Prediction Of Type 1 Diabetes Research Articles

Incidence and predictors of type 1 diabetes among younger adults aged 20–45 years: The diabetes in young adults (DiYA) study

AimsTo estimate incidence of type 1 diabetes (T1D) and to develop a T1D prediction model among young adults. MethodsAdults 20–45 years newly-diagnosed with diabetes in 2017 were identified within Kaiser Permanente’s healthcare systems in California and invited for diabetes autoantibody (DAA) testing. Multiple imputation was conducted to assign missing DAA status. The primary outcome for incidence rates (IR) and the prediction model was T1D defined by ≥1 positive DAA. ResultsAmong 2,347,989 persons at risk, 7862 developed diabetes, 2063 had DAA measured, and 166 (8.0%) had ≥1 positive DAA. T1D IR (95% CI) per 100,000 person-years was 15.2 (10.2–20.1) for ages 20–29 and 38.2 (28.6–47.8) for ages 30–44 years. The age-standardized IRs were 32.5 (22.2–42.8) for men and 27.2 (21.0–34.5) for women. The age/sex-standardized IRs were 30.1 (23.5–36.8) overall; 41.4 (25.3–57.5) for Hispanics, 37.0 (11.6–62.4) for Blacks, 21.4 (14.3–28.6) for non-Hispanic Whites, and 19.4 (8.5–30.2) for Asians. Predictors of T1D among cases included female sex, younger age, lower BMI, insulin use and having T1D based on diagnostic codes. ConclusionsT1D may account for up to 8% of incident diabetes cases among young adults. Follow-up is needed to establish the clinical course of patients with one DAA at diagnosis.

Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes

Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine samples from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test sample, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.

One-Hour Oral Glucose Tolerance Tests for the Prediction and Diagnostic Surveillance of Type 1 Diabetes.

Once islet autoantibody-positive individuals are identified, predicting which individuals are at highest risk for type 1 diabetes (T1D) is important. A metabolic risk score derived from 2-hour oral glucose tolerance test (OGTT) data, the Diabetes Prevention Trial-Type 1 risk score (DPTRS), can accurately predict T1D. However, 2-hour OGTTs are time-consuming and costly. We aimed to determine whether a risk score derived from 1-hour OGTT data can predict T1D as accurately as the DPTRS. Secondarily, we evaluated whether a 1-hour glucose value can be used for diagnostic surveillance. The DPTRS was modified to derive a 1-hour OGTT risk score (DPTRS60) using fasting C-peptide, 1-hour glucose and C-peptide, age, and body mass index. Areas under receiver operating curves (ROCAUCs) were used to compare prediction accuracies of DPTRS60 with DPTRS in Diabetes Prevention Trial-Type 1 (DPT-1) (n = 654) and TrialNet Pathway to Prevention (TNPTP) (n = 4610) participants. Negative predictive values (NPV) for T1D diagnosis were derived for 1-hour glucose thresholds. ROCAUCs for T1D prediction 5 years from baseline were similar between DPTRS60 and DPTRS (DPT-1: 0.805 and 0.794; TNPTP: 0.832 and 0.847, respectively). DPTRS60 predicted T1D significantly better than 2-hour glucose (P < .001 in both cohorts). A 1-hour glucose of less than 180 mg/dL had a similar NPV, positive predictive value, and specificity for T1D development before the next 6-month visit as the standard 2-hour threshold of less than 140 mg/dL (both ≥ 98.5%). A 1-hour OGTT can predict T1D as accurately as a 2-hour OGTT with minimal risk of missing a T1D diagnosis before the next visit.

A combined risk score enhances prediction of type 1 diabetes among susceptible children.

