scholarly journals T1DBase: integration and presentation of complex data for type 1 diabetes research

2007 ◽  
Vol 35 (Database) ◽  
pp. D742-D746 ◽  
Author(s):  
E. M. Hulbert ◽  
L. J. Smink ◽  
E. C. Adlem ◽  
J. E. Allen ◽  
D. B. Burdick ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetes Research ◽  
Complex Data

209-OR: ADA Presidents' Select Abstract: Type 1 Diabetes and the Developing Brain—A Longitudinal Study of Brain Growth by the Diabetes Research in Children Network (DirecNet)

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 209-OR ◽  
Author(s):  
ANA MARIA ARBELAEZ ◽  
STEFANI O’DONOGHUE ◽  
NELLY MAURAS ◽  
BRUCE A. BUCKINGHAM ◽  
NEIL H. WHITE ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Longitudinal Study ◽  
Developing Brain ◽  
Diabetes Research ◽  
Brain Growth ◽  
Select Abstract

scholarly journals Luminex and Other Multiplex High Throughput Technologies for the Identification of, and Host Response to, Environmental Triggers of Type 1 Diabetes

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Sharad Purohit ◽  
Ashok Sharma ◽  
Jin-Xiong She
Keyword(s):  
Type 1 Diabetes ◽  
High Throughput ◽  
Host Response ◽  
Environmental Pollutants ◽  
Silent Period ◽  
Dietary Factors ◽  
Systematic Evaluation ◽  
Complex Nature ◽  
Diabetes Research

Complex interactions between a series of environmental factors and genes result in progression to clinical type 1 diabetes in genetically susceptible individuals. Despite several decades of research in the area, these interactions remain poorly understood. Several studies have yielded associations of certain foods, infections, and immunizations with the onset and progression of diabetes autoimmunity, but most findings are still inconclusive. Environmental triggers are difficult to identify mainly due to (i) large number and complex nature of environmental exposures, including bacteria, viruses, dietary factors, and environmental pollutants, (ii) reliance on low throughput technology, (iii) less efforts in quantifying host response, (iv) long silent period between the exposure and clinical onset of T1D which may lead to loss of the exposure fingerprints, and (v) limited sample sets. Recent development in multiplex technologies has enabled systematic evaluation of different classes of molecules or macroparticles in a high throughput manner. However, the use of multiplex assays in type 1 diabetes research is limited to cytokine assays. In this review, we will discuss the potential use of multiplex high throughput technologies in identification of environmental triggers and host response in type 1 diabetes.

scholarly journals Approaches in type 1 diabetes research: A status report

2009 ◽  
Vol 29 (2) ◽  
pp. 85 ◽  
Author(s):  
VR Rao ◽  
Oindrila Raha ◽  
Subhankar Chowdhury ◽  
Samir Dasgupta ◽  
P Raychaudhuri ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Status Report ◽  
Diabetes Research

scholarly journals Recent advances in understanding Type 1 Diabetes

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 110 ◽  
Author(s):  
Gustaf Christoffersson ◽  
Teresa Rodriguez-Calvo ◽  
Matthias von Herrath
Keyword(s):  
Type 1 Diabetes ◽  
Environmental Factors ◽  
Cell Mass ◽  
Beta Cell Mass ◽  
Diabetes Research ◽  
Mhc I ◽  
Dependence Versus ◽  
Genetic And Environmental Factors ◽  
P Type

Type 1 diabetes is a multifactorial disease in which genetic and environmental factors play a key role. The triggering event is still obscure, and so are many of the immune events that follow. In this brief review, we discuss the possible role of potential environmental factors and which triggers are believed to have a role in the disease. In addition, as the disease evolves, beta cells are lost and this occurs in a very heterogeneous fashion. Our knowledge of how beta cell mass declines and our view of the disease’s pathogenesis are also debated. We highlight the major hallmarks of disease, among which are MHC-I (major histocompatibility complex class I) expression and insulitis. The dependence versus independence of antigen for the immune infiltrate is also discussed, as both the influence from bystander T cells and the formation of neo-epitopes through post-translational modifications are thought to influence the course of the disease. As human studies are proliferating, our understanding of the disease’s pathogenesis will increase exponentially. This article aims to shed light on some of the burning questions in type 1 diabetes research.

scholarly journals Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus

Diabetologia ◽  
2019 ◽  
Vol 63 (3) ◽  
pp. 636-647 ◽  
Author(s):  
Marco Colombo ◽  
◽  
Stuart J. McGurnaghan ◽  
Samira Bell ◽  
Finlay MacKenzie ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Type 1 Diabetes ◽  
Kidney Disease ◽  
Renal Disease ◽  
Adult Population ◽  
Diabetes Duration ◽  
Research Network ◽  
Diabetes Research ◽  
Renal Disease Progression

