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      Plasminogen activator inhibitor-1 4G/5G polymorphism and retinopathy risk in type 2 diabetes: a meta-analysis

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          Abstract

          Background

          Mounting evidence has suggested that plasminogen activator inhibitor-1 (PAI-1) is a candidate for increased risk of diabetic retinopathy. Studies have reported that insertion/deletion polymorphism in the PAI-1 gene may influence the risk of this disease. To comprehensively address this issue, we performed a meta-analysis to evaluate the association of PAI-1 4G/5G polymorphism with diabetic retinopathy in type 2 diabetes.

          Methods

          Data were retrieved in a systematic manner and analyzed using Review Manager and STATA Statistical Software. Crude odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of associations.

          Results

          Nine studies with 1, 217 cases and 1, 459 controls were included. Allelic and genotypic comparisons between cases and controls were evaluated. Overall analysis suggests a marginal association of the 4G/5G polymorphism with diabetic retinopathy (for 4G versus 5G: OR 1.13, 95%CI 1.01 to 1.26; for 4G/4G versus 5G/5G: OR 1.30, 95%CI 1.04 to 1.64; for 4G/4G versus 5G/5G + 4G/5G: OR 1.26, 95%CI 1.05 to 1.52). In subgroup analysis by ethnicity, we found an association among the Caucasian population (for 4G versus 5G: OR 1.14, 95% CI 1.00 to 1.30; for 4G/4G versus 5G/5G: OR 1.33, 95%CI 1.02 to 1.74; for 4G/4G versus 5G/5G + 4G/5G: OR 1.41, 95%CI 1.13 to 1.77). When stratified by the average duration of diabetes, patients with diabetes histories longer than 10 years have an elevated susceptibility to diabetic retinopathy than those with shorter histories (for 4G/4G versus 5G/5G: OR 1.47, 95%CI 1.08 to 2.00). We also detected a higher risk in hospital-based studies (for 4G/4G versus 5G/5G+4G/5G: OR 1.27, 95%CI 1.02 to 1.57).

          Conclusions

          The present meta-analysis suggested that 4G/5G polymorphism in the PAI-1 gene potentially increased the risk of diabetic retinopathy in type 2 diabetes and showed a discrepancy in different ethnicities. A higher susceptibility in patients with longer duration of diabetes (more than 10 years) indicated a gene-environment interaction in determining the risk of diabetic retinopathy.

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          Most cited references34

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          Molecular networks as sensors and drivers of common human diseases.

          The molecular biology revolution led to an intense focus on the study of interactions between DNA, RNA and protein biosynthesis in order to develop a more comprehensive understanding of the cell. One consequence of this focus was a reduced attention to whole-system physiology, making it difficult to link molecular biology to clinical medicine. Equipped with the tools emerging from the genomics revolution, we are now in a position to link molecular states to physiological ones through the reverse engineering of molecular networks that sense DNA and environmental perturbations and, as a result, drive variations in physiological states associated with disease.
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            Diabetes incidence and prevalence in Pima Indians: a 19-fold greater incidence than in Rochester, Minnesota.

            The incidence and prevalence of diabetes mellitus were determined in 3733 Pima Indians aged 5 years or over by periodic examinations over a 10-year period. The examinations included modified glucose tolerance tests and medical record review. The age-sex adjusted prevalence rate was 21.1% (SE = 0.7%). Prevalence was low in childhood and plateaued at 40--50% in adults over 35 years of age. The age-sex adjusted incidence rate of 26.5 cases/1000 person-years (SE = 1.9) is the highest reported diabetes incidence known to the authors. Incidence increased from low levels in childhood to peak at age 40 (males) or 50 (females) and then gradually declined. Diabetes incidence was 19 times that in the predominantly white population of Rochester, Minnesota (95% confidence interval, 16 to 22 times). The high incidence rate was found despite using a more stringent diagnostic criterion than customarily employed, and was shown not be due to biased follow-up of subjects.
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              Genetic associations in large versus small studies: an empirical assessment.

              Advances in human genetics could help us to assess prognosis on an individual basis and to optimise the management of complex diseases. However, different studies on the same genetic association sometimes have discrepant results. Our aim was to assess how often large studies arrive at different conclusions than smaller studies, and whether this situation arises more frequently when findings of first published studies disagree with those of subsequent research. We examined the results of 55 meta-analyses (579 study comparisons) of genetic associations and tested whether the magnitude of the genetic effect differs in large versus smaller studies. We noted significant between-study heterogeneity in 26 (47%) meta-analyses. The magnitude of the genetic effect differed significantly in large versus smaller studies in ten (18%), 20 (36%), and 21 (38%) meta-analyses with tests of rank correlation, regression on SE, and regression on inverse of variance, respectively. The largest studies generally yielded more conservative results than the complete meta-analyses, which included all studies (p=0.005). In 14 (26%) meta-analyses the proposed association was significantly stronger in the first studies than in subsequent research. Only in nine (16%) meta-analyses was the genetic association significant and replicated without hints of heterogeneity or bias. There was little concordance in first versus subsequent discrepancies, and large versus small discrepancies. Genuine heterogeneity and bias could affect the results of genetic association studies. Genetic risk factors for complex diseases should be assessed cautiously and, if possible, using large scale evidence.
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                Author and article information

                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central
                1741-7015
                2013
                2 January 2013
                : 11
                : 1
                Affiliations
                [1 ]Tianjin Medical University, 22 Qixiangtai Road, Tianjin, 300020, PR China
                [2 ]Tianjin Eye Hospital, Tianjin Key Laboratory of Ophthalmology and Visual Science, Tianjin Eye Institute, Clinical College of Ophthalmology Tianjin Medical University, 4 Gansu Road, Tianjin, 300020, PR China
                [3 ]Department of Ophthalmology, Medical School of Yale University, 300 George Street, New Haven, 06511, USA
                [4 ]Department of Thoracic Surgery, Tianjin Cancer Institute and Hospital, Huanhu West Road, Tianjin, 300060, PR China
                [5 ]Saybrook College, Yale University, 242 Elm Street, New Haven, 06511, USA
                Article
                1741-7015-11-1
                10.1186/1741-7015-11-1
                3565939
                23281898
                59819eba-3d02-4111-9178-d6998ea2a820
                Copyright ©2013 Zhang et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 May 2012
                : 2 January 2013
                Categories
                Research Article

                Medicine
                diabetic retinopathy,meta-analysis,pai-1,polymorphism,type 2 diabetes
                Medicine
                diabetic retinopathy, meta-analysis, pai-1, polymorphism, type 2 diabetes

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