A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk.

Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.

Genome-wide association study and polygenic score assessment of insulin resistance.

Insulin resistance (IR) and beta cell dysfunction are the major drivers of type 2 diabetes (T2D). Genome-Wide Association Studies (GWAS) on IR have been predominantly conducted in European populations, while Middle Eastern populations remain largely underrepresented. We conducted a GWAS on the indices of IR (HOMA2-IR) and beta cell function (HOMA2-%B) in 6,217 non-diabetic individuals from the Qatar Biobank (QBB; Discovery cohort; n = 2170, Replication cohort; n = 4047) with and without body mass index (BMI) adjustment. We also developed polygenic scores (PGS) for HOMA2-IR and compared their performance with a previously derived PGS for HOMA-IR (PGS003470). We replicated 11 loci that have been previously associated with HOMA-IR and 24 loci that have been associated with HOMA-%B, at nominal statistical significance. We also identified a novel locus associated with beta cell function near VEGFC gene, tagged by rs61552983 (P = 4.38 × 10-8). Moreover, our best performing PGS (Q-PGS4; Adj R2 = 0.233 ± 0.014; P = 1.55 x 10-3) performed better than PGS003470 (Adj R2 = 0.194 ± 0.014; P = 5.45 x 10-2) in predicting HOMA2-IR in our dataset. This is the first GWAS on HOMA2 and the first GWAS conducted in the Middle East focusing on IR and beta cell function. Herein, we report a novel locus in VEGFC that is implicated in beta cell dysfunction. Inclusion of under-represented populations in GWAS has potentials to provide important insights into the genetic architecture of IR and beta cell function.

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

Circulating immune cell phenotyping is potentially relevant for diabetic retinopathy risk assessment.

BACKGROUND: Inflammation is believed to play a central role in the development of diabetes mellitus and is a common feature of type 2 diabetes mellitus (T2DM). However, the association with diabetic retinopathy (DR) remains a topic of debate. METHODS: This study employed two-sample bidirectional Mendelian randomization (MR) analyses to establish causal associations between immune cell characteristics and DR. Using publicly available GWAS genetic data, we investigated the causal relationship between 731 immune cell characteristics and the risk of DR. A total of four types of immune features, including relative cell (RC), absolute cell (AC), median fluorescence intensities (MFI), and morphological parameters (MP), were included. Sensitivity analysis was conducted to assess the robustness, heterogeneity, and potential horizontal pleiotropy of the results. RESULTS: Thirty-five immune cell phenotypes were correlated with the risk of developing DR among four immune traits (MFI, RC, AC, and MP), and DR resulted in altered expression of twenty-six immune cells. CONCLUSION: We have demonstrated a strong correlation between immune cell traits and DR using a genetic approach. This finding offers valuable insights for early DR prevention and future clinical research and treatment.

Incorporating polygenic risk into the Leicester Risk Assessment score for 10-year risk prediction of type 2 diabetes.

AIMS: We evaluated whether incorporating information on ethnic background and polygenic risk enhanced the Leicester Risk Assessment (LRA) score for predicting 10-year risk of type 2 diabetes. METHODS: The sample included 202,529 UK Biobank participants aged 40-69 years. We computed the LRA score, and developed two new risk scores using training data (80% sample): LRArev, which incorporated additional information on ethnic background, and LRAprs, which incorporated polygenic risk for type 2 diabetes. We assessed discriminative and reclassification performance in a test set (20% sample). Type 2 diabetes was ascertained using primary care, hospital inpatient and death registry records. RESULTS: Over 10 years, 7,476 participants developed type 2 diabetes. The Harrell's C indexes were 0.796 (95% Confidence Interval [CI] 0.785, 0.806), 0.802 (95% CI 0.792, 0.813), and 0.829 (95% CI 0.820, 0.839) for the LRA, LRArev and LRAprs scores, respectively. The LRAprs score significantly improved the overall reclassification compared to the LRA (net reclassification index [NRI] = 0.033, 95% CI 0.015, 0.049) and LRArev (NRI = 0.040, 95% CI 0.024, 0.055) scores. CONCLUSIONS: Polygenic risk moderately improved the performance of the existing LRA score for 10-year risk prediction of type 2 diabetes.

