Evidence map›Paper›PMID 40140895›Full record

ArticleBMC medicine2025

Association of circulating metabolic biomarkers with risk of lung cancer: a population-based prospective cohort study.

Lan Wu, Jun Yang, Yu Chen, Jiahao Lin, Wenkai Huang, Mengmeng Li

Abstract read
In one paragraph

Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Associations between Metabolomic Signatures of Persistent Smoking Effects and Lung Cancer Risk among Former Smokers.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 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

6 authors.

Lan WuDepartment of Cancer Prevention, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Jun YangSchool of Public Health, Guangzhou Medical University, Guangzhou, China.
Yu ChenDepartment of Cancer Prevention, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Jiahao LinDepartment of Cancer Prevention, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Wenkai HuangNational Central Cancer Registry Office, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Mengmeng LiDepartment of Cancer Prevention, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, China. limm@sysucc.org.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is emerging evidence that metabolites might be associated with risk of lung cancer, but their relationships have not been fully characterized. We aimed to investigate the association between circulating metabolic biomarkers and lung cancer risk and the potential underlying pathways.

methodsNuclear magnetic resonance metabolomic profiling was conducted on baseline plasma samples from 91,472 UK Biobank participants without cancer and pregnancy. Multivariate Cox regression models were employed to assess the hazard ratios (HRs) of 164 metabolic biomarkers (including metabolites and lipoprotein subfractions) and 9 metabolic biomarker principal components (PCs) for lung cancer, after adjusting for covariates and false discovery rate (FDR). Pathway analysis was conducted to investigate the potential metabolic pathways.

resultsDuring a median follow-up of 11.0 years, 702 participants developed lung cancer. A total of 109 metabolic biomarkers (30 metabolites and 79 lipoprotein subfractions) were associated with the risk of lung cancer. Glycoprotein acetyls demonstrated a positive association with lung cancer risk [HR = 1.13 (95%CI: 1.04, 1.22)]. Negative associations with lung cancer were found for albumin [0.78 (95%CI: 0.72, 0.83)], acetate [0.91 (95%CI: 0.85, 0.97)], valine [0.90 (95%CI: 0.83, 0.98)], alanine [0.88 (95%CI: 0.82, 0.95)], glucose [0.91 (95%CI: 0.85, 0.99)], citrate [0.91 (95%CI: 0.85, 0.99)], omega-3 fatty acids [0.83 (95%CI: 0.77, 0.90)], linoleic acid [0.83 (95%CI: 0.77, 0.89)], etc. Nine PCs represented over 90% of the total variances, and among those with statistically significant estimates, PC1 [0.85 (95%CI: 0.80, 0.92)], PC2 [0.88 (95%CI: 0.82, 0.95)], and PC9 [0.87 (95%CI: 0.80, 0.93)] were negatively associated with lung cancer risk, whereas PC7 [1.08 (95%CI: 1.00, 1.16)] and PC8 [1.16 (95%CI: 1.08, 1.26)] showed positive associations with lung cancer risk. The pathway analysis showed that the "linoleic acid metabolism" was statistically significant after the FDR adjustment (p value 0.0496).

conclusionsGlycoprotein acetyls had a positive association with lung cancer risk while other metabolites and lipoprotein subfractions showed negative associations. Certain metabolites and lipoprotein subfractions might be independent risk factors for lung cancer. Our findings shed new light on the etiology of lung cancer and might aid the selection of high-risk individuals for lung cancer screening.

Indexed as

Biomarkers, TumorLung NeoplasmsAdultAgedBiomarkersFemaleHumansMaleMetabolomicsMiddle AgedProspective StudiesRisk FactorsUnited KingdomBiomarkersBiomarkers, TumorLipoprotein subfractionsLung cancerMetabolic biomarkersMetabolites

Identifiers

PMID40140895
PMCPMC11948749

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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 Socratic graph.