Prediction Of Type 1 Diabetes Research Articles

Prediction and Prevention of Type 2 Diabetes in Women with a History of GDM.

Women with a history of gestational diabetes mellitus (GDM) have an alarmingly high risk of developing type 2 diabetes (T2D); yet, mechanisms underlying this progression are largely unknown. Clinical characteristics of a GDM pregnancy and postpartum metabolomics may contribute to risk prediction of T2D to identify those women at highest risk of progression and need for intervention. Evidence for effective postpartum lifestyle interventions from observational studies include adherence to a healthy dietary pattern, increasing physical activity, and maintaining a healthy body weight. Larger clinical trials with greater participant engagement are warranted to confirm the effectiveness of lifestyle interventions in women with recent GDM. Research is needed to refine prediction models of T2D after GDM, and to determine the most effective strategies to delay or prevent T2D onset. Incorporating novel biomarkers in the postpartum period, such as metabolomics, could offer a powerful approach.

Time-to-Peak C-Peptide and Peak Glucose during an Oral Glucose Tolerance Test (OGTT) Are Independent Risk Factors for Type 1 Diabetes (T1D) in Antibody-Positive (Ab+) Individuals

Peak C-peptide (C-P) and glucose levels are known predictors of T1D, but T1D prediction by times to those peak levels has not been studied. We thus assessed T1D prediction by times to peak C-P or glucose during OGTTs in 709 Ab+ Diabetes Prevention Trial-type 1 participants with 2-hour OGTTs [mean age: 13.9±9.6 years; BMI percentile (%ile): 59.3±29.0; 56% male]. Log rank testing and Cox regression with adjustments for age, sex, BMI %ile, proband relation, HLA, and Ab number were used. Risks were higher for time to peak C-P >60 mins [HR: 1.96 (CI: 1.49-2.63), p<0.001)] and for time to peak glucose >30 mins [HR: 2.22 (1.67-2.94), p<0.001)]. With time to peak C-P and peak C-P as covariates, T1D associations were greater for time to peak C-P (X2=22.8 vs. 9.3, both p<0.001). Time to peak glucose and peak glucose associations also remained significant (both p<0.001). Findings were validated in Ab+ relatives in the TrialNet Pathway to Prevention study (Figure); those with a peak C-P >60 mins or a peak glucose >30 mins had higher risk (both p<0.001). In conclusion, Ab+ relatives with delayed peak C-P or glucose are at higher T1D risk, independent of peak levels. Moreover, the time to peak C-P appears to be more predictive than the peak level. Times to peak C-P and glucose have potential use as predictors of T1D and as markers for progression. Disclosure H.M. Ismail: None. P. Xu: None. C. Evans-Molina: None. J.P. Palmer: None. J. Sosenko: None.

Continuous Glucose Monitoring Predicts Progression to Diabetes in Autoantibody-Positive Children

Children with islet autoantibodies (Ab+) are at high risk for developing clinical type 1 diabetes (T1D) and need accurate surveillance for prediction and diagnosis of T1D. Monitoring typically utilizes repeated 2-hour oral glucose tolerance tests. The objective of this study was to explore whether continuous glucose monitoring (CGM) could be an alternative measure for T1D prediction. In the Diabetes Autoimmunity Study in the Young (DAISY), 23 Ab+ subjects with a baseline CGM were followed for development of T1D for a mean of 28 months (8 of these progressed to T1D at a mean age of 15.2 years). Compared to non-progressors, subjects who progressed to T1D had significantly increased glycemic variability (SD 29 vs. 21 mg/dl, p=0.047), daytime sensor average (122 vs. 1mg/dl, p=0.02), and daytime sensor AUC (p=0.047). They spent 24% of time above 140 mg/dl and 12% above 160 mg/dl compared to, respectively, 8% and 3% (both p=0.005) for non-progressors; time spent over 200 mg/dl was minimal and not different between groups. Performance of prediction by receiver operating curve (ROC) analyses showed AUC of 0.85 for both %time spent above 140 mg/dl and 160 mg/dl. The cutoff of 18% time spent above 140 mg/dl had 100% specificity and 100% PPV with good sensitivity for diabetes prediction (Table). In conclusion, ≥18% CGM time spent above 140 mg/dl accurately predicts progression to clinical diabetes in islet Ab+ children.TableIndexCutoffPPVNPVSensitivitySpecificity% time > 12036%0.7140.8120.6250.867% time > 14018%1.0000.8830.7501.000% time > 1606%0.8330.8240.6250.933% time > 1802%0.7140.8130.6250.867% time > 2000.2%0.5460.8130.7500.667 Disclosure A. Steck: None. F. Dong: None. I. Taki: None. M.R. Hoffman: None. K. Simmons: None. B.I. Frohnert: None. M. Rewers: None.

