Evidence map›Paper›PMID 40691512›Full record

ArticleScientific reports2025

Lysophospholipid metabolism, clinical characteristics, and artificial intelligence-based quantitative assessments of chest CT in patients with stable COPD and healthy smokers.

Qiqiang Zhou, Lvxinhe Xing, Mi Ma, Bianba Qiongda, Deyuan Li, Ping Wang, Yahong Chen, Ying Liang, Meilang ChuTso, Yongchang Sun

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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 Lipidomic Signatures Across the Healthy-Pre-COPD-COPD Continuum Identified by Machine Learning.International journal of chronic obstructive pulmonary disease · 2026
    Observational
  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

10 authors.

Qiqiang ZhouDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, People's Republic of China.
Lvxinhe XingDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, People's Republic of China.
Mi MaDepartment of Respiratory and Critical Care Medicine, Tibet Autonomous Region People's Hospital, Lhasa, 850000, People's Republic of China.
Bianba QiongdaDepartment of Respiratory and Critical Care Medicine, Tibet Autonomous Region People's Hospital, Lhasa, 850000, People's Republic of China.
Deyuan LiYizhiyuan Health Technology (Hangzhou) Co., Ltd.,, Hangzhou, 311121, People's Republic of China.
Ping WangYizhiyuan Health Technology (Hangzhou) Co., Ltd.,, Hangzhou, 311121, People's Republic of China.
Yahong ChenDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, People's Republic of China.
Ying LiangDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, People's Republic of China. bysyliangying@126.com.
Meilang ChuTsoDepartment of Respiratory and Critical Care Medicine, Tibet Autonomous Region People's Hospital, Lhasa, 850000, People's Republic of China.
Yongchang SunDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, People's Republic of China.

Funding

Innovation& Transfer Fund of Peking University Third Hospital Y82499-07National Natural Science Foundation of China 82090014Natural Science Foundation of the Tibet Autonomous Region XZ2021ZR-ZY19(Z)
6 · The paper itself

Abstract

The specific role of lysophospholipids (LysoPLs) in the pathogenesis of chronic obstructive pulmonary disease (COPD) is not yet fully understood. We determined serum LysoPLs in 20 patients with stable COPD and 20 healthy smokers using liquid chromatography-mass spectrometry (LC-MS) and matching with the lipidIMMS library, and integrated these data with spirometry, systemic inflammation markers, and quantitative chest CT generated by an automated 3D-U-Net artificial intelligence algorithm model. Our findings identified three differential LysoPLs, lysophosphatidylcholine (LPC) (18:0), LPC (18:1), and LPC (18:2), which were significantly lower in the COPD group than in healthy smokers. Significant negative correlations were observed between these LPCs and the inflammatory markers C-reactive protein and Interleukin-6. LPC (18:0) and (18:2) correlated with higher post-bronchodilator FEV1, and the latter also correlated with FEV1% predicted, forced vital capacity (FVC), and FEV1/FVC ratio. Additionally, these three LPCs were negatively correlated with the volume and percentage of low attenuation areas (LAA), high-attenuation areas (HAA), honeycombing, reticular patterns, ground-glass opacities (GGO), and consolidation on CT imaging. In the patients with COPD, the three LPCs were most significantly associated with HAA and GGO. In conclusion, patients with stable COPD exhibited a unique LysoPL metabolism profile, with LPC (18:0), LPC (18:1), and LPC (18:2) being the most significantly altered lipid molecules. The reduction in these three LPCs was associated with impaired pulmonary function and were also linked to a greater extent of emphysema and interstitial lung abnormalities.

Indexed as

Artificial IntelligenceLysophospholipidsPulmonary Disease, Chronic ObstructiveSmokingTomography, X-Ray ComputedAgedBiomarkersCase-Control StudiesFemaleHumansLysophosphatidylcholinesMaleMiddle AgedSmokersBiomarkersLysophosphatidylcholinesLysophospholipidsChest CTChronic obstructive pulmonary diseaseLipidomicsLysophospholipidsSystemic inflammation

Identifiers

PMID40691512
PMCPMC12279966

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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.