Type 1 diabetes (T1D)-an autoimmune disease that destroys the pancreatic islets, resulting in insulin deficiency-often begins early in life when islet autoantibody appearance signals high risk1. However, clinical diabetes can follow in weeks or only after decades, and is very difficult to predict. Ketoacidosis at onset remains common2,3 and is most severe in the very young4,5, in whom it can be life threatening and difficult to treat6-9. Autoantibody surveillance programs effectively prevent most ketoacidosis10-12 but require frequent evaluations whose expense limits public health adoption13. Prevention therapies applied before onset, when greater islet mass remains, have rarely been feasible14 because individuals at greatest risk of impending T1D are difficult to identify. To remedy this, we sought accurate, cost-effective estimation of future T1D risk by developing a combined risk score incorporating both fixed and variable factors (genetic, clinical and immunological) in 7,798 high-risk children followed closely from birth for 9.3 years. Compared with autoantibodies alone, the combined model dramatically improves T1D prediction at ≥2 years of age over horizons up to 8 years of age (area under the receiver operating characteristic curve ≥ 0.9), doubles the estimated efficiency of population-based newborn screening to prevent ketoacidosis, and enables individualized risk estimates for better prevention trial selection.

Arrhythmic Gut Microbiome Signatures Predict Risk of Type 2 Diabetes

Lifestyle, obesity, and the gut microbiome are important risk factors for metabolic disorders. We demonstrate in 1,976 subjects of a German population cohort (KORA) that specific microbiota members show 24-h oscillations in their relative abundance and identified 13 taxa with disrupted rhythmicity in type 2 diabetes (T2D). Cross-validated prediction models based on this signature similarly classified T2D. In an independent cohort (FoCus), disruption of microbial oscillation and the model for T2D classification was confirmed in 1,363 subjects. This arrhythmic risk signature was able to predict T2D in 699 KORA subjects 5 years after initial sampling, being most effective in combination with BMI. Shotgun metagenomic analysis functionally linked 26 metabolic pathways to the diurnal oscillation of gut bacteria. Thus, a cohort-specific risk pattern of arrhythmic taxa enables classification and prediction of T2D, suggesting a functional link between circadian rhythms and the microbiome in metabolic diseases.

Validating the doubly weighted genetic risk score for the prediction of type 2 diabetes in the Lifelines and Estonian Biobank cohorts.

As many cases of type 2 diabetes (T2D) are likely to remain undiagnosed, better tools for early detection of high‐risk individuals are needed to prevent or postpone the disease. We investigated the value of the doubly weighted genetic risk score (dwGRS) for the prediction of incident T2D in the Lifelines and Estonian Biobank (EstBB) cohorts. The dwGRS uses an additional weight for each single nucleotide polymorphism in the risk score, to correct for “Winner's curse” bias in the effect size estimates. The traditional (single‐weighted genetic risk score; swGRS) and dwGRS were calculated for participants in Lifelines (n = 12,018) and EstBB (n = 34,129). The dwGRS was found to have stronger association with incident T2D (hazard ratio [HR] = 1.26 [95% confidence interval: 1.10–1.43] and HR = 1.35 [1.28–1.42]) compared to the swGRS (HR = 1.21 [1.07–1.38] and HR = 1.25 [1.19–1.32]) in Lifelines and EstBB, respectively. Comparing the 5‐year predicted risks from the models with and without the dwGRS, the continuous net reclassification index was 0.140 (0.034–0.243; p = .009 Lifelines), and 0.257 (0.194–0.319; p < 2 × 10−16 EstBB). The dwGRS provided incremental value to the T2D prediction model with established phenotypic predictors. It clearly distinguished the risk groups for incident T2D in both biobanks thereby showing its clinical relevance.

Prediction and Prevention of Type 1 Diabetes.

