Diabetes Consortium Research Articles

Consortium-based approach to receiving an EMA qualification opinion on the use of islet autoantibodies as enrichment biomarkers in type 1 diabetes clinical studies.

The development of medical products that can delay or prevent progression to stage 3 type 1 diabetes faces many challenges. Of note, optimising patient selection for type 1 diabetes prevention clinical trials is hindered by significant patient heterogeneity and a lack of characterisation of the time-varying probability of progression to stage 3 type 1 diabetes in individuals positive for two or more islet autoantibodies. To meet these needs, the Critical Path Institute's Type 1 Diabetes Consortium was launched in 2017 as a pre-competitive public-private partnership between stakeholders from the pharmaceutical industry, patient advocacy groups, philanthropic organisations, clinical researchers, the National Institutes of Health and the Food and Drug Administration. The Type 1 Diabetes Consortium acquired and aggregated data from three longitudinal observational studies, Environmental Determinants of Diabetes in the Young (TEDDY), Diabetes Autoimmunity Study in the Young (DAISY) and TrialNet Pathway to Prevention (TN01), and used analysis subsets of these data to support the model-based qualification of islet autoantibodies as enrichment biomarkers for patient selection in type 1 diabetes prevention trials, including registration studies. The Type 1 Diabetes Consortium has now received a qualification opinion from the European Medicines Agency for the use of these biomarkers, a major success for the field of type 1 diabetes. This endorsement will improve product developers' ability to design clinical trials of agents intended to prevent or delay type 1 diabetes that are reduced in size and/or length, while being adequately powered.

JC Viruria Is Associated With Reduced Risk of Diabetic Kidney Disease

African Americans who shed JC polyomavirus (JCV) in their urine have reduced rates of nondiabetic chronic kidney disease (CKD). We assessed the associations between urinary JCV and urine BK polyomavirus (BKV) with CKD in African Americans with diabetes mellitus. African Americans with diabetic kidney disease (DKD) and controls lacking nephropathy from the Family Investigation of Nephropathy and Diabetes Consortium (FIND) and African American-Diabetes Heart Study (AA-DHS) had urine tested for JCV and BKV using quantitative PCR. Of the 335 individuals tested, 148 had DKD and 187 were controls. JCV viruria was detected more often in the controls than in the patients with DKD (FIND: 46.6% vs 32.2%; OR, 0.52; 95% CI, 0.29 to 0.93; P = 0.03; AA-DHS: 30.4% vs 26.2%; OR, 0.63; 95% CI, 0.27 to 1.48; P = 0.29). A joint analysis adjusted for age, sex, and study revealed that JC viruria was inversely associated with DKD (OR, 0.56; 95% CI, 0.35 to 0.91; P = 0.02). Statistically significant relationships between BKV and DKD were not observed. The results from the present study extend the inverse association between urine JCV and nondiabetic nephropathy in African Americans to DKD. These results imply that common pathways likely involving the innate immune system mediate coincident chronic kidney injury and restriction of JCV replication. Future studies are needed to explore causative pathways and characterize whether the absence of JC viruria can serve as a biomarker for DKD in the African American population.

Research Resource: dkCOIN, the National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) Consortium Interconnectivity Network: A Pilot Program to Aggregate Research Resources Generated by Multiple Research Consortia

The National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) supports multiple basic science consortia that generate high-content datasets, reagent resources, and methodologies, in the fields of kidney, urology, hematology, digestive, and endocrine diseases, as well as metabolic diseases such as diabetes and obesity. These currently include the Beta Cell Biology Consortium, the Nuclear Receptor Signaling Atlas, the Diabetic Complications Consortium, and the Mouse Metabolic Phenotyping Centers. Recognizing the synergy that would accrue from aggregating information generated and curated by these initiatives in a contiguous informatics network, we created the NIDDK Consortium Interconnectivity Network (dkCOIN; www.dkcoin.org). The goal of this pilot project, organized by the NIDDK, was to establish a single point of access to a toolkit of interconnected resources (datasets, reagents, and protocols) generated from individual consortia that could be readily accessed by biologists of diverse backgrounds and research interests. During the pilot phase of this activity dkCOIN collected nearly 2000 consortium-curated resources, including datasets (functional genomics) and reagents (mouse strains, antibodies, and adenoviral constructs) and built nearly 3000 resource-to-resource connections, thereby demonstrating the feasibility of further extending this database in the future. Thus, dkCOIN promises to be a useful informatics solution for rapidly identifying useful resources generated by participating research consortia.

Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest

We study the number of causal variants and associated regions identified by top SNPs in rankings given by the popular 1 df chi-squared statistic, support vector machine (SVM) and the random forest (RF) on simulated and real data. If we apply the SVM and RF to the top 2r chi-square-ranked SNPs, where r is the number of SNPs with P-values within the Bonferroni correction, we find that both improve the ranks of causal variants and associated regions and achieve higher power on simulated data. These improvements, however, as well as stability of the SVM and RF rankings, progressively decrease as the cutoff increases to 5r and 10r. As applications we compare the ranks of previously replicated SNPs in real data, associated regions in type 1 diabetes, as provided by the Type 1 Diabetes Consortium, and disease risk prediction accuracies as given by top ranked SNPs by the three methods. Software and webserver are available at http://svmsnps.njit.edu.