Comparison of Novel MicroRNA and Cell-Free DNA Biomarkers of Beta-Cell Death and Diabetes Progression

Type 1 diabetes (T1D) is characterized by the loss of insulin-producing beta-cells. We currently lack diagnostic tools to measure T1D progression. Using systematic approaches involving unprejudiced discovery analyses in human tissues and clinical samples, we identify microRNAs that are: i) associated with insulin expression during embryonic development (N>60); ii) predictive of and necessary for insulin transcription (N=698); and iii) dysregulated in individuals with (vs. without) T1D (N=200). Insulin cfDNA (assessed by ddPCR) and microRNA (assessed by TaqMan qPCR) biomarkers were measured in around; i) N=400 Australian T1D individuals; ii) N=30 longitudinal samples from children at risk of T1D; iii) N=100 clinical trial samples from individuals receiving IFN-a or Exenatide vs. placebo; iv) N=400 Hong Kong Chinese individuals with T1D; v) N=700 Danish individuals at diagnosis of T1D and vi) N=400 Asian Indian T1D subjects. Among N=600 Australian samples, we identified miRNAs (N=5) that were significantly increased in the circulation of T1D individuals (vs. controls), decreased (at least 2 Fold Change/FC, P<0.05, N=7miRNAs) in T1DCx+ vs. T1DCx- (no T1D complications) or significantly increased (2 FC, P<0.05, N=5) in T1D individuals with detectable C-peptide vs. non-detectable C-peptide. Our studies highlight the potential of circulating microRNAs and cfDNA in quantifying beta cell death before clinical diagnosis of T1D. These data demonstrate that microRNA/cfDNA signatures not only facilitate the potential prediction of diabetes progression, but can also help in stratifying individuals at risk of T1D as well as assess treatment efficacies in trials aiming to retard beta-cell death. We also present our preliminary analysis of cross-ethnic variation in these cfDNA/miRNA biomarkers and present ongoing/future studies to develop a diagnostic test for T1D prediction. Disclosure M. Joglekar: None. R. Farr: None. W. Wong: None. K. Rother: None. A. Jenkins: Research Support; Self; Medtronic, Mylan, Sanofi-Aventis. C.S. Yajnik: None. R.C. Ma: Research Support; Self; AstraZeneca, Bayer AG, Merck Sharp & Dohme Corp., Pfizer Inc.. Advisory Panel; Self; Boehringer Ingelheim GmbH, Nippon Boehringer Ingelheim Co. Ltd.. M.E. Craig: None. T. Jones: None. A. Hardikar: None.

Early and Later C-Peptide Responses during Oral Glucose Tolerance Testing (OGTT) Are Independently and Oppositely Predictive of Type 1 Diabetes (T1D) in Autoantibody-Positive (Ab+) Individuals

