Evidence map›Paper›PMID 40084379›Full record

ArticleAmerican journal of cancer research2025

Impact of long-term N-acetylcysteine use on cancer risk.

Cherng-Chia Yang, Wan-Ming Chen, Ben-Chang Shia, Szu-Yuan Wu

Abstract read
In one paragraph

Article in American journal of cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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.

No citing paper in PubMed yet.

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

4 authors.

Cherng-Chia YangDepartment of Obstetrics and Gynecology, St. Paul's Hospital Taoyuan, Taiwan.
Wan-Ming ChenGraduate Institute of Business Administration, College of Management, Fu Jen Catholic University Taipei, Taiwan.
Ben-Chang ShiaGraduate Institute of Business Administration, College of Management, Fu Jen Catholic University Taipei, Taiwan.
Szu-Yuan WuGraduate Institute of Business Administration, College of Management, Fu Jen Catholic University Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic obstructive pulmonary disease (COPD) patients face an increased risk of developing various malignancies due to shared risk factors and underlying systemic inflammation. N-acetylcysteine (NAC) has shown potential anticancer properties in preclinical studies, but clinical evidence in COPD patients is limited. We conducted a nationwide propensity score-matched cohort study using data from Taiwan's National Health Insurance Research Database to evaluate the anticancer effects of NAC in COPD patients. Patients diagnosed with COPD between 2008 and 2019 were included, and those with pre-existing cancer were excluded. NAC use was defined as consistent administration for most days with an average dose exceeding 28 cumulative defined daily doses (cDDDs) annually. Cox regression models were adjusted for various covariates was employed. PSM yielded 91,546 patients, evenly distributed between NAC and non-NAC groups. Multivariate Cox regression analysis revealed a lower cancer risk in patients with long-term NAC use compared to non-users (adjusted hazard ratio [aHR] 0.69, 95% confidence interval [CI] 0.66-0.72; P<0.001). Dose-dependent relationships were observed, with higher daily NAC intake associated with reduced cancer risk. Time-varying Cox regression analysis demonstrated significant reductions in the risk of specific cancers, including hepatocellular carcinoma, colorectal cancer, and breast cancer, among NAC users compared to non-users. Our study provides clinical evidence supporting the potential anticancer effects of NAC in COPD patients. These findings highlight the importance of exploring NAC as a chemopreventive agent in high-risk populations and inform clinical practice and future research endeavors.

Indexed as

cancer riskchemopreventionCOPDdose-dependentN-acetylcysteine

Identifiers

PMID40084379
PMCPMC11897643

What OpenQuestion holds

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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.