Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review

Genome-wide polygenic risk score, cardiometabolic risk factors, and type 2 diabetes mellitus in the Chinese population

OBJECTIVE

Type 2 diabetes (T2D) is caused by both genetic and cardiometabolic risk factors. However, the magnitude of the genetic predisposition of T2D in the Chinese population remains largely unknown.

METHODS

This study included 93,488 participants from the China Kadoorie Biobank, and multiple polygenic risk scores (PRS) were calculated. A common cardiometabolic risk score (CRS) using smoking, alcohol consumption, physical activity, diet, obesity, blood pressure, and blood lipids was constructed to investigate the effects of cardiometabolic risk factors on T2D. Furthermore, an equation based on ideal PRS, CRS, and their interaction was established to explore the combined effects on T2D.

RESULTS

An ideally fitting PRS model (variance explained, R2  = 7.6%) was reached based on multiple PRS calculation methods. An additive interaction between PRS and CRS (coefficient = 28%, 95% CI: 0.20-0.36, p < 0.001) was found. The R2 of the T2D predictive model could increase to 8.3% when CRS and the interaction terms of PRS × CRS were considered. In the etiological composition of T2D, the ratio of genetic risk effect, cardiometabolic risk effect, and interaction between genetic and cardiometabolic factors was 67:16:17.

CONCLUSIONS

This study identified an ideally fitting PRS model for identifying and predicting the risk of T2D suitable for the Chinese population. The quantified proportional structure of genetic risk factors, cardiometabolic risk factors, and their interaction was detected, which elucidated the critical effect of genetic factors.

Role of Delta/Notch-like EGF-related receptor in blood glucose homeostasis

Cell-cell interactions are necessary for optimal endocrine functions in the pancreas. β-cells, characterized by the expression and secretion of the hormone insulin, are a major constituent of functional micro-organs in the pancreas known as islets of Langerhans. Cell-cell contacts between β-cells are required to regulate insulin production and glucose-stimulated insulin secretion, which are key determinants of blood glucose homeostasis. Contact-dependent interactions between β-cells are mediated by gap junctions and cell adhesion molecules such as E-cadherin and N-CAM. Recent genome-wide studies have implicated Delta/Notch-like EGF-related receptor (Dner) as a potential susceptibility locus for Type 2 Diabetes in humans. DNER is a transmembrane protein and a proposed Notch ligand. DNER has been implicated in neuron-glia development and cell-cell interactions. Studies herein demonstrate that DNER is expressed in β-cells with an onset during early postnatal life and sustained throughout adulthood in mice. DNER loss in adult β-cells in mice (β-Dner cKO mice) disrupted islet architecture and decreased the expression of N-CAM and E-cadherin. β-Dner cKO mice also exhibited impaired glucose tolerance, defects in glucose- and KCl-induced insulin secretion, and decreased insulin sensitivity. Together, these studies suggest that DNER plays a crucial role in mediating islet cell-cell interactions and glucose homeostasis.

Multi-omics profiling: the way towards precision medicine in metabolic diseases

Metabolic diseases including type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and metabolic syndrome (MetS) are alarming health burdens around the world, while therapies for these diseases are far from satisfying as their etiologies are not completely clear yet. T2DM, NAFLD, and MetS are all complex and multifactorial metabolic disorders based on the interactions between genetics and environment. Omics studies such as genetics, transcriptomics, epigenetics, proteomics, and metabolomics are all promising approaches in accurately characterizing these diseases. And the most effective treatments for individuals can be achieved via omics pathways, which is the theme of precision medicine. In this review, we summarized the multi-omics studies of T2DM, NAFLD, and MetS in recent years, provided a theoretical basis for their pathogenesis and the effective prevention and treatment, and highlighted the biomarkers and future strategies for precision medicine.

The association between plasma proneurotensin and glucose regulation is modified by country of birth

The prevalence of type 2 diabetes (T2D) has increased dramatically in Middle Eastern populations that represent the largest non-European immigrant group in Sweden today. As proneurotensin predicts T2D, the aim of this study was to investigate differences in proneurotensin levels across populations of Middle Eastern and Caucasian origin and to study its associations with indices of glucose regulation. Participants in the age 30 to 75 years, living in Malmö, Sweden, and born in Iraq or Sweden, were recruited from the census register. Anthropometrics and fasting samples were collected and oral glucose tolerance tests conducted assessing insulin secretion (DIo) as well as insulin sensitivity (ISI). A total of 2155 individuals participated in the study, 1398 were Iraqi-born and 757 were Swedish-born participants. Higher fasting proneurotensin levels were observed in Iraqi- compared to Swedish-born participants (137.5 vs. 119.8 pmol/L; p < 0.001) data adjusted for age, sex and body mass index. In Iraqi participants only, plasma proneurotensin was associated with impaired glucose regulation assessed as ISI, DIo and HbA1c, and significant interactions between country of birth and proneurotensin were observed (Pinteraction ISI = 0.048; Pinteraction DIo = 0.014; PinteractionHbA1c = 0.029). We report higher levels of proneurotensin in the general Middle Eastern population. The finding that Middle Eastern origin modifies the relationship of proneurotensin with indices of glucose regulation suggests that proneurotensin may be a stronger determinant of T2D in Middle Eastern as compared to Caucasian populations. These findings may explain part of the excess T2D risk in the Middle Eastern population but needs to be explored further.

Association Between Genetic Risk and Development of Type 2 Diabetes in a General Japanese Population: The Hisayama Study

CONTEXT

Although recent genetic studies have identified many susceptibility loci associated with type 2 diabetes (T2D), the usefulness of such loci for precision medicine remains uncertain.

