Genetic screening for the risk of type 2 diabetes: worthless or valuable?

Circular RNAs and the regulation of gene expression in diabetic nephropathy (Review)

Circular RNAs (circRNAs) are non‑coding single‑stranded covalently closed RNA molecules that are considered important as regulators of gene expression at the transcriptional and post‑transcriptional levels. These molecules have been implicated in the initiation and progression of multiple human diseases, ranging from cancer to inflammatory and metabolic diseases, including diabetes mellitus and its vascular complications. The present article aimed to review the current knowledge on the biogenesis and functions of circRNAs, as well as their role in cell processes associated with diabetic nephropathy. In addition, novel potential interactions between circRNAs expressed in renal cells exposed to high‑glucose concentrations and the transcription factors c‑Jun and c‑Fos are reported.

Identifying top ten predictors of type 2 diabetes through machine learning analysis of UK Biobank data

The study aimed to identify the most predictive factors for the development of type 2 diabetes. Using an XGboost classification model, we projected type 2 diabetes incidence over a 10-year horizon. We deliberately minimized the selection of baseline factors to fully exploit the rich dataset from the UK Biobank. The predictive value of features was assessed using shap values, with model performance evaluated via Receiver Operating Characteristic Area Under the Curve, sensitivity, and specificity. Data from the UK Biobank, encompassing a vast population with comprehensive demographic and health data, was employed. The study enrolled 450,000 participants aged 40-69, excluding those with pre-existing diabetes. Among 448,277 participants, 12,148 developed type 2 diabetes within a decade. HbA1c emerged as the foremost predictor, followed by BMI, waist circumference, blood glucose, family history of diabetes, gamma-glutamyl transferase, waist-hip ratio, HDL cholesterol, age, and urate. Our XGboost model achieved a Receiver Operating Characteristic Area Under the Curve of 0.9 for 10-year type 2 diabetes prediction, with a reduced 10-feature model achieving 0.88. Easily measurable biological factors surpassed traditional risk factors like diet, physical activity, and socioeconomic status in predicting type 2 diabetes. Furthermore, high prediction accuracy could be maintained using just the top 10 biological factors, with additional ones offering marginal improvements. These findings underscore the significance of biological markers in type 2 diabetes prediction.

The inverse association between DNA gaps and HbA1c levels in type 2 diabetes mellitus

Naturally occurring DNA gaps have been observed in eukaryotic DNA, including DNA in nondividing cells. These DNA gaps are found less frequently in chronologically aging yeast, chemically induced senescence cells, naturally aged rats, D-galactose-induced aging model rats, and older people. These gaps function to protect DNA from damage, so we named them youth-associated genomic stabilization DNA gaps (youth-DNA-gaps). Type 2 diabetes mellitus (type 2 DM) is characterized by an early aging phenotype. Here, we explored the correlation between youth-DNA-gaps and the severity of type 2 DM. Here, we investigated youth-DNA-gaps in white blood cells from normal controls, pre-DM, and type 2 DM patients. We found significantly decreased youth-DNA-gap numbers in the type 2 DM patients compared to normal controls (P = 0.0377, P = 0.0018 adjusted age). In the type 2 DM group, youth-DNA-gaps correlate directly with HbA1c levels. (r = - 0.3027, P = 0.0023). Decreased youth-DNA-gap numbers were observed in patients with type 2 DM and associated with increased HbA1c levels. Therefore, the decrease in youth-DNA-gaps is associated with the molecular pathogenesis of high blood glucose levels. Furthermore, youth-DNA-gap number is another marker that could be used to determine the severity of type 2 DM.

Cumulative socioeconomic status and incident type 2 diabetes among African American adults from the Jackson heart study

Background

The cumulative socioeconomic status (SES) model posits that childhood and adult experiences accumulate to influence disease risk. While individual SES indicators such as education and income are independently associated with incident type 2 diabetes (T2D), the association of cumulative SES and incident T2D is unclear, especially in African American adults.

Methods

We utilized cohort data of African American participants (n = 3681, mean age 52.6 years) enrolled in the Jackson Heart Study from 2000 to 2013 free of T2D or cardiovascular disease at baseline (2000-2004). Cumulative SES scores at baseline were derived using six SES indicators (education, wealth, income, occupation, employment status, and mother's education) categorized as low, middle, and high. Incident T2D was defined at exam 2 (2005-2008) or exam 3 (2009-2013) based on fasting glucose ≥126 mg/dL, HbA1c ≥ 6.5, reported diabetic medication use, or self-reported physician diagnosis. Proportional hazards regression, allowing for interval censoring, was used to estimate the association between cumulative SES and incident T2D (hazard ratio(HR), 95% confidence interval (CI)) after adjustment for covariates. Sex and age differences were tested using interaction terms.

Results

There were 544 incident T2D cases. The association between low (versus high) cumulative SES and incident T2D was not significant (HR 1.04 [95% CI 0.85, 1.28]) and did not differ by sex (p value for interaction>0.05). However, there were differences by (age p value for interaction = 0.0052 for middle-aged adults and 0.0186 for older adults). Low (versus high) cumulative SES was associated a greater hazard of incident T2D among those 20-46 years (HR 1.12 [95% CI 1.03, 1.21]), 47-59 years (HR 1.25 [95% CI 1.06, 1.47]) and those 60-93 years (HR 1.39 [95% CI 1.09, 1.78]) after adjustment for sex and family history of diabetes. Associations attenuated after adding behavioral and lifestyle risk factors.

Conclusion

The association of low cumulative SES and incident T2D differed by age, which may suggest interventionist should consider impacts of SES on T2D by age.

Polygenic Prediction of Type 2 Diabetes in Africa

OBJECTIVE

Polygenic prediction of type 2 diabetes (T2D) in continental Africans is adversely affected by the limited number of genome-wide association studies (GWAS) of T2D from Africa and the poor transferability of European-derived polygenic risk scores (PRSs) in diverse ethnicities. We set out to evaluate if African American, European, or multiethnic-derived PRSs would improve polygenic prediction in continental Africans.

RESEARCH DESIGN AND METHODS

Using the PRSice software, ethnic-specific PRSs were computed with weights from the T2D GWAS multiancestry meta-analysis of 228,499 case and 1,178,783 control subjects. The South African Zulu study (n = 1,602 case and 981 control subjects) was used as the target data set. Validation and assessment of the best predictive PRS association with age at diagnosis were conducted in the Africa America Diabetes Mellitus (AADM) study (n = 2,148 case and 2,161 control subjects).

RESULTS

The discriminatory ability of the African American and multiethnic PRSs was similar. However, the African American-derived PRS was more transferable in all the countries represented in the AADM cohort and predictive of T2D in the country combined analysis compared with the European and multiethnic-derived scores. Notably, participants in the 10th decile of this PRS had a 3.63-fold greater risk (odds ratio 3.63; 95% CI 2.19-4.03; P = 2.79 × 10-17) per risk allele of developing diabetes and were diagnosed 2.6 years earlier than those in the first decile.

CONCLUSIONS

African American-derived PRS enhances polygenic prediction of T2D in continental Africans. Improved representation of non-European populations (including Africans) in GWAS promises to provide better tools for precision medicine interventions in T2D.

