Evidence map›Paper›PMID 25603868›Full record

ArticleZhongguo fei ai za zhi = Chinese journal of lung cancer2015

[Molecular epidemiology study in Xuanwei: the relationship among 
coal type, genotype and lung cancer risk].

Jihua Li, Jun He, Rui Tang, Wei Hu, Qing Lan, Xingzhou He, Yun Li, Yunsheng Zhang

Open access · greenAbstract readEnglish Abstract
In one paragraph

Article in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2015. 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 citations in OpenAlex.

  1. Review
  2. Article
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

8 authors at 2 institutions in 1 country.

Jihua LiQujing Centers for Disease Control and Prevention, Qujing 655000, China.
Jun HeQujing Centers for Disease Control and Prevention, Qujing 655000, China.
Rui TangQujing Municipal Bureau of Health, Qujing 655000, China.
Wei HuNational Cancer Institute, National Institutes of Health, Bethesda 20892, MD, USA.
Qing LanNational Cancer Institute, National Institutes of Health, Bethesda 20892, MD, USA.
Xingzhou HeChinese Centers for Disease Control and Prevention, Beijing 100050, China.
Yun LiQujing Centers for Disease Control and Prevention, Qujing 655000, China.
Yunsheng ZhangQujing Centers for Disease Control and Prevention, Qujing 655000, China.
Centers for Disease Control and Prevention · USNational Institutes of Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt has been proven that the lung cancer mortality rate in Xuanwei County, China was among the highest in the country and has been associated with exposure to indoor smoky coal emissions that contain high levels of polycyclic aromatic hydrocarbons. This risk may be modified by variation in genetic polymorphisms and coal subtypes. Our objective was to use molecular epidemiological techniques to investigate the relationship among genetic polymorphisms, coal subtype and lung cancer risk in Xuanwei County.

methodsOn the basis of two population-based case-control studies in residents of Xuanwei County, China, questionnaires covering demographic information, smoking history, family and personal medical history, and information on other variables were administered and buccal cells and sputum samples were collected separately from each subject enrolled to extract DNA. GST superfamily, AKR1C3 superfamily, OGG1 superfamily and other genotype were scanned by useing PCR method. ORs and 95%CIs were used to estimate the association between genotypes, coal subtypes and lung cancer risk factors by conditional Logistic regression using Statistical Analysis Software.

resultsCompared with subjects who using smokeless coal or wood, smoky coal use was statistically significantly associated with lung cancer risk (OR=7.7, 95%CI: 4.5-13.3). There was marked heterogeneity in risk estimates for specific subtypes of smoky coal. Estimates were highest for coal from the Laibin (OR=24.8), Longtan (OR=11.6) and Baoshan (OR=6.0) coal types, and lower for coal from other types; the risk within the same subtype of coal in male and female were similar. The GSTM1-null genotype, the AKR1C3 (Ex1-70C>G), OGG1 (Ex6-315C>G) genotypes were closely associated with increased risk of lung cancer in Xuanwei County, and their odds ratios (95%CI) were 2.3 (1.3-4.2), 1.8 (1.0-3.5) and 1.9 (1.1-3.3), respectively. Compared to subjects who with GSTM1-positive and used less than 130 tons of smoky coal during their lifetime, higher risks were closely associated with GSTM1-null and heavier users (≥130 tons), with the OR was 4.9 (95%CI: 1.3-18.2) and 2.7 (95%CI: 1.0-7.4) for female and male, respectively. However, higher risks were only found within female for AKR1C3 (Ex1-70C>G) and OGG1 (Ex6-315C>G), with OR (95%CI)=12.9 (2.2-107.8) and 5.7 (1.1-34.2), respectively.

conclusionsLung cancer risks varied among coal subtypes; however, risks were similar between men and women exposed to the same type of coal. The GSTM1-null genotype may enhance susceptibility to air pollution from indoor smoky coal combustion emissions. AKR1C3 and OGG1 genotypes were significantly associated with higher risk of lung cancer, especially among heavily exposed women.

Indexed as

20-Hydroxysteroid DehydrogenasesAdultCase-Control StudiesChinaCoalDNA GlycosylasesFemaleGenotypeGlutathione TransferaseHumansLung NeoplasmsMaleMiddle AgedMolecular EpidemiologyOdds RatioRisk Factors20-Hydroxysteroid Dehydrogenases3 alpha-beta, 20 beta-hydroxysteroid dehydrogenaseCoalDNA GlycosylasesGlutathione Transferaseoxoguanine glycosylase 1, human

Identifiers

PMID25603868
PMCPMC5999738
OpenAlexW148920147

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.