A longitudinal study showed that early C-peptide (C-P) responsiveness during OGTTs declined during progression to T1D, whereas later C-P responsiveness increased until the diagnosis was imminent. Here we exmaine: 1) whether the baseline C-P response pattern predicts T1D as suggested by the findings from that longitudinal study, and 2) the basis for the higher later C-peptide response during progression. Baseline 2-hour OGTTs from 670 Diabetes Prevention Trial-type 1 participants (age: 13.8±9.6 years; BMI-z: 0.3±1.1; 56% male) were analyzed. In univariate regression models, the early C-P response (30-0 min) was inversely predictive of T1D (X2=28.8, p<0.001), whereas the later C-P response (120-60 min) was positively predictive (X2= 23.3, p<0.001). Each was at least as predictive as the AUC C-peptide (inverse association; X2 = 20.4; p<0.001), a standard prediction measure. In a model including both early and late C-P responses, both remained oppositely predictive of T1D with a stronger overall association (X2=41.1; p<0.001 for both). To explain the positive association of greater T1D risk with a high later C-P, we examined the association between the sum of glucose levels from 60 to 120 min and the 120-60 min C-P; there was a strong positive association (r=0.44, p<0.001). All associations persisted after age and BMI-z adjustments. In conclusion, the findings show that lower early and higher later C-P responses are both indicators of risk for progression to T1D. Moreover, the findings suggest that the increasing later C-P response reflects an attempt by the β-cell to compensate for higher glucose levels due to a deficient early C-P response. These findings are similar to the evolution of C-P responses during type 2 diabetes development. Partitioning C-peptide into early and later responses appears beneficial for T1D prediction and for studying β-cell deterioration during progression. Disclosure H.M. Ismail: None. D.J. Becker: None. S. Geyer: None. P. Xu: None. I. Libman: Consultant; Self; Novo Nordisk A/S. K.C. Herold: None. M.A. Atkinson: Other Relationship; Self; Patent Issued. M.J. Redondo: None. J.P. Palmer: None. J. Sosenko: None.

Quarterly HbA1c Surveillance in Children with Islet Antibodies (IA) Accurately Predicts Type 1 Diabetes (T1D) Timing

IA sensitively predict future T1D, but onset often occurs many years after seroconversion. Early diagnosis results in less DKA and milder initial disease, especially in young children. Using the international TEDDY cohort, we asked if quarterly HbA1c testing could predict T1D among pre-pubertal subjects with ≥1 persistent IA (GADA, IA2A, IAA). Mean(SD) age at seroconversion and T1D onset was 43(29) and 73(22) months respectively. Of 8,504 HLA high risk children, 456 had persistent confirmed IA, had undergone at least 3 quarterly HbA1c tests in the prior 12 months, and were not on diabetes drugs or diets. Of these, 104 progressed to T1D and 352 did not. Subjects were split into training (292 total, 62 progressors) and test (164 total, 40 progressors) datasets with similar characteristics. The optimal maximum HbA1c cutpoint within 1 year pre-onset to predict T1D, by ROC analysis, was similar in training and test datasets (p=0.66). For both, HbA1c ≥5.6% gave the best Youden index. Other factors significant for T1D risk in the training dataset included age at HbA1c test, month of HbA1c test, continent and IA2A titer, but not gender, family history, HLA type, seroconversion age, IAA titer nor T1D-related SNP genotypes. Adjusted for significant factors, the optimal HbA1c cutpoint of ≥5.6% for quarterly testing was 91% sensitive, 91% specific, 73% PPV and 98% NPV to predict T1D within 1 year in children with HLA and IA risk. The final predictive model fit both training and test data similarly (p=0.28). Median(SD) time from first HbA1c ≥5.6% to diagnosis was 8.6(4.5) months, enabling monitoring and education for earlier treatment. Quarterly HbA1c surveillance is cost-effective and clinically accessible. A hierarchical plan for pediatric T1D prediction is proposed comprising initial genetic screening, then IA surveillance of those at genetic risk, then HbA1c surveillance of those with IA, and finally close glycemic surveillance of those with HbA1c ≥5.6% to ascertain metabolic onset. Disclosure M. Killian: None. K. Vehik: None. R.R. Little: None. H. Elding Larsson: None. M.J. Haller: None. M. Rewers: None. J. She: None. J. Toppari: None. B. Akolkar: None. J. Krischer: None. W. Hagopian: None.