Guidelines for genetic testing of healthy adults who deposit samples and related data in bioresource collections and biobanks

Currently, a significant part of research in the fields of human and medical genetics is carried out using tissue samples, genealogical, population, medical and personal data. Their use is of particular relevance in the “genome era”, since only joint analysis of genomic data and health status of the population is crucial for understanding how genes are associated with health and disease. Genetic studies of adults without symptoms of diseases are carried out to obtain data on a possible predisposition to multifactorial diseases, to establish the carrier status of autosomal recessive mutations as part of preconception care and to assess individual sensitivity to drugs. In addition, healthy individuals can be tested to detect an inherited disease at presymptomatic stage. This situation increasingly emphasizes the importance of storing data on genome sequencing or any other patient tests for subsequent data reanalysis, as well as their safety, including biosamples from an individual and one’s family. The review article, based on international experience, summarizes guidelines for genetic testing of healthy individuals. The options for storing biological samples and related data are considered.

Non-skilled occupation as a risk factor of diabetes among working population: A population-based study of community-dwelling adults in Hong Kong

Diabetes among working population brings to society concerns on productivity and social welfare cost, in addition to healthcare burden. While lower socio-economic status has been recognised as a risk factor of diabetes; occupation, compared with other socio-economic status indicators (e.g., education and income), has received less attention. There is some evidence from studies conducted in Europe that occupation is associated with diabetes risk, but less is known in Asia, which has different organisational cultures and management styles from the West. This study examines the association between occupation and diabetes risk in a developed Asian setting, which is experiencing an increasing number of young onset of diabetes and aging working population at the same time. This is a cross-sectional study of working population aged up to 65 with data from a population-based survey collecting demographic, socio-economic, behavioural and metabolic data from Hong Kong residents, through both self-administered questionnaires and clinical health examinations (1,429 participants). Non-skilled occupation was found to be an independent risk factor for diabetes, with an odds ratio (OR) of 3.38 (p < 0.001) and adjusted OR of 2.59 (p = 0.022) after adjusting for demographic, behavioural and metabolic risk factors. Older age (adjusted OR = 1.08, p < 0.001), higher body mass index (adjusted OR = 1.23, p < 0.001) and having hypertriglyceridemia (adjusted OR = 1.93, p = 0.033) were also independently associated with diabetes. Non-skilled workers were disproportionately affected by diabetes with the highest age-standardized prevalence (6.3%) among all occupation groups (4.9%-5.0%). This study provides evidence that non-skilled occupation is an independent diabetes risk factor in a developed Asian setting. Health education on improving lifestyle practices and diabetes screening should prioritise non-skilled workers, in particular through company-based and sector-based diabetes screening programmes. Diabetes health service should respond to the special needs of non-skilled workers, including service at non-office hour and practical health advice in light of their work setting.

RNF6 Targeted by miR-26a-5p Protects Pancreatic β-Cell Function Against Type 2 Diabetes

BACKGROUND

Type 2 diabetes (T2D) is characterized by progressive β-cell dysfunction. Regulatory microRNAs (miRNAs) may be associated with this.

METHODS

Serum miR-26a-5p and RNF6 levels were detected in T2D patients and healthy volunteers via qRT-PCR. Subsequently, the role of specific dysregulated miR-26a-5p or RNF6 in regulating insulin content, cell proliferation, and apoptosis was studied in INS-1 cells. The targeting correlation between miR-26a-5p and RNF6 was detected using a luciferase assay.

RESULTS

RNF6 expression was significantly decreased in T2D individuals and INS-1 cells treated with high glucose, while miR-26a-5p expression was increased. In INS-1 cells, RNF6 overexpression or miR-26a-5p downregulation significantly increased insulin content and secretion, induced proliferation, and inhibited apoptosis. RNF6 has been identified as an miR-26a-5p target, which negatively regulates RNF6 to worsen INS-1 cell function.

CONCLUSION

RNF6 promoted insulin secretion and induced cell proliferation in INS-1 cells. This may be related to miR-26a-5p targeting and negatively regulating T2D pathogenesis.

Genetic testing for suicide risk assessment: Theoretical premises, research challenges and ethical concerns

We explore ethical premises and practical implications of using genetic testing to predict suicide risk. Twin studies indicate heritable components of suicide risk, intertwined with the heritability of mental disorders, and possibly other traits. Current genetics research has abandoned searching for single gene Mendelian determinants, in favour of complex probabilistic epigenetic models. Genome-Wide Association Studies (GWAS) might identify thousands of single nucleotide polymorphisms (SNPs), each contributing very little to the variance associated with behavioral phenotypes. However, suicide is a behavioral outcome rather than a phenotype, with so many different causal aetiologies, that it is impossible to predict the behaviors of individuals. We analyse practical and ethical issues that would arise if future research were to identify genetic information that will accurately predict suicide. Applying ACCE guidelines that specify when genetic tests should and should not be used, we examine the Analytic Validity, Clinical Validity, Clinical Utility and Ethical, Legal, and Social Implications. Low sensitivity and specificity for predicting suicide diminish potential advantages and exacerbate risks. Key considerations include the likelihood that testing will result in effective preventive interventions, which are not currently available, and unreliable positive results increasing hopelessness, stigma, and psychosocial risks. If the unregulated direct-to-consumer genetic testing services include suicide risk assessments, their use risks negative impacts. In the future, if genetic testing could accurately identify suicide risk in individuals, its use would be contraindicated if we cannot provide effective preventive interventions and mitigate the negative impacts of informing people about their risk level.

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.

Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis

CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of CD36 gene rs1761667 (G>A) and rs1527483 (C>T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups (p > 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (≥ 0.75) and Cohen's kappa (κ) (≥ 0.5). Interestingly, in K-star tool, the accuracy and Cohen's κ values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between CD36 polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases.

Mediation model with a categorical exposure and a censored mediator with application to a genetic study

Mediation analysis is a statistical method for evaluating the direct and indirect effects of an exposure on an outcome in the presence of a mediator. Mediation models have been widely used to determine direct and indirect contributions of genetic variants in clinical phenotypes. In genetic studies, the additive genetic model is the most commonly used model because it can detect effects from either recessive or dominant models (or any model in between). However, the existing approaches for mediation model cannot be directly applied when the genetic model is additive (e.g. the most commonly used model for SNPs) or categorical (e.g. polymorphic loci), and thus modification to measures of indirect and direct effects is warranted. In this study, we proposed overall measures of indirect, direct, and total effects for a mediation model with a categorical exposure and a censored mediator, which accounts for the frequency of different values of the categorical exposure. The proposed approach provides the overall contribution of the categorical exposure to the outcome variable. We assessed the empirical performance of the proposed overall measures via simulation studies and applied the measures to evaluate the mediating effect of a women’s age at menopause on the association between genetic variants and type 2 diabetes.

Multi-omics analysis identifies CpGs near G6PC2 mediating the effects of genetic variants on fasting glucose

AIMS/HYPOTHESIS

An elevated fasting glucose level in non-diabetic individuals is a key predictor of type 2 diabetes. Genome-wide association studies (GWAS) have identified hundreds of SNPs for fasting glucose but most of their functional roles in influencing the trait are unclear. This study aimed to identify the mediation effects of DNA methylation between SNPs identified as significant from GWAS and fasting glucose using Mendelian randomisation (MR) analyses.

