Prediction Of Type 1 Diabetes Research Articles

Dysregulation of glucose metabolism in preclinical type 1 diabetes.

Long-term prospective studies have provided valuable information about preclinical type 1 diabetes (T1D). Children who have seroconverted to positive for islet autoantibodies have also, in follow-up, had metabolic tests to understand the timing and development of abnormal glucose tolerance and declining insulin secretion before the clinical diagnosis of T1D. First phase insulin response (FPIR) in the intravenous glucose tolerance test (IVGTT) is lower in the progressors positive for multiple islet autoantibodies in all age groups and as early as 4-6 years before the diagnosis when compared with the non-progressors positive for only islet cell antibodies (ICA). An accelerated decline in FPIR is seen in the progressors during the last 1.5 years before the diagnosis. These results indicate that the progressors may have an early intrinsic defect in beta cell development or function. In the oral glucose tolerance test (OGTT) the peak C-peptide response is delayed in the progressors at least 2 years before diagnosis. Glucose levels and HbA1c are increasing about 2 years before clinical diagnosis. An increase in HbA1c and detection of abnormal glucose tolerance in OGTT are useful in the prediction of the timing of clinical onset of T1D. Continuous glucose monitoring (CGM) may be useful in the prediction of T1D as an early indicator of increased glycemic variability but more data from larger series are needed for confirmation. Children followed in the prospective studies are diagnosed earlier and have a decreased frequency of ketoacidosis at the diagnosis of T1D when compared with age-matched cases from the population.

Early signs of disease in type 1 diabetes.

As a severe chronic disease with long-term complications, type 1 diabetes (T1D) is a burden to the patients and their families as well as a challenge to the health care system. T1D is a heterogeneous disease with a variety of etiologies and a wide range in the rate of progression to the disease. In order to prevent and treat T1D it would be important to identify measures that could be used to predict and monitor disease progression, as well as to further understand the molecular mechanisms involved. During the past 20 yr since its initiation, the Finnish Diabetes Prediction and Prevention Project (DIPP) has collected longitudinal biological samples from children with a human leukocyte antigen gene-conferred risk of T1D. This large sample collection has provided detailed sample series that enable studies to map the progression from health to disease, as well as the healthy maturation of risk-matched children. The DIPP samples have been used in a large body of research to elucidate the factors involved in the development of T1D. Interestingly, results from recent studies exploiting omics platforms have revealed that signs of the disease process can be detected very early on, even prior to appearance of the first T1D-associated antibodies, which are currently considered the earliest indications of the emerging disease. Identification and validation of multi-modal molecular markers will we hope provide a means to subgroup the heterogeneous group of T1D patients and enable prediction, diagnosis, and monitoring of T1D. Discovery of such markers is important in the design and testing of prevention and therapies for T1D.

Validation of a metabolite panel for early diagnosis of type 2 diabetes

BackgroundAccurate, early diagnosis of type 2 diabetes (T2D) would enable more effective clinical management and a reduction in T2D complications. Therefore, we sought to identify plasma metabolite and protein biomarkers that, in combination with glucose, can better predict future T2D compared with glucose alone. MethodsIn this case–control study, we used plasma samples from the Bavarian Red Cross Blood Transfusion Center study (61 T2D cases and 78 non-diabetic controls) for discovering T2D-associated metabolites, and plasma samples from the Personalized Medicine Research Project in Wisconsin (56 T2D cases and 445 non-diabetic controls) for validation. All samples were obtained before or at T2D diagnosis. We tested whether the T2D-associated metabolites could distinguish incident T2D cases from controls, as measured by the area under the receiver operating characteristic curve (AUC). Additionally, we tested six metabolic/pro-inflammatory proteins for their potential to augment the ability of the metabolites to distinguish cases from controls. ResultsA panel of 10 metabolites discriminated better between T2D cases and controls than glucose alone (AUCs: 0.90 vs 0.87; p=2.08×10−5) in Bavarian samples, and associations between these metabolites and T2D were confirmed in Wisconsin samples. With use of either a Bayesian network classifier or ridge logistic regression, the metabolites, with or without the proteins, discriminated incident T2D cases from controls marginally better than glucose in the Wisconsin samples, although the difference in AUCs was not statistically significant. However, when the metabolites and proteins were added to two previously reported T2D prediction models, the AUCs were higher than those of each prediction model alone (AUCs: 0.92 vs 0.87; p=3.96×10−2 and AUCs: 0.91 vs 0.71; p=1.03×10−5, for each model, respectively). ConclusionsCompared with glucose alone or with previously described T2D prediction models, a panel of plasma biomarkers showed promise for improved discrimination of incident T2D, but more investigation is needed to develop an early diagnostic marker.

