A methodological perspective on genetic risk prediction studies in type 2 diabetes: recommendations for future research

Predictive model for aminoglycoside induced ototoxicity

Background

Irreversible hearing loss is a well-known adverse effect of aminoglycosides, however, inability to accurately predict ototoxicity is a major limitation in clinical care. We addressed this limitation by developing a prediction model for aminoglycoside ototoxicity applicable to the general population.

Methods

We employed a prospective non-drug-resistant tuberculosis (TB), non-HIV/AIDS cohort of 153 adults on Streptomycin based anti-TB therapy. High frequency pure-tone audiometry was done at regular intervals throughout the study. Clinical and audiological predictors of ototoxicity were collated and ototoxic threshold shift from the baseline audiogram computed. The prediction model was developed with logistic regression method by examining multiple predictors of ototoxicity. Series of models were fitted sequentially; the best model was identified using Akaike Information Criterion and likelihood ratio test. Key variables in the final model were used to develop a logit model for ototoxicity prediction.

Results

Ototoxicity occurred in 35% of participants. Age, gender, weight, cumulative Streptomycin dosage, social class, baseline pure tone average (PTA) and prior hearing symptoms were explored as predictors. Multiple logistic regression showed that models with age, cumulative dosage and baseline PTA were best for predicting ototoxicity. Regression parameters for ototoxicity prediction showed that yearly age increment raised ototoxicity risk by 5% (AOR = 1.05; CI, 1.01-1.09), and a gram increase in cumulative dosage increased ototoxicity risk by 7% (AOR = 1.05; CI, 1.05-1.12) while a unit change in baseline log (PTA) was associated 254% higher risk of ototoxicity (AOR = 3.54, CI: 1.25, 10.01). Training and validation models had area under the receiver operating characteristic curve as 0.84 (CI, 0.76-0.92) and 0.79 (CI, 0.62-0.96) respectively, showing the model has discriminatory ability.

Conclusion

This model can predict aminoglycoside ototoxicity in the general population.

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.

The Potential Role for Host Genetic Profiling in Screening for Chlamydia-Associated Tubal Factor Infertility (TFI)-New Perspectives

Host immunogenetic factors can affect late complications of urogenital infections with Chlamydia trachomatis. These findings are creating new avenues for updating existing risk prediction models for C. trachomatis-associated tubal factor infertility (TFI). Research into host factors and its utilization may therefore have future implications for diagnosing C. trachomatis-induced infertility. We outline the epidemiological situation regarding C. trachomatis and TFI in high-income countries. Thereupon, we review the main characteristics of the population undergoing fertility work-up and identify screening and diagnostic strategies for TFI currently in place. The Netherlands is an exemplary model for the state of the art in high-income countries. Within the framework of existing clinical approaches, we propose a scenario for the translation of relevant genome-based information into triage of infertile women, with the objective of implementing genetic profiling in the routine investigation of TFI. Furthermore, we describe the state of the art in relevant gene- and single nucleotide polymorphism (SNP) based clinical prediction models and place our perspectives in the context of these applications. We conclude that the introduction of a genetic test of proven validity into the assessment of TFI should help reduce patient burden from invasive and costly examinations by achieving a more precise risk stratification.

Roles of NUCKS1 in Diseases: Susceptibility, Potential Biomarker, and Regulatory Mechanisms

Nuclear casein kinase and cyclin-dependent kinase substrate 1 (NUCKS1) is a 27 kD chromosomal, highly conserved, and vertebrate-specific protein. NUCKS1 gene encodes a nuclear protein and the conserved regions of NUCKS1 contain several consensus phosphorylation sites for casein kinase II (CK2) and cyclin-dependent kinases (Cdk) and a basic DNA-binding domain. NUCKS1 is similar to the high mobility group (HMG) family which dominates chromatin remodeling and regulates gene transcription. Meanwhile, NUCKS1 is a RAD51 associated protein 1 (RAD51AP1) paralog that is significant for homologous recombination (HR) and genome stability and also a transcriptional regulator of the insulin signaling components. NUCKS1 plays an important role in DNA damage response and metabolism, participates in inflammatory immune response, and correlates with microRNA. Although there is still not enough functional information on NUCKS1, evidences suggest that NUCKS1 can be used as the biomarker of several cancers. This review summarizes the latest research on NUCKS1 about its susceptibility in diseases, expression levels, and regulatory mechanisms as well as the possible functions in reference to diseases.

