Evidence map›Paper›PMID 33302528›Full record

ArticleMetabolites2020

Prediction of Autoimmune Diseases by Targeted Metabolomic Assay of Urinary Organic Acids.

Dimitris Tsoukalas, Vassileios Fragoulakis, Evangelos Papakonstantinou, Maria Antonaki, Athanassios Vozikis, Aristidis Tsatsakis, Ana Maria Buga, Mihaela Mitroi, Daniela Calina

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

28 citing papers in PubMed, 43 citations in OpenAlex.

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  16. The oral microbiome in autoimmune diseases: friend or foe?Journal of translational medicine · 2023
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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

9 authors at 4 institutions in 5 countries.

Dimitris TsoukalasDepartment of Clinical Pharmacy, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.ORCID 0000-0001-6885-6209
Vassileios FragoulakisThe Golden Helix Foundation, London WC2N 5AP, UK.
Evangelos PapakonstantinouMetabolomic Medicine, Health Clinic for Autoimmune and Chronic Diseases, 10674 Athens, Greece.
Maria AntonakiLaboratory of Health Economics & Management, Economics Department, University of Piraeus, 18534 Piraeus, Greece.
Athanassios VozikisLaboratory of Health Economics & Management, Economics Department, University of Piraeus, 18534 Piraeus, Greece.ORCID 0000-0002-8342-3839
Aristidis TsatsakisLaboratory of Toxicology and Forensic Sciences, Medical School, University of Crete, 71003 Heraklion, Greece.ORCID 0000-0003-3824-2462
Ana Maria BugaDepartment of Biochemistry, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.ORCID 0000-0003-0082-6956
Mihaela MitroiENT Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.
Daniela CalinaDepartment of Clinical Pharmacy, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.ORCID 0000-0002-1523-9116
University of Medicine and Pharmacy of Craiova · ROUniversity of Piraeus · GRGolden Helix Foundation · GBUniversity of Crete · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autoimmune diseases (ADs) are chronic disorders characterized by the loss of self-tolerance, and although being heterogeneous, they share common pathogenic mechanisms. Self-antigens and inflammation markers are established diagnostic tools; however, the metabolic imbalances that underlie ADs are poorly described. The study aimed to employ metabolomics for the detection of disease-related changes in autoimmune diseases that could have predictive value. Quantitative analysis of 28 urine organic acids was performed using Gas Chromatography-Mass Spectrometry in a group of 392 participants. Autoimmune thyroiditis, inflammatory bowel disease, psoriasis and rheumatoid arthritis were the most prevalent autoimmune diseases of the study. Statistically significant differences were observed in the tricarboxylate cycle metabolites, succinate, methylcitrate and malate, the pyroglutamate and 2-hydroxybutyrate from the glutathione cycle and the metabolites methylmalonate, 4-hydroxyphenylpyruvate, 2-hydroxyglutarate and 2-hydroxyisobutyrate between the AD group and the control. Artificial neural networks and Binary logistic regression resulted in the highest predictive accuracy scores (66.7% and 74.9%, respectively), while Methylmalonate, 2-Hydroxyglutarate and 2-hydroxybutyrate were proposed as potential biomarkers for autoimmune diseases. Urine organic acid levels related to the mechanisms of energy production and detoxification were associated with the presence of autoimmune diseases and could be an adjunct tool for early diagnosis and prediction.

Indexed as

artificial intelligenceautoimmune diseasesdisease predictionglutathione cyclemetabolomicsorganic acidstricarboxylate cycle

Identifiers

PMID33302528
PMCPMC7764183
OpenAlexW3112548223

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.