scholarly journals Research Trends in Diabetes Applying VOS Viewer: A Scientometric Profile

2021 ◽  
Vol 9 (2) ◽  
pp. 93-103
Author(s):  
C Catherin Beula ◽  
Chandran Velmurugan
Keyword(s):  
Type 1 Diabetes ◽  
World Literature ◽  
Research Output ◽  
Communicable Disease ◽  
Research Trends ◽  
Scientometric Analysis ◽  
Diabetes Research ◽  
The Core ◽  
Number Of Authors

As diabetes is a non-communicable disease, many scientists try to cure it in different ways throughout the globe. Due to the remarkable scientific growth in this area, we focus on evaluating the scholarly publications and their current research trends in diabetes, particularly type 1 diabetes published by Indian scientists using statistical tools and scientometric analysis. A total of 83 318 global research productivity and a total number of research publications from India was 2 381 with 36, 408 global citations and the Period between 2009 and 2018. Further, the total number of authors (17712) and its average number of authors is 7.44, a total number of 782 core journals and 95 228 cited references found during the research period. To evaluate data, various scientometric techniques or indicators were used such as Authorship Pattern (Single vs Multiple), Degree of Collaboration, Relative growth rate (RGR) and Doubling Time (DT) and many more indicators used. This study limits with Indian research output did not include world literature. We try to identify the information in different types of type 1 diabetes between 2009 and 2018. This study will help to find out the core journals for collection management and for promoting diabetes research and developments in future. We chose the core keywords in type 1 diabetes with full records with abstracts, types of manuscripts, cited references using the Web of Science database.

Genetic Risk for Type 1 Diabetes Profoundly Influences the Core Gut Microbiome in Children

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 209-LB ◽  
Author(s):  
JORDAN RUSSELL ◽  
LUIZ ROESCH ◽  
MARK A. ATKINSON ◽  
DESMOND SCHATZ ◽  
ERIC W. TRIPLETT ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Gut Microbiome ◽  
Genetic Risk ◽  
The Core

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.

Type 1 diabetes structured education: what are the core self-management behaviours?

2013 ◽  
Vol 30 (6) ◽  
pp. 724-730 ◽  
Author(s):  
L. Grant ◽  
J. Lawton ◽  
D. Hopkins ◽  
J. Elliott ◽  
S. Lucas ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Self Management ◽  
Structured Education ◽  
The Core ◽  
Core Self

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.

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