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 novel approach for identifying causal models of complex diseases from family data

Causal models including genetic factors are important for understanding the presentation mechanisms of complex diseases. Familial aggregation and segregation analyses based on polygenic threshold models have been the primary approach to fitting genetic models to the family data of complex diseases. In the current study, an advanced approach to obtaining appropriate causal models for complex diseases based on the sufficient component cause (SCC) model involving combinations of traditional genetics principles was proposed. The probabilities for the entire population, i.e., normal-normal, normal-disease, and disease-disease, were considered for each model for the appropriate handling of common complex diseases. The causal model in the current study included the genetic effects from single genes involving epistasis, complementary gene interactions, gene-environment interactions, and environmental effects. Bayesian inference using a Markov chain Monte Carlo algorithm (MCMC) was used to assess of the proportions of each component for a given population lifetime incidence. This approach is flexible, allowing both common and rare variants within a gene and across multiple genes. An application to schizophrenia data confirmed the complexity of the causal factors. An analysis of diabetes data demonstrated that environmental factors and gene-environment interactions are the main causal factors for type II diabetes. The proposed method is effective and useful for identifying causal models, which can accelerate the development of efficient strategies for identifying causal factors of complex diseases.

Prediction model for knee osteoarthritis incidence, including clinical, genetic and biochemical risk factors

OBJECTIVE

To develop and validate a prognostic model for incident knee osteoarthritis (KOA) in a general population and determine the value of different risk factor groups to prediction.

METHODS

The prognostic model was developed in 2628 individuals from the Rotterdam Study-I (RS-I). Univariate and multivariate analyses were performed for questionnaire/easily obtainable variables, imaging variables, genetic and biochemical markers. The extended multivariate model was tested on discrimination (receiver operating characteristic curve and area under the curve (AUC)) in two other population-based cohorts: Rotterdam Study-II and Chingford Study.

RESULTS

In RS-I, there was moderate predictive value for incident KOA based on the genetic score alone in subjects aged <65 years (AUC 0.65), while it was only 0.55 for subjects aged ≥65 years. The AUC for gender, age and body mass index (BMI) in prediction for KOA was 0.66. Addition of the questionnaire variables, genetic score or biochemical marker urinary C-terminal cross-linked telopeptide of type II collagen to the model did not change the AUC. However, when adding the knee baseline KL score to the model the AUC increased to 0.79. Applying external validation, similar results were observed in the Rotterdam Study-II and the Chingford Study.

CONCLUSIONS

Easy obtainable 'Questionnaire' variables, genetic markers, OA at other joint sites and biochemical markers add only modestly to the prediction of KOA incidence using age, gender and BMI in an elderly population. Doubtful minor radiographic degenerative features in the knee, however, are a very strong predictor of future KOA. This is an important finding, as many radiologists do not report minor degenerative changes in the knee.

A combined analysis of 48 type 2 diabetes genetic risk variants shows no discriminative value to predict time to first prescription of a glucose lowering drug in Danish patients with screen detected type 2 diabetes

OBJECTIVE

To investigate the genetic influence of 48 type 2 diabetes susceptibility variants on disease progression measured as risk of early prescription redemption of glucose lowering drugs in screen-detected patients with type 2 diabetes.

METHODS

We studied type 2 diabetes progression in 1,480 patients with screen-detected type 2 diabetes from the ADDITION-Denmark study using information of redeemed prescriptions from the Register of Medicinal Products Statistics from 2001-2009 in Denmark. Patients were cluster randomized by general practitioners, who were randomized to treat type 2 diabetes according to either a conventional or a multifactorial intensive treatment algorithm. We investigated the genetic influence on diabetes progression by constructing a genetic risk score (GRS) of all 48 validated type 2 diabetes susceptibility variants, a GRS of 11 variants linked to β-cell function and a GRS of 3 variants linked to insulin sensitivity and assessed the association between number of risk alleles and time from diagnosis until first redeemed prescription of either any glucose lowering drug or an insulin drug.