DNA methylation of loci within ABCG1 and PHOSPHO1 in blood DNA is associated with future type 2 diabetes risk

ABSTRACTIdentification of subjects with a high risk of developing type 2 diabetes (T2D) is fundamental for prevention of the disease. Consequently, it is essential to search for new biomarkers that can improve the prediction of T2D. The aim of this study was to examine whether 5 DNA methylation loci in blood DNA (ABCG1, PHOSPHO1, SOCS3, SREBF1, and TXNIP), recently reported to be associated with T2D, might predict future T2D in subjects from the Botnia prospective study. We also tested if these CpG sites exhibit altered DNA methylation in human pancreatic islets, liver, adipose tissue, and skeletal muscle from diabetic vs. non-diabetic subjects. DNA methylation at the ABCG1 locus cg06500161 in blood DNA was associated with an increased risk for future T2D (OR = 1.09, 95% CI = 1.02–1.16, P-value = 0.007, Q-value = 0.018), while DNA methylation at the PHOSPHO1 locus cg02650017 in blood DNA was associated with a decreased risk for future T2D (OR = 0.85, 95% CI = 0.75–0.95, P-value = 0.006, Q-value = 0.018) after adjustment for age, gender, fasting glucose, and family relation. Furthermore, the level of DNA methylation at the ABCG1 locus cg06500161 in blood DNA correlated positively with BMI, HbA1c, fasting insulin, and triglyceride levels, and was increased in adipose tissue and blood from the diabetic twin among monozygotic twin pairs discordant for T2D. DNA methylation at the PHOSPHO1 locus cg02650017 in blood correlated positively with HDL levels, and was decreased in skeletal muscle from diabetic vs. non-diabetic monozygotic twins. DNA methylation of cg18181703 (SOCS3), cg11024682 (SREBF1), and cg19693031 (TXNIP) was not associated with future T2D risk in subjects from the Botnia prospective study.

Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts.

BackgroundDetection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening.MethodsPlasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia – the AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D.ResultsThe LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76 %. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention.ConclusionsConsidering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.Electronic supplementary materialThe online version of this article (doi:10.1186/s12944-016-0234-3) contains supplementary material, which is available to authorized users.

Oxidative stress, inflammation, endothelial dysfunction and incidence of type 2 diabetes.

BackgroundOxidative stress, inflammation and endothelial dysfunction are interrelated factors in the etiology of cardiovascular disease, but their linkage to type 2 diabetes is less clear. We examined the association of these biomarkers with incident type 2 diabetes (T2D).MethodsAnalysis of 2339 participants in the community-based coronary artery risk development in young adults (CARDIA) study. Participants (age 40.1 ± 3.6 years, 44 % Black, 58 % women) were free of diabetes, and were followed 10 years. Cox regression was used to estimate hazard ratios (HRs) for incident T2D adjusting for the other biomarkers under study, demographic and lifestyle measures, dietary biomarkers, BMI (kg/m2) and metabolic syndrome components.ResultsF2-isoprostanes and oxidized LDL (oxidative stress) were positively associated with incident T2D, but the associations were attenuated by adjustment for BMI. C-reactive protein was positively associated with T2D even with full adjustment: HR (95 % CI) = 2.21 (1.26–3.88) for quartile 4 (Q4) v. quartile 1 (Q1). The HR (95 % CI) for T2D for biomarkers of endothelial dysfunction ICAM-1 and E-selectin for Q4 v. Q1 were 1.64 (0.96–2.81) and 1.68 (1.04–2.71) respectively, with full adjustment. Including these two markers in a common risk score incorporating BMI and clinical measures improved the prediction probability of T2D: relative risk for the average person classified up compared to the average person classified down: 1.09, (1.06–1.13), P < 0.0001.ConclusionsBiomarkers of inflammation and endothelial dysfunction were positively associated with incident T2D. ICAM-1 and E-selectin add to the prediction of T2D beyond a common risk score.

Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application.

Type 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions, which are most effective against T2D and its comorbidities. In metabolomics, the abundance of a comprehensive set of small biomolecules (metabolites) is measured, thus giving insight into disease-related metabolic alterations. This review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of T2D. We summarized key metabolites changing in response to T2D. Despite large variations in predictive biomarkers, many studies have replicated elevated plasma levels of branched-chain amino acids and their derivatives, aromatic amino acids and α-hydroxybutyrate ahead of T2D manifestation. In contrast, glycine levels and lysophosphatidylcholine C18:2 are depressed in both predictive studies and with overt disease. The use of metabolomics for predicting T2D comorbidities is gaining momentum, as are our approaches for translating basic metabolomics research into clinical applications. As a result, metabolomics has the potential to enable informed decision-making in the realm of personalized medicine.

Plasma metabolomic profiles in association with type 2 diabetes risk and prevalence in Chinese adults.

Metabolomic studies have identified several metabolites associated with type 2 diabetes (T2D) in populations of European ancestry. East Asians, a population of particular susceptibility to T2D, were generally not included in previous studies. We examined the associations of plasma metabolites with risk and prevalence of T2D in 976 Chinese men and women (40-74 years of age) who were participants of two prospective cohort studies and had no cardiovascular disease or cancer at baseline. Sixty-eight prevalent and 73 incident T2D cases were included. Non-targeted metabolomics was conducted that detected 689 metabolites with known identities and 690 unknown metabolites. Multivariable logistic and Cox regressions were used to evaluate the associations of standardized metabolites with diabetes risk and prevalence. We identified 36 known metabolites and 10 unknown metabolites associated with prevalent and/or incident T2D at false discovery rate <0.05. The known metabolites are involved in metabolic pathways of glycolysis/gluconeogenesis, branched-chain amino acids, other amino acids, fatty acids, glycerophospholipids, androgen, and bradykinin. Six metabolites showed independent associations with incident T2D: 1,5-anhydroglucitol, mannose, valine, 3-methoxytyrosine, docosapentaenoate (22:5n3), and bradykinin-hydroxy-pro(3). Each standard deviation increase in these metabolites was associated with a 40-150 % change in risk of developing diabetes (30-80 % after further adjustment for glucose). Risk prediction was significantly improved by adding these metabolites in addition to known T2D risk factors, including central obesity and glucose. These findings suggest that hexoses, branched-chain amino acids, and yet to be validated novel plasma metabolites may improve risk prediction and mechanistic understanding of T2D in Chinese populations.

Characterization of immune response to novel HLA-A2-restricted epitopes from zinc transporter 8 in type 1 diabetes

