Association between transcription factor 7-like 2 rs7903146 polymorphism and diabetic retinopathy in type 2 diabetes mellitus: A meta-analysis

Association of transcription factor 7-like 2 (rs7903146) gene polymorphism with diabetic retinopathy

BACKGROUND

Diabetic retinopathy (DR) is one of the most common diabetic complications. Genetic factors play an important role in the development and progression of DR. So, the present study aimed to investigate the association of TCF7L2 (rs7903146) gene polymorphism with the risk of DR in type1 and type2 DM (T1DM and T2DM) in the Egyptian population.

MATERIALS AND METHODS

This work is a case-control study in which 550 diabetic patients were enrolled. Among them, 280 diabetics with DR (120 T1DM and 160 with T2DM) and 270 diabetic patients without DR (120 T1DM and 150 with T2DM). Besides, 120 healthy subjects as a control group. Genotyping of TCF7L2 (rs7903146) (C/T) was done following DNA extraction using polymerase chain reaction-restriction fragment length polymorphism.

RESULTS

C allele and CC genotype of TCF7L2 (rs7903146) were significantly associated with increased risk for DR within T2DM in multiplicative and recessive models. While dominant model showed no significant association with DR. Although TC may be associated with a decreased risk for DR in T1DM and T2DM in over dominant model, there was no significant association of TCF7L2 (rs7903146) with the risk of DR susceptibility within T1DM in multiplicative, dominant, and recessive models.

CONCLUSION

The present study revealed the association of TCF7L2 (rs7903146) polymorphism with DR susceptibility within diabetic patients. Therefore, TCF7L2 (rs7903146) gene polymorphism may have a prognostic value for diabetic retinopathy in the Egyptian population. Further work is required to confirm the association of this polymorphism as a risk for DR.

Association of transcription factor 7-like 2 (TCF7L2) gene polymorphism with type 2 diabetes mellitus in Chinese Korean ethnicity population

Presently, data on the type 2 diabetes mellitus (T2DM) in Chinese Korean ethnicity are very scarce. This study aimed to explore the relationship between the transcription factor 7-like 2 (TCF7L2) and T2DM in Chinese Korean ethnicity population. This case-control study involved 43 T2DM Chinese Korean ethnicity patients (T2DM group) and 43 healthy Chinese Korean ethnicity normoglycemic subjects as controls (Control group). All included participants aged from 40 to 75 years old. Clinical and biological data were collected to determine the phenotypic traits. The restriction fragment length polymorphism-polymerase chain reaction was used to analyze the TCF7L2 by genotyping for rs7903146 (C/T). Spectrophotometer with Chronolab kits was used to conduct the biochemical analyses. TCF7L2 was associated with T2DM in the Chinese Korean ethnicity population (P < .01 for alleles, and P < .05 for genotypes). Significant differences were found 2 groups regarding the T allele (37.2% T2DM patients vs 15.1% healthy subjects, P < .01), and G allele (62.8% T2DM patients vs 84.9% healthy subjects, P < .01). The risk genotypes were GG (83.7% T2DM patients, vs 44.2% healthy control, P < .01), GT (4.7% T2DM patients, vs 20.9% healthy control, P = .04), and TT (11.6% T2DM patients, vs 34.9% healthy control, P = .01). The results of this study demonstrated that TCF7L2 is associated with T2DM in the Chinese Korean ethnicity population, which is an important risk factor for T2DM in this population.

Multilocus genetic risk score for diabetic retinopathy in the Han Chinese population of Taiwan

The aim of this study is to explore the effect of genetic variation on diabetic retinopathy (DR) risk in a Taiwanese population. The logistic regression model was used to evaluate the relationship between DR status and risk factors, including the conventional parameters and genetic risk score (GRS). Candidate single nucleotide polymorphisms (SNPs) in GRS were selected based on previous reports with a combined P < 10^-4 (genome-wide association) and P < 0.05 (meta-analysis). In total, 58 SNPs in 44 susceptibility loci were selected, and four were used to calculate GRS. After adjustment for age, systolic blood pressure, diabetes duration, and HbA1c, the DR risk was 4.95 times higher for patients in the top GRS third tile than for those in the bottom third tile (95% CI = 2.99-8.18; P < 0.001). The addition of genetic information improved DR prediction, increasing the area under the curve (AUC) from 0.72 to 0.77 (P = 0.0024) and improving the sensitivity of the model such that 40 more subjects were reclassified into DR status. The developed multivariate logistic regression model combining conventional risk factors and the multilocus GRS can predict DR, thus enabling timely treatment to reduce blindness in T2D patients.

Diabetic macular oedema: under-represented in the genetic analysis of diabetic retinopathy

Diabetic retinopathy, a complication of both type 1 and type 2 diabetes, is a complex disease and is one of the leading causes of blindness in adults worldwide. It can be divided into distinct subclasses, one of which is diabetic macular oedema. Diabetic macular oedema can occur at any time in diabetic retinopathy and is the most common cause of vision loss in patients with type 2 diabetes. The purpose of this review is to summarize the large number of genetic association studies that have been performed in cohorts of patients with type 2 diabetes and published in English-language journals up to February 2017. Many of these studies have produced positive associations with gene polymorphisms and diabetic retinopathy. However, this review highlights that within this large body of work, studies specifically addressing a genetic association with diabetic macular oedema, although present, are vastly under-represented. We also highlight that many of the studies have small patient numbers and that meta-analyses often inappropriately combine patient data sets. We conclude that there will continue to be conflicting results and no meaningful findings will be achieved if the historical approach of combining all diabetic retinopathy disease states within patient cohorts continues in future studies. This review also identifies several genes that would be interesting to analyse in large, well-defined cohorts of patients with diabetic macular oedema in future candidate gene association studies.