RESULTS

The GRS linked to insulin sensitivity only nominally increased the risk of an early prescription redemption with an insulin drug by 39% (HR [95% C.I.] = 1.39 [1.09-1.77], p = 0.009] in patients randomized to the intensive treatment group. Furthermore, the strongest univariate predictors of diabetes progression for the intensive treatment group (measured as time to first insulin) were younger age (HR [95% C.I.] = 0.96 [0.93-0.99]), increased BMI (1.05 [1.01-1.09]), increased HbA1c (1.50 [1.36-.66]), increased TG (1.24 [1.11-1.39]) and reduced fasting serum HDL (0.37 [0.17-0.80]) at baseline. Similar results were obtained for the conventional treatment group.

CONCLUSION

Higher levels of HbA1c, fasting circulating levels of triglyceride, lower HDL, larger BMI and younger age are significant determinants of early pharmacological intervention in type 2 diabetes. However, known common type 2 diabetes-associated gene variants do not appear to significantly affect disease progression.

Personalized medicine in Type 2 Diabetes

Type 2 diabetes (T2D) is a global public health concern, its prevalence in Asia, especially Taiwan, rising every year. The risk of developing T2D and diabetes complications is not only controlled by environmental but also by genetic factors. Genetic association studies have shown polymorphisms at specific loci may help identify individuals at greatest risk and response to oral antidiabetic drugs. This review probes effect of genetic profiling on T2D and its complications, using our study population as examples. Also, pharmacogenetics and pharmacogenomics of oral anitdiabetic drug will be explored.

Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease

We conducted a meta-analysis of Parkinson's disease genome-wide association studies using a common set of 7,893,274 variants across 13,708 cases and 95,282 controls. Twenty-six loci were identified as having genome-wide significant association; these and 6 additional previously reported loci were then tested in an independent set of 5,353 cases and 5,551 controls. Of the 32 tested SNPs, 24 replicated, including 6 newly identified loci. Conditional analyses within loci showed that four loci, including GBA, GAK-DGKQ, SNCA and the HLA region, contain a secondary independent risk variant. In total, we identified and replicated 28 independent risk variants for Parkinson's disease across 24 loci. Although the effect of each individual locus was small, risk profile analysis showed substantial cumulative risk in a comparison of the highest and lowest quintiles of genetic risk (odds ratio (OR) = 3.31, 95% confidence interval (CI) = 2.55-4.30; P = 2 × 10(-16)). We also show six risk loci associated with proximal gene expression or DNA methylation.

Estimating the predictive ability of genetic risk models in simulated data based on published results from genome-wide association studies

Background: There is increasing interest in investigating genetic risk models in empirical studies, but such studies are premature when the expected predictive ability of the risk model is low. We assessed how accurately the predictive ability of genetic risk models can be estimated in simulated data that are created based on the odds ratios (ORs) and frequencies of single-nucleotide polymorphisms (SNPs) obtained from genome-wide association studies (GWASs).

Methods: We aimed to replicate published prediction studies that reported the area under the receiver operating characteristic curve (AUC) as a measure of predictive ability. We searched GWAS articles for all SNPs included in these models and extracted ORs and risk allele frequencies to construct genotypes and disease status for a hypothetical population. Using these hypothetical data, we reconstructed the published genetic risk models and compared their AUC values to those reported in the original articles.

Results: The accuracy of the AUC values varied with the method used for the construction of the risk models. When logistic regression analysis was used to construct the genetic risk model, AUC values estimated by the simulation method were similar to the published values with a median absolute difference of 0.02 [range: 0.00, 0.04]. This difference was 0.03 [range: 0.01, 0.06] and 0.05 [range: 0.01, 0.08] for unweighted and weighted risk scores.

Conclusions: The predictive ability of genetic risk models can be estimated using simulated data based on results from GWASs. Simulation methods can be useful to estimate the predictive ability in the absence of empirical data and to decide whether empirical investigation of genetic risk models is warranted.

Environmental risk score as a new tool to examine multi-pollutants in epidemiologic research: an example from the NHANES study using serum lipid levels

Objective

A growing body of evidence suggests that environmental pollutants, such as heavy metals, persistent organic pollutants and plasticizers play an important role in the development of chronic diseases. Most epidemiologic studies have examined environmental pollutants individually, but in real life, we are exposed to multi-pollutants and pollution mixtures, not single pollutants. Although multi-pollutant approaches have been recognized recently, challenges exist such as how to estimate the risk of adverse health responses from multi-pollutants. We propose an “Environmental Risk Score (ERS)” as a new simple tool to examine the risk of exposure to multi-pollutants in epidemiologic research.

