基于研究组均值提取与统计推断法研究西格列汀降糖效果的影响因素
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摘要
目的:探索药物疗效应答差异的影响因素研究模式,寻找西格列汀降糖效果的关键影响因素,为该类药物的个体化选择提供依据。方法:系统检索二肽基肽酶-Ⅳ抑制剂(DPP-Ⅳi)相关的临床研究,采用MINORS表进行文献质量评价。以研究组为单位,提取西格列汀组各变量的平均值。采用Pearson相关或Spearman秩相关分析连续型原因变量与结果变量的相关性;参数检验与非参数检验法研究定性原因变量的不同组间结果是否具有统计学差异;对可能影响西格列汀降糖效果的原因变量进行两两相关性分析,以确定原因变量对结果的影响是否独立存在;对联用药物、降糖药物数量及降糖疗效间的关系进行多水平分析,以确定降糖药物数量对西格列汀疗效的影响。结果:共纳入西格列汀相关文献43篇,研究组47个。结果显示,高糖化血红蛋白(HbA1c)和胰岛素敏感性指标(HOMAIR),或未联用二甲双胍(Met)的患者能降低更多的HbA1c;基线期体质量越低的患者人群用药后HbA1c<7%的可能性增加;高C-肽、空腹血糖(FBG)和HbA1c,或未联用Met的患者降低FBG更甚;低体质量、BMI、HOMAIR和胰岛素水平,或未联用Met或磺酰脲类药物(SU)的人群能获得更多的餐后血糖(PBG)降低。BMI和体质量,HbA1c和FBG间存在相关性,提示可能存在共同作用。基础降糖药物数量越少的患者,用药后降低PBG的效果越好。结论:本法用于药物疗效影响因素的分析,具有更高的灵敏度。患者对西格列汀疗效的应答可能与其基线期HbA1c、血糖、BMI、胰岛素分泌和敏感性及联用降糖药物等有关。
OBJECTIVE To explore a research model for influential factors of drug efficacy response differences, and to locate the key contributory factors of sitagliptin hypoglycemic effect, thus providing a basis for individualized selection of such drugs. METHODS Clinical studies related to dipeptidyl peptidase-Ⅳ inhibitors(DPP-Ⅳi) were systematically searched and literature quality was evaluated by using the MINORS table. Each variable mean value of sitagliptin was extracted from different study groups. Pearson correlation or Spearman rank correlation was used to analyze the correlations between continuous causal variables and outcome variables; parametric test and nonparametric test were used to study whether the results of different groups of qualitative causal variables were statistically different. Pairwise correlation analysis was performed on the causal variables that may affect the hypoglycemic effect of sitagliptin to determine whether the effect of cause variables on the outcome was independent;multilevel analysis was performed on the relationship between the number of combined drugs, hypoglycemic drugs and hypoglycemic efficacy to determine the effect of the number of hypoglycemic drugs on the efficacy of sitagliptin. RESULTS A total of 43 literatures related to sitagliptin were included, with 47 study groups involved. The results showed that patients with high hemoglobin A1 c(HbA1 c) and homeostasis optimal model assessment for insulin action(HOMAIR), or without Metformin(Met) used in combination could have more HbA1 c reduced; The possibility of HbA1 c<7% would rise among patients with low baseline weight; A significant FBG's reduction was observed among patients with high C-peptide, fasting blood glucose(FBG) and HbA1 c, or without Metformin(Met) used in combination; patients with low weight, BMI, HOMAIR and insulin, or without Met or Sulfonylureas(SU)used in combinationcould obtain more postprandial blood glucose(PBG) reduction. There was a correlation between BMI and weight, HbA1 c and FBG, which indicated that a possible interaction existed. A better efficacy of reducing PBG was observed in patients with fewer basic hypoglycemic drugs after administration. CONCLUSION The method used to analyze the influencing factors of drug efficacy exhibited a higher sensitivity. The patient's response to sitagliptin may be related to baseline HbA1 c, blood glucose, BMI, insulin secretion and sensitivity, combination with hypoglycemic agents, etc.
