Evidence map›Paper›PMID 37590326›Full record

ArticlePLoS computational biology2023

BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus.

Agnete T Lundgaard, Frédéric Burdet, Troels Siggaard, David Westergaard, Danai Vagiaki, Lisa Cantwell, Timo Röder, Dorte Vistisen, Thomas Sparsø, Giuseppe N Giordano and 3 more

Abstract read
In one paragraph

Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Plasma Proteomic Profiles Predict Subsequent Obstructive Sleep Apnea Diagnosis in Adults With Metabolic Dysfunction-Associated Steatotic Liver Disease.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
    Article
  2. 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

13 authors.

Agnete T LundgaardNovo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark.ORCID 0000-0001-7447-6560
Frédéric BurdetVital-IT, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
Troels SiggaardNovo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark.
David WestergaardNovo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark.
Danai VagiakiNovo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark.ORCID 0000-0002-1674-5141
Lisa CantwellNovo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark.
Timo RöderNovo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark.
Dorte VistisenClinical Epidemiological Research, Steno Diabetes Center Copenhagen, Herlev, Denmark.ORCID 0000-0001-5045-5351
Thomas SparsøBioinformatics and Data Mining, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark.
Giuseppe N GiordanoGenetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Sweden.
Mark IbbersonVital-IT, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
Karina BanasikNovo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark.ORCID 0000-0003-2489-2499
Søren BrunakNovo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark.ORCID 0000-0003-0316-5866

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website https://baldr.cpr.ku.dk.

Indexed as

Diabetes Mellitus, Type 2AnimalsBiomarkersData MiningHumansInternetMicePandemicsBiomarkers

Identifiers

PMID37590326
PMCPMC10464978

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.