Evidence map›Paper›PMID 38061785›Full record

ArticleThe European respiratory journal2024

Associations of combined phenotypic ageing and genetic risk with incidence of chronic respiratory diseases in the UK Biobank: a prospective cohort study.

Ting Wang, Weiwei Duan, Xinying Jia, Xinmei Huang, Yi Liu, Fanqing Meng, Chunhui Ni

Open access · greenAbstract read
In one paragraph

Article in The European respiratory journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 1 pooled it
22.2field-weighted citation impact, top 1% 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

64 citing papers in PubMed, 1 synthesis or guideline pooled it, 86 citations in OpenAlex.

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4 more citing papers are in PubMed but not listed here.

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

7 authors at 3 institutions in 2 countries.

Ting WangDepartment of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
Weiwei DuanDepartment of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Xinying JiaDepartment of Occupational Medical and Environmental Health, Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China.
Xinmei HuangDepartment of Respiratory Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.ORCID 0000-0002-7540-7361
Yi LiuDepartment of Occupational Medical and Environmental Health, Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China.
Fanqing MengDepartment of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
Chunhui NiDepartment of Occupational Medical and Environmental Health, Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China chninjmu@126.com.
Nanjing Drum Tower Hospital · CNNanjing Medical University · CNMinistry of Education · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccelerated biological ageing has been associated with an increased risk of several chronic respiratory diseases. However, the associations between phenotypic age, a new biological age indicator based on clinical chemistry biomarkers, and common chronic respiratory diseases have not been evaluated.

methodsWe analysed data from 308 592 participants at baseline in the UK Biobank. The phenotypic age was calculated from chronological age and nine clinical chemistry biomarkers, including albumin, alkaline phosphatase, creatinine, glucose, C-reactive protein, lymphocyte percent, mean cell volume, red cell distribution width and white blood cell count. Furthermore, phenotypic age acceleration (PhenoAgeAccel) was calculated by regressing phenotypic age on chronological age. The associations of PhenoAgeAccel with incident common chronic respiratory diseases and cross-sectional lung function were investigated. Moreover, we constructed polygenic risk scores and evaluated whether PhenoAgeAccel modified the effect of genetic susceptibility on chronic respiratory diseases and lung function.

resultsThe results showed significant associations of PhenoAgeAccel with increased risk of idiopathic pulmonary fibrosis (IPF) (hazard ratio (HR) 1.52, 95% CI 1.45-1.59), COPD (HR 1.54, 95% CI 1.51-1.57) and asthma (HR 1.18, 95% CI 1.15-1.20) per 5-year increase and decreased lung function. There was an additive interaction between PhenoAgeAccel and the genetic risk for IPF and COPD. Participants with high genetic risk and who were biologically older had the highest risk of incident IPF (HR 5.24, 95% CI 3.91-7.02), COPD (HR 2.99, 95% CI 2.66-3.36) and asthma (HR 2.07, 95% CI 1.86-2.31). Mediation analysis indicated that PhenoAgeAccel could mediate 10∼20% of the associations between smoking and chronic respiratory diseases, while ∼10% of the associations between particulate matter with aerodynamic diameter <2.5 µm and the disorders were mediated by PhenoAgeAccel.

conclusionPhenoAgeAccel was significantly associated with incident risk of common chronic respiratory diseases and decreased lung function and could serve as a novel clinical biomarker.

Indexed as

AsthmaIdiopathic Pulmonary FibrosisPulmonary Disease, Chronic ObstructiveRespiration DisordersAgingBiological Specimen BanksBiomarkersCross-Sectional StudiesHumansIncidenceProspective StudiesRisk FactorsUK BiobankBiomarkers

Identifiers

PMID38061785
PMCPMC10882326
OpenAlexW4389431767

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 OpenQuestion graph.