Methods and Results

We examined 134 environmental pollutants in relation to serum lipids (total cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL) and triglycerides) using data from the National Health and Nutrition Examination Survey between 1999 and 2006. Using a two-stage approach, stage-1 for discovery (n = 10818) and stage-2 for validation (n = 4615), we identified 13 associated pollutants for total cholesterol, 9 for HDL, 5 for LDL and 27 for triglycerides with adjustment for sociodemographic factors, body mass index and serum nutrient levels. Using the regression coefficients (weights) from joint analyses of the combined data and exposure concentrations, ERS were computed as a weighted sum of the pollutant levels. We computed ERS for multiple lipid outcomes examined individually (single-phenotype approach) or together (multi-phenotype approach). Although the contributions of ERS to overall risk predictions for lipid outcomes were modest, we found relatively stronger associations between ERS and lipid outcomes than with individual pollutants. The magnitudes of the observed associations for ERS were comparable to or stronger than those for socio-demographic factors or BMI.

Conclusions

This study suggests ERS is a promising tool for characterizing disease risk from multi-pollutant exposures. This new approach supports the need for moving from a single-pollutant to a multi-pollutant framework.

The construction of risk prediction models using GWAS data and its application to a type 2 diabetes prospective cohort

Recent genome-wide association studies (GWAS) have identified several novel single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D). Various models using clinical and/or genetic risk factors have been developed for T2D risk prediction. However, analysis considering algorithms for genetic risk factor detection and regression methods for model construction in combination with interactions of risk factors has not been investigated. Here, using genotype data of 7,360 Japanese individuals, we investigated risk prediction models, considering the algorithms, regression methods and interactions. The best model identified was based on a Bayes factor approach and the lasso method. Using nine SNPs and clinical factors, this method achieved an area under a receiver operating characteristic curve (AUC) of 0.8057 on an independent test set. With the addition of a pair of interaction factors, the model was further improved (p-value 0.0011, AUC 0.8085). Application of our model to prospective cohort data showed significantly better outcome in disease-free survival, according to the log-rank trend test comparing Kaplan-Meier survival curves (p--value 2:09 x 10(-11)). While the major contribution was from clinical factors rather than the genetic factors, consideration of genetic risk factors contributed to an observable, though small, increase in predictive ability. This is the first report to apply risk prediction models constructed from GWAS data to a T2D prospective cohort. Our study shows our model to be effective in prospective prediction and has the potential to contribute to practical clinical use in T2D.

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

The incidence of type 2 diabetes can be reduced substantially by implementing preventive measures in high-risk individuals, but this requires prior knowledge of disease risk in the individual. Various diabetes risk models have been designed, and these have all included a similar combination of factors, such as age, sex, obesity, hypertension, lifestyle factors, family history of diabetes and metabolic traits. The accuracy of prediction models is often assessed by the area under the receiver operating characteristic curve (AROC) as a measure of discrimination, but AROCs should be complemented by measures of calibration and reclassification to estimate the incremental value of novel biomarkers. This review discusses the potential of novel biomarkers to improve model accuracy. The range of molecules that serve as potential predictors of type 2 diabetes includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites. Some of these biomarkers lead to a statistically significant increase of model accuracy, but their incremental value currently seems too small for routine clinical use. However, only a fraction of potentially relevant biomarkers have been assessed with regard to their predictive value. Moreover, serial measurements of biomarkers may help determine individual risk. In conclusion, current risk models provide valuable tools of risk estimation, but perform suboptimally in the prediction of individual diabetes risk. Novel biomarkers still fail to have a clinically applicable impact. However, more efficient use of biomarker data and technological advances in their measurement in clinical settings may allow the development of more accurate predictive models in the future.

Association of environmental and genetic factors and gene-environment interactions with risk of developing rheumatoid arthritis

OBJECTIVE

We developed rheumatoid arthritis (RA) risk models based on validated environmental factors (E), genetic risk scores (GRS), and gene-environment interactions (GEI) to identify factors that can improve accuracy and reclassification.