ObjectiveZnT8-specific CD8+ T cells in human type 1 diabetes (T1D) have been reported recently, although the results from different laboratories are inconsistent. We aimed to characterize these ZnT8 specific CD8+ T cells and validate assays to screen peptide libraries. MethodsWe screened HLA-A2-restricted T cell candidate peptides of ZnT8 with different methods including computer algorithms, MHC-peptide binding and dissociation assays in T2 cell line, identification in HLA-A2 transgenic (Tg) mice and in vivo CTL assays. Then ELISpot assay was used to measure peptide-reactive T cell responses in 49 HLA-A2-restricted T1D patients. ResultsWe demonstrated that ZnT8107–116(115), ZnT8110–118, and ZnT8177–186 were novel HLA-A*0201-restricted CTL epitopes in T1D patients. ZnT8107–116(115), ZnT8115–123, ZnT8153–161, ZnT8177–186 and ZnT8291–300 represent potentially major biomarkers for T1D. T cell responses against these epitopes showed different distributions between recently diagnosed and long-standing patients. Furthermore, they displayed discriminating performance among different ethnicities. We also compared the performance of the epitope identification strategies used herein. The epitopes which exhibited strong immunogenicity in HLA-A2 Tg mice were also well recognized by T1D patients. ConclusionsThe differences in autoimmune T cell responses among T1D individuals may open new avenues toward T1D prediction and prevention. It also provides efficient strategies for immune intervention.

Non-HLA type 1 diabetes genes modulate disease risk together with HLA-DQ and islet autoantibodies.

The possible interrelations between HLA-DQ, non-HLA single nucleotide polymorphisms (SNPs) and islet autoantibodies were investigated at clinical onset in 1-34 year old type 1 diabetes (T1D) patients (n=305) and controls (n=203). Among the non-HLA SNPs reported by the Type 1 Diabetes Genetics Consortium, 24% were supported in this Swedish replication set including that the increased risk of minor PTPN22 allele and high risk HLA was modified by GAD65 autoantibodies. The association between T1D and the minor AA+AC genotype in ERBB3 gene was stronger among IA-2 autoantibody-positive patients (comparison p=0.047). The association between T1D and the common insulin (AA) genotype was stronger among insulin autoantibody (IAA)-positive patients (comparison p=0.008). In contrast, the association between T1D and unidentified 26471 gene was stronger among IAA-negative (comparison p=0.049) and IA-2 autoantibody-negative (comparison p=0.052) patients. Finally, the association between IL2RA and T1D was stronger among IAA-positive than among IAA-negative patients (comparison p=0.028). These results suggest that the increased risk of T1D by non-HLA genes is often modified by both islet autoantibodies and HLA-DQ. The interactions between non-HLA genes, islet autoantibodies and HLA-DQ should be taken into account in T1D prediction studies as well as in prevention trials aimed at inducing immunological tolerance to islet autoantigens.

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.

LBP25: TYPE 1 DIABETES: AROUND THE WORLD WITH HLA

Aims HLA association with type 1 diabetes (T1D) is well established. HLA alleles and haplotype combinations vary among populations; however, the vast majority of T1D association studies have focused on subjects of European descent. Even the Type 1 Diabetes Genetics Consortium collection is predominantly comprised of European samples. The aim of this study is to determine HLA association with T1D in understudied populations around the globe, with a particular focus on developing countries, to elucidate differences in HLA-associated T1D susceptibility among populations and increase predictive power for T1D for all. Methods In collaboration with the Life for a Child program, we are collecting 100 blood samples from T1D patients and 200 saliva samples from unrelated controls from each of six countries, including Azerbaijan, Pakistan, Bangladesh, Mali, Sudan, and Haiti. For 469 samples collected to date, DNA was extracted and HLA DRB1 genes were sequenced with exon-based NGS technology on the Roche 454 GS Junior platform. Association analyzes were performed by gene counting, followed by chi-square analysis. Results To date, we have received 20 patients and 209 controls from Azerbaijan, 20 patients and 110 controls from Pakistan, and 110 controls from Bangladesh; DRB1 was genotyped for all samples. In preliminary analyzes, for Azerbaijan, DRB1 ∗ 03:01 (OR = 8.22; p = 1.35 × 10 - 9 ) and DRB1 ∗ 04:02 (OR = 6.93; p = 1.39 × 10 - 7 ) are significantly predisposing for T1D, while both DRB1 ∗ 15:01 and DRB1 ∗ 15:02 appear protective, but do not yet reach statistical significance. For Pakistan, the DRB1 ∗ 03:01 association is even stronger (OR = 10.37; p = 1.26 × 10 - 9 ), and DRB1 ∗ 15:02 protection is nearly significant (OR = 0.00, p = 0.063); however, no T1D association was observed for any DRB1 ∗ 04 allele. Conclusion These data illustrate the differences in HLA associated T1D susceptibility among populations. Additional differences will be revealed as we continue to receive and genotype samples. These differences are relevant for understanding the basic biology of HLA-associated T1D susceptibility, for prediction and differential diagnosis of T1D, and for eventual development of precision medicine-based treatment strategies for all individuals, regardless of ethnic heritage.