Risk Assessment for Gastrointestinal Diseases via Clinical Dimension and Genome-Wide Polygenic Risk Scores of Type 2 Diabetes: A Population-Based Cohort Study.

OBJECTIVE: We aimed to evaluate whether individuals with type 2 diabetes (T2D) were at higher risk of developing a wide range of gastrointestinal diseases based on a population-based cohort study. RESEARCH DESIGN AND METHODS: This study included 374,125 participants free of gastrointestinal disorders at baseline; of them, 19,719 (5.27%) with T2D were followed-up by linking to multiple medical records to record gastrointestinal disease diagnoses. Multivariable Cox models were used to estimate the hazard ratios (HRs) and CIs. Logistic models were used to examine the associations between polygenic risk scores (PRS) and clinical gastrointestinal phenotypes. RESULTS: During a median follow-up of 12.0 years, we observed the new onset of 15 gastrointestinal diseases. Compared with nondiabetes, participants with T2D had an increased risk of gastritis and duodenitis (HR 1.58, 95% CI 1.51-1.65), peptic ulcer (HR 1.56, 95% CI 1.43-1.71), diverticular disease (HR 1.19, 95% CI 1.14-1.24), pancreatitis (HR 1.45, 95% CI 1.24-1.71), nonalcoholic fatty liver disease (HR 2.46, 95% CI 2.25-2.69), liver cirrhosis (HR 2.92, 95% CI 2.58-3.30), biliary disease (HR 1.18, 95% CI 1.10-1.26), gastrointestinal tract cancers (HR 1.28, 95% CI 1.17-1.40), and hepatobiliary and pancreatic cancer (HR 2.32, 95% CI 2.01-2.67). Positive associations of PRS of T2D with gastritis, duodenitis, and nonalcoholic fatty liver disease were also observed. CONCLUSIONS: In this large cohort study, we found that T2D was associated with increased risks of a wide range of gastrointestinal outcomes. We suggest the importance of early detection and prevention of gastrointestinal disorders among patients with T2D.

Genetics and epigenetics of diabetes and its complications in India.

Diabetes mellitus (DM) has become a significant health concern with an increasing rate of morbidity and mortality worldwide. India ranks second in the number of diabetes cases in the world. The increasing burden of DM can be explained by genetic predisposition of Indians to type 2 diabetes mellitus (T2DM) coupled with rapid urbanization and socio-economic development in the last 3 decades leading to drastic changes in lifestyle. Environment and lifestyle changes contribute to T2DM development by altering epigenetic processes such as DNA methylation, histone post-translational modifications, and long non-coding RNAs, all of which regulate chromatin structure and gene expression. Although the genetic predisposition of Indians to T2DM is well established, how environmental and genetic factors interact and lead to T2DM is not well understood. In this review, we discuss the prevalence of diabetes and its complications across different states in India and how various risk factors contribute to its pathogenesis. The review also highlights the role of genetic predisposition among the Indian population and epigenetic factors involved in the etiology of diabetes. Lastly, we review current treatments and emphasize the knowledge gap with respect to genetic and epigenetic factors in the Indian context. Further understanding of the genetic and epigenetic determinants will help in risk prediction and prevention as well as therapeutic interventions, which will improve the clinical management of diabetes and associated macro- and micro-vascular complications.

The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care.