OBJECTIVE

This study investigated the impact of genetic risk score (GRS) on the development of T2D in a general Japanese population.

PARTICIPANTS

The current study consists of 1465 subjects aged 40 to 79 years without diabetes who underwent a health examination in 2002.

DESIGN

The GRS was generated using the literature-based effect size for T2D of 84 susceptibility loci for the Japanese population, and the risk estimates of GRS on the incidence of T2D were computed by using a Cox proportional hazard model in a 10-year follow-up study. The influence of GRS on the predictive ability was estimated with Harrell C statistics, integrated discrimination improvement (IDI), and continuous net reclassification improvement (cNRI).

RESULTS

During the 10-year follow-up, 199 subjects experienced T2D. The risk of developing T2D increased significantly with elevating quintiles of GRS (multivariable-adjusted hazard ratio for the fifth vs first quintile, 2.85; 95% CI, 1.83 to 4.44). When incorporating GRS into the multivariable model comprising environmental risk factors, the Harrell C statistics (95% CI) increased from 0.681 (0.645 to 0.717) to 0.707 (0.672 to 0.742) and the predictive ability of T2D was significantly improved (IDI, 0.0376; 95% CI, 0.0284 to 0.0494; cNRI, 0.3565; 95% CI, 0.1278 to 0.5829). GRS was also associated with the risk of T2D independently of environmental risk factors.

CONCLUSIONS

These findings suggest the usefulness of GRS for identifying a high-risk population together with environmental risk factors in the Japanese population.

Genetic Privacy, Disease Prevention, and the Principle of Rescue

Suppose that you have deeply personal information that you do not want to share. Further suppose that this information could help others, perhaps even saving their lives. Should you reveal the information or keep it secret? With the increasing prevalence of genetic testing, more and more people are finding themselves in this situation. Although a patient's genetic results are potentially relevant to all her biological family members, her first-degree relatives-parents, children, and full siblings-are most likely to be affected. This is especially true for genetic mutations-like those in the BRCA1 and BRCA2 genes-that are associated with a dramatically increased risk of disease. Fortunately, people are usually willing to share results with their at-risk relatives. Occasionally, however, a patient refuses to disclose her findings to anyone outside her clinical team. Ethicists have written little on patients' moral duties to their at-risk relatives. Moreover, the few accounts that have been advanced are problematic. Some unnecessarily expose patients' genetic information to relatives who are unlikely to benefit from it, and others fail to ensure that patients' most vulnerable relatives are informed of their genetic risks. Patients' duty to warn can be defended in a way that avoids these problems. I argue that the duty to share one's genetic results is grounded in the principle of rescue-the idea that one ought to prevent, reduce, or mitigate the risk of harm to another person when the expected harm is serious and the cost or risk to oneself is sufficiently moderate. When these two criteria are satisfied, a patient will most likely have a duty to warn.

Predictive performance of a genetic risk score using 11 susceptibility alleles for the incidence of Type 2 diabetes in a general Japanese population: a nested case-control study

AIMS

To assess the predictive ability of a genetic risk score for the incidence of Type 2 diabetes in a general Japanese population.

METHODS

This prospective case-control study, nested within a Japan Public Health Centre-based prospective study, included 466 participants with incident Type 2 diabetes over a 5-year period (cases) and 1361 control participants, as well as 1463 participants with existing diabetes and 1463 control participants. Eleven susceptibility single nucleotide polymorphisms, identified through genome-wide association studies and replicated in Japanese populations, were analysed.

RESULTS

Most single nucleotide polymorphism loci showed directionally consistent associations with diabetes. From the combined samples, one single nucleotide polymorphism (rs2206734 at CDKAL1) reached a genome-wide significance level (odds ratio 1.28, 95% CI 1.18-1.40; P = 1.8 × 10^-8 ). Three single nucleotide polymorphisms (rs2206734 in CDKAL1, rs2383208 in CDKN2A/B, and rs2237892 in KCNQ1) were nominally significantly associated with incident diabetes. Compared with the lowest quintile of the total number of risk alleles, the highest quintile had a higher odds of incident diabetes (odds ratio 2.34, 95% CI 1.59-3.46) after adjusting for conventional risk factors such as age, sex and BMI. The addition to the conventional risk factor-based model of a genetic risk score using the 11 single nucleotide polymorphisms significantly improved predictive performance; the c-statistic increased by 0.021, net reclassification improved by 6.2%, and integrated discrimination improved by 0.003.

CONCLUSIONS

Our prospective findings suggest that the addition of a genetic risk score may provide modest but significant incremental predictive performance beyond that of the conventional risk factor-based model without biochemical markers.

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.

Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication

The incidence of type 2 diabetes (T2D) has rapidly increased over recent decades, and T2D has become a leading public health challenge in China. Compared with European descents, Chinese patients with T2D are diagnosed at a relatively young age and low BMI. A better understanding of the factors contributing to the diabetes epidemic is crucial for determining future prevention and intervention programs. In addition to environmental factors, genetic factors contribute substantially to the development of T2D. To date, more than 100 susceptibility loci for T2D have been identified. Individually, most T2D genetic variants have a small effect size (10-20% increased risk for T2D per risk allele); however, a genetic risk score that combines multiple T2D loci could be used to predict the risk of T2D and to identify individuals who are at a high risk. Furthermore, individualized antidiabetes treatment should be a top priority to prevent complications and mortality. In this article, we review the epidemiological trends and recent progress in the understanding of T2D genetic etiology and further discuss personalized medicine involved in the treatment of T2D.

