Evidence map›Paper›PMID 42554201›Full record

ArticleAllergy2026

Plasma Metabolomes Identify Distinct Asthma Metabotypes: Findings From the COREA Cohort.

Woori Chae, Jin An, Chae Eun Lee, Seo-Young Kim, Eunse Kim, Hyouk-Soo Kwon, Woo-Jung Song, You Sook Cho, Joo-Youn Cho, Tae-Bum Kim

Abstract read
In one paragraph

Article in Allergy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Woori ChaeDepartment of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-7968-1504
Jin AnDepartment of Pulmonary, Allergy and Critical Care Medicine, College of Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-5416-2660
Chae Eun LeeDepartment of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-6164-8140
Seo-Young KimDepartment of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0009-0008-9834-6531
Eunse KimDepartment of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-2915-1497
Hyouk-Soo KwonDepartment of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-7695-997X
Woo-Jung SongDepartment of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-4630-9922
You Sook ChoDepartment of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-8767-2667
Joo-Youn ChoDepartment of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-9270-8273
Tae-Bum KimDepartment of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-5663-0640

Funding

Korea Health Industry Development Institute RS-2024-00403700
6 · The paper itself

Abstract

backgroundPhenotype-based asthma classification has limitations that have motivated endotype-based approaches grounded in pathophysiology. However, existing biomarkers offer limited specificity and clinical accessibility. Plasma metabolomics, which reflects disease-specific metabolic states, offers a promising strategy for asthma classification. This study aimed to identify metabolic subgroups (metabotypes) of adult asthma using plasma metabolomics, with potential implications for personalized treatment.

methodsPlasma samples from 407 patients with asthma in the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA) were analyzed using Biocrates AbsoluteIDQ p400 HR kit with liquid chromatography-mass spectrometry. After preprocessing, 281 metabolites were natural-log-transformed, standardized, and partitioned by k-means clustering. The number of clusters was determined by a multi-criteria assessment combining cluster validity indices, consensus clustering, stability analysis, and cross-algorithm agreement.

resultsFour metabotypes with distinct lipid-class signatures were identified. Group 1 (n = 123) was characterized by elevated ether-linked phosphatidylcholines, comprising younger patients with the earliest symptom onset and female predominance. Group 2 (n = 53) showed elevated lyso-phosphatidylcholines and altered amino acid metabolism (elevated glutamate, reduced glutamine), representing a metabolically intermediate, non-T2-high subgroup. Group 3 (n = 158) exhibited globally reduced sphingomyelins and broadly lower phosphatidylcholines in middle-aged, non-obese patients. Group 4 (n = 73) showed markedly elevated triacylglycerols (TG) and diacylglycerols (DG) with the highest body mass index (BMI), consistent with a non-T2, obesity-related metabotype. The TG/DG signature of Group 4 remained robust after adjustment for BMI, age, and sex.

conclusionPlasma metabolomics identifies clinically meaningful asthma metabotypes, supporting integration of metabolomic profiling into personalized asthma management.

Indexed as

AsthmaMetabolomeMetabolomicsAdultBiomarkersCluster AnalysisClustering AlgorithmsCohort StudiesFemaleHumansMaleMiddle AgedPhenotypeBiomarkersadult asthmaclusteringCOREAmetabotypeplasma metabolomics

Identifiers

PMID42554201
PMCPMC13569652

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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.