Polygenic risk scores (PRSs) hold promise for disease risk assessment and prevention. The Genomic Medicine at Veterans Affairs (GenoVA) Study is addressing three main challenges to the clinical implementation of PRSs in preventive care: defining and determining their clinical utility, implementing them in time-constrained primary care settings, and countering their potential to exacerbate healthcare disparities. The study processes used to test patients, report their PRS results to them and their primary care providers (PCPs), and promote the use of those results in clinical decision-making are modeled on common practices in primary care. The following diseases were chosen for their prevalence and familiarity to PCPs: coronary artery disease; type 2 diabetes; atrial fibrillation; and breast, colorectal, and prostate cancers. A randomized clinical trial (RCT) design and primary outcome of time-to-new-diagnosis of a target disease bring methodological rigor to the question of the clinical utility of PRS implementation. The study's pragmatic RCT design enhances its relevance to how PRS might reasonably be implemented in primary care. Steps the study has taken to promote health equity include the thoughtful handling of genetic ancestry in PRS construction and reporting and enhanced recruitment strategies to address underrepresentation in research participation. To date, enhanced recruitment efforts have been both necessary and successful: participants of underrepresented race and ethnicity groups have been less likely to enroll in the study than expected but ultimately achieved proportional representation through targeted efforts. The GenoVA Study experience to date offers insights for evaluating the clinical utility of equitable PRS implementation in adult primary care.

Assessment of common risk factors of diabetes and chronic kidney disease: a Mendelian randomization study.

Background: The increasing prevalence of diabetes and its significant impact on mortality and morbidity rates worldwide has led to a growing interest in understanding its common risk factors, particularly in relation to chronic kidney disease (CKD). This research article aims to investigate the shared risk factors between type 1 diabetes (T1D), type 2 diabetes (T2D), and CKD using a Mendelian randomization (MR) design. Methods: The study utilized genome-wide association study (GWAS) datasets for T1D, T2D, and CKD from the FinnGen research project. GWAS summary statistics datasets for 118 exposure traits were obtained from the IEU OpenGWAS database. MR analyses were conducted to examine the causal relationships between exposure traits and each of the three outcomes. Multiple methods, including inverse-variance weighted, weighted median, and MR-Egger, were employed for the MR studies. Results: Phenome-wide MR analyses revealed that eosinophil percentage exhibited a significant and suggestive causal association with T1D and CKD, respectively, suggesting its potential as a shared risk factor for T1D and CKD. For T2D, 34 traits demonstrated significant associations. Among these 34 traits, 14 were also significantly associated with CKD, indicating the presence of common risk factors between T2D and CKD, primarily related to obesity, height, blood lipids and sex hormone binding globulin, blood pressure, and walking pace. Conclusion: This research has uncovered the eosinophil percentage as a potential common risk factor for both T1D and CKD, while also identifying several traits, such as obesity and blood lipids, as shared risk factors for T2D and CKD. This study contributes to the understanding of the common risk factors between diabetes and CKD, emphasizing the need for targeted interventions to reduce the risk of these diseases.

Cost-effectiveness of genetic-based screening strategies for maturity-onset diabetes of the young.

Maturity-onset diabetes of the young (MODY) is often misdiagnosed as Type I or II diabetes. This study was designed to assess the cost-effectiveness of MODY screening strategies in Hungary, which included a recent genetic test compared with no routine screening for MODY. A simulation model that combined a decision tree and an individual-level Markov model was constructed to assess the costs per quality-adjusted life year of screening strategies. Stratifying patients based on age and insulin treatment followed by a risk assessment questionnaire, a laboratory test and genetic testing was the most cost-effective strategy, saving EUR 12 and generating 0.0047 quality-adjusted life years gained per screened patient. This screening strategy could be considered for reimbursement, especially in countries with limited resources.

Computational assessment of the biological response of curcumin to type 2 diabetes mellitus induced by metal exposure.

The current study aimed to identify the molecular mechanisms of a metal mixture (cadmium, nickel, and lead) involved in type 2 diabetes mellitus (T2DM) development and the therapeutic effect of curcumin in this metal mixture-induced T2DM. To accomplish this, SwissADME assessed the physicochemical and pharmacokinetic properties of curcumin and the Prediction of Activity Spectra for Substances evaluates its biological activities. The Comparative Toxicogenomics Database, Cytoscape, AutoDock Vina, and MicroRNA ENrichment TURned NETwork were used as tools to perform data-mining approaches and molecular docking. Curcumin properties were fitted within the acceptable range to be a promising drug candidate. The mixed metal altered 23 genes linked to T2DM development and targeted by curcumin. Curcumin had a dual-natured effect or antagonistic effect for most of the involved genes in T2DM and metal mixture. The most prominent biological processes were identified as ''response to external stimulus'', ''regulation of programmed cell death'', ''programmed cell death'', ''cell death'', and ''response to stress''. Three highly interacted miRNAs related to metal mixture-induced T2DM and targeted by curcumin (hsa-miR-98-5p, hsa-miR-34a-5p, and hsa-miR-155-5p) were identified. These findings could pave the way for further studies to evaluate the link between these genes and T2DM.