Multiple Score Comparison: a network meta-analysis approach to comparison and external validation of prognostic scores

BACKGROUND

Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them.

METHODS

Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC) which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD) arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined.

RESULTS

We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small.

CONCLUSIONS

We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores. Our large-scale external validation indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.

Enrichment of minor allele of SNPs and genetic prediction of type 2 diabetes risk in British population

Type 2 diabetes (T2D) is a complex disorder characterized by high blood sugar, insulin resistance, and relative lack of insulin. The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in T2D using previously published SNP datasets and found higher MAC in cases relative to matched controls. A set of 357 SNPs was found to have the best predictive accuracy in a British population. A weighted risk score calculated by using this set produced an area under the curve (AUC) score of 0.86, which is comparable to risk models built by phenotypic markers. These results identify a novel genetic risk element in T2D susceptibility and provide a potentially useful genetic method to identify individuals with high risk of T2D.

Predicting Polygenic Obesity Using Genetic Information

Except in rare cases, obesity tends to be a consequence of both an unhealthy lifestyle and a genetic susceptibility to gain weight. With more than 200 common genetic variants identified, there is a growing interest in developing personalized preventive and treatment strategies to predict an individual's obesity risk. We review the literature on the prediction of obesity and show that models based on the established genetic variants have poorer predictive ability than traditional predictors, such as family history of obesity and childhood obesity. Current findings suggest that opportunities for precision medicine in common obesity may be limited.

Predictive utility of a genetic risk score of common variants associated with type 2 diabetes in a black South African population

AIMS

To determine the predictive utility of polygenic risk scores of common variants associated with type 2 diabetes derived from the European and Asian ethnicities among a black South African population.

METHOD

Our study was a case-control study nested within the Prospective Urban and Rural Epidemiological (PURE) study of 178 male and female cases, matched for age and gender with 178 controls. Four types of genetic risk scores (GRS) were developed from 66 selected SNPs. These comprised of beta cell related variants (GRSb), variants which had significant associations with T2D in our study (GRSn), variants from the trans-ethnic meta-analysis (GRStrans) and all the 66 selected SNPs (GRSt).

RESULTS

Of the GRS's, only GRSn was associated with increased risk of T2D as indicated by an OR (95CI) of 1.21 (1.02-1.43) p-value=0.015. Stratified analysis of age and BMI, indicated the GRSn to be significantly associated with T2D among the non-obese and participants less than 50years. The area under the ROC of the T2D risk factors only was 0.652 (p value<0.001) and with the addition of GRSn it was 0.665 (p value<0.001).

CONCLUSIONS

The GRS of European and Asian derived variants have limited clinical utility in the black South African population. The inclusion of population specific variants in the GRS is pivotal.

Glucose tolerance female-specific QTL mapped in collaborative cross mice

Type-2 diabetes (T2D) is a complex metabolic disease characterized by impaired glucose tolerance. Despite environmental high risk factors, host genetic background is a strong component of T2D development. Herein, novel highly genetically diverse strains of collaborative cross (CC) lines from mice were assessed to map quantitative trait loci (QTL) associated with variations of glucose-tolerance response. In total, 501 mice of 58 CC lines were maintained on high-fat (42 % fat) diet for 12 weeks. Thereafter, an intraperitoneal glucose tolerance test (IPGTT) was performed for 180 min. Subsequently, the values of Area under curve for the glucose at zero and 180 min (AUC_0-180), were measured, and used for QTL mapping. Heritability and coefficient of variations in glucose tolerance (CVg) were calculated. One-way analysis of variation was significant (P < 0.001) for AUC_0-180 between the CC lines as well between both sexes. Despite Significant variations for both sexes, QTL analysis was significant, only for females, reporting a significant female-sex-dependent QTL (~2.5 Mbp) associated with IPGTT AUC_0-180 trait, located on Chromosome 8 (32-34.5 Mbp, containing 51 genes). Gene browse revealed QTL for body weight/size, genes involved in immune system, and two main protein-coding genes involved in the Glucose homeostasis, Mboat4 and Leprotl1. Heritability and coefficient of genetic variance (CVg) were 0.49 and 0.31 for females, while for males, these values 0.34 and 0.22, respectively. Our findings demonstrate the roles of genetic factors controlling glucose tolerance, which significantly differ between sexes requiring independent studies for females and males toward T2D prevention and therapy.

Preventing Diabetes: Early Versus Late Preventive Interventions

There are a number of arguments in support of early measures for the prevention of type 2 diabetes (T2D), as well as for concepts and strategies at later intervention stages. Diabetes prevention is achievable when implemented in a sustainable manner. Sustainability within a T2D prevention program is more important than the actual point in time or disease process at which prevention activities may start. The quality of intervention, as well as its intensity, should vary with the degree of the identified T2D risk. Nevertheless, preventive interventions should start as early as possible in order to allow a wide variety of relatively low- and moderate-intensity programs. The later the disease risk is identified, the more intensive the intervention should be. Public health interventions for diabetes prevention represent an optimal model for early intervention. Late interventions will be targeted at people who already have significant pathophysiological derangements that can be considered steps leading to the development of T2D. These derangements may be difficult to reverse, but the worsening of dysglycemia may be halted, and thus the clinical onset of T2D can be delayed.

