Evidence map›Paper›PMID 41758435›Full record

ArticleBiological trace element research2026

The Genetic Exploration of the Association Between Heavy Metal Exposure in Environmental Pollution and Multi-system Diseases.

Xiaochun Zeng, Qiong Li, Chenyang Zhao, Meng Li, Chaoyan Yue

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Article in Biological trace element research, 2026. 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
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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

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.

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

5 authors.

Xiaochun Zeng *The First People's Hospital of Chenzhou, Chenzhou, China.
Qiong Li *The First People's Hospital of Chenzhou, Chenzhou, China.
Chenyang ZhaoThe First People's Hospital of Chenzhou, Chenzhou, China.
Meng LiObstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases, Shanghai, 200000, China. limeng1701@fckyy.org.cn.
Chaoyan YueObstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases, Shanghai, 200000, China. 20111250007@fudan.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Excessive exposure to heavy metals due to environmental pollution presents a significant challenge to public health. Prior studies have reported potential links between heavy metal exposure and multiple diseases, but the results have not been consistent and have lacked genetic evidence to prove causality. In this study, we employed a two-sample Mendelian randomization (MR) approach based on updated large-scale genome-wide association study (GWAS) data to comprehensively explore the causal relationships between common heavy metal exposures and multiple diseases. We identified 22 causal associations between six heavy metals (lead, arsenic, chromium, copper, cadmium, and nickel) and multiple diseases. Furthermore, sensitivity analyses confirmed the robustness of our findings. Our study provides insights into the susceptibility to multi-system diseases associated with excessive heavy metal exposure due to environmental pollution, highlighting the need for vigilance in populations exposed to high levels of these metals in polluted areas. This work offers potential evidence for the mechanisms underlying the relationship between heavy metals and disease onset.

Indexed as

Environmental ExposureEnvironmental PollutionMetals, HeavyGenome-Wide Association StudyHumansMendelian Randomization AnalysisMetals, HeavyCausal relationshipDiseaseHeavy metalMendelian randomization

Identifiers

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Registered trials

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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.