Evidence map›Paper›PMID 41444232›Full record

ArticleNature communications2025

Proteomic signatures of smoking and their associations with risk of incident diseases and mortality in diverse populations.

Sihao Xiao, Bowen Liu, M Austin Argentieri, Lazaros Belbasis, Claire L Shovlin, Jennifer A Collister, Siyi Wang, Eilis Hannon, Jun Liu, Kahung Chan and 13 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 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. Review
  2. Review
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

23 authors.

Sihao XiaoNuffield Department of Population Health, University of Oxford, Oxford, UK. xiao@broadinstitute.org.
Bowen LiuNuffield Department of Population Health, University of Oxford, Oxford, UK.
M Austin ArgentieriAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0003-0242-853X
Lazaros BelbasisNuffield Department of Population Health, University of Oxford, Oxford, UK.
Claire L ShovlinNational Heart and Lung Institute, Imperial College London, London, UK.ORCID http://orcid.org/0000-0001-9007-5775
Jennifer A CollisterNuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-8010-2503
Siyi WangDepartment of Clinical & Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK.
Eilis HannonDepartment of Clinical & Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK.ORCID http://orcid.org/0000-0001-6840-072X
Jun LiuNuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0001-5288-3042
Kahung ChanNuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-3700-502X
Rami Muath MosaoaCentre of Artificial Intelligence in Precision Medicine (CAIPM), King Abdulaziz University, Jeddah, Saudi Arabia.
Liming LiDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China.
Jun LvDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China.ORCID http://orcid.org/0000-0001-7916-3870
Canqin YuDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China.
Dianjianyi SunDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China.ORCID http://orcid.org/0000-0003-3651-6693
Jonathan MillDepartment of Clinical & Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK.ORCID http://orcid.org/0000-0003-1115-3224
Robert ClarkeNuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-9802-8241
David J HunterNuffield Department of Population Health, University of Oxford, Oxford, UK.
Derrick BennettNuffield Department of Population Health, University of Oxford, Oxford, UK.
Alejo J Nevado-HolgadoCentre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Zhengming ChenNuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0001-6423-105X
Najaf AminNuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-8944-1771
Cornelia M van DuijnNuffield Department of Population Health, University of Oxford, Oxford, UK. cornelia.vanduijn@ndph.ox.ac.uk.ORCID http://orcid.org/0000-0002-2374-9204

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Smoking is the most important behavioural determinant of morbidity and mortality. Using machine learning on plasma levels of 2,917 proteins in the UK Biobank (n = 43,914), we develop a proteomic Smoking Index (pSIN) comprising 51 proteins that accurately distinguish current from never smokers (AUC = 0.95; 95% CI 0.94-0.95). Validation in the China Kadoorie Biobank (n = 3,977) shows similar accuracy (AUC = 0.91; 95% CI 0.89-0.92). pSIN is significantly associated with the risk of all-cause mortality and 18 major chronic diseases, including cardiovascular, renal, pulmonary, neurodegenerative, and cancer outcomes. Among current and former smokers, pSIN predicts death and 11 diseases independently of self-reported smoking history and lifestyle factors. Genome-wide analysis identifies 125 genes (e.g., ALPP, CST5, IL12B) associated with pSIN, while exposome analysis highlights maternal smoking, diet, physical activity, and air pollution as key modifiers. Notably, pSIN tracks recovery among former smokers and identifies those whose disease risks remain comparable to current smokers. These findings demonstrate that plasma proteomics effectively capture the biological imprint of smoking and predict smoking-related morbidity and mortality, offering a more nuanced, molecularly grounded assessment of individual variation in biological response to smoking.

Indexed as

ProteomicsSmokingAdultAgedBiomarkersChinaFemaleGenome-Wide Association StudyHumansMachine LearningMaleMiddle AgedRisk FactorsUnited KingdomBiomarkers

Identifiers

PMID41444232
PMCPMC12830939

What OpenQuestion holds

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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.