Optimal cut-points of different anthropometric indices and their joint effect in prediction of type 2 diabetes: results of a cohort study

BackgroundTo determine the anthropometric indices that would predict type 2 diabetes (T2D) and delineate their optimal cut-points.MethodsIn a cohort study, 7017 Iranian adults, aged 20–60 years, free of T2D at baseline were investigated. Using Cox proportional hazard models, hazard ratios (HRs) for incident T2D per 1 SD change in body mass index (BMI), waist circumference (WC), waist to height ratio (WHtR), waist to hip ratio (WHR), and hip circumference (HC) were calculated. The area under the receiver operating characteristics (ROC) curves (AUC) was calculated to compare the discriminative power of anthropometric variables for incident T2D. Cut-points of each index were estimated by the maximum value of Youden’s index and fixing the sensitivity at 75%. Using the derived cut-points, joint effects of BMI and other obesity indices on T2D hazard were assessed.ResultsDuring a median follow-up of 12 years, 354 men, and 490 women developed T2D. In both sexes, 1 SD increase in anthropometric variables showed significant association with incident T2D, except for HC in multivariate adjusted model in men. In both sexes, WHtR had the highest discriminatory power while HC had the lowest. The derived cut-points for BMI, WC, WHtR, WHR, and HC were 25.56 kg/m2, 89 cm, 0.52, 0.91, and 96 cm in men and 27.12 kg/m2, 87 cm, 0.56, 0.83, and 103 cm in women, respectively. Assessing joint effects of BMI and each of the obesity measures in the prediction of incident T2D showed that among both sexes, combined high values of obesity indices increase the specificity for the price of reduced sensitivity and positive predictive value.ConclusionsOur derived cut-points differ between both sexes and are different from other ethnicities.

HbA1c presents low sensitivity as a post-pregnancy screening test for both diabetes and prediabetes in Greek women with history of gestational diabetes mellitus.

Women with a history of gestational diabetes mellitus (GDM) are at increased risk for type 2 diabetes (T2D). It is thus recommended that an oral glucose tolerance test (OGTT) be performed after delivery. Recently, the use of glycated haemoglobin A1c (HbA1c) has been proposed as a simpler and faster method to diagnose glucose disorders. The aim of this study was to investigate whether HbA1c measurement can replace OGTT in the detection of prediabetes and T2D in women with a history of GDM. We studied 1336 women (35.3 ± 5.8years old) with a history of GDM 16.6 ± 28.2months after delivery. All women were evaluated through an OGTT and a simultaneous HbA1c measurement. American Diabetes Association (ADA) criteria were used for the assessment of glucose disorders. Sensitivity and specificity of HbA1c were measured for the prediction of T2D and prediabetes, while Cohen's coefficient of agreement (k) was calculated. ROC analysis was performed to evaluate the sensitivity and specificity of HbA1c. Based on OGTT, 725 women (54.3%) were normal, 406 (30.4%) presented impaired fasting glucose (IFG), 48 (3.6%) impaired glucose tolerance (IGT), 74 (5.5%) combined IFG+IGT, and 83 presented with T2D (6.2%). By contrast, using HbA1c as a screening test, 1150 women (94.1%) were normal, while 49 (4.0%) had prediabetes and 23 (1.9%) T2D. Sensitivity of HbA1c for the diagnosis of prediabetes was 5.3% in comparison to OGTT, specificity was 99.2%, while for the diagnosis of T2D, the percentages were 29.6 and 100%, respectively. The consistency in classifying impaired glucose tolerance between HbA1c and OGTT was 59.7%. Cohen's coefficient of agreement was k = 0.116, indicating slight agreement. Performing a ROC curve, the optimal value of distinctive ability of HbA1c was 4.6% in the case of prediabetes, while for diabetes, it was 5.5%. This study provided evidence that HbA1c can identify fewer women with prediabetes and T2D than OGTT, indicating that HbA1c cannot be recommended as an alternative post-pregnancy screening method.

Genetic Risk Scores for Type 1 Diabetes Prediction and Diagnosis.

