of Capital Medical University, 2019, 40(6): 875-880.

参考文献

[1] 何权瀛. 弥漫性肺间质纤维化临床诊断思路[J]. 临床肺科杂志, 2018(1):1-4.
[2] Thomeer M,Grutters J C,Wuyts W A,et al. Clinical use of biomarkers of survival in pulmonary fibrosis[J]. Respirat Res,2010,11(1):89-90.
[3] Jia S,Junwei S,Yirui X,et al. Plasma IL-6/IL-10 Ratio and IL-8,LDH,and HBDH level predict the severity and the risk of death in AIDS patients with\,pneumocystis\,pneumonia[J]. J Immunol Res,2016,2016:1-10.
[4] Scott L K, Toner R. Clinically promising biomarkers in cystic fibrosis pulmonary exacerbations[J]. Lung, 2017,195(4):397-401.
[5] Raghu G, Rochwerg B, Zhang Y, et al. An official ATS/ERS/JRS/ALAT Clinical Practice Guideline:Treatment of Idiopathic Pulmonary Fibosis:An Update of the 2011 Clinical Practice Guideline[J]. Am J Respirat Crit Care Med, 2015, 192(5):e3
[6] 胡良平. 基于SAS与R软件的主成分分析[J]. 四川精神卫生, 2018, 125(2):31-36.
[7] Ben S Q,Ni S S,Shen H H,et al. The dynamic changes of LDH isoenzyme 3 and D-dimer following pulmonary thromboembolism in canine[J]. Thrombos Res,2007,120(4):580-583.
[8] Hagadorn J E,Bloor C M,Yang M S. Elevated plasma activity of lactate dehydrogenase isoenzyme-3(LDH3) in experimentally induced immunologic lung injury[J]. Am J Pathol,1971,64(3):575-581.
[9] Emad A,Emad V. The value of BAL fluid LDH level in differentiating benign from malignant solitary pulmonary nodules[J]. J Cancer Res Clin Oncol,2008,134(4):489-493.
[10] 张永锋, 路跃武. 结缔组织病相关自发性纵隔气肿的临床研究[J]. 中华医学杂志, 2019, 99(23):1824-1826.
[11] Preus M,Bhargava A,Elrahmankhater A,et al. Diagnostic value of serum creatine kinase and lactate dehydrogenase isoenzyme determinations for monitoring early cardiac damage in rats[J]. Toxicol Lett,1988,42(2):225-233.
[12] 王贵勤,张军,刘靖丰,等. 呼吸衰竭心肌酶谱变化的研究[J]. 临床肺科杂志,2005,10(5):580-581.

肺纤维化风险预测的临床生物化学模型

A functional laboratory biochemical model for the prediction of pulmonary fibrosis risk

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	于晓敏;王辰;王军. Wnt信号通路的生物学效应及其与肺部疾病的关系[J]. 首都医科大学学报, 2009, 30(2): 182-184.
[2]	彭堃;庞宝森;危天倪;是若春;王虹;庄蝶微;严梅. 严重急性呼吸综合征的临床特点及遗留肺纤维化相关因素分析[J]. 首都医科大学学报, 2006, 27(1): 43-47.
[3]	陈颖;代华平;王辰;马仕芹;庞宝森;马力. 特发性肺纤维化患者蛋白C系统的研究[J]. 首都医科大学学报, 2006, 27(1): 59-62.