Evidence map›Paper›PMID 38873983›Full record

ArticleMolecular medicine reports2024

Serum metabolomics analysis of patients with chronic obstructive pulmonary disease and 'frequent exacerbator' phenotype.

Huan-Zhang Ding, Hui Wang, Di Wu, Fan-Chao Zhou, Jie Zhu, Jia-Bing Tong, Ya-Ting Gao, Ze-Geng Li

Abstract read
In one paragraph

Article in Molecular medicine reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Exploring the Roles of Wnt Signaling Pathway and Non-Coding RNAs in COPD.International journal of chronic obstructive pulmonary disease · 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

8 authors.

Huan-Zhang Ding *College of Traditional Chinese Medicine, Anhui University of Chinese Medicine, Hefei, Anhui 230012, P.R. China.
Hui Wang *College of Traditional Chinese Medicine, Anhui University of Chinese Medicine, Hefei, Anhui 230012, P.R. China.
Di WuCollege of Traditional Chinese Medicine, Anhui University of Chinese Medicine, Hefei, Anhui 230012, P.R. China.
Fan-Chao ZhouCollege of Traditional Chinese Medicine, Anhui University of Chinese Medicine, Hefei, Anhui 230012, P.R. China.
Jie ZhuCollege of Traditional Chinese Medicine, Anhui University of Chinese Medicine, Hefei, Anhui 230012, P.R. China.
Jia-Bing TongCollege of Traditional Chinese Medicine, Anhui University of Chinese Medicine, Hefei, Anhui 230012, P.R. China.
Ya-Ting GaoDepartment of Respiratory Medicine, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui 230012, P.R. China.
Ze-Geng LiAnhui Provincial Key Laboratory for the Application and Transformation of Traditional Chinese Medicine in the Prevention and Treatment of Major Respiratory Diseases, Hefei, Anhui 230012, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic obstructive pulmonary disease (COPD) exacerbations accelerate loss of lung function and increased mortality. The complex nature of COPD presents challenges in accurately predicting and understanding frequent exacerbations. The present study aimed to assess the metabolic characteristics of the frequent exacerbation of COPD (COPD‑FE) phenotype, identify potential metabolic biomarkers associated with COPD‑FE risk and evaluate the underlying pathogenic mechanisms. An internal cohort of 30 stable patients with COPD was recruited. A widely targeted metabolomics approach was used to detect and compare serum metabolite expression profiles between patients with COPD‑FE and patients with non‑frequent exacerbation of COPD (COPD‑NE). Bioinformatics analysis was used for pathway enrichment analysis of the identified metabolites. Spearman's correlation analysis assessed the associations between metabolites and clinical indicators, while receiver operating characteristic (ROC) analysis evaluated the ability of metabolites to distinguish between two groups. An external cohort of 20 patients with COPD validated findings from the internal cohort. Out of the 484 detected metabolites, 25 exhibited significant differences between COPD‑FE and COPD‑NE. Metabolomic analysis revealed differences in lipid, energy, amino acid and immunity pathways. Spearman's correlation analysis demonstrated associations between metabolites and clinical indicators of acute exacerbation risk. ROC analysis demonstrated that the area under the curve (AUC) values for D‑fructose 1,6‑bisphosphate (AUC=0.871), arginine (AUC=0.836), L‑2‑hydroxyglutarate (L‑2HG; AUC=0.849), diacylglycerol (DG) (16:0/20:5) (AUC=0.827), DG (16:0/20:4) (AUC=0.818) and carnitine‑C18:2 (AUC=0.804) were >0.8, highlighting their discriminative capacity between the two groups. External validation results demonstrated that DG (16:0/20:5), DG (16:0/20:4), carnitine‑C18:2 and L‑2HG were significantly different between patients with COPD‑FE and those with COPD‑NE. In conclusion, the present study offers insights into early identification, mechanistic understanding and personalized management of the COPD‑FE phenotype.

Indexed as

BiomarkersMetabolomicsPhenotypePulmonary Disease, Chronic ObstructiveAgedCarnitineDisease ProgressionFemaleHumansMaleMetabolomeMiddle AgedROC CurveBiomarkersCarnitinebiomarkersCOPD‘frequent exacerbator’ phenotypemetabolitesmetabolomics

Identifiers

PMID38873983
PMCPMC11200052

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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.