Maruthur Nisa M, Gribble Matthew O, Bennett Wendy L, Bolen Shari, Wilson Lisa M, Balakrishnan Poojitha, Sahu Anita, Bass Eric, Kao W H Linda, Clark Jeanne M
Corresponding author: Nisa M. Maruthur,
Diabetes Care. 2014;37(3):876-86. doi: 10.2337/dc13-1276.
We performed a systematic review to identify which genetic variants predict response to diabetes medications.
We performed a search of electronic databases (PubMed, EMBASE, and Cochrane Database) and a manual search to identify original, longitudinal studies of the effect of diabetes medications on incident diabetes, HbA1c, fasting glucose, and postprandial glucose in prediabetes or type 2 diabetes by genetic variation. Two investigators reviewed titles, abstracts, and articles independently. Two investigators abstracted data sequentially and evaluated study quality independently. Quality evaluations were based on the Strengthening the Reporting of Genetic Association Studies guidelines and Human Genome Epidemiology Network guidance.
Of 7,279 citations, we included 34 articles (N = 10,407) evaluating metformin (n = 14), sulfonylureas (n = 4), repaglinide (n = 8), pioglitazone (n = 3), rosiglitazone (n = 4), and acarbose (n = 4). Studies were not standalone randomized controlled trials, and most evaluated patients with diabetes. Significant medication-gene interactions for glycemic outcomes included 1) metformin and the SLC22A1, SLC22A2, SLC47A1, PRKAB2, PRKAA2, PRKAA1, and STK11 loci; 2) sulfonylureas and the CYP2C9 and TCF7L2 loci; 3) repaglinide and the KCNJ11, SLC30A8, NEUROD1/BETA2, UCP2, and PAX4 loci; 4) pioglitazone and the PPARG2 and PTPRD loci; 5) rosiglitazone and the KCNQ1 and RBP4 loci; and 5) acarbose and the PPARA, HNF4A, LIPC, and PPARGC1A loci. Data were insufficient for meta-analysis.
We found evidence of pharmacogenetic interactions for metformin, sulfonylureas, repaglinide, thiazolidinediones, and acarbose consistent with their pharmacokinetics and pharmacodynamics. While high-quality controlled studies with prespecified analyses are still lacking, our results bring the promise of personalized medicine in diabetes one step closer to fruition.
我们进行了一项系统综述,以确定哪些基因变异可预测对糖尿病药物的反应。
我们检索了电子数据库(PubMed、EMBASE和Cochrane数据库)并进行了手动检索,以识别关于糖尿病药物通过基因变异对糖尿病前期或2型糖尿病患者新发糖尿病、糖化血红蛋白(HbA1c)、空腹血糖和餐后血糖影响的原始纵向研究。两名研究人员独立审查标题、摘要和文章。两名研究人员依次提取数据并独立评估研究质量。质量评估基于加强遗传关联研究报告指南和人类基因组流行病学网络指南。
在7279篇文献中,我们纳入了34篇文章(N = 10407),评估了二甲双胍(n = 14)、磺脲类药物(n = 4)、瑞格列奈(n = 8)、吡格列酮(n = 3)、罗格列酮(n = 4)和阿卡波糖(n = 4)。这些研究并非独立的随机对照试验,且大多数评估的是糖尿病患者。血糖结局的显著药物 - 基因相互作用包括:1)二甲双胍与溶质载体家族22成员1(SLC22A1)、溶质载体家族22成员2(SLC22A2)、溶质载体家族47成员1(SLC47A1)、蛋白激酶A调节亚基β2(PRKAB2)、蛋白激酶A催化亚基α2(PRKAA2)、蛋白激酶A催化亚基α1(PRKAA1)和丝氨酸/苏氨酸蛋白激酶11(STK11)基因座;2)磺脲类药物与细胞色素P450 2C9(CYP2C9)和转录因子7样蛋白2(TCF7L2)基因座;3)瑞格列奈与内向整流钾通道蛋白11(KCNJ11)、溶质载体家族30成员8(SLC30A8)、神经源性分化因子1/β2(NEURODl/BETA2)、解偶联蛋白2(UCP2)和配对盒基因4(PAX4)基因座;4)吡格列酮与过氧化物酶体增殖物激活受体γ2(PPARG2)和蛋白酪氨酸磷酸酶受体D(PTPRD)基因座;5)罗格列酮与钾通道蛋白Q1(KCNQ1)和视黄醇结合蛋白4(RBP4)基因座;以及5)阿卡波糖与过氧化物酶体增殖物激活受体α(PPARA)、肝细胞核因子4α(HNF4A)、肝脂酶(LIPC)和过氧化物酶体增殖物激活受体γ辅激活因子1α(PPARGC1A)基因座。数据不足以进行荟萃分析。
我们发现了二甲双胍、磺脲类药物、瑞格列奈、噻唑烷二酮类药物和阿卡波糖的药物遗传学相互作用证据,这与其药代动力学和药效学一致。虽然仍缺乏进行预先设定分析的高质量对照研究,但我们的结果使糖尿病个性化医疗的前景又向实现迈进了一步。