Evidence map›Paper›PMID 36424572›Full record

SynthesisBMC medicine2022

Identifying and ranking causal biochemical biomarkers for breast cancer: a Mendelian randomisation study.

Sonja N Tang, Verena Zuber, Konstantinos K Tsilidis

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
5.4field-weighted citation impact, top 4% 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

13 citing papers in PubMed, 29 citations in OpenAlex.

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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 3 institutions in 2 countries.

Sonja N TangDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. sonja.tang19@imperial.ac.uk.ORCID 0000-0002-2761-207X
Verena Zuber *Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Konstantinos K Tsilidis *Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
NIHR Imperial Biomedical Research Centre · GBUK Dementia Research Institute · GBUniversity of Ioannina · GR

Funding

Cancer Research UK 29019Cancer Research UK C18281/A29019Medical Research Council MC_PC_17228Medical Research Council MC_QA137853Medical Research Council MR/S019669/1Medical Research Council MR/W029790/1
6 · The paper itself

Abstract

backgroundOnly a few of the 34 biochemical biomarkers measured in the UK Biobank (UKB) have been associated with breast cancer, with many associations suffering from possible confounding and reverse causation. This study aimed to screen and rank all UKB biochemical biomarkers for possible causal relationships with breast cancer.

methodsWe conducted two-sample Mendelian randomisation (MR) analyses on ~420,000 women by leveraging summary-level genetic exposure associations from the UKB study (n = 194,174) and summary-level genetic outcome associations from the Breast Cancer Association Consortium (n = 228,951). Our exposures included all 34 biochemical biomarkers in the UKB, and our outcomes were overall, oestrogen-positive, and oestrogen-negative breast cancer. We performed inverse-variance weighted MR, weighted median MR, MR-Egger, and MR-PRESSO for 30 biomarkers for which we found multiple instrumental variables. We additionally performed multivariable MR to adjust for known risk factors, bidirectional MR to investigate reverse causation, and MR Bayesian model averaging to rank the significant biomarkers by their genetic evidence.

resultsIncreased genetic liability to overall breast cancer was robustly associated with the following biomarkers by decreasing importance: testosterone (odds ratio (OR): 1.12, 95% confidence interval (CI): 1.04-1.21), high-density lipoprotein (HDL) cholesterol (OR: 1.08, 95% CI: 1.04-1.13), insulin-like growth factor 1 (OR: 1.08, 95% CI: 1.02-1.13), and alkaline phosphatase (ALP) (OR: 0.93, 95% CI: 0.89-0.98).

conclusionsOur findings support a likely causal role of genetically predicted levels of testosterone, HDL cholesterol, and IGF-1, as well as a novel potential role of ALP in breast cancer aetiology. Further studies are needed to understand full disease pathways that may inform breast cancer prevention.

Indexed as

Breast NeoplasmsBayes TheoremBiomarkersCholesterol, HDLEstrogensFemaleHumansTestosteroneBiomarkersCholesterol, HDLEstrogensTestosteroneBiomarkersBreast cancerCausal inferenceEpidemiologyInstrumental variablesMendelian randomisation

Identifiers

PMID36424572
PMCPMC9685978
OpenAlexW4309890149

What OpenQuestion holds

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LicenceCC BY
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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.