Developing and evaluating polygenic risk prediction models for stratified disease prevention

Knowledge of genetics and its implications for human health is rapidly evolving in accordance with recent events, such as discoveries of large numbers of disease susceptibility loci from genome-wide association studies, the US Supreme Court ruling of the non-patentability of human genes, and the development of a regulatory framework for commercial genetic tests. In anticipation of the increasing relevance of genetic testing for the assessment of disease risks, this Review provides a summary of the methodologies used for building, evaluating and applying risk prediction models that include information from genetic testing and environmental risk factors. Potential applications of models for primary and secondary disease prevention are illustrated through several case studies, and future challenges and opportunities are discussed.

Reclassification of genetic-based risk predictions as GWAS data accumulate

Background

Disease risk assessments based on common genetic variation have gained widespread attention and use in recent years. The clinical utility of genetic risk profiles depends on the number and effect size of identified loci, and how stable the predicted risks are as additional loci are discovered. Changes in risk classification for individuals over time would undermine the validity of common genetic variation for risk prediction. In this analysis, we quantified reclassification of genetic risk based on past and anticipated future GWAS data.

Methods

We identified disease-associated SNPs via the NHGRI GWAS catalog and recent large scale genome-wide association study (GWAS). We calculated the genomic risk for a simulated cohort of 100,000 individuals based on a multiplicative odds ratio model using cumulative GWAS-identified SNPs at four time points: 2007, 2009, 2011, and 2013. Individuals were classified as Higher Risk (population adjusted odds >2), Average Risk (between 0.5 and 2), and Lower Risk (<0.5) for each time point and we compared classifications between time points for breast cancer (BrCa), prostate cancer (PrCa), diabetes mellitus type 2 (T2D), and cardiovascular heart disease (CHD). We estimated future reclassification using the anticipated number of undiscovered SNPs.

Results

Risk reclassification occurred for all four phenotypes from 2007 to 2013. During the most recent interval (2011-2013), the degree of risk reclassification ranged from 16.3 % for CHD to 24.4 % for PrCa. Many individuals classified as Higher Risk at earlier time points were subsequently reclassified into a lower risk category. From 2011 to 2013, the degree of such downward risk reclassification ranged from 24.9 % for T2D to 55 % for CHD. The percent of individuals classified as Higher Risk increased as more SNPs were discovered, ranging from an increase of 5 % for CHD to 9 % for PrCa from 2007 to 2013. Reclassification continued to occur when we modeled the discovery of anticipated SNPs based on doubling current sample size.

Conclusion

Risk estimates from common genetic variation show large reclassification rates. Identifying disease-associated SNPs facilitates the clinically relevant task of identifying higher-risk individuals. However, the large amount of reclassification that we demonstrated in individuals initially classified as Higher Risk but later as Average Risk or Lower Risk, suggests that caution is currently warranted in basing clinical decisions on common genetic variation for many complex diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0272-5) contains supplementary material, which is available to authorized users.

Lack of association between rs1800795 (-174 G/C) polymorphism in the promoter region of interleukin-6 gene and susceptibility to type 2 diabetes in Isfahan population

BACKGROUND

Type 2 diabetes mellitus (T2DM) is an inflammatory autoimmune disease that mostly affects older adults. The etiology of T2DM includes both genetic and environmental factors. rs1800795 (-174 G/C) single nucleotide polymorphism (SNP) linked with autoimmune disorders predispositions, identified by Genome-Wide Association Study among genes, which immunologically related is considerably over signified. The goal of this study was to evaluate the association between rs1800795 (-174 G/C) polymorphisms in the promoter of interleukin-6 (IL-6) gene with susceptibility to T2DM in a subset of the Iranian population.

MATERIALS AND METHODS

In this case-control study, 120 healthy subjects and 120 patients with T2DM were included. Genomic DNA obtained from whole blood samples and the polymerase chain reaction was used to amplify the fragment of interest contain rs1800795 SNP, restriction fragment length polymorphism method was applied for genotyping of the DNA samples with NlaIII as a restriction enzyme. SPSS for Windows software (version 18.0, SPSS, Chicago, IL, USA) was performed for statistical analysis.

RESULTS

No significant differences were found between healthy controls and T2DM patients with respect to the frequency distribution of the cytokine gene polymorphism investigated. Odds ratio, adjusted for sex, age, and smoking status has displayed similar outcomes.

CONCLUSION

These results indicated that the rs1800795 SNP is not a susceptibility gene variant for the development of T2DM in the Isfahan population. Further studies using new data on complex transcriptional interactions between IL-6 polymorphic sites are necessary to determine IL-6 haplotype influence on susceptibility to T2DM.

Meta-analytic research on the relationship between cumulative risk alleles and risk of type 2 diabetes mellitus

BACKGROUND

Our aim is to examine the dose-response association between cumulative genetic risk and actual risk of type 2 diabetes mellitus (T2DM) and the influence of adjustment for covariates on T2DM risk through a comprehensive meta-analysis of observational studies.

METHODS

Electronic literature search using EMBASE and MEDLINE (from 2003 to 2014) was conducted for cross-sectional or longitudinal studies that presented the odds ratio (OR) for T2DM in each group with categories based on the total number of risk alleles (RAs) carried (RAtotal ) using at least two single-nucleotide polymorphisms. Spline regression model was used to determine the shape of the relationship between the difference from the referent group of each study in RAtotal (ΔRAtotal ) and the natural logarithms of ORs (log OR) for T2DM.

RESULTS

Sixty-five eligible studies that included 68 267 cases among 182 603 participants were analysed. In both crude and adjusted ORs, defined by adjusting the risk for at least two confounders among age, gender and body mass index, the slope of the log OR for T2DM became less steep as the ΔRAtotal increased. In the analysis limited to 14 cross-sectional and four longitudinal studies presenting both crude and adjusted ORs, regression curves of both ORs in relation to ΔRAtotal were almost identical.