Abstract Aims/hypothesis The aim of this study was to provide data from a contemporary population-representative cohort on rates and predictors of renal decline in type 1 diabetes. Methods We used data from a cohort of 5777 people with type 1 diabetes aged 16 and older, diagnosed before the age of 50, and representative of the adult population with type 1 diabetes in Scotland (Scottish Diabetes Research Network Type 1 Bioresource; SDRNT1BIO). We measured serum creatinine and urinary albumin/creatinine ratio (ACR) at recruitment and linked the data to the national electronic healthcare records. Results Median age was 44.1 years and diabetes duration 20.9 years. The prevalence of CKD stages G1, G2, G3 and G4 and end-stage renal disease (ESRD) was 64.0%, 29.3%, 5.4%, 0.6%, 0.7%, respectively. Micro/macroalbuminuria prevalence was 8.6% and 3.0%, respectively. The incidence rate of ESRD was 2.5 (95% CI 1.9, 3.2) per 1000 person-years. The majority (59%) of those with chronic kidney disease stages G3–G5 did not have albuminuria on the day of recruitment or previously. Over 11.6 years of observation, the median annual decline in eGFR was modest at −1.3 ml min−1 [1.73 m]−2 year−1 (interquartile range [IQR]: −2.2, −0.4). However, 14% experienced a more significant loss of at least 3 ml min−1 [1.73 m]−2. These decliners had more cardiovascular disease (OR 1.9, p = 5 × 10−5) and retinopathy (OR 1.3 p = 0.02). Adding HbA1c, prior cardiovascular disease, recent mean eGFR and prior trajectory of eGFR to a model with age, sex, diabetes duration, current eGFR and ACR maximised the prediction of final eGFR (r2 increment from 0.698 to 0.745, p < 10−16). Attempting to model nonlinearity in eGFR decline or to detect latent classes of decliners did not improve prediction. Conclusions These data show much lower levels of kidney disease than historical estimates. However, early identification of those destined to experience significant decline in eGFR remains challenging.

scholarly journals Challenges to Reshape the Future of Type 1 Diabetes Research

2018 ◽  
Vol 103 (8) ◽  
pp. 2838-2842 ◽  
Author(s):  
David Bleich ◽  
David H Wagner
Keyword(s):  
Type 1 Diabetes ◽  
T Cell ◽  
Trial Design ◽  
Evidence Synthesis ◽  
Clinical Trial Design ◽  
Preventive Treatment ◽  
Diabetes Research ◽  
Knowledge Gaps ◽  
Disease Stratification

Abstract Context Immunotherapy trials to prevent type 1 diabetes have been unsuccessful for >15 years. Understanding pitfalls and knowledge gaps in the immunology of type 1 diabetes should lead us in new directions that will yield better trial outcomes. A proposal is made for precision medicine trial design in future type 1 diabetes studies. Evidence Acquisition High-quality peer-reviewed basic science and clinical research trials for type 1 diabetes were used in this Perspective article. Type 1 diabetes publications were reviewed from 2000 to 2018 by using Google Scholar and PubMed reference databases. Evidence Synthesis Personalized medicine for type 1 diabetes should recognize that each individual has phenotypic and genotypic quirks that distinguish them from other study participants. A uniform protocol for antigen-specific immunotherapy has consistently failed to prevent disease. An alternative approach using molecular tools to personalize the preventive treatment strategy might be a road forward for type 1 diabetes research. Assumptions or lack of knowledge about disease stratification (not all type 1 diabetes is the same disease), individualized antigen-specific T cells, regulatory T-cell populations, and T-cell receptor rearrangement are just a few aspects of immunology that require integration with clinical trial design. Conclusions The type 1 diabetes research community continues to bring forward novel immunotherapy trials to prevent disease, but this approach is unlikely to succeed until several fundamental aspects of clinical immunology are recognized and addressed. Here, we identify several knowledge gaps that could rectify type 1 diabetes trial design and lead to future success.

scholarly journals Eight-Point Glucose Testing Versus the Continuous Glucose Monitoring System in Evaluation of Glycemic Control in Type 1 Diabetes

2005 ◽  
Vol 90 (6) ◽  
pp. 3387-3391 ◽  
Keyword(s):  
Type 1 Diabetes ◽  
Monitoring System ◽  
Continuous Glucose Monitoring ◽  
Hemoglobin A1c ◽  
Glucose Monitoring ◽  
Continuous Glucose Monitoring System ◽  
Diabetes Research ◽  
Glucose Profiles ◽  
Mean Glucose

Context: Advantages/disadvantages of continuous vs. discrete glucose monitoring are not well documented. Objective: Compare glucose profiles from home meters vs. continuous sensors. Design: Randomized clinical trial conducted by the Diabetes Research in Children Network (DirecNet) to assess the utility of the GlucoWatch G2 Biographer. Setting: Home glucose measurements. Patients: Two hundred children (age, 7 to &lt; 18 yr) with type 1 diabetes. Intervention: At baseline, subjects were asked to wear the continuous glucose monitoring system (CGMS) sensor and perform meter tests at eight prespecified times of the day (eight-point testing) each for 3 d (2 d using both, 1 d eight-point testing only, 1 d CGMS only). Hemoglobin A1c was measured in a central laboratory. Main Outcome Measure: Six-month hemoglobin A1c. This analysis looked at baseline glucose profiles/hemoglobin A1c. Results: Only 10% of subjects completed full eight-point testing for 3 d, but median CGMS use was 70 h. Mean glucose was lower when measured by the CGMS compared with eight-point testing (183 ± 37 vs. 188 ± 41 mg/dl; 10.2 ± 2.1 vs.10.4 ± 2.3 mmol/liter; P = 0.009), especially overnight (2400–0400 h; 174 vs. 199 mg/dl; 9.7 vs. 11.1 mmol/liter; P &lt; 0.001). Associations of hemoglobin A1c with mean glucose were similar for eight-point testing [slope 23 mg/dl per 1% (1.3 mmol/liter); correlation 0.40; P &lt; 0.001] and CGMS [slope 19 mg/dl per 1% (1.1 mmol/liter); correlation 0.39; P &lt; 0.001]. Postprandial excursions were lower for eight-point testing vs. CGMS, especially after dinner (mean excursion −17 vs. 63 mg/dl; −1.0 vs. 3.5 mmol/liter; P &lt; 0.001). Conclusions: Both methods gave similar mean glucose profiles and associations with hemoglobin A1c. Advantages of the CGMS were higher density of data and better detection of postprandial peaks. However, the CGMS may overestimate the frequency of low glucose levels, especially overnight.

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