Evidence map›Paper›PMID 30144424›Full record

ReviewTranslational research : the journal of laboratory and clinical medicine2018

Serum biomarkers for diagnosis and prediction of type 1 diabetes.

Lian Yi, Adam C Swensen, Wei-Jun Qian

Open access · greenAbstract readReview
In one paragraph

Review in Translational research : the journal of laboratory and clinical medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 3 pooled it
4.2field-weighted citation impact, top 6% 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

31 citing papers in PubMed, 3 syntheses or guidelines pooled it, 70 citations in OpenAlex.

  1. Pooled it
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  5. Review
  6. Review
  7. Narra J · 2025
    Article
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  15. ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Article
  16. Article
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  18. Review
  19. Posttranslational modifications in diabetes: Mechanisms and functions.Reviews in endocrine & metabolic disorders · 2022
    Review
  20. Review
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

3 authors at 1 institution in 1 country.

Lian YiBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington.
Adam C SwensenBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington.
Wei-Jun QianBiological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington. Electronic address: weijun.qian@pnnl.gov.
Pacific Northwest National Laboratory · US

Funding

Regulatory Networks and Biomarkers of Beta-cell Dysfunction and ApoptosisUC4DK104167 · NIDDK · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI KULKARNI, ROHIT N., MATHEWS, CLAYTON E · 2014 to 2016
$2.7M
Serum protein biomarkers for predicting type 1 diabetesDP3DK110844 · NIDDK · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI QIAN, WEI-JUN · 2016 to 2016
$1.8M
NIDDK NIH HHS DP3 DK110844NIDDK NIH HHS UC4 DK104167
6 · The paper itself

Abstract

Type 1 diabetes (T1D) culminates in the autoimmune destruction of the pancreatic βcells, leading to insufficient production of insulin and development of hyperglycemia. Serum biomarkers including a combination of glucose, glycated molecules, C-peptide, and autoantibodies have been well established for the diagnosis of T1D. However, these molecules often mark a late stage of the disease when ∼90% of the pancreatic insulin-producing β-cells have already been lost. With the prevalence of T1D increasing worldwide and because of the physical and psychological burden induced by this disease, there is a great need for prognostic biomarkers to predict T1D development or progression. This would allow us to identify individuals at high risk for early prevention and intervention. Therefore, considerable efforts have been dedicated to the understanding of disease etiology and the discovery of novel biomarkers in the last few decades. The advent of high-throughput and sensitive "-omics" technologies for the study of proteins, nucleic acids, and metabolites have allowed large scale profiling of protein expression and gene changes in T1D patients relative to disease-free controls. In this review, we briefly discuss the classical diagnostic biomarkers of T1D but mainly focus on the novel biomarkers that are identified as markers of β-cell destruction and screened with the use of state-of-the-art "-omics" technologies.

Indexed as

AutoantibodiesBiomarkersBlood ProteinsC-PeptideCytokinesDiabetes Mellitus, Type 1HumansMetabolomicsMicroRNAsAutoantibodiesBiomarkersBlood ProteinsC-PeptideCytokinesMicroRNAs

Identifiers

PMID30144424
PMCPMC6177288
OpenAlexW2885711808

What OpenQuestion holds

Textmetadata
LicenceTDM
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.