METHODS

Models including E, GRS, and GEI were developed among 317 white seropositive RA cases and 551 controls from the Nurses' Health Studies (NHS) and validated in 987 white anti-citrullinated protein antibody-positive cases and 958 controls from the Swedish Epidemiologic Investigation of Rheumatoid Arthritis (EIRA), stratified by sex. Primary analyses included age, smoking, alcohol, parity, weighted GRS using 31 non-HLA alleles and 8 HLA-DRB1 alleles, and the HLA × smoking interaction. Expanded models included reproductive, geographic, and occupational factors and additional GEI terms. Hierarchical models were compared for discriminative accuracy using the area under the receiver operating characteristic curve (AUC) and reclassification using the integrated discrimination improvement (IDI) and the continuous net reclassification improvement.

RESULTS

The mean age at RA diagnosis was 56 years in the NHS and 51 years in the EIRA. Primary models produced AUCs of 0.716 in the NHS, 0.716 in women in the EIRA, and 0.756 in men in the EIRA. Expanded models produced improvements in discrimination with AUCs of 0.738 in the NHS, 0.724 in women in the EIRA, and 0.769 in men in the EIRA. Models including genetic factors (G) or G + GEI improved reclassification over E models; the full E + G + GEI model provided the optimal predictive ability by IDI analyses.

CONCLUSION

We have developed comprehensive RA risk models incorporating E, G, and GEI that have improved the discriminative accuracy for RA. Further work developing and assessing highly specific prediction models in prospective cohorts is still needed to inform primary RA prevention trials.

Addition of host genetic variants in a prediction rule for post meningitis hearing loss in childhood: a model updating study

BACKGROUND

Sensorineural hearing loss is the most common sequela in survivors of bacterial meningitis (BM). In the past we developed a validated prediction model to identify children at risk for post-meningitis hearing loss. It is known that host genetic variations, besides clinical factors, contribute to severity and outcome of BM. In this study it was determined whether host genetic risk factors improve the predictive abilities of an existing model regarding hearing loss after childhood BM.

METHODS

Four hundred and seventy-one Dutch Caucasian childhood BM were genotyped for 11 single nucleotide polymorphisms (SNPs) in seven different genes involved in pathogen recognition. Genetic data were added to the original clinical prediction model and performance of new models was compared to the original model by likelihood ratio tests and the area under the curve (AUC) of the receiver operating characteristic curves.

RESULTS

Addition of TLR9-1237 SNPs and the combination of TLR2 + 2477 and TLR4 + 896 SNPs improved the clinical prediction model, but not significantly (increase of AUC's from 0.856 to 0.861 and from 0.856 to 0.875 (p = 0.570 and 0.335, respectively). Other SNPs analysed were not linked to hearing loss.

CONCLUSIONS

Although addition of genetic risk factors did not significantly improve the clinical prediction model for post-meningitis hearing loss, AUC's of the pre-existing model remain high after addition of genetic factors. Future studies should evaluate whether more combinations of SNPs in larger cohorts has an additional value to the existing prediction model for post meningitis hearing loss.

The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity

BACKGROUND

Genome-wide association studies have identified numerous single nucleotide polymorphisms associated with type 2 diabetes through the past years. In previous studies, the usefulness of these genetic markers for prediction of diabetes was found to be limited. However, differences may exist between substrata of the population according to the presence of major diabetes risk factors. This study aimed to investigate the added predictive value of genetic information (42 single nucleotide polymorphisms) in subgroups of sex, age, family history of diabetes, and obesity.

METHODS

A case-cohort study (random subcohort N = 1,968; incident cases: N = 578) within the European Prospective Investigation into Cancer and Nutrition Potsdam study was used. Prediction models without and with genetic information were evaluated in terms of the area under the receiver operating characteristic curve and the integrated discrimination improvement. Stratified analyses included subgroups of sex, age (<50 or ≥50 years), family history (positive if either father or mother or a sibling has/had diabetes), and obesity (BMI< or ≥30 kg/m(2)).

