本文针对二分类变量结局指标相对（而非绝对）治疗效果的不一致性。证据本身不会因不同研究结果具有一致性而升级，但可能因不一致而降低质量级别。衡量一致性的标准包括点估计值的相似性、可信区间的重叠程度以及统计学判定标准包括异质性检验和I2。系统评价作者应提出并检验少数几个与患者、干预措施、结局指标以及方法学相关的先验假设以探寻异质性来源。当不一致性很大且无法解释时，因不一致性而降低质量级别是恰当的，特别当某些研究显示有显著益处而其他显示无益甚至有害时（而非仅是疗效大与疗效小的比较）。明显的亚组效应可能不可靠。如果亚组效应满足以下条件，其可信度将会增加：基于少数几个有具体方向的先验假设、亚组比较来自研究内而非研究间、交互检验的P值小、结果有生物学意义。
Globally, the incidence of diabetes has grown rapidly. The prevalence of diabetes in China was 11.6% in 2010. Diabetes has become a huge challenge for public health. The cause of diabetes is not yet completely clear. Potential risk factors include genetic factors, environmental factors, and interactions between the two. Genome-wide association studies have found a series of genetic variants that are closely related to diabetes. Several environmental factors, such as excessive energy intake and lack of physical activity, have been associated with increased risk of diabetes. In the meanwhile, genetic and environmental factors could modify each other’s effect on diabetes risk. With the advent of molecular techniques, post-genomics research, gut microbiota, and trans-omics have provided novel perspectives for the study of diabetes risk factors.
ObjectiveTo explore the association of elongase of very long chain fatty acids family member 6 (ELOVL6) gene with increased risk of large-artery atherosclerosis stroke (LAA) in Han Chinese population in Chengdu.MethodsHan Chinese populations in Chengdu, Sichuan were chosen for this study using the case-control design between January 2015 and December 2017. The genotypes and haplotypes of six single nucleotides polymorphisms (SNPs) of ELOVL6 gene (rs3813825, rs17041272, rs4141123, rs9997926, rs6824447, and rs12504538) were analyzed in different genetic models in entire samples, and gene-enviromental interaction analyses were also carried out to get an insight of the risk factors for LAA. At the same time, we also analyzed the gene expression profile in peripheral blood mononuclear cells between groups.ResultsA total of 240 LAA cases and 211 healthy controls were enrolled in this study. All the enrolled subjects presented CC genotype of rs9997926, while the other five SNPs (rs3813825, rs17041272, rs4141123, rs6824447, and rs12504538) were genotyped successfully in all the enrolled subjects. rs17041272 polymorphism and TGTTG haplotype were significantly associated with LAA risk in studied population [CC/(CG+GG): odds ratio (OR)=0.640, 95% confidence interval (CI) (0.423, 0.968), P=0.034; TGTTG: OR=1.776, 95%CI (1.069, 2.951), P=0.024], and the interaction among rs17041272, rs6824447 SNPs and dyslipidemia increased susceptibility to LAA [OR=2.737, 95%CI (1.715, 4.368), P<0.001]. The ELOVL6 gene expression level was higher in LAA subjects (t=−3.167, P=0.003).ConclusionsELOVL6 gene is associated with LAA risk in Han nationality of Chinese population in Chengdu, and the interaction of gene-environmental risk factors could be of great importance in pathophysiology of LAA.
The interaction mechanism between mental disorders and diabetes is complex, involving genetics, endocrine metabolism, inflammation, oxidative stress and other aspects, which makes it difficult to treat patients with mental disorders complicated by diabetes. Such patients mostly use drugs and non-drug interventions to relieve symptoms of mental disorders and improve blood sugar levels, but the mechanism of mental disorders and diabetes needs to be systematically summarized and needs practical means to intervene. This article starts with the pathogenesis of diabetes and then describes the interaction mechanism of schizophrenia, bipolar disorder, depression and diabetes in detail. Finally, the intervention measures for patients with mental disorders complicated by diabetes are summarized, which aims to provide a reference for medical staff engaged in related work.