Meta-analysis of association between TCF7L2 polymorphism rs7903146 and type 2 diabetes mellitus

BACKGROUND

Large scale association studies have found a significant association between type 2 diabetes mellitus (T2DM) and transcription factor 7-like 2 (TCF7L2) polymorphism rs7903146. However, the quality of data varies greatly, as the studies report inconsistent results in different populations. Hence, we perform this meta-analysis to give a more convincing result.

METHODS

The articles, published from January 1st, 2000 to April 1st, 2017, were identified by searching in PubMed and Google Scholar. A total of 56628 participants (34232 cases and 22396 controls) were included in the meta-analysis. A total of 28 studies were divided into 4 subgroups: Caucasian (10 studies), East Asian (5 studies), South Asian (5 studies) and Others (8 studies). All the data analyses were analyzed by the R package meta.

RESULTS

The significant association was observed by using the dominant model (OR = 1.41, CI = 1.36 - 1.47, p < 0.0001), recessive model (OR = 1.58, CI = 1.48 - 1.69, p < 0.0001), additive model(CT vs CC) (OR = 1.34, CI = 1.28-1.39, p < 0.0001), additive model(TT vs CC) (OR = 1.81, CI = 1.69-1.94, p < 0.0001)and allele model (OR = 1.35, CI = 1.31-1.39, p < 0.0001).

CONCLUSION

The meta-analysis suggested that rs7903146 was significantly associated with T2DM in Caucasian, East Asian, South Asian and other ethnicities.

Clinical worthlessness of genetic prediction of common forms of diabetes mellitus and related chronic complications: A position statement of the Italian Society of Diabetology

AIM

We are currently facing several attempts aimed at marketing genetic data for predicting multifactorial diseases, among which diabetes mellitus is one of the more prevalent. The present document primarily aims at providing to practicing physicians a summary of available data regarding the role of genetic information in predicting diabetes and its chronic complications.

DATA SYNTHESIS

Firstly, general information about characteristics and performance of risk prediction tools will be presented in order to help clinicians to get acquainted with basic methodological information related to the subject at issue. Then, as far as type 1 diabetes is concerned, available data indicate that genetic information and counseling may be useful only in families with many affected individuals. However, since no disease prevention is possible, the utility of predicting this form of diabetes is at question. In the case of type 2 diabetes, available data really question the utility of adding genetic information on top of well performing, easy available and inexpensive non-genetic markers. Finally, the possibility of using the few available genetic data on diabetic complications for improving our ability to predict them will also be presented and discussed. For cardiovascular complication, the addition of genetic information to models based on clinical features does not translate in a substantial improvement in risk discrimination. For all other diabetic complications genetic information are currently very poor and cannot, therefore, be used for improving risk stratification.

CONCLUSIONS

In all, nowadays the use of genetic testing for predicting diabetes and its chronic complications is definitively of little value in clinical practice.

Biomarkers of Diabetic Retinopathy

Diabetic retinopathy (DR), a leading cause of acquired vision loss, is a microvascular complication of diabetes. While traditional risk factors for diabetic retinopathy including longer duration of diabetes, poor blood glucose control, and dyslipidemia are helpful in stratifying patient's risk for developing retinopathy, many patients without these traditional risk factors develop DR; furthermore, there are persons with long diabetes duration who do not develop DR. Thus, identifying biomarkers to predict DR or to determine therapeutic response is important. A biomarker can be defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Incorporation of biomarkers into risk stratification of persons with diabetes would likely aid in early diagnosis and guide treatment methods for those with DR or with worsening DR. Systemic biomarkers of DR include serum measures including genomic, proteomic, and metabolomics biomarkers. Ocular biomarkers including tears and vitreous and retinal vascular structural changes have also been studied extensively to prognosticate the risk of DR development. The current studies on biomarkers are limited by the need for larger sample sizes, cross-validation in different populations and ethnic groups, and time-efficient and cost-effective analytical techniques. Future research is important to explore novel DR biomarkers that are non-invasive, rapid, economical, and accurate to help reduce the incidence and progression of DR in people with diabetes.

Candidate gene studies of diabetic retinopathy in human

Diabetic retinopathy (DR) is a multifactorial disease with complex pathophysiology. It is the main cause of blindness among the people in productive age. The purpose of this literature review is to highlight recent achievements in the genetics of diabetic retinopathy with particular focus on candidate gene studies. We summarized most of the available published data about candidate genes for diabetic retinopathy with the goal to identify main genetic aspects. We conclude that genetic studies reported contradictory findings and no genetic variants meet criteria of a diagnostic marker, or significantly elucidate the root of DR development. Based on these findings it is important to continue with the research in the field of DR genetics, mainly due to the fact that currently new possibilities and approaches associated with utilization of next-generation sequencing are available.