Evidence map›Paper›PMID 34661687›Full record

ArticleJournal of molecular medicine (Berlin, Germany)2022

Genetically determined blood lead is associated with reduced renal function amongst individuals with type 2 diabetes mellitus: insight from Mendelian Randomisation.

Mohsen Mazidi, Richard Kirwan, Ian G Davies

Open access · hybridAbstract read
In one paragraph

Article in Journal of molecular medicine (Berlin, Germany), 2022. 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
0.4field-weighted citation impact, top 39% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. 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

3 authors at 2 institutions in 1 country.

Mohsen MazidiDepartment of Twin Research and Genetic Epidemiology, King's College London, London, UK.ORCID http://orcid.org/0000-0003-3059-229X
Richard KirwanSchool of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool, UK. r.p.kirwan@2018.ljmu.ac.uk.ORCID http://orcid.org/0000-0003-4645-0077
Ian G DaviesResearch Institute of Sport and Exercise Science, Liverpool John Moores University, Liverpool, UK.ORCID http://orcid.org/0000-0003-3722-8466
Liverpool John Moores University · GBKing's College London · GB

Funding

Medical Research Council G9815508Medical Research Council MC_PC_15018Medical Research Council MC_PC_19009
6 · The paper itself

Abstract

Some observational studies indicate a link between blood lead and kidney function although results remain controversial. In this study, Mendelian randomisation (MR) analysis was applied to obtain unconfounded estimates of the casual association of genetically determined blood lead with estimated glomerular filtration rate (eGFR) and the risk of chronic kidney disease (CKD). Data from the largest genome-wide association studies (GWAS) on blood lead, eGFR and CKD, from predominantly ethnically European populations, were analysed in total, as well as separately in individuals with or without type 2 diabetes mellitus. Inverse variance weighted (IVW) method, weighted median (WM)-based method, MR-Egger, MR-Pleiotropy RESidual Sum and Outlier (PRESSO) as well as the leave-one-out method were applied. In a general population, lifetime blood lead levels had no significant effect on risk of CKD (IVW: p = 0.652) and eGFR (IVW: p = 0.668). After grouping by type 2 diabetes status (no diabetes vs. diabetes), genetically higher levels of blood lead had a significant negative impact among subjects with type 2 diabetes (IVW = Beta: -0.03416, p = 0.0132) but not in subjects without (IVW: p = 0.823), with low likelihood of heterogeneity for any estimates (IVW p > 0.158). MR-PRESSO did not highlight any outliers. Pleiotropy test, with very negligible intercept and insignificant p-value, indicated a low likelihood of pleiotropy for all estimations. The leave-one-out method demonstrated that links were not driven by a single SNP. Our results show, for the first time, that among subjects with type 2 diabetes, higher blood lead levels are potentially related to less favourable renal function. Further studies are needed to confirm our results. KEY MESSAGES: What is already known about this subject? Chronic kidney disease is associated with unfavourable lifestyle behaviours and conditions such as type 2 diabetes. Observational studies have reported an association between blood lead and reduced estimated glomerular filtration rate, but the relationship between lead exposure and renal function remains controversial. What is the key question? Using Mendelian randomisation with data from 5433 individuals from the UK and Australian populations, does genetically determined blood lead have a potentially causal effect on estimated glomerular filtration rate and the risk of chronic kidney disease? What are the new findings? Blood lead levels have a potentially causal effect on reduced renal function in individuals with type 2 diabetes. In subjects without diabetes, no such causal relationship was identified. How might this impact on clinical practice in the foreseeable future? This highlights the risk of elevated blood lead, for example, due to environmental exposure, amongst those with type 2 diabetes, which may predispose them to impaired renal function.

Indexed as

AdultDiabetes Mellitus, Type 2Environmental PollutantsFemaleGenome-Wide Association StudyGlomerular Filtration RateHumansLeadMaleMendelian Randomization AnalysisMiddle AgedRenal Insufficiency, ChronicEnvironmental PollutantsLeadBlood leadChronic kidney diseaseDiabetesEstimated glomerular filtration rateMendelian randomisationNephrology

Identifiers

PMID34661687
PMCPMC8724171
OpenAlexW3207637024

What OpenQuestion holds

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LicenceCC BY
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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.