Type 1 Diabetes (T1D) is one of the most common chronic autoimmune diseases in children. The disease is characterized by the destruction of beta cells, leading to hyperglycemia, and to a lifelong insulin-dependent state. Although several studies in the last decades have added relevant insights, the complex pathogenesis of the disease is not yet completely understood. Recent studies have been focused on several factors, including family history and genetic predisposition (HLA and non-HLA genes) as well as environmental and metabolic biomarkers, with the aim of predicting the development and progression of T1D. Once a child becomes symptomatic, beta cell mass has already reached a critical threshold (usually a residual of 20–30% of normal amounts), thus representing only the very late phase of the disease. In particular, this final stage follows two preceding asymptomatic stages, which have been precisely identified. In view of the long natural history and complex pathogenesis of the disease, many strategies may be proposed for primary, secondary, and tertiary prevention. Strategies of primary prevention aim to prevent the onset of autoimmunity against beta cells in asymptomatic individuals at high risk for T1D. In addition, the availability of novel humoral and metabolic biomarkers that are able to characterize subjects at high risk of progression, have stimulated several studies on secondary and tertiary prevention, aimed to preserve residual beta cell destruction and/or to prolong the remission phase after the onset of T1D. This review focuses on the major current knowledge on prediction and prevention of T1D in children.

94-OR: Postpartum Circulating MicroRNAs Can Predict the Development of Type 2 Diabetes in Women with Previous Gestational Diabetes

Type 2 diabetes (T2D) is a major cause of morbidity and death worldwide. Women with gestational diabetes mellitus (GDM) have a seven-fold higher risk of developing T2D later in life. However, accurate methods for post-partum T2D risk stratification of GDM women, are lacking. MicroRNAs are now well recognised as circulating biomarkers or mediators of metabolic disease. We aimed to determine if post-partum circulating microRNAs can predict the devolvement of T2D in women with previous GDM. Participants were part of the GDM follow-up program at the Mercy Hospital for Women, Australia. Plasma samples were collected at 12-week post-partum from 103 women following an index GDM pregnancy. Women were followed for 10 years to assess development of T2D. In a discovery approach, we measured 754 miRNAs in plasma from T2D non-progressors (N=11) and in T2D progressors (N=10) using TaqMan-based real time PCR on an OpenArray platform. Data were analysed using penalised logistic regression followed by bootstrapping. Top 15 miRNAs with high frequencies in this discovery bootstrap analysis were selected for validation in the remaining samples (N=82; 71 non-progressors and 11 progressors). Validation data were normalized to spike-in control (ath-miR-172a). Four miRNAs (miR-543, miR-410, miR-491 and miR-369-3p) were significantly dysregulated (p&amp;lt;0.05) between progressors and non-progressors to T2D, identifying these as risk factors. In a clinical model of prediction of T2D that included six traditional risk factors (age, BMI, pregnancy fasting glucose, post-OGTT change, cholesterol and triglycerides), the addition of these four circulating microRNA biomarkers measured at 12-weeks post-partum improved the prediction of future T2D from traditional AUC=0.8259 (95% CI: 0.68-0.9717) to an AUC= 0.9155 (95% CI: 0.8307-1). Replication of our findings in other cohorts is merited. Improved prediction will be beneficial in facilitating early intervention for T2D. Disclosure M. Joglekar: None. W.K. Wong: None. F. Ema: None. H.M. Georgiou: None. A. Hardikar: None. M. Lappas: None.

1280-P: Improving Type 1 Diabetes (T1D) Prediction by Incorporating Growth Features into Landmark Models

We explored the association between growth features and T1D development using landmark analysis at different ages. Analysis included individuals from 2 birth cohort studies: DAISY and BABYDIAB (n=2,664; 129 progressed to T1D). Using height and weight measured over time, percentiles for age were calculated. Missing values were imputed using LMS parameters of CDC growth charts. Rates of change of percentiles were computed over the prior year. Twelve ages (1-12 years) were used for landmark analysis with Random Survival Forest to predict probability of T1D onset in the 1- to 19-year follow up windows. The baseline model used HLA risk group, sex, T1D family history and breastfeeding history. The full model added growth features: height and weight percentiles at age and change in percentiles. Performance was measured using C-index and feature importance was ranked. Incorporating growth features significantly improved prediction accuracy of T1D onset for 95% of combinations of landmark ages and prediction window sizes (Table 1, not all ages and windows shown). The order of features from most to least predictive is: HLA group, rates of height and weight changes, height and weight percentiles, family history, breastfeeding and sex. This analysis demonstrates that using growth features can significantly improve prediction of T1D. Table 1: C-Index with and without inclusion of growth features for landmark age of 3 years. Disclosure Z. Li: None. V. Anand: None. J.L. Dunne: None. B.I. Frohnert: None. W. Hagopian: Consultant; Self; Novo Nordisk Inc. H. Hyoty: None. M. Maziarz: None. A. Ziegler: None. J. Toppari: None. Funding JDRF (1-IND-2019-717-I-X, 1-SRA-2019-722-I-X, 1-SRA-2019-720-I-X, 1-SRA-2019-721-I-X, 1-SRA-2019-719-I-X, 1-SRA-2019-723-I-X)