RESULTS

A genetic risk score did not improve prediction above classic and metabolic markers, but - compared to a non-invasive prediction model - genetic information slightly improved the area under the receiver operating characteristic curve (difference [95%-CI]: 0.007 [0.002-0.011]). Stratified analyses showed stronger improvement in the older age group (0.010 [0.002-0.018]), the group with a positive family history (0.012 [0.000-0.023]) and among obese participants (0.015 [-0.005-0.034]) compared to the younger participants (0.005 [-0.004-0.014]), participants with a negative family history (0.003 [-0.001-0.008]) and non-obese (0.007 [0.000-0.014]), respectively. No difference was found between men and women.

CONCLUSION

There was no incremental value of genetic information compared to standard non-invasive and metabolic markers. Our study suggests that inclusion of genetic variants in diabetes risk prediction might be useful for subgroups with already manifest risk factors such as older age, a positive family history and obesity.

Type 2 diabetes: prevalence and relevance of genetic and acquired factors for its prediction

BACKGROUND

The epidemiology of type 2 diabetes in Germany is of major societal interest, as is the question of the predictive value of genetic and acquired risk factors.

METHODS

We present clinically relevant aspects of these topics on the basis of a selective review of pertinent literature retrieved by a PubMed search that centered on population-based studies.

RESULTS

The German Health Interview and Examination Survey for Adults (Studie zur Gesundheit Erwachsener in Deutschland [DEGS1], 2008-2011) revealed that diabetes was diagnosed in 7.2% of the population aged 18 to 79 years (women 7.4%, men 7.0%). These figures are two percentage points higher than those found in the preceding national survey (1998). The percentage of cases that were not captured by these surveys is estimated at 2% to 7% depending on the method. Independently of personal factors (the individual's life style), it seems that living in a disadvantaged region characterized by high unemployment, air pollution, and poor infrastructure raises the risk of diabetes. Moreover, type 2 diabetes has a substantial hereditary component. More than 60 genetic regions have been identified to date that affect the risk of type 2 diabetes, yet all of them together account for only 10% to 15% of the genetic background of the disease.

CONCLUSION

The prevalence of type 2 diabetes in Germany has risen in recent years. The discovery of new genetic variants that confer a higher risk of developing the disease has improved our understanding of insulin secretion in diabetes pathogenesis rather than the prediction of individual diabetes risk ("personalized medicine").

How can polygenic inheritance be used in population screening for common diseases?

Advances in genomics have near-term impact on diagnosis and management of monogenic disorders. For common complex diseases, the use of genomic information from multiple loci (polygenic model) is generally not useful for diagnosis and individual prediction. In principle, the polygenic model could be used along with other risk factors in stratified population screening to target interventions. For example, compared to age-based criterion for breast, colorectal, and prostate cancer screening, adding polygenic risk and family history holds promise for more efficient screening with earlier start and/or increased frequency of screening for segments of the population at higher absolute risk than an established screening threshold; and later start and/or decreased frequency of screening for segments of the population at lower risks. This approach, while promising, faces formidable challenges for building its evidence base and for its implementation in practice. Currently, it is unclear whether or not polygenic risk can contribute enough discrimination to make stratified screening worthwhile. Empirical data are lacking on population-based age-specific absolute risks combining genetic and non-genetic factors, on impact of polygenic risk genes on disease natural history, as well as information on comparative balance of benefits and harms of stratified interventions. Implementation challenges include difficulties in integration of this information in the current health-care system in the United States, the setting of appropriate risk thresholds, and ethical, legal, and social issues. In an era of direct-to-consumer availability of personal genomic information, the public health and health-care systems need to prepare for an evidence-based integration of this information into population screening.

Novel risk factors and the prediction of type 2 diabetes in the Atherosclerosis Risk in Communities (ARIC) study

OBJECTIVE

The objective of this study was to determine potential added value of novel risk factors in predicting the development of type 2 diabetes beyond that provided by standard clinical risk factors.

RESEARCH DESIGN AND METHODS

The Atherosclerosis Risk in Communities (ARIC) Study is a population-based prospective cohort study in four U.S. communities. Novel risk factors were either measured in the full cohort or in a case-control sample nested within the cohort. We started with a basic prediction model, previously validated in ARIC, and evaluated 35 novel risk factors by adding them independently to the basic model. The area under the curve (AUC), net reclassification index (NRI), and integrated discrimination index (IDI) were calculated to determine if each of the novel risk factors improved risk prediction.