METHODS

We first performed GWAS analyses for three cohorts (Taiwan Biobank with 18,122 individuals, the Healthy Aging Longitudinal Study in Taiwan with 1989 individuals and the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance with 416 individuals) with individuals of Han Chinese ancestry in Taiwan, followed by a meta-analysis for combining the three GWAS analysis results to identify significant and independent SNPs for fasting glucose. We determined whether these SNPs were methylation quantitative trait loci (meQTLs) by testing their associations with DNA methylation levels at nearby CpG sites using a subsample of 1775 individuals from the Taiwan Biobank. The MR analysis was performed to identify DNA methylation with causal effects on fasting glucose using meQTLs as instrumental variables based on the 1775 individuals. We also used a two-sample MR strategy to perform replication analysis for CpG sites with significant MR effects based on literature data.

RESULTS

Our meta-analysis identified 18 significant (p < 5 × 10^-8) and independent SNPs for fasting glucose. Interestingly, all 18 SNPs were meQTLs. The MR analysis identified seven CpGs near the G6PC2 gene that mediated the effects of a significant SNP (rs2232326) in the gene on fasting glucose. The MR effects for two CpGs were replicated using summary data based on the European population, using an exonic SNP rs2232328 in G6PC2 as the instrument.

CONCLUSIONS/INTERPRETATION

Our analysis results suggest that rs2232326 and rs2232328 in G6PC2 may affect DNA methylation at CpGs near the gene and that the methylation may have downstream effects on fasting glucose. Therefore, SNPs in G6PC2 and CpGs near G6PC2 may reside along the pathway that influences fasting glucose levels. This is the first study to report CpGs near G6PC2, an important gene for regulating insulin secretion, mediating the effects of GWAS-significant SNPs on fasting glucose.

Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records. We conducted a retrospective analysis on a derivation cohort with 3749 DR and 94,127 non-DR diabetic patients. In the analysis, an ensemble predictor selection method was employed to find essential predictors among 26 variables in demographics, duration of diabetes, complications and laboratory results. A predictive model and a risk index were built based on the selected, essential predictors, and then validated using another independent validation cohort with 869 DR and 6448 non-DR diabetic patients. Out of the 26 variables, 10 were identified to be essential for predicting DR. The predictive model achieved a 0.85 AUC on the derivation cohort and a 0.77 AUC on the validation cohort. For the risk index, the AUCs were 0.81 and 0.73 on the derivation and validation cohorts, respectively. The predictive technologies can provide an early warning sign that motivates patients to comply with eye examinations for early screening and potential treatments.

A Genetic Risk Score Improves the Prediction of Type 2 Diabetes Mellitus in Mexican Youths but Has Lower Predictive Utility Compared With Non-Genetic Factors

Background

Type 2 diabetes (T2D) is a multifactorial disease caused by a complex interplay between environmental risk factors and genetic predisposition. To date, a total of 10 single nucleotide polymorphism (SNPs) have been associated with pediatric-onset T2D in Mexicans, with a small individual effect size. A genetic risk score (GRS) that combines these SNPs could serve as a predictor of the risk for pediatric-onset T2D.

Objective

To assess the clinical utility of a GRS that combines 10 SNPs to improve risk prediction of pediatric-onset T2D in Mexicans.

Methods

This case-control study included 97 individuals with pediatric-onset T2D and 84 controls below 18 years old without T2D. Information regarding family history of T2D, demographics, perinatal risk factors, anthropometric measurements, biochemical variables, lifestyle, and fitness scores were then obtained. Moreover, 10 single nucleotide polymorphisms (SNPs) previously associated with pediatric-onset T2D in Mexicans were genotyped. The GRS was calculated by summing the 10 risk alleles. Pediatric-onset T2D risk variance was assessed using multivariable logistic regression models and the area under the receiver operating characteristic curve (AUC).

Results

The body mass index Z-score (Z-BMI) [odds ratio (OR) = 1.7; p = 0.009] and maternal history of T2D (OR = 7.1; p < 0.001) were found to be independently associated with pediatric-onset T2D. No association with other clinical risk factors was observed. The GRS also showed a significant association with pediatric-onset T2D (OR = 1.3 per risk allele; p = 0.006). The GRS, clinical risk factors, and GRS plus clinical risk factors had an AUC of 0.66 (95% CI 0.56-0.75), 0.72 (95% CI 0.62-0.81), and 0.78 (95% CI 0.70-0.87), respectively (p < 0.01).

Conclusion

The GRS based on 10 SNPs was associated with pediatric-onset T2D in Mexicans and improved its prediction with modest significance. However, clinical factors, such the Z-BMI and family history of T2D, continue to have the highest predictive utility in this population.

Assessment of genetic risk of type 2 diabetes among Pakistanis based on GWAS-implicated loci

Genome-wide association studies (GWAS) have identified multiple type 2 diabetes (T2D) loci, mostly among populations of European descent. There is a high prevalence of T2D among Pakistanis. Both genetic and environmental factors may be responsible for this high prevalence. In order to understand the shared genetic basis of T2D among Pakistanis and Europeans, we examined 77 genome-wide significant variants previously implicated among European populations. We genotyped 77 single-nucleotide polymorphisms (SNPs) by iPLEX® Gold or TaqMan® assays in a case-control sample of 1,683 individuals. Association analysis was performed using logistic regression. A total of 16 SNPs (TCF7L2/rs7903146, GLIS3/rs7041847, CHCHD9/rs13292136, PLEKHA1/rs2292626, FTO/rs9936385, CDKAL1/rs7756992, KCNJ11/rs5215, LOC105372155/rs12970134, KCNQ1/rs163182, CTRB1/rs7202877, ST6GAL1/rs16861329, ADAMTS9-AS2/rs6795735, LOC105370275/rs1359790, C5orf67/rs459193, ZBED3-AS1/rs6878122 and UBE2E2/rs7612463) showed statistically significant associations after controlling for the false discovery rate. While KCNQ1/rs163182 and ZBED3-AS1/rs6878122 showed opposite allelic effects, the remaining significant SNPs had the same allelic effects as reported previously. Our data indicate that a selected number of T2D loci previously identified among populations of European descent also affect the risk of T2D in the Pakistani population.

Type 2 diabetes mellitus: pathogenesis and genetic diagnosis

Type 2 diabetes mellitus (T2DM) is a heterogeneous condition that is related to both defective insulin secretion and peripheral insulin resistance. Beta cells are the major organ for secreting insulin hence, it is important to maintain an adequate beta-cell mass in response to various changes. Insulin resistance is a major cause of T2DM leads to elevated free fatty acid (FFA) levels which increases beta-cell mass and insulin secretion to compensate for insulin insensitivity. Chronic increase of plasma FFA levels results in disturbances in lipid metabolism, which contributes to decreased beta-cell function and lipotoxicity thus promoting T2DM. In the present review, we have discussed the process of beta-cell destruction, the role of genes in contributing to the fast increase in the progression of T2DM in detail. More than 130 variants in various T2DM susceptibility and candidate genes have been discovered to be associated with T2DM. Still, these variants elucidate only a small amount of total heritability of T2DM. Further, there is also an inventory of presently used therapeutic tools and a review of novel therapeutic approaches like incretin-based therapies or sodium-glucose transporter-2 inhibitors. Additionally, providing a concise but comprehensive update, this review will be essential to every clinician involved in the treatment of diabetes mellitus.

Could personalised risk prediction for type 2 diabetes using polygenic risk scores direct prevention, enhance diagnostics, or improve treatment?