240-OR: Combining Type 2 Diabetes Polygenic Scores Improves Prediction in 36,000 European and Non-European Participants in the Partners Healthcare Biobank

Polygenic scores constructed by the aggregation of effect sizes of associated genetic variants have been shown to predict type 2 diabetes (T2D). Polygenic scores can be defined as “restricted-to-significant polygenic scores” (rsPS), and “globally expanded polygenic scores” (gePS), which can include millions of variants beyond those that are genome-wide significant. Most polygenic scores have been generated based on genome-wide association studies (GWAS) conducted predominantly in participants of European ancestry (EU); how these polygenic scores perform in non-European individuals in the healthcare system has not been thoroughly evaluated. We applied the rsPS and gePS for T2D in 30,240 EU participants, 3,029 Latinos (LA), and 2,201 African Americans (AA) from the Partners HealthCare Biobank, which maintains blood and DNA samples from patients seen at Partners HealthCare-affiliated hospitals in Boston. We compared rsPS based on the DIAMANTE consortium meta-analysis, with 398 variants (rsPS_398), rsPS based on the latest Million Veterans Program meta-analysis, with 682 variants (rsPS_682), and a gePS based on ldpred, including 6.9M variants adjusted for their effects based on linkage disequilibrium. rsPS_682 was most strongly associated with T2D in all ancestries (OR per SD in EU=1.73 p=7×10-142, OR per SD in LA =1.88, p=7×10-19, OR per SD in AA =1.47, p=1×10-8 compared to the other polygenic scores. We observed that rsPS_682 and the gePS provided both complementary information, and that combining them into the model improved the prediction in all ancestries, with 16%, 22%, and 16% increase in accuracy in EU, LA, and AA, respectively. We show the advantage of combining rsPS and gePS to improve prediction of T2D in multi-ethnic populations within a healthcare system. The results suggest that larger and multi-ethnic T2D GWAS are needed to avoid major health disparities. Disclosure J.M. Mercader: None. J.C. Florez: Advisory Panel; Self; Doris Duke Charitable Foundation. Other Relationship; Self; Novo Nordisk Inc., Park Street School. A. Leong: None. V. Kaur: None. M. Udler: None. J.B. Meigs: Consultant; Self; Quest Diagnostics. B. Porneala: None. Funding American Diabetes Association (1-19-ICTS-068 to J.M.M.)

Epigenetic regulation of insulin action and secretion – role in the pathogenesis of type 2 diabetes

The prevalence of type 2 diabetes (T2D) is rapidly increasing worldwide. Obesity, physical inactivity and ageing increase the risk of T2D. Epigenetic modifications can change due to environmental exposures and may thereby predispose to disease. This review aims at summarizing recent advances in epigenetics related to T2D, with a special focus on impaired insulin action and secretion in humans. There will be an emphasis on analyses in human tissues; both from T2D case-control cohorts and intervention studies. Current data support an important role for epigenetics in the pathogenesis of T2D. Numerous studies have found differential DNA methylation and gene expression in skeletal muscle, adipose tissue, the liver and pancreatic islets from subjects with T2D compared with nondiabetic controls. For example, PDX1 has increased DNA methylation and decreased expression in pancreatic islets from patients with T2D compared with nondiabetic controls. Nongenetic risk factors for T2D such as ageing, unhealthy diets and physical activity do also impact the epigenome in human tissues. Interestingly, physical activity altered DNA methylation of candidate genes for T2D such as THADA in muscle and FTO, KCNQ1 and TCF7L2 in adipose tissue. There is also a strong interaction between genetic and epigenetic factors that together seem to affect T2D. mQTL studies in human adipose tissue and pancreatic islets showed that SNPs associated with DNA methylation levels in numerous sites. Several of these SNPs are also associated with T2D. Recent data also support that DNA methylation of some sites in blood may be developed into biomarkers that predict T2D since methylation of, for example TXNIP, ABCG1 and SREBF1 associated with future T2D. Future studies should use this information for development of new therapies and biomarkers and thereby improve prediction, prevention and treatment of T2D and its complications.

