Evidence map›Paper›PMID 35937890›Full record

ArticleFrontiers in neuroscience2022

Assessing the Association Between Lead Pollution and Risk of Alzheimer's Disease by Integrating Multigenomics.

Chunying Li, Yuwei Zhang, Jiandong Liang, Changyan Wu, Xiao Zou

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 38% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Metals in Alzheimer's Disease.Biomedicines · 2023
    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

5 authors at 2 institutions in 1 country.

Chunying LiInstitute of Fungus Resources, College of Life Sciences, Guizhou University, Guiyang, China.
Yuwei ZhangInstitute of Fungus Resources, College of Life Sciences, Guizhou University, Guiyang, China.
Jiandong LiangBasic Medical School, Guizhou University of Traditional Chinese Medicine, Guiyang, China.
Changyan WuBasic Medical School, Guizhou University of Traditional Chinese Medicine, Guiyang, China.
Xiao ZouInstitute of Fungus Resources, College of Life Sciences, Guizhou University, Guiyang, China.
Guizhou University · CNChongqing Vocational Institute of Engineering · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is a life-threatening neurodegenerative disease of the elderly. In recent observations, exposure to heavy metals environmental may increase the risk of AD. However, there are few studies on the causal relationship between heavy metal exposure and AD. In this study, we integrated two large-scale summaries of AD genome-wide association study (GWAS) datasets and a blood lead level GWAS dataset and performed the two-sample Mendelian randomization analysis to assess the causality of blood lead level and AD risk. The results showed that there is a significantly positive causality between blood lead level and AD risk both in the inverse-variance weighted (IVW) model and the weighted median estimator (WME) model. An independent additional verification also reached a consistent conclusion. These findings further confirm the conclusions of previous studies and improve the understanding of the relationship between AD pathogenesis and the toxicity of lead in environmental pollution.

Indexed as

Alzheimer’s diseaseblood leadheavy metal pollutionMendelian randomizationmultigenomics

Identifiers

PMID35937890
PMCPMC9353516
OpenAlexW4286461760

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.