Evidence map›Paper›PMID 37066248›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Prediction and stratification of longitudinal risk for chronic obstructive pulmonary disease across smoking behaviors.

Yixuan He, David C Qian, James A Diao, Michael H Cho, Edwin K Silverman, Alexander Gusev, Arjun K Manrai, Alicia R Martin, Chirag J Patel

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 1 country.

Yixuan HeAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
David C QianDepartment of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, USA.
James A DiaoDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02215.
Michael H ChoChanning Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
Edwin K SilvermanDepartment of Medicine, Harvard Medical School, Boston, MA, USA.
Alexander GusevDepartment of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA.
Arjun K ManraiDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02215.
Alicia R MartinAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02215.
Harvard University · USBrigham and Women's Hospital · USBroad Institute · USDana-Farber Cancer Institute · USEmory University · US

Funding

Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Data science tools to identify robust exposure-phenotype associations for precision medicineR01ES032470 · NIEHS · HARVARD MEDICAL SCHOOL · PI MANRAI, ARJUN KUMAR, PATEL, CHIRAG J. · 2021 to 2025
$3.5M
NHGRI NIH HHS T32 HG002295NIEHS NIH HHS R01 ES032470
6 · The paper itself

Abstract

Smoking is the leading risk factor for chronic obstructive pulmonary disease (COPD) worldwide, yet many people who never smoke develop COPD. We hypothesize that considering other socioeconomic and environmental factors can better predict and stratify the risk of COPD in both non-smokers and smokers. We performed longitudinal analysis of COPD in the UK Biobank to develop the Socioeconomic and Environmental Risk Score (SERS) which captures additive and cumulative environmental, behavioral, and socioeconomic exposure risks beyond tobacco smoking. We tested the ability of SERS to predict and stratify the risk of COPD in current, previous, and never smokers of European and non-European ancestries in comparison to a composite genome-wide polygenic risk score (PGS). We tested associations using Cox regression models and assessed the predictive performance of models using Harrell's C index. SERS (C index = 0.770, 95% CI 0.756 to 0.784) was more predictive of COPD than smoking status (C index = 0.738, 95% CI 0.724 to 0.752), pack-years (C index = 0.742, 95% CI 0.727 to 0.756). Compared to the remaining population, individuals in the highest decile of the SERS had hazard ratios (HR) = 7.24 (95% CI 6.51 to 8.05, P < 0.0001) for incident COPD. Never smokers in the highest decile of exposure risk were more likely to develop COPD than previous and current smokers in the lowest decile with HR=4.95 (95% CI 1.56 to 15.69, P=6.65×10

Identifiers

PMID37066248
PMCPMC10104210
OpenAlexW4362604225

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.