Questionnaire-Based Polyexposure Assessment Outperforms Polygenic Scores for Classification of Type 2 Diabetes in a Multiancestry Cohort.

OBJECTIVE: Environmental exposures may have greater predictive power for type 2 diabetes than polygenic scores (PGS). Studies examining environmental risk factors, however, have included only individuals with European ancestry, limiting the applicability of results. We conducted an exposome-wide association study in the multiancestry Personalized Environment and Genes Study to assess the effects of environmental factors on type 2 diabetes. RESEARCH DESIGN AND METHODS: Using logistic regression for single-exposure analysis, we identified exposures associated with type 2 diabetes, adjusting for age, BMI, household income, and self-reported sex and race. To compare cumulative genetic and environmental effects, we computed an overall clinical score (OCS) as a weighted sum of BMI and prediabetes, hypertension, and high cholesterol status and a polyexposure score (PXS) as a weighted sum of 13 environmental variables. Using UK Biobank data, we developed a multiancestry PGS and calculated it for participants. RESULTS: We found 76 significant associations with type 2 diabetes, including novel associations of asbestos and coal dust exposure. OCS, PXS, and PGS were significantly associated with type 2 diabetes. PXS had moderate power to determine associations, with larger effect size and greater power and reclassification improvement than PGS. For all scores, the results differed by race. CONCLUSIONS: Our findings in a multiancestry cohort elucidate how type 2 diabetes odds can be attributed to clinical, genetic, and environmental factors and emphasize the need for exposome data in disease-risk association studies. Race-based differences in predictive scores highlight the need for genetic and exposome-wide studies in diverse populations.

Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics.

OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

Development and assessment of diabetic nephropathy prediction model using hub genes identified by weighted correlation network analysis.

Diabetic nephropathy (DN) is one microvascular complication of diabetes. About 30% of diabetic patients can develop DN, which is closely related to the high incidence and mortality of heart diseases, and then develop end-stage renal diseases. Therefore, early detection and screening of high-risk patients with DN is important. Herein, we explored the differences of serum transcriptomics between DN and non-DN in type II diabetes mellitus (T2DM) patients. We obtained 110 target genes using weighted correlation network analysis. Gene Ontology enrichment analysis indicates these target genes are mainly related to membrane adhesion, alpha-amino acid biosynthesis, metabolism, and binding, terminus, inhibitory synapse, clathrinid-sculpted vesicle, kinase activity, hormone binding, receptor activity, and transporter activity. Kyoto Encyclopedia of Genes and Genomes analysis indicates the process of DN in diabetic patients can involve synaptic vesicle cycle, cysteine and methionine metabolism, N-Glycan biosynthesis, osteoclast differentiation, and cAMP signaling pathway. Next, we detected the expression levels of hub genes in a retrospective cohort. Then, we developed a risk score tool included in the prediction model for early DN in T2DM patients. The prediction model was well applied into clinical practice, as confirmed by internal validation and several other methods. A novel DN risk model with relatively high prediction accuracy was established based on clinical characteristics and hub genes of serum detection. The estimated risk score can help clinicians develop individualized intervention programs for DN in T2DM. External validation data are required before individualized intervention measures.

Estimating the causal effect of liability to disease on healthcare costs using Mendelian Randomization.