Over the past three decades, the number of people globally with diabetes mellitus has more than doubled. It is estimated that by 2030, 439 million people will be suffering from the disease, 90-95% of whom will have type 2 diabetes (T2D). In 2017, 5 million deaths globally were attributable to T2D, placing it in the top 10 global causes of death. Because T2D is a result of both genetic and environmental factors, identification of individuals with high genetic risk can help direct early interventions to prevent progression to more serious complications. Genome-wide association studies have identified ~400 variants associated with T2D that can be used to calculate polygenic risk scores (PRS). Although PRSs are not currently more accurate than clinical predictors and do not yet predict risk with equal accuracy across all ethnic populations, they have several potential clinical uses. Here, we discuss potential usages of PRS for predicting T2D and for informing and optimising interventions. We also touch on possible health inequality risks of PRS and the feasibility of large-scale implementation of PRS in clinical practice. Before PRSs can be used as a therapeutic tool, it is important that further polygenic risk models are derived using non-European genome-wide association studies to ensure that risk prediction is accurate for all ethnic groups. Furthermore, it is essential that the ethical, social and legal implications of PRS are considered before their implementation in any context.

Biomarkers for type 2 diabetes

Background

The prevalence and incidence of type 2 diabetes (T2D), representing >90% of all cases of diabetes, are increasing rapidly worldwide. Identification of individuals at high risk of developing diabetes is of great importance as early interventions might delay or even prevent full-blown disease. T2D is a complex disease caused by multiple genetic loci in interplay with lifestyle and environmental factors. Recently over 400 distinct association signals were published; these explain 18% of the risk of T2D.

Scope of review

In this review there is a major focus on risk factors and genetic and non-genetic biomarkers for the risk of T2D identified especially in large prospective population-based studies, and studies testing causality of the biomarkers for T2D in Mendelian randomization studies. Another focus is on understanding genome-phenome interplay in the classification of individuals with T2D into subgroups.

Major conclusions

Several recent large population-based studies and their meta-analyses have identified multiple potential genetic and non-genetic biomarkers for the risk of T2D. Combination of genetic variants and physiologically characterized pathways improves the classification of individuals with T2D into subgroups, and is also paving the way to a precision medicine approach, in T2D.

Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes

Diabetes mellitus, also known simply as diabetes, has been described as a chronic and complex endocrine metabolic disorder that is a leading cause of death across the globe. It is considered a key public health problem worldwide and one of four important non-communicable diseases prioritized for intervention through world health campaigns by various international foundations. Among its four categories, Type 2 diabetes (T2D) is the commonest form of diabetes accounting for over 90% of worldwide cases. Unlike monogenic inherited disorders that are passed on in a simple pattern, T2D is a multifactorial disease with a complex etiology, where a mixture of genetic and environmental factors are strong candidates for the development of the clinical condition and pathology. The genetic factors are believed to be key predisposing determinants in individual susceptibility to T2D. Therefore, identifying the predisposing genetic variants could be a crucial step in T2D management as it may ameliorate the clinical condition and preclude complications. Through an understanding the unique genetic and environmental factors that influence the development of this chronic disease individuals can benefit from personalized approaches to treatment. We searched the literature published in three electronic databases: PubMed, Scopus and ISI Web of Science for the current status of T2D and its associated genetic risk variants and discus promising approaches toward a personalized management of this chronic, non-communicable disorder.

Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes

Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.

Factors associated with progression to pre-diabetes: a recurrent events analysis

AIMS

Pre-diabetes is a strong risk factor for type 2diabetes (T2D). The aim of this study was to explore factors associated with normal glucose maintenance and pre-diabetes prevention or delay.

METHODS

Data of 1016 first-degree relatives of T2D patients were retrieved from the Isfahan Diabetes Prevention Study (IDPS). Association of various variables including nutrients, serum tests and physical activity with the risk of pre-diabetes was assessed using recurrent events approach.

RESULTS

Cumulative incidence of diabetes was 8.17, 9.44, and 4.91% for total sample and individuals with and without pre-diabetes experience in the follow-up. Risk of progression to pre-diabetes was higher in women and older people (p < 0.01). Additionally, BMI and blood pressure had significant association with the risk (p < 0.01) and individuals with higher intake of fat were at higher risk (HR = 2.26; 95% CI 1.66-3.07 for high-intake and HR = 1.52; 95% CI 1.27-1.83 for medium-intake compared to low-intake group). Carbohydrates and protein intake were positively associated with the risk of pre-diabetes with HR = 8.63 per 49 g extra carbohydrates per day and HR = 1.32 per 6 g extra protein per day (p < 0.01). The association was also significant for triglyceride (TG) with 7% risk increase per 1 SD = 1.14 increase in TG level.

CONCLUSION

Despite frequent studies on lifestyle modification for pre-diabetes prevention, less information is available about the role of nutritional components. We observed direct effects for intake of macronutrients including fat, carbohydrates, and protein in first-degree relatives. Further research is warranted to assess these associations in general populations.

LEVEL OF EVIDENCE

Level III: Evidence obtained from a single-center cohort study.

Association of polymorphism in heat shock protein 70 genes with type 2 diabetes in Bangladeshi population

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a chronic disorder of which stress is a major contributor. Under stressful condition, body synthesizes a family of molecular chaperone called Heat-shock proteins (HSPs). Current study assessed the frequency and association of HSP70-hom + 2,437 T/C polymorphism with T2DM risk among Bangladeshis.

METHODS

This polymorphism was selected through bioinformatics analyses and identified by PCR-RFLP method.

RESULTS

Bioinformatics analysis identified this SNP as missense mutation which could destabilize the final HSP product. Heterozygous mutant (CT) genotype was significantly associated with T2DM incidence among the studied populations (p = .015). Further analysis revealed a strong association with female patients (p = .002), while the male group showed no association (p = .958). Moreover, the C allele was significantly associated among all diabetic patients (p = .016) and particularly in the female patient group (p = .001). However, under stressful condition, males with CT genotype were at high risk for T2DM incidence whereas, females with CT genotype showed no significant association.

CONCLUSIONS

HSP70-hom + 2,437 T/C polymorphism was found to be significantly associated with T2DM incidence in the Bangladeshi population in both stress-dependent and independent manners.

Risk of diabetes among related and unrelated family members

AIMS

The aim was to explore familial aggregation of diabetes in genetically related and unrelated individuals.

METHODS

We included citizens from Danish nationwide registries between 1995 and 2018 and calculated rate ratios (RR) of diabetes based on family relation using Poisson regression.

RESULTS

Of 7.3 million individuals eligible for inclusion, we identified 343,237 (4.7%) with diabetes. The RR of diabetes was 2.02 (95% CI: 1.99-2.05; p < 0.0001) if any relative had diabetes, 1.79 (95% CI: 1.76-1.83) if a father had diabetes, and 2.06 (95% CI: 2.02-2.10) if a mother had diabetes. If both parents had diabetes, the RR was 3.40 (95% CI: 3.24-3.56). Among full siblings, the RR for developing diabetes was 2.77 (95% CI: 2.71-2.84) and 5.76 (95% CI: 5.00-6.63) for twins. For second-degree relatives, half siblings with a common mother had a RR of 2.35 (95% CI: 2.15-2.56), and with a common father 1.99 (95% CI: 1.81-2.17). Furthermore, the RR was 1.60 (95% CI: 1.56-1.64) if a wife had diabetes, and 1.41 (95% CI: 1.38-1.44) if a husband had diabetes. A subgroup analysis of individuals receiving insulin only treatment (N = 23,054) demonstrated a similar risk pattern, although with slightly higher risk estimates.