RESULTS

There were 1,457 incident cases of diabetes with a mean of >7.6 years of follow-up among 12,277 participants at risk. None of the novel risk factors significantly improved the AUC. Forced expiratory volume in 1 s was the only novel risk factor that resulted in a significant NRI (0.54%; 95% CI: 0.33-0.86%). Adiponectin, leptin, γ-glutamyl transferase, ferritin, intercellular adhesion molecule 1, complement C3, white blood cell count, albumin, activated partial thromboplastin time, factor VIII, magnesium, hip circumference, heart rate, and a genetic risk score each significantly improved the IDI, but net changes were small.

CONCLUSIONS

Evaluation of a large panel of novel risk factors for type 2 diabetes indicated only small improvements in risk prediction, which are unlikely to meaningfully alter clinical risk reclassification or discrimination strategies.

The genetic and epigenetic basis of type 2 diabetes and obesity

Type 2 diabetes (T2D) and obesity are complex disorders that constitute major public health problems. The evidence for familial aggregation of both T2D and obesity is substantial. To date, more than 150 genetic loci are associated with the development of monogenic, syndromic, or multifactorial forms of T2D or obesity. However, the proportion of overall trait variance explained by these associated loci is modest (~5-10% for T2D, ~2% for body mass index (BMI)). Some of the familial aggregation not attributable to known genetic variation, as well as many of the effects of environmental exposures, may reflect epigenetic processes. In this review, we discuss the evidence concerning the genetic contribution to individual risk of T2D and obesity, and explore the potential role of epigenetic mechanisms. We also explain how genetics, epigenetics, and environment are likely to interact to define the individual risk of disease.

Type 2 Diabetes Genetics: Beyond GWAS

The global epidemic of type 2 diabetes mellitus (T2D) is one of the most challenging problems of the 21(st) century leading cause of and the fifth death worldwide. Substantial evidence suggests that T2D is a multifactorial disease with a strong genetic component. Recent genome-wide association studies (GWAS) have successfully identified and replicated nearly 75 susceptibility loci associated with T2D and related metabolic traits, mostly in Europeans, and some in African, and South Asian populations. The GWAS serve as a starting point for future genetic and functional studies since the mechanisms of action by which these associated loci influence disease is still unclear and it is difficult to predict potential implication of these findings in clinical settings. Despite extensive replication, no study has unequivocally demonstrated their clinical role in the disease management beyond progression to T2D from impaired glucose tolerance. However, these studies are revealing new molecular pathways underlying diabetes etiology, gene-environment interactions, epigenetic modifications, and gene function. This review highlights evolving progress made in the rapidly moving field of T2D genetics that is starting to unravel the pathophysiology of a complex phenotype and has potential to show clinical relevance in the near future.

The combined effect of the T2DM susceptibility genes is an important risk factor for T2DM in non-obese Japanese: a population based case-control study

Background

Type 2 diabetes mellitus (T2DM) is a complex endocrine and metabolic disorder. Recently, several genome-wide association studies (GWAS) have identified many novel susceptibility loci for T2DM, and indicated that there are common genetic causes contributing to the susceptibility to T2DM in multiple populations worldwide. In addition, clinical and epidemiological studies have indicated that obesity is a major risk factor for T2DM. However, the prevalence of obesity varies among the various ethnic groups. We aimed to determine the combined effects of these susceptibility loci and obesity/overweight for development of T2DM in the Japanese.

Methods

Single nucleotide polymorphisms (SNPs) in or near 17 susceptibility loci for T2DM, identified through GWAS in Caucasian and Asian populations, were genotyped in 333 cases with T2DM and 417 control subjects.

Results

We confirmed that the cumulative number of risk alleles based on 17 susceptibility loci for T2DM was an important risk factor in the development of T2DM in Japanese population (P < 0.0001), although the effect of each risk allele was relatively small. In addition, the significant association between an increased number of risk alleles and an increased risk of T2DM was observed in the non-obese group (P < 0.0001 for trend), but not in the obese/overweight group (P = 0.88 for trend).

Conclusions

Our findings indicate that there is an etiological heterogeneity of T2DM between obese/overweight and non-obese subjects.