Evidence map›Paper›PMID 31109325›Full record

ArticleJournal of translational medicine2019

Values of integration between lipidomics and clinical phenomes in patients with acute lung infection, pulmonary embolism, or acute exacerbation of chronic pulmonary diseases: a preliminary study.

Danyan Gao, Linlin Zhang, Dongli Song, Jiapei Lv, Linyan Wang, Shuang Zhou, Yanjun Li, Tao Zeng, Yiming Zeng, Jiaqiang Zhang and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

13 citing papers in PubMed, 33 citations in OpenAlex.

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  11. Advancing Semen Evaluation Using Lipidomics.Frontiers in veterinary science · 2021
    Review
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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

11 authors at 8 institutions in 1 country.

Danyan GaoDepartment of Pulmonary Diseases, The First Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Linlin ZhangDepartment of Pulmonary Diseases, The First Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Dongli SongZhongshan Hospital Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Jiapei LvDepartment of Pulmonary Diseases, The First Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Linyan WangZhongshan Hospital Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Shuang ZhouClinical Center for Molecular Diagnosis and Therapy, The Second Hospital of Fujian Medical University, Quanzhou, Fujian, China.
Yanjun LiDepartment of Anesthesiology, Center for Clinical Single Cell Biomedicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China.
Tao ZengKey Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy Science, Shanghai, China.
Yiming ZengClinical Center for Molecular Diagnosis and Therapy, The Second Hospital of Fujian Medical University, Quanzhou, Fujian, China. zeng_yi_ming@126.com.
Jiaqiang ZhangDepartment of Anesthesiology, Center for Clinical Single Cell Biomedicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China. jqzhang@henu.edu.cn.
Xiangdong WangDepartment of Pulmonary Diseases, The First Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China. xdwang@fuccb.com.
Henan Provincial People's Hospital · CNWenzhou Medical University · CNCenter for Excellence in Molecular Cell Science · CNFirst Affiliated Hospital of Wenzhou Medical University · CNFujian Medical University · CNSecond Affiliated Hospital of Fujian Medical University · CNShanghai Medical College of Fudan University · CNZhongshan Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe morbidity and mortality of patients with critical illnesses remain high in pulmonary critical care units and a poorly understood correlation between alterations of lipid elements and clinical phenomes remain unelucidated.

methodsIn the present study, we investigated plasma lipidomic profiles of 30 patients with severe acute pneumonia (SAP), acute pulmonary embolism (APE), and acute exacerbation of chronic pulmonary diseases (AECOPD) or 15 healthy with the aim to compare disease specificity of lipidomic patterns. We defined the specificity of lipidomic profiles in SAP by comparing it to both APE and AECOPD. Analysis of the correlation between altered lipid elements and clinical phenotypes using the lipid-QTL model was then carried out.

resultsWe integrated lipidomic profiles with clinical phenomes measured by score values from the digital evaluation score system and found phenome-associated lipid elements to identify disease-specific lipidomic profiling. The present study demonstrates that lipidomic profiles of patients with acute lung diseases are different from healthy lungs, and there are also disease-specific portions of lipidomics among SAP, APE, or AECOPD. The comprehensive profiles of clinical phenomes or lipidomics are valuable in describing the disease specificity of patient phenomes and lipid elements. The combination of clinical phenomes with lipidomic profiles provides more detailed disease-specific information on panels of lipid elements When compared to the use of each separately.

conclusionsIntegrating biological functions with disease specificity, we believe that clinical lipidomics may create a new alternative way to understand lipid-associated mechanisms of critical illnesses and develop a new category of disease-specific biomarkers and therapeutic targets.

Indexed as

Disease ProgressionLipidomicsPhenomicsAcute DiseaseArea Under CurveDiscriminant AnalysisHumansLeast-Squares AnalysisPhenotypePrincipal Component AnalysisPulmonary Disease, Chronic ObstructivePulmonary EmbolismRespiratory Tract InfectionsAcute pneumoniaChronic pulmonary diseaseLipidomicsPhenomesPulmonary embolism

Identifiers

PMID31109325
PMCPMC6528323
OpenAlexW2946919573

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.