OBJECTIVE To explore a research model for influential factors of drug efficacy response differences, and to locate the key contributory factors of sitagliptin hypoglycemic effect, thus providing a basis for individualized selection of such drugs. METHODS Clinical studies related to dipeptidyl peptidase-Ⅳ inhibitors(DPP-Ⅳi) were systematically searched and literature quality was evaluated by using the MINORS table. Each variable mean value of sitagliptin was extracted from different study groups. Pearson correlation or Spearman rank correlation was used to analyze the correlations between continuous causal variables and outcome variables; parametric test and nonparametric test were used to study whether the results of different groups of qualitative causal variables were statistically different. Pairwise correlation analysis was performed on the causal variables that may affect the hypoglycemic effect of sitagliptin to determine whether the effect of cause variables on the outcome was independent;multilevel analysis was performed on the relationship between the number of combined drugs, hypoglycemic drugs and hypoglycemic efficacy to determine the effect of the number of hypoglycemic drugs on the efficacy of sitagliptin. RESULTS A total of 43 literatures related to sitagliptin were included, with 47 study groups involved. The results showed that patients with high hemoglobin A1 c(HbA1 c) and homeostasis optimal model assessment for insulin action(HOMAIR), or without Metformin(Met) used in combination could have more HbA1 c reduced; The possibility of HbA1 c<7% would rise among patients with low baseline weight; A significant FBG's reduction was observed among patients with high C-peptide, fasting blood glucose(FBG) and HbA1 c, or without Metformin(Met) used in combination; patients with low weight, BMI, HOMAIR and insulin, or without Met or Sulfonylureas(SU)used in combinationcould obtain more postprandial blood glucose(PBG) reduction. There was a correlation between BMI and weight, HbA1 c and FBG, which indicated that a possible interaction existed. A better efficacy of reducing PBG was observed in patients with fewer basic hypoglycemic drugs after administration. CONCLUSION The method used to analyze the influencing factors of drug efficacy exhibited a higher sensitivity. The patient's response to sitagliptin may be related to baseline HbA1 c, blood glucose, BMI, insulin secretion and sensitivity, combination with hypoglycemic agents, etc.
引文
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[2] Kim YG, Hahn S, Oh TJ,et al.Differences in the glucose-lowering efficacy of dipeptidyl peptidase-4 inhibitors between Asians and non-Asians: a systematic review and meta-analysis[J].Diabetologia, 2013, 56(4): 696-708.
[3] Nomiyama T, Akehi Y, Takenoshita H,et al.Contributing factors related to efficacy of the dipeptidyl peptidase-4 inhibitor sitagliptin in Japanese patients with type 2 diabetes.[J].Diabetes Res Clin Pract, 2012, 95(2): e27-e28.
[4] Esposito K, Chiodini P, Capuano A,et al.Baseline glycemic parameters predict the hemoglobin A1c response to DPP-4 inhibitors[J].Endocrine, 2014, 46(1): 43-51.
[5] Stratton IM, Adler AI, Neil HA,et al.Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study[J].BMJ, 2000, 321(7258): 405-412.
[6] Dan HL, Bai Y, Zhang YL,et al.Medical research value and evaluation of Meta analysis[J].Chin J Med Sci Res Manage(中国医学科研管理杂志), 2003, 16(1): 12-15.
[7] Sun X, Wang L, Li YP.Using Individual Patients Data in Meta-analyses for Assessing Effects of Health care Interventions[J].Chin J Evid-based Med(中国循证医学杂志), 2010, 10(8): 998-1003.
[8] Zeng XT, Zhuang LP, Yang GZ,et al.Meta Analysis Series Seven: Quality assessment tool of non-randomized experimental, diagnostic, and animal experiments.[J].Chin J Evid Based Cardiovasc Med(中国循证心血管医学杂志), 2012, 4(6): 496-499.
[9] Murad MH, Montori VM, Ioannidis JPA,et al.How to Read a Systematic Review and Meta-analysis and Apply the Results to Patient Care Users' Guides to the Medical Literature[J].JAMA, 2014, 312(2): 171-179.
[10] Group OLOE.The Oxford 2011 Levels of Evidence[J].OCEBM, 2016.
[11] Higgins JPT, Thompson SG.Controlling the risk of spurious findings from meta-regression[J].Statis Med, 2004, 23(11): 1663-1682.
[12] Wang D, Qu JX, Mou ZY,et al.Discussing on the Research of Heterogeneity in Meta-analysis[J].Chin J Evid-based Med(中国循证医学杂志), 2009, 9(10): 1115-1118.
[13] Guo YF.Robust Statistic and Comparative Analysis of the Robustness of the Statistic[J].Stat Res(统计研究), 2007, 24(9): 82-85.
[14] Zhang TS, Liu JB, Zhong WZ.Application of Stata in Exploring Sources of Heterogeneity: Meta-Regression Analysis[J].J Evid-Based Med(循证医学), 2009, 9(1): 48-50.
[15] Bihan H, Ng WL, Magliano DJ,et al.Predictors of efficacy of GLP-1 agonists and DPP-4 inhibitors: A systematic review[J].Diabetes Res Clin Pract, 2016, 121: 27-34.