Metabolomics Signatures in Type 2 Diabetes: A Systematic Review and Integrative Analysis.

Metabolic signatures have emerged as valuable signaling molecules in the biochemical process of type 2 diabetes (T2D). To summarize and identify metabolic biomarkers in T2D, we performed a systematic review and meta-analysis of the associations between metabolites and T2D using high-throughput metabolomics techniques. We searched relevant studies from MEDLINE (PubMed), Embase, Web of Science, and Cochrane Library as well as Chinese databases (Wanfang, Vip, and CNKI) inception through 31 December 2018. Meta-analysis was conducted using STATA 14.0 under random effect. Besides, bioinformatic analysis was performed to explore molecule mechanism by MetaboAnalyst and R 3.5.2. Finally, 46 articles were included in this review on metabolites involved amino acids, acylcarnitines, lipids, carbohydrates, organic acids, and others. Results of meta-analysis in prospective studies indicated that isoleucine, leucine, valine, tyrosine, phenylalanine, glutamate, alanine, valerylcarnitine (C5), palmitoylcarnitine (C16), palmitic acid, and linoleic acid were associated with higher T2D risk. Conversely, serine, glutamine, and lysophosphatidylcholine C18:2 decreased risk of T2D. Arginine and glycine increased risk of T2D in the Western countries subgroup, and betaine was negatively correlated with T2D in nested case-control subgroup. In addition, slight improvements in T2D prediction beyond traditional risk factors were observed when adding these metabolites in predictive analysis. Pathway analysis identified 17 metabolic pathways may alter in the process of T2D and metabolite-related genes were also enriched in functions and pathways associated with T2D. Several metabolites and metabolic pathways associated with T2D have been identified, which provide valuable biomarkers and novel targets for prevention and drug therapy.

Biomarker Discovery in Pre-Type 1 Diabetes; Th40 Cells as a Predictive Risk Factor.

The incidence of type 1 diabetes (T1D) is increasing worldwide. The quest to understand T1D etiology and how to predict diabetes is ongoing; and, in many ways, those goals intertwine. Although genetic components associate with T1D, not all individuals with T1D have those components, and T1D does not develop in all subjects with those components. More robust methods for prediction of T1D are needed. We investigated if high CD4+CD40+ T-cell (Th40) levels can be used as a biomarker. Th40 levels were assessed along with other parameters in blood collected from prediabetic subjects in TrialNet. In prediabetic subjects stratified according to Th40 cell level, patterns paralleled those seen between control subjects and those with T1D. Cytokine patterns were significantly different between those with high Th-40 levels (Th40-high) and those with low levels, and a CD4/CD8 double-positive population was more represented in Th40-high groups. Subjects experiencing impaired glucose tolerance had a significantly higher Th40 level than did control subjects. HLA DR4/DR4 and DQ8/DQ8 were more likely found among Th40-high subjects. Interestingly, HLA DR4/DR4 subjects were significantly older compared with all other subjects, suggesting that this haplotype, together with a high Th40 level, may represent someone in whom T1D will develop after age 30 years, which is reported for 42% of T1D cases. Considering the differences found in relation to prediabetic Th40 cell level, it may be possible to devise methods that more accurately predict who will proceed toward diabetes and, possibly, indicate prediabetic stage.

