Evidence map›Paper›PMID 19079665›Full record

ArticleInternational journal of clinical and experimental medicine2008

Biomarkers for type 1 diabetes.

Sharad Purohit, Jin-Xiong She

Open access · diamondAbstract read
In one paragraph

Article in International journal of clinical and experimental medicine, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.3field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 45 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Immune cell trafficking to the islets during type 1 diabetes.Clinical and experimental immunology · 2019
    Review
  5. Serum biomarkers for diagnosis and prediction of type 1 diabetes.Translational research : the journal of laboratory and clinical medicine · 2018
    Review
  6. Proteomic analysis to identify candidate biomarkers associated with type 1 diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2018
    Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Diagnostic accuracy of interferon gamma-induced protein 10 for tuberculosis: a meta-analysis.International journal of clinical and experimental medicine · 2014
    Article
  12. Review
  13. Article
  14. Article
  15. Novel biomarkers in type 1 diabetes.The review of diabetic studies : RDS · 2012
    Review
  16. Article
  17. Article
  18. Article
  19. Proteomic technologies for the discovery of type 1 diabetes biomarkers.Journal of diabetes science and technology · 2010
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sharad Purohit
Jin-Xiong She

Funding

Project 3P01AI042288 · NIAID · UNIVERSITY OF FLORIDA · PI Todd Michael Brusko · 1997 to 2026
$32.9M
Studies of the Immunopathogenesis of Type 1 DiabetesR01HD037800 · NICHD · UNIVERSITY OF FLORIDA · PI SHE, JIN-XIONG · 1999 to 2006
$4.6M
NIDDK BIOTECHNOLOGY CENTER AT THE UNIVERSITY OF FLORIDAU24DK058778 · NIDDK · UNIVERSITY OF FLORIDA · PI SHE, JIN-XIONG · 2000 to 2002
$1.7M
Proteomic changes/progression of human type 1 diabetesR33HD050196 · NICHD · AUGUSTA UNIVERSITY · PI SHE, JIN-XIONG · 2006 to 2008
$1.5M
Development of microarray-based biomarkers for type 1 diR33DK069878 · NIDDK · AUGUSTA UNIVERSITY · PI SHE, JIN-XIONG · 2006 to 2008
$1.1M
NIAID NIH HHS P01 AI042288NICHD NIH HHS R01 HD037800NICHD NIH HHS R33 HD050196NIDDK NIH HHS R33 DK069878NIDDK NIH HHS U24 DK058778
6 · The paper itself

Abstract

Type 1 diabetes (T1D) is an autoimmune disorder characterized by the immune destruction of the insulin producing beta cells of the pancreatic islets. Autoimmunity towards pancreatic antigens results from complex interactions between multiple genes, environmental factors and the immune system. The autoimmune process may occur many years before the onset of clinical diabetes and this long asymptomatic period provides excellent opportunities for the prediction and prevention of the disease. Research in past four decades has identified a number of risk factors including susceptibility genes, gene and protein expression changes, cellular changes as well as environmental triggers, which may serve as excellent biomarkers for risk assessment. Furthermore, demographic and clinical parameters such as age and family history of diabetes and other autoimmune diseases are also important for risk assessment. Despite the identification of multiple useful biomarkers, the existing tests for T1D prediction are still imperfect and earlier biomarkers are also urgently needed. Because of the insufficient predictive power of individual risk factors, future biomarkers with better predictive power will most likely take advantage of the combinatorial power of multiple biomarkers of different nature and the integration of various biomarkers and demographic/clinical information will be the key to success.

Indexed as

bioinformaticsbiomarkersgenomicspredictionproteomicsType-1 diabetes

Identifiers

PMID19079665
PMCPMC2596319
OpenAlexW1823860522

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.