胃癌前病变病证结合风险预测模型的构建研究
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摘要
目的筛查胃癌前病变危险因素,构建病证结合风险预测模型。方法选择胃癌前病变患者76例和非胃癌前病变(单纯慢性萎缩性胃炎和/或伴胃窦轻度肠化)患者214例,通过临床问卷调查采集饮食、家族史、合并病、症状、胃镜及病理诊断等信息,归纳中医证候要素,检测血清胃蛋白酶原Ⅰ(PGⅠ)、胃蛋白酶原Ⅱ(PGⅡ),胃泌素17(G-17)、Hp-Ig G抗体、癌胚抗原(CEA)、糖类抗原724(CA724),以血清学指标、危险因素、中医证候要素为协变量,以胃癌前病变为因变量,运用Logistic单因素、多因素回归分析,构建病证结合胃癌前病变风险评估模型。结果胃癌前病变患者胃癌家族史比例高,合并胃食管反流病、冠心病比例高,幽门螺杆菌(Hp)阳性率低,辛辣饮食、饮浓茶、饮酒、焦虑比例高;证候要素中血瘀比例较高,PGⅠ、PGⅡ、PGR(PGⅠ/PGⅡ)、G-17低于非胃癌前病变患者。筛选出血瘀(X1)、胃癌家族史(X2)、胃食管反流病(X3)、冠心病(X4)、易怒(X5)、浓茶(X6)、辛辣饮食(X7)为高危因素,得出胃癌前病变风险预测模型为:ln(p/1|P)=-3.005+0.763X1+0.911X2+0.690X3+1.133X4+0.966X5+1.077X6+0.726X7,该模型拟合度较好,整体预测有效率为73.1%。结论该研究为胃癌前病变风险评估研究的初步探索,所建立的预测模型有可能为临床监测和针对性治疗提供简单、有效的测评工具。
Objective To establish a combination of syndrome and disease risk predicting model for precancerous lesion of gastric cancer( PLGC) by screening PLGC risk factors. Methods Questionnaire survey data was collected on the risk factors and TCM syndromes of 76 PLGC patients and 214 simple chronic atrophic gastritis and/or mild intestinal metaplasia,the serum pepsinogen( PG) Ⅰ,PG Ⅱ,gastrin-17( G-17),Ig G anti-Helicobacter pylori antibody( Hp-Ig G antibody),CEA,and CA724 were investigated. The serum indices,risk factors and TCM syndrome essentials were selected as the covariates,the PLGC as outcome variable,a prediction model to assess the risk of PLGC among chronic gastritis patients was established,the relationship between the serum indices,risk factors and TCM syndrome essentials were analyzed. Results We found that PLGC patients were educated at higher level,had higher rate of having family history of gastric cancer. They had higher ratios with complicated gastroesophageal reflux disease or coronary heart disease,and lower Hp positive ratio. They ate more spicy food,drank strong tea and alcohol. They were apt to be nervous and anxiety. Blood stasis ratio occupied higher level in syndrome elements. Levels of PGⅠ,PGⅡ,PGR( PGⅠ/PGⅡ),G-17 were lower than non-PLGC patients. Blood stasis( X1),family history of gastric cancer( X2),gastroesophageal reflux disease( X3),coronary heart disease( X4),liability to anger( X5),strong tea( X6),spicy food( X7) were screened as risk factors. The risk predicting model of PLGC was: ln(p/1 | P) =-3. 005+ 0. 763 X1 + 0. 911 X2 + 0. 690 X3 + 1. 133 X4 + 0. 966 X5 + 1. 077 X6 + 0. 726 X7. It was well agreeable to fit and the overall predicting rate was 73. 1%. Conclusions This study was a preliminary research on risk predicting model for PLGC. The risk predicting model of PLGC might become an effective and simple instrument for clinical monitoring and treatment.