CONCLUSIONS/INTERPRETATION

Family aggregation of diabetes is associated with genetic disposition with maternal status being the predominant factor. Furthermore, we observed increased risk of diabetes in second-degree relatives, and between unrelated spouses, indicating that environmental factors influence diabetes risk substantially.

Probiotic mediated NF-κB regulation for prospective management of type 2 diabetes

Diabetes and other lifestyle disorders have been recognized as the leading cause of morbidity and mortality globally. Nuclear factor kappa B (NF-κB) is a major factor involved in the early pathobiology of diabetes and studies reveal that hyperglycemic conditions in body leads to NF-κB mediated activation of several cytokines, chemokines and inflammatory molecules. NF-κB family comprises of certain DNA-binding protein factors that elicit the transcription of pro-inflammatory molecules. Various studies have identified NF-κB as a promising target for diabetic management. Probiotics have been proposed as bio-therapeutic agents for treatment of inflammatory disorders and many other chronic clinical stages. The precise mechanisms by which probiotics acts is yet to be fully understood, however research findings have indicated their role in NF-κB modulation. The current review highlights NF-κB as a bio-therapeutic target for probable management of type 2 diabetes through probiotic intervention.

Pure additive contribution of genetic variants to a risk prediction model using propensity score matching: application to type 2 diabetes

The achievements of genome-wide association studies have suggested ways to predict diseases, such as type 2 diabetes (T2D), using single-nucleotide polymorphisms (SNPs). Most T2D risk prediction models have used SNPs in combination with demographic variables. However, it is difficult to evaluate the pure additive contribution of genetic variants to classically used demographic models. Since prediction models include some heritable traits, such as body mass index, the contribution of SNPs using unmatched case-control samples may be underestimated. In this article, we propose a method that uses propensity score matching to avoid underestimation by matching case and control samples, thereby determining the pure additive contribution of SNPs. To illustrate the proposed propensity score matching method, we used SNP data from the Korea Association Resources project and reported SNPs from the genome-wide association study catalog. We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. The accuracy of the predictions was compared in terms of area under the receiver operating characteristic curve (AUC). The contribution of SNPs to T2D was evaluated by the difference in the AUC between models using only demographic variables and models that included the SNPs. The largest difference among our models showed that the AUC of the model using genetic variants with demographic variables could be 0.107 higher than that of the corresponding model using only demographic variables.

Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients

We evaluated whether genetic information could offer improvement on risk prediction of diabetic nephropathy (DN) while adding susceptibility variants into a risk prediction model with conventional risk factors in Han Chinese type 2 diabetes patients. A total of 995 (including 246 DN cases) and 519 (including 179 DN cases) type 2 diabetes patients were included in derivation and validation sets, respectively. A genetic risk score (GRS) was constructed with DN susceptibility variants based on findings of our previous genome-wide association study. In derivation set, areas under the receiver operating characteristics (AUROC) curve (95% CI) for model with clinical risk factors only, model with GRS only, and model with clinical risk factors and GRS were 0.75 (0.72-0.78), 0.64 (0.60-0.68), and 0.78 (0.75-0.81), respectively. In external validation sample, AUROC for model combining conventional risk factors and GRS was 0.70 (0.65-0.74). Additionally, the net reclassification improvement was 9.98% (P = 0.001) when the GRS was added to the prediction model of a set of clinical risk factors. This prediction model enabled us to confirm the importance of GRS combined with clinical factors in predicting the risk of DN and enhanced identification of high-risk individuals for appropriate management of DN for intervention.

Type 2 diabetes care: Improvement by standardization at a diabetes rehabilitation clinic. An observational report

BACKGROUND

Outcome of type 2 diabetes care depends on the acceptance of self-responsibility by informed patients, as treatment goals will otherwise be missed.

AIMS AND METHODS

This pre/post-observational report describes the clinical outcome of type 2 diabetes care in patients with type 2 diabetes (N =930) admitted consecutively to a diabetes rehabilitation clinic (DRC) between June 2013, and June 2016, where they were exposed to standardized lifestyle modification with meals low in salt and rich in vegetables and fruits, totaling 1,200 to 1,600 kcal/d, and an add-on exercise load equivalent to 400-600 kcal/d.

RESULTS

At admission, patients presented with multiple treatment modes, elevated HbA1c levels (7.6±1.5%, 60±16 mmol/mol), a high prevalence of co-morbidities dominated by obesity (79%), a low rate of influenza and pneumococcal immunization (<9%) and underuse of lipid-lowering drugs (-29%). Analysis of clinical and metabolic outcome after 3 weeks shows that simple standardization of and better adherence to treatment recommendations improved (p<0.0001) glucose (HbA1c -0.4±0.4%) and lipid metabolism (LDL/HDL ratio, -0.58±0.03), permitting a 39% reduction in insulin dosage, omission of insulin in 36/232 patients and omission of oral antidiabetic drugs (OADs) other than metformin and DPP4-inhibitors, while the use of GLP-1 analogs doubled to 5.2%. Improved outcome was independent of treatment strategy and more marked at initially high HbA1c at costs less than 25% of those encountered at a standard hospital.

CONCLUSIONS

Our observations support the clinical notion that adherence to basic treatment recommendations is indispensable in type 2 diabetes care if metabolic and clinical treatment goals are to be met, and if inappropriate add-on over-medicalization with OADs and/or insulin is to be avoided. To this end, 'imprinting' patients at a DRC could be of considerable help.

Potential Impact of MicroRNA Gene Polymorphisms in the Pathogenesis of Diabetes and Atherosclerotic Cardiovascular Disease

MicroRNAs (miRNAs) are endogenous, small (18–23 nucleotides), non-coding RNA molecules. They regulate the posttranscriptional expression of their target genes. MiRNAs control vital physiological processes such as metabolism, development, differentiation, cell cycle and apoptosis. The control of the gene expression by miRNAs requires efficient binding between the miRNA and their target mRNAs. Genome-wide association studies (GWASs) have suggested the association of single-nucleotide polymorphisms (SNPs) with certain diseases in various populations. Gene polymorphisms of miRNA target sites have been implicated in diseases such as cancers, diabetes, cardiovascular and Parkinson’s disease. Likewise, gene polymorphisms of miRNAs have been reported to be associated with diseases. In this review, we discuss the SNPs in miRNA genes that have been associated with diabetes and atherosclerotic cardiovascular disease in different populations. We also discuss briefly the potential underlining mechanisms through which these SNPs increase the risk of developing these diseases.

Decreased expression of microRNAs targeting type-2 diabetes susceptibility genes in peripheral blood of patients and predisposed individuals

AIM

Certain microRNA molecules (miRNAs) that target genes involved in beta-cell growth and insulin resistance are found deregulated in patients with type-2 diabetes mellitus (T2D) and correlate with its complications. However, the expression profile of miRNAs that regulate genes bearing T2D-related single-nucleotide polymorphisms has been hardly studied. We recently reported that the mRNA patterns of specific T2D-susceptibility genes are impaired in patients, and associate with disease parameters and risk factors. The aim of this study was to explore the levels of miRNAs that target those genes, in peripheral blood of patients versus controls.

METHODS

A panel of 14 miRNAs validated to target the CDKN2A, CDK5, IGF2BP2, KCNQ1, and TSPAN8 genes, was developed upon combined search throughout the DIANNA TarBase v7.0, miRTarBase, miRSearch v3.0-Exiqon, miRGator v3.0, and miRTarget Link Human algorithms. Specifically developed poly(A)polyadenylation(PAP)-reverse transcription(RT)-qPCR protocols were applied in peripheral blood RNA samples from patients and controls. Possible correlations with the disease, clinicopathological parameters and/or risk factors were evaluated.