CONCLUSION

Using only single-nucleotide polymorphisms for T2DM screening was of limited value. However, when genotypic T2DM risk was considered independently from risk in relation to covariates, it was suggested that genetic profiles might have a supplementary role related to conventional T2DM risk factors in identifying individuals at high risk of T2DM. Copyright © 2015 John Wiley & Sons, Ltd.

Genetic markers of type 2 diabetes: Progress in genome-wide association studies and clinical application for risk prediction

Type 2 diabetes (T2D) has become a leading public health challenge worldwide. To date, a total of 83 susceptibility loci for T2D have been identified by genome-wide association studies (GWAS). Application of meta-analysis and modern genotype imputation approaches to GWAS data from diverse ethnic populations has been key in the effort to discover T2D loci. Genetic information is expected to play a vital role in the prediction of T2D, and many efforts have been made to develop T2D risk models that include both conventional and genetic risk factors. Yet, because most T2D genetic variants identified have small effect size individually (10%-20% increased risk of T2D per risk allele), their clinical utility remains unclear. Most studies report that a genetic risk score combining multiple T2D genetic variants does not substantially improve T2D risk prediction beyond conventional risk factors. In this article, we summarize the recent progress of T2D GWAS and further review the incremental predictive performance of genetic markers for T2D.

A Review of Emerging Technologies for the Management of Diabetes Mellitus

OBJECTIVE

High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making.

METHODS

A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels.

RESULTS

Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM.

CONCLUSION

Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care.

SIGNIFICANCE

The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.

Predictive or not predictive: understanding the mixed messages from the patient's DNA sequence

AIMS AND OBJECTIVES

The aim of this discussion paper is to enable nurses to understand how deoxyribonucleic acid analysis can be predictive for some diseases and not predictive for others. This will facilitate nurses to interpret genomic test results and explain them to patients.

BACKGROUND

Advances in technology mean that genetic testing is now commonly performed by sequencing the majority of an individual's genome or exome. This results in a huge amount of data, some of which can be used to predict or diagnose disease.

DESIGN

This is a discussion paper.

METHODS

This paper emerged from multiple discussions between the three authors over many months, culminating in a writing workshop to prepare this text.

RESULTS

The results of DNA analysis can be used to diagnose or predict rare diseases that are caused by a mutation in a single gene. However, while there are a number of genetic factors that contribute to common diseases, the ability to predict whether an individual will develop that condition is limited by the overall heritability of the condition. Environmental factors (such as lifestyle) are likely to be more useful in predicting common disease than genomic testing. Genomic tests may be of use to inform management of diseases in specific situations.

CONCLUSIONS

Genomic testing will be of use in diagnosing disorders due to single gene mutations, but the use of genomic testing to predict the chance of a patient being affected in the future by a common disease is unlikely to be a realistic option within a health service setting.

RELEVANCE TO CLINICAL PRACTICE

Nurses will increasingly be involved in the use of genomic tests in mainstream patient care. However, they need to understand and be able to explain to patients the practical applications of and limitations of such tests.

Utilizing Genetic Predisposition Score in Predicting Risk of Type 2 Diabetes Mellitus Incidence: A Community-based Cohort Study on Middle-aged Koreans

Contribution of genetic predisposition to risk prediction of type 2 diabetes mellitus (T2DM) was investigated using a prospective study in middle-aged adults in Korea. From a community cohort of 6,257 subjects with 8 yr' follow-up, genetic predisposition score with subsets of 3, 18, 36 selected single nucleotide polymorphisms (SNPs) (genetic predisposition score; GPS-3, GPS-18, GPS-36) in association with T2DM were determined, and their effect was evaluated using risk prediction models. Rs5215, rs10811661, and rs2237892 were in significant association with T2DM, and hazard ratios per risk allele score increase were 1.11 (95% confidence intervals: 1.06-1.17), 1.09 (1.01-1.05), 1.04 (1.02-1.07) with GPS-3, GPS-18, GPS-36, respectively. Changes in AUC upon addition of GPS were significant in simple and clinical models, but the significance disappeared in full clinical models with glycated hemoglobin (HbA1c). For net reclassification index (NRI), significant improvement observed in simple (range 5.1%-8.6%) and clinical (3.1%-4.4%) models were no longer significant in the full models. Influence of genetic predisposition in prediction ability of T2DM incidence was no longer significant when HbA1c was added in the models, confirming HbA1c as a strong predictor for T2DM risk. Also, the significant SNPs verified in our subjects warrant further research, e.g. gene-environmental interaction and epigenetic studies.

A Mendelian randomization study of the effect of type-2 diabetes on coronary heart disease

In observational studies, type-2 diabetes (T2D) is associated with an increased risk of coronary heart disease (CHD), yet interventional trials have shown no clear effect of glucose-lowering on CHD. Confounding may have therefore influenced these observational estimates. Here we use Mendelian randomization to obtain unconfounded estimates of the influence of T2D and fasting glucose (FG) on CHD risk. Using multiple genetic variants associated with T2D and FG, we find that risk of T2D increases CHD risk (odds ratio (OR)=1.11 (1.05–1.17), per unit increase in odds of T2D, P=8.8 × 10⁻⁵; using data from 34,840/114,981 T2D cases/controls and 63,746/130,681 CHD cases/controls). FG in non-diabetic individuals tends to increase CHD risk (OR=1.15 (1.00–1.32), per mmol·per l, P=0.05; 133,010 non-diabetic individuals and 63,746/130,681 CHD cases/controls). These findings provide evidence supporting a causal relationship between T2D and CHD and suggest that long-term trials may be required to discern the effects of T2D therapies on CHD risk.

