Type 2 diabetes (T2DM) patients are susceptible to Helicobacter pylori (HP), and it has been reported that the occurrence of proteinuria is associated with HP infection in T2DM patients; however, this view remains controversial. This meta-analysis aimed to explore the association between HP infection and the occurrence of proteinuria in T2DM patients. In addition, we hope to provide some recommendations to readers in clinical or related fields.
Our meta-analysis was conducted with the methodology of the Cochrane Collaboration. Search strategies were formulated by relevant professionals. Case–control studies that compared the occurrence of proteinuria in T2DM patients with and without HP infection were involved in our meta-analysis. Relevant English or Chinese studies were searched on online databases before 2018, including PubMed, the Cochrane library, Medline, Google Scholar, the China National Infrastructure, and Wanfang database. The search strategies were “diabetic proteinuria, diabetic microalbuminuria, diabetic albuminuria, diabetic kidney disease, diabetic renal dysfunction, diabetic renal disease, diabetic nephropathy, diabetic complications, and diabetic mellitus, combined with HP.” The quality of these involved articles was separately assessed by two investigators using the Newcastle–Ottawa Scale (NOS). Odds ratios ( ORs) and associated 95% confidence intervals ( CIs) were extracted and pooled using fixed-effects models.
Seven studies involving 1029 participants were included. The quality of these seven articles was all above five stars as assessed by NOS, and there was no significant publication bias in our meta-analysis. We found that T2DM patients with HP infection had a 2.00 times higher risk of the occurrence of proteinuria than patients without HP infection ( OR: 2.00, 95% CI: 1.48–2.69).
本荟萃分析纳入与2型糖尿病患者出现蛋白尿和HP感染有关的病例对照研究。在以下数据库搜索了2018年以前的英文 或中文研究:Pubmed,Cochrane图书馆,Medline,谷歌学术,中国知网(CNKI)和万方数据库,检索策略为:糖尿病蛋白 尿,糖尿病微量白蛋白尿,糖尿病白蛋白尿,糖尿病肾病,糖尿病肾功能不全,糖尿病并发症和糖尿病,分别结合幽门螺旋 杆菌。纳入文章的质量由2名实验人员分别使用NOS量表进行评估。 本研究采用固定效应模型分析整合相关数据。