Evidence map›Paper›PMID 25536107›Full record

ArticlePloS one2014

Benchmark dose estimation for cadmium-induced renal tubular damage among environmental cadmium-exposed women aged 35-54 years in two counties of China.

Jia Hu, Mei Li, Tian-xu Han, Jian-wei Chen, Lin-xiang Ye, Qi Wang, Yi-kai Zhou

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.3field-weighted citation impact, top 22% 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

10 citing papers in PubMed, 21 citations in OpenAlex.

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  9. Change of urinary cadmium and renal tubular protein in female works after cessation of cadmium exposure.International archives of occupational and environmental health · 2017
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  10. 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

7 authors at 1 institution in 1 country.

Jia HuMOE Key Lab of Environment and Health, Institute of Environmental Medicine, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
Mei LiMOE Key Lab of Environment and Health, Institute of Environmental Medicine, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
Tian-xu HanMOE Key Lab of Environment and Health, Institute of Environmental Medicine, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
Jian-wei ChenMOE Key Lab of Environment and Health, Institute of Environmental Medicine, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
Lin-xiang YeDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
Qi WangDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
Yi-kai ZhouMOE Key Lab of Environment and Health, Institute of Environmental Medicine, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China.
Huazhong University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA number of factors, including gender, age, smoking habits, and occupational exposure, affect the levels of urinary cadmium. Few studies have considered these influences when calculating the benchmark dose (BMD) of cadmium. In the present study, we aimed to calculate BMDs and their 95% lower confidence bounds (BMDLs) for cadmium-induced renal tubular effects in an age-specific population in south-central China.

methodsIn this study, urinary cadmium, β2-microglobulin, and N-acetyl-β-D-glucosaminidase levels were measured in morning urine samples from 490 randomly selected non-smoking women aged 35-54 years. Participants were selected using stratified cluster sampling in two counties (counties A and B) in China. Multiple regression and logistic regression analyses were used to investigate the dose-response relationship between urinary cadmium levels and tubular effects. BMDs/BMDLs corresponding to an additional risk (benchmark response) of 5% and 10% were calculated with assumed cut-off values of the 84th and 90th percentile of urinary β2-microglobulin and N-acetyl-β-D-glucosaminidase levels of the controls.

resultsUrinary levels of β2-microglobulin and N-acetyl-β-D-glucosaminidase increased significantly with increasing levels of urinary cadmium. Age was not associated with urinary cadmium levels, possibly because of the narrow age range included in this study. Based on urinary β2-microglobulin and N-acetyl-β-D-glucosaminidase, BMDs and BMDLs of urinary cadmium ranged from 2.08 to 3.80 (1.41-2.18) µg/g cr for subjects in county A and from 0.99 to 3.34 (0.74-1.91) µg/g cr for those in county B. The predetermined benchmark response of 0.05 and the 90th percentiles of urinary β2-microglobulin and N-acetyl-β-D-glucosaminidase levels of the subjects not exposed to cadmium (i.e., the control group) served as cut-off values.

conclusionsThe obtained BMDs of urinary cadmium were similar to the reference point of 1 µg/g cr, as suggested by the European Food Safety Authority, indicating that cadmium exposure must be reduced to protect human health.

Indexed as

AcetylglucosaminidaseAdultBenchmarkingbeta 2-MicroglobulinCadmiumChinaEnvironmental ExposureEnvironmental PollutantsFemaleHumansKidney DiseasesKidney TubulesMiddle AgedAcetylglucosaminidasebeta 2-MicroglobulinCadmiumEnvironmental Pollutants

Identifiers

PMID25536107
PMCPMC4275258
OpenAlexW1999547427

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.