About 50% of the heritability of type 1 diabetes (T1D) is attributed to human leukocyte antigen (HLA) alleles and the remainder to several (close to 50) non-HLA loci. A current challenge in the field of the genetics of T1D is to apply the knowledge accumulated in the last 40years towards differential diagnosis and risk assessment. T1D genetic risk scores seek to combine the information from HLA and non-HLA alleles to improve the accuracy of diagnosis, prediction, and prognosis. Here, we describe genetic risk scores that have been developed and validated in various settings and populations. Several genetic scores have been proposed that merge disease risk information from multiple genetic factors to optimize the use of genetic information and ultimately improve prediction and diagnosis of T1D.

Utilization of genetic data can improve the prediction of type 2 diabetes incidence in a Swedish cohort.

The aim of this study was to measure the impact of genetic data in improving the prediction of type 2 diabetes (T2D) in the Malmö Diet and Cancer Study cohort. The current study was performed in 3,426 Swedish individuals and utilizes of a set of genetic and environmental risk data. We first validated our environmental risk model by comparing it to both the Finnish Diabetes Risk Score and the T2D risk model derived from the Framingham Offspring Study. The area under the curve (AUC) for our environmental model was 0.72 [95% CI, 0.69–0.74], which was significantly better than both the Finnish (0.64 [95% CI, 0.61–0.66], p-value < 1 x 10−4) and Framingham (0.69 [95% CI, 0.66–0.71], p-value = 0.0017) risk scores. We then verified that the genetic data has a statistically significant positive correlation with incidence of T2D in the studied population. We also verified that adding genetic data slightly but statistically increased the AUC of a model based only on environmental risk factors (RFs, AUC shift +1.0% from 0.72 to 0.73, p-value = 0.042). To study the dependence of the results on the environmental RFs, we divided the population into two equally sized risk groups based only on their environmental risk and repeated the same analysis within each subpopulation. While there is a statistically significant positive correlation between the genetic data and incidence of T2D in both environmental risk categories, the positive shift in the AUC remains statistically significant only in the category with the lower environmental risk. These results demonstrate that genetic data can be used to increase the accuracy of T2D prediction. Also, the data suggests that genetic data is more valuable in improving T2D prediction in populations with lower environmental risk. This suggests that the impact of genetic data depends on the environmental risk of the studied population and thus genetic association studies should be performed in light of the underlying environmental risk of the population.

Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

AimThe aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers.MethodsGas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D.ResultsEstablished markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738–0.850]) and 0.808 [0.749–0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577–0.736]). Prediction based on non-blood based measures was 0.638 [0.565–0.711]).ConclusionsEstablished measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.

MiRNAs: novel regulators of autoimmunity-mediated pancreatic β-cell destruction in type 1 diabetes.

MicroRNAs (miRNAs) are a series of conserved, short, non-coding RNAs that modulate gene expression in a posttranscriptional manner. miRNAs are involved in almost every physiological and pathological process. Type 1 diabetes (T1D) is an autoimmune disease that is the result of selective destruction of pancreatic β-cells driven by the immune system. miRNAs are also important participants in T1D pathogenesis. Herein, we review the most recent data on the potential involvement of miRNAs in T1D. Specifically, we focus on two aspects: the roles of miRNAs in maintaining immune homeostasis and regulating β-cell survival and/or functions in T1D. We also discuss circulating miRNAs as potent biomarkers for the diagnosis and prediction of T1D and investigate potential therapeutic approaches for this disease.