Objective To establish a combination of syndrome and disease risk predicting model for precancerous lesion of gastric cancer( PLGC) by screening PLGC risk factors. Methods Questionnaire survey data was collected on the risk factors and TCM syndromes of 76 PLGC patients and 214 simple chronic atrophic gastritis and/or mild intestinal metaplasia,the serum pepsinogen( PG) Ⅰ,PG Ⅱ,gastrin-17( G-17),Ig G anti-Helicobacter pylori antibody( Hp-Ig G antibody),CEA,and CA724 were investigated. The serum indices,risk factors and TCM syndrome essentials were selected as the covariates,the PLGC as outcome variable,a prediction model to assess the risk of PLGC among chronic gastritis patients was established,the relationship between the serum indices,risk factors and TCM syndrome essentials were analyzed. Results We found that PLGC patients were educated at higher level,had higher rate of having family history of gastric cancer. They had higher ratios with complicated gastroesophageal reflux disease or coronary heart disease,and lower Hp positive ratio. They ate more spicy food,drank strong tea and alcohol. They were apt to be nervous and anxiety. Blood stasis ratio occupied higher level in syndrome elements. Levels of PGⅠ,PGⅡ,PGR( PGⅠ/PGⅡ),G-17 were lower than non-PLGC patients. Blood stasis( X1),family history of gastric cancer( X2),gastroesophageal reflux disease( X3),coronary heart disease( X4),liability to anger( X5),strong tea( X6),spicy food( X7) were screened as risk factors. The risk predicting model of PLGC was: ln(p/1 | P) =-3. 005+ 0. 763 X1 + 0. 911 X2 + 0. 690 X3 + 1. 133 X4 + 0. 966 X5 + 1. 077 X6 + 0. 726 X7. It was well agreeable to fit and the overall predicting rate was 73. 1%. Conclusions This study was a preliminary research on risk predicting model for PLGC. The risk predicting model of PLGC might become an effective and simple instrument for clinical monitoring and treatment.
引文
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[14]Terasawa T,Nishida H,Kato K,et al.Prediction of gastric cancer development by serum pepsinogen test and Helicobacter pylori seropositivity in Eastern Asians:A systematic review and meta-analysis[J].PLo S One,2014,9(10):el09783.
[15]Yoshida T,Kato J,Inoue I,et al.Cancer development based on chronic active gastritis and resulting gastric atrophy as assessed by serum levels of pepsinogen and Helicobacter pylori antibody titer[J].Cancer,2014,134(6):1445-1457.
[16]Kondo H,Yoneda M,Inoue K.Application of risk evaluation for gastric cancer(ABC classification)to populationbased gastric cancer screening:Current status and issues in Maniwa City,Okayama Prefecture[J].Gastrointest Cancer Screen,2015,53:589-599.
[17]Suzuki H,Saito Y.Current situation of gastric cancer risk evaluation system"ABC classification"in Japanese local governments[J].Gastrointes Cancer Screen,2015,53(4):463-470.
[18]Yanaoka K,Oka M,Mukoubayashi C,et al.Cancer highrisk subjects identified p H by serum pepsinogen tests:outcomes after 10-year follow-up in asymptomatic middle-aged males[J].Cancer Epidemiol Biomarkers Prev,2008,17(4):838-845.
[19]Hamashimal C,Sasazuki S,Inoue M.Receiver operating characteristic analysis of prediction for gastric cancer development using serum pepsinogen and Helicobacter pylori antibody tests[J].BMC Cancer,2017,17(1):183-192.
[20]Kikuchi R,Abe Y,Iijima K,et al.Low serum levels of pepsinogen and gastrin 17 are predictive of extensive gastric atrophy with high-risk of early gastric cancer[J].Exp Med,2011,223(1):35-44.
[21]曹雪源,贾志芳,马洪喜,等.血清抗幽门螺杆菌抗体、胃蛋白酶原水平与胃癌发病的相关性分析[J].中国实验诊断学,2012,16(6):1026-1028.
[22]田剑波,缪小平,林东昕.中国人群常见恶性肿瘤遗传风险预测模型研究进展[J].生物产业技术,2016,10-15.
[23]贺佳,张智坚,贺宪民.肝癌术后无瘤生存期的人工神经网络预测[J].数理统计与管理,2002,21(4):14-16.
[24]刘建平,程锦泉,张仁利,等.应用分类树模型构建缺血性脑卒中发病风险的预测模型[J].中国慢性病预防与控制,2012,20(3):254-258.
[25]高建伟.多状态Markov模型在轻度认知障碍向阿尔茨海默病转归研究中的应用[D].太原:山西医科大学,2011.
[26]Chang J,Huang Y,Wei L,et al.Risk prediction of esophageal squamous cell carcinoma with common genetic variants and lifestyle factors in Chinese population[J].Carcinogenesis,2013,34(8):1782-1786.
[2]范尧夫,吴燕敏,刘皓,等.中国华东地区人群胃癌癌前病变发病相关危险因素分析[J].胃肠病学和肝病学杂志,2014,23(2):143-146.
[3]Rugge M,Genta RM.Staging and grading of chronic gastritis[J].Hum Pathol,2005,36(3):228-223.
[4]Schlemper RJ,Riddell RH,Kato Y,et al.The Vienna classification of gastrointestinal epithelial neoplasia[J].Gut,2000,10(2):128-139.
