Evidence map›Paper›PMID 38558806›Full record

ArticleFrontiers in immunology2024

Integrated plasma metabolomic and cytokine analysis reveals a distinct immunometabolic signature in atopic dermatitis.

Emily Z Ma, Junwen Deng, Varsha Parthasarathy, Kevin K Lee, Thomas Pritchard, Shenghao Guo, Cissy Zhang, Madan M Kwatra, Anne Le, Shawn G Kwatra

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
5.9field-weighted citation impact, top 4% 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

12 citing papers in PubMed, 11 citations in OpenAlex.

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  2. Biomedicines · 2026
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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

10 authors at 4 institutions in 1 country.

Emily Z MaDepartment of Dermatology, University of Maryland School of Medicine, Baltimore, MD, United States.
Junwen DengDepartment of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Varsha ParthasarathyDepartment of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Kevin K LeeDepartment of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Thomas PritchardDepartment of Dermatology, University of Maryland School of Medicine, Baltimore, MD, United States.
Shenghao GuoPathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Cissy ZhangPathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Madan M KwatraPharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, United States.
Anne LePathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Shawn G KwatraDepartment of Dermatology, University of Maryland School of Medicine, Baltimore, MD, United States.
Johns Hopkins Medicine · USJohns Hopkins University · USUniversity of Maryland, Baltimore · USDuke University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Disease models for atopic dermatitis (AD) have primarily focused on understanding underlying environmental, immunologic, and genetic etiologies. However, the role of metabolic mechanisms in AD remains understudied. Objective: To investigate the circulating blood metabolomic and cytokine profile of AD as compared to healthy control patients. Design: This study collected plasma from 20 atopic dermatitis with moderate-to-severe itch (score of ≥5 on the itch Numeric Rating Scale and IGA score ≥3) and 24 healthy control patients. Mass-spectrometry based metabolite data were compared between AD and healthy controls. Unsupervised and supervised machine learning algorithms and univariate analysis analyzed metabolic concentrations. Metabolite enrichment and pathway analyses were performed on metabolites with significant fold change between AD and healthy control patients. To investigate the correlation between metabolites levels and cytokines, Spearman's rank correlation coefficients were calculated between metabolites and cytokines. Setting: Patients were recruited from the Johns Hopkins Itch Center and dermatology outpatient clinics in the Johns Hopkins Outpatient Center. Participants: The study included 20 atopic dermatitis patients and 24 healthy control patients. Main outcomes and measures: Fold changes of metabolites in AD vs healthy control plasma. Results: In patients with AD, amino acids isoleucine, tyrosine, threonine, tryptophan, valine, methionine, and phenylalanine, the amino acid derivatives creatinine, indole-3-acrylic acid, acetyl-L-carnitine, L-carnitine, 2-hydroxycinnamic acid, N-acetylaspartic acid, and the fatty amide oleamide had greater than 2-fold decrease (all P-values<0.0001) compared to healthy controls. Enriched metabolites were involved in branched-chain amino acid (valine, leucine, and isoleucine) degradation, catecholamine biosynthesis, thyroid hormone synthesis, threonine metabolism, and branched and long-chain fatty acid metabolism. Dysregulated metabolites in AD were positively correlated cytokines TARC and MCP-4 and negatively correlated with IL-1a and CCL20. Conclusions and relevance: Our study characterized novel dysregulated circulating plasma metabolites and metabolic pathways that may be involved in the pathogenesis of AD. These metabolic pathways serve as potential future biomarkers and therapeutic targets in the treatment of AD.

Indexed as

Dermatitis, AtopicCytokinesHumansIsoleucinePruritusThreonineValineCytokinesIsoleucineThreonineValineatopic dermatitiscytokinesimmunologyinflammationitchmetabolomics

Identifiers

PMID38558806
PMCPMC10978712
OpenAlexW4392859667

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.