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.

Type 2 Diabetes Prevention: Implications of Hemoglobin A1c Genetics.

Hemoglobin A1c (HbA1c) is a biomarker used for population-level screening of type 2 diabetes (T2D) and risk stratification. Large-scale, genome-wide association studies have identified multiple genomic loci influencing HbA1c. We discuss the challenges of classifying these genomic loci as influencing HbA1c through glycemic or nonglycemic pathways, based on their probable biology and pleiotropic associations with erythrocyte traits. We show that putative nonglycemic genetic variants have a measurable, albeit small, impact on the classification of T2D status by HbA1c in white and Asian populations. Accounting for their effect on HbA1c may be relevant when screening populations with higher frequencies of nonglycemic HbA1c-altering alleles. As carriers of such HbA1c-altering alleles have HbA1c levels that may not accurately reflect overall glycemia, we describe how accounting for genotype may improve the performance of HbA1c in T2D prediction models and risk stratification, allowing for lifestyle intervention strategies to be directed towards those who are truly at elevated risk for developing T2D. In a Mendelian randomization framework, genetic variants can be used as instrumental variables to estimate causal relationships between HbA1c and T2D-related complications. This approach may help to support or refute HbA1c as an appropriate biomarker for long-term health outcomes in the general population.

Identification of allelic heterogeneity at type-2 diabetes loci and impact on prediction.

Although over 60 single nucleotide polymorphisms (SNPs) have been identified by meta-analysis of genome-wide association studies for type-2 diabetes (T2D) among individuals of European descent, much of the genetic variation remains unexplained. There are likely many more SNPs that contribute to variation in T2D risk, some of which may lie in the regions surrounding established SNPs - a phenomenon often referred to as allelic heterogeneity. Here, we use the summary statistics from the DIAGRAM consortium meta-analysis of T2D genome-wide association studies along with linkage disequilibrium patterns inferred from a large reference sample to identify novel SNPs associated with T2D surrounding each of the previously established risk loci. We then examine the extent to which the use of these additional SNPs improves prediction of T2D risk in an independent validation dataset. Our results suggest that multiple SNPs at each of 3 loci contribute to T2D susceptibility (TCF7L2, CDKN2A/B, and KCNQ1; p<5×10−8). Using a less stringent threshold (p<5×10−4), we identify 34 additional loci with multiple associated SNPs. The addition of these SNPs slightly improves T2D prediction compared to the use of only the respective lead SNPs, when assessed using an independent validation cohort. Our findings suggest that some currently established T2D risk loci likely harbor multiple polymorphisms which contribute independently and collectively to T2D risk. This opens a promising avenue for improving prediction of T2D, and for a better understanding of the genetic architecture of T2D.

Prevention of type 2 diabetes - where is the evidence?