[5]中华医学会消化内镜学分会.慢性胃炎的内镜分型分级标准及治疗的试行意见[J].中华消化内镜杂志,2004,21(2):77-78.
[6]中华中医药学会脾胃病分会.慢性萎缩性胃炎中医诊疗共识意见[J].中医杂志,2010,51(8):749-753.
[7]魏戌,谢雁鸣,田峰,等.病证结合构建慢病风险预测模型的思路与方法[J].中国中医基础医学杂志,2017,23(2):180-183.
[8]张万岱,李军祥,陈治水,等.慢性胃炎中西医结合诊疗共识意见[J].中国中西医结合杂志,2012,32(6):738-743.
[9]叶彬,来丽群.慢性萎缩性胃炎中医辨证分型与胃镜表现的相关性研究[J].浙江中医杂志,2014,49(3):159-160.
[10]刘华一,张莎,杨阔,等.胃癌前病变中医证候分型与胃蛋白酶原的相关性研究[J].中国中西医结合消化杂志,2016,24(6):449-454.
[11]高国林,杨原,任彩文,等.MNNG加吐温20和维生素D3对大鼠前胃的影响[J].世界华人消化杂志,1999,7(9):764.
[12]李卫强,朱西杰,魏雪红.冑癌前病变中医证型与甲皱微循环的相关性研究[J].实用中医内科杂志,2012,26(12):8-9.
[13]Chang YW,Huangbo Y,Lee JW,et al.Clinical parameters including serum pepsinogen level and management strategy in patients with premalignant gastric dysplasia[J].Gastroenterol Hepatol,2011,23(5):405-410.
[14]Terasawa T,Nishida H,Kato K,et al.Prediction of gastric cancer development by serum pepsinogen test and Helicobacter pylori seropositivity in Eastern Asians:A systematic review and meta-analysis[J].PLo S One,2014,9(10):el09783.
[15]Yoshida T,Kato J,Inoue I,et al.Cancer development based on chronic active gastritis and resulting gastric atrophy as assessed by serum levels of pepsinogen and Helicobacter pylori antibody titer[J].Cancer,2014,134(6):1445-1457.
[16]Kondo H,Yoneda M,Inoue K.Application of risk evaluation for gastric cancer(ABC classification)to populationbased gastric cancer screening:Current status and issues in Maniwa City,Okayama Prefecture[J].Gastrointest Cancer Screen,2015,53:589-599.
[17]Suzuki H,Saito Y.Current situation of gastric cancer risk evaluation system"ABC classification"in Japanese local governments[J].Gastrointes Cancer Screen,2015,53(4):463-470.
[18]Yanaoka K,Oka M,Mukoubayashi C,et al.Cancer highrisk subjects identified p H by serum pepsinogen tests:outcomes after 10-year follow-up in asymptomatic middle-aged males[J].Cancer Epidemiol Biomarkers Prev,2008,17(4):838-845.
[19]Hamashimal C,Sasazuki S,Inoue M.Receiver operating characteristic analysis of prediction for gastric cancer development using serum pepsinogen and Helicobacter pylori antibody tests[J].BMC Cancer,2017,17(1):183-192.
[20]Kikuchi R,Abe Y,Iijima K,et al.Low serum levels of pepsinogen and gastrin 17 are predictive of extensive gastric atrophy with high-risk of early gastric cancer[J].Exp Med,2011,223(1):35-44.
[21]曹雪源,贾志芳,马洪喜,等.血清抗幽门螺杆菌抗体、胃蛋白酶原水平与胃癌发病的相关性分析[J].中国实验诊断学,2012,16(6):1026-1028.
[22]田剑波,缪小平,林东昕.中国人群常见恶性肿瘤遗传风险预测模型研究进展[J].生物产业技术,2016,10-15.
[23]贺佳,张智坚,贺宪民.肝癌术后无瘤生存期的人工神经网络预测[J].数理统计与管理,2002,21(4):14-16.
[24]刘建平,程锦泉,张仁利,等.应用分类树模型构建缺血性脑卒中发病风险的预测模型[J].中国慢性病预防与控制,2012,20(3):254-258.
[25]高建伟.多状态Markov模型在轻度认知障碍向阿尔茨海默病转归研究中的应用[D].太原:山西医科大学,2011.
[26]Chang J,Huang Y,Wei L,et al.Risk prediction of esophageal squamous cell carcinoma with common genetic variants and lifestyle factors in Chinese population[J].Carcinogenesis,2013,34(8):1782-1786.