In order to effectively design interventions, it is useful to understand the complex interplay between multiple syndromes. Here, Ahmad et al. use genome-wide association study data and Mendelian randomisation to examine the influence of Type 2 diabetes and fasting glucose levels on coronary heart disease.

A weighted polygenic risk score using 14 known susceptibility variants to estimate risk and age onset of psoriasis in Han Chinese

With numbers of common variants identified mainly through genome-wide association studies (GWASs), there is great interest in incorporating the findings into screening individuals at high risk of psoriasis. The purpose of this study is to establish genetic prediction models and evaluate its discriminatory ability in psoriasis in Han Chinese population. We built the genetic prediction models through weighted polygenic risk score (PRS) using 14 susceptibility variants in 8,819 samples. We found the risk of psoriasis among individuals in the top quartile of PRS was significantly larger than those in the lowest quartile of PRS (OR = 28.20, P < 2.0×10^-16). We also observed statistically significant associations between the PRS, family history and early age onset of psoriasis. We also built a predictive model with all 14 known susceptibility variants and alcohol consumption, which achieved an area under the curve statistic of ~ 0.88. Our study suggests that 14 psoriasis known susceptibility loci have the discriminating potential, as is also associated with family history and age of onset. This is the genetic predictive model in psoriasis with the largest accuracy to date.

Integrated genomic and BMI analysis for type 2 diabetes risk assessment

Type 2 Diabetes (T2D) is a chronic disease arising from the development of insulin absence or resistance within the body, and a complex interplay of environmental and genetic factors. The incidence of T2D has increased throughout the last few decades, together with the occurrence of the obesity epidemic. The consideration of variants identified by Genome Wide Association Studies (GWAS) into risk assessment models for T2D could aid in the identification of at-risk patients who could benefit from preventive medicine. In this study, we build several risk assessment models, evaluated with two different classification approaches (Logistic Regression and Neural Networks), to measure the effect of including genetic information in the prediction of T2D. We used data from to the Original and the Offspring cohorts of the Framingham Heart Study, which provides phenotypic and genetic information for 5245 subjects (4306 controls and 939 cases). Models were built by using several covariates: gender, exposure time, cohort, body mass index (BMI), and 65 SNPs associated to T2D. We fitted Logistic Regressions and Bayesian Regularized Neural Networks and then assessed their predictive ability by using a ten-fold cross validation. We found that the inclusion of genetic information into the risk assessment models increased the predictive ability by 2%, when compared to the baseline model. Furthermore, the models that included BMI at the onset of diabetes as a possible effector, gave an improvement of 6% in the area under the curve derived from the ROC analysis. The highest AUC achieved (0.75) belonged to the model that included BMI, and a genetic score based on the 65 established T2D-associated SNPs. Finally, the inclusion of SNPs and BMI raised predictive ability in all models as expected; however, results from the AUC in Neural Networks and Logistic Regression did not differ significantly in their prediction accuracy.

The genomic load of deleterious mutations: relevance to death in infancy and childhood

The human diploid genome has approximately 40,000 functioning conserved genes distributed within 6 billion base pairs of DNA. Most individuals carry a few heterozygous deleterious mutations and this leads to an increased risk of recessive disease in the offspring of cousin unions. Rare recessive disease is more common in the children of cousin marriages than in the general population, even though <1% of marriages in the Western World are between first cousins. But more than 90% of the children of cousin marriages do not have recessive disease and are as healthy as the rest of the population. A mathematical model based on these observations generates simultaneous equations linking the mean number of deleterious mutations in the genome of adults (M), the mean number of new deleterious mutations arising in gametogenesis and passed to the next generation (N) and the number of genes in the human diploid genome (L). The best estimates are that M is <7 and N is approximately 1. The nature of meiosis indicates that deleterious mutations in zygotes will have a Poisson distribution with a mean of M + N. There must be strong selective pressure against zygotes at the upper end of the Poisson distribution otherwise the value of M would rise with each generation. It is suggested that this selection is based on synergistic interaction of heterozygous deleterious mutations acting in large complex highly redundant and robust genetic networks. To maintain the value of M in single figures over many thousands of generations means that the zygote loss must be of the order of 30%. Most of this loss will occur soon after conception but some will occur later; during fetal development, in infancy and even in childhood. Selection means genetic death and this is caused by disease to which the deleterious mutations predispose. In view of this genome sequencing should be undertaken in all infant deaths in which the cause of death is not ascertained by standard techniques.

Diet, lifestyle, and genetic risk factors for type 2 diabetes: a review from the Nurses' Health Study, Nurses' Health Study 2, and Health Professionals' Follow-up Study

The epidemiological evidence collected from three large US cohorts (Nurses' Health Study, Nurses' Health Study 2, and Health Professionals' Follow-up Study) has yielded important information regarding the roles of overall diet, individual foods and nutrients, physical activity and other lifestyle factors in the development of type 2 diabetes. Excess adiposity is a major risk factor for diabetes, and thus, maintaining a healthy body weight and avoidance of weight gain during adulthood is the cornerstone of diabetes prevention. Independent of body weight, the quality or type of dietary fat and carbohydrate is more crucial than the quantity in determining diabetes risk. Higher consumption of coffee, whole grains, fruits, and nuts is associated with lower risk of diabetes, whereas regular consumption of refined grains, red and processed meats, and sugar-sweetened beverages including fruits juices is associated with increased risk. Dietary patterns rich in fruits and vegetables, whole grains, and nuts and legumes but lower in red and processed meats, refined grains, and sugar-sweetened beverages are consistently associated with diabetes risk, even after adjustment for body mass index. The genome-wide association studies conducted in these cohorts have contributed substantially to the discoveries of novel genetic loci for type 2 diabetes and other metabolic traits, although the identified common variants explain only a small proportion of overall diabetes predisposition. Taken together, these ongoing large cohort studies have provided convincing epidemiologic evidence that a healthy diet, together with regular physical activity, maintenance of a healthy weight, moderate alcohol consumption, and avoidance of sedentary behaviors and smoking would prevent the majority of type 2 diabetes cases.

Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes

OBJECTIVE

A genetic risk score (GRS) comprised of single nucleotide polymorphisms (SNPs) and metabolite biomarkers have each been shown, separately, to predict incident type 2 diabetes. We tested whether genetic and metabolite markers provide complementary information for type 2 diabetes prediction and, together, improve the accuracy of prediction models containing clinical traits.

RESEARCH DESIGN AND METHODS

Diabetes risk was modeled with a 62-SNP GRS, nine metabolites, and clinical traits. We fit age- and sex-adjusted logistic regression models to test the association of these sources of information, separately and jointly, with incident type 2 diabetes among 1,622 initially nondiabetic participants from the Framingham Offspring Study. The predictive capacity of each model was assessed by area under the curve (AUC).

RESULTS

Two hundred and six new diabetes cases were observed during 13.5 years of follow-up. The AUC was greater for the model containing the GRS and metabolite measurements together versus GRS or metabolites alone (0.820 vs. 0.641, P < 0.0001, or 0.820 vs. 0.803, P = 0.01, respectively). Odds ratios for association of GRS or metabolites with type 2 diabetes were not attenuated in the combined model. The AUC was greater for the model containing the GRS, metabolites, and clinical traits versus clinical traits only (0.880 vs. 0.856, P = 0.002).

CONCLUSIONS

Metabolite and genetic traits provide complementary information to each other for the prediction of future type 2 diabetes. These novel markers of diabetes risk modestly improve the predictive accuracy of incident type 2 diabetes based only on traditional clinical risk factors.

Type 2 diabetes as a disease of ectopic fat?

BACKGROUND

Although obesity and diabetes commonly co-exist, the evidence base to support obesity as the major driver of type 2 diabetes mellitus (T2DM), and the mechanisms by which this occurs, are now better appreciated.

DISCUSSION

This review briefly examines several sources of evidence - epidemiological, genetic, molecular, and clinical trial - to support obesity being a causal risk factor for T2DM. It also summarises the ectopic fat hypothesis for this condition, and lists several pieces of evidence to support this concept, extending from rare conditions and drug effects to sex- and ethnicity-related differences in T2DM prevalence. Ectopic liver fat is the best-studied example of ectopic fat, but more research on pancreatic fat as a potential cause of β-cell dysfunction seems warranted. This ectopic fat concept, in turn, broadly fits with the observation that individuals of similar ages can develop diabetes at markedly different body mass indexes (BMIs). Those with risk factors leading to more rapid ectopic fat gain - for example, men (compared with women), certain ethnicities, and potentially those with a family history of diabetes, as well as others with genes linked to a reduced subcutaneous adiposity - are more likely to develop diabetes at a younger age and/or lower BMI than those without.

SUMMARY

Obesity is the major risk factor for T2DM and appears to drive tissue insulin resistance in part via gain of ectopic fat, with the best-studied organ being the liver. However, ectopic fat in the pancreas may contribute to β-cell dysfunction. In line with this observation, rapid resolution of diabetes linked to a preferential and rapid reduction in liver fat has been noted with significant caloric reduction. Whether these observations can help develop better cost-effective and sustainable lifestyle /medical interventions in patients with T2DM requires further study.

Genetic architecture of type 2 diabetes

Genome-wide association studies (GWAS) have identified over 70 loci associated with type 2 diabetes (T2D). Most genetic variants associated with T2D are common variants with modest effects on T2D and are shared with major ancestry groups. To what extent the genetic component of T2D can be explained by common variants relies upon the shape of the genetic architecture of T2D. Fine mapping utilizing populations with different patterns of linkage disequilibrium and functional annotation derived from experiments in relevant tissues are mandatory to track down causal variants responsible for the pathogenesis of T2D.

Genetic-based prediction of disease traits: prediction is very difficult, especially about the future

Translation of results from genetic findings to inform medical practice is a highly anticipated goal of human genetics. The aim of this paper is to review and discuss the role of genetics in medically-relevant prediction. Germline genetics presages disease onset and therefore can contribute prognostic signals that augment laboratory tests and clinical features. As such, the impact of genetic-based predictive models on clinical decisions and therapy choice could be profound. However, given that (i) medical traits result from a complex interplay between genetic and environmental factors, (ii) the underlying genetic architectures for susceptibility to common diseases are not well-understood, and (iii) replicable susceptibility alleles, in combination, account for only a moderate amount of disease heritability, there are substantial challenges to constructing and implementing genetic risk prediction models with high utility. In spite of these challenges, concerted progress has continued in this area with an ongoing accumulation of studies that identify disease predisposing genotypes. Several statistical approaches with the aim of predicting disease have been published. Here we summarize the current state of disease susceptibility mapping and pharmacogenetics efforts for risk prediction, describe methods used to construct and evaluate genetic-based predictive models, and discuss applications.