RESULTS

T2D patients expressed decreased levels of let-7b-5p, miR-1-3p, miR-24-3p, miR-34a-5p, miR-98-5p, and miR-133a-3p, compared with controls. Moreover, these levels correlated with certain disease features including insulin and % HbA1c levels in patients, as well as BMI, triglycerides' levels and family history in controls.

CONCLUSIONS

A T2D-specific expression profile of miRNAs that target disease-susceptibility genes is for the first time described. Future studies are needed to elucidate the associated transcription-regulatory mechanisms, perchance involved in T2D pathogenesis, and to evaluate the potential of these molecules as possible biomarkers for this disorder.

Whole-Exome Sequencing (WES) for Illumina Short Read Sequencers Using Solution-Based Capture

Next-generation sequencing (NGS) is transforming clinical research and diagnostics, vastly enhancing our ability to identify novel disease-causing genetic mutations and perform comprehensive diagnostic testing in the clinic. Whole-exome sequencing (WES) is a commonly used method which captures the majority of coding regions of the genome for sequencing, as these regions contain the majority of disease-causing mutations. The clinical applications of WES are not limited to diagnosis; the technique can be employed to help determine an optimal therapeutic strategy for a patient considering their mutation profile. WES may also be used to predict a patient's risk of developing a disease, e.g., type 2 diabetes (T2D), and can therefore be used to tailor advice for the patient about lifestyle choices that could mitigate those risks. Thus, genome sequencing strategies, such as WES, underpin the emerging field of personalized medicine. Initiatives also exist for sharing WES data in public repositories, e.g., the Exome Aggregation Consortium (ExAC) database. In time, by mining these valuable data resources, we will acquire a better understanding of the roles of both single rare mutations and specific combinations of common mutations (mutation signatures) in the pathology of complex diseases such as diabetes.Herein, we describe a protocol for performing WES on genomic DNA extracted from blood or saliva. Starting with gDNA extraction, we document preparation of a library for sequencing on Illumina instruments and the enrichment of the protein-coding regions from the library using the Roche NimbleGen SeqCap EZ Exome v3 kit; a solution-based capture method. We include details of how to efficiently purify the products of each step using the AMPure XP System and describe how to use qPCR to test the efficiency of capture, and thus determine finished library quality.

Gene-diet quality interactions on haemoglobin A1c and type 2 diabetes risk: The Airwave Health Monitoring Study

INTRODUCTION

Type 2 diabetes (T2D) is multifactorial involving lifestyle, environmental and genetic risk factors. This study aims to investigate the impact of genetic interactions with alcohol and diet quality on glycated haemoglobin A1c (HbA1c) independent of obesity, in a British population.

METHODS

Cross-sectional study of 14 089 white British participants from Airwave Health Monitoring Study and a subsample of 3733 participants with dietary data. A T2D genetic risk score (GRS) was constructed, and its interactions with diet on HbA1c were assessed.

RESULTS

GRS was associated with a higher HbA1c% (β = 0.03, P < 0.0001) and a higher risk of prediabetes (OR = 1.09, P < 0.0001) and T2D (OR = 1.14, P = 0.006). The genetic effect on HbA1c% was significantly higher in obese participants (β = 1.88, P interaction = 0.03). A high intake of wholegrain attenuated the effect on HbA1c% in high-risk individuals P interaction = 0.04.

CONCLUSION

The genetic effect on HbA1c was almost doubled in obese individuals, compared with those with a healthy weight, and independent of weight, there was a modest offset on HbA1c in high-genetic-risk individuals consuming a diet high in wholegrain. This supports the importance of a healthy diet high in wholegrains and along with maintaining a healthy weight in controlling HbA1c among high-genetic-risk groups.

Recent advances and perspectives in next generation sequencing application to the genetic research of type 2 diabetes

Type 2 diabetes (T2D) mellitus is a common complex disease that currently affects more than 400 million people worldwide and has become a global health problem. High-throughput sequencing technologies such as whole-genome and whole-exome sequencing approaches have provided numerous new insights into the molecular bases of T2D. Recent advances in the application of sequencing technologies to T2D research include, but are not limited to: (1) Fine mapping of causal rare and common genetic variants; (2) Identification of confident gene-level associations; (3) Identification of novel candidate genes by specific scoring approaches; (4) Interrogation of disease-relevant genes and pathways by transcriptional profiling and epigenome mapping techniques; and (5) Investigation of microbial community alterations in patients with T2D. In this work we review these advances in application of next-generation sequencing methods for elucidation of T2D pathogenesis, as well as progress and challenges in implementation of this new knowledge about T2D genetics in diagnosis, prevention, and treatment of the disease.

Healthy eating index versus alternate healthy index in relation to diabetes status and health markers in U.S. adults: NHANES 2007-2010

BACKGROUND

It remains to be determined whether the Alternate Healthy Eating Index 2010 (AHEI-2010) or the Healthy Eating Index 2010 (HEI-2010) is preferably recommended as means to assess dietary quality in people with type 2 diabetes (T2DM).

METHODS

The purpose of this study was to determine whether the AHEI-2010 provides a more accurate assessment of dietary quality than the HEI-2010 in relation to diabetes status, while controlling for health markers, sociodemographic and lifestyle factors. The 2007-2010 National Health and Nutrition Examination Survey (NHANES) was used as a representative sample of U.S. adults age 20+ years (n = 4097). HEI-2010 and the AHEI-2010 scores were used as measures of dietary quality and were calculated using data from the first 24-h dietary recall. Health markers evaluated include anthropometrics, blood pressure, lipid and inflammatory markers, and presence of co-morbid diseases. Least Squares Means were computed to determine differences across diabetes status (nondiabetes, prediabetes, T2DM) for total and sub-component HEI-2010 and AHEI-2010 scores, and to determine differences across total HEI-2010 and AHEI-2010 quartiles for health markers. Covariate-adjusted logistic regression was used to examine the association between total HEI-2010 and AHEI-2010 scores and diabetes status.

RESULTS

Adults with T2DM showed higher HEI-2010 and AHEI-2010 scores compared to adults with prediabetes and nondiabetes but did not have better health markers. For HEI-2010 component scores, adults with T2DM had highest consumption (highest score) of total protein foods and lowest consumption (highest score) for empty calories (p < 0.01). For AHEI-2010 component scores, adults with T2DM had the lowest consumption (highest score) for sugar-sweetened beverages and fruit juice, sodium, and alcohol (lowest score). In addition, adults with T2DM had the highest consumption (lowest score) for red and/or processed meats (p < 0.01). However, neither total HEI-2010 nor AHEI-2010 scores were significantly associated with diabetes status (p > 0.05). Results suggest that neither index was clearly superior to the other in terms of its predictive ability in relation to T2DM.

CONCLUSION

Neither total HEI-2010 nor AHEI-2010 scores performed better in terms of their relationship with diabetes status. However, the significant relationships between 1) diabetes status and health markers and 2) between HEI-2010 and AHEI-2010 scores and health markers suggest that diet has some influence on T2DM.