Objective To evaluate the risk factors for cognitive impairment and their interactions in acute ischemic stroke (IS) patients. Methods IS patients admitted to the Department of Neurology, the People’s Hospital of Mianyang between January 2019 and January 2022 were selected. Patients were divided into a cognitive impairment group and a cognitive normal group. The demographic characteristics and clinical data of the subjects were collected, and the traditional risk factors for cognitive impairment were determined by univariate and multivariate logistic regression analysis. The multifactor dimensionality reduction test was used to detect the possible interactions between risk factors. Results A total of 255 patients were included. Among them, 88 cases (34.5%) in the cognitive impairment group and 167 cases (65.5%) in the cognitive normal group. The results of factor logistic regression analysis showed that after adjusting for covariates, big and medium infarction volume, severe IS, moderate to severe carotid artery stenosis as well as high hypersensitive C-reactive protein (hs-CRP) were associated with post-IS cognitive impairment (P<0.05). The cognitive impairment increased by 22.632 times [odds ratio=22.632, 95% confidence interval (5.980, 85.652), P<0.001] in patients with big and medium infarction volume, severe IS and high hs-CRP. Conclusions The cognitive impairment is common in acute IS. Patients with big and medium infarction volume, non-mild stroke, carotid artery stenosis, high hs-CRP, and non-right sided infarction are prone to cognitive impairment, and there are complex interactions among these risk factors.
ObjectiveTo investigate the factors that influence Chinese residents, self-rated health and the effects of the multilevel health insurance system and neighborhood social capital on self-rated health. MethodsBased on the 2018 China labor-force dynamics survey data, and Stata 15.0 software was used to conduct χ2 test, univariate analysis and multiple logistic regression model were used to analyze the influencing factors of self-rated health of Chinese residents. An interaction model was used to analyze the interactive effects of the multilevel health insurance system and the social capital of the neighborhood on self-rated health. ResultsA total of 10 201 people were investigated in this study, and 39.20% of them were self-rated unhealthy. After adjusting for confounders, the results of the multivariate logistic regression model showed that having social health insurance (OR=0.8, 95%CI 0.7 to 1.0) and having neighborhood social capital (OR=0.7, 95%CI 0.6 to 0.8) were more inclined to self-rated health. In addition, the results showed that being male, having a college degree or higher, having a job, and drinking alcohol increased the risk of self-rated unhealthy (P<0.05); whereas being 45-59 years of age, 60 years of age or older, in the central and western regions, exercising regularly, and having a disease or injury within two weeks decreased the risk of self-rated unhealthy (P<0.05). There was a positive multiplicative interaction effect between health insurance and neighborhood social capital on residents’ self-rated health (univariate: OR=1.5, 95%CI 1.1 to 3.7, P<0.05; multivariate: OR=1.7, 95%CI 1.2 to 2.4, P<0.05), and negative additive interactions (RERI=−0.8, 95%CI −1.4 to −0.1; AP=−0.3, 95%CI −0.6 to −0.1; SI=0.6, 95%CI 0.5 to 0.8). ConclusionAttention should be paid to the self-rated health status of key populations through means such as health promotion and education, and healthy behavior lifestyles should be promoted. The health insurance system should be further improved, and attention should be paid to the role of social capital in the neighborhood, encouraging residents to actively build a good social neighborhood, and realizing the coordinated development of the multilevel health insurance system and the social capital in the neighborhood.
Subpopulation treatment effect pattern plot (STEPP) method is a method for examining the relationship between treatment effects and continuous covariates and is characterized by dividing the study population into multiple overlapping subpopulations to be analyzed based on continuous covariate values. STEPP method has a different purpose than traditional subgroup analyses, and STEPP has a clear advantage in exploring the relationship between treatment effects and continuous covariates. In this study, the concepts, advantages, and subpopulation delineation methods of the STEPP method are introduced, and the specific operation process and result interpretation methods of STEPP method analysis using the STEPP package in R language are presented with examples.