Evidence map›Paper›PMID 39332904›Full record

ArticleGenome research2024

Genetics-driven risk predictions leveraging the Mendelian randomization framework.

Daniel Sens, Liubov Shilova, Ludwig Gräf, Maria Grebenshchikova, Bjoern M Eskofier, Francesco Paolo Casale

Abstract read
In one paragraph

Article in Genome research, 2024. 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
–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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Daniel SensInstitute of AI for Health, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany.
Liubov ShilovaInstitute of AI for Health, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany.
Ludwig GräfInstitute of AI for Health, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany.
Maria GrebenshchikovaInstitute of AI for Health, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany.
Bjoern M EskofierInstitute of AI for Health, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany.ORCID 0000-0002-0417-0336
Francesco Paolo CasaleInstitute of AI for Health, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany; francescopaolo.casale@helmholtz-munich.de.ORCID 0000-0002-5450-1981

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate predictive models of future disease onset are crucial for effective preventive healthcare, yet longitudinal data sets linking early risk factors to subsequent health outcomes are limited. To overcome this challenge, we introduce a novel framework,

Indexed as

Alzheimer DiseaseDiabetes Mellitus, Type 2Genome-Wide Association StudyMendelian Randomization AnalysisGenetic Predisposition to DiseaseHumansParkinson DiseasePolymorphism, Single NucleotideRisk AssessmentRisk Factors

Identifiers

PMID39332904
PMCPMC11529896

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.