Accurate measurement of the effects of disease status on healthcare costs is important in the pragmatic evaluation of interventions but is complicated by endogeneity bias. Mendelian Randomization, the use of random perturbations in germline genetic variation as instrumental variables, can avoid these limitations. We used a novel Mendelian Randomization analysis to model the causal impact on inpatient hospital costs of liability to six prevalent diseases and health conditions: asthma, eczema, migraine, coronary heart disease, Type 2 diabetes, and depression. We identified genetic variants from replicated genome-wide associations studies and estimated their association with inpatient hospital costs on over 300,000 individuals. There was concordance of findings across varieties of sensitivity analyses, including stratification by sex and methods robust to violations of the exclusion restriction. Results overall were imprecise and we could not rule out large effects of liability to disease on healthcare costs. In particular, genetic liability to coronary heart disease had substantial impacts on costs.

Assessment of the bi-directional relationship between blood mitochondrial DNA copy number and type 2 diabetes mellitus: a multivariable-adjusted regression and Mendelian randomisation study.

AIMS/HYPOTHESIS: Mitochondrial dysfunction, which can be approximated by blood mitochondrial DNA copy number (mtDNA-CN), has been implicated in the pathogenesis of type 2 diabetes mellitus. Thus far, however, insights from prospective cohort studies and Mendelian randomisation (MR) analyses on this relationship are limited. We assessed the association between blood mtDNA-CN and incident type 2 diabetes using multivariable-adjusted regression analyses, and the associations between blood mtDNA-CN and type 2 diabetes and BMI using bi-directional MR. METHODS: Multivariable-adjusted Cox proportional hazard models were used to estimate the association between blood mtDNA-CN and incident type 2 diabetes in 285,967 unrelated European individuals from UK Biobank free of type 2 diabetes at baseline. Additionally, a cross-sectional analysis was performed to investigate the association between blood mtDNA-CN and BMI. We also assessed the potentially causal relationship between blood mtDNA-CN and type 2 diabetes (N=898,130 from DIAGRAM, N=215,654 from FinnGen) and BMI (N=681,275 from GIANT) using bi-directional two-sample MR. RESULTS: During a median follow-up of 11.87 years, 15,111 participants developed type 2 diabetes. Participants with a higher level of blood mtDNA-CN are at lower risk of developing type 2 diabetes (HR 0.90 [95% CI 0.89, 0.92]). After additional adjustment for BMI and other confounders, these results attenuated moderately and remained present. The multivariable-adjusted cross-sectional analyses showed that higher blood mtDNA-CN was associated with lower BMI (-0.12 [95% CI -0.14, -0.10]) kg/m2. In the bi-directional MR analyses, we found no evidence for causal associations between blood mtDNA-CN and type 2 diabetes, and blood mtDNA-CN and BMI in either direction. CONCLUSIONS/INTERPRETATION: The results from the present study indicate that the observed association between low blood mtDNA-CN and higher risk of type 2 diabetes is likely not causal.

Search for a time- and cost-saving genetic testing strategy for maturity-onset diabetes of the young.

AIMS: Correct genetic diagnosis of maturity-onset diabetes of the young (MODY) is beneficial for person's diabetes management compared to no genetic testing. Aim of the present study was a search for optimal time- and cost-saving strategies by comparing two approaches of genetic testing of participants with clinical suspicion of MODY. METHODS: A total of 121 consecutive probands referred for suspicion of MODY (Group A) were screened using targeted NGS (tNGS), while the other 112 consecutive probands (Group B) underwent a single gene test based on phenotype, and in cases of negative findings, tNGS was conducted. The study was performed in two subsequent years. The genetic results, time until reporting of the final results and financial expenses were compared between the groups. RESULTS: MODY was confirmed in 30.6% and 40.2% probands from Groups A and B, respectively; GCK-MODY was predominant (72.2% in Group A and 77.8% in Group B). The median number of days until results reporting was 184 days (IQR 122-258) in Group A and 91 days (44-174) in Group B (p < 0.00001). Mean costs per person were higher for Group A (639 ± 30 USD) than for Group B (584 ± 296 USD; p = 0.044). CONCLUSIONS: The two-step approach represented a better strategy for genetic investigation of MODY concerning time and costs compared to direct tNGS. Although a single-gene investigation clarified the diabetes aetiology in the majority of cases, tNGS could reveal rare causes of MODY and expose possible limitations of both standard genetic techniques and clinical evaluation.