Diabetes is an “old disease”. The first description of diabetes written on papyrus about 3500 years ago is found in the Ebers papyrus, which was discovered in Egypt in 1873 by the German archeologist Georg Ebers. Humans, like most mammals, may develop diabetes, if sufficient environmental exposure to physical inactivity, unhealthy diet and obesity are present. Genetic effects are important for the individual risk of type 2 diabetes (T2D) but twin studies have shown that the genetic effects combined explain less than 50% of the risk of T2D (1). Today we know almost 100 susceptibility genes associated with T2D but the relative importance of each of these genes is low (2), and together they explain only a small part of the disease risk. Thus, genetic screening cannot be used for the prediction of T2D for individuals. On the other hand, healthy lifestyle can also prevent T2D in people who are genetically at high risk, e.g. those with positive family history (3). Thus, the importance of lifestyle factors for the development and prevention of T2D is overwhelming. The strongest evidence in medicine is considered to come from randomized controlled trials (RCTs). They are also important in providing the link to causal inference that in most observational studies cannot be derived. At the same time, observational studies are necessary for the identification of factors associated with an increased or decreased risk of a disease, and for generation of hypotheses on potential means for prevention based on modifiable risk factors. Observational studies have by now identified a large number of factors that are associated with the development of T2D or with the protection against it. Most of them are related to physical activity, diet and obesity. It has also been shown unequivocally that exposures (poor nutrition, infections, smoking, etc.) during fetal life and early infancy resulting in insufficient development (often shown as low birth weight) also predict T2D later in life (4). Traditionally, T2D has been considered to be a disease of the elderly. However, with increasing obesity and sedentary lifestyle in children, there are increasing numbers of cases of T2D already occurring at a young age in many populations (5). In the past two decades several innovative RCTs have repeatedly shown that interventions on modifiable risk factors can reduce the incidence of diabetes in high-risk groups. The evidence has been summarized in several systematic reviews and meta-analyses (6,7). There is compelling scientific evidence from RCTs that T2D can be prevented or its onset delayed. The key for prevention is a multimodal package of lifestyle changes that is a sum outcome of dietary and physical activity behaviours. The power of lifestyle was dramatically illustrated by the Finnish Diabetes Prevention Study (DPS) that explicitly showed that when the study participants with impaired glucose tolerance reached all five modest lifestyle intervention targets, none of them progressed to T2D (8). Importantly, lifestyle intervention lasting for 3–6 years has been shown to have a carry-over effect on T2D incidence that lasts for several years; up to 15 in the Finnish Diabetes Prevention Study (9) and 20 years in the China Da Qing Diabetes Prevention Study (10). The observed sustained risk reduction seems to be a result of sustained lifestyle changes, but the legacy effect of improved glycaemia during the early intervention may also have contributed to the long-term effect. The follow-up data from the Chinese study indicate moreover that vascular risks are also reduced by lifestyle management. Several prognostic questionnaires exist for detecting people at risk of T2D (11). One of the screening tools with an adequately high sensitivity and specificity is the Finnish Diabetes Risk Score (FINDRISC) developed in Finland (12). The FINDRISC has been successfully implemented in the Finnish primary health care system and used also in many other countries. The FINDRISC was also tested in the Omani population and as a result a slightly modified Omani Diabetes Risk Score was developed (13) which may be a suitable screening tool for the Arab populations in the Middle East. Lifestyle interventions aiming at translating evidence from efficacy RCTs on T2D prevention into “real world” intervention programmes have also been carried out in various populations. Such data were recently combined in a meta-analysis considering the relationship between intervention effectiveness and adherence to guidelines (14). The primary meta-analysis included 22 studies with outcome data for weight loss at 12 months. The pooled result of the direct pairwise meta-analysis shows that lifestyle

Lessons from type 1 diabetes for understanding natural history and prevention of autoimmune disease.

Type 1 diabetes (T1D) is a chronic autoimmune disorder resulting from immune-mediated destruction of insulin-producing beta cells within the pancreatic islets. Prediction of T1D is now possible, as having 2 or more islet autoantibodies confers a 100% risk of diabetes development. With the ability to predict disease development, clinical trials to prevent diabetes onset have been completed and are currently under way. This review focuses on the natural history, prediction, and prevention trials in T1D. We review the lessons learned from these attempts at preventing a chronic autoimmune disease and apply the paradigm from T1D prevention to other autoimmune disorders.

Differentially Expressed Micro-RNAs as Biomarkers for Prediction and Prognosis in Autoimmune Type 1 Diabetes.