Mediation analysis in a case-control study when the mediator is a censored variable

Mediation analysis is an approach for assessing the direct and indirect effects of an initial variable on an outcome through a mediator. In practice, mediation models can involve a censored mediator (eg, a woman's age at menopause). The current research for mediation analysis with a censored mediator focuses on scenarios where outcomes are continuous. However, the outcomes can be binary (eg, type 2 diabetes). Another challenge when analyzing such a mediation model is to use data from a case-control study, which results in biased estimations for the initial variable-mediator association if a standard approach is directly applied. In this study, we propose an approach (denoted as MAC-CC) to analyze the mediation model with a censored mediator given data from a case-control study, based on the semiparametric accelerated failure time model along with a pseudo-likelihood function. We adapted the measures for assessing the indirect and direct effects using counterfactual definitions. We conducted simulation studies to investigate the performance of MAC-CC and compared it to those of the naïve approach and the complete-case approach. MAC-CC accurately estimates the coefficients of different paths, the indirect effects, and the proportions of the total effects mediated. We applied the proposed and existing approaches to the mediation study of genetic variants, a woman's age at menopause, and type 2 diabetes based on a case-control study of type 2 diabetes. Our results indicate that there is no mediating effect from the age at menopause on the association between the genetic variants and type 2 diabetes.

Plasma tyrosine and its interaction with low high-density lipoprotein cholesterol and the risk of type 2 diabetes mellitus in Chinese

AIMS/INTRODUCTION

Metabolomic markers have the potential to improve the predicting accuracy of existing risk scores for type 2 diabetes mellitus. The present study aimed to test the associations between plasma tyrosine and type 2 diabetes mellitus with special attention to identifying possible cut-off points for type 2 diabetes mellitus, and its interactive effects with low high-density lipoprotein cholesterol (HDL-C) and/or high triglyceride for type 2 diabetes mellitus.

METHODS

From 27 May 2015 to 3 August 2016, we retrieved the medical notes of 1,898 inpatients with type 2 diabetes mellitus as the cases, and 1,522 individuals without diabetes as the controls who attended annual medical checkups from the same tertiary care center in Jinzhou, China. Logistic regression analyses were carried out to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Restricted cubic spline analysis nested in the logistic regression analysis was used to identify possible cut-off points of tyrosine for type 2 diabetes mellitus. The additive interaction was used to estimate interactions between high tyrosine and low HDL-C in type 2 diabetes mellitus patients.

RESULTS

The OR of tyrosine for type 2 diabetes mellitus did not increase until 46 μmol/L and after that point, the OR rapidly rose with increasing tyrosine in a nearly linear manner. If 46 μmol/L was used to define high tyrosine, high tyrosine was associated with an increased OR of type 2 diabetes mellitus (adjusted OR 1.88, 95% CI 1.44-2.45). The presence of low HDL-C greatly enhanced the ORs of tyrosine for type 2 diabetes mellitus from 1.11 (95% CI 0.82-1.51) to 54.11 (95% CI 33.96-86.22) with significant additive interaction.

CONCLUSIONS

In Chinese adults, tyrosine >46 μmol/L was associated with increased odds of type 2 diabetes mellitus, which was contingent on low HDL-C.

Blood-based analysis of type-2 diabetes mellitus susceptibility genes identifies specific transcript variants with deregulated expression and association with disease risk

Despite significant progress by genome-wide association studies, the ability of genetic variants to conduce to the prediction or prognosis of type-2 diabetes (T2D) is weak. Expression analysis of the corresponding genes may suggest possible links between single-nucleotide polymorphisms and T2D phenotype and/or risk. Herein, we investigated the expression patterns of 24 T2D-susceptibility genes, and their individual transcript variants (tv), in peripheral blood of T2D patients and controls (CTs), applying RNA-seq and real-time qPCR methodologies, and explore possible associations with disease features. Our data revealed the deregulation of certain transcripts in T2D patients. Among them, the down-regulation of CAPN10 tv3 was confirmed as an independent predictor for T2D. In patients, increased expression of CDK5 tv2, CDKN2A tv3 or THADA tv5 correlated positively with serum insulin levels, of CDK5 tv1 positively with % HbA1c levels, while in controls, elevated levels of TSPAN8 were associated positively with the presence of T2D family history. Herein, a T2D-specific expression profile of specific transcripts of disease-susceptibility genes is for the first time described in human peripheral blood. Large-scale studies are needed to evaluate the potential of these molecules to serve as disease biomarkers.

A Perception on Genome-Wide Genetic Analysis of Metabolic Traits in Arab Populations

Despite dedicated nation-wide efforts to raise awareness against the harmful effects of fast-food consumption and sedentary lifestyle, the Arab population continues to struggle with an increased risk for metabolic disorders. Unlike the European population, the Arab population lacks well-established genetic risk determinants for metabolic disorders, and the transferability of established risk loci to this population has not been satisfactorily demonstrated. The most recent findings have identified over 240 genetic risk loci (with ~400 independent association signals) for type 2 diabetes, but thus far only 25 risk loci (ADAMTS9, ALX4, BCL11A, CDKAL1, CDKN2A/B, COL8A1, DUSP9, FTO, GCK, GNPDA2, HMG20A, HNF1A, HNF1B, HNF4A, IGF2BP2, JAZF1, KCNJ11, KCNQ1, MC4R, PPARγ, SLC30A8, TCF7L2, TFAP2B, TP53INP1, and WFS1) have been replicated in Arab populations. To our knowledge, large-scale population- or family-based association studies are non-existent in this region. Recently, we conducted genome-wide association studies on Arab individuals from Kuwait to delineate the genetic determinants for quantitative traits associated with anthropometry, lipid profile, insulin resistance, and blood pressure levels. Although these studies led to the identification of novel recessive variants, they failed to reproduce the established loci. However, they provided insights into the genetic architecture of the population, the applicability of genetic models based on recessive mode of inheritance, the presence of genetic signatures of inbreeding due to the practice of consanguinity, and the pleiotropic effects of rare disorders on complex metabolic disorders. This perspective presents analysis strategies and study designs for identifying genetic risk variants associated with diabetes and related traits in Arab populations.

Plasma Levels of Amino Acids Related to Urea Cycle and Risk of Type 2 Diabetes Mellitus in Chinese Adults

Objective: This study aimed to test associations between type 2 diabetes mellitus (T2DM) and metabolites in urea cycle including arginine, citrulline and ornithine. Methods:This study used a hospital-based cross-sectional study design. We retrieved medical notes of 401 in-patients with onset of T2DM within 2 years and 1,522 healthy subjects who attended annual physical examination. All cases were admitted to a tertiary care center in Jinzhou, China from May 2015 to August 2016. Binary logistic regression analyses were performed to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Results:Patients with T2DM had higher arginine, and lower ornithine than control subjects. Levels of citrulline were similar in two groups. Arginine was positively associated with T2DM (ORs: 1.20, 1.17-1.23) while ornithine was negatively associated with T2DM (OR: 0.89, 0.88-0.91). After adjustment for other amino acids and traditional risk factors, these associations were still significant and persistent for arginine and ornithine. The association between citrulline and T2DM was not significant. Their ratios of pairs of two amino acids were associated with increased risk of T2DM. After adjustment for other ratios of amino acids, effect size for T2DM remained significant. Further adjustment for traditional risk factors did not lead to large changes (ORs: 1.78, 1.20-2.65 for the ratio of arginine to ornithine; ORs: 1.59, 1.37-1.86 for the ratio of citrulline to ornithine, respectively) except the ratio of arginine to citrulline. Conclusions: Plasma levels of amino acids related to urea cycle and their ratios of these amino-acids were associated with T2DM in Chinese adults.