Biomarker discovery in pre-Type 1 Diabetes; Th40 cells as a predictive risk factor

Abstract The incidence of Type 1 Diabetes (T1D) is on the rise. Quests to understand T1D etiology as well as how to predict onset are ongoing and, in many ways, those goals intertwine. CD4+CD40+ T cell (Th40) percentages are expanded in T1D subjects compared to controls. Antibodies to β-cell antigens are considered risk factors for T1D development but risk of seroconversion to antibody-positivity declines after age 10. While genetic components associate with T1D, not all T1D subjects have those components and not all subjects with those components develop T1D. Therefore, more robust methods for prediction of T1D are needed. Can high Th40 cell percentages be a biomarker in addition to other markers? We assessed Th40 cell percentages, along with other parameters, in blood collected from TrialNet preT1D subjects and stratified them according to their percentages. Th40-low versus Th40-high preT1D subjects demonstrate patterns of CD4 T cell levels that parallel those seen between control and T1D. Cytokine patterns are significantly different between the Th40 groups and a CD4+CD8+ population, described previously in Rheumatoid Arthritis, is more common in Th40-high subjects. Subjects experiencing impaired glucose tolerance have significantly higher Th40 cell percentages than controls. PreT1Ds expressing T1D associated HLA DR4/DR4 or DQ8/DQ8 are more likely found among Th40-high subjects. Interestingly, DR4/DR4 subjects were significantly older compared to all other preT1Ds, suggesting that this haplotype together with a high Th40 cell percentage may represent someone who will onset after age 30, which is reported for 42% of T1D cases. It will be important to determine if preT1D subjects with high Th40 cell percentages will develop overt T1D.

Predicting Islet Cell Autoimmunity and Type 1 Diabetes: An 8-Year TEDDY Study Progress Report.

Assessment of the predictive power of The Environmental Determinants of Diabetes in the Young (TEDDY)-identified risk factors for islet autoimmunity (IA), the type of autoantibody appearing first, and type 1 diabetes (T1D). A total of 7,777 children were followed from birth to a median of 9.1 years of age for the development of islet autoantibodies and progression to T1D. Time-dependent sensitivity, specificity, and receiver operating characteristic (ROC) curves were calculated to provide estimates of their individual and collective ability to predict IA and T1D. HLA genotype (DR3/4 vs. others) was the best predictor for IA (Youden's index J = 0.117) and single nucleotide polymorphism rs2476601, in PTPN22, was the best predictor for insulin autoantibodies (IAA) appearing first (IAA-first) (J = 0.123). For GAD autoantibodies (GADA)-first, weight at 1 year was the best predictor (J = 0.114). In a multivariate model, the area under the ROC curve (AUC) was 0.678 (95% CI 0.655, 0.701), 0.707 (95% CI 0.676, 0.739), and 0.686 (95% CI 0.651, 0.722) for IA, IAA-first, and GADA-first, respectively, at 6 years. The AUC of the prediction model for T1D at 3 years after the appearance of multiple autoantibodies reached 0.706 (95% CI 0.649, 0.762). Prediction modeling statistics are valuable tools, when applied in a time-until-event setting, to evaluate the ability of risk factors to discriminate between those who will and those who will not get disease. Although significantly associated with IA and T1D, the TEDDY risk factors individually contribute little to prediction. However, in combination, these factors increased IA and T1D prediction substantially.

Antibodies to oxidized insulin improve prediction of type 1 diabetes in children with positive standard islet autoantibodies.