Purpose of the Study: Autoimmune Type 1 diabetes (T1D) is a multi-factorial disease involving the progressive destruction of pancreatic beta-cells, resulting in loss of insulin production. While pancreas or islet transplantation remains a reliable treatment approach, gradual attrition of allograft function over time and recurrence of autoimmunity are major limitations. Therefore, identifying individuals at risk for autoimmune T1D development as well as developing biomarkers that predict recurrence of autoimmunity after islet transplantation is important. Herein, the role of pancreatic microRNAs (miRNA) as potential biomarkers for T1D disease risk prediction was investigated. Methods: The female non-obese diabetic (NOD) mouse was used to study miRNA profiles. Pancreases harvested at different T1D development stages were grouped (n=4/group) as follows: non-diabetic (4-5weeks-old), pre-hyperglycemic (14weeks-old) and diabetic (22-23weeks-old). Age/sex-matched NOD-SCIDs were controls. Total RNA was isolated for miRNA microarray hybridization. miRNA expression summaries were obtained using RMA algorithm. Pairwise comparisons analyses were fit using two-sample t-test (p≤0.01; FDR<5%). IPA tool was used for biological analyses. Results: Pairwise comparison between prehyperglycemic vs. non-diabetic mice identified 28 miRNAs (20 upregulated; 8 downregulated) that were significantly and differentially expressed at the prehyperglycemic stage. Pairwise comparison between new-onset-diabetic vs. non-diabetic identified 7 up-regulated (miR-1224, miR-1940, miR-138, miR-29a, miR-200b, miR-5128, let-7a) and 13 down-regulated miRNAs (miR-23b, miR-185, miR-107, miR-24, miR-1196, let-7e, miR-99b, miR-130b, miR-103, miR-214, miR-31, miR-127, miR-379) in new-onset-diabetic mice. Of those, 8 miRNA were directly modulated by insulin and hydrogen peroxide (H2O2) levels. miR-29a and miR-130a-3p expression regulation appeared sensitive to decreased insulin while miR-99a, miR-103-3p, miR-31-5p, miR-214-3p, miR-130a-3p, and mir-21a-3p expression trends responded to H2O2 increments. Identified miRNA profiles were associated with insulin resistance, non-insulin-dependent diabetes, and hyperglycemia. Conclusions: The prehyperglycemic and new-onset-diabetic stages are associated with dysregulated pancreatic miRNAs expression profiles, demonstrating the potential of miRNA as biomarkers for risk prediction and prognosis of T1D.

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.

Cross-Sectional and Longitudinal Replication Analyses of Genome-Wide Association Loci of Type 2 Diabetes in Han Chinese

This study aimed to examine genomic loci of type 2 diabetes (T2D) initially identified by genome-wide association studies in populations of European ancestry for their associations with T2D and quantitative glycemic traits, as well as their effects on longitudinal change in fasting plasma glucose (FPG) and T2D development, in the Chinese population. Single nucleotide polymorphisms (SNP) from 25 loci were genotyped in a large case-control sample of 10,001 subjects (5,338 T2D cases and 4,663 controls) and a prospective cohort of 1,881 Chinese. In the case-control sample, 8 SNPs in or near WFS1, CDKAL1, CDKN2A/2B, CDC123, HHEX, TCF7L2, KCNQ1, and MTNR1B were significantly associated with T2D (P<0.05). Thirteen SNPs were associated with quantitative glycemic traits. For example, the most significant SNP, rs10811661 near CDKN2A/2B (P = 1.11×10−8 for T2D), was also associated with 2-h glucose level of an oral glucose tolerance test (P = 9.11×10−3) and insulinogenic index (P = 2.71×10−2). In the cohort study, individuals carrying more risk alleles of the replicated SNPs had greater FPG increase and T2D incidence in a 7.5-year follow-up period, with each quartile increase in the number of risk alleles being associated with a 0.06 mmol/l greater increase in FPG (P = 0.03) and 19% higher odds of developing T2D (P = 0.058). Our study identified the associations of several established T2D-loci in Europeans with T2D and quantitative glycemic traits in the Chinese population. The prospective data also suggest their potential role in the risk prediction of T2D in the Chinese population.