Evidence map›Paper›PMID 40504703›Full record

ArticleThe Journal of clinical endocrinology and metabolism2025

No More Free Lunch: Challenges to Mendelian Randomization Due to Sample Selection and Complex Methods.

Tianyuan Lu, Wenmin Zhang, Fergus W Hamilton, Guillaume Butler-Laporte, Nicholas J Timpson, George Davey Smith, J Brent Richards

Abstract read
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In one paragraph

Article in The Journal of clinical endocrinology and metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Genetic evidence suggests a protective role of immunoglobulin M in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  2. Review
  3. 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

7 authors.

Tianyuan LuDepartment of Population Health Sciences, University of Wisconsin-Madison, Madison, WI 53726, USA.ORCID 0000-0002-5664-5698
Wenmin ZhangMontreal Heart Institute, University of Montreal, Montreal, QC H1T 1C8, Canada.ORCID 0000-0002-4472-8859
Fergus W HamiltonMRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK.
Guillaume Butler-LaporteLady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada.ORCID 0000-0001-5388-0396
Nicholas J TimpsonMRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK.
George Davey SmithMRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK.ORCID 0000-0002-1407-8314
J Brent RichardsLady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada.ORCID 0000-0002-3746-9086

Funding

BioResourceCalcul Québec and Compute CanadaCanadian Foundation for InnovationCancer Research UK C18281/A29019CIHR 100558CIHR 365825CIHR 409511Clinical Research Facility and Biomedical Research CentreDepartment of Population Health Sciences at the University of Wisconsin-MadisonEuropean UnionFonds de Recherche Québec SantéFRQS Clinical Research ScholarshipGenome QuébecGuy's and St Thomas' NHS Foundation TrustIVADO Postdoctoral FellowshipJewish General HospitalKing's College LondonLady Davis InstituteMcGill UniversityMedical Research Council MC_UU_00032/01MRC Integrative Epidemiology UnitNational Institute for Health ResearchNIH FoundationNIHR Clinical Lectureship ProgrammeOffice of the Vice Chancellor for Research and Graduate EducationPublic Health Agency of CanadaSchool of Medicine and Public HealthUniversity of BristolWelcome Trust
6 · The paper itself

Abstract

Mendelian randomization (MR) is increasingly used in epidemiological studies to investigate causal relationships. MR depends on 3 fundamental instrumental variable assumptions: relevance, independence, and exclusion restriction. Studies often assume that MR mitigates bias from confounding due to the random allocation of genetic variants at conception. In this perspective, using causal directed acyclic graphs, we discuss several scenarios where biases in MR analyses may arise due to the nature of the data or methods being used. These include (1) collider bias due to the nonrandom selection of participants into study populations used for conducting genome-wide association studies (GWAS), (2) indirect genetic effects arising from population-based GWAS rather than within-family studies, and (3) collider bias due to gene-environment interaction effects on the exposure in nonlinear MR analyses. We provide practical considerations for examining and reducing these biases in MR analyses.

Indexed as

BiasMendelian Randomization AnalysisResearch DesignCausalityGene-Environment InteractionGenome-Wide Association StudyHumanscollider biasindirect genetic effectsinstrumental variable assumptionsMendelian randomizationnonlinear analyses

Identifiers

PMID40504703
PMCPMC12342386

What OpenQuestion holds

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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.