Evidence map›Paper›PMID 38844923›Full record

ArticleBMC pulmonary medicine2024

Metabolomic insights into pulmonary fibrosis: a mendelian randomization study.

Wuyinuo Tang, Huanyu Jiang, Xinhui Wu, Guanyi Wu, Chenchong Zhao, Wenbo Lin, Ying Zhu, Guowei Jiang, Xiuhua Chen, Hang Huang and 1 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Wuyinuo Tang *Department of Pulmonology, Jinhua TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jinhua, Zhejiang, China.
Huanyu Jiang *School of basic medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Xinhui WuDepartment of Geriatric, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Guanyi WuDepartment of Pulmonology, Jinhua TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jinhua, Zhejiang, China.
Chenchong ZhaoDepartment of Pulmonology, Jinhua TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jinhua, Zhejiang, China.
Wenbo LinDepartment of Pulmonology, Jinhua TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jinhua, Zhejiang, China.
Ying ZhuDepartment of Pulmonology, Jinhua TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jinhua, Zhejiang, China.
Guowei JiangDepartment of Pulmonology, Jinhua TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jinhua, Zhejiang, China.
Xiuhua ChenDepartment of Pulmonology, Jinhua TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jinhua, Zhejiang, China.
Hang HuangDepartment of Pulmonology, Jinhua TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jinhua, Zhejiang, China.
Lvyuan HeDepartment of Pulmonology, Jinhua TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jinhua, Zhejiang, China. 1185403237@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study leverages a two-sample Mendelian Randomization (MR) approach to explore the causal relationships between 1,400 metabolites and pulmonary fibrosis, using genetic variation as instrumental variables. By adhering to stringent criteria for instrumental variable selection, the research aims to uncover metabolic pathways that may influence the risk and progression of pulmonary fibrosis, providing insights into potential therapeutic targets.

methodsUtilizing data from the OpenGWAS project, which includes a significant European cohort, and metabolite GWAS data from the Canadian Longitudinal Aging Study (CLSA), the study employs advanced statistical methods. These include inverse variance weighting (IVW), weighted median estimations, and comprehensive sensitivity analyses conducted using the R software environment to ensure the robustness of the causal inferences.

resultsThe study identified 62 metabolites with significant causal relationships with pulmonary fibrosis, highlighting both risk-enhancing and protective metabolic factors. This extensive list of metabolites presents a broad spectrum of potential therapeutic targets and biomarkers for early detection, underscoring the metabolic complexity underlying pulmonary fibrosis.

conclusionsThe findings from this MR study significantly advance our understanding of the metabolic underpinnings of pulmonary fibrosis, suggesting that alterations in specific metabolites could influence the risk and progression of the disease. These insights pave the way for the development of novel diagnostic and therapeutic strategies, emphasizing the potential of metabolic modulation in managing pulmonary fibrosis.

Indexed as

Mendelian Randomization AnalysisMetabolomicsPulmonary FibrosisBiomarkersCanadaDisease ProgressionFemaleGenome-Wide Association StudyHumansLongitudinal StudiesMalePolymorphism, Single NucleotideBiomarkersCausal inferenceGenetic variantsGWASMendelian randomizationMetabolitesPulmonary fibrosisStatistical analysis

Identifiers

PMID38844923
PMCPMC11155030

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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.