English

This study aimed to assess HbA1c performance against single fasting blood glucose (FBG) for diagnosis of undiagnosed type 2 diabetes (T2D) and impaired fasting glycaemia (IFG) among general medical outpatients in Botswana. Participants aged, &ge;20 years were cross-sectionally surveyed from August to October, 2014. All the participants underwent testing for HbA1c and FBG. The HbA1c sensitivity, specificity and predictive values in the diagnosis of T2D and IFG were computed and their Pearson&rsquo;s correlation and scatter diagrams determined. A total of 291 participants (74.2% women) with a mean age of 50.1&plusmn;11.0 years provided data for the current analysis. HbA1c at cut-off of &ge; 6.5% (48 mmol/mol) had a sensitivity and specificity for T2D of 100 (15.81 to 100.00) and 86.3% (86.16 to 89.92), respectively. Similarly, for IFG, the sensitivity and specificity was 100 (2.5 to 100) and 36.3% (30.3 to 42.6), respectively. The positive predictive value (PPV) was 4.8 (0.58 to 16.16) and 0.6% (0.02 to 3.45) for T2D and IFG screening, respectively. The negative predictive value (NPV) was 100% in both cases of T2D and IFG screening. HbA1c had a modest, positive correlation (r) with FBG for the overall population (r = 0.536, p &lt; 0.001); for women, (r = 0 .578, p &lt; 0.001) and men (r =0 .336, p =0.003). HbA1c had high sensitivity but widely varying specificity, high proportion of discordant results and poor prediction of T2D and IFG in this setting. Although, HbA1c correlation with fasting glucose was modest, both tests are required to improve diagnostic reliability in asymptomatic T2D screening program. Key words: Diabetes screening, HbA1c test, Botswana.

Sex differences, endogenous sex-hormone hormones, sex-hormone binding globulin, and exogenous disruptors in diabetes and related metabolic outcomes.

In assessing clinical and pathophysiological development of type 2 diabetes (T2D), the critical role of the sex steroids axis is underappreciated, particularly concerning the sex-specific relationships with many relevant cardiometabolic outcomes. In this issue of the Journal of Diabetes, we provide a comprehensive overview of these significant associations of germline variants in the genes governing the sex steroid pathways, plasma levels of steroid hormones, and sex hormone-binding globulin (SHBG) with T2D risk that have been observed in many clinical and high-quality large prospective cohorts of men and women across ethnic populations. Together, this body of evidence indicates that sex steroids and SHBG should be routinely incorporated into clinical characterization of T2D patients, particularly in screening prediabetic patients, such as those with metabolic syndrome, using plasma levels of SHBG. Given that several germline mutations in the SHBG gene have also been directly related to both plasma concentrations of SHBG and clinical manifestation of T2D, targeting signals in the sex steroid axis, particularly SHBG, may have significant utility in the prediction and treatment of T2D. Further, many of the environmental endocrine disrupting chemicals may exert their potential adverse effects on cardiometabolic outcomes via either estrogenic or androgenic signaling pathways, highlighting the importance of using the sex steroids and SHBG as important biochemical markers in both clinical and population studies in studying sex-specific mechanisms in the pathogenesis of T2D and its complications, as well as the need to equitably allocate resources in studying both men and women.

A Systematic Review of Biomarkers and Risk of Incident Type 2 Diabetes: An Overview of Epidemiological, Prediction and Aetiological Research Literature.

BackgroundBlood-based or urinary biomarkers may play a role in quantifying the future risk of type 2 diabetes (T2D) and in understanding possible aetiological pathways to disease. However, no systematic review has been conducted that has identified and provided an overview of available biomarkers for incident T2D. We aimed to systematically review the associations of biomarkers with risk of developing T2D and to highlight evidence gaps in the existing literature regarding the predictive and aetiological value of these biomarkers and to direct future research in this field.Methods and FindingsWe systematically searched PubMed MEDLINE (January 2000 until March 2015) and Embase (until January 2016) databases for observational studies of biomarkers and incident T2D according to the 2009 PRISMA guidelines. We also searched availability of meta-analyses, Mendelian randomisation and prediction research for the identified biomarkers. We reviewed 3910 titles (705 abstracts) and 164 full papers and included 139 papers from 69 cohort studies that described the prospective relationships between 167 blood-based or urinary biomarkers and incident T2D. Only 35 biomarkers were reported in large scale studies with more than 1000 T2D cases, and thus the evidence for association was inconclusive for the majority of biomarkers. Fourteen biomarkers have been investigated using Mendelian randomisation approaches. Only for one biomarker was there strong observational evidence of association and evidence from genetic association studies that was compatible with an underlying causal association. In additional search for T2D prediction, we found only half of biomarkers were examined with formal evidence of predictive value for a minority of these biomarkers. Most biomarkers did not enhance the strength of prediction, but the strongest evidence for prediction was for biomarkers that quantify measures of glycaemia.ConclusionsThis study presents an extensive review of the current state of the literature to inform the strategy for future interrogation of existing and newly described biomarkers for T2D. Many biomarkers have been reported to be associated with the risk of developing T2D. The evidence of their value in adding to understanding of causal pathways to disease is very limited so far. The utility of most biomarkers remains largely unknown in clinical prediction. Future research should focus on providing good genetic instruments across consortia for possible biomarkers in Mendelian randomisation, prioritising biomarkers for measurement in large-scale cohort studies and examining predictive utility of biomarkers for a given context.