Antibodies to posttranslationally modified insulin (oxPTM-INS-Ab) are a novel biomarker of type 1 diabetes (T1D). Here, we evaluated whether oxPTM-INS-Ab can improve T1D prediction in children with positive standard islet autoantibodies (AAB). We evaluated sensitivity, specificity, accuracy, and risk for progression to T1D associated with oxPTM-INS-Ab and the standard islet AAB that include insulin (IAA), GAD (GADA), and tyrosine phosphatase 2 (IA-2A) in a cohort of islet AAB-positive (AAB+ ) children from the general population (median follow-up 8.8years). oxPTM-INS-Ab was the most sensitive and specific autoantibody biomarker (74% sensitivity, 91% specificity), followed by IA-2A (71% sensitivity, 91% specificity). GADA and IAA showed lower sensitivity (65% and 50%, respectively) and specificity (66% and 68%, respectively). Accuracy (AUC of ROC) of oxPTM-INS-Ab was higher than GADA and IAA (P=0.003 and P=0.017, respectively), and similar to IA-2A (P=0.896). oxPTM-INS-Ab and IA-2A were more effective than IAA for detecting progr-T1D when used as second-line biomarker in GADA+ children. Risk for diabetes was higher (P=0.03) among multiple AAB+ who were also oxPTM-INS-Ab+ compared with those who were oxPTM-INS-Ab- . Importantly, when replacing IAA with oxPTM-INS-Ab, diabetes risk increased to 100% in children with oxPTM-INS-Ab+ in combination with GADA+ and IA-2A+ , compared with 84.37% in those with IAA+ , GADA+ , and IA-2A+ (P=0.04). Antibodies to oxidized insulin (oxPTM-INS-Ab), compared with IAA which measure autoantibodies to native insulin, improve T1D risk assessment and prediction accuracy in AAB+ children.

Genetic risk score combining six genetic variants associated with the cellular NRF2 expression levels correlates with Type 2 diabetes in the human population.

Type 2 diabetes (T2D) is known as an inflammatory disease. NRF2 (Nuclear Factor Erythroid 2 Like2) encodes a transcription factor that binds to antioxidant response elements (AREs) and regulates the expression of genes involved in many antioxidant responses. This study aimed to gain insight into individual anti-inflammatory activity to prevent T2D development in humans. We performed a genome-wide association study (GWAS) to identify genetic variants influencing NRF2 expression in LCLs (lymphoblastoid cell lines) generated from 74 different individuals. Association analyses between T2D or its related traits and genetic risk score (GRS) calculated by combining genetic variants detected from GWAS for cellular NRF2 expression were performed using data from 8715 subjects. The T2D prediction model using GRS was evaluated by measuring the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. Our GWAS identified six genetic variants (SNP) showing suggestive evidence of associations with cellular NRF2 expression (P < 10- 6). Logistic regression analysis demonstrated that GRS was associated with an increased risk of T2D (P value = 0.003, OR = 1.13). In addition, linear regression analyses showed positive associations between GRS and fasting glucose (P value = 0.028, β = 0.62), 2-h glucose (P value = 0.0004, β = 1.13) and HbA1C (P value = 0.033, β = 0.03). In the T2D prediction model using GRS, the AUC of the ROC curve was 0.69. This study highlights genetic variants associated with cellular NRF2 expression and suggests that the GRS of NRF2 expression-associated variants is likely to be a useful indicator of T2D development in the human population.

Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis.

OBJECTIVEPreviously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, at HLA risk loci. We aimed to more completely incorporate HLA alleles, their interactions, and recently discovered non-HLA loci into an improved T1D GRS (termed the “T1D GRS2”) to better discriminate diabetes subtypes and to predict T1D in newborn screening studies.RESEARCH DESIGN AND METHODSIn 6,481 case and 9,247 control subjects from the Type 1 Diabetes Genetics Consortium, we analyzed variants associated with T1D both in the HLA region and across the genome. We modeled interactions between variants marking strongly associated HLA haplotypes and generated odds ratios to create the improved GRS, the T1D GRS2. We validated our findings in UK Biobank. We assessed the impact of the T1D GRS2 in newborn screening and diabetes classification and sought to provide a framework for comparison with previous scores.RESULTSThe T1D GRS2 used 67 single nucleotide polymorphisms (SNPs) and accounted for interactions between 18 HLA DR-DQ haplotype combinations. The T1D GRS2 was highly discriminative for all T1D (area under the curve [AUC] 0.92; P < 0.0001 vs. older scores) and even more discriminative for early-onset T1D (AUC 0.96). In simulated newborn screening, the T1D GRS2 was nearly twice as efficient as HLA genotyping alone and 50% better than current genetic scores in general population T1D prediction.CONCLUSIONSAn improved T1D GRS, the T1D GRS2, is highly useful for classifying adult incident diabetes type and improving newborn screening. Given the cost-effectiveness of SNP genotyping, this approach has great clinical and research potential in T1D.