Polygenic type 2 diabetes prediction at the limit of common variant detection

Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)-associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk Development in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared with previous less inclusive GRSt; 2) separate GRS for β-cell (GRSβ) and insulin resistance (GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRSβ, or GRSIR do not differ between blacks and whites. Among 1,650 young white adults in CARDIA, 820 young black adults in CARDIA, and 3,471 white middle-aged adults in FOS, cumulative T2D incidence was 5.9%, 14.4%, and 12.9%, respectively, over 25 years. The 62-locus GRSt was significantly associated with incident T2D in all three groups. In FOS but not CARDIA, the 62-locus GRSt improved the model C statistic (0.698 and 0.726 for models without and with GRSt, respectively; P < 0.001) but did not materially improve risk reclassification in either study. Results were similar among blacks compared with whites. The GRSβ but not GRSIR predicted incident T2D among FOS and CARDIA whites. At the end of the era of common variant discovery for T2D, polygenic scores can predict T2D in whites and blacks but do not outperform clinical models. Further optimization of polygenic prediction may require novel analytic methods, including less common as well as functional variants.

PINK1 deficiency in β-cells increases basal insulin secretion and improves glucose tolerance in mice

The Parkinson's disease (PD) gene, PARK6, encodes the PTEN-induced putative kinase 1 (PINK1) mitochondrial kinase, which provides protection against oxidative stress-induced apoptosis. Given the link between glucose metabolism, mitochondrial function and insulin secretion in β-cells, and the reported association of PD with type 2 diabetes, we investigated the response of PINK1-deficient β-cells to glucose stimuli to determine whether loss of PINK1 affected their function. We find that loss of PINK1 significantly impairs the ability of mouse pancreatic β-cells (MIN6 cells) and primary intact islets to take up glucose. This was accompanied by higher basal levels of intracellular calcium leading to increased basal levels of insulin secretion under low glucose conditions. Finally, we investigated the effect of PINK1 deficiency in vivo and find that PINK1 knockout mice have improved glucose tolerance. For the first time, these combined results demonstrate that loss of PINK1 function appears to disrupt glucose-sensing leading to enhanced insulin release, which is uncoupled from glucose uptake, and suggest a key role for PINK1 in β-cell function.

The effect of genetic counseling for adult offspring of patients with type 2 diabetes on attitudes toward diabetes and its heredity: a randomized controlled trial

The aim of this study is to investigate the effect of diabetes genetic counseling on attitudes toward diabetes and its heredity in relatives of type 2 diabetes patients. This study was an unmasked, randomized controlled trial at a medical check-up center in Japan. Subjects in this study are healthy adults between 30 and 60 years of age who have a family history of type 2 diabetes in their first degree relatives. Participants in the intervention group received a brief genetic counseling session for approximately 10 min. Genetic counseling was structured based on the Health Belief Model. Both intervention and control groups received a booklet for general diabetes prevention. Risk perception and recognition of diabetes, and attitude towards its prevention were measured at baseline, 1 week and 1 year after genetic counseling. Participants who received genetic counseling showed significantly higher recognition about their sense of control over diabetes onset than control group both at 1 week and 1 year after the session. On the other hand, anxiety about diabetes did not change significantly. The findings show that genetic counseling for diabetes at a medical check center helped adults with diabetes family history understand they are able to exert control over the onset of their disease through lifestyle modification.

Likelihood ratio-based integrated personal risk assessment of type 2 diabetes

To facilitate personalized health care for multifactorial diseases, risks of genetic and clinical/environmental factors should be assessed together for each individual in an integrated fashion. This approach is possible with the likelihood ratio (LR)-based risk assessment system, as this system can incorporate manifold tests. We examined the usefulness of this system for assessing type 2 diabetes (T2D). Our system employed 29 genetic susceptibility variants, body mass index (BMI), and hypertension as risk factors whose LRs can be estimated from openly available T2D association data for the Japanese population. The pretest probability was set at a sex- and age-appropriate population average of diabetes prevalence. The classification performance of our LR-based risk assessment was compared to that of a non-invasive screening test for diabetes called TOPICS (with score based on age, sex, family history, smoking, BMI, and hypertension) using receiver operating characteristic analysis with a community cohort (n = 1263). The area under the receiver operating characteristic curve (AUC) for the LR-based assessment and TOPICS was 0.707 (95% CI 0.665-0.750) and 0.719 (0.675-0.762), respectively. These AUCs were much higher than that of a genetic risk score constructed using the same genetic susceptibility variants, 0.624 (0.574-0.674). The use of ethnically matched LRs is necessary for proper personal risk assessment. In conclusion, although LR-based integrated risk assessment for T2D still requires additional tests that evaluate other factors, such as risks involved in missing heritability, our results indicate the potential usability of LR-based assessment system and stress the importance of stratified epidemiological investigations in personalized medicine.

Stem cells: are we ready for therapy?

Cell therapy as a replacement for diseased or destroyed endogenous cells is a major component of regenerative medicine. Various types of stem cells are or will be used in clinical settings as autologous or allogeneic products. In this chapter, the progress that has been made to translate basic stem cell research into pharmaceutical manufacturing processes will be reviewed. Even if in public perception, embryonic stem (ES) cells and more recently induced pluripotent stem (iPS) cells dominate the field of regenerative medicine and will be discussed in great detail, it is the adult stem cells that are used for decades as therapeutics. Hence, these cells will be compared to ES and iPS cells. Finally, special emphasis will be placed on the scientific, technical, and economic challenges of developing stem cell-based in vitro model systems and cell therapies that can be commercialized.