Association between the ratio of triglyceride to high-density lipoprotein cholesterol and incident type 2 diabetes in Singapore Chinese men and women.

Increased triglycerides (TG) and decreased high-density lipoprotein cholesterol (HDL-C) are risk factors for type 2 diabetes (T2D). The relationship between TG:HDL-C ratio and T2D risk is not clear, and it is not known whether the association is modified by gender, body mass index, or fasting status. This study examined the relationship between TG:HDL-C ratio and risk of incident T2D, and the predictive ability of the ratio on top of other diabetes risk factors. Blood biomarkers were determined in 571 T2D cases and 571 controls nested within a prospective, population-based cohort study, the Singapore Chinese Health Study. Participants were free of diagnosed diabetes, cardiovascular disease, and cancer at the time of blood collection (1999-2004). Incident self-reported T2D cases were identified at follow-up interview (2006-10). Controls were matched 1: 1 for age, sex, dialect group, and date of blood collection. Multivariable logistic regression was used to compute the odds ratio (OR) and 95% confidence interval (CI). The ORs (95% CI) of T2D per 1-SD increment in TG and TG: HDL-C ratio were 1.70 (1.39-2.09) and 1.72 (1.37-2.17), respectively. The relationships were stronger among females than males (Pinteraction = 0.028 and 0.017, respectively), and stronger among lean (<23 kg/m2 ) than overweight/obese participants (Pinteraction = 0.051 and 0.046, respectively). Both TG and TG: HDL-C improved T2D prediction to a similar degree. Both TG and TG:HDL-C ratio are independent risk factors for incident T2D, and confer greater risk in women and lean participants. The TG: HDL-C ratio is not a better predictor of diabetes than TG alone.

Association between liver enzymes and incident type 2 diabetes in Singapore Chinese men and women.

AimsTo assess the association between liver enzymes and the risk of type 2 diabetes (T2D) in a Chinese population.MethodsA nested case–control study comprising 571 T2D cases and 571 matched controls was conducted within the Singapore Chinese Health Study. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were quantified in baseline plasma collected from them, while γ-glutamyltransferase (GGT) was assayed among 255 T2D cases with baseline hemoglobin A1c <6.5% and 255 matched controls. Participants were free of diagnosed diabetes, cardiovascular disease, and cancer at blood collections (1999–2004). Incident self-reported T2D cases were identified at follow-up II interview (2006–2010). Controls were matched to cases on age, sex, dialect group, and date of blood collection.ResultsHigher levels of ALT and GGT were significantly associated with increased risk of T2D (p for trend <0.001 for ALT, p for trend=0.03 for GGT), and the ORs (95% CIs) comparing highest versus lowest tertiles of ALT and GGT were 2.00 (1.01 to 3.96) and 2.38 (1.21 to 4.66), respectively. A null association was observed for AST, ALP, and LDH with T2D risk. Adding GGT (<23 vs ≥23 IU/L) or ALT (<21 vs ≥21 IU/L) to a prediction model resulted in significant gain in net reclassification improvement and integrated discrimination improvement of T2D prediction (all p<0.001).ConclusionsHigher levels of GGT and ALT are associated with increased T2D risk. GGT ≥23 IU/L and ALT ≥21 IU/L may identify people at higher risk of developing T2D in this Chinese population.

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.