Biobanking in Latinos: current status, principles for conduct, and contribution of a new biobank, El Banco por Salud, designed to improve the health of Latino patients of Mexican ancestry with type 2 diabetes.

Underserved Latino communities experience a greater burden of type 2 diabetes mellitus (T2DM) than the general population. Predictors of glycemic control are likely to include both biological/genetic and social determinants of health (SDOH). A variety of approaches have been used with cohorts of Latino patients to study aspects of this health disparity, and those are reviewed briefly here. Such projects range from cohorts that are studies for a primary purpose, for example, to discover genetic variation associated with T2DM or to examine a particular aspect of SDOH that might be involved. Other studies have been conducted more as infrastructure that is broadly based in order to provide a resource that can be used by many investigators to address a variety of questions. From our experience and those of others, we propose a set of principles to ensure that needs of the community are identified and taken into account during the conduct of these studies. As an example of the implementation of these principles, we also describe a new biobank El Banco por Salud (El Banco), which was designed to improve access to studies designed to improve glycemic control and health in Latinos in partnership with Federally Qualified Health Centers in Arizona.

A Case Control Study of Risk Assessment of Diabetes and Nephropathy with eNOS (T786C and 27bp VNTR) Gene Polymorphisms.

OBJECTIVES: To determine the association of eNOS (T786C and 27bp VNTR) gene polymorphism with the risk of type II diabetes mellitus and diabetic nephropathy in North India. METHODS: The prospective case control study was conducted over a period of 18 months. A total of 100 patients of Type 2 Diabetes Mellitus (A1: 50 cases without Diabetic nephropathy-DN and 50 cases with DN) aged 18-75 years and 50 healthy adults as control (Group B) were included. The endothelial nitric oxide gene variant (T786C and 27bp VNTR) genotypes and alleles were studied. Odds ratio with 95% CI was calculated for genotype and alleles for the occurrence of diabetes and DN. p value of less than 0.05 was considered as significant. RESULTS: With Bb as reference(27bp VNTR), the odds ratio for Ab in the three groups (A1,A2,B) was 2.243, 1.545 and 0.746 respectively; and for Aa was 3.043, 3.058 and 1.878 respectively; with TT as reference (T786C), it was 1.573, 1.55 and 1.055 respectively for TC; and for CC it was 2.121, 2.063 and 2.348 respectively. The OR was comparable among the study groups and control for all genotypes and alleles (p>0.05). CONCLUSION: In conclusion, there was a trend towards higher predilection of DN with aa genotype and a allele in 27 VNTR, CC genotype and C allele of -786T>C polymorphism however it was not found to be statistically significant. Future large sample studies are required to account for the ethnic variation for a clearer association of the genes and their associated risk with Diabetes and its complications.

DNA Methylation and Type 2 Diabetes: Novel Biomarkers for Risk Assessment?

Diabetes is a severe threat to global health. Almost 500 million people live with diabetes worldwide. Most of them have type 2 diabetes (T2D). T2D patients are at risk of developing severe and life-threatening complications, leading to an increased need for medical care and reduced quality of life. Improved care for people with T2D is essential. Actions aiming at identifying undiagnosed diabetes and at preventing diabetes in those at high risk are needed as well. To this end, biomarker discovery and validation of risk assessment for T2D are critical. Alterations of DNA methylation have recently helped to better understand T2D pathophysiology by explaining differences among endophenotypes of diabetic patients in tissues. Recent evidence further suggests that variations of DNA methylation might contribute to the risk of T2D even more significantly than genetic variability and might represent a valuable tool to predict T2D risk. In this review, we focus on recent information on the contribution of DNA methylation to the risk and the pathogenesis of T2D. We discuss the limitations of these studies and provide evidence supporting the potential for clinical application of DNA methylation marks to predict the risk and progression of T2D.