An association between IL-10 promoter polymorphisms and diabetic nephropathy: a meta-analysis of case-control studies

Background

This study aimed to synthesize evidence on the association between IL-10 gene (-819 C/T, -1082 A/G, -592 A/C) polymorphisms and the risk of developing diabetic nephropathy.

Methods

A systematic literature search was done in health-related electronic databases. The search was limited to studies published in English until September 2017. We also checked the references of retrieved articles and relevant reviews for any additional studies. The methodological quality of the studies included in this review was assessed using the 'Scales for Quality Assessment'. The I ² test was used to quantify between-study heterogeneity. A value of I ² > 50% indicated substantial heterogeneity. For the pooled analysis, summary odds ratio (OR) and its 95% confidence interval (CI) in random effect model were used.

Results

Eight case-control studies (1192 cases with diabetic nephropathy and 2399 controls) met the inclusion criteria. Three groups of people namely Africans, Asians and Caucasians were included in this review. There were significant protective effects of SNP -819 C/T in overall population (OR 0.32, 95% CI 0.26-0.4) and - 1082 A/G SNP in the Asian population (OR 0.64, 95% CI 0.47-0.86) on diabetic nephropathy in the recessive model. There was no significant effect of -592 A/C on diabetic nephropathy.

Conclusion

The findings suggest the protective effects of -1082A/G and -819G/A polymorphisms on the risk of developing diabetic nephropathy in type 2 diabetes mellitus, especially in the Asian population. Well- designed, prospective studies with sufficient number of participants are recommended to substantiate these findings.

Association of the PCK2 Gene Polymorphism With New-onset Glucose Intolerance in Japanese Kidney Transplant Recipients

BACKGROUND

New-onset diabetes mellitus after transplantation (NODAT) is a risk factor for both cardiovascular disease and poor graft survival after kidney transplantation (KTx). In this study, we identified single-nucleotide polymorphisms (SNPs) in genes involved in glucose metabolism and examined the correlation between these SNPs and glucose intolerance after KTx.

METHODS

Thirty-eight patients with normal glucose tolerance before KTx were included in this study. Patients with plasma glucose levels of >140 mg/dL at 120 minutes on the 75-g oral glucose tolerance test at 1 year after KTx were classified as having new-onset impaired glucose tolerance (NIGT). We identified 8 SNPs in 7 genes that are involved in glucose metabolism among the patients included in this study, and compared the prevalence rate of NIGT among SNPs in each gene.

RESULTS

Of the 38 patients, 11 (28.9%) were diagnosed with NIGT. For rs4982856 in the PCK2 gene, the distribution of genotypes among the total patient population was as follows: T/T, 12 (31.6%); T/C, 22 (57.9%); and C/C, 4 (10.5%). Seven of 11 patients with NIGT had the T/T genotype of rs4982856, whereas only 5 of 27 patients with normal glucose tolerance had this genotype. The T allele frequency of the rs4982856 was significantly higher in the NIGT group than in the normal group (81.8 vs 52.8%, respectively; P = .015).

CONCLUSION

Our study indicates that the T allele of the rs4982856 SNP in the PCK2 gene may be a risk factor for glucose intolerance after KTx.

High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes

Type 2 diabetes (T2D) is a complex disorder that is caused by a combination of genetic, epigenetic, and environmental factors. High-throughput approaches have opened a new avenue toward a better understanding of the molecular bases of T2D. A genome-wide association studies (GWASs) identified a group of the most common susceptibility genes for T2D (i.e., TCF7L2, PPARG, KCNJ1, HNF1A, PTPN1, and CDKAL1) and illuminated novel disease-causing pathways. Next-generation sequencing (NGS)-based techniques have shed light on rare-coding genetic variants that account for an appreciable fraction of T2D heritability (KCNQ1 and ADRA2A) and population risk of T2D (SLC16A11, TPCN2, PAM, and CCND2). Moreover, single-cell sequencing of human pancreatic islets identified gene signatures that are exclusive to α-cells (GCG, IRX2, and IGFBP2) and β-cells (INS, ADCYAP1, INS-IGF2, and MAFA). Ongoing epigenome-wide association studies (EWASs) have progressively defined links between epigenetic markers and the transcriptional activity of T2D target genes. Differentially methylated regions were found in TCF7L2, THADA, KCNQ1, TXNIP, SOCS3, SREBF1, and KLF14 loci that are related to T2D. Additionally, chromatin state maps in pancreatic islets were provided and several non-coding RNAs (ncRNA) that are key to T2D pathogenesis were identified (i.e., miR-375). The present review summarizes major progress that has been made in mapping the (epi)genomic landscape of T2D within the last few years.

A Point System for Predicting 10-Year Risk of Developing Type 2 Diabetes Mellitus in Japanese Men: Aichi Workers' Cohort Study

Background

Relatively little evidence exists for type 2 diabetes mellitus (T2DM) prediction models from long-term follow-up studies in East Asians. This study aims to develop a point-based prediction model for 10-year risk of developing T2DM in middle-aged Japanese men.

Methods

We followed 3,540 male participants of Aichi Workers’ Cohort Study, who were aged 35–64 years and were free of diabetes in 2002, until March 31, 2015. Baseline age, body mass index (BMI), smoking status, alcohol consumption, regular exercise, medication for dyslipidemia, diabetes family history, and blood levels of triglycerides (TG), high density lipoprotein cholesterol (HDLC) and fasting blood glucose (FBG) were examined using Cox proportional hazard model. Variables significantly associated with T2DM in univariable models were simultaneously entered in a multivariable model for determination of the final model using backward variable selection. Performance of an existing T2DM model when applied to the current dataset was compared to that obtained in the present study’s model.

Results

During the median follow-up of 12.2 years, 342 incident T2DM cases were documented. The prediction system using points assigned to age, BMI, smoking status, diabetes family history, and TG and FBG showed reasonable discrimination (c-index: 0.77) and goodness-of-fit (Hosmer-Lemeshow test, P = 0.22). The present model outperformed the previous one in the present subjects.

Conclusion

The point system, once validated in the other populations, could be applied to middle-aged Japanese male workers to identify those at high risk of developing T2DM. In addition, further investigation is also required to examine whether the use of this system will reduce incidence.

Evaluating the glucose raising effect of established loci via a genetic risk score

Recent genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) associated with glucose levels. We tested the hypothesis here whether the cumulative effect of glucose raising SNPs, assessed via a score, is associated with glucose levels. A total of 1,434 participants of Greek descent from the THISEAS study and 1,160 participants form the GOMAP study were included in this analysis. We developed a genetic risk score (GRS), based on the known glucose-raising loci, in order to investigate the cumulative effect of known glucose loci on glucose levels. In the THISEAS study, the GRS score was significantly associated with increased glucose levels (mmol/L) (β ± SE: 0.024 ± 0.004, P = 8.27e-07). The effect of the genetic risk score was also significant in the GOMAP study (β ± SE: 0.011 ± 0.005, P = 0.031). In the meta-analysis of the two studies both scores were significantly associated with higher glucose levels GRS: β ± SE: 0.019 ± 0.003, P = 1.41e-09. Also, variants at the SLC30A8, PROX1, MTNR1B, ADRA2A, G6PC2, LPIN3 loci indicated nominal evidence for association with glucose levels (p < 0.05). We replicate associations of the established glucose raising variants in the Greek population and confirm directional consistency of effects (binomial sign test p = 6.96e-05). We also demonstrate that the cumulative effect of the established glucose loci yielded a significant association with increasing glucose levels.