Plasma Acylcarnitines and Risk of Type 2 Diabetes in a Mediterranean Population at High Cardiovascular Risk.

The potential associations between acylcarnitine profiles and incidence of type 2 diabetes (T2D) and whether acylcarnitines can be used to improve diabetes prediction remain unclear. To evaluate the associations between baseline and 1-year changes in acylcarnitines and their diabetes predictive ability beyond traditional risk factors. We designed a case-cohort study within the PREDIMED Study including all incident cases of T2D (n = 251) and 694 randomly selected participants at baseline (follow-up, 3.8 years). Plasma acylcarnitines were measured using a targeted approach by liquid chromatography-tandem mass spectrometry. We tested the associations between baseline and 1-year changes in individual acylcarnitines and T2D risk using weighted Cox regression models. We used elastic net regressions to select acylcarnitines for T2D prediction and compute a weighted score using a cross-validation approach. An acylcarnitine profile, especially including short- and long-chain acylcarnitines, was significantly associated with a higher risk of T2D independent of traditional risk factors. The relative risks of T2D per SD increment of the predictive model scores were 4.03 (95% CI, 3.00 to 5.42; P < 0.001) for the conventional model and 4.85 (95% CI, 3.65 to 6.45; P < 0.001) for the model including acylcarnitines, with a hazard ratio of 1.33 (95% CI, 1.08 to 1.63; P < 0.001) attributed to the acylcarnitines. Including the acylcarnitines into the model did not significantly improve the area under the receiver operator characteristic curve (0.86 to 0.88, P = 0.61). A 1-year increase in C4OH-carnitine was associated with higher risk of T2D [per SD increment, 1.44 (1.03 to 2.01)]. An acylcarnitine profile, mainly including short- and long-chain acylcarnitines, was significantly associated with higher T2D risk in participants at high cardiovascular risk. The inclusion of acylcarnitines into the model did not significantly improve the T2D prediction C-statistics beyond traditional risk factors, including fasting glucose.

Gene polymorphism of Il-18 as a pro-inflammatory mediator and susceptibility of diabetes type 1in Egyptian children.

Type 1 diabetes (T1D) is a chronic immune-mediated disease and is the most common autoimmune disorder in childhoodbut the disease may become manifest at any age. It is characterized by selective loss of insulin-producing β-cells in the pancreatic islets in genetically susceptible subjects.Interleukin- 18 (IL-18) is a proinflammatory cytokinethat was originally identified as an interferon-γ inducing factor.It plays a critical role in chronic inflammation, in autoimmune diseases, and in a number of infectious diseases. Functional variants of IL18 gene has been reported as associated with T1D.the purpose of this was to investigate the role of IL-18 gene polymorphism (-607 A/C)in the prediction of T1D in the Egyptian children. One hundred and four patients with T1D and 116 healthy controls, IL- 18 promoter genotyping at -607 (rs1946518) position was analyzed with sequence-specific PCR(PCR/SSP).Therewas no significant association between polymorphisms in SNPs of IL-18 (rs1946518A/C) and T1D risk in Egyptian population,However, the results suggest an association of genotypes with age at onset of T1D.Our preliminary data suggested that polymorphisms in the IL-18 gene may not contribute to T1D susceptibility. To the best of our knowledge, this study is the first one that deals with IL-18 gene polymorphism in T1D patients in Egypt. Although, further studies with large sample size should be conducted to validate these results in Egyptian population.